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International Conference on Software Engineering (ICSE)

作者： Zhen Li Ning Wang Deqing Zou Yating Li Ruqian Zhang Shouhuai Xu Chao Zhang Hai Jin 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 School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Jin YinHu Laboratory Wuhan China Department of Computer Science University of Colorado Colorado Springs Colorado Springs Colorado USA Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter -Procedural Vulnerability Dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers; and (ii) function-level vulner-ability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities.

关键词： Deep learning Detectors Open source software Software engineering

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Petri net-based scheduling analysis of dual-arm cluster tools with wafer revisiting

Petri net-based scheduling analysis of dual-arm cluster tool...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Yan Qiao NaiQi Wu MengChu Zhou Department of Industrial Engineering School of Electro-Mechanical Engineering Guangdong University of Technology Guangzhou China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

With wafer revisit, it is complicated to schedule cluster tools in semiconductor fabrication. In wafer fabrication processes, such as atomic layer deposition (ALD), the wafers need to visit some process modules for a number of times. The existing swap-based strategy can be used to operate a dual-arm cluster tool for such a process. It results in a 3-wafer cyclic schedule. However, it is not optimal in the sense of cycle time. Thus, to search for a better schedule, a Petri net model is developed for a dual-arm cluster tool with wafer revisit. With it, the properties of the 3-wafer schedule are analyzed. It is found that, to improve the performance, it is necessary to reduce the number of wafers completed in a cycle. Thus, a 1-wafer schedule is developed by using a new swap-based strategy.

关键词： Robots Schedules Semiconductor device modeling Firing Steady-state Load modeling Transient analysis

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DAMPER: A Dual-Stage Medical Report Generation Framework with Coarse-Grained MeSH Alignment and Fine-Grained Hypergraph Matching

arXiv

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arXiv 2024年

作者： Huang, Xiaofei Chen, Wenting Liu, Jie Lu, Qisheng Luo, Xiaoling Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Medical report generation is crucial for clinical diagnosis and patient management, summarizing diagnoses and recommendations based on medical imaging. However, existing work often overlook the clinical pipeline involved in report writing, where physicians typically conduct an initial quick review followed by a detailed examination. Moreover, current alignment methods may lead to misaligned relationships. To address these issues, we propose DAMPER, a dual-stage framework for medical report generation that mimics the clinical pipeline of report writing in two stages. In the first stage, a MeSH-Guided Coarse-Grained Alignment (MCG) stage that aligns chest X-ray (CXR) image features with medical subject headings (MeSH) features to generate a rough keyphrase representation of the overall impression. In the second stage, a Hypergraph-Enhanced Fine-Grained Alignment (HFG) stage that constructs hypergraphs for image patches and report annotations, modeling high-order relationships within each modality and performing hypergraph matching to capture semantic correlations between image regions and textual phrases. Finally,the coarse-grained visual features, generated MeSH representations, and visual hypergraph features are fed into a report decoder to produce the final medical report. Extensive experiments on public datasets demonstrate the effectiveness of DAMPER in generating comprehensive and accurate medical reports, outperforming state-of-the-art methods across various evaluation metrics. Copyright © 2024, The Authors. All rights reserved.

关键词： Coarse-grained modeling

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Generalized Visual Quality Assessment of GAN-Generated Face Images

arXiv

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arXiv 2022年

作者： Tian, Yu Ni, Zhangkai Chen, Baoliang Wang, Shiqi Wang, Hanli Kwong, Sam The Department of Computer Science City University of Hong Kong 999077 Hong Kong The Department of Computer Science & Technology Tongji University Shanghai 200092 China The Department of Computer Science & Technology Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai 200092 China The City University of Hong Kong Shenzhen Research Institute Shenzhen518057 China

Recent years have witnessed the dramatically increased interest in face generation with generative adversarial networks (GANs). A number of successful GAN algorithms have been developed to produce vivid face images towards different application scenarios. However, little work has been dedicated to automatic quality assessment of such GAN-generated face images (GFIs), even less have been devoted to generalized and robust quality assessment of GFIs generated with unseen GAN model. Herein, we make the first attempt to study the subjective and objective quality towards generalized quality assessment of GFIs. More specifically, we establish a large-scale database consisting of GFIs from four GAN algorithms, the pseudo labels from image quality assessment (IQA) measures, as well as the human opinion scores via subjective testing. Subsequently, we develop a quality assessment model that is able to deliver accurate quality predictions for GFIs from both available and unseen GAN algorithms based on meta-learning. In particular, to learn shared knowledge from GFIs pairs that are born of limited GAN algorithms, we develop the convolutional block attention (CBA) and facial attributes-based analysis (ABA) modules, ensuring that the learned knowledge tends to be consistent with human visual perception. Extensive experiments exhibit that the proposed model achieves better performance compared with the state-of-the-art IQA models, and is capable of retaining the effectiveness when evaluating GFIs from the unseen GAN algorithms. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

