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arXiv 2025年

作者： Liu, Guangyuan Du, Hongyang Wang, Jiacheng Niyato, Dusit Kim, Dong In College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

The rapid advancement of immersive technologies has propelled the development of the Metaverse, where the convergence of virtual and physical realities necessitates the generation of high-quality, photorealistic images to enhance user experience. However, generating these images, especially through Generative Diffusion Models (GDMs), in mobile edge computing environments presents significant challenges due to the limited computing resources of edge devices and the dynamic nature of wireless networks. This paper proposes a novel framework that integrates contract-inspired contest theory, Deep Reinforcement Learning (DRL), and GDMs to optimize image generation in these resource-constrained environments. The framework addresses the critical challenges of resource allocation and semantic data transmission quality by incentivizing edge devices to efficiently transmit high-quality semantic data, which is essential for creating realistic and immersive images. The use of contest and contract theory ensures that edge devices are motivated to allocate resources effectively, while DRL dynamically adjusts to network conditions, optimizing the overall image generation process. Experimental results demonstrate that the proposed approach not only improves the quality of generated images but also achieves superior convergence speed and stability compared to traditional methods. This makes the framework particularly effective for optimizing complex resource allocation tasks in mobile edge Metaverse applications, offering enhanced performance and efficiency in creating immersive virtual environments. © 2025, CC BY.

关键词： Mobile edge computing

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Construction Process Quality Control Assisted Mobile Application 8

Construction Process Quality Control Assisted Mobile Applica...

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8th IEEE International Conference on Industrial Engineering and Applications, ICIEA 2021

作者： Kruachottikul, Pravee Cooharojananone, Nagul Phanomchoeng, Gridsada Wongsanao, Keetapat Thabsuwan, Sirada Suwannatrai, Kuptipong Pongsuttisatta, Watcharapong Ganyaporngul, Sirapavee Kovitanggoon, Kittikul Vajarodaya, Napatsapa Graduate School Chulalongkorn University Technopreneurship and Innovation Management Program Bangkok Thailand Chulalongkorn University Faculty of Science Department of Mathematics and Computer Science Bangkok Thailand Chulalongkorn University Faculty of Engineering Department of Mechanical Engineering Bangkok Thailand Chulalongkorn University Faculty of Science Department of Mathematics and Computer Science Bangkok Thailand Solitech Innovations and Technologies Co. Ltd. Bangkok Thailand

ISBN: (纸本)9781665428958

Digital transformation plays a big role in many industries, including construction. One of the obstacles to manage large-scale and complex construction projects is inefficient communication between involving personnel. Most problems are such as misunderstanding contexts, incomplete information, ignorance, missing multimedia files, etc. In Thailand, organizations adopt digital transformation by utilizing a variety of applications and software to complete their projects, such as LINE, G-Suite, Asana, etc. Users have to spend a lot of time to learn each application and software to be able to achieve maximum performance. Construction process quality control assisted application is the new tool developed for uniting all needs and solving communication problems. Requirements are gathered by five interviews with three major organizations, the Thailand department of Highway, Team Built Co., Ltd., and one major construction company. Requirements were analyzed to extract the most useful features that are beneficial to all users. React Native and the Expo framework were used to develop the frontend part of the application. For the backend part, the application used microservice architecture with various cloud services, like Amazon AWS, Expo Server, and Firebase Cloud Messaging. In the end, the application was put through hand-on tests by 13 on-site personnel. The final results showed the average scores of the user interface, hands-on experience, and benefits for the users were 4, 4.16, and 3.92 (out of 5), respectively, in which the total satisfaction score is 4.03(out of 5). © 2021 IEEE.

关键词： Construction

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Measure the Level of Success in Using Google Drive with the Kuder Richardson (KR) Reliability Method

Measure the Level of Success in Using Google Drive with the ...

