Reviewerly/exHarmony huggingface dataset:
Rethinking the Defense Against Free-rider Attack From the Perspective of Model Weight Evolving Frequency

Federated learning (FL) is a distributed machine learning approach where multiple clients collaboratively train a joint model without exchanging their data. Despite FL's unprecedented success in data privacy-preserving, its vulnerability to free-rider attacks has attracted increasing attention. Existing defenses may be ineffective against highly camouflaged or high percentages of free riders. To address these challenges, we reconsider the defense from a novel perspective, i.e., model weight evolving frequency.Empirically, we gain a novel insight that during the FL's training, the model weight evolving frequency of free-riders and that of benign clients are significantly different. Inspired by this insight, we propose a novel defense method based on the model Weight Evolving Frequency, referred to as WEF-Defense.Specifically, we first collect the weight evolving frequency (defined as WEF-Matrix) during local training. For each client, it uploads the local model's WEF-Matrix to the server together with its model weight for each iteration. The server then separates free-riders from benign clients based on the difference in the WEF-Matrix. Finally, the server uses a personalized approach to provide different global models for corresponding clients. Comprehensive experiments conducted on five datasets and five models demonstrate that WEF-Defense achieves better defense effectiveness than the state-of-the-art baselines.
IMPLEMENTATION OF THE TREND MOMENT METHOD IN ESTIMATING BREAD SALES

The trend moment method is a forecasting method for producing an estimated number of future bread supplies so that there is no excess or shortage of bread stock in the coming month. Data on bread sales is used in this study every month from January to December 2021. Each month's sales records are useful for determining whether sales have increased or decreased. The study's findings include the development of a computerized system that can generate approximate numbers in predicting deals for the following month utilizing the PHP and MySQL programming dialects, making it simpler to determine how much bread will be sold while taking into account the stock of goods and how much will be produced in the coming months. the following month with the goal that there is no lack or abundance of bread stock. The results of sales predictions for 12 months in 2021, produce predictions in January 2022, in the 13th period with MAD (Mean Absolute Deviation) results of 40.08% and MSE (Mean Squared Error) rates of 27.64%.
Secure Counting Query Protocol for Genomic Data

Statistical analysis on genomic data can explore the relationship between gene sequence and phenotype. Particularly, counting the genomic mutation samples and associating with related phenotypes for statistical analysis can annotate the variation sites and help to diagnose genovariation. Expansion of the size of variation sample data helps to increase the accuracy of statistical analysis. It is feasible to securely share data from genomic databases on cloud platforms. In this paper, we design a secure counting query protocol that can securely share genomic data on cloud platforms. Our protocol supports statistical analysis of the genomic data in VCF (Variant Call Format) files by counting query. There are three participants of data owner, cloud platform and query party. Firstly, the genomic data is preprocessed to reduce the data size. Secondly, Paillier homomorphic is used so that genomic data can be securely shared and calculated on cloud platform. Finally, the results which be decrypted is used to implement counting function of the protocol. Experimental results show that the protocol can implement the query counting function after homomorphic encryption. The query time is less than 1 s, which provide a feasible solution to share genomic data securely on cloud platform for statistical analysis.
Learning-Based Data Storage [Vision] (Technical Report)

Deep neural network (DNN) and its variants have been extensively used for a wide spectrum of real applications such as image classification, face/speech recognition, fraud detection, and so on. In addition to many important machine learning tasks, as artificial networks emulating the way brain cells function, DNNs also show the capability of storing non-linear relationships between input and output data, which exhibits the potential of storing data via DNNs. We envision a new paradigm of data storage, "DNN-as-a-Database", where data are encoded in well-trained machine learning models. Compared with conventional data storage that directly records data in raw formats, learning-based structures (e.g., DNN) can implicitly encode data pairs of inputs and outputs and compute/materialize actual output data of different resolutions only if input data are provided. This new paradigm can greatly enhance the data security by allowing flexible data privacy settings on different levels, achieve low space consumption and fast computation with the acceleration of new hardware (e.g., Diffractive Neural Network and AI chips), and can be generalized to distributed DNN-based storage/computing. In this paper, we propose this novel concept of learning-based data storage, which utilizes a learning structure called learning-based memory unit (LMU), to store, organize, and retrieve data. As a case study, we use DNNs as the engine in the LMU, and study the data capacity and accuracy of the DNN-based data storage. Our preliminary experimental results show the feasibility of the learning-based data storage by achieving high (100%) accuracy of the DNN storage. We explore and design effective solutions to utilize the DNN-based data storage to manage and query relational tables. We discuss how to generalize our solutions to other data types (e.g., graphs) and environments such as distributed DNN storage/computing.
IMPLEMENTATION OF E-COMMERCE IN PERCETAKAN CITRA BERSAMA TANJUNGBALAI USING THE CRM METHOD

