Reviewerly/exHarmony huggingface dataset:
Hand Gesture-Based AI System for Accessing Windows Applications

Actions speak more than words. In the context of the above statement, the importance of gestures and using them to control a system has become popular. The hand gesture recognition system for opening applications in Windows is a computer vision application that uses a webcam and machine learning models to recognize hand gestures and open specific applications. This system allows users to control their computers using simple hand gestures instead of relying on traditional input devices such as keyboards or mouse. The system uses the Tensor Flow and Media-Pipe libraries for training and detecting hand gestures, respectively. The trained machine learning model recognizes the gestures, and specific applications are opened based on the recognized gesture. The system has potential applications in accessibility, gaming, and virtual reality. In this work, we will discuss potential applications of the hand gesture recognition system for opening applications in Windows, and the second proposed system, which may also act as the control system of PowerPoint presentations, can be made easy and convenient using these gestures-based control. Thus, the proposed model improves the interaction between the human and the computer.
Real-time Sentiment Analysis on Social Networks using Meta-model and Machine Learning Techniques

Sentiment analysis is a critical task in social media analysis, enabling the understanding of user attitudes and opinions towards various topics. This paper proposes a real- time sentiment analysis system for social networks that utilizes a meta-model and machine learning techniques to accurately classify user sentiment. The proposed system integrates textual and visual data from social media posts to improve sentiment classification accuracy. The methodology includes data collection and preprocessing, feature extraction and selection, and the proposed meta-model for sentiment analysis. The system utilizes several machine learning techniques, including SVM, CNN, and LSTM networks. We evaluated the proposed system on a large-scale dataset and compared its performance with several state- of-the-art methods. The evaluation metrics, including accuracy, precision, recall, and F1-score, showed that our proposed system outperforms existing methods. The proposed system’s ability to handle multimodal data and achieve high accuracy in real- time makes it suitable for various applications, including social media monitoring and marketing analysis. The proposed system’s limitations provide opportunities for further research, such as developing more efficient algorithms and models that require less training data, and improving techniques for handling noisy and ambiguous data, such as sarcasm and irony. In conclusion, the proposed real-time sentiment analysis system using a meta-model and machine learning techniques provides a robust and efficient solution for sentiment analysis on social networks. The proposed system's performance and potential applications demonstrate its importance in the field of social media analysis.
An Adversarial Dual-Branch Network for Nonhomogeneous Dehazing in Tunnel Construction

The tunnel construction area poses significant challenges for the use of vision technology due to the presence of nonhomogeneous haze fields and low-contrast targets. However, existing dehazing algorithms display weak generalization, leading to dehazing failures, incomplete dehazing, or color distortion in this scenario. Therefore, an adversarial dual-branch convolutional neural network (ADN) is proposed in this paper to deal with the above challenges. The ADN utilizes two branches of the knowledge transfer sub-network and the multi-scale dense residual sub-network to process the hazy image and then aggregate the channels. This input is then passed through a discriminator to judge true and false, motivating the network to improve performance. Additionally, a tunnel haze field simulation dataset (Tunnel-HAZE) is established based on the characteristics of nonhomogeneous dust distribution and artificial light sources in the tunnel. Comparative experiments with existing advanced dehazing algorithms indicate an improvement in both PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity) by 4.07 dB and 0.032 dB, respectively. Furthermore, a binocular measurement experiment conducted in a simulated tunnel environment demonstrated a reduction in the relative error of measurement results by 50.5% when compared to the haze image. The results demonstrate the effectiveness and application potential of the proposed method in tunnel construction.
CLASSIFICATION OF ARTICLES USING MACHINE LEARNING: CASE STUDY OF TRA VINH UNIVERSITY JOURNAL OF SCIENCE, VIETNAM

The rapid development of technologies has led to an increasing number of research works submitted to journals or conferences. However, the process of submitting articles can be challenging for authors due to the wide range of subjects covered by submission systems, such as the Association for Computing Machinery, with 2,000 subjects. This challenge arises from the need to accurately categorize the manuscript into the appropriate subject area before submission. This article proposes an automatic solution that extracts information and categorizes scientific papers into relevant topics to address this issue. The proposed approach employs pre-processing, extraction, vectorization, and classification techniques using three machine learning methods: support vector machines, Naïve Bayes, and decision trees. The experiments conducted on a dataset of articles published in the Tra Vinh University Journal of Science show promising results. The support vector machines technique, in particular, achieved an accuracy rate of over 75%, demonstrating its potential as a tool for developing an automatic classification system for scientific papers.
DEVELOPING A SUPPLEMENTARY READING TEXT BASED ON KARO LOCAL CULTURE