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2019 International Congress on Applied Information Technology, AIT 2019

作者： Nugroho, Andi Warnars, Harco Leslie Hendric Spits Heriyadi, Yaya Tanutama, Lukas Mercu Buana University and Binus Graduate Program - Doctor of Computer Science Computer Science Department Jakarta Indonesia Binus Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781728158624

The Google Sheets is a cloud technology part of Google Drive. Google Drive is a cloud operating system owned by Google's search engine. National Elementary School (SDN) to make a report book or Basic Education Databook (Dapodik) still use Microsoft Excel, which has the effect of delaying the delivery of report results to parents. This happens because to fill in the DAPODIK score must be filled by teachers who teach subjects in class. This study aims to provide knowledge to teachers in collaborating to fill DAPODIK and measure how reliable the delivery of material received by each teacher. Using the method of reliability Kuder Richardson KR-20 and KR-21 produces a value before the training on reliability values KR-20 = 0.6328, and KR-21 = 0.6000, after the training, the reliability values KR-20 = 0.5788 and KR-21 = 0.6231. From these results, it is known that the value after training for KR-21 has increased and according to the conventional correlation coefficient. In future research can be re-developed by making application system recommendations in determining teachers in schools to become instructors in accordance with the capabilities possessed based on the reliability testing results of the KR-20 and KR-21. © 2019 IEEE.

关键词： Search engines

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Factors Affecting Knowledge Transfer between Project Managers: A Conceptual Framework 13

Factors Affecting Knowledge Transfer between Project Manager...

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13th International Conference on Knowledge Management and Information Systems, KMIS 2021 as part of 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2021

作者： Srisuksa, Nontouch Wiriyapinit, Mongkolchai Bhattarakosol, Pattarasinee Technopreneurship and Innovation Management Program Graduate School Chulalongkorn University Bangkok Thailand Department of Commerce Chulalongkorn Business School Chulalongkorn University Bangkok Thailand Department of Mathematics and Computer Science Faculty of Science Chulalongkorn University Bangkok Thailand

ISBN: (纸本)9789897585333

The purpose of this study is to identify factors from the literature review that influence knowledge transfer among project managers at all levels. Findings indicate nine factors to potentially benefit knowledge transfer amongst project managers. They can be used by project managers involved in government agencies and private sector organizations as a preliminary guidance for the practice, improvement, and growth of project managers in order to achieve a competitive edge in business. Copyright © 2021 by SCITEPRESS – science and Technology Publications, *** rights reserved.

关键词： Project management

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Towards Emotionally Intelligent Software Engineers: Understanding Students' Self-Perceptions After a Cooperative Learning Experience

Towards Emotionally Intelligent Software Engineers: Understa...

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ICSE Workshop on Cooperative and Human Aspects on Software Engineering, CHASE

作者： Allysson Allex Araújo Marcos Kalinowski Matheus Paixao Daniel Graziotin Center for Science and Technology Federal University of Cariri Juazeiro do Norte Brazil Department of Informatics Pontifical Catholic University of Rio de Janeiro Rio de Janeiro Brazil Graduate Program in Computer Science State University of Ceará Fortaleza Brazil Department of Information Systems and Digital Technologies University of Hohenheim Stuttgart Germany

ISBN: (数字)9798331538712

ISBN: (纸本)9798331502539

Background] Emotional Intelligence (EI) can impact Software Engineering (SE) outcomes through improved team communication, conflict resolution, and stress management. SE workers face increasing pressure to develop both technical and interpersonal skills, as modern software development emphasizes collaborative work and complex team interactions. Despite EI's documented importance in professional practice, SE education continues to prioritize technical knowledge over emotional and social competencies. [Objective] This paper analyzes SE students' self-perceptions of their EI after a twomonth cooperative learning project, using Mayer and Salovey's four-ability model to examine how students handle emotions in collaborative development. [Method] We conducted a case study with 29 SE students organized into four squads within a projectbased learning course, collecting data through questionnaires and focus groups that included brainwriting and sharing circles, then analyzing the data using descriptive statistics and open coding. [Results] Students demonstrated stronger abilities in managing their own emotions compared to interpreting others' emotional states. Despite limited formal EI training, they developed informal strategies for emotional management, including structured planning and peer support networks, which they connected to improved productivity and conflict resolution. [Conclusion] This study shows how SE students perceive EI in a collaborative learning context and provides evidence-based insights into the important role of emotional competencies in SE education.

关键词： Training Productivity Federated learning Software Encoding Data models Planning Faces Software engineering Software development management

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Optimized Transmission Strategy for UAV-RIS 2.0 Assisted Communications Using Rate Splitting Multiple Access

Optimized Transmission Strategy for UAV-RIS 2.0 Assisted Com...

