Efficient Search and Detection of Faint Moving Objects in Image Data

Abstract: Abstract The search and detection of faint moving objects in image data can enable discoveries of small solar system bodies. To detect objects fainter than the single-frame sensitivity limit, track-before-detect methods can improve the signal-to-noise ratio of the object of interest by incoherently adding the object’s signal across multiple frames. However, traditional track-before-detect techniques can become computationally intensive over large search volumes. In this work, we present a computational approach to significantly speed up the search process by applying dynamic-programming techniques to implement the discrete X-ray transform. In this approach, image frames are processed in stages, in each of which pairs of frames are shifted and added to generate short-track segments, which are combined in later stages to form longer tracks. The algorithm speedup comes from the fact that a single short track segment can be reused multiple times for different longer tracks without the need for recomputing. Benchmark testing with simulated data shows that the method presented in this paper results in a significant reduction in runtime in comparison to a traditional track-before-detect approach. As a proof of concept, we demonstrated the applicability of the technique in performing a blind search for faint asteroids in image data collected from the Transiting Exoplanet Survey Satellite, leading to the detection of more than a thousand asteroids below the single-frame detection limit with moderate computational resources. The approach presented in this work has the potential to enable efficient discovery of previously undetected faint solar system objects across multiple orbit classes.


The effect of AI-powered chatbots in social studies education

Abstract: Abstract The aim of this research is to investigate the educational potential of AI-powered chatbots in Social Studies learning-teaching processes. The study was conducted using embedded design, evaluated within the framework of mixed methods research. The study group consists of 78 6th-grade students studying in three different classes, along with one teacher who implemented the practices and guided the process. During the experimental phase of the study, lessons in the experimental group were taught by the practitioner teacher using an AI-powered chatbot named “SosyalciBot”. Meanwhile, lessons in two control groups were taught following the constructivist approach. At the end of the process, it was determined that the experimental group students’ post-test and permanency test scores were significantly higher than the control group students’ scores in both post-test and permanency test. In the semi-structured interviews, both students and the practitioner teacher provided positive evaluations of the pedagogical and design features of the chatbot, indicating that these features positively impacted the learning-teaching process. They also identified some shortcomings while offering suggestions for improvement. Based on the study findings, it is evident that chatbots have a high potential to contribute to Social Studies education. However, to fully harness this potential and achieve optimal effectiveness, further advancements and refinements in chatbot technology are required. At this point, it is recommended to conduct theoretical or applied studies focusing on developing chatbots with high communication capacity and explore innovative and constructive ideas regarding the integration of chatbots into educational environments.


A diachronic cluster analysis of Danish museum websites

Abstract: Earlier studies describing and categorising the development of museum websites either build on theoretical knowledge or the analysis of a qualitative sample of relatively few, but prominent museum websites. The present study takes a broader approach and aims to characterise the development across Danish museum websites through the years 2005–2020. The methodological approach is a diachronic (temporal) analysis of Danish museum websites using data extracted from the Danish Web. A cluster analysis was used to identify similarities between museum websites. The cluster analysis shows that the museum website representations possess distinct attributes pivotal to their degree of resemblance with others, such as collection type, geographical location, organisational structure and whether the communication and dissemination strategy focus on disseminating collection content or extends to a broader communication scope. However, the quantitative text analysis approach to studying archived museum websites did not identified a new typology. Instead, the study suggests how a cluster analysis of the Danish museum web sphere can constitute an initial step in comprehending domain development, and ultimately discerning the attributes that characterise the different museum website clusters.


Deepfakes: current and future trends

Abstract: Abstract Advances in Deep Learning (DL), Big Data and image processing have facilitated online disinformation spreading through Deepfakes. This entails severe threats including public opinion manipulation, geopolitical tensions, chaos in financial markets, scams, defamation and identity theft among others. Therefore, it is imperative to develop techniques to prevent, detect, and stop the spreading of deepfake content. Along these lines, the goal of this paper is to present a big picture perspective of the deepfake paradigm, by reviewing current and future trends. First, a compact summary of DL techniques used for deepfakes is presented. Then, a review of the fight between generation and detection techniques is elaborated. Moreover, we delve into the potential that new technologies, such as distributed ledgers and blockchain, can offer with regard to cybersecurity and the fight against digital deception. Two scenarios of application, including online social networks engineering attacks and Internet of Things, are reviewed where main insights and open challenges are tackled. Finally, future trends and research lines are discussed, pointing out potential key agents and technologies.


SCGFormer: Semantic Chebyshev Graph Convolution Transformer for 3D Human Pose Estimation

Abstract: With the rapid advancement of deep learning, 3D human pose estimation has largely freed itself from reliance on manually annotated methods. The effective utilization of joint features has become significant. Utilizing 2D human joint information to predict 3D human skeletons is of paramount importance. Effectively leveraging 2D joint data can improve the accuracy of 3D human skeleton prediction. In this paper, we propose the SCGFormer model to reduce the error in predicting human skeletal poses in three-dimensional space. The network architecture of SCGFormer encompasses Transformer and two distinct types of graph convolution, organized into two interconnected modules: SGraAttention and AcChebGconv. SGraAttention extracts global feature information from each 2D human joint, thereby augmenting local feature learning by integrating prior knowledge of human joint relationships. Simultaneously, AcChebGconv broadens the receptive field for graph structure information and constructs implicit joint relationships to aggregate more valuable adjacent features. SCGraFormer is tested on widely recognized benchmark datasets such as Human3.6M and MPI-INF-3DHP and achieves excellent results. In particular, on Human3.6M, our method achieves the best results in 9 actions (out of a total of 15 actions), with an overall average error reduction of about 1.5 points compared to state-of-the-art methods, demonstrating the excellent performance of SCGFormer.


USING MACHINE LEARNING ALGORITHMS FOR NATURAL HABITATS ASSESSMENT

Abstract: The potential of AI to process and interpret large volumes of data can provide researchers with a powerful tool to understand and monitor biodiversity on a global scale. In this paper we aimed to identify dominant individual plant species in natural protected habitats. Mapping the dominant species from the targeted natural habitats was followed by testing machine learning algorithm for differentiating similar species using satellite images. In the end we validated the data generated by machine learning algorithms through extensive field observations. Using the Sentinel-2 mission 10m resolution data and comprehensive field mapping we managed to see different phenology variations between diverse types of plant communities. Using the NDVI and NDII vegetation indexes and Random Forest algorithm during the dominant species phenology stages for each consecutive 10-day periods between May 1st and September 10th, revealed distinct responses to climate fluctuations and environmental factors. The natural habitats different signatures are strongly influenced by their ecological and conservation status and are not yet suitable for identification, but could help improve AI’s automatic detection for multiannual analysis if a favorable conservation trend is reached. The main achievement of the proposed methodology is the ability to differentiate between different species of deciduous trees, with machine learning training accuracy generally exceeding 95% and classification accuracy surpassing 90%.


