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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Huang, Sujia Xiao, Shunxin Liu, Wenzhe Lu, Jielong Wu, Zhihao Wang, Shiping Rajapakse, Jagath C. Fuzhou University College of Computer and Data Science Fuzhou China Huzhou University School of Information Engineering Huzhou China Nanyang Technological University School of Computer Science and Engineering Singapore

ISBN: (纸本)9798350337488

Multi-omics data provides a wealth of information concerning disease mechanisms, which benefits the exploration of the intricate molecular phenomena underlying diseases. In recent years, considerable endeavors have been directed towards the combination of graph convolutional network, which has the powerful ability to gather information, with multi-omics learning methods to obtain more reliable results. For achieving this pursuit, an essential challenge is data integration. Against this backdrop, we propose a unified framework named multi-level knowledge integration with graph convolutional network, which effectively incorporates multiple prior knowledge and omics data to learn an intrinsic representation. In specific, the model consists of two subnetworks: an attribute-level module and a sample-level module. The former firstly aggregates the knowledge given by the prior biological graphs into low-dimensional embeddings, and then maximizes the consistency between these prior views via optimizing a contrastive loss for attaining the attribute-based representations. The latter leverages an encoder to dimensionalize the original multi-omics data to attain more dominant sample knowledge, and subsequently utilizes another contrastive loss to align these representations between multiple omics for learning the global sample-level information. Comprehensive experiments are performed to show that the proposed model surpasses other state-of-the-art methods. © 2023 IEEE.

关键词： cancer molecular subtype classification contrastive learning graph convolutional network Multi-omics data

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A DL Intrusion Detection Method for Low-Rate Denial of Service Attacks

A DL Intrusion Detection Method for Low-Rate Denial of Servi...

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2023 Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2023

作者： Wu, Yuhong Lv, Yunshan Hu, Xiangdong School of Big Data & Computer Science Engineering Chongqing College of Mobile Communication China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Key Laboratory of Public Big Data Security Technology Chongqing Chongqing China School of Big Data & Computer Science Engineering Chongqing College of Mobile Communication Key Laboratory of Public Big Data Security Technology Chongqing China School of Automation Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781665457033

Denial of service (DoS) attacks is among the most harmful network threats. Low-rate DoS (LDoS) attacks feature good concealment and are likely to evade detection by conventional signal analysis-based methods. A LDoS attack detection method that combines the convolutional neural network and the gated recurrent unit is proposed in the present work. To evaluate the proposed method, we collect real user traffic from a university's website, label the traffic with LDoS attacks as normal traffic. In order to get the real attack traffic, this paper has collected some new LDoS attack programs, such as slowloris, slowhttptest, pwnlloris, torhammer and httplog, which can be obtained from public channels, and constructed a LAN environment that conformed to the working scenario of the program. By running the attack program, the attack traffic could be obtained. In a large sample scenario, the average detection rate of our proposed method can reach 98.68%. This article focuses on the research: the average detection rate of the detection method proposed in this article in the case that the security agency can only intercept a small number of attack samples. That is, in the small sample scenario, the detection of the collected traffic shows that the average detection rate of the proposed detection method can reach 89.54% in a small sample scenario. Its effective detection rate is obviously superior to the existing similar detection methods. © 2023 IEEE.

关键词： Denial-of-service attack

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Improved Particle Swarm Algorithm Using Multiple Strategies 1

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14th International Symposium on Intelligence Computation and Applications, ISICA 2023

作者： Yi, Yunfei Wang, Zhiyong Shi, Yunying School of Big Data and Computer Hechi University Guangxi Yizhou546300 China College of Computer Science and Engineering Guangxi Normal University Guangxi Guilin541001 China

