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6th International Conference on intelligent Computing and Signal Processing (ICSP)

作者： Yunyu Chen Yuan Zhang College of Big Data and Artificial Intelligence China Telecom Research Institute Shanghai China College of Information Science and Electronic Engineering Zhejiang University College of Big Data and Artificial Intelligence China Telecom Research Institute Hangzhou China

An image encryption algorithm based on chaotic scrambling and DNA sequence multi-rule coding is proposed. Through the research of related algorithms, the original image and the chaotic sequence generated by Logistic chaos are encoded, fused and decoded by using the multi-rule coding principle of DNA sequence, and the pixel value is changed to encrypt. Then, the PWLCM algorithm is used to generate new chaos to scramble the pixel blocks of the image, so as to obtain the encrypted image. The experimental results show that the algorithm not only improves the encryption efficiency of the traditional encryption algorithm, increases the key space, but also resists the exhaustive and statistical attacks.

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Sentiment Analysis on Drug Reviews Using Diverse Classification Techniques 3

Sentiment Analysis on Drug Reviews Using Diverse Classificat...

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3rd International Conference on Communication, Computing and Industry 4.0, C2I4 2022

作者： Duraisamy, Premkumar Natarajan, Yuvaraj Preethaa, K.R.Sri Mouthami, K. KPR Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Research Professor Intelligent Construction Automation Center Kyungpook National University Deagu Korea Republic of

ISBN: (纸本)9781665474979

Free text reviews are abundantly distributed over the internet among the standard population of the world. Although online doctors were consulted during the pandemic, many drugs are taken without prescription by ordinary people. Reviews are distributed about the medicines through online feeds, posts and blogs, which are merely helpful in identifying the side effects and their adverse reactions in combination with another drug or standalone adverse reactions. Spelling mistakes, jargon and short names are normalized throughout the medical terminologies using Natural language processing (NLP) and RxNorm from the reviews extracted from these sources. The concept of Bidirectional Encoder Representations from Transformers (BERT) is used to mine the association rules for the drug interactions when used in conjugation with another drug, and the idea of Adaptive Fuzzy logic neural networks is used to validate the results of association rules derived from the BERT. Inconsistencies identified by the regulations generated were rejected efficiently by the Support vector machines (SVM), Radial Basis Function (RBF)and the system shows 60.11 percentage of accuracy with 10 cross-fold validation, whereas the results are equivalent to the accuracy obtained using SVM with RBF kernel. The data set is reduced from its dimension using Linear discriminant analysis (LDA) by applying the mining process and BERT on two drugs, namely 'Azithromycin' in conjugation with Buprenorphine has Type 'A' side effects, and Prozac in conjugation with Metronidazole shows different side effects. Deplin and Zoloft give very positive symptoms and are independent of dosage interactions. © 2022 IEEE.

关键词： Bidirectional Encoder Representations from Transformers (BERT) Linear discriminant analysis (LDA) Radial Basis Function (RBF) Support vector machines (SVM)

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Federated Learning for 6G:Applications,Challenges,and Opportunities

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engineering 2022年第1期8卷 33-41页

作者： Zhaohui Yang Mingzhe Chen Kai-Kit Wong H.Vincent Poor Shuguang Cui Department of Electronic and Electrical Engineering University College LondonLondon WC1E 6BTUK Department of Electrical and Computer Engineering Princeton UniversityPrincetonNJ 08544USA Shenzhen Research Institute of Big Data The Chinese University of Hong KongShenzhen 518172China School of Science and Engineering and Future Network of Intelligence Institute The Chinese University of Hong KongShenzhen 518172China

Standard machine-learning approaches involve the centralization of training data in a data center,where centralized machine-learning algorithms can be applied for data analysis and ***,due to privacy restrictions and limited communication resources in wireless networks,it is often undesirable or impractical for the devices to transmit data to parameter *** approach to mitigate these problems is federated learning(FL),which enables the devices to train a common machine learning model without data sharing and *** paper provides a comprehensive overview of FL applications for envisioned sixth generation(6G)wireless *** particular,the essential requirements for applying FL to wireless communications are first *** potential FL applications in wireless communications are *** main problems and challenges associated with such applications are ***,a comprehensive FL implementation for wireless communications is described.

