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Data-Filtered Prediction with Decomposition and Amplitude-Aware Permutation Entropy for Workload and Resource Utilization in Cloud Data Centers

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IEEE Internet of Things Journal 2025年

作者： Yuan, Haitao Hu, Qinglong Wang, Meijia Wang, Shen Bi, Jing Buyya, Rajkumar Shi, Shuyuan Yang, Jinhong Zhang, Jia Zhou, MengChu Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Computer Science Hong Kong999077 Hong Kong Beijing University of Technology School of Software Engineering The Faculty of Information Technology Beijing100124 China Lab School of Computing and Information Systems MelbourneVIC3010 Australia Beihang University School of Integrated Circuit Science and Engineering Beijing China CSSC Systems Engineering Research Institute Beijing China Southern Methodist University Department of Computer Science The Lyle School of Engineering DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

In recent years, cloud computing has witnessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in quality of service and cost efficiency. However, these data often exhibit non-linearity, high volatility, and interdependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVAPI for achieving high-accuracy workload prediction in cloud computing systems. SVAPI employs the Savitzky-Golay filter, Variational mode decomposition, and the mode selection based on Amplitude-aware Permutation entropy for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVAPI improves the prediction accuracy by 37.7% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVAPI enhances the prediction accuracy of workload, CPU, and memory by 65.6%, 66.9%, and 70.8%, respectively, demonstrating that SVAPI owns strong abilities in noise filtering, feature processing, and multivariate joint analysis for achieving higher prediction accuracy. © 2014 IEEE.

关键词： Quality of service

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Minimizing Occlusion Effect on Multi-View Camera Perception in BEV with Multi-Sensor Fusion

arXiv

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arXiv 2025年

作者： Kumar, Sanjay Truong, Hiep Sharma, Sushil Sistu, Ganesh Scanlan, Tony Grua, Eoin Eising, Ciarán Department of Electronic and Computer Engineering University of Limerick Ireland Research Centre University of Limerick Ireland Lero The Irish Software Research Centre University of Limerick Ireland DSW Valeo Kronach Germany Valeo Vision Systems Ireland

Autonomous driving technology is rapidly evolving, offering the potential for safer and more efficient transportation. However, the performance of these systems can be significantly compromised by the occlusion on sensors due to environmental factors like dirt, dust, rain, and fog. These occlusions severely affect vision-based tasks such as object detection, vehicle segmentation, and lane recognition. In this paper, we investigate the impact of various kinds of occlusions on camera sensor by projecting their effects from multi-view camera images of the nuScenes dataset into the Bird’s-Eye View (BEV) domain. This approach allows us to analyze how occlusions spatially distribute and influence vehicle segmentation accuracy within the BEV domain. Despite significant advances in sensor technology and multi-sensor fusion, a gap remains in the existing literature regarding the specific effects of camera occlusions on BEV-based perception systems. To address this gap, we use a multi-sensor fusion technique that integrates LiDAR and radar sensor data to mitigate the performance degradation caused by occluded cameras. Our findings demonstrate that this approach significantly enhances the accuracy and robustness of vehicle segmentation tasks, leading to more reliable autonomous driving systems. https://***/OmX2NEeOzAE © 2025, CC BY.

关键词： Image segmentation

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A Consumer Electronics-Enhanced UAV System for Agricultural Farm Tracking With Fuzzy SMO and Actuator Fault Detection Control Algorithms

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IEEE Transactions on Consumer Electronics 2025年

作者： Bilal, Hazrat Aslam, Muhammad Shamrooz Tian, Yibin Ullah, Inam Ayouni, Sarra Vasilakos, Athanasios V. College of Mechatronics and Control Engineering College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China China University of Mining and Technology Artificial Intelligence Research Institute Xuzhou2211106 China Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Gachon University Department of Computer Engineering Seongnam13120 Korea Republic of Tashkent State University of Economics Department of Artificial Intelligence Tashkent100066 Uzbekistan Princess Nourah bint Abdulrahman University Department of Information Systems College of Computer and Information Sciences Saudi Arabia Grimstad Norway

