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IEEE International Conference on computer Design: VLSI in computers and Processors, (ICCD)

作者： Yi Zhang Yunpeng Song Wentong Li Yiyang Huang Dingcui Yu Mengyang Ma Liang Shi MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application East China Normal University Shanghai China School of Computer Science and Technology East China Normal University Shanghai China

ISBN: (数字)9798350380408

ISBN: (纸本)9798350380415

Mobile devices always cache numerous files during application runtime, which can be trimmed to improve the user experience. However, existing cache file trimming methods are unaware of the cleaning cost within the file system and storage devices, which degrades the system performance and storage lifetime, resulting in low benefits of trimming cache files. Motivated by this, an adaptive cache file trimming (CacheTrimmer) scheme is proposed to trim cache files for performance and lifetime improvement. The basic idea is to determine the trimming timing based on the cleaning cost of the file system and storage device, maximizing the benefit of trimming cache files. Specifically, CacheTrimmer includes two components: First, a cleaning cost-aware trimming method is proposed to trim cache files by recording the index information of cache files in a list and determining the timing and size of file trimming. Second, to avoid trimming-induced intra-segment fragmentation and improve trimming efficiency, a log-structured cache scheme is further proposed to maintain the cache files in separate segments. We prototype CacheTrimmer with a real mobile platform. Experimental results under real workloads show that CacheTrimmer achieves encourage performance and lifetime improvement compared to the state-of-the-art.

关键词： Performance evaluation Costs Runtime File systems System performance Cleaning Mobile handsets User experience Timing Recording

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Patch Target Guided Dual-Branch Deep Multiple Instance Learning for 3D MRI Analysis

Patch Target Guided Dual-Branch Deep Multiple Instance Learn...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Dai, Mingzhe Shi, Xiaoshuang Zhu, Xiaofeng Pan, Tingrui Li, Kang University of Science and Technology of China School of Software Engineering Hefei China West China Hospital of Sichuan University West China Biomedical Big Data Center Chengdu China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China Sichuan Artificial Intelligence Research Institute Yibin China University of Science and Technology of China Suzhou Institute for Advanced Research Suzhou China Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (纸本)9798350386226

Deep multiple instance learning (MIL) has attracted considerable attention in medical image analysis, since it only requires image-level labels for model training without using fine-grained (or patch) annotations. Unfortunately, MIL-based methods might lose some significant patch features. Although pseudo-label-based methods, which assign a pre-defined label to each patch, can explore more patch-level features, they might bring label noise and make the patch-level features lose diversity, thereby possibly restricting the model performance. To overcome this issue, we propose a novel gradient-based patch target generation (PTG) module to dynamically produce a feature vector for each patch as its target. Additionally, based on the PTG module, we propose a patch-target guided dual-branch deep MIL framework for 3D MRI data analysis, where both the two branches consist of a CNN model to extract patch-level features, an attention module to interpret the significance of patches, and a bag-level classifier, while the second branch also contains the PTG module to generate patch targets of patches. Moreover, the two branches are alternatively updated in our framework, resulting in a bi-level optimization problem, and thus we design a bi-level optimization algorithm to solve our proposed objective function. Extensive experiments demonstrate the superior classification and interpretation performance of the proposed framework over recent state-of-the-art methods. Codes are available at https://***/daimz1213/mil2024. © 2024 IEEE.

关键词： Image annotation

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Time-aware Medication Recommendation via Intervention of Dynamic Treatment Regimes 25

Time-aware Medication Recommendation via Intervention of Dyn...

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34th ACM Web Conference, WWW 2025

作者： Li, Yishuo Zhang, Qi Lu, Wenpeng Peng, Xueping Zhang, Weiyu Si, Jiasheng Gong, Yongshun Hu, Liang Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Department of Computer Science and Technology Tongji University Shanghai China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia School of Software Shandong University Jinan China

ISBN: (纸本)9798400712746

Medication recommendation aims to suggest personalized drug combinations to patients based on their longitudinal medical histories stored in electronic health record (EHR) datasets. Patients’ Dynamic Treatment Regimes (DTRs) determine how patients’ drug combinations change along with the evolution of disease treatment. DTRs are effective for comprehending disease-treatment dynamics and for recommending a timely and personalized combination of medications for patients. However, existing medication recommender systems (MRSs) overlook the multiple treatment pathways generated by the intervention of DTRs and can only recommend a single treatment paradigm, ignoring the fact that patients may be at different treatment stages and thus require different treatment regime. Such disregard leads to a significant limitation in recommending personalized medication combinations tailored to different treatment stages, yielding greatly compromised accuracy and applicability of MRSs. Moreover, existing methods often overlook the time interval information over patients’ successive visits, which is critical to indicate patients’ treatment evolution. To address these significant gaps, we propose a Time-aware Medication Recommendation Framework via Intervention of Dynamic Treatment Regimes, called MR-DTR. To explicitly illustrate the intervention processes of DTRs on similar patients, we employ a co-guided graph to connect various patient sequences. In addition, to fully utilize the time interval information, we design a time-aware guidance mechanism dedicated to the co-guided graph to efficiently learn medication representation using the patient’s guidance information. We also introduce relative time intervals in the encoder to act as positional information. Extensive experiments on two real-world datasets demonstrate that MR-DTR surpasses state-of-the-art models in terms of recommendation performance. © 2025 Copyright held by the owner/author(s).