Cross‐modal knowledge learning with scene text for fine‐grained image classification

Abstract: Abstract Scene text in natural images carries additional semantic information to aid in image classification. Existing methods lack full consideration of the deep understanding of the text and the visual text relationship, which results in the difficult to judge the semantic accuracy and the relevance of the visual text. This paper proposes image classification based on Cross modal Knowledge Learning of Scene Text (CKLST) method. CKLST consists of three stages: cross‐modal scene text recognition, text semantic enhancement, and visual‐text feature alignment. In the first stage, multi‐attention is used to extract features layer by layer, and a self‐mask‐based iterative correction strategy is utilized to improve the scene text recognition accuracy. In the second stage, knowledge features are extracted using external knowledge and are fused with text features to enhance text semantic information. In the third stage, CKLST realizes visual‐text feature alignment across attention mechanisms with a similarity matrix, thus the correlation between images and text can be captured to improve the accuracy of the image classification tasks. On Con‐Text dataset, Crowd Activity dataset, Drink Bottle dataset, and Synth Text dataset, CKLST can perform significantly better than other baselines on fine‐grained image classification, with improvements of 3.54%, 5.37%, 3.28%, and 2.81% over the best baseline in mAP, respectively.


Strategic Approaches to Cybersecurity Learning: A Study of Educational Models and Outcomes

Abstract: As the demand for cybersecurity experts in the industry grows, we face a widening shortage of skilled professionals. This pressing concern has spurred extensive research within academia and national bodies, who are striving to bridge this skills gap through refined educational frameworks, including the integration of innovative information applications like remote laboratories and virtual classrooms. Despite these initiatives, current higher education models for cybersecurity, while effective in some areas, fail to provide a holistic solution to the root causes of the skills gap. Our study conducts a thorough examination of established cybersecurity educational frameworks, with the goal of identifying crucial learning outcomes that can mitigate the factors contributing to this skills gap. Furthermore, by analyzing six different educational models, for each one that can uniquely leverage technology like virtual classrooms and online platforms and is suited to various learning contexts, we categorize these contexts into four distinct categories. This categorization introduces a holistic dimension of context awareness enriched by digital learning tools into the process, enhancing the alignment with desired learning outcomes, a consideration sparsely addressed in the existing literature. This thorough analysis further strengthens the framework for guiding education providers in selecting models that most effectively align with their targeted learning outcomes and implies practical uses for technologically enhanced environments. This review presents a roadmap for educators and institutions, offering insights into relevant teaching models, including the opportunities for the utilization of remote laboratories and virtual classrooms, and their contextual applications, thereby aiding curriculum designers in making strategic decisions.


Comparative Analysis of GF-5 and Sentinel-2A Fusion Methods for Lithological Classification: The Tuanjie Peak, Xinjiang Case Study

Abstract: This study investigates the application of hyperspectral image space-spectral fusion technology in lithologic classification, using data from China's GF-5 and Europe's Sentinel-2A. The research focuses on the southern region of Tuanjie Peak in the Western Kunlun Range, comparing five space-spectral fusion methods: GSA, SFIM, CNMF, HySure, and NonRegSRNet. To comprehensively evaluate the effectiveness and applicability of these fusion methods, the study conducts a comprehensive assessment from three aspects: evaluation of fusion effects, lithologic classification experiments, and field validation. In the evaluation of fusion effects, the study uses an index analysis and comparison of spectral curves before and after fusion, concluding that the GSA fusion method performs the best. For lithologic classification, the Random Forest (RF) classification method is used, training with both area and point samples. The classification results from area sample training show significantly higher overall accuracy compared to point samples, aligning well with 1:50,000 scale geological maps. In field validation, the study employs on-site verification combined with microscopic identification and comparison of images with actual spectral fusion, finding that the classification results for the five lithologies are essentially consistent with field validation results. The "GSA+RF" method combination established in this paper, based on data from GF-5 and Sentinel-2A satellites, can provide technical support for lithological classification in similar high-altitude regions.


Exploring 3D Object Detection for Autonomous Factory Driving: Advanced Research on Handling Limited Annotations with Ground Truth Sampling Augmentation

Abstract: Autonomously driving vehicles in car factories and parking spaces can represent a competitive advantage in the logistics industry. However, the real-world application is challenging in many ways. First of all, there are no publicly available datasets for this specific task. Therefore, we equipped two industrial production sites with up to 11 LiDAR sensors to collect and annotate our own data for infrastructural 3D object detection. These form the basis for extensive experiments. Due to the still limited amount of labeled data, the commonly used ground truth sampling augmentation is the core of research in this work. Several variations of this augmentation method are explored, revealing that in our case, the most commonly used is not necessarily the best. We show that an easy-to-create polygon can noticeably improve the detection results in this application scenario. By using these augmentation methods, it is even possible to achieve moderate detection results when only empty frames without any objects and a database with only a few labeled objects are used.


A Novel Method for Monocular Depth Estimation Using an Hourglass Neck Module

Abstract: In this paper, we propose a novel method for monocular depth estimation using the hourglass neck module. The proposed method has the following originality. First, feature maps are extracted from Swin Transformer V2 using a masked image modeling (MIM) pretrained model. Since Swin Transformer V2 has a different patch size for each attention stage, it is easier to extract local and global features from images input by the vision transformer (ViT)-based encoder. Second, to maintain the polymorphism and local inductive bias of the feature map extracted from Swin Transformer V2, a feature map is input into the hourglass neck module. Third, deformable attention can be used at the waist of the hourglass neck module to reduce the computation cost and highlight the locality of the feature map. Finally, the feature map traverses the neck and proceeds through a decoder, comprised of a deconvolution layer and an upsampling layer, to generate a depth image. To evaluate the objective reliability of the proposed method in this paper, we used the NYU Depth V2 dataset to compare and evaluate the methods published in other papers. As a result of the experiment, the RMSE value of the novel method for monocular depth estimation using the hourglass neck module proposed in this paper was 0.274, which was lower than those published in other papers. The lower the RMSE value, the better the depth estimation method; therefore, its efficiency compared to other techniques has been proven.


Holographic matrix method for rapid material structural feature extraction and detection

Abstract: The extraction of the structural features of materials is fundamental for investigating novel properties in fields such as electronic information and biochemistry. However, existing experimental methods have limitations in analyzing material structures with sufficient depth. Therefore, rapid and accurate extraction and analysis of structural features from atomic coordinates obtained through simulation calculations are crucial for advancing the exploration of new material properties. Herein, we propose an approach for extracting the structural features of materials by combining the holographic matrix method with Bayesian optimization and tensor flow operations. The proposed algorithm efficiently classifies and statistically analyzes cluster structures within materials. Experimental validation conducted on a system comprising 8000 atoms demonstrated a correct recognition rate exceeding 99.213%. Moreover, the algorithm achieved an average recognition time of approximately [Formula: see text][Formula: see text]s. The proposed analytical framework exhibits scalability and robustness, establishing an algorithmic foundation for future advancements in big data analytics for complex materials.


How Does VR Affect Emotional Appeal and Persuasiveness of Gain Versus Loss-Framed Messages?

