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2022 2nd International Conference on Communication Technology and Information Technology, ICCTIT 2022

作者： Lv, Haifeng Ji, Xiaoyu Ding, Yong Guangxi Key Laboratory of Machine Vision and Intelligent Control WuZhou University Wuzhou China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Colleges and Universities Key Laboratory of Industry Software Technology Wuzhou University Wuzhou China

The intrusion detection system (IDS) plays an important part because it offers an efficient way to prevent and mitigate cyber attacks. Numerous deep learning methods for intrusion anomaly detection have been developed as a result of recent advances in artificial intelligence (AI) in order to strengthen internet security. The balance among the high detection rate (DR), the low false alarm rate (FAR) and disaster of dimensionality is the crucial apprehension while devising an effective IDS. For the binary classification of intrusion detection systems, we present in this study a mixed model called K-means-XGBoost consisting of K-means and (Extreme Gradient Boosting, XGBoost) algorithms. The distributed computation of our method is achieved in Spark platform to rapidly separate normal events and anomaly events. In phrases of accuracy, DR, F1-score, recall, precision, and error indices FAR, the proposed model's performance is measured via the well-known dataset of NSL-KDD. The experimental outcomes indicate that our method is outstandingly better among accuracy, DR, F1-score, training time, and processing speed, compared to other models which are recently created. In particular, the accuracy, F1-score, and DR of the proposed model can achieve as high as 93.28%, 94.39%, and 99.22% in the NSL-KDD dataset, respectively. © Published under licence by IOP Publishing Ltd.

关键词： Network security

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Multi-task Collaborative Network for Image-Text Retrieval 1

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Qin, Xueyang Li, Lishuang Hao, Jing Ge, Meiling Huang, Jiayi Pang, Guangyao School of Computer Science and Technology Dalian University of Technology Dalian116024 China School of Computer Engineering Weifang University Weifang261061 China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou543002 China

ISBN: (数字)9783031533112

ISBN: (纸本)9783031533105

Image-text retrieval aims to capture semantic relevance between images and texts. Most existing approaches rely solely on the image-text pairs to learn visual-semantic representation through fine-grained alignments while neglecting the potential beneficial impact of unimodal tasks on cross-modal retrieval. To this end, we present a Multi-Task Collaborative Network (MTCN) that leverages the synergy between multiple tasks to enhance the performance of image-text retrieval. Specifically, we introduce three unimodal tasks, including text-text matching, image multi-label classification, and text multi-label classification, and train together with target task image-text retrieval from the perspective of semantic constraints. Additionally, we employ a modality interaction module for image-text retrieval to discover interrelationships between these two modalities. Subsequently, a cascaded graph convolutional network combined with a multi-layer perceptron is used to infer the correlation scores between images and texts. We conduct comprehensive experiments on two benchmark datasets, Flickr30K and MSCOCO, and the quantitative and qualitative experimental results demonstrate the effectiveness of the proposed method. The source code is available at https://***/FlyCuteBird/MTCN. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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An Efficient Method for Finding Mislabeled Samples of Similar Chinese Characters 21

An Efficient Method for Finding Mislabeled Samples of Simila...

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21st IEEE International Conference on Ubiquitous Computing and Communications, IUCC-CIT-DSCI-SmartCNS 2022

作者： Chen, Yue Zhu, Xiaoying Huang, Jihong Pang, Guangyao Pu, Baoxing Wuzhou University Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou543002 China Guangxi Colleges and Universities Key Laboratory of Industry Software Technology Wuzhou University Wuzhou543002 China

ISBN: (纸本)9781665477260

Convolutional neural networks for offline handwritten Chinese character recognition require a large number of samples to train the model, the mislabeled samples in the training set will reduce the recognition accuracy of the model, while similar Chinese character samples are the most prone to be mislabeled. In this paper, we propose a method to find the mislabeled samples of similar handwritten Chinese character pairs by differential features based recognition combined with manual verification, which can significantly reduce the time required to find the mislabeled samples. The method was applied to the similar character pair of E-B' whose handwritten samples are easy to be mislabeled. The experiments show that the proposed method can narrow down the scope of candidate mislabeled samples and improve the efficiency of manual verification, and find four times more mislabeled samples than convolutional neural network based method. © 2022 IEEE.

关键词： Convolutional neural networks

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A control Strategy for Passing and Receiving in Virtual Human Soccer 21

A Control Strategy for Passing and Receiving in Virtual Huma...

