Broad-based Network for Protecting the Rights and Interests of Women and Children——A story about the Women's Federation of Qinhuangdao City

The room is in utter chaos, with chairs piling up in a comer and the cable TV not connected to the power source. But one thing is put in place： a slogan on the wall ＂Creating an outstanding WFQ team for the benefit of all women and children.＂

Employment and development levels in rural areas of the Russian Federation

The development of agro-industrial complex is important for ensuring national food security and national health.The development of rural areas is subject to the development of agriculture and local infrastructure,as well as the availability of various services.This study selected 15 indicators in 2021 to analyze the employment and development levels in rural areas of 71 regions of the Russian Federation using the analytical grouping method.The results indicated that 20 regions(Group 1)had the highest percentage of rural population(33.10%).The percentage of population engaged in agriculture had the highest value(12.40%)in 31 regions(Group 2).Moreover,20 regions(Group 3)had the highest investments in fixed assets at the expense of municipal budget(11.80 USD/person).Increasing the investments in fixed assets carried out from the budget of the municipality can improve the employment level in rural areas.Then,we used cluster analysis to divide 14 regions of the Volga Federal District in the Russian Federation into 3 clusters.Cluster 1 covered Kirov Region and Republic of Mari El;Cluster 2 included Ulyanovsk Region,Saratov Region,Nizhny Novgorod Region,Perm Territory,Orenburg Region,Chuvash Region,and Republic of Mordovia;and Cluster 3 contained Republic of Tatarstan,Samara Region,Udmurtian Republic,Penza Region,and Republic of Bashkortostan.Results indicated that the 2 regions of Cluster 1 need to increase the availability of resources and natural gas and improve the investment attractiveness of rural areas.The 7 regions of Cluster 2 needed to develop infrastructure,public services,and agricultural production.We found the highest employment level in rural areas,the largest investments in fixed assets at the expense of municipal budget,the largest residential building area per 10,000 persons,and the largest individual residential building area in the 5 regions of Cluster 3.This study makes it possible to draw up a comprehensive regional development program and proves the need for the development of rural areas,which is especially important for the sustainable development of the Russian Federation.

A game-theoretic approach for federated learning:A trade-off among privacy,accuracy and energy

Benefiting from the development of Federated Learning(FL)and distributed communication systems,large-scale intelligent applications become possible.Distributed devices not only provide adequate training data,but also cause privacy leakage and energy consumption.How to optimize the energy consumption in distributed communication systems,while ensuring the privacy of users and model accuracy,has become an urgent challenge.In this paper,we define the FL as a 3-layer architecture including users,agents and server.In order to find a balance among model training accuracy,privacy-preserving effect,and energy consumption,we design the training process of FL as game models.We use an extensive game tree to analyze the key elements that influence the players’decisions in the single game,and then find the incentive mechanism that meet the social norms through the repeated game.The experimental results show that the Nash equilibrium we obtained satisfies the laws of reality,and the proposed incentive mechanism can also promote users to submit high-quality data in FL.Following the multiple rounds of play,the incentive mechanism can help all players find the optimal strategies for energy,privacy,and accuracy of FL in distributed communication systems.

Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of Vehicles

High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility environment.In order to protect data privacy and improve data learning efficiency in knowledge sharing,we propose an asynchronous federated broad learning(FBL)framework that integrates broad learning(BL)into federated learning(FL).In FBL,we design a broad fully connected model(BFCM)as a local model for training client data.To enhance the wireless channel quality for knowledge sharing and reduce the communication and computation cost of participating clients,we construct a joint resource allocation and reconfigurable intelligent surface(RIS)configuration optimization framework for FBL.The problem is decoupled into two convex subproblems.Aiming to improve the resource scheduling efficiency in FBL,a double Davidon–Fletcher–Powell(DDFP)algorithm is presented to solve the time slot allocation and RIS configuration problem.Based on the results of resource scheduling,we design a reward-allocation algorithm based on federated incentive learning(FIL)in FBL to compensate clients for their costs.The simulation results show that the proposed FBL framework achieves better performance than the comparison models in terms of efficiency,accuracy,and cost for knowledge sharing in the IoV.

A Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications

Federated learning is an innovative machine learning technique that deals with centralized data storage issues while maintaining privacy and security.It involves constructing machine learning models using datasets spread across several data centers,including medical facilities,clinical research facilities,Internet of Things devices,and even mobile devices.The main goal of federated learning is to improve robust models that benefit from the collective knowledge of these disparate datasets without centralizing sensitive information,reducing the risk of data loss,privacy breaches,or data exposure.The application of federated learning in the healthcare industry holds significant promise due to the wealth of data generated from various sources,such as patient records,medical imaging,wearable devices,and clinical research surveys.This research conducts a systematic evaluation and highlights essential issues for the selection and implementation of federated learning approaches in healthcare.It evaluates the effectiveness of federated learning strategies in the field of healthcare.It offers a systematic analysis of federated learning in the healthcare domain,encompassing the evaluation metrics employed.In addition,this study highlights the increasing interest in federated learning applications in healthcare among scholars and provides foundations for further studies.

