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Proceedings of the 41st International Conference on Machine Learning

作者： Huilai Chen Yuanbo Wen Limin Cheng Shouxu Kuang Yumeng Liu Weijia Li Ling Li Rui Zhang Xinkai Song Wei Li Qi Guo Yunji Chen State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China and University of Chinese Academy of Sciences Beijing China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China Intelligent Software Research Center Institute of Software CAS Beijing China University of Chinese Academy of Sciences Beijing China and Intelligent Software Research Center Institute of Software CAS Beijing China

With the rapid development of Artificial Intelligence of Things (AIoT), customizing and optimizing operating system (OS) kernel configurations for various AIoT application scenarios is crucial for maximizing system performance. However, existing approaches falter due to the overwhelming problem complexity (i.e., over 15,000 configuration options in the Linux kernel), together with the huge evaluation costs and error-prone options that may result in OS boot-up failure, which all make it an unresolved problem to optimize the Linux kernel automatically. In this paper, we introduce AutoOS, a novel framework exploiting Large Language Models for customizing and optimizing OS kernel configurations automatically for various AIoT application scenarios. Inspired by the inherently directory-structured kernel configuration process, we first formulate our research problem as optimizing on a dynamic tree. We then propose a novel framework integrating a state machine-based traversal algorithm as the observe-prune-propose-act-correct loop, which can effectively refine the optimization space and ensure a successful OS boot-up. Experimental results show that AutoOS can automatically customize and optimize the OS kernel configurations without human effort. More importantly, AutoOS even achieves better performance by up to 25% than vendor-provided configuration.

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TH-SLP: Web Service Link Prediction Based on Topic-aware Heterogeneous Graph Neural Network

TH-SLP: Web Service Link Prediction Based on Topic-aware Het...

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IEEE International Conference on Web Services (ICWS)

作者： Qian Peng Buqing Cao Xiang Xie Shanpeng Liu Guosheng Kang Jianxun Liu School of Computer Science and Engineering & Hunan Provincial Key Lab. For Services Computing and Novel Software Technology Hunan University of Science and Technology Xiangtan Hunan China

With the emergence of more and more Web services, finding suitable services becomes a difficult problem. Service link prediction is employed to disclose relationships among services, which facilitates the further development of service composition, selection, and recommendation. But the existing link prediction approaches simply utilize the structural features of the service network. In reality, the rich text content in service node description documents also carries latent but fine-grained semantics generated by multifaceted topic-aware factors, yet few efforts are committed to mining them. In this paper, we propose a Web service link prediction method based on a topic-aware heterogeneous graph neural network. Specifically, the method consists of two main layers, including the meta-path intra-decomposition and the meta-path inter-mergence. Meta-path intra-decomposition aims to mine the topic distribution of the meta-paths-based context while capturing fine-grained topic-aware semantics. Meta-path inter-mergence uniquely aggregates topic-aware factors according to the mined distribution and adopts a multifaceted attention mechanism to aggregate different meta-paths, enabling service nodes to generate multifaceted topic-aware embeddings that preserve not only the structure and but also the topic-aware semantics. In addition, a topic prior guidance regularization item is set up for quality assurance of multifaceted topic-aware embedding that depends on global knowledge of the unstructured text content in description documents. Experimental results on real datasets show that our proposed model outperforms other existing baselines methods in the link prediction task, successfully validating the effectiveness of our proposed method.

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Organization-Driven Business Process Configurable Modeling for Spatial Variability

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China Communications 2021年第11期18卷 229-242页

作者： Guosheng Kang Liqing Yang Liang Zhang Jianxun Liu Yiping Wen Hunan Provincial Key Lab.for Services Computing and Novel Software Technology Hunan University of Science and TechnologyXiangtan 411201China School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan 411201China School of Computer Science Fudan UniversityShanghai 201203China

