Reviewerly/exHarmony huggingface dataset:
#5490 GENERATIVE ARTIFICIAL INTELLIGENCE FOR CREATION OF SYNTHETIC HYPERTENSION TRIAL DATA

Abstract Background and Aims Synthetic data can be an effective supplement or alternative to real data for the training of machine learning models. Synthetic data may also be used to evaluate new tools, develop educational curricula, or remove undesirable biases in datasets. We aim to evaluate four synthetic data generation methods applied to hypertension randomized clinical trial data. Method The Systolic Blood Pressure Intervention Trial (SPRINT) trial showed that intensive BP control to SBP &amp;lt;120 mm Hg results in significant cardiovascular benefits in high-risk patients with hypertension compared with routine BP control to &amp;lt;140 mm Hg. The Synthetic Data Vault (SDV) is a Synthetic Data Generation ecosystem of libraries that allows users to easily generate new Synthetic Data that has the same format and statistical properties as the original dataset. SDV supports multiple types of data, including date-times, discrete-ordinal, categorical, and numerical. SPRINT data was pre-processed to create a single table of 140,000 patient visits with baseline variables (age, sex, race, aspirin use, estimated Glomerular Filtration Rate (eGFR)) and visit level variables (systolic and diastolic blood pressure, heart rate and total number of antihypertensive medications at end of visit). Using the SDV library for python, we used four generative models to create synthetic SPRINT data, 1. Gaussian copula model, 2. Conditional Tabular Generative adversarial network (CTGAN), 3. CopulaGan model, and 4. Tabular Variational Auto-encode (TVAE). We evaluated the results using the SDMetrics library which includes the shapes of the columns (marginal distributions), the pairwise trends between the columns (correlations), reproduce mathematical properties from your original data and new row synthesis. Finally, an overall quality score which represents an amalgamation of the marginal distribution and correlations was computed, where 0 indicates the lowest quality and 1 indicates the highest. Results Two hundred thousand synthetic patient visits were created for each method. The overall quality scores in order were 90.67% for Gaussian copula, 86.77% for TVAE, 81.03% for CTGAN’, and 79.7% for CopulaGAN. The column shape score which represents the marginal distribution was highest for Gaussian Copula (94.54%), followed by TVAE (88.44%), CTGAN (82.35%), and Copula GAN (80.27%). The column pair trend which corresponds to correlations was highest for Gaussian Copula (86.8%), followed by TAVE (85.1%), CTGAN (79.72%), and Copula GAN (79.12%). Conclusion Gaussian copula created the highest scoring synthetic SPRINT data based on the marginal distribution, correlations, and overall score. The Synthetic Data Vault is a feasible collection of methods for generation of synthetic clinical trial data for training future machine learning and AI models.
Gtnet: guided transformer network for detecting human-object interactions

The human-object interaction (HOI) detection task refers to localizing humans, localizing objects, and predicting the interactions between each human-object pair. HOI is considered one of the fundamental steps in truly understanding complex visual scenes. For detecting HOI, it is important to utilize relative spatial configurations and object semantics to find salient spatial regions of images that highlight the interactions between human object pairs. This issue is addressed by the novel self-attention based guided transformer network, GTNet. GTNet encodes this spatial contextual information in human and object visual features via self-attention while achieving state of the art results on both the V-COCO<sup>1</sup> and HICO-DET<sup>2</sup> datasets. Code is available online<sup>∗</sup>.
Quantum Optimization Techniques and It’s Comparison with Classical Optimization

Quantum computers are best known for its outstanding performance. Harnessing the last 10 years of advancement of technology in hardware and software, the computational complexity a measure of time needed to execute complex optimization problems plays a significant role in performance of the system. Some of the classical optimization algorithms use random seeds to converge and sometimes bad random seeds lead to non-convergence and utilize lots of computational resources to solve complex optimization problems. As quantum computing comes with supremacy of computing, in this paper an attempt has been made to explicate and review quantum optimization algorithms such as Genetic Quantum Algorithm (GQA) and Quantum Approximate Optimization algorithm (QAOA) and its comparison with classical optimization algorithms.
A Study on the Use of AI in Interviews

