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arXiv 2024年

作者： Yu, Xingrui Wan, Zhenglin Bossens, David Mark Lyu, Yueming Guo, Qing Tsang, Ivor W. CFAR IHPC Agency for Science Technology and Research Singapore Singapore School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen China College of Computing and Data Science NTU Singapore Singapore

Learning diverse and high-performance behaviors from a limited set of demonstrations is a grand challenge. Traditional imitation learning methods usually fail in this task because most of them are designed to learn one specific behavior even with multiple demonstrations. Therefore, novel techniques for quality diversity imitation learning, which bridges the quality diversity optimization and imitation learning methods, are needed to solve the above challenge. This work introduces Wasserstein Quality Diversity Imitation Learning (WQDIL), which 1) improves the stability of imitation learning in the quality diversity setting with latent adversarial training based on a Wasserstein Auto-Encoder (WAE), and 2) mitigates a behavior-overfitting issue using a measure-conditioned reward function with a single-step archive exploration bonus. Empirically, our method significantly outperforms state-of-the-art IL methods, achieving near-expert or beyond-expert QD performance on the challenging continuous control tasks derived from MuJoCo environments. Copyright © 2024, The Authors. All rights reserved.

关键词： Engineering education

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Estimation of Facial Emotion Based on Landmark Points by Applying Artificial Intelligence and Machine Learning 7

Estimation of Facial Emotion Based on Landmark Points by App...

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Osman Abdalraheem, Mohammed Hassan Hossain, Mohammad Alamgir Ahmed Hamdan, Alfadil Kechadi, M. Tahar Limker, Suresh Jazan University Department of Information Technology Saudi Arabia Jazan University Department of Computer Science Saudi Arabia Sudan University of Science and Technology Department of Computer Science Sudan School of Computer Science University College Dublin Dublin Ireland Aissms Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India

ISBN: (纸本)9798350304268

In the science of emotion processing, it is hard to figure out how to predict an emotion from naturalistic facial expressions in a multidimensional space. While many studies and works have been done to develop methods for making accurate predictions, relatively little has been done to put the uniqueness of the human face to use in reading and responding to people's expressions of emotion. This work proposes an emotional image retrieval strategy for multimodal emotion detection. This strategy is based on the fact that face features are always there and that the human face has regional redundancy that is independent of face features. Our method uses landmark points to get rid of unnecessary information about facial expressions and focus on the most attractive ones in each frame. It's possible that by making use of the landmarks, we'll be able to predict facial emotions more precisely. This is especially helpful for long-term emotion prediction since it allows us to recover time and space representations by introducing an emotional face. The preliminary results based on a benchmark dataset show that the proposed method exceeds its competitors in terms of prediction accuracy. © 2023 IEEE.

关键词： emotion prediction facial expression landmark point LBP method

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Customized Movie Recommendation for Enhanced Entertainment

Customized Movie Recommendation for Enhanced Entertainment

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Cybernation and Computation (CYBERCOM), International Conference on

作者： M.S. Minu Srikar Talapalli Saahithi Chevuri Medhasri T.S. Department of Computer Science Engineering SRM Institute of Science And Technology Ramapuram Chennai India Department of Computer Science Engineering with Specialization in Big Data Analytics SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (数字)9798331542344

ISBN: (纸本)9798331542351

The surging popularity of online movie databases has created a challenge for viewers: choosing a film from a massive library can be overwhelming. In this paper, it proposes to design a new hybrid movie recommendation powered by machine learning techniques as a solution to improve user experience. Among those techniques, two are combined into this paper: content-based filtering, using the real features of a movie such as genre and a director, and collaborative filtering, which uses the rating provided by users. This hybrid approach thus improves the intrinsic weaknesses that reside in both these methods individually. On the other hand, content-based filtering tends to reduce exploration to movies of the exact type one is doing well at, whereas, inversely, collaborative filtering settings often mean that a user will be exposed only to films within his or her already-recognized taste profile. It is expected that the proposed hybrid system will solve both problems by balancing this line between exploring familiar cinematic territory and making new recommendations based on both user preferences and the entire collective intelligence of the user base. The approach would aim to enrich user satisfaction by facilitating the selection of movies and providing a richer and more diversified movie-viewing experience.

关键词： Hands Filtering Collaborative filtering Transforms Machine learning Motion pictures User experience Libraries Hamming distances Recommender systems

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Software Testing Combining the Fusion of Surrogate Model and Evolutionary Algorithm

Software Testing Combining the Fusion of Surrogate Model and...

