As the amount of sensor data made available online increases, it becomes more difficult for users to identify useful datasets. Semantic web technologies improve discovery with meaningful ontologies, but the decision of suitability remains with the users. The GEO label provides a visual summary of the standardised metadata to aid users in this process. This work presents novel rules for deriving the information for the GEO label's multiple facets, such as user feedback or quality information, based on the Semantic Sensor Network Ontology and related ontologies. It enhances an existing implementation of the GEO label API to generate labels for resources of the Semantic Sensor Web. The prototype is deployed to serverless cloud infrastructures. We find that serverless GEO label generation is capable of handling two evaluation scenarios for concurrent users and burst generation. More real-world semantic sensor descriptions and an integration into large scale discovery platforms are needed to develop the presented solutions further.

Strategic intelligence covers the examination of what impacts and influences the business and the related SWOT analysis, (strengths, weaknesses, opportunities, and threats mapping). Through this research, leaders will be able to properly allocate and re-route resources, identify areas of improvement, and ensure that strategic initiatives are aligned with the organization's mission. Strategic intelligence is, therefore, effective through the use of critical thought and data-driven decisions and seeing the outcomes of different positioned alternatives. It must also form and support a culture of cooperation and open communication within the organizations, stimulating employees' inputs and learning curves. The article's subject is the necessity for strategic intelligence, a crucial competence for leaders, managers, and companies striving to remain competitive, to be utilized and best leveraged in today's volatile and dynamic business contexts. It helps the leadership turn proactive, foresee trends, and make correct, agile decisions that enforce companies' long-term growth and success. This study follows a research design that includes a thorough literature review and bibliometric research, executed via VoSViewer, illustrating the concept of strategic intelligence from a leadership point of view as a driver of competitive advantage. Further, the article points out that the limitations are acknowledged because the literature on the matter is limited, as strategic intelligence is relatively new to the business sector; moreover, more research is required to fully understand and use the potential of strategic intelligence in business growth. By overcoming these limitations and continuing to study this concept, businesses can gain a competitive edge and make informed decisions that fuel their success in an ever-evolving market. As a result, this study contributes to developing strategic intelligence in leadership and business and paves the road for further research and applications of strategic intelligence theories in organizational contexts.

The purpose of this paper is to explore the implementation of Retrieval-Augmented Gen- 14 eration (RAG) technology with open-source Large Language Models (LLMs). For this purpose, a 15 web-based application named PaSSER that integrates RAG with three models - Mistral:7b, 16 Llama2:7b: Orca2:7b, is developed. In its development various technologies are used, including 17 Blockchain for managing and storing assessment results of built-in testing modules. PaSSER em- 18 ploys a set of evaluation metrics for LLMs’ performance - METEOR, ROUGE, BLEU, Perplexity, 19 Cosine similarity, Pearson correlation, and F1 score. The specialized knowledge base is in the smart 20 agriculture domain. As an illustration, the paper presents results and analysis of two tests. First one 21 is assessing the performance of LLMs across different hardware configurations, the other is deter- 22 mining the model that delivers most accurate and contextually relevant responses within the RAG. 23 The paper also discusses the integration of blockchain technology with LLMs to manage and store 24 assessment results within a blockchain environment. The tests revealed that Mistral:7b demon- 25 strates the best results across most of the evaluated metrics. The discussion section comments on 26 technical and hardware considerations that affect the performance of LLMs. Future development 27 will focus in leveraging new larger pre-trained open-source LLMs, exploring different finetuning 28 approaches, and further integration with Blockchain and IPFS.

The task of weakly supervised point cloud semantic segmentation has received widespread attention and also has been widely used in autonomous driving, robotics, and modern industries. Due to the high dimensionality and large volume of point cloud data, many technical difficulties appeal to weakly supervised semantic processing, especially segmentation. However, weakly-supervised schemes usually provide only partial labelling information of the underlying point cloud data and thus need to effectively extract local features, geometric information, and contextual relationships only using these limited labelled data for supervised learning. To improve weakly-supervised semantic segmentation, we propose a novel segmentation network through the boundary-based feature aggregation based on a K-NN algorithm with down-sampling operation, and also introduce the smoothness loss and Siamese loss for effective segmentation. The experiments on public datasets demonstrate that our presented segmentation method exceeds most of the existing fully supervised and weakly-supervised methods in terms of mIoU. Specifically, our network has high segmentation accuracy on the labels of objects with similar geometrical structures, such as ceiling, wall, floor, chair and table, reaching 91.2%, 98.8%, 83.3%, 75.3% and 80.2%, respectively. Extensive experiments also illustrate its robustness, effectiveness, and generalization of the proposed weakly supervised segmentation network.

There are rich proper noun derivative semantic relationships in geographic entity vector data. The identification of traditional proper noun derivative semantic relationships usually requires toponym researchers to examine the source of derived toponyms by consulting a large number of toponym documents. This method cannot adapt to the mining of proper noun derivative semantic relationships for large-scale geographical entity. In this regard, this paper proposes a method of identifying the proper noun derivative semantic relationships for geographical entity by combining prompt learning and XGBoost model. This method transforms the extraction problem of proper noun derivative semantic relationships into a binary classification problem by analyzing the characteristics of the derivation relationship of toponyms in the form of toponyms and the spatial and temporal distribution of geography. Firstly, prompt learning and gaussian mixture model are used to extract and cluster the derivation words of toponyms, so as to realize the recognition of derived toponyms collections. Then, enumerate the possible proper noun derivations in the toponym set, and then the XGBoost model is used to identify the semantic relations derived from the proper noun of geographical entities. In the experiment, the recognition method of proper noun derivative semantic relationships based on prompt learning and XGBoost proposed in this paper has achieved good relationship recognition effect in the experiment. This method has certain practical value in the fields of toponym management and toponym translation.

Prestressed concrete sleepers (or railroad ties) are principally designed in order to carry wheel loads from the rails to the ground of railway tracks. Their design takes into account static and dynamic loading conditions. In spite of the most common use of the prestressed concrete crossties in railway tracks, there have always been many demands from rail engineers to improve serviceability and functionality of concrete crossties. For example, signaling, fiber optic, equipment cables are often damaged either by ballast corners or by tamping machine. There has been a need to re-design concrete crosstie to cater cables internally so that they would not experience detrimental or harsh environments. Also, many concrete crossties need a retrofit for automatic train control device and similar signaling equipment. In contrast, the effects of holes and web openings on structural capacity of concrete crossties have not been thoroughly investigated. This paper accordingly highlights the effect of holes and web openings on the toughness and ductility of concrete crossties. The outcome of this research enables better decision making process for retrofiting prestressed concrete crossties with holes and web opening in practice.

In this paper, a new approach to retrieve semantic images based on shape and geometric features of image in conjunction with multi-class support vector machine is proposed. Zernike moment as shape feature is to verify the invariance of objects for silhouette image. In addition, a set of geometrical features is to explore the objects shape using two features of rectangularity and circularity. Then the extracted features are normalized and employed for multi-class support vector machine either for learning or retrieving processes. The retrieving process relies on three main tasks which namely Query Engine, Matching Module and Ontology Manger, respectively. Query Engine is to build the input text or image query using SPARQL language. The matching module extracts the shape and geometric features of image’s objects and employ them to Ontology Manger which in turn inserts them in ontology knowledge base. Benchmark mammals have been conducted to empirically conclude the outcome of proposed approach. Our experiment on text and image retrieval yields efficient results to problematic phenomena than previously reported.

The essence of cross-modal generation and retrieval modeling for image-text interactions lies in semantic consistency. Among these, cross-modal representation alignment serves as the foundational requirement for achieving cross-modal semantic consistency. The crux of our proposed method entails the collaborative training of an image captioning model(referred to as the ’captioner’) and a pair of contrastive encoders for image-text matching(referred to as the ’concoder’). This synergistic approach yields concurrent enhancements in concoder’s performance, captioner’s proficiency, and cross-modal semantic coherence. We coin the proposed method as ‘ReCap’. To begin, we initialize the concoder through knowledge distillation from a pre-trained model. During the training process, we use the output of the initialized concoder as input to a residual attention network, jointly training it with the captioner to achieve semantic consistency. Subsequently, we iteratively update the concoder during the image-text momentum encoding phase using the residual attention network, creating a closed-loop for semantic consistency resolution. Upon completion of training, the concoder and captioner modules can be used independently to perform tasks related to cross-modal retrieval and cross-modal generation of text and images. In order to substantiate the efficacy of our proposed method, we initially conducted empirical experiments encompassing image-text retrieval and image captioning tasks, employing a widely recognized benchmark dataset, MSCOCO. Subsequently, we assessed the fine-grained cross-modal alignment performance of the concoder through an image classification task on the iNaturalist 2018 dataset. The achieved performance metrics notably surpassed those achieved by several incumbent state-of-the-art models, thus validating the proficiency of our method. Finally, motivated by the practical requirements of handling the nature conservation image data, we performed caption annotation for the iNaturalist 2018 dataset. Subsequently, we trained the ReCap on this annotated data. Experimental results demonstrate that our proposed method can maintain semantic consistency between cross-modal retrieval and image caption generation for species with similar visual features but distinct semantics. This achievement signifies a meaningful exploration in the field of cross-modal semantic consistency representation, holding significant practical value in the domain of biological research.

Bio-molecular reagents like antibodies required in experimental biology are expensive and their effectiveness, among other things, is critical to the success of the experiment. Although such resources are sometimes donated by one investigator to another through personal communication between the two, there is no previous study to our knowledge on the extent of such donations, nor a central platform that directs resource seekers to donors. In this paper, we describe, to our knowledge, a first attempt at building a web-portal titled Bio-Resource Exchange that attempts to bridge this gap between resource seekers and donors in the domain of experimental biology. Users on this portal can request for or donate antibodies, cell-lines and DNA Constructs. This resource could also serve as a crowd-sourced database of resources for experimental biology. Further, in order to index donations outside of our portal, we mined scientific articles to find instances of donations of antibodies and attempted to extract information about these donations at the finest granularity. Specifically, we extracted the name of the donor, his/her affiliation and the name of the antibody for every donation by parsing the acknowledgements sections of articles. To extract annotations at this level, we propose two approaches – a rule based algorithm and a bootstrapped relation learning algorithm. The algorithms extracted donor names, affiliations and antibody names with average accuracies of 57% and 62% respectively. We also created a dataset of 50 expert-annotated acknowledgements sections that will serve as a gold standard dataset to evaluate extraction algorithms in the future. Contact: [email protected], [email protected] Database URL: http://tonks.dbmi.pitt.edu/brx Supplementary information: Supplementary data are available at Database online.

Wikipedia, as "social machine", is a privileged place to observe the collective construction of concepts without central control. Based on Dahlberg's theory of concept, and anchored in the pragmatism of Hjørland - in which the concepts are socially negotiated meanings - the evolution of the concept of Semantic Web (SW) was analyzed in the English version of Wikipedia. An exploratory, descriptive and qualitative study was designed and we identified 26 different definitions (between 7-12-2001 and 12/31/2017) of which 8 are of particular relevance for their duration, with the latter being the two recorded at the end of the analyzed period. According to them, SW: "is a extension of the web"and "is a Web of Data"; the latter, used as a complementary definition, links to Berners-Lee's publications. In Wikipedia, the evolution of the SW concept appears to be based on the search for the use of non-technical vocabulary and the control of authority carried out by the debate. As a space for collective bargaining of meanings, the Wikipedia study may bring relevant contributions to a community's understanding of a particular concept and how it evolves over time.

Semantic segmentation of high-resolution remote sensing images provides detailed and precise feature information, playing a pivotal role in decision-making and analysis in sectors such as water management, agriculture, military, and environmental protection. However, most methods merely combine features from various branches directly, without a mechanism for spatial location feature screening, and treat all extracted features as equally important. To overcome these limitations, we introduce a novel spatially adaptive interaction network (SAINet) for dynamic interaction across different features in remote sensing semantic segmentation task. Specifically, we propose a spatial refined module that leverages local context information to filter spatial locations and extract salient regions. Following this, we introduce an innovative spatial interaction module that uses a spatial adaptive modulation mechanism to dynamically select and allocate spatial position weights. This facilitates interaction between local salient areas and global information, allowing the network to focus more on relevant regions. This flexible approach enables the network to capture more informative features, thereby enhancing segmentation accuracy. Experiments conducted on the DeepGlobe, Vaihingen, and Potsdam public datasets confirm the effectiveness and capability of SAINet. Our code and models will be publicly available

This paper's aim is to extend horizons in symbolic AI. The key to doing so is to establish a scientific basis for knowledge foundations, and specifically for the concept of meaning, and its computation. This led to the creation of the Meaning Computation AI (MCAI). Knowledge structures are at the heart of MCAI. The semantic triple chain, one of such structures, is the subject of this paper. This structure is the core of introducing computational semantic reasoning models across a variety of domains. To efficiently apply knowledge structures to create semantic reasoning applications MCAI contains a development framework consisting of four components. The first component stems from our discovery that knowledge is relational in nature. It can be expressed through implicit relations (which reflect meaning) and semantic relations (which describe how implicit relations act between entities). An algebraical apparatus for relational knowledge, specifically for implicit and semantic relations, represents the first component. Axiomatic models of relational domain ontologies and domain-oriented semantic reasoning models comprise the second and third components. The fourth component contains domain-oriented semantic reasoning engines.

