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共 7042 条记录，以下是301-310 订阅

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A Comprehensive Approach for Detecting and Classifying Loitering Events in Fishing Vessels Using Supervised and Semi-Supervised Learning 3

A Comprehensive Approach for Detecting and Classifying Loite...

引用

3rd IEEE Asian Conference on Innovation in Technology, ASIANCON 2023

作者： Sira, Rishi Kumar Tanjavur, Irthiqua Moin Guru Prasad, M.S. Rajan, Abraham Laxman Mohan Kumar, T.G. Dept. of Computer Science and Engg. Dehradun India Nagarjuna College of Engineering and Technology Dept. of Computer Science and Engg. Bengaluru India School of Computing and Information Technology Reva University Bengaluru India Nitte Meenakshi Institute of Technology Dept. of Information Science and Engg. Bengaluru India

ISBN: (纸本)9798350302288

Illegal, unreported, and unregulated (IUU) fishing practices pose significant threats to marine ecosystems and global fisheries sustainability. The detection and classification of loitering events, where fishing vessels spend an extended period in a specific area, are critical for identifying potential IUU activities. This research proposes a comprehensive approach that combines supervised and semi-supervised learning techniques to effectively detect and classify loitering events. By leveraging various machine learning algorithms, including logistic regression, Gaussian mixture models, support vector machines, and random forests, accurate predictions can be made to enhance surveillance and combat IUU fishing. © 2023 IEEE.

关键词： Logistic regression

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Convolutional Sequence to Sequence Learning for English-Khasi Neural Machine Translation 4

Convolutional Sequence to Sequence Learning for English-Khas...

引用

4th International Conference on Computing and Communication Systems, I3CS 2023

作者： Hujon, Aiusha Vellintihun Amitab, Khwairakpam Singh, Thoudam Doren St. Anthony's College Dept. of Computer Science Meghalaya Shillong India North Eastern Hill University Dept. of Information Technology Meghalaya Shillong India NIT Silchar Dept. of Computer Science and Engineering Assam India

ISBN: (纸本)9798350323771

The convolutional neural network is relatively prevalent for visual recognition tasks. Implementing CNN for neural machine translation is still challenging compared to the predominant approach such as recurrent neural networks and transformers. This paper presents an empirical study of the convolutional sequence to sequence learning model to translate English to Khasi. The approach uses convolution for both encoders and decoders with multi-step attention. The study also includes a model using a transfer learning approach on which the parent model is trained using the English-French dataset. The quantitative and qualitative results are significant thus suggesting that the convolutional approach for neural machine translation tasks can perform remarkably well as other tasks using convolution neural networks in the case of English-Khasi language pair. © 2023 IEEE.

关键词： Neural machine translation

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Applicability of Deep Learning Model Trainings on Embedded GPU Devices: An Empirical Study

Applicability of Deep Learning Model Trainings on Embedded G...

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12th Mediterranean Conference on Embedded Computing, MECO 2023

作者： Chou, Po-Hsuan Wang, Chao Mei, Chih-Shuo National Taiwan Normal University Dept. of Computer Science and Information Engineering Taipei City Taiwan

ISBN: (纸本)9798350322910

The wide applications of deep learning techniques have motivated the inclusion of both embedded GPU devices and workstation GPU cards into contemporary Industrial Internet-of-Things (IIoT) systems. Due to substantial differences between the two types of GPUs, deep-learning model training in its current practice is run on GPU cards, and embedded GPU devices are used for inferences or partial model training at best. To supply with empirical evidence and aid the decision of deep learning workload placement, this paper reports a set of experiments on the timeliness and energy efficiency of each GPU type, running both Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model training. The results suggest that embedded GPUs did save the total energy cost despite the longer response time, but the amount of energy saving might not be significant in a practical sense. Further in this paper we report a case study for prognostics applications using LSTM. The results suggest that, by comparison, an embedded GPU may save about 90 percent of energy consumption at the cost of doubling the application response time. But neither the save in energy cost nor the increase in response time is significant enough to impact the application. These findings suggest that it may be feasible to place model training workload on either workstation GPU or embedded GPU. © 2023 IEEE.

