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11th International Conference on information Technology in Medicine and Education, ITME 2021

作者： Huang, Yun Luo, Zhiming Zhang, Miaohui Liu, Wei Li, Shaozi Xiamen University Department of Artificial Intelligence Xiamen China Institute of Energy Research Jiangxi Academy of Sciences Nanchang China School of Software East China Jiaotong University Nanchang China Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Wuyi University Nanping China

ISBN: (纸本)9781665406796

With the rapid development of Deepfake technology, face video forgery can produce highly deceptive video content and bring serious security threats. The detection of this kind of fake video is more urgent and challenging. Most of the existing detection methods regard this problem as a common binary classification problem, and using a simple average or maximum as the prediction of video results can easily lead to missed detection or false detection. While the video-based detection work such as LSTM, in Deepfake detection, too much focus on timing modeling will affect the performance of Deepfake video detection to a certain extent. Based on this, this paper proposes a VLAD-based aggregation module DF-VLAD, which advances the aggregation of multiple frames from the output layer to the feature layer, which on the one hand makes the aggregation more flexible, on the other hand, it also uses the objective function of forgery detection to directly guide the learning of frame-level depth representation;On the other hand, this paper deals with this problem as a special fine-grained classification problem, because the difference between fake face and real face is very subtle. It is found that the existing face forgery methods such as Face2Face and NeuralTextures leave some common artifacts in the spatial domain. Different forgery methods produce different artifacts, while natural faces have more similar features. To make the model pay more attention to artifacts, a forgery trace capture model based on the fusion of self-attention mechanism and channel attention mechanism is proposed in this paper. Like other fine-grained classification methods, note intentions are used to guide the network to pay attention to key parts of the face. Experimental results on different public data sets show that the proposed method achieves the latest performance. © 2021 IEEE.

关键词： Long short-term memory

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Gradually Vanishing Gap in Prototypical network for Unsupervised Domain Adaptation

arXiv

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arXiv 2024年

作者： Wang, Shanshan Zhou, Hao Yang, Xun He, Zhenwei Wang, Mengzhu Zhang, Xingyi Wang, Meng The Information Materials and Intelligent Sensing Laboratory of Anhui Province Institutes of Physical Science and Information Technology Anhui University Hefei230601 China The Department of Electronic Engineering and Information Science School of Information Science and Technology University of Science and Technology of China Hefei230026 China The College of Computer Science and Engineering Chongqing University of Technology Chongqing400054 China The School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China The School of Artificial Intelligence Hebei University of Technology Tianjin China The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education The School of Computer Science and Technology Anhui University Hefei230601 China

Unsupervised domain adaptation (UDA) is a critical problem for transfer learning, which aims to transfer the semantic information from labeled source domain to unlabeled target domain. Recent advancements in UDA models have demonstrated significant generalization capabilities on the target domain. However, the generalization boundary of UDA models remains unclear. When the domain discrepancy is too large, the model can not preserve the distribution structure, leading to distribution collapse during the alignment. To address this challenge, we propose an efficient UDA framework named Gradually Vanishing Gap in Prototypical network (GVG-PN), which achieves transfer learning from both global and local perspectives. From the global alignment standpoint, our model generates a domain-biased intermediate domain that helps preserve the distribution structures. By entangling cross-domain features, our model progressively reduces the risk of distribution collapse. However, only relying on global alignment is insufficient to preserve the distribution structure. To further enhance the inner relationships of features, we introduce the local perspective. We utilize the graph convolutional network (GCN) as an intuitive method to explore the internal relationships between features, ensuring the preservation of manifold structures and generating domain-biased prototypes. Additionally, we consider the discriminability of the inner relationships between features. We propose a pro-contrastive loss to enhance the discriminability at the prototype level by separating hard negative pairs. By incorporating both GCN and the pro-contrastive loss, our model fully explores fine-grained semantic relationships. Experiments on several UDA benchmarks validated that the proposed GVG-PN can clearly outperform the SOTA models. Copyright © 2024, The Authors. All rights reserved.

