Reviewerly/exHarmony huggingface dataset:
Research on the Method of Artistic Image Restoration Based on Artificial Intelligence

Authors:
raw_author_name: Jian Wang
raw_affiliation_string: Changsha University of science and technology, School of Design Art, Changsha, 410114, China

Abstract: In order to improve the current inpainting algorithm for natural art images, an artistic image inpainting model is proposed by integrating artificial intelligence and embedding multi-scale attention expansion convolution. According to the uniqueness of artistic images, the network structure of DMFB is improved, and its repair model combines two plug-and-play optimization modules: extended convolution block and coordinate attention mechanism. The extended convolution module is used to capture multi-scale context information, and the coordinate attention mechanism is used to improve the remote migration ability of features of the repaired network. The combination of the two makes the repair results meet the visual visibility and semantic rationality. The experimental results show that ablation experiments are carried out to evaluate the effectiveness of each module in this model. In this paper, the hybrid extended convolution and coordinated attention mechanism are used to train the network, and the effectiveness of MADC is verified. When the expansion rate changes to 2, 4, 6 and 8, the method in this paper will lead to moderately distorted structure and fuzzy texture. On the contrary, there is no coordination attention. In the branch of mechanism, the output of this method shows texture defects and discontinuities. By using these two branches, the method in this paper has achieved good results in structure and texture. The quantitative evaluation of branches without coordinated attention mechanism is given when the expansion rate is fixed at 2, 4, 6 and 8. Conclusion: The results restored by this method are more consistent with the real image in human senses, and the objective evaluation of PSNR, SSIM and MSE has also been improved to some extent.
Research on Intelligent Assistant Teaching System for National Musical Instruments Based on PSS

Authors:
raw_author_name: Na Lu
raw_affiliation_string: Zhejiang University of Finance and Economics, Hangzhou, 310024, China

Abstract: In order to improve the efficiency and quality of the teaching service of the national musical instruments, a design method of product service system (PSS) for national musical instruments is proposed to build a tool for integrating teaching resources, autonomous practice feedback, and social interaction. The basic characteristics of the national musical instruments of the balance between the improvement and the inheritance of traditional music is summarized, the shortcomings of digital transformation of national musical instruments under the condition of smart technology are compared and pointed out, there is still room for development in precision teaching services. Based on the design method of PSS, the musical instrument products and teaching services are integrated into an overall solution, a model of teaching service and value co-creation is constructed. A service process is formed with the song as the basic teaching unit and gesture skill training as the key. The human-smart system collaboration mode is analyzed. The smart product subsystem and teaching service subsystem are established. The relationship among the stakeholders is discussed. The prototype scheme of the smart assistant teaching system of Pipa based on this method is built up to verify.
Research on Improving the Quality of Japanese Chinese Machine Translation Based on Deep Learning

Authors:
raw_author_name: Jialing Lu
raw_affiliation_string: Shenyang Aerospace University
raw_author_name: Fengxian Yin
raw_affiliation_string: Shenyang Aerospace University

Abstract: In order to improve the quality of Japanese-Chinese machine translation, the research on improving the quality of Japanese-Chinese machine translation based on deep learning is put forward. The maximum entropy model is used to effectively classify Japanese possible states and passive states, and then the features of Japanese possible states and passive states are effectively integrated into the log-linear model to improve the translation model, so as to improve the accuracy of selecting possible states and passive states in translation rules. The experimental results show that this method can effectively improve the translation quality of Japanese possible sentences and passive sentences. On large-scale Japanese Chinese materials, the maximum translation BLEU value can be increased from 41.50 to 42.01, and the overall intelligibility of translation results has been improved by 2.71% in manual evaluation. Conclusion: This method can improve the accuracy of rule selection.
Spillover Effect of Network Public Opinion on Market Prices of Small-Scale Agricultural Products

Authors:
raw_author_name: Jun Meng
raw_affiliation_string: College of Arts and Sciences, Northeast Agricultural University, Harbin 150030, China

Abstract: Network public opinion plays a crucial role in the behavior and decision making of various stakeholders, including farmers, middlemen, and consumers. It also affects the price fluctuations of small-scale agricultural products. Understanding the transmission path and spillover effect of network public opinion on the price fluctuations of these products is essential for ensuring their sustainable development and price stability. This paper selects the monthly data of network public opinion and related market prices of small-scale agricultural products from January 2014 to December 2021, constructs a network public opinion value through the sentiment classification results of deep learning models, and uses the trivariate VAR-BEKK-GARCH(1,1) model and spillover index model to study the spillover effect and spillover index of network public opinion on the market prices of small-scale agricultural products (national average price and origin price). The results show that: (1) There is a bidirectional volatility spillover effect between public opinion sentiment and the market prices of small-scale agricultural products. Additionally, this two-way volatility spillover effect is also evident between the average market prices and the origin prices of these commodities. (2) The influence of network public opinion on the market prices of small-scale agricultural products is substantial, with the spillover index being more pronounced for origin prices than for national average prices and reaching its zenith earlier. Consequently, based on these results, recommendations are provided to adapt planting and inventory strategies, enhance vigilance towards price risk transmission amongst small-scale agricultural product markets, and improve the comprehensive information platform encompassing the entire industry chain.
Success Factors in Management of IT Service Projects: Regression, Confirmatory Factor Analysis, and Structural Equation Models

