检索结果-内蒙古大学图书馆

A Survey on Intelligent Solutions for Increased Video Delivery Quality in Cloud-Edge-End Networks

IEEE Communications Surveys and Tutorials 2025年第2期27卷 1363-1394页

作者： Shi, Wanxin Li, Qing Yu, Qian Wang, Fulin Shen, Gengbiao Jiang, Yong Xu, Yang Ma, Lianbo Muntean, Gabriel-Miro Tsinghua University Tsinghua Shenzhen International Graduate School Shenzhen518055 China Fudan University School of Computer Science Shanghai200433 China Peng Cheng Laboratory Department of Strategic and Advanced Interdisciplinary Research Shenzhen518066 China Institute of Future Networks Southern University of Science and Technology Shenzhen518055 China Tecorigin Software Department Shanghai200120 China Peng Cheng Laboratory Department of Strategic and Advanced Interdisciplinary Research Shenzhen518055 China Northeastern University College of Software Shenyang110819 China Dublin City University Performance Engineering Laboratory School of Electronic Engineering DublinD09 DD7R Ireland

The digital age has brought a significant increase in video traffic. This traffic growth, driven by rapid Internet advancements and a surge in multimedia applications, presents both challenges and opportunities to video transmissions. Users seek high-quality video content, prompting service providers to offer high-definition options to improve user experience and increase profits. However, traditional end-to-end best-effort networks struggle to meet the demands of extensive video streaming and ensure good user Quality of Experience (QoE), especially in high user mobility scenarios or fluctuating network conditions. Addressing some of these challenges, content delivery networks (CDN) are instrumental in delivering video content, but they are under increased pressure to support high quality and reduce their deployment and maintenance costs. Currently, cloud-edge-end fusion technologies have become one of the optimization directions for network services due to their flexibility and scalability. At the same time, in the context of the recent advancements in computing-focused network paradigms, intelligent enhancement techniques (e.g., super-resolution), commonly utilized in image optimization, have been adopted as a pivotal solution for increasing video delivery quality. To illustrate the essence and employment of the intelligent enhancement solutions for video streaming, this paper first outlines the video streaming process, discusses relevant evaluation metrics, and examines aspects related to the intelligent solutions. Then the paper presents the intelligent enhancement process of video streaming, analyzes various typical intelligent models for content enhancement and highlights their distinct characteristics. This exploration delves deeper into various intelligent quality-improved solutions, scrutinizing their applicability across different transmission scenarios like Video on Demand (VoD) and live streaming, and shedding light on their strengths and weaknesses from

关键词： Video streaming

来源：评论

学校读者我要写书评

暂无评论

Comparison of machine learning techniques to predict students’ CGPA by using course learning outcomes datasets

引用

Human-Intelligent Systems Integration 2025年 1-11页

作者： Siraj, Saima Dahri, Fida Hussain Chandio, Jamil Ahmed Jalbani, Akhtar Hussain Laghari, Asif Ali Quaid-E-Awam University of Engineering Science & Technology Nawabshah Pakistan School of Computer Science and Engineering Southeast University Nanjing China Shah Abdul Latif University Khairpur Pakistan Software College Shenyang Normal University Shenyang China

As technology advances, traditional teaching, training, and performance evaluation approaches are being replaced by computer-based tools (CBT) such as online video courses, online exams, offline video classes, and flip classes. To improve the teaching and learning performance of the students, the education system (ES) assesses the course learning outcomes (CLO) by using the bloom taxonomy (BT). Owing to the abrupt COVID-19 epidemic, traditional classrooms are molded into online classes; therefore, it is very hard to assess the CLO because physical and online teaching pedagogies are heterogeneous, and it is very hard to classify either content has been generated by the students or any other online sources used to answer the questions. Thus, it is necessary to construct the classification model using artificial intelligence (AI) techniques and to assist educational stakeholders like schools, colleges, universities, NGOs, and the government in measuring student performance. This research proposes a comparative analysis of machine learning techniques to mitigate all the above-stated problems. It proposes methods to perform data cleaning in its first phase, whereas the second phase constructs classification models using machine learning algorithms such as decision tree, support vector machine, and random forest. In the third phase, performance evaluation is performed to estimate the results. The highest accuracy was achieved using a decision tree with 96.99% and 10 k-fold cross-validation. This research contributes (a) a real-world dataset and (b) the highest classification accuracy to analyze education.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Resilience to deception attacks in consensus tracking control of incommensurate fractional-order power systems via adaptive RBF neural network

