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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Chen, Ke Han, Xiaosong Li, Xiaoran Liang, Yanchun Xu, Dong Guan, Renchu Jilin University Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Software Changchun China Jilin University Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Changchun China Zhuhai College of Science and Technology Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education School of Computer Science Zhuhai China University of Missouri Christopher S. Bond Life Sciences Center Department of Electrical Engineering and Computer Science Columbia United States

ISBN: (纸本)9798350386226

Drug-Drug Interaction (DDI) task plays a crucial role in clinical treatment and drug development. Recently, deep learning methods have been successfully applied for DDI prediction. However, training deep learning models always need large amount of data, while known DDIs are scarce. To address this challenge, a graph neural network-based DDI prediction model named PNESR-DDI is proposed, which compensates for the lack of DDIs by enriching drug representations. First, to obtain initial node representations that incorporate rich semantic information from the biomedical knowledge graph (KG), a link prediction pre-training method on external KG is proposed in the node embedding pre-training module. Then, considering the large scale of the KG, subgraph extraction for the target drug pairs is introduced to reduce noise and decrease computational complexity in the subgraph anchoring module. After that, the subgraph is updated, and node similarities are propagated in the subgraph reconstruction module. Based on the node similarity scores, the subgraph is pruned and reconstructed, which adjusts node representations to be more conducive to DDI prediction. Finally, the drug embeddings, subgraph representations, and drug fingerprint features are concatenated to predict DDIs. PNESRDDI is evaluated on two benchmark DDI datasets: DrugBank and TWOSIDES. Experiment results show that PNESR-DDI achieves better performance than baselines. Ablation results validate the effectiveness of the pre-training method and the adaptive subgraph reconstruction strategy. © 2024 IEEE.

关键词： Knowledge graph

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Micro-expression Recognition based on Spatiotemporal Local Gabor Binary Patterns on Three Orthogonal Planes

Micro-expression Recognition based on Spatiotemporal Local G...

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2023 International Conference on Image, Signal Processing, and Pattern Recognition, ISPP 2023

作者： Fan, Ling Lin, Chenhan Computer Science and Information Engineering School Xiamen Institute of Technology Xiamen361021 China Center for Digital Media Computation of Xiamen University Xiamen361005 China

ISBN: (纸本)9781510666351

Analyzing facial micro-expressions based on computer vision has become an active research area in recent years. Subtle changes and short duration of micro-expressions bring great challenges to this issue, and extracting effective spatiotemporal features is the key to improving recognition performance. This paper proposes an approach for micro-expression recognition, Spatiotemporal Local Gabor Binary Patterns on Three Orthogonal Planes (STLGBP-TOP), by combining spatiotemporal Gabor filtering and LBP-TOP. In this approach, LBP-TOP is employed to encode local dynamic features of micro-expression videos on top of spatiotemporal Gabor filtering. Experimental results on CASME and CASME2 datasets demonstrate the effectiveness of our method. © 2023 SPIE.

关键词： Local binary pattern

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High-Resolution Noise Artifact Tracking Network for Image Splicing Forgery Detection

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Journal of Information science and engineering 2023年第4期39卷 759-775页

作者： Yu, Haibo Zhang, Tao Zhu, Wenhuan Zhang, Li Jin, Jiali School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China China Ship Scientific Research Center Wuxi214122 China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China

Aiming at the problems that existing methods lose a lot of spatial information and are easily interfered by image content, this paper proposes an end-to-end trainable high-resolution noise artifact tracking network (HRNAT-Net) for image splicing forgery detection. The network adopts HRNet as the network backbone, which can always retain high-resolution features, thereby effectively reducing the loss of important spatial information such as edge features. Besides, the network utilizes dilated convolution, which further preserves rich tampering information. Noise information are used as auxiliary information to build a dual-stream network to exploit visual and noise features simultaneously. Through multiple multi-scale feature fusion, the expressive ability of features is further enhanced. Finally, the visual features and noise features of corresponding resolutions are concatenated, and through multi-scale feature fusion, visual features and noise features are combined to locate the tampered area. The proposed network effectively solves the loss of spatial information, and simultaneously seeks forensic clues from the visual and noise levels. We evaluate the network performance on the CASIA v2.0 dataset and the Columbia dataset using metrics such as pixel-level Precision, Recall, and F1, and the detection accuracy (F1) reaches 0.845 and 0.950, respectively. Experimental results show that the proposed network outperforms state-of-the-art methods and has good robustness to JPEG compression, Gaussian noise, and resizing attacks. © 2023 Institute of Information science. All rights reserved.

