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arXiv 2023年

作者： Lin, Bo Liu, Yepang Wang, Shangwen Xia, Xin Liu, Zhongxin Mao, Xiaoguang College of Computer Science National University of Defense Technology Changsha China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Zhejiang University Hangzhou China Key Laboratory of Software Engineering for Complex Systems China Research Institute of Trustworthy Autonoumous Systems United Kingdom

software is constantly changing, requiring developers to perform several derived tasks in a timely manner, such as writing a description for the intention of the code change, or identifying the defect-prone code changes. Considering that the cost of dealing with these tasks can account for a large proportion (typically around 70 percent) of the total development expenditure, automating such processes will significantly lighten the burdens of developers. To achieve such a target, existing approaches mainly rely on training deep learning models from scratch or fine-tuning existing pre-trained models on such tasks, both of which have weaknesses. Specifically, the former uses comparatively small-scale labelled data for training, making it difficult to learn and exploit the domain knowledge of programming language hidden in the large-amount unlabelled code in the wild;the latter is hard to fully leverage the learned knowledge of the pre-trained model, as existing pre-trained models are designed to encode a single code snippet rather than a code change (i.e., the difference between two code snippets). We propose to pre-train a model specially designed for code changes to better support developers in software maintenance. To this end, we first collect a large-scale dataset containing 1.5M+ pairwise data of code changes and commit messages. Based on these data, we curate five different tasks for pre-training, which equip the model with diverse domain knowledge about code changes. We fine-tune the pre-trained model, CCT5, on three widely-studied tasks incurred by code changes and two tasks specific to the code review process. Results show that CCT5 outperforms both conventional deep learning approaches and existing pre-trained models on these tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Domain Knowledge

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Hybrid Digital-Analog Semantic Communications

arXiv

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arXiv 2024年

作者： Xie, Huiqiang Qin, Zhijin Han, Zhu Letaief, Khaled B. College of Information Science and Technology Jinan University Guangzhou China Department of Electronic Engineering Tsinghua University Beijing China State Key Laboratory of Space Network and Communications Beijing China Department of Electrical and Computer Engineering University of Houston Houston United States Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Digital and analog semantic communications (Sem- Com) face inherent limitations such as data security concerns in analog SemCom, as well as leveling-off and cliff-edge effects in digital SemCom. In order to overcome these challenges, we propose a novel SemCom framework and a corresponding system called HDA-DeepSC, which leverages a hybrid digitalanalog approach for multimedia transmission. This is achieved through the introduction of digital-analog allocation and fusion modules. To strike a balance between data rate and distortion, we design new loss functions that take into account long-distance dependencies in the semantic distortion constraint, essential information recovery in the channel distortion constraint, and optimal bit stream generation in the rate constraint. Additionally, we propose denoising diffusion-based signal detection techniques, which involve carefully designed variance schedules and sampling algorithms to refine transmitted signals. Through extensive numerical experiments, we will demonstrate that HDADeepSC exhibits robustness to channel variations and is capable of supporting various communication scenarios. Our proposed framework outperforms existing benchmarks in terms of peak signal-to-noise ratio and multi-scale structural similarity, showcasing its superiority in semantic communication quality. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Research on Liver Segmentation of CT Image Based on Deep Active Contour Model 3

Research on Liver Segmentation of CT Image Based on Deep Act...

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3rd International Conference on computer Vision, Image and Deep Learning and International Conference on computer Engineering and Applications, CVIDL and ICCEA 2022

作者： He, Shanshan Song, Jinfang Zhang, Yonggang Tian, Xin Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun China Jilin University College of Software Changchun China Second Hospital of Jilin University Department of Pediatrics Changchun China Jilin University College of Computer Science and Technology Changchun China

ISBN: (纸本)9781665459112

Liver cancer is one of the liver diseases with a high morbidity and mortality. At present, surgery is still the main treatment method for liver cancer. Therefore, it is particularly important to accurately segment the liver on CT imaging. Deep learning-based algorithms struggle to derive shape priors due to the irregular shape of the liver and blurring organ borders in CT images. We present a two-stage 3D deep active contour model 3DAC based on the V-net and CV models, in which the neural network is guided to learn the global features by regressing the signed distance map. The signed distance function can also be used as an initialization level set function. Then, the active contour model is extended to three dimensions to enable evolution of the overall liver surface. The advantages of the proposed method for liver segmentation were validated on the AbdomenCT-lK dataset. Compared with the DALS method, the Dice coefficient and the Jaccard coefficient are improved by 4.29% and 4.58%, the ASD and 95HD indicators are improved by 5. 96mm and 10. 04mm, which proves the effectiveness and robustness of the proposed method. © 2022 IEEE.

