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International Conference on Data engineering

作者： Jianshun Zhang Fang Wang Sheng Qiu Yi Wang Jiaxin Ou Junxun Huang Baoquan Li Peng Fang Dan Feng Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of data storage systems and Technology Ministry of Education of China School of Computer Science and Technology Huazhong University of Science and Technology China Shenzhen Huazhong University of Science and Technology Research Institute Bytefrance Inc.

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Key- Value Stores (KVS) implemented with log- structured merge-tree (LSM-tree) have gained widespread ac-ceptance in storage systems. Nonetheless, a significant challenge arises in the form of high write amplification due to the compaction process. While KV-separated LSM-trees successfully tackle this issue, they also bring about substantial space am-plification problems, a concern that cannot be overlooked in cost-sensitive scenarios. Garbage collection (GC) holds significant promise for space amplification reduction, yet existing GC strategies often fall short in optimization performance, lacking thorough consideration of workload characteristics. Additionally, current KV-separated LSM-trees also ignore the adverse effect of the space amplification in the index LSM-tree. In this paper, we systematically analyze the sources of space amplification of KV- separated LSM-trees and introduce Scavenger, which achieves a better trade-off between performance and space amplification. Scavenger initially proposes an I/O-efficient garbage collection scheme to reduce I/O overhead and incorporates a space-aware compaction strategy based on compensated size to minimize the space amplification of index LSM-trees. Extensive experiments show that Scavenger significantly improves write performance and achieves lower space amplification than other KV-separated LSM-trees (including BlobDB, Titan, and TerarkDB).

关键词： Data engineering Compaction Indexes Optimization

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Semantic Data Augmentation for Few-Shot Biomedical Named Entity Recognition

Semantic Data Augmentation for Few-Shot Biomedical Named Ent...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ying Zhang Weihua Wang College of Computer Science Inner Mongolia University Hohhot China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology Hohhot China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Biomedical Named Entity Recognition (BioNER) aims to identify and classify entities in biomedical text. This task struggles with data scarcity due to limited annotated data. Although data augmentation is effective, existing methods fail to handle the complex semantic mappings on biomedical terminology, which results in the generated samples lacking semantic diversity. In this paper, we propose a data augmentation method to create semantically diverse and coherent training samples for few-shot BioNER. The method utilizes the Unified Medical Language System (UMLS) for entity replacement and combines with a Masked Language Model (MLM) to generate contextually relevant words. Experimental results show that our method improves BioNER performance in few-shot scenarios. Compared to all baseline models, our method achieves an average F1 score improvement of 7.6% and 11.6% on the NCBI and JNLPBA datasets, respectively. Our source code and data are available at https://***/ABC8184/SDA

关键词： Training Terminology Biological system modeling Source coding Unified modeling language Semantics Named entity recognition Signal processing Data augmentation Speech processing

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Exploit your Latents: Coarse-Grained Protein Backmapping with Latent Diffusion Models 39

Exploit your Latents: Coarse-Grained Protein Backmapping wit...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Rongchao Huang, Yu Lou, Yiwei Xin, Yi Chen, Haixu Cao, Yongzhi Wang, Hanpin Key Laboratory of High Confidence Software Technologies Peking University Ministry of Education School of Computer Science Peking University Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China Institute of Geriatrics National Clinical Research Center of Geriatrics Disease Chinese PLA General Hospital Beijing China

ISBN: (纸本)157735897X

Coarse-grained (CG) molecular dynamics of proteins is a preferred approach to studying large molecules on extended time scales by condensing the entire atomic model into a limited number of pseudo-atoms and preserving the thermodynamic properties of the system. However, the significantly increased efficiency impedes the analysis of substantial physicochemical information, since high-resolution atomic details are sacrificed to accelerate simulation. In this paper, we propose LatCPB, a generative approach based on diffusion that enables high-resolution backmapping of CG proteins. Specifically, our model encodes an all-atom into discrete latent embeddings, aligned with learnable multimodal discrete priors for circumventing posterior collapse and maintaining the discrete properties of the protein sequence. During the generation, we further design a latent diffusion process within the continuous latent space due to the potential stochastics in the data. Moreover, LatCPB performs a contrastive learning strategy in latent space to separate feature representations of various molecules and conformations of the same molecule, thus enhancing the comprehension of molecular representational diversity. Experimental results demonstrate that LatCPB is able to backmap CG proteins effectively and achieve outstanding performance. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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On-device High Accuracy Plant Classifier 9

On-device High Accuracy Plant Classifier

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9th International Conference on Digital Home, ICDH 2022

作者： Zheng, Qifeng Luo, Xiaonan Lan, Rushi Guilin University of Electronic Technology School of Computer Science and Information Security Guilin541004 China Guilin Huigu Institute of Artificial Intelligence Industrial Technology Guilin China Guilin University of Electronic Technology National Local Joint Engineering Research Center of Satellite Navigation and Location Service Guilin541004 China

