Reviewerly/exHarmony huggingface dataset:
Hybrid deep learning model using SPCAGAN augmentation for insider threat analysis

Cyberattacks from within an organization’s trusted entities are known as insider threats. Anomaly detection using deep learning requires comprehensive data, but insider threat data is not readily available due to confidentiality concerns of organizations. Therefore, there arises demand to generate synthetic data to explore enhanced approaches for threat analysis. We propose a linear manifold learning-based generative adversarial network, SPCAGAN, that takes input from heterogeneous data sources and adds a novel loss function to train the generator to produce high-quality data that closely resembles the original data distribution. Furthermore, we introduce a deep learning-based hybrid model for insider threat analysis. We provide extensive experiments for data synthesis, anomaly detection, adversarial robustness, and synthetic data quality analysis using benchmark datasets. In this context, empirical comparisons show that GAN-based oversampling is competitive with numerous typical oversampling regimes. For synthetic data generation, our SPCAGAN model overcame the problem of mode collapse and converged faster than previous GAN models. Results demonstrate that our proposed approach has a lower error, is more accurate, and generates substantially superior synthetic insider threat data than previous models.
VFL-Chain: Bulletproofing Federated Learning in the V2X environments

Federated Learning (FL) has gained significant traction as a promising approach to enable collaborative machine learning (ML) while safeguarding data privacy across diverse applications, with the Vehicle-to-Everything (V2X) environment being a notable use case. However, conventional FL systems remain susceptible to model poisoning attacks, wherein malicious participants can introduce inaccurate or deliberately misleading local model updates to the central aggregator. In the context of V2X, such attacks could spawn catastrophic outcomes and jeopardize safety-critical applications. To tackle this pressing concern, we introduce a privacy-preserving and verifiable FL framework, dubbed VFL-Chain, designed to facilitate secure and efficient collaboration among intelligent connected vehicles (ICVs). VFL-Chain aspires to deliver a privacy-centric, verifiable FL experience complemented by a robust incentive mechanism tailored for ICVs. Our proposed solution leverages Bulletproofs to enable efficient verification of model update integrity, which are assimilated within smart contracts on an underlying permissioned blockchain. Furthermore, VFL-Chain presents a fair incentive mechanism that rewards honest participation, fostering a resilient and efficient FL implementation. We conduct an exhaustive security analysis and performance assessment of our proposed system, which underscores its efficacy in countering data poisoning attacks and augmenting the accuracy of FL.
TDS-NA: Blockchain-based trusted data sharing scheme with PKI authentication

Data sharing has received much attention and research as an excellent way to unlock the value of data. Trusted data storage servers will participate in the data-sharing system to provide users with convenient data access and storage services. Currently, the vast majority of researchers design data-sharing systems based on centralized trusted authorities and key management centers, but they tend to ignore the problems of trust dependency and data leakage that exist in centralized trust and lead to the overall untrustworthiness of the system. To solve the above problems, this paper considers the use of public key infrastructure (PKI) to provide trusted authentication for data-sharing entities, but the traditional PKI has CA root trust and scenario adaptation problems, so we optimize the traditional PKI model for data-sharing scenarios and call it an improved PKI. Combining the decentralized trust property of blockchain, this paper proposes a TDS-NA scheme based on blockchain and improved PKI to build a distributed trusted, and secure data-sharing system in a semi-trusted network environment. TDS-NA can secure shared data in data sharing, and provide digital certificates that support entity-trusted authentication and reliable access control while designing digest blocks for efficient data auditing. In this paper, we demonstrate that the TDS-NA scheme is able to resist man-in-the-middle attacks and certificate forgery attacks through formal security analysis while satisfying the necessary security properties of data-sharing systems. We implement a prototype of the TDS-NA scheme in ethereum smart contracts and finally verify the security and feasibility of TDS-NA through experimental comparison and analysis.
Smart choice with smart device: the use of apps in accelerated online education for nontraditional students

Purpose Based on student responses to a set of customized questionnaires, this study aims to present evidence that while student evaluations of instructors and courses remain consistent, a designated mobile app enhances perceived online learning experience. Design/methodology/approach This study addresses quality assurance issues in accelerated online graduate-level education by identifying factors that influence nontraditional adult student preferences for using mobile applications (apps). Findings It is evident that affordability and functionality are the two most important determinants of nontraditional student preferences for app-based learning, followed by mobility and ease of purchase. Originality/value These findings underscore the potential of app learning to bolster positive perceptions of online education. Findings of this study imply that integrating additional app learning tools can be used as a quality assurance mechanism and enhance nontraditional students’ satisfaction through improving their perceived online learning experience.
Dual-Branch Multi-Scale Relation Networks with Tutorial Learning for Few-Shot Learning

