Reviewerly/exHarmony huggingface dataset:
ACS: An Efficient Messaging System with Strong Tracking-Resistance

The increasingly rampant network monitoring and tracing bring a huge challenge on the privacy protection, because even if the message data is encrypted, the communication privacy is difficult to be hidden. Existing anonymous systems sacrifice anonymity for efficient communication, or vice versa. In this paper, we present ACS, an efficient messaging system which leverages a two-layer framework to provide tracking-resistance. The first layer is the entry layer, which consists of entry servers to relay messages. The second layer is the exchange layer, which consists of exchange servers to exchange messages. Users divide its message into different shares and send each share to exchange server via a randomly chosen entry server. Users only provide their pseudonyms to exchange servers for message exchange. Then, entry servers have no information about the message exchange, and exchange servers have no information about users’ identities. The exchange servers also provide message storage service in case that the receiver of these messages are offline, in which way, the communication becomes more simple and flexible. The experimental results show that our proposed system guarantees the strong tracking-resistance and high communication efficiency.
IMPORT SUBSTITUTION OF OPERATING SYSTEMS ON THE EXAMPLE OF WINDOWS AND LINUX DISTRIBUTIONS

Currently, import substitution has affected all areas, including information technology. Russian developers are creating domestic operating systems to replace Windows that has left Russia. The article considers one of the domestic Linux distributions, a comparative analysis is carried out. It is revealed that the distribution is well comparable with the most popular and well-known operating system. The reasons why a full-fledged transition from Windows is currently impossible have been identified.
Variational Inference and Variational Expectation Maximization

This self-contained introduction to machine learning, designed from the start with engineers in mind, will equip students with everything they need to start applying machine learning principles and algorithms to real-world engineering problems. With a consistent emphasis on the connections between estimation, detection, information theory, and optimization, it includes: an accessible overview of the relationships between machine learning and signal processing, providing a solid foundation for further study; clear explanations of the differences between state-of-the-art techniques and more classical methods, equipping students with all the understanding they need to make informed technique choices; demonstration of the links between information-theoretical concepts and their practical engineering relevance; reproducible examples using Matlab, enabling hands-on student experimentation. Assuming only a basic understanding of probability and linear algebra, and accompanied by lecture slides and solutions for instructors, this is the ideal introduction to machine learning for engineering students of all disciplines.
Transfer Learning, Multi-task Learning, Continual Learning, and Meta-learning

This self-contained introduction to machine learning, designed from the start with engineers in mind, will equip students with everything they need to start applying machine learning principles and algorithms to real-world engineering problems. With a consistent emphasis on the connections between estimation, detection, information theory, and optimization, it includes: an accessible overview of the relationships between machine learning and signal processing, providing a solid foundation for further study; clear explanations of the differences between state-of-the-art techniques and more classical methods, equipping students with all the understanding they need to make informed technique choices; demonstration of the links between information-theoretical concepts and their practical engineering relevance; reproducible examples using Matlab, enabling hands-on student experimentation. Assuming only a basic understanding of probability and linear algebra, and accompanied by lecture slides and solutions for instructors, this is the ideal introduction to machine learning for engineering students of all disciplines.
New Algorithms and Analyses for Sum-Preserving Encryption

We continue the study of sum-preserving encryption schemes, in which the plaintext and ciphertext are both integer vectors with the same sum. Such encryption schemes were recently constructed and analyzed by Tajik, Gunasekaran, Dutta, Ellia, Bobba, Rosulek, Wright, and Feng (NDSS 2019) in the context of image encryption. Our first main result is to prove a mixing-time bound for the construction given by Tajik et al. using path coupling. We then provide new sum-preserving encryption schemes by describing two practical ways to rank and unrank the values involved in sum-preserving encryption, which can then be combined with the rank-encipher-unrank technique from format-preserving encryption. Finally, we compare the efficiency of the Tajik et al. construction and our new ranking constructions based on performance tests we conducted on prototype implementations.
A Negative Sampling-Based Service Recommendation Method

