Reviewerly/exHarmony huggingface dataset:
Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks

Massive open online courses (MOOCs) , which offer open access and widespread interactive participation through the internet, are quickly becoming the preferred method for online and remote learning. Several MOOC platforms offer the service of course recommendation to users, to improve the learning experience of users. Despite the usefulness of this service, we consider that recommending courses to users directly may neglect their varying degrees of expertise. To mitigate this gap, we examine an interesting problem of concept recommendation in this paper, which can be viewed as recommending knowledge to users in a fine-grained way. We put forward a novel approach, termed HinCRec-RL, for C oncept Rec ommendation in MOOCs, which is based on H eterogeneous I nformation N etworks and R einforcement L earning . In particular, we propose to shape the problem of concept recommendation within a reinforcement learning framework to characterize the dynamic interaction between users and knowledge concepts in MOOCs. Furthermore, we propose to form the interactions among users, courses, videos, and concepts into a heterogeneous information network (HIN) to learn the semantic user representations better. We then employ an attentional graph neural network to represent the users in the HIN, based on meta-paths. Extensive experiments are conducted on a real-world dataset collected from a Chinese MOOC platform, XuetangX , to validate the efficacy of our proposed HinCRec-RL. Experimental results and analysis demonstrate that our proposed HinCRec-RL performs well when compared with several state-of-the-art models.
Collaborative Privacy-preserving Approaches for Distributed Deep Learning Using Multi-Institutional Data

Deep learning (DL) algorithms have shown remarkable potential in automating various tasks in medical imaging and radiologic reporting. However, models trained on low quantities of data or only using data from a single institution often are not generalizable to other institutions, which may have different patient demographics or data acquisition characteristics. Therefore, training DL algorithms using data from multiple institutions is crucial to improving the robustness and generalizability of clinically useful DL models. In the context of medical data, simply pooling data from each institution to a central location to train a model poses several issues such as increased risk to patient privacy, increased costs for data storage and transfer, and regulatory challenges. These challenges of centrally hosting data have motivated the development of distributed machine learning techniques and frameworks for collaborative learning that facilitate the training of DL models without the need to explicitly share private medical data. The authors describe several popular methods for collaborative training and review the main considerations for deploying these models. They also highlight publicly available software frameworks for federated learning and showcase several real-world examples of collaborative learning. The authors conclude by discussing some key challenges and future research directions for distributed DL. They aim to introduce clinicians to the benefits, limitations, and risks of using distributed DL for the development of medical artificial intelligence algorithms. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.
The role of education in modulating the effect of ICT on governance in Africa

This study investigates the role of education in modulating the effect of ICT on governance in 53 African countries between 2002 and 2020. The Two-Step System Generalized Method of Moment (GMM) strategy is adopted to address the potential endogeneity problem. Governance is computed as a composite index that encompasses the six indicators of the Worldwide Governance Indicators (Control of corruption, rule of law, political stability, regulatory quality, government effectiveness, and voice and accountability). ICT is measured by the number of individuals using the internet, mobile cellular subscribers and fixed broadband subscription. The findings of the study reveal that the quality of governance in Africa is enhanced by growth in ICT. The findings further indicate that the interaction between ICT and education procure positive net effects on governance. In addition, we observed that ICT still enhances the quality of governance in African countries that have adopted the French civil law and the British common law system. The study suggests the design of policies for enhancing e-governance and ICT in African institutions, and are recommended to be used as part of the school curriculum for quality management.
Integrated photonics in quantum technologies

Abstract Quantum information processing is nowadays an established branch of quantum mechanics applications. Recent results in different areas, ranging from communications to computing, show that quantum technologies are advancing towards being mature platforms with substantial advantages over traditional classical protocols. Quantum optics and photonic apparatuses are one of the best candidates for the realization of quantum processors. In this context, integrated photonic technologies are essential components towards miniaturisation of such complex optical systems. Integrated optical devices enabled an important improvement in the level of complexity in the generation and processing of quantum states of light. This review aims at providing an exhaustive framework of the advances of integrated quantum photonic platforms, for what concerns the integration of sources, manipulation, and detectors, as well as the contributions in quantum computing, cryptography and simulations.
Securing the Cyber Resilience of a Blockchain-Based Railroad Non-Stop Customs Clearance System