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21st IEEE International Conference on Ubiquitous Computing and Communications, IUCC-CIT-DSCI-SmartCNS 2022

作者： Liao, Jingjie Gong, Hai Xiao Lu, Hongyan Yang, Yuling Lin, Jiliang Wuzhou University Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou China Xiamen University of Technology School of International Education Xiamen China

ISBN: (纸本)9781665477260

Virtual human soccer is the concentrated embodiment of virtual human technology and kinematics. In order to realize the nature and reality of controlling the passing and receiving of the ball, the passing and receiving control of the virtual human and the movement track of the soccer ball is studied, and a message mechanism combined with inverse dynamics control strategy is proposed. The interaction of virtual human passing and receiving the ball is divided into execution behavior and contact ball effect. The execution behavior is controlled by the message mechanism to inform and receive the message, which is transmitted to the virtual human. The contact ball effect is in the charge of inverse dynamics, which adjusts the contact part when passing and receiving the ball. Finally, the simulation of football passing and receiving is realized, and the screen is smooth and natural. The experimental results verify the feasibility of this method. guangxi key laboratory of machine vision and intelligent control. © 2022 IEEE.

关键词： Inverse problems

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DMHFR:Decoder with Multi-Head Feature Receptors for Tract Image Segmentation

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Computers, Materials & Continua 2025年第3期82卷 4841-4862页

作者： Jianuo Huang Bohan Lai Weiye Qiu Caixu Xu Jie He Department of Endoscopy Center Zhongshan Hospital(Xiamen)Fudan UniversityXiamen361015China School of Computing and Data Science Xiamen University MalaysiaSepang43900Malaysia School of Computer Science and Techonology Tongji UniversityShanghai200092China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou UniversityWuzhou543002China Xiamen Clinical Research Center for Cancer Therapy Xiamen361015China

The self-attention mechanism of Transformers,which captures long-range contextual information,has demonstrated significant potential in image ***,their ability to learn local,contextual relationships between pixels requires further *** methods face challenges in efficiently managing multi-scale fea-tures of different granularities from the encoder backbone,leaving room for improvement in their global representation and feature extraction *** address these challenges,we propose a novel Decoder with Multi-Head Feature Receptors(DMHFR),which receives multi-scale features from the encoder backbone and organizes them into three feature groups with different granularities:coarse,fine-grained,and full *** groups are subsequently processed by Multi-Head Feature Receptors(MHFRs)after feature capture and modeling *** include two Three-Head Feature Receptors(THFRs)and one Four-Head Feature Receptor(FHFR).Each group of features is passed through these MHFRs and then fed into axial transformers,which help the model capture long-range dependencies within the *** three MHFRs produce three distinct feature *** output from the FHFR serves as auxiliary auxiliary features in the prediction head,and the prediction output and their losses will eventually be *** results show that the Transformer using DMHFR outperforms 15 state of the arts(SOTA)methods on five public ***,it achieved significant improvements in mean DICE scores over the classic Parallel Reverse Attention Network(PraNet)method,with gains of 4.1%,2.2%,1.4%,8.9%,and 16.3%on the CVC-ClinicDB,Kvasir-SEG,CVC-T,CVC-ColonDB,and ETIS-LaribPolypDB datasets,respectively.

关键词： Medical image segmentation feature exploration feature aggregation deep learning multi-head feature receptor

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Fast Fractal Image Compression Algorithm Based on Compression Perception 6th

Fast Fractal Image Compression Algorithm Based on Compressio...

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6th EAI International Conference on machine Learning and intelligent Communications, MLICOM 2021

作者： Zhang, Lixian Xu, Caixu He, Jie School of Electronic and Information Engineering Wuzhou University Wuzhou543002 China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou543002 China

ISBN: (纸本)9783031044083

To address the problem of long coding time of fractal image compression algorithm, this paper proposes a fractal image compression algorithm based on compression perception. Firstly, the algorithm is coded in the wavelet domain by separating the high and low frequency signals of the image, then, the low frequency information is fractally coded, while the sparse high frequency signals are sampled and coded in a compression-aware manner, and finally, a better image reconstruction compensation effect is achieved with the premise of reducing the number of coding searches and coding time. The experimental results show that this algorithm has a slight decrease in coding quality and compression ratio compared to fractal coding image compression, but has a superior improvement in coding speed. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Image compression

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Color Image Fast Encryption Algorithm Based on JPEG Encoding 6th

Color Image Fast Encryption Algorithm Based on JPEG Encoding

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6th EAI International Conference on machine Learning and intelligent Communications, MLICOM 2021

作者： Rong, Ma Gaohua, Yao Hui, Guo School of Electronics and Information Engineering Wuzhou University Wuzhou543002 China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou543002 China

