Reviewerly/exHarmony huggingface dataset:
A Secure and Efficient Renewable Energy Sharing Framework for Distributed Prosumers

A microgrid incorporates renewable energy sources, generating and storing power in a distributed manner. Traditionally, a microgrid can only sell the surplus of energy to the grid at a much lower price, potentially discouraging people from participating in the energy revolution. Enabling direct energy trading among microgrids and consumers improves the return on investment. However, it is challenging to swap transaction information. Thus, we leverage carefully-selected blockchain technologies to tackle the above concern and develop an off-grid energy-sharing framework for prosumers. In detail, a combination of technologies is adopted to design the framework, including permisionless chain, proof of state algorithm, smart contract, lightweight cryptosystem, and Raiden network. Evaluation showcases the benefits and validity of our framework, where a prosumer makes up to 10 times of profits as the conventional trading strategy in the Australian market. Furthermore, security and efficiency are enhanced in comparison to classic solutions.
τSQWRL: A TSQL2-Like Query Language for Temporal Ontologies Generated from JSON Big Data

Temporal ontologies allow to represent not only concepts, their properties, and their relationships, but also time-varying information through explicit versioning of definitions or through the four-dimensional perdurantist view.They are widely used to formally represent temporal data semantics in several applications belonging to different fields (e.g., Semantic Web, expert systems, knowledge bases, big data, and artificial intelligence).They facilitate temporal knowledge representation and discovery, with the support of temporal data querying and reasoning.However, there is no standard or consensual temporal ontology query language.In a previous work, we have proposed an approach named JOWL (temporal OWL 2 from temporal JSON, where OWL 2 stands for "OWL 2 Web Ontology Language" and JSON stands for "JavaScript Object Notation").JOWL allows (1) to automatically build a temporal OWL 2 ontology of data, following the Closed World Assumption (CWA), from temporal JSON-based big data, and (2) to manage its incremental maintenance accommodating their evolution, in a temporal and multi-schema-version environment.In this paper, we propose a temporal ontology query language for JOWL , named SQWRL (temporal SQWRL), designed as a temporal extension of the ontology query language-Semantic Query-enhanced Web Rule Language (SQWRL).The new language has been inspired by the features of the consensual temporal query language TSQL2 (Temporal SQL2), well known in the temporal (relational) database community.The aim of the proposal is to enable and simplify the task of retrieving any desired ontology version or of specifying any (complex) temporal query on time-varying ontologies generated from time-varying big data.Some examples, in the Internet of Healthcare Things (IoHT) domain, are provided to motivate and illustrate our proposal.
Context-aware query derivation for IoT data streams with DIVIDE enabling privacy by design

Integrating Internet of Things (IoT) sensor data from heterogeneous sources with domain knowledge and context information in real-time is a challenging task in IoT healthcare data management applications that can be solved with semantics. Existing IoT platforms often have issues with preserving the privacy of patient data. Moreover, configuring and managing context-aware stream processing queries in semantic IoT platforms requires much manual, labor-intensive effort. Generic queries can deal with context changes but often lead to performance issues caused by the need for expressive real-time semantic reasoning. In addition, query window parameters are part of the manual configuration and cannot be made context-dependent. To tackle these problems, this paper presents DIVIDE, a component for a semantic IoT platform that adaptively derives and manages the queries of the platform’s stream processing components in a context-aware and scalable manner, and that enables privacy by design. By performing semantic reasoning to derive the queries when context changes are observed, their real-time evaluation does require any reasoning. The results of an evaluation on a homecare monitoring use case demonstrate how activity detection queries derived with DIVIDE can be evaluated in on average less than 3.7 seconds and can therefore successfully run on low-end IoT devices.
Contradictions of digital higher education in the information culture of modern society

