Reviewerly/exHarmony huggingface dataset:
Package CovRegpy: Regularised Covariance Regression and Forecasting in Python

This paper will outline the functionality available in the CovRegpy package for actuarial practitioners, wealth managers, fund managers, and portfolio analysts written in Python 3.7. The major contributions of CovRegpy can be found in the CovRegpy_DCC.py, CovRegpy_IFF.py, CovRegpy_RCR.py, CovRegpy_RPP.py, CovRegpy_SSA.py, CovRegpy_SSD.py, and CovRegpy_X11.py. These seven scripts contain the Dynamic Conditional Correlation (DCC) framework, Instantaneous Frequency Forecasting (IFF) framework, Regularised Covariance Regression (RCR) framework, Risk Premia Parity (RPP) weighting functions, Singular Spectrum Analysis (SSA), and Singular Spectrum Decomposition (SSD), and X11 decomposition framework, respectively. These techniques can be used sequentially or independently with other techniques to extract implicit factors to use them as covariates in the RCR framework to forecast covariance and correlation structures and finally apply portfolio weighting strategies based on the portfolio risk measures based on forecasted covariance assumptions. Explicit financial factors can be used in the covariance regression framework, implicit factors can be used in the traditional explicit market factor setting and RPP techniques with long/short equity weighting strategies can be used in traditional covariance assumption frameworks. The CovRegpy_IFF.py, CovRegpy_SSA.py, CovRegpy_SSD.py, and CovRegpy_X11.py scripts were developed originally out of the study of Empirical Mode Decomposition (EMD) and the development of the AdvEMDpy package. The CovRegpy_DCC.py, CovRegpy_RCR.py, and CovRegpy_RPP.py scripts were developed out of seeking to develop Covariance Regression for financial applications.
Human Activity Recognition Utilizing Ensemble of Transfer-Learned Attention Networks and a Low-Cost Convolutional Neural Architecture

Throughout the last decades, human activity recognition has been considered one of the most complex tasks in the domain of computer vision. Previously, many works have suggested different machine learning models for the recognition of human actions from sensor-based data and video-based data which is not cost-efficient. The recent advancement of the convolutional neural network (CNN) has opened the possibility of accurate human activity recognition from still images. Although many researchers have already proposed some deep learning-based approaches addressing the problem, due to the high diversity in human actions, those approaches failed to achieve decent performance for all human actions under consideration. Some researchers argued that an ensemble of different models may work better in this regard. However, as the images used for recognition in this domain are mostly captured by security cameras, often, the deep models couldn’t extract valuable features resulting in misclassifications. To resolve these issues, in this study, we have considered three transfer-learned models i.e., DenseNet201, Xception, and EfficientNetB6, and applied a multichannel attention module to extract more distinguishable features. Moreover, a custom-made low-cost CNN has been proposed that works with small images extracting features that often get lost due to deep computations. Finally, the fusion of features extracted by attention-based transfer-learned models and the low-cost CNN has been used for the final prediction. We validated the proposed ensemble model on Stanford 40 actions, BU-101, and Willow datasets, and it achieved 97.48%, 98.29%, and 94.19% overall accuracy respectively which outperformed the previous performances by notable margins.
Verifiably Exact Solution of the Electronic Schrödinger Equation on Quantum Devices

Quantum computers have the potential for an exponential speedup of classical molecular computations. However, existing algorithms have limitations; quantum phase estimation (QPE) algorithms are intractable on current hardware while variational quantum eigensolvers (VQE) are dependent upon approximate wave functions without guaranteed convergence. In this Article we present an algorithm that yields verifiably exact solutions of the many-electron Schr\"odinger equation. Rather than solve the Schr\"odinger equation directly, we solve its contraction over all electrons except two, known as the contracted Schr\"odinger equation (CSE). The CSE generates an exact wave function ansatz, constructed from a product of two-body-based non-unitary transformations, that scales polynomially with molecular size and hence, provides a potentially exponential acceleration of classical molecular electronic structure calculations on ideal quantum devices. We demonstrate the algorithm on both quantum simulators and noisy quantum computers with applications to H$_{2}$ dissociation and the rectangle-to-square transition in H$_{4}$. The CSE quantum algorithm, which is a type of contracted quantum eigensolver (CQE), provides a significant step towards realizing verifiably accurate but scalable molecular simulations on quantum devices.
Gestalt Modeling of Ponteio No. 30 by Camargo Guarnieri