Abstract: Can virtual reality (VR) lead to more desirable persuasion effects than videos in communicating the science of contentious issues like climate change? And, will these effects differ between messages that offer hope versus fear? We addressed these questions with a 2 (Modality: Desktop VR vs. Unidirectional video) × 2 (framing: Gain vs. Loss) between-subjects experiment ( N = 130), and found that VR triggered more fearful responses, which in turn led to more persuasive outcomes. It also increased individuals’ attitudes toward climate change mitigation policy when the experience was loss-framed, but backfired when the experience was gain-framed. Theoretical and practical implications are discussed.


Motion Planning for Autonomous Vehicles in Unanticipated Obstacle Scenarios at Intersections Based on Artificial Potential Field

Abstract: In unanticipated obstacle scenarios at intersections, the safety and mobility of autonomous vehicles (AVs) are negatively impacted due to the conflict between traffic law compliance and obstacle avoidance. To solve this problem, an obstacle avoidance motion planning algorithm based on artificial potential field (APF) is proposed. An APF-switching logic is utilized to design the motion planning framework. Collision risk and travel delay are quantified as the switching triggers. The intersection traffic laws are digitalized and classified to construct compliance-oriented potential fields. A potential violation cost index (PVCI) is designed according to theories of autonomous driving ethics. The compliance-oriented potential fields are reconfigured according to the PVCI, forming violation cost potential fields. A cost function is designed based on compliance-oriented and violation cost potential fields, integrated with model predictive control (MPC) for trajectory optimization and tracking. The effectiveness of the proposed algorithm is verified through simulation experiments comparing diverse traffic law constraint strategies. The results indicate that the algorithm can help AVs avoid obstacles safely in unanticipated obstacle scenarios at intersections.


MSIE-Net: Associative Entity-Based Multi-Stage Network for Structured Information Extraction from Reports

Abstract: Efficient document recognition and sharing remain challenges in the healthcare, insurance, and finance sectors. One solution to this problem has been the use of deep learning techniques to automatically extract structured information from paper documents. Specifically, the structured extraction of a medical examination report (MER) can enhance medical efficiency, data analysis, and scientific research. While current methods focus on reconstructing table bodies, they often overlook table headers, leading to incomplete information extraction. This paper proposes MSIE-Net (multi-stage-structured information extraction network), a novel structured information extraction method, leveraging refined attention transformers and associated entity detection to enhance comprehensive MER information retrieval. MSIE-Net includes three stages. First, the RVI-LayoutXLM (refined visual-feature independent LayoutXLM) targets key information extraction. In this stage, the refined attention accentuates the interaction between different modalities by adjusting the attention score at the current position using previous position information. This design enables the RVI-LayoutXLM to learn more specific contextual information to improve extraction performance. Next, the associated entity detection module, RIFD-Net (relevant intra-layer fine-tuned detection network), identifies each test item’s location within the MER table body. Significantly, the backbone of RIFD-Net incorporates the intra-layer feature adjustment module (IFAM) to extract global features while homing in on local areas, proving especially sensitive for inspection tasks with dense and long bins. Finally, structured post-processing based on coordinate aggregation links the outputs from the prior stages. For the evaluation, we constructed the Chinese medical examination report dataset (CMERD), based on real medical scenarios. MSIE-Net demonstrated competitive performance in tasks involving key information extraction and associated entity detection. Experimental results validate MSIE-Net’s capability to successfully detect key entities in MER and table images with various complex layouts, perform entity relation extraction, and generate structured labels, laying the groundwork for intelligent medical documentation.


Meetor: A Human-Centered Automatic Video Editing System for Meeting Recordings

Abstract: Widely adopted digital cameras and smartphones have generated a large number of videos, which have brought a tremendous workload to video editors. Recently, a variety of automatic/semi-automatic video editing methods have been proposed to tackle these issues in some specific areas. However, for the production of meeting recordings, the existing studies highly depend on extra equipment in the conference venues, such as the infrared camera or special microphone, which are not practical. In this paper, we design and implement Meetor, a human-centered automatic video editing system for meeting recordings. The Meetor mainly contains three parts: an audio-based video synchronization algorithm, human-centered video content flaw detection algorithms, and an automatic video editing algorithm. Two main experiments are conducted from both objective and subjective aspects to evaluate the performance of the Meetor. The experimental results on a testbed illustrate that the proposed algorithms could achieve state-of-the-art (SOTA) performance in video content flaw detection. On the other hand, the conducted user study demonstrates that Meetor could generate meeting recordings with a satisfactory quality compared with professional video editors. Moreover, we also present a practical application of the Meetor in a university campus prototype, in which the Meetor is applied in the automatic editing of lecture recordings. All in all, the proposed Meetor can be utilized in practical applications to release the workload of professional video editors.


Exploring the Role of Sociability, Ownership, and Affinity for Technology in Shaping Acceptance and Intention to Use Personal Assistance Robots.

Abstract: Abstract Personal assistance robots are making inroads into our private and public life. At the same time, most humans are still unfamiliar with this technology and hesitate to accept and use it for daily tasks. Fortunately, the designs of robots can be adjusted to yield greater acceptance, subsequently enabling their utilization across various tasks. Using a scenario-based online experiment, we explored how sociability (low vs. high), ownership (private vs. public), and affinity for technology influence the acceptance and intention to use a robot for grocery shopping. Moreover, to assess users’ preference for robots’ morphology, participants were asked to choose a robot (technical vs. anthropomorphic design) that they would prefer to use in a supermarket. We found that low sociability of the service robot and a higher affective affinity for technology led to a higher level of acceptance. For more sociable robots, higher levels of anthropomorphism were preferred. Our results point to the importance of task-specific robot design that exceeds functional considerations.


Digital citizenship and its relevance for literacy education: perspectives of preservice teachers

Abstract: This research examines preservice teachers' perspectives on digital citizenship and its relevance for literacy education. Digital citizenship has been explored in various ways in recent decades, primarily in the educational technology literature, and prominent themes of digital citizenship include the use of safely, responsibly, and productively using technology to engage in digital spaces. However, limited research has explored preservice teachers' perspectives on digital citizenship, particularly as relates to literacy education. This qualitative case study investigates the views of 111 preservice teachers enrolled in a teacher preparation program in the Midwestern United States on digital citizenship and its relevance for literacy education. Data analysis of participants' written reflections revealed four primary themes of digital citizenship that align with concepts, skills, and goals in literacy education: well-informed citizens, community engagement and activism, safety, and technological know-how. These themes also connect to digital literacies. Given the commonalities between digital citizenship and literacy education, explicit integration of digital citizenship curricula into literacy education can connect important and discrete digital literacy skills into more cohesive educational units that empower children to safely and productively utilize digital technologies to promote meaningful change in their communities.


