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

共 68658 条记录，以下是271-280 订阅

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Motion Planning of the Autonomous Vehicles with Multi-view Images and GRUs 3rd

Motion Planning of the Autonomous Vehicles with Multi-view I...

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3rd international conference on Computing and Communication Networks, ICCCN 2023

作者： Singh, Divya Srivastava, Rajeev Gupta, Umesh School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science and Engineering Indian Institute of Technology Banaras Hindu University Uttar Pradesh Varanasi India SR University Telangana Warangal India

ISBN: (纸本)9789819726707

Motion planning is arguably the most significant module in autonomous driving. Image-based and LiDAR-based representations are very successful but costly. The proposed method considers three images from different angles to understand the ego vehicle's surroundings. The proposed approach decreases the occurrence of infractions, to reduce the violation of traffic signals. The proposed model generates a sequence of coordinates representing the waypoints in the predicted path of the vehicle for the upcoming few time steps and uses an inverse dynamic algorithm to derive the values of the driving parameters, like steering angle, throttle, and brake value, from the coordinates of the waypoints generated by the model. The CARLA simulator is used for data extraction to train and evaluate the model and simulate the motion planning. The proposed approach shows an improvement in the Route Completion and Driving Score metrics compared to the existing methods on the dataset of this simulator. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Traffic signals

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Commonsense Subgraph for Inductive Relation Reasoning with Meta-learning 31

Commonsense Subgraph for Inductive Relation Reasoning with M...

引用

31st international conference on Computational Linguistics, COLING 2025

作者： Zhao, Feng Zhang, Zhilu Yan, Cheng Liu, Xianggan Natural Language Processing and Knowledge Graph Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798891761964

In knowledge graphs (KGs), predicting missing relations is a critical reasoning task. Recent subgraph-based models have delved into inductive settings, which aim to predict relations between newly added entities. While these models have demonstrated the ability for inductive reasoning, they only consider the structural information of the subgraph and neglect the loss of semantic information caused by replacing entities with nodes. To address this problem, we propose a novel Commonsense Subgraph Meta-Learning (CSML) model. Specifically, we extract concepts from entities, which can be viewed as high-level semantic information. Unlike previous methods, we use concepts instead of nodes to construct commonsense subgraphs. By combining these with structural subgraphs, we can leverage both structural and semantic information for more comprehensive and rational predictions. Furthermore, we regard concepts as meta-information and employ meta-learning to facilitate rapid knowledge transfer, thus addressing more complex few-shot scenarios. Experimental results confirm the superior performance of our model in both standard and few-shot inductive reasoning. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Enhancing Zero-Shot Relation Extraction through Staged Interaction with Large Language Models

Enhancing Zero-Shot Relation Extraction through Staged Inter...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Yifang Duan, Pengfei Yang, Yiwen Xiong, Shengwu Sanya Science and Education Innovation Park Wuhan University of Technology Sanya China School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China

ISBN: (纸本)9798350368741

Zero-shot Relation Triplet Extraction (ZeroRTE) is a challenging yet valuable task that extracts relation triplets from unstructured texts for new relation types, significantly reducing the time and effort needed for data labeling. With the advancement of the zero-shot capabilities of large language models, the performance of many zero-shot tasks has been further improved only simply by interacting with large language models (LLMs). In this work, we transform the zero-shot triplet extraction task into a two-stage chat with LLMs. Specifically, in the first stage, we prompt the LLMs to perform Named Entity Recognition (NER). In the second stage, we prompt the LLMs to perform Relation Classification (RC) using the results from the first stage. Experiments on Wiki-ZSL and FewRel datasets show the efficacy of Relation Prompt for the ZeroRTE task. Notably, our method significantly outperforms strong baselines, achieving an impressive 15.89% increase in F1 scores, particularly on Wiki-ZSL with 15 unseen relations. © 2025 IEEE.

关键词： Instruction-tuning LLMs Prompt Relation Triplet Extraction Zero-Shot

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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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An Extended Euclidean Algorithm Implementation over Ciphertext Space 2nd

An Extended Euclidean Algorithm Implementation over Ciphert...

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2nd international conference on Security and Information Technologies with AI, Internet Computing and Big-data Applications, SITAIBA 2023

作者： Chi, Po-Wen Lin, Yuan-Pang Wang, Ming-Hung Chuang, Yun-Hsin Department of Computer Science and Information Engineering National Taiwan Normal University Taiwan Taipei Taiwan Department of Computer Science and Information Engineering National Chung Cheng University Taiwan Chiayi Taiwan Engineering Management Graduate Program College of Engineering National Cheng Kung University Taiwan Tainan Taiwan

ISBN: (纸本)9789819777853

Fully homomorphic encryption (FHE) is a useful cryptographic primitive. It makes a third-party manipulate data in the ciphertext space and therefore is a key technology for privacy-preserving outsourcing services. Theoretically, FHE can evaluate any circuits. However, the implementation is not trivial. The reason it lacks lots of basic operations like conditional check and branch control. In this paper, we focus on extended Euclidean algorithm operation over FHE. We introduce the problems we encounter and show how we solve these issues. We implement our design and evaluate its performance on a real computer. Finally, we propose some research directions for our future work. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ciphertext

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BAV-DSS: Blockchain Assisted Verifiable Data Sharing Scheme with Fast Encryption and Outsourced Decryption for IoT 24th

BAV-DSS: Blockchain Assisted Verifiable Data Sharing Scheme ...

