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International Joint Conference on Neural Networks (IJCNN)

作者： Chaofan Chen Wenhao Zhong Kezhong Lu College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Extreme learning machines (ELM) has been widely used in data stream processing, but because the parameters of ELM are generated randomly, the general approximation ability of randomized neural network depends on hidden layer nodes and random parameter range, if the parameters are improperly set, the objective function can not be approximated with high probability. Stochastic Configuration Network(SCN) solves this problem. In order to deal with the problem of data stream classification better, this paper proposes SSOE-FW-SCN, which combines the advantages of SCN and online elastic ELM. The algorithm adds class weights in the initialization phase and online update phase to adapt to the problem of class imbalance. The confusion matrix is used to dynamically calculate the class weight and forgetting factor, which makes the algorithm adapt to the problem of concept drift and class imbalance. In addition to parameter update, the algorithm also adds semi-supervised structure update in the online phase, which makes the algorithm adapt to more concept drift scenarios and class imbalance problems. Compared with the existing data stream classification algorithms, the proposed algorithm has better approximation and robustness.

关键词： Extreme learning machines Annotations Heuristic algorithms Concept drift Neural networks Approximation algorithms Linear programming

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Rainfall Process Similarity Algorithm Research Based on Deep Learning by Using Precipitation Images

Rainfall Process Similarity Algorithm Research Based on Deep...

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computer Information science and Application Technology (CISAT), International Conference on

作者： Yufeng Yu Panqi Wu Difei Cai Weifu Liu Xuhang Wang College of computer science and software engineering Hohai University Nanjing China

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

Rainfall is the main cause of flood disasters, and analyzing its features plays a crucial role in preventing flood disasters. How to extract rainfall process features and conduct rainfall similarity analysis is a challenging work. Based on the similarity search method for daily precipitation images, this paper proposes a rainfall process similarity search method, which extracts rainfall duration, cumulative precipitation, temporal distribution, spatial distribution and center movement trajectory as the features of rainfall process, and defines a set-weighting method based on the Grey Wolf Optimization algorithm (IGWO) to weight and fuse the five extracted features as the similarity measure of rainfall process. The similarity search results of rainfall process images on the Jialing Basin illustrate that the method proposed in this paper can efficiently and accurately identify similar rainfall processes in historical scenes, thereby providing a new technical support for intelligent rainfall-flood forecasting.

关键词： Weight measurement Rain Graphical models Search methods Process control Feature extraction Trajectory Floods Forecasting Optimization

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QoS-aware Dynamic Service Caching and Updating in Cost-efficient Multi-Access Edge Computing 22

QoS-aware Dynamic Service Caching and Updating in Cost-effic...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Lu, Shuaibing Jin, Xin Wu, Jie Zhou, Shuyang Wu, Shen Yang, Jackson Yan, Ran Beijing University of Technology College of Computer Science Beijing China Temple University Center for Networked Computing United States Beijing Jiaotong University School of Software Engineering Beijing China

ISBN: (纸本)9798331509712

In the context of mobile edge computing, achieving dynamic service caching and updating to guarantee the QoS of users and reduce system costs is a challenging problem. However, existing research still has certain deficiencies in considering the dynamic behavior of users and the limited storage resources of edge servers. To address this problem, this paper proposes three novel strategies for the different stages of service caching and updating to jointly optimize the delay and cost. At the initial service caching stage, we propose a caching strategy based on dynamic programming, taking into account the constraint of limited memory resources. Given the dynamic behavior of users, we formulate the joint optimization problem as a Markov Decision Process (MDP) and design a service extension strategy based on Q-learning at the service updating decision-making stage and a replacement strategy taking both the distribution of service replications and service access frequency into account at the service updating replacement stage to guarantee the QoS of users. We effectively tackle the challenges arising from the dynamic behavior of users and limited storage resources. Through extensive comparative experiments, our approach outperforms traditional strategies by significantly reducing user latency and system cost. © 2024 IEEE.

关键词： Markov processes

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Emotion recognition research of eye-movement feature extraction classification network in online video learning environment 3

Emotion recognition research of eye-movement feature extract...

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3rd International Seminar on Artificial Intelligence, Networking, and Information Technology, AINIT 2022

作者： Liu, Shengxi Li, Ze Tao, Xiaomei College of Computer Engineering Henan Institute of Economy and Trade Henan Zhengzhou China School of information science and engineering Guilin University of Technology Guangxi Guilin China School of Computer Science and Engineering School of Software Guangxi Normal University Guangxi Guilin China

ISBN: (数字)9781510662971

ISBN: (纸本)9781510662964

With the rapid development of artificial intelligence technology, emotion recognition has been applied in all aspects of life, using eye movement tracking technology for emotion recognition has become an important branch of emotion computing. In order to explore the relationship between eye movement signals and learners' emotional states in the online video learning environment, we used machine learning and convolutional neural network methods to recognize eye movement signals, and classify learners' emotional states into two categories, positive and negative. The study of eye movement data under different time windows mainly includes four stages: data collection, data preprocessing, classifier modeling, training and testing. In this paper, a Eye-movement Feature Extraction Classification Network(EFECN) based on convolutional neural network is proposed for small sample data and the classification of emotion state based on eye movement. The eye movement data were transformed into images through cross-modal conversion as input of multiple different deep convolutional neural networks, and the emotional states were classified in positive and negative directions. The accuracy was used as the evaluation index to evaluate and compare the different models. The accuracy of the eye movement emotion recognition model reached 72% in the SVM model and 91.62% in the EFECN model. Experimental results show that the convolutional neural network based on deep learning has a significant improvement in recognition accuracy compared with traditional machine learning methods. © 2023 SPIE.

