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2023 SAR in Big data Era, BIGSARdata 2023

作者： Feng, Xiaolin Tian, Sirui Abhadiomhen, Stanley Ebhohimhen Xu, Zhiyong Shen, Xiang-Jun Wang, Jing Wang, Chao School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China School of Computer Science and Communication Engineering Jiangsu University Zhenjiang212013 China University of Nigeria Department of Computer Science Nsukka410001 Nigeria Aerospace Information Research Institute Chinese Academy of Sciences Key Laboratory of Digital Earth Science Beijing100094 China

ISBN: (纸本)9798350326239

Synthetic Aperture Radar (SAR) images often suffer from inherent speckle noise that can significantly degrades their quality, and makes it difficult to identify important targets or extract useful information from them. To solve this problem, a novel SAR image despeckling model called HM-SIDLR is proposed, which can suppress noise and preserve image edges from low-rank residues simultaneously. Specifically, our method considers that there is some structural information discarded as noise in residues. Therefore, we construct a hierarchical model to extract more edge details which can compensate for the over-smoothing problem caused by removing most of the speckle noise in the low-rank part. So as to learn the information from the edge subspace, a tunable prior knowledge matrix is designed, allowing for differentiation between clean and noisy pixels. Experimental results with the Virtual SAR dataset indicate that HM-SIDLR can achieve comparable despeckling performance to the state-of-the-art (SOTA) results. © 2023 IEEE.

关键词： Synthetic aperture radar

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Real-time Stream Processing in IoT Environments 9

Real-time Stream Processing in IoT Environments

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Rajasekar, P. Bhosale, Rupali Shekar Indhumathi, C. Sandeep, K.V. Prasanthi Kumari, N. Rajendiran, M. School of Computing Srm Institute of Science And Technology Department of Data Science And Business Systems Tamil Nadu Kattankulathur603203 India Bharati Vidyapeeth Institute of Technology Department of Electronics And Telecommunication Engineering Maharashtra Navi Mumbai400614 India Rmk College of Engineering And Technology Department of Computer Science And Business Systems Tamil Nadu Puduvoyal601206 India Dayananda Sagar College of Engineering Department of Electronics & Telecommunication Engineering Karnataka Bengaluru560111 India School of Computer Sciences University of Petroleum And Energy Studies Uttarakhand 248007 Dehradun India Panimalar Engineering College Department of Computer Science And Engineering Tamil Nadu Chennai600123 India

ISBN: (纸本)9798350365092

New IoT devices generate massive real-time data. Fast, real-time stream processing is needed for IoT data insight extraction. There are pros and downsides to real-time stream processing for the Internet of Things (IoT). This article analyzes present methods and technologies. The inquiry begins with large-scale, real-time IoT data analysis. Real-time analytics and bulk processing limits are important for IoT decision-making. We then investigate IoT stream processing frameworks, methodologies, and systems' complexity. We explore Internet of Things technology selection and tradeoffs between scalability, accuracy, latency, and real-time processing system scalability. It covers real-time stream processing system design and deployment and risk mitigation. Case studies and use cases highlight real-time IoT stream processing. Healthcare, smart city, and industrial IoT operational efficiency and proactive decision-making improve with real-time data. Finally, the report suggests the Internet of Things real-time stream processing research. New IoT data analytics protocols, methods, and technologies are highlighted. We teach real-time stream processing to IoT academics, practitioners, and decision-makers. They could then develop more efficient and scalable IoT data stream optimization solutions. © 2024 IEEE.

关键词： Edge computing

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Community Search Based on Containment Control of Multi-Agent System with Opinion Leaders 43

Community Search Based on Containment Control of Multi-Agent...

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43rd Chinese Control Conference, CCC 2024

作者： Liu, Siyang Li, Tong Shao, Jinliang Zheng, Wei Xing University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing100089 China Western Sydney University School of Computer Data and Mathematical Sciences SydneyNSW2751 Australia

ISBN: (纸本)9789887581581

Community search is the problem of identifying the community in which a given node resides. Different from traditional community search methods based on specific topological structures, this paper proposes a community search algorithm based on containment control of multi-agent system by studying the influence of opinion leaders with competitive relationships in community formation. Firstly, the Breadth-First Search method is used to locally sample the network. Secondly, containment control of multi-agent system is applied to obtain the final state of each node for local community partitioning. Finally, grounded in practical considerations, an effective leadership transfer mechanism is proposed to update the results and enhance search precision. Through experiments on real-world networks and synthetic networks in comparison with three local community detection algorithms, the effectiveness and rationality of our algorithm are demonstrated. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Multi agent systems

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Reed-Solomon Codes over Ring with Lower Computational Complexity

Reed-Solomon Codes over Ring with Lower Computational Comple...

