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2023 International Conference on cloud computing, Performance computing, and Deep Learning, CCPCDL 2023

作者： Huang, Xinyi Zhao, Gansen School of Computer Science South China Normal University Guang Zhou China Guangzhou Key Lab on Cloud Computing Security and Assessment Technology Guang Zhou China

ISBN: (纸本)9781510666474

Mashup is an information system development project based on Web services. It can build new business processes by mixing data, resources and APIs from different sites or applications, and meet new user needs. However, in recent years, the number of Web API is increasing quickly, and it is difficult for developers and users to find suitable Mashups, which influences the development and functional efficiency of information systems. Therefore, designing an accurate and efficient Web service recommendation method to improve the functional characteristics of information systems has become a research hotspot. At present, many algorithms, like matrix factorization and collaborative filtering, often have serious cold start and sparse problems. In order to alleviate the above problems and capture as much information as possible, we proposed a multi-modal knowledge graph enhancement recommendation method (MKE), which combines structured information and unstructured information of Mashup and Web API. Firstly, the feature information is mined from the text description of API and Mashup to assist the construction of the service recommendation system. Then, the knowledge graph is constructed by using the related attributes and relationships of Mashup and API, as well as the entity and relationship of Mashup and API, and the text features. The low-dimensional embedded Trans model provides Top-K recommendation results for the specified Mashup description. Finally, experiments are carried out on the latest ProgrammableWeb dataset, and the experimental results show that the recommendation performance of this method is better than that of RippleNet, NCF, CKE, KGCN. © 2023 SPIE.

关键词： Knowledge graph

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Learned Fingerprint Embedding for Large-Scale Peptide Mass Spectra Retrieval

Learned Fingerprint Embedding for Large-Scale Peptide Mass S...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wang, Yongshuai Cai, Xiaojun Li, Defeng Sun, Shiwei Chen, Cheng Cui, Xuefeng Shandong University School of Computer Science and Technology Qingdao China Chinese Academy of Sciences Key Lab of Intelligent Information Processing Institute of Computing Technology Beijing China

ISBN: (纸本)9798350337488

Tandem mass spectrometry (MS/MS) is a widely used technique for protein identification, post-translational modifications, immunotherapy, and other applications. As the amount of MS/MS spectra data increases, new computational methods are needed to efficiently search through these databases. This study introduces MS2VEC, a novel fingerprint embedding model designed to facilitate large-scale retrieval of peptide mass spectra. MS2VEC captures the relationships between distant peaks and incorporates position-aware fingerprint features from all peaks. To do this, dilated convolutions are used to capture remote relationships, and a novel position-aware multi-head attention pooling mechanism is used to abstract fingerprint features. The results demonstrate that MS2VEC achieves a top-1 retrieval accuracy of 0.810, outperforming existing methods by 5.1%. Interestingly, the precursor charge is not essential for the retrieval task, as the spectra itself contains enough information to accurately predict the charge. Additionally, the results suggest that weight-balanced fragment ions and water losses are important contributors to fingerprint features. © 2023 IEEE.

关键词： deep learning embedding information retrieval peptide mass spectra tandem mass spectrometry

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A Circuit Domain Generalization Framework for Efficient Logic Synthesis in Chip Design 41

A Circuit Domain Generalization Framework for Efficient Logi...

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41st International Conference on Machine Learning, ICML 2024

作者： Wang, Zhihai Chen, Lei Wang, Jie Bai, Yinqi Li, Xing Li, Xijun Yuan, Mingxuan Hao, Jianye Zhang, Yongdong Wu, Feng CAS Key Laboratory of Technology in GIPAS & MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition University of Science and Technology of China China Noah's Ark Lab Huawei Technologies China College of Intelligence and Computing Tianjin University China

Logic Synthesis (LS) plays a vital role in chip design. A key task in LS is to simplify circuits-modeled by directed acyclic graphs (DAGs)-with functionality-equivalent transformations. To tackle this task, many LS heuristics apply transformations to subgraphs-rooted at each node on an input DAG-sequentially. However, we found that a large number of transformations are ineffective, which makes applying these heuristics highly time-consuming. In particular, we notice that the runtime of the Resub and Mfs2 heuristics often dominates the overall runtime of LS optimization processes. To address this challenge, we propose a novel data-driven LS heuristic paradigm, namely PruneX, to reduce ineffective transformations. The major challenge of developing PruneX is to learn models that well generalize to unseen circuits, i.e., the out-of-distribution (OOD) generalization problem. Thus, the major technical contribution of PruneX is the novel circuit domain generalization framework, which learns domain-invariant representations based on the transformation-invariant domain-knowledge. To the best of our knowledge, PruneX is the first approach to tackle the OOD problem in LS heuristics. We integrate PruneX with the aforementioned Resub and Mfs2 heuristics. Experiments demonstrate that PruneX significantly improves their efficiency while keeping comparable optimization performance on industrial and very large-scale circuits, achieving up to 3.1× faster runtime. Copyright 2024 by the author(s)

关键词： Logic Synthesis

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REVISITING GRAPH ADVERSARIAL ATTACK AND DEFENSE FROM A DATA DISTRIBUTION PERSPECTIVE 11

REVISITING GRAPH ADVERSARIAL ATTACK AND DEFENSE FROM A DATA ...

