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IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Kang Chen Ming Wen Haoxiang Jia Rongxin Wu Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology (HRUST) Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security Jin YinHu Laboratory Wuhan China School of Informatics Xiamen University Xiamen China School of Computer Science and Technology HUST Wuhan China Cluster and Grid Computing Lab

ISBN: (数字)9798350330663

ISBN: (纸本)9798350330670

Software systems often encounter various errors or exceptions in practice, and thus proper error handling code is essential to ensure the reliability of software systems. Unfortunately, error handling code is often bug-prone, while sufficiently testing them is challenging as such code often cannot be triggered under normal conditions. Motivated by this, recent studies have proposed to leverage software fault injection (SFI) based fuzzing to discover potential bugs in complicated error handling code. Despite the promising results achieved, their effectiveness and efficiency are still compromised in practice due to the huge search space of error sites, inadequate fuzzing guidance, and the overhead induced by context-sensitive SFI. To achieve effective and efficient testing of error handling code, this study presents AFL-FI, which first utilizes a similarity-based method to identify suspicious error sites, and then incorporates the idea of error site coverage to guide the fuzzing process. Finally, the design of lightweight context-sensitive SFI enables AFL-FI to execute test cases efficiently. We evaluate AFL-FI on eight large-scale open-source projects, and the results show that it can outperform existing state-of-the-art fuzzing tools significantly in terms of branch code coverage. More importantly, AFL-FI has discovered 13 previously unknown bugs, and all of them have been confirmed while 12 of them have been fixed. Besides, our evaluation also demonstrates that all the key designs of AFL- F I are effective that contribute significantly to its overall performance.

关键词： Codes computer bugs Fuzzing Software systems Software reliability Testing

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L2GC: Lorentzian Linear Graph Convolutional Networks For Node Classification

arXiv

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arXiv 2024年

作者： Liang, Qiuyu Wang, Weihua Bao, Feilong Gao, Guanglai College of Computer Science Inner Mongolia University Hohhot China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Mongolian Information Processing Technology Hohhot China

Linear Graph Convolutional Networks (GCNs) are used to classify the node in the graph data. However, we note that most existing linear GCN models perform neural network operations in Euclidean space, which do not explicitly capture the tree-like hierarchical structure exhibited in real-world datasets that modeled as graphs. In this paper, we attempt to introduce hyperbolic space into linear GCN and propose a novel framework for Lorentzian linear GCN. Specifically, we map the learned features of graph nodes into hyperbolic space, and then perform a Lorentzian linear feature transformation to capture the underlying tree-like structure of data. Experimental results on standard citation networks datasets with semi-supervised learning show that our approach yields new state-of-the-art results of accuracy 74.7% on Citeseer and 81.3% on PubMed datasets. Furthermore, we observe that our approach can be trained up to two orders of magnitude faster than other nonlinear GCN models on PubMed dataset. Our code is publicly available at https://***/llqy123/LLGC-master. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolution

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Sonar Object Detection Based on Global Context Feature Fusion and Extraction

Sonar Object Detection Based on Global Context Feature Fusio...

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International Conference on Intelligent Control and Information Processing (ICICIP)

作者： Wenwei Wu Xiaonan Luo School of Computer Science and Information Security Guilin University of Electronic Technology Guilin Huigu Institute of Artificial Intelligence Industrial Technology Guilin China National Local Joint Engineering Research Center of Satellite Navigation and Location Service Guilin University of Electronic Technology Guilin China

Automatic underwater target detection is a key task of sonar image processing and analysis, which aims to identify the target category and achieve accurate localization. However, sonar images are affected by seabed reverberation noise and complex background, resulting in great challenges for sonar target detection. YOLO network has shown excellent performance in the field of object detection, but its network structure is too complex, and its detection effect on sonar targets is not good. To address these difficulties, this paper proposes a sonar image target detection network based on Global Context Feature Fusion and Extraction (GCFFE-Net), which can globally extract and fuse semantic features from sonar images at multiple scales, and bidirectionally transfer features between different levels, thus highlighting the target area. Specifically, the multi-scale channel feature fusion module (MCFF-Block) in the network uses dilated convolution, and enhances the semantic information interaction between the feature layers required for different anchor detection by adaptively adjusting the weights of different channels. The spatial context feature pyramid fusion module (SCPFF-Block) adopts a pyramid structure, and realizes the fusion of shallow features and deep features of different levels of size through bidirectional information transfer from top to bottom and from bottom to top, improving the network's perception ability of the spatial context information of the target. The hierarchical region of interest attention module (HAA-Block) aligns and pools the regions of interest of different levels of size, making the network more focused on the location of the target, and enhancing the accuracy and robustness of target detection. The paper conducts experiments on the public dataset UADT, and the results show that the network proposed in this paper has high performance and superiority in sonar image target detection.