ISBN: (纸本)9783031044083

In order to strengthen the security of standard JPEG format color images in network transmission, this paper proposes a kind of JPEG color image encryption algorithm which combines the efficient and stable compression encoding with encrypting selectively. First, it selects out mapping better chaotic characteristics as a pseudo-random number generator, and uses the generated chaotic sequence for the encryption algorithm. Then, it uses the edge detection algorithm to find the blocks with rich image contour information, which provides a basis for selection and encryption of later images. It Encrypts the quantized image coefficients and embeds in the important block information. The experimental results shows that the algorithm in this paper can hide the color information and contour information of the plaintext image effectively. At the same time, the key is more sensitive. Compared with the classic encryption algorithm AES, it has better performance in terms of time complexity and encryption quality. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Cryptography

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Application Methods and Practices of Deep Learning in Network Intrusion Detection 23

Application Methods and Practices of Deep Learning in Networ...

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Proceedings of the 2023 International Conference on Artificial Intelligence, Systems and Network Security

作者： Shouan Wei Zhiheng Zhang Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University China Network Security Laboratory Public Security Bureau of Lianyungang City China

ISBN: (纸本)9798400716966

In the realm of cybersecurity, this study presents a novel approach to intrusion detection by harnessing the power of artificial intelligence (AI) and machine learning (ML). We delve into the critical domain of network intrusion detection, emphasizing the significance of our research. Existing methodologies face challenges, prompting the need for advanced solutions. Our work addresses this gap by employing an innovative method. Beginning with the collection and preprocessing of extensive network traffic data, we extract key features crucial for characterizing network behavior. Our approach utilizes a deep learning model to effectively categorize network behavior, distinguishing between normal traffic and malicious attacks. Through rigorous experimental validation against established technologies, we demonstrate substantial improvements in key metrics such as accuracy, recall, and F1 score. Beyond performance metrics, we explore the practical deployment potential of our approach in real-world network environments. This research contributes to the ongoing discourse on effective intrusion detection solutions driven by AI and ML in the realm of network security. In conclusion, our study not only enhances the understanding of network intrusion detection but also provides a robust and efficient solution for bolstering cybersecurity in an increasingly interconnected world.

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Identifying driver genes in cancer based on Pareto optimality consensus

Identifying driver genes in cancer based on Pareto optimalit...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Deng, Zheng Wu, Jingli Chen, Xiaorong Li, Gaoshi Guangxi Normal University College of Computer Science Information Technology Guilin China Guangxi Normal University Guangxi Key Lab of Multi-source Information Mining & Security Guilin China Wuzhou University Guangxi Key Laboratory of Machine Vision Intelligent Control Wuzhou China

ISBN: (纸本)9781665468190

An important issue in cancer genomics is the identification of driver genes. It is significant for the discovery of key biomarkers and the development of effective personalized therapies. In this paper, a computated method PGScore is proposed. It scores genes at multilayer and integrates the scores to identify cancer driver genes based on Pareto Optimality Consensus(POC) strategy. PGScore uses random walks to reevaluate gene mutations, and integrates differential expression of mRNA and miRNA in normal and cancer samples. It measures the centrality of the gene in the network according to the weight of its direct and indirect neighbors, and finally integrates the above layers to get the final priority of the genes. We compare PGScore with state-of-the-art cancer driver genes prioritization methods on two real cancer datasets. The results show that PGScore can obtain better performance in identification accuracy and the partial area under the ROC(pAUC) curve on multiple reference databases. © 2022 IEEE.

关键词： Gene expression

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Design and Implementation of Water Environment Monitoring System Based on Bionic Robot Fish

Design and Implementation of Water Environment Monitoring Sy...

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control Engineering and Robotics (ISCER), International Symposium on

作者： Meihuan Lin Weilin Wu Department of Electronic Information Guangxi Minzu University Nanning Guangxi China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Guangxi China

In this paper, a small robotic fish system with single joint caudal fin propulsion is designed and developed independently by imitating the tail fin swing mode of real fish in nature. It has human-computer interaction and ornamental. The whole design process is divided into mechanical structure design, software/hardware design. In this paper, the three-dimensional design software Solidworks is used to model the mechanical structure, and the mechanical structure is simulated. After many experiments and optimizations, the caudal fin with higher propulsion efficiency was finally developed, and the maximum navigation speed, turning radius, maximum floating/diving speed and other performance indicators of the robotic fish under high-efficiency caudal fin propulsion were tested. At the same time, appropriate remote control communication technology was selected to realize the free remote control of the robotic fish within a certain water depth range.

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