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检索条件"任意字段=14th IEEE International Conference on Computer Research and Development, ICCRD 2022"

共 1452 条记录，以下是1-10 订阅

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2022 ieee 14th international conference on computer research and development, iccrd 2022

2022 IEEE 14th International Conference on Computer Research...

引用

14th ieee international conference on computer research and development, iccrd 2022

ISBN: (纸本)9781728177212

the proceedings contain 70 papers. the topics discussed include: a survey of machine learning technologies for COVID-19 pandemic;research on the relationship between mobile network data and COVID-19 cases;deep learning techniques for financial fraud detection;impact of resampling and deep learning to detect anomaly in imbalance time-series data;quantization algorithm in neural network training;deep learning and data mining for book recommendation: retrospect and expectation;deep neural networks for stock price prediction;deep learning techniques for aspect based sentiment analysis;real-time distortion prediction and optimization of weld sequence using artificial neural network;a survey on implicit bias of gradient descent;and a robust multiple network attacks detection method based on artificial neural network.

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2022 ieee 14th international conference on computer research and development (iccrd) [Title page]

2022 IEEE 14th International Conference on Computer Research...

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international conference on computer research and development (iccrd)

Presents the title page of the proceedings record.

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EchoGCN: An Echo Graph Convolutional Network for Skeleton-Based Action Recognition 27th

EchoGCN: An Echo Graph Convolutional Network for Skeleton-Ba...

引用

27th international conference on Pattern Recognition, ICPR 2024

作者： Qian, Weiwen Huang, Qian Li, Chang Chen, Zhongqi Mao, Yingchi College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (纸本)9783031783531

Graph Convolutional Networks (GCNs) have attracted considerable attention in the realm of human action recognition. However, conventional GCNs-based methods typically struggle to construct adjacency matrices that capture diverse semantics, thus leading to performance limitations. To tackle this issue, we propose the Echo Graph as a set of adjacency matrices, which includes both hierarchical and global graphs. Specifically, our hierarchical graph exploits the hierarchical information based on the selected central broadcast nodes, aggregating joints dispersed across considerable physical distances into a unified semantic space. the global graph we construct transcends the limitations imposed by physically defined topological structures, delving into comprehensive information exchange among nodes. Additionally, we propose node activation and hierarchical activation model. these activations aim to prominently highlight crucial nodes and edges for specific samples. Finally, we incorporate Margin ReLU distillation to improve computational efficiency and design a four-stream integration using only the joint and bone data streams from two central broadcast nodes. Based on the aforementioned components, we propose EchoGCN, capable of extracting representative skeleton features. Experimental results on three datasets (NTU-RGB+D 60, NTU-RGB+D 120, and NW-UCLA) demonstrate that our model achieves state-of-the-art performance. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Musculoskeletal system

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Integrated Sensing and Communication Empowered Secure Computation Offloading in Integrated Satellite-Terrestrial Networks 14th

Integrated Sensing and Communication Empowered Secure Compu...

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14th EAI international conference on Wireless and Satellite Systems, WiSATS 2024

作者： Dou, Chenglong Huang, Xumin Kang, Jiawen Wu, Yuan Qian, Liping State Key Laboratory of Internet of Things for Smart City University of Macau Macau China School of Automation Guangdong University of Technology Guangzhou510006 China Zhuhai UM Science and Technology Research Institute Zhuhai519301 China College of Information Engineering Zhejiang University of Technology Hangzhou310023 China

ISBN: (纸本)9783031861956

Integrated satellite-terrestrial edge computing networks have emerged as a promising solution to enhance data processing capabilities and connectivity in remote and underserved areas. However, the physical layer security of computation offloading in such networks is increasingly challenged by potential eavesdroppers. In this paper, we propose an integrated sensing and communication (ISAC) empowered secure computation offloading in integrated satellite-terrestrial networks, in which the satellite can utilize the ISAC signal to sense the malicious eavesdropper with an uncertain location while offloading data with improved secrecy rates. To investigate this problem, we formulate a joint optimization of the transmit beamforming, the receive beamforming, the computation offloading strategies and the associated allocations of the communication and computing resources, with the objective of maximizing the minimum sensing performance for all possible eavesdropper locations, while guaranteeing the secrecy offloading transmission rate. Despite the nonconvexity of the formulated problem, we propose an efficient algorithm to obtain its solutions. Numerical results validate the performance advantages of our ISAC empowered secure computation offloading in secrecy and robustness. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Beamforming

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research on the Improvement of Human-Machine Collaborative Machine Translation Model Based on Internet 14th

Research on the Improvement of Human-Machine Collaborative M...