As the number of services has increased dramatically in recent years, it has become a challenge to provide users with high quality services. At present, the collaborative filtering technology based on graph neural network has achieved good results in service recommendation. However, these methods ignore the influence of negative sample neighbor nodes. Therefore, we propose a service recommendation method based on negative sampling, which uses the information of neighbor nodes of non-interacted services to generate negative samples to improve the recommendation accuracy. In this method, firstly, we construct the dataset for training based on the interaction records between users and services; second, we use the interpolation mixing technique to pollute the negative samples, and generate synthetic hard negative samples by fusing the negative samples selected by the hard negative selection strategy through the pooling operation; then, the samples are used in GNN-based recommender systems to obtain more accurate user and service embedding representations; next, calculate the inner product score embedded by the user and the service, and recommend high inner product score services to user. Finally, experiments were conducted on three real datasets, Shopify, Programmable, and 360Apps, and the experimental results demonstrate that the proposed method can achieve better recommendation results such as average relative increase of 30% for LightGCN and 8% for NGCF in terms of NDCG@20.
Self-gated FM: Revisiting the Weight of Feature Interactions for CTR Prediction

With the successful application of factorization machine models in click-through rate prediction, the automatic selection of feature interactions has attracted extensive attention. As the most commonly-used strategies, automatic construction of limited high-order cross-feature and automatic learning of feature interaction weight have made great progress. However, most studies still face challenges of complex training and search process. Therefore, we propose a self-gating mechanism for automatic feature selection of factorization machine models and implement a portable self-gating layer. In the self-gating layer, the weight of feature interaction is revisited through the attention network with different attentive aspects, and then the gate status is dynamically determined according to the attention score to achieve the effect of automatic selection. Our method can be easily ported to FM and DeepFM. The experimental results on two real-world data sets show that our proposed methods are superior to many state-of-the-art methods.
Effective Blockchain-Based Asynchronous Federated Learning for Edge-Computing

Since massive data are generated at the network’s edge, the Internet of Things devices can exploit edge computing and federated learning to train artificial intelligence (AI) models while protecting data privacy. However, heterogeneous devices lead to low efficiency and single-point-of-failure. Moreover, malicious nodes may affect training accuracy. Therefore, we propose FedLyra, an effective blockchain-based asynchronous federated learning architecture, to improve the efficiency of aggregation and resist malicious nodes in a trusted and decentralized manner. We then propose a reputation mechanism that combines historical behaviors and the quality of local updates to resist disagreements and adversaries. With the help of the reputation mechanism, we propose a council-based decentralized aggregation mechanism to exclude malicious nodes. Experiments show that FedLyra can resist malicious nodes and ensure the accuracy of training results.
An API Recommendation Method Based on Beneficial Interaction

With the wide application of Mashup technology, it has become one of the hot and challenging problems in the field of service computing that how to recommend the API to developers to satisfy their Mashup requirements. The existing service recommendation methods based on Graph Neural Network (GNN) usually construct feature interaction graph by the interactions of service features, and regard it as the input of GNN to achieve service prediction and recommendation. In fact, there are some distinctions in the interactions between service features, and the importance of interactions is also different. To address this problem, this paper proposes an API recommendation method based on beneficial feature interaction, which can distinguish and extract beneficial feature interaction pairs from a large number of service feature interaction relationships. Firstly, feature extraction of Mashup requirements and API services is performed, and the correlation between API services is calculated based on the label and description document of the API services and used as a basis for recommending API services to Mashup requirements. Secondly, edge prediction component is used to extract beneficial feature pairs from input features of Mashup requirements and API services to generate beneficial feature interaction diagram between features. Thirdly, the beneficial feature interaction diagram is used as input of the graph neural network to predict and generate the API services set of recommendations for the Mashup requirements. Finally, the experiment on ProgrammableWeb dataset shows that the AUC of the proposed method has increased 20%, 24%, 27%, 13% and 21% respectively than that of AFM, NFM, DeepFM, FLEN and DCN, which means the proposed method improves the accuracy and quality of service recommendation.
Basic Regressions and Model Checking

We introduce regression models that are widely used, including multivariate regression, binomial logistic regression, logistic regression, and Poisson regression. Further, we discuss how to use visualization to check whether a model is proper.
The role of the facilitator in the context of subject-subject pedagogical communication