Extending the Framework for Developing Intelligent Virtual Environments (FIVE) with Artifacts for Modeling Internet of Things Devices and a New Decentralized Federated Learning Based on Consensus for Dynamic Networks

Abstract: One of the main lines of research in distributed learning in recent years is the one related to Federated Learning (FL). In this work, a decentralized Federated Learning algorithm based on consensus (CoL) is applied to Wireless Ad-hoc Networks (WANETs), where the agents communicate with other agents to share their learning model as they are available to the wireless connection range. When deploying a set of agents, it is essential to study whether all the WANET agents will be reachable before the deployment. The paper proposes to explore it by generating a simulation close to the real world using a framework (FIVE) that allows the easy development and modification of simulations based on Unity and SPADE agents. A fruit orchard with autonomous tractors is presented as a case study. The paper also presents how and why the concept of artifact has been included in the above-mentioned framework as a way to highlight the importance of some devices used in the environment that have to be located in specific places to ensure the full connection of the system. This inclusion is the first step to allow Digital Twins to be modeled with this framework, now allowing a Digital Shadow of those devices.


Short-Term Photovoltaic Power Prediction Using Nonlinear Spiking Neural P Systems

Abstract: To ensure high-quality electricity, improve the dependability of power systems, reduce carbon emissions, and promote the sustainable development of clean energy, short-term photovoltaic (PV) power prediction is crucial. However, PV power is highly stochastic and volatile, making accurate predictions of PV power very difficult. To address this challenging prediction problem, in this paper, a novel method to predict the short-term PV power using a nonlinear spiking neural P system-based ESN model has been proposed. First, we combine a nonlinear spiking neural P (NSNP) system with a neural-like computational model, enabling it to effectively capture the complex nonlinear trends in PV sequences. Furthermore, an NSNP system featuring a layer is designed. Input weights and NSNP reservoir weights are randomly initialized in the proposed model, while the output weights are trained by the Ridge Regression algorithm, which is motivated by the learning mechanism of echo state networks (ESNs), providing the model with an adaptability to complex nonlinear trends in PV sequences and granting it greater flexibility. Three case studies are conducted on real datasets from Alice Springs, Australia, comparing the proposed model with 11 baseline models. The outcomes of the experiments exhibit that the model performs well in tasks of PV power prediction.


Solar Energy Forecasting With Performance Optimization Using Machine Learning Techniques

Abstract: The objective of this paper is proposed a forecasting technique system constitute the data integrity phase using Machine Learning (ML) techniques and post-processing output optimization to improve the performance. The outcomes demonstrated that the Photo Voltaic (PV) energy manufacture neural network performed more accurate by reducing solar irradiance prediction errors of linear regression characteristics. Through the adoption of both the optimized method settings and chosen function of ML methods were evaluated for predicting the performance. The accuracy of solar radiation estimation could be assessed in terms of statistical error measurement and verification measurements. The approach was tested in two climates hot and cold and the findings demonstrated using absolute error percentage with a contribution of 6.3% and 4.7%, respectively to improve the performance of the system based on data-driven ML algorithms and analytical post-processing.


Digital competence profiles of first year, pre-service teachers. Analysis in the Catalan university system

Abstract: Like many other countries around the world, Catalonia is currently facing an urgent need for digitally competent teachers. Understanding the profile of first-year, pre-service teachers in relation to their teacher digital competence (TDC) can help design effective training programmes for future educators. The present study aims to measure the TDC profile of first-year, pre-service teachers along with the demographic variables related. A quantitative approach with a not experimental design was conducted in the 2018–19 academic year, to perform a cluster analysis. Three TDC groups emerge from a sample of 947 participants: Confident (n = 525), Average (n = 311), and Realistic (n = 111). Two variables show significant differences among groups: age and access to university. Both younger students and those coming from vocational training should be guided intensively during their initial training. On the other hand, gender and type of studies are distributed similarly across the three profiles. These findings have implications for developing pre-service teachers’ digital competence. In particular, the findings of this study highlight the need for all Catalan universities to develop a critical and common perspective, considering personal and professional factors of TDC as a key aspect for designing and implementing training in the curriculum.


Dark Light Image-Enhancement Method Based on Multiple Self-Encoding Prior Collaborative Constraints

Abstract: The purpose of dark image enhancement is to restore dark images to visual images under normal lighting conditions. Due to the ill-posedness of the enhancement process, previous enhancement algorithms often have overexposure, underexposure, noise increases and artifacts when dealing with complex and changeable images, and the robustness is poor. This article proposes a new enhancement approach consisting in constructing a dim light enhancement network with more robustness and rich detail features through the collaborative constraint of multiple self-coding priors (CCMP). Specifically, our model consists of two prior modules and an enhancement module. The former learns the feature distribution of the dark light image under normal exposure as an a priori term of the enhancement process through multiple specific autoencoders, implicitly measures the enhancement quality and drives the network to approach the truth value. The latter fits the curve mapping of the enhancement process as a fidelity term to restore global illumination and local details. Through experiments, we concluded that the new method proposed in this article can achieve more excellent quantitative and qualitative results, improve detail contrast, reduce artifacts and noise, and is suitable for dark light enhancement in multiple scenes.


Digital and Social-Civic Skills in Future Primary Education Teachers: A Study from the Didactics of Social Sciences for the Improvement of Teacher Training in Competences

Abstract: The use of technology, especially among young people, is providing new possibilities, including in the academic field, and requires teacher training through the development of skills and competences. At this point, Social Science Didactics plays a fundamental role, as it prepares future teachers to teach social knowledge in order to achieve useful and meaningful learning for students and society. Using an online questionnaire, structured with the Likert scale, which had previously been validated and published by Professors Peart, Gutiérrez-Esteban, and Cubo-Delgado, 156 students of the Degree in Primary Education of the subject Didactics of Social Sciences at the University of Granada (Spain) participated (academic year 2023–2024), with the aim of investigating the digital and socio-civic competences of trainee teachers in order to seek ways to improve their training. The results were processed in the IBM SPSS Statistics 25 programme, carrying out a descriptive statistical analysis, considering the mode and the variance ratio. The participating students mainly use digital environments to communicate with acquaintances and, although they know and value a democratic society, they do not exercise their citizenship on the Internet. This makes it even more necessary to train future teachers in digital competences, based on digital and socio-civic skills, as only in this way will they be able to train citizens capable of facing the challenges of the knowledge society.


Research on course teaching evaluation and academic early warning based on data mining

Abstract: Based on the development requirements of modern vocational education and the development trend of information-based teaching, the purpose of the paper is to build a three-dimensional and diversified teaching evaluation method by introducing data mining technology, use appropriate data mining algorithms to summarize the knowledge as well as master rules, predict the learning status of students, timely carry out early warning and teaching intervention, and explore a teaching evaluation and academic early warning method suitable for vocational education courses through practical classroom practice so as to improve curriculum mastery level of the students and optimize the quality of personnel training technical skills.


Song Emotion Intelligence Analysis for Psychological Stress Relief

Abstract: In today's digital and networked era, multimedia content such as images, audio, and video have become an important part of the data transmitted on the Internet's information superhighway. How to manage the stress in the college student population is directly related to the future life and development of college students. In this article, based on domestic and international data mining technology, the authors designed an intelligent analysis system for college students' mental health, pre-processed the data, then analysed these data in detail by using the outlier analysis algorithm in clustering algorithm, and finally mined the intrinsic connection between the psychological problems and attributes by using the Apriori association rule algorithm, so as to provide the decision makers with a reliable basis. This study puts forward reasonable solutions and suggestions for the problems existing in the psychological level of contemporary college students.


