Reviewerly/exHarmony huggingface dataset:
Interval-enhanced Graph Transformer solution for session-based recommendation

In many online recommendation services (e.g., multimedia streaming, e-commerce), predicting user’s next behavior based on anonymous sessions remains a challenging problem, mainly due to the lack of basic user information and limited behavioral information. The existing typical methods either model user behavior sequences based on RNN or capture potential relationships among items based on GNN. However, these pioneers ignore the importance of different time intervals in the behavior sequence, which implies the user preferences and makes the session sequence more distinguishable. Towards this end, we contribute an Interval-enhanced Graph Transformer (IGT) solution for the session-based recommendation, which takes both item relations and corresponding time intervals into consideration. Specifically, IGT consists of three modules: (i) Interval-enhanced session graph, which constructs all session sequences as session graphs with time intervals; (ii) Graph Transformer, which is embedded with time intervals is adopted to learn the complex interaction information among items. Among them, we design various time interval embedding functions, which can be flexibly injected into the framework; (iii) Preference representation and prediction, which uses an attention network to fuse the user’s long-term preferences and short-term preferences to predict the next click. By conducting extensive experiments on the DIGINETICA, YOOCHOOSE and Last.FM three real-world datasets, we validate that IGT outperforms state-of-the-art solutions.
The Brussels Effect and Artificial Intelligence

The European Union is likely to introduce among the first, and most comprehensive AI regulatory regimes of the world’s major jurisdictions. We ask whether the EU’s upcoming regulation for AI will diffuse globally, producing a so-called “Brussels Effect”. Extending Anu Bradford’s work, we outline the mechanisms by which such regulatory diffusion may occur. We consider both the possibility that the EU’s AI regulation will incentivise changes in products offered in non-EU countries (a de facto Brussels Effect) and the possibility it will influence regulation adopted by other jurisdictions (a de jure Brussels Effect). Focusing on the proposed EU AI Act, we tentatively conclude that both de facto and de jure Brussels effects are likely. A de facto effect is particularly likely to arise in large US tech companies with “high-risk” AI systems. The upcoming regulation might be important in offering the first operationalisation of developing and deploying trustworthy AI.
On the explanatory power of Boolean decision trees

Decision trees have long been recognized as models of choice in sensitive applications where interpretability is of paramount importance. In this paper, we examine the computational ability of Boolean decision trees for the explanation purpose. We focus on both abductive explanations (suited to explaining why a given instance has been classified as such by the decision tree at hand) and on contrastive explanations (suited to explaining why a given instance has not been classified by the decision tree as it was expected). More precisely, we are interested in deriving, minimizing, and counting abductive explanations and contrastive explanations. We prove that the set of all irredundant abductive explanations (also known as PI-explanations or sufficient reasons) for an instance given a decision tree can be exponentially larger than the size of the input (the instance and the decision tree). Therefore, generating the full set of sufficient reasons for an instance can be out of reach. In addition, deriving a single sufficient reason, though computationally easy when dealing with decision trees, does not prove enough in general; indeed, two sufficient reasons for the same instance may differ on many features. To deal with this issue and generate synthetic views of the set of all sufficient reasons, we define notions of relevant features and of necessary features that characterize the (possibly negated) features appearing in at least one or in every sufficient reason for an instance, and we show that they can be computed in polynomial time. We also introduce the notion of explanatory importance, that indicates how frequent each (possibly negated) feature is in the set of all sufficient reasons. We show how the explanatory importance of a (possibly negated) feature and the number of sufficient reasons for an instance can be obtained via a model counting operation, which turns out to be practical in many cases. We also explain how to enumerate minimum-size sufficient reasons. We finally show that, unlike sufficient reasons, the set of all contrastive explanations for an instance given a decision tree can be derived, minimized and counted in polynomial time.
Jordanian Media's Use of Data Journalism in Newsrooms: A Descriptive Study

The current study dealt with identifying the nature of employing data journalism in newsrooms in the Jordanian media, and to achieve the goals of the study (53%) of the workers in the five local media institutions, who numbered (56), were taken through a simple random sample, and developed a questionnaire consisting of (56) Paragraph and seven dimensions. After verifying the psychometric properties of the questionnaire from honesty and consistency, the questionnaire was applied to the members of the sample, and the results have concluded that the concept of data journalism from the point of view of workers inside the newsrooms in the local media was low, and that the degree of employment Data journalism within G. The news in the local media from the point of view of its employees was of a moderate degree, and the degree of implications that the data journalism addresses within the newsroom In the local media it was a moderate degree, and the degree of tools used in data journalism in newsrooms in the local media from the viewpoint of its employees was a high degree, and that the degree of the most prominent difficulties facing the use of data journalism in newsrooms in a media Local media from the viewpoint of workers in it, and that the degree of the future view of the success of data journalism in the future from the viewpoint of workers in it, was a moderate degree.
Investigating science teachers’ intention to adopt virtual reality through the integration of diffusion of innovation theory and theory of planned behaviour: the moderating role of perceived skills readiness

