Reviewerly/exHarmony huggingface dataset:
The Decision-Support Modeling with Fuzzy Analytic Hierarchy Process (AHP) to Determine the Career Path for Bachelor Informatics Students

Authors:
raw_author_name: Ezra Karuna Wijaya
raw_affiliation_string: Department of Computer Science, Bina Nusantara University, Jakarta,
raw_author_name: Ford Lumban Gaol
raw_affiliation_string: Department of Computer Science, Bina Nusantara University, Jakarta,
raw_author_name: Tokuro Matsuo
raw_affiliation_string: Advanced Institute of Industrial Technology (AIIT), Tokyo,

Abstract: The pursuit of success in one's chosen profession is a universal aspiration that requires individuals to engage in competition, not just domestically but also internationally, particularly in today's era of globalization. The rapid advancement of technology presents both opportunities and challenges, necessitating proactive management to achieve professional success. In the field of Informatics Engineering, there is a wide range of professional pathways, each offering unique opportunities and requiring distinct skill sets. For university graduates, it is crucial to have a comprehensive understanding of the specific requirements and demands associated with various job paths to make informed decisions. This knowledge enables them to select a professional route that aligns with their individual aspirations and goals, thereby avoiding potential discontent in their chosen career. Lack of knowledge or awareness during the decision-making process can negatively impact productivity and overall performance. There is a growing demand among university graduates, especially at the undergraduate level, for career-related information. This has led to heightened competition among graduates in terms of skills, knowledge, and availability. To effectively navigate this competitive landscape and engage in meaningful competition with fellow graduates, individuals must possess a comprehensive understanding of their desired career trajectory. To address these challenges, the Decision Support Model (MPK) can be utilized, employing the Fuzzy Analytical Hierarchy Process methodology. This approach considers four primary criteria—Financial Compensation, Non-Financial Compensation, Soft Skills, and Hard Skills—each with several sub-criteria. These criteria are evaluated based on the perspectives of certified graduates of Informatics Engineering and industry specialists. The study successfully identified the primary criteria influencing career decisions, such as job conditions, incentives, and prospects. It also highlighted the most favorable career path, with the role of a Data Analyst being identified as particularly promising. This career path involves roles within the field of Informatics Engineering that focus on processing data according to specific requirements, highlighting the importance of understanding the evolving landscape of technology and its impact on professional opportunities. Doi: 10.28991/ESJ-2024-08-01-011 Full Text: PDF
An Adaptive Harmony Search Part-of-Speech tagger for Square Hmong Corpus

Authors:
raw_author_name: Di-Wen Kang
raw_author_name: Shao-Qiang Ye
raw_author_name: Sharifah Zarith Rahmah Syed Ahmad
raw_author_name: Li-Ping Mo
raw_author_name: Feng Qin
raw_author_name: Pan Zhou

Abstract: Data-driven models perform poorly on part-of-speech tagging problems with the square Hmong language, a low-resource corpus. This paper designs a weight evaluation function to reduce the influence of unknown words. It proposes an improved harmony search algorithm utilizing the roulette and local evaluation strategies for handling the square Hmong part-of-speech tagging problem. The experiment shows that the average accuracy of the proposed model is 6%, 8% more than HMM and BiLSTM-CRF models, respectively. Meanwhile, the average F1 of the proposed model is also 6%, 3% more than HMM and BiLSTM-CRF models, respectively.
N-N-E-Trending Tectonic Systems

Authors:
raw_author_name: Yue Zhao
raw_affiliation_string: Institute of Geomechanics, Chinese Academy of Geological Sciences, Beijing, China
raw_author_name: Zhihu Ling
raw_affiliation_string: Institute of Geomechanics, Chinese Academy of Geological Sciences, Beijing, China
raw_author_name: Huijun Li
raw_affiliation_string: Institute of Geomechanics, Chinese Academy of Geological Sciences, Beijing, China

