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A Hybrid Deep Learning Approach for Software Just-in-Time Defects Prediction

International Journal of Network Security 2025年第3期27卷 476-486页

作者： Yu, Yingting Chen, Mingliang Zeng, Chuanhan Xie, Guoqiang Pan, Benren Cui, Liu Sun, Heyang Li, Yuancheng State Grid Jiangxi Electric Power Co. Ltd. Research Institute Jiangxi Province Nanchang330096 China Xi'an Jiaotong University Shaanxi Province Xi'an710049 China School of Control and Computer Engineering North China Electric Power University Changping District Beijing102206 China

To address the challenges of high maintenance costs, time-consuming processes, and delayed defect repairs in software projects, we introduce a Just-In-Time (JIT) Defect Prediction method tailored for software. Initially, we established an enriched code change metrics system. Building upon existing change metrics, we incorporated two novel dimensions—time and review—to mitigate errors and reduce data deviations during feature extraction. Recognizing the prevalent imbalance where defect-free samples in open-source software outweigh defective ones, we innovatively utilize the Boundary Equilibrium Generative Adversarial Networks (BEGAN) model. This strategy aims to produce synthetic defect data, rectifying the dataset imbalance. Concluding our methodology, we present the CBGRU model for JIT defect prediction. This model synergistically integrates the Convolutional Neural Network (CNN) for data properties and the Bidirectional Gated Recurrent Unit (BGRU) to capture temporal correlations within diverse data sets. Empirical evaluations across three open-source software platforms reveal that BEGAN surpasses conventional imbalance-handling techniques. Remarkably, our proposed CBGRU model enhances Precision by 17% and F-measure by 9.6% compared to prior methodologies. © (2025), (Femto Technique Co., Ltd.). All rights reserved.

关键词： Open source software

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DISTANCE BETWEEN SPECIES BY CONFUSION OPERATORS OF MULTI-CLASS CLASSIFIERS

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2025年第1期2025卷 171-182页

作者： Tarbă, Nicolae Irimescu, Ionela N. Pleavă, Ana M. Scarlat, Eugen N. Mihăilescu, Mona Doctoral School Computer Science and Engineering Department Faculty of Automatic Control and Computers National University of Science and Technology POLITEHNICA Bucharest Romania Applied Sciences Doctoral School National University of Science and Technology POLITEHNICA Bucharest Romania CAMPUS Research Center National University of Science and Technology POLITEHNICA Bucharest Romania Physics Dept National University of Science and Technology POLITEHNICA Bucharest Romania Physics Dept Research Center for Applied Sciences in Engineering National University of Science and Technology POLITEHNICA Bucharest Romania

We introduce a method to evaluate the similarities between classes of objects based on the confusion matrices coming from the multi-class machine learning (ML) predictors that operate in the vector space generated by the classes. Operation in the confusion domain allows us to use the distance as evaluation metric of the closeness of the relevant properties carried by the classes. We applied the method to semiquantitatively evaluate the effects of X-ray and carbon ion radiation on cultured SW1353 chondrosarcoma cells with respect to the non-irradiated reference. We prove that it is possible to draw useful information on the strength of cells nuclei changes. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Radiation effects

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Diffusion-based framework for weakly-supervised temporal action localization

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Pattern Recognition 2025年 160卷

作者： Zou, Yuanbing Zhao, Qingjie Sarker, Prodip Kumar Li, Shanshan Wang, Lei Liu, Wangwang School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Beijing Institute of Control Engineering Beijing100190 China

Weakly supervised temporal action localization aims to localize action instances with only video-level supervision. Due to the absence of frame-level annotation supervision, how effectively separate action snippets and backgrounds from semantically ambiguous features becomes an arduous challenge for this task. To address this issue from a generative modeling perspective, we propose a novel diffusion-based network with two stages. Firstly, we design a local masking mechanism module to learn the local semantic information and generate binary masks at the early stage, which (1) are used to perform action-background separation and (2) serve as pseudo-ground truth required by the diffusion module. Then, we propose a diffusion module to generate high-quality action predictions under the pseudo-ground truth supervision in the second stage. In addition, we further optimize the new-refining operation in the local masking module to improve the operation efficiency. The experimental results demonstrate that the proposed method achieves a promising performance on the publicly available mainstream datasets THUMOS14 and ActivityNet. The code is available at https://***/Rlab123/action_diff. © 2024

关键词： Self-supervised learning

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An Entropy-Integrated Adaptive Coding and Scheduling Framework for Optimized Data Transmission in Fog-Cloud IoT Architectures

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IEEE Internet of Things Journal 2025年

