Reviewerly/exHarmony huggingface dataset:
TDS-NA: Blockchain-based trusted data sharing scheme with PKI authentication

Authors:
raw_author_name: Xiaofei Xing
raw_affiliation_string: School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China
raw_author_name: Siqi He
raw_affiliation_string: School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China
raw_author_name: Guojun Wang
raw_affiliation_string: School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China

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.
Dual-Branch Multi-Scale Relation Networks with Tutorial Learning for Few-Shot Learning

Authors:
raw_author_name: Chuanyun Xu
raw_affiliation_string: College of Computer and Information Science, Chongqing Normal University, Chongqing 401331, China; School of Artificial Intelligence, Chongqing University of Technology, Chongqing 401135, China
raw_author_name: Hang Wang
raw_affiliation_string: School of Artificial Intelligence, Chongqing University of Technology, Chongqing 401135, China
raw_author_name: Yang Zhang
raw_affiliation_string: College of Computer and Information Science, Chongqing Normal University, Chongqing 401331, China
raw_author_name: Zheng Zhou
raw_affiliation_string: School of Artificial Intelligence, Chongqing University of Technology, Chongqing 401135, China
raw_author_name: Gang Li
raw_affiliation_string: School of Artificial Intelligence, Chongqing University of Technology, Chongqing 401135, China

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.
A Natural Language Processing System for Text Classification Corpus Based on Machine Learning

Authors:
raw_author_name: Yawen Su
raw_affiliation_string: Normal College, Jimei University, Xiamen 361000, Fujian, China

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

Authors:
raw_author_name: Yu Sun
raw_affiliation_string: School of Computer and Electronics and Information, Guangxi University, Nanning 530004, China
raw_author_name: Zhiqiang Zhang
raw_affiliation_string: School of Computer and Electronics and Information, Guangxi University, Nanning 530004, China

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.
Design of human lower limb motion data acquisition system based on multi-sensor

Authors:
raw_author_name: Gang Liu

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

Authors:
raw_author_name: Ziqi Zhu

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

Authors:
raw_author_name: Zhenjie Yao
raw_affiliation_string: Institute of Microelectronics, Key Laboratory of Fabrication Technologies for Integrated Circuits, Chinese Academy of Sciences, Beijing, China and Purple Mountain Laboratory, Nanjing, China
raw_author_name: Yixin Chen
raw_affiliation_string: Washington University in St.Louis, St. Louis, USA
raw_author_name: Jinwei Wang
raw_affiliation_string: Renal Division, Department of Medicine, Peking University First Hospital, Beijing, China and National Institute of Health Data Science at Peking University, Beijing, China
raw_author_name: Shouling Wu
raw_affiliation_string: Department of Cardiology, Kailuan General Hospital Affiliated to North China University of Science and Technology, Tangshan, China
raw_author_name: Yanhui Tu
raw_affiliation_string: Shandong Future Network Research Institute, Jinan, China
raw_author_name: Ming-Hui Zhao
raw_affiliation_string: National Institute of Health Data Science at Peking University, Beijing, China and Renal Division, Department of Medicine, Peking University First Hospital, Beijing, China and Chinese Academy of Medical Sciences, Beijing, China
raw_author_name: Luxia Zhang
raw_affiliation_string: National Institute of Health Data Science at Peking University, Beijing, China and Renal Division, Department of Medicine, Peking University First Hospital, Beijing, China and Chinese Academy of Medical Sciences, Beijing, China

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

Authors:
raw_author_name: Jiayu Gong

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

Authors:
raw_author_name: Oksana Karabin
raw_affiliation_string: epartment of Informatics and Methods of its Teaching, Faculty of Physics and Mathematics, Volodymyr Hnatiuk National Pedagogical University of Ternopil, 2, Maxyma Kryvonosa Str., 46027, Ternopil, UKRAINE
raw_author_name: Viktoriia Bielova
raw_affiliation_string: Department of Modern European Languages, Faculty of International Trade and Law, State University of Trade and Economics, 19, Kioto Str., 02156, Kyiv, UKRAINE
raw_author_name: Tetiana Hladun
raw_affiliation_string: Department of General and Medical Psychology, Bogomolets National Medical University, 13, T. Shevchenko Blvd., 01601, Kyiv, UKRAINE
raw_author_name: Lesia Makarenko
raw_affiliation_string: Department of Information Systems and Technologies, Dragomanov Ukrainian State University, 9, Pyrohova Str., 01601, Kyiv, UKRAINE

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

Authors:
raw_author_name: R. M. Zhitin
raw_affiliation_string: Derzhavin Tambov State University
raw_author_name: A. G. Topilsky
raw_affiliation_string: Derzhavin Tambov State University

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

Authors:
raw_author_name: Zhiyuan Yang
raw_affiliation_string: School of Sport Engineering, Beijing Sport University, Beijing, China.
raw_author_name: Yuanyuan Shen
raw_affiliation_string: School of Sport Engineering, Beijing Sport University, Beijing, China.
raw_author_name: Yanfei Shen
raw_affiliation_string: School of Sport Engineering, Beijing Sport University, Beijing, China.

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.