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General capacity scaling of wireless networks

General capacity scaling of wireless networks

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

作者： Cheng Wang Changjun Jiang Xiang-Yang Li Shaojie Tang Panlong Yang Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Department of Computer Science University of Tongji Shanghai China Department of Computer Science Illinois Institute of Technology Chicago IL USA TNLIST School of Software Tsinghua University China People''s Liberation Army University of Science and Technology China

We study the general scaling laws of the capacity for random wireless networks under the generalized physical model. The generality of this work is embodied in three dimensions denoted by (λ ∈ [1, n], n d ∈ [1, n], n s ∈ (1, n]). It means that: (1) We study the random network of a general node density λ ∈ [1, n], rather than only study either random dense network (RDN, λ = n) or random extended network (REN, λ = 1) as in the literature. (2) We focus on the multicast capacity to unify unicast and broadcast capacities by setting the number of destinations for each session as a general value n d ∈ [1, n]. (3)We allow the number of sessions changing in the range n s ∈ (1, n], rather than assume that n s = Θ(n) as in the *** derive the general lower bounds on the capacity for the arbitrary case of (λ, n d , n s ). Particularly, we show that for the special cases (λ = 1, n d ∈ [1, n], n s = n) and (λ = n, n d ∈ [1, n], n s = n), our schemes achieve the highest multicast throughputs proposed in the existing works.

关键词： Roads Routing Throughput Lattices Schedules Mathematical model

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Capacity-shared heterogeneous CMP cache

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Jisuanji Yanjiu yu Fazhan/computer Research and Development 2008年第5期45卷 877-885页

作者： Gao, Xiang Zhang, Longbing Hu, Weiwu Key Laboratory of Computer System and Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Department of Computer Science and Technology University of Science and Technology of China Hefei 230027 China

The characteristics of advanced integrated circuit technologies require architects to look for new ways to utilize large numbers of gates and mitigate the effects of high interconnect delays. Chip multiprocessors (CMPs) exploit increasing transistor counts by placing multiple processors on a single die. As the chip multiprocessors (CMPs) have become the trend of high performance microprocessors, the target workloads become more and more diversified. Due to the wire delay problem and diversity of applications, neither private nor shared caches can provide both large capacity and fast access in CMPs. A novel CMP cache design, the heterogeneous CMP cache (HCC) is presented, in which chips are constructed by tiles of two different categories. L2 caches of private tiles provide lowest hit latency and L2 cache of shared tiles increases the effective cache capacity for shared data. Incorporating indirect-index cache technology to share capacity between different hierarchies, HCC provide a both capacity-effective and access-fast on-chip memory subsystem. Detailed full-system simulations are used to analyze the HCC performance for various programs, including SPEC CPU2000, SPLASH2 and commercial workloads. The result shows that HCC improves performance by 16% for single-threaded benchmarks and 9% for multi-thread benchmarks. HCC is easy to implement and the design ideas will be used in the future multi-core processors of Godson series.

关键词： Microprocessor chips

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Optimal Linear Crossover for Mitigating Negative Transfer in Evolutionary Multitasking

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IEEE Transactions on Evolutionary Computation 2024年

作者： Liu, Zhaobo Yuan, Jianhua Zhang, Haili Zeng, Tao Zhu, Zexuan Shenzhen University Institute for Advanced Study Shenzhen518060 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Shenzhen Polytechnic University School of Undergraduate Education Shenzhen518055 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Shenzhen University Shenzhen City Key Laboratory of Embedded System Design Shenzhen518060 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education of China Chongqing400044 China

Evolutionary multitasking algorithms use information exchange among individuals in a population to solve multiple optimization problems simultaneously. Negative transfer is a critical factor that affects the performance of evolutionary multitasking algorithms. In this study, we propose an innovative approach to mitigate negative transfer in evolutionary multitasking algorithms. The proposed approach is grounded in rigorous theoretical analysis, which provides valuable theoretical insights into the design of an optimal linear crossover operator for mitigating negative transfer. By identifying interpretable conditions, we establish a solid theoretical foundation to prevent negative transfer in diverse scenarios. Building upon these findings, we theoretically derive a closed-form expression for the optimal crossover operator and propose practical design methods based on approximations. Furthermore, we integrate the proposed optimal crossover operator into a fundamental evolutionary multitasking algorithm framework. The resultant algorithm is comparable or superior to other state-of-the-art methods. Empirical validation through comprehensive experiments confirms the effectiveness of our theoretical findings. © 1997-2012 IEEE.

关键词： Optimization algorithms

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An Automatic Tooth Position and Dental Disease Detection Algorithm Based on YOLOv8

An Automatic Tooth Position and Dental Disease Detection Alg...

