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arXiv 2023年

作者： Zhao, Bowen Chen, Wei-Neng Li, Xiaoguo Liu, Ximeng Pei, Qingqi Zhang, Jun Guangzhou Institute of Technology Xidian University Guangzhou China The School of Computer Science and Technology South China University of Technology Guangzhou China The School of Computing and Information Systems Singapore Management University Singapore Singapore The College of Computer and Data Science Fuzhou University Fujian China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen China School of Telecommunications Engineering Xidian University Xian China Zhejiang Normal University Jinhua China Hanyang University Ansan Korea Republic of

Recently, evolutionary computation (EC) has been promoted by machine learning, distributed computing, and big data technologies, resulting in new research directions of EC like distributed EC and surrogate-assisted EC. These advances have significantly improved the performance and the application scope of EC, but also trigger privacy leakages, such as the leakage of optimal results and surrogate model. Accordingly, evolutionary computation combined with privacy protection is becoming an emerging topic. However, privacy concerns in evolutionary computation lack a systematic exploration, especially for the object, motivation, position, and method of privacy protection. To this end, in this paper, we discuss three typical optimization paradigms (i.e., centralized optimization, distributed optimization, and data-driven optimization) to characterize optimization modes of evolutionary computation and propose BOOM (i.e., oBject, mOtivation, pOsition, and Method) to sort out privacy concerns in evolutionary computation. Specifically, the centralized optimization paradigm allows clients to outsource optimization problems to the centralized server and obtain optimization solutions from the server. While the distributed optimization paradigm exploits the storage and computational power of distributed devices to solve optimization problems. Also, the data-driven optimization paradigm utilizes data collected in history to tackle optimization problems lacking explicit objective functions. Particularly, this paper adopts BOOM to characterize the object and motivation of privacy protection in three typical optimization paradigms and discusses potential privacy-preserving technologies balancing optimization performance and privacy guarantees in three typical optimization paradigms. Furthermore, this paper attempts to foresee some new research directions of privacy-preserving evolutionary computation. © 2023, CC BY.

关键词： Optimization

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A Clustering-Based Routing Protocol Using Path Pattern Discovery Method to Minimize Delay in VANET

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Wireless Communications and Mobile computing 2023年第1期2023卷

作者： Xie, Xiaoyun Navaei, Yahya Dorostkar Einy, Sajad School of Electronic Information Engineering Gannan University of Science and Technology Jiangxi Ganzhou341000 China Ganzhou Key Laboratory of Cloud Computing and Big Data Jiangxi Ganzhou341000 China Computer and Information Technology Engineering Qazvin Branch Islamic Azad University Qazvin Iran Istanbul Aydin University Department of Application and Research Center for Advanced Studies Istanbul Turkey

In vehicular ad hoc networks (VANETs), vehicle-to-vehicle (V2V) communications can link vehicles to each other, and vehicle-to-infrastructure (V2I) messaging and communications can link roadside infrastructure such as routers. The vehicles in these networks act as relays that transmit critical messages in the network. Due to the high-speed movement of vehicles on the road, real-time messaging and minimizing the delay in sending messages is one of the most important objectives of VANET developers. On the other hand, the high mobility of vehicles causes communication interruptions and decreases the data delivery rate in VANET. To overcome this issue, predicting the path of vehicles can play an important role in sending data from the source to the destination. When an accident occurs on the road, the messages that are sensed by the imbedded sensors in the vehicles need to be sent, and if they are sent by the vehicles that change their route, these messages will not be sent to the destination and the performance of the network will be disturbed. Previous methods in the literature for data transmission in intervehicular networks have focused more on reliability and trust, and little attention has been paid to the prediction of vehicle movement paths in these types of networks. Therefore, for fast and reliable data transmission in VANET, accurate prediction of vehicle movement and creation of movement patterns can be effective in message transmission delay and data delivery rate. In this paper, we present an approach using a combination of cluster-based routing protocols and pattern discovery methods to minimize latency in VANETs. The outline of the proposed method has four modules: primary data collection and analysis, primary data preparation and analysis, pattern extraction and vehicle route discovery, and vehicle clustering and data/information transmission routing. The simulation results show that the proposed method with a delivery rate of 88.56% has significantly i

关键词： Vehicular ad hoc networks

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Usage: Uncertain Flow Graph and Spatio-Temporal Graph Network-Based Saturation Attack Detection Method

