Reviewerly/exHarmony huggingface dataset:
A Modified Meta-Learner for Few-Shot Learning.

Meta-Learning, or so-called Learning to learn, has become another important research branch in Machine Learning. Different from traditional deep learning, meta-learning can be used to solve one-to-many problems and has a better performance in few-shot learning which only few samples are available in each class. In these tasks, meta-learning is designed to quickly form a relatively reliable model through very limited samples. In this paper, we propose a modified LSTM-based meta-learning model, which can initialize and update the parameters of classifier (learner) considering both short-term knowledge of one task and long-term knowledge across multiple tasks. We reconstruct a Compound loss function to make up for the deficiency caused by the separate one in original model aiming for a quick start and better stability, without taking expensive operation. Our modification enables meta-learner to perform better under few-updates. Experiments conducted on the Mini-ImageNet demonstrate the improved accuracies.
PENERAPAN VISUALISASI DATA TERHADAP KLASIFIKASI TINDAK KRIMINAL DI INDONESIA

Crime still occurs in every region in Indonesia. Various classifications of crimes that occur and cause unrest for people in every region in Indonesia. The purpose of this study is to analyze and visualize data on crimes that occur in every region in Indonesia so as to facilitate the Indonesian government in making decisions. The method used for this research is the clustering method. The clustering stage is carried out by grouping data on crimes that occur in Indonesia which are classified from each region. The results of the data that have been visualized show that the crime rate in each area is different according to the classification of crime, so it is necessary to increase security in each area that has a level of criminality data according to the classification of crime.
Generalized Brain Image Synthesis with Transferable Convolutional Sparse Coding Networks

High inter-equipment variability and expensive examination costs of brain imaging remain key challenges in leveraging the heterogeneous scans effectively. Despite rapid growth in image-to-image translation with deep learning models, the target brain data may not always be achievable due to the specific attributes of brain imaging. In this paper, we present a novel generalized brain image synthesis method, powered by our transferable convolutional sparse coding networks, to address the lack of interpretable cross-modal medical image representation learning. The proposed approach masters the ability to imitate the machine-like anatomically meaningful imaging by translating features directly under a series of mathematical processings, leading to the reduced domain discrepancy while enhancing model transferability. Specifically, we first embed the globally normalized features into a domain discrepancy metric to learn the domain-invariant representations, then optimally preserve domain-specific geometrical property to reflect the intrinsic graph structures, and further penalize their subspace mismatching to reduce the generalization error. The overall framework is cast in a minimax setting, and the extensive experiments show that the proposed method yields state-of-the-art results on multiple datasets.
Blockchain-based photovoltaic subsidy trading mechanism design

Due to the large number of internal and external collaborators, complex business processes and long cycle chains, the domestic PV subsidy issuance process generally suffers from problems such as subsidy delinquency, settlement difficulties and low regulatory transparency, which cannot meet the business objectives of accurate fund disbursement, immediate business response and transparent subsidy issuance. Blockchain technology has become a new type of distributed bookkeeping and clearing application mode by virtue of its security, decentralization and difficulty in tampering. Scholars at home and abroad have also put forward architectural ideas for the application of blockchain technology in the energy sector. In this paper, we propose a blockchain-based PV subsidy transaction mechanism. In order to finally achieve consistency in the settlement results of metering data by multiple parties of subsidies, we study mechanisms such as PV power generation deposit method, PV subsidy metering data pre-settlement verification, and consensus on the consistency of subsidy amount, and form a decision on whether to submit a completed settlement transaction by endorsing the verification of the results of pre-settlement of metering data by power grids, power generation enterprises and governments. In order to verify the feasibility of the above technology, simulations are conducted for a network topology of 8 nodes for 3 types of subjects, which can support PV subsidy settlement nd PV subsidy business issuance.
The Force of Compensation, a Multi-stage Incentive Mechanism Model for Federated Learning

In federated learning, data owners ‘provide’ their local data to model owners to train a mature model in a privacy-preserving way. A critical factor in the success of a federated learning scheme is an optimal incentive mechanism that motivates all participants to fully contribute. However, the privacy protection inherent to federated learning creates a dual ethical risk problem in that there is information asymmetry between the two parties, so neither side’s effort is observable. Additionally, there is often an implicit cost associated with the effort contributed to training a model, which may lead to self-interested, opportunistic behaviour on both sides. Existing incentive mechanisms have not addressed this issue. Hence, in this paper, we analyse how dual ethical risk affects the performance of federated learning schemes. We also derive an optimal multi-stage contract-theoretic incentive mechanism that minimises this risk, and experiment with calculating an optimal incentive contract for all participants. To our best knowledge, this is the first time that dual ethical risk for federated learning participants has been discussed. It is also the first time that an optimal incentive mechanism to overcome this issue has been developed.
TACTILE – A Mixed Reality Solution for Staying Active and Socially Included