关键词： Federated learning 6G Reconfigurable intelligent surface Semantic communication Sensing Communication and computing

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Reversible data Hiding in Encrypted Images Based on Multi-Predictor and Quad-tree Block Encoding 2022

Reversible Data Hiding in Encrypted Images Based on Multi-Pr...

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7th International Conference on intelligent Information Technology, ICIIT 2022

作者： Zhang, Huiqi Li, Lin Li, Qingyan School Of Science Jimei University Xiamen361021 China Computer Engineering College Jimei University Xiamen361021 China Digital Fujian Big Data Modeling And Intelligent Computing Institute Jimei University Xiamen361021 China

ISBN: (纸本)9781450396172

With the rapid development of the Internet, people's awareness of privacy protection is also gradually improved. Reversible data hiding of encrypted image is a technique that encrypts the original image and allows the embedding of secret data without knowing the original image information. This method can accurately extract the secret data and reconstruct the original image completely. In this paper, a reversible data hiding algorithm of encrypted image based on multi-predictor and quad-tree block encoding is proposed. First, the original image is divided into 8×8 non-overlapping blocks. Then, multi-predictor is used to predict each block to get the prediction error image, then block scrambling is used to encrypt it. Since the pixel values of the encrypted image are small integer closes to zero, the quad-tree block encoding method can be used to embed a large amount of data. Experimental results show that the embedding rate of this method is better than that of some new algorithms. © 2022 ACM.

关键词： Embeddings

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Generalizing Personalized Federated Graph Augmentation via Min-max Adversarial Learning 25

Generalizing Personalized Federated Graph Augmentation via M...

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Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and data Mining V.1

作者： Liang Zhang Tao Long Yang Liu Lei Zhang Laizhong Cui Qingjiang Shi Shenzhen Research Institute of Big Data Shenzhen Guangdong China College of Computer Science and Software Engineering Shenzhen University Shenzhen Guangdong China Data Science Research Center Dukekunshan University Kunshan Jiangsu China School of Software Engineering Tongji University Yangpu Shanghai China

ISBN: (纸本)9798400712456

Federated learning (FL) enables the training of a global machine learning model among multiple local clients in a collaborative fashion without directly sharing the details of their data. Due to this advantage, it has been utilized in a wide range of applications where privacy is a critical concern and has attracted great attention for graph representation learning (GRL). Despite the offered advances, there still exist two major challenges in the FL for GRL across distributed graph data, including heterogeneity and complementarity. In order to tackle these challenges, a novel personalized federated graph augmentation (PFGA) framework is proposed in this work. Unlike existing techniques, it utilizes generative models as bridges to enable information sharing among clients, thereby facilitating the collaborative training of GRL models. Instead of directly using the generative model trained on each client individually, we aggregate them into the globally generative model to gain a global view of the entire graph, which effectively alleviates the heterogeneity and complementarity issues simultaneously. We formulate the training of the generative and GRL models as a min-max adversarial learning problem and theoretically prove the convergence. Furthermore, the effectiveness of the method is demonstrated using experimental results on six real-world datasets.

关键词： federated learning

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Passenger Waiting Time Prediction Based on Density Spatiotemporal Clustering and Time Series Feature Processing 6

Passenger Waiting Time Prediction Based on Density Spatiotem...

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6th International Conference on Electronic engineering and Informatics, EEI 2024

作者： Huang, Ruixi Xia, Dawen Geng, Jian Zhang, Wenyong Hu, Yang Li, Yantao Li, Huaqing College of Data Science and Information Engineering Guizhou Minzu University Guiyang550025 China Engineering Research Center of Micro-nano and Intelligent Manufacturing Ministry of Education Kaili University Kaili556011 China College of Big Data Engineering College of Microelectronics and Artificial Intelligence Kaili University Kaili556011 China Party School of the Anshun Municipal Committee of C.P.C Anshun561000 China Guizhou Traffic Technician and Transportation College Department of Automotive Engineering Guiyang550008 China College of Computer Science Chongqing University Chongqing400044 China College of Electronic and Information Engineering Southwest University Chongqing400715 China

