Reviewerly/exHarmony huggingface dataset:
A 4D Convolutional Neural Networks for Video Violence Detection

As global crime has escalated, surveillance cameras have become widespread and will continue to proliferate. Due to the large amount of video, there must be systems that automatically look for suspicious activity and send out an online alert if they find it. This paper presents a deep learning architecture based on video-level four-dimensional convolution neural networks. The suggested architecture consists of residual blocks, which are combined with three-dimensional Convolutional Neural Networks (3D CNNs). The architecture aims to learn short-term and long-term representations of spatiotemporal from video, in addition to interactivity between clips. ResNet50 serves as the foundation for three-dimensional convolution networks and Dense optical flow in the region of concern. The proposed architecture is tested on the RWF2000 dataset with a test accuracy of 94.75. This research achieved higher results compared to other methods in the state of the art.
Interactive Development of Medical Robot Using Voice Chatbot Based on Deep Learning

Voice-based technology is one of the fields that is always being developed. This will be applied in the field of Health in the construction of medical robots to help hospital workers. This development became the idea to create a Voice Chatbot program. Natural language processing methods, Speech to Text, and Text Speech will be used to complete the functions of the Voice Chatbot. This Voice Chatbot was built to complement the Voice Interaction function of medical robots. The test results obtained the success rate in testing was 85.56%.
Research on calibration method for mirror attitude and tilt monitoring system

This paper investigates a method for measuring and calibrating the mirror attitude, tilt monitoring system, and compensation lens in a diffraction camera. Firstly, a measurement network is established by installing a reference prism and a theodolite on the load-bearing plate, enabling the overall measurement of the position of the mirror relative to the camera. Then, by changing the attitude of the mirror and recording the changes in the detector point coordinates, the relationship between the direction angle of the mirror and the detector point coordinates is calculated. Then, by adjusting the compensation lens in its local coordinate system, the relationship between the compensation lens and the detector point coordinates is obtained. Finally, by substituting the above matrix into the conversion matrix formula, the conversion matrix between the direction angle of the secondary mirror and the compensation lens translation is obtained, thereby completing the calibration of the attitude. This method can directly drive the compensation lens according to the change in the position of the mirror, ensuring the imaging quality of the camera.
The Concept of Anthropotechnics in the Social and Humanitarian Dimension

Purpose. This research defines the conceptual foundations of anthropotechnics as a science that studies modern processes of interaction between humans and technologies in the socio-humanitarian dimension. Theoretical basis. The authors use the method of anthropological analysis, which allows generalizing the approaches of anthropotechnics in the socio-cultural context in the "human-technology" system. Originality. Based on the results of the research, the understanding of the essence of anthropotechnics as a science that studies human interaction with technologies and technical systems has been clarified. The idea of implementing anthropotechnical approaches to the management of the educational process gained further development, in particular in the context of practical training of applicants for education, distance learning, and dual education forms. The concept of professional activity and professional self-realization of the individual is taken into account, and promising directions in the development of anthropotechnics in the context of the application of artificial intelligence are determined. Conclusions. Considering anthropotechnics in a philosophical and sociocultural dimension is an important approach to understanding the interaction of technologies, people, and society as a whole, where technologies affect human perception of the world and interaction with it. The range of approaches chosen by the authors, the disclosure of their principles and categories allows for considering the object of this research – anthropotechnics as a modern science – in a multifaceted and holistic way, to interpret the results of the research based on the use of the basic categories of approaches: a person, equipment, technology, activity, development, personality, system.
Philosophical and Anthropological Foundations of Psychosynthesis by Roberto Assaggioli

Purpose. The authors aim to reveal the influence of philosophical and esoteric principles on the formation and further development of Roberto Assagioli’s concept of psychosynthesis. The theoretical basis of the study is determined by the latest methodological approaches in the study of the relationship between philosophical, psychological, and esoteric approaches in the study of the unconscious and the formation of a harmonious personality. Originality. For the first time, a systematic analysis of the anthropological foundations of Roberto Assagioli’s work has been carried out in Ukrainian philosophical literature, the specificity of the connection between esoteric teachings and psychosynthesis has been revealed on the basis of a thorough study of primary sources and biographical evidence of the life and work of the famous Italian thinker. Conclusions. Psychosynthesis, which is one of the leading directions of modern psychology, has a rather multicultural philosophical and methodological basis. Its founder Roberto Assagioli, while developing his concept, was influenced by theosophical-esoteric, pragmatic kabbalistic, and Eastern religious concepts and practices. Unlike psychoanalysis, which saw the unconscious as physiological-naturalistic determinants of the human psyche and behavior, psychosynthesis considers the unconscious as a hidden potential for self-development of the individual. Roberto Assagioli went through a long and difficult way to finally form his own concept, which became the basis of the psycho-techniques of disidentification and work with subpersonalities, which help to discover the true transpersonal self, which is the source of human self-improvement. Modern discussions about the relationship between psychosynthesis and various forms of parapsychology once again testify to the synthetic nature of Assagioli’s teaching, which combines various psychotherapeutic practices. This is what gives psychosynthesis a new push for development in the conditions of wars, disasters, loneliness, and related psychological disorders.
Effects of System Parameters on the Measurement Accuracy of Optical Surgical Navigation System