关键词： Convolution

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Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems:A Medical Case Study

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Journal of Bionic engineering 2024年第1期21卷 409-425页

作者： Adel Got Djaafar Zouache Abdelouahab Moussaoui Laith Abualigah Ahmed Alsayat Faculty of informatics University of Science and Technology Houari BoumedieneAlgiersAlgeria LRIA Laboratory University of Science and Technology Houari BoumedieneAlgiersAlgeria Computer Science Department University of Mohamed El Bachir El IbrahimiBordj Bou ArreridjAlgeria Computer Science Department University of Ferhat AbbasSetifAlgeria Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq 25113Jordan Department of Computer Science College of Computer and Information SciencesJouf UniversityJoufSaudi Arabia Department of Electrical and Computer Engineering Lebanese American University13-5053ByblosLebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman 19328Jordan MEU Research Unit Middle East UniversityAmman 11831Jordan Applied science research center Applied science private universityAmman 11931Jordan School of Computer Sciences Universiti Sains Malaysia11800 PenangMalaysia School of Engineering and Technology Sunway University Malaysia27500 Petaling JayaMalaysia

Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification ***,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good *** the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of *** this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset *** proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking ***,it is applied to a disease Covid-19 *** experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters,and its acceptable performance to deal with feature selection problem.

关键词： Support vector machine Parameters tuning Feature selection Bioinspired algorithms Manta ray foraging optimizer

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Optimizing Secure Multi-User ISAC Systems With STAR-RIS: A Deep Reinforcement Learning Approach for 6G Networks

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IEEE Access 2025年 13卷 31472-31484页

作者： Kamal, Mian Muhammad Zain Ul Abideen, Syed Al-Khasawneh, M.A. Alabrah, Amerah Sohail Ahmed Larik, Raja Irfan Marwat, Muhammad Southeast University School of Electronic Science and Engineering Jiangning Jiangsu Nanjing211189 China Qingdao University College of Computer Science and Technology Qingdao266071 China Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman19111 Jordan Skyline University College School of Computing University City Sharjah Sharjah United Arab Emirates King Saud University College of Computer and Information Science Department of Information Systems Riyadh11543 Saudi Arabia Ilma University Department of Computer Science Sindh Karachi75190 Pakistan University of Science and Technology Bannu Department of Software Engineering Bannu28100 Pakistan

The rapid evolution of wireless communication technologies and the increasing demand for multi-functional systems have led to the emergence of integrated sensing and communication (ISAC) as a key enabler for future 6G networks. Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have recently garnered significant attention for their ability to enhance signal coverage and improve system efficiency. This paper investigates a STAR-RIS-assisted ISAC system designed to secure communication for multiple legitimate users (LUs) while safeguarding against multiple eavesdroppers (Eves). By jointly optimizing the base station (BS) transmit beamforming, STAR-RIS transmission and reflection coefficients, and receive filters, the proposed framework aims to maximize the long-term average secrecy rate for all LUs. Constraints are imposed to ensure minimum echo signal-to-noise ratios (SNRs) for sensing and meet the achievable rate requirements of LUs. To address the inherent complexity of this non-convex problem, two deep reinforcement learning (DRL) algorithms are proposed. Numerical results demonstrate that the proposed system achieves significant improvements in secrecy rate compared to conventional RIS setups. This work provides a scalable and efficient approach for secure multi-user ISAC systems, making it highly relevant for future 6G networks, smart cities, and IoT applications. © 2025 The Authors.

关键词： Deep reinforcement learning

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MANAGING PROSUMER EXPORTS IN DISTRIBUTION NETWORKS THROUGH PARTICIPATORY EXPORT LIMITS

MANAGING PROSUMER EXPORTS IN DISTRIBUTION NETWORKS THROUGH P...