关键词： Emotion Recognition

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ClipComb: Global-Local Composition Network based on CLIP for Composed Image Retrieval

ClipComb: Global-Local Composition Network based on CLIP for...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yingying Zhu Dafeng Li Zhihang Liu Hong Zhou College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

In this paper, we focus on the task of composed image retrieval. To develop a comprehensive understanding of image and text, we propose a novel global-local composition network (ClipComb) based on the vision-language pretraining CLIP model. The two main phases of ClipComb are the fine-tuning training stage and the composition training stage. First, in the fine-tuning training step, we fine-tune the text and image encoders of the CLIP model to transfer CLIP to this task. We also perform contrastive loss alignment on both global and local features. Second, during the composition training phase, we devise a global-local composition module (GLC) based on the fine-tuned composition learning framework. The GLC module make full use of the CLIP’s pretraining knowledge to generate a composite representation aligned with the target representation. Extensive experimental results demonstrate that our method achieves state-of-the-art performance on two benchmark datasets.

关键词： Training Image retrieval Footwear Benchmark testing

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Effective and Imperceptible Adversarial Textual Attack Via Multi-objectivization

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ACM Transactions on Evolutionary Learning and Optimization 2024年第3期4卷 1-23页

作者： Liu, Shengcai Lu, Ning Hong, Wenjing Qian, Chao Tang, Ke Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen College of Engineering South Tower 1088 Xueyuan Avenue Guangdong Shenzhen518055 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong999077 Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen 10F Zhizhen Building Canghai Campus 3688 Nanhai Avenue Guangdong Shenzhen China National Key Laboratory for Novel Software Technology School of Artificial Intelligence Nanjing University Nanjing International College Area Xianlin Campus 163 Xianlin Avenue Jiangsu Nanjing China

The field of adversarial textual attack has significantly grown over the past few years, where the commonly considered objective is to craft adversarial examples (AEs) that can successfully fool the target model. However, the imperceptibility of attacks, which is also essential for practical attackers, is often left out by previous studies. In consequence, the crafted AEs tend to have obvious structural and semantic differences from the original human-written text, making them easily perceptible. In this work, we advocate leveraging multi-objectivization to address such an issue. Specifically, we reformulate the problem of crafting AEs as a multi-objective optimization problem, where the attack imperceptibility is considered as an auxiliary objective. Then, we propose a simple yet effective evolutionary algorithm, dubbed HydraText, to solve this problem. HydraText can be effectively applied to both score-based and decision-based attack settings. Exhaustive experiments involving 44,237 instances demonstrate that HydraText consistently achieves competitive attack success rates and better attack imperceptibility than the recently proposed attack approaches. A human evaluation study also shows that the AEs crafted by HydraText are more indistinguishable from human-written text. Finally, these AEs exhibit good transferability and can bring notable robustness improvement to the target model by adversarial training. © 2024 Copyright held by the owner/author(s).

关键词： Multiobjective optimization

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CNN Based Features Extraction and Selection Using EPO Optimizer for Cotton Leaf Diseases Classification

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computers, Materials & Continua 2023年第9期76卷 2779-2793页

作者： Mehwish Zafar JaveriaAmin Muhammad Sharif Muhammad Almas Anjum Seifedine Kadry Jungeun Kim Department of Computer Science COMSATS University IslamabadWah CampusWah Cantt47040Pakistan Department of Computer Science University ofWahWah Cantt47040Pakistan National University of Technology(NUTECH) Islamabad44000Pakistan Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea

Worldwide cotton is the most profitable cash *** year the production of this crop suffers because of several *** an early stage,computerized methods are used for disease detection that may reduce the loss in the production of *** several methods are proposed for the detection of cotton diseases,however,still there are limitations because of low-quality images,size,shape,variations in orientation,and complex *** to these factors,there is a need for novel methods for features extraction/selection for the accurate cotton disease *** in this research,an optimized features fusion-based model is proposed,in which two pre-trained architectures called EfficientNet-b0 and Inception-v3 are utilized to extract features,each model extracts the feature vector of length N×*** that,the extracted features are serially concatenated having a feature vector lengthN×*** prominent features are selected usingEmperor PenguinOptimizer(EPO)*** method is evaluated on two publically available datasets,such as Kaggle cotton disease dataset-I,and Kaggle *** EPO method returns the feature vector of length 1×755,and 1×824 using dataset-I,and dataset-II,*** classification is performed using 5,7,and 10 folds *** Quadratic Discriminant Analysis(QDA)classifier provides an accuracy of 98.9%on 5 fold,98.96%on 7 fold,and 99.07%on 10 fold using Kaggle cotton disease dataset-I while the Ensemble Subspace K Nearest Neighbor(KNN)provides 99.16%on 5 fold,98.99%on 7 fold,and 99.27%on 10 fold using Kaggle cotton-leaf-infection dataset-II.