Resource management at the network edge for federated learning

Federated learning has been explored as a promising solution for training machine learning models at the network edge,without sharing private user data.With limited resources at the edge,new solutions must be developed to leverage the software and hardware resources as the existing solutions did not focus on resource management for network edge,specially for federated learning.In this paper,we describe the recent work on resource manage-ment at the edge and explore the challenges and future directions to allow the execution of federated learning at the edge.Problems such as the discovery of resources,deployment,load balancing,migration,and energy effi-ciency are discussed in the paper.

Privacy Enhanced Mobile User Authentication Method Using Motion Sensors

With the development of hardware devices and the upgrading of smartphones,a large number of users save privacy-related information in mobile devices,mainly smartphones,which puts forward higher demands on the protection of mobile users’privacy information.At present,mobile user authenticationmethods based on humancomputer interaction have been extensively studied due to their advantages of high precision and non-perception,but there are still shortcomings such as low data collection efficiency,untrustworthy participating nodes,and lack of practicability.To this end,this paper proposes a privacy-enhanced mobile user authentication method with motion sensors,which mainly includes:(1)Construct a smart contract-based private chain and federated learning to improve the data collection efficiency of mobile user authentication,reduce the probability of the model being bypassed by attackers,and reduce the overhead of data centralized processing and the risk of privacy leakage;(2)Use certificateless encryption to realize the authentication of the device to ensure the credibility of the client nodes participating in the calculation;(3)Combine Variational Mode Decomposition(VMD)and Long Short-TermMemory(LSTM)to analyze and model the motion sensor data of mobile devices to improve the accuracy of model certification.The experimental results on the real environment dataset of 1513 people show that themethod proposed in this paper can effectively resist poisoning attacks while ensuring the accuracy and efficiency of mobile user authentication.

A Tutorial on Federated Learning from Theory to Practice:Foundations,Software Frameworks,Exemplary Use Cases,and Selected Trends

When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized environment.FL allows ML models to be trained on local devices without any need for centralized data transfer,thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third parties.This paradigm has gained momentum in the last few years,spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data sources.By virtue of FL,models can be learned from all such distributed data sources while preserving data privacy.The aim of this paper is to provide a practical tutorial on FL,including a short methodology and a systematic analysis of existing software frameworks.Furthermore,our tutorial provides exemplary cases of study from three complementary perspectives:i)Foundations of FL,describing the main components of FL,from key elements to FL categories;ii)Implementation guidelines and exemplary cases of study,by systematically examining the functionalities provided by existing software frameworks for FL deployment,devising a methodology to design a FL scenario,and providing exemplary cases of study with source code for different ML approaches;and iii)Trends,shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL landscape.The ultimate purpose of this work is to establish itself as a referential work for researchers,developers,and data scientists willing to explore the capabilities of FL in practical applications.

Hierarchical Federated Learning Architectures for the Metaverse

In the context of edge computing environments in general and the metaverse in particular,federated learning(FL)has emerged as a distributed machine learning paradigm that allows multiple users to collaborate on training a shared machine learning model locally,eliminating the need for uploading raw data to a central server.It is perhaps the only training paradigm that preserves the privacy of user data,which is essential for computing environments as personal as the metaverse.However,the original FL architecture proposed is not scalable to a large number of user devices in the metaverse community.To mitigate this problem,hierarchical federated learning(HFL)has been introduced as a general distributed learning paradigm,inspiring a number of research works.In this paper,we present several types of HFL architectures,with a special focus on the three-layer client-edge-cloud HFL architecture,which is most pertinent to the metaverse due to its delay-sensitive nature.We also examine works that take advantage of the natural layered organization of three-layer client-edge-cloud HFL to tackle some of the most challenging problems in FL within the metaverse.Finally,we outline some future research directions of HFL in the metaverse.

Implementing and evaluating an automatic centroid moment tensor procedure for the Indonesia region and surrounding areas