The recently proposed and logically developed paradigm of deep genetical affinity of scientific terms "energy-entropy-information" harmonizes classical and quantum physics with general and special theories of relativity. For the first time in engineering practice, a complex vector-tensor-matrix description of gravitational, thermal and electromagnetic fields in ideal solid, liquid and gaseous media was performed analytically and without preliminary measurements. The basic set of fundamental quantum constants of the standard physical model has been computed with an extreme precision of 1/10^64, which is a natural limit arising due to elementary recursive arithmetic and elementary functional analysis in digital notation.

The internet has changed the way people perceived fiction to a new level. For instance, online forums have given people the opportunity to write without revealing their real identities. Especially in the Saudi context, online users were using these forums to write web novels that reflect their culture, lives, concerns, hopes and dreams. In this paper, we describe a dataset that was collected from one of the online forums that was used for sharing web novels among its readers. The collected dataset contains 1,267 novels between 2003-2015. This data set is available to the research community to analyze to gain a better understanding of the social, economical, and behavioral mindset that was manifested in the community in that decade.

Web-Based GIS is a platform that deals with the representation, management and storage of locational information for real-time online retrieval, viewing and manipulations. Web-based GIS incorporate all the functions of information systems: gathering, storing, retrieving, visualizing and dissemination of spatial information on the Internet. In modern-day GIS serve as vital means to not only geo-referenced locational information that are been retrieved but also the development in web-based technologies as well as there involvements in decision making processes. In recent time the increased used of GIS on the Internet have totally transformed and made three distinct changes across our GIS environment. These are: (i) how are GIS data being accessed (ii) what are modes of its transmission and (iii) what available GIS functionalities are within the environment for analysis. This paper examines the applications and technologies associated with web-based GIS architectures with an emphasis on the internet GIS based structure and case studies of GIS Web-based applications in MapInfo, Microsoft Silverlight Technology and the ArcGIS Server using ArcGIS JavaScript API.

A novel method for finding linear mappings among word embeddings for several languages, taking as pivot a shared, universal embedding space, is proposed in this paper. Previous approaches learn translation matrices between two specific languages, but this method learn translation matrices between a given language and a shared, universal space. The system was first trained on bilingual, and later on multilingual corpora as well. In the first case two different training data were applied; Dinu’s English-Italian benchmark data, and English-Italian translation pairs extracted from the PanLex database. In the second case only the PanLex database was used. The system performs on English-Italian languages with the best setting significantly better than the baseline system of Mikolov et al. [1], and it provides a comparable performance with the more sophisticated systems of Faruqui and Dyer [2] and Dinu et al. [3]. Exploiting the richness of the PanLex database, the proposed method makes it possible to learn linear mappings among an arbitrary number of languages.

In recent years, semantic communication has received significant attention from both academia and industry, driven by the growing demands for ultra-low latency and high-throughput capabilities in emerging intelligent services. Nonetheless, a comprehensive and effective theoretical framework for semantic communication has yet to be established. In particular, finding the fundamental limits of semantic communication, exploring the capabilities of semantic-aware networks, or utilizing theoretical guidance for deep learning in semantic communication are very important yet still unresolved issues. In general, the mathematical theory of semantic communication and the mathematical representation of semantics are referred to as semantic information theory. In this paper, we introduce the pertinent advancements in semantic information theory. Grounded in the foundational work of Claude Shannon, we present the latest developments in semantic entropy, semantic rate-distortion, and semantic channel capacity. Additionally, we analyze some open problems in semantic information measurement and semantic coding, providing a theoretical basis for the design of a semantic communication system. Furthermore, we carefully review several mathematical theories and tools and evaluate their applicability in the context of semantic communication. Finally, we shed light on the challenges encountered in both semantic communication and semantic information theory.

The paper presents the final results from the research project “Urban Abacus of Building Energy performances (Abaco Urbano Energeticodegli Edifci – AUREE)” aimed at supporting the renovation and energy efficiency enhancement of urban building heritage financed by the “Ricerca Sistema Elettrico (MISE) program”. The crux of AUREE project is a Web – GIS GeoBlog portal with customized semantic dashboards aimed at sharing information on urban built environment and promoting the participation of local stakeholders to its improvement. As the latest development of this research, a workflow integrating the AUREE portal with BIM authoring and an open source IoT platform has been implemented and applied to an experimental case study concerning a public building in Carbonia (Italy). The headquarters of the sustainable energy research center Sotacarbo was selected as case study. The presented results proved that it was possible to create an open system, which could be easily consulted by "non-expert" users as well, to make available real-time monitoring data obtained from IoT sensors measuring the energy consumption, the indoor environmental conditions and the usage profiles of public buildings. The proposed open system could also be suitable to be later used as an effective tool to support the rising “energy communities”.

Abstract: A new method for constructing exact solutions of the General Relativity equations for a dusty matter with fractal property is proposed. This method allows to find the solution of the GR-equations in terms of matter velocities : the connection coefficients and the Ricci tensor of space-time are expressed in terms of matter velocities; the metric tensor and the matter density are found as functions of velocity from the GR-equations. The connection coefficients and the Ricci tensor are invariant with respect to the discrete scaling transformation of velocity , where is constant. Therefore, the found solution can be used to simulate the fractal properties of the cosmic web in terms of matter velocities. This solution includes isotropic and anisotropic distributions of matter density. In an isotropic case, there is a class of exact solutions including both the well-known Friedmann’s solution and a solution with a periodic distribution of the matter density in space. This last solution may be used to simulate the quasi-periodic distribution of matter in the cosmic web. It is possible that, the cosmic web and its fractal properties are the space-time primary properties. These properties are described with a deformation tensor of the space-time.

This study examines the relationship between semantic memory and social cognition in schizophrenia, addressing how these cognitive domains intersect. Semantic memory, which includes general world knowledge and word meanings, was evaluated using verbal fluency tasks and the Camels and Cactus Test. Social cognition, essential for social interaction, was assessed through emotion recognition (Faces Test) and Theory of Mind (Hinting Task). Participants included 50 individuals with schizophrenia and 30 controls. The schizophrenia group showed significantly lower performance on both semantic memory and social cognition tasks. Notably, strong correlations were found between the Camels and Cactus Test and social cognition measures, suggesting that social cognition deficits in schizophrenia may be linked to semantic memory impairments. Regression analyses highlighted that the Camels and Cactus Test significantly predicted social cognition performance, independent of symptomatology. These findings underscore the interconnectedness of semantic memory and social cognition in schizophrenia, suggesting that semantic memory deficits, particularly in non-categorical associations, play a important role in social cognitive impairments. This study provides new insights into the cognitive underpinnings of schizophrenia, emphasizing the need for further research to explore these relationships and their implications for cognitive models and therapeutic interventions.

Accurately estimating the current state of local traffic scenes is one of the key problems in the development of software components for automated vehicles. In addition to details on free space and drivability, static and dynamic traffic participants, information on the semantics may also be included in the desired representation. Multi-layer grid maps allow to include all this information in a common representation. However, most existing grid mapping approaches only process range sensor measurements such as LIDAR and Radar and solely model occupancy without semantic states. In order to add sensor redundancy and diversity it is desired to add vision based sensor setups in a common grid map representation. In this work, we present a semantic evidential grid mapping pipeline including estimates for eight semantic classes that is designed for straightforward fusion with range sensor data. Unlike in other publication our representation explicitly models uncertainties in the evdiential model. We present results of our grid mapping pipeline based on a monocular vision setup and a stereo vision setup. Our mapping resulsts are accurate and dense mapping due to the incorporation of a disparity- or depth-based ground surface estimation in the inverse perspective mapping. We conclude this paper by providing a detailed quantitative evaluation based on real traffic scenarios in the Kitti odometry benchmark and demonstrating the advantages compared to other semantic grid mapping approaches.

In the field of visualization, understanding users’ analytical process is important to determine the effectiveness of a designed visualization application. To understand the users’ analytical process, numerous studies have been performed to capture and analyze user interactions. Although many studies have emphasized the importance of analyzing user interactions to understand users’ analytical reasoning processes, few have successfully linked these interactions to users’ reasoning processes. This paper introduces an approach that bridges this gap by correlating semantic user interactions with analysis decisions through an interactive wire transaction analysis system and a visual state transition matrix. With the designed analysis system, interactive analysis can be performed to evaluate financial fraud in wire transactions. It also allows the mapping of captured user interactions and analytical decisions back onto the visualization, revealing users’ distinct results. The visual state transition matrix further aids to help understanding users’ analytical flows, revealing their decision-making processes. Classification machine learning algorithms are applied to assess the effectiveness of our approach in understanding analysts’ strategies by connecting them to their decisions. From the study, we observed an average of 72% accuracy in clearly classifying the semantic user interactions. For classifying individual decisions, we observed an average of 70% classification accuracy. This emphasizes the importance of capturing semantic user interactions to understand users’ analytical processes. Overall, it is determined that the proposed approach not only enhances the understanding of analytical behaviors but also offers a robust approach for evaluating user interactions in visualization tools.

When discussing the concepts of risk, safety, and security, people have an intuitive understanding of what these concepts mean, and, to a certain level, this understanding is universal. However, when delving into the real meaning of these concepts, one is likely to fall into semantic debates and ontological discussions. In industrial parks, it is important that (risk) managers from dierent companies, belonging to one and the same park, have the same understanding of the concepts of risk, safety, and security. It is even important that all companies in all industrial parks share a common understanding regarding these issues. As such, this paper explores the similarities and dierences behind the perceptions of these concepts, to come to a fundamental understanding of risk, safety, and security, proposing a semantic and ontological ground for safety and security science, based on an etymological and etiological study of the concepts of risk and safety. The foundation has been induced by the semantics used in the ISO 31000 risk management guidance standard. Hence, this article proposes a coherent, standardized set of concepts and definitions with a focus on the notion “objectives” that can be used as an ontological foundation for safety and security science, linking “objectives” with the concepts of safety, security, risk, performance and also failure and success, theoretically allowing for an increasingly more precise understanding and measurement of (un)safety across the whole range of individuals, sectors and organizations, or even society as a whole.

Waste management is a significant issue that Indonesia must face. The presence of waste will always exist as long as life continues. One of the main challenges in waste management is the proper separation of organic and inorganic waste. As a solution to address this issue, the Ministry of Environment has made efforts to develop Waste Banks. However, it also creates new problems related to manual data collection, lack of monitoring by the community in implementing waste management activities, and a lack of specific reports regarding waste management conducted by officers. We propose the creation of a website using the Agile Development SDLC method. This website involve several steps and incorporate functions for data digitalization and expedited data collection, such as the addition of QR Code functionality, Monthly Reporting Function, and enhanced data security. The method used in this research is Agile Development. The application has been developed with a user-friendly interface and the latest web development technologies to ensure smooth performance. Overall, the web-based application for organic and inorganic waste using Laravel is an important step to promoting sustainable waste management practices.

Next generation sequencing and metabolomics have become very cost and work efficient and are integrated into an ever growing number of life science research projects. Typically, well established software pipelines provide quantitative data informing about gene expression or concentrations of metabolites from the raw data. This data needs to be visualized and further analyzed in order to support scientific hypothesis building and identification of underlying biological patterns. Some tools exist, but require installation or manual programming. We developed “Gene Expression Plotter” (GXP), an RNA-Seq and Metabolomics data visualization and analysis tool entirely running in the user’s web browsers, thus not needing any custom installation, manual programming or upload of confidential data to third party servers. GXP enables the user to generate interactive plots, visually summarize genetic or metabolic responses in scientific sketches (Mapman), carry out cluster and principal component analysis, and conduct overrepresentation analyses. GXP can be used to publish research data along with interactive plots and results of analyses carried out with it. GXP is freely available on GitHub: https://github.com/usadellab/GeneExpressionPlots

“Dark Web” usually describes a new world of e-living space perceived as a place, free from con-straints, in which its main actors generally are criminal, unstable, and mentally disturbed individ-uals. Nowadays, advancements in information and internet technologies have made the Dark Web more accessible in recent decades. The Dark Web is a virtual world component; therefore, as-sessing users’ behavior may be crucial in understanding motivation to use it. This study aims to discuss the behavior patterns of Dark Web users within the theoretical framework of the technol-ogy acceptance model via a structural equation model. The resulting structural equation model showed that considered causal relationships were statistically significant.