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Layered Structure Aware Dependent Microservice Placement Toward Cost Efficient Edge Clouds

Layered Structure Aware Dependent Microservice Placement Tow...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Deze Zeng Hongmin Geng Lin Gu Zhexiong Li School of Computer Science China University of Geosciences Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Although the containers are featured by light-weightness, it is still resource-consuming to pull and startup a large container image, especially in relatively resource-constrained edge cloud. Fortunately, Docker, as the most widely used container, provides a unique layered architecture that allows the same layer to be shared between microservices so as to lower the deployment cost. Meanwhile, it is highly desirable to deploy dependent microservices of an application together to lower the operation cost. Therefore, the balancing of microservice deployment cost and the operation cost should be considered comprehensively to achieve minimal overall cost of an on-demand application. In this paper, we first formulate this problem into a Quadratic Integer Programming form (QIP) and prove it as a NP-hard problem. We further propose a Randomized Rounding-based Microservice Deployment and Layer Pulling (RR-MDLP) algorithm with low computation complexity and guaranteed approximation ratio. Through extensive experiments, we verify the high efficiency of our algorithm by the fact that it significantly outperforms existing state-of-the-art microservice deployment strategies.

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A Joint Visual Compression and Perception Framework for Neuralmorphic Spiking Camera

arXiv

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arXiv 2025年

作者： Feng, Kexiang Jia, Chuanmin Ma, Siwei Gao, Wen Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China National Engineering Research Center of Visual Technology Peking University Beijing100871 China Wangxuan Institute of Computer Technology Peking University Beijing100080 China

The advent of neuralmorphic spike cameras has garnered significant attention for their ability to capture continuous motion with unparalleled temporal resolution. However, this imaging attribute necessitates considerable resources for binary spike data storage and transmission. In light of compression and spike-driven intelligent applications, we present the notion of Spike Coding for Intelligence (SCI), wherein spike sequences are compressed and optimized for both bit-rate and task performance. Drawing inspiration from the mammalian vision system, we propose a dual-pathway architecture for separate processing of spatial semantics and motion information, which is then merged to produce features for compression. A refinement scheme is also introduced to ensure consistency between decoded features and motion vectors. We further propose a temporal regression approach that integrates various motion dynamics, capitalizing on the advancements in warping and deformation simultaneously. Comprehensive experiments demonstrate our scheme achieves state-of-the-art (SOTA) performance for spike compression and analysis. We achieve an average 17.25% BD-rate reduction compared to SOTA codecs and a 4.3% accuracy improvement over SpiReco for spike-based classification, with 88.26% complexity reduction and 42.41% inference time saving on the encoding side. Copyright © 2025, The Authors. All rights reserved.

关键词： Image compression

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Analysis of Deep Learning Algorithms for Prawn Aquaculture in a Challenging Environment 6

Analysis of Deep Learning Algorithms for Prawn Aquaculture i...

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6th International Conference on Applied Computational Intelligence in Information Systems, ACIIS 2023

作者： Zariful, Wafiq Tashim, Najeebah Az-Zahra Lim, Tiong Hoo Basri, Aida Maryam Chuprat, Suriayati Putra, Seno Adi Liu, Pengcheng Fakhrurroja, Hanif Universiti Teknologi Brunei Electrical and Electronic Engineering Bandar Seri Begawan Brunei Universiti Teknologi Brunei School of Applied Sciences and Mathematics Bandar Seri Begawan Brunei Universiti Teknologi Malaysia Razak Faculty of Technology and Informatics Johor Bahru Malaysia Telkom Universiti School of Industrial and System Engineering Bandung Indonesia University of York Department of Computer Science York United Kingdom Telkom University Research Center for Smart Mechatronics National Research and Innovation Agency Bandung Indonesia