关键词： Embedded systems

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Towards Empathetic Care Robots

Towards Empathetic Care Robots

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2023 Italia Intelligenza Artificiale - Thematic Workshops, Ital-IA 2023

作者： Costantini, Stefania Dell'Acqua, Pierangelo Dyoub, Abeer Monaldini, Andrea Department of Information Engineering Computer Science and Mathematics University of L'Aquila Italy Dept. of Science and Technology Linköping University Sweden

Care Robots are the future of healthcare giving for patients with chronic diseases and disabilities. For improving the quality of care-giving and the trustworthiness of those robots, they should be equipped with emotion recognition capabilities and empathetic behavior. In this work, we propose an framework for an empathy module to be incorporated in every care robot, and then demonstrate the effectiveness of our proposal by means of an example. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Emotion Recognition

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Into 3D-Reconstructed Apparel over Virtual Try-On with Virtual Reality

Into 3D-Reconstructed Apparel over Virtual Try-On with Virtu...

引用

2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Doungtap, Surasachai Petchhan, Jirayu Phanichraksaphong, Varinya Wang, Jenq-Haur International Graduate Program of Electrical Engineering and Computer Science Taiwan National Taiwan University of Science & Technology Dept. of Electrical Engineering Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350324174

Virtualization technologies are still growing bigger and faster. Despite the greatness of its advancement, the costume industry is still very accessible when it comes to real trials. Off-the-shelf stuff are inadequate details for the desired individual to assess its in-dept. utility for each garment trying on for a second, including custom stuff are much harder to try out right away. To this end, 2D image-based 3D reconstruction inclusive of touchable-virtualized space is accessible easier to stuff details for mans' decision making in purchasing. We establish the overall end-to-end pipeline from reconstruction until visualization for one instance to be triable on its stuff for a moment. As an expectation, our proposed approach can bring objects into the experimental area and use them immediately without any obstacle. © 2023 IEEE.

关键词： Virtual reality

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Discriminative Vector Learning with Application to Single Channel Speech Separation 48

Discriminative Vector Learning with Application to Single Ch...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Tan, Ha Minh Liang, Kai-Wen Wang, Jia-Ching National Central University Dept. of Computer Science and Information Engineering Taiwan Nguyen Tat Thanh University Faculty of Information Technology Viet Nam

ISBN: (纸本)9781728163277

In this paper, we introduce a discriminative vector learning method and apply it to single-channel speech separation. First, speech samples are transformed into discriminative vectors using two backbone networks. These vectors are easily separated by simple clustering algorithms. Among them, vectors with lower similarity are separated into different clusters, while vectors in the same cluster have higher similarity. This property is very important in image segmentation, audio separation, and data clustering problems. In our work, we design the network architecture to improve the discriminativeness of vectors through learning, taking this task as spectrogram segmentation. Experiments show that our method significantly improves performance compared to other deep clustering methods for speech separation. © 2023 IEEE.

关键词： Vectors

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Dynamic View Synthesis of Thin Structures with Short-term Movements from Monocular Videos Using Neural Radiance Fields 25

Dynamic View Synthesis of Thin Structures with Short-term Mo...

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25th International Conference on Digital Image Computing: Techniques and Applications, DICTA 2024

作者： Rajapaksha, Uchitha Laga, Hamid Diepeveen, Dean Bennamoun, Mohammed Sohel, Ferdous School of Information Technology Murdoch University MurdochWA Australia The University of Western Australia Dept. of Computer Science and Software Engineering CrawleyWA Australia

ISBN: (纸本)9798350379037

Learning to generate motions of thin structures such as plant leaves in dynamic view synthesis is challenging. This is because thin structures usually undergo small but fast, non-rigid motions as they interact with air and wind. When given a set of RGB images or videos of a scene with moving thin structures as input, existing methods that map the scene to its corresponding canonical space for rendering novel views fail as the object movements are too subtle compared to the background. Disentangling the objects with thin parts from the background scene is also challenging when the parts show fast and rapid motions. To address these issues, we propose a Neural Radiance Field (NeRF)-based framework that accurately reconstructs thin structures such as leaves and captures their subtle, fast motions. The framework learns the geometry of a scene by mapping the dynamic images to a canonical scene in which the scene remains static. We propose a ray masking network to further decompose the canonical scene into foreground and background, thus enabling the network to focus more on foreground movements. We conducted experiments using a dataset containing thin structures such as leaves and petals, which include image sequences collected by us and one public image sequence. Experiments show superior results compared to existing methods. Video outputs are available at https://***/. © 2024 IEEE.

关键词： Rendering (computer graphics)

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Advancing Robust Few-shot Surface Defect Detection through Meta-learning 9

Advancing Robust Few-shot Surface Defect Detection through M...