Unsupervised Graph Anomaly Detection with Discriminative Embedding Similarity for Viscoelastic Sandwich Cylindrical Structures

Abstract: In the detection of slipping anomalies in viscoelastic sandwich cylindrical structures (VSCS), conventional methods may encounter challenges due to the extremely rare and weak nature of slipping signals. This study focuses on normal signals and introduces unsupervised graph representation learning (UGRL) with discriminative embedding similarity for VSCS's detection. UGRL involves data preprocessing, model embedding, and matrix reconstructing. Association graphs are constructed based on sample similarities for yielding adjacency and attribute matrices. Subsequently, the matrices undergo embedding and reconstruction via various network modules to enhance graph data characterization. Detection indicators are derived by calculating embedding similarities and reconstruction errors, and thresholds are constructed using these indicators to enable efficient anomaly detection. The experiments in VSCS slipping dataset effectively indicate the superiority of the proposed method.


Artificial Intelligence and the Modelling of Teachers Competencies

Abstract: This research explores educators' perceptions of their role in shaping competencies and also presents the key challenges and measures perceived by educators in the AI system development landscape.The study employs a partial least squarestructural equation modelling (PLS-SEM) as the primary analytical tool.A set of hypotheses was derived from the literature, formulated and tested to substantiate the relationship between educators' attitudes and competencies.The findings highlight a significant positive relationship between teachers' attitudes toward AI and competence development, with the most pronounced impact on educational management competencies.Thus, teachers' positive attitudes toward AI statistically significant influence cognitive, fundamental and educational management competencies, underscoring the critical role of instilling positive attitude towards AI among educators to foster the development of multifaceted competencies.Furthermore, the research highlights key challenges to integrating AI into education, including the imperative for professional development for educators and ensuring equitable access to educational resources and technology.From a practical point of view, the study advocates for initiatives aimed at bridging the digital divide and integrating AI education into curricula as indispensable policy measures.The originality of our research lies in the unitary approach of the relationship between positive attitudes of teachers toward artificial intelligence and a series of competencies, such as cognitive, fundamental and educational management.Although the study provides valuable information, it has certain limitations.The findings are based on educators' perceptions and attitudes, which may vary among individuals, the study scope is limited to a specific region, and the generalisability of findings to other settings may require further investigation.This research contributes to the existing literature by exploring the relationship between teachers' attitudes toward AI and the development of their skills, with the help of an exploratory model using structural equations, a model that has the teachers in the foreground.


Adversarial attacks and defenses in explainable artificial intelligence: A survey

Abstract: Explainable artificial intelligence (XAI) methods are portrayed as a remedy for debugging and trusting statistical and deep learning models, as well as interpreting their predictions. However, recent advances in adversarial machine learning (AdvML) highlight the limitations and vulnerabilities of state-of-the-art explanation methods, putting their security and trustworthiness into question. The possibility of manipulating, fooling or fairwashing evidence of the model’s reasoning has detrimental consequences when applied in high-stakes decision-making and knowledge discovery. This survey provides a comprehensive overview of research concerning adversarial attacks on explanations of machine learning models, as well as fairness metrics. We introduce a unified notation and taxonomy of methods facilitating a common ground for researchers and practitioners from the intersecting research fields of AdvML and XAI. We discuss how to defend against attacks and design robust interpretation methods. We contribute a list of existing insecurities in XAI and outline the emerging research directions in adversarial XAI (AdvXAI). Future work should address improving explanation methods and evaluation protocols to take into account the reported safety issues.


In Conversation with Sir David Spiegelhalter and Professor Sylvia Richardson

Abstract: Sir David Spiegelhalter and Professor Sylvia Richardson are two eminent statisticians of our time who have made pioneering contributions to Statistics and Data Science with careers spanning over more than four decades. They have a long and celebrated legacy built through foundational research in Bayesian statistics, impactful collaborations, steadfast professional service and superb scientific communications. They have won many prestigious awards and recognitions throughout their distinguished careers. During my sabbatical in 2022 at the University of Cambridge I had the honor of sitting down for a conversation with these two remarkable individuals. We discussed early career influences and digressions, research philosophy, role of mentors and advice for the future generation. We hope this conversation with David and Sylvia will inspire many future statisticians.


Emerging Directions in Bayesian Computation

Abstract: Bayesian models are powerful tools for studying complex data, allowing the analyst to encode rich hierarchical dependencies and leverage prior information. Most importantly, they facilitate a complete characterization of uncertainty through the posterior distribution. Practical posterior computation is commonly performed via MCMC, which can be computationally infeasible for high-dimensional models with many observations. In this article, we discuss the potential to improve posterior computation using ideas from machine learning. Concrete directions are explored in vignettes on normalizing flows, statistical properties of variational approximations, Bayesian coresets and distributed Bayesian inference.


Outlier detection using flexible categorisation and interrogative agendas

Abstract: Categorization is one of the basic tasks in machine learning and data analysis. Building on formal concept analysis (FCA), the starting point of the present work is that different ways to categorize a given set of objects exist, which depend on the choice of the sets of features used to classify them, and different such sets of features may yield better or worse categorizations, relative to the task at hand. In their turn, the (a priori) choice of a particular set of features over another might be subjective and express a certain epistemic stance (e.g. interests, relevance, preferences) of an agent or a group of agents, namely, their interrogative agenda. In the present paper, we represent interrogative agendas as sets of features, and explore and compare different ways to categorize objects w.r.t. different sets of features (agendas). We first develop a simple unsupervised FCA-based algorithm for outlier detection which uses categorizations arising from different agendas. We then present a supervised meta-learning algorithm to learn suitable (fuzzy) agendas for categorization as sets of features with different weights or masses. We combine this meta-learning algorithm with the unsupervised outlier detection algorithm to obtain a supervised outlier detection algorithm. We show that these algorithms perform at par with commonly used algorithms for outlier detection on commonly used datasets in outlier detection. These algorithms provide both local and global explanations of their results.


Quantum Annealing-driven Branch and Bound for the single machine total weighted number of tardy jobs scheduling problem

Abstract: In the paper we present a new approach to solving NP-hard problems of discrete optimization adapted to the architecture of quantum processors (QPU, Quantum Processor Unit) implementing hardware quantum annealing. This approach is based on the use of the quantum annealing metaheuristic in the exact branch and bound algorithm to compute the lower and upper bounds of the objective function. To determine the lower bound, a new method of defining the Lagrange function for the dual problem (the generalized discrete knapsack problem) was used, the value of which is calculated on the QPU of a quantum machine. In turn, to determine the upper bound, we formulate an appropriate task in the form of binary quadratic programming with constraints. Despite the fact that the results generated by the quantum machine are probabilistic, the hybrid method of algorithm construction proposed in the paper, using alternately a CPU and QPU, guarantees the optimal solution. As a case study we consider the NP-hard single machine scheduling problem with minimizing the weighted number of tardy jobs. The performed computational experiments showed that optimal solutions were already obtained in the root of the solution tree, and the values of the lower and upper bounds differ by only a few percent.