The adoption of agricultural robots, or agrobots, has revolutionized modern farming operations, ranging from crop monitoring to automated harvesting, significantly boosting productivity. Motivated by the rapid advancements in agrobots and their integration into smart agricultural practices, this study proposes an autonomous trajectory tracking system for wheat farms using quadcopter UAVs. To address actuator fault detection, including stuck faults and partial loss of efficiency, a TSF-H∞-SMO (Takagi-Sugeno Fuzzy-based H∞ Sliding Mode Observer) fault detection framework is introduced. The approach initiates with the derivation of a TSF (Takagi-Sugeno Fuzzy) attitude control model that integrates an uncertainty term, constructed from the original nonlinear dynamics of the UAV and approximated through local linear models at four equilibrium positions. An actuator fault model is subsequently integrated to develop a comprehensive TSF-UAV model, accounting for actuator faults. The TSF-H∞-SMO is then designed using matrix coordinate transformation to enable precise fault detection. The fault detection capabilities of the TSF-H∞-SMO are evaluated through simulations on the TSF-UAV model under SISO (single-input single-output) actuator fault scenarios. The experimental results validate the proposed system, demonstrating its ability to detect a range of actuator faults accurately and promptly. The analysis reveals a proportional relationship between the amplitude of the state change and the severity of the fault, attributed to the interaction between system states and actuator flaps. This approach underscores the potential for deploying autonomous UAV-based fault detection and trajectory tracking systems in agricultural applications. Furthermore, integrating such advanced fault-tolerant control algorithms holds promise for consumer technology applications, where precision, reliability, and robustness are critical to enhancing system performance and operational efficiency. © 1

关键词： Agricultural robots

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How Do Hackathons Foster Creativity? Towards AI Collaborative Evaluation of Creativity at Scale

arXiv

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arXiv 2025年

作者： Falk, Jeanette Chen, Yiyi Rafner, Janet Zhang, Mike Bjerva, Johannes Nolte, Alexander Aalborg University Copenhagen Department of Computer Science Copenhagen Denmark Center for Hybrid Intelligence Department of Management and Aarhus Institute for Advanced Studies Aarhus University Aarhus Denmark Aalborg University Copenhagen Department of Computer Science Pioneer Centre for Artificial Intelligence Copenhagen Denmark Software Engineering and Technology Eindhoven University of Technology Eindhoven Netherlands and Software and Societal Systems Carnegie Mellon University PittsburghPA United States

Hackathons have become popular collaborative events for accelerating the development of creative ideas and prototypes. There are several case studies showcasing creative outcomes across domains such as industry, education, and research. However, there are no large-scale studies on creativity in hackathons which can advance theory on how hackathon formats lead to creative outcomes. We conducted a computational analysis of 193,353 hackathon projects. By operationalizing creativity through usefulness and novelty, we refined our dataset to 10,363 projects, allowing us to analyze how participant characteristics, collaboration patterns, and hackathon setups influence the development of creative projects. The contribution of our paper is twofold: We identified means for organizers to foster creativity in hackathons. We also explore the use of large language models (LLMs) to augment the evaluation of creative outcomes and discuss challenges and opportunities of doing this, which has implications for creativity research at large. © 2025, CC BY.

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Wheelchair Wheel Design with Transformable “Wheel-Leg” Configuration for Climbing Stairs and Various Surfaces

Wheelchair Wheel Design with Transformable “Wheel-Leg” Con...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Islam Magomedov Timur Aygumov Anzor A. Kanametov Bissnes informatics Kadyrov Chechen state university Grozny Russia Department of software engineering computer technology and automated systems Dagestan State Technical University Dagestan Russia Department of Electronics and Digital Information Technologies Kabardino-Balkarian State University Nalchik Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

The dynamic and uneven terrain of our environment introduces complexity for locomotion vehicles to navigate easily across different settings. Wheelchairs belong to the category of vehicles that require locomotion across various uneven terrains, such as stairs and other partitions. Therefore, the aim of this work is to design a wheel that can travel through various environments without using complex mechanisms. This aim was achieved by creating a novel transformable wheel. This wheel can turn into a legged wheel without using a complex mechanism and, when unnecessary, transform back into a normal wheel. Several simulations were carried out to understand the design's capability to overcome obstacles. In this case, the stair was the obstacle, and the results illustrated that the wheel is capable of climbing stairs without serious issues. Different parameters, such as speed and force, were examined with various values. The results showed that the weight on the wheelchair plays a significant role in climbing. For instance, with lighter weight, the structure moves without major slipping. The roughness of the surface is also related to the wheelchair's ability to reach the top of the stairs. A finer depth of cuts on the surface of the stairs increases the chance of the legged wheel firmly gripping and moving up the stairs.