ISBN: (纸本)9798350353594

Accurate passenger waiting time prediction addresses the taxi supply-demand imbalance in intelligent Transport Systems (ITS). To capture the dependencies of passenger waiting time on urban taxi distribution, we propose a Density Spatiotemporal Clustering and Time Series Feature Processing (DSC-TSFP) method that discovers potential feature relationships of taxi GPS trajectory data. Specifically, a Multilevel Spatiotemporal Density-Based Spatial Clustering of Applications with Noise (MST-DBSCAN) is proposed to cluster taxi GPS trajectory data to obtain waiting time series with spatiotemporal correlations. Furthermore, a time series processing module based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Normalization (CEEMDANN) is designed to enhance prediction accuracy by handling non-stationarity and numerical discrepancy, which solves the residual white noise problem in Intrinsic Modal Functions (IMFs) of EEMD and CEEMD. Next, a waiting time prediction module oriented to a Convolutional Long Short-Term Memory Network (ConvLSTM) is incorporated to compensate for the defects of spatially localized dependencies that LSTM cannot capture. Finally, a CEEMDANN-ConvLSTM model is constructed to predict the passenger waiting time by combining the time series processing and waiting time prediction module. Based on real-world taxi GPS trajectory data, experimental results demonstrate that DSC-TSFP outperforms comparable methods regarding prediction accuracy. © 2024 IEEE.

关键词： Global positioning system

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Integrated Interpolation and Matrix Completion for Radio Map Estimation: A Convex Optimization Approach

Integrated Interpolation and Matrix Completion for Radio Map...

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

作者： Hongcheng Dong Wenqiang Pu Rui Zhou Xiao Fu Feng Yin Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China School of Electrical Engineering and Computer Science Oregon State University Corvallis OR USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Radio map estimation (RME) is crucial for effective planning and optimization of wireless networks. Traditional approaches such as interpolation excel at capturing local smoothness in densely populated data but struggle with sparse or irregular data. Conversely, matrix completion (MC) approaches utilize global structures but require huge number of samples and may produce non-smooth estimates. To integrate these strengths, we propose a convex optimization approach for RME (IIMC-RME) that merges interpolation with MC. This approach formulates the RME task as a low-rank MC problem constrained by interpolated results. Additionally, we have developed a convergent algorithm utilizing the alternating direction method of multipliers (ADMM) to efficiently solve the IIMC-RME problem. Experimental evaluations on both synthetic and real-world datasets have shown that IIMC-RME surpasses existing approaches, thereby achieving superior accuracy in RME.

关键词： Interpolation Wireless networks Estimation Signal processing algorithms Signal processing Convex functions Planning Sparse matrices Speech processing Optimization

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Unveiling Spectrum-Structure Correlation in Vibrational Spectroscopy: Task-Driven Deep Learning Classification Balancing Global Fusion and Local Extraction

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Analytical Chemistry 2025年第21期97卷 10988-10998页

作者： Shi, Guoyang Guo, Haoyu Gao, Tianchu Wu, Haoping Luo, Si-Heng Wang, Sanlei Lei, Yong Zhang, Yun Xie, Yangfan Ren, Bin Tian, Zhong-Qun Shao, Gui-Fang Liu, Guo-Kun State Key Laboratory of Marine Environmental Science Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies Center for Marine Environmental Chemistry & Toxicology College of the Environment and Ecology Xiamen University Xiamen361102 China Key Laboratory of Big Data Intelligent Analysis and Decision Pen-Tung Sah Institute of Micro-Nano Science and Technology Xiamen University Fujian Xiamen361102 China State Key Laboratory of Physical Chemistry of Solid Surfaces College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China Conservation Technology Department Palace Museum Beijing100009 China