Current railroad customs clearance systems are problematic in that the movement of trains is occasionally restricted for extended periods during inspections to verify cargo integrity at customs clearance. Consequently, significant human and material resources are consumed to obtain customs clearance to the destination, considering different processes exist for cross-border trade. Therefore, we developed a cross-border blockchain-based non-stop customs clearance (NSCC) system to address these delays and reduce resource consumption for cross-border trains. The integrity, stability, and traceability of blockchain technology are used to construct a stable and reliable customs clearance system to address these problems. The proposed method connects diverse trade and customs clearance agreements in a single blockchain network, which ensures integrity and minimal resource consumption, and includes railroads, freight vehicles, and transit stations in addition to the current customs clearance system. The integrity and confidentiality of customs clearance data are protected using sequence diagrams and the blockchain to strengthen the resilience of the NSCC process against attacks; the blockchain-based NSCC system structurally verifies the attack resilience based on matching sequences. The results confirm that the blockchain-based NSCC system is time- and cost-efficient compared with the current customs clearance system and offers improved attack resilience.
The Impact of ICT on Financial Sector Development Under Structural Break: An Empirical Analysis of the Turkish Economy

This study is to examine the relationship between information and communication technology (ICT) and financial development in the Turkish economy during the period of 1986–2018. By empirical literature, economic growth, technological innovation, and financial globalization are added to the financial development model as explanatory variables. The autoregressive distributed lag (ARDL) model and Hatemi-J cointegration test with two structural breaks are applied to examine the presence of cointegration between the variables. Dynamic ordinary least squares (DOLS), fully modified least squares (FMOLS), and canonical cointegrating regression (CCR) estimation techniques are applied for long-term estimates. The vector error correction model (VECM) Granger causality approach is used for causality analysis. Our empirical results show that under the structural break, ICT, economic growth, technological innovation, and financial globalization are cointegrated with financial development. In the presence of a structural break, ICT and technological innovation negatively affect financial development, while economic growth and financial globalization have a positive impact on financial development. The causality analysis determines that there is a one-way causality relationship running from ICT and economic growth to financial development. In addition, technological innovation and financial globalization lead to long-term financial development. Empirical findings have important policy recommendations for financial development in the Turkish economy.
Discriminative sparse least square regression for semi-supervised learning

The various variants of the classical least square regression (LSR) have been extensively utilized in numerous applications. However, most previous linear regression methods only consider the fitting between the original features and the corresponding label information. They ignore the correlations among data points. Another problem with these methods is that the strict zero-one binary label matrix is utilized as the final regression target. The degree of freedom is minimal, and the binary label information cannot be adequately fitted. A semi-supervised learning discriminative sparse least square regression (DSLSR) algorithm with some notable characteristics is presented to address the above mentioned issues. Firstly, we utilize the estimated label of the observed samples to design a new objective function in the generalized regression form, further generalizing the previous least square regression framework. Secondly, a novel jointly sparse regularized term is designed to take full use of the estimated label information, which is expected to force the extracted features of each class to be jointly sparse instead of the learned projection being jointly sparse. Thirdly, Label estimation, label relaxation, locality, and joint sparsity are seamlessly integrated into the least square regression (LSR) framework. This property can make the margins of the observed data with the same class label as minimal as feasible while concurrently maximizing the margins of observed data from other classes. The experimental findings reveal that DSLSR outperforms state-of-the-art linear regression-based approaches.
Health insurance fraud detection by using an attributed heterogeneous information network with a hierarchical attention mechanism