Abstract: Introduction to Global Tectonic Systems, pp. 117-145 (2024) No AccessChapter 5: N-N-E-Trending Tectonic SystemsYuzhu Kang, Shuwen Xing, Zhihong Kang, Yue Zhao, Zhihu Ling, Zhijiang Kang, and Huijun LiYuzhu KangSinopec Petroleum Exploration and Production Research Institute, China Petrochemical Corporation, Beijing, China, Shuwen XingInstitute of Geomechanics, Chinese Academy of Geological Sciences, Beijing, China, Zhihong KangChina University of Geosciences (Beijing), Beijing, China, Yue ZhaoInstitute of Geomechanics, Chinese Academy of Geological Sciences, Beijing, China, Zhihu LingInstitute of Geomechanics, Chinese Academy of Geological Sciences, Beijing, China, Zhijiang KangSinopec Petroleum Exploration and Production Research Institute, Beijing, China, and Huijun LiInstitute of Geomechanics, Chinese Academy of Geological Sciences, Beijing, Chinahttps://doi.org/10.1142/9789811285561_0005Cited by:0 (Source: Crossref) PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Abstract: In this chapter, N-N-E-trending tectonic systems are introduced, including those in China, New Zealand–Tonga, the Eastern United States and the east coast of South America. Keywords: Tectonic systemN-N-E-trendingType FiguresReferencesRelatedDetails Recommended Introduction to Global Tectonic SystemsMetrics History KeywordsTectonic systemN-N-E-trendingTypePDF download
Multi-genre Digital Music Based on Artificial Intelligence Automation Assisted Composition System

Authors:
raw_author_name: Anna Liu
raw_affiliation_string: Dance School Affiliated to Shanghai Theatre Academy (Shanghai Dance School); Shanghai, 200336, China

Abstract: In the present paper, we conduct an in-depth study on multi-styled digital music and design an artificial intelligence-based multi-styled digital theme automation-assisted composition system. In the process of system development, B/S software design architecture is selected, J2EE ecosystem-related technology is used, and the system framework is built following the principle of sub-module development. A web hosting platform is provided for the whole Internet publishing system to realise the online and real-time functions. In addition, to improve the robustness, scalability, security, and ultimately, the system's quality of service, the functional design analysis, software architecture system, development technology, and optimisation strategy are carefully studied and discussed. After learning and analysing the file format of MIDI digital music, we chose the feature extraction method for each instrument to preserve the instrument's characteristics. We used its parameter model for each device during the composition to ensure the instrument's characteristics. After optimisation of the model, composition experiments were conducted, and the compositional effect was measured; the average ratio of adjacent notes with intervals within one octave was 83.57% for the composed pieces. The composition system eliminates using only a single algorithmic composition technique method in the system platform. Instead, it adopts the direction of a hybrid system that integrates multiple processes, an inevitable trend. The composition system provides flexible human-computer interaction at all music composition levels to improve the system's usefulness and effectiveness.
Enhancing Disease Prediction with a Hybrid CNN-LSTM Framework in EHRs

Authors:
raw_author_name: Jingxiao Tian
raw_affiliation_string: Electrical and Computer Engineering, San Diego State University, San Diego, US
raw_author_name: Ao Xiang
raw_affiliation_string: Digital Media Technology, University of Electronic Science and Technology of China, Chengdu, China
raw_author_name: Yuan Feng
raw_affiliation_string: Interdisciplinary Data Science, Duke University, Durham, US
raw_author_name: Qin Yang
raw_affiliation_string: Microelectronics Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

