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International Conference on Software Engineering (ICSE)

作者： Siyue Feng Wenqi Suo Yueming Wu Deqing Zou Yang Liu Hai Jin Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab Jinyinhu Laboratory Wuhan China School of Cyber Science and Engineering HUST Wuhan China Nanyang Technological University Singapore School of Computer Science and Technology HUST Wuhan China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

As software engineering advances and the code demand rises, the prevalence of code clones has increased. This phenomenon poses risks like vulnerability propagation, underscoring the growing importance of code clone detection techniques. While numerous code clone detection methods have been proposed, they often fall short in real-world code environments. They either struggle to identify code clones effectively or demand substantial time and computational resources to handle complex clones. This paper introduces a code clone detection method namely Toma using tokens and machine learning. Specifically, we extract token type sequences and employ six similarity calculation methods to generate feature vectors. These vectors are then input into a trained machine learning model for classification. To evaluate the effectiveness and scalability of Toma, we conduct experiments on the widely used BigCloneBench dataset. Results show that our tool outperforms token-based code clone detectors and most tree-based clone detectors, demonstrating high effectiveness and significant time savings.

关键词： Codes Scalability Computational modeling Cloning Machine learning Detectors Feature extraction

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Adapting Crop Recommendations to Local Climate Variability Through Machine Learning 4

Adapting Crop Recommendations to Local Climate Variability T...

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4th IEEE International Conference on Smart Technologies in computing, Electrical and Electronics, ICSTCEE 2023

作者： Thirumala Akash, K. Mohammed Usman, F. Nitesh Kumar, T. Ahmed, Mohammed Riyaz Dxc Technology Analyst I Infrastructure Services Bengaluru India Reva University School Of Computing And Information Technology Bengaluru India Reva University School Of Multidisciplinary Studies Bengaluru India Hkbk College Of Engineering Department Of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350384628

Crop plantation has undergone significant transformations fueled by technological advancements and scientific knowledge, resulting in improved crop yields and quality. Traditionally reliant on manual labor and basic tools, such as plows, hoes, and sickles, farmers employed crop rotation and organic fertilization methods to enhance soil fertility. However, modern crop cultivation has embraced mechanization such as employing tractors, combines heavy machinery for planting, cultivation, and harvesting operations. Furthermore, precision agriculture techniques have been developed to optimize resource utilization and maximize crop production, including precision planting, irrigation, and fertilization. This research focuses on developing a machine-learning model for tailoring crop recommendations to local climate variability. By analyzing recorded weather data and crop yields, the model generates customized crop recommendations based on specific locations and conditions. Experimental results demonstrate the model's capability to incorporate weather patterns and accurately predict crop performance, leading to enhanced yields and sustainable agricultural practices. Future enhancements to the model could encompass additional factors like soil conditions and market demand, further refining the crop recommendation system. © 2023 IEEE.

关键词： Machine learning

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Research on Causal Model Construction and Fault Diagnosis of Control systems 7th

Research on Causal Model Construction and Fault Diagnosis of...

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7th International Conference on computing, Control and Industrial Engineering, CCIE 2023

作者： Zhu, Qun Chen, Zhi Liu, Jie Xu, Zhuoran Wen, Juan Yang, Jinghua Jian, Yifan School of Computer Science University of South China Hengyang421200 China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610064 China School of Electrical Engineering University of South China Hengyang421200 China

ISBN: (纸本)9789819927296

Fault diagnosis of complex control systems is a difficult and hot topic of research at this stage. In order to explore the internal operation characteristics of complex control systems and fault propagation methods to achieve efficient and accurate fault diagnosis, this paper studies control system fault diagnosis methods based on cause-effect relationships, analyzes the role relationship between system components from the perspective of control equation structure, proposes the method of constructing cause-effect diagram models with control equations, and uses the algorithm of way to implement, and then combine the information flow to quantify the causal effect quantifiably, when the system steady state is disrupted, the information flow produces a jump, so as to extend the fault diagnosis reasoning method. The fault diagnosis is verified by simulation of the pressure control system of the nuclear power plant regulator, and the experimental results verify the effectiveness of the proposed method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Failure analysis

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Integrated Device for Controllable Droplet Generation and Detection on Open Array Chip 16th

Integrated Device for Controllable Droplet Generation and De...

