检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Li, Zhaoqing Jiang, Maiqi Chen, Shengyuan Li, Bo Chen, Guorong Huang, Xiao Department of Systems Engineering and Engineering Management Chinese University of Hong Kong Hong Kong Department of Computing Hong Kong Polytechnic University Hong Kong Department of Computing The Hong Kong Polytechnic University Hong Kong School of Intelligent Technology and Engineering Chongqing University of Science and Technology China

Heterogeneous information networks (HINs) can be used to model various real-world systems. As HINs consist of multiple types of nodes, edges, and node features, it is nontrivial to directly apply graph neural network (GNN) techniques in heterogeneous cases. There are two remaining major challenges. First, homogeneous message passing in a recursive manner neglects the distinct types of nodes and edges in different hops, leading to unnecessary information mixing. This often results in the incorporation of "noise" from uncorrelated intermediate neighbors, thereby degrading performance. Second, feature learning should be handled differently for different types, which is challenging especially when the type sizes are large. To bridge this gap, we develop a novel framework - AutoGNR, to directly utilize and automatically extract effective heterogeneous information. Instead of recursive homogeneous message passing, we introduce a non-recursive message passing mechanism for GNN to mitigate noise from uncorrelated node types in HINs. Furthermore, under the non-recursive framework, we manage to efficiently perform neural architecture search for an optimal GNN structure in a differentiable way, which can automatically define the heterogeneous paths for aggregation. Our tailored search space encompasses more effective candidates while maintaining a tractable size. Experiments show that AutoGNR consistently outperforms state-of-the-art methods on both normal and large scale real-world HIN datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

A Scalable, Efficient, and Robust Dynamic Memory Management Library for HLS-based FPGAs

A Scalable, Efficient, and Robust Dynamic Memory Management ...

引用

IEEE/ACM International Symposium on Microarchitecture (MICRO)

作者： Qinggang Wang Long Zheng Zhaozeng An Shuyi Xiong Runze Wang Yu Huang Pengcheng Yao Xiaofei Liao Hai Jin Jingling Xue National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Zhejiang Lab Hangzhou China School of Computer Science and Engineering University of New South Wales Sydney NSW Australia

ISBN: (数字)9798350350579

ISBN: (纸本)9798350350586

Nowadays, high-level synthesis (HLS) has gained prominence for FPGA-based architecture prototyping, enhancing productivity significantly. Despite this advancement, HLS tools are impeded by a critical drawback: they lack support for dynamic memory management (DMM), leading to static mem-ory allocation and suboptimal use of memory resources. In response, numerous efforts have been made to develop DMM solutions compatible with HLS. However, our analysis indicates that existing solutions fail to concurrently meet the desired trifecta of scalability (efficient management of memory of any size), efficiency (minimal latency in memory (de-)allocation), and robustness (low allocation failure rates). This limitation hampers their applicability in real-world scenarios. In this paper, we introduce GraDMM, a “three-birds-one- stone” solution that comprehensively enhances the scalability, efficiency, and robustness of DMM. The key insight is to formulate memory (de-)allocation as graph analytics and lever-age sophisticated FPGA-based graph processing techniques. To achieve scalability, GraDMM specializes a simplified pipeline that significantly suppresses resource utilization expansion caused by managed memory scaling. This is crucial for managing arbitrarily sized memory on resource-limited FPGA platforms. For efficiency, GraDMM implements a data-centric concurrent traversal scheme and a shortcut-assisted fast traversal policy to accelerate (de-)allocation-guided graph traversal, reducing mem-ory (de-)allocation latency. To enhance robustness, GraDMM incorporates an adaptive memory defragmenter that defragments managed memory to minimize fragmentation-induced allocation failures. GraDMM is encapsulated as a library, providing high- level interfaces for users and ensuring synthesizability with Vi- vado HLS. Experimental results demonstrate that GraDMM out-performs three state-of-the-art HLS-compatible DMM solutions by significant margins: 56.71 %-85.59% in resource consumption sa

关键词：

来源：评论

学校读者我要写书评

暂无评论

Empowering Cyber Defense by Unveiling AI-Powered Expert Systems and Software engineering 2

Empowering Cyber Defense by Unveiling AI-Powered Expert Syst...

