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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Li, Zhaowen Lin, Shujin Zhou, Fan School of Computer Science and Engineering National Engineering Research Center of Digital Life China Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion China School of Communication and Design Sun Yat-sen University Guangzhou China

ISBN: (纸本)9798350337020

The task of image-guided point cloud completion aims to leverage information from images to address uncertainty issues in the completion inference of point clouds. The key challenge in this setting lies in how to effectively combine features extracted from both modalities. Due to the large domain discrepancy between the image and point cloud, existing methods that use cross-modal attention to directly fuse features have increased attention on redundant information and noise from different modalities, resulting in poor feature fusion performance. Hence, by introducing multi-modal fusion transformers that use bottleneck tokens, we enabled point cloud feature to learn image feature through information bridges, leading to improved point cloud completion performance. Our method can not only benefit from RGB images, but also from sketches with less feature information but more emphasis on edge information. Extensive experiments demonstrate that our proposed method enhances the quality of point cloud completion and outperforms other state-of-the-art methods. © 2023 IEEE.

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A Distributed Framework for Large-scale Protein-protein Interaction data Analysis and Prediction Using MapReduce

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 160-172页

作者： Lun Hu Shicheng Yang Xin Luo Huaqiang Yuan Khaled Sedraoui MengChu Zhou School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China School of Computer Science and Technology Wuhan University of TechnologyWuhan 430070China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of SciencesUrumqi 830000China Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

Protein-protein interactions are of great significance for human to understand the functional mechanisms of *** the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them *** address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using *** do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and *** solutions are then devised to overcome these *** particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of *** that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and *** results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

关键词： Distributed computing large-scale prediction machine learning MapReduce protein-protein interaction(PPI)

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Internet Public Safety Event Grading and Hybrid Storage Based on Multi-feature Fusion for Social Media Texts 28th

Internet Public Safety Event Grading and Hybrid Storage Bas...

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Hu, Die Xie, Yulai Feng, Dan Zhao, Shixun Fu, Pengyu Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China School of Cyber Science and Engineering Hubei Engineering Research Center on Big Data Security Huazhong University of Science and Technology Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9783031306365

With the rapid development of mobile Internet technology, various public events appear on social media platforms and attract a lot of attention. Since netizens can express their opinions freely on the Internet, some events that cause negative public opinion seriously threaten public security, which are called Internet Public Safety events (IPSe). Existing solutions use a single metric to realize event detection, which has a high false detection rate for Internet Public Safety events. In addition, they lack countermeasures after the outbreak of public opinion, resulting in inefficient information management. This paper proposes a novel Internet Public Safety event grading method based on multi-feature, which measures events from the three perspectives of heat, emotion, and sensitivity. In order to improve the retrieval efficiency of events information, we implement a smart dynamic hot/cold data migration mechanism using a hybrid storage system containing solid-state drives and hard-disk drives, which realizes real-time data adjustment in the storage layer and ensures efficient events management. In the experiments, we verify our method through several real internet events, and the results show that our method achieves the state-of-the-art accuracy in the detection of Internet Public Safety events and realizes efficient retrieval with a low query overhead. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Grading

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Joint Near Field Uplink Communication and Localization Using Message Passing-Based Sparse Bayesian Learning

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 7666-7675页

作者： Liu, Fei Yuan, Zhengdao Guo, Qinghua Zhang, Yuanyuan Wang, Zhongyong Zhang, J. Andrew Zhengzhou University School of Geoscience and Techonology The School of Electrical and Information Engineering Zhengzhou450000 China Open University of Henan Artificial Intelligence Technology Engineering Research Center Zhengzhou450000 China University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia Zhengzhou University of Light Industry School of Electronics and Information Henan Zhengzhou450002 China University of Technology Sydney Global Big Data Technologies Centre SydneyNSW2007 Australia

This work deals with the problem of uplink communication and localization in an integrated sensing and communication system, where users are in the near field (NF) of antenna aperture due to the use of high carrier frequency and large antenna arrays at base stations. We formulate joint NF signal detection and localization as a problem of recovering signals with a sparse pattern, and we adopt differentiation modulations to avoid the frequent use of pilot signals. To solve the problem, we develop a message passing based sparse Bayesian learning (SBL) algorithm, where multiple unitary approximate message passing (UAMP)-based sparse signal estimators work jointly to recover the sparse signals with low complexity. Simulation results demonstrate the effectiveness of the proposed method. © 1967-2012 IEEE.

