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Quantified emotion analysis based on design principles of color feature recognition in pictures

Multimedia Tools and Applications 2024年第19期83卷 57243-57267页

作者： Chen, Chin-Ling Huang, Qing-Yang Zhou, Ming Huang, Der-Chen Liu, Ling-Chun Deng, Yong-Yuan School of Information Engineering Changchun Sci-Tech University Jilin Province Changchun130600 China Department of Computer Science and Information Engineering Chaoyang University of Technology Taichung City413310 Taiwan Xiamen Academy of Arts and Design Fuzhou University Fujian Xiamen361024 China School of Computer and Information Engineering Xiamen University of Technology Fujian Xiamen361024 China Department of Computer Science and Engineering National Chung-Hsing University Taichung402 Taiwan

When people observe pictures, different pictures will generate different emotions, and the painters often convey emotional energy to the audience through the media. Through the effect of this emotional transfer, people get emotional satisfaction psychologically, and the author can better express the emotion he needs to express by using the principle. For those who do not know art appreciation, it is difficult to accurately describe the feelings brought by pictures and describe them accurately. It is also difficult to quickly and accurately find the picture of the corresponding feeling sought by oneself. This paper presents a quantitative picture emotion analysis method. Based on the analysis of the characteristics and colors of the pictures that arouse people's emotions, the classification planning is carried out in RGB space and HSV color space respectively. And the combination analysis of the color features with strong influence is carried out to select a more appropriate color space for the expression of human feelings. At the same time, the paper also discusses the effect of the black-and-white filling and the brightness saturation of the image. Finally, it makes use of the corresponding design principles to carry out a quantitative analysis of the emotion generated by it. The system performs a combined analysis of a picture from these directions and presents the results to the user. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Color

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Blending Visitor Familiarity and Site Significance: RAG Enhanced Heritage Interpretation 13

Blending Visitor Familiarity and Site Significance: RAG Enha...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Chao, August F.Y. National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu Taiwan

ISBN: (纸本)9798350355079

Cultural heritage interpretation holds a critical role in enriching the comprehension and admiration of historical sites in small towns by visitors. Nevertheless, the lack of adequate human resources in remote areas may pose a challenge to the effective interpretation of heritage. In this research, a new methodology is suggested, which utilizes Retrieval-Augmented Generation (RAG) technology to deliver personalized cultural heritage narratives that are customized to suit the preferences of tourists. Through the consolidation of extensive language models with retrieval capabilities, the system can connect visitors' existing knowledge with the local attractions, objects, and activities, thereby nurturing a stronger bond with the abundant history of the location. The proposed system helps overcome the challenges of reaching diverse audiences and highlighting the cultural importance, while also reducing the need for specialized and trained staff members. A trial run employing the Llama3 model and resources linked to Nanliao village in Penghu Island showcases the system's proficiency in producing pertinent and culturally sensitive outputs. The research shows the potential benefits of combining advanced language analysis technology with explanations of cultural heritage. This could provide a scalable and efficient way to enhance the experiences of visitors to small towns. © 2024 IEEE.

关键词： Tourism

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RAPID-AV: Realtime Adaptive and Intelligent Detection For AV's Against DDoS Attacks 18

RAPID-AV: Realtime Adaptive and Intelligent Detection For AV...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Kotha, Aditya Venkanna, U. International Institute of Information Technology Department of Computer Science and Engineering Naya Raipur India National Institute of Technology Department of Computer Science and Engineering Warangal India

ISBN: (纸本)9798350391725

Autonomous vehicles (AVs) increasingly rely on vehicle-to-everything (V2X) networks for communication. However, due to the devices' heterogeneity, they are more susceptible to attacks like distributed denial of service (DDoS), resulting in packet loss and transmission delays. Existing solutions to these issues often create bottlenecks and lack post-attack recovery mechanisms. To solve these problems, this paper proposes RAPID-AV, an ML-based approach that uses programmable P4 switches for real-time detection and mitigation of DDoS attacks in AV networks. The proposed system uses a threefold strategy: collect traffic statistics from AV, use P4-based programmable packet processing for anomaly detection, and use majority voting to mitigate attacks. In our experiments, we found that the decision tree algorithm achieves an accuracy of 94.33% in distinguishing traffic from DDoS traffic. In addition, RAPID-AV demonstrated a nearly 10% reduction in CPU load during attacks and maintained consistent communication between AV and RSU. This approach can help improve AV network security while addressing the limitations of existing solutions. © 2024 IEEE.

关键词： Network security

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Utilizing Social Trust Mechanisms to Minimize the Influence of False Ratings on Recommendation Systems 16

Utilizing Social Trust Mechanisms to Minimize the Influence ...

