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15th International Conference on Performance Evaluation Methodologies and Tools, VALUETOOLS 2022

作者： Nakamura, Ayane Phung-Duc, Tuan Graduate School of Science and Technology University of Tsukuba Tsukuba Japan Institute of Systems and Information Engineering University of Tsukuba Tsukuba Japan

ISBN: (纸本)9783031312335

We consider an observable multi-server batch service queueing model where a shared server admission fee and maintenance cost for the monopolist are considered. Specifically, customers observe the number of full batches and waiting customers for an uncompleted batch, and then afterward decide to join or balk depending on their utility function. We analyze strategic customer behavior and find the equilibrium strategy. We also show numerical results for social welfare and monopolist’s revenue in various settings depending on fee, batch size, and number of servers. Based on our results, we discuss the existence of optimal fee, batch size, and number of servers. © 2023, ICST Institute for Computer sciences, Social Informatics and Telecommunications engineering.

关键词： Costs

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Highly efficient flexible perovskite solar cells with vacuum-assisted low-temperature annealed SnO2 electron transport layer

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Journal of Energy Chemistry 2022年第4期31卷 1-7页

作者： Xiaoguo Li Zejiao Shi Fatemeh Behrouznejad Mohammad Hatamvand Xin Zhang Yaxin Wang Fengcai Liu Haoliang Wang Kai Liu Hongliang Dong Farhan Mudasar Jiao Wang Anran Yu Yiqiang Zhan Center for Micro Nano Systems School of Information Science and Technology(SIST)Fudan UniversityShanghai 200433China Academy for Engineering&Technology Fudan UniversityShanghai 200433China Center for High Pressure Science and Technology Advanced Research PudongShanghai 201203China

The demand for lightweight, flexible, and high-performance portable power sources urgently requires high-efficiency and stable flexible solar cells. In the case of perovskite solar cells(PSCs), most of the common electron transport layer(ETL) needs to be annealed for improving the optoelectronic properties,while conventional flexible substrates could barely stand the high temperature. Herein, a vacuumassisted annealing SnO_(2) ETL at low temperature(100℃) is utilized in flexible PSCs and achieved high efficiency of 20.14%. Meanwhile, the open-circuit voltage(V_(oc)) increases from 1.07 V to 1.14 V. The flexible PSCs also show robust bending stability with 86.8% of the initial efficiency is retained after 1000 bending cycles at a bending radius of 5 mm. X-ray photoelectron spectroscopy(XPS), atomic force microscopy(AFM), and contact angle measurements show that the density of oxygen vacancies, the surface roughness of the SnO_(2) layer, and film hydrophobicity are significantly increased, respectively. These improvements could be due to the oxygen-deficient environment in a vacuum chamber, and the rapid evaporation of solvents. The proposed vacuum-assisted low-temperature annealing method not only improves the efficiency of flexible PSCs but is also compatible and promising in the large-scale commercialization of flexible PSCs.

关键词： Flexible perovskite solar cells Vacuum-assisted Electron transport layer Trap-assisted recombination

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Discovering Frequency Bursting Patterns in Temporal Graphs 39

Discovering Frequency Bursting Patterns in Temporal Graphs

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Zhang, Qianzhen Guo, Deke Zhao, Xiang Yuan, Long Luo, Lailong National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory China National University of Defense Technology Laboratory for Big Data and Decision China Nanjing University of Science and Technology China