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Cognitive Computing and Complex data (ICCD), International Conference on the

作者： Xiangying Dang Wenzheng Bao Qian Qu Dan Li School of Information Engineering (School of Big Data) Xuzhou University of Technology Xuzhou China School of Information Engineering Yancheng Institute Of Technology Yancheng China

The primary challenge is the extensive number of iterations required to converge on suitable solutions in using Genetic Algorithms (GA) for test data generation. Each iteration involves executing candidate solutions and evaluating their fitness values, resulting in prolonged time consumption. This limitation can impede the scalability and efficiency of the evolutionary algorithm, particularly in large-scale or time-critical software testing scenarios. Addressing this issue is crucial for enhancing test data generation efficiency. The method we proposed to mitigate this problem is by fusing surrogate models that approximate the fitness values of candidate solutions instead of executing them on the actual software system. The proposed method typically begins with initial candidate solutions (test data), which along with their fitness values, can be used as a training set to develop the surrogate model. Additional samples may be collected during the evolutionary process to enhance the model’s accuracy. Subsequently, the surrogate model is employed to evaluate the fitness values of new candidate solutions. Solutions with high predicted fitness values are then selected as the initial population to execute the program and undergo the genetic operations. The preliminary experimental results show that by utilizing surrogate models, the need for numerous evaluations on the actual system can be significantly reduced, enabling faster convergence and improved efficiency in test data generation.

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Large Language Model Enhanced Fuzzy Logic Fusion Framework for Stance Detection 2nd

Large Language Model Enhanced Fuzzy Logic Fusion Framework f...

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2nd CSIG Conference on Emotional Intelligence, CEI 2024

作者： Zhang, Bowen Dai, Genan Ma, Jun Lin, Hui Huang, Hu College of Big Data and Internet Shenzhen Technology University Shenzhen518000 China Electronic Science Research Institute of China Electronics Technology Group Corporation Beijing100084 China School of Cyberspace Security University of Science and Technology of China Hefei230000 China

ISBN: (纸本)9789819650835

Analyzing public attitudes on social media is crucial for opinion-mining systems. Stance detection aims to identify the attitude of an opinionated text towards a given target. Despite advances in conventional stance detection methods, handling sentences with ambiguous stances remains a significant challenge. Meanwhile, fuzzy logic theory can improve knowledge representation under uncertainty, but learning logical rules from samples requires a large corpus of annotated data, which is impractical for stance detection tasks involving numerous targets. Inspired by the impressive zero-shot performance of large language models (LLM) on commonsense reasoning tasks, we propose the LLM-enhanced Fuzzy Logic Fusion (LLM-FLF) Framework, which synergizes the strengths of LLMs and fuzzy logic to improve performance in complex reasoning tasks. LLM-FLF consists of two main components: Logical Schema Graph construction and Logic-integrated fuzzy relational graph convolutional network. Our approach achieves state-of-the-art results across zero-shot, cross-target, and in-target settings. Notably, it demonstrates a substantial performance gain, outperforming the best baseline methods by 5.1% and 5.5% on Semeval-2016 and VAST benchmark datasets in the most challenging zero-shot scenario. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image representation

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EG17: Accurate linear-array dispersion imaging of passive surface waves in urban environments 7

EG17: Accurate linear-array dispersion imaging of passive su...

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7th International Conference on Engineering Geophysics, ICEG 2023

作者： Liu, Ya Cheng, Feng Mi, Binbin Pang, Jingyin Ning, Ling Zhang, Hao Xi, Chaoqiang Chen, Xinhua Guan, Bo You, Baiyang Hong, Yu Ma, Yulong Zhou, Jie Xia, Jianghai Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province School of Earth Sciences Zhejiang University Hangzhou China State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines School of Earth and Environment Anhui University of Science and Technology Huainan China GBA Branch of Aerospace Information Research Institute Chinese Academy of Sciences Guangzhou510700 China

Passive surface-wave methods are gaining more and more attention in near-surface geophysical and geotechnical applications. The key to the success of surface-wave methods is accurate dispersion imaging. In urban environments, a linear receiver array is commonly used for dispersion analysis for its high efficiency and low cost. Non-ideal distribution of noise sources can lead to inaccurate imaging results. For obtaining accurate phase-velocity estimation with a linear array, we propose a new workflow for dispersion imaging. Two schemes are used for handling nonuniform source distributions. With three-component data, the source azimuth can be determined and utilized for apparent velocity correction during dispersion imaging. Without three-component data, the strength of the stationary-phase-zone sources is estimated by single-component data and used for the stacking of crosscorrelations. Two synthetic examples demonstrate that the proposed workflow is effective for accurate linear-array dispersion imaging when noise sources are nonuniformly distributed. Our workflow would be an alternative to the applications of passive surface-wave methods in urban areas, especially for nonuniform source distribution. © 2024 7th International Conference on Engineering Geophysics, ICEG 2023. All rights reserved.

关键词： Surface waves

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Advancements in Liver Tumor Detection:A Comprehensive Review of Various Deep Learning Models

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Computer Modeling in Engineering & sciences 2025年第1期142卷 91-122页

作者： Shanmugasundaram Hariharan D.Anandan Murugaperumal Krishnamoorthy Vinay Kukreja Nitin Goyal Shih-Yu Chen Deptartment of Artificial Intelligence and Data Science Vardhaman College of EngineeringHyderabad501218India Deptartment of CSE V.S.B College of EngineeringKarur639111India Deptartment of Electrical and Electronics Engineering Vardhaman College of EngineeringHyderabad501218India Centre for Research Impact&Outcome Chitkara UniversityRajpura140401PunjabIndia Department of Computer Science and Engineering School of Engineering and TechnologyCentral University of HaryanaMahendergarh123031HaryanaIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan

Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment *** Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and *** review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and *** metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical *** review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging *** suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular *** paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.

