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3rd International Conference on Artificial Intelligence, Human-computer Interaction and Robotics, AIHCIR 2024

作者： Wang, Jimin Zhu, Ying Sun, Jiahao College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (纸本)9798331534035

Existing time series variable-length motif mining algorithms based on suffix arrays suffer from long running times and are prone to premature termination of matching due to variations in individual characters, which hampers the discovery of longer motifs. To address these issues, we propose a novel time series variable-length motif mining algorithm based on suffix array indexing. This algorithm employs PAA and SAX methods for the symbolic representation of time series data, while the DC3 algorithm is utilized for the rapid construction of suffix arrays. Similarity matching is performed based on edit distance, enabling the extraction of variable-length motifs. This approach facilitates the identification of longer motifs with an edit distance less than specified parameters. Experimental results demonstrate that, compared to representative algorithms, our method significantly enhances mining accuracy, maximizes motif length, and improves time efficiency. © 2024 IEEE.

关键词： Indexing (materials working)

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Statistics-Fused Learning for Classification of Randomized Eeg Trials

SSRN

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SSRN 2025年

作者： Zhang, Wuxia Tian, Junchao Zhang, Xiaoyan Jiang, Jianmin College of Computer Science and Software Engineering Shenzhen University Shenzhen518000 China

Following the publication of EEG-ImageNet with 40 class labels by Spampinato et. al. [1], where natural images with multiple semantics are directly used to stimulate human brains in a deterministic order, extensive research has been attempted and widely reported in the literature over the topic of decoding human brains via EEG classifications, making people feel optimistic that mind reading via computational intelligence is emerging on the horizon [2, 3, 4, 5]. In 2020, however, Li et al. [6] published their investigation over EEG-ImageNet and reported that, when the order of brain stimulation is randomized, the classification rate in decoding brain perception of natural images drops to chances [7]. Ahmed et al. then updated EEG-ImageNet by increasing the number of samples to 10 times more and tested a range of classifiers, where the maximum classification rate achieved is 7% [8], presenting an extremely difficult challenge for this direction of *** this paper, we report our latest attempt to address this challenge with a new deep framework. Compared with the existing efforts, our proposed framework features in: (i) representational projection to transform decoding brain perception of natural images into a problem of image classification, paving the way for introducing ResNet as the primary backbone to exploit its success in image classification across the areas of computer vision and pattern recognition;(ii) replacing ReLU with ELU to increase its robustness to noises, and add a focused attention to improve its selectivity and intensiveness in learning;(iii) statistics-fused learning to coordinate four ResNet-based deep classifiers and maximize their collective efforts to produce the final decoding output. Extensive experiments and ablation studies are carried out, and the accuracy of classifying the randomized EEG trials has almost doubled the maximum rate reported so far in the literature. © 2025, The Authors. All rights reserved.

关键词： Image enhancement

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Clustered Federated Learning Based on Client’s Prototypes

Clustered Federated Learning Based on Client’s Prototypes

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International Conference on computer Supported Cooperative Work in Design

作者： Weimin Lai Zirong Xu Qiao Yan College of Computer Science & Software Engineering ShenZhen University Shenzhen China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Federated learning empowers multiple parties to train machine learning models collaboratively while preserving data within local confines. However, due to the intrinsic non-independent and identically distributed (Non-IID) nature of client-side data, aggregating these diverse local models into a global model remains a significant challenge. Clustered Federated Learning (CFL) offers a compelling solution to mitigate the impact of data heterogeneity by organizing clients into clusters. Existing CFL methods often involve unstable, time-consuming cluster identity estimation during training. In this work, we propose an innovative federated learning approach that efficiently identifies similarities among client data distributions by analyzing client’s prototypes. Our method accurately identifies client cluster identities during the initialization phase. Clients employ the same randomly initialized model to compute the client’s prototypes for their local data and provide it to the server. The server performs one-shot client clustering by comparing the similarity of the client’s prototypes. We further design a unique inter-cluster aggregation strategy that adapts inter-cluster aggregation weights based on the similarity of the client’s prototypes, thereby enhancing model convergence. We evaluate two mixed datasets with three Non-IID settings, and our approach outperforms several popular baseline methods. Compared to IFCA and FlexCFL, our approach yields more reasonable clustering results. It achieves an average test accuracy improvement of over 11.51% in the feature and label shift Non-IID setting of the Digits-5 dataset.