Few-shot learning refers to training a model with a few labeled data to effectively recognize unseen categories. Recently, numerous approaches have been suggested to improve the extraction of abundant feature information at hierarchical layers or multiple scales for similarity metrics, especially methods based on learnable relation networks, which have demonstrated promising results. However, the roles played by image features in relationship measurement vary at different layers, and effectively integrating features from different layers and multiple scales can improve the measurement capacity of the model. In light of this, we propose a novel method called dual-branch multi-scale relation networks with tutoring learning (DbMRNT) for few-shot learning. Specifically, we first generate deep multiple features using a multi-scale feature generator in Branch 1 while extracting features at hierarchical layers in Branch 2. Then, learnable relation networks are employed in both branches to measure the pairwise similarity of features at each scale or layer. Furthermore, to leverage the dominant role of deep features in the final classification, we introduce a tutorial learning module that enables Branch 1 to tutor the learning process of Branch 2. Ultimately, the relation scores of all scales and layers are integrated to obtain the classification results. Extensive experiments on popular few-shot learning datasets prove that our method outperforms other similar methods.
THE PHENOMENON OF THE "INFORMED PATIENT": ACCESS TO HEALTH-CARE CONTENT AVAILABLE ON THE WEB AND RECONFIGURATIONS IN THE RELATIONSHIPS BETWEEN HEALTH-CARE PROFESSIONALS AND USERS

The popularization of the internet has significantly expanded access to information, giving rise to a more active and well-informed society. In the health area, it is possible to find, on the web, more and more information available about diseases, symptoms, diagnoses, prevention, medicines, treatment, professionals, etc. This has given rise to a more informed, active, questioning and critical user, who seeks information about their health on the internet, in order to gain more autonomy in the face of these processes: the “expert patient” or the “informed patient”. This research aims to identify and analyze the forms of interaction between people (users and health professionals in general) and health content available on the internet, seeking to understand the changes and reconfigurations that access to this information has been promoting in relationships between users of health systems, professionals and services in general. This is a qualitative research of an ethnomethodological nature, which was based on a bibliographical review and a field study, organized in two stages: an exploratory one, with the application of an online questionnaire, and a systematic one, with interviews in depth. The analysis of the research results was based on the methodology adapted from the Collective Subject Discourse (CSD), according to Lefèvre, which made it possible to create the categories. Through a cross-sectional analysis of the collected material, it was possible to identify numerous aspects that surround the search for health information on the web. Among them, the autonomy and “empowerment” of users, who, in an attempt to move towards the humanization of health care, begin to seek contributions from external means, in order to satisfy their desires and needs, to fill the gaps left by the services of health and to find other agents to place trust and credibility.
A Natural Language Processing System for Text Classification Corpus Based on Machine Learning

A classification system for hazardous materials in air traffic control was investigated using the Human Factors Analysis and Classification System (HFACS) framework and natural language processing to prevent hazardous situations in air traffic control. Based on the development of the HFACS standard, an air traffic control hazard classification system will be created. The dangerous data of the aviation safety management system is selected by dead bodies, classified and marked in 5 levels. TFIDF TextRank text classification method based on key content extraction and text classification model based on CNN and BERT model were used in the experiment to solve the problem of small samples, many labels and random samples in hazardous environment of air pollution control. The results show that the total cost of model training time and classification accuracy is the highest when the keywords are around 8. As the number of points increases, the time spent in dimensioning decreases and affects accuracy. When the number of points reaches about 93, the time spent in determining the size increases, but the accuracy of the allocation remains close to 0.7, but the increase in the value of time leads to a decrease in the total cost. It has been proven that extracting key content can solve text classification problems for small companies and contribute to further research in the development of security systems.
Automatic Feature Construction-Based Genetic Programming for Degraded Image Classification