关键词： Liver tumor detection liver tumor segmentation image processing liver tumor diagnosis feature extraction tumor classification deep learning machine learning

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Fast Moving Consumer Goods Object Detection Using YOLOV8

Fast Moving Consumer Goods Object Detection Using YOLOV8

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Electronic Systems and Intelligent Computing (ICESIC), International Conference on

作者： A. Umamageswari Mukund. P.U Abhishek. V.B Y. Devi Vaishnavi Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer Science and Engineering With Specialization in Big Data Analytics SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (数字)9798331522988

ISBN: (纸本)9798331522995

The basis of this project is to investigate whether the YOLO, an object detection algorithm where 'You Only Look Once' constitutes the name, could be applied to develop FMCG management; together with the management of FMCG, it becomes the gateway to increasing retail precision, efficiency, and agility. Under real-time processing, the system detects and counts FMCG products on retail shelves with an accuracy rate of 92.5%. This accuracy ensures that the optimum stock levels are maintained and there will be no discrepancies. The system processes video feeds at 0.08 seconds per frame, thus allowing inventory data to be updated in real-time to enable quick, data-driven decisions. YOLO's flexibility ensures consistent performance across different store layouts and product types. This system integrates with other retail management tools and allows integrating inventory data in the real time. This means that it would be made available with invaluable insights which would prevent stock depletion while helping to curtail costs. This project takes YOLO ahead of just being a game changer in the FMCG inventory management while operating to reduce expenses and customer satisfaction due to product availability.

关键词： YOLO Training Accuracy Scalability Refining Streaming media Logic gates Real-time systems Mobile handsets Monitoring

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Test data Generation based on Multiprocess Enhanced Multi-Population Genetic Algorithm

Test Data Generation based on Multiprocess Enhanced Multi-Po...

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Cognitive Computing and Complex data (ICCD), International Conference on the

作者： Xiangying Dang Dan Li Qian Qu School of Information Engineering (School of Big Data) Xuzhou University of Technology Xuzhou China School of Information Engineering Yancheng Institute Of Technology Yancheng China

To improve the efficiency of test data generation, this paper proposes a method for test data generation based on a multi-process enhanced multi-population genetic algorithm(MPMGA). This method involves parallel execution of multiple populations by running multiple processes simultaneously, thereby accelerating the search process. In this approach, the problem of generating test data for multipaths is divided into several subproblems, and an independent population is created for each subproblem. What we have improved is that during the evolutionary process for test data generation, not only does each subpopulation operate in its own independent process, conducting its own evolutionary and selection operations, but also when a subpopulation fails to find a solution to its specific problem, it can migrate to other subproblems. Migrating subpopulations have tried to solve other subproblems. By utilizing Python’s multi-process approach, we can facilitate the exchange of information between processes and the sharing of evolutionary results. This enables efficient communication and collaboration among the different populations. The preliminary exploration results indicate that the proposed method achieves a high success rate in generating test data while maintaining low test costs. It is particularly effective in generating test data for difficult paths.

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Machine Learning Model for Sentimental Analysis of Amazon Reviews

Machine Learning Model for Sentimental Analysis of Amazon Re...

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Electronic Systems and Intelligent Computing (ICESIC), International Conference on

作者： A. Umamageswari R.J. Pratishwaran M. Poojitha Reddy R. Yuvan Sankar Raj Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer Science and Engineering With Specialization in Big Data Analytics SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (数字)9798331522988

ISBN: (纸本)9798331522995

The goal of this project is to draw a deeper understanding of the subjective nature behind online product reviews, largely by examining a large dataset received from Amazon that contains numerous star ratings and comments. Since there is such a massive amount of data on the Amazon platform, it was perfect for the analysis. Our methodology is platform-agnostic and can be easily extended to other comparable datasets from other web platforms with comparable guarantees in achieving wide applicability. Our primary motivation is improving the dependability of online shopping. Since product reviews are currently widely used by many consumers as guides in making decisions to buy products, such reviews ought to be fair representations of the actual dependability and quality of the products. The goal of our effort is to identify patterns in the language and ratings of such reviews that would directly reflect the real customer sentiment and also the reliability of the product. Our approach has yielded already a rate of about 92%, which shows it has the potential greatly to raise the legitimacy of online reviews. We'd like to make our customers feel more confident and better equipped with information to make better and safer purchases on a website like Amazon and others. In the final analysis, we strive to make online shopping for our customers worldwide safer and more trustworthy.

关键词： Analytical models Sentiment analysis Reviews Stacking Stars Predictive models Boosting Ensemble learning Electronic commerce Random forests

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