关键词： Electronic health record

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KRLGI: Knowledge Representation Learning Based on Global Information for Reasoning

KRLGI: Knowledge Representation Learning Based on Global Inf...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Bo Wu Yingchi Mao Fudong Chi Yingjie Liu Silong Ding Rongzhi Qi College of Computer Science and Software Engineering Hohai University Nanjing China Technology Research and Development Center Huaneng Lancang River Hydropower Inc Kunming China

ISBN: (数字)9798331527235

ISBN: (纸本)9798331527242

Knowledge Graph Reasoning based on Representation Learning maps the entities and relations into a vector space, assessing entity-pair similarities to deduce unknown facts. However, existing methods focus solely on the local importance of entities and ignore isolated entities, leading to the issue of missing feature information. To solve this, we propose Knowledge Representation Learning based on Global Information (KRLGI). KRLGI adopts an attention-based biased random walk algorithm to obtain global information and determine the importance of global entities. The global entity importance goes through a conversion into attention weights, and these weights are integrated with the local entity importance. Subsequently, the local and global entity importance can be used together to represent entity embeddings. Globally integrated representations can reveal richer semantics and enhance reasoning capabilities. Experimental results indicate that KRLGI outperforms other baselines in reasoning accuracy on four public datasets. Notably, on the FB15k-237 dataset, KRLGI shows significant improvements over the best baseline, with increases of 6.37% in MRR, 2.5% in HITS@1, and 5.5% in HITS@10.

关键词： Representation learning Accuracy Semantics Knowledge graphs Learning (artificial intelligence) Cognition Vectors

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LRIRL: Improving Knowledge Graph Reasoning through Representation Learning-Based Rule Induction

LRIRL: Improving Knowledge Graph Reasoning through Represent...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Yingjie Liu Yingchi Mao Fudong Chit Bo Wu Silong Ding Rongzhi Qi College of Computer Science and Software Engineering Hohai University Nanjing China Technology Research and Development Center Huaneng Lancang River Hydropower Inc Kunming China

ISBN: (数字)9798331527235

ISBN: (纸本)9798331527242

Rule induction is an important approach for reasoning over Knowledge Graphs. Existing works mainly rely on searching for rule instances within the Knowledge Graph to induce rules. However, this approach may generate a vast search space, leading to inefficiency and difficulty in discovering rules that lack instance support. We propose a Logical Rule Induction based on Representation Learning (LRIRL) method, which can mine rules at the pattern level. By computing rule scores through vector representations of the rules, LRIRL can avoid the inefficiency caused by directly searching for rule instances in a vast search space. Furthermore, by incorporating the deductive nature of logical rules into the rule induction process, LRIRL can mine and evaluate rules even in the absence of rule instances. The generated rules can be used to perform more efficient and accurate reasoning tasks on the Knowledge Graph. Experimental results demonstrate that LRIRL outperforms baselines in both reasoning accuracy and rule mining efficiency on public datasets. Compared to the best baseline, LRIRL can achieve an average accuracy improvement of 3.43% in MRR, 1.40% in HITS@1, and 1.80% in HITS@10. Moreover, LRIRL can reduce rule mining time by an average of approximately 35% compared to the best baseline.

关键词： Representation learning Accuracy Knowledge graphs Cognition Vectors Logic Computational complexity Artificial intelligence

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A Survey on Anomaly Detection with Few-Shot Learning 8th

A Survey on Anomaly Detection with Few-Shot Learning

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8th International Conference on Cognitive Computing, ICCC 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Chen, Junyang Wang, Changbo Hong, Yifan Mi, Rui Zhang, Liang-Jie Wu, Yirui Wang, Huan Zhou, Yue College of Computer Science and Software Engineering Shenzhen University Shenzhen China Center of Health Administration and Development Studies Hubei University of Medicine Shiyan China College of Informatics Huazhong Agricultural University Wuhan China Hohai University Nanjing China Department of Obstetrics and Gynecology Tianyou Hospital Affiliated to Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9783031779534

The primary objective of anomaly detection is to identify abnormal or unusual patterns within a dataset, where the number of normal samples typically exceeds that of abnormal samples. Due to the scarcity of labeled abnormal samples, traditional methods face challenges when dealing with anomaly detection. To overcome these limitations, few-shot learning has emerged as a promising solution. By leveraging a limited number of labeled anomaly samples, few-shot learning enables the construction of models that enhance anomaly detection performance and generalization. This paper provides a comprehensive investigation of anomaly detection, covering its definition, fundamental principles, methods, and challenges. Furthermore, it introduces few-shot learning as a solution and explores its principles, applications, and technical categorization, including meta-learning, transfer learning, generative models, prototypical learning, and siamese networks. The paper explores the utilization of few-shot learning in anomaly detection across diverse data types This paper delves into significance across different domains. Additionally, it addresses the challenges faced by few-shot learning in the field of anomaly detection and proposes future directions for development. This comprehensive analysis aims to provide profound insights and guidance for prospective research and application in anomaly detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Zero-shot learning

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Message from conference chairs

Proceedings - 20th IEEE/ACIS International Summer Conference...