关键词： Training Adaptation models Federated learning Computational modeling Prototypes Estimation Data aggregation

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Age of Information in Linear Multi-Hop Wireless Networks

Age of Information in Linear Multi-Hop Wireless Networks

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IEEE Conference on Vehicular Technology (VTC)

作者： Xinhui Han Haoyuan Pan College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

We investigate the information freshness, measured by age of information (AoI), of a linear multi-hop wireless network, where a source tries to deliver status update packets to a destination via a series of half-duplex relays. Sending update packets frequently from the source is critical to achieve a low AoI at the destination. However, if the packet generation rate is not designed properly, an intermediate relay may receive mutual wireless interference from its previous and subsequent nodes. When the previous node sends a new packet to the intermediate relay, the subsequent node simultaneously forwards an old packet to the next hop in which the wireless signal is also received by the intermediate relay. A key motivation to solve this problem is that the intermediate relay has previously received the old packet (which thus can be forwarded to the subsequent node for further relaying). Hence, successive interference cancellation (SIC) can be used to remove the interference of the old packet and then recover the new packet. Taking a three-hop network as an example, we design an SIC-enabled packet relaying scheme tailored to low AoI, which can be generalized to scenarios beyond three hops. We model the three-hop relaying scheme using a Markov chain to derive the theoretical average AoI (AAoI) and peak AoI (PAoI). Theoretical and simulation results show that the SIC-enabled packet relaying scheme significantly reduces the AAoI and PAoI compared to the non-SIC counterpart, thanks to a more frequent packet generation rate at the source and the SIC technique at the relays.

关键词： Vehicular and wireless technologies Analytical models Interference cancellation Wireless networks Simulation Half-duplex system Spread spectrum communication

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Reducing Age of Collection with Dynamic-Frame Time Division Multiple Access

Reducing Age of Collection with Dynamic-Frame Time Division ...

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IEEE Conference on Vehicular Technology (VTC)

作者： Yurong Lai Xinhui Han Xueer Wang Haoyuan Pan College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

This paper introduces a dynamic-frame time division multiple access (DF-TDMA) scheme aimed at decreasing the age of collection (AoC) in collaborative monitoring scenarios. Unlike the conventional age of information (AoI) metric, AoC decreases only when partial information from multiple sources is aggregated to form a complete observation. Previous studies on AoC predominantly assumed that complete observations are derived from aggregating information from all sources. However, this assumption does not hold in scenarios where sources are correlated, and a complete observation can be achieved with partial information from only a subset of sources. To address this, new channel access protocols are necessary to achieve a low network-wide average peak AoC. DF-TDMA is proposed as a solution, featuring dynamically adjustable TDMA frame sizes to minimize time wastage and thereby reduce AoC. While the dynamic frame size adjustment complicates AoC analysis, we theoretically derive the average peak AoC of DF-TDMA. Simulations show that DF-TDMA maintains a stable average peak AoC across varying numbers of sources N, and notably reduces the average peak AoC compared to a fixed-frame TDMA scheme, particularly under conditions with a large N.

关键词： Measurement Time division multiple access Vehicular and wireless technologies Data integrity Access protocols Traffic control Information age Reliability Vehicle dynamics Monitoring

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Improved Hybrid Collaborative Fitering Algorithm Based on Spark Platform

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Wuhan University Journal of Natural sciences 2023年第5期28卷 451-460页

作者： YOU Zhen HU Hongwen WANG Yutao XUE Jinyun YI Xinwu College of Computer Information Engineering Jiangxi Normal UniversityNanchang 330022JiangxiChina National-Level International Science and Technology Cooperation Base of Networked Supporting Software Nanchang 330022JiangxiChina

An improved Hybrid Collaborative Filtering algorithm(H-CF)is proposed,addressing the issues of data sparsity,low recommendation accuracy,and poor scalability present in traditional collaborative filtering *** core of H-CF is a linear weighted hybrid algorithm based on the Latent Factor Model(LFM)and the Improved Item Clustering and Similarity Calculation Collaborative Filtering Algorithm(ITCSCF).To begin with,the items are clustered based on their attribute dimension,which accelerates the computation of the nearest neighbor ***,H-CF enhances the formula for scoring similarity by penalizing popular items and optimizing unpopular *** improvement enhances the rationality of scoring similarity and reduces the impact of data ***,a weighting function is employed to combine the various improved *** balance factor of the weighting function is dynamically adjusted to attain the optimal recommendation *** address the real-time and scalability concerns,the algorithm leverages the Spark big data distributed cluster computing *** were conducted using the public dataset Movie Lens,where the improved algorithm’s performance was compared against the algorithm before enhancement and the algorithm running on a single *** experimental results demonstrate that the improved algorithm outperforms in terms of data sparsity,recommendation personalization,accuracy,recall,and efficiency.

关键词： recommendation algorithm collaborative filtering latent factor model score weighting item clustering spark similarity calculation

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Research on CUDA-based Multidimensional Time Series Motif Mining

Research on CUDA-based Multidimensional Time Series Motif Mi...

