Reviewerly/exHarmony huggingface dataset:
Face Recognition based on Frontalization of Multiple Poses and Expressions using GGAN and PCA

Face identification is an essential application in recent times as it has several applications like surveillance, criminal identification, etc. Many face recognition methods are still facing various challenges by researchers in real-time applications because of multiple rotational angles of faces and different expressions. To overcome this issue, we come up with the novel approach of Global Generative Adversarial Networks (G-GAN) to convert various expressions and side images into frontal images and Principal Component Analysis (PCA) to recognize the face in quick time with low cost, high accuracy and with use of less memory. The unique error loss algorithm is introduced in G-GAN during training which preserves identical features of profile images and photo-realistic good resolution front image. The single ground truth frontal image of an individual and G-GAN converted frontal pictures of individuals are considered, and PCA is applied to obtain features. The features are compared using Euclidean Distance (ED) to recognize an individual. The module used publicly available datasets like Indian male and female, ORL, and Multi PIE and achieved good results with a high recognition rate. Keywords: Biometrics, Face Recognition, GAN, Face Profile Images, PCA
Cybersocialization as a factor of university students’ adaptation to distance learning conditions

This paper examines the problem of cyberspace, which is becoming a field for discussion of both political, economic, social problems and issues related to personal life, learning and development processes. The process of spontaneous socialization, where the agents have traditionally been the institutions of family and education, is complicated by modern global information computer technology, which has naturally led to cyberevolution. The necessity of entering cyberspace in the sphere of education is considered. The authors describe the need to include the student community in the learning process through online technologies using modern learning platforms (Moodle, Microsoft Teams, etc.). The paper considers cybersocialization phenomenon as the main factor of socio-pedagogical adaptation of students with the need to exit to a positive cybersocialization. Particular emphasis is placed on the positive aspects of cybersocialization of students, and the problems of students adaptation to distance learning conditions are studied. The paper presents the results of the development and implementation of socio-pedagogical program of students adaptation to distance learning conditions. The conducted research showed the orientation of students to distance learning format, where a certain role is given to curators of study groups and teachers in solving the problems of students cybersocialization. At the same time, the independence and motivation of students in solving this problem is important. The obtained materials can serve as a basis for understanding the process of socialization of modern students to the conditions of distance education.
Rashomon Capacity: A Metric for Predictive Multiplicity in Probabilistic  Classification

Predictive multiplicity occurs when classification models with nearly indistinguishable average performances assign conflicting predictions to individual samples. When used for decision-making in applications of consequence (e.g., lending, education, criminal justice), models developed without regard for predictive multiplicity may result in unjustified and arbitrary decisions for specific individuals. We introduce a new measure of predictive multiplicity in probabilistic classification called Rashomon Capacity. Prior metrics for predictive multiplicity focus on classifiers that output thresholded (i.e., 0-1) predicted classes. In contrast, Rashomon Capacity applies to probabilistic classifiers, capturing more nuanced score variations for individual samples. We provide a rigorous derivation for Rashomon Capacity, argue its intuitive appeal, and demonstrate how to estimate it in practice. We show that Rashomon Capacity yields principled strategies for disclosing conflicting models to stakeholders. Our numerical experiments illustrate how Rashomon Capacity captures predictive multiplicity in various datasets and learning models, including neural networks. The tools introduced in this paper can help data scientists measure, report, and ultimately resolve predictive multiplicity prior to model deployment.
Generalized Reductions: Making any Hierarchical Clustering Fair and Balanced with Low Cost

Clustering is a fundamental building block of modern statistical analysis pipelines. Fair clustering has seen much attention from the machine learning community in recent years. We are some of the first to study fairness in the context of hierarchical clustering, after the results of Ahmadian et al. from NeurIPS in 2020. We evaluate our results using Dasgupta's cost function, perhaps one of the most prevalent theoretical metrics for hierarchical clustering evaluation. Our work vastly improves the previous $O(n^{5/6}poly\log(n))$ fair approximation for cost to a near polylogarithmic $O(n^\delta poly\log(n))$ fair approximation for any constant $\delta\in(0,1)$. This result establishes a cost-fairness tradeoff and extends to broader fairness constraints than the previous work. We also show how to alter existing hierarchical clusterings to guarantee fairness and cluster balance across any level in the hierarchy.
Implementasi Manajemen Pendidikan Berbasis Prilaku di MTS Yatashi Kota Bogor