Motivation: The visualization of biological data is a fundamental technique that enables researchers to understand and explain biology. Some of these visualizations have become iconic, for instance: tree views for taxonomy, cartoon rendering of 3D protein structures, or tracks to represent features in a gene or protein, for instance in a genome browser. Nightingale provides visualizations in the context of proteins and protein features. Results: Nightingale is a library of re-usable data visualization web components that are currently used by UniProt and InterPro, among other projects. The components can be used to display protein sequence features, variants, interaction data, 3D structure, etc. These components are flexible, allowing users to easily view multiple data sources within the same context, as well as compose these components to create a customized view. Availability: Nightingale examples and documentation are freely available at https://ebi-webcomponents.github.io/nightingale/. It is distributed under the MIT license, and its source code can be found at https://github.com/ebi-webcomponents/nightingale.

Enzymes are in high demand for very diverse biotechnological applications. However, natural biocatalysts often need to be engineered for fine-tuning their properties towards the end applications, such as the activity, selectivity, stability to temperature or co-solvents, and solubility. Computational methods are increasingly used in this task, providing predictions that narrow down the space of possible mutations significantly and can enormously reduce the experimental burden. Many computational tools are available as web-based platforms, making them accessible to non-expert users. These platforms are typically user-friendly, contain walk-throughs, and do not require deep expertise and installations. Here we describe some of the most recent outstanding web-tools for enzyme engineering and formulate future perspectives in this field.

Nowadays, semantic segmentation is increasingly used in exploration by underwater robots, for example in autonomous navigation, so that the robot can recognise the nature and elements of its environment during the mission and act according to this classification to avoid collisions. Other applications can be found in the search for archaeological artefacts, in the inspection of underwater structures or in species monitoring. Therefore, it is necessary to try to improve the performance in these tasks as much as possible. To this end, we compare some methods for improving image quality and for data augmentation and test whether higher performance metrics can be achieved with both strategies. The experiments are performed with the SegNet implementations and the SUIM dataset with 8 common underwater classes to compare the obtained results with the already known ones. The results obtained with both strategies show that they are beneficial and lead to better performance results by achieving a mean IoU of 56% and an increased overall accuracy of 81.8%. The single result shows that there are 5 classes with an IoU value above 60% and only one class with an IoU value below 30%, which is a more reliable result and easier to use in real contexts.

Cross-institutional secondary use of medical data benefits from structured semantic annotation, which, ideally, enables matching and merging semantically related data items from different sources and sites. While numerous medical terminologies and ontologies, as well as some tooling, exist to support such annotation, cross-institutional data usage based on independently annotated datasets is challenging for multiple reasons: the annotation process is resource intensive and requires a combination of medical and technical expertise since it often requires judgment calls to resolve ambiguities resulting from non-uniqueness of potential mappings to various levels of ontological hierarchies and relational and representational systems. Divergent resolution of such ambiguities can inhibit joint cross-institutional data usage based on semantic annotation since data items with related content from different sites will not be identifiable based on their respective annotations if different choices were made without further steps such as ontological inference, which is still an active area of research. We hypothesize that a collaborative approach to semantic annotation of medical data can contribute to more resource efficient and high-quality annotation by utilizing prior annotational choices of others to inform the annotation process, thus both speeding up the annotation itself and fostering a consensus approach to resolving annotational ambiguities by enabling annotators to discover and follow pre-existing annotational choices. Therefore, we have implemented a prototypical Collaborative Annotation Tool (CoAT), evaluated its usability, and present first inter-institutional experiences with this novel approach to promoting practically relevant interoperability driven by use of standardized ontologies.

Spatio-temporal aspects of data lead to critical information. Sensors capture data at all scales continually so it is imperative that useful information be extracted ubiquitously and regularly. Location plays a vital part by helping understand relations between datasets. It is crucial to link developmental works with spatial attributes and current challenge is to create an open platform that manages real-time sensor data and provides critical spatial analytics atop expert domain knowledge provided in the system. That is a two-faced problem where the solution tackles not only data from multiple sources but also runs data management platform, a spatial data infrastructure(SDI) as backbone framework able to harness sensor web(SW). The paper proposes development of such a globally shared open spatial expert system(ES), SmaCiSENS, a first of a kind geo-enabled knowledge based(KB) ES for multiple fields, smarter cities to climate modeling. SmaCiSENS is integration of SW and SDI with domain KB on data and problems, ready to infer solutions. The paper describes an architecture for semantic enablement for SW, SDI; connect interfaces, functions of SDI and SW, and sensor data application program interfaces (APIs) to better manage climate modeling, geohazard, global changes, and other vital areas of attention and action.

Nowadays, one of the greatest challenges in Data Meshes revolves around detecting and creating Data Domains and Data Products for providing the ability to adapt easily and quickly to changing business needs. This requires a disciplined approach to identify, differentiate and prioritize distinct data sources according to their content and diversity. The current paper tackles this highly com-plicated issue and suggests a standardized approach that integrates the concept of Data Blueprints with Data Meshes. In essence, a novel standardization framework is proposed that creates Data Products using a metadata semantic enrichment mechanism, the latter also offering Data Domain readiness and alignment. The approach is demonstrated using real-world data produced by mul-tiple sources in a poultry meat production factory. A set of functional attributes is used to compare qualitatively the proposed approach against existing data structures utilized in storage architectures with quite promising results. Finally, experimentation with different scenarios varying in data product complexity and granularity suggests successful performance.

Interhemispheric and frontoparietal functional connectivity have been reported to increase during explicit information processing. However, it is unclear how and when interhemispheric and frontoparietal functional connectivity interact during explicit semantic processing. Here, we tested the neural coupling hypothesis that explicit semantic processing promotes neural activity in the nondominant right hemispheric areas owing to synchronization with enhanced frontoparietal functional connectivity at later processing stages. We analyzed electroencephalogram data obtained using a semantic priming paradigm, which comprised visual priming and target words successively presented under direct or indirect attention to semantic association. Scalp potential analysis demonstrated that the explicit processing of congruent targets reduced negative event-related potentials, as previously reported. Current source density analysis showed that explicit semantic processing activated the right temporal area during later temporal intervals. Subsequent dynamic functional connectivity and neural coupling analyses revealed that explicit semantic processing increased the correlation between right temporal source activities and frontoparietal functional connectivity in later temporal intervals. These findings indicate that explicit semantic processing increases neural coupling between the interhemispheric and frontoparietal functional connectivity during later processing stages.

Lung cancer is a deadly disease if not diagnosed in its early stages. However, early detection of lung cancer is a challenging task due to the shape and size of its nodules. Radiologists need support from automated tools for precise opinion. Automated detection of the affected lungs nodule is difficult because of the shape similarity among healthy tissues. Over the years, several expert systems have been developed that help radiologists to diagnose lung cancer. In this article, we propose a framework to precisely detect lungs cancer by classifying it between benign and malignant nodules. The framework is tested using the subset of the publicly available at the Lung Image Database Consortium image collection (LIDC-IDRI). Multiple techniques including filtering and noise removing are applied for pre-processing. Subsequently, the OTSU and the semantic segmentation are used to accurately detect the unhealthy lungs nodules. In total, 13 nodules features were extracted using Principal Components Analysis (PCA) algorithm. Four optimal features are selected based on the classification performance. In the classification phase, 9 different classifiers are used along with two types of validation schemes i.e. train test holdout validation with 70-30 data split and 10 fold cross-validation. Our experiments show that the proposed system provides 99.23\% accuracy using logic boost classifier.

The Semantic-Enhanced Tree-LSTM (SeT-LSTM) network, a novel advancement in the realm of linguistic modeling, marks a significant step forward from traditional Tree-based Long Short Term Memory (LSTM) networks. By intricately weaving in the nuances of typed grammatical dependencies, SeT-LSTMs offer a more nuanced understanding of language semantics. Traditional models often overlook the semantic shift caused by variations in word or phrase roles, a gap this paper aims to fill by focusing on the types of grammatical connections, or typed dependencies, within sentences. Our proposed architecture, dubbed the Semantic Relationship-Guided LSTM (SRG-LSTM), leverages a control mechanism to model the interplay between sequence elements. Additionally, we present a novel Tree-LSTM variant, the Semantic Dependency Tree-LSTM (SDT-LSTM), which integrates dependency parse structures with dependency types for more robust sentence embedding. The SDT-LSTM demonstrates superior performance in Semantic Relatedness Scoring and Sentiment Analysis compared to its predecessors. Qualitatively, it shows resilience to changes in sentence voice and heightened sensitivity to nominal alterations, aligning well with human intuition. This research underlines the pivotal role of grammatical relationships in sentence understanding and paves the way for further exploration in this domain.

As we all know, many more dynamic objects appear almost continuously in real world, which are immensely able to impair the performance of the majority of vision-based SLAM systems that are based on the static-world assumption. In order to improve the robustness and accuracy of visual SLAM in the high dynamic environment, a real-time and robust stereo SLAM system for dynamic scenes was proposed. To weaken influences of dynamic content, the moving object detection method was put forward in our visual odometry, then the semantic segmentation network was combined into our stereo SLAM to extract pixel-level contours of dynamic objects. Then influences of dynamic objects were extremely weaken and the performance of our system was increased markedly in dynamic, complex and crowed city spaces. Experiment with both on KITTI Odometry dataset and in a real-life scene, the results show that our method can dramatically decrease the tracking error or drift, improve the robustness and stability of our stereo SLAM in high dynamic outdoor scenarios.

Embedding representation models characterize each word as a vector of numbers with a fixed length. These models have been used in tasks involving text classification, such as recommen- dation and question-answer systems. Semantic relationships are words with a relationship between them providing a complete idea to a text. Therefore, it is hypothesized that an embedding model involving semantic relationships will provide better performance for tasks that use them. This paper presents three embedding models based on semantic relations extracted fromWikipedia to classify texts. The synonym, hyponym, and hyperonym semantic relationships were the ones considered in this work since previous experiments have shown that they are the ones that provide the most semantic knowledge. Lexical-syntactic patterns present in the literature were implemented and subsequently applied to the Wikipedia corpus to obtain the semantic relationships present in it. Several semantic relationships are used in different models: synonymy, hyponym-hyperonym, and a combination of the first two. A convolutional neural network was trained for text classification to evaluate the performance of each model. The results obtained were evaluated with the metrics of precision, accuracy, recall, and F1-measure. The best values obtained with the second model were accuracy of 0.79 for the 20-Newsgroup corpus. F1-measure and recall of 0.87 respectively for the Reuters corpus.

The paper describes model of human affect based on quantum theory of semantics. The model considers emotion as subjective representation of behavioral context relative to a basis binary choice, organized by cyclical process structure and an orthogonal evaluation axis. The resulting spherical space, generalizing well-known circumplex models, accommodates basic emotions in specific angular domains. Predicted process-semantic structure of affect is observed in the word2vec data, as well as in the previously obtained spaces of emotion concepts. The established quantum-theoretic structure of affective space connects emotion science with quantum models of cognition and behavior, opening perspective for synergetic progress in these fields.

The paper describes an algorithm for semantic representation of behavioral contexts relative to a dichotomic decision alternative. The contexts are represented as quantum qubit states in two-dimensional Hilbert space visualized as points on the Bloch sphere. The azimuthal coordinate of this sphere functions as a one-dimensional semantic space in which the contexts are accommodated according to their subjective relevance to the considered uncertainty. The contexts are processed in triples defined by knowledge of a subject about a binary situational factor. The obtained triads of context representations function as stable cognitive structure at the same time allowing a subject to model probabilistically-variative behavior. The developed algorithm illustrates an approach for quantitative subjectively-semantic modeling of behavior based on conceptual and mathematical apparatus of quantum theory.