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16th International Conference on Intelligent Robotics and Applications, ICIRA 2023

作者： Zhou, Zijian Wang, Jie Li, Yuxin Zhou, Jia Du, Lin School of Mechanical Engineering University of Shanghai for Science and Technology Shanghai200433 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200433 China State Key Laboratory of ASIC and System School of Microelectronics Fudan University Shanghai200433 China

ISBN: (纸本)9789819965007

The open array chip is an incredibly valuable tool for analyzing single molecule levels. Its efficiency, speed, compatibility, and precision make it an essential part of this process. However, efficient sample addition has become a significant challenge in the development of open array chips due to the coupling of the solid-liquid interface and the need for high-precision control. To address this challenge, we have developed an integrated adding sample device using technology of generates and detects controllable droplets array based on an open array chip with a biomimetic structure. This device combines a microfluidic chip, image detection, and smear speed regulation. We have studied the factors that affect the efficiency of sample loading, such as the number, speed and fluctuation of smears, and microwell size. The experiments have shown that the open array chip with the microwell’s diameter of 350 µm can generate arrays of ~470/cm2 ~8 nL droplets, significantly reducing controlled speed fluctuation errors by7.24% and volume fluctuation errors by 24% compared to traditional manual ways. The device has also displayed excellent performance on chips with microwell diameters of 120, 350, 700, and 1300 µm, respectively, with a notable first-time success rate of up to 91% on chips with microwell diameters of 120 µm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Microfluidic chips

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User-Level Large Scale Load Forecasting Based on GPU 9

User-Level Large Scale Load Forecasting Based on GPU

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9th Annual International Conference on Network and Information systems for computers, ICNISC 2023

作者： Qi, Jinshan Wang, Jiangtao Liang, Xun Xu, Zhiming Huaiyin Normal University Huai'an Key Laboratory of Big Data Intelligent Computing and Analysis School of Computer Science and Technology Huai'an China Renmin University of China School of Information Beijing China

ISBN: (纸本)9798350306682

Load forecasting is an essential part in power system management and planning. In recent years, more and more researchers started to focus on the behavior of terminal customers, trying to improve the accuracy of load forecasting. This paper proposes to use the neuron network model to predict each user's load first, and then aggregate all the forecasting results to get higher-level prediction. In order to speed up the process, we also design a model based on CUDA. We conducted several experiments on real-world data (160 thousand consumers' load profiles) and the results confirm the effectiveness and efficiency of our method. © 2023 IEEE.

关键词： Graphics processing unit

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Tracking Objects and Activities with Attention for Temporal Sentence Grounding 48

Tracking Objects and Activities with Attention for Temporal ...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Xiong, Zeyu Liu, Daizong Zhou, Pan Zhu, Jiahao Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Peking University Wangxuan Institute of Computer Technology China

ISBN: (纸本)9781728163277

Temporal sentence grounding (TSG) aims to localize the temporal segment which is semantically aligned with a natural language query in an untrimmed video. Most existing methods extract frame-grained features or object-grained features by 3D ConvNet or detection network under a conventional TSG framework, failing to capture the subtle differences between frames or to model the spatio-temporal behavior of core persons/objects. In this paper, we introduce a new perspective to address the TSG task by tracking pivotal objects and activities to learn more fine-grained spatio-temporal behaviors. Specifically, we propose a novel Temporal Sentence Tracking Network (TSTNet), which contains (A) a Cross-modal Targets Generator to generate multi-modal templates and search space, filtering objects and activities, and (B) a Temporal Sentence Tracker to track multi-modal targets for modeling the targets' behavior and to predict query-related segment. Extensive experiments and comparisons with state-of-the-arts are conducted on challenging benchmarks: Charades-STA and TACoS. And our TSTNet achieves the leading performance with a considerable real-time speed. © 2023 IEEE.

关键词： computer vision

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Towards DDIs Identification by Knowledge Graph with BiRW and Back Aggregation

Towards DDIs Identification by Knowledge Graph with BiRW and...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Yin, Zhuang Lin, Xiaoli Zhang, Xiaolong Wuhan University of Science and Technology Hubei Key Laboratory of Intelligent Information Processing and Real-time Industrial System School of Computer Science and Technology Wuhan China

ISBN: (纸本)9781665468190

Effective identification of potential drug-drug interactions (DDIs) can prevent adverse effects caused by DDIs to a certain extent. This paper proposes the new hybrid method for predicting DDIs, which is called BiRW-KGBAN that combines bidirectional random walk and back aggregation based on knowledge graph. BiRW-KGBAN first constructs two knowledge graph KG-DrugBank and KG-KEGG that integrate data related to drugs. Then, an improved sampling method based on bidirectional random walk (BiRW) is used to sample the neighborhood information of drugs, including directly connected entities, related semantic relations and potential information. In addition, the path with drug as the center node is also extracted. Finally, two improved back aggregation model KGBAN1 and KGBAN2 are used to obtain the final embedded representation of the drugs. The experiments show that, compared with other existing methods, our method could obtain the higher-order topological information and the deeper potential neighborhood information of drugs, and has great improvements in DDIs prediction. The case study also shows that the proposed method has the potential for actual DDIs prediction. © 2022 IEEE.