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IEEE Conference on Vehicular Technology (VTC)

作者： Aamer Mohamed Huroon Yu-Chih Huang Li-Chun Wang Department of Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Institute of Communications Engineering National Yang Ming Chiao Tung University Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University

In this paper, we study the transmission strategy of a ground-based beyond diagonal reconfigurable intelligent surface (BD-RIS), a.k.a RIS 2.0, in a network where multiple unmanned aerial vehicles (UAVs) simultaneously transmit signals to the respective groups of users. It is assumed that each group is assigned subcarriers orthogonal to those assigned to other groups and rate splitting multiple access (RSMA) is adopted within each group. A corresponding mixed integer nonlinear programming problem (MINLP) is formulated, which aims to jointly optimize 1) allocation of BD-RIS elements to groups, 2) BD-RIS phase rotations, 3) rate allocation in RSMA, and 4) precoders. To solve the problem, we propose using generalized benders decomposition (GBD) augmented with a manifold-based algorithm. GBD splits the MINLP problem into two sub-problems, namely the primal and the relaxed master problem, which are solved alternately and iteratively. In the primal problem, we apply block coordinate descent (BCD) to manage the coupling of variables effectively. Moreover, we recognize the manifold structure in the phase rotation constraint of BD-RIS, enabling the Riemannian conjugate gradient (RCG). Simulation results demonstrate the effectiveness of the proposed approach in maximizing spectral efficiency.

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Psychological Stress Classification Using Extreme Gradient Boosting Algorithm

Psychological Stress Classification Using Extreme Gradient B...

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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Maria Mahardini Sakanti Viacheslav Siniaev Aurelia Amaris Win-Jet Luo Suhartono C. Bambang Dwi Kuncoro Graduate Institute of Precision Manufacturing National Chin-Yi University of Technology Taichung Taichung Taiwan Institute of Applied Computer Science ITMO University St. Petersburg Russian Federation Department of Informatics Engineering Faculty of Computer Science Universitas Brawijaya Malang Indonesia Ph.D. Program Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology Taichung Taichung Taiwan Department of Refrigeration Air Conditioning and Energy Engineering National Chin-Yi University of Technology Taichung Taiwan

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

Stress, as a reaction to threatening situations, can raise heart rate and result in serious conditions that might cause significant damage or even be life-threatening. Traditional methods for evaluating stress, which rely on subjective self-reporting and clinical assessments, often suffer from biases and inconsistencies. Artificial intelligence models have been explored to predict stress levels more accurately. This paper investigates the application of Extreme Gradient Boosting in classifying psychological stress using the WESAD dataset, which includes parameters such as acceleration, electrocardiogram, electromyography, electrodermal activity, temperature, and respiration. The dataset was balanced and sampled to create a manageable subset for experimental. Extreme Gradient Boosting was chosen for its efficiency and scalability in handling complex datasets. The model was trained and validated, achieving a 95% accuracy in predicting stress levels. This study highlights the potential of integrating Extreme Gradient Boosting models into wearable devices for real-time stress monitoring. Future work involves optimizing the model to utilize fewer sensors without decreasing accuracy, ensuring it can be integrated into portable/wearable systems using tiny microcontrollers.

关键词： Accuracy Microcontrollers Psychology Human factors Predictive models Boosting Prediction algorithms Real-time systems Sensors Wearable devices

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Lung Nodule Analysis in CT Images: Deep Learning for Segmentation and Measurement 24

Lung Nodule Analysis in CT Images: Deep Learning for Segment...

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8th International Conference on Medical and Health Informatics, ICMHI 2024

作者： Li, Ai-Hsien Adams Aruperes, Cristian Daniel Lai, Yen-Jun Chien, Ting-Ying Chiu, Yen-Ling Chan, Chien-Lung Division of Cardiology Far Eastern Memorial Hospital New Taipei TaiwanGraduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan TaiwanDepartment of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan TaiwanInnovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan Taiwan Department of Information Management Yuan Ze University Taoyuan Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan TaiwanGraduate Institute of Medicine Yuan Ze University Taoyuan Taiwan TaiwanDepartment of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Department of Information Management Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400716874