Authors:
raw_author_name: Rafał Michalski
raw_affiliation_string: Faculty of Management, Wrocław University of Science and Technology, 50-370 Wrocław, Poland

Abstract: Although there have been some studies on the success factors for IT software projects, there is still a lack of coherent research on the success factors for IT service projects. Therefore, this study aimed to identify and understand the factors and their relationships that contribute to the success of IT service projects. For this purpose, multivariate regressions and structural equation models (SEMs) were developed and analyzed. The regression models included six project management success criteria used as dependent variables (quality of the delivered product, scope realization and requirements, timeliness of delivery, delivery within budget, customer satisfaction, and provider satisfaction) and four independent variables (agile techniques and change management, organization and people, stakeholders and risk analysis, work environment), which had been identified through exploratory factor analysis. The results showed that not all success factors were relevant to all success criteria, and there were differences in their importance. An additional series of exploratory and confirmatory factor analyses along with appropriate statistical measures were employed to evaluate the quality of these four factors. The SEM approach was based on five latent constructs with a total of twenty components. The study suggests that investing in improving people’s knowledge and skills, using agile methodologies, creating a supportive work environment, and involving stakeholders in regular risk analysis are important for project management success. The results also suggest that the success factors for IT service projects depend on both traditional and agile approaches. The study extensively compared its findings with similar research and discussed common issues and differences in both the model structures and methodologies applied. The investigation utilized mathematical methods and techniques that are not commonly applied in the field of project management success modeling. The comprehensive methodology that was applied may be helpful to other researchers who are interested in this topic.
ESARS: A Situation-Aware Multi-Agent System for Real-Time Emergency Response Management

Authors:
raw_author_name: Dumnamene Jolley Sunday Sako
raw_affiliation_string: Rivers State University, Nigeria
raw_author_name: Chima Godknows Igiri
raw_affiliation_string: Rivers State University, Nigeria

Abstract: This paper describes ESARS, a real-time situation-aware social media- enabled emergency situation alert and reporting system, as a decision support system built on multi-agent software design architecture for emergency situation management. The impact of an incident or disruption due to the incident could be minimized by implementing real-time intervention strategies that involve event monitoring, detection and situation identification via classification and prediction, notification, visualization and reporting that culminate in providing emergency support within time. The nature of agent behavior, which is autonomous, proactive and cooperative, makes them a suitable method for the design and deployment of a dynamic system of this nature. The system relies on historical and streamed real-time geolocation-enabled Twitter data stream for the target emergency events to provide decision-makers with dynamic, comprehensive, and timely information specific to the emergency situation.
A Syntactic Proof of the Decidability of First-Order Monadic Logic

Authors:
raw_author_name: Eugenio Orlandelli
raw_affiliation_string: University of Bologna, Department of the Arts
raw_author_name: Matteo Tesi
raw_affiliation_string: Vienna University of Technology

Abstract: Decidability of monadic first-order classical logic was established by Löwenheim in 1915. The proof made use of a semantic argument, but a purely syntactic proof has never been provided. In the present paper we introduce a syntactic proof of decidability of monadic first-order logic in innex normal form which exploits G3-style sequent calculi. In particular, we introduce a cut- and contraction-free calculus having a (complexity optimal) terminating proof-search procedure. We also show that this logic can be faithfully embedded in the modal logic T.
Ant Colony Optimization for Efficient Distance and Time Optimization in Swarm Drone Formation

Authors:
raw_author_name: Ronny Mardiyanto
raw_affiliation_string: Institut Teknologi Sepuluh Nopember
raw_author_name: Andri Suhartono
raw_affiliation_string: Institut Sains dan Teknologi Terpadu Surabaya
raw_author_name: Heri Suryoatmojo
raw_affiliation_string: Institut Teknologi Sepuluh Nopember