关键词： Knowledge graph

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Detector Collapse: Physical-World Backdooring Object Detection to Catastrophic Overload or Blindness in Autonomous Driving

arXiv

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arXiv 2024年

作者： Zhang, Hangtao Hu, Shengshan Wang, Yichen Zhang, Leo Yu Zhou, Ziqi Wang, Xianlong Zhang, Yanjun Chen, Chao School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology 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 China Hubei Key Laboratory of Distributed System Security China Cluster and Grid Computing Lab China School of Information and Communication Technology Griffith University Australia University of Technology Sydney Australia RMIT University Australia

Object detection tasks, crucial in safety-critical systems like autonomous driving, focus on pinpointing object locations. These detectors are known to be susceptible to backdoor attacks. However, existing backdoor techniques have primarily been adapted from classification tasks, overlooking deeper vulnerabilities specific to object detection. This paper is dedicated to bridging this gap by introducing Detector Collapse (DC), a brand-new backdoor attack paradigm tailored for object detection. DC is designed to instantly incapacitate detectors (i.e., severely impairing detector's performance and culminating in a denial-of-service). To this end, we develop two innovative attack schemes: SPONGE for triggering widespread misidentifications and BLINDING for rendering objects invisible. Remarkably, we introduce a novel poisoning strategy exploiting natural objects, enabling DC to act as a practical backdoor in real-world environments. Our experiments on different detectors across several benchmarks show a significant improvement (∼10%-60% absolute and ∼2-7× relative) in attack efficacy over state-of-the-art attacks. Copyright © 2024, The Authors. All rights reserved.

关键词： Denial-of-service attack

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An end-to-end repair-based joint training framework for weakly supervised pavement crack segmentation

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Multimedia Tools and Applications 2025年第16期84卷 16281-16299页

作者： Zhou, Hui Tao, Huanjie Duan, Qianyue Hu, Zhenwu Deng, Yishi School of Computer Science Northwestern Polytechnical University Xi’an710129 China School of Cyberspace Security Northwestern Polytechnical University Xi’an710129 China Engineering Research Center of Embedded System Integration Ministry of Education Xi’an710129 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’an710129 China

Weakly supervised pavement crack segmentation aims to assign each pixel of pavement surface images a category label (crack or non-crack) using limited annotation information. Most existing methods adopt the multi-stage approach based on the class activation map (CAM), which outputs from a pre-trained crack classification model, to generate the pixel-level pseudo labels, and then train a segmentation model using these pseudo labels. However, these methods have two notable drawbacks: (i) CAM focuses primarily on the most discriminative regions of the image, potentially resulting in an incomplete estimation of crack regions, and (ii) the process of obtaining pixel-level pseudo labels from CAM is complex and may introduce noise. To avoid these issues, we propose an end-to-end repair-based joint training framework for weakly supervised pavement crack segmentation. Specifically, to better guide the model to pay more attention to the repair process of crack regions, we introduce the segmentation loss, which assigns different weights for crack regions and background regions, through the Residual Project Network. Additionally, to alleviate the impact of the domain gap between synthetic and real datasets, we introduce the consistency loss through the joint training paradigm of unpaired data that incorporates both crack and non-crack images into the training process simultaneously. Furthermore, to enable the model to effectively extract local features and reduce the model's computational complexity, we introduce the Lightweight Multi-head Self-attention module. The experiment results on the CRACK500 Dataset, the CFD Dataset, and the AEL Dataset demonstrate that our model achieves better performance than some existing weakly supervised pavement segmentation methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Repair

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Charging Dynamic Sensors through Online Learning 42

Charging Dynamic Sensors through Online Learning

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Sun, Yu Lin, Chi Yang, Wei Ren, Jiankang Wang, Lei Wu, Guowei Zhang, Qiang Dalian University of Technology School of Software Technology Dalian116024 China Xidian University The State Key Laboratory of Integrated Services Networks Xi'an710071 China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian116621 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China

ISBN: (纸本)9798350334142

As a novel solution for IoT applications, wireless rechargeable sensor networks (WRSNs) have achieved widespread deployment in recent years. Existing WRSN scheduling methods have focused extensively on maximizing the network charging utility in the fixed node case. However, when sensor nodes are deployed in dynamic environments (e.g., maritime environments) where sensors move randomly over time, existing approaches are likely to incur significant performance loss or even fail to execute normally. In this work, we focus on serving dynamic nodes whose locations vary randomly and formalize the dynamic WRSN charging utility maximization problem (termed MATA problem). Through discretizing candidate charging locations and modeling the dynamic charging process, we propose a near-optimal algorithm for maximizing charging utility. Moreover, we point out the long-short-term conflict of dynamic sensors that their location distributions in the short-term usually deviate from the long-term expectations. To tackle this issue, we further design an online learning algorithm based on the combinatorial multi-armed bandit (CMAB) model. It iteratively adjusts the charging strategy and adapts well to nodes' short-term location deviations. Extensive experiments and simulations demonstrate that the proposed scheme can effectively charge dynamic sensors and achieve a higher charging utility compared to baseline algorithms in both long-term and short-term. © 2023 IEEE.

关键词： Location

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