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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wang, Chenhang Jin, Yujian Liang, Jiayi Yang, YuHan Nie, Shanling Hai, Yang East China University of Science and Technology Department of Computer Science and Engineering Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China The University of Sydney Faculty of Engineering Sydney Australia

ISBN: (纸本)9798350337488

Hepatocellular Carcinoma (HCC) holds a record of high incidence and severe global harm. In tasks of liver cancer segmentation based on 3D medical images, the majority of methods have endeavored to enhance the 3D U-net by integrating the latest modules from the field of computer Vision (such as the transformer), often overlooking the distinct characteristics of liver components. We introduced a novel deep learning architecture, Modif-SegUnet, to circumvent this limitation. This architecture extends the U-net model by incorporating a 3D-modifiable attention module, thereby fostering a heightened focus on distinguishing between normal liver sections and lesions. Furthermore, Modif-SegUnet ingeniously amalgamates the 3D-modifiable attention module with the Modif-transformer block, enabling efficient capture of relevant and valuable full-text information in CT images that contain liver or tumor regions. We subjected the proposed Modif-SegUnet to evaluation on the Liver Tumor Segmentation benchmark dataset. Experimental outcomes indicate that our methodology surpasses state-of-the-art approaches in liver tumor segmentation, suggesting potential pathways for advancing the diagnosis and treatment of liver cancer. © 2023 IEEE.

关键词： 3D medical image attention CT Liver cancer Segmentation transformer U-Net

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VALAR: Streamlining Alarm Ranking in Static Analysis with Value-Flow Assisted Active Learning

VALAR: Streamlining Alarm Ranking in Static Analysis with Va...

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IEEE International Conference on Automated software Engineering (ASE)

作者： Pengcheng Liu Yifei Lu Wenhua Yang Minxue Pan State Key Laboratory for Novel Software Technology Software Institute Nanjing University China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China

Static analyzers play a critical role in program defects and security vulnerabilities detection. Despite their importance, the widespread adoption of static analysis techniques in industrial development faces numerous obstacles, among which the high rate of false alarms constitutes a significant one. To address this issue, we propose a novel approach called Valar, which performs alarm ranking for advanced value-flow analysis using the active learning technique. Active learning algorithms minimize the manual effort for alarm inspection by maximizing the effect of each user labeling in recognizing true/false alarms. Meanwhile, the value-flows provide Valar with a concise and comprehensive summary of the operational semantics about programs. Based on this, Valar is able to reason about the potential correlations between alarms and prioritize the most profitable unlabeled alarm. Additionally, the accuracy of Valar increases as more user labels are given and Valar's active learning model is further refined. We evaluate Valar on 20 real-world C/C++ programs using three value-flow based checkers. Our experimental results demonstrated that Valar significantly lowers the priorities of false alarms with most true alarms ranked high. Notably, Valar ranked all true alarms in the top 47% in 90% projects and ranked 90% true alarms in the top 22% in 75% projects. Furthermore, Valar has no requirement for pretraining and has a negligible computation time of less than 0.1s for each alarm prioritization.

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Question Selection for Multi-Modal Code Search Synthesis using Probabilistic Version Spaces

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IEEE Transactions on software Engineering 2025年

作者： Wu, Jiarong Jiang, Yanyan Wei, Lili Xu, Congying Cheung, Shing-Chi Xu, Chang The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong McGill University Department of Electrical and Computer Engineering Montreal Canada Nanjing University State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing China

Searching the occurrences of specific code patterns (code search) is a common task in software engineering, and programming by example (PBE) techniques have been applied to ease customizing code patterns. However, previous PBE tools only synthesize programs meeting the input-output examples, which may not always align with the user intent. To bridge this gap, this paper proposes Excalibur, a multi-modal (example and natural language description) and interactive synthesizer for code search. Excalibur ensures that the generated programs are correct for the provided examples (soundness) and include the user-intended program (bounded completeness). Furthermore, Excalibur helps the user identify the user-intended program through question-answer interaction. To minimize the required interaction efforts, question selection is crucial. To improve question selection for code search, we propose probabilistic version spaces (ProbVS), in which the user-intended program’s probability is high and others are low. ProbVS combines traditional version spaces for compactly representing extensive programs and large language models (on the user-provided natural language description) for adjusting programs’ probabilities to align with users’ intents. Extensive experiments on a benchmark of 44 tasks demonstrated the effectiveness of Excalibur and ProbVS and demystified how ProbVS affects probability distributions and how the configurable parameters affect ProbVS. © 1976-2012 IEEE.