The article is devoted to the problem of pedagogical facilitation. The dominant role of information technologies, the transition to mixed (face-to-face-distance) learning, the necessity to create a safe educational environment, caused by the full-scale war of russia against Ukraine, determine the urgent need for the teacher to master a new role - the facilitator. It is substantiated that pedagogical facilitation is the effect of pedagogical interaction that occurs in subject-subject communication, the essence of which is to actualize personal creative potential, improve the productivity of interaction by facilitating the process of educational and professional activity. Determinants of the specified process are a special style of interaction, the presence of the teacher's personal qualities that determine the possibility of performing the role of facilitator. It has been proven that the most acceptable professional position of a teacher is a partner position, since it is a position that gives the opportunity to implement the principle of equality (subjectivity). In order to realize the professional role of a teacher-facilitator, it is important to be aware of social expectations: a dignified attitude to the student's personality as the highest value, centering in pedagogical activity on the needs of the student and on the realization of one's own professional need for self-development, self-improvement, establishing harmonious relationships on the basis of morality and spirituality. The content of the activity of a teacher-facilitator is to create a psychological atmosphere, an intellectual field, to observe the activities of students, to model the interaction of students, and to activate their activities. The congruence, acceptance and empathy of the teacher will contribute to the formation of trusting relations between the teacher and the students, which is a guarantee of the opportunity for each partner to manifest his own subjectivity.
Effect of zero hour exercise type on brain function related factor, and learning attitude factor: Focusing on middle school male students

The purpose of this study was to analyze the effects of jump band exercise(JEx) and yoga exercise (YEx) for 12 weeks on brain function related factor, and learning attitude factor in male middle school students aged 12-15 years by dividing them into a JEx(N=15), YEx group(N=14), and control group (N=14). The JEx and YEx grograms included 45-minute sessions 3 times each week at the following intesities: 40-50%HRR(RPE 9-10) for 1-4 weeks, 50-60%HRR(RPE 11-12) for 5-8 weeks, 60-70%HRR(RPE13-14) for 9-12 weeks. For the comparision of mean value differences in each item before and after 12-week exercise, Two-way repeated measures ANOVA was used to test the interactions between each period in the JEX, YEx, and control groups. A paired t-test was used to analyze the within-group differences, and one-way analysis of variance(ANOVA) was used to analyze the differences between the JEX, YEx, and control groups with a post-hoc Scheffe’s test. The alpha level of p.05 was set for all tests of significance. The findings of this study were as follows; BDNF, total attitude, concentrating attention, and learning method showed the interaction between time and groups. In conclusion, this study suggested that regular JEx and YEx has effective methods for the development of brain function and the improvement of quality of learning attitude due to increasing BDNF and total learning attitude score, and decreasing cortisol in male middle school students.
Exploring countermeasures to realize active aging in Japan, a super-aged society : Focusing on physical health index

The purpose of this study is to explore countermeasures to realize active aging in Japan, a super-aged society. In the process of aging Japanese society, the emphasis was on comparing and analyzing the characteristics of physical health evaluation indicators developed to induce individual physical independence and successful longevity and confirming their usefulness. In particular, by tracking the actual use cases of the physical health evaluation index developed by Tanaka et al. and others in the community, present study tried to present implications for the applicability in our community. This study applied the literature review method from January 1990 to August 2022, focusing on papers published in academic journals among the documents published for the Japanese people. In order to search for literature subject to research analysis, various Japanese web-based search engines such as the Tsukuba University Library. Through this study, it is meaningful to review the need for domestic application of Japan
Effect of 12 weeks of walking exercise on changes in muscle mass, blood pressure and heart rate in obese college students

This study analyzed the effects of 12-week walking exercise on muscle mass changes, blood pressure, and heart rate in obese college students, and the following conclusions were obtained.BRBR 1. The change in skeletal muscle mass (kg) increased by about 3.6% from 28.59±9.67kg before the walking exercise to 29.61±8.10kg after the walking exercise, but no statistically significant difference was found.BR 2. In the change of SBP (mmHg), it decreased by about 0.1% from 122.43±31.81mmHg before the walking exercise to 122.29±14.26mmHg after the walking exercise, but there was no statistically significant difference.BR 3. The change in DBP (mmHg) decreased by about 5.4% from 87.57±14.23mmHg before the walking exercise to 82.86±7.65mmHg after the walking exercise, but there was no statistically significant difference.BR 4. Changes in heart rate increased by 1.1% from 91.00±13.34bpm before walking to 92.00±11.60bpm after walking, but no statistically significant difference was observed.BRBR As such, in this study, it is considered that the 12-week walking exercise program had a positive effect on the improvement of muscle mass, blood pressure, and heart rate in obese college students.
Open Access: State surveillance and the COVID-19 crisis