引用

2nd International Conference on Foundation and Large Language Models, FLLM 2024

作者： Tawalbeh, Lo'Ai Franklin, John Ramirez, Christian Garcia, Jonathan Tawalbeh, Abdelrahman Muheidat, Fadi Jordan University of Science and Technology Department of Computer Engineering Irbid22110 Jordan Texas A&M University Department of Computing and Cyber Security San AntonioTX78224 United States Johnson High School San AntonioTX78259 United States School of Computer Science and Engineering California State University San BernardinoCA92407 United States

ISBN: (纸本)9798350354799

People all over the world are subject to cyber-attacks. The abundance of information on the internet gives hackers the tools to exploit vulnerabilities in computer systems, from home desktops to the most considerable servers hosted by organizations. Cyber security is a practice dedicated to defending electronic devices from cybercrime. Top cyber security practices apply expert systems that can detect patterns and adapt to changing variables in hacking methods. An expert system in computing is a complex program that utilizes artificial intelligence (AI) to simulate human tasks or calculate intricate functions. Artificial Intelligence techniques can be used to determine any abnormal activities and unusual data traffic in computer network which can be utilized to protect these networks against any malicious activities. Today, organizations seek the latest expert systems for cyber defense. This paper aims to describe the software engineering behind artificial intelligence and how it supports expert machines used in cyber defense systems. The intricate functions of artificial intelligence in cyber security are discussed, such as deep learning, intrusion detection trees, natural language processing, and artificial immune systems. © 2024 IEEE.

关键词： Cyber attacks

来源：评论

学校读者我要写书评

暂无评论

Abusing iOS Permissions: A Security Perspective 21

Abusing iOS Permissions: A Security Perspective

引用

21st International Multi-Conference on Systems, Signals and Devices, SSD 2024

作者： Alanazy, Fares Saifan, Ramzi Al-Akhras, Mousa Atawneh, Samer Alhomoud, Adeeb College of Computing and Informatics Saudi Electronic University Riyadh11673 Saudi Arabia School of Engineering The University of Jordan Amman11942 Jordan King Abdullah II School of Information Technology The University of Jordan Amman11942 Jordan College of Science and Theoretical Studies Saudi Electronic University Riyadh11673 Saudi Arabia

ISBN: (纸本)9798350374131

Smartphone users have become accustomed to installing Apps of different categories such as gaming, business, educational, lifestyle, and many more Apps. However, even with the growing awareness of developing more secure Apps, there is no evidence that the available Apps users are completely safe. This paper explores the correlation between iOS permissions that can be abused and the safety of user information. More specifically, we study the permissions of the most popular iOS educational Apps. The study findings revealed that there is a positive correlation between abusing iOS permissions and the safety of user information. About 98 % of the Apps have abused permissions and consequently, they are classified as suspicious. In addition, the results of the study showed that the users used to download Apps and grant them inordinate permissions. Thus, placing their personal data at risk of exposure to unscrupulous third parties. © 2024 IEEE.

关键词： Safety engineering

来源：评论

学校读者我要写书评

暂无评论

FLARE: A New Federated Learning Framework with Adjustable Learning Rates over Resource-Constrained Wireless Networks

引用

IEEE Transactions on Wireless Communications 2025年

作者： Xiao, Bingnan Zhang, Jingjing Ni, Wei Wang, Xin Fudan University School of Information Science and Technology Department of Communication Science and Engineering Shanghai200433 China CSIRO Data61 MarsfieldNSW2122 Australia School of Computing Science and Engineering KenningtonNSW2052 Australia

Wireless federated learning (WFL) suffers from heterogeneity prevailing in the data distributions, computing powers, and channel conditions of participating devices. This paper presents a new Federated Learning with Adjusted leaRning ratE (FLARE) framework to mitigate the impact of the heterogeneity. The key idea is to allow the participating devices to adjust their individual learning rates and local training iterations, adapting to their instantaneous computing powers. The convergence upper bound of FLARE is established rigorously under a general setting with non-convex models in the presence of non-i.i.d. datasets and imbalanced computing powers. By minimizing the upper bound, we further optimize the scheduling of FLARE to exploit the channel heterogeneity. A nested problem structure is revealed to facilitate iteratively allocating the bandwidth with binary search and selecting devices with a new greedy method. A linear problem structure is also identified and a low-complexity linear programming scheduling policy is designed when training models have large Lipschitz constants. Experiments demonstrate that FLARE consistently outperforms the baselines in test accuracy, and converges much faster with the proposed scheduling policy. © 2002-2012 IEEE.

