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2024 7th International Symposium on Big data and Applied Statistics, ISBDAS 2024

作者： He, Yanzhong Zhang, Yanjiao Zhu, Lin School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China School of Computer Wuhan Qingchuan University Wuhan China

As societal development progresses and individual travel needs evolve, the demand for acquiring multi-modal transportation status information has steadily increased. The present domain of transportation status encompasses a wealth of multi-modal information, including vehicle trajectory data, traffic condition visual imagery, and textual information. Acquiring multi-modal transportation status information facilitates a rapid understanding of the prevailing traffic conditions at a given location. In this study, we investigate multi-modal transportation status data encompassing trajectories, textual descriptions, and visual imagery. We establish a multi-modal dataset comprising circuit, text, and image data and enhance the state-of-the-art cross-modal retrieval model within the Chinese domain. We utilize hash codes to compute the similarity between images and textual descriptions. During model evaluation using our collected dataset, the Chinese-CLIP-Hash model (our model) demonstrates performance improvements over the Chinese-CLIP model. Specifically, it exhibits a 1.0% improvement in R@10 for the Text-to-Image task and a 0.4% improvement in R@5 for the Image-to-Text task, along with a 0.2% enhancement in R@10. Furthermore, during the cross-modal retrieval process, it achieves a notable speed increase, exhibiting an 11% improvement in speed compared to the Chinese-CLIP model when handling queries from a set of 400 textual descriptions. © Published under licence by IOP Publishing Ltd.

关键词： Network security

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Cat Swarm Algorithm Generated Based on Genetic Programming Framework Applied in Digital Watermarking

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computers, Materials & Continua 2025年第5期83卷 3135-3163页

作者： Shu-Chuan Chu Libin Fu Jeng-Shyang Pan Xingsi Xue Min Liu School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210000China College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266000China Department of Information Management Chaoyang University of TechnologyTaichung40601TaiwanChina School of Information Science and Engineering Fujian University of TechnologyFuzhou350000China Key Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resource Nanjing University of Information Science and TechnologyNanjing210000China

Evolutionary algorithms have been extensively utilized in practical ***,manually designed population updating formulas are inherently prone to the subjective influence of the *** programming(GP),characterized by its tree-based solution structure,is a widely adopted technique for optimizing the structure of mathematical models tailored to real-world *** paper introduces a GP-based framework(GPEAs)for the autonomous generation of update formulas,aiming to reduce human *** modifications to tree-based GP have been instigated,encompassing adjustments to its initialization process and fundamental update operations such as crossover and mutation within the *** designing suitable function sets and terminal sets tailored to the selected evolutionary algorithm,and ultimately derive an improved update *** Cat Swarm Optimization Algorithm(CSO)is chosen as a case study,and the GP-EAs is employed to regenerate the speed update formulas of the *** validate the feasibility of the GP-EAs,the comprehensive performance of the enhanced algorithm(GP-CSO)was evaluated on the CEC2017 benchmark ***,GP-CSO is applied to deduce suitable embedding factors,thereby improving the robustness of the digital watermarking *** experimental results indicate that the update formulas generated through training with GP-EAs possess excellent performance scalability and practical application proficiency.

关键词： Cat swarm algorithm genetic programming digital watermarking update mode mode generation framework

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The Classification of Software Requirements using Large Language Models: Emerging Results and Future Directions 3

The Classification of Software Requirements using Large Lang...

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3rd International Conference on Cloud Computing, Big data Application and Software Engineering, CBASE 2024

作者： Zhang, Yizhuo Li, Yanhui School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China School of Computer Wuhan Qingchuan University Wuhan China

ISBN: (纸本)9798331506582

In recent years, large language models (LLMs) have demonstrated exceptional capabilities across various tasks. However, the effectiveness of LLMs in software requirement classification has yet to be thoroughly explored. This paper aims to bridge this gap by evaluating the performance of multiple LLMs (including GPT-3.5, GPT-4, GPT-4o, HuggingChat, Gemini, and DeepSeek) under different task prompts, focusing specifically on the binary classification of functional requirements (FR) versus non-functional requirements (NFR), as well as the multi-class classification of non-functional requirements. Experimental results show that HuggingChat achieved the highest accuracy and F1 score in the binary classification task, outperforming other models;meanwhile, GPT-4 performed relatively well in the multi-class classification task. Through the analysis of these results, this paper provides important references for future research and applications in software requirement classification and explores potential directions for LLM-based software requirement classification. © 2024 IEEE.

关键词： Application programs

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ML-Based College Predictor Telegram Bot Indian Engineering Colleges 1

ML-Based College Predictor Telegram Bot Indian Engineering C...