引用

Expert Systems with Applications 2025年 283卷

作者： Sharafian, Amin Naeem, H.M. Yasir Ullah, Inam Ali, Ahmad Qiu, Li Bai, Xiaoshan College of Mechatronics and Control Engineering Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China

This paper addresses the consensus control problem for incommensurate fractional-order multi-agent power systems vulnerable to sensor deception attacks within their communication networks. To overcome this problem, an adaptive Radial Basis Function neural network (RBFNN) state observer is employed to estimate the system uncertainties in the presence of malicious attacks. Additionally, a compensation mechanism is implemented to mitigate the effects of unknown time-varying gains in the system output induced by deception attacks. Within the adaptive backstepping framework, the proposed control strategy ensures that all system signals remain bounded while enabling all followers to achieve consensus with the leader's trajectory. The effectiveness of the proposed control approach is further validated through MATLAB simulations, demonstrating its ability to enhance stability and reliability in power systems. © 2025 Elsevier Ltd

关键词： State estimation

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Differential Privacy Implementations on Synthetic Data

Comparative Analysis of Differential Privacy Implementations...

引用

IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Huwida E. Said Qusay H. Mahmoud Mandeep Goyal Faiza Hashim Department of Computing and Applied Technolgy College of Technological Innovation Zayed University Dubai UAE Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa ON Canada Department of Computer Science and Software Engineering College of Information Technology UAE University AI-Ain UAE

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

Differential privacy offers a promising solution to balance data utility and user privacy. This paper compares two prominent differential privacy tools-PyDP and IBM's diffprivlib-that are applied to a synthetic dataset with medical attributes. We evaluate these tools based on their effectiveness in maintaining data privacy while preserving the statistical integrity of the data. Our results reveal that PyDP provides synthetic data that closely matches real-world data, making it appropriate for tasks requiring accuracy while striking a better balance between data utility and privacy. However, IBM's diffprivlib is more suitable for privacy-critical applications due to its stronger privacy guarantees, but at the cost of increased noise and decreased data utility. This paper contributes to the practical understanding of implementing differential privacy in machine learning and software applications and enhances the tools available for developers in sensitive data environments.

关键词： Differential privacy Costs Accuracy Conferences Noise Machine learning software Synthetic data

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Partitioning-Based Inter-Chip Redistribution Layer Routing for Fan-Out Wafer-Level Packaging

引用

IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Xu, Haoyang Huang, Xing Zhuang, Zhen Yu, Zhiwen Guo, Bin Chao, Kai-Yuan Yu, Bei Ho, Tsung-Yi Wong, Martin D. F. Northwestern Polytechnical University School of Computer Science Xi’an China Harbin Engineering University College of Computer Science and Technology Harbin China The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Siemens Digital Industries Software WilsonvilleOR United States Hong Kong Baptist University Hong Kong

In modern advanced fan-out wafer-level packaging (FOWLP), redistribution layers (RDLs) are used to implement efficient interconnections among chips, where both flexible vias and irregular pad structures can be deployed to realize highly integrated heterogeneous systems. With the sharp increase in chip densities, however, it becomes challenging to fully utilize the routing resources in RDLs. The routing graph without optimized resource planning can lead to significant deterioration in the solution quality of RDL design. Furthermore, prior work determines via locations in the RDLs before identifying wire routes, thus offsetting the advantages of flexible vias and irregular pads. To systematically solve the RDL routing problem in FOWLP, in this paper, we propose a hierarchical partitioning-based inter-chip routing method considering both flexible vias and irregular pads, so that optimized RDL solutions can be generated automatically and efficiently. The proposed method includes the following key techniques: 1) a channel planning method for optimized routing region partition and a dynamic routing graph-based pathfinding method for efficient global routing, 2) a binarysearch-based hierarchical tile segmentation method for conflict removal and wirelength reduction, 3) a novel monotonicityanalysis-based access point optimization method and a staggered via planning strategy for both wirelength and detour reduction, and 4) a geometry-based detailed routing algorithm for innertile wire routing. The experimental results demonstrate that the proposed algorithm leads to 100% routablility and 4.8% wirelength reduction on average compared to the state-of-the-art work, with a maximum reduction rate of 10.3% across all the benchmarks. © 1982-2012 IEEE.