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2023 IEEE International Conference on Big data, Bigdata 2023

作者： Tan, Shicheng Hou, Hanxu Yu, Leilei Dongguan University of Technology School of Computer Science & Technology Dongguan China Dongguan University of Technology School of Electrical Engineering & Intelligentization Dongguan China University of Electronic Science & Technology of China Shenzhen Institute for Advanced Study China

ISBN: (纸本)9798350324457

Reed-Solomon (RS) codes are widely used in storage systems to provide high data reliability. The existing RS codes are constructed over finite field with field size larger than the code length that have high encoding/decoding complexity. In this paper, we propose a new construction for RS codes over a cyclic ring instead of finite field. Our new RS codes only incurs XOR and cyclic-shift operation in the encoding/decoding processes, and therefore have lower encoding/decoding complexity than the existing RS codes. In addition, we show that we can employ the efficient Reed-Muller (RM) transform in our new RS codes that reduces the encoding/decoding complexity. Moreover, we implement our new RS codes by C++ and show that our new RS codes have better encoding/decoding performance than the existing RS codes for the evaluated parameters. © 2023 IEEE.

关键词： cyclic ring cyclic-shift encoding/decoding complexity Reed-Muller transform Reed-Solomon codes

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Brillouin Frequency Shift Extraction for BOTDA Based on Gramian Angular Fields 4

Brillouin Frequency Shift Extraction for BOTDA Based on Gram...

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4th International Computational Imaging Conference, CITA 2024

作者： Zhu, Borong Zhang, Zhihao Qian, Yuhao Ma, Xiaole Wang, Liang Yu, Kuanglu Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Institute of Information Science School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China National Engineering Laboratory for Next Generation Internet Access System School of Optics and Electronic Information Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781510688834

To accurately extract Brillouin frequency shift of BOTDA with large sweeping step sizes, a novel structure of GAFCNN is proposed, combining time series coding with convolutional neural networks. The experimental data validation results demonstrate that the GAFCNN is capable of obtaining more accurate results under large sweep steps and low signal-to-noise ratios (SNRs) than other current methods. © 2025 SPIE.

关键词： Convolutional neural networks

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DeepGCN based on variable multi‐graph and multimodal data for ASD diagnosis

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 879-893页

作者： Shuaiqi Liu Siqi Wang Chaolei Sun Bing Li Shuihua Wang Fei Li College of Electronic and Information Engineering Hebei UniversityBaodingChina Machine Vision Technology Innovation Center of Hebei Province BaodingChina The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Computer Science and Technology Henan Polytechnic UniversityJiaozuoChina

Diagnosing individuals with autism spectrum disorder(ASD)accurately faces great chal-lenges in clinical practice,primarily due to the data's high heterogeneity and limited sample *** tackle this issue,the authors constructed a deep graph convolutional network(GCN)based on variable multi‐graph and multimodal data(VMM‐DGCN)for ASD ***,the functional connectivity matrix was constructed to extract primary ***,the authors constructed a variable multi‐graph construction strategy to capture the multi‐scale feature representations of each subject by utilising convolutional filters with varying kernel ***,the authors brought the non‐imaging in-formation into the feature representation at each scale and constructed multiple population graphs based on multimodal data by fully considering the correlation between *** extracting the deeper features of population graphs using the deep GCN(DeepGCN),the authors fused the node features of multiple subgraphs to perform node classification tasks for typical control and ASD *** proposed algorithm was evaluated on the Autism Brain Imaging data Exchange I(ABIDE I)dataset,achieving an accuracy of 91.62%and an area under the curve value of 95.74%.These results demon-strated its outstanding performance compared to other ASD diagnostic algorithms.

关键词： machine learning medical image processing medical signal processing

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Recent Recommender Systems and Analysis of the Machine Learning Public Instances

Recent Recommender Systems and Analysis of the Machine Learn...

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International Conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Raja Marappan Saraswatikaniga R School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology Chennai M.Tech Computer Science and Engineering (Business Analytics) School of Computer Science and Engineering Vellore Institute of Technology Chennai

Recommendation systems have become increasingly important in various online applications. One of the significant types of research in machine learning (ML) is recommendation systems. A recommender system, alternatively referred to as a recommendation system, is an information filtering system that supports users in identifying their “rating” or “preference” for an object and makes predictions based on that rating or preference. This research analyzes current recommendation systems applied in ML public instances to explore knowledge discovery. The suggestions systems are developed for musical, online dating, and restaurant applications. The recommendation system serves as a tool to assist users in discovering what they are interested in by providing them with appropriate ideas. To provide personalized recommendations to users, primarily employ collaborative, content-based, session-based, demographic, and hybrid filtering. Classification, clustering, and association rule discovery are the critical data mining and ML techniques most widely employed in recommendation systems. In general, the accuracy of the recent models lies at (75%, 99 % ) and the error rate occurs at (5%, 25 % ) for the ML public instances.