The present work proposes the gestalt modeling (hereinafter GM) of Ponteio No. 30 by Camargo Guarnieri with the purpose of elaborating a compositional system to be used in the planning of a work for solo piano. The creation of this new work was possible based on observations of the structural dispositions of the ponteio in question, following the suggestions by Tenney (1988) for the musical application of the gestalt law of objective group. The initial purpose of this work was to identify and describe in Ponteio No. 30 these musical suggestions and the relationships between the musical elements in this context. Based on a generalization of these relationships, definitions of the hypothetical system that generated the intertext (Ponteio No. 30) were prepared, which served as the basis for planning the new work. The similarities between the two works occur at a deep level, in an abstract way, while the differences between them reside in the different elements used to fill in the structural archetypes and in the decisions regarding parameters not included in the systemic model. We understand that GM is effective for the creation of new works, since it provides a consistent starting point for compositional planning and gives freedom in choosing the elements that must be inserted in the structures of the models and also outside them. In addition, GM has its analytical potential for coherently presenting new perspectives based on the listener's perception.
PKM Utilization of Learning Resources in Improving the Quality of the Learning Process in the Mutiara Bahari Play Group

Community Service Activities (PKM) are carried out in one unit of the Mutiara Bahari Playgroup because it has a large number of students with limited learning resources and teacher competence. PKM is conducted from October to November by going through several stages including: initial identification on October 22 2022, debriefing on learning concepts on November 5 2022, workshop on implementing the use of learning resources in improving the quality of the learning process on November 11 2022, testing field trials from November 14 to 28, program evaluation in the first week of December. The change felt by the teacher is that for sufficient learning resources from the natural surroundings, there is no need to use materials or media from the manufacturer, and the most enjoyable thing is that children usually ask to go home during the learning process after activities are carried out using learning resources from the surrounding environment in an attractive arrangement. Children are more enthusiastic to come to school and do various creativity according to the imagination of each child.
The VVAD-LRS3 Dataset for Visual Voice Activity Detection

Robots are becoming everyday devices, increasing their interaction with humans. To make human-machine interaction more natural, cognitive features like Visual Voice Activity Detection (VVAD), which can detect whether a person is speaking or not, given visual input of a camera, need to be implemented. Neural networks are state of the art for tasks in Image Processing, Time Series Prediction, Natural Language Processing and other domains. Those Networks require large quantities of labeled data. Currently there are not many datasets for the task of VVAD. In this work we created a large scale dataset called the VVAD-LRS3 dataset, derived by automatic annotations from the LRS3 dataset. The VVAD-LRS3 dataset contains over 44K samples, over three times the next competitive dataset (WildVVAD). We evaluate different baselines on four kinds of features: facial and lip images, and facial and lip landmark features. With a Convolutional Neural Network Long Short Term Memory (CNN LSTM) on facial images an accuracy of 92% was reached on the test set. A study with humans showed that they reach an accuracy of 87.93% on the test set.
Leveraging Large Text Corpora for End-to-End Speech Summarization

End-to-end speech summarization (E2E SSum) is a technique to directly generate summary sentences from speech. Compared with the cascade approach, which combines automatic speech recognition (ASR) and text summarization models, the E2E approach is more promising because it mitigates ASR errors, incorporates nonverbal information, and simplifies the overall system. However, since collecting a large amount of paired data (i.e., speech and summary) is difficult, the training data is usually insufficient to train a robust E2E SSum system. In this paper, we present two novel methods that leverage a large amount of external text summarization data for E2E SSum training. The first technique is to utilize a text-to-speech (TTS) system to generate synthesized speech, which is used for E2E SSum training with the text summary. The second is a TTS-free method that directly inputs phoneme sequence instead of synthesized speech to the E2E SSum model. Experiments show that our proposed TTS- and phoneme-based methods improve several metrics on the How2 dataset. In particular, our best system outperforms a previous state-of-the-art one by a large margin (i.e., METEOR score improvements of more than 6 points). To the best of our knowledge, this is the first work to use external language resources for E2E SSum. Moreover, we report a detailed analysis of the How2 dataset to confirm the validity of our proposed E2E SSum system.
Overcome Ethnic Discrimination with Unbiased Machine Learning for Facial Data Sets

AI-based prediction and recommender systems are widely used in various industry sectors.However, general acceptance of AI-enabled systems is still widely uninvestigated.Therefore, firstly we conducted a survey with 559 respondents.Findings suggested that AI-enabled systems should be fair, transparent, consider personality traits and perform tasks efficiently.Secondly, we developed a system for the Facial Beauty Prediction (FBP) benchmark that automatically evaluates facial attractiveness.As our previous experiments have proven, these results are usually highly correlated with human ratings.Consequently they also reflect human bias in annotations.An upcoming challenge for scientists is to provide training data and AI algorithms that can withstand distorted information.In this work, we introduce AntiDiscriminationNet (ADN), a superior attractiveness prediction network.We propose a new method to generate an unbiased convolutional neural network (CNN) to improve the fairness of machine learning in facial dataset.To train unbiased networks we generate synthetic images and weight training data for anti-discrimination assessments towards different ethnicities.Additionally, we introduce an approach with entropy penalty terms to reduce the bias of our CNN.Our research provides insights in how to train and build fair machine learning models for facial image analysis by minimising implicit biases.Our AntiDiscriminationNet finally outperforms all competitors in the FBP benchmark by achieving a Pearson correlation coefficient of PCC = 0.9601.
Use of ontologies and semantic mediawiki for representation and retrival of scientific data in the fair paradigm