Introduction. Digital education today is a new phenomenon for many countries of the world. In its most general form, it represents the next stage of informatization of education, but it has its own specific features. The author continues a number of previous studies on the problems of informatization of education, information socialization of personality, formation of information culture. Scientists are actively studying the problems associated with the digitalization of higher education. The range of these problems is quite wide and each of them arises on the basis of various contradictions that are studied in different areas of scientific knowledge: sociology, economics, pedagogy, philosophy, cultural studies, etc. To see the possible negative consequences of the transition to digital education and take measures to level them, it is necessary to identify and systematize these contradictions and problems. This will make it possible to develop programs and proposals to improve the quality and effectiveness of digital higher education based on an integrated system approach. The aim of the study is to analyze and systematize the main contradictions and problems of digital higher education in Russia. Materials and Methods. The methodology and methods of the study were the methods of analytical review of domestic and foreign literature on the development of digital education. Further, on the basis of methods of analysis, comparison, generalization and principles of dialectics, a systematization of the main contradictions and problems that arise in the system of modern higher education in Russia in the process of its digitalization was carried out. Results. The study showed that the main contradictions and related problems of digital higher education can be divided into three groups: within pedagogical activity between traditional and digital forms of education, training and upbringing; between digital and traditional forms of elements of education (subject, object, means, environment, conditions, goals, result); between elements of the main subsystems of culture (information, praxiological and axiological), the formation of which is the main goal of pedagogical activity. Discussion and Conclusions. The study can be used to build models of digital educational activities that enable you to learn new principles of the relationship of digital education, training, and education in a digital information culture of a modern society, a new status and role of subjects of pedagogical activity, a new paradigm of pedagogical interaction, given a system of contradictions between traditional and digital mechanisms of socialization.
Complete monogamy of multipartite quantum mutual information

Quantum mutual information (QMI) not only displays the mutual information in the system but also demonstrates some quantum correlation beyond entanglement. We explore here the two alternatives of multipartite quantum mutual information (MQMI) based on the von Neumann entropy according to the framework of the complete measure of multi-particle quantum system. We show that these two MQMI are complete, monogamous on pure states, and one of them is not only completely monogamous but also tightly complete monogamous while another one is not. Moreover, we present another two MQMI by replacing the von Neumann entropy with the Tasllis $q$-entropy from the former two ones. It is proved that one of them displays some degree of ``completeness'' as a measure of multi-particle quantum system, but the other one is not even non-negative and thus it can not be a alternative of MQMI. We also discuss the triangle relation for these three alternatives of MQMI. It is shown that the triangle inequalities hold for the former two MQMI as that of entanglement measure but the later one fails. By comparison, we found that the von Neumann entropy is better than other versions of entropy as desired when we characterize the quantum correlation in multi-particle system.
CoSumNet: A video summarization-based framework for COVID-19 monitoring in crowded scenes

The outbreak of COVID-19 pandemic poses new challenges to research community to investigate novel mechanisms for monitoring as well as controlling its further spread via crowded scenes. Moreover, the contemporary methods of COVID-19 preventions are enforcing strict protocols in the public places. The emergence of robust computer vision-enabled applications leverages intelligent frameworks for monitoring of the pandemic deterrence in public places. The employment of COVID-19 protocols via wearing face masks by human is an effective procedure that is implemented in several countries across the world. It is a challenging task for authorities to manually monitor these protocols particularly in densely crowded public gatherings such as, shopping malls, railway stations, airports, religious places etc. Thus, to overcome these issues, the proposed research aims to design an operative method that automatically detects the violation of face mask regulation for COVID-19 pandemic. In this research work, we expound a novel technique for COVID-19 protocol desecration via video summarization in the crowded scenes (CoSumNet). Our approach automatically yields short summaries from crowded video scenes (i.e., with and without mask human). Besides, the CoSumNet can be deployed in crowded places that may assist the controlling agencies to take appropriate actions to enforce the penalty to the protocol violators. To evaluate the efficacy of the approach, the CoSumNet is trained on a benchmark "Face Mask Detection ∼12K Images Dataset" and validated through various real-time CCTV videos. The CoSumNet demonstrates superior performance of 99.98 % and 99.92 % detection accuracy in the seen and unseen scenarios respectively. Our method offers promising performance in cross-datasets environments as well as on a variety of face masks. Furthermore, the model can convert the longer videos to short summaries in nearly 5-20 s approximately.
Arabic Captioning for Images of Clothing Using Deep Learning

Fashion is one of the many fields of application that image captioning is being used in. For e-commerce websites holding tens of thousands of images of clothing, automated item descriptions are quite desirable. This paper addresses captioning images of clothing in the Arabic language using deep learning. Image captioning systems are based on Computer Vision and Natural Language Processing techniques because visual and textual understanding is needed for these systems. Many approaches have been proposed to build such systems. The most widely used methods are deep learning methods which use the image model to analyze the visual content of the image, and the language model to generate the caption. Generating the caption in the English language using deep learning algorithms received great attention from many researchers in their research, but there is still a gap in generating the caption in the Arabic language because public datasets are often not available in the Arabic language. In this work, we created an Arabic dataset for captioning images of clothing which we named "ArabicFashionData" because this model is the first model for captioning images of clothing in the Arabic language. Moreover, we classified the attributes of the images of clothing and used them as inputs to the decoder of our image captioning model to enhance Arabic caption quality. In addition, we used the attention mechanism. Our approach achieved a BLEU-1 score of 88.52. The experiment findings are encouraging and suggest that, with a bigger dataset, the attributes-based image captioning model can achieve excellent results for Arabic image captioning.
Time-Series-Based Air Temperature Forecasting Based on the Outlier Robust Extreme Learning Machine