Citra Bersama Tanjungbalai Printing is a business engaged in selling printing products and services, established in 2006, located on Jalan Peninsula, Sirantau Village, Datuk Bandar District, Tanjungbalai City. The problem with Citra Bersama Printing is that it has not implemented an online sales system, it is difficult for customers to know about sales information in the printing press. There is no information system to carry out promotions, and the process of recording customer order data at the printing press is still doing it manually. Using the CRM method is a web-based printing product and service sales system that can help buyers find out various information and make it easier for buyers to place orders without coming directly to the printer. With the application of E-commerce using the CRM method, it can help Tanjungbalai Citra Bersama Printing carry out promotions and provide information related to the sale of products and services it markets to customers, and expand sales of products and services it markets because of promotional features, discount prices, customer data, live chat, and product ratings.
REVEALING THE TRUE AUTHORSHIP: A CASE STUDY

As digital media is growing, the problem of text proliferation is becoming a big problem. Therefore, the identification of true ownership of a document has become a cumbersome task. In the digital era, it is very easy to copy someone's document and publish it in their name. So it is very necessary to check the true authorship. Authorship attribution becomes difficult when we conduct it manually. However, this process needs automation, when the document size becomes large. AA is a mixture of art, science and technology that helps to discover the genuine authorship of an unknown text/document, based on its specific writing features. These specific features can reflect the author's mood, education, gender, age, ideology, religion, or motivation. Many kinds of characteristics, like lexical, character, structural, syntactic, and semantic are used in authorship recognition. In this experiment, we used approximately 120 different kinds of feature sets. In our experiment, we examined that the logistic classifier was working well and gave good results in the form of accuracy.
EFEKTIVITAS APLIKASI IPB MOBILE FOR STUDENT SEBAGAI PEMENUHAN KEBUTUHAN INFORMASI TENTANG KAMPUS BAGI MAHASISWA

This research aims to analyze the level of correlation between the effectiveness of the IPB Mobile for Student as the fulfillment of campus information needs for students. This research consisted of variable X namely, program success, the success of the intended, satisfication the program, level of output and input and achieving the overall objective and variable Y namely perception of the problem by user, intent for use of the information by user, internal knowledge state of the user in respect to the problem at hand, and public knowledge expectations or estimate by the user. This study uses descriptive quantitative research. Theory used in this study is Uses and Gratification theory. This study was distributing questionnaires to 101 students of IPB University. The method used to test correlation uses the Spearman Rank formula, data processing is assisted with SPSS version 25. The result of this research is there’s a significance level of .000 and a correlation of .760 **. This means that there is a correlation between the two variables with a positive direction so that the higher the effectiveness of applications the higher the fulfillment of information needs for students.
Characterizing the thermal quantum correlations in a two-qubit Heisenberg XXZ spin-1/2 chain under Dzyaloshinskii-Moriya and Kaplan-Shekhtman-Entin-Wohlman-Aharony interactions

Abstract We considered the two-qubit Heisenberg XXZ spin chain in the presence of Dzyaloshinskii--Moriya (DM) and Kaplan--Shekhtman--Entin--Wohlman--Aharony (KSEA) interactions in thermal equilibrium. As discord-type quantifiers of thermal non-classical correlations, we used trace distance discord and local quantum uncertainty. Depending on the thermal state of the system considered, the explicit analytical expressions of the suggested quantifiers are derived and their behaviors with respect to the various parameters of the system are examined. The results obtained showed that the DM and KSEA interactions considerably improve the quantum correlations contained in the bipartite system while the temperature tends to decrease their quantity.
Grey Wolf Optimizer-based decentralized service discovery in Internet of Things applications

Abstract Discovery of service is one of the main challenge in Internet of Things due to the increasing of services and applications, which is especially tricky for the lookup of service (or set of services). Therefore , in this paper , we propose a new distributed discovering algorithm based on Grey Wolf Optimizer ( GWO ) for IoT . GWO is one of recent metaheuristic , the field of swarm intelligence to solve combinatorial optimization problem. To show the different effective performances of GWO in accurate discovering of services, The simulation results show that GWO can achieve a high discovery success with small number of hops used to discover service. Our approach maintains their performance and achieve good scalability as the number of object in decentralized approach for IoT .
INDEPENDENT WORK OF STUDENTS AS ONE OF THE METHODS FOR PROFESSIONAL COMPETENCE DEVELOPMENT//Social Sciences: Achievements and Prospects Journal 1(14), 2022 (ISSN 2733-3825)/ Chief Editor Vitalii Vl. Klevtsov /OEAPS OU - Tallinn, Estonia (Open European Academy of Public Sciences): Berlin, Germany, 30.04.2022: OEAPS OU., 2022 - P. 9-13.

