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Edge Intelligence Based Garbage Classification Detection Method 1

3rd EAI International Conference on Edge Computing and IoT, EAI ICECI 2022

作者： Zhu, Ruijia Liu, Yiwen Gao, Yanxia Shi, Yuanquan Peng, Xiaoning School of Computer and Artificial Intelligence Huaihua University Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua418000 China

ISBN: (数字)9783031289903

ISBN: (纸本)9783031289897

To address the problem that the classification and cleaning of garbage in city streets is always ineffective nowadays, the paper proposes a garbage detection method based on edge intelligence. The edge intelligence not only reduces the computational load of the cloud and speeds up the data transmission, but also greatly reduces the data transmission cost. First, images of city streets are collected and uploaded to the edge device via mobile devices in various locations in the city. Then, the edge server is used to temporarily store the image information, and the PeleeNet model deployed on it is used to identify and classify various kinds of garbage, and then visualize the information of each street. Finally, the street garbage information is transmitted to the cloud, which provides a detailed picture of the city’s garbage situation and facilitates city management. In this paper, the PeleeNet model is compared with ResNet, DenseNet and MobileNet models. The results show that the edge devices equipped with PeleeNet model not only have the fastest computation speed and the highest accuracy, but also occupy the least memory. It is fully demonstrated that the method studied in the paper can be applied to the problem of litter detection in urban streets. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Smart city

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Intrusion Detection Based on Deep Belief Network and Stacking Method 3

Intrusion Detection Based on Deep Belief Network and Stackin...

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3rd International Conference on computer Communication and Network Security, CCNS 2022

作者： Wang, Jia Bao Miao, Xiang Hua Li, Xiang Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming650500 China Yunnan Key Laboratory of Computer Technology Application Kunming University of Science and Technology Yunnan Kunming650500 China

ISBN: (纸本)9781510660113

After data preprocessing, the feature dimension of NSL-KDD dataset increases from 42 dimensions to 122 dimensions. High dimensional data will make it more difficult for the model to learn the characteristics of the data, and there will be a lot of redundant data in the data set. Therefore, this paper uses the deep belief network to reduce the dimension of the characteristics of the intrusion detection data set after data preprocessing, and uses the stacking algorithm as the classifier to construct the intrusion detection model. Through comparative experiments, it is proved that the model has good performance in the four evaluation indexes of accuracy, precision, recall and F1 score, and effectively improves the performance of intrusion detection model. © 2022 SPIE.

关键词： Intrusion detection

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A Hybrid Blockchain Scheme for Tracing Manufacturing Processes and Trading Transactions 22

A Hybrid Blockchain Scheme for Tracing Manufacturing Process...

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22nd IEEE International Symposium on Parallel and Distributed Processing with applications, ISPA 2024

作者： Yuan, Ye Yang, Mingxing Xiong, Ruoting Sun, Yani Zhang, Xianchao Ren, Wei HuBei Land Resources Vocational College China China University of Geosciences Gemological Institute Wuhan China University of East Anglia School of Computing Science United Kingdom Yunnan Innovation Institute Beihang University Yunnan Key Laboratory of Blockchain Application Technology Kunming China Jiaxing University Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province Jiaxing China State Key Laboratory of Geo-Information Engineering Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology Mnr Beijing China Ministry of Education China China University of Geosciences School of Computer Science Wuhan China

ISBN: (纸本)9798331509712

Blockchain can be envisioned as an enabling framework in terms of cryptography and distributed computing to construct trusted data records among un-trusted users. It can be applied in two typical scenarios - historical process provenance and transaction tracing, by immutably sequentialized blocks. In the former there exists no tokens;but in the latter token handover among accounts should be cryptographically guaranteed. In some situations, above two applications may be both required. E.g., in the manufacture of a jewel, processing flow by different operators should be recorded for quality guaranteeing, and in its trading stage, the ownership handover should be recorded for value confirmation. To tackle above two requirements, two chains are usually thus demanded. In this paper, we propose a single chain solution to avoid cross-chain burden, by a hybrid block structure. The process data can be imported from the legacy (non-anonymous) information processing system, and transaction data stems from anonymous payment demand for ownership handover. We formally describe and prove token handover model with cryptographical authenticity. We also propose several security enhancements in the design for shortening the length of the chain, and protecting the data confidentiality in the chain. Our design is general and not limited to single application. The experimental results and extensive analysis justified that the performance and security of our proposed scheme is efficient. © 2024 IEEE.