ISBN: (数字)9789819743933

ISBN: (纸本)9789819743926

In order to address the issues of premature convergence and low search efficiency in the basic particle swarm algorithm, this paper analyzes the improved particle swarm optimization algorithms proposed by previous researchers. Based on this analysis, a modified algorithm for particle swarm optimization is proposed. The enhanced algorithm adjusts the inertia weight parameter in a nonlinear manner and dynamically changes the self-learning factor using chaos, allowing it to continuously change during iterations and achieve better optimization results. Additionally, to prevent the algorithm from getting trapped in local optima, a mutation operation is introduced into the improved algorithm. Finally, several classical test functions are used to conduct experiments, and the results are compared with the improved particle swarm optimization algorithms described in related literature. The experimental results demonstrate that the newly proposed algorithm achieves a certain degree of improvement in optimization accuracy compared to the basic particle swarm algorithm. This indicates that the modified algorithm effectively avoids premature convergence and possesses high search efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Particle swarm optimization (PSO)

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Probabilistic Reliability via Subsystem Structures of Arrangement Graph Networks

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IEEE Transactions on Reliability 2024年第1期73卷 279-289页

作者： Huang, Yanze Lin, Limei Xu, Li Hsieh, Sun-Yuan Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fuzhou350118 China Fujian Normal University College of Computer and Cyber Security Key Laboratory of Network Security and Cryptology Fuzhou350117 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan

With the rapid growth of the number of processors in a multiprocessor system, faulty processors occur in it with a probability that rises quickly. The probability of a subsystem with an appropriate size being fault-free in a definite time interval is a significant and practical measure of the reliability for a multiprocessor system, which characterizes the functionality of a multiprocessor system well. Motivated by the study of subgraph reliability, as well as the attractive structure and fault tolerance properties of (n, k)-arrangement graph A n, k, we focus on the subgraph reliability for A n, k under the probabilistic fault model in this article. First, we investigate intersections of no more than four subgraphs in A n, k, and classify all the intersecting modes. Second, we focus on the probability P(q, A n, k n-1, k-1) with which at least one (n-1, k-1)-subarrangement graph is fault-free in A n, k, when given a uniform probability q with which a single vertex is fault-free, and we establish the P(q, A n, kn-1, k-1) by adopting the principle of inclusion-exclusion under the probabilistic fault model. Finally, we study the probabilistic fault model involving a nonuniform probability with which a single vertex is fault-free, and we prove that the P(q, A n, kn-1, k-1) under both models is very close to the asymptotic value by both theoretical arguments and experimental results. © 1963-2012 IEEE.

关键词： Fault tolerance

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Insider Trading with a Random Deadline under Partial Observations:Maximal Principle Method

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Acta Mathematicae Applicatae Sinica 2022年第4期38卷 753-762页

作者： Kai XIAO Yong-hui ZHOU School of Mathematical Sciences Guizhou Normal UniversityGuiyang 550001China School of Big Data and Computer Science Guizhou Normal UniversityGuiyang 550001China

For a revised model of Caldentey and Stacchetti(Econometrica,2010)in continuous-time insider trading with a random deadline which allows market makers to observe some information on a risky asset,a closed form of its market equilibrium consisting of optimal insider trading intensity and market liquidity is obtained by maximum principle *** shows that in the equilibrium,(i)as time goes by,the optimal insider trading intensity is exponentially increasing even up to infinity while both the market liquidity and the residual information are exponentially decreasing even down to zero;(ii)the more accurate information observed by market makers,the stronger optimal insider trading intensity is such that the total expect profit of the insider is decreasing even go to zero while both the market liquidity and the residual information are decreasing;(iii)the longer the mean of random time,the weaker the optimal insider trading intensity is while the more both the residual information and the expected profit are,but there is a threshold of trading time,half of the mean of the random time,such that if and only if after it the market liquidity is increasing with the mean of random time increasing.

关键词： continuous-time insider trading random deadline partial observations filtering theory maximal principle

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Cross-modal image text retrieval based on Cross-optimization and Joint strategy

Cross-modal image text retrieval based on Cross-optimization...

