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SSRN 2022年

作者： Sun, Lilei Wen, Jie Liu, Chengliang Fei, Lunke State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen Shenzhen518000 China School of Computer Science and Technology Guangdong University of Technology Guangzhou510000 China

There is a large amount of incomplete multi-view data in the real-world. How to cluster these incomplete multi-view data is an urgent realistic problem since almost all of the conventional multi-view clustering methods are inapplicable to the case with missing views. In this paper, we propose a new graph learning based incomplete multi-view clustering method to address this issue. Different from the existing works, our method seeks to learn a common consensus graph from all of the incomplete views and obtain a clustering indicator matrix in a unified framework. Specifically, to obtain a stable clustering result, a relaxed spectral clustering model is introduced to obtain a unique probability consensus representation whose all elements are positive to reflect the data clustering result. In view of the different contributions of views in the clustering task, we introduce a weighted multi-view learning mechanism to automatically balance their effects in the model optimization. In such a way, the intrinsic information in these incomplete multi-view data can be exploited sufficiently. The experimental results on several incomplete multi-view datasets illustrate that the proposed method achieves the best clustering performance among all of the compared state-of-the-art methods, which verifies the effectiveness of the proposed method for incomplete multi-view clustering. © 2022, The Authors. All rights reserved.

关键词： Cluster analysis

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MSONoC: A Metasurface-assisted Hybrid Waveguide/FSO Network-on-Chip Architecture

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年

作者： Sun, Wei Li, Jingsi Guo, Pengxing He, Xiangyu Wang, Zimo Hu, Cong Liu, Weichen Hou, Weigang Guo, Lei School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Institute of Intelligent Communications and Network Security Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guilin University of Electronic Technology Guilin541004 China School of Computer Science and Engineering Nanyang Technological University Singapore

Optical networks-on-chips (ONoCs) have emerged as the most promising solution for on-chip interconnection networks, offering distinct advantages such as high bandwidth and low latency. However, traditional wired-based ONoCs often suffer from a high average number of hops, leading to challenges such as increased insertion loss, crosstalk noise, and energy consumption. Therefore, this paper presents a novel ONoC structure known as the metasurface ONoC (MSONoC), which integrates wired waveguide and free-space optical (FSO) transmission with the assistance of metasurfaces. Specifically, this work encompasses the design of a passive metasurface structure tailored for ONoCs, a virtual network cluster partitioning method, an optical metasurface access points select strategy, and a shortest-path routing algorithm. Performance verification is conducted using a modified Noxim simulator. The simulation results demonstrate that the proposed MSONoC offers advantages over traditional NoCs, radio frequency (RF)-based hybrid wired/wireless NoCs, and wired-based ONoCs in terms of throughput, delay, energy consumption, and signal-to-noise ratio under both synthetic traffic and real traffic models. These improvements proves the effectiveness of the MSONoC architecture in optimizing network performance, making it a promising solution for high-performance, energy-efficient communication in on-chip networks. © 2017 IEEE.

关键词： Network-on-chip

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Applying Parallel Processing to Improve the Computation Speed of K-Nearest Neighbor Algorithm

Applying Parallel Processing to Improve the Computation Spee...

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2020 International Conference on data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020

作者： Alanezi, Maha A. Alqaddoumi, Abdulla University of Bahrain Collage of Science Big Data Science and Analytics Master Program Sakhir Bahrain University of Bahrain Collage of Information Technology Department of Information System Sakhir Bahrain

ISBN: (纸本)9781728196756

K-Nearest Neighbor (KNN) is a widely used algorithm to gain an accurate and efficient classification. One of the drawbacks of the algorithm is the time required to calculate the distance for each point. In this paper, the aim is to speed up the KNN algorithm with the implementation of multi threads and multiprocessors to reduce the time to execute the algorithm. There are two medical datasets used to apply the KNN algorithm for the comparison of the sequential and parallel performance. The datasets utilized are Heart Test and Breast Cancer Wisconsin (Diagnostic) data Sets. The multiprocessing is used to parallelize the computation of the distance of KNN. The parallel KNN outperforms the sequential version with the speedup increase, leading to reduction in time for both the processes and threads. © 2020 IEEE.