Reading is one of the 4 critical language talents regarding the potential to interpret the words that are expressed. This research relates to the development of locally based teaching materials culture. According to data presented by UNESCO in 2020 the State of Indonesia ranks second lowest in terms of Literacy. During the Covid-19 pandemic, Indonesia experienced a learning loss. The situation experienced increasingly makes students have no interest in reading. To overcome this problem, the researcher took action in order to foster students' interest in reading by developing reading texts based on Folklore. Method in the researcher is Descriptive Qualitative, results and discussion in accordance with field conditions. This research focuses on one of the people from Karo, namely the story of Lake Lau Kawar. The researcher took data from SMP Smart School Swari Class VII SMP. The conclusion of this study is that culture-based learning materials are appropriate for use based on design experts and material experts.
A Blockchain-Based Intelligent Data Management Platform for Power Grid Applications

With the access of a large number of renewable energy sources in the power system, the increasingly complex power system operations bring considerable safety concerns for data management. While measurement data analytics in the power grid play an important role in different control and alarming applications, smart and reliable data management is required. Blockchain technology is a decentralized database based on cryptography and distributed storage, which is immutable and traceable. This paper analyzes the matching degree between the characteristics of blockchain technology and the demand for measurement data management, showing the advantages of blockchain for power grid data management. In this paper, a data intelligent management platform based on blockchain technology is designed. A standardized interface design of data service is elaborated, and the application scenarios of intelligent data are listed, providing new possibilities for developing advanced applications based on the measurement data.
METHODOLOGICAL APPROACH AND PRINCIPLES OF RESEARCH ACTIVITY DEVELOPMENT OF UNIVERSITY TEACHERS BASED ON ACTION RESEARCH

The article is devoted to the problem of developing the research activity of university teachers based on the integration of the Action Research approach into professional practice. The relevance of improving the quality of education by involving teachers in research activities with the involvement of internal resources of professional practice is substantiated. The methodological approaches underlying the development of research activity (activity, synergetic) are considered.An analysis of modern research is given, revealing modern technologies for the formation of research competencies of teachers. The possibilities of the Action Research method in the development of the research activity of teachers are revealed. The principles for the development of research activity (proactivity, self-reflection, creativity, self-development and self-realization, continuity, integration of all types of educational activities, actualization of learning resources, synergy of efforts of the subjects of the educational process) are formulated. Prospects for integrating Action Research into the educational practice of universities are shown.
Study of Financial Fraud in Guangzhou Longkey Based on Fraud Risk Factors

With the development of the economy and society, China's capital market has become more open and transparent. In recent years, listed companies have gone to great lengths to whitewash statements and convey false information in order to raise funds, improve performance and boost share prices, which has led to frequent instances of financial fraud such as Ruixing Coffee and Wanfushengke. Despite increased supervision by regulators, a series of punitive measures and regular training for executives and finance staff to improve their professional ethics, the huge benefits of financial fraud still drive practitioners to commit fraud on their own initiative. This paper analyses the causes of fraud at Guangzhou Longkey based on the more established fraud risk factor theory and proposes effective countermeasures to address the causes.
Moving from piecemeal to systematic: Reprioritizing how academic libraries approach research information management

This paper is based on a lightening talk given at the 2023 NISO Plus conference. The authors explore how research information management (RIM) workflows at universities are increasingly intersecting with library workflows, particularly regarding compliance with federal funding and preservation of the institution’s scholarly record. This paper outlines one academic library’s plan to shift from thinking about library-supported RIM services as a piecemeal approach to a more systematic approach that prioritizes open infrastructure. Though in its early stages of implementation, the authors seek to examine current RIM systems and services at their institution, expand on the current findings of U.S. activities brought forth in reports produced by OCLC and Lyrasis, and to identify local opportunities for library-supported RIM tools/services that are open and transparent. Specifically, this paper emphasizes the implementation and adoption of persistent identifiers, such as ORCID, but discussion will also cover other RIM systems and services.
A Hybrid Technique for Detecting Extremism in Arabic Social Media Texts

Today, social media sites like Twitter provide effective platforms to share opinions and thoughts in public with millions of other users. These opinions shared on such sites influence a large number of people who may easily retweet them and accelerate their spread. Unfortunately, some of these opinions were expressed by extremists who promoted hateful content. Since Arabic is one of the most spoken languages, it is crucial to automate the process of monitoring Arabic content published on social sites. Therefore, this study aims to propose a hybrid technique to detect extremism in Arabic social media texts and articles to monitor the situation of published extremist content. The proposed technique combines the lexicon-based approach with the rough set theory approach. The rough set theory is employed with two approximation strategies: lower approximation and accuracy approximation. The hybrid technique used the rough set theory as a classifier and the lexicon-based as a vector. Furthermore, this study built three types of corpuses (V1, V2, and V3) collected from Twitter. The experimental findings show that among the proposed hybrid methods, the accuracy approximation was superior to the lower approximation with seed vector. It was also revealed that hybrid methods outperformed machine learning techniques in terms of efficiency. Moreover, the study recommends using an accuracy approximation method with seed vector to identify the polarity of the text.
Contrastive Learning based Item Representation with Asymmetric Augmentation for Sequential Recommendation

