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检索条件"任意字段=2023 International Conference on Computer Science and Automation Technology, CSAT 2023"

共 68642 条记录，以下是311-320 订阅

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TranslateKar: An Efficient Transformer Based Konkani Translator 3rd

TranslateKar: An Efficient Transformer Based Konkani Transla...

引用

3rd international conference on Emerging Electronics and automation, E2A 2023

作者： Pai, Shreya Mandrekar, Sajal Phadte, Saylee Naik, Atit Prasad, Shitala Computer Science Goa College of Engineering Goa Ponda India School of Mathematics and Computer Science IIT Goa Goa Ponda India

ISBN: (纸本)9789819730896

In this research, we introduce a bidirectional Neural Machine Translation (NMT) model that employs the Transformer architecture to facilitate translations between Konkani and English languages. Inspired by "Attention is All You Need", we delve into variations in hyperparameters, including dropout rates and vocabulary size, to assess their impact on translation performance. The Transformer model, renowned for its capacity to handle long-range dependencies efficiently and in parallel, proves highly suitable for sequence-to-sequence tasks such as machine translation. We meticulously conduct a series of experiments, training the model with diverse hyperparameters configuration, and subsequently compare the outcomes to discern the optimal setup. Our findings underscore the superiority of the dash model, as it attains the highest BLEU score on the 3k test dataset, demonstrating its enhanced translation accuracy for both Konkani to English and English to Konkani translations. These results shed light on the paramount importance of hyperparameters selection in bidirectional NMT systems, underscoring the Transformer architecture’s potential in multilingual translation tasks. The study’s outcomes represent a noteworthy contribution to the advancement of machine translation techniques and set the stage for more effective communication tools in multilingual contexts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Neural machine translation

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Effect of EKF Localization on Path Tracking in Autonomous Vehicle Navigation Systems

Effect of EKF Localization on Path Tracking in Autonomous Ve...

引用

2025 international conference on Electronics, Information, and Communication, ICEIC 2025

作者： Cho, Eun Ho Yang, Seung Geon Kwon, Girim Lim, Seung-Chan School of ICT Robotics and Mechanical Engineering Hankyong National University Anseong Korea Republic of School of Electrical and Computer Engineering University of Seoul Seoul Korea Republic of

ISBN: (纸本)9798331510756

This paper presents an integrated approach to evalu-ating the interplay between localization and path-tracking performances in autonomous vehicle navigation systems. Considering stochastic uncertainties in motion and measurement models, we design an extended Kalman filter (EKF) for localization and employ the pure pursuit method for steering control. We analyze how localization accuracy impacts path-tracking performance through extensive simulations, varying the number of landmarks and noise covariance settings. Our results highlight the critical role of accurate localization in improving path tracking, offering valuable insights for designing robust autonomous navigation systems. © 2025 IEEE.

关键词： Stochastic systems

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Unknown Category Malicious Traffic Detection Based on Contrastive Learning 6th

Unknown Category Malicious Traffic Detection Based on Contra...

引用

6th international conference on Machine Learning for Cyber Security, ML4CS 2024

作者： Yan, Leiming Zhou, Tao Chen, Xianyi Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China School of Computer Science and Cyber Science and Engineering Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (纸本)9789819645657

In response to the continuous emergence of novel unknown malicious traffic and the limitations of traditional detection methods, this paper presents an unknown-category malicious traffic detection approach based on contrastive learning. The proposed method utilizes contrastive learning to train an encoder for distinguishing normal traffic and multiple types of known malicious traffic, thereby enhancing inter-class separability. Subsequently, the model is fine-tuned to optimize the representations of malicious traffic categories with limited samples, amplifying the separation between different classes in the feature space. This ensures that the distance between unknown malicious traffic and other categories exceeds a predefined threshold, thereby achieving effective detection. Experimental results on the CICIoT2023 and UNSW-NB15 datasets demonstrate the superior performance of the proposed method, achieving multi-class F1-scores of 93.23 and 89.93, respectively, outperforming traditional approaches. Additionally, the method achieves an accuracy of over 83% in simulated scenarios involving unknown-category malicious traffic detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Siamese Network for EEG Classification of Motor Execution and Imagery 5

Siamese Network for EEG Classification of Motor Execution an...