SSRN

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SSRN 2023年

作者： Wang, Kaixi Cui, Yunhe Qian, Qing Chen, Yi Guo, Chun Shen, Guowei Engineering Research Center of Text Computing & Cognitive Intelligence Guizhou University China State Key Laboratory of Public Big Data Guizhou University China Guizhou Provincial Characteristic Key Laboratory of Software Engineering and Information Security Guizhou University China College of Computer Science and Technology Guizhou University China School of Information Guizhou University of Finance and Economics China

With the development of Software-Defined Networking (SDN), its security problems have become important research content. Among these security problems, saturation attack is one of the most destructive problems. Saturation attacks can destroy SDN networks. Currently, neural graph networks have already been used for detecting saturation attacks in SDN. When constructing the graph based on K-Nearest Neighbors (KNN) algorithm, the number of edges in the graph is fixed. That cannot reflect the different nodes’ relationship well. In addition, when using the Graph Convolutional Network (GCN) to detect saturation attack against SDN, only a partial graph structure and node features will be learned, causing the flow graph nodes information cannot be fully utilized. To overcome these issues, in this work, we propose USAGE, an uncertain flow graph and spatio-temporal graph network-based saturation attack detection method. More specifically, USAGE designs the CRAM algorithm and EGG model. The CRAM algorithm is designed to generate an uncertain flow graph for each traffic flow, which can represent the uncertain relationship among different nodes. EGG is a spatio-temporal graph convolutional network model, which is proposed to detect saturation attacks towards SDN switches. The evaluation results demonstrate that USAGE can generate a better graph than KNN and achieve high detection performance. Meanwhile, USAGE outperforms other methods in terms of TPR, TNR, accuracy, precision, recall, F-score, confusion matrix, ROC curves, and PR curves. © 2023, The Authors. All rights reserved.

关键词： Flow graphs

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Revocable and certificateless public auditing for cloud storage

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science China(information sciences) 2020年第10期63卷 282-284页

作者： Yinghui ZHANG Tiantian ZHANG Shengmin XU Guowen XU Dong ZHENG School of Cyberspace Security Xi'an University of Posts and Telecommunications Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology National Engineering Laboratory for Wireless Security Xi'an University of Posts and Telecommunications Secure Mobile Centre School of Information SystemsSingapore Management University School of Computer Science and Engineering University of Electronic Science and Technology of China

Dear editor,Plenty of computing and storage resources in the cloud are provided for users with restricted computing and storage resources,which has attracted the attention of many researchers[1,2].A generic blockchain-based cloud data auditing scheme[3] is proposed,which is compatible with any blockchains including the bitcoin *** the data integrity checking scheme,certificate-

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Artificial intelligence assisted recognition and diagnosis of Magnetically controlled capsule gastroscopy

Artificial intelligence assisted recognition and diagnosis o...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Susu Chen Chuyu Wei Dongqi Li Glen M. Borchert Zhong Yu Jiawen Lin Jingshan Huang College of Computer and Data Science Fuzhou University Fuzhou China Department of Gastroenterology Sun Yat-sen Memorial Hospital Sun Yat-sen University Guangzhou China School of Information & Computer Sciences School of Social & Behavioral Sciences University of California Irvine Irvine CA U.S.A Department of Pharmacology College of Medicine University of South Alabama Mobile AL U.S.A School of Computing & College of Medicine University of South Alabama Mobile U.S.A

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

To enhance the diagnostic efficiency and accuracy of gastric mucosal lesions in magnetically controlled capsule endoscopy (MCCE) images, a Jigsaw Guided Deep Feature Fusion (JG-DFF) artificial intelligence (AI)-assisted recognition model based on the ResNet-50 convolutional neural network (CNN) is presented in this paper. Based on a dataset consisting of 4,053 MCCE images retrospectively collected from Sun Yat-sen Memorial Hospital, we constructed a ResNet-50 AI recognition model, as well as a JG-DFF AI recognition model under the jigsaw guided deep feature fusion. Additionally, the diagnostic ability and run time of the JG-DFF model were compared against a digestive endoscopist using the same data. The results indicate that the JG-DFF model has the potential for clinical application in improving diagnostic efficiency and reducing false positive rates. The overall accuracy of the JG-DFF artificial intelligence assisted recognition model established in this study is 92.1%. Compared to the ResNet-50 model, recognition by the JG-DFF model was more consistent; Compared with the digestive endoscopist, observed specificity of the JG-DFF model in identifying positive lesions was similar (P>0.05). That said, the JG-DFF model was more sensitive in identifying certain classes (P<0.05), and the time required to identify a single image was significantly shorter. 1