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Deep Learning Approaches for Medical Imaging Under Varying Degrees of Label Availability: A Comprehensive Survey

arXiv

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arXiv 2025年

作者： Ma, Siteng Du, Honghui An, Yu Wang, Jing Wang, Qinqin Wu, Haochang Lawlor, Aonghus Dong, Ruihai The Insight Centre for Data Analytics School of Computer Science University College Dublin Dublin Ireland College of Computer Science North China Institute of Aerospace Engineering Langfang China Hithink RoyalFlush Information Network Co. Ltd Hangzhou China School of Electrical and Electronic Engineering University College Dublin Dublin Ireland

Deep learning has achieved significant breakthroughs in medical imaging, but these advancements are often dependent on large, well-annotated datasets. However, obtaining such datasets poses a significant challenge, as it requires time-consuming and labor-intensive annotations from medical experts. Consequently, there is growing interest in learning paradigms such as incomplete, inexact, and absent supervision, which are designed to operate under limited, inexact, or missing labels. This survey categorizes and reviews the evolving research in these areas, analyzing around 600 notable contributions since 2018. It covers tasks such as image classification, segmentation, and detection across various medical application areas, including but not limited to brain, chest, and cardiac imaging. We attempt to establish the relationships among existing research studies in related areas. We provide formal definitions of different learning paradigms and offer a comprehensive summary and interpretation of various learning mechanisms and strategies, aiding readers in better understanding the current research landscape and ideas. We also discuss potential future research *** Codes 68T07, 68T45, 92C50, 92C55 Copyright © 2025, The Authors. All rights reserved.

关键词： Semi-supervised learning

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Finding Simplex Items in data Streams 39

Finding Simplex Items in Data Streams

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39th IEEE International Conference on data engineering, ICDE 2023

作者： Fan, Zhuochen Guo, Jiarui Li, Xiaodong Yang, Tong Zhao, Yikai Wu, Yuhan Cui, Bin Xu, Yanwei Uhlig, Steve Zhang, Gong Peking University School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Beijing China Peng Cheng Laboratory Shenzhen China Central Research Institute 2012 Labs Huawei Technologies Co. Ltd. Theory Lab Hong Kong Queen Mary University of London School of Electronic Engineering and Computer Science London United Kingdom

ISBN: (纸本)9798350322279

In this paper, we propose a new type of item in data streams, called simplex items. Simplex items have frequencies in consecutive p windows that can be approximated by a polynomial of degree at most k, where k = 0, 1, 2. These low-order representable simplex items have a wide range of potential applications. For example, when k = 1, we can leverage these items whose frequency has obvious linear increase or decrease to speed up the running time of a class of machine learning models and detect network attacks such as distributed denial-of-service (DDoS), etc. To find k-degree simplex items in real time, we propose a novel sketch, namely X-Sketch, to accurately record simplex items in a compact space. The key idea of X-Sketch is to effectively filter out non-simplex items with less memory overhead, and then monitor the remaining potential simplex items and keep those items with more consecutive windows. We conduct extensive experiments, and the experimental results show that the F1 Score of X-Sketch is on average 68.6%, 57.9%, and 42.2% higher than the baseline solution for k = 0, 1, 2, respectively. Finally, we also provide a case study that applies X-Sketch to "accelerate"the two machine learning models through end-to-end experiments. We have released our source code at GitHub. © 2023 IEEE.

关键词： Denial-of-service attack

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MAAS-Net: Efficient Multi-Scale Attention Aggregation Supervision Network for Tire Tread Segmentation 4

MAAS-Net: Efficient Multi-Scale Attention Aggregation Superv...

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4th International Conference on Industrial Automation, Robotics and Control engineering, IARCE 2024

作者： Ji, Xu Tian, Yongqin Xu, Yibo Liu, Dehua Zhao, Wenyi Zhao, Liang Yang, Mu Yang, Huihua School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China School of Information Engineering Henan Institute of Science and Technology Xinxiang China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Industrial Technology Co. Ltd. Beijing China

ISBN: (纸本)9798350380323

Overlap detection plays a critical role in ensuring tire safety performance, as defects like overlapping anomalies due to mold aging and wear elevate the risk of tire blowouts. However, traditional edge detection algorithms entail a cumbersome manual parameter adjustment process and are limited to fixed hardware equipment scenarios, thereby compromising the flexibility and robustness of surface defect detection. Moreover, conventional image segmentation methods often fall short in achieving high-precision segmentation of the tread area. In this paper, we introduce MAAS-Net, a network employing a U-shaped encoder-decoder architecture. Integrated within are Convolutional Block Attention Modules (CBAM) after each decoding stage to generate multi-resolution attention feature maps. These maps are progressively fused into Multi-scale Aggregated Supervision (MAS) to enhance the model's capacity to discern features across various levels and scales. Additionally, to enhance the model's accuracy near the tread edge area, we propose a post-processing method named ContourFix. Leveraging data augmentation and multi-task learning, ContourFix aims to replace unstable prediction values near the edges with high-confidence prediction values inside the target area. The efficacy of these techniques is evaluated using a dataset of 3D tire tread. Experimental results demonstrate that MAAS-Net significantly outperforms conventional semantic segmentation models in segmenting tire treads, and ContourFix effectively enhances the accuracy of the segmentation model near the regional boundary. © 2024 IEEE.

关键词： Semantic Segmentation

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