International scientific conference «Information technologies and management in higher education and sciences» : conference proceedings (November 28, 2022. Fergana, the Republic of Uzbekistan). Riga, Latvia : "Baltija Publishing", 2022. Part 3. 432 pages.
Modern selection of educational materials for students of Ukrainian higher schools

International scientific conference «Information technologies and management in higher education and sciences» : conference proceedings (November 28, 2022. Fergana, the Republic of Uzbekistan). Riga, Latvia : “Baltija Publishing”, 2022. Part 3. 432 pages.
Prediction of Student Scholarship Recipients Using the K-Means Algorithm and C4.5

The government has a program called "KIP Lectures" to assist students in financing education. Where PTS makes the selection manually, with the Data Mining technique, a process will be carried out to speed up the manual process. This research will apply the clustering method with the K-Means algorithm and the classification method with the C4.5 algorithm, as well as a test using the RapidMiner application, which utilizes applicant data in 2022 with a total of 1298 participants. The test results found that 327 participants were in the highest cluster, "cluster_0", then the results of the c4.5 algorithm test obtained a decision tree if the participant has a KIP Card and the value obtained from the "Total Income" Criterion is more than 70 points, then the participant concerned is entitled to scholarship where 317 participants meet the criteria, and the university only has to choose participants from the results obtained in accordance with a predetermined quota.&#x0D;
TAARA Method for Processing on the Network Forensics in the Event of an ARP Spoofing Attack

According to reports in 2021 by Kaspersky, requests for investigations into suspicious network activity, such as ARP Spoofing, which can result in sophisticated attacks, reached up to 22%. Several difficulties with examining network systems have been overcome thanks to network forensic investigations. This study aims to perform a network forensic analysis of ARP spoofing attacks using Wireshark forensic tools and Network Miner with a sniffer design process to capture traffic on the router side. In order to gather reliable evidence, this study employs the TAARA method as a network forensic investigation process. Based on the research conducted, it can be demonstrated that an attack took place from eight PCAP files. The information that was gathered, such as the IP address and MAC address of the attacker, the IP address and MAC address of the target, and the date and time of the attack are examples of evidence information that was gathered. This study also shows that network forensic operations can use the Wireshark forensic tool to obtain more detailed data.&#x0D;
Analisis Sentimen Pengguna Sosial Media Twitter Terhadap Perokok Di Indonesia

One of the tools web users use to access, share, and discuss subjects of interest is social media. One social networking site, Twitter, is often used in real time to communicate this. Due to its significant negative impacts on both health and the economy, smoking is still a topic of regular debate and debate in Indonesia. This research was conducted to assess sentiment towards smokers and differentiate between positive and negative emotions. The data used in this study were obtained by crawling the Twitter social media network. Three Bayes techniques (NB), Support Vector Machine (SVM), and Logistic Regression are used in this study. In this study, 40.25% of Twitter users agreed with the existence of smokers in Indonesia, while 59.74% disagreed. The Naive Bayes method was used in this study, giving the highest accuracy value = 62.1% using 60% training data and 40% test data.
Development of research interests of high school students in the process of joint creative activities with students of pedagogical university

Introduction. The study is aimed at solving the problem of substantiation of ways to design continuous educational activities of high school students and students, leading them to the maximum disclosure of their creative potential. The proposed approach to solving this problem consists of using the potential of joint creative activity of high school students and students as a means of developing their common research interests. The purpose of this article is to substantiate the model of organization of this activity. Materials and methods. The study was conducted by methods of scientific literature analysis and modeling of informal educational environment as a system of psychological and pedagogical conditions of development of joint creative activity of high school and college students. The model was tested in the form of the Festival of Creative Ideas. The total number of participants was 150 people. The effectiveness of the created model was evaluated according to the following indicators: satisfaction of the participants with the obtained results; productivity of participation in the process of discussions; expression of research interests of high school and college students; expression of psychological barriers to subject-subjective interactions. Results. The principles underlying the organization of joint creative activity of senior pupils and students: dialogicality, productivity of subject-subject interactions and gamification are substantiated. The model of educational event as a structural unit of joint creative activity of senior pupils and students, including 4 blocks: diagnostic, communicative, content and reflexive, is created. The results of its experimental testing are characterized. It is shown that the majority of surveyed participants are most satisfied with the results of joint creative activity (84.1%), but insufficiently satisfied with their own contribution to this activity (36.4%). A wide range of research interests of high school students participating in joint creative activity with students has been revealed. The dominant interest is in the problems of choosing and mastering the future profession in the modern world (79.5%). The psychological barriers of the subjects of joint creative activity of senior pupils and students are reflected: fear of an unfamiliar situation (81,8%), fear of looking inadequate (75%), difficulty in establishing interpersonal contacts (68,2%). Conclusion. The general conclusion is made that the joint creative activity of high school students and students, carried out in an informal educational environment, has the result of the development of their research interests to the level comparable to the state of modern science.
DESIGN AND BUILD COMPANY PROFILE WEBSITE BAGASKARA ART STORE WONOSOBO