Clinically, knowing the number of red blood cells (RBCs) and white blood cells (WBCs) helps doctors to make the better decision on accurate diagnosis of numerous diseases. The manual cell counting is a very time-consuming and expensive process, and it depends on the experience of specialists. Therefore, a completely automatic method supporting cell counting is a viable solution for clinical laboratories. This paper proposes a novel blood cell counting procedure to address this challenge. The proposed method adopts SegNet - a deep learning semantic segmentation to simultaneously segment RBCs and WBCs. The global accuracy of the segmentation of WBCs, RBCs, and the background of peripheral blood smear images obtains 89% when segment WBCs and RBCs from the background of blood smear images. Moreover, an effective solution to separate grouped or overlapping cells and cell count is presented using Euclidean distance transform, local maxima, and connected component labeling. The counting result of the proposed procedure achieves an accuracy of 93.3% for red blood cell count using dataset 1 and 97.38% for white blood cell count using dataset 2.

Fast 3D reconstruction with semantic information on road scenes is of great requirements for autonomous navigation. It involves issues of geometry and appearance in the field of computer vision. In this work, we propose a method of fast 3D semantic mapping based on the monocular vision. At present, due to the inexpensive price and easy installation, monocular cameras are widely equipped on recent vehicles for the advanced driver assistance and it is possible to acquire semantic information and 3D map. The monocular visual sequence is used to estimate the camera pose, calculate the depth, predict the semantic segmentation, and finally realize the 3D semantic mapping by combination of the techniques of localization, mapping and scene parsing. Our method recovers the 3D semantic mapping by incrementally transferring 2D semantic information to 3D point cloud. And a global optimization is explored to improve the accuracy of the semantic mapping in light of the spatial consistency. In our framework, there is no need to make semantic inference on each frame of the sequence, since the mesh data with semantic information is corresponding to sparse reference frames. It saves amounts of the computational cost and allows our mapping system to perform online. We evaluate the system on naturalistic road scenes, e.g., KITTI and observe a significant speed-up in the inference stage by labeling on the mesh.

The advancement of mass spectrometry technologies has revolutionised plant metabolomics research by enabling the acquisition of raw metabolomics data. However, the identification, analysis, and visualisation of these data require specialised tools. Existing solutions lack a dedicated plant-specific metabolite database and pose usability challenges. To address these limitations, we developed PlantMetSuite, a web-based tool for comprehensive metabolomics analysis and visualisation. PlantMetSuite encompasses interactive bioinformatics tools and databases specifically tailored for plant metabolomics data, facilitating upstream-to-downstream analysis in metabolomics and supporting integrative multi-omics investigations. PlantMetSuite can be accessed directly through a user's browser without the need for installation or programming skills. The tool is freely available at https://plantmetsuite.verygenome.com/ and will undergo regular updates and expansions to incorporate additional libraries and newly published metabolomics analysis methods. The tool's significance lies in empowering researchers with an accessible and customisable platform for unlocking plant metabolomics insights.

Objective: This paper aims to build an Effort Estimation Model for design, coding and testing Web Applications Based Fuzzy and Practical Models, which will help in optimizing the efforts in software development. Methods/Analysis: Soft computing approach is adopted and applied in the effort estimation and then compared with practical efforts in the development process with interpreting the historical data available for the existing functionalities. Findings: The effort estimation model presented in this paper focuses on the first level estimates published by Project Managers and the second level estimates presented by Project Leaders or Developers for any new requirement or enhancement for a web application built on 3-tier architecture using Microsoft technologies. The model considers the classification of each task as either Low or Medium or High complexity. These tasks pertain to the lowest level parts in bottom-up estimation. Efforts are estimated for designing, coding and unit testing of these tasks and the efforts are summed up to get the effort estimation for the higher level which is a feature to be implemented. Novelty/Improvement: The paper also discusses about the application of the effort estimation model by taking a new requirement as a case study. The first level estimates calculated using the effort estimation model has a variance of about 25% when compared with the actual effort. This variance is very much acceptable considering the fact that the first level estimates can be tolerable up to 35%. The proposed effort estimation tool would help the project managers to efficiently control the project, manage the resources effectively, and improve the software development process and also trade off analyses among schedule, performance, quality and functionality. Fuzzy logic is used to verify the claims made in efforts estimation. It is proposed a new relation between the number of data and efforts value membership for actual data. And converts it into crisp value in the range [0…1] which helps to classify the complexity of the task and subtask in the design, coding and testing phases.

Electric energy has become essential nowadays not only for the daily life of each of us but also for the economy of different countries. The dissemination of geographic information plays an important role in national development as it facilitates communication between managers, investors, and consumers in this sector. Since the management of electricity network data was previously done in Tunisia based on paper maps and plans, the purpose of this article is to present a case of planning based on GIS, Web, and 3D Web GIS, which would have significant positive consequences on this sector from a technical and financial sides with an improvement in customer satisfaction and the creation of an intelligent electricity network which will be a real decision-making tool. This work draws up an inventory of the network MV (Medium Voltage)/LV (Low Voltage) of the region of Medjez El Bab which routes electricity to the big centers of consumption with access to MV/LV subscribers. The analysis of the network's impedance allowed carrying out different scenarios to optimize performance and obtain more realistic routes. Many thematic maps were produced as part of this project (Slope map, Land use map, map of the MV voltage domains, map of the MV/LV transformer stations power, etc.). A three-dimensional virtual city has been developed to visualize the graphical and attribute data for the study area. A Web and 3D Web GIS applications that allows the publication of the interactive maps on the Web as well as the database information have been developed to offer users the possibility of consulting the produced products by internet. Finally, a website related to the study was developed.

A new requirement for the manufacturing companies in Industry 4.0 is to be flexible with respect to changes in demands, requiring them to react rapidly and efficiently on the production capacities. Together with the trend to use Service-Oriented Architectures (SOA), this requirement induces a need for agile collaboration among supply chain partners, but also between different divisions or branches of the same company. In order to address this collaboration challenge, we~propose a novel pragmatic approach for the process analysis, implementation and execution. This~is achieved through sets of semantic annotations of business process models encoded into BPMN 2.0 extensions. Building blocks for such manufacturing processes are the individual available services, which are also semantically annotated according to the Everything-as-a-Service (XaaS) principles and stored into a common marketplace. The optimization of such manufacturing processes combines pattern-based semantic composition of services with their non-functional aspects. This is achieved by means of Quality-of-Service (QoS)-based Constraint Optimization Problem (COP) solving, resulting in an automatic implementation of service-based manufacturing processes. The produced solution is mapped back to the BPMN 2.0 standard formalism by means of the introduced extension elements, fully detailing the enactable optimal process service plan produced. This approach allows enacting a process instance, using just-in-time service leasing, allocation of resources and dynamic replanning in the case of failures. This proposition provides the best compromise between external visibility, control and flexibility. In this way, it provides an optimal approach for business process models' implementation, with a full service-oriented taste, by implementing user-defined QoS metrics, just-in-time execution and basic dynamic repairing capabilities. This paper presents the described approach and the technical architecture and depicts one initial industrial application in the manufacturing domain of aluminum forging for bicycle hull body forming, where the advantages stemming from the main capabilities of this approach are sketched.

Fibromyalgia is a complex and often misunderstood chronic pain disorder. It is characterized by widespread musculoskeletal pain, fatigue, and heightened sensitivity, and has evolved in diag-nostic criteria and understanding over the years. Initially met with skepticism, fibromyalgia is now recognized as a global health concern affecting millions of people, with a prevalence trans-cending demographic boundaries. The clinical features and diagnosis of fibromyalgia encompass a range of symptoms beyond pain, including sleep disturbances and cognitive difficulties. The study emphasizes the importance of a comprehensive evaluation for accurate diagnosis, considering the shift from tender point reliance to a more holistic approach. Etiology and pathophysiology involve genetic predisposition, neurotransmitter dysregulation, central sensitization, and immune system involvement. Risk factors such as gender, age, family history, and comorbid conditions contribute to susceptibility. The impact on quality of life is profound, affecting physical and social aspects, often accompanied by mood disorders. Management approaches include pharmacological interventions, non-pharmacological therapies, lifestyle modifications, and alternative treatments. The study also delves into emerging research, exploring advances in neurobiological understanding, brain imaging, genetic markers, glutamate modulation, cannabinoids, gut microbiome, and digital health tools for fibromyalgia manage-ment. Overall, this study provides a nuanced and up-to-date overview of the complexities sur-rounding fibromyalgia, aiming to enhance understanding and support for individuals grappling with this challenging condition.

The development of storage standards for databases of different nature and origin makes it possible to aggregate and interact with different data sources in order to obtain and show complex and thematic information to the end user. This article aims to analyze some possibilities opened up by new applications and hypothesize their possible developments. With this work, using the currently available Web technologies, we would like to verify the potential of the use of Linked Open Data in the world of WebGIS and illustrate an application that allows the user to interact with Linked Open Data through their representation on a map. Italy has a artistic and cultural heritage unique in the world and the italian Ministry of Cultural Heritage and Activities and Tourism has created and made freely available a dataset in Linked Open Data format that represents it. With the aim of enhancing and making this heritage more usable, the National Research Council (CNR) has created an application that presents this heritage via WebGIS on a map. Following criteria definable by the user, such as the duration, the subject of interest and the style of the trip, tourist itineraries are created through the places that host this heritage. New possibilities open up where the tools made available by the Web can be used together, according to pre-established sequences, to create completely new applications. This can be compared to the use of words, all known in themselves, which, according to pre-established sequences, allow us to create ever new texts.

The conditions for safe Internet access and the development of skills enabling full participation in online environments are recognized in the Council of Europe’s strategy for child rights, from 2022. The guarantee of this right has implications for experiences inside and outside the school context. Therefore, this article aims to compare the perceptions of students from different educational levels, who participated in a digital storytelling workshop, regarding online safety, searching habits, and digital competences. Data were collected through a questionnaire survey completed by 84 Portuguese students from elementary and secondary schools. A non-parametric multivariate analysis of variance was used to identify differences as children advance across educational stages. The results revealed that secondary students tend to spend more time online, and demonstrated more advanced search skills. Interestingly, the youngest children exhibited higher competences in creating games and practicing safety measures regarding online postings. These findings emphasize the importance of schools, in a joint action with the educational community, including parents, teachers and students, developing a coordinated and vertically integrated approach to digital education that considers the children's current knowledge, attitudes, and skills as a starting point for pedagogical intervention.

The Dark Web is known as a place triggering a variety of criminal activities. Anonymization techniques enable illegal operations, leading to the loss of confidential information and its further use as bait, a trade product or even a crime tool. Despite technical progress, there is still not enough awareness of the Dark Web and its secret activity. In this study, we introduced the Dark Web Enhanced Analysis (DWEA), in order to analyze and gather information about the content accessed on the Dark Net based on data characteristics. The research was performed to identify how the Dark Web has been influenced by recent global events, such as the COVID-19 epidemic. The research included the usage of a crawler, which scans the network and collects data for further analysis with machine learning. The result of this work determines the influence of the COVID-19 epidemic on the Dark Net.

This paper proposes a solution for the problem of mobile robot navigation and trajectory interpolation in dynamic environments with large scenes. The solution combines a semantic laser SLAM system that utilizes deep learning and a trajectory interpolation algorithm. The paper first introduces some open-source laser SLAM algorithms and then elaborates in detail on the general framework of the SLAM system used in this paper. Secondly, the concept of voxel is introduced into the occupation probability map to enhance the ability of local voxel maps to represent dynamic objects. Then, in this paper, we propose a PointNet++ point cloud semantic segmentation network combined with deep learning algorithms to extract deep features of dynamic point clouds in large scenes and output semantic information of points on static objects. A descriptor of the global environment is generated based on its semantic information. Closed-loop completion of global map optimization is performed to reduce cumulative error. Finally, T-trajectory interpolation is utilized to ensure the motion performance of the robot and improve the smooth stability of the robot trajectory. The experimental results indicate that the combination of the semantic laser SLAM system with deep learning and the trajectory interpolation algorithm proposed in this paper yields better graph building and loop closure effects in large scenes at SIASUN large scene campus. The use of T-trajectory interpolation ensures vibration-free and stable transitions between target points.