关键词： Learning algorithms

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Online Discriminative Semantic-Preserving Hashing for Large-Scale Cross-Modal Retrieval 18th

Online Discriminative Semantic-Preserving Hashing for Large-...

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18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021

作者： Yi, Jinhan He, Yi Liu, Xin Department of Computer Science and Technology Huaqiao University Xiamen361021 China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing210094 China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Xiamen China Fujian Key Laboratory of Big Data Intelligence and Security Xiamen China

ISBN: (纸本)9783030891879

Cross-modal hashing has drawn increasing attentions for efficient retrieval across different modalities, and existing methods primarily learn the hash functions in a batch based mode, i.e., offline methods. Nevertheless, the multimedia data often comes in a streaming fashion, which makes the batch based learning methods uncompetitive for large-scale streaming data due to the large memory consumption and calculation. To address this problem, we present an Online Discriminative Semantic-Preserving Hashing (ODSPH) method for large-scale cross-modal retrieval, featuring on fast training speed, low memory consumption and high retrieval accuracy. Within the proposed ODSPH framework, we utilize the newly coming data points to learn the hash codes and update hash functions in a stream manner. When new data comes, the corresponding hash codes are obtained by regressing the class label of the training examples. For hash function, we update it with the accumulated information from each round. Besides, we design a novel momentum updating method to adaptively update the hash function and reduce quantization loss, which can produce discriminative hash codes for high retrieval precision. Extensive experiments on three benchmark datasets show that the proposed ODSPH method improves the retrieval performance over the state-of-the-arts. © 2021, Springer Nature Switzerland AG.

关键词： Semantics

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Extremely Short Chinese Text Classification Method Based on Bidirectional Semantic Extension 2

Extremely Short Chinese Text Classification Method Based on ...

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2019 2nd International Symposium on big data and Applied Statistics, ISBDAS 2019

作者： Yue, Yongzeng Zhang, Yuhong Hu, Xuegang Li, Peipei Key Laboratory of Knowledge Engineering with Big Data [Hefei University of Technology] Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei23061 China

Short text classification methods have achieved significant progress and wide application on text data such as Twitter and Weibo. However, the extremely short chinese texts like tax invoice data are different with traditional short texts in lackness of contextual semantic information, feature sparseness and extremely short length. The existing short text classification methods are difficult to achieve a satisfactory performance in these texts. To address these problems, this paper proposes a text classification method based on bidirectional semantic extension for extremely short texts like Chinese tax invoice data. More specifically, firstly, the Chinese knowledge graph is introduced for extending bidirectional semantic of texts and label data to expand the extremely short texts and ease the problem of feature sparseness;secondly, the hash vectorization is used to avoid the semantic problem caused by the lackness of contextual information. Experimental results conducted the real tax invoice dataset demonstrate the effectiveness of our proposed method. © Published under licence by IOP Publishing Ltd.

关键词： Classification (of information)

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Optimizing Read Performance in Lightweight Blockchain with Cooperative Storage Scheme

Optimizing Read Performance in Lightweight Blockchain with C...

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Blockchain Computing and Applications (BCCA), International Conference on

作者： Han Wang Hui Li Zixian Wang Feng Zhao Yao Yao Yuhong Chen Tuanfa Qin Ping Lu Jiaoli Shi School of Electronic and Computer Engineering Peking University Shenzhen China School of Computer and Big Data Science Jiujiang University Jiujiang China Tencent Technology (Beijing) Co. LTD Beijing China School of Computer and Electronic Information Guangxi University Nanning China ZTE Corporation Shenzhen China