Feature selection based on dataset variance optimization using Hybrid Sine Cosine – Firehawk Algorithm (HSCFHA)

Abstract: Feature selection plays a pivotal role in preprocessing data for machine learning (ML) models. It entails choosing a subset of pertinent features to enhance the model’s accuracy and minimize overfitting. Wrapper methods based on metaheuristics are one approach to feature selection, leveraging the predictive accuracy of a learning algorithm to form a condensed set of features. Traditionally, this method uses K-Nearest Neighbour (KNN) for maximizing accuracy as its cost function. However, this approach often yields less than optimal results in large sample spaces and demands considerable computational resources. To circumvent the shortcomings of this approach, this work proposes a novel metaheuristic algorithm, termed the Hybrid Sine Cosine Firehawk Algorithm. Furthermore, a novel feature selection technique is designed that uses this hybrid algorithm to eliminate insignificant and redundant features by incorporating the minimization of dataset variance in the cost function. Additionally, the hybridization of multiple metaheuristic algorithms produces the best features of each algorithm to improve the exploration ability. The proposed technique is tested on 22 University of California Irvine datasets containing low, medium and high dimensional datasets and compared to the traditional KNN-based approach. The technique is also compared with other state-of-the-art metaheuristic techniques, namely Particle Swarm Optimizer, Grey Wolf Optimizer, Whale Optimization Algorithm, Hybrid Ant Colony Optimizer and Improved Binary Bat Algorithm. The results show significant improvements over previous techniques in terms of minimal loss in essential data while reducing the size of the raw data in considerably less time, as well as a well-balanced confusion matrix.


Hybrid deep learning model using SPCAGAN augmentation for insider threat analysis

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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)

Abstract: 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

Abstract: 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: 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: 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

Abstract: 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

Abstract: 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

Abstract: 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.


Private initiative as a growth factor of Book publishing activity in Russia in the last third of the 18th century

Abstract: Importance. A landmark event in the history of Russia in the last third of the 18th century was the development of private printing houses, which destroyed the state and church monopoly on printing publications. The work of publishing houses played a decisive role in satisfying the spiritual needs of the broad masses of the population, contributed to the secularization of the national book culture. At the same time, the richness and diversity of fiction, educational and scientific literature, produced both in Russian and in foreign languages, thoroughly influenced the emergence and dissemination of new information and facts, increased the level of education of all segments of the reading population. Despite the existence of extensive research on the book publishing of this period, there is a well-known imbalance in the description of the activities of famous domestic printers and their foreign colleagues. Meanwhile, it was foreign specialists (I.M. Hartung, J.J. Weitbrecht, etc.) who played a key role in opening the first private printing houses in Russia. The study of their activities contributes to the increment of knowledge on the Russian book culture history, allows you to consider unknown pages of Russian book publishing and book trade. Research Methods. The main sources of the study are the materials of the Russian State Archive of Ancient Acts (Fund 17, Fund 248), as well as legislative acts and orders of that time that influenced the book trade. Results and Discussion. Being a characteristic feature of the development of information culture in the first half of the 18th century, censorship could not stop the transformative role of books in the history of Russia. As a result of the intensification of book printing in the country, the emergence of free book trade, the successful operation of new education models, interest in printed products has noticeably increased. Book business has ceased to be only a capital occupation and has become the property of the whole country. By allowing everyone to start printing houses at will, the state equated them with factories, which facilitated the processes of creating and developing their own printing business. Conclusion. The activity of private book publishers in Russia in the second half of the 18th century created opportunities for the formation of the national book market, filling it with various scientific, fiction and educational literature. Thus, the printers contributed to the dissemination of the widest knowledge among the Russian population, increased the variety and quality of the literature sold.


Football referee gesture recognition algorithm based on YOLOv8s

Abstract: Gesture serves as a crucial means of communication between individuals and between humans and machines. In football matches, referees communicate judgment information through gestures. Due to the diversity and complexity of referees’ gestures and interference factors, such as the players, spectators, and camera angles, automated football referee gesture recognition (FRGR) has become a challenging task. The existing methods based on visual sensors often cannot provide a satisfactory performance. To tackle FRGR problems, we develop a deep learning model based on YOLOv8s. Three improving and optimizing strategies are integrated to solve these problems. First, a Global Attention Mechanism (GAM) is employed to direct the model’s attention to the hand gestures and minimize the background interference. Second, a P2 detection head structure is integrated into the YOLOv8s model to enhance the accuracy of detecting smaller objects at a distance. Third, a new loss function based on the Minimum Point Distance Intersection over Union (MPDIoU) is used to effectively utilize anchor boxes with the same shape, but different sizes. Finally, experiments are executed on a dataset of six hand gestures among 1,200 images. The proposed method was compared with seven different existing models and 10 different optimization models. The proposed method achieves a precision rate of 89.3%, a recall rate of 88.9%, a mAP@0.5 rate of 89.9%, and a mAP@0.5:0.95 rate of 77.3%. These rates are approximately 1.4%, 2.0%, 1.1%, and 5.4% better than those of the newest YOLOv8s, respectively. The proposed method has right prospect in automated gesture recognition for football matches.


Development of a Decommissioning Robot with a Simple Structure Capable of Traversing Steps Using Two Different Drive Systems

Abstract: In response to the imperative need for robotic involvement in the decommissioning of the Fukushima Daiichi Nuclear Power Plant, a proactive initiative was undertaken. This initiative led to the organization of the “7th Decommissioning Creative Robot Contest,” in which pivotal challenges related to nuclear plant decommissioning were addressed. Recognizing inaccessible areas within a decommissioned plant, the primary objective of this study was to develop a remotely operated robot capable of executing decontamination tasks in such environments. Two distinct locomotion systems were designed: mecanum wheels and crawlers. The mecanum wheels, characterized by their ability to execute approximately 100 mm vertical movements, facilitated the robot to surmount obstacles, particularly by negotiating 100 mm steps by engaging the tracks. Simultaneously, to address decontamination tasks at elevated locations, a threefold expandable ladder truck system was integrated, augmenting the operational range of the robot. Participation in the contest provided a platform for showcasing the developed technologies and validating their practicality. Despite encountering challenges during competition, such as damage to one locomotion system, the other locomotion system can be used to reach the destination. Experience serves as a crucial means of verifying technological advancements. Issues such as malfunctions in the range of motion control due to faulty limit switch contacts and unexpected loads on the tire elevation motors were noted, which hindered the achievement of the anticipated results. Despite these drawbacks, the adopted structural design demonstrates notable advantages, particularly in terms of maneuverability. The combination of mecanum wheels and crawlers along with a ladder truck system demonstrated adaptability to various scenarios. This adaptability holds significant promise for future nuclear power plant decontamination applications. While the competition may not have yielded the desired outcome, the lessons learned and the verified practicality of the developed technologies reinforce their potential utility in addressing the future challenges of nuclear plant decommissioning.