The optical surgical navigation system, which plays a crucial role in ensuring the safety and success rate of medical treatment, is heavily dependent on measurement accuracy determined by system parameters. To investigate the measurement error distribution pattern of the optical surgical navigation system and to guide the installation of the optical surgical navigation system, this paper analyses the influence of system parameters on the measurement accuracy of the optical surgical navigation system. First, aiming at the spatial geometric model of the optical surgical navigation system, the measurement error model of the system is established by using the triangulation method. Then, the system parameters are divided into positional parameters and structural parameters. Based on the established measurement error model, the effects of system parameters such as baseline, focal length, the angle between the baseline and the optical axis of the camera, distance, horizontal view angle, and vertical view angle, on the measurement accuracy of the system are discussed, and the simulation is carried out. Finally, based on the simulation results, a guideline is given for the construction of the optical surgical navigation system.
A Probabilistic Solar Irradiance Interval-Valued Prediction Model with Multi-Objective Optimization of Reliability, Sharpness and Stability

Improved interval-valued prediction models for solar power and irradiance forecasting allow enhanced planning and operation of solar power systems. Highly uncertain atmospheric and environmental factors are major challenges of solar irradiance forecasting. Existing upper and lower bound estimation methods mainly focus on narrowing the prediction intervals and minimizing forecasting errors. However, the sensitivity of the interval-valued prediction model is not considered. Sensitivity is described as the model's output fluctuations due to unseen samples. Models with high sensitivity may not perform well in real-life applications under uncertain environments. This paper presents a novel interval-valued prediction model, P _RSS, by simultaneously optimizing the reliability, sharpness, and stability (RSS) for probabilistic solar irradiance interval-valued prediction. With sensitivity regularization, P _RSS has reduced sensitivity to unseen samples with perturbations from training samples and enhanced robustness. An Extreme learning machine (ELM) model is constructed to directly output prediction intervals (PIs) of solar irradiance via a multi-objective optimization of the RSS. An evaluation framework is proposed to verify the RSS performance. Moreover, a new comprehensive evaluation indicator is proposed to evaluate the PIs. Case studies on three American solar irradiance datasets show that P _RSS yields outstanding performance against state-of-the-art methods.
MDIASE-Autoencoder: A Novel Anomaly Detection Method for Increasing The Performance of Credit Card Fraud Detection Models

Machine learning has presented diverse solutions for identifying credit card fraud by analyzing datasets with anonymous transactions. However, an unexplored avenue lies in employing autoencoders for anomaly detection in credit card fraud identification. Our research objective is to apply and assess autoencoders to enhance credit card fraud detection through machine learning-based classification methods. The study utilizes the credit card fraud dataset available on Kaggle, applying various preprocessing techniques such as standardization and over-sampling using the Synthetic Minority Oversampling Technique (SMOTE). Subsequently, a 5-layer autoencoder is applied to the modified dataset. A novel metric for evaluating reconstruction errors, Mean Decrease in the Impurity of Absolute and Squared Error (MDIASE), is employed for feature selection in the new dataset, combining mean reduction in impurity (MDI), square error, and absolute error concepts. Finally, we assess the influence of the autoencoder on four classification methods: decision tree, KNN, logistic regression, and MLP. The evaluation metrics used for comparison are AUC maximization and geometric mean (g-mean). The test results demonstrate that the autoencoder enhances the performance of four state-of-the-art models in credit card fraud classification. Mainly, MLP exhibits the most significant improvement, with its AUC increasing from 0.558 without the autoencoder to 0.999 after its implementation.
Hyperparamaters Fine Tuning for Bidirectional Long Short Term Memory on Food Delivery