This study contributes to the extant literature on instructional technology by investigating the relationships between the social and personal factors and behavioral intention to use virtual reality. Moreover, the current study examined the links between perceived characteristics of virtual reality and attitude and the moderating role that can be played by perceived skills readiness between those links. Inspired by the Theory of Planned Behaviour and Diffusion of Innovation Theory, a set of hypotheses was formed to test the proposed relationships using structural equation modeling partial least square to a sample of 171 science teachers in Oman. The results showed that attitude, social norms and perceived behavioral control can predict behavioral Intention to use virtual reality with attitude as the strongest predictor. Furthermore, the results indicated that relative advantage could predict attitude towards using virtual reality while compatibility and observability cannot. Finally, perceived skills readiness can strengthen the relationship between the perceived characteristics of virtual reality applications (relative advantage, compatibility and observability) and attitude towards using the virtual reality in the science classroom. Thus, this study highlights the importance of focussing on science teachers' skills readiness to use virtual reality so that they can use it confidently. Implications and future research studies are discussed.
Metropolitan single-photon distribution at 1550 nm for random number generation

Quantum communication networks will connect future generations of quantum processors, enable metrological applications, and provide security through quantum key distribution. We present a testbed that is part of the municipal fiber network in the greater Stockholm metropolitan area for quantum resource distribution through a 20 km long fiber based on semiconductor quantum dots emitting in the telecom C-band. We utilize the service to generate random numbers passing the NIST test suite SP800-22 at a subscriber 8 km outside of the city with a bit rate of 23.4 kbit/s.
Status, Challenges and Future Directions of Blockchain Technology in Power System: A State of Art Review

Intermittent distributed energy resources (DERs) add challenges to the modern power system network. On the other hand, information and communication technology (ICT) is changing traditional electricity grids into smart grids, which facilitates a decentralized system in which prosumers may participate in energy trading. Smart grids, DER integration, and network connectivity are adding complexity to the power system network day by day; Blockchain technology might be a great tool to manage the network’s operational complexity. The Blockchain provides for quicker, frictionless, secure, and transparent transactions. With the addition of smart contracts, it may be utilized to manage the expanding complexity of the contemporary power system. In this study, the authors focus on the scope, challenges, and potential future direction of Blockchain technology application in the power system. Blockchain has received interest and has been used for decentralized power system applications in recent years, but it is still young and has scalability, decentralization, and security concerns. This article discusses the interfaces and the possibilities that can assure trust, security, and transparency in decentralized power system applications and make a decentralized power system and power market possible.
The Use and Ethics of Digital Twins in Medicine

Digital Health Technologies (DHTs) are currently the subject of much debate both in terms of their technological frontiers as well as their ethical, legal and societal implications (ELSI). Regulation of such technologies as medical devices currently lacks behind their level of adoption. Digital Twins are the next evolution step of such DHTs and provide an opportunity to anticipate and act on ELSI before adoption again leaps before the necessary review. This paper introduces the concept and use cases of digital twins in medicine, then frames the debate through the lens of related technologies, machine learning and personalized medicine, and maps ethical challenges stemming from those. Finally, we lay out how digital twins may change and challenge the future practice of medicine.
A Machine-Learning–Blockchain-Based Authentication Using Smart Contracts for an IoHT System

Nowadays, finding genetic components and determining the likelihood that treatment would be helpful for patients are the key issues in the medical field. Medical data storage in a centralized system is complex. Data storage, on the other hand, has recently been distributed electronically in a cloud-based system, allowing access to the data at any time through a cloud server or blockchain-based ledger system. The blockchain is essential to managing safe and decentralized transactions in cryptography systems such as bitcoin and Ethereum. The blockchain stores information in different blocks, each of which has a set capacity. Data processing and storage are more effective and better for data management when blockchain and machine learning are integrated. Therefore, we have proposed a machine-learning-blockchain-based smart-contract system that improves security, reduces consumption, and can be trusted for real-time medical applications. The accuracy and computation performance of the IoHT system are safely improved by our system.
Efficient Output Photovoltaic Power Prediction Based on MPPT Fuzzy Logic Technique and Solar Spatio-Temporal Forecasting Approach in a Tropical Insular Region

