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IEEE Open Journal of the Communications Society 2024年 5卷 3568-3580页

作者： Zahoor, Amina Mahmood, Khalid Saleem, Muhammad Asad Badar, Hafiz Muhammad Sanaullah Le, Tuan-Vinh Das, Ashok Kumar Department of Computer Science Sahiwal57000 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Emerson University Multan Department of Emerging Computing Technologies Multan61000 Pakistan Fu Jen Catholic University Bachelor's Program of Artificial Intelligence and Information Security New Taipei City24206 Taiwan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India

The Smart Power Grid (SPG) is pivotal in orchestrating and managing demand response in contemporary smart cities, leveraging the prowess of Information and Communication Technologies (ICTs). Within the immersive SPG environment, the ubiquitous deployment of smart meters stands as a testament to their paramount importance in the realm of vigilant monitoring and oversight. These smart meters are installed on high-tension electricity lines and transmit information about electricity outages and other issues to the utility center. To access services from utility centers, smart meters need to communicate securely over a public channel, even though the network itself is insecure. However, potential attacks from adversaries ( Ad) can exploit this communication. Therefore, protecting this communication is of utmost importance. Several privacy-preserving authentication protocols designed for SPG have been introduced in the literature. Nevertheless, a significant number of these protocols exhibit vulnerabilities to diverse security attacks. This article introduces a lightweight and anonymous authentication protocol specifically designed to address these concerns in the SPG environment. Our protocol ensures both security and efficiency, surpassing other comparable protocols in terms of its lightweight nature. By employing both formal and informal analysis, we showcase the robustness of our protocol against significant attacks while remaining lightweight. The proposed protocol provides added security features and incurs 23.8879% lower computation cost than related protocols. Consequently, our protocol is highly suitable for implementation in the SPG system. © 2020 IEEE.

关键词： Hash functions

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TMCSpeech: A Chinese TV and Movie Speech dataset with Character Descriptions and a Character-Based Voice Generation Model 27th

TMCSpeech: A Chinese TV and Movie Speech Dataset with Charac...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Liu, Dong Lin, Yueqian Xu, Yunfei Li, Ming Suzhou Municipal Key Laboratory of Multimodal Intelligent Systems Data Science Research Center Duke Kunshan University Kunshan China Guangdong OPPO Mobile Telecommunications Corp. Ltd. Dongguan China

ISBN: (纸本)9783031781711

Recent research on text-guided speech synthesis has sparked considerable interest. This study explores the potential of leveraging publicly available internet video data for speech synthesis and character-based new voice generation. We introduce a multi-modal extraction pipeline for automating the creation of speech synthesis datasets, extracting accurate character speech segments and descriptions from online videos. Additionally, we propose a person-description-based controllable voice synthesis system, establishing a mapping from character descriptions to speaker representation vectors. This system transforms character descriptions into new vectors, serving as input for zero-shot VITS to generate character-specific voices. Both objective and subjective metrics affirm our approach’s capability to generate previously unheard character-specific voices with acceptable naturalness. We plan to release the annotation set of TMCSPEECH (We only provide our collected original video links and our annotated labels for non-commercial research purposes. Our shared annotation set does not contain any audio or video data. It is the user’s responsibility to decide whether to download the video data and whether their intended purpose with the downloaded data is allowed in their country). Our audio samples can be accessed online (https://***/TMCSPEECH/). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： data description

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Predicting CircRNA-Disease Associations Through Non-negative Matrix Factorization and Adversarially Regularized Variational Graph Autoencoder

Predicting CircRNA-Disease Associations Through Non-negative...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lu, Mianshuo Wang, Lei Sun, Jinzhu Li, Yang Wei, Mengmeng Wei, Yu Liu, Changchun Li, Zhengwei China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China Guangxi Academy of Sciences Big Data and Intelligent Computing Research Center Nanning530007 China Zaozhuang Mental Health Center Zaozhuang277100 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China Zaozhuang Univerity College of Information Science and Enginnering 277100 China

