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International Conference on Big Data and Artificial Intelligence (BDAI)

作者： Yufeng Yu Difei Cai Panqi Wu Weifu Liu Xuhang Wang College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (数字)9798350352009

ISBN: (纸本)9798350352016

Precipitation images can clearly reflect the rainfall spatio-temporal features and play an important role in hydrological analysis and flood forecasting. However, it is challenging to mine the association response relationship between rainfall and flood events. This paper proposed a rainfall-flood association pattern database construction method based on rainfall image features. First, the rainfall pattern database and flood pattern database are constructed respectively through the steps of hydrological process segmentation, feature extraction, similarity measurement and cluster analysis. Additionally, association rule mining algorithm is used to conduct rainfall-flood association pattern mining and consrtuct the rainfall-flood association pattern database. The experimental results in the Jialing River basin show that the method proposed in this paper can effectively mine the rainfall-flood association pattern database and provide technical support for intelligent flood prediction based on historical scene patterns.

关键词： Rain Databases Time series analysis Clustering algorithms Feature extraction Prediction algorithms Rivers Association rule learning Floods Forecasting

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Predicting Plain Text Imageability for Faithful Prompt-Conditional Image Generation 21st

Predicting Plain Text Imageability for Faithful Prompt-Cond...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Chen, Yufeng Yue, Guanghui Liu, Weide Lv, Chenlei Wang, Ruomei Zhou, Fan Zhao, Baoquan School of Artificial Intelligence Sun Yat-sen University Zhuhai China School of Biomedical Engineering Shenzhen University Shenzhen China Harvard Medical School Harvard University Boston United States College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (纸本)9789819601219

Text imageability is often used to quantize the ease with which a natural language description can invoke a mental image in a reader. With the proliferation of artificial intelligence powered text-to-image generation models, it will likely play an even more significant role in bridging the gap between language and visual representation. Unfortunately, automatically suggesting proper imageable textural prompts from a piece of plain text has scarcely been systematically investigated. In this paper, we narrow the gap by introducing a novel framework for text imageability assessment to automatically predict whether a piece of plain text and a prompt is highly imageable to be fed into a text-to-image model for faithful image generation. We have also developed a new visual-text dataset, named Ted1.6k, to facilitate model training and validation. Experiment results demonstrate the effectiveness of the proposed method in promoting prompt-guided image generation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image representation

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SFTCS: Multiform Semantic Fusion Based on Transformer for Code Summarization

SFTCS: Multiform Semantic Fusion Based on Transformer for Co...

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International Conference on software Technology and engineering (ICSTE)

作者： Jun Yang Longhao Ao Rongzhi Qi College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (数字)9798350378955

ISBN: (纸本)9798350378962

Automatic code summarization aims to create co-herent natural language descriptions for code snippets. Recent studies indicate that integrating additional code representation structures improve the quality of generated summaries beyond token sequence-based approaches. Current technical research mainly concentrates on the graph structure information of code snippets. However, using a graph method makes it challenging to extract meaningful semantic information from the code snippets. To address the issue, we investigate how to learn more about code semantics and information from the perspective of code text feature representation. Consequently, we propose a novel model named SFTCS. SFTCS employs a conjoint feature fusion strategy, focusing on two types of information in code snippets: code token sequence features and FGT features based on Abstract Syntax Trees (AST) sequence and graph sequence. An N-adjacency ma-trix based on AST features is constructed, and the corresponding feature for AST block are optimized through a graph-based structure. Subsequently, a FGC decoder processes the two types of features. With the aid of an attention mechanism for feature enhancement, the SFTCS model ultimately predicts the results concretely. Evaluations on two publicly available Java datasets validate the effectiveness of SFTCS, showing its superiority over six advanced code summarization models. Ablation studies further elucidate the contributions of each component within the SFTCS model.

关键词： Java Codes Source coding Semantics Natural languages Syntactics Predictive models Transformers software Python

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Innovization - Based Evolutionary Large-Scale Multiobjective Optimization

Innovization - Based Evolutionary Large-Scale Multiobjective...

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Data-driven Optimization of Complex Systems (DOCS), International Conference on

作者： Songbai Liu Qianqiang Yan Zeyi Wang College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350377842

ISBN: (纸本)9798350377859

How to handle the high dimensional search space effectively is a burning research question in evolutionary computation when solving large-scale multiobjective optimization problems. To alleviate it, we propose a new evolutionary algorithm with a tactic of innovization-based representation learning, which aims to discover innovative solution principles from compressed representations. Specifically, a neural net, including an encoder and a decoder, is iteratively learned to transform input solutions into the predictions that dominate them. The encoder thus can capture their compressed representations that retain the information necessary for making good predictions. The decoder can create innovative promising solutions from newly explored representations by evolutionary search. Finally, the proposed algorithm is tested on synthetic and real-world benchmarks with the dimension of variables up to $10^{4}$ . Comparison results show the superior performance of the proposed optimizer in solving these benchmarks within $10^{5}$ function evaluations.

关键词： Representation learning Neural networks Evolutionary computation Transforms Benchmark testing Search problems Prediction algorithms Distance measurement Decoding Optimization

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AMCheX: Accurate Analysis of Missing-Check Bugs for Linux Kernel

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Journal of computer science & Technology 2021年第6期36卷 1325-1341页

作者： Ying-Jie Wang Liang-Ze Yin Wei Dong Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense TechnologyChangsha 410073China

The Linux kernel adopts a large number of security checks to prevent security-sensitive operations from being executed under unsafe *** a security-sensitive operation is unchecked,a missing-check issue *** check is a class of severe bugs in software programs especially in operating system kernels,which may cause a variety of security issues,such as out-of-bound accesses,permission bypasses,and privilege *** to the lack of security specifications,how to automatically identify security-sensitive operations and their required security checks in the Linux kernel becomes a challenge for missing-check *** this paper,we present an accurate missing-check analysis method for Linux kernel,which can automatically infer possible security-sensitive ***,we first automatically identify all possible security check functions of *** according to their callsites,a two-direction analysis method is leveraged to identify possible security-sensitive operations.A missing-check bug is reported when the security-sensitive operation is not protected by its corresponding security *** have implemented our method as a tool,named AMCheX,on top of the LLVM(Low Level Virtual Machine)framework and evaluated it on the Linux *** reported 12 new missing-check bugs which can cause security *** of them have been confirmed by Linux maintainers.

关键词： security check function security-sensitive operation program analysis missing-check

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Semi-supervised Online Elastic Stochastic Configuration Network to Deal with Concept Drift and Class Imbalance in Data Streams

Semi-supervised Online Elastic Stochastic Configuration Netw...

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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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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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