ISBN: (纸本)9798350317428

Deep Learning plays a vital role in various domains, spanning surveillance, agriculture, and healthcare. However, the current landscape lacks comprehensive comparative studies, underscoring the needs to explore various convolutional neural networks (CNNs) for aquaculture industries. This study conducts a comprehensive comparative analysis on state-of-the-art object detection models to identify the most suitable approach for detecting freshwater prawns, specifically in challenging turbid and murky water conditions as there is a lack of comparison studies for underwater images of freshwater prawns. The evaluated models, including YOLOv8, Faster R-CNN (FRCNN), Single Shot Detector (SSD), and EfficientDet, are assessed based on the following metrics namely inference time, accuracy, image cropping size, and model memory footprint. Using a dataset comprising 2976 manually collected images of freshwater prawns, augmented to a total of 7148 images, our results demonstrate that YOLOv8 outperforms its counterparts, achieving an impressive mean Average Precision (mAP) accuracy of 83.71% with an inference time of 32.9 milliseconds. This positions YOLOv8 as a dependable choice for real-time object detection in challenging underwater environments and contributes to addressing the limited research comparing YOLOv8 with other popular object detection algorithms, thereby aiding model selection for similar object detection applications. © 2023 IEEE.

关键词： Object detection

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A Comparison of the Instructor-Trainee Dance Dataset Using Cosine similarity, Euclidean distance, and Angular difference

A Comparison of the Instructor-Trainee Dance Dataset Using C...

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International computer Science and engineering Conference (ICSEC)

作者： Thanawat Srikaewsiew Khatadet Khianchainat Atima Tharatipyakul Suporn Pongnumkul Sarunya Kanjanawattana Department of Computer Engineering Institute of Engineering Suranaree University of Technology Nakhon Ratchasima Thailand Data Science and Analytics Research Group National Electronics and Computer Technology Center (NECTEC) Pathumthani Thailand

ISBN: (纸本)9781665462730

The COVID-19 outbreak has restricted most outdoor activities, leads to increasing interest in exercise at home with online trainers. One issue of online exercise technology is the safety since improper motion might result in injury. As a basis to prevent improper motion, methods for evaluating the motion similarity between an instructor and a trainee are essential. Cosine similarity, Angular difference, and Euclidean distance are three general ways for the motion evaluation. This study aimed to determine the most effective way for analyzing the similarity of human motion on the dataset of instructor-led dances. We first experimented with the data to find the appropriate cut-off value for classifying posture into two classes based on the similarity score. Confusion matrix, precision, recall, F1-score, accuracy of the results were then used to compare the efficiency. We discovered that Cosine similarity had the highest accuracy, 82.77 percent at cut-off 93.

关键词： computer science COVID-19 Humanities Euclidean distance Safety Injuries

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Real-Time Scheduling in IoT Applications: A Systematic Review

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Sensors 2023年第1期23卷 232页

作者： Abolhassani Khajeh, Sima Saberikamarposhti, Morteza Rahmani, Amir Masoud Department of Computer Science Islamic Azad University Qeshm International Branch Qeshm 79515/1393 Iran Department of Computer Engineering Islamic Azad University South Tehran Branch Tehran*** Iran Future Technology Research Center National Yunlin University of Science and Technology Douliu64002 Taiwan

The Internet of Things (IoT) is a telecommunication network in the next generation of applications with the rapid progress of wireless sensor network techniques that have touched many spheres of life today. Hardware, telephony, communications, storage, secure platforms, software and services, and data processing platforms are all part of the IoT environment. IoT sensors collect data from their environment and share it by connecting to the Internet gateway. These sensors often perform tasks without human intervention. This article aims to review real-time scheduling in the IoT to fully understand the issues raised in this area published from 2018 to 2022. A classification for IoT applications based on practical application is provided for selected studies. Selected studies include healthcare, infrastructure, industrial applications, smart city, commercial applications, environmental protection, and general IoT applications. Studies are sorted into groups based on related applications and compared based on indicators such as performance time, energy consumption, makespan, and assessment environments depending on the provided classification. Finally, this paper discusses all reviewed studies’ main concepts, disadvantages, advantages, and future work. © 2022 by the authors.

