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Understanding and predicting types of bugs are of practical importance for developers to improve the testing efficiency and take appropriate steps to address bugs in software releases. However, due to the complex conditions under which faults manifest and the complexity of the classification rules, the automatic classification of Mandelbugs is a difficult task. In this article, we present a deep semantic information-based Mandelbug classification method that combines a semantic model with a deep learning classifier and makes use of both labeled and unlabeled bug reports. By training the bug report semantic model on millions of bug reports, each word in the text of a bug report is represented as a word embedding that preserves the semantic relationship among the words. Then, a convolutional neural network model is designed to capture the high-level features of bug reports to obtain a more accurate classification. Moreover, the effects of the semantic model size and domain on the classification results are investigated, and the quality of word embeddings is evaluated by analyzing several important parameters.

Relative localization is a fundamental requirement for the coordination of multiple robots. To date, existing research in relative localization mainly depends on the extraction of low-level geometry features such as planes, lines, and points, which may fail in challenging cases when initial error is large and overlapping area is low. In this paper, a novel approach named collaborative semantic map matching (COSEM) is proposed to estimate the relative transformation between robots. COSEM jointly performs multimodal information fusion, semantic data association, and optimization in a unified framework. Firstly, each robot applies a multimodal information fusion model to generate local semantic maps. Since the correspondences between local maps are latent variables, a flexible semantic data association strategy is proposed using Expectation-Maximization. Instead of assigning hard geometry data association, semantic association and geometry association are jointly estimated. Then, the minimization of the expected cost results in a rigid transformation matrix between two semantic maps. Evaluations on Semantic KITTI benchmarks and real world experiments show the improved accuracy, convergence, and robustness.

In general, people interpret a rope as a tool for binding. In the extensive Indonesian dictionary, rope means goods with long threads, made of various materials (coconut fiber, palm fiber, plastic, etc.) used for binding, pulling. Whereas in Arabic and the Quran, rope mean hablun, and it does not always mean rope. In the interpretation of hablun it is known as al-jama'ah, agreement, the Quran, Islam, necklaces of commands and prohibitions, neck veins. Whereas the word rope is only a tool for binding, pulling, pulling, tightening, etc. The theory used in this research is semantics. Semantics is a science that is used to explore or study a meaning in words. The semantics used is the encyclopedic model introduced by Dadang Darmawan and Irma Riyani in their journal article, which refutes the Izutsu semantic model with several shortcomings. The word hablun has a primary meaning in the form of a long tool used to bind. In contrast, in relational meaning, it means abstract in jahiliyah poetry and mass media, while in interpretation, it means concrete in Makkiyah letters and abstract meaning in madaniyah surahs. The concept of the word hablun is fundamental to learn because it is a rope and has important lessons for the safety of the world and the hereafter. Among them is that the concept of hablun commands to hold on to the Prophet Muhammad's two inheritances, avoid division, and understand and practice the concept of Islamic sociology.

There are several kinds of smart devices, such as smartphones, sensors, and smart wearable devices, included in the Human-in-the-Loop (HITL) system, but different devices have their own data processing and programming paradigm. Programmers usually need to design the same data processing logic for different devices by using a different programming model. How to mapping the same code to different devices without any change is an emerging topic in the HITL system. Furthermore, the intelligent data processing for the smart CPS sector is experiencing significant growth in data volume, driven by a large number of smart devices that are anticipated in the near further. All these smart devices are expected to improve the overall HITL system performance marvelously. A large number of devices can also outstandingly increase the data volume, which needs to be processed in real time. How to process large-scale data on a smart device in real time is another challenge. Focused on these challenges, this article proposed a computing device-aware HITL CPS data processing framework, named Barge, aiming to map the regular code to the different hardware without any change. In Barge, a semantic model, an architecture-driven programming model, and a graph partition scheme are included. The semantic model is used to express the user-defined graph algorithms by using the domain-specific language. The architecture-driven programming model will execute the graph algorithms on a different device in parallel. Furthermore, the graph partition scheme will partition the large-scale graphs into suitable partitions by aware of the topology to make the partitioned data suitable for kinds of smart devices. We believe that our work would open a wide range of opportunities to improve the performance of large-scale graph processing for HITL systems.

Massive ocean data acquired by various observing platforms and sensors poses new challenges to data management and utilization. Typically, it is difficult to find the desired data from the large amount of datasets efficiently and effectively. Most of existing methods for data discovery are based on the keyword retrieval or direct semantic reasoning, and they are either limited in data access rate or do not take the time cost into account. In this paper, we creatively design and implement a novel system to alleviate the problem by introducing semantics with ontologies, which is referred to as Data Ontology and List-Based Publishing (DOLP). Specifically, we mainly improve the ocean data services in the following three aspects. First, we propose a unified semantic model called OEDO (Ocean Environmental Data Ontology) to represent heterogeneous ocean data by metadata and to be published as data services. Second, we propose an optimized quick service query list (QSQL) data structure for storing the pre-inferred semantically related services, and reducing the service querying time. Third, we propose two algorithms for optimizing QSQL hierarchically and horizontally, respectively, which aim to extend the semantics relationships of the data service and improve the data access rate. Experimental results prove that DOLP outperforms the benchmark methods. First, our QSQL-based data discovery methods obtain a higher recall rate than the keyword-based method, and are faster than the traditional semantic method based on direct reasoning. Second, DOLP can handle more complex semantic relationships than the existing methods.

International audience; This paper presents a novel formal semantics, mechanized in Coq, for a large, sequential subset of the LLVM IR. In contrast to previous approaches, which use relationally-specified operational semantics, this new semantics is based on monadic interpretation of interaction trees, a structure that provides a more compositional approach to defining language semantics while retaining the ability to extract an executable interpreter. Our semantics handles many of the LLVM IR's non-trivial language features and is constructed modularly in terms of event handlers, including those that deal with nondeterminism in the specification. We show how this semantics admits compositional reasoning principles derived from the interaction trees equational theory of weak bisimulation, which we extend here to better deal with nondeterminism, and we use them to prove that the extracted reference interpreter faithfully refines the semantic model. We validate the correctness of the semantics by evaluating it on unit tests and LLVM IR programs generated by HELIX.