As a popular and innovative technology in fields including digital currencies and the Internet of Things, blockchain provides decentralized and immutable trust to multiple parties through an append-only chained structure and redundant replicas. However, the increasing length of the blockchain leads to extreme storage costs. While some lightweight storage schemes compress block data on-chain, most of them suffer from poor generality, limited compression capabilities, and imperfect read performance. To alleviate these issues, we propose a cooperative storage scheme as a lightweight solution for resource-limited blockchains. It incorporates a history-based node assignment mechanism and a timeline-based adaptive read mechanism to reduce the decoding probability when accessing transactions. The former maps chunks of erasure codes to nodes based on their history scores, while the latter redundantly caches blocks at certain nodes for easy access. Experiments demonstrate that the proposed scheme actively mitigates the influence of anomalous nodes on read requests. In contrast to typical schemes, it achieves extremely low read time with less storage cost, which is especially valuable in the long term.

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Fusion of Terahertz Spectroscopy and Raman Spectroscopy Combined with Support Vector Machine to Distinguish Different Pericarpium Citri Reticulatae Species

SSRN

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SSRN 2024年

作者： Yang, Qiyu Xia, Congzi Cui, Yuanwu Zhang, Xiaoqiuyan Zhang, Tianyu Huang, Yong Liu, Diwei Wei, Yanyu Gong, Yubin Fang, Guangyou Wang, Zhenyou Chen, Xuequan Hu, Min Terahertz Center School of Electronic Science and Engineering University of Electronic Science and Technology Sichuan611731 China Key Laboratory of Terahertz Technology Ministry of Education Sichuan611731 China Institute of Big Data for Vocational Education Guangdong Polytechnic of Science and Technology Zhuhai519000 China GBA Branch of Aerospace Information Research Institute Chinese Academy of Sciences Guangzhou510700 China Guangdong Provincial Key Laboratory of Terahertz Quantum Electromagnetics Guangzhou51 0700 China Shenzhen Authentic Traditional Chinese Medicine Treatment Hospital Shenzhen518100 China

Pericarpium citri reticulatae (PCR, also called Chenpi) is an important medical and food resource in China. The medicinal value and market price of Chenpi varies greatly among different origins and years. Traditional sensory identification methods involve a great deal of subjectivity, while chemical methods are cumbersome and have low repeatability. In recent years, the classification method using electromagnetic spectrum combined with machine learning has become a new means of identifying the classification of PCR, but problems such as the limited accuracy and the unclear criterion of classification hinder their practical usage. This paper explores the classification of PCR based on the fusion of terahertz and Raman spectral data combined with machine learning methods. The fused spectral data from the two frequency bands will be used together to build a model for the classification of seven PCR species of different ages, origins, and artificial skins in Jiangmen, Guangdong, China. Model selection includes K-nearest neighbor (KNN) classification algorithms, convolutional neural networks (CNN), and support vector machine (SVM). We initially obtain a good classification model by analyzing the model's metrics. These performance metrics include the classification accuracy of the model and the area under receiver operating characteristic curve (AUC). To further improve the overall performance of the model, we use an innovative Manhattan data preprocessing approach. Through the Manhattan distance of the data between different species, the method further attenuates the data errors within the same species of PCR due to instrumental, environmental and human factors, which makes the indicators of the model improved. Ultimately, we built a PCR classification model with high AUC metrics as well as a high accuracy of 96.8%. We used the T-distributed stochastic neighbor embedding (t-SNE) algorithm to realize the downscaling visualization of the data, which intuitively obtains the

关键词： Support vector machines

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Paddy Diseases Multi-Class Classification using CNN Variants

Paddy Diseases Multi-Class Classification using CNN Variants

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Information technology (ACIT)

作者： Danial Izzat Anwar Bin Abdul Aziz Marina Yusoff Nurain Ibrahim Raed Alazaidah College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Malaysia Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Malaysia Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan

Agriculture is critical to ensuring and securing food supply for human consumption. Paddy crops are an agricultural practice that produces one of the highest food yields in agriculture. Paddy diseases must now be detected and diagnosed as soon as possible rather than later. Farmers currently use the naked eye to observe suspected infected paddies. The suspected infected paddy will then be sampled and sent to the laboratory for further examination. These methods are highly inefficient, time-consuming, and error prone. Most paddy farmers lack the knowledge to make an informed and accurate diagnosis of which paddies are infected by which diseases. This paper explores using deep learning algorithms to detect and classify various paddy diseases automatically. This method will take an image of paddy leaves and classify it based on the disease category indicated by the idea. The PaddyDoctor dataset from the Kaggle website was used to train and test the deep-learning models. Convolutional Neural Network (CNN) and Transfer Learning models, including MobileNet, MobileNetv2, and InceptionV3, have been constructed. The results of an experiment involving different dataset split ratios, learning rates, and batch sizes. CNN with MobileNetV2 outperformed CNN with MobileNetV1 and CNN with InceptionV3 by 98.05 percent, 80.08 percent, and 83.98 percent, respectively.

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DCSF-KD: Dynamic Channel-wise Spatial Feature Knowledge Distillation for Object Detection 39

DCSF-KD: Dynamic Channel-wise Spatial Feature Knowledge Dist...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Dai, Tao Lin, Yang Guo, Hang Wang, Jinbao Zhu, Zexuan College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China Shenzhen City Key Laboratory of Embedded System Design Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (纸本)157735897X

Knowledge distillation (KD) has recently gained great success in the field of object detection. By transferring the knowledge of the spatial or channel domain from the teacher model to the student model, it allows for a more compact representation with minimal performance loss. Despite this progress, existing KD methods typically treat knowledge from spatial or channel domains independently, ignoring the exploitation of the mutual relationship between these domains. In this work, we first explore the connection between spatial and channel domains and find there exists a strong correlation between them, i.e. the salient channels tend to contain significant object regions in the spatial domain. Motivated by this observation, we propose DCSF-KD, a novel Dynamic Channel-wise Spatial Feature Knowledge Distillation framework for object detection by fully exploiting both spatial and channel knowledge. Specifically, we introduce channel-wise spatial feature distillation and global channel attention distillation, using information from both domains to improve the accuracy of the student network. Experiments demonstrate that our DCSF-KD outperforms existing detection methods on both homogeneous and heterogeneous teacher-student network pairs. For example, when using the MaskRCNN-Swin detector as the teacher, and based on RetinaNet and FCOS with ResNet-50 on MS COCO, our DCSF-KD can achieve 41.9% and 44.1% mAP, respectively. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Students

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A Novel data Augmentation Method for Robotic Surgical Instrument Small Part Segmentation in Complex Scenes 23

A Novel Data Augmentation Method for Robotic Surgical Instru...

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Proceedings of the 6th International Conference on Machine Learning and Machine Intelligence

作者： Dewei Yang Yongjian Li Xin Wang Jie Liu Xiaobo Zhou College of Advanced Manufacturing Engineering Chongqing University of Posts and Telecommunications China Biomedical Big Data Center West China Hospital of Sichuan University China Division of Pancreatic Surgery West China Hospital of Sichuan University China Chengdu Withai Innovations Technology Company China Center for Computational Systems Medicine University of Texas Health Science Center at Houston USA

ISBN: (纸本)9798400709456

Accurate and robust segmentation of surgical instrument parts is urgently required by AI enhanced intelligent surgery and automatic surgery skill evaluation. However, it is still a challenging problem due to the multiple small areas of frontal clasper and wrist parts, complicated object occlusion and light variation issues. Increasing high quality labeled data is effective to improve the performance but labor-costing. A novel instrument data augmentation method is proposed to generate high quality training data through planar instrument replacement and orientation adjustment. Moreover, image repainting methods are utilized to recover the occluded parts by instruments and generate clean tissue images. Then joint angle reconfigured instruments are placed on recovered clean tissue images to generate simulated surgery images, and we control the occlusion rates and overlapping areas by which high quality instrument manipulation augmented training data are generated automatically. Detailed experiment results show our proposed data augmentation method make existing methods achieve better segmentation accuracy and robustness of wrist and clasper part, especially the fault detection errors decreased significantly in instrument overlapping and complicated lighting conditions.

关键词： data augmentation Robotic instrument segmentation Small part segmentation

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