Seniors are often affected by a decline in physical and mental abilities, resulting in social isolation, anxiety, and frailty. They often live alone, spatially separated from family members and friends, who cannot visit them regularly. Moreover, seniors often suffer from physical ailments. Thus, they cannot go outside anymore on their own and feel even more isolated or depressed. Various studies show that playing board games and doing physical exercises keep the mind and body trained and thus have the potential to prevent loneliness and diseases. Therefore, we developed TACTILE – a mixed-reality (MR) solution that connects seniors with family members and friends even when apart. The TACTILE system offers two basic functionalities: (i) board games that can be played with a remote partner and, (ii) physical exercises that are done together with a virtual avatar. While using the system, users can also talk to each other when playing games and interact with the virtual avatar that accompanies the physical exercises. Such interaction strengthens the intuitiveness of the system and the social component even more. Three prototype versions were evaluated within three end-user workshops and a final prototype during long-lasting field trials in two test countries: Austria and the Netherlands. This paper describes the status of the TACTILE system, its components, the strong end-user involvement, and the results and findings from the field trials where the final prototype has been tested for an extended period by 64 end-users in their home and life setting.
State policy of sustainable digitalization of Kazakhstan education in the experience of transformation during the pandemic period

The modern world cannot be imagined without the Internet and digital devices; sustainable digitalization is a global trend that covers all areas of life and is gaining momentum from year to year. The digitalization of the educational sphere in Ka-zakhstan began relatively recently, but the global pandemic and the associated transition to distance education have significantly accelerated the process of digi-tal transformation of the education system. The article provides an analysis of the problems that Kazakhstani education had to face in the transition to online learn-ing, and emergency measures taken by the state and the business environment to solve them. Based on the materials of in-depth interviews with experts in the edu-cational field, an analysis was made of the impact of the pandemic on the process of digital transformation of the educational sector and the prospects for the devel-opment of education in the changed conditions, and key areas for further improv-ing the quality of digital education were identified.
Serious Game Application Development for Learning Battle of Surabaya

The main purpose of this research is to educate the society, especially those who are still in school about Indonesian history and to increase public interest to learn the history of Indonesia, especially the history of the battle of Surabaya which took place on November 10, 1945. Where in this game the player will play as an Indonesian warrior character who fights foreign soldiers. So that the authors make an android-based game that is an educational genre. This game is based on android, because currently android-based smartphones are easier to access. This study uses the Black-Box Testing evaluation method, where this evaluation method only uses the upper and lower limits as values to test functionality. The game engine used in the development of this game is “Unity” which helps writers to develop Indonesian historical games to make the learning process of Indonesian history can be learned more simply and in a more enjoyable way in the hope that public interest, especially those who are still in school can increase and encourage the interest of a wider society in accessing other educational games.
Towards a Web-based Membership Information System to Manage Product Marketing Content

The company with a direct selling technique uses membership marketing as a sales method. Members establish no digital promotion and transaction management in terms of product marketing. It causes a problem when doing product promotion activities for each member since there is no standard from the company. Therefore, members can vary marketing content, such as product prices and discounts. Also, the provision of rewards for member sales cannot do automatically. This research aims to create a system that provides a marketing platform for companies with a membership business model. The web-based system is entitled Membership Information System (MIS). MIS makes it easier for the company to control and manage the transactions handled by members, conducting promotions and giving rewards automatically from member sales. The system develops using the Agile method that consists of several phases: system requirements, system design, development, testing, deployment, and review phase. The developed system has several features to manage product marketing content, including category data management, product data management, order management, reward management, retail order management, retail sales data recap, shopping carts, payment gateways, and shipping gateways. The unit and API automation testing of MIS were running as expected.
VALUE AND MOTIVATION COMPONENTS IN THE STRUCTURE OF THE PROFESSIONAL ORIENTATION OF FUTURE PSYCHOLOGISTS

