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XRL-SHAP-Cache:an explainable reinforcement learning approach for intelligent edge service caching in content delivery networks

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science China(Information sciences) 2024年第7期67卷 46-71页

作者： Xiaolong XU Fan WU Muhammad BILAL Xiaoyu XIA Wanchun DOU Lina YAO Weiyi ZHONG School of Software Nanjing University of Information Science and Technology School of Computing and Communications Lancaster University School of Computing Technologies Royal Melbourne Institute of Technology State Key Laboratory for Novel Software Technology Nanjing University School of Computer Science and Engineering University of New South Wales Data 61 Commonwealth Scientific and Industrial Research Organization School of Computer Science Qufu Normal University

Content delivery networks(CDNs) play a pivotal role in the modern internet infrastructure by enabling efficient content delivery across diverse geographical regions. As an essential component of CDNs, the edge caching scheme directly influences the user experience by determining the caching and eviction of content on edge servers. With the emergence of 5G technology, traditional caching schemes have faced challenges in adapting to increasingly complex and dynamic network environments. Consequently, deep reinforcement learning(DRL) offers a promising solution for intelligent zero-touch network governance. However, the blackbox nature of DRL models poses challenges in understanding and making trusting decisions. In this paper,we propose an explainable reinforcement learning(XRL)-based intelligent edge service caching approach,namely XRL-SHAP-Cache, which combines DRL with an explainable artificial intelligence(XAI) technique for cache management in CDNs. Instead of focusing solely on achieving performance gains, this study introduces a novel paradigm for providing interpretable caching strategies, thereby establishing a foundation for future transparent and trustworthy edge caching solutions. Specifically, a multi-level cache scheduling framework for CDNs was formulated theoretically, with the D3QN-based caching scheme serving as the targeted interpretable model. Subsequently, by integrating Deep-SHAP into our framework, the contribution of each state input feature to the agent's Q-value output was calculated, thereby providing valuable insights into the decision-making process. The proposed XRL-SHAP-Cache approach was evaluated through extensive experiments to demonstrate the behavior of the scheduling agent in the face of different environmental *** results demonstrate its strong explainability under various real-life scenarios while maintaining superior performance compared to traditional caching schemes in terms of cache hit ratio, quality of service(QoS),a

关键词： deep reinforcement learning (DRL) explainable artificial intelligence (XAI) multi-level cache content delivery network (CDN) D3QN algorithm Deep-SHAP

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Nimbus++: Revisiting E±cient Function Signature Recovery with Depth Data Analysis

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International Journal of software Engineering and Knowledge Engineering 2023年第10期33卷 1537-1565页

作者： Chen, Ligeng Qian, Yi Wang, Yuyang Mao, Bing State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Function signature recovery is vital for many binary analysis tasks, led by control-°ow integrity enhancement. To minimize human e®ort, existing works attempt to replace rule-based methods with learning-based methods. These works put a lot of work into improving the system's performance, but this had the unintended consequence of increasing resource usage. However, recovering the function signature is more about providing information for subsequent tasks, e.g. reverse engineering, so both e±ciency and performance are signi¯cant. To identify the fundamental factors that increase e±ciency, we attempt to optimize data-driven systems throughout their lifecycle from a data perspective. To this end, we perform detailed data analysis on a carefully collected dataset. After analysis and exploration, selective input is adopted and a multi-task learning (MTL) structure is introduced for function feature recovery to make full use of mutual information, and the computing resource overhead is optimized based on the observation of information deviation and sub-task relationship. The resource usage of the entire process is signi¯cantly reduced by our suggested solution, named Nimbus++ for e±cient function signature recovery, without sacri¯cing performance. Our test ¯ndings demonstrate that we even surpass the state-of-the-art method's prediction accuracy across all function signature recovery tasks by about 1% with just about 12.5% of the processing time. © World Scientific Publishing Company.

关键词： Recurrent neural networks

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How far are we to GPT-4V?Closing the gap to commercial multimodal models with open-source suites

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science China(Information sciences) 2024年第12期67卷 5-22页

作者： Zhe CHEN Weiyun WANG Hao TIAN Shenglong YE Zhangwei GAO Erfei CUI Wenwen TONG Kongzhi HU Jiapeng LUO Zheng MA Ji MA Jiaqi WANG Xiaoyi DONG Hang YAN Hewei GUO Conghui HE Botian SHI Zhenjiang JIN Chao XU Bin WANG Xingjian WEI Wei LI Wenjian ZHANG Bo ZHANG Pinlong CAI Licheng WEN Xiangchao YAN Min DOU Lewei LU Xizhou ZHU Tong LU Dahua LIN Yu QIAO Jifeng DAI Wenhai WANG State Key Laboratory for Novel Software Technology Nanjing University Shanghai AI Laboratory School of Computer Science Fudan University SenseTime Research Department of Information Engineering The Chinese University of Hong Kong Department of Electronic Engineering Tsinghua University