关键词： Data Provenance Data Tracing Hybrid Blockchain Token Handover

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Breaking Speaker Recognition with Paddingback

Breaking Speaker Recognition with Paddingback

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhe Ye Diqun Yan Li Dong Kailai Shen Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Key Laboratory of Mobile Network Application Technology China

Machine Learning as a Service (MLaaS) has gained popularity due to advancements in Deep Neural Networks (DNNs). However, untrusted third-party platforms have raised concerns about AI security, particularly in backdoor attacks. Recent research has shown that speech backdoors can utilize transformations as triggers, similar to image backdoors. However, human ears can easily be aware of these transformations, leading to suspicion. In this paper, we propose PaddingBack, an inaudible backdoor attack that utilizes malicious operations to generate poisoned samples, rendering them indistinguishable from clean ones. Instead of using external perturbations as triggers, we exploit the widely-used speech signal operation, padding, to break speaker recognition systems. Experimental results demonstrate the effectiveness of our method, achieving a significant attack success rate while retaining benign accuracy. Furthermore, Padding-Back demonstrates the ability to resist defense methods and maintain its stealthiness against human perception.

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Highly Accurate Dynamic Gesture Recognition Method Based on Edge Intelligence 1

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3rd EAI International Conference on Edge Computing and IoT, EAI ICECI 2022

作者： Changkai, Lu Yiwen, Liu Yanxia, Gao Yuanquan, Shi Xiaoning, Peng School of Computer and Artificial Intelligence Huaihua University Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua418000 China

ISBN: (数字)9783031289903

ISBN: (纸本)9783031289897

In recent years, gestures have been widely used in many fields. For example, human-computer interaction, virtual reality, gesture translation, etc. The reason for its rapid development is due to the emergence of deep learning and artificial intelligence under today’s society. Due to dynamic gestural interactions, such large intelligent models are often characterized by many parameters, large sample size, frequent parameter updates, and high communication volume. Based on this feature, we propose a cloud-based edge design architecture approach for gesture recognition based on an improved YOLOv5 network model optimization by changing different gestures, background interference, which uses a 21-layer model for the neural network. With the use of edge intelligence, the computational accuracy can be improved by 10.6% over the traditional YOLOv5 with a MAP value of 93.3%. The final recall rate is also improved by 3.6%. The parameter model is only 43.6% of the original one. This shows the practicality and operability of using edge computing as a technique in gesture recognition, as well as the small improvement cost and obvious effect. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Gesture recognition

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Multiple Attention-Guided Neural Architecture Search for Hyperspectral Image Classification 4

Multiple Attention-Guided Neural Architecture Search for Hyp...

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4th International Conference on Video, Signal and Image Processing, VSIP 2022

作者： Song, Yingluo Wang, Aili Dai, Shiyu Wu, Haibin Iwahori, Yuji Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application Harbin University of Science and Technology Harbin150080 China Department of Computer Science Chubu University Aichi487-8501 Japan

ISBN: (纸本)9781450397810

Hyperspectral images(HSIs) have rich spatial and spectral information, which provide a strong basis for distinguishing different landcover objects. The HSI classification methods based on deep learning have achieved good performance. Recently, several Neural Architecture Search (NAS) algorithms have been proposed for HSI to further improve the accuracy to a new level. In this paper, the neural structure search method based onmultiple attention mechanism search space is proposed for hyperspectral images. In the search process, the network selects attention mechanism operation at an appropriate layer, which can help the network focus on useful features and discard useless features. In our experiments, we have verified the superiority of this method on two hyperspectral datasets. © 2022 ACM.

关键词： Image classification

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A 3D Geometry Model of Vocal Tract Based on Smart Internet of Things

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computer Systems Science & Engineering 2023年第7期46卷 783-798页

作者： Ming Li Kuntharrgyal Khysru Haiqiang Shi Qiang Fang Jinrong Hu Yun Chen Tianjin Medical University General Hospital Tianjin300041China Key Laboratory of Artificial Intelligence Application Technology State Ethnic Affairs Commission Qinghai Minzu UniversityXining810007China Chinese Academy of Sciences Beijing300308China School of Computer Science Chengdu University of Information and TechnologyChengduSichuan610225China College of Intelligence and Computing Tianjin UniversityTianjin300350China

The Internet of Things(IoT)plays an essential role in the current and future generations of information,network,and communication development and *** research focuses on vocal tract visualization and modeling,which are critical issues in realizing inner vocal tract *** is applied in many fields,such as speech training,speech therapy,speech analysis and other speech production-related *** work constructed a geometric model by observation of Magnetic Resonance Imaging data,providing a new method to annotate and construct 3D vocal tract *** proposed method has two advantages compared with previous *** it has a uniform construction protocol for all speech ***,this method can build correspondent feature points between different speech *** are less than three control parameters can be used to describe every speech organ accurately,for which the accumulated contribution rate is more than 88%.By means of the reconfiguration,the model error is less than 1.0 *** to the data from Chinese Magnetic resonance imaging(MRI),this is the first work of 3D vocal tract *** will promote the theoretical research and development of the intelligent Internet of Things facing speech generation-related issues.

关键词： Virtual reality vocal tract visualization articulatory modeling IoT

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Efficient Deep learning-based Real-time Migration Strategies for Containers in SDN

Efficient Deep learning-based Real-time Migration Strategies...