Within the GEOFIT project (Smart GeothermalSystems1), BIM environment has been defined asGeoBIM platform. This term refers to those specificgeothermal applications which are included in a tailormadeBIM platform to manage the geothermal systems, building, site and assets information from models,sensors installed and simulations. In GEOFIT project,the demo-sites location is enriched up to the holisticview of the retrofitted buildings with all the geothermalfacilities designed, simulated, installed, commissioned,and monitored, from inception onward, during thelifecycle of a facility and includes all stakeholders who need facility information – from the designers to theoccupants with the building in operation. This holisticview includes the execution control and the permanentgeographical reference because the simulation,monitoring and design processes happen in a specificgeographical context. The definition andimplementation of a GEOBIM platform is paramountfor the project and it is one of the main outcomes ofGEOFIT project. While BIM implementation isubiquitous in the architectural issues of the project, relying mostly on CAD designs, geographicalinformation has a limited role particularly inconstruction projects, it is often restricted to somespecific tasks or seen as a potential redundancy to BIM. Considering the geographical dependent tasks inGEOFIT, GIS can bring a valuable complementarycontribution to the BIM process by providing spatialinput and geospatial visualization, adding informationon the retrofitting demo-site’s surroundingenvironment and underground thermal information thatis essential for design decisions and the approvalprocesses regarding building integrity and geothermalenergy availability. In this paper, an interdisciplinarycooperation, data exchange, and data transfer occurs among the different professionals and disciplinesinvolved for the successful retrofitting project planningand energy efficiency demonstration throughout theGEOBIM platform. This is implemented to assemblethis set of powerful assessment, inspection and groundresearch, testing, and real time monitoring tools. Raw datais available for download here.

As the use of autonomous Unmanned Ground Vehicles (UGV) for automated data collection from construction projects increases, construction stakeholders have become aware of a problem with inter-disciplinary semantic data sharing and exchanges between construction and robotic. Cross-domain data translation requires detailed specifications especially when it comes to semantic data translation. Building Information Modeling (BIM) and Geographic Information System (GIS) are the two digital building technologies used to capture and store semantic information for indoor structures and outdoor environments respectively. In the absence of a standard format for data exchanges between the construction and robotic domains, the tools of both industries have yet to be integrated into a coherent deployment infrastructure. In other words, the semantics of BIM-GIS cannot be automatically integrated by the robotic platforms currently being used. To enable semantic data transfer across domains, semantic web technology has been widely used in multi-disciplinary areas for interoperability. This paves the way to smarter, quicker and more precise robot navigation on construction sites. This paper develops a semantic web ontology integrating robot navigation and data collection to convey the meanings from BIM-GIS to the robot. The proposed Building Information Robotic System (BIRS) provides construction data that are semantically transferred to the robotic platform and can be used by the robot navigation software stack on construction sites. To meet this objective, first, knowledge representation between construction and robotic domains is bridged. Then, a semantic database integrated with the Robot Operating System (ROS) is developed, which can communicate with the robot and the navigation system to provide the robot with semantic building data at each step of data collection. Finally, the BIRS proposed system is validated through four case studies.

The issue of interoperability in the Architecture, Engineering, and Construction (AEC) industry represents a challenge on a scale that spans across the project life cycle. This is predominant in the infrastructure sector that usually comprises a more versatile Operations and Maintenance (O&M) phase in comparison with the buildings sector. To this end, an important stage in the information life cycle is the asset information capture and validation during product procurement at the O&M phase. The water industry in the United Kingdom relies on Product Data Templates (PDTs) to fulfil such task, which is usually an error prone manual process. This paper presents an ongoing research, which investigates the application of Semantic Web Technologies (SWT) for improving product data exchange during product procurement at the O&M phase for the water industry in the United Kingdom (UK). Therefore, focus group sessions with industry experts were held to discuss current inefficiencies and solution requirements. Based on these results, a semantic common model named Asset Specification Ontology (ASO) was developed to capture and validate asset information during product procurement at the O&M phase. The common model (ontology) is based on available technologies, namely Web Ontology Language (OWL) and Shapes Constraint Language (SHACL). This gives the advantage of semantically rich data which can be linked and queried in a meaningful way to facilitate the exchange and validation of water industry assets’ data. The uniqueness of this paper is manifested in the issue it tackles, as efficient product procurement, and hence, data exchange in the water industry is an industrial challenge that is seldom researched. Results from the focus group sessions showed that information exchange within the UK water industry is impeded due to the lack of structured and semantic data. However, for a robust semantic interoperability, there needs to be a robust semantic data infrastructure, which would require semantic mappings from standards to product properties, from standards to other standards, and from standards to dictionaries. These conclusions were further supported by the common model, which was created from existing schemas, standards, and dictionaries. Generally, this paper recommends a common model/product library for phase-specific product data exchange in the water industry.

Recent years have witnessed a convergence of data and methods that allow us to approximate the shape, size, and functional attributes of biological organisms. This is not only limited to traditional model species: given the ability to culture and visualize a specific organism, we can capture both its structural and functional attributes. We present a quantitative model for the colonial diatom Bacillaria paradoxa, an organism that presents a number of unique attributes in terms of form and function. To acquire a digital model of B. paradoxa, we extract a series of quantitative parameters from microscopy videos from both primary and secondary sources. These data are then analyzed using a variety of techniques, including two rival deep learning approaches. We provide an overview of neural networks for non-specialists as well as present a series of analysis on Bacillaria phenotype data. The application of deep learning networks allow for two analytical purposes. Application of the DeepLabv3 pre-trained model extracts phenotypic parameters describing the shape of cells constituting Bacillaria colonies. Application of a semantic model trained on nematode embryogenesis data (OpenDevoCell) provides a means to analyze masked images of potential intracellular features. We also advance the analysis of Bacillaria colony movement dynamics by using templating techniques and biomechanical analysis to better understand the movement of individual cells relative to an entire colony. The broader implications of these results are presented, with an eye towards future applications to both hypothesis-driven studies and theoretical advancements in understanding the dynamic morphology of Bacillaria.