In this paper, we introduce InternVL 1.5, an open-source multimodal large language model(MLLM) to bridge the capability gap between open-source and proprietary commercial models in multimodal understanding. We introduce three simple improvements.(1) Strong vision encoder: we explored a continuous learning strategy for the large-scale vision foundation model — InternViT-6B, boosting its visual understanding capabilities, and making it can be transferred and reused in different LLMs.(2) Dynamic high-resolution: we divide images into tiles ranging from 1 to 40 of 448×448 pixels according to the aspect ratio and resolution of the input images, which supports up to 4K resolution input.(3) High-quality bilingual dataset: we carefully collected a high-quality bilingual dataset that covers common scenes, document images,and annotated them with English and Chinese question-answer pairs, significantly enhancing performance in optical character recognition(OCR) and Chinese-related tasks. We evaluate InternVL 1.5 through a series of benchmarks and comparative studies. Compared to both open-source and proprietary commercial models, InternVL 1.5 shows competitive performance, achieving state-of-the-art results in 8 of 18 multimodal benchmarks. Code and models are available at https://***/OpenGVLab/InternVL.

关键词： multimodal model open-source vision encoder dynamic resolution bilingual dataset

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Heuristic Search with Cut Point Based Strategy for Critical Node Problem

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Journal of computer science & technology 2024年第6期39卷 1328-1340页

作者： Zhi-Han Chen Shao-Wei Cai Jian Gao Shi-Ke Ge Chan-Juan Liu Jin-Kun Lin School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 101408China Key Laboratory of System Software(Chinese Academy of Sciences)and State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China College of Information Science and Technology Northeast Normal UniversityChangchun 130024China School of Computer Science and Technology Dalian University of TechnologyDalian 116024China SeedMath Technology Limited Beijing 100086China

The critical node problem(CNP)aims to deal with critical node identification in a graph,which has extensive applications in many *** CNP is a challenging task due to its computational complexity,and it attracts much attention from both academia and *** this paper,we propose a population-based heuristic search algorithm called CPHS(Cut Point Based Heuristic Search)to solve CNP,which integrates two main *** first one is a cut point based greedy strategy in the local search,and the second one involves the functions used to update the solution pool of the ***,a mutation strategy is applied to solutions with probability based on the overall average similarity to maintain the diversity of the solution *** are performed on a synthetic benchmark,a real-world benchmark,and a large-scale network benchmark to evaluate our *** with state-of-the-art algorithms,our algorithm has better performance in terms of both solution quality and run time on all the three benchmarks.

关键词： local search cut point heuristic search critical node problem

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Multi-agent policy transfer via task relationship modeling

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science China(Information sciences) 2024年第8期67卷 102-114页

作者： Rongjun QIN Feng CHEN Tonghan WANG Lei YUAN Xiaoran WU Yipeng KANG Zongzhang ZHANG Chongjie ZHANG Yang YU National Key Laboratory for Novel Software Technology Nanjing University Polixir Technologies School of Engineering and Applied Sciences Harvard University Department of Computer Science and Technology Tsinghua University Institute for Interdisciplinary Information Sciences Tsinghua University

Team adaptation to new cooperative tasks is a hallmark of human intelligence, which has yet to be fully realized in learning agents. Previous studies on multi-agent transfer learning have accommodated teams of different sizes but heavily relied on the generalization ability of neural networks for adapting to unseen tasks. We posit that the relationship among tasks provides key information for policy *** utilize this relationship for efficient transfer by attempting to discover and exploit the knowledge among tasks from different teams, proposing to learn an effect-based task representation as a common latent space among tasks, and using it to build an alternatively fixed training scheme. Herein, we demonstrate that task representation can capture the relationship among teams and generalize to unseen tasks. Thus, the proposed method helps transfer the learned cooperation knowledge to new tasks after training on a few source ***, the learned transferred policies help solve tasks that are difficult to learn from scratch.

关键词： multi-agent reinforcement learning cooperative transfer learning task relationship modeling multi-agent policy reuse multi-agent multi-task learning

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Iterative Android automated testing

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Frontiers of computer science 2023年第5期17卷 15-26页

作者： Yi ZHONG Mengyu SHI Youran XU Chunrong FANG Zhenyu CHEN State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210093China School of Big Data and Computer Science Chongqing College of Mobile CommunicationChongqing 400065China

With the benefits of reducing time and workforce,automated testing has been widely used for the quality assurance of mobile applications(APPs).Compared with automated testing,manual testing can achieve higher coverage in complex interactive *** the effectiveness of manual testing is highly dependent on the user operation process(UOP)of experienced *** on the UOP,we propose an iterative Android automated testing(IAAT)method that automatically records,extracts,and integrates UOPs to guide the test logic of the tool across the complex Activity *** feedback test results can train the UOPs to achieve higher coverage in each *** extracted 50 UOPs and conducted experiments on 10 popular mobile APPs to demonstrate IAAT’s effectiveness compared with Monkey and the initial automated *** experimental results show a noticeable improvement in the IAAT compared with the test logic without human *** the 60 minutes test time,the average code coverage is improved by 13.98%to 37.83%,higher than the 27.48%of Monkey under the same conditions.