The purpose of this research was to suggest an applicable procedure for computing the centroid moment tensor(CMT)automatically and in real time from earthquakes that occur in Indonesia and the surrounding areas.Gisola software was used to estimate the CMT solution by selecting the velocity model that best suited the local and regional geological conditions in Indonesia and the surrounding areas.The data used in this study were earthquakes with magnitudes of 5.4 to 8.0.High-quality,real-time broadband seismographic data were provided by the International Federation of Digital Seismograph Networks Web Services(FDSNWS)and the European Integrated Data Archive(EIDA)Federation in Indonesia and the surrounding areas.Furthermore,the inversion process and filter adjustment were carried out on the seismographic data to obtain good CMT solutions.The CMT solutions from Gisola provided good-quality solutions,in which all earthquake data had A-level quality(high quality,with good variant reduction).The Gisola CMT solution was justified with the Global CMT(GCMT)solution by using the Kagan angle value,with an average of approximately 11.2°.This result suggested that the CMT solution generated from Gisola was trustworthy and reliable.The Gisola CMT solution was typically available within approximately 15 minutes after an earthquake occurred.Once it met the quality requirement,it was automatically published on the internet.The catalog of local and regional earthquake records obtained through this technology holds great promise for improving the current understanding of regional seismic activity and ongoing tectonic processes.The accurate and real-time CMT solution generated by implementing the Gisola algorithm consisted of moment tensors and moment magnitudes,which provided invaluable insights into earthquakes occurring in Indonesia and the surrounding areas.

Integrative medicine:the guide line in women's reproductive health and the treatment of gynecological disease

Integrative medicine follows the principles in TCM that nature and human beings being correspondent with its holistic and dialectic views and explores the integrating point with theories and scientific data in modern medicine. During study on 16 gynecological diseases, the deficiency. In patients with pubertal uterine bleeding, ovulation is induced by acupuncture via decrease in central β-endorphin (β-EP) levels, which leads to the cure of anorexia nervosa by acupuncture via decrease in serum cortisol levels in early 1980s. These results in TCM lead to the idea that neuro-endocrine system is directly related to metabolic system which is also proved to be true in the treatment of polycystic ovary syndrome (PCOS) with or without hyperinsulinemia by two formulas (Yu's Tonifying Ricipe and Tian Gui Recipe,TGR) respectively according to differential diagnosis in TCM. Ovulation resulted with decrease in obesity, acanthosis nigricans, serum androgen, insulin, leptin and hyperthalamic neuropeptide Y (NPY), proopiomalanocortin (POMC) levels based on scientific data from both patients and an animal modal. It suggests that metabolic network, and the incidence of obesity, hypertension, diabetes and cancers of the endometrium and breast may be reduced among patients with PCOS in their future life while these recipes being taken long period. For perimenopausal syndrome, formula Geng Nian Chun (GNC)without increase in serum estrogen levels. In aging rats, it was found that GNC increased the ER and ERmRNA expressions in the ovarian axis and the spleen accompanying with increase in central β-EP, serotonin (5-HT) and peripheral interlutin-2 (IL-2) levels in aging rats without changes of estrogen levels. These data may refer to the release of symptoms in women with postmenopausal syndome. It suggests that GNC up-regulates the fading of neuro-endocrine-immune network. Prevention, alleviation of osteoporosis, cardiovascular disease and Alzheimer disease may be expected in aging women with long period taking GNC.

Research and Practice on the Training Mode of Innovative and Entrepreneurial Talents in Colleges and Universities:A Case Study of Academic Association of"Federation of Life Science Research and Innovation"

Taking the academic association of the Federation of Life Science Research and Innovation of the College of Life Science and Biotechnology of Heilongjiang Bayi Agricultural University as the research platform,it is necessary to organically combine personalized education with the cultivation of innovative and entrepreneurial ability.On the basis of unified teaching ideas,we should pay attention to the individual differences of educational objects,eliminate the disadvantages of traditional education,and break the conventional"spoon-feeding"teaching mode.It is necessary to shift from only paying attention to one-way indoctrination to paying attention to the cultivation of students'innovative quality,and to enhance students'interest in scientific research and practical ability of innovation and entrepreneurship through the rich scientific research activities of academic associations.

On the Chinese Characteristics of Literary Federation System

The literary federation system is a cultural system with Chinese characteristics. The Chinese characteristics of the literary federation system are mainly embodied in: it is the embodiment of the United Front under the leadership of the Communist Party of China in the field of literature and art, an important system to promote the popularization of literature and art, and a supplement to the system of honors in the cultural field of new China.

Legal Frame for Criminal Proceedings Against Juveniles in the Federation of Bosnia and Herzegovina

The paper analyzes applicability of legal frame of international standards on the protection of juvenile rights which are expressed through the concept of protection of ＂the best interests of a child and juvenile＂, in view of the question whether the Federation of Bill performs appropriate activities and to which extent, and are there controversy points that need to be resolved separately. It points out the unknowns which the practices of the courts in the Federation of Bill have not yet completely resolved, and are related to the applicability of the new rules adopted by the Law on Protection and Treatment of Children and Juveniles in Criminal Proceedings of the Federation of Bill. Also, it presents a set of legal rules that regulate criminal proceedings against juveniles within the Federation of Bill, with a special emphasis on the basic characteristics of this process. Finally, possible solutions to the mentioned ambiguities and dilemmas are proposed in accordance with the principle of legal certainty as essential to addressees of the relevant legal norms