Nowadays,enterprises need to continually adjust their business processes to adapt to the changes of business environments,especially when one business needs to be deployed in different application scenarios,which is called spatial variability in this *** the field of BPM(Business Process Management),configurable business process models have demonstrated their effectiveness in aspects of process modeling and model ***,we found that the existing techniques lead to complex configurable models,and are inadequate for model reuse especially for the spatial variability issue because they neglect the root impact of organizations on control flow.S-BPM(Subject-oriented Business Process Management)models provide a solid foundation for dealing with complex applications and help to bridge the gap between business and IT for process *** this paper,we propose an organization-driven business process configurable modeling approach for spatial variability by integrating both restriction and extension operations based on the S-BPM paradigm,in which business objects are also *** approach is validated with a general business process developed for the Real Estate Administration(REA)in a certain province of *** resulting configurable modeling framework can express the heterogeneous activity sequences for one business and has the potential to generate process models for uncertain environments in a new organization structure.

关键词： S-BPM configurable modeling organization-driven spatial variability business objects services

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Neuro-symbolic Learning Yielding Logical Constraints

arXiv

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arXiv 2024年

作者： Li, Zenan Huang, Yunpeng Li, Zhaoyu Yao, Yuan Xu, Jingwei Chen, Taolue Ma, Xiaoxing Lü, Jian State Key Lab of Novel Software Technology Nanjing University China Department of Computer Science University of Toronto Canada School of Computing and Mathematical Sciences Birkbeck University of London United Kingdom

Neuro-symbolic systems combine neural perception and logical reasoning, representing one of the priorities of AI research. End-to-end learning of neuro-symbolic systems is highly desirable, but remains to be challenging. Resembling the distinction and cooperation between System 1 and System 2 of human thought (à la Kahneman), this paper proposes a framework that fuses neural network training, symbol grounding, and logical constraint synthesis to support learning in a weakly supervised setting. Technically, it is cast as a game with two optimization problems which correspond to neural network learning and symbolic constraint learning respectively. Such a formulation naturally embeds symbol grounding and enables the interaction between the neural and the symbolic part in both training and inference. The logical constraints are represented as cardinality constraints, and we use the trust region method to avoid degeneracy in learning. A distinguished feature of the optimization lies in the Boolean constraints for which we introduce a difference-of-convex programming approach. Both theoretical analysis and empirical evaluations substantiate the effectiveness of the proposed framework. © 2024, CC0.

关键词： Self-supervised learning

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LIBALCHEMY: A Two-Layer Persistent Summary Design for Taming Third-Party Libraries in Static Bug-Finding Systems

LIBALCHEMY: A Two-Layer Persistent Summary Design for Taming...

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International Conference on software Engineering (ICSE)

作者： Rongxin Wu Yuxuan He Jiafeng Huang Chengpeng Wang Wensheng Tang Qingkai Shi Xiao Xiao Charles Zhang Xiamen Key Laboratory of Intelligent Storage and Computing School of Informatics Xiamen University Xiamen China The Hong Kong University of Science and Technology Hong Kong China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Person Hang Zhou China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Despite the benefits of using third-party libraries (TPLs), the misuse of TPL functions raises quality and security concerns. Using traditional static analysis to detect bugs caused by TPL function is nontrivial. One promising solution would be to automatically generate and persist the summaries of TPL functions offline and then reuse these summaries in compositional static analysis online. However, when dealing with millions of lines of TPL code, the summaries designed by existing studies suffer from an unresolved paradox. That is, a highly precise form of summary leads to an unaffordable space and time overhead, while an imprecise one seriously hurts its precision or recall. To address the paradox, we propose a novel two-layer summary design. The first layer utilizes a line-sized program representation known as the program dependence graph to compactly encode path conditions, while the second layer encodes bug-type-specific properties. We implemented our idea as a tool called Libalchemy and evaluated it on fifteen mature and extensively checked open-source projects. Experimental results show that Libalchemy can check over ten million lines of code within ten hours. Libalchemy has detected 55 true bugs with a high precision of 90.16%, eleven of which have been assigned CVE IDs. Compared to whole-program analysis and the conventional design of path-sensitively precise summaries, Libalchemy achieves an 18.56× and 12.77× speedup and saves 91.49% and 90.51% of memory usage, respectively.