Abstract: This study developed a novel hybrid deep learning framework aimed at enhancing the accuracy of disease prediction using temporal data from Electronic Health Records (EHRs). The framework integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, leveraging the strength of CNNs in extracting hierarchical feature representations from complex data and the capability of LSTMs in capturing long-term dependencies in temporal information. An empirical investigation on real-world EHR datasets revealed that, compared to Support Vector Machine (SVM) models, standalone CNNs, and LSTMs, this hybrid deep learning network demonstrated significantly higher prediction accuracy in disease prediction tasks. This research not only advances the performance of predictive models in the health data analytics domain but also underscores the importance of adopting and further developing advanced deep learning technologies to address the complexity of modern medical data. Our findings advocate for a shift towards integrating complex neural network architectures in developing predictive models, potentially offering avenues for more personalized and proactive disease management and care, thereby setting new standards for future health management practices.
Enhancing Credit Card Fraud Detection: A Neural Network and SMOTE Integrated Approach

Authors:
raw_author_name: Mengran Zhu
raw_affiliation_string: Miami University, Computer Engineering, Oxford, OH, USA
raw_author_name: Yulu Gong
raw_affiliation_string: Northern Arizona University, Computer & Information Technology, Flagstaff, AZ, USA
raw_author_name: Changxin Xu
raw_affiliation_string: Northern Arizona University, Independent researcher, Flagstaff, AZ, USA
raw_author_name: Yafei Xiang
raw_affiliation_string: Northeastern University, Computer Science, Boston, MA, USA

Abstract: Credit card fraud detection is a critical challenge in the financial sector, demanding sophisticated approaches to accurately identify fraudulent transactions. This research proposes an innovative methodology combining Neural Networks (NN) and Synthetic Minority Over-sampling Technique (SMOTE) to enhance the detection performance. The study addresses the inherent imbalance in credit card transaction data, focusing on technical advancements for robust and precise fraud detection. Results demonstrate that the integration of NN and SMOTE exhibits superior precision, recall, and F1-score compared to traditional models, highlighting its potential as an advanced solution for handling imbalanced datasets in credit card fraud detection scenarios. This research contributes to the ongoing efforts to develop effective and efficient mechanisms for safeguarding financial transactions from fraudulent activities.
Deployment of optoelectronic devices based on improved NSGA-II

Authors:
raw_author_name: Hua Gong
raw_affiliation_string: Liaoning Provincial Key Laboratory of Intelligent Optimization and Control of Ordnance Industry (China); Shenyang Ligong University (China)
raw_author_name: Ke Xu
raw_affiliation_string: Liaoning Provincial Key Laboratory of Intelligent Optimization and Control of Ordnance Industry (China); Shenyang Ligong University (China)
raw_author_name: Wenjuan Sun
raw_affiliation_string: Liaoning Provincial Key Laboratory of Intelligent Optimization and Control of Ordnance Industry (China); Shenyang Ligong University (China)

Abstract: In order to improve the security of the defended targets in our region and efficiently utilize various types of optoelectronic devices, the research is conducted on optoelectronic devices deployment within the region. With the objectives of maximizing the protection effectiveness and minimizing devices operational cost, a multi-objectives optimization model is established by considering constraints such as devices-target visibility conditions and device types, quantities, etc. Non-dominated sorting genetic algorithmⅡ (NSGA-Ⅱ) improved by Q-learning is designed to solve the model. To address the difficulty of fixed parameter settings adapting to dynamic changes, Q-learning is adopted to adaptively adjust the crossover probability and variation probability. In order to search for Pareto front solutions that are close to the global optimal more efficiently. The correctness of the model and the effectiveness of the algorithm are verified through simulation examples.
Fuzzy-PID-based trajectory tracking for 3WIS robot

Authors:
raw_author_name: Chaobin Xu
raw_affiliation_string: Hubei University of Technology (China)

Abstract: This paper presents a trajectory tracking approach for a three-wheel independent steering (3WIS) robot using a fuzzy PID controller. The kinematic model of the 3WIS robot is introduced, highlighting its capability for omnidirectional motion control. A comparison is made between traditional PID controllers and fuzzy PID controllers, emphasizing the advantages of the latter in handling nonlinear systems. The concept of fuzzy PID control is explained, and fuzzy rules for parameter adjustment are proposed. The accuracy of the kinematic model and the effectiveness of the fuzzy PID controller are verified through simulation using the Webots platform and real-world experiment. Results indicate successful trajectory tracking, although slight fluctuations in the heading angle are observed due to frictional torque and gear backlash. The findings validate the accuracy of the kinematic model and the effectiveness of fuzzy PID controller in trajectory tracking.
Supplemental Material: The Zaozigou orogenic gold-antimony deposit, West Qinling Orogen, China: Structural controls on multiple mineralization events

Authors:
raw_author_name: Kun-Feng Qiu
raw_author_name: et al.