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5th IEEE international conference on Power, Electronics and computer Applications, ICPECA 2025

作者： Li, Zhi Chen, Cai Peng, Fulai Lv, Danyang Tian, Chongxuan Meng, Xiangwei Zhang, Ningling Wu, Fengxia Shandong Institute of Advanced Technology Chinese Academy of Sciences Jinan250000 China Biomedical Engineering Institute School of Control Science and Engineering Shandong University Jinan250061 China School of Basic Medical Sciences Shandong University Jinan250000 China

ISBN: (纸本)9798331533694

Motor imagery (MI), the mental simulation of movement without physical execution, is a pivotal aspect of brain-computer interface (BCI) research, facilitating direct communication between the brain and external devices. Non-invasive BCIs, particularly those utilizing electroencephalogram (EEG) signals, face inherent challenges, including high-dimensional data, temporal dependencies, and variability across subjects. This study investigates the potential of Siamese networks-A deep learning framework grounded in contrastive learning-for single-channel EEG classification in MI tasks. Through a series of comparative experiments employing identical parameter settings, including ten-fold cross-validation, we demonstrate that Siamese networks outperform convolutional neural networks (CNNs), yielding an average classification accuracy of approximately 10% higher. These findings underscore the superior capability of Siamese networks in capturing subtle temporal patterns and mitigating inter-subject variability, positioning them as a promising alternative to traditional CNN-based approaches in EEG-based MI classification. © 2025 IEEE.

关键词： Electroencephalography

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Knowledge Distillation Dealing with Sample-Wise Long-Tail Problem 17th

Knowledge Distillation Dealing with Sample-Wise Long-Tail P...

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17th Asian conference on computer Vision, ACCV 2024

作者： Yu, Tao Zhao, Xu An, Yongqi Tang, Ming Wang, Jinqiao Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Wuhan AI Research Wuhan China

ISBN: (纸本)9789819609710

We discover that while knowledge distillation improves the overall performance of student models, the performance improvement for some samples in the tail is limited, which is a rarely addressed issue. These tail samples can lead to poor learning of the teacher’s feature distribution in the corresponding regions of the feature space, thereby limiting the alignment between the student and the teacher. Since tail samples often lack clear label definitions in many tasks, we identify them by analyzing the average feature similarity from the teacher model. To improve knowledge distillation, we propose a Sample-wise Re-weighting (SRW) method, assigning different loss function weights to samples based on their average similarity. Experimental results show that our method enhances the performance of student models across different tasks and can be combined with various knowledge distillation methods. Additionally, our approach demonstrates advantages in foundational models such as Segmentation Anything Models (SAM) and Contrastive Language–Image Pretraining (CLIP) models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Visual languages

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Making Strides Security in Multimodal Fake News Detection Models: A Comprehensive Analysis of Adversarial Attacks 31st

Making Strides Security in Multimodal Fake News Detection M...

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31st international conference on Multimedia Modeling, MMM 2025

作者： Si, Jiahua Wang, Youze Hu, Wenbo Liu, Qiang Hong, Richang University of Science and Technology of China Hefei China Hefei University of Technology Hefei China Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9789819620609

With the rise of social media as a crucial data source, fake news has proliferated, posing significant challenges to data accuracy and societal well-being. Our research investigates the impact of multimedia on the rapid dissemination of fake news, highlighting the need for effective detection models. We focus on developing algorithms to detect fake news, emphasizing a multimodal approach to accommodate the complexity of modern media formats. Our study underscores the urgent need for real-time detection models to control the swift spread of misinformation. We prioritize usability across various platforms, ensuring accessibility and efficiency for both large networks and small organizations. Additionally, we address the ethical implications of fake news detection, emphasizing adherence to social norms and legal frameworks to prevent abuse and ensure reliable information. A critical aspect of our research is examining adversarial attack techniques targeting multimodal fake news detection models. We analyze the vulnerability of current models to both unimodal and multimodal attacks and emphasize the necessity for advanced security measures to reliably counter substantial adversarial threats. Our study aims to advance the development of adaptable and robust systems to combat the spread of fake news by evaluating the resilience of multimodal detection models against various adversarial attack techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fake detection

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A Review of Human Mesh Reconstruction: Beyond 2D Video Object Segmentation 16th

A Review of Human Mesh Reconstruction: Beyond 2D Video Obje...