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14th international conference on Frontier Computing, FC 2024

作者： Sun, Wei Yao, Man Foreign Language School Anhui Xinhua University Anhui Hefei China

ISBN: (纸本)9789819627936

Machine translation is a key study subject in computer science that has drawn interest from a wide range of academic disciplines, including linguistics, philosophy, psychology, physics, and engineering. Its journey over the last seventy years has been from the limelight to the back burner to the vanguard, then back to the forefront, demonstrating the use of modern research techniques and its continued importance in commerce and academics. the two basic strategies in machine translation are rule-based and corpus-based methods, and hybrid models that combine the two have grown in popularity over the last decade. the development of corpus-based methods that take advantage of breakthroughs in computational linguistics and natural language processing has enabled the emergence of niche machine translation solutions that are well-suited to specific fields and market segments. In this paper, we introduce a new, collaborative human-machine translation model based on an Internet scenario that differs from traditional methods to MT. Unlike the traditional MT model, in which all learning is automatically machine-driven, the suggested technique mixes human review with C-value methods to improve translation quality. In other words, this innovative approach integrates human experience with all stages of the MT process to restore machine performance to levels comparable to those of human translators. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Machine translation

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Joint Signal Adaptive Modulation Recognition and Radio Frequency Fingerprinting Based on Multi-task Learning 14th

Joint Signal Adaptive Modulation Recognition and Radio Freq...

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14th EAI international conference on Wireless and Satellite Systems, WiSATS 2024

作者： Li, Zhuo He, Zhongqiu Xu, Congan Zhang, Wei Zha, Haoran Wang, Yu Tang, Zeyu Harbin Engineering University Harbin150001 China Naval Aviation University Yantai26400 China Advanced Technology Research institute Beijing institute of Technology Jinan250300 China National Key Laboratory of Electromagnetic SpaceSecurity Sichuan Chengdu610036 China Nanjing University of Posts and Telecommunications NanJing210003 China

ISBN: (纸本)9783031861956

Radio Frequency Fingerprinting Identification (RFFI) leverages inherent discrepancies in radiation source hardware, which are challenging to mimic and counterfeit. this attribute enhances the security of wireless networks and ensures the protection of data privacy, vital for secure communications. Inherent challenges such as channel fading and frequency drift affect radio signals. this paper explores the synergy between Automatic Modulation Recognition (AMR) and RFFI by employing a multi-label dataset to strategically influence the relationship between signal labels and individual radiation sources. We propose an advanced multi-gate mixture-of-experts convolutional neural network model, the MMOE-CNN-Transformer, which operates within a multi-task soft sharing framework. Our empirical results reveal that this model significantly enhances RFFI classification accuracy, particularly at a 0dB signal-to-noise ratio, outperforming traditional single-task learning (STL) approaches and surpassing the efficacy of hard sharing architectures, exemplified by the Shared-Bottom model. this study underscores the potential of integrating sophisticated neural network architectures in enhancing the robustness and precision of radio frequency identification systems. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Signal to noise ratio

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Advance Collaborative AI-Driven Bots Using RPA to Enhance Workplace Inclusivity for Employees with Disabilities in India

Advance Collaborative AI-Driven Bots Using RPA to Enhance Wo...

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international conference on Communication Systems and Network Technologies (CSNT)

作者： Sweta Nigam Akhilesh A. Waoo Department of Computer Science and Engineering AKS University Satna India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

AI-powered bots are designed to assist workers with disabilities in various work environments. It explores adaptive interfaces and supportive features that enable these bots to meet the diverse needs of individuals facing visual, auditory, motor, and cognitive challenges. By synthesizing existing research, this review emphasizes the possibility of AI to explore workplace inclusivity, assesses the effectiveness of different assistive technologies, and identifies critical gaps in current studies. Furthermore, the paper highlights emerging trends in AI-driven accessibility tools and provides recommendations for future research and development initiatives.