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16th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2024

作者： Wu, Sin-Hua Lin, Lien-Fa National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering Kaohsiung Taiwan

ISBN: (纸本)9798350377903

In today's recommendation systems, people's ratings of products are important criteria for analysis. For instance, a recommendation system based on content analysis may be utilized. However, this can lead to a decrease in recommendation accuracy due to the presence of a large number of false ratings. In this study, we propose a recommendation system that utilizes two different neural networks. The first model is based on users' ratings of items for rating prediction, while the second model incorporates users' social trust data to determine the trust level of item ratings. Finally, the results of the two models are integrated using an integrated learning approach to mitigate the effect of false ratings. © 2024 IEEE.

关键词： Prediction models

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Resource Allocation and Task Offloading for Slicing-Based Communication and Computing in Space-Air-Ground Integrated Networks 21

Resource Allocation and Task Offloading for Slicing-Based Co...

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21st Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2024

作者： Song, Yueqian Yuan, Guiyuan Zeng, Qingtian Chen, Geng College of Electronic and Information Engineering Shandong University of Science and Technology China College of Computer Science and Engineering Shandong University of Science and Technology China

ISBN: (纸本)9798331519186

In the upcoming 6G era, the demand for network communication and computing is expected to surge and diversify. A Space-Air-Ground Integrated Network (SAGIN) is introduced as a solution to provide seamless global connectivity. Meanwhile, network slicing technology can further enhance network capabilities by supporting customized services. However, the joint optimization of multi-dimensional resource allocation and task offloading decisions presents a significant challenge in dynamic and complex environments with diverse task types. In this work, we establish an SAGIN paradigm that integrates network slicing. A multi-level optimization scheme for resource allocation and task offloading is proposed to improve Key Performance Indicators (KPIs) and Quality of Service (QoS). Specifically, we propose a State Encoding-based Multi-Agent Soft Actor-Critic algorithm (SE-MASAC) within a Centralized Training with Decentralized Execution (CTDE) architecture. This algorithm processes current sensing states and historical information, leveraging reinforcement learning to make inter-slice resource and offloading scheduling decisions. Intra-slice decisions for intelligent User Equipments (iUEs) are determined based on their sensing data-driven priority. Simulation results demonstrate that our approach outperforms baselines in terms of system utility, QoS satisfaction, and various KPIs. © 2024 IEEE.

关键词： Quality of service

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A combinatorial algorithm for finding straight skeleton of a digital object

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Multimedia Tools and Applications 2025年 1-21页

作者： Maity, Anukul Dutt, Mousumi Biswas, Arindam Department of Computer Science and Engineering Narula Institute of Technology 81 Nilgunj Road Agarpara West Bengal Kolkata700109 India Department of Computer Science and Engineering St. Thomas’ College of Engineering and Technology 4 Diamond Harbour Road Kidderpore West Bengal Kolkata700023 India Department of Information Technology Indian Institute of Engineering Science and Technology Botanical Garden Road Shibpur West Bengal Howrah711103 India

The paper presents a combinatorial algorithm to find the straight skeleton of the inner isothetic cover of a digital object imposed on a uniform background grid. The isothetic polygon (orthogonal polygon) tightly inscribes the given digital object. The algorithm stated here finds the straight skeleton of any isothetic polygon (simple or non-simple) in O(nglogng) time in a single traversal by applying combinatorial rules, where n is the number of pixels on the boundary of the digital object and g is the grid size on which the digital object is imposed. To find the straight skeleton of non-simple orthogonal polygon, the polygon is divided into sub-parts which are a set of simple orthogonal polygons. The straight skeleton is generated for each of the sub-parts and the corresponding results are merged. The orthogonal polygon containing holes is cut at each holes and the polygon becomes hole-free. The algorithm to obtain the straight skeleton is applied on it. The disconnected hole parts are rejoined and the result is merged. The straight skeleton is useful shape descriptor of digital object. This algorithm stated here has applications in shape analysis. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Combinatorial optimization

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A Lightweight Reverse Distillation for Texture Anomaly Detection 9

A Lightweight Reverse Distillation for Texture Anomaly Detec...

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9th International Conference on Intelligent Informatics and BioMedical sciences, ICIIBMS 2024

作者： Lu, Chang Li, Wenju Wang, Xubin Shanghai Institute of Technology School of Computer Science and Information Engineering Shanghai China

ISBN: (数字)9798350363043

ISBN: (纸本)9798350363043

Reverse Distillation (RD) has achieved effective results in the challenge of unsupervised anomaly detection on the textured surfaces of industrial products. However, original RD has very large numbers of parameters, making it unsuitable for practical deployment. To address this issue, we proposed a lightweight reverse distillation called RD-Lite. Efforts to reduce the model's weight were made in two parts. First, the depth and the number of feature channels of the model were significantly reduced. Additionally, the student network and the bottleneck layer were reconstructed using depthwise separable convolution modules. The streamlined model maintains excellent anomaly detection performance while keeping the model size at a tiny level. Experiments on benchmark texture datasets show that our RDLite achieved a sample-level AUROC of 98.92%, which is very close to the accuracy of the original RD, while the number of our model's parameters is only 3.3% of the original one. © 2024 IEEE.