关键词： Image recognition Accuracy Magnetic resonance imaging Manuals Predictive models Lesions Convolutional neural networks Artificial intelligence Optimization Periodic structures

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An Effective Learning Management System for Revealing Student Performance Attributes

arXiv

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arXiv 2024年

作者： Zhang, Xinyu Lee, Vincent C.S. Xu, Duo Chen, Jun Obaidat, Mohammad S. The School of Electronics and Information Northwestern Polytechnical University Xi’an710072 China The Department of Data Science and Artificial Intelligence Faculty of IT Monash University Melbourne3800 Australia Guangyuan Foreign Language School Guangyuan628018 China Chongqing Institute for Brain and Intelligence Guangyang Bay Laboratory Chongqing400064 China The King Abdullah II School of Information Technology The University of Jordan Amman11942 Jordan School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Department of Computational Intelligence School of Computing SRM University SRM Nagar TN Kattankulathur603203 India School of Engineering The Amity University UP Noida201301 India

Contribution: A learning management system (LMS) incorporated with an advanced educational data mining module is proposed, as a means to mine efficiently from student performance records to provide valuable insights for educators in helping plan effective learning pedagogies, improve curriculum design, and guarantee quality of teaching. Background: An LMS streamlines the management of the teaching process in a centralized place, recording, tracking, and reporting the delivery of educational courses and student performance. Educational knowledge discovery from such an elearning system plays a crucial role in rule regulation, policy establishment, and system development. However, existing LMSs do not have embedded mining modules to directly extract knowledge. As educational modes become more complex, educational data mining efficiency from those heterogeneous student learning behaviours is gradually degraded. Intended outcomes: The design and application of the LMS enable educators to learn from past student performance experiences, empowering them to guide and intervene with students in time, and eventually improve their academic success. Application design: This study proposes an effective LMS which processes the stored data through an advanced educational data mining module. The mining module utilizes probability-based frequent pattern mining for generating candidate itemsets and updating LMS databases in each iteration for complexity reduction. In addition, the conditional probability increment ratio (CPIR) is deployed as an exceptionality measure to mine effective common and exception rules simultaneously. The iteratively generated rules can reveal academic performance patterns so that have merits in learning personal goal achievement. Findings: Through two illustrative case studies, experimental results demonstrate increased mining efficiency of the proposed mining module without information loss compared to classic educational mining algorithms. The mined knowl

关键词： data mining

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Training of Spiking Neural Network joint Curriculum Learning Strategy

arXiv

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arXiv 2023年

作者： Tang, Lingling Chu, Jielei Gong, Zhiguo Li, Tianrui The School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China The State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China

Starting with small and simple concepts, and gradually introducing complex and difficult concepts is the natural process of human learning. Spiking Neural Networks (SNNs) aim to mimic the way humans process information, but current SNNs models treat all samples equally, which does not align with the principles of human learning and overlooks the biological plausibility of SNNs. To address this, we propose a CL-SNN model that introduces Curriculum Learning(CL) into SNNs, making SNNs learn more like humans and providing higher biological interpretability. CL is a training strategy that advocates presenting easier data to models before gradually introducing more challenging data, mimicking the human learning process. We use a confidence-aware loss to measure and process the samples with different difficulty levels. By learning the confidence of different samples, the model reduces the contribution of difficult samples to parameter optimization automatically. We conducted experiments on static image datasets MNIST, Fashion-MNIST, CIFAR10, and neuromorphic datasets N-MNIST, CIFAR10-DVS, DVS-Gesture. The results are promising. To our best knowledge, this is the first proposal to enhance the biologically plausibility of SNNs by introducing CL. Copyright © 2023, The Authors. All rights reserved.

关键词： Curricula

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The minority matters: a diversity-promoting collaborative metric learning algorithm 22

The minority matters: a diversity-promoting collaborative me...

