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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Li, Ying Shan, Chun Liu, Zhen Liao, Shuyan Beijing Key Laboratory of Software Security Engineering Technology Beijing Institute of Technology Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China Computer School Beijing Information Science and Technology University Beijing China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9789819615476

Deep learning(DL) systems exhibit multiple behavioral characteristics such as correctness, robustness, and fairness. Ensuring that these behavioral characteristics function properly is crucial for maintaining the accuracy of DL systems’ outputs. As a specialized form of software, DL systems’ security testing techniques have increasingly become a focus of research in quality assurance. We analyze and organize the testing techniques for DL systems based on an investigation of the current state of the art both domestically and internationally. This paper categorizes existing approaches as component-oriented and attribute-oriented methods, providing a detailed review based on this classification. Additionally, we forecast the future development of testing techniques for DL systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Sparse is Enough in Fine-tuning Pre-trained Large Language Models 41

Sparse is Enough in Fine-tuning Pre-trained Large Language M...

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41st International Conference on Machine Learning, ICML 2024

作者： Song, Weixi Li, Zuchao Zhang, Lefei Zhao, Hai Du, Bo National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China Hubei Luojia Laboratory Wuhan430072 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China

With the prevalence of pre-training-fine-tuning paradigm, how to efficiently adapt the pre-trained model to the downstream tasks has been an intriguing issue. Parameter-Efficient Fine-Tuning (PEFT) methods have been proposed for low-cost adaptation. Although PEFT has demonstrated effectiveness and been widely applied, the underlying principles are still unclear. In this paper, we adopt the PAC-Bayesian generalization error bound, viewing pre-training as a shift of prior distribution which leads to a tighter bound for generalization error. We validate this shift from the perspectives of oscillations in the loss landscape and the quasi-sparsity in gradient distribution. Based on this, we propose a gradient-based sparse finetuning algorithm, named Sparse Increment Fine-Tuning (SIFT), and validate its effectiveness on a range of tasks including the GLUE Benchmark and Instruction-tuning. The code is accessible at https://***/song-wx/SIFT. Copyright 2024 by the author(s)

关键词： Benchmarking

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Hypergraph-based Self-supervised Multi-channel Network for Drug-target Interaction Prediction

Hypergraph-based Self-supervised Multi-channel Network for D...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zeng, Xiaoting Liang, Yu Yang, Peng Chen, Weilin Lei, Baiying Shenzhen University School of Computer Science and Software Engineering Shenzhen China Shenzhen University School of Biomedical Engineering Shenzhen China Shenzhen University Medical School Shenzhen University Institute of Biological Therapy Shenzhen China Shenzhen University School of Biomedical Engineering Health Science Center Shenzhen China

ISBN: (纸本)9798350386226

Drug-target interaction (DTI) prediction is vital for drug discovery and repurposing. Hypergraph is utilized in DTI prediction for modeling higher-order relationships in biomedical networks. Although the strategies of modeling hypergraph-based drug-related interactions with multi-channel and utilizing self-supervised learning task to improve DTI prediction performance have been proven promising, current researches fail to effectively model feature interaction across different channels and fully exploit cross-channel information for self-supervised task. In this study, we propose a hypergraph-based self-supervised multi-channel interaction framework HSMI-DTI for DTI prediction. HSMI-DTI aims to extract hypergraph features effectively and model cross-channel correlations, leveraging hierarchical self-supervised learning to uncover the discover correlations between different channels. We compare HSMI-DTI with advanced baselines, and experiment results show our model outperforms existing methods, thereby optimizing DTI prediction performance. © 2024 IEEE.

关键词： Self-supervised learning

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IEEE 802.11 VANETs for Safety of Autonomous Driving: QoS Requirements & Benchmarking 100

IEEE 802.11 VANETs for Safety of Autonomous Driving: QoS Req...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Ma, Xiaomin Ding, Shengli Zhang, Tingyu Oral Roberts University School of Engineering TulsaOK74171 United States Purdue University Elmore Family School of Electrical and Computer Engineering United States Dalian University of Science and Technology College of Software Engineering China

ISBN: (纸本)9798331517786

It is anticipated that wireless vehicular ad hoc networks (VANETs) can further enhance the safety of autonomous vehicles. Recently, two major standards for the next generation of VANET technologies have been suggested and tested: IEEE 802.11p/bd and 3GPP NR-V2X. Compared with human driving VANET, autonomous driving VANET poses big challenges for more stringent requirements on reliability and delay of message transmissions. Up to date, the quality of service (QoS) requirements of VANETs for the safety services of autonomous driving vehicles have not been investigated systematically. Whether or not the current proposed communication systems can meet such high QoS requirements is unclear. This paper reviews the communication requirements for safety use cases, and the QoS requirements for level 5 autonomous driving VANETs are deduced. Then, the QoS of IEEE 802.11-based VANETs, including 802.11p, 802.11bd, and other 802.11 cutting-edge versions, are evaluated by simulations and analyses on a few selected safety use cases and benchmarked against the corresponding QoS requirements. The numerical results reveal that the current IEEE 802.11p/bd cannot meet the QoS requirements for some selected safety applications in autonomous driving vehicles. However, IEEE 802.11 systems with new enhancements can potentially support safety services with assured QoS. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Business Scenario Driven Reinforcement Learning Testing Method 26

Business Scenario Driven Reinforcement Learning Testing Meth...