Women's Liberation during the Initial Stage of P. R. China: A Human Rights Perspective

The liberation of women is the historical process in which women have striven for gender equality, freedom from oppression, confinement and discrimination, and are becoming the masters of the society and their own destinies. According to the diversity in history and the state, the process can be divided into a serial of correlated phases, with each phase focusing on different issues. Sexism order in China had remained stable in the traditional society for centuries through a set of institutions. Women had believed in a sexual order and the rules of propriety, which advocated that men were superior to women. After the People’s Republic of China was founded in 1949, women’s rights in politics, economy, society, and even family were recognized and secured by the Constitution and laws just as men’s during a short period. Women’s liberation in China has made a significant progress.

Design and Implementation of the Federation Load Simulator

The importance and necessity of emulating the federation load during simulating was analyzed firstly.Then,the special federation load emulation tool,federation load simulator (FLS),was designed and implemented,by which all of the vital essential characteristics of a simulation could be tested,e.g.the amount of federates,the joint/resigned speed of federate,the amount of object instances,the registered/deleted speed of instance in one single program,etc..The applications proved that FLS could provide a convenient,effective and adjustable simulation load testing environment during the procedure of run-time infrastructure(RTI)and interrelated tool federates researching,developing and performance testing.Furthermore,the FLS utilized all kinds of resources with high efficiency.

A Cognitive Study on the Category Fruit——Taking under 35-year-old Women's Cognitions as Informants

This paper probes into the category fruit, aiming at revealing the categorization of fruit to find them prototype in under 35-year-old women's cognitions at present. And author uses related category theories, such as prototype category to analyze the outcome of the research. In addition, author also locates women who under 35 years old as this research's informants to find whether the prototype changed in their cognition or not. The method of research is questionnaires, and hands out the questionnaires by the Internet. And this research also uses SSPS to analyze the effective data.

The Development of Legal Protection of Women's Human Rights in China

The legal confirmation process of women’s human rights has been undergoing great difficulties both in the United Nations and among nations. The Fourth World Conference on Women plays a role in promoting the legal protection of women’s human rights. Currently, the protection of women’s human rights has undoubtedly become the consensus of both international and domestic law. International conventions on human rights have become an important launching area for promoting international law on women’s human rights, owing to its legal binding force and supervising mechanism established under it. Domestic law has also become the first line of defense for protecting women’s human rights. However, it needs to be pointed out that, for international conventions on human rights and domestic legislation, the cognition and acceptance of social gender conception has not only promoted changes on forms, but, more importantly, on revolution at substantial level.

Women's Rights in the Context of the Three-child Policy:Analyzing from the Constitutional Connotation of“the State Shall Protect the Mother”

Women's rights are essential human rights recognized by the international covenants on human rights and protected in the constitutions and laws of countries.In the context of implementing the three-child policy in China,women are facing pressures from childbearing and bringing up children as well as trying to have a career.It is clearly stipulated in Article 49 of China's Constitution"the mother is protected by the state,"the constitutional principle of equality of men and women shall be incorporated into the interpretation of this article,fully respecting women's subjectivity and equal rights and affirming the social benefits of childbearing and bringing up children.With the introduction of the three-child policy,the protection of women's rights shall focus on their reproductive rights,right to health,right to work and equal rights within the family.The system of women's rights is complex,relates to identity and must be open to the future.The state has the obligation to fully respect,actively promote and promote in a narrow sense the realization of women's rights.To better coordinate the implementation of the three-child policy and the protection of women's rights,we should introduce the concept of social gender equality,improve the system ofp ublic childcare services,home care leave and equal employment,and promote the implementation of relevant laws and policies with special attention being paid to women in rural areas and single mothers.

Federation Boosting Tree for Originator Rights Protection

The problem of data island hinders the application of big data in artificial intelligence model training,so researchers propose a federated learning framework.It enables model training without having to centralize all data in a central storage point.In the current horizontal federated learning scheme,each participant gets the final jointly trained model.No solution is proposed for scenarios where participants only provide training data in exchange for benefits,but do not care about the final jointly trained model.Therefore,this paper proposes a newboosted tree algorithm,calledRPBT(the originator Rights Protected federated Boosted Tree algorithm).Compared with the current horizontal federal learning algorithm,each participant will obtain the final jointly trained model.RPBT can guarantee that the local data of the participants will not be leaked,while the final jointly trained model cannot be obtained.It is worth mentioning that,from the perspective of the participants,the scheme uses the batch idea to make the participants participate in the training in random batches.Therefore,this scheme is more suitable for scenarios where a large number of participants are jointly modeling.Furthermore,a small number of participants will not actually participate in the joint training process.Therefore,the proposed scheme is more secure.Theoretical analysis and experimental evaluations show that RPBT is secure,accurate and efficient.