检索结果-内蒙古大学图书馆

Artificial Intelligence (SLAAI-ICAI), SLAAI International Conference on

作者： R.G.C. Upeksha M.W.P. Maduranga N.W. Chanka Lasantha N.G.S. Aminda Department of Software Engineering Sri Lanka Technology Campus Padukka Sri Lanka Department of Electrical and Electronic Engineering University of Sri Jayewardenepura Nugegoda Sri Lanka Cybersecurity and GRC Division Kerner Norland Pvt (Ltd) Colombo Sri Lanka Department of Data Science Sri Lanka Technology Campus Padukka Sri Lanka

ISBN: (数字)9798331509170

ISBN: (纸本)9798331509187

This paper presents a multi-faceted framework to detect and forestall ARP poisoning attacks in switchport details enhanced by the MTD strategy incorporated with the real-time attack detection system DIPRM under dynamic IP randomization and managed by machine learning to counter new threats. The innovation of this work is the combination of dynamic IP randomization together with ML-based real-time attack detection that creates a strong defense strategy against ARP poisoning, the logical continuation of each static defense avert array, and making a point of the defensive-offensive balance typical for the old-fashioned static security systems. The proposed solution, if applied, exhibited a significant enhancement in addressing ARP poisoning attack identification and prevention aspects, as evinced by the findings on the UNSW-NB15, KDD’Cup99, and NSL-KDD datasets, where the receiver operating characteristic (ROC) curves of the selected hybrid models outperformed other single algorithmic approaches across precision, recall, and overall net accuracy lines. Consequently, the research study substantiates the efficacy of the discussed hybrid model for eliminating false positives and false negatives conducive to the stability and performance of the network without a significant decline in the system’s throughput.

关键词： Technological innovation Machine learning algorithms Computational modeling Scalability Switches Throughput NSL-KDD Real-time systems Stability analysis IP networks

来源：评论

学校读者我要写书评

暂无评论

Large-scale group decision-making method based on knowledge differentiation and the grouping of alternatives into modifiable scenarios

引用

Procedia Computer science 2022年 214卷 927-934页

作者： José Ramón Trillo Enrique Herrera-Viedma María José Higueras-Ruiz Sergio Alonso Juan Antonio Morente-Molinera Francisco Javier Cabrerizo Department of Computer Science and Artificial Intelligence Andalusian Research Institute of Data Science and Computational Intelligence DaSCI University of Granada Granada 18071 Spain Department of Information and Communication University of Granada Granada 18071 Spain Department of Software Engineering Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada 18071 Spain

Nowadays, group decision-making is an everyday event, since we usually live in a community. Nevertheless, when the number of participants is large, it starts to generate a lot of information that is complex to manage, and unlike group decision-making, where the participants are usually experts, when the number of participants increases, not all of them are, which generates a conflict of interest because the value of the vote of an expert and a (non-expert) user is worth the same. Moreover, it may happen that not all participants who start a process will finish it and not all of them will be able to make all the assessments, since they do not know all the options. Therefore, to solve all the drawbacks, this paper presents a large-scale group decision-making system in a modifiable scenario environment that groups participants according to their preferred alternative and also performs knowledge differentiation, clustering them into experts and users. This allows for optimising the consensus process, as the consensus is made by comparing the clusters and the participants with each other.

关键词： Large-Scale Group Decision-Making Consensus Clustering Modifiable Scenarios Knowledge Differentiation

来源：评论

学校读者我要写书评

暂无评论

Online Collaborative Resource Allocation and Task Offloading for Multi-access Edge Computing