Food delivery is growing rapidly in Indonesia. Every food delivery order holds big promotions to attract users’ attention, so it has advantages and disadvantages. However, users only focus on evaluating drivers and restaurants, so the company does not get feedback on its services. This research aimed to understand user sentiment and maximize model accuracy with hyperparameters and fine-tuning. Sentiment analysis can be used to determine user sentiment based on reviews, and the results of this analysis can provide suggestions for companies. The bidirectional long short-term memory method was used for sentiment analysis to understand a word’s meaning better. The Bidirectional Short-Term Memory model andWord2Vec extraction features were proven to be better than several other extraction modelsand features. The dataset was balanced, and the hyperparameters in the model and optimization could also improve accuracy. So, the Gofood and Shopeefood research results had an accuracy of 98.1%, and Grabfood’s was 97.4%.
Mobile Application Security in the Health Care and Finance Sector with Static Analysis (Tools: MobSF)

This research focuses on assessing the security of applications in sensitive sectors like healthcare and finance, where user privacy data is at risk due to the increasing cyber threats in the digital age. The study employs the Mobile Security Framework (MobSF) tool for static analysis and uses the Common Vulnerability Scoring System (CVSS) to calculate vulnerability scores for the tested applications. The research identifies common vulnerabilities in storage, cryptography, network, platform, and resilience applications. The study will provide documented analyses and scores, enabling readers to compare application security in their respective sectors. Additionally, a prototype related to the research and scoring of these applications will be developed.
Navigating the Data Security Landscape: Challenges and Solutions in Financial Markets amid Digitalization and Artificial Intelligence

Financial markets have the risk of data mishandling or leakage of information. Regarding consumer, privacy breach and information mismanagement affects the overall trust in the system. With faster digitalization and online usage of information, a large section of economic markets require that consumers reveal their individual information constituting a part of regulatory requirements and privacy agreements. Financial systems gather information such as identity, payment passwords, shopping choices, and other similar details that can be shared online. Financial data is prone to misuse and leakage through big data pricing discrimination and over-marketing. Furthermore, the adoption of artificial intelligence/ Machine learning (AI/ML) creates new, distinct cyber risks and expands possibilities for cyber-attacks. AI/ML systems are susceptible to new threats along with the usual cyber threats posed by human error or software malfunctions. Encroachment of personal details and disruption of law by certain organizations make it difficult for individuals to assess the challenges or problems encountered within financial markets. Therefore, the lack of systematic arrangement of the financial system results in financial losses and psychological disruption presenting as mistrust of the government’s accountability, impairment of the social balance, and erratic behavior. The present study aims to assess: 1) The scope of data security in financial markets; 2) the Challenges encountered by financial markets in maintaining data security, 3) the Role of Artificial intelligence in data breaches and Current methods (Technical and non-technical) utilized for data security.
Devising an approach to the use of distance education technologies in performing control measures for technical students

The object of this study is the process of implementation of control measures with the help of distance learning technologies for students of technical specialties. The problem of the application of information and communication technologies for the effective implementation of final control during distance learning has been studied. An authentic algorithm for creating a test on the Moodle platform has been developed. The proposed algorithm is based on the use of the classical structure of the ticket, which allows implementing an approach to final control that is closest to the traditional one. This is important for assessing the acquired practical students competences of engineering specialties since for them the control measure may include a combination of textual, graphic, and computational tasks. The application of the algorithm in practice allows one to improve the quality of the final assessment by ensuring equal conditions for students, attracting the optimal set of tools for this, as well as identifying the student and thus ensuring the transparency of the control measure. Verification of the developed algorithm during the quarantine period sessions at the Faculty of Transport Construction of the National Transport University demonstrated a better adaptability of students to conduct control measures in remote form. This is confirmed by an increase in quality (from 1.16 % to 6.8 %) and success (to 7.4 %) rates among students, as well as a high level of student satisfaction in 2022 with the effectiveness of online learning, which was determined through an anonymous questionnaire. Promising steps regarding the use of the algorithm in the final evaluation are the development of measures for the implementation of automated recognition of the emotional status of students
Pengaruh Model Pembelajaran Role Playing terhadap Hasil Belajar Siswa pada Pembelajaran Tematik Kelas IV SD pada gugus XVII Kota Bengkulu

The study aims to determine the influence of Role Playing learning models on thematic learning of student learning outcomes. This research is quantitative study. The research method used is an experimental Quasi with design of The Matching Only Pretest Posttest Control Group Design. The population was grade IV elementary school students in the XVII group of the city of Bengkulu. The sample was class IVA SDN 04 Bengkulu and IVB SDN 11 Bengkulu with total students as much as 56 people. The sampling technique uses purposive sampling. The research instrument used a Multiple Choices given through the pretest, posttest and observation sheet. The data analysis technique this study is quantitative analysis using descriptive statistics and inferential statistics . The hypothesis test on this study uses test-t. Based on the results of this study obtained t count of 7.61 with t tabel at a significant level of 5% by 2.01 where t count is greater than t_tabel with the details of the average value in Indonesian subjects is 85.35, the subject of IPS is 86.15 and PPKN is 84.54. Thus it can be concluded that there is a significant effect of student learning outcomes on Thematic learning using role playing models.Keywords : Thematic, Role Playing learning models, Learning Outcomes
On the Importance and Necessity of Implementing Modern Educational Technology in Applied Curriculum of Higher Vocational Colleges