关键词： Visualization Technological innovation Reviews Employment Collaboration Transforms Stakeholders Usability Robots Testing

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Enhancing Socio-Economic development through Digital Literacy: a Strategic Imperative for Rural Communities

Enhancing Socio-Economic Development Through Digital Literac...

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international conference on Communication Systems and Network Technologies (CSNT)

作者： Priyam Aryan Bhavya Gaba Akarsh Katiyar Shashwat Ambar Pathak Manish Kumar Anubhava Srivastava Department of Computer Science and Engineering Sharda University Greater Noida India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Modern society requires digital literacy because it operates under IT leadership. Various locations across rural territory continue to experience technology shortages alongside insufficient training regarding proper IT utilization. the communities encounter multiple obstacles because they have inadequate internet connectivity and insufficient infrastructure alongside poor cybersecurity capabilities. A mobile-based digital platform targeting rural needs was proposed to solve this problem. Digital literacy training helps people gain abilities that enhance their access to education and healthcare services and government programs. the research examines digital literacy fundamentals alongside practical applications and educational and social effects that digital literacy creates for employment alongside society at large. the research evaluates methods which can improve digital literacy knowledge delivery to different population segments.

关键词： Training Leadership Government Employment Medical services Mobile applications Internet Digital intelligence Socioeconomics computer security

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Real-time Posture Correction System in sitting using Deep Learning Techniques

Real-time Posture Correction System in sitting using Deep Le...

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international conference on Communication Systems and Network Technologies (CSNT)

作者： Rupesh Kumar Dewang Abid Ali Divya Kumar Ranvijay Atul Kumar Sanskar Omar Dept. Computer Science & Engineering Motilal Nehru National Institute of Technology Allahabad Prayagraj India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

In today's workplace, desk employees often spend long hours sitting in front of computers, leading to issues such as poor posture, back pain, neck strain, and other musculoskeletal problems. this research presents a solution using wearable devices and computer vision technology to tackle these health concerns. the system monitors the user's body position in real-time by analyzing images captured by the wearable device. It calculates body angles and relevant data to evaluate posture and offers corrective feedback when poor posture is detected. Maintaining good posture is important not only for physical well-being but also for enhancing productivity. Recognizing this, researchers and developers are creating technological solutions to help desk workers develop healthier posture habits throughout the workday. this paper introduces a system designed to promote better posture using computer vision. the camera capture real-time images of the user, and the system uses algorithms to identify key body points and calculate posture angles. If improper posture is identified, the system provides the user with guidance to make adjustments, helping prevent posture-related health issues. this technology offers a practical approach to reducing the physical strain associated with prolonged sitting, benefiting both individual health and workplace efficiency.

关键词： Productivity computer vision Employment Predictive models Real-time systems Wearable devices Monitoring research and development Strain Testing

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AI-Driven Time Series Models for Rainfall Prediction: A Machine Learning Approach

AI-Driven Time Series Models for Rainfall Prediction: A Mach...

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international conference on Communication Systems and Network Technologies (CSNT)

作者： Akshithaa Parvathavardhini K P Chandra J Department of Computer Science Christ (Deemed to be University) Bangalore India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Rainfall variability plays a critical role in agriculture, water resources, and disaster management in India. this study focuses on the usage of a SARIMAX model to analyze and predict rainfall patterns while accounting for both non-seasonal and seasonal components. the model adequately captures seasonal variations, illustrating the relevance of incorporating seasonal factors into forecasting. However, the non-seasonal components did not considerably improve the model’s performance. Diagnostic tests demonstrate that the model handles autocorrelation satisfactorily; however, the residuals exhibit irregularities, as evidenced by skewness and large tails. While the model has fair prediction accuracy, the findings show areas for development, particularly in fine-tuning non-seasonal dynamics and eliminating residual abnormalities. this research emphasizes the importance of seasonality in rainfall forecasting and lays the framework for future model improvements. the study provides valuable information on rainfall patterns, supporting better planning and management

关键词： Analytical models Rain Temperature Wind speed Time series analysis Humidity Predictive models Data models Forecasting Wind forecasting

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