ISBN: (纸本)9798350322279

A frequency bursting pattern (FBP) in temporal graphs represents some interaction behavior that accumulates its frequency at the fastest rate. Mining FBPs is essential to early warning of emergencies. However, existing studies on frequency-based pattern mining in graphs do not consider the temporal information and bursting features of a subgraph pattern. As a result, they may not provide effective and efficient mining algorithms for FBP discovery. In this paper, we study the problem of discovering top-k FBPs in temporal graphs. We present a novel model, referred to as maximal (m, θ)-bursting pattern, to describe FBPs in a temporal graph, which is a subgraph with a size larger than m that accumulates its frequency at the fastest rate during a time interval of length no less than θ. A naive solution for top-k FBPs discovery is to use the best-first search algorithm, where the burstiness threshold changes as more patterns are mined. However, this method will result in huge search space since we need to check every possible time interval for a candidate pattern in the temporal graph. To tackle this problem, we devise an online top-k framework in which k candidate results are maintained from the initial timestamp to the end in the temporal graph. Under the new framework, we further conceive two optimization strategies by exploiting incremental subgraph matching and Evolutionary Game Theory to boost the performance. Extensive experiment results on five real temporal graphs show that our algorithm has higher efficiency, effectiveness and scalability. © 2023 IEEE.

关键词： Game theory

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Efficient Brain Tumor Classification Leveraging Modified UNet and ResNet-50 Model 6th

Efficient Brain Tumor Classification Leveraging Modified UNe...

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6th International Conference on Smart Computing and Informatics, SCI 2024

作者： Yuganesan, R. Rajkumar, S. Allen Gilchrist, J. Gowseelan, C. Varun Manickam, S. Soy, Theja School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9789819619801

Identification and classification of brain tumors have become a concern for the medical community as the treatment planning and patients’ prognosis is highly dependent on their classification. In the past few years, deep learning models have become the most discussed and widely used tools to achieve higher accuracy and efficiency in this area. This study gives a comparison of the two widely used deep learning networks, the modified UNet and ResNet-50 for the task of brain tumor classification. The study assesses the efficiency of these architectures on the benchmark dataset and indicates the strengths and limitations of each. The evaluation process is made of extremely rigorous testing and validation procedures, which are aimed at making the data reliable. The outcomes of our study indicate that both modified UNet and ResNet-50 can reasonably be applied to the brain tumor classification. Modified UNet surpasses ResNet-50 in overall accuracy, which implies its effectiveness in high-precision classifications. This comparison revealed the feasibility of the architectures for classifying brain tumors. It helps both researchers and practitioners in the field by providing them with information to find the model appropriate for their specific needs. Moreover, the results of the study could be used to build the discussion on the application of the deep learning models in the diagnosis and classification of brain tumors as a part of the medical image analysis. Therefore, this paper provides a thorough guidance for individuals who want to use deep learning to enhance healthcare outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electrotherapeutics

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Optimal Bandwidth Utilization Techniques for IoT Device Networks using Edge Computing 4

Optimal Bandwidth Utilization Techniques for IoT Device Netw...

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4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Dankan Gowda, V. Vijayakumar, G. Chandrasekar, J. Shekhar, R. Prasad, K.D.V. Senthil Kumar, C. Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India V.S.B. Engineering College Department of Information Technology Tamilnadu Karur India Tamilnadu Chennai India Alliance University Department of Computer Science and Engineering Karnataka Bangalore India Symbiosis Institute of Business Management Hyderabad India Pune India Vellore Institute of Technology School of Computer Science Engineering & Information Systems Department of Information Technology Vellore632014 India

ISBN: (纸本)9798350354218

The IoT devices have persisted on growing at a very fast pace and this has proven very challenging in managing the network bandwidth. This paper spins the new concept of increasing the IoT networks bandwidth by adopting the edge computing strategies. It also offers data processing near the sources which reduces the load on the central servers and cuts down data transfer requirements. The usage of the Dynamic Bandwidth Allocation algorithm in the proposed method also enhances the priority of the imperative data and scales up the transmission rates according to the network status. The number of data units achieve higher values, the latency is less and overall, better results have been obtained for the total network performance. Consequently, the results obtained presented pathways through which the bandwidth challenge in IoT could be addressed by means of edge computing to enhance IoT capacity in the corresponding surroundings. © 2024 IEEE.