Navigation Path Following Platform for a Greenhouse Shuttle Robot Using the State-flow Method

Abstract: Localization is an important method for autonomous indoor robots to recognize their positions. Generally, the navigation of a mobile robot is conducted using a camera, Lidar, and global positioning system. However, for an indoor environment, GPS is unavailable. Therefore, a, state-trajectory tracking method is utilized based on a Lidar map. This paper presents the path following of an autonomous indoor mobile robot, that is, a shuttle robot, using a state-flow method via a robot operating system network. MATLAB and Linux high-level computers and an inertial measurement unit sensor are used to obtain the Cartesian coordinate information of a bicycle-type mobile robot. The path following problem can be solved in the state-flow block by setting appropriate time and linear and angular velocity variables. After the predetermined time, the linear and angular velocities are set based on the length of the path and radius of the quarter-circle of the left and right turns in the state-flow block, path planning, which can execute the work effectively, is established using the state-flow algorithm. The state-flow block produces time-series data that are sent to Linux system, which facilitates real-time mobile platform path following scenario. Several cases within the path-following problem of the mobile robot were considered, depending on the linear and angular velocity settings: the mobile robot moved forward and backward, turned in the right and left directions on the circular path. The effectiveness of the method was demonstrated using the desktop-based indoor mobile robot control results. Thus, the paper focuses on the application of the state-flow algorithm to the shuttle robot specifically in the narrow indoor environment.


The role of the motivational aspect in vocal students’ professional development

Abstract: Importance. The necessity of using the motivational aspect in the professional training of music specialists, which contributes to the effectiveness of the educational process, is determined. A motivated student strives to constantly improve his skills, which allows him to become a professional in his field. The types of motivation that influence the vocal students’ professional development and various aspects such as vocal training, musical abilities development, music theory studying, working on performance technique and much more that contribute the vocalist’s professional development are revealed. Research Methods. Based on a theoretical analysis of specialized literature and systematization of materials from scientific sources, the role of the motivational aspect in vocal students’ professional development is substantiated. Results and Discussion. The possibilities of motivation that contribute to improving the quality of education and developing the personality of a vocal student are considered. The characteristics of the main methods that teachers use to motivate vocal students are given. Recommendations are offered for the use of motivation in the process of vocal students training. Conclusion. It is concluded that motivation contributes to the education quality improving, vocal student’s personality development and his successful adaptation in the modern world.


Deep Learning-Based Vehicle Type and Color Classification to Support Safe Autonomous Driving

Abstract: This technology can prevent accidents involving large vehicles, such as trucks or buses, by selecting an optimal driving lane for safe autonomous driving. This paper proposes a method for detecting forward-driving vehicles within road images obtained from a vehicle’s DashCam. The proposed method also classifies the types and colors of the detected vehicles. The proposed method uses a YOLO deep learning network for vehicle detection based on a pre-trained ResNet-50 convolutional neural network. Additionally, a Resnet-50 CNN-based object classifier, using transfer learning, was used to classify vehicle types and colors. Vehicle types were classified into four categories based on size whereas vehicle colors were classified into eight categories. During autonomous driving, vehicle types are used to determine driving lanes, whereas vehicle colors are used to distinguish the road infrastructure, such as lanes, vehicles, roads, backgrounds, and buildings. The datasets used for learning consisted of road images acquired in various driving environments. The proposed method achieved a vehicle detection accuracy of 91.5%, vehicle type classification accuracy of 93.9%, and vehicle color classification accuracy of 94.2%. It accurately detected vehicles and classified their types and colors. These can be applied to autonomous and safe driving support systems to enhance the safety of autonomous vehicles.


Zhang neural networks: an introduction to predictive computations for discretized time-varying matrix problems

Abstract: Abstract This paper wants to increase our understanding and computational know-how for time-varying matrix problems and Zhang Neural Networks. These neural networks were invented for time or single parameter-varying matrix problems around 2001 in China and almost all of their advances have been made in and most still come from its birthplace. Zhang Neural Network methods have become a backbone for solving discretized sensor driven time-varying matrix problems in real-time, in theory and in on-chip applications for robots, in control theory and other engineering applications in China. They have become the method of choice for many time-varying matrix problems that benefit from or require efficient, accurate and predictive real-time computations. A typical discretized Zhang Neural Network algorithm needs seven distinct steps in its initial set-up. The construction of discretized Zhang Neural Network algorithms starts from a model equation with its associated error equation and the stipulation that the error function decrease exponentially fast. The error function differential equation is then mated with a convergent look-ahead finite difference formula to create a distinctly new multi-step style solver that predicts the future state of the system reliably from current and earlier state and solution data. Matlab codes of discretized Zhang Neural Network algorithms for time varying matrix problems typically consist of one linear equations solve and one recursion of already available data per time step. This makes discretized Zhang Neural network based algorithms highly competitive with ordinary differential equation initial value analytic continuation methods for function given data that are designed to work adaptively. Discretized Zhang Neural Network methods have different characteristics and applicabilities than multi-step ordinary differential equations (ODEs) initial value solvers. These new time-varying matrix methods can solve matrix-given problems from sensor data with constant sampling gaps or from functional equations. To illustrate the adaptability of discretized Zhang Neural Networks and further the understanding of this method, this paper details the seven step set-up process for Zhang Neural Networks and twelve separate time-varying matrix models. It supplies new codes for seven of these. Open problems are mentioned as well as detailed references to recent work on discretized Zhang Neural Networks and time-varying matrix computations. Comparisons are given to standard non-predictive multi-step methods that use initial value problems ODE solvers and analytic continuation methods.


Technologies of literary and methodical teacher education

Abstract: Importance. The purpose of the study is to reflect the search for the most productive ways of teaching literature. To achieve this goal, it is necessary to include modern technologies in the methodology of literary reading in primary and secondary schools to prepare teachers for effective teaching. Research Methods. The technologization methods of the educational process are related to the typical features’ understanding of technology, the classification of technologies in pedagogy and, in particular, in didactics, with the development of educational and training technologies that are available to primary school teachers and teachers of philology. Results and Discussion. The technologies of multilevel perception of the text by the teacher, complex analysis of the work in preparation for the lesson, achievement of expressive reading of the text, technology of co-creative reading, discrete reading, immersion in the artistic text, preparation for retelling, carrying out dictionary work, etc. have been developed and tested in teaching practice. Special attention should be paid to the technology of artistic and speech accompaniment of children’s life activities during the day, which has not only an educational, but also a healthsaving, educating character. Each technology cannot be described in detail within the scope of the study, therefore, the list of references contains sources containing detailed theoretical justifications of technologies and multiple examples of applications. Conclusion. The conclusion of the study on the technologization of the educational process is related to the self-education of the teacher, who is currently provided with a variety of opportunities for this.