关键词： quality assurance automated testing UOP test coverage

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GTE: learning code AST representation efficiently and effectively

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science China(Information sciences) 2025年第3期68卷 393-394页

作者： Yihao QIN Shangwen WANG Bo LIN Kang YANG Xiaoguang MAO College of Computer Science and Technology National University of Defense Technology Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology

With the development of deep learning in recent years, code representation learning techniques have become the foundation of many software engineering tasks such as program classification [1] and defect detection. Earlier approaches treat the code as token sequences and use CNN, RNN, and the Transformer models to learn code representations.

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Rethinking confidence scores for source-free unsupervised domain adaptation

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Neural Computing and Applications 2024年第24期36卷 14951-14966页

作者： Tian, Qing Sun, Canyu School of Software Nanjing University of Information Science and Technology Nanjing210044 China Wuxi Institute of Technology Nanjing University of Information Science and Technology Wuxi214000 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China

Source-free unsupervised domain adaptation (SFUDA) aims to achieve target domain predictions through a source model instead of source data. One of the representative ideas for the SFUDA problem is to apply a self-supervised pseudo-labeling strategy (SSPL) to achieve target domain adaptation, but it is prone to being plagued by noisy labels, which can lead to negative transfer. Therefore, many methods attempt to improve the SSPL and leverage confidence scores to weaken the impact of low-confidence samples on the model, which are potentially noisy samples. However, they are unable to completely overcome the problem of noisy labels because the pseudo-labels of high-confidence samples may also be incorrect. Besides, they rarely allow low-confidence samples to be added to training, which can lead to sample selection bias and thus limit the model generalization ability. In this work, we propose information re-exploitation based on confidence scores (RECS) for the SFUDA problem, in which we rethink the information brought by confidence scores and take advantage of them to solve the shortcomings of the improved SSPL. Specifically, we realize cross-domain target adaptation by the symmetric SSPL with dual denoising, and reduce the intra-domain distribution discrepancy by the discriminative class-balanced feature alignment. In this way, the model robustness and generalization are enhanced. Extensive experiments conducted on three standard datasets have demonstrated the effectiveness and superiority of our proposed method. The code is available at https://***/lingyuxuan1234/RECS. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Confidence scores Noisy labels Sample selection bias Source-free unsupervised domain adaptation

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Learning Hierarchical Adaptive Code Clouds for Neural 3D Shape Representation

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Machine Intelligence Research 2025年第2期22卷 304-323页

作者： Yuanxun Lu Xinya Ji Hao Zhu Xun Cao State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China School of Electronic Science and Engineering Nanjing UniversityNanjing 210023China School of Intelligence Science and Technology Nanjing UniversitySuzhou 215163China

Neural implicit representation(NIR)has attracted significant attention in 3D shape representation for its efficiency,generalizability,and flexibility compared with traditional explicit *** works usually parameterize shapes with neural feature grids/volumes,which prove to be inefficient for the discrete position constraints of the *** recent advances make it possible to optimize continuous positions for the latent codes,they still lack self-adaptability to represent various kinds of shapes *** this paper,we introduce a hierarchical adaptive code cloud(HACC)model to achieve an accurate and compact implicit 3D shape ***,we begin by assigning adaptive influence fields and dynamic positions to latent codes,which are optimizable during training,and propose an adaptive aggregation function to fuse the contributions of candidate latent codes with respect to query *** addition,these basic modules are stacked hierarchically with gradually narrowing influence field thresholds and,therefore,heuristically forced to focus on capturing finer structures at higher *** formulations greatly improve the distribution and effectiveness of local latent codes and reconstruct shapes from coarse to fine with high *** qualitative and quantitative evaluations both on single-shape reconstruction and large-scale dataset representation tasks demonstrate the superiority of our method over state-of-the-art approaches.

关键词： Representation learning shape analysis deep implicit function 3D reconstruction 3D modeling

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CAGNet:a context-aware graph neural network for detecting social relationships in videos

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Visual Intelligence 2024年第1期2卷 259-271页

作者： Fan Yu Yaqun Fang Zhixiang Zhao Jia Bei Tongwei Ren Gangshan Wu State Key Laboratory for Novel Software Technology Nanjing UniversityNanjingChina

Social relationships,such as parent-offspring and friends,are crucial and stable connections between individuals,especially at the person level,and are essential for accurately describing the semantics of *** this paper,we analogize such a task to scene graph generation,which we call video social relationship graph generation(VSRGG).It involves generating a social relationship graph for each video based on person-level *** propose a context-aware graph neural network(CAGNet)for VSRGG,which effectively generates social relationship graphs through message passing,capturing the context of the ***,CAGNet detects persons in the video,generates an initial graph via relationship proposal,and extracts facial and body features to describe the detected individuals,as well as temporal features to describe their ***,CAGNet predicts pairwise relationships between individuals using graph message ***,we construct a new dataset,VidSoR,to evaluate VSRGG,which contains 72 h of video with 6276 person instances and 5313 relationship instances of eight relationship *** experiments show that CAGNet can make accurate predictions with a comparatively high mean recall(mRecall)when using only visual features.

关键词： Video analysis Social relationship detection Scene graph generation Message passing

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