Symbolic semantic understanding of staff images is an important part in music information retrieval. Due to the complex composition of staff symbols and the strong semantic correlation between symbol spaces, it is difficult to understand the pitch and duration of each note during performances. In this paper, we design a semantic understanding system for optical staff symbols. The system uses the YOLOv5 to implement optical staff’s low-level semantic understanding stage, which understands the pitch and duration in natural scales and other symbols that affect the pitch and duration. The proposed note encoding reconstruction algorithm is used to implement high-level semantic understanding stage. Such algorithm understands the logical, spatial, and temporal relationships between natural scales and other symbols based on music theory, and outputs digital codes for the pitch and duration of main notes during performances. The model is trained with a self-constructed SUSN dataset. Experimental results of YOLOv5 show that the precision is 0.989 and the recall is 0.972. For the system, the error rate is 0.031 and the omission rate is 0.021. The paper concludes by analysing the causes of semantic understanding errors and offers recommendations for further research. The results of this paper provide a method for multimodal music artificial intelligence applications such as notation recognition through listening, intelligent score flipping and automatic performance.

Semantic segmentation is a very popular topic in modern computer vision and it has applications to many fields. Researchers proposed a variety of architectures over time, but the most common ones exploit an encoder-decoder structure that aims to capture the semantics of the image and it low level features. The encoder uses convolutional layers, in general with a stride larger than one, to extract the features, while the decoder recreates the image by upsampling an using skip connections with the first layers. In this work, we use DeepLab as architecture to test the effectiveness of creating an ensemble of networks by randomly changing the activation functions inside the network multiple times. We also use different backbone networks in our DeepLab to validate our findings. We manage to reach a dice coefficient of 0.888, and a mean Intersection over Union (mIoU) of 0.825, in the competitive Kvasir-SEG dataset. Results in skin detection also confirm the performance of the proposed ensemble, which is ranked first with respect to other state-of-the-art approaches (including HardNet) in a large set of testing datasets. The developed code will be available at https://github.com/LorisNanni.

A statistical analysis of semantic memory should reflect the complex, multifactorial structure of the relations among its items. Still, a dominant paradigm in the study of semantic memory has been the idea that the mental representation of concepts is structured along a simple branching tree spanned by superordinate and subordinate categories. We propose a generative model of item representation with correlations that overcomes the limitations of a tree structure. The items are generated through "factors" that represent semantic features or real-world attributes. The correlation between items has its source in the extent to which items share such factors and the strength of such factors: if many factors are balanced, correlations are overall low; whereas if a few factors dominate, they become strong. Our model allows for correlations that are neither trivial nor hierarchical, but may reproduce the general spectrum of correlations present in a data-set of nouns. We provide an estimate of the number of concepts that can be stored and retrieved by a large-scale cortical network, the Potts network, which is perhaps approximately 10⁷ with human cortical parameters. When this storage capacity is exceeded, however, retrieval fails completely only for balanced factors; above a critical degree of imbalance, a phase transition leads to a regime where the network still extracts considerable information about the cued item, even if not recovering its detailed representation: partial categorization seems to emerge spontaneously as a consequence of the dominance of particular factors, rather than being imposed ad hoc. We argue this to be a relevant model of semantic memory resilience in Tulving’s remember/know paradigms.

The ubiquity of mobile sensors (such as GPS, accelerometer and gyroscope) together with increasing computational power have enabled an easier access to contextual information, which proved its value in next generation of the recommender applications. The importance of contextual information has been recognized by researchers in many disciplines, such as ubiquitous and mobile computing, to filter the query results and provide recommendations based on different user status. A context-aware recommendation system (CoARS) provides a personalized service to each individual user, driven by his or her particular needs and interests at any location and anytime. Therefore, a contextual recommendation system changes in real time as a user’s circumstances changes. CoARS is one of the major applications that has been refined over the years due to the evolving geospatial techniques and big data management practices. In this paper, a CoARS is designed and implemented to combine the context information from smartphones’ sensors and user preferences to improve efficiency and usability of the recommendation. The proposed approach combines user’s context information (such as location, time, and transportation mode), personalized preferences (using individuals past behavior), and item-based recommendations (such as item’s ranking and type) to personally filter the item list. The context-aware methodology is based on preprocessing and filtering of raw data, context extraction and context reasoning. This study examined the application of such a system in recommending a suitable restaurant using both web-based and android platforms. The implemented system uses CoARS techniques to provide beneficial and accurate recommendations to the users. The capabilities of the system is evaluated successfully with recommendation experiment and usability test.

Background: Producing, distributing or discussing child sexual abuse materials (CSAM) is often committed through the dark web in order to remain hidden from search engines and regular users. Additionally, on the dark web, the CSAM creators employ various techniques to avoid detection and conceal their activities. The large volume of CSAM on the dark web presents a global social problem and poses a significant challenge for helplines, hotlines and law enforcement agencies. Objective: Identifying CSAM discussions on the dark web and uncovering associated metadata insights into characteristics, behaviours and motivation of CSAM creators. Participants and Setting: We have conducted an analysis of more than 353,000 posts generated by 35,400 distinct users and written in 118 different languages across eight dark web forums in 2022. Out of these, approximately 221,000 posts were written in English and contributed by around 29,500 unique users. Method: We propose a CSAM detection intelligence system. The system uses a manually labelled dataset to train, evaluate and select an efficient CSAM classification model. Once we identify CSAM creators and victims through CSAM posts on the dark web, we proceed to analyze, visualize and uncover information concerning the behaviors of CSAM creators and victims. Result: The CSAM classifier, based on Support Vector Machine model, exhibited good performance, achieving the highest precision of 92.3\%, accuracy of 87.6\% and recall of 84.2\%. Its prediction time is fast, taking only 0.3 milliseconds to process a single post on our laptop. While, the Naive Bayes combination is the best in term of recall, achieving 89\%, and its prediction time is just 0.1 microseconds per post. Across the eight forums in 2022, our Support Vector Machine model detected around 63,000 English CSAM posts and identified near 10,500 English CSAM creators. The analysis of metadata of CSAM posts revealed meaningful information about CSAM creators and their victims, such as: (1) the ages and nationalities of the victims typically mentioned by CSAM creators, (2) forum topics where the CSAM creators assign their posts, and (3) online platforms preferred by CSAM creators for sharing or uploading CSAM. Conclusion: Our CSAM detection system exhibits high performance in precision, recall, and accuracy in real-time when classifying CSAM and non-CSAM posts. Additionally, it can extract and visualize valuable and unique insights about CSAM creators and victims by employing advanced statistical methods. These insights prove beneficial to our partners, i.e. national hotlines and child agencies.

Expressive clinical terminologies are of utmost importance for achieving an semantically interoperable data exchange and reuse in healthcare. SNOMED CT, widely respected as the most comprehensive terminology in medicine, provides formal concept definitions based on description logic which not only allows for advanced querying of SNOMED CT-coded data but also for flexibly augmenting its 350,000 concepts. This ability for postcoordination largely increases the expressivity of the terminology but correlates with an intrinsic complexity. Complicated by the current lack of tooling support, postcoordination is widely either ignored or applied in an error-prone way. To help facilitate the adoption of postcoordination, we implemented a web application that guides through the creation of Postcoordinated expressions (PCEs) while ensuring the adherence to syntactic and semantic constraints. Our approach was largely facilitated by making use of the extensive SNOMED CT specifications as well as advanced HL7 FHIR Terminology Services. Qualitative evaluations confirmed the usability of the developed application and the correctness of PCEs created with it.

Sustainable bio-economy is considered to be one of the most promising routes towards the transition to a circular and climate-neutral economy. The valorization industry of bio-waste and agri-food by-products represents a key player in bioeconomy. In this article the design and the development of a web platform aiming at promoting synergies enabling the agricultural waste valorization is presented. The platform consists of: (i) the AgriPLaCE Waste Management Database which provides the users with an extended list of potential utilization measures for a variety of agricultural waste streams (ii) the AgriPLaCE Synergies Tool which aims to create synergies between different actors involved in the value chain from agricultural waste production to waste treatment and new valuable products’ exploitation. At the initial stage, the conceptual design of both tools took place by conducting an in-depth analysis for the user needs and services alongside the system architecture. Following, the AgriPLaCE platform development stage took place, with the implementation of all the necessary subsystems. The results showed that multiple potential collaborations can arise by the use of the AgriPLaCE platform, while users can also deepen on alternative and emerging treatment technologies and valuable products that can derive from a wide range of agricultural waste streams.

Aiming at the problem that dynamic targets in indoor environments lead to low accuracy and large errors in the localization and position estimation of visual SLAM systems and the inability to build maps containing semantic information, a semantic visual SLAM algorithm based on the semantic segmentation network DeepLabV3+ and LK optical flow is proposed based on ORB-SLAM2 system. First, the dynamic target feature points are detected and rejected based on the lightweight semantic segmentation network DeepLabV3+ and LK optical flow method. second, the static environment occluded by the dynamic target is repaired using the time-weighted multi-frame fusion background repair technique. Lastly, the filtered static feature points are used for feature matching and position calculation. Meanwhile, the semantic labeling information of static objects obtained based on the lightweight semantic segmentation network DeepLabV3+ is fused with the static environment information after background repair to generate dense point cloud maps containing semantic information, and the semantic dense point cloud maps are transformed into semantic octree maps using the octree spatial segmentation data structure. The localization accuracy of the visual SLAM system and the construction of the semantic maps are verified using the TUM RGB-D widely used dataset and real scene data, respectively. The experimental results show that the proposed semantic visual SLAM algorithm can effectively reduce the influence of dynamic targets on the system, has a higher localization accuracy, and compared with other advanced algorithms, such as DynaSLAM, has the highest performance in indoor dynamic environments while considering both localization accuracy and real-time performance. In addition, semantic maps can be constructed so that the robot can better understand and adapt to the indoor dynamic environment.

Probabilistic latent semantic analysis is a statistical technique developed for information retrieval and spanned many fields. It yields intuitive and solid results. However, the rigidity of the assumptions and the iterative nature derived from the Expectation-maximization algorithm generate several problems, dividing detractors and enthusiasts. In this manuscript, we first describe the Probabilistic latent semantic analysis. After, we discuss reformulations that attempt to solve these problems. We pay special attention to the works relating Probabilistic latent semantic analysis and the Singular value decomposition Theorem. Also, Probabilistic latent semantic analysis can be the basis for other techniques, such as kernelization or probabilistic transfer learning, and those that extend the descriptive character of the Principal component analysis to an inferential tool and open a window of opportunities.

The deaf-mute and individuals with hearing disabilities who communicate through sign language should not be overlooked or excluded. It is a social responsibility to strive for an inclusive world without inequality. Advances in technology offer various solutions to address the communication gap, one of which is the use of computer-based sign language recognition systems. This project proposes a Malaysian Sign Language recognition model utilizing MobileNetV1, a convolutional neural network-based algorithm, to interpret sign language in real-time settings through a website application using Streamlit. The outcome of the model test shows that it has achieved a recognition rate of 96 percent. Although the proposed model has demonstrated the ability to recognize sign language, it is suggested that further improvement can be achieved by enhancing the quality and diversity of the dataset. The implementation of a more comprehensive dataset would lead to improved performance and increased accuracy of the model. This project underscores the importance of considering and accommodating the needs of the deaf-mute and individuals with hearing disabilities and highlights the potential of technology to bridge communication barriers and achieve a more inclusive society.

Choosing a college major is a crucial issue for twelfth-grade students. Many students feel confused and influenced by their surroundings when making this decision. This can have negative consequences if it does not align with the students interests and talents. This research proposes a web application as a solution, providing introductory information for each major. We employed a design and developement approach to create an application that assists prospective students in identifying majors based on their interests. The primary objective is to alleviate students discomfort during their studies, which often leads to decreased performance. With this application, students can avoid selecting majors solely due to peer pressure or parental coercion.

Advances in technology and information systems have had a major impact on people's lives and activities. Smartphone use plays an important role in many areas, including housing governance. One of the problems faced by housing administrators is administrative management, which is still manual. To overcome this, it is proposed to use a web-based application. A literature study was conducted to support the development of this application. Data collection through interviews and observations was carried out to obtain system requirements. The Agile Scrum method is used in making applications. These applications have non-functional requirements such as security, availability, performance, usability, and compatibility. Implementation using PHP and MySQL. Testing is done to ensure the application functions properly. This application allows administrators to manage citizen data, payment of contributions, billing, and print financial reports. Residents can upload proof of transfer and print proof of payment. It is hoped that this application will facilitate housing administration, improve the quality of life in the community, and create a better housing environment.

The internet can broadly be divided into three parts: surface, deep and dark among which the latter offers anonymity to its users and hosts [1]. Deep Web refers to an encrypted network that is not detected on search engine like Google etc. Users must use Tor to visit sites on the dark web [2]. Ninety six percent of the web is considered as deep web because it is hidden. It is like an iceberg, in that, people can just see a small portion above the surface, while the largest part is hidden under the sea [3, 4, and 5]. Basic methods of graph theory and data mining, that deals with social networks analysis can be comprehensively used to understand and learn Deep Web and detect cyber threats [6]. Since the internet is rapidly evolving and it is nearly impossible to censor the deep web, there is a need to develop standard mechanism and tools to monitor it. In this proposed study, our focus will be to develop standard research mechanism to understand the Deep Web which will support the researchers, academicians and law enforcement agencies to strengthen the social stability and ensure peace locally & globally.