引用

24th international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Huang, Yao Yu, Zhongyuan Wang, Guijuan Chai, Baobao Zhang, Hongliang Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250353 China School of Information Science and Engineering Lanzhou University Lanzhou730000 China School of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9789819615414

Many sophisticated cloud platforms are available to provide services such as storage and computation for Internet of Things (IoT) applications. However, with the concerns about the semi-trusted nature of cloud platforms, data security and privacy have been gaining broad attention. In this paper, we propose a blockchain-assisted verifiable data sharing scheme (BAV-DSS) for IoT. The BAV-DSS features fine-grained access control, fast encryption, and outsourced decryption with verifiability. Specifically, with the application of ciphertext-policy attribute-based encryption (CP-ABE), flexible access control is enabled. Blockchain is employed to store public keys, generate account addresses, and perform data verification. Furthermore, the selective replayable chosen ciphertext attacks (sRCCA) security of BAV-DSS is proved, and the simulation results indicate that BAV-DSS obtains high efficiency and practicality compared to other schemes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

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Burning Neural Networks with Harary Graphs for Optimal Connectivity Control

Burning Neural Networks with Harary Graphs for Optimal Conne...

引用

international conference on Computational Engineering, ICCE 2023

作者： Rajan, R. Sundara Kannan, V. Kamala Navaneetan, M. Department of Mathematics Hindustan Institute of Technology and Science Tamil Nadu Chennai India Department of Computer Science and Engineering Hindustan Institute of Technology and Science Tamil Nadu Chennai India

ISBN: (纸本)9783031777639

Embark on a journey into the neural landscape where fires ignite, spread, and reveal the secrets of connectivity. This groundbreaking exploration introduces "NeuroBurning," a novel approach that computes the burning number for Harary graphs, unraveling the minimum activation needed to set the entire neural network ablaze. From controlling disease spread to optimizing brain stimulation, this study delves into the computation of the burning number and its captivating applications in understanding the intricate web of connections within the brain. In this study, we have calculated the burning number for Harary graphs and explored the dynamics of neural connectivity by examining this concept within the framework of Harary graphs. Additionally, we have determined the burning number for pencil graphs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Graph neural networks

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PCOS Detection Using CNN and ML Algorithms 5th

PCOS Detection Using CNN and ML Algorithms

引用

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

作者： Roja, G. Spandana, B. Divya, N. Anusha, R. Pushpa Rani, K. Narayanamma Institute of Technology and Science Telangana Hyderabad India Department of CSE G. Narayanamma Institute of Technology and Science Telangana Hyderabad India Narayanamma Institute of Technology and Science Telangana Hyderabad India Department of CSE MLR Institute of Technology and Science Telangana Hyderabad India

ISBN: (纸本)9789819780426

Polycystic Ovarian Syndrome (PCOS) is a complicated menstrual sickness that affects a wide range of women of procreative lifetime. Its miles connected with a combination of signs, including abnormal menstrual cycles, hormonal imbalances, and the presence of more than one cyst on the ovaries. The current methods for diagnosing PCOS involve a comprehensive evaluation that includes medical history assessment, physical examinations, and laboratory tests. However, these approaches have limitations. They can be time-consuming, relying on the subjective judgment and expertise of healthcare professionals. In light of these challenges, there is a growing need for more advanced and efficient methods of diagnosing PCOS. By utilizing machine learning algorithms and computer vision techniques, such a system can provide an objective and reliable approach to identify PCOS based on ultrasound images. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diagnosis

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An Optimal Feature Selection-Based Approach to Predict Cervical Cancer Using Machine Learning 3rd

An Optimal Feature Selection-Based Approach to Predict Cervi...

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3rd international conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Al Mamun, Abdullah Uddin, Khandaker Mohammad Mohi Chakrabarti, Anamika Nur-A-Alam, Md. Mahbubur Rahman, Md. Department of Computer Science and Engineering Dhaka International University Dhaka Bangladesh Department of Computer Science and Engineering Southeast University Dhaka1208 Bangladesh Department of Computer Science and Engineering University of Business and Technology Dhaka Bangladesh

ISBN: (纸本)9789819739363

Cervical cancer has been one of the leading causes of female early death in recent years. In developing countries, cervical cancer occurs in more than 85% of cases. Cervical cancer has been associated with several risk factors. For forecasting the prognosis of cervical cancer patients, we created a prediction model in this study based on early screening and risk trends in individual health records. In this paper, we analyze the risk variables for cervical cancer using ML classification methods. The study’s dataset is very unbalanced and includes missing values. Consequently, the ROS approach, a sampling method was used. The effectiveness of class imbalance was demonstrated by comparing the suggested model’s accuracy, sensitivity, and specificity. The RF, GB, and MLP machine learning models perform better with 99.07, 98.57, and 98.51% accuracy when using the Random Oversampling technique, and XGBoost. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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Investigating the Applications of Sentimental Analysis Algorithms for Highly-Automated Delivery Applications 5th

Investigating the Applications of Sentimental Analysis Algor...

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

作者： Bhuvana, J. Ali, Arif Pawaskar, Praveen Giridhar Savita Karnataka Bangalore India Department of Computer Science & 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 & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Sentimental evaluation algorithms enable computer systems to stumble on, classify, and degree textual content sentiment. This era can be leveraged to evaluate consumer evaluations and provide remarks to corporations to be able to improve provider shipping. This observe pursuits to analyze the capability packages of those algorithms in pretty automatic shipping packages, inclusive of self sufficient drones and robots. Reading consumer sentiments using those algorithms can assist to stumble on client satisfaction or dissatisfaction in a timely fashion. This evaluation may be used to inform decision-making and to enhance carrier revel in for customers. The take a look at will hire a mixture of strategies, consisting of interviews, surveys, and quantitative analyses, to evaluate the effectiveness of this era in incredibly automatic shipping programs. The outcomes of this study will offer precious perception into the potential applications of sentimental evaluation algorithms for reinforcing customer support in computerized shipping systems.. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decision making

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