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1st International Conference on AIML-Applications for Engineering and technology, ICAET 2025

作者： More, Amruta Patil, Satchal Savale, Aditya Satle, Prafull Tilwane, Krishna Shinde, Sanket Ajeenkya DY Patil School of Engineering Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350355611

This Research aimed at the challenge of finding college for students, but this is complex task as students are unable to find best institutes based on entrance exam scores in India. To bounce on that, we focus on the development of a College Predictor Bot, which uses some key factors notably JEE and CET scores and predicts admission to different Indian colleges. You are trained on years of admissions data from hundreds of colleges, with students of all shapes and sizes and performance levels. In terms of methodology used in the study, a combination of random forest and decision tree models were used for analysing the exam scores and make findings. The ML-model is subject to a lot of statistics and testing, and the analysis results in highly valuable co-relations between performance on entrance exams and admitting results. Our results show that College Predictor Bot accurately predicts admissions, therefore helping students know what colleges are suitable. The larger implications [of this tool] are that this might make the college selection process easier and provide a slightly more transparent and informed way of getting deserved seat in the Eductaion system of Bharat. © 2025 IEEE.

关键词： Students

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Leveraging Support Vector Machines for Detection of Network Traffic Attacks 4

Leveraging Support Vector Machines for Detection of Network ...

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4th International Conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Tiwari, Anurag Indian Institute of Technology School of Artificial Intelligence & Data Science Jodhpur India Babu Banarasi das Institute of Technology& Management Department of Computer Science & Engineering Lucknow India

ISBN: (纸本)9798350364729

Network security is crucial to ensure the protection of information systems in an increasingly connected world, and network traffic attack detection is an important aspect of maintaining system integrity. This study addresses this by investigating the use of a powerful and flexible machine learning algorithm, namely Support Vector Machines (SVM), to detect network traffic attacks. As a result, SVM has been successfully applied in different domains like network security, etc. In this study, SVM is applied for normal and malicious network traffic classification, including several attack types like Denial-of-Service (DoS), User-to-Root (U2R), and Remote-to-Local (R2L) attacks. To this end, the KDD Cup 99 dataset is employed for training and testing the model. These metrics are then used to evaluate the performance of the SVM-based intrusion detection system. The results demonstrate that SVM is able to recognize a high level of accurateness on network data traffic attacks as well as it has performed well in a difficult attack trends. In this case, the F1-scores for the Linear and RBF did result in 96.65/99.24, respectively, indicating that SVM also proved to be an effective algorithm choice in this example. Finally, the study either propose new algorithms or improve existing algorithms for SVM and demonstrate their effectiveness in the area of network security. In conclusion, this study reinforces the importance of SVM as an effective instrument in the detection of attacks on network traffic and strengthening cyber defense capabilities. © 2024 IEEE.

关键词： Network intrusion

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Multi-Objective Genetic-Programming Hyper-Heuristic for Evolving Interpretable Flexible Job Shop Scheduling Rules 13

Multi-Objective Genetic-Programming Hyper-Heuristic for Evol...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Pang, Junwei Mei, Yi Zhang, Mengjie Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Victoria University of Wellington PO Box 600 Wellington New Zealand

ISBN: (纸本)9798350308365

The job shop scheduling problem is an important combinatorial optimisation problem in the real world. Genetic programming hyper-heuristic has been successfully applied to automatically evolve effective dispatching rules to make a schedule in real time without much domain knowledge. However, the interpretability of GP-evolved rules has been largely neglected, which could lead to the lack of reliability and trustworthiness of the evolved rules in practice. Current work related to interpretable genetic programming algorithms primarily uses the model size as the interpretability metric. This could not fully reflect the interpretability of evolved rules. To overcome the limitation, we employ structural complexity and dimension gap as more comprehensive interpretability measures. In addition, a new multi-objective genetic programming algorithm, which applies the a non-dominated sorting method to solve the objective selection bias issue, is proposed to optimise the makespan (scheduling objective), structural complexity and dimension gap simultaneously. A variety of experiments demonstrates the competitive performance of our proposed algorithm based on effectiveness, convergence and diversity. Furthermore, the semantics of evolved dispatching rules are analysed to show their better interpretability. © 2024 IEEE.

关键词： Genetic programming

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Design and Optimization of Loss Functions in Fine-grained Sentiment and Emotion Analysis 5

Design and Optimization of Loss Functions in Fine-grained Se...