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11th International Conference on Learning Representations, ICLR 2023

作者： Li, Kuan Liu, Yang Ao, Xiang He, Qing Key Lab of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences China

Recent studies have shown that structural perturbations are significantly effective in degrading the accuracy of Graph Neural Networks (GNNs) in the semi-supervised node classification (SSNC) task. However, the reasons for the destructive nature of gradient-based methods have not been explored in-depth. In this work, we discover an interesting phenomenon: the adversarial edges are not uniformly distributed on the graph, and a majority of perturbations are generated around the training nodes in poisoning attacks. Combined with this phenomenon, we provide an explanation for the effectiveness of the gradient-based attack method from a data distribution perspective and revisit both poisoning attack and evasion attack in SSNC. From this new perspective, we empirically and theoretically discuss some other attack tendencies. Based on the analysis, we provide nine practical tips on both attack and defense and meanwhile leverage them to improve existing attack and defense methods. Moreover, we design a fast attack method and a self-training defense method, which outperform the state-of-the-art methods and can effectively scale to large graphs like ogbn-arxiv. We validate our claims through extensive experiments on four benchmark datasets. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Graph neural networks

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A Semantic Overlay on Blockchain for Supporting Traceability in Cross-border Logistics 2

A Semantic Overlay on Blockchain for Supporting Traceability...

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2nd IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2022

作者： Sun, Xiaoping Institute of Computing Technology Chinese Academy of Sciences Key Lab of Intelligent Information Processing Beijing China

ISBN: (纸本)9781665492270

Traceability is important for maintaining quality of logistics transactions and improving logistics efficiency. Implementing traceability for cross-border logistics is facing challenges when it lacks global centralized logistics information management services. Blockchain techniques enable a decentralized cross-border logistics management by publishing logistics transaction on blockchain transactions to support privacy protection and traceability. Logistics transactions involves complex interactions among participants. How to publish those interactions on a blockchain platform is a challenge. We propose a semantic overlay model that publishes a logistics transaction as a semantic link on a blockchain platform for supporting traceability. A semantic link schema model is designed and published on the blockchain platform to validate semantics links for ensuring the correctness of logistics transaction execution. The proposed semantic overlay on blockchain supports further extension of semantic computing in decentralized applications. © 2022 IEEE.

关键词： Blockchain

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Offloading approach for mobile edge computing based on chaotic quantum particle swarm optimization strategy

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Journal of Ambient Intelligence and Humanized computing 2023年第10期14卷 14333-14347页

作者： Zhang, DeGan Sun, GuiXiang Zhang, Jie Zhang, Ting Yang, Peng Tianjin Key Lab of Intelligent Computing and Novel Software Technology Tianjin University of Technology Tianjin300384 China School of Sports Economics and Management Tianjin University of Sport Tianjin301617 China School of Electronics and Information Engineering Beijing Jiaotong University Beijing100093 China

With the development of 5 g, computing-intensive and complex applications in smart-city is growing rapidly. Due to the limited resources of mobile terminal devices in smart-city, new applications have higher requirements for delay, bandwidth, security, and energy consumption. Computation offloading in mobile edge computing (MEC) is effective to reduce delay and energy consumption of Real-time video analysis. An improved chaos quantum-behaved particle swarm optimization (ICQPSO) algorithm is proposed for multi-user and multi-MEC edge Computation offloading scenarios. Compared with other heuristic algorithms, the improved chaos quantum-behaved particle swarm optimization algorithm can effectively reduce the delay and energy consumption of edge computing offloading. Experimental results show that the improved chaotic quantum-behaved particle swarm optimization (ICQPSO) can effectively avoid premature convergence, has stronger global searchability, and can solve multi-dimensional complex NP-hard problems more efficiently. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Energy utilization

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VF2VF: Improving Precision while Maintaining Accuracy

VF2VF: Improving Precision while Maintaining Accuracy

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Zhang, Zhenyu Chen, Xuebin Zhu, Haogang Beihang University State Key Laboratory of Software Development Environment Lab Beijing China Wesin Cloud Intelligent Technology Research Institute Wesin Cloud Ltd Xiamen China

ISBN: (纸本)9781665480451

Visual Field (VF), a measurement of retinal function, is one of the most important references in modern glaucoma diagnosis and treatment. While VF is a typical physical-psychological measurement, the results are often imprecise. How to improve measurement accuracy is always one of the most significant issues in clinical practice. This study proposes a VF2VF training method based on the assumption that the pattern of multiple VF measurements in a short period does not change, and designs a neural network to transform the VF measurements. We analyze the VFs after transformation through experiments. Experiments show that the training method of VF2VF can significantly improve the precision while maintaining the accuracy of original VFs. Besides, the VFs after transformation achieve a significant performance improvement in downstream deterioration detection tasks. When the false positive rate is 5%, the Hit Rate increases by 20%. And the transformed VFs can give a warning 1.8 times ahead of the original VFs. © 2022 IEEE.