Spectrum-structure correlation is crucial to identify and quantify chemicals, in which classification of mixtures and identification of functional groups are two central tasks. Deep learning-driven algorithms have made significant strides to these two tasks. However, many of these algorithms are merely adaptations of models originally designed for computer vision applications. As a result, the models often suffer from either low accuracy or limited generality when applied to spectral data due to the overlooked inherent limitations in feature richness and volume of spectral data. Here, in light of the distinctive difference in the attention of global and local information in spectral data between these two tasks, we developed contrapuntally two CNN-based algorithms, incorporating multiscale convolution and attention mechanism, to address the unique requirements of each task. It was found that the lightweight CNN-Peak algorithm is favored for the classification of a mixture, a type of single-label task, in which the feature fusion of global information is more important. Meanwhile, the more complex ResNet-ResPeak algorithm is ideally suited for the identification of functional groups, a type of multilabel task, in which the feature extraction of local information takes precedence. The task-oriented, conceptual design of deep learning algorithms not only enhances the efficacy and accuracy of spectrum-structure correlation analysis but also feeds back to achieve a more rigorous experimental design and implementation, forming a closed loop of AI for science. © 2025 American Chemical Society.

关键词： Deep learning

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Task Separation and Knowledge Sharing for Class Incremental Learning

Task Separation and Knowledge Sharing for Class Incremental ...

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intelligent Computing and Human-Computer Interaction (ICHCI), International Conference on

作者： Jiali Zhang Xiaoyan Qiao Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Technology and Evaluation Shandong Engineering Research Center School of Computer Science and Technology Shandong Technology and Business University Yantai Shandong China Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Technology and Evaluation Shandong Engineering Research Center School of Mathematics and Information Science Shandong Technology and Business University Yantai Shandong China

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

Methods based on dynamically expanding architectures can effectively mitigate catastrophic forgetting in class incremental learning (CIL), but they often overlook information sharing and integration between subnetworks, leading to performance degradation and increased parameter count and memory consumption as the network expands. To address these complex issues, we introduce a new method called Task Separation and Knowledge Sharing (TSKS), which transfers knowledge at both the logical and feature levels through Hybrid Knowledge Distillation (HKD). In logical distillation, we combine Global Knowledge Distillation (GKD) and Task Knowledge Distillation (TKD) to establish connections between tasks. To prevent the model from favoring new classes, we design an Attention- Based Feature Fusion (ABFF) mechanism to capture important features. Additionally, we introduce a Soft-Margin Auxiliary Loss (SMAL) to help the model learn new concepts. Extensive experiments show that TSKS outperforms existing models on CIFAR100 and ImageNet100, regardless of whether memory samples are used. Notably, TSKS demonstrates superior performance in memory-free settings, indicating a reduced reliance on rehearsal memory.

关键词： Knowledge engineering Human computer interaction Degradation Incremental learning Computational modeling Memory management Information sharing Network architecture Feature extraction Knowledge transfer

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Audio-Visual Active Speaker Extraction for Sparsely Overlapped Multi-Talker Speech

Audio-Visual Active Speaker Extraction for Sparsely Overlapp...

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

作者： Junjie Li Ruijie Tao Zexu Pan Meng Ge Shuai Wang Haizhou Li Shenzhen Research Institute of Big Data Shenzhen China Department of Electrical and Computer Engineering National University of Singapore Singapore School of Data Science The Chinese University of Hong Kong Shenzhen China

Target speaker extraction aims to extract the speech of a specific speaker from a multi-talker mixture as specified by an auxiliary reference. Most studies focus on the scenario where the target speech is highly overlapped with the interfering speech. However, this scenario only accounts for a small percentage of real-world conversations. In this paper, we aim at the sparsely overlapped scenarios in which the auxiliary reference needs to perform two tasks simultaneously: detect the activity of the target speaker and disentangle the active speech from any interfering speech. We propose an audio-visual speaker extraction model named ActiveExtract, which leverages speaking activity from audio-visual active speaker detection (ASD). The ASD directly provides the frame-level activity of the target speaker, while its intermediate feature representation is trained to discriminate speech-lip synchronization that could be used for speaker disentanglement. Experimental results show our model outperforms baselines across various overlapping ratios, achieving an average improvement of more than 4 dB in terms of SI-SNR.

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