arXiv

引用

arXiv 2025年

作者： Sun, Geng Yuan, Minghua Sun, Zemin Wang, Jiacheng Du, Hongyang Niyato, Dusit Han, Zhu Kim, Dong In College of Computer Science and Technology Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore College of Software Engineering Jilin University Changchun130012 China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Multi-access edge computing (MEC) is emerging as a promising paradigm to provide flexible computing services close to user devices (UDs). However, meeting the computation-hungry and delay-sensitive demands of UDs faces several challenges, including the resource constraints of MEC servers, inherent dynamic and complex features in the MEC system, and difficulty in dealing with the time-coupled and decision-coupled optimization. In this work, we first present an edge-cloud collaborative MEC architecture, where the MEC servers and cloud collaboratively provide offloading services for UDs. Moreover, we formulate an energy-efficient and delay-aware optimization problem (EEDAOP) to minimize the energy consumption of UDs under the constraints of task deadlines and long-term queuing delays. Since the problem is proved to be non-convex mixed integer nonlinear programming (MINLP), we propose an online joint communication resource allocation and task offloading approach (OJCTA). Specifically, we transform EEDAOP into a real-time optimization problem by employing the Lyapunov optimization framework. Then, to solve the real-time optimization problem, we propose a communication resource allocation and task offloading optimization method by employing the Tammer decomposition mechanism, convex optimization method, bilateral matching mechanism, and dependent rounding method. Simulation results demonstrate that the proposed OJCTA can achieve superior system performance compared to the benchmark approaches. Copyright © 2025, The Authors. All rights reserved.

关键词： Convex optimization

来源：评论

学校读者我要写书评

暂无评论

Traffic Aware Power Saving Communication Assisted By Double-Faced Active RIS

Traffic Aware Power Saving Communication Assisted By Double-...

引用

IEEE International Conference on Communications (ICC)

作者： Yuyan Zhou Yang Liu Qingqing Wu Qingjiang Shi Jun Zhao School of Information and Communication Engineering Dalian University of Technology Department of Electronic Engineering Shanghai Jiao Tong University School of Software Engineering Tongji University and Shenzhen Research Institute of Big Data Shenzhen China School of Computer Science and Engineering Nanyang Technological University

Despite its high energy and hardware efficiency, some defects of the reconfigurable intelligence surface (RIS) technology have come to be realized, including the severe fading loss and restricted-to-half-space coverage. This paper proposes a novel double-faced-active (DFA)-RIS structure to overcome these defects. Besides, we utilize this novel DFA-RIS to improve power saving of the communication system. Unlike traditional power saving literature, we aim at fulfilling queueing stability and long-term power minimization in a downlink system assisted by the DFA-RIS, with a realistic data arriving process taken into consideration. Enlightened by Lyapunov control theory, we propose an online optimization strategy that adaptively adjusts DFA-RIS configuration. Each online problem can be efficiently solved by leveraging alternative directional method of multipliers (ADMM) method. Numerical results demonstrate the effectiveness of our proposed Lyapunov-guided strategy and DFA-RIS’ superiority over the classical passive RIS.

关键词：

来源：评论

学校读者我要写书评

暂无评论

DAMPER: A Dual-Stage Medical Report Generation Framework with Coarse-Grained MeSH Alignment and Fine-Grained Hypergraph Matching 39

DAMPER: A Dual-Stage Medical Report Generation Framework wit...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Huang, Xiaofei Chen, Wenting Liu, Jie Lu, Qisheng Luo, Xiaoling Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

ISBN: (纸本)157735897X

Medical report generation is crucial for clinical diagnosis and patient management, summarizing diagnoses and recommendations based on medical imaging. However, existing work often overlook the clinical pipeline involved in report writing, where physicians typically conduct an initial quick review followed by a detailed examination. Moreover, current alignment methods may lead to misaligned relationships. To address these issues, we propose DAMPER, a dual-stage framework for medical report generation that mimics the clinical pipeline of report writing in two stages. In the first stage, a MeSH-Guided Coarse-Grained Alignment (MCG) stage that aligns chest X-ray (CXR) image features with medical subject headings (MeSH) features to generate a rough keyphrase representation of the overall impression. In the second stage, a Hypergraph-Enhanced Fine-Grained Alignment (HFG) stage that constructs hypergraphs for image patches and report annotations, modeling high-order relationships within each modality and performing hypergraph matching to capture semantic correlations between image regions and textual phrases. Finally,the coarse-grained visual features, generated MeSH representations, and visual hypergraph features are fed into a report decoder to produce the final medical report. Extensive experiments on public datasets demonstrate the effectiveness of DAMPER in generating comprehensive and accurate medical reports, outperforming state-of-the-art methods across various evaluation metrics. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Split Learning without Local Weight Sharing To Enhance Client-side data Privacy