Abstract: &lt;p&gt;Table S1: Detailed operating parameters and conditions for dolomite U-Pb dating. Table S2: LA-ICP-MS U-Pb isotope data for dolomite. &lt;/p&gt;
An all-in-one lightweight image restoration network based on polarized attention mechanism and efficient feature extraction

Authors:
raw_author_name: Chunhua Yuan
raw_affiliation_string: Shenyang Ligong University (China)

Abstract: Removing noise from images is a challenging task in front-end computer vision applications, especially in snowy, rainy, foggy, and underwater environments, which pose significant difficulties for various visual tasks. Existing image restoration methods often struggle to achieve robust generalization and are burdened with immense computational complexity. This paper introduces a lightweight image restoration network based on polarized attention mechanisms and efficient feature extraction, capable of addressing tasks like rain removal, snow removal, fog removal, and underwater image enhancement. Initially, a polarized self-attention mechanism is proposed to intricately learn weather noise, followed by an efficient noise removal module complementing the initial network. This addition compensates for any noise information overlooked by the polarized self-attention mechanism. Furthermore, residual connections are integrated into each module of the network, along with grouped convolutions, to prevent network degradation and reduce computational load. In comparison to existing models, this network not only learns noise with similar shapes but also adapts to noise with significant shape variations. Additionally, the introduced residual structure ensures the network's stability, avoiding performance degradation before achieving expected results. The collaborative use of the efficient noise removal module and polarized self-attention mechanism sets a high standard for noise learning in this network. Experimental results demonstrate the efficacy of this algorithm on publicly available datasets including Snow100K, CSD, Rain200H, Rain200L, Rain800, RSID, and EUVP. The proposed method efficiently removes various image noises, effectively achieving the desired image restoration objectives.
ADVANTAGES AND DRAWBACKS OF TWO-STEP APPROACH TO PROVIDING DESIRED PARAMETERS IN LOSSY IMAGE COMPRESSION

Authors:
raw_author_name: Fangfang Li
raw_affiliation_string: Nanchang Hang-kong University, Nanchang
raw_author_name: Sergey Abramov
raw_affiliation_string: National Aerospace University, Kharkiv
raw_author_name: Vladimir Lukin
raw_affiliation_string: National Aerospace University, Kharkiv

Abstract: The object of the study is the process of lossy image compression. The subject of the study is the two-step approach to providing desired parameters (quality and compression ratio) for different coders. The goals of the study are to review advantages of the two-step approach to lossy compression, to analyze the reasons of drawbacks, and to put forward possible ways to get around these shortcomings. Methods used: linear approximation, numerical simulation, statistical analysis. Results obtained: 1) the considered approach main advantage is that, in most applications, it provides substantial improvement of accuracy of providing a desired value of a controlled compression parameter after the second step compared to the first step; 2) the approach is quite universal and can be applied for different coders and different parameters of lossy compression to be provided; 3) the main problems and limitations happen due to the use of linear approximation and essential difference in behavior of rate/distortion curves for images of different complexity; 4) there are ways to avoid the approach drawbacks that employ adaptation to image complexity and/or use certain restrictions at the second step. Conclusions: based on the results of the study, it is worth 1) considering more complex approximations of rate-distortion curves; 2) paying more attention to adequate and fast algorithms of characterizing image complexity before compression; 3) using quality metrics that have quasi-linear rate/distortion curves for a given coder.
Horizontal Learning Approach to Discover Association Rules

