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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Fang Cherry Liu Michael D. Weiner Kevin Manalo Aaron Jezghani Christopher J. Blanton Christopher Stone Kenneth Suda Nuyun Zhang Dan Zhou Mehmet Belgin Semir Sarajlic Ruben Lara Partnership for an Advanced Computing Environment (PACE) Georgia Institute of Technology Atlanta U.S.A Research Computing and Data Facilitation Service North Carolina State University Raleigh U.S.A Microsoft "Present Affiliation" Atlanta U.S.A

ISBN: (纸本)9781665458429

Most HPC centers face a lack of expertise of data center migrations, as it’s a rare event that only a small portion of HPC professionals experience more than once in their entire professional careers. This paper presents how the Georgia Institute of Technology (Georgia Tech) Partnership for an Advanced computing Environment (PACE) team employed automation to migrate research computing data from the old Rich computing center (Rich) to the new Coda data center (Coda) in 2020. PACE successfully migrated 1844 TB of data for 3550 users without loss of user data. PACE implemented a ‘human-in-the-loop’ automatic workflow to facilitate the migration, interleaving automated scripts with human-driven reviews, significantly reducing staff time commitment while ensuring the integrity and accuracy of data migrations. PACE deployed a cached data movement strategy which reduced the migration downtime significantly. We share our one-year migration journey for the benefit of the HPC community.

关键词： data centers Automation Uncertainty Scientific computing Soft sensors Standardization Human in the loop

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RE-SEGNN:recurrent semantic evidence-aware graph neural network for temporal knowledge graph forecasting

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Science China(Information Sciences) 2025年第2期68卷 103-119页

作者： Wenyu CAI Mengfan LI Xuanhua SHI Yuanxin FAN Quntao ZHU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and Technology

Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both entity and relation embedding to make predictions, ignoring the semantic correlations among different entities and relations within the same timestamp. This can lead to random and nonsensical predictions when unseen entities or relations occur. Furthermore, many existing models exhibit limitations in handling highly correlated historical facts with extensive temporal depth. They often either overlook such facts or overly accentuate the relationships between recurring past occurrences and their current counterparts. Due to the dynamic nature of TKG, effectively capturing the evolving semantics between different timestamps can be *** address these shortcomings, we propose the recurrent semantic evidenceaware graph neural network(RE-SEGNN), a novel graph neural network that can learn the semantics of entities and relations simultaneously. For the former challenge, our model can predict a possible answer to missing quadruples based on semantics when facing unseen entities or relations. For the latter problem, based on an obvious established force, both the recency and frequency of semantic history tend to confer a higher reference value for the current. We use the Hawkes process to compute the semantic trend, which allows the semantics of recent facts to gain more attention than those of distant facts. Experimental results show that RE-SEGNN outperforms all SOTA models in entity prediction on 6 widely used datasets, and 5 datasets in relation prediction. Furthermore, the case study shows how our model can deal with unseen entities and relations.

关键词： knowledge graph reasoning temporal knowledge graph Hawkes process semantic evidence

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Situational Scene Graph for Structured Human-Centric Situation Understanding

Situational Scene Graph for Structured Human-Centric Situati...

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2025 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2025

作者： Sugandhika, Chinthani Li, Chen Rajan, Deepu Fernando, Basura College of Computing and Data Science Nanyang Technological University Singapore Centre for Frontier Ai Research Agency for Science Technology and Research Singapore Institute of High-Performance Computing Agency for Science Technology and Research Singapore