ISBN: (纸本)9798350386226

Circular RNA (circRNA) is an RNA molecule that plays an important role in both pathology and physiology. Accurate identification of associations between circRNAs and diseases is crucial for further physiology research. However, verifying the circRNA-disease associations (CDA) through biological experimental methods is time-consuming. Here, we propose a novel method combines Non-negative Matrix Factorization (NMF) and Adversarially Regularized Variational Graph Autoencoder (ARVGA) to accurately predict CDA. Our model first fuses multi-source information in order to build circRNA similarity, disease similarity and circRNA-disease association matrices. Thereby our model constructs graphs for circRNA and disease respectively and optimize them using a K-means clustering algorithm. We obtain linear features using NMF and non-linear features using ARVGA. Finally, an Extremely Randomized Trees classifier is employed to predict CDA. On the gold standard dataset CircR2Disease, our model achieved a prediction accuracy of 94.8% and an AUC of 0.984 under 5-fold cross-validation. In case study, 19 of top 20 predicted circRNAs associated with Hepatocellular Carcinoma were confirmed in relevant literature. Furthermore, ablation experiment, classifier experiment and independent datasets test fully demonstrate the effectiveness and robustness of our model. © 2024 IEEE.

关键词： Non-negative matrix factorization

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Automatic Segmentation Method for the Nuclei Contour of High Resolution Rat Brain 23

Automatic Segmentation Method for the Nuclei Contour of High...

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6th International Conference on Big data Technologies, ICBDT 2023

作者： Xu, Xiaofeng Zheng, Wei Henan Engineering Research Center of Intelligent Processing for Big Data of Digital Image School of Computer Science and Technology Nanyang Normal University Nanyang473061 China

ISBN: (纸本)9798400707667

The brain consists of massive nuclei with different functions. In neuroscience research, the precise recognition and delineation of nucleus boundaries is the crux of brain atlas illustration. Here, we propose a method based on Markov Random Field. This method introduces fractional differentiation into texture feature extraction and a new potential energy function is defined based on kernel region information. To handle the problem of large data volume, we propose a strategy of dual MRF processing with down sampling for pre classification. Finally, the fuzzy entropy criterion is used to optimize the segmentation results. Experiments on model data and real tissue slice data show that this method can not only segment the regions with obvious differences in the density of the nuclei with insignificant differences in the density of nuclei, but also divide the nuclei without insignificant density differences by the cell shapes and textures. In addition, this method can not only segment a specific target kernel in one calculation, but also simultaneously partition multiple target kernels. The segmentation algorithm shows great potential for illustrating high-resolution 3D brain atlases. © 2023 ACM.

关键词： Markov processes

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Generative Image Steganography Based on Latent Space Vector Coding and Diffusion Model 1

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2nd International Conference on Artificial Intelligence Security and Privacy, AIS and P 2024

作者： Liu, Weisong Yang, Jianfeng Huang, Jiewen El-Latif, Ahmed A. Abd Zhou, Zhili School of Artificial Intelligence Guangzhou University Guangzhou China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China EIAS Data Science Lab College of Computer and Information Sciences and Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia

ISBN: (数字)9789819611485

ISBN: (纸本)9789819611478

Image steganography is a technology that embed secret information within a cover image to obtain a stego-image for covert communication. The transmission of undetectable stego-images via social media can facilitate secure and efficient covert communication. Recently, the generative image steganography has achieved promising performance with the rapid development of generative models. However, existing generative image steganography suffers from issues, i.e., the low quality of the generated stego-image and the limited hiding capacity. To address these issues, this paper proposes a generative image steganography scheme based on latent space vector coding and diffusion model. It consists of an optimized diffusion denoising network, the latent space vector coding mapping network, and the information extraction network. In this scheme, the secret information is encoded as a latent space vector, which is then transformed in the input diffusion model to generate the stego-image. Experimental results demonstrate that this scheme achieves superior performance compared with existing generative image steganography methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Vectors

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Deep Learning Approaches for Road Damage Detection Using YOLOv8 and Custom Convolutional Neural Network 6

Deep Learning Approaches for Road Damage Detection Using YOL...