关键词： Carrier communication

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Efficient Remote Memory Paging for Disaggregated Memory Systems 1

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22nd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Wang, Tao Liu, Haikun Jin, Hai 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 Wuhan430074 China

ISBN: (数字)9783031226779

ISBN: (纸本)9783031226762

Memory disaggregation has attracted increasing attention in recent years because it is a cost-efficient approach to scale memory capacity for applications in a data center. However, the latency of remote memory access is a major concern in disaggregated memory systems. This paper presents VANDI, a virtual memory paging mechanism that allows applications to use remote memory pools transparently. VANDI enables effective data caching and prefetching mechanisms to address the problem of high access latency in disaggregated memory systems. VANDI exploits a low-complexity cache replacement strategy to optimize the asynchronous staging queue so that the remote write latency can be significantly reduced. VANDI can also prefetch data in multi-granularity from a remote memory pool in an adaptive manner, and thus further improves the hit rate of the local cache to reduce the read latency of remote memory. Our extensive experiments using micro-benchmarks show that VANDI can improve the performance of typical remote paging system–Infiniswap by up to 15 × –102 ×. VANDI can also improve the performance of state-of-the-art disaggregated memory system–Valet by 1.2 × –2.7 ×. For typical machine learning workloads, VANDI can achieve 20% to 80% performance improvement compared with the state-of-the-art Valet. © 2023, Springer Nature Switzerland AG.

关键词： Paging systems

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Lightweight object detection algorithm for road scenes based on YOLOV8 4

Lightweight object detection algorithm for road scenes based...

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4th International Conference on Machine Learning and computer Application, ICMLCA 2023

作者： Huang, Chengqin Yang, Degang Zhang, Xin College of Computer and Information Science Chongqing Normal University Chongqing401331 China Chongqing Engineering Research Center of Educational Big Data Intelligent Perception and Application Chongqing Normal University Chongqing401331 China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

Object detection in road scenes is a crucial component of autonomous driving. Due to the significant variations in target scales, it is prone to false positives, false negatives, and some underperforming devices cannot deploy the state-of-the-art detectors. To address these issues, we propose a lightweight algorithm based on an improved YOLOv8. We simplify the model by using the FasterNet Block from FasterNet to replace the BottleNeck module in YOLOv8's backbone network C2f, reducing parameters and floating-point computations. We also enhance feature extraction by substituting RFCAConv for the downsampling standard convolution in C2f. Additionally, we introduce Wise-IoU to replace the original activation function, directing the network's focus towards anchor boxes of average *** promote effective fusion of original, shallow, and deep features, we introduce the BiFPN structure to replace YOLOv8's PAN structure. Furthermore, a small object detection layer is added to the head to handle the drastic scale variations in road scenes. Experimental results on the SODA10M dataset demonstrate that the improved YOLOv8 model achieves a 55.8% mAP@0.5 and a 35.5% mAP@0.5:0.95. The model's parameter count, size, and floating-point computations decrease by 58.3%, 57.0%, and 21.1%, respectively. Analysis of the experimental results confirms that the proposed model is effective and superior, striking a balance between detection accuracy and model lightweightness. © 2024 SPIE.

关键词： Object detection

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data Mining and Bioinformatics Approaches to Elucidate Bacterial Communities in the Extreme Environment

Data Mining and Bioinformatics Approaches to Elucidate Bacte...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Trinugroho, Joko Pebrianto Hidayat, Alam Ahmad Nirwantono, Rudi Asadi, Faisal Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Mathematics Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Bioinformatics and Data Science Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307306

Bacteria are microscopic organisms that can be found in many environments. They are abundant and have many roles in our life. Studying bacteria is essential so that we can identify the bacteria that are needed for many industrial applications. However, the main problem is that majority of the bacteria are unculturable, hampering the exploration of bacteria from different environments. Metagenomics approach which employs Next Generation Sequencing technology could help study bacteria by utilizing 16S rRNA marker gene. This study aims to demonstrate data mining and bioinformatics approaches to analyze 16S rRNA sequencing data. The raw sequencing data of 16S rRNA was collected from biological database. Then, the data were trimmed, denoised, and clustered to generate Amplicon Sequence Variants (ASVs). Diversity (alpha and beta) and taxonomic analyses were then conducted to elucidate the bacterial diversity and taxonomic profile of ASVs. The results of this work showed that Shannon and PD indices in China's hot spring were higher than Singapore and the USA. Furthermore, a significant difference was observed in the PD index. The unweighted UniFrac distance also showed there was a significant difference of the bacterial communities between the three locations. In addition, the taxonomic investigation unveiled prevalent bacterial groups in the ecosystem, namely Proteobacteria, Chloroflexi, Cyanobacteria, and Crenarchaeota. The research outcomes have the potential to serve as a foundational resource for subsequent bacterial metagenomic research, particularly the hot spring environment. © 2023 IEEE.