The paper addresses the identification and description of the structure of professional orientation for future psychologists studying at correspondence and full-time departments for baccalaureate/master’s degree, as well as the determination of the value and motivation components in it. The research aims to examine the value and motivation components in the structure of the professional orientation of future psychologists for different courses, forms and levels of professional education. Materials and methods. The research employs the method of “Motivation for learning at university” by T.I. Ilyin, the method of “Motivation of professional activity” by K. Zamfir, the method of diagnosing value orientations by S. Schwartz, the method of “Career Anchors” by E. Shein, and methods of mathematical-statistical processing and interpretation of data. Research results. An empirical study of the professional orientation of future psychologists enrolled on different courses and choosing various forms of education has revealed the specific features of the value and motivation components in the structure of professional orientation, and indicated that the identified hierarchy of motives is a personality characteristic of a student majoring in Psychology. Conclusion. This empirical research has made it possible to formulate a conclusion about the significance of the value and motivation components in the structure of the professional orientation of the personality of future psychologists, as well as the importance of the educational process organization, in which professional motives dominate in the student’s motivational structure. Prospects for further research are associated with an identification of the relationship between the value-motivation components of the professional orientation of future psychologists and their employment.
Qualitology in the system of higher education, conceptual foundations of assessment as a function of the study of educational systems

The purpose of the study is to propose a direction for improving the conceptual foundations of qualitology in the higher education system. The article focuses on the necessity to develop the interaction of the labor market and education in order to improve the quality of education. The scientific novelty lies in the formation of a new direction in qualitology in the system of higher education, taking into account the relationship of the modern labor market and educational programs of the university. As a result, the necessity of improving the conceptual foundations of qualitology in the field of higher education, the need for the formation of communicative interaction between the labor market and training, taking into account the close correlation dependence, is revealed.
Evaluation of Performance Service Quality at Private Universities

In today's marketplace, whether in manufacturing or the service business, it is vital to prioritize customer demands.Managers in the service industry are subject to external influences; they place a premium on customer satisfaction, which has resulted in the continuous growth of service organizations' performance.While this is occurring, it is vital to comprehend and quantify customer expectations.Numerous efforts at quantifying service quality using the SERVQUAL paradigm have been attempted.The goal of this study is to analyze the ideas and elements impacting the quality of services using a modified SERVQUAL model, and then to assess the quality of services using the suggested Grey Analytic Hierarchy Process (G-AHP (Grey Analytic Hierarchy Process)) and Multilevel Grey Evaluation models (GST).To demonstrate our strategy, we will conduct a case study on the performance of service quality in Kurdistan Sulaymaniyah's higher education institutions, as the data indicates that the most crucial factor for universities is the communication quality of staff with students, followed by service depth and intensity, and the least crucial factor for universities is external building beautification.
USING THE COMPETENCE MODEL IN BUILDING THE IMAGE OF A MODERN CIVIL TEACHER IN THE SYSTEM OF THE MINISTRY OF INTERNAL AFFAIRS OF RUSSIA

The article raises the question of the relevance of applying a competence-based approach to understanding the image of modern civil staff performing their professional activities in higher education institutions of the Ministry of Internal Affairs of Russia. The authors emphasize the expediency of studying such a less-studied topic, since the current situation on the world political stage requires rethinking the image of a teacher and their qualitative characteristics, especially if they work in military or police educational institutions. The authors note that the creation of a specialist’s competencies profile, which involves not only hard skills and soft skills, but also actual external and internal settings (vision of the state; mission and philosophy of the educational organization itself) makes it possible to switch from an abstract, sketched image of a teacher to a justified, concrete model of their qualities. On the basis of theoretical and empirical methods of collecting information, the authors developed a list of competencies of a civil teacher in the system of the Ministry of Internal Affairs of Russia (on the example of an employee of foreign languages at the St. Petersburg University of the Ministry of Internal Affairs of Russia). They worked out in details the ranked behavioral indicators for the highlighted competencies and formed a model (profile) of competencies. The area between the permissible minimum and maximum levels of competences formation was obtained with the method of expert assessments based on data analysis. This zone marked on a petal diagram contributes to awareness of the required teacher’s level of development of different knowledge, skills and abilities. In the conclusion the authors emphasize that the competence profile gives an opportunity to both the organization itself to find “its” employee, and the teacher themselves, through self-reflection, to adjust their professional activity and build their future in the workplace.
Asymptotically good $ \mathbb{Z}_{p}\mathbb{Z}_{p}[u]/\langle u^{t}\rangle $-additive cyclic codes