Accurately classifying degraded images is a challenging task that relies on domain expertise to devise effective image processing techniques for various levels of degradation. Genetic Programming (GP) has been proven to be an excellent approach for solving image classification tasks. However, the program structures designed in current GP-based methods are not effective in classifying images with quality degradation. During the iterative process of GP algorithms, the high similarity between individuals often results in convergence to local optima, hindering the discovery of the best solutions. Moreover, the varied degrees of image quality degradation often lead to overfitting in the solutions derived by GP. Therefore, this research introduces an innovative program structure, distinct from the traditional program structure, which automates the creation of new features by transmitting information learned across multiple nodes, thus improving GP individual ability in constructing discriminative features. An accompanying evolution strategy addresses high similarity among GP individuals by retaining promising ones, thereby refining the algorithm’s development of more effective GP solutions. To counter the potential overfitting issue of the best GP individual, a multi-generational individual ensemble strategy is proposed, focusing on constructing an ensemble GP individual with an enhanced generalization capability. The new method evaluates performance in original, blurry, low contrast, noisy, and occlusion scenarios for six different types of datasets. It compares with a multitude of effective methods. The results show that the new method achieves better classification performance on degraded images compared with the comparative methods.
Twelve tips for Natural Language Processing in medical education program evaluation

With the increasing application of Natural Language Processing (NLP) in Medicine at large, medical educators are urged to gain an understanding and implement NLP techniques within their own education programs to improve the workflow and make significant and rapid improvements in their programs. This paper aims to provide twelve essential tips inclusive of both conceptual and technical factors to facilitate the successful integration of NLP in medical education program evaluation. These twelve tips range from advising on various stages of planning the evaluation process, considerations for data collection, and reflections on preprocessing of data in preparation for analysis and interpretation of results. Using these twelve tips as a framework, medical researchers, educators, and administrators will have an understanding and reference to navigating applications of NLP and be able to unlock its potential for enhancing the evaluation of their own medical education programs.
Opportunities for Using Artificial Intelligence in the Higher Education System (in the field of international relations)

The article examines the role and the influence of artificial intelligence in the digital age in the field of higher education, in particular in the field of International relations. The article, which comprehensively presents the issue under discussion, also makes the focus on the growing role of artificial intelligence in the field of international relations, which has become one of the important issues on the agenda of superpower relations. At the same time, the advantages and risks of digital technologies and artificial intelligence systems are discussed, which, on the one hand, ensure technological progress, and on the other, create dividing barriers between developed and less developed countries. The author researches how the foreign policy departments of the leading world powers use the opportunities provided by artificial intelligence systems in their work, and what advantages this provides. The above-mentioned questions indicate that the current generation of International relations scholars must be capable to use the instruments of digital technologies and artificial intelligence systems in the course of their professional activities, which will save time and increase the efficiency of the work done. However, the important question is how to use digital technologies and artificial intelligence systems in the educational process of training specialists in International relations, so that this does not have negative consequences and does not lead to overload of students or tutors. It is necessary to identify the relevant components of education in this regard, as well as to estimate the proportion of using the artificial intelligence in education. It is important that the university is also a research environment and the functioning of a continuous chain of science and education is really crucial, and in which the use of artificial intelligence capabilities is also important. The article also analyzes and presents the views of researchers involved in the issue regarding the positive and negative consequences of the use of artificial intelligence in higher education. Accordingly, conclusions were drawn regarding the prospects for the use of artificial intelligence in higher education.
Happiness Management

Uncertainty, innovation, competitive advantage, globalization, and digitalization are some of the changes that have forced managers to rethink the way they manage their organizations. So, management models are beginning to emerge, focusing on human capital (employees). Happiness management arose as a new form of organizational culture, and the main objective is to ensure the well-being of employees by promoting positive experiences/emotions in the workplace. Artificial intelligence combines the ability to learn similarly to humans, with an even more extensive capacity than humans. Although its effects on employees still divide opinions, what is certain is that innovation in this area is increasingly common in organizations in a broad field.
Design of human lower limb motion data acquisition system based on multi-sensor

Abstract In order to protect the health of older people, a wearable lower extremity motion data acquisition system was designed. With STM32F103C8T6 as the core, the system uses an inertial attitude sensor to collect lower limb motion attitude information. In view of the large difference in plantar pressure data of different fall types, the distributed flexible film plantar pressure sensor is used to collect plantar pressure information as an auxiliary discrimination means, which can accurately distinguish the fall behavior from other behaviors, and carry out data transmission through the wireless module. The device is small in size, light in weight, low in power consumption, and wearable, which is easy to apply in the field of health monitoring, and can also be used as a reference for the implementation of corresponding protection strategies for active lower limb prosthetics.
TLS-RWKV: Real-Time Online Action Detection with Temporal Label Smoothing