ISBN: (数字)9781665454780

ISBN: (纸本)9781665454780

In this paper, we present an on-device high accuracy plant classifier (HAPC) system, which classifies plant species from leaf images. The system generally uses a classic pre-trained ResNet variant called ResNet50-D as the backbone with several refinements. We use the EDA (Exploratory Data Analysis) approach to comprehend the dataset and explore the data structure to extract the dataset's main characteristics. Besides EDA, we also implement image augmentation for the dataset in order to improve model performance and generalize ability. The model, pre-trained with ImageNet, has 19 million parameters and 6 billion FLOPs. With 5-fold cross-validation at 50 epochs, we reached the accuracy of 98.522% on the validation set. Our baseline only has 85.681% accuracy, showing that a classic architecture with refinements can still gain a significant performance boost. © 2022 IEEE.

关键词： Image enhancement

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An Exoskeleton BCI System for Stroke Rehabilitation Using Multi-modality Training Mode

An Exoskeleton BCI System for Stroke Rehabilitation Using Mu...

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2023 International Automatic Control Conference, CACS 2023

作者： Su, Kai-Hsiang Chen, Chiao-Hsin Chen, Chia-Hsin Zhang, Yong-Liang Ko, Li-Wei National Yang Ming Chiao Tung University Former National Chiao Tung University Institute of Biological Science and Technology Hsinchu30010 Taiwan National Yang Ming Chiao Tung University Former National Chiao Tung University Institute of Molecular Medicine and Bioengineering Hsinchu30010 Taiwan Medical University Chung-Ho Memorial Hospital Rehabilitation Department of Kaohsiung Taiwan College of Biological Science and Technology Hsinchu30010 Taiwan National Yang Ming Chiao Tung University Former National Chiao Tung University Institute of Electrical and Control Engineering Hsinchu30010 Taiwan Brain Research Center Department of Electrical and Computer Engineering Hsinchu30010 Taiwan

ISBN: (纸本)9798350306354

With the advent of the global aging era, the impact of stroke on humans is continuously increasing. Therefore, new therapeutic approaches to enhance patient recovery are receiving growing attention. Stroke is a severe neurological disorder that causes brain damage, requiring extended periods of rehabilitation for patients to return to normal life. Currently, common clinical methods largely rely on traditional passive limb rehabilitation, primarily targeting peripheral nerve training. Nowadays, an increasing number of people are recognizing the importance of post-stroke neuroplasticity. This study aims to activate patients' central nervous system and proposes a brain-machine interface (BCI) exoskeleton system based on human cognition. By designing three cognitive brainwave training models, including an attention-based standing-up model detecting θ-band power decrease, a relaxation-based sitting-down model detecting α-band power increase, and a walking model detecting MRCP, patients can actively control lower limb exoskeleton movement using *** study provides cross-validation accuracy through both normal subjects and stroke patients, with accuracies of 87.13±9.38 and 80.83±9.68, respectively. Envisioning a rehabilitation system guided by human cognition, our team has developed an intuitive BCI exoskeleton system. © 2023 IEEE.

关键词： Brain

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GeckoDAG: Towards a Lightweight DAG-Based Blockchain via Reducing Data Redundancy

GeckoDAG: Towards a Lightweight DAG-Based Blockchain via Red...

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International Conference on Distributed Computing Systems

作者： Xiaohai Dai Yifan Zhou Jiang Xiao Feng Cheng Xia Xie Hai Jin Bo Li National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China School of Computer Science and Technology Hainan University China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

To overcome the scaling and performance limitations, the Directed Acyclic Graph (DAG) is utilized as the underlying storage model of blockchain systems, which enables concurrent transaction processing and confirmation. However, accompanied by high performance, DAG-based blockchains still suffer from the severe challenge of constrained storage scalability, i.e., expensive storage overhead. Based on an in-depth analysis of the data, we discover that the root cause of storage overhead stems from the considerable data redundancy in the DAG-based blockchains. In this paper, we propose GeckoDAG, a lightweight DAG-based blockchain, whose design consists of two steps. First, we abstract a storage model named Basic from the existing DAG-based blockchain systems, which offers both high performance and security. On top of Basic, we then devise GeckoDAG, which merges previous transactions into Transaction Union (TU) and reduces the data redundancy in TU, thus lowering the storage overhead. To evaluate our design, we implement a prototype of GeckoDAG and conduct various experiments on it. The experimental results demonstrate that GeckoDAG can offer storage scalability while maintaining the security and efficiency of DAG-based blockchains.

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A Triangular Stable Node Network based on Self-supervised Learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qing Liu Qian Gao Jun Fan Zhiqiang Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China China Telecom Digital Intelligence Techonology Co Ltd Jinan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN— a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model’s effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets.

