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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Zhu, Ke Wang, Zhixin Jiang, Tao Xiao, Yingyuan Zheng, Wenguang Hsu, Ching-Hsien Tianjin University of Technology Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China He'nan University of Economics and Law School of Computer and Information Engineering Zhengzhou China Asia University Department of Computer Science and Information Engineering Taichung Taiwan

ISBN: (纸本)9781665405270

As a representative of nonfunctional features, the Quality-of-Service (QoS) plays an important role in recommending the best service to users. To obtain the QoS value of web services, researchers have proposed many web service QoS prediction methods. However, most existing methods, e.g., the collaborative filtering method, leverage the historical user service invocation information to predict the QoS value, and only consider the QoS information of similar users or services, and ignore the attributes and characteristics of users and services. In this paper, we propose a Deep Feature Interaction Learning method, called DFIL for short, for web service QoS prediction. Specifically, (1) DFIL constructs a feature extraction method to effectively obtain the features of users and services. (2) DFIL trains a novel neural network for QoS prediction, which can accurately find the potential relationship between users and services based on the feature information, and provide good web service recommendation. © 2022 IEEE.

关键词： Quality of service

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Design and Implementation of an On-Board system for a Spider Robot 1st

Design and Implementation of an On-Board System for a Spider...

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1st International Conference on Computing and Emerging Technologies, ICCET 2023

作者： Nauman, Nasir Mustafa, Hirra Ashraf, Abrar Khalil, Hisham Department of Computer Science Superior University Lahore Pakistan Department of Computer Science Sharif College of Engineering and Technology Lahore Lahore Pakistan Embedded System and Robotics Lab Department of Technology The University of Lahore Lahore Pakistan

ISBN: (纸本)9783031776199

This paper presents the design of a system onboard spider robot for rescue services. The spider has the six-leg walking pattern that contains the total number of 18 servo motors. In order to reduce the payload, the abdomen of the spider is made with RF4 printed circuit board (PCB). Whereas, the abdomen is also used as the main structure of the robot to attach the rest of the system. Wherein, two pulse-width modulations (PWM) module are controlled by Arduino Nano, the Bluetooth module is used for communication link and bulk converter for the power supply. The spider robot is operated with an in-house developed Android application. It has been observed that the spider robot has a stable walking response along with the multiple excise performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Machine design

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Efficient Adaptive Batching of DNN Inference Services for Improved Latency

Efficient Adaptive Batching of DNN Inference Services for Im...

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International Conference on Information Networking

作者： Osama Khan Junyeol Yu Yeonjae Kim Euiseong Seo Dept. of AI System Engineering Sungkyunkwan University Suwon Republic of Korea Dept. of Computer Science and Engineering Sungkyunkwan University Suwon Republic of Korea

ISBN: (数字)9798350330946

ISBN: (纸本)9798350330953

With the rising success of deep neural network (DNN) applications, GPU servers are increasingly utilized to provide DNN inference services. Batching, in which multiple incoming requests are grouped together for simultaneous processing, is a common practice for the DNN inference serving systems to enhance GPU utilization and thus lower the service cost. While batching enhances GPUs’ efficiency, it can extend the end-to-end latency for each individual request. This delay is especially noticeable when requests are infrequently incoming to form a complete batch. On the other hand, when the request rate is high, a consistently small batch size may not fully harness the GPU’s capacity. Handling requests one by one might decrease this latency but also raise energy consumption. Therefore, it’s crucial to strike a balance between energy efficiency and latency, optimizing the batch size to ensure timely processing without compromising performance under varying request rates. Many existing solutions aim to fully use user-set deadlines, which, even though they maximize, can heighten perceived latency, particularly in larger pipelines. In this paper, we introduce a dynamic batching strategy tailored to adapt batch sizes in real-time for varying request rates. Our method aims to balance the time taken to assemble a batch and the time required to execute it, ensuring efficient energy consumption and prompt response times. By dynamically adjusting batch size relative to the current request rate, we strive to ensure that the collection duration for a batch coincides with its execution duration. This will minimize the latency as much as possible while ensuring we utilize the batching to provide an efficient solution. We built our approach using Triton, a platform for AI model deployment and execution. Our evaluation with four different DNN models shows that our technique achieved marginally close latency as the batching deployed by the Triton inference server and improved the energy