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16th international conference on Social Robotics, ICSR + InnoBiz 2024

作者： Wu, Peng Wang, Zhicheng Pan, Feiyu Li, Fangkai Hu, Hao Lu, Xiankai Guo, Yiyou School of Software Shandong University Shandong Jinan250000 China School of Mathematics and Computer Science Fujian Provincial Key Laboratory of Data-Intensive Computing Fujian University Laboratory of Intelligent Computing and Information Processing Quanzhou Normal University Quanzhou China

ISBN: (纸本)9789819611508

Video object segmentation aims to extract 2D object masks by segmenting video frames into multiple objects, which is crucial in various practical applications such as medical imaging, etc.. However, traditional video object segmentation methods produce 2D masks, which are not suitable for 3D scenarios where depth information is essential, such as in robotic grasping, virtual reality, and autonomous driving, etc.. In this paper, we present a comprehensive review of 3D human mesh reconstruction (HMR) as an extension beyond 2D video object segmentation. We begin by reviewing the mainstream video object segmentation methods, then transition from 2D video object segmentation to 3D HMR. We further categorize recent HMR methods based on key characteristics that define this research field, including the type of model input and the use of statistical models. Finally, we provide detailed information on HMR datasets and evaluation metrics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mesh generation

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Bi-directional Power Flow Simulation of Permanent Magnet Linear Machine for Gravity Energy Storage

Bi-directional Power Flow Simulation of Permanent Magnet Lin...

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2025 international conference on Electrical automation and Artificial Intelligence, ICEAAI 2025

作者： Feng, Yi Chi, Song Yan, Jianhu School of Business Nanjing Institute of Technology Nanjing China School of Automation Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798331506797

Solid gravity energy storage (GES) can realize the bi-directional power flow according to grid dispatching by using permanent magnet linear machine (PMLM). When the power of grid is excessive, electricity can be inputted to lift a heavy block to a certain height to store the gravitational potential energy of this heavy block. On the contrast, when the power of grid is shortage, the heavy block is lowered and the stored gravitational potential energy can be converted into electrical energy through the generator mode by controlling PMLM. According to this direct driven type with PMLM, the GES has higher energy conversion efficiency compared with the traditional GES type. This paper uses the principle of GES to design a PMLM structure for motor/generation operation, establishes a dynamic mathematical model of PMLM, and proposes a three-loop control system of power, speed, and current as the main control part of the entire GES system. The bi-directional power flow operation characteristics of the proposed PMLM is simulated by using Matlab/Simulink to implement the GES study in the future. © 2025 IEEE.

关键词： bi-directional power flow closed-loop control dynamic mathematical model gravity energy storage permanent magnet linear machine

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Deep Learning in Medical Imaging: A Comparative Review of Models for Ischemic and Hemorrhagic Stroke Lesion Segmentation

Deep Learning in Medical Imaging: A Comparative Review of Mo...

引用

international Health Informatics conference, IHIC 2023

作者： Sulaiman, Sadiya Thanka, M. Roshni Edwina, E. Bijolin Salam, Nader Department of Computer Science and Engineering Karunya Institute of Technology and Science Tamil Nadu Coimbatore India

ISBN: (纸本)9789819771899

Stroke, primarily categorized as ischemic and hemorrhagic, stands among the top global causes of mortality and can lead to severe neurological conditions if left untreated. The emergence of machine intelligence, especially advanced neural networks, has brought significant advancements in medical imaging, including the automated segmentation of stroke lesions. This automation aids radiologists and surgeons in more effective diagnosis and treatment planning. This review paper concentrates on deep learning methods used in segmenting ischemic and hemorrhagic stroke lesions, examining these models from an architectural standpoint. The paper begins with a discussion on the types of strokes. Subsequently, delve into a review of deep learning models, specifically emphasizing their application in ischemic and hemorrhagic stroke lesion segmentation. The performance, advantages, and limitations of these models are assessed and comparisons among them are presented. The paper concludes by spotlighting the current needs in this field and suggesting potential avenues for future research. The study aims to provide insights to both advancedresearchers and newcomers in the field, thereby fostering future advancements in stroke lesion segmentation using deep learning methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Transillumination

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Implementing Encryption Algorithms for the Protection of Wireless Networks 5th

Implementing Encryption Algorithms for the Protection of Wir...

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5th international conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Ahmad, Naseem Madhura, K. Agarwal, Trapty Zaidi, Taskeen Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India

ISBN: (纸本)9789819780426

The use of encryption algorithms is more and more turning into a crucial issue of defensive wireless networks. Encryption algorithms may be used to cozy facts that is transferred over a Wi-Fi network while also presenting authentication and integrity protocols for the systems connected to the network. This paper discusses the significance of enforcing encryption algorithms for the safety of a wireless community. It describes the distinct encryption algorithms and protocols which are available, and the criteria for selecting the proper algorithm for exclusive varieties of Wi-Fi networks. It additionally provides unique implementation eventualities based totally on numerous sorts of Wi-Fi community structure in addition to the vast security threats in Wi-Fi networks that may be averted by imposing encryption algorithms. Finally, this paper offers guidelines on selecting and imposing the perfect encryption set of rules for safety of wireless networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Encryption algorithms

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