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CIRED 2024 Vienna Workshop

作者： Karmaker, Ashish Kumar Behrens, Sam Sturmberg, Bjorn Pota, Hemanshu School of Engineering and Technology University of New South Wales Canberra Australia Energy Center Commonwealth Scientific and Industrial Research Organization Newcastle Australia College of Engineering and Computer Science Australian National University Canberra Australia

ISBN: (纸本)9781837242672

This paper proposes an innovative stakeholder-centric approach for managing prosumer exports in distribution networks ensuring stakeholder benefits. The rapid inclusion of photovoltaic (PV) systems leads to excessive exports, affecting the stability and performance of the distribution networks and fostering effective export management. In addition, the emergence of vehicle-to-grid (V2G) services presents challenges that may exacerbate export-related issues. Several distribution companies use fixed export limits for PV prosumers without looking at network performance. Current studies suggest dynamic limits to unlock export capabilities, providing variable export opportunities while neglecting diverse capabilities and nodal disparities of prosumers. By amalgamating dynamic export limits and prosumer priority indexing, this paper endeavours to derive individualized export limits in distribution networks. This research examined dynamic export capabilities on a real Australian suburban distribution feeder with 966 customers utilizing diverse capacities, nodal situations, and network uncertainties. The prosumer priority ranking is based on nodal constraints, time, and individual capabilities. The paper highlights participatory export limits, utilizing dynamic export limits and individual rankings, to offer enhanced export flexibility, benefiting stakeholders both distribution network operators and prosumers. These stakeholder-centric export limits provide a promising avenue for seamless PV and V2G integration toward a sustainable and resilient energy ecosystem. © The Institution of engineering & Technology 2024.

关键词： Vehicle-to-grid

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KSKV:Key-Strategy for Key-Value Data Collection with Local Differential Privacy

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computer Modeling in engineering & sciences 2024年第6期139卷 3063-3083页

作者： Dan Zhao Yang You Chuanwen Luo Ting Chen Yang Liu Artificial Intelligence Development Research Center Institute of Scientific and Technical Information of ChinaBeijing100038China Industry Development Department NSFOCUS Inc.BeijingChina School of Information Science and Technology Beijing Forestry UniversityBeijing100083China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu610054China Institute of Computing Technology China Academy of Railway Sciences Corporation LimitedBeijing10081China

In recent years,the research field of data collection under local differential privacy(LDP)has expanded its focus fromelementary data types to includemore complex structural data,such as set-value and graph ***,our comprehensive review of existing literature reveals that there needs to be more studies that engage with key-value data *** studies would simultaneously collect the frequencies of keys and the mean of values associated with each ***,the allocation of the privacy budget between the frequencies of keys and the means of values for each key does not yield an optimal utility *** the importance of obtaining accurate key frequencies and mean estimations for key-value data collection,this paper presents a novel framework:the Key-Strategy Framework forKey-ValueDataCollection under ***,theKey-StrategyUnary Encoding(KS-UE)strategy is proposed within non-interactive frameworks for the purpose of privacy budget allocation to achieve precise key frequencies;subsequently,the Key-Strategy Generalized Randomized Response(KS-GRR)strategy is introduced for interactive frameworks to enhance the efficiency of collecting frequent keys through group-anditeration *** strategies are adapted for scenarios in which users possess either a single or multiple key-value ***,we demonstrate that the variance of KS-UE is lower than that of existing *** claims are substantiated through extensive experimental evaluation on real-world datasets,confirming the effectiveness and efficiency of the KS-UE and KS-GRR strategies.

关键词： Key-value local differential privacy frequency estimation mean estimation data perturbation

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IDSDeep-CCD: intelligent decision support system based on deep learning for concrete cracks detection

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Multimedia Tools and Applications 2025年第12期84卷 10013-10026页

作者： Abualigah, Sayel M. Al-Naimi, Ahmad F. Sachdeva, Gagan AlAmri, Omran Abualigah, Laith Department of Computer Information Systems Jordan University of Science and Technology Irbid Jordan Gagan Sachdeva Dubai United Arab Emirates Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Computer Science Department Al al-Bayt University Mafraq25113 Jordan Research Center University of Tabuk Tabuk71491 Saudi Arabia MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Applied Science Research Center Applied Science Private University Amman11931 Jordan College of Engineering Yuan Ze University Taoyuan32003 Taiwan