关键词： Anomaly detection

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Behavioral insights into compression: a study of Move-to-Front-or-Middle deterministic online algorithm through sequence classification and characterization

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International Journal of information technology (Singapore) 2025年第1期17卷 189-203页

作者： Baisakh Mohapatra, Hitesh Guru, Abhishek Department of Computer Science and Engineering Sambalpur University Institute of Information Technology Sambalpur India Computer Science and Engineering GMR Institute of Technology Andhra Pradesh Rajam India School of Computer Engineering KIIT Deemed to Be University Bhubanseswar India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

Since the list update problem was applied to data compression as an effective encoding technique, numerous deterministic algorithms have been studied and analyzed. A powerful strategy, Move-to-Front (MTF), involves moving an accessed item to the front of the list immediately. Theoretical analysis has demonstrated MTF’s potential, showing it to be 2-competitive through competitive analysis. This success has inspired researchers to develop various intriguing MTF variants and assess their performance both theoretically and experimentally. One such variant, Move-to-Front-or-Middle (MFM), has been experimentally shown that MFM performed better than the classic MTF on Calgary and Cantebury Datasets. However, the experimental performance alone does not provide sufficient insight into the internal behavior of the algorithm or explain why its performance surpasses MTF on certain datasets. Therefore, in this paper, we conduct a detailed behavioral study of MFM through sequence classification and characterization to gain deeper understanding of the algorithm and aims to reconcile the disparity between theoretical and experimental outcomes. This paper also by classifying and characterizing MFM’s performance across various request sequences, considering both the presence and absence of locality of reference. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Competitive analysis Deterministic list update algorithm List update problem Sequence classification

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Gene selection and clustering of single-cell data based on Fisher score and genetic algorithm

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Journal of Supercomputing 2023年第7期79卷 7067-7093页

作者： Feng, Junhong Zhang, Jie Zhu, Xiaoshu Wang, Jian-Hong School of Computer and Information Engineering Guangdong Songshan Polytechnic Guangdong Shaoguan512126 China School of Computer Science and Engineering Yulin Normal University Guangxi Yulin537000 China Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung Taiwan

Huge amounts of genes in single-cell RNA sequencing (scRNA-seq) data may influence the performance of data clustering. To obtain high-quality genes for data clustering, the study proposes a novel gene selection algorithm based on Fisher score and genetic algorithms with dynamic crossover (abbreviated as FDCGA). To reduce time and space complexity, FDCGA first employs Fisher score to gain the preliminary candidate genes and then utilizes genetic algorithms with dynamic crossover to select beneficial genes to data clustering and analysis. The experimental results conducted on several publicly real-world scRNA-seq datasets demonstrate that FDCGA outperforms the other several competitors in terms of both NMI and ARI metrics and possesses significant optimization performances. The experimental convergence shows that the fitness of FDCGA can increase and converge to a fixed state versus the number of iterations. The statistical analysis demonstrates that FDCGA statistically significantly outperforms the other competing methods. © 2022, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Genetic algorithms

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Analysis of Employee Attrition using Statistical and Machine Learning Approaches 1st

Analysis of Employee Attrition using Statistical and Machine...

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1st International Conference on Artificial Intelligence of Things, ICAIoT 2023

作者： Singh, Akash Kumar Thakral, Prateek Department of Computer Science and Engineering and Information Technology Jaypee University of Information Technology Waknaghat Himachal Pradesh Solan India

ISBN: (纸本)9783031487804

Attrition means exit of employees from an organization due to any reason. The exit may be voluntary or involuntary in nature. Increased rate of employee attrition leads to loss of an organization’s resources in terms of time, cost, customer satisfaction as well as talent acquisition team’s efforts. The pattern of statistical approaches used in this paper to study the causes of attrition provides insights for the decision makers of organizations about the possible constraints due to which employees may think of leaving. This paper analyses an employee dataset and proposes a machine learning based model with an F1 Score of 0.99 and accuracy of 98.63% that can be used and deployed by the companies on their datasets to predict attrition. The timely prediction of attrition using this method will help the companies to prevent the possible attrition of employees and will help in retaining them for good. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Decision trees

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