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5th International Conference on Natural Language Processing, ICNLP 2023

作者： Shiren, Ye Li, Ding Rinku, Ali Md Changzhou University School of Computer Science and Artificial Intelligence Aliyun School of Big Data School of Software Changzhou China

ISBN: (纸本)9798350302219

The unbalanced distribution of category labels and the correlation between these labels tend to cause over-learning issues in deep learning models. In fine-grained sentiment analysis datasets, the correlation between category labels and the heterogeneity of tag distribution are prominent. In the deep learning model, we use the adjusted circle-loss to introduce margin and gradient attenuation in the loss function to handle the challenges caused by unbalanced label distribution and non-independence between labels. This method can be well combined with pre-trained models and adapt to various learning models and algorithms. Compared with the state-of-the-art typical models, our loss function mechanism achieves significant improvement using SemEval18 and GoeEmotions by measure of Jaccard coefficient, micro-F1, and macro-F1. It implies that our solution could work efficiently for sentiment analysis and sentiment analysis tasks. © 2023 IEEE.

关键词： Sentiment analysis

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Feature Selection for GPSR Based on Maximal Information Coefficient and Shapley Values 13

Feature Selection for GPSR Based on Maximal Information Coef...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Rimas, Mohamad Anfar, Mohamad Chen, Qi Zhang, Mengjie School of Engineering and Computer Science Victoria University of Wellington Centre for Data Science and Artificial Intelligence PO Box 600 Wellington New Zealand

ISBN: (纸本)9798350308365

Feature selection is a critical aspect of improving the interpretability of machine learning models. Genetic Programming (GP) has a built-in feature selection mechanism that explores the search space to include informative features in models. However, this built-in mechanism is insufficient for identifying important features, when dealing with high-dimensional feature spaces. To overcome this limitation, the paper introduces a novel feature importance measurement based on the Maximal Infor-mation Coefficient and Shapley Values. The proposed algorithm operates in two stages. In the first stage, it identifies the best individuals from different populations. In the second stage, the best individuals from the first stage are utilized for the calculation of the novel individual feature importance measurement. The new feature importance measurement offers valuable insights into the significance and relevance of the selected features. Regression experiments were conducted on six datasets to assess the effectiveness of the proposed method. Furthermore, comparisons were made with two other algorithms to evaluate its performance. The results indicate that the proposed approach enhances GP performance for high dimensional datasets while maintaining GP trees of similar size compared to standard GP. © 2024 IEEE.

关键词： Genetic programming

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AFRLNet: Adaptive-context Feature Representation Learning for Facial Expressions recognition via Transformers 4

AFRLNet: Adaptive-context Feature Representation Learning fo...

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4th International Conference on Electronic Information Engineering and computer Communication, EIECC 2024

作者： Liu, Xionghua He, Lulu Liu, Tingting Liu, Hai He, Dingxin Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan China Wuhan Technology and Business University School of Computer Science and Automation Wuhan China

ISBN: (纸本)9798331534622

Facial expression recognition (FER) is a critical task in intelligent educational technologies, enabling adaptive learning environments to respond to learners' emotional states and support more personalized instruction. This article introduces AFRLNet, a novel FER framework that integrates convolutional neural networks (CNNs) and transformer architectures to enhance the identification and interpretation of student facial expressions. The proposed framework includes two key components: ContextAware Local Feature Learning (CALL) and Adaptive Feature Relationship Induction (AFRI). CALL leverages attention mechanisms to emphasize symmetrical and contextually meaningful facial regions relevant to students' affective responses, while AFRI employs a Transformer-based approach to capture global features and model long-range dependencies, thus addressing challenges such as facial occlusions or varied head poses commonly found in classroom or remote learning settings. Extensive evaluations on the RAF-DB and KDEF datasets demonstrate that AFRLNet achieves state-of-the-art accuracy of 89.44% and 94.76%, respectively, surpassing existing methods. © 2024 IEEE.

关键词： Convolutional neural networks

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FSRNet: Facial Structural-aware Representation Learning for Facial Expression Recognition via Swin Transformers 4

FSRNet: Facial Structural-aware Representation Learning for ...

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4th International Conference on Electronic Information Engineering and computer Communication, EIECC 2024

作者： He, Lulu Liu, Xionghua Liu, Tingting Liu, Hai He, Dingxin Wuhan Technology and Business University School of Computer Science and Automation Wuhan China Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan China

ISBN: (纸本)9798331534622

Dynamic facial expression recognition (DFER) is an essential upstream task in the areas of human-machine interaction, driving safety, education and health aids. However, the inherent uncertainty between variations significantly impacts the effectiveness of DFER in practical applications, particularly due to the similar facial changes associated with different facial expressions. To address this challenge, we introduced a novel relationship-driven approach based on the Transformer architecture that can learn the relationships between facial expressions and facial variations. Specifically, we designed several facial variation tokens to explicitly encode the variation status of key facial regions. In addition, we developed a novel module to model the interactions between facial variation tokens and help the model better focus on the specific facial variations of facial expressions. Experimental results on two challenging benchmark DFER datasets show that our proposed FSRNet achieves state-of-the-art performance. © 2024 IEEE.

关键词： Human robot interaction

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