Formal Security Analysis of ISA100.11a Standard Protocol Based on Colored Petri Net Tool

Abstract: This paper presents a formal security analysis of the ISA100.11a standard protocol using the Colored Petri Net (CPN) modeling approach. Firstly, we establish a security threat model for the ISA100.11a protocol and provide a detailed description and analysis of the identified security threats. Secondly, we use the CPN tool to model the protocol formally and conduct model checking and security analysis. Finally, we analyze and discuss the results of the model checking, which demonstrate that the ISA100.11a standard protocol may have vulnerabilities when certain security threats exist, and provide some suggestions to enhance the security of the protocol. This research provides a certain level of security assurance for the ISA100.11a standard protocol and serves as a reference for similar security research on protocols.


Dual-side Adversarial Learning based Fair Recommendation for Sensitive Attribute Filtering

Abstract: With the development of recommendation algorithms, researchers are paying increasing attention to fairness issues such as user discrimination in recommendations. To address these issues, existing works often filter users’ sensitive information that may cause discrimination during the process of learning user representations. However, these approaches overlook the latent relationship between items’ content attributes and users’ sensitive information. In this paper, we propose a fairness-aware recommendation algorithm (DALFRec) based on user-side and item-side adversarial learning to mitigate the effects of sensitive information in both sides of the recommendation process. Firstly, we conduct a statistical analysis to demonstrate the latent relationship between items’ information and users’ sensitive attributes. Then, we design a dual-side adversarial learning network that simultaneously filters out users’ sensitive information on the user and item side. Additionally, we propose a new evaluation strategy that leverages the latent relationship between items’ content attributes and users’ sensitive attributes to better assess the algorithm’s ability to reduce discrimination. Our experiments on three real datasets demonstrate the superiority of our proposed algorithm over state-of-the-art methods.


Evaluation of Problems While Marketing Clothing on Instagram with the Pythagorean Fuzzy AHP Method

Abstract: Abstract Due to the fierce international competition in today’s clothing market, businesses have started to pay more attention to marketing activities. One of the most popular of these marketing activities is Instagram marketing. For this reason, the aim of this study was to identify the problems faced by clothing businesses that perform garment marketing on Instagram, to rank them in order of importance and to develop solutions to the most important problems. Within the scope of the research, firstly, the problems encountered by businesses while marketing clothing on Instagram were determined through data obtained from 14 businesses through a designed questionnaire. The problems identified are technical problems, cargo-related problems, difficulty in increasing the number of followers, and various other issues. Secondly, all the problems identified in the research were ranked by the Pythagoras Fuzzy AHP Method according to the degree of importance; increasing the number of followers, technical problems, lack of trust and others. In the last stage of the research, expert opinion from Instagram marketing experts of 14 businesses was used to develop solutions to the three most important problems previously identified. The solution suggestions developed are as follows: to increase the number of followers of businesses, to increase the number and variety of advertisements and to make advertising campaigns continuous; for technical problems - to share purchase links to customers, to keep the programs used constantly updated, to get professional support and to focus more on story ads instead of post ads; and for the problem of lack of trust - honesty, phenomenon support, product photo shoots within the business.


Secure Aggregation Protocol Based on DC-Nets and Secret Sharing for Decentralized Federated Learning

Abstract: In the era of big data, millions and millions of data are generated every second by different types of devices. Training machine-learning models with these data has become increasingly common. However, the data used for training are often sensitive and may contain information such as medical, banking, or consumer records, for example. These data can cause problems in people’s lives if they are leaked and also incur sanctions for companies that leak personal information for any reason. In this context, Federated Learning emerges as a solution to the privacy of personal data. However, even when only the gradients of the local models are shared with the central server, some attacks can reconstruct user data, allowing a malicious server to violate the FL principle, which is to ensure the privacy of local data. We propose a secure aggregation protocol for Decentralized Federated Learning, which does not require a central server to orchestrate the aggregation process. To achieve this, we combined a Multi-Secret-Sharing scheme with a Dining Cryptographers Network. We validate the proposed protocol in simulations using the MNIST handwritten digits dataset. This protocol achieves results comparable to Federated Learning with the FedAvg protocol while adding a layer of privacy to the models. Furthermore, it obtains a timing performance that does not significantly affect the total training time, unlike protocols that use Homomorphic Encryption.


An adaptive dynamic window approach for UUV obstacle avoidance planning in 3D environments

Abstract: Abstract Unmanned Underwater Vehicles (UUVs) are essential equipment for Marine development, widely used in Marine scientific research, Marine resource survey, and Marine security. The autonomous navigation planning ability in unknown environments is a critical indicator for UUV intelligence. This paper focuses on the particularity of UUV motion and the complexity of the underwater environment and proposes an adaptive Dynamic Window Approach (DWA) for UUV obstacle avoidance planning. The adaptive DWA introduces novel heading angle evaluation and adaptive dynamic strategies to solve the problems of poor adaptability of traditional DWA to complex environments and unreasonable path selection in encountering dynamic obstacles and approaching targets. Simulations verify the effectiveness and superiority of the proposed method.


Academic community’s use of university digital library and their searching behaviour

Abstract: Purpose This study aims to explore the evidence-based usage patterns of higher education commission (HEC) subscribed e-journal databases in the university digital library used by the scholarly community and the academics’ online searching behaviour at a higher education institution in Pakistan. Design/methodology/approach The study used an explanatory sequential mixed methods approach. Raw transaction log data were collected for quantitative analysis, and the interview technique was used for qualitative data collection and thematic analysis. Findings Log analysis revealed that HEC subscribed databases were used significantly, and among those, scholarly databases covering various subjects were more frequently used than subject-specific society-based databases. Furthermore, the users frequently accessed the needed e-journal articles through search engines like Google and Google Scholar, considering them sources of free material instead of the HEC subscribed databases. Practical implications It provides practical implications for examining the evidence-based use patterns of e-journal databases. It suggests the need for improving the access management of HEC databases, keeping in view the usage statistics and the demands of the scholars. The study may also help create market venues for the publishers of scholarly databases by offering attractive and economical packages for researchers of various disciplines in developing and underdeveloped countries. The study results also guide the information professionals to arrange orientation and information literacy programs to improve the searching behaviour of their less frequent users and enhance the utilization of these subscribed databases. Originality/value The study is part of a PhD project and, to the best of the authors’ knowledge, is the first such work in the context of a developing country like Pakistan.


Distributed maximum correntropy unscented Kalman filter under hybrid attacks in non‐Gaussian environment

Abstract: Abstract This paper addresses a distributed nonlinear filtering issue based on maximum correntropy for dealing with randomly occurring hybrid cyber‐attacks in non‐Gaussian environment. The types of cyber‐attacks include denial of service attacks and deception attacks. First, a modified distributed unscented Kalman filter is proposed, using Cauchy kernel‐based maximum correntropy criterion instead of the traditional mean square error criterion, against cyber‐attacks and non‐Gaussian noise. Then based on fixed‐point iterative rules and information fusion strategy, the update equations of state estimates and covariance matrices of the proposed filter are obtained. Furthermore, the sufficient condition guaranteeing its convergence and computational complexity are also given. Finally, an illustrative example is presented to verify the effectiveness of the proposed filter under hybrid cyber‐attacks in non‐Gaussian environment.