Bagaskara Art Studio is a studio that provides training in acting, mime, painting and dance classes. It is located in Tanjunganom Village, Kaliwiro District, Wonosobo Regency, Central Java. Bagaskara Art Studio currently does not have a company profile website. Company profile is a product written by public relations (PR) practitioners that contains an overview of the company so that the company can choose the points that it wants to convey openly to the public. The company profile provides an overview of the company profile, vision and mission, as well as the products or services offered. The problem with Bagaskara Art Studio is that the client does not know in detail what services are offered because the client knows Bagaskara Art Studio only through social media. Information about the studio that is published on social media is only Whatsapp contact for ordering and only mentions the services offered without being explained in detail. so this research has designed a company profile website for Bagaskara Wonosobo Art Studio by applying a PHP framework that focuses on Laravel and Boostrap and MySQL database. The method chosen in this research is Extreme Programming which consists of 5 stages. The first stage is planning, which is to determine the needs of the system by collecting and analyzing existing data at Bagaskara Wonosobo Art Studio through interviews and literature studies. The second stage is design, which is the creation of a system model based on the results of the needs analysis that has been obtained. The third stage is coding, namely the application of the system model design into program code. The fourth stage is testing using the blackbox testing method. The fifth stage is software increment. The results of the research are in the form of the Wonosobo Bagaskara Art Studio website which was built by implementing Backend Hypertext Preprocessor (PHP) with the Laravel framework, Frontend CSS with the Bootstrap framework and MySQL database. The website features of Bagaskara Wonosobo Art Studio are displaying the home page, services, news, portfolio, and my account which displays information so that it can advance Bagaskara Art Studio. All features made on the website can run well and in accordance with the expected functions
An Encryption Approach Based on Formal Method for Securing Distributed Big Data Storage in Cloud Environment

Cloud computing is the technology used to store massive amounts of data (Big data) on Servers which locations are unknown to the user of the service. One of the major source of concern in security and privacy is the fact that cloud operators have chances to reach the sensitive data. This makes Cloud computing adoption by organizations with users’ sensitive details including the banking sector and government agencies marred by resentment. Therefore, a cryptography approach is proposed, named Secured Efficient Distributed Storage SecDcloud) model that is designed to obtain an efficient mass distributed service, and is supported by the Modified Alternative Data Distribution (MAD2) Algorithm Modified Secured Data distribution (MSED2) Algorithm as well as Improved Data Conflation (IDCon) Algorithm based on formal method. Our proposed mechanism aims to split, encrypt sensitive data and store the data to the different cloud servers without causing big overheads using formal method which prevents cloud service providers from directly accessing the user’s data. Our experiments, evaluated on security and efficiency of our technique, and is compared with state of the art Advanced Encryption Standard(AES) Algorithm and the results show that it is capable of effectively defending against the most common cloud-based threats while still maintaining a reasonable amount of processing time.&#x0D;
UTILIZING WHATSAPP APPLICATION AS A LEARNING MEDIA IN THE ONLINE ENGLISH CLASS

WhatsApp application as the foremost prevalent application for the education field is helpful to support the online teaching and learning process. During the Covid-19 pandemic, WhatsApp application is one of the media to help students in the learning process in online classes. WhatsApp gives useful highlights such as content, call, send videos, sounds, links, locations, documents, and pictures. The purpose of this study is to know if the use of WhatsApp applications enhances English learning during online classes. In this research, a descriptive qualitative method was employed, and to gather the data the researcher used a questionnaire to collect the data. The findings supported the use of WhatsApp applications to help students in learning English during online classes, using WhatsApp to increase students’ enthusiasm for learning English, and using WhatsApp helped students to develop English skills. In conclusion, the use of WhatsApp applications is helpful for students in learning English during online classes.