引用

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Pham, Ngoc Duy Phan, Tran Khoa Abuadbba, Alsharif Gao, Yansong Nguyen, Van-Doan Chilamkurti, Naveen La Trobe University School of Computing Engineering and Mathematical Sciences VIC Australia CSIRO's Data61 Cybersecurity CRC Australia The University of Western Australia Department of Computer Science and Software Computing Australia

Split learning (SL) aims to protect user data privacy by distributing deep models between the client-server and keeping private data locally. In SL training with multiple clients, the local model weights are shared among the clients for the local model updates. This paper first reveals data privacy leakage exacerbated by local weight sharing among the clients in SL through model inversion attacks. Then, to reduce the data privacy leakage issue, we propose and analyze privacy-enhanced SL (P-SL) (or SL without local weight sharing). We further propose a parallelized P-SL to expedite the training process by duplicating multiple server-side model instances without compromising accuracy. Finally, we explore P-SL with late participating clients and devise a server-side cache-based training method to address the forgetting phenomenon in SL when late clients join. The experiment results demonstrate that P-SL helps reduce up to 50\% of client-side data leakage, which essentially achieves a better privacy-accuracy trade-off than the current trend by using differential privacy mechanisms. Moreover, P-SL and its cache-based version achieve comparable accuracy to baseline SL under various data distributions, while incurring lower costs for computation and communication. Additionally, caching-based training in P-SL mitigates the negative effect of forgetting, stabilizes learning, and enables practical and low-complexity training in a dynamic environment with late-arriving clients. © 2004-2012 IEEE.

关键词： Sensitive data

来源：评论

学校读者我要写书评

暂无评论

CLDG: Contrastive Learning on Dynamic Graphs

arXiv

引用

arXiv 2024年

作者： Xu, Yiming Shi, Bin Ma, Teng Dong, Bo Zhou, Haoyi Zheng, Qinghua Department of Computer Science and Technology Xi’an Jiaotong University China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University China Department of Distance Education Xi’an Jiaotong University China School of Software Beihang University China Advanced Innovation Center for Big Data and Brain Computing Beihang University China

The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of the representative paradigms is graph contrastive learning. It constructs self-supervised signals by maximizing the mutual information between the statistic graph’s augmentation views. However, the semantics and labels may change within the augmentation process, causing a significant performance drop in downstream tasks. This drawback becomes greatly magnified on dynamic graphs. To address this problem, we designed a simple yet effective framework named CLDG. Firstly, we elaborate that dynamic graphs have temporal translation invariance at different levels. Then, we proposed a sampling layer to extract the temporally-persistent signals. It will encourage the node to maintain consistent local and global representations, i.e., temporal translation invariance under the timespan views. The extensive experiments demonstrate the effectiveness and efficiency of the method on seven datasets by outperforming eight unsupervised state-of-the-art baselines and showing competitiveness against four semi-supervised methods. Compared with the existing dynamic graph method, the number of model parameters and training time is reduced by an average of 2,001.86 times and 130.31 times on seven datasets, respectively. The code and data are available at: https://***/yimingxu24/CLDG. © 2024, CC BY.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

On Selection of Optimal Kernel Function for software Defect Prediction

On Selection of Optimal Kernel Function for Software Defect ...

引用

International Conference on Machine Learning and Applications (ICMLA)

作者： Mohammad Azzeh Ali Bou Nassif Shadi Banitaan Department of Data Science Princess Sumaya University for Technology Amman Jordan Department of Computer Engineering University of Sharjah Sharjah UAE Department of Software Engineering University of Detroit Mercy Detroit USA

software defect prediction is used to identify the most likely defect-prone modules. This activity can help the quality assurance team to distribute limited resources efficiently as well as reduce the time and cost of the testing phase. Support Vector Machine (SVM) has been widely used to build software defect prediction models. Nevertheless, the accuracy of such method depends on multiple factors such as the type of SVM method, choice of kernel functions and dimensionality of the dataset. This paper continues along that research line and examines the impact and stability of four kernel functions and feature dimensionality reduction on the performance of SVM for software defect pre- diction. Comprehensive experiments have been conducted using four kernel functions, ten feature selection thresholds based on the Information gain algorithm, and seven public datasets. This has resulted in 280 experiments. The findings demonstrate that there is no kernel function that can show stable performance across different experimental settings. However, both Radial basis and Sigmoid kernel functions showed more stability than Polynomial and Linear kernel functions. In addition, changing the feature set from high to low dimensionality for each kernel function did not always improve the accuracy of SVM, which contradicts the findings that say using a small set of features can be as effective as using a large set of features.