With the rapid growth of healthcare services, health insurance fraud detection has become an important measure to ensure efficient use of public funds. Traditional fraud detection methods have tended to focus on the attributes of a single visit and have ignored the behavioural relationships of multiple visits by patients.We propose a health insurance fraud detection model based on a multilevel attention mechanism that we call MHAMFD. Specifically, we use an attributed heterogeneous information network (AHIN) to model different types of objects and their rich attributes and interactions in a healthcare scenario. MHAMFD selects appropriate neighbour nodes based on the behavioural relationships at different levels of a patient's visit. We also designed a hierarchical attention mechanism to aggregate complex semantic information from the interweaving of different levels of behavioural relationships of patients. This increases the feature representation of objects and makes the model interpretable by identifying the main factors of fraud.Experimental results using real datasets showed that MHAMFD detected health insurance fraud with better accuracy than existing methods.Experiment suggests that the behavioral relationships between patients' multiple visits can also be of great help to detect health care fraud. Subsequent research fraud detection methods can also take into account the different behavioral relationships between patients.
Near-deterministic hybrid generation of arbitrary photonic graph states using a single quantum emitter and linear optics

Since linear-optical two-photon gates are inherently probabilistic, measurement-based implementations are particularly well suited for photonic platforms: a large highly-entangled photonic resource state, called a graph state, is consumed through measurements to perform a computation. The challenge is thus to produce these graph states. Several generation procedures, which use either interacting quantum emitters or efficient spin-photon interface, have been proposed to create these photonic graph states deterministically. Yet, these solutions are still out of reach experimentally since the state-of-the-art is the generation of a linear graph state. Here, we introduce near-deterministic solutions for the generation of graph states using the current quantum emitter capabilities. We propose hybridizing quantum-emitter-based graph state generation with all-photonic fusion gates to produce graph states of complex topology near-deterministically. Our results should pave the way towards the practical implementation of resource-efficient quantum information processing, including measurement-based quantum communication and quantum computing.
Maximum Correntropy Kalman Filtering for Non-Gaussian Systems With State Saturations and Stochastic Nonlinearities

This paper tackles the maximum correntropy Kalman filtering problem for discrete time-varying non-Gaussian systems subject to state saturations and stochastic nonlinearities. The stochastic nonlinearities, which take the form of state-multiplicative noises, are introduced in systems to describe the phenomenon of nonlinear disturbances. To resist non-Gaussian noises, we consider a new performance index called maximum correntropy criterion (MCC) which describes the similarity between two stochastic variables. To enhance the “robustness” of the kernel parameter selection on the resultant filtering performance, the Cauchy kernel function is adopted to calculate the corresponding correntropy. The goal of this paper is to design a Kalman-type filter for the underlying systems via maximizing the correntropy between the system state and its estimate. By taking advantage of an upper bound on the one-step prediction error covariance, a modified MCC-based performance index is constructed. Subsequently, with the assistance of a fixed-point theorem, the filter gain is obtained by maximizing the proposed cost function. In addition, a sufficient condition is deduced to ensure the uniqueness of the fixed point. Finally, the validity of the filtering method is tested by simulating a numerical example.
Artificial Intelligence in Engineering

Artificial intelligence (AI) has moved past its primitive stages and is now poised to revolutionize various fields, making it a disruptive technology. This technology is expected to completely transform traditional engineering in design, electrical, communication, and renewable energy approaches that have been human-centred. Despite being in its early stages, AI-powered engineering applications can work with vague design parameters and resolve intricate engineering problems that cannot be tackled using traditional design, electrical, communication, and renewable energy methods. This article aims to shed light on the current progress and future research trends in AI applications in engineering concepts, focusing on the ramp-up period of the last 5 years. Various methods such as machine learning, genetic algorithm, and fuzzy logic have been carefully evaluated from an engineering standpoint. AI-powered design studies have been reviewed and categorized for different design stages such as inspiration, idea and concept generation, evaluation, optimization, decision-making, and modeling. The review shows that there has been an increased interest in data-based design methods and explainable artificial intelligence in recent years. The use of AI methods in engineering applications has proven to be efficient, fast, accurate, and comprehensive, particularly with the use of deep learning methods and combinations that address situations where human capacity is inadequate. However, it is crucial to choose the appropriate AI method for an engineering problem to achieve successful results.&#x0D;
The use of immersive technologies in distance education: A systematic review