作者： Lu, Yang Zang, Yuting Bian, Ziyi Zhao, Shuai Zheng, Yan Northeast Forestry University College of Computer and Control Engineering Harbin China Harbin Normal University College of Computer and Information Engineering Harbin China La Trobe University School of Computing Engineering and Mathematical Sciences MelbourneVIC Australia

Entropy-driven network coding has been used as a basis for optimizing data transmission and enhancing resource utilization in Fog-Cloud IoT architectures, including applications in smart cities, industrial automation, environmental monitoring, and healthcare. In Fog-Cloud IoT architectures, conventional data transmission protocols are inefficient because they cannot adapt to dynamic entropy levels, resulting in underutilized bandwidth, increased latency, and higher energy consumption. In this paper, we propose an Enhanced Entropy-Driven Network Coding (E-EDNC) framework to address these problems. Our framework integrates real-time entropy estimation with adaptive coding strategies and employs a hybrid evolutionary-reinforcement learning (HE-RL) algorithm to dynamically optimize coding parameters and scheduling decisions. Experimental results demonstrate that E-EDNC improves bandwidth utilization by 25%, reduces latency by 25%, and decreases energy consumption by 12%, thereby enhancing overall data transmission efficiency and reliability in Fog-Cloud IoT environments. © 2014 IEEE.

关键词： Network coding

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Safe platooning control of connected and autonomous vehicles on curved multi-lane roads

arXiv

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arXiv 2025年

作者： Chen, Xiao Tang, Zhiqi Johansson, Karl H. Mårtensson, Jonas Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

This paper investigates the safe platoon formation tracking and merging control problem of connected and automated vehicles (CAVs) on curved multi-lane roads. The first novelty is the separation of the control designs into two distinct parts: a lateral control law that ensures a geometrical convergence towards the reference path regardless of the translational velocity, and a longitudinal control design for each vehicle to achieve the desired relative arc length and velocity with respect to its neighboring vehicle. The second novelty is exploiting the constructive barrier feedback as an additive term to the nominal tracking control, ensuring both lateral and longitudinal collision avoidance. This constructive barrier feedback acts as a dissipative term, slowing down the relative velocity toward obstacles without affecting the nominal controller’s performance. Consequently, our proposed control method enables safe platoon formation of vehicles on curved multi-lane roads, with theoretical guarantees for safety invariance and stability analysis. Simulation and experimental results on connected vehicles are provided to further validate the effectiveness of the proposed method. Copyright © 2025, The Authors. All rights reserved.

关键词： Feedback

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Vision-Language Model Predictive control for Manipulation Planning and Trajectory Generation

arXiv

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arXiv 2025年

作者： Chen, Jiaming Zhao, Wentao Meng, Ziyu Mao, Donghui Song, Ran Pan, Wei Zhang, Wei School of Control Science and Engineering Shandong University China Department of Computer Science The University of Manchester United Kingdom

Model Predictive control (MPC) is a widely adopted control paradigm that leverages predictive models to estimate future system states and optimize control inputs accordingly. However, while MPC excels in planning and control, it lacks the capability for environmental perception, leading to failures in complex and unstructured scenarios. To address this limitation, we introduce Vision-Language Model Predictive control (VLMPC), a robotic manipulation planning framework that integrates the perception power of vision-language models (VLMs) with MPC. VLMPC utilizes a conditional action sampling module that takes a goal image or language instruction as input and leverages VLM to generate candidate action sequences. These candidates are fed into a video prediction model that simulates future frames based on the actions. In addition, we propose an enhanced variant, Traj-VLMPC, which replaces video prediction with motion trajectory generation to reduce computational complexity while maintaining accuracy. Traj-VLMPC estimates motion dynamics conditioned on the candidate actions, offering a more efficient alternative for long-horizon tasks and real-time applications. Both VLMPC and Traj-VLMPC select the optimal action sequence using a VLM-based hierarchical cost function that captures both pixel-level and knowledge-level consistency between the current observation and the task input. We demonstrate that both approaches outperform existing state-of-the-art methods on public benchmarks and achieve excellent performance in various real-world robotic manipulation tasks. Code is available at https://***/PPjmchen/VLMPC. Copyright © 2025, The Authors. All rights reserved.

关键词： Predictive control systems

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Tissue P Systems with Look-Ahead Mode

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Chinese Journal of Electronics 2025年第1期23卷 81-86页

作者： Yun Jiang Tao Song Zheng Zhang School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing China Department of Control Science and Engineering Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan China

Tissue P systems are a class of distributed and parallel computing models inspired from inter-cellular communication and cooperation between cells. In this work, a variant of tissue P system, named tissue P system with look-ahead mode, is discussed for decreasing the inherent non-determinism of tissue P systems and helping implementing tissue P systems on computers. Such systems are proved to be universal by simulating register machine, and they are also proved to be able to efficiently solve computationally hard problems by means of a space-time tradeoff, which is illustrated with a polynomial solution to 3-coloring problem.