ISBN: (纸本)9798331510831

Graph based representation has been widely used in modelling spatio-temporal relationships in video understanding. Although effective, existing graph-based approaches focus on capturing the human-object relationships while ignoring fine-grained semantic properties of the action components. These semantic properties are crucial for understanding the current situation, such as where does the action takes place, what tools are used and functional properties of the objects. In this work, we propose a graph-based representation called Situational Scene Graph (SSG) to encode both human-object relationships and the corresponding semantic properties. The semantic details are represented as predefined roles and values inspired by situation frame, which is originally designed to represent a single action. Based on our proposed representation, we introduce the task of situational scene graph generation and propose a multi-stage pipeline Interactive and Complementary Network (InComNet) to address the task. Given that the existing datasets are not applicable to the task, we further introduce a SSG dataset whose annotations consist of semantic role-value frames for human, objects and verb predicates of human-object relations. Finally, we demonstrate the effectiveness of our proposed SSG representation by testing on different downstream tasks. Experimental results show that the unified representation can not only benefit predicate classification and semantic role-value classification, but also benefit reasoning tasks on human-centric situation understanding. Code and data are available at https://***/LUNAProject22/SSG. © 2025 IEEE.

关键词： Human engineering

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A numerical magnitude aware multi-channel hierarchical encoding network for math word problem solving

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Neural computing and Applications 2025年第3期37卷 1651-1672页

作者： Xu, Junjie Chen, Yuzhong Xiao, Lingsheng Liao, Hongmiao Zhong, Jiayuan Dong, Chen College of Computer and Data Science Fuzhou University Fujian Province Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fujian Province Fuzhou350108 China

Math word problem (MWP) represents a critical research area within reading comprehension, where accurate comprehension of math problem text is crucial for generating math expressions. However, current approaches still grapple with unresolved challenges in grasping the sensitivity of math problem text and delineating distinct roles across various clause types, and enhancing numerical representation. To address these challenges, this paper proposes a Numerical Magnitude Aware Multi-Channel Hierarchical Encoding Network (NMA-MHEA) for math expression generation. Firstly, NMA-MHEA implements a multi-channel hierarchical context encoding module to learn context representations at three different channels: intra-clause channel, inter-clause channel, and context-question interaction channel. NMA-MHEA constructs hierarchical constituent-dependency graphs for different levels of sentences and employs a Hierarchical Graph Attention Neural Network (HGAT) to learn syntactic and semantic information within these graphs at the intra-clause and inter-clause channels. NMA-MHEA then refines context clauses using question information at the context-question interaction channel. Secondly, NMA-MHEA designs a number encoding module to enhance the relative magnitude information among numerical values and type information of numerical values. Experimental results on two public benchmark datasets demonstrate that NMA-MHEA outperforms other state-of-the-art models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Semantics

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A Method for Constructing research data Provenance in High-Performance computing Systems

A Method for Constructing Research Data Provenance in High-P...

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IEEE International Conference on e-Science and Grid computing

作者： Yuta Namiki Takeo Hosomi Hideyuki Tanushi Akihiro Yamashita Susumu Date Joint Research Laboratory for Integrated Infrastructure of High Performance Computing and Data Analysis Cybermedia Center Osaka University Osaka Japan NEC Corporation Tokyo Japan

research must be reproducible to be verifiable. Provenance, which describes how data was produced, is one of the metadata that can improve reproducibility. In this paper, we propose a method to construct the provenance of research data produced in high-performance computing (HPC) systems. Our method can construct a high-level and user-perspective provenance by integrating information available in HPC systems, such as a workload manager, with low-level data about running programs' behavior captured in an operating system kernel. The method enables users of HPC systems to collect the provenance without modifying assets such as programs and scripts.

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Soft-GNN:towards robust graph neural networks via self-adaptive data utilization

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Frontiers of Computer Science 2025年第4期19卷 1-12页

作者： Yao WU Hong HUANG Yu SONG Hai JIN National Engineering Research Center for Big Data Technology and System Service Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China College of Information and Communication National University of Defense TechnologyWuhan 430019China Department of Computer Science and Operations Research Universitéde MontréalMontreal H3C 3J7Canada

Graph neural networks(GNNs)have gained traction and have been applied to various graph-based data analysis tasks due to their high ***,a major concern is their robustness,particularly when faced with graph data that has been deliberately or accidentally polluted with *** presents a challenge in learning robust GNNs under noisy *** address this issue,we propose a novel framework called Soft-GNN,which mitigates the influence of label noise by adapting the data utilized in *** approach employs a dynamic data utilization strategy that estimates adaptive weights based on prediction deviation,local deviation,and global *** better utilizing significant training samples and reducing the impact of label noise through dynamic data selection,GNNs are trained to be more *** evaluate the performance,robustness,generality,and complexity of our model on five real-world datasets,and our experimental results demonstrate the superiority of our approach over existing methods.