关键词： Energy consumption Adaptation models Pipelines Graphics processing units Artificial neural networks Energy efficiency User experience

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SumPA: Efficient Pattern-Centric Graph Mining with Pattern Abstraction 21

SumPA: Efficient Pattern-Centric Graph Mining with Pattern A...

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Proceedings of the 30th International Conference on Parallel Architectures and Compilation Techniques

作者： Chuangyi Gui Xiaofei Liao Long Zheng Pengcheng Yao Qinggang Wang Hai Jin National Engineering Research Center for Big Data Technology and System/Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781665442787

Graph mining aims to explore interesting structural information of a graph. Pattern-centric systems typically transform a generic-purpose graph mining problem into a series of subgraph matching problems for high performance. Existing pattern-centric mining systems reduce the substantial search space towards a single pattern by exploring a highly-optimized matching order, but inherent computational redundancies of such a matching order itself still suffer severely, leading to significant performance degradation. The key innovation of this work lies in a general redundancy criterion that characterizes computational redundancies arising in not only handing a single pattern but also matching multiple patterns simultaneously. In this paper, we present SumPA, a high-performance pattern-centric graph mining system that can sufficiently remove redundant computations for any complex graph mining problems. SumPA features three key designs: (1) a pattern abstraction technique that can simplify numerous complex patterns into a few simple abstract patterns based on pattern similarity, (2) abstraction-guided pattern matching that completely eliminates (totally and partially) redundant computations during subgraph enumeration, and (3) a suite of system optimizations to maximize storage and computation efficiency. Our evaluation on a wide variety of real-world graphs shows that SumPA outperforms the two state-of-the-art systems Peregrine and GraphPi by up to 61.89× and 8.94×, respectively. For many mining problems on large graphs, Peregrine takes hours or even days while SumPA finishes in only a few minutes.

关键词： data reuse

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Pilot Study on Aerial Wireless Superdirectional Sound Sources Utilizing Parametric Array Loudspeakers via Local 5G Networks

Pilot Study on Aerial Wireless Superdirectional Sound Source...

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International Symposium on Antennas and Propagation (ISAP)

作者： Yohei Iihoshi Kan Okubo Department of Electrical Engineering and Computer Science Faculty of System Design Tokyo Metropolitan University Hino Tokyo

This study presents a pilot study on the potential future use of aerial sound sources utilizing parametric array loudspeakers (PALs) in drones via fifth-generation (5G) networks. Integrating superdirectional loudspeakers with high-speed wireless communication is essential for projecting sound from the air to specific locations using drones. Herein, as an experimental approach, we have evaluated the performance of wireless communication, specifically local 5G, for realizing aerial sound sources. The results demonstrated the high performance of the local 5G connection intended for PALs in the air.

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Solar Harmoni Air: The Integration of Solar Power for Intelligent Outdoor Air Purification and Monitoring system

Solar Harmoni Air: The Integration of Solar Power for Intell...

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Recent Advances in science and engineering Technology (ICRASET), International Conference on

作者： B M Hemanth Kumar Neha Prakash C Namitha H R Chandana G Jeevan G Manjunath Electronics and Communication Engineering Malnad College of Engineering (Affiliated by VTU) Hassan INDIA Computer Science and Business System Enginnering Malnad College of Engineering (Affiliated by VTU) Hassan INDIA

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

The escalating global concerns over air pollution necessitate innovative solutions to mitigate its adverse effects on public health and the environment. In response, we present a pioneering solar-powered air purifier integrated with an advanced air quality monitoring system. An outdoor air purifier with dual sensors capable of detecting various pollutants, including ammonia, sulphide, benzene, smoke, and carbon monoxide, is developed. The system activates its filtration mechanisms when air quality falls below certain thresholds, utilizing a two-tiered filtration system comprising pre-filter and fine filter layers, effectively reducing volatile organic compound (VOC) pollutants. Real-time monitoring and effective purification capabilities, achieving purification levels ranging approximately from 70% to 80%, were accomplished. This innovative solution offers a comprehensive approach to addressing air quality concerns in outdoor environments, providing efficient purification capabilities and operating for up to 14 hours a day.