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26th ACIS International Winter Conference on software engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD-Winter 2023

作者： Cai, Lizhi Wang, Jin School of Information Science and Engineering East China University of Science and Technology Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai China

ISBN: (纸本)9798350345865

Reinforcement learning has been successfully applied in software testing, but the existing testing methods cannot perform effective testing according to the characteristics of applications, and using outdated interactive experience during training, resulting in inefficient testing. In this paper, we propose BSDRTesting. Firstly, the demonstration experience of human users is collected according to the functional scenarios and business logic of each application, and combining reinforcement learning and imitation learning to maximize rewards while imitating user behavior, experience replay aims to sample experiences from the agent's self-exploration and expert demonstrations to improve sampling efficiency. At the same time, according to the input rules, the black-box testing method is used to fully test the input events, and finally an experience filtering mechanism is proposed, and the reward value and TD-Error are used as the basis for priority sampling. The experimental results on 10 open source applications show BSDRTesting has achieved significant improvements in code coverage and branch coverage compared with existing methods. © 2023 IEEE.

关键词： Reinforcement learning

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Fast Verification of Network Configuration Updates

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computers, Materials & Continua 2023年第1期74卷 293-311页

作者： Jiangyuan Yao Zheng Jiang Kaiwen Zou Shuhua Weng Yaxin Li Deshun Li Yahui Li Xingcan Cao Scool of Computer Science and Technology Hainan UniversityHaikou570228China School of Cyberspace Security Hainan UniversityHaikou570228China School of Software Engineering Jilin UniversityChangchun130012China School of Software Beijing Jiaotong UniversityBeijing100091China University of British Columbia VancouverV5K1K5Canada

With the expansion of network services,large-scale networks have progressively become *** network status changes rapidly in response to customer needs and configuration changes,so network configuration changes are also very ***,no matter what changes,the network must ensure the correct conditions,such as isolating tenants from each other or guaranteeing essential *** changes occur,it is necessary to verify the after-changed ***,for the verification of large-scale network configuration changes,many current verifiers show poor *** order to solve the problem ofmultiple global verifications caused by frequent updates of local configurations in large networks,we present a fast configuration updates verification tool,FastCUV,for distributed control *** aims to enhance the efficiency of distributed control plane verification for medium and large networks while ensuring *** paper presents a method to determine the network range affected by the configuration *** present a flow model and graph structure to facilitate the design of verification algorithms and speed up *** scheme verifies the network area affected by obtaining the change of the Forwarding Information Base(FIB)before and *** supports rich network attributes,meanwhile,has high efficiency and correctness *** experimental verification and result analysis,our method outperforms the state-of-the-art method to a certain extent.

关键词： Network verification configuration updates network control plane forwarding information base

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Chinese Speech Emotion Recognition Based on Pre-Trained Model 5

Chinese Speech Emotion Recognition Based on Pre-Trained Mode...

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5th International Conference on Big Data and Artificial Intelligence and software engineering, ICBASE 2024

作者： Xu, Sukui Jiang, Nan Lian, Shun Pan, Jingmin Anhui Institute of Information Technology College of Computer and Software Engineering Wuhu China Fujian Polytechnic of Information Technology School of General Education Fuzhou China Anhui University School of Computer Science and Technology Hefei China

ISBN: (纸本)9798331506612

This paper proposes a method based on large-scale speech pre-trained models for the task of Chinese speech emotion recognition. Similar to the transfer learning approach in image classification task, speech pre-trained model is used as a fixed feature extractor to extract features from speech data, followed by a fully connected network for classification. Experimental results show that, on the seven-class speech emotion recognition task of the Chinese M3ED dataset, the proposed method achieves classification accuracies of 0.506 and 0.560 using base and large sized pre-trained models respectively, surpassing the baseline accuracy of 0.454 for the ECAPA-TDNN model. © 2024 IEEE.

关键词： Transfer learning

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Research on Image Style Transfer Algorithm Based on Structural Consistency 8

Research on Image Style Transfer Algorithm Based on Structur...

