Reviewerly/exHarmony huggingface dataset:
Virtual Obstacles for Sensors Incapacitation in Robot Navigation: A Systematic Review of 2D Path Planning

The field of mobile robot (MR) navigation with obstacle avoidance has largely focused on real, physical obstacles as the sole external causative agent for navigation impediment. This paper has explored the possible option of virtual obstacles (VOs) dominance in robot navigation impediment in certain navigation environments as a MR move from one point in the workspace to a desired target point. The systematically explored literature presented reviews mostly between the years 2000 and 2021; however, some outlier reviews from earlier years were also covered. An exploratory review approach was deployed to itemise and discuss different navigation environments and how VOs can impact the efficacy of both algorithms and sensors on a robotic vehicle. The associated limitations and the specific problem types addressed in the different literature sources were highlighted including whether or not a VO was considered in the path planning simulation or experiment. The discussion and conclusive sections further recommended some solutions as a measure towards addressing sensor performance incapacitation in a robot vehicle navigation problem.
Dual-Graph Learning Convolutional Networks for Interpretable Alzheimer’s Disease Diagnosis

In this paper, we propose a dual-graph learning convolutional network (dGLCN) to achieve interpretable Alzheimer’s disease (AD) diagnosis, by jointly investigating subject graph learning and feature graph learning in the graph convolution network (GCN) framework. Specifically, we first construct two initial graphs to consider both the subject diversity and the feature diversity. We further fuse these two initial graphs into the GCN framework so that they can be iteratively updated (i.e., dual-graph learning) while conducting representation learning. As a result, the dGLCN achieves interpretability in both subjects and brain regions through the subject importance and the feature importance, and the generalizability by overcoming the issues, such as limited subjects and noisy subjects. Experimental results on the Alzheimer’s disease neuroimaging initiative (ADNI) datasets show that our dGLCN outperforms all comparison methods for binary classification. The codes of dGLCN are available on https://github.com/xiaotingsong/dGLCN .
ThermoSecure: Investigating the Effectiveness of AI-Driven Thermal Attacks on Commonly Used Computer Keyboards

Thermal cameras can reveal heat traces on user interfaces, such as keyboards. This can be exploited maliciously to infer sensitive input, such as passwords. While previous work considered thermal attacks that rely on visual inspection of simple image processing techniques, we show that attackers can perform more effective artificial intelligence (AI)–driven attacks. We demonstrate this by presenting the development of ThermoSecure and its evaluation in two user studies (N = 21, N = 16), which reveal novel insights about thermal attacks. We detail the implementation of ThermoSecure and make a dataset of 1,500 thermal images of keyboards with heat traces resulting from input publicly available. Our first study shows that ThermoSecure successfully attacks 6-symbol, 8-symbol, 12-symbol, and 16-symbol passwords with an average accuracy of 92%, 80%, 71%, and 55% respectively, and even higher accuracy when thermal images are taken within 30 seconds. We found that typing behavior significantly impacts vulnerability to thermal attacks: hunt-and-peck typists are more vulnerable than fast typists (92% vs. 83% thermal attack success. respectively, if performed within 30 seconds). The second study showed that keycap material has a statistically significant effect on the effectiveness of thermal attacks: ABS keycaps retain the thermal trace of user presses for a longer period of time, making them more vulnerable to thermal attacks, with a 52% average attack accuracy compared with 14% for keyboards with PBT keycaps. Finally, we discuss how systems can leverage our results to protect from thermal attacks and present 7 mitigation approaches that are based on our results and previous work.
Graph Neural Networks: A bibliometrics overview

Recently, graph neural networks (GNNs) have become a hot topic in machine learning community. This paper presents a Scopus-based bibliometric overview of the GNNs’ research since 2004 when GNN papers were first published. The study aims to evaluate GNN research trends, both quantitatively and qualitatively. We provide the trend of research, distribution of subjects, active and influential authors and institutions, sources of publications, most cited documents, and hot topics. Our investigations reveal that the most frequent subject categories in this field are computer science, engineering, and telecommunications. In addition, the most active source of GNN publications is Lecture Notes in Computer Science. The most prolific or impactful institutions are found in the United States, China, and Canada. We also provide must-read papers based on citation count and future directions. Our analysis reveals that node classification is the most popular task, followed by link prediction, and graph classification in the GNN literature. Moreover, the results suggest that the application of graph convolutional networks and attention mechanisms are now among hot topics of GNN research. Finally, scalability, generalization, over-smoothing, and explainability of graph neural networks are some research directions to pursue.
A neurodynamic optimization approach to nonconvex resource allocation problem