关键词： Frequency allocation

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An Optimal DPM Based Energy-Aware Task Scheduling for Performance Enhancement in Embedded MPSoC

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Computers, Materials & Continua 2023年第1期74卷 2097-2113页

作者： Hamayun Khan Irfan Ud Din Arshad Ali Mohammad Husain Department of Electrical Engineering Faculty of Engineering&Technology Superior University Lahore54000Pakistan Department of Computer Science Faculty of Computer Science&IT Superior University Lahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahAl Madinah Al Munawarah42351Saudi Arabia

Minimizing the energy consumption to increase the life span and performance of multiprocessor system on chip(MPSoC)has become an integral chip design issue for multiprocessor *** performance measurement of computational systems is changing with the advancement in *** to shrinking and smaller chip size power densities onchip are increasing rapidly that increasing chip temperature in multi-core embedded *** operating speed of the device decreases when power consumption reaches a threshold that causes a delay in complementary metal oxide semiconductor(CMOS)circuits because high on-chip temperature adversely affects the life span of the *** this paper an energy-aware dynamic power management technique based on energy aware earliest deadline first(EA-EDF)scheduling is proposed for improving the performance and reliability by reducing energy and power consumption in the system on chip(SOC).Dynamic power management(DPM)enables MPSOC to reduce power and energy consumption by adopting a suitable core configuration for task *** migration avoids peak temperature values in the multicore *** utilization factor(ui)on central processing unit(CPU)core consumes more energy and increases the temperature *** technique switches the core bymigrating such task to a core that has less temperature and is in a low power *** proposed EA-EDF scheduling technique migrates load on different cores to attain stability in temperature among multiple cores of the CPU and optimized the duration of the idle and sleep periods to enable the low-temperature *** effectiveness of the EA-EDF approach reduces the utilization and energy consumption compared to other existing methods and *** simulation results show the improvement in performance by optimizing 4.8%on u_(i) 9%,16%,23%and 25%at 520 MHz operating frequency as compared to other energy-aware techniques for MPSoCs when the least number of tasks is in running state and can

关键词： Dynamic power management dynamic voltage&frequency scaling dynamic thermal management multiprocessor system on chip complementary metal oxide semiconductor reliability

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Data Security Storage Mechanism Based on Blockchain Network

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Computers, Materials & Continua 2023年第3期74卷 4933-4950页

作者： Jin Wang WeiOu Wenhai Wang RSimon Sherratt Yongjun Ren Xiaofeng Yu School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410004China School of Control Science and Engineering Zhejiang UniversityHangzhou310058China School of Systems Engineering The University of ReadingReadingRG66AYUK School of Computer Engineering Research Center of Digital Forensics of Ministry of EducationNanjing University of Information Science&TechnologyNanjing210044China School of Business Nanjing UniversityNanjing210093China

With the rapid development of information technology,the development of blockchain technology has also been deeply *** performing block verification in the blockchain network,if all transactions are verified on the chain,this will cause the accumulation of data on the chain,resulting in data storage *** the same time,the security of data is also challenged,which will put enormous pressure on the block,resulting in extremely low communication efficiency of the *** traditional blockchain system uses theMerkle Tree method to store *** verifying the integrity and correctness of the data,the amount of proof is large,and it is impossible to verify the data in batches.A large amount of data proof will greatly impact the verification efficiency,which will cause end-to-end communication delays and seriously affect the blockchain system’s stability,efficiency,and *** order to solve this problem,this paper proposes to replace the Merkle tree with polynomial commitments,which take advantage of the properties of polynomials to reduce the proof size and communication *** realizing the ingenious use of aggregated proof and smart contracts,the verification efficiency of blocks is improved,and the pressure of node communication is reduced.