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

PROPENSITY-DRIVEN UNCERTAINTY LEARNING FOR SAMPLE EXPLORATION IN SOURCE-FREE ACTIVE DOMAIN ADAPTATION

arXiv

引用

arXiv 2025年

作者： Pan, Zicheng Gao, Yongsheng Yu, Xiaohan Zhang, Weichuan School of Engineering and Built Environment Griffith University Brisbane Australia School of Computing Macquarie University Sydney Australia Shaanxi University of Science and Technology Shaanxi China

Source-free active domain adaptation (SFADA) addresses the challenge of adapting a pre-trained model to new domains without access to source data while minimizing the need for target domain annotations. This scenario is particularly relevant in real-world applications where data privacy, storage limitations, or labeling costs are significant concerns. Key challenges in SFADA include selecting the most informative samples from the target domain for labeling, effectively leveraging both labeled and unlabeled target data, and adapting the model without relying on source domain information. Additionally, existing methods often struggle with noisy or outlier samples and may require impractical progressive labeling during training. To effectively select more informative samples without frequently requesting human annotations, we propose the Propensity-driven Uncertainty Learning (ProULearn) framework. ProULearn utilizes a novel homogeneity propensity estimation mechanism combined with correlation index calculation to evaluate feature-level relationships. This approach enables the identification of representative and challenging samples while avoiding noisy outliers. Additionally, we develop a central correlation loss to refine pseudo-labels and create compact class distributions during adaptation. In this way, ProULearn effectively bridges the domain gap and maximizes adaptation performance. The principles of informative sample selection underlying ProULearn have broad implications beyond SFADA, offering benefits across various deep learning tasks where identifying key data points or features is crucial. Extensive experiments on four benchmark datasets demonstrate that ProULearn outperforms state-of-the-art methods in domain adaptation scenarios. Copyright © 2025, The Authors. All rights reserved.

关键词： Active learning

来源：评论

学校读者我要写书评

暂无评论

Exploring Innovations in Autonomous Robotics and Mechatronics: Application in Manufacturing and Healthcare 5

Exploring Innovations in Autonomous Robotics and Mechatronic...

引用

5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Shalini, T.Grace Ganeshabu, L. Chemudugunta, Priyanka Yamsani, Nagendar Kumar, Ch.Ashok Athiraja, A. School of Computing Department of Computational Intelligence Chengalpattu Tamil Nadu Kattankulathur India Rajalakshmi Engineering College Department of Robotics and Automation Thandalam Mevalurkuppam Rajalakshmi Nagar Tamil Nadu Chennai India Karpaga Vinayaga College of Engineering and Technology Department of Robotics and Automation Tamil Nadu Chengalpattu India Sr University Department of School of Computer Science and Artificial Engineering Warangal India Malla Reddy Engineering College Department of Mechanical Engineering Main Campus Hyderabad Telangana Maisammaguda India Telangana Secunderabad India

ISBN: (纸本)9798331504403

Autonomous robotics and mechatronics have drastically changed the manufacturing and healthcare sectors by increasing productivity, precision, and flexibility. This work addresses the pressing need for new methods to advance these sectors by merging intelligent algorithms with autonomous systems. A hybrid strategy was employed, combining Deep Reinforcement Learning (DRL) and Adaptive Control Systems (ACS), to develop and optimize robotic operations. Extensive studies were conducted to assess the success of this strategy, which demonstrated a 25% reduction in operating expenses and a 30% increase in production efficiency in manufacturing settings. The method produced a precision rate of about 99% in the healthcare industry, which is significantly better than the 93% average of current systems. It also reduced patient recovery times by an estimated 20%. These results highlight the improved performance and promise of ACS with DRL combination in autonomous robotics, which may have revolutionary implications for the manufacturing and healthcare industries. The success, highlighted by economic and medical benefits, demonstrates the efficacy of our methodology and provides a strong platform for future developments in autonomous robotics and mechatronics. © 2024 IEEE.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Software Re-engineering using Cloud computing Platform 5

Software Re-Engineering using Cloud Computing Platform

引用

5th International Conference on Smart Systems and Inventive technology, ICSSIT 2023

作者： Raja, A. Ravi Raghunandan, K.R. Dodmane, Radhakrishna Dixit, Asmita Kumar, M. Sunil Kapila, Dhiraj Velagapudi Ramakrishna Siddhartha Engineering College Ece Department Vijayawada India Department of Computer Science and Engineering Nitte India Abes Engineering College Department of Informaton Technology Ghaziabad India Mohan Babu University School of Computing Department of Computer Science and Engineering Vijayawada India Lovely Professional University Department of Computer Science and Engineering Phagwara India