Semantic Web technologies are being increasingly adopted by the geospatial community during last decade through the utilization of open standards for expressing and serving geospatial data. This was also dramatically assisted by an ever increasing access and usage of geographic mapping and location-based services via smart devices in people’s daily activities. In this paper we explore the developmental framework of a pure Javascript client-side GIS platform exclusively based on invocable geospatial Web services. We also extend Javascript utilization on the server side by deploying a node server acting as a bridge between open source WPS libraries and popular geoprocessing engines. The vehicle for such an exploration is a cross platform Web browser capable of interpreting Javascript commands to achieve interaction with geospatial providers. The tool is a generic Web interface providing capabilities of acquiring spatial datasets, composing layouts and applying geospatial processes. In an ideal form the end-user will have to identify those services, which satisfy a geo-related need and put them in the appropriate row. The final output may act as a potential collector of freely available geospatial web services. Its server-side components may exploit geospatial processing suppliers composing that way a light-weight fully transparent open Web GIS platform.

The mental workload induced by a Web page is essential for improving the user’s browsing experience. However, continuously assessing the mental workload during a browsing task is challenging. In order to face this issue, this paper leverages the correlation between stimuli and physiological responses, which are measured with high-frequency, non-invasive psychophysiological sensors during very short span windows. An experiment was conducted to identify levels of mental workload through the analysis of pupil dilation measured by an eye-tracking sensor. In addition, a method was developed to classify real-time mental workload by appropriately combining different signals (electrodermal activity (EDA), electrocardiogram, photoplethysmography (PPG), electroencephalogram (EEG), temperature and eye gaze) obtained with non-invasive psychophysiological sensors. The results show that the Web browsing task involves on average four levels of mental workload. Also, by combining EEG with the PPG and EDA, the accuracy of the classification reaches 95.73 %.

Over the past decade, Knowledge bases (KB) have been increasingly utilized to complete and enrich the representation of queries and documents in order to improve the document retrieval task. Although many approaches have used KB for such purpose, understanding how effectively lev-erage entity-based representation still needs to be resolved. This paper proposes a Purely Enti-ty-based Semantic Search Approach for Information Retrieval (PESS4IR) as a novel solution. The approach includes (i) its own entity linking method, (ii) an inverted indexing method, and for document retrieval and ranking, (iii) an appropriate ranking method is designed to take advantage of all the strengths of the approach. We report the findings on the performance of our approach tested by queries annotated by two known entity linking tools, REL and DBpedia-Spotlight. The experiments are performed on the standard TREC 2004 Robust and MSMARCO collections. By using the REL method, for queries whose all terms are annotated and whose average annotation scores are greater than or equal to 0.75, our approach achieves the maximum nDCG@5 score (1.000). Thus, using our approach with any other document retrieval method would be an added value, unless that method achieves the maximum nDCG@5 score for those highly annotated queries.

Topic discovery is finding the main idea of large amounts of textual data. It indicates the recurring topics in the documents, allowing an overview of the texts. Current topic discovery models receive the texts, with or without pre-processing of Natural Language Processing. The processing consists of stopwords removal, text cleaning and normalization (lowercase conversion). A topic discovery model that receives texts with or without processing generates general topics since the input data is many uncategorized texts. The general topics do not offer a detailed overview of the input texts, and manual text categorization is a time-consuming and tedious task. Accordingly, it is necessary to integrate an automatic text classification task in the topic discovery process to obtain specific topics with their top words that contain relevant relationships based on belonging to a class. Text classification performs a word analysis that makes up a document to decide what class or category is being identified; then, integrating the text classification before a topic discovery process will provide latent topics depicted by top words with a high coherence in each topic based on the previously obtained classes. Therefore, this paper exposes a approach that integrates text classification into topic discovery from large amounts of English textual data, such as 20-Newsgroup and Reuters corpora. The text classification is accomplished with a Convolutional Neural Network(CNN) incorporating three embedding models based on semantic relationships. The topic discovery over categorized texts is realized with Latent Dirichlet Analysis(LDA), Probabilistic Latent Semantic Analysis(PLSA), and Latent Semantic Analysis(LSA) algorithms. An evaluation process was performed based on the normalized topic coherence metric. The 20-Newsgroup corpus was classified, and twenty topics with ten top words were discovered for each class, obtaining 0.1723 normalized topic coherence when applying LDA, 0.1622 with LSA, and 0.1716 with PLSA. The Reuters corpus was also classified, obtaining 0.1441 normalized topic coherence when applying the LDA algorithm to obtain 20 topics for each class.

With the rapid development of Internet and the widely usage of smart devices, massive multimedia data are generated, collected, stored and shared on the Internet. This trend makes cross-modal retrieval problem become a hot issue in this years. Many existing works pay attentions on correlation learning to generate a common subspace for cross-modal correlation measurement, and others uses adversarial learning technique to abate the heterogeneity of multi-modal data. However, very few works combine correlation learning and adversarial learning to bridge the inter-modal semantic gap and diminish cross-modal heterogeneity. This paper propose a novel cross-modal retrieval method, named ALSCOR, which is an end-to-end framework to integrate cross-modal representation learning, correlation learning and adversarial. CCA model, accompanied by two representation model, VisNet and TxtNet is proposed to capture non-linear correlation. Beside, intra-modal classifier and modality classifier are used to learn intra-modal discrimination and minimize the inter-modal heterogeneity. Comprehensive experiments are conducted on three benchmark datasets. The results demonstrate that the proposed ALSCOR has better performance than the state-of-the-arts.

The sentiment of a word varies based on its context of usage: the words used around it and the part-of-speech it is used as. This paper proposes a technique to suggest the sentiment of a word by combining its part-of-speech and the semantic similarities of its co-occurrences with both context-specific and pre-trained embeddings to achieve powerful and fast results. A study was conducted across domains and sub-domains to measure variance of sentiment by switching domains and switching context within the same domain. Re-scoring a commonly used polarity lexicon showed that 10% of words changed scores while switching domains and 8% changed scores within domains while switching context. Part of Speech analysis on 65,353 commonly used sentiment lexicons showed that 81% of sentiment bearing (non-neutral) lexicons were of the tags NN (Common Noun), JJ (Adjective) or NNS (Proper Noun).

Flood events are becoming more severe, causing significant problems to human communities, including physical, psychological, and material damage. For both flood forecasting in emergency response situations and flood mapping, georeferencing and data curation are paramount in the context of prevention or preparedness. Hence, data display, data management, and articulation with numerical simulation results must occur on GIS platforms. Our research is motivated by recent advances in Web and GIS technologies, social sensing and high-performance computing, and an envisaged wider availability of remote sensing data. This paper presents and discusses an innovative Web GIS platform named "RiverCure Portal" or "RCP" for short. This platform combines observations and hydrodynamic modelling tools to support various stages of the flood risk management cycle, including operational response, emergency preparedness, and risk assessment. RCP is a multi-organisation, multi-context digital platform with flexible configuration features to define and support multiple sensor types and modelling options, satisfying the various needs of different organisations and stakeholders. In addition, this paper discusses the RiverCure Approach, which encompasses the following tasks directly supported by the RCP platform: defining the context and involved geometries, associating sensors to the context, pre-processing and generating the context mesh, defining the simulation event, running the simulation event, and analysing the results from the simulation event. Thus, the RCP streamlines and simplifies data analysis and simulation procedures to meet decision-makers' needs. The novelties discussed in this paper include the design and discussion of a Web GIS platform that allows (i) to manage flood data and results of simulations at several contextual levels by different stakeholders such as domain experts, decision-makers, researchers, or students; (ii) to process and curate sensed data obtained from physical and social sensors; and (iii) update the state and values of the parameters of simulation tools through continuous data assimilation techniques for forecasting purposes. Finally, this paper supports the explanation and discussion with a running example, "Águeda 2016 flood" event, which dataset is publicly available for further study and experimentation.

In systems biology, biological phenomena are often modeled by ODE and distributed in the de facto standard file format SBML. The primary analyses performed with such models are dynamic simulation, steady-state analysis, and parameter estimation. These methodologies are mathematically formalized, and libraries for such analyses have been published. Several tools exist to create, simulate, or visualize models encoded in SBML. However, setting up and establishing analysis environments is a crucial hurdle for non-modelers. Therefore, easy access to perform fundamental analyses of ODE models is a significant challenge. We developed SBMLWebApp, a web-based service to execute SBML-based simulations, steady-state analysis, and parameter estimation directly in the browser without the need for any setup or prior knowledge to address this issue. SBMLWebApp visualizes the result and numerical table of each analysis and provides a download of the results. SBMLWebApp allows users to select and analyze SBML models directly from the BioModels Database. Taken together, SBMLWebApp provides barrier-free access to an SBML analysis environment for simulation, steady-state analysis, and parameter estimation for SBML models. SBMLWebApp is implemented in Java™ based on an Apache Tomcat® web server using COPASI, the SBSCL, and LibSBMLSim as simulation engines. SBMLWebApp is licensed under MIT with source code available from https://github.com/TakahiroYamada/SBMLWebApp. The program runs online at http://simulate-biology.org.

The fusion of machine learning, biomedical, and bioengineering research offers novel ways to understand, diagnose, and treat various health conditions. However, the complexities of biomedical and bioengineering data, coupled with the intricate process of developing and deploying machine learning solutions, often pose significant challenges to researchers in these fields. We introduce a new Automatic Semantic Machine Learning Microservice Framework (AIMS) that addresses these challenges by automating key aspects of the machine learning pipeline, from data preprocessing to model evaluation. Our proposed framework emphasizes model interpretability, the integration of domain knowledge, and the efficient handling of biomedical data. Furthermore, it incorporates self-supervised knowledge learning strategies, enabling the automatic generation, optimization, and continuous adaptation of machine learning models in response to new tasks and data. We illustrate the potential of our framework through three case studies in biomedical research, showcasing its capacity to streamline research processes, improve scientific exploration quality, and provide a foundation for significant breakthroughs.

Semantic jokes involve resolving an incongruity emerging from wordplay or from violation of world knowledge. Research has shown individuals with autism spectrum disorder (ASD) demonstrate lower performance on humor tasks involving social situations, but less is known about their semantic joke comprehension. This study examined semantic joke comprehension among adolescents with ASD, and its possible relationship to vocabulary size, theory of mind (ToM), and idiom comprehension. Thirty-two adolescents with ASD and 32 typically developed (TD) peers participated. Semantic joke comprehension was assessed via multiple-choice questionnaire and time-limited computer program. Vocabulary, ToM abilities, and idiom comprehension were also tested. Results revealed that adolescents with ASD were as fast in processing semantic jokes as their age- and vocabulary-matched TD peers but less accurate. Age and idiom comprehension contributed significantly to semantic joke comprehension among both groups. As semantic joke comprehension is based on incongruity resolution, the greater difficulties in comprehension among the adolescents with ASD may be due to deficits in simultaneously retaining two alternative interpretations and selecting the relevant one. Like the TD group, semantic joke comprehension among the ASD group appeared more developed with age. Future neuroimaging studies should test semantic brain region involvement in semantic joke comprehension in ASD.

Semantic communication is not obsessed with improving the accuracy of transmitted symbols, but is concerned with expressing the desired meaning that the symbol sequence exactly carried. However, the generation and measurement of semantic messages are still an open problem. Expansion combines simple things into complex systems and even generates intelligence, which is consistent with the evolution of the human language system. We apply this idea to semantic communication system, quantifying and transmitting semantics by symbol sequences, and investigate the semantic information system in a similar way as Shannon did for digital communication systems. This work was the first to propose the concept of semantic expansion and knowledge collision, which may provide a new paradigm for semantic communications. We believe that expansion and collision will be the cornerstone of semantic information theory.