关键词： Image segmentation

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Analyzing the Expected Hitting Time of Evolutionary Computation-based Neural Architecture Search Algorithms

arXiv

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arXiv 2022年

作者： Lv, Zeqiong Qian, Chao Yen, Gary G. Sun, Yanan The College of Computer Science Sichuan University Chengdu610065 China The State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China The School of Electrical and Computer Engineering Oklahoma State University StillwaterOK74078 United States

Evolutionary computation-based neural architecture search (ENAS) is a populartechnique for automating architecture design of deep neural networks. Despiteits groundbreaking applications, there is no theoretical study for ENAS. Theexpected hitting time (EHT) is one of the most important theoretical issues,since it implies the average computational time complexity. This paper proposesa general method by integrating theory and experiment for estimating the EHT ofENAS algorithms, which includes common configuration, search space partition,transition probability estimation, population distribution fitting, and hittingtime analysis. By exploiting the proposed method, we consider the($\\lambda$+$\\lambda$)-ENAS algorithms with different mutation operators andestimate the lower bounds of the EHT. Furthermore, we study the EHT on theNAS-Bench-101 problem, and the results demonstrate the validity of the proposedmethod. To the best of our knowledge, this work is the first attempt toestablish a theoretical foundation for ENAS algorithms. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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On the Study of Success Serving Probability for Integrated Sensing and Communication (ISAC) Based on Stochastic Geometry

On the Study of Success Serving Probability for Integrated S...

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IEEE International Conference on Communications (ICC)

作者： Wentao Cheng Zhongyuan Zhao Howard H. Yang Wei Hong Tony Q.S. Quek Zhiguo Ding The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China ZJU-UIUC Institute Zhejiang University Haining China Beijing Xiaomi Mobile Software Beijing China Singapore University of Technology and Design Singapore Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi UAE

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Integrated sensing and communication (ISAC) has been proved as a promising technique to further improve the performance for both the communication and the sensing tasks in wireless networks. However, due to the complicated and dynamic interference circumstances, the performance of ISAC cannot be guaranteed. To provide some insights for keeping a sophisticated balance between communication and sensing with considering co-channel interference, the theoretical performance of ISAC is studied in this paper. First, an analytical system model is provided based on stochastic geometry. Second, the success serving probability (SSP) is defined for both the communication and the sensing tasks based on mutual information, which provided a unified analysis framework for ISAC. The tractable expressions of SSP are also derived. Finally, the simulation results are shown to verify the analytical results of SSP, which can provide some insights for the tradeoff between sensing and communication of ISAC.

关键词： Geometry Measurement Monte Carlo methods Wireless networks Simulation Stochastic processes Integrated sensing and communication

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OmniMark: Efficient and Scalable Latent Diffusion Model Fingerprinting 39

OmniMark: Efficient and Scalable Latent Diffusion Model Fing...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Fei, Jianwei Dai, Yunshu Xia, Zhihua Huang, Fangjun Zhou, Jiantao State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China School of Cyber Science and Technology Sun Yat-Sen University China College of Cyberspace Security Jinan University China

ISBN: (纸本)157735897X

We introduce OmniMark, a novel and efficient fingerprinting method for Latent Diffusion Models (LDM). OmniMark can encode user-specific fingerprints across diverse dimensions of the weights of the LDM, including kernels, filters, channels, and spatial domains. The LDM is fine-tuned to encode the invisible fingerprint into generated images, which can be decoded by a decoder. By altering fingerprints and re-encoding the weights, OmniMark supports efficient and scalable adhoc generation ( Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Research on Liver Fat Sequencing Measurement Method Combined with Super-pixel Segmentation 3

Research on Liver Fat Sequencing Measurement Method Combined...

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3rd International Conference on computer Vision, Image and Deep Learning and International Conference on computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Jiao, Xu Song, Jinfang Zhang, Yonggang Tian, Xin Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun China Jilin University College of Software Changchun China Second Hospital of Jilin University Department of Pediatrics Changchun China Jilin University College of Computer Science and Technology Changchun China

ISBN: (纸本)9781665459112

The number of patients with nonalcoholic fatty liver is increasing. Liver fat quantification is the most reliable indicator for the diagnosis of non-alcoholic fatty liver disease, and it is of great value for the early diagnosis and condition monitoring of non-alcoholic fatty liver disease. Therefore, it is of great significance to quantitatively analyze liver fat. In this work, we utilize the superpixel segmentation method based on depth neural network to preprocess the MRI image, we can simplify the representation of the image and obtain good organ boundary information. In addition, we also understand the popular involution operator and improve it through combining the medical image segmentation model u-net to obtain a better region of interest. The segmentation accuracy of the proposed method is 1.82 percentage points higher than that of u-net model. On this basis, the proton density and fat fraction of liver are calculated by medical formula, achieving the sequencing measurement of liver fat. The experimental results show that the proposed method is efficient and feasible. © 2022 IEEE.

关键词： Magnetic resonance imaging

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An Authentication Framework in ICN-Enabled Industrial Cyber-Physical Systems 3rd

An Authentication Framework in ICN-Enabled Industrial Cyber-...