关键词： Chip scale packages

来源：评论

学校读者我要写书评

暂无评论

Stochastic Geometry Based Semantic Performance Analysis For Text Semantic Communication

引用

IEEE Transactions on Communications 2025年

作者： Zhu, Heng Zhu, Kun Zhang, Yang Niyato, Dusit Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing210016 China Collaborative Innovation Center of Novel Software Technology and Industrialization China Nanyang Technological University School of Computer Science and Engineering Singapore639798 Singapore

Semantic communication has recently garnered substantial attention due to its potential to alleviate bandwidth constraints and improve network capacity. Nonetheless, existing studies primarily concentrate on network architecture and overlook the communication performance analysis. Therefore, this paper seeks to derive semantic-oriented error probability. Specifically, we develop a novel text semantic communication framework that comprises distinct semantic and physical layers. In the semantic layer, we employ latent Dirichlet allocation (LDA) to extract text topics and evaluate the topic distribution. Given an expected transmission accuracy, we propose a dichotomy to determine the minimal number of topics. These acquired topics, along with their respective distributions, are defined as the text semantic features. In the physical layer, the semantic features are encoded into a binary sequence and modulated with conventional methods. The relationship between the semantic and physical layers is uncover by associating coding of the semantic features with the symbol error probability (SEP). Considering a scenario wherein base stations (BSs) following a specific Poisson point process (PPP), we derive the approximate SEP and semantic inference error probability (SIEP) for multiple coding strategies. Simulation results show that the proposed text semantic communication network enables effective text transmission and the derived error probability accurately reflect the performance of an actual communication system. © 1972-2012 IEEE.

关键词： Latent Dirichlet allocation Semantic communication Semantic inference error probability Stochastic geometry

来源：评论

学校读者我要写书评

暂无评论

Approximated Coded Computing: Towards Fast, Private and Secure Distributed Machine Learning

引用

IEEE Transactions on Emerging Topics in Computing 2025年

作者： Qiu, Houming Zhu, Kun Luong, Nguyen Cong Niyato, Dusit Jiangxi University of Finance and Economics School of Software and IoT Engineering Jiangxi 330032 China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing210016 China Phenikaa University Faculty of Computer Science Hanoi12116 Viet Nam Nanyang Technological University School of Computer Science and Engineering 639798 Singapore

In a large-scale distributed machine learning system, coded computing has attracted wide-spread attention since it can effectively alleviate the impact of stragglers. However, several emerging problems greatly limit the performance of coded distributed systems. Firstly, an existence of colluding workers who collude results with each other leads to serious privacy leakage issues. Secondly, there are few existing works considering security issues in data transmission of distributed computing systems/or coded distributed machine learning systems. Thirdly, the number of required results for which need to wait increases with the degree of decoding functions. In this paper, we design a secure and private approximated coded distributed computing (SPACDC) scheme that deals with the above-mentioned problems simultaneously. Our SPACDC scheme guarantees data security during the transmission process using a new encryption algorithm based on elliptic curve cryptography. Especially, the SPACDC scheme does not impose strict constraints on the minimum number of results required to be waited for. An extensive performance analysis is conducted to demonstrate the effectiveness of our SPACDC scheme. Furthermore, we present a secure and private distributed learning algorithm based on the SPACDC scheme, which can provide information-theoretic privacy protection for training data. Our experiments show that the SPACDC-based deep learning algorithm achieves a significant speedup over the baseline approaches. © 2013 IEEE.