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Proceedings - 20th IEEE/ACIS International Summer Conference on computer and Information Science, ICIS 2021-Summer 2021年 IX页

作者： Miao, Huaikou Cai, Lizhi Shanghai University China Shanghai Development Center of Computer Software Technology China

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Vehicle Check-In Data-driven POI Recommendation Based on Improved SVD and Graph Convolutional Network 19

Vehicle Check-In Data-driven POI Recommendation Based on Imp...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Liu, Yuwen Zhang, Jie Dou, Ruihan Zhou, Xiaokang Xu, Xiaolong Wang, Shoujin Qi, Lianyong College of Computer Science and Technology Qingdao China Nanjing University State Key Laboratory for Novel Software Technology Department of Computer Science and Technology China Shanghai Meteorological Information and Technology Support Center China University of Waterloo Faculty of Mathematics Waterloo Canada Shiga University Faculty of Data Science Japan Riken Center for Advanced Intelligence Project Riken Japan Nanjing University of Information Science and Technology School of Computer and Software China Macquarie University School of Computing Australia Qufu Normal University School of Computer Science China

ISBN: (纸本)9798350346558

The application of automobile tool software is becoming increasingly widespread, and its functions are becoming increasingly diverse. Using vehicle traffic data to give users with recommendations for Points-of-interest (POI) is becoming an intriguing research challenge. However, the current POI recommendation is either a sequence-based method, ignoring the interaction information between users and POIs. Or it needs a lot of model training time to use graph correlation method to mine the interaction between users or vehicles and POIs. In order to address these issues, we propose a method for vehicle check-in data-driven POI recommendation based on improved Singular Value Decomposition (SVD) and Graph Convolutional Network (GCN). First, we apply SVD to replace the neighborhood aggregation update method in traditional GCN, update the node embedding with fewer parameters and faster speed. Second, we propose an information enhancement strategy to make up for the information loss caused by the SVD. Experiments on large vehicle check-in datasets demonstrate that our method is both faster and more effective when making POI recommendations. © 2022 IEEE.

关键词： Singular value decomposition

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Symbolic Reasoning About Quantum Circuits in Coq

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Journal of computer Science & technology 2021年第6期36卷 1291-1306页

作者： Wen-Jun Shi Qin-Xiang Cao Yu-Xin Deng Han-Ru Jiang Yuan Feng Shanghai Key Laboratory of Trustworthy Computing East China Normal UniversityShanghai 200062China John Hopcroft Center of Computer Science Shanghai Jiao Tong UniversityShanghai 200240China Yanqi Lake Beijing Institute of Mathematical Sciences and Applications Beijing 101408China Centre for Quantum Software and Information University of Technology SydneySydney NSW 2007Australia

A quantum circuit is a computational unit that transforms an input quantum state to an output state.A natural way to reason about its behavior is to compute explicitly the unitary matrix implemented by ***,when the number of qubits increases,the matrix dimension grows exponentially and the computation becomes *** this paper,we propose a symbolic approach to reasoning about quantum *** is based on a small set of laws involving some basic manipulations on vectors and *** symbolic reasoning scales better than the explicit one and is well suited to be automated in Coq,as demonstrated with some typical examples.

关键词： quantum circuit symbolic reasoning Dirac notation Coq

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ConZone: A Zoned Flash Storage Emulator for Consumer Devices

ConZone: A Zoned Flash Storage Emulator for Consumer Devices

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Design, Automation and Test in Europe Conference and Exhibition

作者： Dingcui Yu Jialin Liu Yumiao Zhao Wentong Li Ziang Huang Zonghuan Yan Mengyang Ma Liang Shi MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application East China Normal University Shanghai China College of Computer Science and Technology East China Normal University Shanghai China Software Engineering Institute East China Normal University Shanghai China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Considering the potential benefits to lifespan and performance, zoned flash storage is expected to be incorporated into the next generation of consumer devices. However, due to the limited volatile cache and heterogeneous flash cells of consumer-grade flash storage, adopting a zone abstraction requires additional internal hardware design to maximize its benefits. To understand and efficiently improve the hardware design on consumer-grade zoned flash storage, we present ConZone—the first emulator tailored to the characteristics of consumer-grade zoned flash storage. Users can explore the internal architecture and management strategies of consumer-grade zoned flash storage and integrate the optimization with software. We validate the accuracy of ConZone by realizing a hardware architecture for consumer-grade zoned flash storage and comparing it with the state-of-the-art. We also make a case study for read performance research with ConZone to explore the design of mapping mechanisms and cache management strategies.

关键词： Performance evaluation Accuracy Europe computer architecture Hardware software Planning Optimization Next generation networking

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