Modern research applies the Open Science approach that fosters the production and sharing of Open Data according to the FAIR (Findable, Accessible, Interoperable, Reusable) principles. In the geospatial context this is generally achieved through the setup of OGC Web services that implements open standards that satisfies the FAIR requirements. Nevertheless, the requirement of Findability is not fully satisfied by those services since there’s no use of persistent identifiers and no guarantee that the same dataset used for a study can be immutably accessed in a later period: a fact that hinders the replicability of research. This is particularly true in recent years where data-driven research and technological advances have boosted frequent updates of datasets. Here, we review needs and practices, supported by some real case examples, on frequent data or metadata updates in geo-datasets of different data types. Additionally we assess the currently available tools that support data versioning for databases, files and log-structured tables. Finally we discuss challenges and opportunities to enable geospatial web services that are fully FAIR: a fact that would provide, due to the massive use and increasing availability of geospatial data, a great push toward open science compliance with ultimately impacts on the science transparency and credibility.

The development of sustainable tourism in the territory requires the monitoring of all diverse aspects of the tourism sector and the creation of information technologies that provide the collection, analysis, processing and visualization of relevant information. The aim of this work is to describe the concept of the tourism monitoring service in Irkutsk Oblast as part of the Lake Baikal area, define methods and software of implementing this service, and present individual results of its prototyping. The creation of a service includes the development and software implementation of methods for collecting, processing and visualizing data, as well as ensuring the use of the functionality of existing cloud platforms for the main information processes. Data collection methods are based on Application Programming Interface and web scraping technologies, processing methods include statistical methods and artificial intelligence methods, visualization methods are based on Business Intelligent systems methods. In this paper described the concept of service, justified methods of data analysis and visualization, built an ontological model, defined the content of dashboards, implemented a prototype of a service, and demonstrated the results of data collection, processing and presentation obtained from the research. Currently, information has been collected and processed for the following tourist attractions: 685 accommodation facilities, 16 services, 809 catering establishments, 352 attractions, 94 tourist routes and excursions. The proposed technology (methods and software) of the territory tourism monitoring service has demonstrated its effectiveness.

Nowadays, the selection of web services with uncertain quality of service (QoS) is gaining a lot of attention in the service-oriented computing paradigm (soc). In fact, searching for a service composition that fulfills a complex user’s request is known to be NP-Complete. The search time is mainly dependent on the number of the requested tasks, the size the available services, and the size of the QoS realizations (i.e., sample size). To handle this problem, we propose a two-stage approach that reduces the search space using heuristics for ranking the tasks’ services and a bat algorithm metaheuristic for selecting the final near optimal compositions. The fitness used by the metaheuristic aims to fulfill all the global constraints of the user. The experimental study shows that the ranking heuristics, termed “fuzzy pareto dominance” and "Zero-order stochastic dominance", are highly effective than the other heuristics and most of the existing state-of-the-art methods.

The purpose of this paper is to develop WebSecuDMiner algorithm to discover unusual web access patterns based on analysing the potential rules hidden in web server log and user navigation history. Design/methodology/approach: WebSecuDMiner uses equivalence class transformation (ECLAT) algorithm to extract user access patterns from the web log data, which will be used to identify the user access behaviours pattern and detect unusual one. Data extracted from the web serve log and user browsing behaviour is exploited to retrieve the web access pattern that is produced by the same user. Findings: WebSecuDMiner is used to detect whether any unauthorized access have been posed and take appropriate decisions regarding the review of the original rights of suspicious user. Research limitations/implications: The present work uses the database which is extracted from web serve log file and user browsing behaviour. Although the page is viewed by the user, the visit is not recorded in the server log file, since it can be access from the browser's cache.

The use of the un-indexed web, commonly known as the deep web and dark web, to commit or facilitate criminal activity has drastically increased over the past decade. The dark web is an infamously dangerous place where all kinds of criminal activities take place, despite advances in web forensics techniques, tools, and methodologies, few studies have formally tackled the dark and deep web forensics and the technical differences in terms of investigative techniques and artefacts identification and extraction. This research proposes a novel and comprehensive protocol to guide and assist digital forensics professionals in investigating crimes committed on or via the deep and dark web, the protocol named D2WFP establishes a new sequential approach for performing tasks and subtasks to improve the accuracy and effectiveness of current tools' output. Quantitative and qualitative research has been conducted by testing the protocol following a comprehensive and rigorous process in different scenarios and the obtained results show an apparent increase in the number of artefacts recovered when adopting D2WFP. The second contribution of D2WFP is the artefacts correlation and cross-validation which enables Digital Forensics professionals to better document and structure their analysis of host-based deep and dark web browsing artefacts.

Haze always shrouds satellite images, obscuring valuable geographic information for military surveillance, natural calamity surveillance and mineral resource exploration. Satellite image dehazing (SID) provides the possibility for better applications of satellite images. Most of the existing dehazing methods are tailored for natural images and are not very effective for satellite images with non-homogeneous haze since the semantic structure information and inconsistent attenuation are not fully considered. To tackle this problem, this study proposes a hierarchical semantic-guided contextual structure-aware network (SCSNet) for satellite image dehazing. Specifically, a hybrid CNN-transformer architecture integrated with a hierarchical semantic guidance (HSG) module is presented to learn semantic structure information by synergetically complementing local representation from non-local features. Furthermore, a cross-layer fusion (CLF) module is specially designed to replace the traditional skip connection during the feature decoding stage, so as to reinforce the attention to the spatial regions and feature channels with more serious attenuation. The results on the SateHaze1k, RS-Haze, and RSID datasets demonstrate that the proposed SCSNet can achieve effective dehazing and outperforms existing state-of-the-art methods.

The significance of Linked Open Data datasets for traffic information goes beyond just including open traffic data. It extends to incorporating links to other relevant thematic datasets available on the web. This enables federated queries across different data platforms from various countries and sectors, such as transport, geospatial, environmental, weather, and more. Businesses, researchers, national operators, administrators, and citizens at large can benefit from having dynamic traffic open data connected to heterogeneous datasets across different Member States. This paper focuses on the development of a semantic model that enhances the basic service of accessing open traffic data through a LOD-enhanced Traffic Information System in alignment with the ITS Directive (2010/40/EU). The objective is not limited to just viewing or downloading data but also to improve the extraction of meaningful information and enable other types of services that are only achievable through LOD. By structuring the information using the RDF format meant for machines and employing SPARQL for querying, LOD allows for comprehensive and unified access to all datasets. Considering that the European standard DATEX II is widely used in many priority areas and services mentioned in the ITS Directive, LOD DATEX II was developed as a complementary approach to DATEX II XML. This facilitates the accessibility and comprehensibility of European traffic data and services. As part of this development, an ontological model called dtx_srti, based on the DATEX II Ontology, was created to support these efforts.

Simple measures often couldn’t count a deep complexity. In the case of semantic complexity, conventional readability formulas share a common style, a common sort of achievements and a common borders of limitation: These formulas lack a semantics-aware approach and as a result, a precise measurement of semantic complexity couldn’t be done. In this paper, we introduce DASTEX, a novel semantics-aware complexity measure for semantic complexity of text. By DASTEX, a new layer of complexity analysis are opened for NLP, cognitive and computational tasks. This measure benefits from an intuitionistic underlying formal model which consider semantic as a lattice of intuitions. This yields to a well-defined definition for semantic of a text and its complexity. DASTEX is a practical analysis method upon this formal model. So a complete suite of idea, model and method are prepared to result in a simple but yet deep measure for semantic complexity of text. The evaluation of the proposed approach is done by 4 Experiments. The results show DASTEX is capable of measuring the semantic complexity of text in 6 application-tasks.

Dictionary Learning (DL) and Sparse Representation (SR) based Classifier have impacted greatly on the classification performance and has had good recognition rate on image data. In Video Semantic Analysis (VSA), the local structure of video data contains more vital discriminative information needed for classification. However, this has not been fully exploited by the current DL based approaches. Besides, similar coding findings are not being realized from video features with the same video category. Based on the issues stated afore, a novel learning algorithm, called Sparsity based Locality-Sensitive Discriminative Dictionary Learning(SLSDDL) for VSA is proposed in this paper. In the proposed algorithm, a discriminant loss function for the category based on sparse coding of the sparse coefficients is introduced into structure of Locality-Sensitive Dictionary Learning (LSDL) algorithm. Finally, the sparse coefficients for the testing video feature sample are solved by the optimized method of SLSDDL and the classification result for video semantic is obtained by minimizing the error between the original and reconstructed samples. The experiment results show that, the proposed SLSDDL significantly improves the performance of video semantic detection compared with the comparative state-of-the-art approaches. Moreover, the robustness to various diverse environments in video is also demonstrated, which proves the universality of the novel approach.

With the proliferation of locating devices, more and more raw spatial trajectories are formed, and many works enrich these raw trajectories with semantics, and mine patterns from both raw and semantic trajectories, but access control of spatial trajectories is not considered yet. We present a multi-policy secure model for semantic spatial trajectories. In our model, Mandatory Access Control, Role Based Access Control and Discretionary Access control are all enforced, separately and combined, and we represent the model semi-formally in Ontology Web Language.

The consequences of climate change and anthropogenic stressors, such as habitat loss and overexploitation, are threatening the subsistence of species and communities across the planet. Therefore, it is crucial to analyze the impact of environmental perturbations on the diversity, structure and function of ecosystems. In this work, in silico simulations of biodiversity loss were carried out on the marine food web of Caleta Potter (25 de Mayo Island, Antarctica) where global warming has caused critical changes in the abundance and distribution of benthic and pelagic communities during the last 30 years. We performed species removal considering their degree and trophic level and including four different thresholds on the occurrence of secondary extinctions. We examined the impact of extinctions on connectance, modularity and stability of the food web. We found different responses of these properties depending on the extinction criteria used, e.g., large increase in modularity and rapid decrease in stability when most-connected and relatively high-trophic level species were removed. Additionally, we studied the complexity-stability relationship of the food web founding two regimes: 1) high sensitivity to small perturbations suggesting that Potter Cove would be locally unstable, and 2) high persistence to long-range perturbations suggesting global stability of this ecosystem.

Motivation: UCSC Xena platform provides huge amounts of processed cancer omics data from big public projects like TCGA or individual reserach groups for enabling unprecedented research opportunities. In 2019, we developed UCSCXenaTools, an R package for retrieval of UCSC Xena data. However, an easier dataset exploration and analysis tool is still lack, especially for researchers without programming experience. Results: We develop UCSCXenaShiny, an R Shiny package to quickly explore, download all datasets from UCSC Xena data hubs. In addiction, a module based analysis framework is constructed to analyze and visualize data. Availability: https://github.com/openbiox/UCSCXenaShiny or https://cran.r-project.org/package=UCSCXenaShiny.

The purpose of this study was to report data analysis results from the International Health Regulations (2005) Ship Sanitation Certificates (SSC), recorded in the European Information System (EIS). International sea trade and population movements by ships can contribute to the global spread of diseases. SSC are issued to ensure the implementation of control measures if public health risk exists on board. EIS designed according to the World Health Organization (WHO) “Handbook for Inspection of Ships and Issuance of SSC”. Inspection data were recorded and SSC issued by inspectors working at European ports were analysed. From July 2011-February 2017, 107 inspectors working at 54 ports in 11 countries inspected 5579 ships. Of these, there were 29 types under 85 flags (including 19 EU flags). As per IHR (2005) 10,281 Ship Sanitation Control Exception Certificates (SSCEC) and 296 Ship Sanitation Control Certificates (SSCC) were issued, 74 extensions to existing SSC were given, 7565 inspection findings were recorded, and 47 inspections were recorded without issuing an SSC. The most frequent inspection findings were the lack of potable water quality monitoring reports (23%). Ships aged ≥12 years (Odds Ratio, OR = 1.77, 95%Confidence Intervals, CI = 1.37–2.29) with an absence of cargo at time of inspection (OR = 3.36, 95%CI = 2.51–4.50) had a higher probability of receiving an SSCC, while ships under the EU flag had a lower probability of having inspection findings (OR = 0.72, 95%CI = 0.66–0.79). Risk factors to prioritise the inspections according to IHR were identified by using the EIS. A global information system, or connection of national or regional information systems and data exchange, could help to better implement SSC using common standards and procedures.