A neurodynamic optimization approach characterized by a negative subgradient flow (NSF) is proposed to solve distributed nonconvex resource allocation problem (DNRAP). Under some mild assumptions, it is proved that the state solutions of the proposed approach converge to the critical point set of the considered DNRAP. Moreover, benefiting from the well-known nonsmooth Lojasiewicz inequality, analysis results show that the state solutions converge toward a critical point (not set) of the considered DNRAP. The convergence rate of the state solution, including exponential or finite-time convergence, can be evaluated through the quantitative calculation of Lojasiewicz exponent. Furthermore, we assess the related Lojasiewicz exponent of presented NSF in some special situation, for instance, in nonconvex quadratic programming. Finally, simulation illustrates the well performance of presented neurodynamic optimization approach.
Effectiveness of Distance Learning in Higher Education Institutions under the Martial Law

The aim of the research was an empirical study of the effectiveness of distance learning in pedagogical HEIs of Ukraine under the martial law. The research involved surveying the subjects of the educational process of Ukrainian HEIs. The survey respondents were the students of pedagogical educational institutions, teachers studying at the advanced training courses, academic staff. The criteria for assessing the effectiveness of distance learning in times of war were the possibility and quality of feedback, access to educational/didactic and methodological content, the possibility and frequency of monitoring performance, consultations, meeting deadlines, interactivity, technical capabilities of Internet access. The survey evidenced the negative impact of the martial law on online learning, the limited interactivity of classes, the unsystematic consultations and monitoring, and poor-quality communication between the subjects of the online educational process. At the same time, the positive impact of the varied approaches of teachers to presenting educational materials to students on the effectiveness of distance learning was confirmed. As a result, students with different technical capabilities were able to access the educational content provided by the curricula. The results of the study can be used by other HEIs of Ukraine in the context of sharing progressive pedagogical practices of distance learning organization in times of war. Further studies involve expanding the research, generalizing the unique experience of higher education of Ukraine in providing distance learning in crisis conditions, and sharing it with the world scientific communities.
A Comprehensive Study of Learning Approaches for Author Gender Identification

In recent years, author gender identification is an important yet challenging task in the fields of information retrieval and computational linguistics. In this paper, different learning approaches are presented to address the problem of author gender identification for Turkish articles. First, several classification algorithms are applied to the list of representations based on different paradigms: fixed-length vector representations such as Stylometric Features (SF), Bag-of-Words (BoW) and distributed word/document embeddings such as Word2vec, fastText and Doc2vec. Secondly, deep learning architectures, Convolution Neural Network (CNN), Recurrent Neural Network (RNN), special kinds of RNN such as Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU), C-RNN, Bidirectional LSTM (bi-LSTM), Bidirectional GRU (bi-GRU), Hierarchical Attention Networks and Multi-head Attention (MHA) are designated and their comparable performances are evaluated. We conducted a variety of experiments and achieved outstanding empirical results. To conclude, ML algorithms with BoW have promising results. fast-Text is also probably suitable between embedding models. This comprehensive study contributes to literature utilizing different learning approaches based on several ways of representations. It is also first important attempt to identify author gender applying SF, fastText and DNN architectures to the Turkish language.
Light field quality assessment based on aggregation learning of multiple visual features

Light field imaging is a way to represent human vision from a computational perspective. It contains more visual information than traditional imaging systems. As a basic problem of light field imaging, light field quality assessment has received extensive attention in recent years. In this study, we explore the characteristics of light field data for different visual domains (spatial, angular, coupled, projection, and depth), study the multiple visual features of a light field, and propose a non-reference light field quality assessment method based on aggregation learning of multiple visual features. The proposed method has four key modules: multi-visual representation of a light field, feature extraction, feature aggregation, and quality assessment. It first extracts the natural scene statistics (NSS) features from the central view image in the spatial domain. It extracts gray-level co-occurrence matrix (GLCM) features both in the angular domain and in the spatial-angular coupled domain. Then, it extracts the rotation-invariant uniform local binary pattern (LBP) features of depth map in the depth domain, and the statistical characteristics of the local entropy (SDLE) features of refocused images in the projection domain. Finally, the multiple visual features are aggregated to form a visual feature vector for the light field. A prediction model is trained by support vector machines (SVM) to establish a light field quality assessment method based on aggregation learning of multiple visual features.
Using virtual reality technologies in STEM education: ICT pre-service teachers’ perceptions