关键词： Measurement Bridges Contrastive learning Signal processing Data processing Vectors Space exploration Resource management Speech processing Mutual information

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A Staged Fuzzy Evolutionary Algorithm for Constrained Large-Scale Multiobjective Optimization

SSRN

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SSRN 2024年

作者： Zhou, Jinlong Zhang, Yinggui Yu, Fan Yang, Xu Suganthan, Ponnuthurai Nagaratnam School of Traffic and Transport Engineering Central South University Changsha410075 China College of Computer National University of Defense Technology Changsha410073 China KINDI Center for Computing Research College of Engineering Qatar University Doha Qatar

Almost all multiobjective optimization problems in practical application scenarios involve constraints, and constrained multiobjective optimization has received widespread attention. Based on the experience in constrained single-objective optimization, some classical constraint handling techniques (e.g., constrained dominance principle) are extended to solve constrained multiobjective optimization problems (CMOPs). Nevertheless, they encounter difficulties in addressing CMOPs with complex infeasible regions. Even worse, as the number of decision variables increases, the search efficiency of algorithms deteriorates dramatically. To solve those issues, we propose a staged fuzzy evolutionary algorithm (i.e., SFEA) for constrained large-scale problems. To balance exploration and exploitation, a fuzzy stage adjustment strategy based on the sigmoid function is proposed. Furthermore, this article develops a novel variant of the fuzzy operator to perform fuzzy operations on various vectors (e.g., solutions or constraint violations). Computational experiments were conducted on CMOP test suites with up to 500 decision variables and a set of real-world applications. The experimental results demonstrate that, compared to existing popular algorithms, our algorithm exhibits superior or competitive performance. © 2024, The Authors. All rights reserved.

关键词： Constrained optimization

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OTMEA : Multi-modal Entity Alignment via Optimal Transport

OTMEA : Multi-modal Entity Alignment via Optimal Transport

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Cunda Wang Weihua Wang Xinyu Li Qiuyu Liang Feilong Bao College of Computer Science Inner Mongolia University Hohhot China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology Hohhot China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multi-modal Entity Alignment (MMEA) aims to identify the same entities exhibited in different knowledge graphs (KGs), where the entities are enriched by structure and visual information. Existing MMEA methods learn multi-modal joint entity embeddings by encompassing both modality interaction and modality alignment. However, these approaches predominantly emphasize modality interaction and fail to adequately address the issue of modality heterogeneity. In this paper, we propose a novel approach OTMEA, which leverages optimal transport to mitigate modality heterogeneity from the perspective of modality distributions. Specifically, we view the modality alignment problem as a Wasserstein minimum distance problem involving multimodal distributions. Furthermore, our experiments indicate that employing entity-level attention weights significantly enhances modality alignment through optimal transport. The effectiveness of our method is validated through extensive experiments conducted on five public datasets. The source code is available at https://***/wonderCS1213/OTMEA.

关键词： Visualization Source coding Knowledge graphs Signal processing Acoustics Speech processing

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Exploring Seasonal Coastal Water Quality Parameter Interactions Through the Self-Organizing Map Neural Network in Eastern Mediterranean: Lebanon as a Case Study

SSRN

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SSRN 2025年

作者： Hadjisolomou, Ekaterini El Rahman Hassoun, Abed Fakhri, Milad Ghanem, Abeer Mina, Roula Herodotou, Herodotos Michaelides, Michalis Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol3036 Cyprus National Center for Marine Sciences P.O. Box 189 Jounieh Lebanon GEOMAR Helmholtz Centre for Ocean Research Kiel Kiel Germany

Coastal water pollution poses significant environmental challenges, leading to severe ecological and public health consequences. Thus, new assessment tools should be developed and refined to manage water quality more efficiently. In this paper, we present the results of Self-Organizing Map (SOM) neural network models utilized to simulate several water quality parameter interactions monitored from several coastal stations in the South-Eastern Mediterranean Sea, namely in Lebanon. Four seasonal SOM models were developed and calibrated based on the available dataset. The SOM’s visualization of the Component Planes (CPs) enabled the examination of water quality parameter interactions and associations on a seasonal basis. Additionally, Pearson correlation analysis between the SOM’s Best Matching Units was calculated for each monitored parameter to augment the CPs analysis. SOM’s clustering of the data samples and assessment of anthropogenic activities nearby the associated monitoring stations, enabled the evaluation of the potential effects from the existing human activities in nearby areas for each monitoring station. Specifically, the seasonal models revealed that specific land uses nearby the monitoring stations like the ANT-2 (near river mouth), SEL-2 (near a chemical plant), BEY- 6 (public beach) are responsible for the coastal water quality degradation in Lebanon. Overall, this paper demonstrates that the SOM model is not only capable of revealing the parameters’ interactions, but also to identify sources of anthropogenic pollution. Therefore, the SOM models might be used as valuable tools in coastal water quality management. © 2025, The Authors. All rights reserved.

关键词： Eutrophication

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