关键词： Internet of things

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Astrophysics-based transit search optimization heuristics for parameter estimation of multi-frequency sinusoidal signals

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Modern Physics Letters B 2024年第34期38卷

作者： Malik, Naveed Ahmed Chang, Ching-Lung Chaudhary, Naveed Ishtiaq Raja, Muhammad Asif Zahoor Shu, Chi-Min Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology Yunlin Douliou 64002 Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Douliou 64002 Taiwan Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou 64002 Taiwan Department of Safety Health and Environmental Engineering National Yunlin University of Science and Technology Yunlin Douliou 64002 Taiwan

This study investigates an astrophysics-based Transit search optimization algorithm (TSOA) for solving a challenging engineering problem. This innovative strategy uses the fundamentals of physics to improve the accuracy and efficacy of problem solving techniques. Harmonic distortions in power systems have become a massive challenge because of the nonlinear loads associated with the electrical power distribution system. Overheating of the equipment, motor failure, capacitor failure, and improper power metering are all issues caused by harmonic distortion. A new examination of the causes and consequences of these issues, as well as the status of hardware and software available for harmonic evaluation, is necessary in light of the unprecedented advancements in power electronic devices and their integration at all levels in the power and energy system. In order to estimate phase and amplitude simultaneously with a specified frequency, an objective function of power system harmonics is created. Keeping in mind the adverse effects of harmonics, parameter estimation is carried out under various conditions by taking different particle sizes and signal-To-noise ratios. TSOA proved its efficacy for both phase and amplitude parameters under different situations and precisely estimated the harmonics signal up to an accuracy of 1.1648E-15. Two harmonic signals were taken in this research work, and the best MSE values achieved are 9.748E-4, 8.287E-07, 6.157E-10, and 1.165E-15 for 30, 60, 90, and 150 dB noise, respectively, under case study 1 while varying the particle size from 50 to 450. The results for case study 2 proved to be best up to 7.373E-16, and no significant change occurred by increasing the generations above 500. The proposed study would be a step further in developing a more accurate and robust computing platform for robust estimation of harmonics arising in power and energy systems. © 2024 World Scientific Publishing Company.

关键词： astrophysics Harmonics estimation power systems transit search optimization

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An Energy-Aware Load Balancing Method for IoT-Based Smart Recycling Machines Using an Artificial Chemical Reaction Optimization Algorithm

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Algorithms 2023年第2期16卷 115-115页

作者： Tabaghchi Milan, Sara Darbandi, Mehdi Jafari Navimipour, Nima Yalcın, Senay Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz*** Iran Department of Electrical and Electronic Engineering Eastern Mediterranean University via Mersin 10 Gazimagusa99450 Turkey Department of Computer Engineering Kadir Has University Istanbul34083 Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou64002 Taiwan Department of Computer Engineering Nisantasi University Istanbul34398 Turkey

Recycling is very important for a sustainable and clean environment. Developed and developing countries are both facing the problem of waste management and recycling issues. On the other hand, the Internet of Things (IoT) is a famous and applicable infrastructure used to provide connection between physical devices. It is an important technology that has been researched and implemented in recent years that promises to positively influence several industries, including recycling and trash management. The impact of the IoT on recycling and waste management is examined using standard operating practices in recycling. Recycling facilities, for instance, can use IoT to manage and keep an eye on the recycling situation in various places while allocating the logistics for transportation and distribution processes to minimize recycling costs and lead times. So, companies can use historical patterns to track usage trends in their service regions, assess their accessibility to gather resources, and arrange their activities accordingly. Additionally, energy is a significant aspect of the IoT since several devices will be linked to the internet, and the devices, sensors, nodes, and objects are all energy-restricted. Because the devices are constrained by their nature, the load-balancing protocol is crucial in an IoT ecosystem. Due to the importance of this issue, this study presents an energy-aware load-balancing method for IoT-based smart recycling machines using an artificial chemical reaction optimization algorithm. The experimental results indicated that the proposed solution could achieve excellent performance. According to the obtained results, the imbalance degree (5.44%), energy consumption (11.38%), and delay time (9.05%) were reduced using the proposed method. © 2023 by the authors.

关键词： Energy utilization

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