This study aims to conduct a systematic review that includes studies on the use of immersive technologies in distance education. For this purpose, 132 studies detected by searching Web of Science, Eric, Taylor & Francis and Education Full Text (EBSCO) databases were examined. The studies were analysed using the content analysis method. As a result of the analyses, it was observed that the first study investigating the subject was conducted in 2002, and the number of related studies increased over the years. In addition, these studies were primarily conducted quantitatively, were mainly journal articles, and originated mostly from China and the USA. Moreover, the sample groups of these studies consisted mostly of university students. Therefore, they mainly used academic performance and motivation variables. Furthermore, these studies were conducted primarily in the science and medical education disciplines. When the studies were evaluated in terms of publication journals, it was determined that they were published mostly in "Education Science" and "Computers & Education" journals. They were also included in the proceedings published within the scope of various conferences. When the application platforms in the studies were examined, it was determined that the UNITY and ARTUTOR platforms were mostly used. The findings of the studies revealed that the increase in academic performance and motivation was one of the most reported advantages of such technologies. On the other hand, the problems caused while using these technologies and the internet were the most reported difficulties in the studies. Finally, the review presented suggestions for future studies.
Perspectives on validation of clinical predictive algorithms

The generalizability of predictive algorithms is of key relevance to application in clinical practice. We provide an overview of three types of generalizability, based on existing literature: temporal, geographical, and domain generalizability. These generalizability types are linked to their associated goals, methodology, and stakeholders.
A study on computer vision for facial emotion recognition

Abstract Artificial intelligence has been successfully applied in various fields, one of which is computer vision. In this study, a deep neural network (DNN) was adopted for Facial emotion recognition (FER). One of the objectives in this study is to identify the critical facial features on which the DNN model focuses for FER. In particular, we utilized a convolutional neural network (CNN), the combination of squeeze-and-excitation network and the residual neural network, for the task of FER. We utilized AffectNet and the Real-World Affective Faces Database (RAF-DB) as the facial expression databases that provide learning samples for the CNN. The feature maps were extracted from the residual blocks for further analysis. Our analysis shows that the features around the nose and mouth are critical facial landmarks for the neural networks. Cross-database validations were conducted between the databases. The network model trained on AffectNet achieved 77.37% accuracy when validated on the RAF-DB, while the network model pretrained on AffectNet and then transfer learned on the RAF-DB results in validation accuracy of 83.37%. The outcomes of this study would improve the understanding of neural networks and assist with improving computer vision accuracy.
Artificial Intelligence Technologies in Cardiology

As the world produces exabytes of data, there is a growing need to find new methods that are more suitable for dealing with complex datasets. Artificial intelligence (AI) has significant potential to impact the healthcare industry, which is already on the road to change with the digital transformation of vast quantities of information. The implementation of AI has already achieved success in the domains of molecular chemistry and drug discoveries. The reduction in costs and in the time needed for experiments to predict the pharmacological activities of new molecules is a milestone in science. These successful applications of AI algorithms provide hope for a revolution in healthcare systems. A significant part of artificial intelligence is machine learning (ML), of which there are three main types—supervised learning, unsupervised learning, and reinforcement learning. In this review, the full scope of the AI workflow is presented, with explanations of the most-often-used ML algorithms and descriptions of performance metrics for both regression and classification. A brief introduction to explainable artificial intelligence (XAI) is provided, with examples of technologies that have developed for XAI. We review important AI implementations in cardiology for supervised, unsupervised, and reinforcement learning and natural language processing, emphasizing the used algorithm. Finally, we discuss the need to establish legal, ethical, and methodical requirements for the deployment of AI models in medicine.
Recent advancements in multimodal human–robot interaction

Robotics have advanced significantly over the years, and human–robot interaction (HRI) is now playing an important role in delivering the best user experience, cutting down on laborious tasks, and raising public acceptance of robots. New HRI approaches are necessary to promote the evolution of robots, with a more natural and flexible interaction manner clearly the most crucial. As a newly emerging approach to HRI, multimodal HRI is a method for individuals to communicate with a robot using various modalities, including voice, image, text, eye movement, and touch, as well as bio-signals like EEG and ECG. It is a broad field closely related to cognitive science, ergonomics, multimedia technology, and virtual reality, with numerous applications springing up each year. However, little research has been done to summarize the current development and future trend of HRI. To this end, this paper systematically reviews the state of the art of multimodal HRI on its applications by summing up the latest research articles relevant to this field. Moreover, the research development in terms of the input signal and the output signal is also covered in this manuscript.