关键词： Shape Navigation Wheelchairs Wheels Rotors Transforms Stairs Surface roughness Rough surfaces Vehicle dynamics

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Instructors' Perspectives on Capstone Courses in Computing Fields: A Mixed-Methods Study 2024

Instructors' Perspectives on Capstone Courses in Computing F...

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29th Working Group Reports on Innovation and Technology in computer Science Education, ITiCSE WGR 2024

作者： Hooshangi, Sara Shakil, Asma Dasgupta, Subhasish Davis, Karen C.C. Farghally, Mohammed Fitzpatrick, KellyAnn Gutica, Mirela Hardt, Ryan Riddle, Steve Seyam, Mohammed Department of Computer Science Virginia Tech BlacksburgVA United States School of Computer Science University of Auckland Auckland New Zealand Info Systems and Tech Mgmt Dept George Washington University WashingtonDC United States Computer Science and Software Engineering Dept. Miami University OxfordOH United States School of Computing Instruction Georgia Institute of Technology AtlantaGA United States School of Computing & Academic Studies British Columbia Institute of Technology BurnabyBC Canada Computer & Information Science University of St. Thomas Saint PaulVA United States School of Computing Newcastle University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9798400712081

Team-based capstone courses are integral to many undergraduate and postgraduate degree programs in the computing field. They are designed to help students gain hands-on experience and practice professional skills such as communication, teamwork, and self-reflection as they transition into the real world. Prior research on capstone courses has focused primarily on the experiences of students. The perspectives of instructors who teach capstone courses have not been explored comprehensively. However, an instructor's experience, motivation, and expectancy can have a significant impact on the quality of a capstone course. In this working group, we used a mixed methods approach to understand the experiences of capstone instructors. Issues such as class size, industry partnerships, managing student conflicts, and factors influencing instructor motivation were examined using a quantitative survey and semi-structured interviews with capstone teaching staff from multiple institutions across different continents. Our findings show that there are more similarities than differences across various capstone course structures. Similarities include team size, team formation methodologies, duration of the capstone course, and project sourcing. Differences in capstone courses include class sizes and institutional support. Some instructors felt that capstone courses require more time and effort than regular lecture-based courses. These instructors cited that the additional time and effort is related to class size and liaising with external stakeholders, including industry partners. Some instructors felt that their contributions were not recognized enough by the leadership at their institutions. Others acknowledged institutional support and the value that the capstone brought to their department. Overall, we found that capstone instructors were highly intrinsically motivated and enjoyed teaching the capstone course. Most of them agree that the course contributes to their professional de

关键词： Students

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Research and Development of Kubernetes Operator for Lightweight Multi-Access Edge Computing Platform Simulator

Research and Development of Kubernetes Operator for Lightwei...

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systems of Signals Generating and Processing in the Field of on Board Communications

作者： Ivan Filianin Nikita Borisov Vladimir Bogatyrev Faculty of Software Engineering and Computer Engineering ITMO University St. Petersburg Russia Faculty of Infocommunication Technologies ITMO University St. Petersburg Russia Department of Information Systems Security Saint-Petersburg State University of Aerospace Instrumentation St. Petersburg Russia

ISBN: (数字)9798331532635

ISBN: (纸本)9798331532642

This paper addresses the problem of deploying complex systems in Kubernetes clusters. It discusses using the OperatorSDK framework supported by RedHat as a basis for implementing the Kubernetes operator for Lightweight MultiAccess Edge Computing Platform Simulator (LWMECPS) deployment. Special attention is given to the operator Kubernetes architecture and how to use Operator Lifecycle Manager (OLM) to continuously deploy LWMECPS and machine learning models for it.

关键词： computer languages Multi-access edge computing Codes Computational modeling Machine learning computer architecture Complex systems Research and development

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Integrating AI-based adaptive learning into the flipped classroom model to enhance engagement and learning outcomes

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computers and Education: Artificial Intelligence 2025年 8卷

作者： Katona, Jozsef Gyonyoru, Klara Ida Katonane Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest University of Economics and Business Budapest Hungary Department of Organizational Development and Communication Science Institute of Social Sciences University of Dunaujvaros Dunaujvaros Hungary