This article gives brief review of what independent work is, its types and ways of its implementation in the learning process. It discusses the benefits of self-study and creative attitude to self-development and its importance in the professional personality formation. It reveals that higher education today plays significant role in this process and one of the main tasks of education in general is formation of creative personality of a specialist capable of self-development, self-education, and self- study. To solve this task is hardly possible only by transferring ready-made knowledge from teacher to student. It is necessary to transfer a student from the passive consumer of knowledge to the active creator who knows how to formulate the problem, analyze the ways of its solving, find the optimal result, and prove its correctness. This involves focusing on active methods of mastering knowledge, developing creative abilities of students, moving from online to individualized learning, considering the needs and capabilities of the individual. It’s not a secret that successful training requires a certain level of cognitive activity and independence in self-education of learners and creative application of the acquired knowledge will depend on methods of independent work organization with some elements of professional activities.
The involvement of community members in community policing in Nigeria

While community policing is critical in the battle against crime in every neighbourhood, its efficacy is contingent on the community's preparation and support. The fundamental purpose of all civilizations is to foster an environment of cooperation rather than aggression, and to work for long-term peace and security. As a result, politicians and civic leaders are increasingly convinced that community policing is a crucial component of security strategy, owing to its role in maintaining peace. The purpose of this inquiry is to better understand community members' involvement in community policing in Nigeria. The study used a descriptive research approach and used Odeomu in Osun State's Ayadaade Local Government Area as a case study. Both qualitative and quantitative methodologies were used in the investigation. The multistage sampling strategy was adapted from non-probability methods in this study. In the first instance, purposive sampling was used, with an emphasis on eight notable town residents. Four community leaders/chiefs were also purposefully chosen since they possess adequate understanding of community policing in the area. Respondents were stratified in each of the designated areas. A total of 400 copies of questionnaire were sent to six areas for the quantitative sample, covering only the first and second objectives. The data gathered for this investigation were analyzed qualitatively as well as quantitatively. The study concluded that community policing is becoming a more popular strategy for ensuring Nigeria's long-term peace and security. However, in order for it to work, it must put a high value on the participation of the community members.
Image Geolocation Method Based on Attention Mechanism Front Loading and Feature Fusion

Image geolocation is an important technique for robotics and autonomous systems. The existing methods mainly extract local features from images directly and use global descriptors, which are aggregated by these local features, to retrieve candidate references from all references. Thus, the training efficiency is affected by the image noises and the accuracy is so limited that the further verification is extremely time consuming. To address these issues, this work proposes an image geolocation framework, which adds the noise filtering layer before local feature extraction. Based on this framework, an image geolocation method based on attention mechanism front loading and feature fusion is designed. In the noise filtering layer, the proposed method uses triplet attention to denoise images thus leading to higher training efficiency. In the feature aggregation layer, an improved SPP (spatial pyramid pooling) is designed to extract the local factors reflected by the position relationships among local features. Then, the local factors are incorporated with the global factors extracted by NetVLAD. The fused descriptors contain not only the statistic of the geometric elements but also the position relationships among them. The experimental results show that the proposed method outperforms NetVLAD in terms of the training convergence round and Recall@ <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>N</mi> </math> ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>N</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>5</mn> <mo>,</mo> <mn>10</mn> <mo>,</mo> <mn>20</mn> </math> ); especially, the convergence round of Recall@5 reduces from 25 to 10, the convergence round of Recall@10 reduces from 25 to 7, Recall@1 increases from 79.45% to 84.01%, and Recall@5 increases from 90.10% to 92.81%.
6-D Pose Estimation of Objects in Masked Image using Line Segment Detection

In this paper, for accurate grasping of objects, we propose a pose estimation algorithm that recognizes a three-dimensional object model within the mask area of the object detected by Mask R-CNN and estimates the 6D (degrees of freedom) pose of several instances. In industrial sites, it is challenging to determine the exact grasping position of small objects that lack characteristics when numerous parts are stacked. The proposed algorithm solves this problem by calculating the 6D pose of an object using the vertex detected from the contour of the object plane obtained from the image masked by instance segmentation. A line segmentation detection method in masked images based on low-cost RGB-D (Depth) cameras enables accurate pose estimation of objects for a robot gripper at high speed. The proposed method was verified through various experiments in an environment where rectangular objects were stacked.
HAPPINESS AS A PSYCHOLOGICAL FORMATION