코로나19 팬데믹 이후 비대면(Non Contact) 시대가 도래함에 따라 사람과 사람사이 대화를 통해 진행되는 면접까지 영향을 받게 되었다. 4차 산업혁명과 함께 ICT(Information and Communication Technology)의 급속한 발달로 인해 인공지능(Artificial Intelligence 이하 AI)은 더 정교하게 발전하고 있고, 사회 전반적인 부분에 걸쳐 영향을 미치고 있다. 비대면 면접 과정에서도 AI 도입을 시도하는 기업들이 많이 늘어나고 있다. 본 연구에서는 채용 및 면접을 진행하는 과정 중 AI를 적용할 수 있는 영역에 대해 살펴보고 AI 알고리즘에 있어 주관적 요소가 개입할 가능성과 이에 따라 발생할 수 있는 윤리적 문제에 대해서 고찰해 보았다. 이를 통해 면접 과정에 적용되는 AI 또한 알고리즘의 각 요소에 기업의 가치관과 사람의 주관적 기준이 개입하게 되는 점을 알게 되었다. AI 내에 인간의 존엄성과 기본권이 보장될 수 있도록 하는 것은 앞으로 풀어야 할 숙제 중 하나이다. 국내에서도 인권위원회에서 인공지능 개발 및 활용에 있어서 윤리적 가이드라인을 제시한 만큼 AI 시스템에 대한 윤리적 감시 및 검증은 앞으로 더 필요할 것이다. AI 분야는 앞으로 더 발전할 것이고, AI 편향성 및 윤리적 이슈는 전 분야에 걸쳐 발생할 수 있다. 그렇기에 기술과 윤리의 균형적 시각과 원칙이 더 굳게 정립되어야 할 것이다.With the advent of the non-face-to-face (non-contact) era after the COVID-19 pandemic, interviews conducted through conversations between people have been affected. With the rapid development of ICT (Information and Communication Technology) along with the Fourth Industrial Revolution, artificial intelligence (AI) is developing in more detail and affecting the entire society. In the non-face-to-face interview section, more and more companies are attempting to introduce AI. In this study, the areas where AI can be applied during the recruitment and interview process were examined, and the possibility of subjective elements being involved in the AI algorithm and ethical problems that may arise accordingly were examined. Through this, it was found that AI applied to the interview process also intervenes between corporate values and subjective standards of people in each factor of the algorithm. Ensuring that human dignity and basic rights can be guaranteed within AI is one of the tasks to be solved in the future. In Korea, ethical monitoring and verification of the AI system will be needed more in the future as the Human Rights Commission has presented ethical guidelines for the development and use of artificial intelligence. The AI field will develop further in the future, and AI bias and ethical issues can occur throughout the field. This is why the balanced perspective and principles of technology and ethics should be more firmly established.
The Influence of Role-Playing Method on Social Science Learning Outcomes

This study determined the effect of the role-playing method on social studies learning outcomes in terms of student learning motivation at SMP Negeri 11 Bengkulu Tengah. This is quantitative quasi-experimental. One experimental group and one control group, consisting of 25 members of classes VII B and VII A, respectively, comprised the population of this study. A straightforward random sample method was used to conduct the sampling. The methods for gathering data such as test, questionnaire, and observation. Statistical data analysis procedures, such as the normality test, homogeneity test, and two-way ANOVA test, are used to manage and analyze data. Based on data analysis from the findings of the research, it can be deduced that the F-count value is 4.702 with a p value of 0.035 based on the “Method” value. It can be inferred that the strategy influences learning results in part since the p value is less than 0.05. The F-count value is 10.885 with a p value of 0.002 based on the “Motivation” value. It can be said that motivation effects learning outcomes to some extent because the p value is less than 0.05. The F-count value, based on the “Method with Motivation” value, is 4.995, and the p value is 0.030. It may be deduced that there is an interaction between the role-playing method and student learning motivation on student learning results because the p value is less than 0.05.
Anomaly Detection from Crowded Video by Convolutional Neural Network and Descriptors Algorithm: Survey