关键词： Codes Computer bugs Static analysis Libraries Security software engineering

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Hyperbolic Geometric Latent Diffusion Model for Graph Generation 41

Hyperbolic Geometric Latent Diffusion Model for Graph Genera...

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41st International Conference on Machine Learning, ICML 2024

作者： Fu, Xingcheng Gao, Yisen Wei, Yuecen Sun, Qingyun Peng, Hao Li, Jianxin Li, Xianxian Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Institute of Artificial Intelligence Beihang University Beijing China School of Software Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing China

Diffusion models have made significant contributions to computer vision, sparking a growing interest in the community recently regarding the application of them to graph generation. Existing discrete graph diffusion models exhibit heightened computational complexity and diminished training efficiency. A preferable and natural way is to directly diffuse the graph within the latent space. However, due to the non-Euclidean structure of graphs is not isotropic in the latent space, the existing latent diffusion models effectively make it difficult to capture and preserve the topological information of graphs. To address the above challenges, we propose a novel geometrically latent diffusion framework HypDiff. Specifically, we first establish a geometrically latent space with interpretability measures based on hyperbolic geometry, to define anisotropic latent diffusion processes for graphs. Then, we propose a geometrically latent diffusion process that is constrained by both radial and angular geometric properties, thereby ensuring the preservation of the original topological properties in the generative graphs. Extensive experimental results demonstrate the superior effectiveness of HypDiff for graph generation with various topologies. Copyright 2024 by the author(s)

关键词： Graph theory

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MRIN: Fine-grained Feature Interaction based on Multiple Feature Relationships for CTR Prediction

MRIN: Fine-grained Feature Interaction based on Multiple Fea...

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International Conference on Computer Supported Cooperative Work in Design

作者： Jianbang Ju Yingyuan Xiao Peilin Yang Wenguang Zheng Enginnering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China

CTR prediction is an important task in personalized recommendation and online advertising. In recent years, many models have been applied in CTR prediction systems to imporve the predcition accuracy. Although the current work has achieved results, the diversity of relationships between the different features was ignored: 1) most existing models usually use the vector-product to realize the interaction of each feature pairs, which ignore the potential contexts between the different feature interactions and lack the different interactive behaviors between the same pair of features; 2) most existing models usually only consider the importance of features of the same order, which ignore the weight relationships between the different feature *** this paper, we propose a novel deep learning model called Multi-Relational Interaction Network (MRIN), which learns fine-grained feature interactions and captures latent relationships between features. On the one hand, we design two interactive modules that take different interactive methods to learn the feature interactions in the potential contexts. On the other hand, we flexibly apply attention network to further automatically learn the weight relationships between different feature representations. Extensive experiments conducted on two public datasets prove the effectiveness of our proposed model, which has better performance than the state-of-the-art models.

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Emergent communication for rules reasoning 23

Emergent communication for rules reasoning

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Yuxuan Guo Yifan Hao Rui Zhang Enshuai Zhou Zidong Du Xishan Zhang Xinkai Song Yuanbo Wen Yongwei Zhao Xuehai Zhou Jiaming Guo Qi Yi Shaohui Peng Di Huang Ruizhi Chen Qi Guo Yunji Chen University of Science and Technology of China and State Key Lab of Processors Institute of Computing Technology CAS and Cambricon Technologies State Key Lab of Processors Institute of Computing Technology CAS State Key Lab of Processors Institute of Computing Technology CAS and Shanghai Innovation Center for Processor Technologies State Key Lab of Processors Institute of Computing Technology CAS and Cambricon Technologies University of Science and Technology of China Intelligent Software Research Center Institute of Software CAS State Key Lab of Processors Institute of Computing Technology CAS and University of Chinese Academy of Sciences