关键词： Parallel processing systems

来源：评论

学校读者我要写书评

暂无评论

GLoT: A Novel Gated-Logarithmic Transformer for Efficient Sign Language Translation

arXiv

引用

arXiv 2025年

作者： Shahin, Nada Ismail, Leila Lab Department of Computer Science and Software Engineering College of Information Technology UAE University Al-Ain United Arab Emirates Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates Emirates Center for Mobility Research UAE University Al-Ain United Arab Emirates

Machine Translation has played a critical role in reducing language barriers, but its adaptation for Sign Language Machine Translation (SLMT) has been less explored. Existing works on SLMT mostly use the Transformer neural network which exhibits low performance due to the dynamic nature of the sign language. In this paper, we propose a novel Gated-Logarithmic Transformer (GLoT) that captures the long-term temporal dependencies of the sign language as a time-series data. We perform a comprehensive evaluation of GloT with the transformer and transformer-fusion models as a baseline, for Sign-to-Gloss-to-Text translation. Our results demonstrate that GLoT consistently outperforms the other models across all metrics. These findings underscore its potential to address the communication challenges faced by the Deaf and Hard of Hearing community. © 2025, CC BY.

关键词： Neural machine translation

来源：评论

学校读者我要写书评

暂无评论

Toward Understanding Federated Learning over Unreliable Networks

IEEE Transactions on Machine Learning in Communications and ...

引用

IEEE Transactions on Machine Learning in Communications and Networking 2025年 3卷 80-97页

作者： Feng, Chenyuan Arafa, Ahmed Chen, Zihan Zhao, Mingxiong Quek, Tony Q. S. Yang, Howard H. EURECOM Department of Communication Biot06410 France University of North Carolina at Charlotte Department of Electrical and Computer Engineering CharlotteNC28223 United States Singapore University of Technology and Design Information Systems Technology and Design Pillar Tampines487372 Singapore Yunnan University Engineering Research Center of Cyberspace National Pilot School of Software Kunming650106 China Zhejiang University ZJU-UIUC Institute Haining314400 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310007 China University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering ChampaignIL61820 United States

This paper studies the efficiency of training a statistical model among an edge server and multiple clients via Federated Learning (FL) - a machine learning method that preserves data privacy in the training process - over wireless networks. Due to unreliable wireless channels and constrained communication resources, the server can only choose a handful of clients for parameter updates during each communication round. To address this issue, analytical expressions are derived to characterize the FL convergence rate, accounting for key features from both communication and algorithmic aspects, including transmission reliability, scheduling policies, and momentum method. First, the analysis reveals that either delicately designed user scheduling policies or expanding higher bandwidth to accommodate more clients in each communication round can expedite model training in networks with reliable connections. However, these methods become ineffective when the connection is erratic. Second, it has been verified that incorporating the momentum method into the model training algorithm accelerates the rate of convergence and provides greater resilience against transmission failures. Last, extensive empirical simulations are provided to verify these theoretical discoveries and enhancements in performance. © 2023 CCBY.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

VisioSLR: A Vision Data-Driven Framework for Sign Language Video Recognition and Performance Evaluation on Fine-Tuned YOLO Models

引用

Procedia computer science 2025年 257卷 85-92页

作者： Leila Ismail Nada Shahin Henok Tesfaye Alain Hennebelle Intelligent Distributed Computing and Systems (INDUCE) Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates Independent Researcher

With the emergence of AI for good, there has been an increasing interest in building computer vision data-driven deep learning inclusive AI solutions. Sign language Recognition (SLR) has gained attention recently. It is an essential component of a sign-to-text translation system to support the deaf and hard-of-hearing population. This paper presents a computer VISIOn data-driven deep learning framework for Sign Language video Recognition (VisoSLR). VisioSLR provides a precise measurement of translating signs for developing an end-to-end computational translation system. Considering the scarcity of sign language datasets, which hinders the development of an accurate recognition model, we evaluate the performance of our framework by fine-tuning the very well-known YOLO models, which are built from a signs-unrelated collection of images and videos, using a small-sized sign language dataset. Gathering a sign language dataset for signs training would involve an enormous amount of time to collect and annotate videos in different environmental setups and multiple signers, in addition to the training time of a model. Numerical evaluations of VisioSLR show that our framework recognizes signs with a mean average precision of 97.4%, 97.1%, and 95.5% and 11, 12, and 12 milliseconds of recognition time on YOLOv8m, YOLOv9m, and YOLOv11m, respectively.

关键词： Artificial intelligence computer vision Deep learning Fine-tuning Machine translation Neural machine translation Sign language Sign language recognition Transfer learning YOLO

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：