ISBN: (纸本)9781665474672

Economic and technological progress in the cloud are the main topics of this article. The essay examines the question of whether or not it makes financial sense to build software in the cloud, vs in-house. This research work provides an overview of the current model-based re-engineering practises. A novel and more thorough method is outlined in Business Process Model and Notation (BPMN) with some example activities, inputs, and outputs. As an integral element of the primary re-engineering process, reverse engineering is also something to be considered. Therefore, it expands on the concept of 'SBSE for the cloud,' providing actionable formulations of problems associated with cloud computing. The system has been built in the cloud to track the health of analytical services while they are being called into use. As an example of analytical services, a number of implementations of data mining methods are hosted from a number of packages. Their Quality of Service (QoS) values are also sampled by running them on certain real datasets. Based on the analysis of the KSEA National Math Competition event management system, this project will suggest some guidelines for software reengineering using AWS cloud computing services and web application technologies. Using these best practises, the informational gaps in software reengineering are bridged for web applications built in with cloud computing frameworks. © 2023 IEEE.

关键词： Cloud computing

来源：评论

学校读者我要写书评

暂无评论

Anomaly Detection for Log using AutoM and Auto-Sklearn Classification Algorithm 15

Anomaly Detection for Log using AutoM and Auto-Sklearn Class...

引用

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Karthik, G.M. Gupta, Ankur Gupta, Sh Rajesh Jha, Amaresh Sivasangari, A. Mishra, B.P. School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India Department of CSE RIMT University Godindgarh Mandi India Department of Management Sanskriti University Uttar Pradesh Mathura India School of Modern Media University of Petroleum and Energy Studies Dehradun India School of Computing Sathyabama Institute of Science and Technology Chennai India Department of Electronics JSS Academy of Technical Education NoidaSector-62 India

ISBN: (纸本)9798350370249

— In the realm of application development, logs represent a pivotal resource for the identification and comprehension of application performance, as well as the detection of anomalies, all of which are essential for ensuring the overall efficiency of an application. The proliferation of users has resulted in a substantial increase in the volume of logs available for analysis in the field of log analysis research. Typically, these logs exhibit an unstructured format, largely dictated by the developer's design choices. Consequently, there is a need for log parsing techniques to in still structure into these logs, enabling the application of machine learning algorithms for the prediction of anomalies. In the present research, the primary objective is the prediction of anomalies within Hadoop Distributed File System (HDFS) logs utilizing an AutoML (Automated Machine Learning) tool known as Auto-Sklearn. The research outcomes illustrate the effectiveness of the proposed model, which attains an impressive accuracy rate of 99% when applied to the dataset. This study underscores the critical role of logs in application development, highlights the importance of log structure transformation, and demonstrates the substantial predictive capabilities of the Auto-Sklearn model in the context of HDFS log analysis. ©2024 IEEE.

关键词： Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Rainfall Induced Landslide Detection in Study Area using Machine Learning Model 7

Rainfall Induced Landslide Detection in Study Area using Mac...

引用

7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Rawat, Pradeep Singh Yadav, Anuj Kumar Barthwal, Varun Gaur, Sachin Dit University Dehradun School of Computing Dehradun India H.N.B Garhwal University Pauri Department of Information Technology Pauri Garhwal India B.T Kumaon Institute of Technology Dwarahat Department of Computer Science and Engineering Dwarahat India

ISBN: (纸本)9798350341485

In the present era of computing, communication and technology, natural disaster can be controlled in an efficient manner. The natural disaster in hill sate is common. It can be mitigated using machine learning techniques. In this manuscript out objective is proposed a machine learning model which focus on rainfall induced landslide prediction in uttarakhand state districts using benchmark dataset. There is a good correlation between landslide and antecedent rain fall. The antecedent rain fall supports the machine learning model for better accuracy and correctness. The machine learning model with optimal performance metrics provides the prior information about the level rainfall and its impact level on landslide in a study area of the focused state. The results show that Random Forest model outperforms the linear model, SVR model, and neural network model respectively. The key performance indicators i.e. mean absolute error(MAE), and root mean square error(RMSE) are improved by a factor of 79.05%, and 83.34% respectively. The key performance indicators evaluated and analyzed against state of art methodologies i.e. Random Forest model outperforms the linear model, SVR model, and neural network © 2023 IEEE.

关键词： Landslides

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：