In general, the purpose of this study is to reveal the implementation of behavior-based education management at MTs Yatashi, Bogor City. However, the specific objectives of this research are: (1) To determine the implementation of behavior-based education management in MTs Yatashi Bogor City, (2) To determine the implementation of behavior-based education at MTs Yatashi Bogor City. This research is a field research (Field Research) that is conducting research directly to the location to obtain and collect data. The research carried out in the field is examining qualitative problems, namely research data procedures that produce descriptive data in the form of written or spoken words from people and observed behavior. The implementation of behavior-based education management at MTs Yatashi Bogor City is pursued through four alternative strategies in an integrated manner, namely: (a) integrating behavioral education content that has been formulated into all subjects, (b) integrating behavioral education into daily activities at school, (c) integrate behavioral education into programmed or planned activities, (d) build collaborative communication between schools and parents of students. From the discussion above, it can be concluded that the implementation of behavior-based education management is carried out through two activities, namely Intracurricular, namely through teaching and learning activities that have been scheduled by the Deputy Head of curriculum and extracurricular activities, namely scouting, religion, Islamic arts, Youth Red Cross (PMR) which is scheduled by the Deputy Head of the field. to students and the values embedded are religious, disciplined, honest, responsible, tolerance, love the homeland, hard work, care for the environment, love peace, care for social, creative and independent. Keywords: management, education, behavior
The Impact of Information Communication and Technology (ICT) on Corruption and Social Capital

This research paper examines the argument that Information Communication and Technology (ICT) and social capital serve as major factors to reduce corruption. ICT has the potential to reduce unnecessary human intervention in government work processes, thus reducing the need to monitor corrupt behavior. Furthermore, citizens living in a society with a high level of social capital are more likely to become actively involved in the political decision-making process, increasing the likelihood that public employees' corrupt behaviors will be exposed to the densely connected public. We also examined the relationship between social capital and ICT. Our statistical analysis, which used panels of datasets obtained from various sources, revealed that (1) ICT is an effective tool for reducing corruption; (2) social capital also has positive effects on anti-corruption, but various dimensions of social capital may have different impacts; and (3) the relationship between social capital and ICT is inconclusive. Points for practitioners The theoretical model and empirical results of this research paper shed light on the potential impacts of ICT on corruption, thereby providing practitioners with the opportunity to consider ICT as a useful and practical policy tool for reducing corruption in addition to traditional methods, i.e. administrative reform and law enforcement. Further, although our research findings on the relationship between ICT and social capital were inconclusive, social capital was found to have anticorruption effects independent of ICT, which implies that policies designed to foster trust networks in a society can contribute to the reduction of corruption.
Construction of English Translation Model Based on Improved Fuzzy Semantic Optimal Control of GLR Algorithm

Data point overlap exists in the model translation recognition results of generalized maximum likelihood ratio detection (GLR) algorithm. A fuzzy semantic optimal control intelligent recognition model for English translation based on improved GLR algorithm is proposed. This algorithm is used to create a phrase corpus for marking tens of thousands of English and Chinese words, so that phrases can be searched automatically. The algorithm builds a phrase corpus of about 710,000 Chinese and English words. Phrase structure is constructed through phrase centers. Partial speech recognition results can be obtained. According to the syntactic function of analytic linear list, the ambiguity of Chinese and English structures in part of speech recognition results is corrected. Finally get the content of the identifier on the basis of comprehensive evaluation. The recognition accuracy based on the improved algorithm is more than 95%. The overall score was 92.3. This algorithm overcomes the disadvantages of GLR. Compared with statistical algorithm and dynamic memory algorithm, the algorithm improves the operation speed and processing performance and is more suitable for machine translation tasks. It provides a new idea in the field of machine translation.
Sales and Purchase Accounting Information Systems In Trading Companies

Effective and efficient record of sales and purchase transactions are increasingly needed, especially in large transactions. This research is conducted at the “Toko Beras Dadar” in Peterongan Market. This shop still uses manual records. The difficult for manual record is to keep track of the existing stock of goods. This shop has not compiled trial balance and income statement according to standard. So, this problem must be solved with a website-based sales accounting information system. The program used are Laravel and MySQL. The results of technical testing show the system can run well and help the user to simplify transaction process.
Ontology Extraction from Software Requirements Using Named-Entity Recognition

With the software playing a key role in most of the modern, complex systems it is extremely important to create and keep the software requirements precise and non-ambiguous.One of the key elements to achieve such a goal is to define the terms used in a requirement in a precise way.The aim of this study is to verify if the commercially available tools for natural language processing (NLP) can be used to create an automated process to identify whether the term used in a requirement is linked with a proper definition.We found out, that with a relatively small effort it is possible to create a model that detects the domain specific terms in the software requirements with a precision of 87%.Using such model it is possible to determine if the term is followed by a link to a definition.
Effectiveness of digital interventions with application program to improve nutritional knowledge, mother's behavior and nutrition status of children under five