In this study, an improved version of the outlier robust extreme learning machine (IORELM) is introduced as a new method for multi-step-ahead hourly air temperature forecasting. The proposed method was calibrated and used to estimate the hourly air temperature for one to ten hours in advance after finding its most optimum values (i.e., orthogonality effect, activation function, regularization parameter, and the number of hidden neurons). The results showed that the proposed IORELM has an acceptable degree of accuracy in predicting hourly temperatures ten hours in advance (R = 0.95; NSE = 0.89; RMSE = 3.74; MAE = 1.92).
Generation Z’s response to the virtual reality advertising in China

Purpose Virtual reality (VR), as a new type of media technology, significantly improves the audience experience with product presentation in the marketing communication field. The apartment rental market, particularly in China, has no exception in adopting VR technology in its communication strategy. VR usage has been boosted since the outbreak of COVID-19 and has become a widespread application in the global apartment rental market. Although extant studies have analyzed how real estate companies use VR technology to enhance customer experience, few studies have been made to explore the power of VR in apartment rental advertising, particularly in targeting the youth in China market. To fill this research gap, this study aims to figure out how young consumers perceive VR advertising and characteristics of VR used in apartment rental advertisements, and how VR advertising affects young consumers’ intentions to rent an apartment. Design/methodology/approach A cross-sectional survey was conducted in 2021 with 301 Chinese university students aged 18 to 23. All respondents were invited offline and guided to watch one selected rental advertisement with VR technology featuring an apartment of about 50 square meters and then complete a questionnaire. Findings VR’s media richness in the apartment rental advertising increases its sense of presence perceived by the survey respondents. Both VR’s media richness and sense of presence positively influence respondents’ attitudes toward the advertised apartment. If respondents evaluate the advertised apartment positively, they are more willing to rent the advertised apartment. Research limitations/implications The sample size is not large enough to represent all Generation Z consumers in China. The use of the nonprobability sampling method also limits the generalizability of the study results. Practical implications To counter the challenges created by COVID-19, apartment rental service providers and apartment owners/landlords are suggested to enhance the application of VR technology to the apartment rental advertisements to grow young consumers’ interest in the advertised apartments and even their renting intention. Originality/value To the best of the authors’ knowledge, this is the first quantitative study to assess young consumers’ responses to VR apartment rental advertising in China.
Problems in information classification: insights from practice

Purpose This study aims to identify problems connected to information classification in theory and to put those problems into the context of experiences from practice. Design/methodology/approach Five themes describing problems are discussed in an empirical study, having informants represented from both a public and a private sector organization. Findings The reasons for problems to occur in information classification are exemplified by the informants’ experiences. The study concludes with directions for future research. Originality/value Information classification sustains the basics of security measures. The human–organizational challenges are evident in the activities but have received little attention in research.
Arithmetic optimization with hybrid deep learning algorithm based solar radiation prediction model

Solar radiation affects extreme weather occurrences, as well as on the temperature and mean sea level. Therefore, precise studies and measurements of geographical and temporal variations of solar radiation are required. The development of deep learning and machine learning methods for developing solar radiation predictive models is gaining traction. Therefore, this paper presents an Arithmetic Optimization with Hybrid Deep Learning based Solar Radiation Prediction model. The presented AOHDL-SRP model follows a three-stage process: pre-processing, prediction, and hyperparameter optimization. Primarily, the AOHDL-SRP model involves the min–max normalization technique to normalize the input data to a uniform format. Besides, the presented AOHDL-SRP model applies HDL using a convolutional neural network with an attention-oriented long short-term memory (ALSTM) model. Finally, the arithmetic optimization algorithm (AOA) is applied for the hyperparameter optimization of the HDL model, and it assists in improving the predictive performance. The experimental validation of the AOHDL-SRP model is tested, and the results are lower than different expectations. The AOHDL-SRP model has demonstrated that it is superior to all other models by achieving the highest possible R2-score of 100% and MSE, RMSE, and MAE values of 0.18, 0.43, and 0.32, respectively.
An empirical evaluation of the effectiveness of attack graphs and MITRE ATT&amp;CK matrices in aiding cyber attack perception amongst decision-makers