Research on emergent communication between deep-learning-based agents has received extensive attention due to its inspiration for linguistics and artificial intelligence. However, previous attempts have hovered around emerging communication under perception-oriented environmental settings, that forces agents to describe low-level perceptual features intra image or symbol contexts. In this work, inspired by the classic human reasoning test (namely Raven's Progressive Matrix), we propose the Reasoning Game, a cognition-oriented environment that encourages agents to reason and communicate high-level rules, rather than perceived low-level contexts. Moreover, we propose 1) an unbiased dataset (namely rule-RAVEN) as a benchmark to avoid overfitting, 2) and a two-stage curriculum agent training method as a baseline for more stable convergence in the Reasoning Game, where contexts and semantics are bilaterally drifting. Experimental results show that, in the Reasoning Game, a semantically stable and compositional language emerges to solve reasoning problems. The emerged language helps agents apply the extracted rules to the generalization of unseen context attributes, and to the transfer between different context attributes or even tasks.

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MACRO: Incentivizing Multi-Leader Game-Based Pareto-Efficient Crowdsourcing for Video Analytics 40

MACRO: Incentivizing Multi-Leader Game-Based Pareto-Efficien...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Chen, Yu Zhang, Sheng Zhou, Ziying Wang, Xiaokun Liang, Yu Chen, Ning Yan, Yuting Xiao, Mingjun Wu, Jie Qian, Zhuzhong Xu, Harry Nanjing University State Key Lab. for Novel Software Technology China School of Systems Science Beijing Normal University China School of Computer and Electronic Information School of Artificial Intelligence Nanjing Normal University China School of Computer Science and Technology University of Science and Technology of China China Temple University Center for Networked Computing United States Samueli School of Engineering University of California Computer Science Department Los Angeles United States

ISBN: (纸本)9798350317152

In recent years, many crowdsourcing platforms have emerged, using the resources of recruited workers to perform diverse outsourcing tasks, where the video analytics attracts much attention due to its practical implications. For maximum profits, platforms carefully choose the workers and determine the video analytics configurations to ensure accuracy;meanwhile, workers possess the flexibility to tailor the configurations for their indivi-dual gains, which makes it hard for platforms to optimize their profits considering the platform-worker conflicts. In this paper, we design an incentive mechanism for Multi-leader game-based video Analytics upon CROwdsourcing, named MACRO, to over-come the above situation. Under that mechanism, we first formu-late the utility optimization problems for platforms and workers, respectively. We then propose a dual ascent-based method to op-timally determine the video analytics configurations for a multi-platform game, ensuring Pareto efficiency. Moreover, in the context of a multi-leader game involving platform-worker conflicts, we design an incentive function with its incentive factor update strategy and propose an ADMM-based approach for maximizing incentives that motivate workers to contribute to the platforms' profits. Rigorous proofs demonstrate the linear convergence of the MACRO to the multi-leader Stackelberg equilibrium. Trace-driven experiments show that MACRO improves the Pareto efficiency by 26.3%, outperforming other approaches. © 2024 IEEE.

关键词： Profitability

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Graph-based Multi-view Clustering for Web services

Graph-based Multi-view Clustering for Web services

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

作者： Wang Yang Yuan Zhenzhen Kang Guosheng Cao Buqing Liu JianXun Xiao Yong HNUST Xiangtan China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology Hunan University of Science and Technology Xiangtan China School of Computer Science and Engineering

The number of Web services on the Internet has been steadily increasing in recent years due to their growing popularity. Under the big data environment, how to effectively manage Web services is of significance for service discovery, service recommendation, etc. It is widely studied that Web services clustering is an effective way for service management. However, most of the current Web service clustering only extracts the information of Web services for clustering from one view, such as Web service content descriptions, networks in which Web services participate, and so on. Extracting information from Web services only unilaterally will not be able to provide a three-dimensional and comprehensive description of Web services, which may diminish the effect of Web service clustering. In addition, some Web service resources will be wasted if other information of Web services is not used at the same time. We find that multi-view clustering can simultaneously consider multiple information of a data at the same time, and multiple information can complement and enhance each other according to the characteristics of multiview clustering. Therefore, in this paper, we apply Web services to graph-based multi-view clustering in multi-view clustering to improve the performance of Web service clustering by simultaneously considering multiple feature information about Web services and distributing different weights to different information in the clustering process.

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