关键词： Deep learning cotton disease detection features selection classification efficientnet-b0 inception-v3 quadratic discriminant analysis subspace KNN

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BCLB: A Scalable and Cooperative Layer-4 Load Balancer for Data Centers

BCLB: A Scalable and Cooperative Layer-4 Load Balancer for D...

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International Conference on Distributed Computing Systems

作者： Shu Yang Xinze Wu Laizhong Cui College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

Nowadays, load balancing is more and more important for huge data centers. Most data centers usually adopt a software-based load balancer (LB), which consumes too many server resources and does not scale with the increasing traffic volume. However, there is an increased demand for layer-4 load balancers, which need to check more bits in packet headers. Although hardware-based load balancers can meet the requirements, they are much more expensive. During the past years, data centers frequently update their devices, including kinds of switches. Thus, there exists a huge number of obsolete switches, some of which are in good condition and equipped with high-performance storage like SRAM (Static Random Access Memory). In this paper, we proposed BCLB (Bit-based Collaborative Load Balancer), a new cooperative LB, which builds more powerful load balancing based on existing switches. Different with previous cooperative mechanisms that distribute rules to different LBs, BCLB lets switches cooperate based on bits. Many switches along the data path check different bits, and cooperatively check all bits in 5-tuple (which would be 296 bits in IPv6) for layer-4 packet header. In this way, rule updating will not influence the load balancing in BCLB. To optimize the performance of BCLB, we formulate the problem as an optimization problem, then we propose a dynamic programming algorithm to solve it. Finally, we conduct comprehensive simulations using both real-world traffic datasets and a P4-based prototype, the results show that BCLB performs much better than previous rule-based cooperative schemes.

关键词： Data centers Heuristic algorithms Random access memory Prototypes Distributed databases Load management Dynamic programming

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NAS-SW: Efficient Neural Architecture Search with Stage-Wise Search Strategy

NAS-SW: Efficient Neural Architecture Search with Stage-Wise...

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International Joint Conference on Neural Networks (IJCNN)

作者： Jikun Nie Yeming Yang Qingling Zhu Qiuzhen Lin College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

A number of Neural Architecture Search (NAS) methods have been proposed to automate the design of Convolutional Neural Networks (CNNs). However, they typically require significant computational resources and exhibit search instability. To save computational costs, many NAS methods design only a single group of cell architectures (i.e., a small part of the total network architecture), which are simply stacked to form the final network. This leads to a lack of diversity in network architecture design, thereby reducing network performance. To alleviate this problem, we propose a Particle Swarm Optimization (PSO)-based Stage-Wise NAS algorithm (named NAS-SW). Initially, we introduce a stage-wise architecture search strategy, which can find different cell architectures at various search stages. Then, we propose a grouping architecture update strategy, which can realize effective information transfer between cell architectures rapidly. Importantly, these strategies do not add extra computational costs. Experiments demonstrate that NAS-SW has superior performance and better efficiency. NAS-SW only needs 0.37 GPU days to design a CNN on CIFAR-10. Additionally, on the CIFAR-10 and CIFAR-100 datasets, the network architectures searched by NAS-SW achieve top-1 validation accuracies of 97.55% and 83.78%, respectively.

关键词： Microprocessors Design methodology Neural networks Graphics processing units computer architecture Network architecture Search problems

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FireRobBrain: Planning for a Firefighting Robot using Knowledge Graph and Large Language Model

FireRobBrain: Planning for a Firefighting Robot using Knowle...

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Intelligent Data and Security (IDS), IEEE International Conference on

作者： Jialei Zhang Shubin Cai Zhiwei Jiang Jian Xiao Zhong Ming College of Computer Science and Software Engineering Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Guang Ming Laboratory Shenzhen China College of Computer Science and Software Engineering Shenzhen University Guang Ming Laboratory Shenzhen Technology University Shenzhen China

ISBN: (数字)9798350389487

ISBN: (纸本)9798350389494

Firefighting robots play a crucial role in improving fire safety. However, these robots have limitations in understanding their surroundings and adapting to changing situations. In this paper, we propose the “FireRobBrain”, a combination of a knowledge graph and a large language model, to act as the “brain” of firefighting robots. This approach aims to tackle challenges related to planning robots in dynamic environments. The FireRobBrain consists of a KG with a dynamic information base and a relatively static knowledge base. It also includes a prompting module that helps the language model generate suggestions for the robot's reactions. We evaluated the framework using a dataset of 864 samples and discovered that the combination of LLM and KG, facilitated by a well-designed prompt module, significantly improves the quality of answers, particularly for tasks involving specific contexts and structured information. Furthermore, we noted that providing a task scope in the input prefix contributes to a better understanding of the robot's task, resulting in enhanced performance.

关键词： Large language models Prevention and mitigation Knowledge based systems Knowledge graphs Planning Security Reliability

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