Since the outbreak of the novel coronavirus epidemic, the country has carried out a teaching reform, rapidly using a variety of modern educational technologies to achieve cross-space and cross-time teaching. The research on the application of modern educational technology in teaching has become a hot topic. Taking the course of Nutrition Science as an example, this paper summarizes the relevant teaching and research focuses at home and abroad. The main points are that the application research on the combination of modern educational technology and specific curriculum content is not deep enough in terms of the content that needs special attention in the development of education and teaching. Then it analyzes the disadvantages of traditional teaching and the superiority of modern educational technology applied in teaching, affirms the importance of modern educational technology, traditional teaching and modern educational technology should complement each other and integrate each other. Finally, some suggestions are put forward on the application of modern education technology in classroom teaching, focusing on the details of teaching work, paying attention to comparative analysis, practice, effectiveness, research refinement, the data of teaching effect, school-enterprise cooperation and curriculum digitization etc. Any new educational technology is only a teaching tool and auxiliary means, which cannot completely replace traditional teaching forms, let alone be implemented for the sake of implementation. The importance and necessity of modern educational technology are built on the basis of better serving students. Everything is "student-centered", and teachers can reasonably apply modern educational technology to teaching design and leading classroom implementation.
Iterative Network for Disparity Prediction with Infrared and Visible Light Images Based on Common Features

In recent years, the range of applications that utilize multiband imaging has significantly expanded. However, it is difficult to utilize multichannel heterogeneous images to achieve a spectral complementarity advantage and obtain accurate depth prediction based on traditional systems. In this study, we investigate CFNet, an iterative prediction network, for disparity prediction with infrared and visible light images based on common features. CFNet consists of several components, including a common feature extraction subnetwork, context subnetwork, multimodal information acquisition subnetwork, and a cascaded convolutional gated recurrent subnetwork. It leverages the advantages of dual-band (infrared and visible light) imaging, considering semantic information, geometric structure, and local matching details within images to predict the disparity between heterogeneous image pairs accurately. CFNet demonstrates superior performance in recognized evaluation metrics and visual image observations when compared with other publicly available networks, offering an effective technical approach for practical heterogeneous image disparity prediction.
Improving Reading Comprehension and Writing Achievements of the Eighth Grade Students Through Generating Interaction Between Schemata and Text (GIST) Strategy

The aim of this study was to investigate the effectiveness of (Generating Interaction between schemata and Text) GIST strategy in enhancing the students’ achievement in reading comprehension and writing. This study applied quasi-experimental method with nonequivalent pretest-posttest groups’ design. Forty two students were selected as the sample. They were divided equally into experimental and control groups. Reading and writing tests were used to collect the data. Paired and independent sample t-test, and linear regression were applied to analyze the data. The result showed that GIST strategy improves the students' achievement in most aspects of reading comprehension and writing. However, GIST strategy does not give the significant improvement to the students’ achievement in inference aspect of reading comprehension and developing ideas aspect of writing. It implies that English teacher should use the strategy more carefully to improve all the aspects of reading comprehension and writing, especially the inference aspect of reading and the developing ideas aspect of writing.
The Peculiarities of Improving the System of Comprehensive Acquisition of Professional and Technical Sciences Based on the Competence Approach

This scientific work provides information about the peculiarities of improving the system of comprehensive acquisition of professional and technical sciences based on the competence approach.
Research on Teaching Reform of Intelligent Transportation System based on Internet Learning

Network learning education teaching method is widely used in the new era, is a new method of teacher teaching, new technology, is a new idea of school teaching management, is an important teaching tool for schools to organize online learning. Network learning has the characteristics of high technology value, high integration, high sharing and high personalization. Network teaching has its unique advantages and promotes the new development direction of teaching. At the same time, because of its own shortcomings, network teaching can not fully replace traditional teaching. This paper aims at the problems existing in intelligent transportation courses, combined with the advantages of network learning, to explore the reform of teaching methods and means, so as to improve the efficiency and quality of education and teaching, and enhance students' interest in learning.