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Next Generation Data-driven Networks (NGDN), International Conference on

作者： Shan Huang Yanan Liu Yanuo Li Feili He Xiaodong Duan School of Computer Science and Engineering Dalian Minzu University Key Laboratory of Big Data Application Technology of the State Ethnic Affairs Commission Dalian Ethnic Culture Digital Technology Key Laboratory Dalian China

ISBN: (数字)9798350388374

ISBN: (纸本)9798350388381

Container technology is widely used and improve the efficiency of container real-time migration has become an important research topic. Existing studies mainly focus on optimizing iterative dumping of containers without fully considering the impact of migration node selection on migration efficiency. Software-defined networking (SDN), achieves centralized control of the network by separating the control plane and the forwarding plane. Applying deep learning to SDN makes tasks such as traffic prediction and network optimization more intelligent. This paper proposes Prediction Model based on CLCB (CNN-LSTM-CBAM) uses real-time collected load data as input, extracts feature components of load data using one-dimensional convolutional neural networks, and then predicts using LSTM. To improve prediction accuracy, a lightweight CBAM attention module is introduced, and the prediction results are output through fully connected layers. The Network Traffic Prediction Model based on GCGRU-Conv (GCN-GRU-CNN) considers both spatial and temporal features of network traffic. GCN and GRU are used to extract spatial and temporal features of network traffic, respectively, and then these features are fused together using convolutional neural networks. To address the gradient problem caused by increasing network depth, a residual network structure is introduced. Experimental results show that the method proposed can effectively reduce the container migration time and has better prediction results.

关键词： Deep learning Costs Telecommunication traffic Containers Predictive models Feature extraction Real-time systems Convolutional neural networks Software defined networking Load modeling

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FEMD: Feature Enhancement-aided Multimodal Feature Fusion Approach for Smart Contract Vulnerability Detection 30

FEMD: Feature Enhancement-aided Multimodal Feature Fusion Ap...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Li, Juan Li, Rui Lu, Youshui Cai, Bowen Cao, Yulin Lin, Hang Li, Xingxing Li, Chan Qinghai Normal University School of Computer Science Xining China Xidian University The State Key Laboratory of Tibetan Intelligent Information Processing and Application School of Computer Science Qinghai Normal University School of Computer Science and Technology Xian China School of the Instrument Science and Technology School of Electrical Engineering Xi'an Jiaotong University Xian China School of Science and Engineering University of Minnesota - Twin City Minneapolis United States

ISBN: (纸本)9798331515966

Smart contracts, due to their immutability and transparency upon deployment, entail significant economic and systemic risks from any vulnerabilities present. Traditional vulnerability detection methods suffer from low automation and high false positive rates, while existing deep learning-based approaches inadequately extract contract features, thereby limiting detection accuracy. To address these issues, this paper proposes FEMD: a feature-enhanced aided multimodal feature fusion method for smart contract vulnerability detection. Building on the foundation of addressing the low automation of traditional detection tools, our method improves the model's feature extraction performance and detection capabilities. Specifically, we construct a contract graph through Comprehensive Expert-Graph Fusion, combining multi-modal feature fusion using a multi-head attention mechanism with expert patterns to ensure the capture and effective preservation of all critical information during the fusion process. To delve deeper into potential information within smart contract graphs, we employ a Feature Enhancer that leverages transpose operations on feature matrices to extract complex interaction patterns across different dimensions. Our approach is validated through batches of experiments on the Ethereum open dataset focusing on reentrancy and timestamp dependency vulnerabilities, demonstrating significant improvements in detection accuracy and robustness. © 2024 IEEE.

关键词： Deep learning

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Speech Encryption with Fractional Watermark

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computers, Materials & Continua 2022年第10期73卷 1817-1825页

作者： Yan Sun Cun Zhu Qi Cui Key Laboratory of Artificial Intelligence Application Technology State Ethnic Affairs Commission Qinghai Minzu UniversityXining810007QinghaiChina School of Computer of Qinghai Normal University Xining810003QinghaiChina The Department of Electrical and Computer Engineering University of WindsorWindsorN9B 3P4Canada

Research on the feature of speech and image signals are carried out from two perspectives,the time domain and the frequency *** speech and image signals are a non-stationary signal,so FT is not used for the non-stationary characteristics of the *** short-term stable speech is obtained by windowing and framing the subsequent processing of the signal is completed by the Discrete Fourier Transform(DFT).The Fast Discrete Fourier Transform is a commonly used analysis method for speech and image signal processing in frequency *** has the problem of adjusting window size to a for desired *** the Fractional Fourier Transform can have both time domain and frequency domain processing *** paper performs global processing speech encryption by combining speech with image of Fractional Fourier *** speech signal is embedded watermark image that is processed by fractional transformation,and the embedded watermark has the effect of rotation and superposition,which improves the security of the *** paper results show that the proposed speech encryption method has a higher security level by Fractional Fourier *** technology is easy to extend to practical applications.

关键词： Fractional Fourier Transform watermark speech signal processing image processing

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