Authors:
raw_author_name: Arthur Yosef
raw_affiliation_string: Department of Information Systems, Tel Aviv-Yaffo Academic College, Tel Aviv-Yafo 6818211, Israel
raw_author_name: Amos Baranes
raw_affiliation_string: Department of Information Systems, Tel Aviv-Yaffo Academic College, Tel Aviv-Yafo 6818211, Israel
raw_author_name: Moti Schneider
raw_affiliation_string: Department of Computer Science, Netanya Academic College, Netanya 4223587, Israel

Abstract: Association rule learning is a machine learning approach aiming to find substantial relations among attributes within one or more datasets. We address the main problem of this technology, which is the excessive computation time and the memory requirements needed for the processing of discovering the association rules. Most of the literature pertaining to the association rules deals extensively with these issues as major obstacles, especially for very large databases. In this paper, we introduce a method that requires substantially lowers the run time and memory requirements in comparison to the methods presently in use (reduction from O(2m) to O2m2 in the worst case).
Visual–Inertial Odometry of Structured and Unstructured Lines Based on Vanishing Points in Indoor Environments

Authors:
raw_author_name: Baoquan Li
raw_affiliation_string: Tianjin Key Laboratory of Intelligent Control of Electrical Equipment, School of Control Science and Engineering, Tiangong University, Tianjin 300387, China
raw_author_name: Kexin Liu
raw_affiliation_string: Institute for Higher Education, Jilin University, Changchun 130012, China

Abstract: In conventional point-line visual–inertial odometry systems in indoor environments, consideration of spatial position recovery and line feature classification can improve localization accuracy. In this paper, a monocular visual–inertial odometry based on structured and unstructured line features of vanishing points is proposed. First, the degeneracy phenomenon caused by a special geometric relationship between epipoles and line features is analyzed in the process of triangulation, and a degeneracy detection strategy is designed to determine the location of the epipoles. Then, considering that the vanishing point and the epipole coincide at infinity, the vanishing point feature is introduced to solve the degeneracy and direction vector optimization problem of line features. Finally, threshold constraints are used to categorize straight lines into structural and non-structural features under the Manhattan world assumption, and the vanishing point measurement model is added to the sliding window for joint optimization. Comparative tests on the EuRoC and TUM-VI public datasets validated the effectiveness of the proposed method.
EXPLORING THE DETERMINANTS EFFECTING PROMOTION OF RESEARCH CULTURE AT UNIVERSITY LEVEL

Authors:
raw_author_name: Muhammad Sharif
raw_affiliation_string: PhD Scholar, Qurtuba University of Science & Information Technology, Dera Ismail Khan, Pakistan
raw_author_name: Umar Ali Khan
raw_affiliation_string: Professor, Qurtuba University of Science and Information Technology, Dera Ismail Khan, Pakistan

Abstract: The study in hand was undertaken to explore the determinants which are effecting promotion of research culture at university level. Although many factors are effecting but the most relevant determinants affect was checked which are environmental and institutional determinants. The population was comprised of 1477 the faculty members of BZ University, Multan &amp; Islamia University Bahawalpur as well as Gomal University, Dera Ismail Khan and University of Science and technology, Bannu. The selected sample from the population was comprised of 253 faculty members hailing from the above-mentioned higher education institutions out of 1477 was selected for study. The research design was quantitative in which survey questionnaires were distributed for data collection. T-test and ANOVA were used for analysis of data. The results offered significant information for reaching the conclusion, and making the desired decisions. Also, some recommendations have been extracted from results offered to the policy-makers and future researchers about research issues.
What are the challenges in integrating LLMs into organisations' data workflows?

Authors:
raw_author_name: Amir Aryani

Abstract: Integrating Large Language Models (LLMs) such as GPT into organizations’ data workflows is a complex process with various challenges. These obstacles include but are not limited to technical, operational, ethical, and legal dimensions, each presenting hurdles that organisations must navigate to harness the full potential of LLMs effectively.