Photovoltaic (PV) energy source generation is becoming more and more common with a higher penetration level in the smart grid because of PV energy’s falling production costs. PV energy is intermittent and uncertain due to its dependence on irradiance. To overcome these drawbacks, and to guarantee better smart grid energy management, we need to deal with PV power prediction. The work presented in this paper concerns the study of the performance of the fuzzy MPPT approach to extract a maximum of power from solar panels, associated with PV power estimation based on short time scale irradiance forecasting. It is particularly applied to a case study of a tropical insular region, considering extreme climatic variability. To validate our study with real solar data, measured and predicted irradiance profiles are used to feed the PV system, based on solar forecasting in a tropical insular context. For that, a spatio-temporal autoregressive model (STVAR) is applied. The measurements are collected at three sites located on Guadeloupe island. The high variability of the tropical irradiance profile allows us to test the robustness and stability of the used MPPT algorithms. Solar forecasting associated with the fuzzy MPPT technique allows us to estimate in advance the produced PV power, which is essential for optimal energy management in the case of smart energy production systems. Simulation of the proposed solution is validated under Matlab/Simulink software. The results clearly demonstrate that the proposed solution provides good PV power prediction and better optimization performance: a fast, dynamic response and stable static power output, even when irradiation is rapidly changing.
Comprehensive Analysis of Knowledge Graph Embedding Techniques Benchmarked on Link Prediction

In knowledge graph representation learning, link prediction is among the most popular and influential tasks. Its surge in popularity has resulted in a panoply of orthogonal embedding-based methods projecting entities and relations into low-dimensional continuous vectors. To further enrich the research space, the community witnessed a prolific development of evaluation benchmarks with a variety of structures and domains. Therefore, researchers and practitioners face an unprecedented challenge in effectively identifying the best solution to their needs. To this end, we propose the most comprehensive and up-to-date study to systematically assess the effectiveness and efficiency of embedding models for knowledge graph completion. We compare 13 models on six datasets with different sizes, domains, and relational properties, covering translational, semantic matching, and neural network-based encoders. A fine-grained evaluation is conducted to compare each technique head-to-head in terms of standard metrics, training and evaluation times, memory consumption, carbon footprint, and space geometry. Our results demonstrate the high dependence between performance and graph types, identifying the best options for each scenario. Among all the encoding strategies, the new generation of translational models emerges as the most promising, bringing out the best and most consistent results across all the datasets and evaluation criteria.
Is image-to-image translation the panacea for multimodal image registration? A comparative study

Despite current advancement in the field of biomedical image processing, propelled by the deep learning revolution, multimodal image registration, due to its several challenges, is still often performed manually by specialists. The recent success of image-to-image (I2I) translation in computer vision applications and its growing use in biomedical areas provide a tempting possibility of transforming the multimodal registration problem into a, potentially easier, monomodal one. We conduct an empirical study of the applicability of modern I2I translation methods for the task of rigid registration of multimodal biomedical and medical 2D and 3D images. We compare the performance of four Generative Adversarial Network (GAN)-based I2I translation methods and one contrastive representation learning method, subsequently combined with two representative monomodal registration methods, to judge the effectiveness of modality translation for multimodal image registration. We evaluate these method combinations on four publicly available multimodal (2D and 3D) datasets and compare with the performance of registration achieved by several well-known approaches acting directly on multimodal image data. Our results suggest that, although I2I translation may be helpful when the modalities to register are clearly correlated, registration of modalities which express distinctly different properties of the sample are not well handled by the I2I translation approach. The evaluated representation learning method, which aims to find abstract image-like representations of the information shared between the modalities, manages better, and so does the Mutual Information maximisation approach, acting directly on the original multimodal images. We share our complete experimental setup as open-source (https://github.com/MIDA-group/MultiRegEval), including method implementations, evaluation code, and all datasets, for further reproducing and benchmarking.
An RRT-Dijkstra-Based Path Planning Strategy for Autonomous Vehicles