Abstract: One of the challenges in swarm drone formation is achieving fast and effective formation with optimal distances. In this paper, we propose a swarm drone formation approach utilizing Ant Colony Optimization (ACO) for achieving it. We conducted simulations involving the formation of three or more drones, aiming to identify the best formation based on distance, acceleration, and time criteria. Simulation results demonstrate that formation time is significantly reduced when employing ACO optimization compared to non-optimized methods. Additionally, the optimized formations exhibit shorter inter-drone distances compared to non-optimized formations. By implementing this approach, swarm drone formations can be rapidly established with minimized distances, resulting in substantial battery savings. The simulation encompassed various patterns formed by 3, 5, 10, 15, 20, and 25 drones. The findings indicate that the approach can reduce formation time by varying degrees, ranging from 12% to 51%, across 66% of the conducted experiments, notably for patterns created with a substantial drone count. The degree of diversity observed among the proposed solutions reached 60%, with minimal variances of less than 1% for each.
Analyzing Facebook Mobile Usage: Efficacy and ESL Learners’ Writing Proficiency

Authors:
raw_author_name: Sohaib Alam
raw_affiliation_string: Prince Sattam Bin Abdulaziz University, Alkharj, Kingdom of Saudi Arabia

Abstract: Facebook has leveraged rapid technological and societal changes over the past decade and has emerged as the largest social networking platform. However, research on Facebook has been limited, particularly when examining its potential to improve English as a second language (ESL) writing in comparison to a control group. The study aims to explore the morphological, syntactic, and orthographical aspects of ESL writing through an experimental group [N = 30] using Facebook on mobile and a control group [N = 30] with a traditional teaching approach. The experimental and control groups undergo observation through twice-weekly, in-class-focused free writing sessions for 10 weeks. The progress of the learners’ writing was tracked using pre- and post-tests to assess its impact. The analysis of variance (ANOVA) test revealed that the experimental group made more significant advancements in writing by reducing morphological, syntactic, and orthographical errors compared to the control group. These results validate previous studies that support the use of Facebook on mobile devices in ESL courses and emphasize the need for further research comparing Facebook with other writing platforms.
Convolutional Neural Network Architectures for Gender, Emotional Detection from Speech and Speaker Diarization

Authors:
raw_author_name: Thaer Mufeed Taha
raw_affiliation_string: ATISP research unit,  École Nationale d’Électronique et de Télécommunications de Sfax (ENET'Com) University Sfax – Tunisia
raw_author_name: Mondher Frikha
raw_affiliation_string: Advanced Technologies of Image and Signal Processing’ research lab, Higher Institute of Information and Communication Technologies, Carthage University, Tunisia

Abstract: This paper introduces three system architectures for speaker identification that aim to overcome the limitations of diarization and voice-based biometric systems. Diarization systems utilize unsupervised algorithms to segment audio data based on the time boundaries of utterances, but they do not distinguish individual speakers. On the other hand, voice-based biometric systems can only identify individuals in recordings with a single speaker. Identifying speakers in recordings of natural conversations can be challenging, especially when emotional shifts can alter voice characteristics, making gender identification difficult. To address this issue, the proposed architectures include techniques for gender, emotion, and diarization at either the segment or group level. The evaluation of these architectures utilized two speech databases, namely VoxCeleb and RAVDESS (Ryerson audio-visual database of emotional speech and song) datasets. The findings reveal that the proposed approach outperforms the strategy level in terms of recognition results, despite the real-time processing advantage of the latter. The challenge of identifying multiple speakers engaging in a conversation while considering emotional changes that impact speech is effectively addressed by the proposed architectures. The data indicates that the gender and emotion classification of diarization achieves an accuracy of over 98 percent. These results suggest that the proposed speech-based approach can achieve highly accurate speaker identification.
Decentralised Storage Network by Using Block-Chain

Authors:
raw_author_name: Dattatray G. Takale
raw_affiliation_string: Assistant Professor, Department of Computer Engineering, Vishwakarma Institute of Information Technology,  Pune, Maharashtra, India

Abstract: This paper explores the intersection of network security and blockchain technology in the context of today's digital era. It begins by acknowledging the vulnerabilities and threats contemporary networks face, including data breaches and denial-of-service attacks. The paper then delves into the foundational principles of blockchain, highlighting its decentralization, consensus mechanisms, cryptographic encryption, and immutability as key attributes that can enhance network security The central idea presented is the potential of blockchain to revolutionize network security. It discusses how blockchain can be integrated into network architecture to achieve secure and transparent identity management, combat identity fraud, automate network processes through smart contracts, and enhance real-time threat detection and response. However, the paper also recognizes challenges such as scalability, interoperability, and energy efficiency when implementing blockchain in secure networks and suggests ongoing research efforts and potential solutions to make this integration more practical.
Power System Uncertainty Modeling Based on Gaussian Process

Authors:
raw_author_name: Tianze Zhang
raw_affiliation_string: School of Computer Science and Technology, Xinjiang University, Urumqi, China