关键词： Support vector machines Dimensionality reduction Quality assurance software algorithms Predictive models Feature extraction software

来源：评论

学校读者我要写书评

暂无评论

Di-Net: Decomposed Implicit Garment Transfer Network for Digital Clothed 3d Human

SSRN

引用

SSRN 2024年

作者： Zhong, Xiaojing Su, Yukun Wu, Zhonghua Lin, Guosheng Wu, Qingyao School of Software Engineering South China University of Technology China School of Computer Science and Engineering Nanyang Technological University Singapore Key Laboratory of Big Data and Intelligent Robot Ministry of Education China Peng Cheng Laboratory China Tencent Wechat Department China SenseTime Research

3D virtual try-on tasks aim to generate realistic try-on results for full- body garments, allowing them to be observed from arbitrary perspectives. Recent methods often represent the 3D human form with a fixed topol- ogy, limiting garment representation and leading to higher computational demands. Furthermore, the challenge of flexible transfer over various body parts remains. To address these problems, we introduce DI-Net(Decomposed Implicit Garment Transfer Network), where garment transfer is decomposed into 2D image layout transfer and 3D texture layout transfer. We propose a complementary warping module that leverages dense correspondence learn- ing and sparse flow learning to align the body appearance and garments with the target pose for image layout transfer. Subsequently, the transferred garments are lifted to the 3D human mesh by constructing a pixel-aligned implicit function and predicting the texture based on geometric priors. By applying different semantic masks for garment transfer, we efficiently control the texture of the generated mesh. Experimental results show the effective- ness and superiority of our method in the 3D virtual try-on task. © 2024, The Authors. All rights reserved.

关键词： Textures

来源：评论

学校读者我要写书评

暂无评论

A Secure Authentication Framework for Consumer Mobile Crowdsourcing Networks

引用

IEEE Transactions on Consumer Electronics 2024年

作者： Aldosary, Abdallah Tanveer, Muhammad Ahmad, Musheer Maghrabi, Louai A. Ahmed, Emad A. Hussain, Amir El-Latif, Ahmed A. Abd Department of Computer Engineering Prince Sattam bin Abdulaziz University Riyadh Wadi Addwasir11991 Saudi Arabia University of Management and Technology Department of Computer Science Lahore54770 Pakistan Jamia Millia Islamia Department of Computer Engineering New Delhi110025 India University of Business and Technology College of Engineering Department of Software Engineering Jeddah Saudi Arabia South Valley University Faculty of Computers and Information Department of Computer Science Qena Egypt Edinburgh Napier University United Kingdom Prince Sultan University EIAS Data Science Lab College of Computer and Information Sciences Center of Excellence in Quantum and Intelligent Computing Riyadh11586 Saudi Arabia Menoufia University Faculty of Science Department of Mathematics and Computer Science Shebin El-Koom32511 Egypt

The Mobile crowdsourcing network (MCN) leverages collaborative intelligence to solve complex tasks through group cooperation. It comprises three main components: the end-user, the service provider, and the mobile user. The end-user requests crowd-sensing and computing services from the service provider, who handles task decomposition, distribution, re-composition, and recommendation. Mobile users perform the crowd sensing and computing tasks. Despite its benefits, MCN faces significant security and privacy challenges. Communications between the service provider and the end-user involve sensitive data, and mobile users and service providers communicate over open channels, making them vulnerable to security threats. To address these concerns, we propose a secure authentication framework for MCN, called SAF-MCN. This framework combines the lightweight cryptographic primitive ' ASCON' with elliptic curve cryptography. SAF-MCN allows the service provider to authenticate mobile users before they join the MCN. Once authenticated, a secure channel is established for encrypted communication over the public Internet. The security of SAFMCN is validated using both formal and informal methods, demonstrating its resilience against potential attacks. The use of Scyther further ensures the security of SAF-MCN. Comparative analysis shows that SAF-MCN is superior in terms of security features, computational efficiency, and communication cost. © 1975-2011 IEEE.

关键词： ASCON Authenticated Encryption Authentication Mobile Crowdsourcing Network Scyther Security

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：