It is challenging to plan paths for autonomous vehicles on half-structured roads because of the vast planning area and complex environmental constraints. This work aims to plan optimized paths with high accuracy and efficiency. A two-step path planning strategy is proposed. The classic planning problem is divided into two simpler planning problems: reduction problems for a vast planning area and solving problems for weighted directed graphs. The original planning area is first reduced using an RRT (Rapidly Exploring Random Tree) based guideline planner. Second, the path planning problem in the smaller planning region is expanded into a weighted directed graph and transformed into a discrete multi-source cost optimization problem, in which a potential energy field based discrete cost assessment function was designed considering obstacles, lanes, vehicle kinematics, and collision avoidance performances, etc. The output path is then obtained by applying a Dijkstra optimizer. Comparative simulations are conducted to assess the effectiveness of the proposed strategy. The results shows that the designed strategy balances efficiency and accuracy with enough planning flexibility and a 22% improvement in real-time performance compared to the classic Lattice planner, without significant loss of accuracy.
Virtual Reality in Health Science Education: Professors’ Perceptions

Virtual reality (VR) is a simulated experience in a three-dimensional (3D) computer-simulated world. Recent advances in technology position VR as a multipurpose technology in the healthcare sector and as a critical component in achieving Health 4.0. In this article, descriptive and correlationally quantitative research is carried out on the assessments made by Latin American health sciences university professors on the didactic use of virtual reality technologies. The main objective was to analyze the differences in the perceptions expressed by the public or private tenure of the universities where the professors teach. In addition, gender and age gaps were identified in the assessments obtained from each of the types of universities. The results reveal that Latin American health science professors at private universities have a higher selfconcept of their digital skills for the use of virtual reality in the lectures. This greater selfconcept also leads to a reduction in the gender and age gaps in the participating private universities with respect to the public counterparts. It is advisable to increase both faculty training in the didactic use of virtual reality and funding for its use, mainly in public universities.
Remote Entanglement of Superconducting Qubits via Solid-State Spin Quantum Memories

Quantum communication between remote superconducting systems is being studied intensively to increase the number of integrated superconducting qubits and to realize a distributed quantum computer. Since optical photons must be used for communication outside a dilution refrigerator, the direct conversion of microwave photons to optical photons has been widely investigated. However, the direct conversion approach suffers from added photon noise, heating due to a strong optical pump, and the requirement for large cooperativity. Instead, for quantum communication between superconducting qubits, we propose an entanglement distribution scheme using a solid-state spin quantum memory that works as an interface for both microwave and optical photons. The quantum memory enables quantum communication without significant heating inside the refrigerator, in contrast to schemes using high-power optical pumps. Moreover, introducing the quantum memory naturally makes it possible to herald entanglement and parallelization using multiple memories.
Evaluation of factors affecting university students' satisfaction with e-learning systems used dur-ing Covid-19 crisis: A field study in Jordanian higher education institutions

E-learning results from the integration of technology and education and has become an effective learning medium today. E-learning courses and systems with various services are on the rise owing to its importance. E-learning systems should be evaluated to assure successful delivery, effective usage, and positive impacts on learners. A holistic model that identifies various levels of success on a vast range of success determinants was proposed. The model was empirically validated using data obtained from 724 e-learning student users in Jordan. Structural Equation Modelling (SEM) was used in data analyses. Results showed that perceived usefulness of information systems, user training, system quality, and management support have positive effects on user’s behavioral intention; whereas perceived ease of use has not. Also, SEM displayed that user behavioral intention has a positive effect on information systems use, use on student satisfaction, and the latter on student loyalty. Machine Learning (ML) methods produce high correlation values reaching up to 80% in predicting Behavior Intention (BI) from the input factors, and student loyalty from student satisfaction factors. This indicates that the ML are promising techniques to forecast the future targets based on the input independent features.
Examining the effect of social media interaction, E-WOM, and public relations: Assessing the mediating role of brand awareness

The development of trust among the customers of the organization is a key objective and critical to obtaining a competitive advantage. Therefore, this study was examined to assess the effect of social media interaction, public relations, and electronic word of mouth on brand awareness and trust. This study also examined the mediating role of brand awareness as well. For this purpose, the data was collected from the customers of the retail sector in UAE. This study adopted a quantitative research approach and cross-sectional design. The data was collected in the form of a research questionnaire by adopting simple random sampling. The questionnaire was distributed among 512 respondents. The usable response rate was 69.72%. For the analysis of data, the SEM technique was adopted and the tool used for this purpose was Smart PLS. The findings of the study revealed that all proposed hypotheses are accepted. The findings of the study are helpful for the academicians for further research and development of policies.