Abstract: In order to solve the problem of low accuracy of parameter estimation in expectation maximization algorithm, a modeling method based on Gaussian component number reduction is proposed. Taking the nonparametric kernel density estimation results as the base Gaussian mixture model, the Gaussian mixture model with any number of Gaussian components can be established by reducing the number of Gaussian components by using the density-preserving hierarchical expectation maximization algorithm, which overcomes the problem that the expectation maximization algorithm has low parameter estimation accuracy when there are many Gaussian components. In order to reduce the burden of modeling calculation under large samples, a hierarchical modeling method based on time scale is proposed. In order to solve the problem of Gaussian component number combination explosion of independent random variables, a hierarchical modeling method of “combination-reduction” is proposed. The proposed method is tested by using measured multidimensional wind speed data and load data with complex distribution characteristics. The experimental results show that the Pearson and Spearman correlation coefficients of GMM constructed based on this method are very close to the sample data. The absolute value of Pearson correlation coefficient error is 0.03739, and the root mean square value of error is 0.02388. The absolute value of Spearman correlation coefficient error is 0.11693, and the root mean square error is 0.05797. Conclusion: The accuracy of the proposed method is significantly better than that of Gaussian mixture model and Copula function method based on expectation maximization algorithm.
THE IMPACT OF ARTIFICIAL INTELLIGENCE IN DECISION MAKING: A COMPREHENSIVE REVIEW

Authors:
raw_author_name: None Muhammad Eid BALBAA
raw_author_name: None Marina Sagatovna ABDURASHIDOVA

Abstract: Artificial Intelligence (AI) has emerged as a transformative technology with the potential to revolutionize decision making across various domains. This research article explores the impact of AI in decision making and its implications for individuals, organizations, and society. The article begins by providing an overview of AI and its key components, such as machine learning and natural language processing. It then discusses the role of AI in enhancing decision-making processes by automating tasks, augmenting human capabilities, and providing data-driven insights. The article highlights the benefits of AI in improving decision accuracy, efficiency, and scalability, while also acknowledging the challenges and risks associated with its implementation. These challenges include ethical considerations, biases in AI algorithms, and potential job displacement. The article further explores the importance of transparency, accountability, and interpretability in AI decision-making systems. Additionally, it discusses the role of human-AI collaboration and the need for interdisciplinary approaches to ensure the responsible and ethical deployment of AI in decision making. Drawing on case studies and empirical research, the article provides concrete examples of how AI is transforming decision making in various fields, such as finance, healthcare, and transportation. Finally, the article concludes by discussing future directions and recommendations for policymakers, organizations, and individuals to harness the full potential of AI in decision making while addressing its ethical, social, and economic implications. KEYWORDS: Artificial Intelligence, Decision Making, Machine Learning, Automation, Augmentation, Ethical Considerations, Bias, Transparency, Human-AI Collaboration, Responsible AI.
Yoga Guide: Yoga Pose Estimation Using Machine Learning

Authors:
raw_author_name: Aniket Jagtap

Abstract: Abstract: A deep learning model is proposed which uses convolutional neural networks/LR algorithm for yoga pose identification along with a human joints localization model followed by a process for identification of errors in the pose for developing the system. After obtaining all the information about the pose of the user the system gives feedback to improve or correct the posture of the user. we propose an improved algorithm to calculate scores that can be applied to all poses. Our application is evaluated on different Yoga poses under different scenes, and its robustness is guaranteed.
Pengaruh Sistem Informasi Akuntansi Manajemen terhadap Kinerja Manajerial di Moderasi oleh Ketidakpastian Lingkungan pada PT Perkebunan Nusantara IV (Persero) Medan

Authors:
raw_author_name: None Irfan
raw_affiliation_string: Universitas Muhammadiyah Sumatra Utara

Abstract: The aim of this research is to test and analyze the management accounting information system for managerial performance moderated by environmental uncertainty at PT Perkebunan Nusantara IV (Persero) Medan directly and indirectly. The approach used in this research is a causal approach. The population in this study was all employees of PT Perkebunan Nusantara IV (Persero) Medan. The sample in this study used the Slovin formula so that there were 80 employees of PT Perkebunan Nusantara IV (Persero) Medan. Data collection techniques in this research used documentation, observation and questionnaire techniques. The data analysis technique in this research uses a quantitative approach using statistical analysis using Auter Model Analysis, Inner Model Analysis and Hypothesis Testing. Data processing in this research uses the PLS (Partial Least Square) software program. The results of this research prove that the accounting information system directly influences managerial performance. Indirectly, environmental uncertainty cannot moderate the influence of management accounting information systems on managerial performance
Trustworthy Satellite Earth Observations for Science and Society

Authors:
raw_author_name: Fabrizio Niro
raw_author_name: Jaime Nickeson

Abstract: Enhancing confidence in satellite observations of terrestrial properties like land surface temperature and soil moisture requires advances in validation and data quality assessment practices.