Purpose The purpose of this paper is to reveal the smart collaborative learning service. This concept aims to build teams of learners based on the complementarity of their skills, allowing flexible participation and offering interdisciplinary collaboration opportunities for all the learners. The success of this environment is related to predict efficient collaboration between the different teammates, allowing a smartly sharing knowledge in the Smart University environment. Design/methodology/approach A random forest (RF) approach is proposed, which is based on semantic modelization of the learner and the problem-solving allowing multidisciplinary collaboration, and heuristic completeness processing to build complementary teams. To achieve that, this paper established a Konstanz Information Miner workflow that integrates the main steps for building and evaluating the RF classifier, this workflow is divided into: extracting knowledge from the smart collaborative learning ontology, calculating the completeness using a novel heuristic and building the RF classifier. Findings The smart collaborative learning service enables efficient collaboration and democratized sharing of knowledge between learners, by using a semantic support decision support system. This service solves a frequent issue related to the composition of learning groups to serve pedagogical perspectives. Originality/value The present study harmonizes the integration of ontology, a new heuristic processing and supervised machine learning algorithm aiming at building an intelligent collaborative learning service that includes a qualified classifier of complementary teams of learners.

Purpose The purpose of this paper is to propose a graph-based representation approach for evolutionary knowledge under the big data circumstance, aiming to gradually build conceptual models from data. Design/methodology/approach A semantic data model named meaning graph (MGraph) is introduced to represent knowledge concepts to organize the knowledge instances in a graph-based knowledge base. MGraph uses directed acyclic graph–like types as concept schemas to specify the structural features of knowledge with intention variety. It also proposes several specialization mechanisms to enable knowledge evolution. Based on MGraph, a paradigm is introduced to model the evolutionary concept schemas, and a scenario on video semantics modeling is introduced in detail. Findings MGraph is fit for the evolution features of representing knowledge from big data and lays the foundation for building a knowledge base under the big data circumstance. Originality/value The representation approach based on MGraph can effectively and coherently address the major issues of evolutionary knowledge from big data. The new approach is promising in building a big knowledge base.

The heterogeneity and the increasing amount of the news published on the web create challenges in accessing them. In the authors' previous studies, they introduced a semantic web-based sports news aggregation system called BKSport, which manages to generate metadata for every news item. Providing an intuitive and expressive way to retrieve information and exploiting the advantages of semantic search technique is within their consideration. In this paper, they propose a method to transform natural language questions into SPARQL queries, which could be applied to existing semantic data. This method is mainly based on the following tasks: the construction of a semantic model representing a question, detection of ontology vocabularies and knowledge base elements in question, and their mapping to generate a query. Experiments are performed on a set of questions belonging to various categories, and the results show that the proposed method provides high precision.

Intelligent transportation systems pervasively deploy thousands of video cameras. Analyzing live video streams from these cameras is of significant importance to public safety. As streaming video is increasing, it becomes infeasible to have human operators sitting in front of hundreds of screens to catch suspicious activities or detect objects of interests in real-time. Actually, with millions of traffic surveillance cameras installed, video retrieval is more vital than ever. To that end, this article proposes a long video event retrieval algorithm based on superframe segmentation. By detecting the motion amplitude of the long video, a large number of redundant frames can be effectively removed from the long video, thereby reducing the number of frames that need to be calculated subsequently. Then, by using a superframe segmentation algorithm based on feature fusion, the remaining long video is divided into several Segments of Interest (SOIs) which include the video events. Finally, the trained semantic model is used to match the answer generated by the text question, and the result with the highest matching value is considered as the video segment corresponding to the question. Experimental results demonstrate that our proposed long video event retrieval and description method which significantly improves the efficiency and accuracy of semantic description, and significantly reduces the retrieval time.

An era of exponential technological changes marks the current century. The convergence of different processes has changed the way that companies use technology. Thus, the objective of this study is to reveal how companies can achieve digital transformation at the recruitment and selection process through semantic data analysis. The methodological procedures were elaborated in a descriptive exploratory research, through the qualitative method, which consisted of the literature review and documentary analysis. As a result, it was found that technology, by using artificial intelligence, can assist in the analysis of the candidates' responses. Therefore, if a person uses more the pronoun “we”, it may indicate that the person is more sociable, according to the analyzed case. In this way, it is concluded that technology can corroborate for a more assertive hiring in the management of Human Resources, and then increase efficiency in recruitment and selection activities. Therefore, the future of work will be marked by advanced technologies.

The research in the geospatial data structuring and formats interoperability direction is the crucial task for creating a 3D Geodatabase at the urban scale. Both geometric and semantic data structuring should be considered, mainly regarding the interoperability of objects and formats generated outside the geographical space. Current reflections on 3D database generation, based on geospatial data, are mostly related to visualisation issues and context-related application. The purposes and scale of representation according to LoDs require some reflections, particularly for the transmission of semantic information.This contribution adopts and develops the integration of some tools to derive object-oriented modelling in the HBIM environment, both at the urban and architectural scale, from point clouds obtained by UAV (Unmanned Aerial Vehicle) photogrammetry.One of the paper’s objectives is retracing the analysis phases of the point clouds acquired by UAV photogrammetry technique and their suitability for multiscale modelling. Starting from UAV clouds, through the optimisation and segmentation, the proposed workflow tries to trigger the modelling of the objects according to the LODs, comparing the one coming from CityGML and the one in use in the BIM community. The experimentation proposed is focused on the case study of the city of Norcia, which like many other historic centres spread over the territory of central Italy, was deeply damaged by the 2016-17 earthquake.