The problem of under-five nutrition is still a significant concern in developing countries, including Indonesia. Under-five nutritional problems are caused by multifactorial causes, therefore, nutrition education can be the key to intervention in children’s nutrition problems. With the current development of technology, it will be easier to intervene in nutritional problems through digital platforms, for example, using applications. This review aimed to analyze the effectiveness of digital interventions through applications on nutritional knowledge, mother’s behaviour, and nutritional status of children under-five
A Qualitative Method for Learning Medical Expert Reasoning

The aim of this paper is to propose a qualitative method for learning a model that represents the closest possible experts reasoning and strategies to provide recommendations of antibiotics. The learned model contains an integrity constraint and a preference formula. The former indicates the features that an antibiotic should have to be recommended. The later indicates the rank of recommendation of an antibiotic.
METHODOLOGICAL FOUNDATIONS OF THE ORGANIZATION OF EXTRACURRICULAR INDEPENDENT WORK OF STUDENTS

The article discusses conceptual approaches to organizing independent work of students in higher educational institutions. The concept of "independent work" includes a type of educational activity directed by the teacher with the help of special educational materials, which is performed by the student without direct contact with the teacher or supervisor. There are different approaches to the concept of organizing individual work of students. The analysis of the methodology of organizing independent work of students was carried out through the analysis of many years of information sources and practical experience.
Review of Anomaly Detection and Removal Techniques from Autonomous Car Data

Artificial Intelligence and the development of computers has brought a huge change in our daily lives. Simple tasks that required humans to use precious time and energy are now being done by computers. One aspect of this is using AI in self-driving cars. By introducing such technologies in the world, it can be said that travelling has become a whole new experience. However, Artificial Intelligence cannot be considered perfect. It has its own faults and these faults can lead to undesired circumstances such as car accidents. One of the major faults in Artificial Intelligence is the presence of anomalies in data. Anomalies are values that are either too extreme to be considered, or are just not needed. Detecting these anomalies is a major task and requires algorithms that efficiently extract them. This paper discusses a few of these techniques and further highlights which ones can be used in autonomous self-driving cars.
BRIGHT -- Graph Neural Networks in Real-Time Fraud Detection

Detecting fraudulent transactions is an essential component to control risk in e-commerce marketplaces. Apart from rule-based and machine learning filters that are already deployed in production, we want to enable efficient real-time inference with graph neural networks (GNNs), which is useful to catch multihop risk propagation in a transaction graph. However, two challenges arise in the implementation of GNNs in production. First, future information in a dynamic graph should not be considered in message passing to predict the past. Second, the latency of graph query and GNN model inference is usually up to hundreds of milliseconds, which is costly for some critical online services. To tackle these challenges, we propose a Batch and Real-time Inception GrapH Topology (BRIGHT) framework to conduct an end-to-end GNN learning that allows efficient online real-time inference. BRIGHT framework consists of a graph transformation module (Two-Stage Directed Graph) and a corresponding GNN architecture (Lambda Neural Network). The Two-Stage Directed Graph guarantees that the information passed through neighbors is only from the historical payment transactions. It consists of two subgraphs representing historical relationships and real-time links, respectively. The Lambda Neural Network decouples inference into two stages: batch inference of entity embeddings and real-time inference of transaction prediction. Our experiments show that BRIGHT outperforms the baseline models by >2\% in average w.r.t.~precision. Furthermore, BRIGHT is computationally efficient for real-time fraud detection. Regarding end-to-end performance (including neighbor query and inference), BRIGHT can reduce the P99 latency by >75\%. For the inference stage, our speedup is on average 7.8$\times$ compared to the traditional GNN.
NeuralSympCheck: A Symptom Checking and Disease Diagnostic Neural Model with Logic Regularization

The symptom checking systems inquire users for their symptoms and perform a rapid and affordable medical assessment of their condition. The basic symptom checking systems based on Bayesian methods, decision trees, or information gain methods are easy to train and do not require significant computational resources. However, their drawbacks are low relevance of proposed symptoms and insufficient quality of diagnostics. The best results on these tasks are achieved by reinforcement learning models. Their weaknesses are the difficulty of developing and training such systems and limited applicability to cases with large and sparse decision spaces. We propose a new approach based on the supervised learning of neural models with logic regularization that combines the advantages of the different methods. Our experiments on real and synthetic data show that the proposed approach outperforms the best existing methods in the accuracy of diagnosis when the number of diagnoses and symptoms is large.
Innovation and Practice of Music Education Path in Colleges and Universities Under the Popularization of 5g Network

