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作者： Ma, Chenlin Yang, Hao Wu, Shangyu Wang, Yi Mao, Rui Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China City University of Hong Kong Department of Computer Science Hong Kong Shenzhen University College of Computer Science and Software Engineering The Shenzhen Institute of Computing Sciences Shenzhen518060 China

The log-structured merge-tree (LSM-tree)-based key-value store has been widely adopted by many large-scale data storage applications for its excellent write performance. However, such write performance gains mainly come from scarifying read performance due to the leveled and log-structured intrinsic characteristics of the LSM-tree. Therefore, the critical challenge of the existing LSM-tree is how to improve the read efficiency by reducing read amplification. This article for the first time proposes Tidal-tree-Mem, a novel data structure where data flow inside the LSM-tree-like Tidal waves. First, a floating strategy is proposed to allow frequently accessed files at the bottom of the LSM-tree to move to higher positions, reducing read amplification. Second, a stretching strategy is proposed to vary the shape of the LSM-tree to adapt to workloads with different characteristics. To evaluate the performance of Tidal-tree-Mem, we conduct a series of experiments using standard benchmarks from YCSB. The experimental results show that Tidal-tree-Mem can effectively reduce read amplification and the overall latency by over 71.94% and 47.34%, respectively, compared with representative schemes. © 1982-2012 IEEE.

关键词： Benchmarking

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Event-Based Vision for Robot Soccer

Event-Based Vision for Robot Soccer

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27th RoboCup International Symposium, 2024

作者： Marjani Bajestani, Seyed Ehsan Beltrame, Giovanni Computer and Software Engineering Department Polytechnique Montreal MontrealQCH3T 1J4 Canada

ISBN: (纸本)9783031858581

Object tracking is one of the main challenges in soccer-playing robots. Due to its fast movement, detecting and tracking the soccer ball is challenging for goalkeepers in both humanoid and wheeled robots. To speed up object tracking, we propose the use of Event-based Cameras (ECs). ECs are bio-inspired sensors that asynchronously report changes in brightness for each pixel. Because of their high dynamic range, pixel bandwidth, temporal resolution, low power consumption, and computational simplicity, they are beneficial for vision-based projects in challenging lighting conditions and can detect fast movements with their microsecond response time. We created a dataset using two different event-based cameras from iniVation and Prophesee that recorded events in the lab and during Middle Size League matches at RoboCup 2023. Additionally, we created a Graphical User Interface (GUI) working with the Robot Operating System (ROS) to simplify camera and camera-projector setup calibration for RoboCup participants. The proposed ROS GUI is able to control the camera bias parameters online and publish a stream of events in addition to event "frames" on ROS topics. These advancements will help all RoboCup teams shift from frame-based to event-based technologies, enhancing ball detection regardless of color or lighting. The dataset is available publicly: ***/MISTLab/event_based_data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Graphical user interfaces

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MR Image Quality Assessment via Enhanced Mamba: A Hybrid Spatial-Frequency Approach

MR Image Quality Assessment via Enhanced Mamba: A Hybrid Spa...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lou, Yiwei Xu, Dexuan Zhang, Rongchao Zhang, Jiayu Cao, Yongzhi Wang, Hanpin Huang, Yu Key Laboratory of High Confidence Software Technologies Peking University Ministry of Education China Peking University School of Computer Science Beijing China Peking University School of Software and Microelectronics Beijing China Peking University National Engineering Research Center for Software Engineering Beijing China

ISBN: (纸本)9798350386226

Magnetic resonance (MR) image quality assessment plays a crucial role in disease diagnosis and data analysis. Existing methods typically treat the data as images and process them with convolutional networks, thereby ignoring the sequential characteristics of MR data. In this paper, we propose a hybrid spatial-frequency network (HSFNet) for MR image quality assessment, which extracts MR image quality features in both spatial and frequency domains. Specifically, within each data domain, information from local images and global sequences is iteratively integrated by applying a Mamba-based cascading processing module for multiple times. Extensive experiments on both T1-weighting and T2-weighting MR datasets demonstrate the proposed method's effectiveness and generalization ability in comparison with state-of-the-art MR image quality assessment methods. © 2024 IEEE.