关键词： Bacteria

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ICH-SCNet: Intracerebral Hemorrhage Segmentation and Prognosis Classification Network Using CLIP-guided SAM mechanism

ICH-SCNet: Intracerebral Hemorrhage Segmentation and Prognos...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Yu, Xinlei Elazab, Ahmed Ge, Ruiquan Jin, Hui Jiang, Xinchen Jia, Gangyong Wu, Qing Shi, Qinglei Wang, Changmiao Hangzhou Dianzi University School of Computer Science Hangzhou China Shenzhen University School of Biomedical Engineering Shenzhen China Chinese University of Hong Kong Shenzhen School of Management and Economics Shenzhen China Chinese University of Hong Kong Shenzhen School of Medicine Shenzhen China Shenzhen Research Institute of Big Data Medical Big Data Lab Shenzhen China

ISBN: (纸本)9798350386226

Intracerebral hemorrhage (ICH) is the most fatal subtype of stroke and is characterized by a high incidence of disability. Accurate segmentation of the ICH region and prognosis prediction are critically important for developing and refining treatment plans for post-ICH patients. However, existing approaches address these two tasks independently and predominantly focus on imaging data alone, thereby neglecting the intrinsic correlation between the tasks and modalities. This paper introduces a multi-task network, ICH-SCNet, designed for both ICH segmentation and prognosis classification. Specifically, we integrate a SAM-CLIP cross-modal interaction mechanism that combines medical text and segmentation auxiliary information with neuroimaging data to enhance cross-modal feature recognition. Additionally, we develop an effective feature fusion module and a multi-task loss function to improve performance further. Extensive experiments on an ICH dataset reveal that our approach surpasses other state-of-the-art methods. It excels in the overall performance of classification tasks and outperforms competing models in all segmentation task metrics. © 2024 IEEE.

关键词： Diagnosis

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Anole: A Lightweight and Verifiable Learned-Based Index for Time Range Query on Blockchain Systems 28th

Anole: A Lightweight and Verifiable Learned-Based Index for...

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Chang, Jian Li, Binhong Xiao, Jiang Lin, Licheng Jin, Hai 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 Wuhan430074 China

ISBN: (纸本)9783031306365

Time range query is essential to facilitate a wide range of blockchain applications such as data provenance in the supply chain. Existing blockchain systems adopt the storage-consuming tree-based index structure for better query performance, however, fail to efficiently work for most blockchain nodes with limited resources. In this paper, we propose Anole, a lightweight and verifiable time range query mechanism, to present the feasibility of building up a learned-based index to achieve high performance and low storage costs on blockchain systems. The key idea of Anole is to exploit the temporal characteristics of blockchain data distribution and design a tailored lightweight index to reduce storage costs. Moreover, it uses a digital signature to guarantee the correctness and completeness of query results by considering the learned index’s error bounds, and applies batch verification to further improve verification performance. Experimental results demonstrate that Anole improves the query performance by up to 10 × and reduces the storage overhead by 99.4 % compared with the state-of-the-art vChain+. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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An Integrated Analysis of Yield Prediction Models:A Comprehensive Review of Advancements and Challenges

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computers, Materials & Continua 2024年第7期80卷 389-425页

作者： Nidhi Parashar Prashant Johri Arfat Ahmad Khan Nitin Gaur Seifedine Kadry School of Computer Science and Engineering Galgotias UniversityGreater Noida203201India Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates MEU Research Unit Middle East UniversityAmman11831Jordan Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand

The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction *** learning(DL)and machine learning(ML)models effectively deal with such *** research paper comprehensively analyses recent advancements in crop yield prediction from January 2016 to March *** addition,it analyses the effectiveness of various input parameters considered in crop yield prediction *** conducted an in-depth search and gathered studies that employed crop modeling and AI-based methods to predict crop *** total number of articles reviewed for crop yield prediction using ML,meta-modeling(Crop models coupled with ML/DL),and DL-based prediction models and input parameter selection is *** conduct the research by setting up five objectives for this research and discussing them after analyzing the selected research *** study is assessed based on the crop type,input parameters employed for prediction,the modeling techniques adopted,and the evaluation metrics used for estimatingmodel *** also discuss the ethical and social impacts of AI on ***,various approaches presented in the scientific literature have delivered impressive predictions,they are complicateddue to intricate,multifactorial influences oncropgrowthand theneed for accuratedata-driven ***,thorough research is required to deal with challenges in predicting agricultural output.

关键词： Machine learning crop yield prediction deep learning remote sensing long short-term memory time series prediction systematic literature review

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