The precedent of the study of happiness as a psychological education is the extraordinary relevance of the problem and its interdisciplinary study. The authors of the article emphasize that despite the constant interest in the problem, the phenomenon of happiness today is still not sufficiently studied, there is no clear common definition. Happiness is difficult to rigorous, accurate analysis and psychodiagnostic measurement. Positions of understanding happiness are singled out: as a motive and purpose of life and as a result of human activity. It is noted that a person's desire for happiness is correlated with the idea of self-sufficiency, realization and creativity. The traps that can occur on the path to well-being and happiness are analyzed, as well as the important factors with which happiness correlates and does not correlate. The determinants and a wide range of factors of happiness are determined, which are determined by both the objective circumstances of human life and the uniqueness of the individual. It is objective (health, income, work, social status, leisure, education, age, gender, religion, etc.) and subjective (neuroticism, extraversion, humor, joy, love) factors to varying degrees. your contribution to the overall level of happiness. Also important are the principles of happiness, namely: optimism, love, the ability to forgive, self-development, etc., which simplify the path to a happy life. Happiness is associated with a sense of fullness of life, joy and satisfaction with life, which underlie the optimal, healthy and effective functioning of the individual, as an integral result based on the recognition of the priority of both public and personal interests. Based on integrated models and formulas of happiness, the authors built their own component model of happiness and well-being, which is determined by the variables of physiological, cognitive, affective (emotional), social and personal levels of the individual. A thorough analysis of the psychological phenomenon of happiness as a psychological formation, characterized by satisfaction and awareness of human life, the dominance of a positive emotional state and based on the integration of components of a happy life. Prospects for research in the direction of finding out effective methods of studying happiness, its dynamics and effective development strategies are identified. Keywords: happiness, well-being, psychological education, personality, model of happiness.
Level One English Language Teachers’ Evaluation of the Quality and Suitability of the Super Minds CEFR Textbook for Local Use by Malaysian Pupils

In 2017, the Ministry of Education decided to use Super Minds (Student’s Book 1), an imported textbook published by Cambridge English in the UK, as the English Language textbook for Year 1 and Year 2 pupils in all public schools in Malaysia. The adoption of the book garnered much attention and debate as Malaysians presented opposing views regarding the necessity and usefulness of using an imported textbook rather than a local one. The ongoing debate prompted the researcher to conduct the present study a year after the book came into use, with the aim of evaluating the content, physical and overall quality of the Super Minds textbook. The respondents were 180 Level One primary school teachers who had been actively using the textbook in their English classes for at least a year prior to the study. The instrument was a self-developed rating scale with 25 items measuring the three quality constructs. It was converted into a Google Form and administered as an online survey via several social media platforms. The data were analyzed using exploratory factor analysis (EFA) and descriptive statistics. The EFA results supported the existence of content, physical and overall quality as three mutually exclusive constructs. Overall, the Level One English teachers rated the content quality and physical quality of Super Minds as average and good, while endorsing its overall quality to be moderate. A major contribution of the study is the production of a rating scale with relevant language learning criteria—such as practice activities, content relevance to the target audience, graphics, use of vocabulary and cultural appropriateness—for evaluating the quality of English language textbooks. For future research, the study recommends that Super Minds be assessed for usefulness and relevance to rural students and students from various ethnic groups.
IMPLEMENTASI SOFTWARE METRICS UNTUK PENGUKURAN PERFORMA PADA SISTEM INFORMASI AKADEMIK UNIVERSITAS LAMPUNG (SIAKAD V.4.1 UNILA)

The University of Lampung Academic Information System (SIAKAD) is an information system developed to support the university in managing student academic processes. The absence of a feedback section makes users, especially students, unable to deliver opinions about the system, so that even though the system is running well, the real quality of the system based on user experience is unknown. Performance is one aspect of quality that is important to know, but it cannot be measured directly. The study uses an information system functional scoreboard to assess three aspects of the system, those are system performance, information effectiveness, and service performance. The questionnaire was distributed to 100 respondents from all faculties as a sample, which are expected to represent the answers from the entire university. Based on the research results, it is known that the performance of SIAKAD V.4.1 UNILA is good to a moderate extent. Some things that are needed to be improved are the stages of service procedures, the addition of other academic information such as graphs of student’s grade, transparency of grades, an updated course schedule, integration to the graduation registration system, adding the latest campus information that is displayed clearly and well, also integration with the other systems at the University of Lampung as well as better server replacement and maintenance.