Semantic segmentation is a crucial step for document understanding. In this paper, an NVIDIA Jetson Nano-based platform is applied for implementing semantic segmentation for teaching artificial intelligence concepts and programming. To extract semantic structures from document images, we present an end-to-end dilated convolution network architecture. Dilated convolutions have well-known advantages for extracting multi-scale context information without losing spatial resolution. Our model utilizes dilated convolutions with residual network to represent the image features and predicting pixel labels. The convolution part works as feature extractor to obtain multidimensional and hierarchical image features. The consecutive deconvolution is used for producing full resolution segmentation prediction. The probability of each pixel decides its predefined semantic class label. To understand segmentation granularity, we compare performances at three different levels. From fine grained class to coarse class levels, the proposed dilated convolution network architecture is evaluated on three document datasets. The experimental results have shown that both semantic data distribution imbalance and network depth are import factors that influence the document’s semantic segmentation performances. The research is aimed at offering an education resource for teaching artificial intelligence concepts and techniques.

Urban transportation systems are shaped by factors that include people, vehicles, roads, and the environment, forming a complex and giant system with dynamics, diversity, and uncertainty. Physical signal-driven intelligent transportation systems (ITSs) typically lack the ability to capture social behaviors or crowd willingness, and they achieve only information automation for transportation decision support. The crowdsourcing social signals consist of timely, extensive, comprehensive, and rich intelligence that concern urban dynamics, social behaviors, and traffic environments. Such social signals provide a new paradigm for operating ITS with unstructured semantic data, making knowledge automation for decision intelligence a possibility. This article reviews the knowledge automation paradigms for cyber–physical–social systems (CPSSs) compared with traditional information automation paradigms for cyber–physical systems (CPSs) in ITS, from the perspective of data-driven, modeling space, analytical methodologies, and decision support services. To investigate the key methodology in social spaces that enhance information automation into knowledge automation, we summarize the current research into a multisource heterogeneous social signal-based traffic decision knowledge automation framework and further exploit the computational paradigm and applications scenarios of this framework. Finally, we discuss future challenges for designing and realizing knowledge automation on CPSS in transportation.

Mining user preferences from online reviews to understand the representative preferences of different customer groups plays a critical role in product development and improvement, especially in personalized product design. Previous research on mining user preferences usually assumes that all consumers' preferences are homogenous and does not take differences in consumers’ personalities into account. Besides, traditional online review deep mining methods are too broad to focus on precise and detailed mining of customer preferences. To fill the gaps in existing research, our study develops a template matching deep mining method to segment customers and narrow the mining scope of customer group preference, and then proposes a product family lean improvement model. Firstly, K-means and structural change model are applied to cluster customers reliably based on the similarity of user preferences. Secondly, in order to decrease down mining scope of customer group preference, the Improved Deep Structured Semantic Model is designed to determine sentimental polarity sentimental polarity of different groups by matching the standard sentimental polarity review templates and online reviews. Finally, a KANO mapping model is developed to decide the user preferences for product attributes in each customer group according to their sentimental polarity and further summarize the common preferences and personalized preferences of various groups according to the Preference Commonality Measurement Function. Accordingly, product family lean improvement strategies are proposed to provide product developers with improvement directions. An empirical study is carried out on laptop data on JD.COM to verify the validity of the proposed model and product family lean improvement suggestions are put forward.

Wireless sensor networks (WSN) are ad-hoc wireless networks used in many domains involving the analysis of various properties. Along with the growth in the number of deployments of WSNs, there has been an increase in the size of the networks as well. These systems tend to generate a tremendous amount of data that require further processing and analysis. Transmission of data requires a huge amount of energy considering that the components are powered by tiny batteries with a short life cycle. Consequently, energy efficiency is a dominant factor in the performance measure of WSNs. One major contributor to the overall energy consumption of a WSN is overhearing, a condition, in which the same data is picked up and transmitted by multiple nodes. Reducing overhearing results in a marked decrement in energy usage. This paper proposes to use semantic technology for the observation and collection of sensor data. With the use of an ontology, sensor nodes can be remarkably interoperable and configurable in the receipt and transmission of semantic data. Using this interoperability, we introduce asynchronous semantic preamble listening to avoid overhearing in a semantic sensor network. Performance comparisons to LPL by real experiments show stark improvements in energy consumption.

This paper presents the design of a multi-agent framework that aids engineers in the adaptation of modular production systems. The framework includes general implementations of agents and other software components for self-learning and adaptation, sensor data analysis, system modelling and simulation, as well as human-computer interaction. During an adaptation process, operators make changes to the production system, in order to increase capacity or manufacture a product variant. These changes are automatically captured and evaluated by the framework, building an experience base of adjustments that is then used to infer adaptation knowledge. The architecture of the framework consists of agents divided in two layers: the agents in the lower layer are associated with individual production modules, whereas the agents in the higher layer are associated with the entire production line. Modelling, learning, and adaptations can be performed at both levels, using a semantic model to specify the structure and capabilities of the production system. An evaluation of a prototype implementation has been conducted on an industrial assembly system. The results indicate that the use of the framework in a typical adaptation process provides a significant reduction in time and resources required.

While considered a relatively emergent area of research (Bloch and Sacks in Autom Constr 91:256–272, 2018), Semantic Enrichment (SE) and Semantic Web services are among the prominent topics and trends in BIM research (BIM handbook a guide to building information modeling for owners, designers, engineers, contractors, and facility managers, John Wiley & Sons Inc, New Jersey). SE computational approaches provide valuable means to overcome current BIM limitations such as interoperability, topology relationships, extensions to standard schemas, among many others. Therefore, the study herein consists of a semi-systematic literature review on BIM-based SE systems and applications developed during the last decade. The article describes the computational methods and approaches identified, a classification of the screened papers according to their primary BIM Use ( https://bim.psu.edu/uses/ ), as well as reported limitations and recommendations for future developments. From the selected articles, main developments in SE techniques encompass multidisciplinary approaches comprising the use of Semantic Web technologies; inference rules, and rule processing engines; artificial intelligence methods; ontology mapping and semantic similarity; application of Industry Foundation Classes (IFC) libraries; and custom plugins. Considering BIM Uses, research was mostly focused on "Capture Existing Conditions" and "Validate Code Compliance". Other identified BIM Uses " verified three or fewer occurrences. Reported limitations state that more user-friendly interfaces are required to handle SPARQL queries (Lee et al. in Autom Constr 68:102–113, 2016). Moreover, the ontologies development process is deemed as time-consuming (Zhong et al. in Build Environ 141:127–142, 2018), and constraints were identified when trying to share semantic data between BIM and Geographic Information System (GIS) platforms (Zhong et al. in Build Environ 141:127–142, 2018). Future research may be expected in transitions to a more extended BIM paradigm, such as the formalization of Digital Twinning processes; discussions concerning a standard format for SPARQL query results (Karan and Irizarry in Autom Constr 53:1–12, 2015); as well as enhanced support through the transition to Linked Data and ontology-based systems.