Depending on the context of interest, an anomaly is defined differently. In the case when a video event isn't expected to take place in the video, it is seen as anomaly. It can be difficult to describe uncommon events in complicated scenes, but this problem is frequently resolved by using high-dimensional features as well as descriptors. There is a difficulty in creating reliable model to be trained with these descriptors because it needs a huge number of training samples and is computationally complex. Spatiotemporal changes or trajectories are typically represented by features that are extracted. The presented work presents numerous investigations to address the issue of abnormal video detection from crowded video and its methodology. Through the use of low-level features, like global features, local features, and feature features. For the most accurate detection and identification of anomalous behavior in videos, and attempting to compare the various techniques, this work uses a more crowded and difficult dataset and require light weight for diagnosing anomalies in objects through recording and tracking movements as well as extracting features; thus, these features should be strong and differentiate objects. After reviewing previous works, this work noticed that there is more need for accuracy in video modeling and decreased time, and since attempted to work on real-time and outdoor scenes.
Exploring a new business model for lending processes in the banking sector using Blockchain technology: An Italian case study

Blockchain is a decentralized information technology (IT) architecture that has garnered significant attention across various sectors of the global economy. In the banking sector, blockchain was initially used for cryptocurrency trading and later expanded to encompass smart contracts, peer-to-peer transactions, and other banking services. In recent years, blockchain technology (BT) has been applied to streamline less standardized credit processes and to successfully support mortgage credit through decentralized recording on ledgers. Employing a qualitative research approach, this paper proposes a novel business model for small banks that utilizes new-generation information technologies to enhance loan profitability. While previous research has linked BT to lending processes, this study is the first to propose a BT application for reshaping traditional banking practices, especially for commercial banks. The research findings demonstrate that blockchain implementation offers advantages in containing information asymmetries, managing credit rationing, and driving business innovation.
Geochemical Features of the Kyplatap Ag-Au Ore Occurrence in Central Chukotka

The article considers the geochemical features of unconventional mineralization in secondary quartzite’s of the Kyplatap ore occurrence, located on the southeastern flank of the volcanic dome structure (VDS) of the same name, complicating the Palyavaam-Pykarvaam volcanotectonic depression (VTD) in the Chaun zone of the Central Chukotka sector of the Okhotsk-Chukotka volcanic Belt (OChVB). The Kyplatap VDS was formed due to the intrusion into the Late Cretaceous Alkaquun volcanites of a large laccolith-like subvolcanic body composed of rhyolite-trachyriolite-trachydacites. Based on the geochemical studies of the Kyplatap ore occurrence, the authors compiled a representative database of geochemical data, including the results of analyses of express-RFA, PKSA and XSA with coordinates and composition of ores and rocks. Correlation analysis of the database showed a positive relationship between Au and Ag, as well as Sb for ore mineralization, and a negative correlation of these elements with the association of the rock group of elements (Ti, Y, Zr, Nb and Yb) that were removed during ore formation. The authors also distinguished the second rock group of elements (K, Ga, Sn, Ba, V, W, Pb, Sc, La and B), characterized by medium and weak correlation of the first group of elements. They calculated the coefficients of enrichment and deficiency of ore-forming elements for the samples taken within the ore zones of the Kyplatap ore occurrence. The extreme enrichment group includes elements Au, Ag, Sb, Bi, As, the average contents of which are 100 times or more, and the minimum concentrations are at least 2-10 times higher than the average contents in the upper crust. The normal enrichment group includes elements S, Sn, Cu, Zn, Mn, Mo and Pb, the average contents of which are 2-10 times, and the maximum concentrations are 5-100 times higher than the average concentrations of the upper crust. The obtained values of the enrichment coefficients indicate a sufficiently intensive metensomatoses and ore formation within the Kyplatap ore occurrence.
Implementasi Sekolah Balita Melalui Program Pos Pelayanan Terpadu (Posyandu) (Belajar Kasus Di Desa Gayam)