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2024 International Conference on Image, Signal Processing, and Pattern Recognition, ISPP 2024

作者： Zheng, Wei Han, Na Hu, Yan Kang, Peipei Zhang, Yue Yang, Aqing Zhang, Lei School of Computer Science Guangdong Polytechnic Normal University China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data China School of Computer Science and Technology Guangdong University of Technology China School of Information Engineering East University of Heilongjiang China

ISBN: (纸本)9781510680425

In the fields of computer vision and natural language processing, cross-modal retrieval is of great importance that cannot be ignored. In existing multi-granularity alignment methods, significant progress has been made by globally aligning images and sentences or locally aligning regions and words. However, most of these methods rely on attention mechanisms, which may be affected by noise and cannot balance the differences between modalities, resulting in suboptimal image-text alignment. In addition, training with attention mechanisms consumes a lot of computational resources, and the retrieval process is time-consuming. To address these challenges, we develop a novel multigranularity image-text alignment model, which we call DFVLM. First, we independently train the intra-modal encoder and cross-modal encoder to reduce interference between different modal encoders and better learn intra-modal and inter-modal information. Then, we propose a joint strategy to combine the intra-modal and inter-modal information to better capture the information between image and text. In addition, we introduce a hard negative pair-based method to train the performance of the intra-modal multi-granularity encoder without using attention mechanisms. Unlike existing methods, We treat image and text retrieval as a two-way process and delve into the intrinsic connections between the two matrices. We propose a cross-validation method to optimize the retrieval result based on common image-to-text alignment scenarios in daily life, i.e., during the image-to-text retrieval process, a text-to-image reverse verification is performed on the retrieved corresponding text. Through extensive qualitative experiments and analysis, our approach performs well on both Flickr30K and MSCOCO datasets and also significantly reduces the time required for testing. © 2024 SPIE.

关键词： Natural language processing systems

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Ghost Module Based Residual Mixture of Self-Attention and Convolution for Online Signature Verification

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computers, Materials & Continua 2024年第4期79卷 695-712页

作者： Fangjun Luan Xuewen Mu Shuai Yuan Department of Computer Technology School of Computer Science and EngineeringShenyang Jianzhu UniversityShenyang110168China Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang Jianzhu UniversityShenyang110168China Shenyang Branch of National Special Computer Engineering Technology Research Center Shenyang Jianzhu UniversityShenyang110168China

Online Signature Verification (OSV), as a personal identification technology, is widely used in various ***, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. Toaddress these issues, we propose a novel approach for online signature verification, using a one-dimensionalGhost-ACmix Residual Network (1D-ACGRNet), which is a Ghost-ACmix Residual Network that combines convolutionwith a self-attention mechanism and performs improvement by using Ghost method. The Ghost-ACmix Residualstructure is introduced to leverage both self-attention and convolution mechanisms for capturing global featureinformation and extracting local information, effectively complementing whole and local signature features andmitigating the problem of insufficient feature extraction. Then, the Ghost-based Convolution and Self-Attention(ACG) block is proposed to simplify the common parts between convolution and self-attention using the Ghostmodule and employ feature transformation to obtain intermediate features, thus reducing computational ***, feature selection is performed using the random forestmethod, and the data is dimensionally reducedusing Principal Component Analysis (PCA). Finally, tests are implemented on the MCYT-100 datasets and theSVC-2004 Task2 datasets, and the equal error rates (EERs) for small-sample training using five genuine andforged signatures are 3.07% and 4.17%, respectively. The EERs for training with ten genuine and forged signaturesare 0.91% and 2.12% on the respective datasets. The experimental results illustrate that the proposed approacheffectively enhances the accuracy of online signature verification.