Decision-makers struggle to understand cyber-security reports, sometimes leading to inadequate cyber-attack responses. Attack modelling techniques (AMTs) can aid cyber-attack perception, but their effectiveness in improving comprehension amongst non-experts is still under-researched. Attack graphs are the most popular AMT amongst academics, while MITRE ATT&CK is becoming the most popular tool amongst practitioners. This research evaluates the effectiveness of attack graphs and MITRE ATT&CK in aiding cyber-attack perception after an attack has taken place. 157 participants were divided into expert and non-expert groups and further subdivided within respective groups. Participants underwent a test designed to demonstrate their comprehension of two cyber-attacks. Participants were also required to express personal preferences, in particular outlining which of the two AMTs was better at aiding their cyber-attack perception. Paired T-Tests reveal that both groups performed better with the attack graph compared to MITRE ATT&CK. Furthermore, both groups outlined a personal preference for the attack graph over MITRE ATT&CK.
An Intelligent Heuristic Manta-Ray Foraging Optimization and Adaptive Extreme Learning Machine for Hand Gesture Image Recognition

The development of hand gesture recognition systems has gained more attention in recent days, due to its support of modern human-computer interfaces. Moreover, sign language recognition is mainly developed for enabling communication between deaf and dumb people. In conventional works, various image processing techniques like segmentation, optimization, and classification are deployed for hand gesture recognition. Still, it limits the major problems of inefficient handling of large dimensional datasets and requires more time consumption, increased false positives, error rate, and misclassification outputs. Hence, this research work intends to develop an efficient hand gesture image recognition system by using advanced image processing techniques. During image segmentation, skin color detection and morphological operations are performed for accurately segmenting the hand gesture portion. Then, the Heuristic Manta-ray Foraging Optimization (HMFO) technique is employed for optimally selecting the features by computing the best fitness value. Moreover, the reduced dimensionality of features helps to increase the accuracy of classification with a reduced error rate. Finally, an Adaptive Extreme Learning Machine (AELM) based classification technique is employed for predicting the recognition output. During results validation, various evaluation measures have been used to compare the proposed model's performance with other classification approaches.
Leveraging Data Science and Novel Technologies to Develop and Implement Precision Medicine Strategies in Critical Care

Precision medicine aims to identify treatments that are most likely to result in favorable outcomes for subgroups of patients with similar clinical and biological characteristics. The gaps for the development and implementation of precision medicine strategies in the critical care setting are many, but the advent of data science and multi-omics approaches, combined with the rich data ecosystem in the intensive care unit, offer unprecedented opportunities to realize the promise of precision critical care. In this article, the authors review the data-driven and technology-based approaches being leveraged to discover and implement precision medicine strategies in the critical care setting.
Reviewing Learning and Teaching Content in the Scope of Artificial Intelligence: for Humanities and Social Sciences Majors

Social progress is largely determined by the degree to which digital reality is mastered by society as well as by the development and implementation of innovative technologies related to the use of artificial intelligence. Of particular relevance are issues related to the training of a new generation of specialists, who will be able not only to harmoniously fit into the current digital contexts of social life but also purposefully improve, develop and transform their professional digital tools. It is required to set and solve the tasks of developing students’ competences at a high-quality level, which implies pedagogical specification of the modern educational content. The aim of the study is to elaborate a didactic apparatus for revising the modern educational content for students majoring in the humanities and social sciences, taking into account the current contexts of professional activity in digital reality. The authors have formulated a hypothesis that, in order to prepare humanities students to solve the ever more complex tasks of professional activity and communication in digital reality, it is necessary to update the educational content on an interdisciplinary basis, bringing it into line with the digital contexts of professional activity in this field and introducing a new content outline. This can be done if the following tasks are solved: (1) to define the current areas of work of a specialist in the humanities and social sciences in digital reality and, on this basis, create a digital profile for such a specialist, i.e., an invariant set of tasks relevant in the context of digitalization as well as the corresponding competences; (2) to determine a set of disciplines that have the greatest potential for implementing the digital profile of such a specialist; (3) to substantiate the project of changes in the educational content for students majoring in the humanities and social sciences; and (4) to conduct an experimental test of the draft changes in this educational content. The nominal data comparison method, namely the McNemar test, was used. The solution of the above tasks allowed us: in theoretical terms, to lay the conceptual foundations for selecting the actual educational content for students majoring in the humanities and social sciences in digital reality and, in practical terms, to propose specific tools for updating the subject content. The obtained results confirmed the hypothesis of the study and ways to update the educational content on an interdisciplinary basis, taking into account the current contexts of the professional activity in the social and humanitarian sphere in digital reality.