Contrastive learning has been widely applied in sequential recommendation to improve the recommendation performance. Existing contrastive learning methods focus on adjusting the views number of positive and negative samples to enhance the item representation via data-level augmentation(e.g., MMInfoRec). However, they generally ignore the sampled items still follow a long-tail distribution, i.e., a few popular items have a high-frequency occurrence in contrastive learning, while the majority of unpopular items have a low-frequency in contrastive learning. As a result, this imbalance in sample extraction leads to insufficient contrastive learning between popular and unpopular items, resulting in sub-optimal item representation. In this paper, we propose CA4Rec, a Contrastive learning based item representation with Asymmetric augmentation for Sequential Recommendation, that adopts an asymmetric augmented multi-instance contrastive learning strategy to enhance the item representation. Specifically, we first use the popularity-aware method to divide the entire item set into popular and unpopular items, then we generate k augmented views for popular items, and k + n augmented views for unpopular items. Finally, we use our proposed asymmetric multi-instance noise contrastive estimation (AMINCE) to perform contrastive learning calculation. Make sure there is sufficient contrast between popular and unpopular items to mitigate the issue. we implement the improvements on the state-of-the-art model and conduct extensive experiments on three benchmark datasets. The experimental results demonstrate that CA4Rec outperforms the state-of-the-art baselines.
U-Net-Based Denoising Autoencoder Network for De-Speckling in Fetal Ultrasound Images

Nowadays medical imaging plays an important role in the detection of defects before proceeding with any medical procedures. During pregnancy, medical imaging plays a major role in the detection of any abnormalities/improper development of fetal organs during the gestational period with the help of ultrasound images. As ultrasound images are feeble toward noise interference, it makes difficult for proper intervention for the physician. In this paper, we modeled a U-Net-based denoising autoencoder network to reduce the speckle noise in fetal ultrasound images. To study the performance analysis of U-Net-based denoising autoencoder network, peak signal-to-noise ratio and structural similarity index measure parameters are used. The parameters are analyzed based on different dropout values, different epoch values, and noise levels (σ = 1, 2, 3, 5, 10). It is inferred from the analysis that PSNR = 28.48 dB, SSIM = 90.87%, accuracy = 98.40%, and loss = 0.54 from the proposed network. The proposed network model performs well than other de-speckling techniques.
Multi-label Classification of Mobile Application User Reviews Using Neural Language Models

Mobile application (App) reviews which are provided by users through different App stores are considered as a rich information source for developers to inform about bugs, new feature requests, performance issues, etc. These feedbacks help developers improve the quality of their apps which in turn will significantly impact the user experience and the App’s overall ratings. Popular Apps receive a high number of user reviews daily which makes their manual analysis a very tedious and time-consuming task. Automating the classification of user reviews will save developers time and help them better prioritize the issues that need to be handled. Since an App review is text data in which a user may report more than one issue, we propose a multi-label text classification model which uses neural language models. These models have shown high performance in various natural language processing problems. Experimental results confirm that neural language models outperform frequency-based methods in the context of App reviews classification. In fact, with RoBERTa, we could achieve a 0.87 average F1-score and a 0.16 hamming loss performances.
Deep Learning Model for Computer Vision: Sustainable Image Classification Using Machine Learning

The core challenge within computer vision is image classification, which involves assigning an image a label and categorizing it using the features that have been extracted from it. Even though this work appears rudimentary, it requires the application involving artificial intelligence, specifically Deep Learning, a subfield of Machine Learning. In this paper, we have discussed how Convolutional Neural Networks (CNN), the most modern technique for image classification, are used in Deep Learning to classify images. We have discussed the applications of image classification and drawn the conclusion that using deep learning is preferable to using machine learning. Additionally, we have also presented a comparative study of various CNN models to observe how they could potentially be employed as pre-trained models for the image classification job.
Towards a synthesis of language capability in humans and AI