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8th International Conference on Intelligent Computing and Signal Processing, ICSP 2023

作者： Yang, Chuxiong Chen, Zhuang Chongqing University of Posts and Telecommunications School of Software Engineering Chongqing China Chongqing University of Technology College of Computer Science and Engineering Chongqing China

ISBN: (纸本)9798350302455

The algorithm proposed in this paper aims to address the issue of structural content distortions in images that occur after applying image style transfer. It introduces a structural consistency-based approach called the PoolNet algorithm. This algorithm incorporates saliency regions to perform style transfer at the block level. By employing a saliency detection network, a saliency map is generated for both the synthetic and content images. During the training process, two losses are computed to ensure that the salient regions in the synthetic image remain consistent with those in the content image. Additionally, local affine constraints in the color space are employed to enhance the stylized images by constraining the image transformation. Experimental results demonstrate that the stylized images produced by this style transfer model exhibit improved visual effects and preserve the semantic information of the content images. This improvement is particularly noticeable when dealing with distorted migration images. © 2023 IEEE.

关键词： Convolutional neural networks

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Deep visual-linguistic fusion network considering cross-modal inconsistency for rumor detection

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science China(Information sciences) 2023年第12期66卷 16-32页

作者： Yang YANG Ran BAO Weili GUO De-Chuan ZHAN Yilong YIN Jian YANG School of Computer Science and Engineering Nanjing University of Science and Technology National Key Laboratory for Novel Software Technology Nanjing University School of Software Shandong University

With the development of the Internet, users can freely publish posts on various social media platforms, which offers great convenience for keeping abreast of the world. However, posts usually carry many rumors, which require plenty of manpower for monitoring. Owing to the success of modern machine learning techniques, especially deep learning models, we tried to detect rumors as a classification problem automatically. Early attempts have always focused on building classifiers relying on image or text information, i.e., single modality in posts. Thereafter, several multimodal detection approaches employ an early or late fusion operator for aggregating multiple source information. Nevertheless, they only take advantage of multimodal embeddings for fusion and ignore another important detection factor, i.e., the intermodal inconsistency between modalities. To solve this problem, we develop a novel deep visual-linguistic fusion network(DVLFN) considering cross-modal inconsistency, which detects rumors by comprehensively considering modal aggregation and contrast information. Specifically, the DVLFN first utilizes visual and textual deep encoders, i.e., Faster R-CNN and bidirectional encoder representations from transformers, to extract global and regional embeddings for image and text modalities. Then, it predicts posts' authenticity from two aspects:(1) intermodal inconsistency, which employs the Wasserstein distance to efficiently measure the similarity between regional embeddings of different modalities, and(2) modal aggregation, which experimentally employs the early fusion to aggregate two modal embeddings for prediction. Consequently, the DVLFN can compose the final prediction based on the modal fusion and inconsistency measure. Experiments are conducted on three real-world multimedia rumor detection datasets collected from Reddit, Good News, and Weibo. The results validate the superior performance of the proposed DVLFN.

关键词： multimodal learning Wasserstein distance rumor detection

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GRETO: REMEDYING DYNAMIC GRAPH TOPOLOGY-TASK DISCORDANCE VIA TARGET HOMOPHILY 11

GRETO: REMEDYING DYNAMIC GRAPH TOPOLOGY-TASK DISCORDANCE VIA...

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11th International Conference on Learning Representations, ICLR 2023

作者： Zhou, Zhengyang Huang, Qihe Lin, Gengyu Yang, Kuo Bai, Lei Wang, Yang Hefei China School of Software Engineering USTC China School of Computer Science and Technology USTC China Shanghai AI Laboratory Shanghai China

Dynamic graphs are ubiquitous across disciplines where observations usually change over time. Regressions on dynamic graphs often contribute to diverse critical tasks, such as climate early-warning and traffic controlling. Existing homophily Graph Neural Networks (GNNs) adopt physical connections or feature similarity as adjacent matrix to perform node-level aggregations. However, on dynamic graphs with diverse node-wise relations, exploiting a pre-defined fixed topology for message passing inevitably leads to the aggregations of target-deviated neighbors. We designate such phenomenon as the topology-task discordance, which naturally challenges the homophily assumption. In this work, we revisit node-wise relationships and explore novel homophily measurements on dynamic graphs with both signs and distances, capturing multiple node-level spatial relations and temporal evolutions. We discover that advancing homophily aggregations to signed target-oriented message passing can effectively resolve the discordance and promote aggregation capacity. Therefore, a GReTo is proposed, which performs signed message passing in immediate neighborhood, and exploits both local environments and target awareness to realize high-order message propagation. Empirically, our solution achieves significant improvements against best baselines, notably improving 24.79% on KnowAir and 3.60% on Metr-LA. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Message passing

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