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Clustering Analysis of a Spatiotemporal Dataset with a Novel Kernel Density Estimator

Clustering Analysis of a Spatiotemporal Dataset with a Novel...

引用

International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jen-Chien Yu Chun-Chieh Yang John Reuben Gilbert Rou-Jun Liu Yen-Jen Oyang Meng-Han Yang Department of Computer Science and Information Engineering National Kaohsiung University of Science and Technology Kaohsiung City Taiwan ROC Master Program in Statistics National Taiwan University Taipei Taiwan ROC Department of Computer Science & Information Engineering National Taiwan University Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University

A vast number of spatiotemporal datasets collected from a wide range of sources has motivated scientists to develop effective approaches to identify interesting patterns hidden in these datasets. In this respect, kernel density estimators, which belong to a class of non-parametric estimators in statistics, have been widely exploited in recent years. With this background, we have developed a novel kernel density estimator aiming to provide accurate analysis results. According to the evaluation with a real spatiotemporal dataset, which collected emergency medical service records in a county in the United States, the proposed kernel density estimator can approximate the probability density function significantly more accurately than a conventional kernel density estimator. Furthermore, we have exploited the proposed kernel density estimator to identify interesting patterns hidden in the real spatiotemporal dataset.

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Vision-based Semantic Communications for Metaverse Services: A Contest Theoretic Approach

Vision-based Semantic Communications for Metaverse Services:...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Guangyuan Liu Hongyang Du Dusit Niyato Jiawen Kang Zehui Xiong Boon Hee Soong Energy Research Institute@NTU Interdisciplinary Graduate Program School of Computer Science and Engineering Nanyang Technological University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Electrical and Electronic Engineering Nanyang Technological University Singapore

The popularity of Metaverse as an entertainment, social, and work platform has led to a great need for seamless avatar integration in the virtual world. In Metaverse, avatars must be updated and rendered to reflect users' behaviour. Achieving real-time synchronization between the virtual bilocation and the user is complex, placing high demands on the Metaverse Service Provider (MSP)'s rendering resource allocation scheme. To tackle this issue, we propose a semantic communication framework that leverages contest theory to model the interactions between users and MSPs and determine optimal resource allocation for each user. To reduce the consumption of network resources in wireless transmission, we use the semantic communication technique to reduce the amount of data to be transmitted. Under our simulation settings, the encoded semantic data only contains 51 bytes of skeleton coordinates instead of the image size of 8.243 megabytes. Moreover, we implement Deep Q-Network to optimize reward settings for maximum performance and efficient resource allocation. With the optimal reward setting, users are incentivized to select their respective suitable uploading frequency, reducing down-sampling loss due to rendering resource constraints by 66.076% compared with the traditional average distribution method. The framework provides a novel solution to resource allocation for avatar association in VR environments, ensuring a smooth and immersive experience for all users.

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A Systematic Literature Review of Recent Trends and Challenges in Digital Twin Implementation

A Systematic Literature Review of Recent Trends and Challeng...

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International Conference on ICT For Smart Society (ICISS)

作者： Ikrar Adinata Arin Meyliana Harco Leslie Hendric Spits Warnars Dina Fitria Murad Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia

This paper reviews the latest trends and challenges in implementing digital twin technology. A digital twin is a tool used in various industries to improve efficiency, optimize processes, and enable advanced analysis. The review involved searching major research databases and search engines for articles published between 2018 and 2023. The findings reveal several important trends, including the development of different types of digital twin dimensions, each with its own advantages and limitations. The benefits of digital twin implementation include improved decision-making, increased productivity, and operational efficiency. However, there are challenges, such as data integration, security and privacy concerns, a lack of standardization, and the need for experts to effectively design and operate digital twins. The implications of these trends and challenges are discussed regarding their impact on the successful adoption and implementation of digital twin technology. The review also highlights the need to address these challenges and explore new approaches for maximizing the benefits of digital twin technology. Overall, this comprehensive review is a valuable resource for researchers, practitioners, and organizations seeking to understand the current landscape, identify areas for improvement, and make informed decisions when implementing digital twin technology.

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Recognition of Pedestrians’ Crossing Intentions with a Conv-Transformer Network 8th

Recognition of Pedestrians’ Crossing Intentions with a Conv...