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Proceedings of the 36th International Conference on Neural information Processing Systems

作者： Shilong Bao Qianqian Xu Zhiyong Yang Yuan He Xiaochun Cao Qingming Huang State Key Laboratory of Information Security Institute of Information Engineering CAS and School of Cyber Security University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS School of Computer Science and Tech. University of Chinese Academy of Sciences Alibaba Group School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Laboratory of Big Data Mining and Knowledge Management CAS and Peng Cheng Laboratory

ISBN: (纸本)9781713871088

Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collaborative Filtering. Following the convention of RS, existing methods exploit unique user representation in their model design. This paper focuses on a challenging scenario where a user has multiple categories of interests. Under this setting, we argue that the unique user representation might induce preference bias, especially when the item category distribution is imbalanced. To address this issue, we propose a novel method called Diversity-Promoting Collaborative Metric Learning (DPCML), with the hope of considering the commonly ignored minority interest of the user. The key idea behind DPCML is to include a multiple set of representations for each user in the system. Based on this embedding paradigm, user preference toward an item is aggregated from different embeddings by taking the minimum item-user distance among the user embedding set. Furthermore, we observe that the diversity of the embeddings for the same user also plays an essential role in the model. To this end, we propose a Diversity Control Regularization Scheme (DCRS) to accommodate the multi-vector representation strategy better. Theoretically, we show that DPCML could generalize well to unseen test data by tackling the challenge of the annoying operation that comes from the minimum value. Experiments over a range of benchmark datasets speak to the efficacy of DPCML.

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LSN:Long-Term Spatio-Temporal Network for Video Recognition

LSN:Long-Term Spatio-Temporal Network for Video Recognition

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作者： Zhenwei Wang Wei Dong Bingbing Zhang Jianxin Zhang School of Computer Science and Engineering Dalian Minzu University SEAC Key Laboratory of Big Data Applied Technology Dalian Minzu University Institute of Machine Intelligence and Bio-Computing Dalian Minzu University School of Information and Communication Engineering Dalian University of Technology

Although recurrent neural networks(RNNs) are widely leveraged to process temporal or sequential data,they have attracted too little attention in current video action recognition ***,this work attempts to model the long-term spatio-temporal information of the video based on a variant of RNN,i.e.,higher-order ***,we propose a novel long-term spatio-temporal network(LSN) for solving this video task,the core of which integrates the newly constructed high-order ConvLSTM(HO-ConvLSTM) modules with traditional2D convolutional ***,each HO-Conv LSTM module consists of an accumulated temporary state(ATS) module as well as a standard Conv LSTM module,and several previous hidden states in the ATS module are accumulated to one temporary state that will enter the standard Conv LSTM to determine the output together with the current *** HO-Conv LSTM module can be inserted into different stages of the 2D convolutional neural network(CNN) in a plug-andplay manner,thus well characterizing the long-term temporal evolution at various spatial *** results on three commonly used video benchmarks demonstrate that the proposed LSN model can achieve competitive performance with the representative models.

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Machine Learning for Drug Discovery: Predicting Drug-Protein Binding Affinities using Graph Convolutional Networks

Machine Learning for Drug Discovery: Predicting Drug-Protein...

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Recent Trends in computer science and Technology (ICRTCST), International Conference on

作者： S. RadhaMahendran Akriti Dogra Dinesh Mendhe S. B G Tilak Babu Shriniket Dixit Surya Pratap Singh Department of Bioinformatics School of Life Sciences Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai India Department of Oral & Maxillofacial Surgery Manav Rachna Dental College Faridabad Haryana Computer and Information Research Scientist Office of Research Computing Rutgers State University of New Jersey New Brunswick New Jersey Department of ECE Aditya Engineering College Surampalem School of Computer Science Engineering Vellore Institute of Technology Vellore Tamil Nadu India Department of Multi-Disciplinary Research Unit UPUMS Etawah UP

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

Determining the potential interactions between ligands and proteins is a crucial first step in developing new drugs. Graph BAR, our novel prediction model, makes accurate guesses about the strength of ligand-protein bindings by means of a graph convolutional neural network. When compared to graph convolutional neural networks, standard convolutional neural network models are more resource and time intensive to manage. The interaction between proteins and ligands is shown via a network consisting of many adjacency meshes. The atomic distances dictate the alterations to these matrices. Additionally, the atomic properties can be shown in a feature matrix. Using the PDBbind datasets, we verified the functionality of graph convolution and evaluated Graph BAR's ability to forecast the binding strengths of proteins and ligands. In order to maximize The model's performance was improved by utilizing ways for adding additional input, which took advantage of the processing capability of graph convolutional neural networks. Graph BAR's predictions appear to have potential for use in drug creation due to its great accuracy and quickness. The results of our research suggest that Graph Convolutional Networks have the potential to expedite the discovery of new medications by properly predicting the interactions between pharmaceuticals and proteins.

关键词： Proteins Drugs computer science Accuracy Convolution Machine learning Predictive models

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