Posyandu now provides educational services as well as health care through toddler schools. Toddler education is a link between Posyandu BKB and Pos PAUD activities. Toddlerhood is a critical stage in human development. All human instruments are produced in this toddler age, not only intelligence but also psychological and physical skills, which is known as the golden era of development. The purpose of this research is to determine the implementation of under-five schools via the Posyandu program. It can lessen and prevent early children growth and development failures, from posyandu programs to toddler schools. As a result, cadre training is required to make early childhood more appealing in conducting the toddler school program.
Augmented Reality in Transportation: Demonstration of AR Visualizations at Public Meetings Using a Combination of VISSIM Traffic Models, 3Ds Max Technology, and Roll Plots

The State Highway 20 (SH 20) Alameda Avenue Corridor Study, led by the Texas Department of Transportation (TxDOT) and performed by Jacobs, evaluated the operational, safety, and aesthetic/community needs for a 35-mi stretch of SH 20 (Alameda Avenue) extending from downtown El Paso to Tornillo, TX. Jacobs developed VISSIM traffic models for existing and future conditions to demonstrate operational issues and proposed improvements. The VISSIM simulation data was then integrated into a 3D digital model of the main intersections along the corridor, including roadway aesthetics, and surrounding landscape and buildings. This digital model was used in conjunction with real-time game engine technology to develop an augmented reality (AR) application that let the public visualize the corridor improvements along with their impact on traffic operations and safety. Using handheld tablets and “targets” on roll plots at public meetings, patrons were able to hover over the roll plot and see the traffic animations come to life for that specific area.
USING COMMUNICATION AND COLLABORATION TOOLS IN VIRTUAL LEARNING ENVIRONMENTS MOODLE FOR MATHEMATICS IN PRIMARY SCHOOL

With the advancement of information and communication technologies, teaching mathematics in a real-life classroom is combined with teaching in a virtual learning environment (VLE). It is important to determine how a primary school teacher can use VLE communication and collaboration tools to teach mathematics primary school students. Participants – 4th grade students ((n = 51). Access to quantitative studies has been chosen for the study. Methods of study: Analysis of scientific literature, testing, descriptive statistics, and inference statistics. Data from the pilot study and the educational experiment were processed using version 23 of the IBM SPSS Statistical Package for Social Sciences. The normality of the variable distribution was tested using the Shapiro-Wilk test. Throughout the research, decisions are taken at a value a = 0.05. Study adhered to the fundamental principles of the European Code of Conduct for Study Ethics. The curator of the education was in contact with the students and their parents by e-mail and using the VLE communication and collaboration tools (messages, forums, feedback). The aim was to find out whether the number of emails and messages sent by the curator affected the students’ learning time in the VLE. The hypothesis of zero Pearson coefficient equality in the population is checked. There was a statistically significant weak relationship between the number of emails sent by the curator of the curriculum, the number of messages for students and the time spent by the student for the lessons of the curriculum. There was a mean relationship in the boy’s group, but there was no statistically significant relationship in girls’ group. There was also a statistically significant weak relationship between e-mails sent by the curriculum curator, the number of messages sent to students and the evaluation of the lessons of the curriculum. There was an average relationship in the boy’s group, but in the girl’s group there was no statistically significant relationship between the emails sent by the tutor, the number of messages to students and the evaluation of the lessons of the curriculum. This confirms the theory of constructivism that VLE is suitable for education because teachers can act as learning facilitators to communicate with each other during learning.
INVASION OF INFORMATION SYSTEMS AS A DANGER FOR HUMAN RIGHTS

In recent years, more and more people have been pushed to direct usage of various information systems (IS) developed by governmental, commercial, municipal, and non-governmental organisations. IS were widely used already for a relatively long time – 2 or 3 decades (depending of state), however, people were not pushed to use them directly, they were used by staff members of organisations, so for clients it does not made a difference, do they use an IS or, for example, a paper folder or a file cabinet. Now people are increasingly required to use ISs directly, which makes the process more challenging and potentially dangerous. This article aims to analyse this situation and these processes, trying to classify risks, draw conclusions and provide recommendations.
Explaining a Deep Learning Model for Aspect-Based Sentiment Classification Using Post-hoc Local Classifiers