关键词： Air cleaners Volatile organic compounds Purification Filtration Solar energy Air pollution Real-time systems Internet of Things Solar panels Sustainable development

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MBAPIS: Multi-Level Behavior Analysis Guided Program Interval Selection for Microarchitecture Studies 23

MBAPIS: Multi-Level Behavior Analysis Guided Program Interva...

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Proceedings of the 32nd International Conference on Parallel Architectures and Compilation Techniques

作者： Hongwei Cui Yujie Cui Honglan Zhan Shuhao Liang Xianhua Liu Chun Yang Xu Cheng Engineering Reserach Center of Microprocessor & System Ministry of Education School of Computer Science Peking University Beijing National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Beijing

ISBN: (纸本)9798350342543

Understanding program behavior is crucial in computer architecture research, but the growing size of benchmarks makes analyzing and simulating entire programs increasingly challenging. In practice, researchers often select representative program intervals for analysis and testing. These intervals are different sections of continuous execution of a program. SimPoint is a well-known method for selecting representative intervals using hardware-independent information. However, when focusing on a specific microarchitecture study, it is desirable to select intervals that are more relevant to that study. For instance, intervals with more branch mispredictions are more appropriate for branch prediction studies. We refer to these intervals as "tailored intervals" for branch prediction *** paper presents a Multi-level Behavior Analysis guided Program Interval Selection (MBAPIS) for selecting tailored intervals. For a given microarchitecture study, the first level of MBAPIS uses hardware performance counters to prioritize selecting the intervals that exhibit clearer microarchitectural characteristics relevant to that study. The second level analyzes the processor performance bottlenecks to further select the intervals where the concerned microarchitecture design more strongly impacts performance. Finally, MBAPIS performs clustering analysis with the basic block information of each interval selected by the first two levels, and selects the representative intervals among them while preserving the diverse software behavior. Additionally, we present a general and extensible interval-replaying design to accurately re-execute selected *** SPEC CPU2006 and CPU2017 benchmarks are used for evaluation. The results demonstrate that MBAPIS can select representative and tailored intervals for two typical microarchitecture studies and deliver accurate estimates of the concerned hardware events for all tailored intervals in each benchmark, with an average error rate of le

关键词： Benchmark

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Enhancing Deep Learning Soil Moisture Forecasting Models by Integrating Physics-based Models

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Advances in Atmospheric sciences 2024年第7期41卷 1326-1341页

作者： Lu LI Yongjiu DAI Zhongwang WEI Wei SHANGGUAN Nan WEI Yonggen ZHANG Qingliang LI Xian-Xiang LI School of Atmospheric Sciences Sun Yat-sen UniversityGuangzhou 510275China Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) Guangzhou 510275China Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies Guangzhou 510275China Institute of Surface-Earth System Science School of Earth System ScienceTianjin UniversityTianjin 300072China College of Computer Science and Technology Changchun Normal UniversityChangchun 130123China