Language is a major part of human intelligence and researchers have been focusing strongly on developing such competences of machines. Natural Language Processing (NLP) technologies are a key area of artificial intelligence (AI). This chapter develops a conceptual framework of language competence that allows for comparing human and machine competences. It then maps major available language benchmarks on the framework and discusses the performance of state-of-the-art AI systems on a range of tasks in two broad domains: language understanding and language generation. This exercise is a first step in building an index of AI language capability.
Identificação Caixa-Cinza de Modelo de Wiener-Hammerstein para Pedal de Distorção

The advancement of electronics allowed the development of several types of audio effects, mostly applied to musical instruments. One of these effects is distortion, which shaped the sound of electric guitar. Equipments that provided such effects became more robust and compact, combined with a greater diversity of tones. The digital electronics made possible equipments that have these effects, called multi-effect pedals, and unities that can model effects. One of the challenges in the development of these equipments is to truly replicate famous distortion tones. For that purpose, it is possible to model the desired effect through full knowledge of its electronic circuit or through modeling based on the identification of the system in question. This work proposes the digital modeling of the distortion pedal BOSS Blues Driver based on the identification of the used parametric model. The used model is based on a Wiener-Hammerstein and its parameters were optimized by a genetic algorithm. The results of the simulations were evaluated by time-error calculus and a blind hearing test with 30 people.
Evaluation of The Quality of Physical Assets Batu Mahpar Geopark Recreation Park in Tasikmalaya District

Batu Mahpar Geopark Recreation Park has tourist attractions which include rock expanses, five waterfalls, campsites, mini zoos, outbound rides, museums, interpretation boards, outdoor swimming pools, and photo spots. However, there are a number of indications of problems related to the quality of physical assets, including the difficulty of finding public transportation, car parks, information centers, visitor needs kiosks and souvenir kiosks, there are ten photo spots that are damaged, trash cans are not separated by type of waste, put together places for ablution between men and women, there are footpaths covered with wild plants, and the cell phone signal is unstable. Based on the indications of these problems, it is necessary to conduct research that aims to evaluate the quality of Batu Mahpar's physical assets including the availability and condition of assets which consist of four dimensions including tourist attractions, accessibility, supporting facilities, and infrastructure. This research uses descriptive methods with qualitative and quantitative approaches. Data collection techniques used include observation, interviews, and questionnaires. The results of the evaluation show that the dimensions of tourist attractions, supporting facilities and infrastructure are good quality, while the accessibility dimensions have sufficient quality. The average value of the quality of physical assets resulting from the four dimensions is 64.66%. This means that Batu Mahpar's physical assets are of sufficient quality.Keywords: Facilities, Infrastructure, Physical Assets, Quality Evaluation, Tourism
Optimization of Multilevel Spatial Partitioning Accelerating Structure of Hash Tables and Kd-tree

This research conducts an in-depth analysis of existing types of spatial partitioning acceleration structures. It investigates the properties and characteristics of the original combined spatial partitioning structure, based on hash tables and kd-trees. Three types of use cases for this structure are identified: dynamic, balanced, and static. Methods for optimizing the structure construction and cell search algorithms are proposed. For each of the three usage scenarios, algorithms for determining the size and placement levels of hash tables are introduced. An analysis of the optimal parameter values for these algorithms in each usage case is performed. Furthermore, a comparative efficiency analysis of the modified structure against the original combined structure and kd-tree is carried out. The comparison reveals that the resulting structure significantly enhances cell search efficiency for the considered types of structure usage.
Daily Life, Fear of COVID-19 and Social Support in the Older Adults in Home Isolation: A Cross-Sectional Study

The COVID-19 (infection caused by the SARS-CoV-2 virus) pandemic caused several changes in the everyday life of the older adults, altering the perception of psychosocial varia-bles. Objetive: To evaluate the daily life, fear of COVID-19, and family support of the older adults at home during the COVID-19 pandemic. Methodology: Cross-sectional and exploratory study conducted with older adults people in home social isolation in the municipality of Vitória da Conquista/BA. Data were collected by sociodemographic questionnaire, Fear of COVID-19 scale and Satisfaction with Social Support scale. The STROBE checklist was used to evaluate the study. Results: Of the 101 participants, 68.3% were female and 56.5% in the age group of 60 to 69 years; in relation to daily life before the pandemic 89.1% reported having an active life and 77.2% started to reside with the family during the pandemic. In social support, an overall above aver-age score of 56.04 (9.23) was obtained. Regarding fear, 10.9% reported feeling dizzy or fainting because of the news and 18.8% reported being paralyzed or without reactions when thinking or being exposed to information about the virus. Conclusions: Changes in the daily lives of the older adults were related to feeling less active and being confined to family members. Symp-toms due to fear of COVID-19 and satisfaction with social support were observed to a lesser ex-tent than expected.