The outbreak of COVID-19 has ushered in a global rise in state surveillance. In an effort to trace the spread of the disease and to enforce lockdowns, governments in democracies and autocracies alike have turned to surveillance technologies such as contact tracing apps. Governments have also tightened their hold on communication flows in other ways, through censorship and information manipulation. These kinds of government actions are not new: States have long recognized the value of controlling information in times of crisis. In this article, we consider these tactics in the context of the COVID-19 pandemic, in which, unlike most other security threats, the threat posed is not endogenous to governance and applies to all countries. We consider how observations from this context prompt future research and reflect on the implications of information control for civil liberties.
Author Index Volume 7 (2022)

Journal of Medical Robotics ResearchVol. 07, No. 04, 2299001 (2022) Free AccessAuthor Index Volume 7 (2022)https://doi.org/10.1142/S2424905X2299001XCited by:0 Previous This article is part of the issue: Special Issue on ISMR 2022Guest Editors: Iulian Ioan Iordachita and Elena De Momi AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail FiguresReferencesRelatedDetails Recommended Vol. 07, No. 04 Metrics History PDF download
Force and Motion

In the first part of discussions up to now, we have introduced basic elements of programming and toolboxes for scientific computing. Our goal is not computation in and of itself, although it is an important and necessary component. Rather, we aim to solve physical and engineering problems with computational methods.
Python Tutorial

This chapterPythontutorial introduces the fundamental concepts of the Python language and will teach you to read and write Python code. Later chapters will build off the material covered here.
The Design of Web-Based Management Information System for Gas Drainage in Coal Mining

Before gas drainage, you must understand the basic information of gas and gas extraction parameters to evaluate the feasibility and economy of coal seam gas extraction, as the basis for design of gas drainage. Gas drainage in coal mining must have the appropriate drawings, records, reports. Based on the current management status of coal minning, combined the investigation of the situation in coal mining, using the development method of management information system (MIS), this thesis made a comprehensive needs analysis and exposition of the design of the database for gas drainage. The thesis provided a general ideal which is developing the system based on B/S structure and the desing of some modules. This system runs on Microsoft.NET platform which uses SQL Server 2018 as database server and ASP.NET for programming. It uses ADO.NET, the subassembly of ASP.NET, to access the database. The new system is reliable, efficient, convenient and easy to maintain. The practice shows that the system is helpful and universal.
Research on Noise Reduction Algorithm of Controller Pressure Signal

A noise reduction algorithm for the controller pressure signal of the massage chair head airbag is proposed in this paper. Based on wavelet transform, the characteristics of several wavelet basis functions, threshold functions and threshold estimation methods are analyzed. The target signal is decomposed in three layers by the sym4 wavelet basis function. And the experiment results show that the wavelet threshold noise reduction of the pressure signal is effective.
Improvement of YOLOX Object Detection Algorithm Combined with Adaptive Spatial Feature Fusion

In order to further improve the ability of the YOLOX network to locate and classify objects in an industrial environment, this study proposes an algorithm that combines adaptive spatial feature fusion with the YOLOX object detection model. Handle the problem of the scale-invariance of features between features at different scales by filtering conflicting in the space field. At the same time, the global attention mechanism GAM is introduced, which effectively improves the convolutional feature expression ability of the network. Finally, the original BCE loss function structure is improved to a Focal Loss function, which improves the imbalance of positive and negative samples that may be caused by SimOTA. Through the test on the PASCAL VOC dataset, the experimental results of the improved algorithm in this paper show that:mAP@50 reached 93.4%, an increase of 11.98% compared to the original YOLOX-S, mAP@50_95 reached 77.62%, an increase of 15.45% compared to the original YOLOX-S. Enhance the generalization ability of the model and improve the detection effect.
Research on Visual Servo Positioning System Based on Stewart Platform

Vision servo control has a wide range of applications in industrial production and smart manufacturing, and can perform control tasks excellently under complex working conditions. This work designs and validates a calibration-free vision servo strategy for flexible assembly based on Stewart platform. First, a quantitative relationship between image velocity information and Stewart platform strut velocity is established to realize the basic visual servo control. Then, the dynamic Quasi-Newton methods is used to estimate the image Jacobi matrix, and the servo control strategy is optimized. Finally, a practical experimental environment is built to verify the visual servo control strategy proposed in this paper. The experimental results show that the Stewart platform can move from the initial position to the target position with an error value of less than 1.5 pixels under the image-based visual servo control.