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computer, Big Data and Artificial Intelligence (ICCBD+AI), International Conference on

作者： Jimin Wang Shuaiwei Wang Junru Wang College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (数字)9798331533991

ISBN: (纸本)9798331534004

Traditional serial motif mining methods struggle to quickly identify motif information in large-scale time series data. A CUDA-based multidimensional motif mining algorithm is proposed to discover motifs in multidimensional time series. The algorithm introduces a multidimensional kNN Matrix Profile (kNN mMP) structure to address the limitations of the multidimensional mMP. Parallel computation of kNN mMP using the CUDA architecture enables rapid motif discovery in massive datasets and facilitates detection of additional motif features, such as strong and weak motifs, as well as identification of anomalous motifs. Experiments on public datasets demonstrate that using CUDA for kNN mMP structure achieves a significant speedup; on NVIDIA GeForce RTX 4060, the kNN-mSTOMP-GPU algorithm exhibits a speedup of over 220 times compared to the STOMP algorithm utilizing only kNN mMP when the sequence length is 200,000.

关键词： Time series analysis Graphics processing units computer architecture Big Data Feature extraction Data mining Artificial intelligence

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Contextual Interaction Enhancement Network for Smoke Detection

Contextual Interaction Enhancement Network for Smoke Detecti...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Jiefeng Lin Chenlin Fu Qiang Huang Yingying Zhu College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Smoke detection can warn fires and prevent them. Visual-based smoke detection has gained more attention. However, there is a lack of publicly available smoke datasets in this area. To fill this gap, in this paper, we build a smoke dataset based on various realistic environments that contains 24776 smoke RGB images. Furthermore, a Contextual Interaction Enhancement Network for smoke detection is presented. Re-parameterized large kernel convolutions are utilized in the backbone to increase the receptive field of smoke feature extraction. And a Contextual Interaction Enhancement Module (CIEM)-neck is proposed to get aggregated features for better feature assignment to the decoupled head. Experimental results show that our method has excellent performance, achieving state-of-the-art results on the proposed Smoke 24776 dataset at 83.8% precision, 77.3% AP@0.5 and 49.8% mAP. Additionally, even if it is specialized for smoke detection, our method has still competitive performance and generalization on famous MS-COCO with 66% AP@0.5 and 47.0% mAP. Dataset is available at https://***/linjiefengFutureMediaSZU/Smoke24776.

关键词： Head Annotations Object detection Feature extraction Robustness Kernel

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HTTP/3 over Information-Centric Networking

HTTP/3 over Information-Centric Networking

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International Workshop on Quality of Service

作者： Yaodong Huang Jiahui Luo Changkang Mo Laizhong Cui College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350350128

ISBN: (纸本)9798350350135

HTTP/3 is designed to enhance performance and security by utilizing QUIC as its underlying transport protocol. Integrating HTTP/3 and its features can expand the application scope of ICN. Through an analysis of critical elements of HTTP/3, we implement it on NDN forwarding daemons and run applications in browsers to assess its capabilities and potential benefits in ICN environments. Our system demonstration illustrates compatibility with mainstream browsers while supporting ICN-based transmissions.

关键词： Transport protocols Information-centric networking Quality of service Browsers Security System analysis and design

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Security and Privacy in Solar Insecticidal Lamps Internet of Things:Requirements and Challenges

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 58-73页

作者： Qingsong Zhao Lei Shu Kailiang Li Mohamed Amine Ferrag Ximeng Liu Yanbin Li the College of Artificial Intelligence Nanjing Agricultural UniversityNanjing 210031 the State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023 the State Key Laboratory of Cryptology Beijing 100878China IEEE the School of Engineering University of LincolnLincoln LN67TSUK the Artificial Intelligence and Digital Science Research Center Technology Innovation InstituteMasdar City 9639UAE the College of Computer and Data Science Fuzhou UniversityFuzhou 350108China

Solar insecticidal lamps(SIL) can effectively control pests and reduce the use of pesticides. Combining SIL and Internet of Things(IoT) has formed a new type of agricultural IoT,known as SIL-IoT, which can improve the effectiveness of migratory phototropic pest control. However, since the SIL is connected to the Internet, it is vulnerable to various security *** issues can lead to serious consequences, such as tampering with the parameters of SIL, illegally starting and stopping SIL,etc. In this paper, we describe the overall security requirements of SIL-IoT and present an extensive survey of security and privacy solutions for SIL-IoT. We investigate the background and logical architecture of SIL-IoT, discuss SIL-IoT security scenarios, and analyze potential attacks. Starting from the security requirements of SIL-IoT we divide them into six categories, namely privacy, authentication, confidentiality, access control, availability,and integrity. Next, we describe the SIL-IoT privacy and security solutions, as well as the blockchain-based solutions. Based on the current survey, we finally discuss the challenges and future research directions of SIL-IoT.

关键词： Challenges Internet of Things(IoT) privacy and security security requirements solar insecticidal lamps(SIL)

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