Aspect-Based Sentiment Classification (ABSC) models are increasingly utilised given the surge in opinionated text displayed on the Web. This paper aims to explain the outcome of a black box state-of-the-art deep learning model used for ABSC, LCR-Rot-hop++. We compare two sampling methods that feed an interpretability algorithm which is based on local linear approximations (LIME). One of the sampling methods, SS, swaps out different words from the original sentence with other similar words to create neighbours to the original sentence. The second method, SSb, uses SS and then filters its neighbourhood to better balance the sentiment proportions in the localities created. We use a 2016 restaurant reviews dataset for ternary classification and we judge the interpretability algorithms based on their hit rate and fidelity. We find that SSb can improve neighbourhood sentiment balance compared to SS, reducing bias for the majority class, while simultaneously increasing the performance of LIME.
VIDEO PROJECTS AS A COMPONENT OF INCLUSIVE COMPETENCE OF FUTURE TEACHERS

In our paper, we propose to focus on the use of video products in the educational process of teacher education in Ukraine.First of all, it is important for us to present a justification of the features and importance of using video projects as part of theoretical material / educational content / online and offline courses. After all, the use of video projects allows to deepen the knowledge of future teachers, including audiovisual involvement in the perception of terminology, complex elements of teacher organization, legislation or other theoretical aspects.Secondly, we believe that it is worth noting and presenting the experience of Ukrainian students of pedagogical specialties' involvement in video projects (in particular, the creation of a social video, etc.). After all, such work allows you to reassess your own skills in using technology, acquire new knowledge, formulate a strategy for implementing project-based learning in secondary school practice, and build strong cause-and-effect relationships between the knowledge and experience of using it through technology and social media.All of this together allows us to observe the formation of a future teacher who is not separated from the challenges of the technology world, but who uses the opportunities of our time in a harmonious and high-quality way.
SIMULATED ANNEALING METHOD IN THE CLASSIC BOLTZMANN MACHINES

The classical Boltzmann machine is understood as a neural network proposed by Hinton and his colleagues in 1985. They added noise interferences to the Hopfield model and called this network a Boltzmann machine drawing an analogy between its behaviour and physical systems with the presence of interferences. This study explains the definition of “simulated annealing” and “thermal equilibrium” using the example of a partial network. A technique for calculating the probabilities of transition states at different temperatures using Markov chains is described, an example of the application of the SA - travelling salesman problem is given. Boltzmann machine is used for pattern recognition and in classification problems. As a disadvantage, a slow learning algorithm is mentioned, but it makes it possible to get out of local minima. The main purpose of this article is to show the capabilities of the simulated annealing algorithm in solving practical tasks.
DEVELOPING COMPUTER ENGINEERING AND INFORMATION TECHNOLOGY UNDERGRADUATES’ LEARNER AUTONOMY: INDIVIDUALISED INSTRUCTION IN ENGLISH CLASSROOM

The rapid development of information science and technology encourages lifelong learning and requires university graduates to be able to learn throughout their lives and have special abilities which help them search for continuous education and training opportunities. For students who associate their professional lives with computer sciences, learner autonomy is of immense importance since it helps them take full responsibility for success in professional life and career and contributes to the sustainable development of the current fast-moving digital society. As autonomous learners Computer Engineering and Information Technology undergraduates are able to define their goals clearly, to identify the most optimal learning strategies for achieving these goals and for acquiring new knowledge and skills necessary for their further professional development. The present study is aimed at finding out Computer Engineering and Information Technology undergraduates’ viewpoints on effectiveness of individualised instruction for developing their learner autonomy in English classroom. With this in mind, the team of researchers applied a questionnaire to a sample of Computer Engineering and Information Technology undergraduates who study at three higher education institutions of Ukraine. Data collection lasted for three months and took place in September-December 2022. The obtained results clearly demonstrate that individualised instruction delivered through online teaching tools and apps in English classroom enables university teachers to develop Computer Engineering and Information Technology Undergraduates’ learner autonomy effectively.