关键词： Alignment

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Prediction of exosomal piRNAs based on deep learning for sequence embedding with attention mechanism

Prediction of exosomal piRNAs based on deep learning for seq...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yajun Liu Yulian Ding Aimin Li Rong Fei Xie Guo Fangxiang Wu Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an China Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China Department of Computer Science Department of Mechanical Engineering Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

PIWI-interacting RNAs (piRNAs) are a type of small non-coding RNAs which bind with the PIWI proteins to exert biological effects in various regulatory mechanisms. A growing amount of evidence reveals that exosomal piRNAs are potential biomarkers for diagnosis and treatment of complex diseases. Effective methods for the prediction of exosomal piRNAs are the foundation of piRNA functional research. In this study, we propose an end-to-end deep network for identifying exosomal piRNAs based on features learned from natural language processing (NLP) models for sequence embedding with attention mechanism. First, a benchmark dataset is constructed by processing piRNA subcellular localization annotated data and sequence data. Moreover, bagging positive unlabeled learning is applied to get the reliable negative set. Finally, we treat a piRNA sequence as a sentence and its k-mer subsequence as a token. Sequence embedding models with self-attention mechanism is designed to extract features from exosome piRNA sequences, which are used for the prediction task. Compared with three competing methods, our model achieves the best performance and reveals the key factors of exosomal piRNA sequences by the attention mechanism. Our model characterizes exosomal piRNAs and could be beneficial for researchers to investigate exosomal piRNAs’ functions.

关键词： Proteins Location awareness RNA Biological system modeling Predictive models Feature extraction Natural language processing

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Efficient VVC Intra Prediction Based on Deep Feature Fusion and Probability Estimation

arXiv

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arXiv 2022年

作者： Zhao, Tiesong Huang, Yuhang Feng, Weize Xu, Yiwen Kwong, Sam The Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou University Fuzhou350108 China The Peng Cheng Laboratory Shenzhen518055 China The Department of Computer Science City University of Hong Kong Hong Kong

The ever-growing multimedia traffic has underscored the importance of effective multimedia codecs. Among them, the up-to-date lossy video coding standard, Versatile Video Coding (VVC), has been attracting attentions of video coding community. However, the gain of VVC is achieved at the cost of significant encoding complexity, which brings the need to realize fast encoder with comparable Rate Distortion (RD) performance. In this paper, we propose to optimize the VVC complexity at intra-frame prediction, with a two-stage framework of deep feature fusion and probability estimation. At the first stage, we employ the deep convolutional network to extract the spatial-temporal neighboring coding features. Then we fuse all reference features obtained by different convolutional kernels to determine an optimal intra coding depth. At the second stage, we employ a probability-based model and the spatial-temporal coherence to select the candidate partition modes within the optimal coding depth. Finally, these selected depths and partitions are executed whilst unnecessary computations are excluded. Experimental results on standard database demonstrate the superiority of proposed method, especially for High Definition (HD) and Ultra-HD (UHD) video sequences. Copyright © 2022, The Authors. All rights reserved.

关键词： Video signal processing

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Abnormal network Traffic Detection based on Leaf Node Density Ratio 2019

Abnormal Network Traffic Detection based on Leaf Node Densit...

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Proceedings of the 2019 the 9th International Conference on Communication and network Security

作者： Huajun Liu Zhanghui Liu Yanhua Liu Xiaoling Gao College of Mathematics and Computer Science Fuzhou University Fuzhou Fujian China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou Fujian China College of Mathematics and Computer Science Fuzhou University Fuzhou Fujian China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou Fujian China

ISBN: (纸本)9781450376624

As the network evolves, cyber-attacks become more and more diverse. In the process of detecting network traffic, the most complicated but also the most important task is to find unknown abnormal network traffic data in time. In the existing abnormal network traffic detection method based on Extended Isolation Forest, there are limitations such as unbalanced detection accuracy and insufficient generalization ability. An improved abnormal network traffic detection method EIF-LNDR is proposed for the above problems. Based on the leaf node density ratio, the anomaly score of the instance can be calculated differently for each iTree. The experiments show that EIF-LNDR has significant improvement in precision, false negative rate, and detector efficiency compared with Extended Isolation Forest and LOF methods.

