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49th Annual Conference of the IEEE Industrial electronics Society, IECON 2023

作者： Wu, Renjie Pike, Matthew Chai, Xiaoqing Lee, Boon Giin Chung, Wan-Young Nkenyereye, Lionel School of Computer Science University of Nottingham Ningbo China Ningbo China Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute School of Computer Science University of Nottingham Ningbo China Ningbo China Pukyong National University Department of Electronic Engineering Busan Korea Republic of

ISBN: (纸本)9798350331820

Indoor positioning in the firefighting ground shows promising application prospects for enhancing rescue safety and efficiency. Low visibility and signal interference caused by smoke pose significant difficulties for visual Simultaneous Localization and Mapping (SLAM) systems and radio frequency-based localization methods, while the performance of existing pedestrian dead reckoning (PDR) methods is affected by unpredictable user gaits. This paper introduces a gait analysis-based PDR (GA-PDR) for deriving heading estimation in PDR to improve its localization performance. The proposed method determines the step pattern by analyzing the features of inertial measurement unit data, thereby enabling the classification of forward, left- and right-turn and around-turn from left or right-side movements. In addition, this study introduces a redundant turn elimination method to differentiate false positive patterns via a time-domain heading analysis for turn movements. The location tracking performance with the proposed GA-PDR approach is validated using a self-created dataset established under a smoke-filled experiment, the results of which indicate a lower loop closure error compared with the traditional PDR in all of the tested scenarios. © 2023 IEEE.

关键词： Smoke

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Bridging Gaps: Federated Multi-View Clustering in Heterogeneous Hybrid Views

arXiv

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arXiv 2024年

作者： Chen, Xinyue Ren, Yazhou Xu, Jie Lin, Fangfei Pu, Xiaorong Yang, Yang School of Computer Science and Engineering University of Electronic Science and Technology of China China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China China

Recently, federated multi-view clustering (FedMVC) has emerged to explore cluster structures in multi-view data distributed on multiple clients. Existing approaches often assume that clients are isomorphic and all of them belong to either single-view clients or multi-view clients. Despite their success, these methods also present limitations when dealing with practical FedMVC scenarios involving heterogeneous hybrid views, where a mixture of both single-view and multi-view clients exhibit varying degrees of heterogeneity. In this paper, we propose a novel FedMVC framework, which concurrently addresses two challenges associated with heterogeneous hybrid views, i.e., client gap and view gap. To address the client gap, we design a local-synergistic contrastive learning approach that helps single-view clients and multi-view clients achieve consistency for mitigating heterogeneity among all clients. To address the view gap, we develop a global-specific weighting aggregation method, which encourages global models to learn complementary features from hybrid views. The interplay between local-synergistic contrastive learning and global-specific weighting aggregation mutually enhances the exploration of the data cluster structures distributed on multiple clients. Theoretical analysis and extensive experiments demonstrate that our method can handle the heterogeneous hybrid views in FedMVC and outperforms state-of-the-art methods. The code is available at https://***/5Martina5/FMCSC. © 2024, CC BY-NC-SA.

关键词： Contrastive Learning

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ChirpKey: A Chirp-level Information-based Key Generation Scheme for LoRa Networks via Perturbed Compressed Sensing 42

ChirpKey: A Chirp-level Information-based Key Generation Sch...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Yang, Huanqi Sun, Zehua Liu, Hongbo Xia, Xianjin Zhang, Yu Gu, Tao Hancke, Gerhard Xu, Weitao City University of Hong Kong Shenzhen Research Institute Hong Kong City University of Hong Kong Department of Computer Science Hong Kong University of Electronic Science and Technology of China School of Computer Science and Engineering China The Hong Kong Polytechnic University Department of Computing Hong Kong Macquarie University School of Computing Australia

ISBN: (纸本)9798350334142

Physical-layer key generation is promising in establishing a pair of cryptographic keys for emerging LoRa networks. However, existing key generation systems may perform poorly since the channel reciprocity is critically impaired due to low data rate and long range in LoRa networks. To bridge this gap, this paper proposes a novel key generation system for LoRa networks, named ChirpKey. We reveal that the underlying limitations are coarse-grained channel measurement and inefficient quantization process. To enable fine-grained channel information, we propose a novel LoRa-specific channel measurement method that essentially analyzes the chirp-level changes in LoRa packets. Additionally, we propose a LoRa channel state estimation algorithm to eliminate the effect of asynchronous channel sampling. Instead of using quantization process, we propose a novel perturbed compressed sensing based key delivery method to achieve a high level of robustness and security. Evaluation in different real-world environments shows that ChirpKey improves the key matching rate by 11.03-26.58% and key generation rate by 27-49× compared with the state-of-the-arts. Security analysis demonstrates that ChirpKey is secure against several common attacks. Moreover, we implement a ChirpKey prototype and demonstrate that it can be executed in 0.2 s. © 2023 IEEE.

关键词： Compressed sensing

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Edge-Cloud Cooperation for DNN Inference via Reinforcement Learning and Supervised Learning 15

Edge-Cloud Cooperation for DNN Inference via Reinforcement L...