关键词： Image quality

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Preprocessing Method for Color-Adaptive Pix2Pix Generative Neural Network Data

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Journal of Institute of Control, Robotics and Systems 2023年第6期29卷 521-526页

作者： Lee, Hayoung Son, Bong-Ki Lee, Jaeho Department of IT Media Engineering Duksung Women’s University Korea Republic of Department of Computer Science and Engineering Seowon University Korea Republic of Department of Software Duksung Women’s University Korea Republic of

Adversarial generative neural networks (GANs) have received increased research interest in recent years, and they have been applied in various fields, such as image generation, transformation, and restoration. Pix2Pix, a popular GAN model, trains data in which input and output data are paired. However, because this method is based on sketches, it cannot consider colors once the training is completed. Not only to apply colors requires coloring on edges of all training data, which requires high effort to build data, but also it is difficult to automatically generate edges. In this paper, we proposed a method that considers colors and performs the automatic generation of edges required for Pix2Pix. The proposed method extracts edge data by using the canny edge detection algorithm and constructs color edge and color background data by using the extracted edge data. The applicability of the proposed method was verified by conducting experiments in the fashion field. © ICROS 2023.

关键词： Color

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nPGSAO: A Hybrid Particle Swarm Optimization and Genetic Algorithm With Niching Technology for Edge Server Placement

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IEEE Internet of Things Journal 2025年第12期12卷 19370-19383页

作者： Wang, Bo Zhang, Zhifeng Song, Ying Chen, Ming Liu, Dongqing Zhengzhou University of Light Industry Software Engineering College Zhengzhou450002 China Beijing Information Science and Technology University Computer School Beijing100101 China Tongling University School of Mathematics and Computer Science Tongling China The First Affiliated Hospital of Zhengzhou University National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou450052 China

In this article, we focus on the edge server placement problem (ESPP). ESPP is to decide positions (edge stations) where purchased edge servers (ESs) are placed when a service provider builds or upgrades its edge computing solution. The decision of ESPP largely determines the overall service quality and resource efficiency. We aim at providing ESPP solutions for optimizing the service quality in terms of the system response time, given a set of purchased ESs. To achieve this aim, we first deduce the optimal request dispatch for each edge station based on queue theory and Binomial theorem. Then, based on the deduced result, we formulate ESPP into a discrete nonlinear constrained optimization model. Because ESPP is NP-hard and generally multimodality, we designed a hybrid meta-heuristic algorithm to combine advantages of particle swarm optimization (PSO) and genetic algorithm (GA) as well as the niching technology, which is named nPGSAO. The basic idea of nPGSAO is grouping similar individuals into a niche and finding the niche best. Then, nPGSAO evolves each individual by integrating the swarm intelligence of PSO into the evolutionary process of GA based on its personal and niching best. To evaluate the performance of nPGSAO, we conducted extensive simulated experiments. The results show that nPGSAO achieves 5.78%–40.4% better response time with varied ESPP scale, compared to 11 up-to-date algorithms, and has a linear time complexity with the ESPP scale. These confirm the superior performance and scalability of nPGSAO. © 2014 IEEE.

关键词： Genetic algorithms

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Automation of Monitoring and Optimization of Docker Containers Using Artificial Intelligence 24

Automation of Monitoring and Optimization of Docker Containe...

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24th International Symposium INFOTEH-JAHORINA, INFOTEH 2025

作者： Stanišić, Sava Luković, Vanja Belotić, Branislav Faculty of Technical Sciences Department of Computer and Software Engineering Čačak Serbia Faculty of Technical Sciences Department of Information Technologies Čačak Serbia

ISBN: (纸本)9798331515799

The increasing adoption of Docker containers in modern software development requires effective monitoring and optimization to ensure high performance and efficient resource utilization. This paper provides an overview of the various tools and technologies that leverage artificial intelligence (AI) to automate the monitoring and optimization of Docker containers. The authors explore the capabilities of AI-driven tools such as machine learning-based anomaly detection, predictive analytics, and automated resource management. By examining these tools, the authors highlight their benefits in enhancing container performance, scalability, and availability. Through comparative analysis, the strengths and limitations of each tool and their impact on container management are discussed. The findings underscore the potential of AI to revolutionize container monitoring and optimization, leading to more efficient and robust IT infrastructure. © 2025 IEEE.

关键词： Resource allocation

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A Vulnerability Accessibility Analysis Framework for software Supply Chain 10

A Vulnerability Accessibility Analysis Framework for Softwar...

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10th International Symposium on System Security, Safety, and Reliability, ISSSR 2024

作者： Xiao, Lei Gao, Zhenyu Xu, Qizhen Chang, Le Zhu, Shunzhi Xiamen University of Technology College of Computer and Information Engineering Xiamen361024 China Software Security Company Chengdu610041 China

ISBN: (纸本)9798350362930

The security issues brought by third-party components in software are becoming increasingly prominent. However, current component analysis tools only analyze the existence of the vulnerabilities introduced by components, resulting in much false positives. In this paper, we present a vulnerability accessibility analysis framework to analyze whether these vulnerabilities would be actually called by the project. © 2024 IEEE.