Image semantic data have multilevel feature information. In the actual segmentation, the existing segmentation algorithms have some limitations, resulting in the fact that the final segmentation accuracy is too small. To solve this problem, a segmentation algorithm of image semantic sequence data based on graph convolution network is constructed. The graph convolution network is used to construct the image search process. The semantic sequence data are extracted. After the qualified data points are accumulated, the gradient amplitude forms complete rotation field and no scatter field in the diffusion process, which enhances the application scope of the algorithm, controls the accuracy of the segmentation algorithm, and completes the construction of the data segmentation algorithm. After the experimental dataset is prepared and the semantic segmentation direction is defined, we compare our method with four methods. The results show that the segmentation algorithm designed in this paper has the highest accuracy.

Object Re-Identification (ReID) is the task of matching a given object in the new environment with its image captured in a different environment. The input for a ReID method includes two sets of images. The probe set includes one or more images of the object that must be identified in the new environment. The gallery set includes images that may contain the object from the probe image. The ReID task’s complexity arises from the differences in the object appearance in the probe and gallery sets. Such difference may originate from changes in illumination or viewpoint locations for multiple cameras that capture images in the probe and gallery sets. This paper focuses on developing a deep learning ThermalReID framework for cross-modality object ReID in thermal images. Our framework aims to provide continuous object detection and re-identification while monitoring a region from a UAV. Given an input probe image captured in the visible range, our ThermalReID framework detects objects in a thermal image and performs the ReID. We evaluate our ThermalReID framework and modern baselines using various metrics. We use the IoU and mAP metrics for the object detection task. We use the cumulative matching characteristic (CMC) curves and normalized area-under-curve (nAUC) for the ReID task. The evaluation demonstrated encouraging results and proved that our ThermalReID framework outperforms existing baselines in the ReID accuracy. Furthermore, we demonstrated that the fusion of the semantic data with the input thermal gallery image increases the object detection and localization scores. We developed the ThermalReID framework for cross-modality object re-identification. We evaluated our framework and two modern baselines on the task of object ReID for four object classes. Our framework successfully performs object ReID in the thermal gallery image from the color probe image. The evaluation using real and synthetic data demonstrated that our ThermalReID framework increases the ReID accuracy compared to modern ReID baselines.

The web page recommendation is generated by using the navigational history from web server log files. Semantic Variable Length Markov Chain Model (SVLMC) is a web page recommendation system used to generate recommendation by combining a higher order Markov model with rich semantic data. The problem of state space complexity and time complexity in SVLMC was resolved by Semantic Variable Length confidence pruned Markov Chain Model (SVLCPMC) and Support vector machine based SVLCPMC (SSVLCPMC) meth-ods respectively. The recommendation accuracy was further improved by quickest change detection using Kullback-Leibler Divergence method. In this paper, socio semantic information is included with the similarity score which improves the recommendation accuracy. The social information from the social websites such as twitter is considered for web page recommendation. Initially number of web pages is collected and the similari-ty between web pages is computed by comparing their semantic information. The term frequency and inverse document frequency (tf-idf) is used to produce a composite weight, the most important terms in the web pages are extracted. Then the Pointwise Mutual Information (PMI) between the most important terms and the terms in the twitter dataset are calculated. The PMI metric measures the closeness between the twitter terms and the most important terms in the web pages. Then this measure is added with the similarity score matrix to provide the socio semantic search information for recommendation generation. The experimental results show that the pro-posed method has better performance in terms of prediction accuracy, precision, F1 measure, R measure and coverage.

The increasing growing need for optimization of transportation in a sustainable and green environment is fundamental for the future of mobility. Companies, governments, and non-governmental institutions are trying to find new ways to contribute to this challenge. In particular, the approach is to improve the efficiency and the quality of the movement of diversification of an integrated way of transportation of people and goods. Many people may need to be at some location at some point in time, or many goods may need to be transported to nearby destinations with the same packaging conditions. The main goal of this Special Issue was to integrate the most recent advances in transportation that connect the hybrid point of view. In particular, key contributions integrate several interesting topics related to earning algorithms for the integration of passengers and goods, gamification techniques, semantic data, blockchain, and edge computing. Other topics in urban mobility, railway, and highways were considered, and hot topics on shared autonomous vehicles (SAVs) brought huge value. Moreover, the Internet of Things (IoT) opens the doors for new development in Vehicle to Everything (V2X), which are crucial for the development of new services that generate business and social impact in the future of a diversified way of the integration of passengers and goods mobility.

Technological developments in communication technologies in the form of hardware and software have made unilateral sensor connectivity over Internet access that facilitates data observation and measurement of physical entities. A technology known as Internet of Things (IoT) is commonly referred to as the connectivity of Internet devices that provides the communication interactivity between the physical and the cyber objects. One of the key objectives of Internet computing is to simplify human activities and improve the user experience and device access. To explore its basic challenges, big data is somehow diversified into smart-data intelligence that transforms the raw semantic data into smart-data. The transformation approaches realize the significance of productivity and financial gain, which in turn offers a better decision-making process and privacy preservation. Moreover, the intelligent system collects raw data from different devices that analyze the extracted information. Since IoT plays a significant role in the development of a new source dataset, a seamless authentication protocol (SAP) is preferably chosen to coalesce data inference, algorithm development, and technological advancement. The comparative analysis proves that the proposed SAP consumes less computation and communication overhead as compared to other authentication schemes.