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2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of Things, iThings 2022, 18th IEEE International Conferences on Green Computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2022 and 8th IEEE International Conference on Smart data, Smartdata 2022

作者： Zhang, Tinghao Li, Zhijun Chen, Yongrui Lam, Kwok-Yan Zhao, Jun Nanyang Technological University School of Computer Science and Engineering Singapore Harbin Institute of Technology School of Computer Science and Technology Harbin China University of Chinese Academy of Sciences Department of Electronic and Communication Engineering Beijing China

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

Deep Neural Networks (DNNs) have been widely applied in Internet of Things (IoT) systems for various tasks such as image classification and object detection. However, heavyweight DNN models can hardly be deployed on edge devices due to limited computational resources. In this paper, an edge-cloud cooperation framework is proposed to improve inference accuracy while maintaining low inference latency. To this end, we deploy a lightweight model on the edge and a heavyweight model on the cloud. A reinforcement learning (RL)-based DNN compression approach is used to generate the lightweight model suitable for the edge from the heavyweight model. Moreover, a supervised learning (SL)-based offloading strategy is applied to determine whether the sample should be processed on the edge or on the cloud. Our method is implemented on real hardware and tested on multiple datasets. The experimental results show that (1) The sizes of the lightweight models obtained by RL-based DNN compression are up to 87.6% smaller than those obtained by the baseline method;(2) SL-based offloading strategy makes correct offloading decisions in most cases;(3) Our method reduces up to 78.8% inference latency and achieves higher accuracy compared with the cloud-only strategy. © 2022 IEEE.

关键词： Deep neural networks

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Qmix-Based Cross-Domain Transmission Routing for Time-Sensitive and Deterministic Networks 24

Qmix-Based Cross-Domain Transmission Routing for Time-Sensit...

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24th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2023

作者： Wang, Wenxuan Wang, Wenyong Wu, Bochun Zou, Sai Yu, Haisheng Ni, Wei Zhang, Ji University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China Telecom Co. Ltd Pa Company Sc Branch Chendu China Fudan University Shanghai China Guizhou University College of Big Data and Information Engineering Guiyang China Macau University of Science and Technology International Institute of Next Generation Internet China Csiro Sydney Australia

ISBN: (纸本)9781665496261

This paper studies the design of cross-domain deterministic transmission routing for time-sensitive and deterministic networks (TSDNs) in industrial parks. The unified queue forwarding and resource reservation mechanism have been proved to solve the multi transmission problem effectively. Given the lack of real-time global information, the cross-domain deterministic transmission is described as a decentralized partially observable Markov decision process (Dec-POMDP). Developed from an eponymous multi-agent deep reinforcement learning method, a Qmix-based cross-domain transmission (QCDT) routing algorithm is proposed to improve the arrival ratio of deterministic flows. Numerical tests show that the arrival ratio of QCDT is 37.1% higher than that of the basic border gateway protocol (BGP). QCDT can also transmit deterministic flows with stable convergence in scenarios with multiple heterogeneous networks. © 2023 IEEE.

关键词： Reinforcement learning

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DTGCN: a method combining dependency tree and graph convolutional networks for Chinese long-interval named entity relationship extraction

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 13361-13373页

作者： Li, Taoyuan Ma, Liangli Qin, Jiwei Ren, Wei College of Electronic Engineering Naval University of Engineering Wuhan China School of Computer Science China University of Geosciences Wuhan China Key Laboratory of Network Assessment Technology CAS Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Henan Key Laboratory of Network Cryptography Technology Zhengzhou China

The extraction of relationships between military entities and descriptive entities is a key task for automated construction of military knowledge graphs. This category of text unstructured imposed problems such as unfixed entity length, possibly overlong intervals between entities, and excessive pronouns. Many studies rely on traditional pattern matching and statistical learning methods. Thanks to the expansion of data volume, deep learning methods are generally used to address similar problems. However, the traditional single neural network model lacks the ability to extract the relationship information of long-interval entities and cannot tackle the above problems well. To solve these problems, a new named entity relationship extraction model called DTGCN is proposed combining dependency tree and GCN (Graph Convolutional Networks). By comparing with traditional models, the experimental results demonstrate that the method proposed in this paper can enhance F1-value by 2.28–11.11% and can provide support for the extraction of Chinese long-interval relationships. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Knowledge graph

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Achieving Secure Dynamic Searchable Symmetric Encryption for data Sharing Services via Blockchain 2

Achieving Secure Dynamic Searchable Symmetric Encryption for...