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International Conference on computer and Communications (ICCC)

作者： Hongliang Wang Ke Diao Lin Wu Xuerao Li Xin Zhou Xiaozhou Liu Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China School and Hospital of Stomatology China Medical University Liaoning Provincial Key Laboratory of Oral Diseases Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

With the development of digital dental imaging technology, panoramic tomographic images have become an effective tool for diagnosing dental diseases. However, due to the diversity and complexity of dental lesions, accurately identifying tooth positions and pathological conditions from these images remains challenging. To address the issue of low target detection accuracy caused by the high similarity and complex textures of teeth, this paper introduces YOLOv8-Teeth, an enhanced teeth detection algorithm designed for accurate identification of teeth positions and dental diseases from panoramic radiographs. key innovations include integrating the C2f_ConvNeXtv2 module for improved feature extraction, employing the ASPP module for optimized multi-scale feature capture, and utilizing Focal-EloU Loss for refined bounding box regression. Experimental results on a custom dataset of 476 panoramic radiographs demonstrate significant performance gains, achieving a mean average precision (mAP) of 91.3%, precision of 93.6%, and recall of 88.7%. These improvements offer promising potential for early diagnosis and treatment planning in dental care.

关键词： Technological innovation Accuracy Computational modeling Teeth Tomography Feature extraction Real-time systems Dentistry Detection algorithms Diseases

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GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross Appearance-Edge Learning

arXiv

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arXiv 2024年

作者： Zhang, Yaning Yu, Zitong Wang, Tianyi Huang, Xiaobin Shen, Linlin Gao, Zan Ren, Jianfeng Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Nanyang Technological University 50 Nanyang Ave Block N 4 639798 Singapore Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518129 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University China Jinan250014 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China School of Computer Science University of Nottingham Ningbo China

The rapid advancement of photorealistic generators has reached a critical juncture where the discrepancy between authentic and manipulated images is increasingly indistinguishable. Thus, benchmarking and advancing techniques detecting digital manipulation become an urgent issue. Although there have been a number of publicly available face forgery datasets, the forgery faces are mostly generated using GAN-based synthesis technology, which does not involve the most recent technologies like diffusion. The diversity and quality of images generated by diffusion models have been significantly improved and thus a much more challenging face forgery dataset shall be used to evaluate SOTA forgery detection literature. In this paper, we propose a large-scale, diverse, and fine-grained high-fidelity dataset, namely GenFace, to facilitate the advancement of deepfake detection, which contains a large number of forgery faces generated by advanced generators such as the diffusion-based model and more detailed labels about the manipulation approaches and adopted generators. In addition to evaluating SOTA approaches on our benchmark, we design an innovative Cross Appearance-Edge Learning (CAEL) detector to capture multi-grained appearance and edge global representations, and detect discriminative and general forgery traces. Moreover, we devise an Appearance-Edge Cross-Attention (AECA) module to explore the various integrations across two domains. Extensive experiment results and visualizations show that our detection model outperforms the state of the arts on different settings like cross-generator, cross-forgery, and cross-dataset evaluations. Code and datasets will be available at https://***/Jenine-321/GenFace. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Model checking cooperative multi-agent systems in BDI logic

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Journal of Information and Computational Science 2012年第5期9卷 1185-1194页

作者： Chen, Qingliang Su, Kaile Wu, Lijun Xu, Zhaocheng School of Electronics Engineering and Computer Science Peking University Beijing 100871 China Department of Computer Science Jinan University Guangzhou 510632 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Institute for Integrated and Intelligent Systems Griffith University Brisbane Australia School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

Traditional temporal logics such as LTL (Linear Temporal Logic) and CTL (Computation Tree Logic) have shown tremendous success in specifying and verifying hardware and software systems. However, this kind of logic can only model the dynamic behaviors for a single system, and fall short in capturing the joint and concurrent aspects in the composition of systems, such as the interactions and coordinations of agents in Multi-agent systems. Although ATL (Alternating-time Temporal Logic) can solve the problem to a certain extent, by preceding the temporal operators with a selective path quantifier, to specify that the interactions between entities inside the system can make sure that there is a strategy to reach a certain state, it can not formalize the rational mental attitudes of the agents such as the Belief, Desire and Intention (BDI) which plays an essential role in decision making during cooperation with other agents. This paper will investigate this problem by proposing a logic of ATLBDI (Alternating-time Temporal Logic of Belief Desire and Intention) and discuss the algorithmic issues such as model checking algorithms and the computational complexity, along with examples to show its effectiveness. We conclude that model checking for ATLBDI is completely tractable and is in PTIME-Complete, which is quite an optimistic and promising result for further applications. © 2012 by Binary Information Press.

关键词： Multi agent systems

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