In the realm of research, the global health challenge posed by lung cancer remains pronounced, contributing substantially to annual cancer-related fatalities. The critical imperative lies in the early identification of pulmonary nodules, frequently indicative of impending lung cancer, to enhance patient outcomes and diminish mortality rates. Computed Tomography (CT) imaging stands out as a pivotal diagnostic instrument for the timely detection of these nodules. The swift proliferation of medical imaging data has underscored the pressing necessity for precise and efficient methodologies dedicated to nodule segmentation and measurement. These approaches are crucial in assisting radiologists in their diagnostic and clinical decision-making endeavors. In this study, we introduced a thorough method for analyzing lung nodules, leveraging dataset from Far Eastern Memorial Hospital (FEMH) comprising original CT images and manually annotated ground truth masks obtained with the assistance of radiologists at FEMH. This dataset is utilized for the segmentation of nodules. We employed advanced deep learning models, specifically the U-Net architecture, identified as the optimal model through our training process. We made substantial progress in nodule segmentation, attaining an Intersection over Union (IoU) score of 0.824 and a Dice Coefficient of 0.903 for the FEMH dataset. Furthermore, our performance improved when utilizing the merged dataset comprising FEMH and Luna16, yielding an IoU score of 0.862 and a Dice Coefficient of 0.926. Luna16 has been extensively utilized in numerous studies related to nodule detection and segmentation. In the next phase of the study, the best-performing model from our segmentation phase was utilized to predict nodule masks on the merged dataset. Subsequently, we measured the size of each predicted nodule by comparing it with the size ground truth mask in millimeters. In detail, this study achieved the Pearson Correlation Coefficient (PCC) at 0.

关键词： computerized tomography

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Semi-Automated Classification of Arabic User Requirements into Functional and Non-Functional Requirements using NLP Tools

Semi-Automated Classification of Arabic User Requirements in...

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2021 International Conference on Information Technology, ICIT 2021

作者： Shehadeh, Karmel Arman, Nabil Khamayseh, Faisal Palestine Polytechnic University Deanship of Graduate Studies/Master's of Informatics Program Hebron Palestine Palestine Polytechnic University Department of Computer Science and Information Technology Hebron Palestine

ISBN: (纸本)9781665428705

Functional and non-functional requirements are equally important in software engineering. Both of them are mixed together within the same software requirement document. Usually, they are expressed in natural languages. So, a lot of human effort is required to classify them. Software requirements classification is a challenging task. Requirements classification can help developers to deliver quality software that meets users' expectations completely. In this paper, we present a Semi-Automated classification approach of Arabic functional and non-functional requirements using a natural language processing (NLP) tool. We propose a set of heuristics based on basic constructs of Arabic sentences in order to extract information from Arabic software requirements to classify the requirements into functional and non-functional requirements. This research aims to help software engineers by reducing the cost and time required in performing manual classification of software requirements. © 2021 IEEE.

关键词： Requirements engineering

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High-Performance and Resource-Efficient IoT-based Sleep Monitoring System

High-Performance and Resource-Efficient IoT-based Sleep Moni...

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IEEE Conference on Vehicular Technology (VTC)

作者： Nico Surantha Oei Kurniawan Utomo Sani Muhamad Isa Computer Science Department BINUS Graduate Program-Master of Computer Science Jakarta Indonesia

ISBN: (数字)9781728152073

ISBN: (纸本)9781728140537

Recently, the number of people who have trouble sleeping is on the rise. It results in an increase in the number of car accidents, lower productivity and a higher risk of chronic disease. Advancing wearable and contactless health sensors offers the possibility of daily sleep quality monitoring that can be done on a regular basis by patients in their homes. The goal of this research is to propose the design of an Internet-of-Things (IoT) sleep quality monitoring system that can be integrated with a wearable or contactless sensor. The proposed platform architecture is based on micro-services and is event-driven. The simulation results show that the implementation of the IoT sleep quality monitoring system based on the proposed design decreases the response time by 55.85 per cent while increasing the throughput by 34.76 per cent compared to the traditional approach. On the other hand, the rate of increase in memory usage per instance replication for proposed method is lower than the traditional method.

关键词： Monitoring Logic gates Time factors computer architecture Sensor systems Throughput

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