Abstract Online action detection (OAD)is a challenging task that involves predicting the ongoing action class in real-time streaming videos, which is essential in the field of autonomous driving and video surveillance. In this article, we propose an approach for OAD based on the Receptance Weighted Key Value (RWKV) model with temporal label smooth. The RWKV model captures temporal dependencies and computes efficiently at the same time, which makes it well-suited for real-time applications. Our TLS-RWKV model demonstrates advancements in two aspects. First, we conducted experiments on two widely used datasets, THUMOS’14 and TVSeries. Our proposed approach demonstrates state-of-the-art performance with 71.8% mAP on THUMOS’14 and 89.7% cAP on TVSeries. Second, our proposed approach demonstrates impressive efficiency, running at over 600 FPS and maintaining a competitive mAP of 59.9% on THUMOS’14 with RGB features alone. Notably, this efficiency surpasses the prior state-of-the-art model, TesTra, by more than two times. Even when executed on a CPU, our model maintains a commendable speed, exceeding 200 FPS. This high efficiency makes our model suitable for real-time deployment, even on resource-constrained devices. These results showcase the effectiveness and competitiveness of our proposed approach in OAD.
An Interpretable Trend Analysis Neural Networks for Longitudinal Data Analysis

Cohort study is one of the most commonly used study methods in medical and public health researches, which result in longitudinal data. Conventional statistical models and machine learning methods are not capable of modeling the evolution trend of the variables in longitudinal data. In this paper, we propose a Trend Analysis Neural Networks (TANN), which models the evolution trend of the variables by adaptive feature learning. TANN was tested on dataset of Kaiuan research. The task was to predict occurrence of cardiovascular events within 2 and 5 years, with 3 repeated medical examinations during 2008 and 2013. For 2-year prediction, The AUC of the TANN is 0.7378, which is a significant improvement than that of conventional methods, while that of TRNS, RNN, DNN, GBDT, RF, and LR are 0.7222, 0.7034, 0.7054, 0.7136, 0.7160 and 0.7024, respectively. For 5-year prediction, TANN also shows improvement. The experimental results show that the proposed TANN achieves better prediction performance on cardiovascular events prediction than conventional models. Furthermore, by analyzing the weights of TANN, we could find out important trends of the indicators, which are ignored by conventional machine learning models. The trend discovery mechanism interprets the model well. TANN is an appropriate balance between high performance and interpretability.
Analysis of the Reasons for the Formation of Network Group Polarization and its Implications

With the development of the Internet, the phenomenon of network group polarization has become more and more serious. This paper looks at how network group Polarization has developed from the original concept of group polarization after the Internet was created and popularized. The reasons for the emergence of online groups are categorized as (1) The Internet provides a wealth of information that people can selectively receive, and algorithmic mechanisms on the web push information based on user data. (2) people can communicate virtually on the Internet, (3) the influence of the media and public figures, (4) the platform’s vetting mechanism of the information, and (5) the accumulation of social contradictions for which the Internet provides a platform for catharsis. At the same time, group polarization may exacerbate the antagonism of different groups in society, but it will also help minorities find a sense of identity and increase cultural inclusiveness.
The Role of Digital Technologies in Increasing the Students’ Involvement in the Educational Process

The level of students’ professional education can be increased through motivation. This study aims to determine the role of digital technologies in increasing students’ involvement in the educational process. The aim of the work was achieved by conducting a pedagogical experiment before and after training, a general theoretical method of observation, and a sociological survey. The efficiency ratio, the comparison coefficient, and the standard deviation coefficient were also calculated. The results showed that the highest level of motivation was achieved due to the perception of the general learning process (29%), and the development of creative skills (25%). It was established that digital technologies had a greater influence on knowledge of laws and methods of practical application of theoretical information (0.96). High results are associated with a variety of interactive technologies that were used during training. It was found that motivation in training influenced the in-depth study of the topic (1.6), and the search for new digital technologies for training (1.2). The use of non-standard approaches in education (1.5), structuring of material when doing homework (1.1), and participation in university knowledge contests (1.8) also had an impact. The academic novelty of the work consists in increasing the interest of future teachers in the educational process due to the combination of various digital technologies, which are aimed at the development of theoretical and practical skills, as well as creative thinking. The practical significance is the creation of new learning mechanisms (development of creative thinking using Emaze, and Storyjumper applications) for the development of student motivation using digital technologies. The prospects for further research may be related to the comparison of the achieved level of motivation among students and schoolchildren because of using the developed approaches to learning.