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6th International Conference on Frontier Technologies of Information and Computer, ICFTIC 2024

作者： Khan, Muhammad Waseem Mahmood, Khalid Hussain, Israr Talha Qureshi, Muhammad Muhammad Sanaullah Badar, Hafiz Gao, Wu School of Computer Science Northwestern Poly technical University Center for Bio-inspired System-on-Chip Xi'an China Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Taiwan Institute of Computing Mns University of Agriculture Multan Pakistan Henan University Kaifeng450046 China

ISBN: (纸本)9798331541750

The identification of road damage is deemed very important to the preservation of infrastructure. Since recent development in deep learning provide great potential approaches, in this paper, three deep learning models, namely EfficientNet, CNN and YOLOv8 are considered and their performance is evaluated on a dataset with 8,586 images with labelled road damages consisting of seven subclasses including longitudinal cracks and potholes. Besides, other issues like class imbalance and variabilities in the environment were studied and considered in its assessment based on the Models accuracy, precision, recall, and F1-score. With an accuracy of 64%, YOLOv8 is the most accurate model for identifying various types of damages, particularly in identifying multiple damages present in one or more types of damage, as was seen in the high accuracy rate of a split sample of 44 images of multiple damages. Our findings showcase YOLOv8 as a strong contender for real-time road damage detection with potential of enhancement with better augmentation and fine-tuning. © 2024 IEEE.

关键词： Convolutional neural networks

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Logical Relation Modeling and Mining in Hyperbolic Space for Recommendation 40

Logical Relation Modeling and Mining in Hyperbolic Space for...

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Tan, Yanchao Lv, Hang Zhou, Zihao Guo, Wenzhong Xiong, Bo Liu, Weiming Chen, Chaochao Wang, Shiping Yang, Carl College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Institute for Artificial Intelligence University of Stuttgart Stuttgart Germany College of Computer Science Zhejiang University Hangzhou China Emory University Department of Computer Science Atlanta United States

ISBN: (纸本)9798350317152

The sparse interactions between users and items have aggravated the difficulty of their representations in recommender systems. Existing methods leverage tags to alleviate the sparsity problem but ignore prevalent logical relations among items and tags (e.g., membership, hierarchy, and exclusion), which can be leveraged to enhance the accuracy of modeling user preferences and conducting recommendations. To this end, we propose to extract logical relations among item tags from existing tag taxonomies and exploit the individual strengths of the Poincaré and the Lorentz models in hyperbolic space for logical relation modeling towards enhanced recommendations. Moreover, we find that the logical relations directly extracted from existing tag taxonomies can be inaccurate and coarse. Therefore, we further devise innovative consistency-based and granularity-based weighting mechanisms based on user behavior patterns for data-driven logical relation mining that can be jointly optimized along with recommendations in an end-to-end fashion. Extensive experiments on four real-world benchmark datasets show drastic performance gains brought by our proposed framework, which constantly achieves an average of 8.25% improvement over state-of-the-art competitors regarding both Recall and NDCG metrics. Insightful case studies further demonstrate that our automatically refined logical relations are highly accurate and interpretable. © 2024 IEEE.

关键词： Recommender systems

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Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition

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植物表型组学（英文） 2024年第4期6卷 828-840页

作者： Jianwu Lin Xin Zhang Yongbin Qin Shengxian Yang Xingtian Wen Tomislav Cernava Quirico Migheli Xiaoyulong Chen Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China School of Biological Sciences Faculty of Environmental and Life SciencesUniversity of SouthamptonSouthampton S017 1BJUK Dipartimento di Agraria and NRD-Nucleo di Ricerca sulla Desertificazione Università degli Studi di SassariSassariItaly Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Life Sciences Guizhou UniversityGuiyang 550025China