关键词： graph neural networks node classification label noise robustness

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AegonKV: A High Bandwidth, Low Tail Latency, and Low Storage Cost KV-Separated LSM Store with SmartSSD-based GC Offloading 23

AegonKV: A High Bandwidth, Low Tail Latency, and Low Storage...

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23rd USENIX Conference on File and Storage Technologies, FAST 2025

作者： Duan, Zhuohui Feng, Hao Liu, Haikun Liao, Xiaofei Jin, Hai Li, Bangyu National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9781939133458

The key-value separation is renowned for its significant mitigation of the write amplification inherent in traditional LSM trees. However, KV separation potentially increases performance overhead in the management of Value region, especially for garbage collection (GC) operation that is used to reduce the redundant space occupation. In response, many efforts have been made to optimize the GC mechanism for KV separation. However, our analysis indicates that such solution based on trade-offs between CPU and I/O overheads cannot simultaneously satisfy the three requirements of KV separated systems in terms of throughput, tail latency, and space usage. This limitation hinders their real-world application. In this paper, we introduce AegonKV, a "three-birds-one-stone" solution that comprehensively enhances the throughput, tail latency, and space usage of KV separated systems. AegonKV first proposes a SmartSSD-based GC offloading mechanism to enable asynchronous GC operations without competing with LSM read/write for bandwidth or CPU. AegonKV leverages offload-friendly data structures and hardware/software execution logic to address the challenges of GC offloading. Experiments demonstrate that AegonKV achieves the largest throughput improvement of 1.28-3.3 times, a significant reduction of 37%-66% in tail latency, and 15%-85% in space overhead compared to existing KV separated systems. © 2025 FAST. All Rights Reserved.

关键词： Digital storage

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Correction to: Consistent Prompt Tuning for Generalized Category Discovery (International Journal of Computer Vision, (2025), 10.1007/s11263-024-02343-w)

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International Journal of Computer Vision 2025年 1-10页

作者： Yang, Muli Yin, Jie Gu, Yanan Deng, Cheng Zhang, Hanwang Zhu, Hongyuan Singapore Singapore School of Electronic Engineering Xidian University Xi’an China Norinco Group Testing and Research Institute Xi’an China College of Computing and Data Science Nanyang Technological University Singapore Singapore

The original publication of this article was published with several errors and has been corrected as follows. In the Section 4.3.1, the hyperlink for the Section 5 citation was missing and has been included. In Table 6, the significance ‘Reimplemented using CLIP’s image encoder’ was incorrectly provided for the method ‘CPT (Ours) and has been removed. In Table 12, the method name was incorrectly listed as ‘CLIP-GC’ but should read as ‘CLIP-GCD’. Additionally, the significance ‘Reimplemented using (Open/Meta) CLIP’s image encoder’ was missing for the method ‘CLIP-GCD’ and has been included. In Table 15, the incorrect significance ‘Reimplemented using CLIP’s image encoder’ was provided for the methods GCD (2022a), SimGCD (2023) and GPC (2023). This should be removed and the correct significance ‘Training with estimated category number’ has been included. In Table 22, the species "andrias acropora acuminata (staghorn coral)" was missing and has been included before the species "andrias davidianus (chinese giant salamander)." For completeness and transparency, the old incorrect and the corrected versions of Tables are displayed below. The original article has been corrected. Incorrect Table 6: GCD accuracy (%) in the customized setting on NICO dataset dataset → NICO-10 NICO-33 NICO-100 Method ↓ All Old New All Old New All Old New GCD (2022a) 95.1 97.3 94.0 38.6 58.4 25.1 48.0 67.0 39.1 PromptCAL (2023b) 89.6 80.9 93.6 30.5 42.4 22.5 45.3 53.4 41.5 SimGCD (2023) 97.3 97.0 97.4 45.9 67.7 46.3 65.1 37.4 PIM (2023) 96.4 97.2 96.0 42.2 67.6 24.9 49.5 67.5 41.0 GCD† 98.0 39.3 65.0 21.8 51.6 54.5 PromptCAL† 97.5 97.1 29.2 41.8 20.6 48.9 64.4 41.7 SimGCD† 97.5 46.6 29.1 69.4 44.6 PIM† 97.5 97.0 71.8 50.0 39.2 CPT (Ours)† 98.5 Bold value indicates the best results and underlined value indicates the second best results †Reimplemented using CLIP’s image encoder Corrected Table 6: GCD accuracy (%) in the customized setting on NICO dataset dataset → NICO-10 NICO-33 NICO-100 Method ↓