关键词： Normal distribution

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DeepRank: Test Case Prioritization for Deep Neural Networks 35

DeepRank: Test Case Prioritization for Deep Neural Networks

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35th International Conference on software Engineering and Knowledge Engineering, SEKE 2023

作者： Li, Wei Zhang, Zhiyi Jian, Yifan Liu, Chen Huang, Zhiqiu Collage of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China School of Marxism Yangzhou University Yangzhou China Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing University of Aeronautics and Astronautics Nanjing China

Deep neural networks (DNNs) have been widely used in safety-critical fields such as autonomous driving and medical diagnosis. However, DNNs are easily disturbed to make wrong decisions, which may lead to loss of life or property. Therefore, it is vital to test DNN adequately. In practice, to reveal the incorrect behavior of DNN and improve its robustness, testers usually need massive labeled data to test and optimize DNN. However, labeling test inputs to detect the correctness of DNN predictions is an expensive and time-consuming task that even affects the efficiency of DNN testing. To relieve the labeling-cost problem, we propose DeepRank, a test case prioritization technique based on cross-entropy loss. The key idea of DeepRank is that the higher the loss value of a test case relative to the DNN, the more likely it is to be mispredicted and the more conducive it is to improve the robustness of the DNN through retraining. Therefore, the cross-entropy loss value can be used for test case prioritization. We experimentally validate our approach on two datasets and three DNNs models. The experimental results demonstrate that DeepRank is significantly better than existing test case prioritization methods regarding fault-revealing capability and retraining effectiveness. © 2023 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Deep neural networks

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Graph Convolutional Network with Long Time Memory for Skeleton-based Action Recognition 26

Graph Convolutional Network with Long Time Memory for Skelet...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Qi, Yanpeng Pang, Chen Liu, Yiliang Liu, Hong Lyu, Lei Shandong Normal University School of Information Science and Engineering Jinan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China Shandong Provincial Academy of Education Recruitment and Examination Jinan China

ISBN: (纸本)9798350331684

Skeleton-based action recognition task has been widely studied in recent years. Currently, the most popular researches use graph convolutional network (GCN) to solve this task by modeling human joints data as spatio-temporal graph. However, a large number of long-term temporal motion relationships cannot be effectively captured by GCN. Thus, recurrent neural network (RNN) is introduced to solve this defect. In this work, we propose a model namely graph convolutional network with long time memory (GCN-LTM). Specifically, there are two task streams in our proposed model: GCN stream and RNN stream, respectively. The GCN stream aims to capture the spatial motion relationships as well as the RNN stream focuses on extracting the long-term temporal patterns. In addition, we introduce the contrastive learning strategy to better facilitate feature learning between these two streams. The multiple ablation experiments have verified the feasibility of our proposed model. Numerous experiments show that the proposed model is superior to the current state-of-the-art method under two large-scale datasets including NTU-RGBD and NTU-RGBD-120. © 2023 IEEE.

关键词： Recurrent neural networks

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Wireless Channel Estimation and Equalization Based on Deep Learning

Wireless Channel Estimation and Equalization Based on Deep L...

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Intelligent Data and Security (IDS), IEEE International Conference on

作者： Xueting Huang Linshu Chen Xiaoyan Chen Mo Wang Yuxin Liu Hunan Key Laboratory for Service Computing and Novel Software Technology School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China School of Software Engineering Xiamen University of Technology Xiamen China

ISBN: (数字)9798350389487

ISBN: (纸本)9798350389494

Nowadays, the research on wireless communication is hot, and it has achieved leapfrog development in the past few decades and has made astonishing breakthroughs in emerging applications such as 5G technology, Internet of Things (lOT), and smart cities. Channel estimation and equalization in wireless communication are very important technologies, which are cru-cial for ensuring reliable data transmission and improving system performance. However, with the increasing demand for wireless high-speed data communication, traditional channel estimation and equalization technologies are facing enormous challenges. Nowadays, with the development of artificial intelligence, deep learning has been successfully applied to wireless communication systems. Compared to traditional channel estimation and equalization techniques, deep learning-based channel estimation and equalization techniques have many advantages. For example, traditional channel estimation and equalization methods typically require modeling of channel models and are only applicable to specific channel conditions, making it difficult to achieve optimal performance under different channel conditions. Deep learning technology can improve the performance of channel estimation and equalization by learning a large amount of data and training under different channel conditions, especially in nonlinear and complex channel conditions. Therefore, this article will provide a detailed description of the research on channel estimation and equalization based on deep learning, in order to demonstrate its ultra-high performance and development prospects.

关键词： Wireless communication Deep learning Training Smart cities System performance Channel estimation Data communication

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Optimal Policies for Quantum Markov Decision Processes

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International Journal of Automation and computing 2021年第3期18卷 410-421页

作者： Ming-Sheng Ying Yuan Feng Sheng-Gang Ying Centre for Quantum Software and Information University of Technology SydneyNSW 2007Australia State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China

Markov decision process(MDP)offers a general framework for modelling sequential decision making where outcomes are *** particular,it serves as a mathematical framework for reinforcement *** paper introduces an extension of MDP,namely quantum MDP(q MDP),that can serve as a mathematical model of decision making about quantum *** develop dynamic programming algorithms for policy evaluation and finding optimal policies for q MDPs in the case of *** results obtained in this paper provide some useful mathematical tools for reinforcement learning techniques applied to the quantum world.