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2022 2nd International Conference on Artificial Intelligence, Big data and Algorithms, CAIBDA 2022

作者： Xu, Zonglian Su, Yu Qiu, Yuhe Chen, Ruidong School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China China Mobile Chengdu Institute of Research and Development Chengdu China

ISBN: (纸本)9783800760268

In the era of big data, massive data is generated. The use of a large amount of data has brought great convenience to people's lives and promoted the improvement of production efficiency. However, independent organizations in various fields have their own information systems. The incompatible systems and the lack of trust among organizations lead to data silos, which seriously hinders data sharing and usage. In order to break data barriers and increase the reliability and security of data sharing, we construct a blockchain-based dynamic searchable symmetric encryption scheme for multi-users. Blockchain, serving as a retrieval platform, uses a bit matrix to store the correspondence between keywords and data owners who just upload encrypted keywords rather than all original files to blockchain. At the same time, we verify user identity through random numbers, which not only realizes access control, but also ensures forward security. We tested the performance of the scheme locally. The results show that the encryption, authorization and revocation and query performance of this scheme are good. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Blockchain

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Efficient Anti-Glare Ceramic Decals Defect Detection by Incorporating Homomorphic Filtering

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computer Systems science & engineering 2021年第3期36卷 551-564页

作者： Xin Chen Ying Zhang Lang Lin Junxiang Wang Jiangqun Ni Southeast Digital Economic Development Institute Quzhou324000China School of Mechanical and Electronic Engineering Jingdezhen Ceramic InstituteJingdezhen333400China School of Data and Computer Science Sun Yat-Sen UniversityCanton510000China

Nowadays the computer vision technique has widely found applicationsin industrial manufacturing process to improve their efficiency. However, it ishardly applied in the field of daily ceramic detection due to the following twokey reasons: (1) Low detection accuracy as a result of ceramic glare, and (2) Lackof efficient detection algorithms. To tackle these problems, a homomorphic filtering based anti-glare ceramic decals defect detection technique is proposed in thispaper. Considering that smooth ceramic surface usually causes glare effects andleads to low detection results, in our approach, the ceramic samples are takenin low light environment and their luminance and details restored by a homomorphic filtering based image enhancement technique. With relatively high quality preprocessed images, an effective ceramic decal defect detection algorithm isthen designed to rapidly locate those out-of-bounds defects and further estimatetheir size. The experimental results show that the proposed scheme could achieveits desired performance.

关键词： Ceramic glaring homomorphic filtering ceramic decal border extraction out-of-bounds detection

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Adaptation of Compositional data Analysis in Deep Learning to Predict Pasture Biomass Proportions 29

Adaptation of Compositional Data Analysis in Deep Learning t...

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29th Irish Conference on Artificial Intelligence and Cognitive science, AICS 2021

作者： Narayanan, Badri Saadeldin, Mohamed Albert, Paul McGuinness, Kevin O'Connor, Noel E. Namee, Brian Mac School of Computer Science University College Dublin Ireland School of Electronic Engineering Dublin City University Ireland Insight SFI Centre for Data Analytics VistaMilk

Dry biomass weight measurements from a quadrat in a paddock for grass, clover and weeds when expressed as percentages of total dry herbage mass are compositional in nature. Unlike real valued regression problems, prediction of compositional data is handled differently in statistics because of its closure property where the components of the composition are positive data adding up to a constant sum and is therefore constrained in the simplex space, in our case 100%. Our motivation in this paper was to study whether the adaptation of compositional data analysis (CoDa) techniques in deep learning improves the prediction results over the best performing deep learning model we used in our earlier paper [Narayanan et al., 2021]. Although the log ratio transformation of targets is an appropriate adaptation of CoDa and is interesting for Biomass prediction, our study indicates that the CoDa adaptation does not improve the prediction errors over our earlier method. © 2021 CEUR-WS. All rights reserved.

关键词： Biomass

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Discrepancy and Thematic Bibliometric Analyses of the Remaining Limitations in Artificial Intelligence

Discrepancy and Thematic Bibliometric Analyses of the Remain...

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2024 IEEE International Conference on Computing, ICOCO 2024

作者： Mansor, Mahayaudin M. Ibrahim, Nurain Radi, Noor Fadhilah Ahmad Rahim, Nadirah Abdul Yahaya, Syarul Heiry Bakar, Mohd Aftar Abu Zakaria, Roslinazairimah School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Malaysia Institute of Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA Malaysia Department of Electrical and Computer Engineering Kuliyyah of Engineering International Islamic University Malaysia Department of Science Computer and Mathematics Universiti Teknologi MARA Penang Campus Malaysia Department of Mathematical Sciences Faculty of Science & Technology Universiti Kebangsaan Malaysia Centre for Mathematical Sciences Universiti Malaysia Pahang Al-Sultan Abdullah Malaysia

ISBN: (纸本)9798331530303

As artificial intelligence (AI) advances, it is essential to continuously comprehend its limitations to optimise the integration of AI into autonomous systems that empower humans. The first objective of this study is to highlight the key capabilities of both humans and AI, focusing on the differences and identifying limitations of AI from the literature. Through discrepancy analysis, this study uses a Venn diagram to visualise the remaining limitations of AI into five key domains: (i) emotional intelligence, (ii) consciousness and awareness, (iii) creative imagination, (iv) communication, and (v) ethical decision-making. Furthermore, this study employs thematic bibliometric analysis to provide a more detailed examination of each AI limitation as the second objective. This study has identified underdeveloped and emerging research themes with potential for future development, such as emotion recognition, human-computer interaction, digital health, and situational awareness, which may require further research. Additionally, this study commends the ongoing efforts to harness AI’s computational power and algorithmic innovations to enhance AI’s overall performance and applicability. ©2024 IEEE.

关键词： Emotion Recognition

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