关键词： Supply chains

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Parameter-Efficient Federated Cooperative Learning for 3-D Object Detection in Autonomous Driving

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IEEE Internet of Things Journal 2025年第12期12卷 20314-20325页

作者： Chi, Fangyuan Wang, Yixiao Nasiopoulos, Panos Leung, Victor C. M. The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada Toronto AI Lab LG Electronics TorontoONM5V 1M3 Canada Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

In the rapidly evolving field of autonomous driving, accurately detecting and understanding dynamic environments remains a challenge. Federated learning (FL) offers a promising approach by integrating decentralized models from multiple connected autonomous vehicles (CAVs) to enhance the performance of deep-learning (DL)-based object detection methods. However, traditional FL faces hurdles, such as extensive data synchronization requirements, limited data variance, and high communication costs. This article introduces a federated cooperative learning framework that addresses these challenges by combining data from both CAVs and roadside units. The framework combines local cooperative perception with global FL through a parameter-efficient FL adapter and a lazy communication strategy, improving DL-based object detection capabilities across diverse driving scenarios while significantly reducing bandwidth requirements. We also present a novel multiagent-multitown dataset Vehicle-to-Everything-Fed, specifically developed to validate the effectiveness of our approach under various conditions. Notably, our framework retains 97.51% of the detection accuracy achieved by full-model FL, while utilizing only 1.4% of the bandwidth typically required, demonstrating substantial improvements over conventional FL strategies. This study underscores the potential of our tailored approach to substantially enhance autonomous vehicle technologies with minimal resource utilization. © 2014 IEEE.

关键词： Bandwidth

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Efficient Fingerprint Database Organization for Optimized Recognition 7

Efficient Fingerprint Database Organization for Optimized Re...

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7th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2025

作者： Dhas, Jeffrey Jeeva Koshy, Adhip Dua, Nishit Ajitha, D. Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Vellore Vellore India

ISBN: (纸本)9798331515683

A popular biometric identification method, renowned for its dependability and individuality in personal identification, is fingerprint recognition. This article presents an efficient fingerprint identification system that significantly reduces processing times for 100,000 fingerprints from 8 minutes to less than 5 seconds by utilizing multithreading, ANSI 378 templates, and Source AFIS. The system overcomes the time-consuming image-to-template conversion procedure and increases overall storage efficiency by utilizing parallel processing and preformatted templates (ANSI/ISO). The fingerprint matching system incorporates Directional Field, CNN models for identifying fingerprint types, and Finger Code methods to classify fingerprints, improving precision and efficiency. © 2025 IEEE.

关键词： ANSI 378 biometric identification CNN Fingerprint recognition minutiae multithreading SourceAFIS synthetic fingerprints template matching

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An Exploratory Study on Code Quality, Testing, Data Accuracy, and Practical Use Cases of IoT Wearables 7

An Exploratory Study on Code Quality, Testing, Data Accuracy...

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7th Conference on Cloud and Internet of Things, CIoT 2024

作者： Minani, Jean Baptiste El Fellah, Yahia Ahmed, Sanam Sabir, Fatima Moha, Naouel Gueheneuc, Yann-Gael Concordia University Computer Science and Software Engineering Department MontrealQC Canada École de Technologie Supérieure Département de Génie Logiciel et des Ti MontrealQC Canada University of the Punjab Department of Computer Sciences and Software Engineering Lahore Pakistan

ISBN: (纸本)9798350356717

The growth of the Internet of Things (IoT), particularly in wearable devices like Fitbits, has raised challenges related to source code quality, testing, data accuracy, and practical applications. This paper investigates issues in Fitbit apps by (1) analyzing GitHub repositories of Fitbit projects to identify code quality issues, (2) using Large Language Models (LLMs) to automate testing, (3) comparing data variations across different Fitbit models, and (4) experimenting with real-world use cases for Fitbit devices. Our analysis of 16 GitHub repositories revealed code quality issues in Fitbit apps, highlighting the need for better practices. Using LLMs like ChatGPT-4, we generated unit tests with 100% coverage. Data comparisons across Fitbit Versa models showed consistent accuracy. Finally, we showed the potential of wearable devices in the real-world with two practical use cases: health monitoring with robotic assistance and location-based tracking. These findings open new avenues for research in wearables. © 2024 IEEE.

关键词： Data accuracy

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