This study aims to investigate the perceptions, expectations, and experiences of information and communication technology (ICT) pre-service teachers regarding the potential of using virtual reality (VR) as a learning and teaching tool in STEM education. The research participants included 44 ICT pre-service teachers. The data were collected through the self-efficacy and pedagogical usage questionnaire. Descriptive statistics, t-tests, and content analyses were used for analysis. The results show that most ICT pre-service teachers did not have the experience of using VR technology, although they stated that using VR technology had a positive impact on STEM education in many ways. They felt that using VR technologies in STEM education can be more useful and effective in the process of exploring inaccessible places. Some participants had some concerns about using VR in the classroom, for example, VR is a costly tool, and it might isolate and distract students from the real world.
Teachers' psychological resistance to digital innovation in jordanian entrepreneurship and business schools: Moderation of teachers' psychology and attitude toward educational technologies

The current study aimed to highlight the factors that may influence teachers' psychological resistance to digital technologies in entrepreneurship and business schools. Theoretically grounded in the diffusion of innovations theory and the theory of planned behavior, the current research investigates teachers' psychological resistance to digital innovation, school culture and climate, and moderation of teacher attitudes toward educational technologies. A cross-sectional field survey of 600 business and entrepreneurship school teachers was conducted in Jordan. In this study, partial least square-structural equation modeling (PLS-SEM) was used to assess the variables' "direct and moderating impacts" using the Smart PLS software 3.0. According to the results, school culture and school innovation climate had a considerable positive impact on teachers' resistance to digital innovation. Additionally, teachers' attitudes toward educational technologies moderated the relationship between study constructs in the framework. The study is a significant advance to the literature related to entrepreneurship, business education, and digital innovation. Several key policy insights and recommendations for further research, as well as theoretical and practical implications, are suggested.
IOT integrated smart grid management system for effective energy management

Customers' steadily increasing usage of power equipment causes a mismatch among requirements and supply, which presents a growing issue for power generation. Energy management is an essential tool in minimizing large supply-side shortages and boosting power effectiveness. The present practice in energy systems emphasizes lowering the total price of electricity without restricting use by deciding to decrease electricity usage duringpeaktimings. The aforementioned problem calls for the creation and advancement of an adaptable & moveable technology that serves a broad range of clients and maintains the overall energy balance. The goal of the Intelligent Electricity Management Solution seems to be to compensate for an energy loss of electricity in a territory with managed part-load reduction that caters to consumption habits. The execution of experiments conducted is demonstrated under the assumptions of a power storage occurrence, the maximum demand restriction using various scenarios, and adjusting the preference allocated to each equipment. In Intelligent Power Management System (IPMS), there are price-optimization techniques depending on the duration of use and flexibility using detector information elements. A home location network with efficient ZigBee connectivity has been constructed, and an IoT framework has been created for predictive analytics and archiving.
Calibrate: Interactive Analysis of Probabilistic Model Output

Analyzing classification model performance is a crucial task for machine learning practitioners. While practitioners often use count-based metrics derived from confusion matrices, like accuracy, many applications, such as weather prediction, sports betting, or patient risk prediction, rely on a classifier's predicted probabilities rather than predicted labels. In these instances, practitioners are concerned with producing a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">calibrated</i> model, that is, one which outputs probabilities that reflect those of the true distribution. Model calibration is often analyzed visually, through static reliability diagrams, however, the traditional calibration visualization may suffer from a variety of drawbacks due to the strong aggregations it necessitates. Furthermore, count-based approaches are unable to sufficiently analyze model calibration. We present Calibrate, an interactive reliability diagram that addresses the aforementioned issues. Calibrate constructs a reliability diagram that is resistant to drawbacks in traditional approaches, and allows for interactive subgroup analysis and instance-level inspection. We demonstrate the utility of Calibrate through use cases on both real-world and synthetic data. We further validate Calibrate by presenting the results of a think-aloud experiment with data scientists who routinely analyze model calibration.
The Hybrid Multiple-Criteria Decision-Making Model for Home Healthcare Nurses’ Job Satisfaction Evaluation and Improvement

Objectives: To investigate and evaluate the key factors related to job satisfaction performance of home healthcare nurses (HHNs). Methods: A total of 31 HHNs from three community hospitals in Zhejiang province were invited to participate in the study. They completed a questionnaire survey based on the home healthcare nurse job satisfaction scale (HHNJS) from February to March 2022. Consistent fuzzy preference relation (CFPR) methods and important-performance analysis (IPA) were used to obtain the attribute weights and performance for HHNs job satisfaction. Results: The results showed that the attributes of C13, C14, C15, C23, C24, C42, C51, and C52 were key factors influencing HHNs job satisfaction. Conclusion: The hybrid multiple-criteria decision-making (MCDM) model can help home-healthcare-agency administrators better understand the key factors related to HHNs job satisfaction and establish reasonable improvement strategies.
Creating a digital institution of higher education: theory and practice