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3rd International Conference on Security and Privacy in New Computing Environments, SPNCE 2020

作者： Lu, Yanrong Zhang, Mengshi Zheng, Xi School of Computer Science and Technology Civil Aviation University of China Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China Facebook Inc. Menlo ParkCA United States Autonomous Systems Research Center Australia Brisbane Australia

ISBN: (纸本)9783030669218

Industrial Cyber-Physical Systems (ICPS), as a new industrial revolution, are to provide advanced intellectual foundation for next generation industrial systems. While such systems present substantial security challenges for the host-centric communication with the growing trend of sensor data streams. Information Centric Networking (ICN) architecture suggests features exploitable in ICPS applications, reducing delivery latency and promoting quality of services that applies broadly across Industrial Internet. Emerging available solutions for secure communication, however, few of them have thoroughly addressed concerns related to securing access due to the dependence on an online provider server. In this work, we propose a concrete authentication framework for ICN ICPS based on proxy signature, which guarantees authentic sensor data access only to legitimate users and does not require interaction between users. This framework would help lower the level of the complexity of the entire system and reduce the cost of authentication by leveraging edge cache. We prove the security of the proposed authentication scheme and present performance analysis to show its efficiency. © 2021, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Cyber Physical System

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Non-Cooperative Game for Edge Server Placement in Wireless Body Area Network

Non-Cooperative Game for Edge Server Placement in Wireless B...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Xiaolong Xu Yiming Fei Xiaokang Zhou Xiaoyong Li Yong Cheng Lianyong Qi Wanchun Dou School of Computer Science Nanjing University of Information Science and Technology Nanjing China Faculty of Data Science Shiga University Tokyo Japan College of Meteorology and Oceanography National University of Defense Technology Changsha China College of Computer Science and Technology China University of Petroleum (East China) China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

The outbreak of the coronavirus disease 2019 (COVID-19) has become the worst public health event in the whole world, threatening the physical and mental health of hundreds of millions of people. However, because of the high survivability of the virus, it is impossible for humans to eliminate viruses completely. For this reason, it is particularly important to strengthen the prevention of the transmission of viruses and monitor the physical status of the crowd. Wireless sensors are a key player in the fight against the current global outbreak of the Covid-19 pandemic, where they are playing an important role in monitoring human health. The Wireless Body Area Network (WBAN) composed of these wireless sensor devices can monitor human health data without interference for a long time, and update the data in almost real time through the Internet of Things (IoT). However, because the data monitored by the devices is relatively large and the transmission distance is long, only transmitting the data to medical centers through the personal devices (PB) cannot get feedback in time. We propose a non-cooperative game-based server placement method, which is named ESP-19 to improve the efficiency of transmission data of wireless sensors. In this paper, experimental tests are conducted based on the distribution of Shanghai Telecom's base stations, and then the performance of ESP-19 is evaluated. The results show that the proposed method in this paper outperforms the comparison method in terms of service delay.

关键词： Wireless communication COVID-19 Wireless sensor networks computer viruses Games Body area networks Sensors

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Generalizing to Unseen Speakers: Multimodal Emotion Recognition in Conversations With Speaker Generalization

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IEEE Transactions on Affective Computing 2025年

作者： Tu, Geng Jing, Ran Liang, Bin Yu, Yue Yang, Min Qin, Bing Xu, Ruifeng School of Computer Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies China Chinese University of Hong Kong Hong Kong Hong Kong Peng Cheng Laboratory Shenzhen518066 China National University of Defense Technology College of Computer Changsha410073 China Chinese Academy of Sciences SIAT Shenzhen518055 China Harbin Institute of Technology Department of Computer Science Harbin150006 China

Multimodal Emotion Recognition in Conversations (MERC) aims to identify the emotion expressed in each utterance within conversational videos. Current efforts are directed toward modeling speaker-sensitive context dependencies and multimodal fusion. However, they still struggle to handle utterances from unseen speakers, hampering the model's generalizability. To tackle this challenge, we propose a Speaker Generalization Framework for MERC. Specifically, we build a prototype graph to learn Speaker-based Utterance Representations (SUR), leveraging prototypes as the bridge between seen and unseen speakers. Speaker-aware Contrastive Learning (CL) is then applied to refine SUR, pulling utterances (or prototypes) from the same speaker together while pushing those from different speakers apart. Further, we introduce a prototypical graph CL to generalize SUR to unseen speakers, ensuring that the same speakers exhibit similar graph structures, while dissimilar ones differ. To further enhance model generalization, we introduce Uncertainty-based Generalization for Speakers, randomly sampling SUR statistics from the estimated Gaussian distribution and probabilistically replacing the original SUR. Experimental findings on two datasets highlight that our framework substantially improves the generalization of various MERC models, surpassing state-of-the-art methods. © 2010-2012 IEEE.

关键词： Gaussian distribution

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