关键词： leaf node density ratio extended Isolation Forest network traffic data mining cyber attack Abnormal detection

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Study on Gene Splicing Site Recognition Based on Particle Swarm Optimization Twin Support Vector Machine Algorithm for Smart Healthcare

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Wireless Communications and Mobile computing 2023年第1期2023卷

作者： Zhang, Fuquan Wang, Yiou Mei, Peng Dai, Aibing Wang, Bo Liu, Laiyang Xia, Yong Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350117 China Institute of Science and Technology Information Beijing Academy of Science and Technology Beijing100089 China Beijing100089 China Industrial Digital Finance Department Huaxia Bank Beijing100020 China Digital Performance and Simulation Technology Lab Beijing Institute of Technology Beijing100081 China Fuzhou Maternity and Child Health Care Hospital Fuzhou350005 China Fujian Medical University Fuzhou350122 China

Gene splicing site recognition is a very important research topic in smart healthcare. Gene splicing site recognition is of great significance, not only for the large-scale and high-quality computational annotation of genomes but also for the analysis and recognition of the gene sequences evolutionary process. It is urgent to study a reliable and effective algorithm for gene splice site recognition. Traditional Twin Support Vector Machine (TWSVM) algorithm has advantages in solving small-sample, nonlinear, and high-dimensional problems, but it cannot deal with parameter selection well. To avoid the blindness of parameter selection, particle swarm optimization algorithm was used to find the optimal parameters of twin support vector machine. Therefore, a Particle Swarm Optimization Twin Support Vector Machine (PSO-TWSVM) algorithm for gene splicing site recognition was proposed in this paper. The proposed algorithm was compared with traditional Support Vector Machine algorithm, TWSVM algorithm, and Least Squares Support Vector Machine algorithm. The comparison results show that the positive sample recognition rate, negative sample recognition rate, and correlation coefficient (CC) of the proposed algorithm are the best among the four different support vector machine algorithms. The proposed algorithm effectively improves the recognition rate and the accuracy of splice sites. The comparison experiments verify the feasibility of the proposed algorithm. © 2023 Fuquan Zhang et al.

关键词： Genes

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Self-adaptive resource allocation for cloud-based software services based on progressive QoS prediction model

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Science China(information Sciences) 2019年第11期62卷 192-194页

作者： Xing CHEN Junxin LIN Yun MA Bing LIN Haijiang WANG Gang HUANG College of Mathematics and Computer Science Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University School of Software Tsinghua University College of Physics and Energy Fujian Normal University Key Laboratory of High Confidence Software Technologies Ministry of Education

Dear editor,Self-adaptation is a promising approach to allocate resources for cloud-based software services [1, 2].Traditional self-adaptive resource-allocation methods are rule-driven, which leads to high administrat... 详细信息

关键词： service Self-adaptive resource allocation for cloud-based software services based on progressive QoS prediction model

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Decision making of mobile robot based on multimodal fusion 2020

Decision making of mobile robot based on multimodal fusion

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6th International Conference on computing and Data Engineering, ICCDE 2020 and 2020 International Conference on Artificial Intelligence in Electronics Engineering, AIEE 2020

作者： Hou, Ya Feng, Zhiquan Xu, Tao Department of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan250022 China

ISBN: (纸本)9781450376730

To solve the problem of multimodal information fusion, this paper proposes a method based on the filling of scene main components, and evaluates the channel information according to the knowledge base. The modal channel of this paper chooses vision and hearing, which is more suitable for the information transmission in the actual communication. Firstly, the single mode information is identified by the neural network. After processing, the image and audio expression are transformed into text expression, and the component value describing the scene is filled in according to text analysis. According to the knowledge base, the evaluation model is established to calculate the confidence of each channel when the information conflicts. After getting the scene, query the content of the prior knowledge base again and send the corresponding action instructions to the robot. The experimental results show that this paper can correct the modal fusion results under the guidance of prior knowledge base, and achieve effective human-computer cooperation in specific scenes. It reduces the dependence on single channel information in the interaction process, increases fault tolerance mechanism, and improves user experience evaluation. © 2020 ACM International Conference Proceeding Series. All rights reserved.