The transfer of activities to the virtual space under modern conditions is recognized as an important factor in the success of political, economic, social and cultural relations in the society. Currently, social challenges and strategic directions of professional and personal development of the young generation require the creation of an effective educational system that would take into account the conditions of the digital reality in which the general economic, business and social spheres around the world function. Taking into account the above-mentioned, nowadays, the goal of higher education lies in coordinating the actions of its various subjects for the most effective educational influence on children and youth in the digital space, combining the efforts of the state, education, family and the real economy, creating a safe digital environment conducive to the development of the student’s personality. The issue of organizing the successful operation of digital institutions of higher education is extremely urgent, taking into consideration the global trends of the transition of the educational sphere to blended and distance learning. The purpose of the academic paper lies in analysing the various standpoints of scientists regarding the functioning of a modern digital institution of higher education, as well as investigating the practical aspects of organizing the educational process in higher education by virtue of digital tools. Methodology. In the course of conducting the research, bibliographic and analytical methods have been used in order to analyse the scientific literature on the activities of digital institutions of higher education, as well as an online survey for the practical clarifying the individual features of the activities of a digital institution of higher education. Results. Based on the results of the research, the principal aspects of the operation of digital institutions of higher education have been analysed and the preferences of students of such institutions in the areas of improving the virtual educational process have been established.
A Dynamic Contribution Measurement and Incentive Mechanism for Energy-Efficient Federated Learning in 6G

With 5G being commercialized, researchers have turned attention toward the sixth-generation (6G) network with the vision of connecting intelligence in a green energy-efficient manner. Federated learning triggers an upsurge of green intelligent services such as resources orchestration of communication infrastructures, while preserving privacy and communication efficiency. However, designing effective incentives in federated learning is challenging due to the dynamic available clients and the correlation between clients’ contributions during the learning process. In this paper, we propose a dynamic incentive and contribution mechanism to improve energy efficiency and training performance of federated learning. The proposed incentive based on Stackelberg game can timely adjust optimal energy consumption with changes in available clients during federated learning. Meanwhile, the contributions of clients in contribution management are formulated based on cooperative game to capture the correlation between tasks, which satisfies the availability, fairness and additivity. The simulation results show that the proposed scheme can significantly motivate high-performance clients to participate in federated learning, then improve the accuracy and energy efficiency of the federated learning model.
Audio-Visual Wake Word Spotting in MISP2021 Challenge: Dataset Release and Deep Analysis

In this paper, we describe and release publicly the audio-visual wake word spotting (WWS) database in the MISP2021 Challenge, which covers a range of scenarios of audio and video data collected by near-, mid-, and far-field microphone arrays, and cameras, to create a shared and publicly available database for WWS.The database 1 and the code 2 are released, which will be a valuable addition to the community for promoting WWS research using multi-modality information in realistic and complex conditions.Moreover, we investigated the different data augmentation methods for single modalities on an endto-end WWS network.A set of audio-visual fusion experiments and analysis were conducted to observe the assistance from visual information to acoustic information based on different audio and video field configurations.The results showed that the fusion system generally improves over the single-modality (audio-or video-only) system, especially under complex noisy conditions.
FedBranch: Heterogeneous Federated Learning via Multi-Branch Neural Network

As a privacy-preserving paradigm of decentralized machine learning, federated learning (FL) has become a hot spot in the field of machine learning. Existing FL approaches generally assume that the global model can be deployed and trained on any client. However, in practical applications, the devices participated in FL are often heterogeneous and have different computation capacities, resulting in the difficulty of large neural network model training. The current solutions, such as reducing the scale of the global model to fit all clients or removing weak devices to deploy a larger model, will lead to model accuracy degradation, owing to the limitation of model scale or the loss of data on weak clients. To address the device heterogeneity issue inherent in FL, we propose FedBranch, a heterogeneous FL framework based on multi-branch neural network model. Its core idea is to assign a proper branch model to each client according to their computation capacity. In FedBranch, a layer-wise aggregation method is designed to address aggregation of different branches. Meanwhile, we introduce a model regularization method to improve the convergence efficiency and model performance of FedBranch. Besides, we propose a training task offloading algorithm based on Split Learning to safely and effectively share training tasks among different branch models. Extensive experiments conducted on different datasets demonstrate that our FedBranch method has higher convergence efficiency and model accuracy than existing federated learning methods in various heterogeneous scenarios.