关键词： Blockchain cryptographic commitment smart contract data storage

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Chatbot and its Impact on the Retail Industry 3

Chatbot and its Impact on the Retail Industry

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Tatikonda, Reshmi Kempanna, M. Thatikonda, Ramya Bhuvanesh, A. Thota, Ragupathi Keerthanadevi, R. Department of Computer Information Systems University of the Central Missouri Warrensburg United States Department of AI and ML Bangalore Institute of Technology Bengaluru India Department of Information Technology University of the Cumberlands Williamsburg United States Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli India Department of Information Technology Jawaharlal Technological University Hyderbad India Department of Computer Science and Engineering PSN College of Engineering and Technology Tirunelveli India

ISBN: (纸本)9798331527549

This study explores the transformative potential of chatbots in the retail industry, highlighting their role in enhancing customer engagement, optimizing business processes, and revolutionizing the nature of retail interactions. Through a comprehensive review of existing literature and case studies, the paper delves into the cognitive processes underlying human-chatbot interactions and examines their impact on customer experience, satisfaction, and loyalty. Chatbots serve a wide array of operational functions, including inventory management, automated order processing, data-driven analytics, and employee training. These functionalities streamline workflows, improve efficiency, and maximize resource utilization. The paper further explores the innovative applications of chatbots, including AI-driven personalization, conversational commerce, and seamless integration with emerging technologies. By analyzing both the current applications and challenges of chatbot technology in retail, the study uncovers the exciting opportunities they present for shaping the future of retail by fostering more personalized, efficient, and interactive customer experiences. © 2025 IEEE.

关键词： Inventory control

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Enhancing Robustness in Multi-Agent Actor-Critic with Double Actors 38

Enhancing Robustness in Multi-Agent Actor-Critic with Double...

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38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

作者： Han, Xue Cui, Peng Zhang, Ya Southeast University School of Automation Nanjing210096 China Science and Technology on Information Systems Engineering Laboratory Nanjing210007 China

ISBN: (纸本)9798350303636

In recent years, multi-agent reinforcement learning (MARL) has shown a strong ability to solve complex system tasks. How to solve the overfitting problem of MARL algorithm and how to improve the robustness of MARL algorithm are important issues that need to be considered when the algorithm is turned to practical application. In this paper, we discuss the application potential of two-actor networks in MARL algorithms. First, we design double actors based on MADDPG algorithm, to improve the robustness of learning strategies, which is called DA-MADDPG algorithm. In the DA-MADDPG, the central-decentralized mismatch caused by two actor networks updating with the same Q value is further discussed. A gradient updating optimization method for DA-MADDPG by KL divergence and clipped function is proposed respectively. Experimental results in MPE environment show that DA-MADDPG and DA-MADDPG bycclipped function can achieve better results than MADDPG algorithm. © 2023 IEEE.

关键词： clipped function double actors KL divergence Multi-Agent Reinforcement Learning

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Road Traffic Monitoring from Aerial Images Using Template Matching and Invariant Features

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Computers, Materials & Continua 2024年第3期78卷 3683-3701页

作者： Asifa Mehmood Qureshi Naif Al Mudawi Mohammed Alonazi Samia Allaoua Chelloug Jeongmin Park Department of Creative Technologies Air UniversityIslamabad46000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Engineering Tech University of KoreaGyeonggi-do15073South Korea

Road traffic monitoring is an imperative topic widely discussed among *** used to monitor traffic frequently rely on cameras mounted on bridges or ***,aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger *** this end,different models have shown the ability to recognize and track ***,these methods are not mature enough to produce accurate results in complex road ***,this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image *** extracted frames were converted to grayscale,followed by the application of a georeferencing algorithm to embed coordinate information into the *** masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring ***,Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise *** preprocessing,the blob detection algorithm helped detect the *** of varying sizes have been detected by implementing a dynamic thresholding *** was done on the first image of every ***,to track vehicles,the model of each vehicle was made to find its matches in the succeeding images using the template matching *** further improve the tracking accuracy by incorporating motion information,Scale Invariant Feature Transform(SIFT)features have been used to find the best possible match among multiple *** accuracy rate of 87%for detection and 80%accuracy for tracking in the A1 Motorway Netherland dataset has been *** the Vehicle Aerial Imaging from Drone(VAID)dataset,an accuracy rate of 86%for detection and 78%accuracy for tracking has been achieved.

关键词： Unmanned Aerial Vehicles(UAV) aerial images dataset object detection object tracking data elimination template matching blob detection SIFT VAID

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