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MS-SAM: Multi-scale SAM Based on Dynamic Weighted Agent Attention 31st

MS-SAM: Multi-scale SAM Based on Dynamic Weighted Agent Att...

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31st International Conference on Multimedia Modeling, MMM 2025

作者： Yang, Enhui Zhang, Zhibin Engineering Research Center of Ecological Big Data Ministry of Education Key Laboratory of Wireless Networks and Mobile Computing Inner Mongolia University Hohhot010021 China

ISBN: (纸本)9789819620630

The Segment Anything Model (SAM), introduced in 2023, has made significant advancements in the field of computer vision. However, SAM faces two major challenges: limitations related to single-scale processing and high computational demands for large-scale data handling, which restrict its widespread adoption in practical applications. Therefore, we propose MS-SAM to achieve better segmentation performance at a lower cost. We introduce a multi-scale image encoder to handle images of varying scales and complexities. Additionally, we propose a dynamic weighted agent attention, which can flexibly and efficiently adjust attention distribution while reducing computational costs. We train MS-SAM on a 1/20 subset of the SA-1B dataset for instance segmentation using points or boxes as prompts. The results show that we maintain the accuracy with the SAM method while reducing the parameters by about 6 times. We give sufficient experimental results to demonstrate its effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

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Robotic Cognitive Behavioural Therapy: rCBT 16th

Robotic Cognitive Behavioural Therapy: rCBT

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16th International Conference on Social Robotics, ICSR + BioMed 2024

作者： Samani, Hooman Zaraki, Abolfazl Davies, Daisy-Rose Fernando, Owen Noel Newton Creative Computing Institute University of the Arts London London United Kingdom and Robotics Research Group University of Hertfordshire Hatfield United Kingdom School of Engineering Computing and Mathematics University of Plymouth Plymouth United Kingdom College of Computing and Data Science Nanyang Technological University Singapore Singapore

ISBN: (纸本)9789819789627

This paper introduces Robotic Cognitive Behavioural Therapy (rCBT) as a novel approach to overcoming the limitations of traditional Cognitive Behavioural Therapy (CBT) in treating emotional and psychological issues. After a brief overview of CBT and its inherent challenges, this paper explores the innovative application of physical robots in therapy, building on the success of technology-based CBT methods that have effectively treated various mental health conditions. The proposed rCBT solution is a compact, portable, and cost-effective 3D-printed robot designed to aid individuals with anxiety or depression. This robot includes interactive features such as a diary, health questionnaires, daily mood monitoring, to-do lists, mindfulness exercises, and a crisis helpline, enabling users to track their emotional states and activities. These functions support healthcare professionals and users in identifying emotional patterns and triggers. The robot can monitor physiological signals like heart rate. This research aims to enhance the accessibility and effectiveness of CBT, suggesting that rCBT could significantly broaden the therapy’s reach and improve mental health outcomes for a larger population. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Social robots

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