引用

8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： Yang, Biao Yan, Guocheng Wang, Fujun Yang, Changchun Liu, Xiaofeng College of IoT Engineering Hohai University Changzhou213100 China School of Microelectronics and Control Engineering Changzhou University Changzhou213100 China School of Computer Science and Artificial Intelligence Changzhou University Changzhou213100 China Graduate School of Environment and Information Sciences YOKOHAMA National University Yokohama240-8501 Japan

ISBN: (纸本)9783031067662

Recognizing pedestrians’ crossing intentions is critical for self-driving cars to avoid vehicle-pedestrian collisions. However, it is challenging to recognize pedestrians’ crossing intentions based on their appearance or actions under severe conditions, including occlusions, dim illumination, and pedestrians standing far away. In this work, a Conv-transformer network is proposed to recognize pedestrians’ crossing intentions. Pedestrians’ appearance is modeled by a Resnet network, which will output a feature sequence describing pedestrians at different time steps. A Transformer network is then introduced to model the feature sequence to output the recognition result, which is crossing or not crossing. Evaluations on benchmarking databases, including JAAD and PIE, are performed. Results show that the proposed method can recognize pedestrians’ crossing intentions precisely. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Pedestrian safety

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Evaluation of Source Data Selection for DTL Based CSI Feedback Method in FDD Massive MIMO Systems

Evaluation of Source Data Selection for DTL Based CSI Feedba...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Mayuko Inoue Tomoaki Ohtsuki Kohei Yamamoto Guan Gui Graduate School of Science and Technology Keio University Kanagawa Japan Department of Information and Computer Science Keio University Kanagawa Japan College of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China

In frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO), the downlink channel state information (CSI) feedback method based on deep transfer learning (DTL) has been proposed to obtain the downlink CSI at the Base Station (BS). In the CSI feedback method based on DTL, a target model for one channel environment is obtained by fine-tuning the parameters of a source model trained on a large number of the CSI dataset (source data) of another channel environment. The fine-tuning is done with a small number of the CSI dataset (target data) of the target channel environment. Thus, a target model can be obtained at a low learning cost. However, the performance of the target model could highly depend on the source data. In this paper, we investigate two metrics as criteria for selecting source data to obtain a target model with a high CSI reconstruction performance: (i) Jensen-Shannon Divergence (JSD), which represents the similarity between target and source data, and (ii) entropy, which represents the diversity of source data. The simulation results showed when the target channel model is non line-of-sight (NLOS), the source data with high entropy and low JSD tend to provide higher CSI reconstruction performance of the target model. These results indicate that the JSD and the entropy could be a source data selection metric.

关键词： Measurement Computational modeling Transfer learning Line-of-sight propagation Massive MIMO Downlink Data models

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Design of Retail Product Recognition by Transfer Learning and Fine-tuning Techniques

Design of Retail Product Recognition by Transfer Learning an...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Kuei-Chung Chang Wei-Kai Huang International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology R.O.C. Dept. of Information Engineering and Computer Science Feng Chia University R.O.C.

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

Vision-based deep-learning retail product recognition has increasingly attracted, and plenty of work has been done in this field. However, there are some challenges to these approaches. One challenge is that with the increasing number of new products every season, the appearances of existing products change frequently over time. In this situation, the model must be continually retrained to contain the new products. In this paper, we use a pre-trained model as the base model and apply transfer learning and fine-tuning techniques to quickly and efficiently rebuild the model to contain new products. The results show that the proposed methods can save 76% of the training time compared with the training from scratch and can increase the accuracy by 0.5% and 0.4%, respectively, under two different testing data sets.

关键词： Training Energy consumption Accuracy Transfer learning Consumer electronics Testing Residual neural networks

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StegoFL: Using Steganography and Federated Learning to Transmit Malware

StegoFL: Using Steganography and Federated Learning to Trans...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Rong Wang Junchuan Liang Haiting Jiang Chaosheng Feng Chin-Chen Chang College of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Laboratory of Sichuan Sichuan Normal University Chengdu China Graduate School of Science and Engineering Hosei University Tokyo Japan Department of Information Engineering and Computer Science Feng Chia University Taichung Taiwan

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Federated learning (FL) has gained increasing popularity in today’s privacy-focused world due to its ability to break data silos. Although federated learning can keep all the training data private at each site, it results in clients not being effectively monitored, which may pose security risks to the learning process. Previous studies mainly focus on backdoor and poisoning attacks against federated learning. In this paper, we propose a new stegomalware attack in FL, where an attacker, disguised as a benign client, hides malware into his local model and distributes the malware to other clients through the FL process. Conducting such an attack faces two challenges. First, embedding data into a local model may degrade its model performance, making it easier for a server in FL to detect this anomaly. Second, the local model with embedded malware is recalculated after model aggregation, which unavoidably alters the malware’s carrier and hinders the success of malware extraction. To address these challenges, we propose a method called StegoFL to incorporate steganography into federated learning to transmit malware. Specifically, we split the malware to be transmitted into segments and randomly select a few model parameters as carriers. Each malware segment is concealed within these carriers over several training rounds. We also propose a simple value-mapping method to extract the embedded data by comparing the aggregated carrier values with a threshold. Experimental results demonstrate that StegoFL can circumvent server-side detection mechanisms, i.e., accuracy tests and parametric distribution comparisons. In addition, it increases transmission capacity by at least 50 times compared to state-of-the-art covert communication methods in federated learning.