关键词： Audition

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Automatic Segmentation of Nasopharyngeal Carcinoma in CT Images Using Dual Attention and Edge Detection

Automatic Segmentation of Nasopharyngeal Carcinoma in CT Ima...

引用

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

作者： Qizhi Wang Wei Huang Yuan Zhang Xuanya Li Xiongjun Ye Kai Hu Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan China Department of Radiology the First Hospital of Changsha Changsha China Baidu Inc. Beijing China National Cancer Center Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China

Nasopharyngeal carcinoma (NPC) is a malignant tumor with a high incidence. Accurate segmentation of the tumor region in Computed Tomography (CT) images of NPC is the key to treatment. However, the features of uneven grayscale values and hazy boundaries of NPC regions make accurate NPC segmentation particularly challenging. To address these problems, we propose an accurate and effective NPC segmentation method using Dual Attention and Edge Detection Convolutional Neural network (DAED-Net). Firstly, we combine a 2.5D convolutional neural network with UNet++ and propose a new backbone called Dual-dimension Dense UNet (DD-UNet), which can extract more beneficial features from 3D images. Secondly, a Dual Attention Module (DAM) is proposed to help the model better segment the target region of NPC by efficiently collecting spatial and channel attention information from feature maps. Moreover, an Edge Detection Module (EDM) is introduced in the network to enhance the segmentation of the target contours. Finally, we evaluate the proposed DAED-Net on the public MICCAI 2019 StructSeg NPC dataset from different perspectives. Numerical and visual results show that the proposed method outperforms nine state-of-the-art segmentation methods and yields more accurate NPC segmentation results.

关键词： Image segmentation Visualization Three-dimensional displays Dams Computed tomography Image edge detection Feature extraction

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ESO-Based Control for 2-D networked Control Systems With Markov-Type DoS Attacks

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IEEE Transactions on network Science and Engineering 2025年

作者： Cheng, Peng He, Shuping Xiao, Gaoxi Zhang, Weidong Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Anhui University 230601 China Anhui University National Key Laboratory of Opto-Electronic Information Acquisition and Protection Technology School of Electrical Engineering and Automation Hefei 230601 China Nanyang Technological University School of Electrical and Electronic Engineering 639798 Singapore Shanghai Jiao Tong University Department of Automation Shanghai 200240 China

This article aims to investigate the issue of the extended state observer (ESO)-based controller design for a two-dimensional (2-D) networked control system (NCS), where the communication channel suffers from Markov-type denial-of-service (DoS) attacks. The concerned system is developed by the Fornasini-Marchesini second (FMII) model. Firstly, the evolution of the DoS attacks is characterized with the help of a Markov jump process with two modes, which can eliminate the need for an explicit energy description. Moreover, a 2-D information compensation mechanism is devised, which utilizes the most recently received bidirectional signals unaffected by DoS attacks to ensure continuous input to the ESO, thereby mitigating the impact of communication interruptions. With the aid of the ESO, the developed control scheme ensures system stabilization and guarantees ultimate uniform boundedness under DoS attacks. Finally, a simulation example is conducted to validate the soundness and effectiveness of the presented control strategy, and the analysis of the Markov-type DoS attack intensity on system stability is presented as well. © 2013 IEEE.

关键词： denial-of-service (DoS) attacks extended state observer (ESO) Fornasini-Marchesini second (FMII) model networked control system (NCS) ultimate uniform boundedness

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