BACKGROUND: The increasing use of robotics in the work of co-workers poses some new problems in terms of occupational safety and health. In the workplace, industrial robots are being used increasingly. During operations such as repairs, unmanageable, adjustment, and set-up, robots can cause serious and fatal injuries to workers. Collaborative robotics recently plays a rising role in the manufacturing filed, warehouses, mining agriculture, and much more in modern industrial environments. This development advances with many benefits, like higher efficiency, increased productivity, and new challenges like new hazards and risks from the elimination of human and robotic barriers. OBJECTIVES: In this paper, the Advanced Human-Robot Collaboration Model (AHRCM) approach is to enhance the risk assessment and to make the workplace involving security robots. The robots use perception cameras and generate scene diagrams for semantic depictions of their environment. Furthermore, Artificial Intelligence (AI) and Information and Communication Technology (ICT) have utilized to develop a highly protected security robot based risk management system in the workplace. RESULTS: The experimental results show that the proposed AHRCM method achieves high performance in human-robot mutual adaption and reduce the risk. CONCLUSION: Through an experiment in the field of human subjects, demonstrated that policies based on the proposed model improved the efficiency of the human-robot team significantly compared with policies assuming complete human-robot adaptation.

Regarding programmable logic controller (PLC) development, improving programming efficiency and encouraging code exchange and software reuse are necessary to increase the productivity and safety of smart manufacturing and industrial automation. However, differences between implementations are significant even though all manufacturers claim to conform to the IEC 61131-3 and IEC 61131-10 standards, resulting in incompatibilities inside heterogeneous systems, preventing projects from interoperating between vendors. In this article, we present an approach that utilizes an ontology-based semantic model named OntoPLC to enable automatic porting of PLC projects between development environments and also prevent significant information loss during the translation process. High-level semantics of PLC projects are added into OntoPLC including software resources, which can be required for software reuse leveraging semantic query. To demonstrate the usefulness of the ontology model, the proposed methodology is applied to a turbine-driven boiler feed pump control module.

Knowledge representation is one of the critical problems in knowledge engineering and artificial intelligence, while knowledge embedding as a knowledge representation methodology indicates entities and relations in knowledge graph as low-dimensional, continuous vectors. In this way, knowledge graph is compatible with numerical machine learning models. Major knowledge embedding methods employ geometric translation to design score function, which is weak-semantic for natural language processing. To overcome this disadvantage, in this paper, we propose our model based on multi-view clustering framework, which could generate semantic representations of knowledge elements (i.e., entities/relations ). With our semantic model, we also present an empowered solution to entity retrieval with entity description. Extensive experiments show that our model achieves substantial improvements against baselines on the task of knowledge graph completion, triple classification, entity classification, and entity retrieval.

Currently, Knowledge Base Question Answering (KBQA) is an important research topic in the fields of information retrieval (IR) and natural language processing (NLP). The most common questions asked on the Web are simple questions, which can be answered by a single relational fact in a knowledge base (KB). However, answering simple questions automatically remains a challenging task in the IR and NLP research communities. Based on a review of various studies and a detailed analysis, we surmise that these challenges are primarily related to the following concerns: (1) how to effectively access a large-scale KB; and (2) how to effectively reduce the gap between NL questions and the structured semantics in a KB. Most previous studies have considered these as separate and independent subtasks, subject detection and predicate matching. Here, we propose a deep fused model that combines subject detection and predicate matching under a unified framework. Specifically, we employ a subject detection model to recognize the subject entity in a question, and a multilevel semantic model to learn the semantic representations for questions and predicates. These models share parameters, and can be trained jointly. We evaluated the proposed method on both English and Chinese KBQA datasets. The experimental results demonstrate that the proposed approach significantly outperforms state-of-the-art systems when applied to both datasets.

The subject of study. The paper focuses on the extraction of semantics from texts in Arabic. In particular, the applicability of the Bell test to word pairs is investigated as a measure of the semantic words relatedness in a context. The study applies the quantum formalism to the task of information retrieval in Arabic texts and presents the results of this work. The authors also examine the influence of the context width on the effectiveness of information retrieval. Method. The research is based on the vector representation of the context. It uses the well-known approach based on the HAL (Hyperspace Analogue to Language) matrix and Bell test. The HAL matrix allows taking into account both the frequency of the words occurrence in the context and the distance to the target word. Quantum theory operates with probability density matrices. Quantum theory allows describing probabilities in the vector space in a more natural way, i.e., words can be represented as vectors. Main results. The results demonstrate that using the Bell’s test for texts in Arabic provides a better ranking of search results compared to the results of search services. Practical significance. The research results can be used in the development of the information retrieval systems, as well as for the further development of methods based on the distributive hypothesis.

PurposeTo meet the emerging demand for fine-grained annotation and semantic enrichment of cultural heritage images, this paper proposes a new approach that can transcend the boundary of information organization theory and Panofsky's iconography theory.Design/methodology/approachAfter a systematic review of semantic data models for organizing cultural heritage images and a comparative analysis of the concept and characteristics of deep semantic annotation (DSA) and indexing, an integrated DSA framework for cultural heritage images as well as its principles and process was designed. Two experiments were conducted on two mural images from the Mogao Caves to evaluate the DSA framework's validity based on four criteria: depth, breadth, granularity and relation.FindingsResults showed the proposed DSA framework included not only image metadata but also represented the storyline contained in the images by integrating domain terminology, ontology, thesaurus, taxonomy and natural language description into a multilevel structure.Originality/valueDSA can reveal the aboutness, ofness and isness information contained within images, which can thus meet the demand for semantic enrichment and retrieval of cultural heritage images at a fine-grained level. This method can also help contribute to building a novel infrastructure for the increasing scholarship of digital humanities.