关键词： Training Steganography Federated learning Training data Malware Data models Security Servers Monitoring Faces

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Meal Image Recognition and Healthy Meal Combination Recommendation System Integrated with Generative Artificial Intelligence

Meal Image Recognition and Healthy Meal Combination Recommen...

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IEEE Eurasia Conference on Biomedical engineering, Healthcare and Sustainability (ECBIOS)

作者： Chin-Chih Chang Chi-Hung Wei Sean Hsiao Chyuan-Huei Thomas Yang Department of Computer Science and Information Engineering Chung Hua University Hsinchu Taiwan Ph.D. Program in Engineering Science Chung Hua University Hsinchu Taiwan Bachelor's Program in Applied Foreign Language and Smart Commerce Chung Hua University Hsinchu Taiwan School of Information Engineering Shandong Vocational and Technical University of International Studies Shandong China

ISBN: (数字)9798350396133

ISBN: (纸本)9798350396140

People increasingly prioritize a balanced diet to enhance well-being, yet making informed dietary choices remains challenging amidst the abundance of options. To address this, we developed a meal image recognition and healthy meal combination recommender system, integrating generative artificial intelligence (AI). Convolutional neural networks were used for precise meal image recognition to identify diverse food items. The generative AI-augmented recommendation engine offers personalized meal suggestions aligned with nutritional goals and dietary preferences, utilizing a nutrition knowledge base to ensure overall well-being. The system's feasibility was validated, illustrating its excellence in meal recognition accuracy, recommendation diversity, and user engagement. By integrating generative AI, the system shows its potential to enhance dietary recommendations and public health.

关键词： Image recognition Accuracy Generative AI Knowledge based systems Convolutional neural networks Sustainable development Public healthcare Recommender systems Engines Biomedical engineering

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DViTPolyp: Small Polyp Detection in Colonoscopy Videos based on Deep Vision Transformer

DViTPolyp: Small Polyp Detection in Colonoscopy Videos based...

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Electrical, computer and Energy Technologies (ICECET), International Conference on

作者： Ming Ma Likun Xia Hyunseok Seo Dept. of Graduate Computer Science and Engineering Katz School of Science and Health Yeshiva University New York USA College of Information Engineering Capital Normal University Beijing China Biomedical Research Institute Korea Institute of Science and Technology Seoul South Korea

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

Early detection of colorectal polyps and removal play a crucial role in the prevention of colon cancer. computer-aided detection (CAD) methods can be employed to help endoscopists reduce the polyp miss-rates. Numerous automatic polyp detection methods have been explored. However, it is still challenging to automatically detect colon polyps in small size in colonoscopy videos. This paper presents a method termed DViTPolyp for detecting small colorectal polyp in colonoscopy videos. The proposed method leverages the power of the deep vision transformer as the detection head in the network to aggregate rich global features, and uses the re-attention mechanism to regenerate attention maps to capture the features from the backbone model and DViT block. Also, we propose to use a power regularized Intersection Over Union (IoU) as the loss function to further enhance the accuracy of detection. Experiments are conducted on a mix of several public polyp datasets. Comparative results show that our proposed method has a better performance than other methods.

关键词： computer vision Solid modeling Head Design automation Aggregates Prevention and mitigation Colonoscopy Transformers Feature extraction Videos

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Metamorphopsia Insepction System Based on Relevance Feedback

Metamorphopsia Insepction System Based on Relevance Feedback

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IEEE International Conference on Systems, Man and Cybernetics

作者： Zhenyang Zhu Katsuhito Moritake Kenji Kashiwagi Masahiro Toyoura Kentaro Go Issei Fujishiro Xiaoyang Mao Department of Computer Science and Engineering University of Yamanashi Kofu Japan Graduate School of Engineering University of Yamanashi Kofu Japan Department of Ophthalmology University of Yamanashi Chuo Japan Department of Information and Computer Science Keio University Yokohama Japan

People with metamorphopsia suffer from perceiving things in a distorted way. Various methods for examining metamorphopsia have been suggested in the current literature, with the most advanced techniques demonstrating the ability to yield quantitative measurements. However, these cutting-edge methods necessitate extended examination durations and impose challenging manipulations on patients. In this study, our objective is to enhance the time efficiency of the inspection process and alleviate the burden placed on the user. We propose a novel user-friendly quantitative inspection system which utilizes interactive reinforcement learning. Instead of having users directly operate the system, we ask them to evaluate the stimuli generated by the system. Based on their evaluations, the system gradually refines the deformation map representing the distortion perceived by the user. The reinforcement learning scheme is implemented using relevance feedback approach based on optimum-path forest classifier. To evaluate the effectiveness of the proposed system, subjective evaluation experiments involving simulated and real metamorphopsia participants were conducted in this study. The experimental findings reveal that, when compared to the state-of-the-art method, our proposed system yields comparable inspection out-comes while significantly reducing both the inspection duration and the mental workload.

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