关键词： Online signature verification feature selection ACG block ghost-ACmix residual structure

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DBFIA: Diffusion-Based Face Image Anonymization 24th

DBFIA: Diffusion-Based Face Image Anonymization

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Xue, Hanyu Yuan, Xin Liu, Bo Ding, Ming Australian Artificial Intelligence Institute School of Computer Science University of Technology Sydney Ultimo Australia Data61 CSIRO Eveleigh Australia

ISBN: (纸本)9789819615247

This paper presents a novel approach to address the issue of identity protection in facial image datasets. Our goal is to prevent any violation of privacy for the individuals depicted in the dataset while ensuring that the dataset is still useful for downstream training tasks and maintains the quality of the images. Previous methods have predominantly used Generative Adversarial Network (GAN)-based models to anonymize facial datasets. However, such models suffer from distortion and loss of detail when processing real-life images. To overcome these challenges, we propose a method for anonymizing faces using a diffusion model. Our approach retains the original facial attributes and produces anonymized images with high-quality image details. These attributes are essential when using anonymized images for downstream tasks. Our proposed framework optimizes the latent space vectors of the conditional Denoising diffusion probabilistic model (DDPM) to maintain a certain distance between the identity features (in the feature space of ArcFace) of the anonymized images and the original identity features while preserving the facial attributes. Through qualitative and quantitative experiments, we demonstrate that our method can effectively anonymize the identity of facial images while preserving image details and facial attributes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Anonymity

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AMHF-TP:Multifunctional therapeutic peptides prediction based on multi-granularity hierarchical features

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Quantitative Biology 2025年第1期13卷 127-141页

作者： Shouheng Tuo YanLing Zhu Jiangkun Lin Jiewei Jiang School of Computer Science and Technology Xi’an University of Posts and TelecommunicationsXi’anChina Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’anChina Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’anChina School of Electronic Engineering Xi’an University of Posts and TelecommunicationsXi’anChina

Multifunctional therapeutic peptides(MFTP)hold immense potential in diverse therapeutic contexts,yet their prediction and identification remain challenging due to the limitations of traditional methodologies,such as extensive training durations,limited sample sizes,and inadequate generalization *** address these issues,we present AMHF-TP,an advanced method for MFTP recognition that utilizes attention mechanisms and multi-granularity hierarchical features to enhance *** AMHF-TP is composed of four key components:a migration learning module that leverages pretrained models to extract atomic compositional features of MFTP sequences;a convolutional neural network and selfattention module that refine feature extraction from amino acid sequences and their secondary structures;a hypergraph module that constructs a hypergraph for complex similarity representation between MFTP sequences;and a hierarchical feature extraction module that integrates multimodal peptide sequence *** with leading methods,the proposed AMHF-TP demonstrates superior precision,accuracy,and coverage,underscoring its effectiveness and robustness in MFTP *** comparative analysis of separate hierarchical models and the combined model,as well as with five contemporary models,reveals AMHFTP’s exceptional performance and stability in recognition tasks.

关键词： deep learning hypergraph multifunctional therapeutic peptides multi-granularity hierarchical features

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An Analysis of data Sparsity Resolution Algorithms Used in Recommender Systems 1

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4th International Conference on Advances in Information Communication Technology and Computing, AICTC 2022

作者： Bhardwaj, Shivani Kanwar, Kushal Gupta, Gaurav Yogananda School of AI Computer and Data Science Shoolini University H.P. Solan India

ISBN: (数字)9789811998881

ISBN: (纸本)9789811998874

In previous years, a substantial rise has been observed in the use of YouTube, Netflix, Amazon, and other similar web services that have enabled the utilization of recommender systems. Ranging from e-commerce to online advertisements, recommender systems tend to be unavoidable in routine journeys. Fundamentally, such systems are emphasized by suggesting relevant items to end-users and are of great use in enhancing the engagement rate. However, there are several problems such as Cold Start and data Sparsity that impact the efficacy of the recommender system. A wide range of factors is responsible for such issues. In a similar context, this paper focuses on conducting a systematic literature review and analysis of different algorithms that have been proposed by researchers and practitioners in the field of recommender systems. The preliminary purpose of this paper is associated with obtaining an in-depth and succinct understanding of a wide range of solutions that have emerged from different studies. Based on the algorithms mentioned in the literature, it has been observed that Singular Value Decomposition Plus Plus offers the best possible resolution to data sparsity issues. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Singular value decomposition

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