Accurate identification of plant diseases is important for ensuring the safety of agricultural *** neural networks(CNNs)and visual transformers(VTs)can extract effective representations of images and have been widely used for the intelligent recognition of plant disease ***,CNNs have excellent local perception with poor global perception,and VTs have excellent global perception with poor local *** makes it difficult to further improve the performance of both CNNs and VTs on plant disease recognition *** this paper,we propose a local and global feature-aware dual-branch network,named LGNet,for the identification of plant *** specifically,we first design a dual-branch structure based on CNNs and VTs to extract the local and global ***,an adaptive feature fusion(AFF)module is designed to fuse the local and global features,thus driving the model to dynamically perceive the weights of different ***,we design a hierarchical mixed-scale unit-guided feature fusion(HMUFF)module to mine the key information in the features at different levels and fuse the differentiated information among them,thereby enhancing the model's multiscale perception ***,extensive experiments were conducted on the Al Challenger 2018 dataset and the self-collected corn disease(SCD)*** experimental results demonstrate that our proposed LGNet achieves state-of-the-art recognition performance on both the Al Challenger 2018 dataset and the SCD dataset,with accuracies of 88.74％and 99.08％,respectively.

关键词： global plant disease dual-branch feature-aware local networks recognition

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DeepRisk:A deep learning approach for genome-wide assessment of common disease risk

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Fundamental research 2024年第4期4卷 752-760页

作者： Jiajie Peng Zhijie Bao Jingyi Lia Ruijiang Han Yuxian Wang Lu Han Jinghao Peng Tao Wang Jianye Hao Zhongyu Wei Xuequn Shang AI for Science Interdisciplinary Research Center Schoolof Computer ScienceNorthwestern Polyechnical UniversityXi'an 129China Key Laboratoryof Bg Data Sorage and Management Northwestern Polytechnical UniversityMinisty of Industy and Information TechnologyX'an 129Chin College of Intelligence and Computing Tianjin UniversityTianjin 300072China School of Data Science Fudan UniversityShanghai 200433China Research and Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen 518000China

The potential for being able to identify individuals at high disease risk solely based on genotype data has garnered significant *** widely applied,traditional polygenic risk scoring methods fall short,as they are built on additive models that fail to capture the intricate associations among single nucleotide polymorphisms(SNPs).This presents a limitation,as genetic diseases often arise from complex interactions between multiple *** address this challenge,we developed DeepRisk,a biological knowledge-driven deep learning method for modeling these complex,nonlinear associations among SNPs,to provide a more effective method for scoring the risk of common diseases with genome-wide genotype *** demonstrated that DeepRisk outperforms existing PRs-based methods in identifying individuals at high risk for four common diseases:Alzheimer's disease,inflammatory bowel disease,type 2diabetes,and breast cancer.

关键词： Disease risk prediction Deep learning Polygenic risk score Common disease risk Disease prevention

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A Grouping Strategy-Based Progressive Fusion Network for Hyperspectral Image Super-Resolution

A Grouping Strategy-Based Progressive Fusion Network for Hyp...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Lv, Guohua Zhang, Baodong Gao, Yongbiao Zhao, Guixin Wang, Guotao Wang, Juncan Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Shandong Key Laboratory of Ubiquitous Intelligent Computing Jinan China Ministry of Education China Beihang University Beijing China

ISBN: (纸本)9798350368741

Hyperspectral super-resolution involves combining low-resolution hyperspectral images with high-resolution multispectral images to produce a high-resolution hyperspectral image. Recently, although many methods for hyperspectral image super-resolution have been proposed, they often fail to fully utilize the high similarity among adjacent bands to enhance fusion performance. Therefore, we propose a grouping strategy-based progressive fusion network (GPFNet) for hyperspectral super-resolution. The core of GPFNet is the grouping strategy fusion block (GPF block), in which grouping-based spatial-spectral information fusion and spatial information refinement are performed. We design the spatial-spectral information fusion module (SSIFM) based on grouped convolutions to capture the feature differences from adjacent bands. To refine spatial details, we develop the spatial information enhancement module (SpaEM), which leverages the hierarchical features extracted by the multi-scale feature extraction module (MIEM). Additionally, a progressive fusion strategy, which involves using multiple upsampled hyperspectral images and downsampled multispectral images, further preserves spectral integrity and spatial details. Extensive experiments show that GPFNet outperforms state-of-the-art methods both qualitatively and quantitatively. © 2025 IEEE.

关键词： group convolution Hyperspectral image image fusion super-resolution

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