In an era where Intelligent Decision Support Systems (IDSS) are integral to managing the vast data from Internet of Everything (IoE) systems, this study introduces IDSDeep-CCD, a novel IDSS approach for detecting concrete cracks, a critical issue in civil infrastructure maintenance. Traditional visual inspection methods for crack detection are time-consuming and error-prone. To address this, IDSDeep-CCD employs deep learning, utilizing an open-source dataset of concrete crack images for enhanced accuracy. The system's two-stage approach, featuring feature extraction via pre-trained models and a fully connected network for classification, significantly outperforms existing methods. Our results demonstrate a remarkable 99.9% accuracy rate in crack detection without data augmentation, marking a notable advancement in automatic detection technology. This breakthrough offers practical benefits for the industry by enabling more precise and efficient identification of concrete cracks, paving the way for timely and accurate remedial actions. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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A novel hybrid butterfly optimization algorithm for feature selection with sine cosine velocity in the high-dimensional classification data

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Journal of Intelligent and Fuzzy Systems 2024年第5-6期47卷 369-391页

作者： Zhang, Li Chen, Xiaobo Key Laboratory of Data Science and Intelligence Education Hainan Normal University Ministry of Education Haikou Hainan China School of Computer Engineering Jiangsu University of Technology Changzhou Jiangsu China Changzhou City Center Branch People's Bank of China Changzhou Jiangsu China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China

Aiming at the shortcomings of the traditional butterfly optimization algorithm in solving the high-dimensional classification feature selection problem, which has low convergence and is prone to fall into local optimal solutions, a new hybrid butterfly optimization algorithm is proposed, i.e., HBOA-SCV (A novel hybrid butterfly optimization algorithm with sine cosine velocity). The algorithm is applied to solve a high-dimensional classification feature selection problem. Firstly, the algorithm's global exploration and local exploitation ability can be dynamically balanced by introducing inertia weight coefficients w based on multiple learning strategies. Secondly, using the updated speed position formula of the sine-cosine acceleration strategy, individual butterflies' autonomous search ability and convergence speed can be further improved. Finally, according to the fitness value of each butterfly individual, the moving step length and direction of the butterfly individual are automatically adjusted better to fit the actual search process of the butterfly individual, increase the search ability in the global range, and avoid the algorithm from falling into the local optimum. To verify the algorithm's effectiveness, 18 high-dimensional classification numbers are selected to carry out simulation and comparison experiments between HBOA-SCV and traditional BOA algorithm, five improved BOA algorithms and other comparative algorithms for high-dimensional classification data successively. The experimental results show that the average fitness value and classification accuracy of the HBOA-SCV algorithm are better than the comparison algorithm, thus verifying the superiority of the HBOA-SCV algorithm. © 2024 - IOS Press. All rights reserved.

关键词： Optimization algorithms

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A Model Inference Attack Based on Random Sampling in DLaaS 24th

A Model Inference Attack Based on Random Sampling in DLaaS

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

作者： Wu, Feng Sun, Shouyue Yang, Jiaxun Wu, Liwen Cui, Lei Qu, Youyang Yao, Shaowen Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Engineering Research Center of Cyberspace Yunnan University Kunming China School of Information Science and Engineering Yunnan University Kunming China Data61 Commonwealth Scientific and Industrial Research Organization BurwoodVIC3166 Australia

ISBN: (纸本)9789819615506

Deep learning as a service (DLaaS) has become an effective business solution in numerous domains. Nevertheless, its service form has been proven vulnerable to model inference by previous research. Existing work on model inference attack relies on strong assumptions, such as the attacker knows the sample variance and model structure, which may not thoroughly reflect the attack’s potential threats. In this paper, we propose MC-infer, a zero-knowledge, real-data-free, and black-box model inference method inspired by Monte-Carlo sampling. In particular, MC-infer feeds random noises obtained from different distributions to the target model, and sniffs the corresponding target distributions according to its feedback. Then many samples are taken in these distributions to obtain enough robust noises. Finally, the distribution represented by the target model is fitted through these noises to perform model inference. Our extensive evaluations demonstrate that MC-infer can effectively infer the target model with less information, and general noise perturbation on the model’s outputs cannot defend against MC-infer. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Intelligent systems

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