关键词： Deterioration

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Towards General Algorithm Discovery for Combinatorial Optimization: Learning Symbolic Branching Policy from Bipartite Graph 41

Towards General Algorithm Discovery for Combinatorial Optimi...

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41st International Conference on Machine Learning, ICML 2024

作者： Kuang, Yufei Wang, Jie Zhou, Yuyan Li, Xijun Zhu, Fangzhou Hao, Jianye Wu, Feng CAS Key Laboratory of Technology in GIPAS University of Science and Technology of China China MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition University of Science and Technology of China China Noah's Ark Lab Huawei Technologies China College of Intelligence and Computing Tianjin University China

Machine learning (ML) approaches have been successfully applied to accelerating exact combinatorial optimization (CO) solvers. However, many of them fail to explain what patterns they have learned that accelerate the CO algorithms due to the black-box nature of ML models like neural networks, and thus they prevent researchers from further understanding the tasks they are interested in. To tackle this problem, we propose the first graph-based algorithm discovery framework-namely, graph symbolic discovery for exact combinatorial optimization solver (GS4CO)-that learns interpretable branching policies directly from the general bipartite graph representation of CO problems. Specifically, we mainly focus on the variable selection part of the branching policy. We design a unified representation for symbolic variable selection policies with graph inputs, and then we employ a Transformer with multiple tree-structural encodings to generate symbolic trees end-to-end, which effectively reduces the cumulative error from iteratively distilling graph neural networks. Experiments show that GS4CO learned interpretable and lightweight policies outperform all the baselines on CPU machines, including both the human-designed and the learning-based. GS4CO shows an encouraging step towards general algorithm discovery on modern CO solvers. Codes are available at https://***/MIRAlab-USTC/L2O-GS4CO. Copyright 2024 by the author(s)

关键词： Combinatorial optimization

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Context-Driven Index Trimming: A Data Quality Perspective to Enhancing Precision of RALMs

Context-Driven Index Trimming: A Data Quality Perspective to...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Ma, Kexin Jin, Ruochun Wang, Haotian Wang, Xi Chen, Huan Tang, Yuhua Wang, Qian Institute for Quantum Information State Key Laboratory of High Performance Computing China College of Computer Science and Technology National University of Defense Technology Changsha China Intelligent Game and Decision Lab Academy of Military Science Beijing China

ISBN: (纸本)9798891761681

Retrieval-Augmented Large Language Models (RALMs) have made significant strides in enhancing the accuracy of generated responses. However, existing research often overlooks the data quality issues within retrieval results, often caused by inaccurate existing vector-distance-based retrieval methods. We propose to boost the precision of RALMs' answers from a data quality perspective through the Context-Driven Index Trimming (CDIT) framework, where Context Matching Dependencies (CMDs) are employed as logical data quality rules to capture and regulate the consistency between retrieved contexts. Based on the semantic comprehension capabilities of Large Language Models (LLMs), CDIT can effectively identify and discard retrieval results that are inconsistent with the query context and further modify indexes in the database, thereby improving answer quality. Experiments demonstrate average improvement of 3.75% in accuracy on challenging question-answering tasks. Also, the flexibility of CDIT is verified through its compatibility with various language models and indexing methods, which offers a promising approach to bolster RALMs' data quality and retrieval precision jointly. © 2024 Association for Computational Linguistics.

关键词： Data accuracy

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Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection 61

Learn over Past, Evolve for Future: Forecasting Temporal Tre...

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61st Annual Meeting of the Association for Computational Linguistics, ACL 2023

作者： Hu, Beizhe Sheng, Qiang Cao, Juan Zhu, Yongchun Wang, Danding Wang, Zhengjia Jin, Zhiwei Key Lab of Intelligent Information Processing Chinese Academy of Sciences Institute of Computing Technology China University of Chinese Academy of Sciences China ZhongKeRuijian Technology Co. Ltd. China

ISBN: (纸本)9781959429685

Fake news detection has been a critical task for maintaining the health of the online news ecosystem. However, very few existing works consider the temporal shift issue caused by the rapidly-evolving nature of news data in practice, resulting in significant performance degradation when training on past data and testing on future data. In this paper, we observe that the appearances of news events on the same topic may display discernible patterns over time, and posit that such patterns can assist in selecting training instances that could make the model adapt better to future data. Specifically, we design an effective framework FTT (Forecasting Temporal Trends), which could forecast the temporal distribution patterns of news data and then guide the detector to fast adapt to future distribution. Experiments on the real-world temporally split dataset demonstrate the superiority of our proposed framework. The code is available at https://***/ICTMCG/FTT-ACL23. © ACL *** rights reserved.

关键词： Forecasting

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