In recent years, the use of Historic Building Information Modeling (HBIM) has grown prevalent and thus provided a research opportunity. Differing from newly constructed buildings, structural components of historic buildings come with unique physical configurations and have amassed impressive amount of restoration data, all of which must be taken into consideration when incorporating Building Information Modeling. In terms of modelling, it is critical to determine the appropriate level of detail (LoD), level of information (LoI), especially the comprehensiveness and expandability of the database. International Committee for Documentation/Conceptual Reference Model (CIDOC CRM) is a widely accepted standard for ontology model. This study aims to integrate the HBIM and CIDOC CRM to construct a framework and comprehensive operational procedure for the modeling of traditional Minan architecture and a database with complete semantics archiving the background and restoration data. Autodesk A360 is ideal for collaborative. However, there are limitations when it comes to developing advanced models for data management or query; interactive experience; meeting model applications derived from future scenarios. Therefore, the study also offers a 3D modeling platform constructed using Unity, as well as a comparison of the platforms built with Unity, three.js and Autodesk A360 as a reference for users.

We present an expressive information-flow control type system with recursive types, existential types, label polymorphism, and impredicative type polymorphism for a higher-order programming language with higher-order state. We give a novel semantic model of this type system and show that well-typed programs satisfy termination-insensitive noninterference. Our semantic approach supports compositional integration of syntactically well-typed and syntactically ill-typed---but semantically sound---components, which we demonstrate through several interesting examples. We define our model using logical relations on top of the Iris program logic framework; to capture termination-insensitivity, we develop a novel language-agnostic theory of Modal Weakest Preconditions. We formalize all of our theory and examples in the Coq proof assistant.

Laser scanners can easily acquire the geometric data of physical environments in the form of point clouds. Industrial 3D reconstruction processes generally recognize objects from point clouds, which should include both geometric and semantic data. However, the recognition process is often a bottleneck in 3D reconstruction because it is labor intensive and requires expertise in domain knowledge. To address this problem, various methods have been developed to recognize objects by retrieving their corresponding models from a database via input geometric queries. In recent years, geometric data conversion to images and view-based 3D shape retrieval applications have demonstrated high accuracies. Depth images that encode the depth values as pixel intensities are frequently used for view-based 3D shape retrieval. However, geometric data collected from objects are often incomplete owing to occlusions and line-of-sight limitations. Images generated by occluded point clouds lower the view-based 3D object retrieval performance owing to loss of information. In this paper, we propose a viewpoint and image-resolution estimation method for view-based 3D shape retrieval from point cloud queries. Further, automatic selection of viewpoint and image resolution are proposed using the data acquisition rate and density calculations from sampled viewpoints and image resolutions. The retrieval performances for images generated by the proposed method are investigated via experiments and compared for various datasets. Additionally, view-based 3D shape retrieval performance with a deep convolutional neural network was investigated using the proposed method.

Studying food recipes is indispensable to understand the science of cooking. An essential problem in food computing is the adaptation of recipes to user needs and preferences. The main difficulty when adapting recipes is in determining ingredients relations, which are compound and hard to interpret. Word embedding models can catch the semantics of food items in a recipe, helping to understand how ingredients are combined and substituted. In this work, we propose an unsupervised method for adapting ingredient recipes to user preferences. To learn food representations and relations, we create and apply a specific-domain word embedding model. In contrast to previous works, we not only use the list of ingredients to train the model but also the cooking instructions. We enrich the ingredient data by mapping them to a nutrition database to guide the adaptation and find ingredient substitutes. We performed three different kinds of recipe adaptation based on nutrition preferences, adapting to similar ingredients, and vegetarian and vegan diet restrictions. With a 95% of confidence, our method can obtain quality adapted recipes without a previous knowledge extraction on the recipe adaptation domain. Our results confirm the potential of using a specific-domain semantic model to tackle the recipe adaptation task., European Commission 816303, University of Granada

The provision of the heterogeneous information acquisition and managing of emerging technologies with IoT, cloud-based storage, and improved communication services have filled the data scarcity gap on one hand but raised the challenge to extract, process, and comprehend relevant data of complex integrated multiple domains involving a large number of participants with diverse spatial terminologies and methodologies. To resolve this challenge various big data and natural language processing techniques were applied. Another widely used approach to resolve the challenges of heterogeneity, interoperability, and complexity of integrated domain is ontology-based semantic modeling. We proposed Ontology for River Flow and Flood Mitigation (ORFFM) for semantic knowledge formalization with semantic understandability of irrigation, disaster management, related administrative and agricultural domain concepts by humans and machines. The semantic modeling of distributed river flow network and associated flood disaster mitigation for effective coordination, collaborative response activities leads to reduce the impact of a disaster and improve information representation among stakeholders. Furthermore, semantic formalization and inference are supported by explicitly annotated information. We populated ORFFM with Pakistan’s Indus river system, flood disaster management, and Sindh administrative authorities to develop a knowledgebase for knowledge sharing and representation. The formal semantically enriched knowledgebase would contribute towards streamflow optimization and flood mitigation through effective coordination and common conceptualization during disaster management phases. The semantic model of irrigation networks would also be useful for academic purposes to acquire domain knowledge for new entrants in the irrigation and disaster management field.

Productivity and flexibility are antagonists with opposite goals. Until now, optimal productivity could only be achieved in fully automated inflexible serial production. Increased demand for flexibility due to individualized products e.g. in most SMEs requires an increased level of flexibility – also for robots that should provide improved skills as well as improved means of interaction to simplify programming. Instrumented tools based on 3D tracking technology can be used as interfaces in Programming by Demonstration (PbD) scenarios but are prone to inaccuracies introduced by human demonstration. This paper presents a programming paradigm that combines a semantic model based geometric reasoning paradigms with an instrumented tool based PbD approach in order to compensate those introduced inaccuracies as well as investigates basic accuracies of demonstration of point based operations.