关键词： Quantum Markov decision processes quantum machine learning reinforcement learning dynamic programming decision making

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No-Reference Image Quality Assessment via Multibranch Convolutional Neural Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第1期4卷 148-160页

作者： Pan, Zhaoqing Yuan, Feng Wang, Xu Xu, Long Shao, Xiao Kwong, Sam the School of Electrical and Information Engineering Tianjin University Tianjin300072 China the College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China the Key Laboratory of Solar Activity National Astronomical Observatories Chinese Academy of Sciences Beijing100012 China the School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China the Department of Computer Science City University of Hong Kong Hong Kong

No-reference image quality assessment (NR-IQA) aims to evaluate image quality without using the original reference images. Since the early NR-IQA methods based on distortion types were only applicable to specific distortion scenarios, and lack of practicality, it is challenging to designing a universal NR-IQA method. In this article, a multibranch convolutional neural network (MB-CNN) based NR-IQA method is proposed, which includes a spatial-domain feature extractor, a gradient-domain feature extractor, and a weight mechanism. The spatial-domain feature extractor aims to extract the distortion features from the spatial domain. The gradient-domain feature extractor is used to guide the spatial-domain feature extractor to pay more attention to the distortions of the structure information. Particularly, the spatial-domain feature extractor uses the hierarchical feature merge module to realize multiscale feature representation, and the gradient-domain feature extractor uses pyramidal convolution to extract the multiscale structure information of the distorted image. In addition, a position vector is proposed to build the weight mechanism by considering the position relationships between patches and its entire image for improving the final prediction performance. We conduct the experiments on five representative databases: LIVE, TID2013, CSIQ, LIVE MD and Waterloo Exploration Database, and the experimental results show that the proposed NR-IQA method achieves the state-of-the-art performance, which demonstrate the effectiveness of our proposed NR-IQA method. The code ofthe proposed MB-CNN will be released at https://***/NUIST-Videocoding/MB-CNN. © 2020 IEEE.

关键词： Neural networks

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NfvInsight:A Framework for Automatically Deploying and Benchmarking VNF Chains

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Journal of computer science & technology 2022年第3期37卷 680-698页

作者： Tian-Ni Xu Hai-Feng Sun Di Zhang Xiao-Ming Zhou Xiu-Feng Sui Sa Wang Qun Huang Yun-Gang Bao State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China Peng Cheng Laboratory Shenzhen 518055China Department of Computer Science and Technology Peking UniversityBeijing 100871China

With the advent of virtualization techniques and software-defined networking(SDN),network function virtualization(NFV)shifts network functions(NFs)from hardware implementations to software appliances,between which exists a performance *** to narrow the gap is an essential issue of current NFV ***,the cumbersomeness of deployment,the water pipe effect of virtual network function(VNF)chains,and the complexity of the system software stack together make it tough to figure out the cause of low performance in the NFV *** pinpoint the NFV system performance,we propose NfvInsight,a framework for automatic deployment and benchmarking VNF *** framework tackles the challenges in NFV performance *** framework components include chain graph generation,automatic deployment,and fine granularity *** design and implementation of each component have their *** the best of our knowledge,we make the first attempt to collect rules forming a knowledge base for generating reasonable chain *** deploys the generated chain graphs automatically,which frees the network operators from executing at least 391 lines of bash commands for a single *** diagnose the performance bottleneck,NfvInsight collects metrics from multiple layers of the software ***,we collect the network stack latency distribution ingeniously,introducing only less than 2.2%*** showcase the convenience and usability of NfvInsight in finding bottlenecks for both VNF chains and the underlying *** our framework,we find several design flaws of the network stack,which are unsuitable for packet forwarding inside one single server under the NFV *** optimization for these flaws gains at most 3x performance improvement.

关键词： network function virtualization(NFV) service chain performance bottleneck network stack latency

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Text and Image Are Mutually Beneficial: Enhancing Training-Free Few-Shot Classification with CLIP

arXiv

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arXiv 2024年

作者： Li, Yayuan Guo, Jintao Qi, Lei Li, Wenbin Shi, Yinghuan National Key Laboratory for Novel Software Technology Nanjing University China School of Computer Science and Engineering Southeast University China

Contrastive Language-Image Pretraining (CLIP) has been widely used in vision tasks. Notably, CLIP has demonstrated promising performance in few-shot learning (FSL). However, existing CLIP-based methods in training-free FSL (i.e., without the requirement of additional training) mainly learn different modalities independently, leading to two essential issues: 1) severe anomalous match in image modality;2) varying quality of generated text prompts. To address these issues, we build a mutual guidance mechanism, that introduces an Image-Guided-Text (IGT) component to rectify varying quality of text prompts through image representations, and a Text-Guided-Image (TGI) component to mitigate the anomalous match of image modality through text representations. By integrating IGT and TGI, we adopt a perspective of Text-Image Mutual guidance Optimization, proposing TIMO. Extensive experiments show that TIMO significantly outperforms the state-of-the-art (SOTA) training-free method. Additionally, by exploring the extent of mutual guidance, we propose an enhanced variant, TIMO-S, which even surpasses the best training-required methods by 0.33% with approximately ×100 less time cost. Our code is available at https://***/lyymuwu/TIMO. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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