关键词： computers Computational modeling Medical services Parallel processing Polynomials Computational efficiency Registers Business

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A Trust-Aware and Cost-Optimized Blockchain Oracle Selection Model with Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Zhang, Hengyang Li, Shike Bao, Hang Wu, Sixing Li, Jianbin School of Control and Computer Engineering North China Electric Power University Beijing China Shanxi University China

The rapid development of blockchain technology has driven the widespread application of decentralized applications (DApps) across various fields. However, DApps cannot directly access external data and rely on oracles to interact with off-chain data. As a bridge between blockchain and external data sources, oracles pose potential risks of malicious behavior, which may inject incorrect or harmful data, leading to trust and security issues. Additionally, with the surge in data requests, the disparity in oracle trustworthiness and costs has increased, making the dynamic selection of the most suitable oracle for each request a critical challenge. To address these issues, this paper proposes a Trust-Aware and Cost-Optimized Blockchain Oracle Selection Model with Deep Reinforcement Learning (TCO-DRL). The model incorporates a comprehensive trust management mechanism to evaluate oracle reputation from multiple dimensions and employs an improved sliding time window to monitor reputation changes in real time, enhancing resistance to malicious attacks. Moreover, TCO-DRL uses deep reinforcement learning algorithms to dynamically adapt to fluctuations in oracle reputation, ensuring the selection of high-reputation oracles while optimizing node selection, thereby reducing costs without compromising data quality. We implemented and validated TCO-DRL on Ethereum. Experimental results show that, compared to existing methods, TCO-DRL reduces the allocation rate to malicious oracles by more than 39.10% and saves over 12.00% in costs. Furthermore, simulated experiments on various malicious attacks further validate the robustness and effectiveness of TCO-DRL. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Oral Cancer Classification Using a Hybrid Attention-Aided Deep Learning Model 5th

Oral Cancer Classification Using a Hybrid Attention-Aided D...

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5th International Conference on Medical Imaging and computer Aided Diagnosis, MICAD 2024

作者： Dutta, Srinjan Roy, Jibesh Mandal, Diptarka Kaplun, Dmitrii Shpakovskaya, Irina Sarkar, Ram Department of Computer Science and Engineering Jalpaiguri Government Engineering College Jalpaiguri India School of Computer Engineering and Mathematical Sciences Defence Institute of Advanced Technology Pune India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Process Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India I.P. Pavlov Saint Petersburg First State Medical University St. Petersburg Russia

ISBN: (纸本)9789819638628

Oral cancer remains a critical global health challenge, characterized by high morbidity and mortality due to late-stage diagnosis. This paper addresses the need for improved diagnostic accuracy by introducing a novel hybrid-attention aided deep learning model for oral cancer classification. Our approach integrates the EfficientNetB0 model for feature extraction with a customized hybrid attention module, combining self, spatial, and soft attention mechanisms to emphasize crucial features. The model has been trained and evaluated on both microscopic and macroscopic oral cancer datasets, demonstrating its generalizability. Our proposed model achieves an accuracy of 96.97% on a macroscopic dataset and 92% on a microscopic dataset, with respective F1-scores of 0.97 and 0.89, outperforming baseline models. This hybrid attention-aided deep learning model shows significant potential in improving early detection and treatment planning of oral cancer. Source code and additional results of this work can be found at the GitHub repository. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Computational imaging based on single-photon detection: a survey

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Artificial Intelligence Review 2025年第8期58卷 1-54页

作者： Pu, Yanyun Zhu, Chengyuan Yao, Gongxin Li, Chao Pan, Yu Yang, Kaixiang Yang, Qinmin State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Zheda Road Zhejiang Hangzhou310027 China Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Zheda Road Zhejiang Hangzhou310027 China School of Computer Science and Engineering South China University of Technology Guangzhou510641 China

The rapid advancements in single-photon detectors with picosecond timing resolution over the past decade have significantly driven the development of time-correlated single-photon counting (TCSPC) for computational imaging applications, including bioimaging and remote sensing. In this review, we utilize the CiteSpace tool to create knowledge maps and perform a bibliometric analysis of this research area. Furthermore, we provide a comprehensive overview of the key challenges associated with computational imaging using temporal single-photon counting. We also highlight how these challenges have been addressed under extreme conditions to establish a reference model for future imaging solutions. We examine the performance evaluation parameters of single-photon detectors to enhance the understanding of detector array scaling and their application in constructing efficient computational imaging systems. Lastly, we aim to elucidate the current technical challenges in single-photon detector-based computational imaging and explore their potential future developments. © The Author(s) 2025.

关键词： Computational Imaging

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