This study explores the integration of the AI-powered adaptive learning system within the flipped classroom model and discusses programming education. The increasing demand for the acquisition of programming skills in the digital era generates the development of innovative pedagogical approaches to help students overcome the difficulties in mastering programming concepts. The study investigates how AI-based adaptive feedback influences student engagement, motivation, and learning outcomes. This study was conducted for 13 weeks, with two groups of undergraduate students. In one of the groups, tshe traditional flipped classroom model was applied, while in the other group, AI-driven adaptive learning tools were introduced. In this research methodology, a mixed-methods approach was adopted, integrating quantitative analyses with qualitative data. These showed that the experimental group demonstrated significant improvements in learning results and motivation, thereby realiszing the value of real-time, personalized feedback provided through the AI system. The qualitative findings revealed that with the AI system, there was an increase in student autonomy;thus, there is increased responsibility among students to collaborate in in-class participation, which helps enhance a more dynamic and cooperative learning environment. On the whole, these findings reveal important implications of the integration of AI into flipped classrooms, while at the same time, they reveal how to make the modernization of programming education achieve a better overall experience in student learning. © 2025 The Authors

关键词： Contrastive Learning

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Machine Learning in Higher Education using RF and SVM for Pedagogical Enhancement

Machine Learning in Higher Education using RF and SVM for Pe...

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Integrated Circuits and Communication systems (ICICACS), IEEE International Conference on

作者： Fateh Bahadur Kunwar Satyajee Srivastava R Dhivya Monika Gulati Taruna Anand D Antony Arul Raj Department of Computer Science and Engineering Geetanjali Institute of Technical Studies Udaipur India Department of Computer Science and Engineering School of Engineering and Technology Manav Rachna International Institute of Research and Studies Faridabad India Department of Information Technology M.Kumarasamy College of Engineering Karur India MMIM Maharishi Markandeshwar (Deemed to be University) Ambala India Department of Humanities and Social Sciences Graphic Era (Deemed to be) University Dehradun India Department of Software Systems PSG College of Arts & Science Coimbatore India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

AI's revolutionary potential in higher education is examined in this proposal, including how it could revolutionize teaching, learning, administration, and research. Adaptive learning platforms, intelligent tutoring systems, and virtual learning assistants may change how students learn by improving engagement, providing quick feedback, and enabling tailored learning. The suggested process includes preprocessing, feature extraction, and model training. Preprocessing involves de-anonymizing, tokenizing, and removing punctuation, spaces, and numbers before analysis. The linear transformation method PCA extracts features. Training the systems uses the RF -SVM approach, which combines the advantages of RF and SVM. The suggested RF -SVM model beats two RF and SVM models with an average accuracy of 90.39 *** improves resource use, strategic decision-making, and research capacity in university management. The RF-SVM model's success in academia highlights the practicality of AI-driven solutions.

关键词： Support vector machines Radio frequency Training Accuracy Education Symbols Feature extraction Tokenization Integrated circuit modeling Principal component analysis

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Analysis of Legged Wheels with Spring Suspension for Stair-Climbing Robotics

Analysis of Legged Wheels with Spring Suspension for Stair-C...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Islam Magomedov Timur Aygumov Josè Luis Ordõnez-Avila Bissnes informatics Kadyrov Chechen state university Grozny Russia Department at of software engineering computer technology and automated systems Dagestan State Technical University Dagestan Russia Universidad Tecnol'ogica Centroamericana(UNITEC) San Pedro Sula Honduras

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

The following article looked into legged wheels with spring suspension. The idea behind the work was to see how adding springs in various places and their stiffness would impact the robot's ability to navigate through stairs. Due to the unconventional appearance of legged wheels, they are not suitable for even terrain passage. Hence, the idea was to add springs to minimize shock occurrence each time a leg touches the even environment. It was also important to equip springs throughout the whole path. As the robot progresses through steps, a greater disturbance of the structure occurs. Two different ideas were tested. In the first scenario, springs were attached between the holder of the wheels and the main body. In the second scenario, springs were directly added to the legs. The extruded part of the leg was divided into two, and a spring was embedded between these two parts. Different values of spring stiffness were also examined. The results illustrated that the structure would go further by implementing springs in all four wheels or in the rear. However, the structure was tested only on an upward incline. With other setups, it might be different with the rearrangement of the springs. It was also noted that with higher stiffness of the springs, the structure became unstable. In the second scenario, results illustrated the slightest progress. For instance, when the front wheels' legs were modified, they showed better results. To understand the influence in more depth, further investigation of the problem must be conducted.

关键词： Legged locomotion Industries Navigation Service robots Electric shock Wheels Stairs Boundary conditions Springs

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