Recent advancements in deep learning have spurred the development of numerous novel semantic segmentation models for land cover mapping, showcasing exceptional performance in delineating precise boundaries and producing highly accurate land cover maps. However, to date, no systematic literature review has comprehensively examined semantic segmentation models in the context of land cover mapping. This paper addresses this gap by synthesizing recent advancements in semantic segmentation models for land cover mapping from 2017 to 2023, drawing insights on trends, data sources, model structures, and performance metrics based on a review of 106 extracted articles. Our analysis identifies top journals in the field, including MDPI Remote Sensing, IEEE Journal of Selected Topics in Earth Science, and IEEE Transactions on Geoscience and Remote Sensing, IEEE Geoscience and Remote Sensing Letters, ISPRS Journal Of Photogrammetry And Remote Sensing as the leading journals. We find that research predominantly focuses on land cover, urban areas, precision agriculture, environment, coastal areas, and forests. Geographically, 35.29% of the study areas are located in China, followed by USA (11.76%), France (5.88 %), Spain (4%) and others. Sentinel-2, Sentinel-1, and Landsat satellites emerge as the most commonly used data sources. Benchmark datasets such as ISPRS Vaihingen & Potsdam, LandCover.ai, DeepGlobe, and GID datasets are frequently employed. Model architectures predominantly utilize encoder-decoder, and hybrid convolutional neural network-based structures because of their impressive performances, with limited adoption of transformer-based architectures due to its computational complexity issue, and slow convergence speed. Lastly, this paper highlights existing key research gaps in the field to guide future research directions.

Visual inertial SLAM algorithms enable robots to autonomously explore and navigate in unknown scenes. However, most of the current SLAM systems highly rely on static environment assumptions, which fails in the exsitence of motional objects in the real environment. To improve the robustness and localization accuracy of SLAM systems in dynamic scenes, this paper proposes a visual-inertial SLAM framework that fuses semantic and geometric information, called DA-VINS. First, this paper presents a dynamic object classification method based on feature’s current motion state, which obtains temporary static features in the environment. Secondly, a features dynamics check module based on IMU prior and adjacent frame’s geometry constraint is designed to calculate dynamic factors. It also verifies the classification results of temporary static features. Finally, a dynamic adaptive bundle adjustment module based on the features’ dynamic factors is designed to adjust the weights of features in nonlinear optimization. We evaluated our method in public and our dataset. The results show that D-VINS is one of the most real-time, accurate, and robust systems in dynamic scenes.

We review some semantical aspects of probability bounds from Boole’s “conditions on possible experience” violated by quantum mechanics. We also speculate about emerging space-time categories as an epiphenomenon of quantization and the resulting breakdown of relativity theory by non-unitary and non-linear processes.

The unprecedented growth of publicly available SARS-CoV-2 genome sequence data has increased demand for effective and accessible SARS-CoV-2 data analysis and visualization tools. A majority of the currently available tools either require computational expertise to deploy or limit user input to pre-selected subsets of SARS-CoV-2 genomes. To address these limitations, we developed ViralVar, a publicly available, point-and-click webtool that gives users the freedom to investigate and visualize user-selected subsets of SARS-CoV-2 genomes obtained from the GISAID public database. ViralVar has two primary features that enable: 1) visualization of spatiotemporal dynamics of SARS-CoV-2 lineages, and 2) structural/functional analysis of genomic mutations. As proof-of-principle, ViralVar was used to explore the evolution of the SARS-CoV-2 pandemic in the USA in the pediatric, adult, and elderly population (n > 1.7 million genomes). While the spatiotemporal dynamics of variants did not differ between these age groups, several USA-specific sublineages arose relative to the rest of the world. Our development and utilization of ViralVar to provide insights on the evolution of SARS-CoV-2 in the USA demonstrates the importance of developing accessible tools to facilitate and accelerate large-scale surveillance of circulating pathogens. The ViralVar webserver is freely available at http://viralvar.org/.

In roll-to-roll (R2R) processing, uniformity of the web is a crucial factor that can guarantee high coating quality. To understand web defects due to thermal deformation, we analyzed the effects of web unevenness on the coating quality of an yttria-stabilized zirconia (YSZ) layer, a brittle electrolyte of solid oxide fuel cells (SOFCs). We used finite element analysis to analyze the thermal and mechanical deformations at different drying temperatures. A YSZ layer was also coated using R2R slot-die coating to observe effects of web unevenness on the coating quality. It was seen that web unevenness was generated by thermal deformation due the conduction and convection heat from the dryer. Owing to varying web unevenness with time, the YSZ layer developed cracks. At higher drying temperatures, more coating defects having larger widths were generated. Results indicated that web unevenness at the coating section led to coating defects, which could damage the SOFC and decrease its yield in the R2R process. From this study, we suggest that coating defects, generated by the web unevenness owing to the convection and conduction heat, should be considered for the high-volume production of brittle electrolytes using the R2R process.

(1) Background: A global pandemic due to COVID-19 emerged in November 2019 and hit France in early March 2020. It not only resulted in a loss of lives but also in very strict confinement measures. The objective of this study was to understand what the determinants of the change in participants’ behavior and mental state were during the confinement. (2) Methods: An online survey was launched on April 23rd, 2020, and closed on May 7th, 2020. The final sample included 1454 participants from 24 to 65 years old. Descriptive and multivariate analyses were then performed. (3) Results: 28.7% reported having a more balanced diet, against 17.1% with a less balanced diet. 22.7% of respondents reported an increased alcohol consumption, as opposed to only 12.2% declaring a decrease and 11.2% of respondents increased their tobacco consumption, while 6.3% decreased it. 50.6% of the participants reported being more depressed, stressed, or irritable since the beginning of the lockdown. Confinement had a negative effect on every behavior studied in this survey, except for nutrition. We also found that negative mental state changes were strongly associated with nutrition, sleep, physical activity, and alcohol consumption changes.

Summary CoV-GLUE is an online web application for the interpretation and analysis of SARS-CoV-2 virus genome sequences, with a focus on amino acid sequence variation. It is based on the GLUE data-centric bioinformatics environment and provides a browsable database of amino acid replacements and coding region indels that have been observed in sequences from the pandemic. Users may also analyse their own SARS-CoV-2 sequences by submitting them to the web application to receive an interactive report containing visualisations of phylogenetic classification and highlighting genomic variation of potentially high impact, for example linked to primer mismatches.Availability and implementation Available at http://cov-glue.cvr.gla.ac.uk. Implemented using GLUE, an open source framework for the development of virus sequence data resources. Contact [email protected]

During virus outbreaks in the recent past web behavior mining, modeling, and analysis have served as means to examine, explore, interpret, assess, and forecast the worldwide perception, readiness, reactions, and response linked to these virus outbreaks. The recent outbreak of the Marburg Virus disease (MVD), the high fatality rate of MVD, and the conspiracy theory linking the FEMA alert signal in the United States on October 4, 2023, with MVD and a zombie outbreak, resulted in a diverse range of reactions in the general public which has transpired in a surge in web behavior in this context. This resulted in “Marburg Virus” featuring in the list of the top trending topics on Twitter on October 3, 2023, and “Emergency Alert System” and “Zombie” featuring in the list of top trending topics on Twitter on October 4, 2023. No prior work in this field has mined and analyzed the emerging trends in web behavior in this context. The work presented in this paper aims to address this research gap and makes multiple scientific contributions to this field. First, it presents the results of performing time series forecasting of the search interests related to MVD emerging from 216 different regions on a global scale using ARIMA, LSTM, and Autocorrelation. The results of this analysis present the optimal model for forecasting web behavior related to MVD in each of these regions. Second, the correlation between search interests related to MVD and search interests related to zombies (in the context of this conspiracy theory) was investigated. The findings show that there were several regions where there was a statistically significant correlation between MVD-related searches and zombie-related searches (in the context of this conspiracy theory) on Google on October 4, 2023. Finally, the correlation between zombie-related searches (in the context of this conspiracy theory) in the United States and other regions was investigated. This analysis helped to identify those regions where this correlation was statistically significant.

Lactobacillus plantarum has been observed to play a crucial role in shaping the sensory properties of chili sauce. However, the specific taste-active metabolites responsible for the desirable flavor profile of chili sauce remain inadequately characterized. This study employed a combination of metabolomics and web-based computational tools analysis to investigate the dynamic changes in taste-active metabolites during chili sauce fermentation. Initially, metabolites were rapidly annotated using a feature-based molecular network, leading to the tentative annotation of 206 metabolites, of which a significant proportion had not been previously reported. Subsequently, the VirtualTaste tool identified dihydrosphingosine, lactic acid, isoleucine, phytosphingosine, and gluconic acid as potential taste markers for quality control. Finally, pathway enrichment analysis revealed that these components were primarily associated with amino acid tRNA, phenylalanine, tyrosine, and tryptophan biosynthesis, as well as sphingolipid metabolism. This study provides valuable insights into the mechanisms underlying the formation of the distinctive flavor of chili sauce.

In recent years, Web APIs have become a de facto standard for exchanging machine-readable data on the Web. Despite this success though, they often fail in making resource descriptions interoperable due to the fact that they rely on proprietary vocabularies that lack formal semantics. The Linked Data principles similarly seek the massive publication of data on the Web, yet with the specific goal of ensuring semantic interoperability. Given their complementary goals, it is commonly admitted that cross-fertilization could stem from the automatic combination of Linked Data and Web APIs. Towards this goal, in this paper we leverage the micro-service architectural principles to define a SPARQL Micro-Service architecture, aimed at querying Web APIs using SPARQL. A SPARQL micro-service is a lightweight SPARQL endpoint that provides access to a small, resource-centric, virtual graph. In this context, we argue that full SPARQL Query expressiveness can be supported efficiently without jeopardizing servers availability. Furthermore, we demonstrate how this architecture can be used to dynamically assign dereferenceable URIs to Web API resources that do not have URIs beforehand, thus literally ``bringing'' Web APIs into the Web of Data. We believe that the emergence of an ecosystem of SPARQL micro-services published by independent providers would enable Linked Data-based applications to easily glean pieces of data from a wealth of distributed, scalable and reliable services. We describe a working prototype implementation and we finally illustrate the use of SPARQL micro-services in the context of two real-life use cases related to the biodiversity domain, developed in collaboration with the French National Museum of Natural History.

Green ICT has received significant attention in organizations to reduce global warming since last decade. Several maturity models have been proposed for tracking green ICT practices in organizations. Current literature shows that SMEs around the world try to follow some common strategies such as virtualization, consolidation of devices, energy efficiency and disposal of ICT equipment for greening ICT. The increasing interest for green ICT practices in organizations is not only due to the desire to attain environment-friendly atmosphere but mainly because of sustaining business goals such as cost reduction, competitive advantages and stakeholders’ pressure. Nevertheless, due to the lack of green ICT knowledge, organizations, specially SMEs, tend to ignore those practices. Therefore, this research proposes a framework that provides combination of existing green and sustainable ICT maturity models by mapping them with the strategies the businesses are already following. Out of this framework, a web application has been developed that provides questionnaire for SMEs to identify their present situation of green ICT practices and guidelines for improvement. The results have been analyzed by testing the application in some SMEs of Finland and Bangladesh. Finally, a survey has been conducted to attain SMEs’ perception about the possibility of sustainable development of businesses through this application.

Affective computing in general and human activity and intention analysis in particular, is a rapidly growing field of research. Head pose and emotion changes, present serious challenges when applied to player’s training and ludology experience in serious games or analysis of customer satisfaction regarding broadcast and web services or monitoring a driver’s attention. Given the increasing prominence and utility of depth sensors, it is now feasible to perform large-scale collection of three-dimensional (3D) data for subsequent analysis. Discriminative random regression forests was selected in order to rapidly and accurately estimate head pose changes in unconstrained environment. In order to complete the secondary process of recognising four universal dominant facial expressions (happiness, anger, sadness and surprise), emotion recognition via facial expressions (ERFE) was adopted. After that, a lightweight data exchange format (JavaScript Object Notation-JSON) is employed, in order to manipulate the data extracted from the two aforementioned settings. Motivated by the need of generating comprehensible visual representations from different sets of data, in this paper we introduce a system capable of monitoring human activity through head pose and emotion changes, utilising an affordable 3D sensing technology (Microsoft Kinect sensor).

This paper deals with wh-in situ elements (i.e. wh-elements that stay in the argument position) in islands such as adverbial clauses, conditionals, relative clauses, complement clauses, etc. extracted from corpora of Italian and Spanish. I will show that there are two types of wh-in situ embedded inside an island in these languages. One type is a real genuine question. Another type is an echo question. There are several approaches that convincingly account for the insensitivity of echo questions to islands. The genuine question type is still a puzzle for semantic and syntactic approaches. It turns out that wh-in situ questions with a genuine question interpretation are only possible inside islands that are not marked for sentence type and do not contain any operator that could block the question interpretation of the wh-in situ. This way question alternatives can be passed higher up the clause until they meet a question operator in the matrix clause that interprets them. Consequently, the question is well-formed and interpreted without any problem. Real islands contain semantic operators or quantifiers that interpret question alternatives triggered by wh-in situ until they reach the question morpheme to be interpreted as question alternatives. The result is that the derivation crashes.