This paper proposes an interacting Takagi–Sugeno(T-S) fuzzy semantic model estimator (ITS-FSM) for maneuvering target tracking, and constructs a framework for a generic interacting T-S fuzzy semantic model to incorporate semantic information concerning the target. To adaptively calculate the transition probability matrix of the models, a probabilistic switching model based on semantic fuzzy sets is derived by using the degree of intersection between fuzzy sets, which enables switching from one semantic fuzzy set to another. We also propose an efficient kernel maximum entropy fuzzy clustering method to identify the premise parameters of the model. This enables the proposed algorithm to recursively estimate the premise parameters in case of a limited number of samples. Moreover, a modified extended forgetting factor recursive least squares (MEFRLS) estimator is used to identify the parameters of the T-S fuzzy semantic model. The results of experiments on three simulation datasets show that the proposed ITS-FSM algorithm is efficient, and is excellent at handling non-Gaussian noise.

The control loop model and a new method for reproducing information transfer processes in the digital platform ecosystem are developed and proposed. The presented innovative solutions are based on a semantic model created for the functional architecture of a component in the ecosystem of a digital platform that provides information interaction of organizational systems. The novelty of the method is to provide the possibility of coordinated centralized management in a single information management environment of the digital platform ecosystem.

With the target of simultaneously segmenting semantically related videos to identify the common objects, video object cosegmentation has attracted the attention of researchers in recent years. Existing methods are primarily based on pair-wise relations between adjacent pixels and regions, which are susceptible to performance degradation from object entries/exists or occlusions. Specifically, we refer these video frames without the common objects present as the “empty” frames. In this paper, we propose a multilevel hypergraph-based full Video object CoSegmentation (VCS) method, which incorporates high-level semantics and low-level appearance/motion/saliency to construct the hyperedge among multiple spatially and temporally adjacent regions. Specifically, the high-level semantic model fuses multiple object proposals from each frame instead of relying on a single object proposal per frame. A hypergraph cut is subsequently utilized to calculate the object cosegmentation. Experiments on four video object segmentation/cosegmentation datasets against state-of-the-art methods with both objective and subjective results manifest the effectiveness of the proposed VCS method, including the SegTrack and VCoSeg datasets without “empty” frames, the XJTU-Stevens dataset with 3.7% “empty” frames, and the Noisy-ViCoSeg dataset proposed together with our method with 30.3% “empty” frames.

We argue that that the use of the Carnap - Bar-Hillel principle of indexed expressions provides a suitable cognitive model to undergo the spread of information processes in the Semantic Net. To understand the stratification of the net into the surface Web and the hidden Web, a model structure is proposed that provides navigation through the net as a whole. For logical expressions, a relativized evaluation map is given, which is parameterized by the indexing system. It is allowed to use concepts that are generated using partial functions, the variables of which run over the range of possible individual information processes. A generalized operator providing virtualization is presented for individuals. The process is accompanied with the dynamic generation of Web-pages, which triggers their indexing. This way they are thereby transferred to the surface layer of the network. A commutative diagram is proposed that establishes a cognitive model of semantic constructions of variable domains, the connection of virtual individuals with actual ones which corresponds to transitions over the stages of knowledge. The knowledge stages are modeled by indexing systems.

The purpose of the article: development of semantic model of metrics and data and technique for security assessment based on of this model to get objective scores of information system security. Research method: theoretical and system analysis of open security data sources and security metrics, semantic analysis and classification of security data, development of the security assessment technique based on the semantic model and methods of logical inference, functional testing of the developed technique. The result obtained: an approach based on the semantic model of metrics and data is proposed. The model is an ontology generated considering relations among the data sources, information system objects and data about them, primary metrics of information system objects and integral metrics and goals of assessment. The technique for metrics calculation and assessment of unspecified information systems security level in real-time using the proposed model is developed. The case study demonstrating applicability of the developed technique and ontology to answer security assessment questions is provided. The area of use of the proposed approach are security assessment components of information security monitoring and management systems aimed at increasing their efficiency.

This work presents the development and field testing of a novel adaptive visual information gathering (AVIG) framework for autonomous exploration of benthic environments using AUVs. The objective is to adapt dynamically the robot exploration using the visual information gathered online. This framework is based on a novel decision-time adaptive replanning (DAR) behavior that works together with a sparse Gaussian process (SGP) for environmental modeling and a Convolutional Neural Network (CNN) for semantic image segmentation. The framework is executed in mission time. The SGP uses semantic data obtained from stereo images to probabilistically model the spatial distribution of certain species of seagrass that colonize the sea bottom forming widespread meadows. The uncertainty of the probabilistic model provides a measure of sampling informativeness to the DAR behavior. The DAR behavior has been designed to execute successive informative paths, without stopping, considering the newest information obtained from the SGP. We solve the information path planning (IPP) problem by means of a novel depth-first (DF) version of the Monte Carlo tree search (MCTS). The DF-MCTS method has been designed to explore the state-space in a depth-first fashion, provide solution paths of a given length in an anytime manner, and reward smooth paths for field realization with non-holonomic robots. The complete framework has been integrated in a ROS environment as a high level layer of the AUV software architecture. A set of simulations and field testing show the effectiveness of the framework to gather data in P. oceanica environments.

The paper considers the possibility of constructing a specialized computing system oriented at the transmission of data through information channels, that are determined taking into account the semantics of the selected data. In the process of computations the data is connected with semantic characteristics that describe the channel of computations, which can be considered as a method of semantic data enrichment. The system of information channels as a whole can be considered as an information graph describing the structuring of the processed data. The information graph supports the data model in the form of a network, the framework of which are objects and the relationships between them. The paper proposes language tools for determining the information graph and interpretation tools that provide practical computations. The set of information channels that make up the information graph can be considered as a low-level tool for data enrichment. The paper studies the possibility of determining tools of higher level. An applicative type language is proposed for defining information graphs, the syntax and semantics of the language are specified. The proposed language can be considered as an intermediate level tool for defining semantics. A procedure is proposed for compiling the language into a low-level construct, preserving the semantics of the language. The supporting system for the proposed computing system includes a low-level language interpreter, as well as an intermediate-level language compiler into a low-level language. The supporting system is implemented in an applicative programming environment. Some elements of the supporting system were tested when developing applied information systems in the field of jurisprudence.