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9th International Conference on Integrated Circuits, Design, and Verification, ICDV 2024

作者： Putri, Wenny Ramadha Li, Yung-Hui Wang, Jia Ching National Central University Dept. of Computer Science and Information Engineering Taoyuan Taiwan Hon Hai Research Institute AI Research Center Taipei Taiwan

ISBN: (纸本)9798350371864

The primary limitation of deep learning (DL) techniques in detecting manufacturing defects is the requirement for a substantial volume of labeled data. Nevertheless, obtaining precise annotations is an arduous process due to the diverse range of defect categories. To tackle this issue, we propose MetaFormer, a transformer-based few-shot surface defect detection that learns the meta-features from a large-scale dataset of base classes. This method enables generalization to a novel class with only a few training examples. We demonstrate the effectiveness of our model on the steel surface defect dataset, surpassing the performance of the baseline model despite using only a limited number of annotated examples. © 2024 IEEE.

关键词： Zero-shot learning

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Telemedicine System Based on Mixed Reality and Cognitive Speech Service Technologies 4

Telemedicine System Based on Mixed Reality and Cognitive Spe...

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4th IEEE Eurasia Conference on Biomedical engineering, Healthcare and Sustainability, ECBIOS 2022

作者： Lin, Pei-Jung Tsai, Bo-Chang Tsai, Yung-Wei Hungkuang University Dept. of Computer Science and Information Engineering Taichung Taiwan

ISBN: (数字)9781728195797

ISBN: (纸本)9781728195797

Because of the prevalence of COVID-19, medical capacity reduction, and inconvenience due to epidemic prevention policies, the development of telemedicine has been emphasized. An AI sensing device (i.e., Azure Kinect DK) with a mixed reality device (i.e., HoloLens 2) and window application are combined to design a system that completes the consultation process from a distance. This registering system uses an SQL server database, which reads the data of registered patients, selects the registration time and department on its own, and converts the data by PC/SC with an ISO7816 database. For the window application, the patient side screen and audio content for the window screen are captured through the lens and microphone array of Azure Kinect DK. HoloLens 2 is used as the main device on the doctor's side to design the window application for dictating case conversion and transmission, whereas the Unity engine is used to create mixed-reality scenes for viewing medical records and photos of patients. In the consultation part, doctor and patient devices use Dynamic 365 Assist to conduct video consultations via Microsoft Teams, transforming the traditional consultation form into a remote consultation service via the Internet. The consultation process records the consultation data by voice and then the prescription by Speech to Text and Azure cognitive voice service. It is expected to develop a hybrid-reality telemedicine system and combine metaverse-related technologies for more online interactive service functions. The future development goal is to integrate the virtual reality rehabilitation training system further to make the application of telemedicine more complete. © 2022 IEEE.

关键词： Mixed reality

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Neural Network-Based Analysis of Forest Fire Aftermath in Class-Imbalanced Remote Sensing Earth Image Classification

Neural Network-Based Analysis of Forest Fire Aftermath in Cl...

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2024 Symposium on Beyond the Canopy: Technologies and Applications of Remote Sensing

作者： Hnatushenko, Viktoriia Hnatushenko, Volodymyr Soldatenko, Dmytro Dept. Information Technologies and Systems Ukrainian State University of Science and Technologies Dnipro Ukraine Dept. Information Technologies and Computer Engineering Dnipro University of Technology Dnipro Ukraine Institute of Photogrammetry and GeoInformation Leibniz Universität Hannover Germany

Today's agricultural sector is characterized by an important role of accurate mapping and monitoring of agriculture with the help of satellite imagery, which allows to optimize the use of resources, to plan crop areas and to forecast productivity. Classification of satellite images with unbalanced sample distribution is a critical problem in this regard. Traditional machine learning algorithms in particular have limitations in dealing with sample imbalance. In this paper, we proposed convolution neural networks for semantic segmentation, where sample imbalance is considered based on a particular loss function coupled with data augmentation. To illustrate our method, we use Sentinel-2 remote sensing (RS) images covering a number of regions in Ukraine, and then we create an image dataset of the region and for training and testing make data augmentation. The models with different architectural features were investigated. The results demonstrate that the proposed CNN has a higher classification accuracy than the ones discussed in the paper: the classification accuracy on the test dataset reached 96.7% with intersection-over-union values of up to 89.7%. This opens the way for further research in the direction of refining algorithms for classify satellite data with an imbalanced class structure. © Author(s) 2024.

关键词： Semantic Segmentation

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