$ \mathbb{Z}_{2}(\mathbb{Z}_{2}+u\mathbb{Z}_{2}) $-additive cyclic codes were proved to be asymptotically good in 2020 by Yao et al., where $ u^{2} = 0 $. We extend the study to the double cyclic codes over two finite commutative chain rings. Let $ R_{t} = \mathbb{Z}_{p}[u]/\langle u^{t}\rangle = \mathbb{Z}_{p}+u\mathbb{Z}_{p}+u^{2}\mathbb{Z}_{p}+\ldots+u^{t-1}\mathbb{Z}_{p} $ be a chain ring, where $ u^{t} = 0 $. We construct a class of $ \mathbb{Z}_{p}R_{t} $-additive cyclic codes generated by pairs of polynomials, where $ p $ is a prime number. By using probabilistic methods, we study the asymptotic behaviour of the rates and relative minimum distances of a certain class of the codes. We show that there exists an asymptotically good infinite sequence of $ \mathbb{Z}_{p}R_{t} $-additive cyclic codes with the relative minimum distance of the code is convergent to $ \delta $, and the rate is convergent to $ \frac{1}{1+p^{t-1}} $ for $ 0&lt; \delta&lt; \frac{1}{1+p^{t-1}} $, and $ t\geq1 $.
Analisis Kinematik Singularty Pada Manipulator 7 DOF Dengan Software Simulasi ROBOAnalyzer

Studies related to manipulators are still being carried out, along with the times and needs. Utilization in manufacturing and fabrication has led to the development of many manipulators. Smooth, fast, and accurate movement continue to be developed. In the case of the multi manipulator, 7 DOF (Degree of Freedom) is a type of manipulator that has special features in terms of joints. Inverse kinematic is a parameter used to control the orientation and movement of the manipulator. 7 DOF has three singularity wrist, elbow, and shoulder. In the simulation, each singularity produces an inverse kinematic matrix which is used to control the movement of the manipulator. The method used is to determine the angle of motion of the joint (continues joint angel) with simulation to produce calculations that are fast, precise, accurate, and stable. The target of each joint with the theta value that has been determined by the singularity was successfully carried out with an error of 0%.
An Application of Knowledge Graph for Enterprise Risk Prediction

The continuous development of artificial intelligence has brought new ideas to enterprise management. Knowledge graph is a special way of data storage and presentation. In recent years, it has received more and more attention, and the application scenario and scope are also expanding. This paper proposes an enterprise risk prediction method based on knowledge graph, which extracts and analyzes the internal business data and real-time news of the enterprise through knowledge graph, so as to realize the risk discovery and early warning analysis of the enterprise itself and related enterprises. It is a typical application of knowledge graph in the field of enterprise risk early warning analysis.
A Data-Centric Anomaly-Based Detection System for Interactive Machine Learning Setups

A major concern in the use of Internet of Things (IoT) technologies in general is their reliability in the presence of security threats and cyberattacks.Particularly, there is a growing recognition that IoT environments featuring virtual sensing and interactive machine learning may be subject to additional vulnerabilities when compared to traditional networks and classical batch learning settings.Partly, this is as adversaries could more easily manipulate the user feedback channel with malicious content.To this end, we propose a data-centric anomaly-based detection system, based on machine learning, that facilitates the process of identifying anomalies, particularly those related to poisoning integrity attacks targeting the user feedback channel of interactive machine learning setups.We demonstrate the capabilities of the proposed system in a case study involving a smart campus setup consisting of different smart devices, namely, a smart camera, a climate sensmitter, smart lighting, a smart phone, and a user feedback channel over which users could furnish labels to improve detection of correct system states, namely, activity types happening inside a room.Our results indicate that anomalies targeting the user feedback channel can be accurately detected at 98% using the Random Forest classifier.
Risk Detection of Heart Disease

Cardio Vascular Disease (CVD) is the leading cause of death worldwide and a significant contributor to the rising cost of health care. Due to the growing patient population, existing healthcare systems are becoming increasingly burdened and unable to effectively manage the increased demand for health care services. The purpose of this paper is to demonstrate the design of a fuzzy logic-based system for a patient’s detection of heart disease risk at home. The system is implemented in the MATLAB software, using the fuzzy logic toolbox. This paper provides the technical description of the system and evaluates its performance. The dataset from the Framingham Heart Study was used for the system’s implementation.
Managing the information and educational environment of an educational institution under the influence of digitalization

The book discusses the processes taking place in pedagogical education, analyzes modern approaches, ideas, technologies for the transformation of the management of the information and educational environment of an educational institution under the influence of digitalization. Materials from scientific articles and reports at conferences, dissertation research of authors on the problems of the information and educational environment of the educational institution are presented. It is addressed to teachers, scientists, teachers, graduate students, employees of educational institutions, the general pedagogical community.