Accurate soil moisture(SM)prediction is critical for understanding hydrological ***-based(PB)models exhibit large uncertainties in SM predictions arising from uncertain parameterizations and insufficient representation of land-surface *** addition to PB models,deep learning(DL)models have been widely used in SM predictions ***,few pure DL models have notably high success rates due to lacking physical ***,we developed hybrid models to effectively integrate the outputs of PB models into DL models to improve SM *** this end,we first developed a hybrid model based on the attention mechanism to take advantage of PB models at each forecast time scale(attention model).We further built an ensemble model that combined the advantages of different hybrid schemes(ensemble model).We utilized SM forecasts from the Global Forecast system to enhance the convolutional long short-term memory(ConvLSTM)model for 1–16 days of SM *** performances of the proposed hybrid models were investigated and compared with two existing hybrid *** results showed that the attention model could leverage benefits of PB models and achieved the best predictability of drought events among the different hybrid ***,the ensemble model performed best among all hybrid models at all forecast time scales and different soil *** is highlighted that the ensemble model outperformed the pure DL model over 79.5%of in situ stations for 16-day *** findings suggest that our proposed hybrid models can adequately exploit the benefits of PB model outputs to aid DL models in making SM predictions.

关键词： soil moisture forecasting hybrid model deep learning ConvLSTM attention mechanism

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Integration of SDN and Digital Twin for the Intelligent Detection of DoC Attacks in WRSNs

arXiv

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arXiv 2025年

作者： Qaisar, Muhammad Umar Farooq Yuan, Weijie Han, Guangjie Ahmed, Adeel Liu, Chang Piran, Md. Jalil School of Computer Science Northwestern Polytechnical University China School of System Design and Intelligent Manufacturing Southern University of Science and Technology China Department of Internet of Things Engineering Hohai University China School of Computer Science and Technology University of Science and Technology of China China School of Information Engineering Guangdong University of Technology Guangzhou China Department of Computer Science and Engineering Sejong University Seoul05006 Korea Republic of

Wireless rechargeable sensor networks (WRSNs), supported by recent advancements in wireless power transfer (WPT) technology, hold significant potential for extending network lifetime. However, traditional approaches often prioritize scheduling algorithms and network optimization, overlooking the security risks associated with the charging process, which exposes the network to potential attacks. This paper addresses this gap by integrating Software-Defined Networking (SDN) and Digital Twin technologies for the intelligent detection of Denial of Charging (DoC) attacks in WRSNs. First, it leverages the flexibility and intelligent control of SDN, in combination with Digital Twin, to enhance real-time detection and mitigation of DoC attacks. Second, it employs four key metrics to detect such attacks including charging request patterns, energy consumption, behavioral and reputation scores, and charging behavior and efficiency. The numerical results demonstrate the superior performance of the proposed protocol in terms of energy usage efficiency, survival rate, detection rate, and travel distance. © 2025, CC BY.

关键词： State of charge

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Dynamic Memorable Password Generation Algorithm (DyMP-Gen)

Dynamic Memorable Password Generation Algorithm (DyMP-Gen)

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International Conference on Computing, Networking and Communications (ICNC)

作者： Baker Al Smadi Dileon Saint-Jean Majd Tahat Hosam Alamleh Syed Raza Ali Abdullah S. AlQahtani Computer Science and Digital Technologies Grambling State University Grambling LA USA Computer Science and Data Science American Public University System Computer Science and Digital Tech Grambling State University Grambling LA USA Cyberspace Engineering Louisiana Tech University Ruston LA USA Computer Science University of North Carolina Wilmington Wilmington North Carolina Computer Science and Data Science American Public University System Charles Town West Virginia Computer Systems Technology North Carolina A&T State University Greensboro North Carolina

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

The increasing frequency and sophistication of cyberattacks necessitate a reevaluation of traditional password practices. This paper introduces DyMP-Gen (Dynamic Memorable Password Generation) Algorithm, a novel algorithm designed to enhance password security while prioritizing usability. DyMP-Gen allows users to generate strong, unique passwords that evolve over time, mitigating the risks associated with static, password reuse, and leaked passwords. By combining a user pre-selected static component with dynamically generated elements, a password based on current time and date is created. DyMP-Gen ensures that passwords are both memorable and resilient to cracking attempts. The algorithm’s simplicity and offline functionality further enhance its usability, eliminating the need for additional devices or software. Through its innovative approach, DyMP-Gen offers a promising solution to the pervasive challenges of password security.

关键词： Surveys Heuristic algorithms Software algorithms Authentication Passwords Software Security Synchronization Servers Usability

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