Abstract The value of music education in society and culture cannot be overstated. For students with exceptional needs, music education plays a vital role. Student benefits from community-based arts education programmes range from social to academic. The issue is that music education, like other arts programmes, is frequently not taught in special education classrooms. We propose fifth-generation (5G)-based music education for cognitive computing for tailored behavioural intervention utilizing 5G in this article. Cognitive computing is a multidisciplinary sector of study that aims to develop computational designs and decision-making procedures depending on brain neurobiology, cognitive science, and psychology. Cognitive-behavioral techniques aim to change students' perceptions, alter their behaviour, and increase their pain control. Music and other cognitive therapies try to regulate the stress response by effectively mediating emotional responses. These objectives can be met by using behavioural treatments that influence the individual's affective response, which is influenced by prior experiences, prior knowledge, and emotions. The suggested system's performance is evaluated and compared to existing approaches.
Pemilihan Metode Predictive Analytics dengan Machine Learning untuk Analisis dan Strategi Peningkatan Kualitas Kredit Perbankan

&lt;p align="justify"&gt;As a factor that determines bank’s profitability, loan quality, that is categorized based on debtor’s collectability classification, always gets attention and become main analysis topic in banking industry. Through recent development of statistics and data science, especially in predictive analytics using machine learning techniques, more comprehensive analysis and prediction in loan quality can be conducted. This research is intended to give example on application of predictive analytics using machine learning technique for analysis and strategy recommendation in increasing bank’s loan quality improvement. In this research, some machine learning classification methods are compared to conduct predictive analytics in loan quality with big data size (big data analytics). Computation result of different methods are compared and summarized, resulted in recommendation on most appropriate method to achieve this research objective. This research concluded that for imbalanced big data size such as bank’s loan collectability, Tree Ensemble method, further development of Decision Tree method that is commonly used in machine learning, is one of appropriate methods to get satisfactory result in this research. Imbalanced data that can result in false positive may be overcame by oversampling Synthetic Minority Oversampling Technique (SMOTE). This research scope is limited to analysis and prediction of debtor’s collectability for the next several months, combined with analysis and strategy recommendations based on product type, gender, and debtor’s occupation. Further predictive analytics for the next several years by including external factors, such as economic growth, is not covered in this research and possible to be conducted. As machine learning application in Indonesian banking industry analysis is still in early phase, this research is expected to become one of reference in application of predictive analytics using machine learning in banking industry. &lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords&lt;/strong&gt;&lt;strong&gt;: &lt;/strong&gt;predictive analytics; machine learning; loan collectability; loan quality&lt;/p&gt;
DOMINANT PROTECTION STRATEGIES OF STUDENTS' COMMUNICATION AND THEIR RELATION TO CHARACTER ACCENTATIONS

The development level of the modern higher education system highlights the problems associated with the structure of the dominant protection strategies in communication among students, and especially in those specialties that involve working with people (physicians, psychologists, social workers, etc.). Within the research of the above stated problem, the study of the degree of connection between the dominant protection strategies in communication and accentuations of character is of particular interest.
Sentiments Analysis Using Tweets

Abstract Data is a powerful weapon in today’s society, and it can assess if something is a success or a failure. This enables the company to examine its customer base and deliver results as promptly as workable. This concept is known as Sentiment Analysis of Data. Sentimental Analysis can be performed using historical or present data. There are many tools and machine learning approaches available, but most people find that natural language processing is a great tool for assessing data for opinion mining, and machine learning is a great solution if they know how to use it correctly.
Sub-optimal Approaches to Heteroscedasticity in Silicon Strip Detectors: the Lucky Model and the Super-Lucky Model

The approach to heteroscedasticity of ref.1(Instruments 2022, 6(1), 10) contains a sketchy application of a sub-optimal method of very easy implementation: the lucky model. The supporting proof of this method could not be inserted in ref.1. The proof requires the analytical forms of the probability of ref.2 for the two strip center of gravity. However, those analytical forms suggest also a completion of the lucky-model for the absence of a scaling constant, relevant for combinations of different detector types. The advanced lucky-model (the super-lucky model) can be directly used for track fitting in trackers composed of non-identical detectors. The construction of the weights for the fits is very simple. Simulations of track fitting with this upgraded tool show resolution improvements also for combination of two types of very different detectors, near to the resolutions of the schematic model of ref.1.