检索结果-内蒙古大学图书馆

2025 2nd International Conference on Algorithms, Software engineering and Network Security (ASENS)

作者： Peichao Qiu Lifeng Huang Ning Liu School of Computer Science and Engineering Sun Yat-sen University Guangzhou China College of Mathematics and Informatics South China Agricultural University Guangzhou China

ISBN: (数字)9798331520298

ISBN: (纸本)9798331520304

Although adversarial training proves to be an effective method for training robust neural networks, its application is constrained by model capacity, making it challenging to implement in scenarios with limited computing and storage resources. To tackle this issue, adversarial distillation emerges as a viable solution to transfer knowledge from a robust teacher model to a smaller-capacity student model. However, most existing research overlooks the importance of inconsistencies in adversarial example features during distillation, which can potentially lead to a degradation in the student model's performance. In this paper, we propose a novel method named importance-aware intermediate distillation method (IID), which enhances the efficiency of distillation by leveraging the teacher model's prior knowledge to perceive the varying importance of features in different samples. Through extensive experiments on different datasets, we demonstrate the effectiveness of IID in improving model robustness.

关键词： Training Degradation Frequency modulation Computational modeling Software algorithms Neural networks Network security Feature extraction Robustness Software engineering

来源：评论

学校读者我要写书评

暂无评论

Imitation Game for Adversarial Disillusion with Multimodal Generative Chain-of-Thought Role-Play

arXiv

引用

arXiv 2025年

作者： Chang, Ching-Chun Chen, Fan-Yun Gu, Shih-Hong Gao, Kai Wang, Hanrui Echizen, Isao Information and Society Research Division National Institute of Informatics Tokyo Japan Department of Information Engineering and Computer Science Feng Chia University Taichung Taiwan

As the cornerstone of artificial intelligence, machine perception confronts a fundamental threat posed by adversarial illusions. These adversarial attacks manifest in two primary forms: deductive illusion, where specific stimuli are crafted based on the victim model’s general decision logic, and inductive illusion, where the victim model’s general decision logic is shaped by specific stimuli. The former exploits the model’s decision boundaries to create a stimulus that, when applied, interferes with its decision-making process. The latter reinforces a conditioned reflex in the model, embedding a backdoor during its learning phase that, when triggered by a stimulus, causes aberrant behaviours. The multifaceted nature of adversarial illusions calls for a unified defence framework, addressing vulnerabilities across various forms of attack. In this study, we propose a disillusion paradigm based on the concept of an imitation game. At the heart of the imitation game lies a multimodal generative agent, steered by chain-of-thought reasoning, which observes, internalises and reconstructs the semantic essence of a sample, liberated from the classic pursuit of reversing the sample to its original state. As a proof of concept, we conduct experimental simulations using a multimodal generative dialogue agent and evaluates the methodology under a variety of attack scenarios. © 2025, CC BY.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Evolving Multi-Branch Attention Convolutional Neural Networks for Online RIS Configuration

引用

IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Stamatelis, George Stylianopoulos, Kyriakos Alexandropoulos, George C. National and Kapodistrian University of Athens Department of Informatics and Telecommunications Panepistimiopolis Ilissia Athens15784 Greece University of Illinois Chicago Department of Electrical and Computer Engineering IL60601 United States

In this paper, we consider the problem of jointly controlling the configuration of a Reconfigurable Intelligent Surface (RIS) with unit elements of discrete responses and a codebook-based transmit precoder in RIS-empowered Multiple-Input Single-Output (MISO) communication systems. The adjustable elements of the RIS and the precoding vector need to be jointly modified in real time to account for rapid changes in the wireless channels, making the application of complicated discrete optimization algorithms impractical. We present a novel Multi-Branch Attention Convolutional Neural Network (MBACNN) architecture for this design objective which is optimized using NeuroEvolution (NE), leveraging its capability to effectively tackle the non-differentiable problem arising from the discrete phase states of the RIS elements. The channel matrices of all involved links are first passed to separate self-attention layers to obtain initial embeddings, which are then concatenated and passed to a convolutional network for spatial feature extraction, before being fed to a per-element multi-layered perceptron for the final RIS phase configuration calculation. Our MBACNN architecture is then extended to multi-RIS-empowered MISO communication systems, and a novel NE-based optimization approach for the online distributed configuration of multiple RISs is presented. The superiority of the proposed single-RIS approach over both learning-based and classical discrete optimization benchmarks is showcased via extensive numerical evaluations over both stochastic and geometrical channel models. It is also demonstrated that the proposed distributed multi-RIS approach outperforms both distributed controllers with feedforward neural networks and fully centralized ones. © 2015 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Bibliometric and Content Analysis of Large Language Models Research in Software engineering: The Potential and Limitation in Software engineering

引用

International Journal of Advanced computer Science and Applications 2025年第4期16卷 344-356页

作者： Damayanti, Annisa Dwi Gani, Hamdan Zhipeng, Feng Gani, Helmy Zuhriyah, Sitti Nurani Djabir, St. Nurhayati Wardani, Nur Ilmiyanti Department of Environmental Engineering Faculty of Engineering Hasanuddin University Makassar Indonesia Department of Machinery Automation System ATI Makassar Polytechnic Makassar Indonesia School of Culture Creativity and Media Hangzhou Normal University Zhejiang Hangzhou China Department of Industrial Hygiene Faculty of Public Health Occupational Health and Safety Makassar College of Health Sciences Indonesia Department of Computer System Universitas Handayani Makassar Makassar Indonesia Department of Information Systems and Technology Institut Teknologi dan Bisnis Nobel Indonesia Jl. Sultan Alauddin No.212 Makassar90221 Indonesia Department of Informatics Engineering Universitas Handayani Makassar Makassar Indonesia

Large Language Models (LLM) is a type of artificial neural network that excels at language-related tasks. The advantages and disadvantages of using LLM in software engineering are still being debated, but it is a tool that can be utilized in software engineering. This study aimed to analyze LLM studies in software engineering using bibliometric and content analysis. The study data were retrieved from Web of Science and Scopus. The data were analyzed using two popular bibliometric approaches: bibliometric and content analysis. VOS Viewer and Bibliometrix software were used to conduct the bibliometric analysis. The bibliometric analysis was performed using science mapping and performance analysis approaches. Various bibliometric data, including the most frequently referenced publications, journals, and nations, were evaluated and presented. Then, the synthetic knowledge method was utilized for content analysis. This study examined 235 papers, with 836 authors contributing. The publications were published in 123 different journals. The average number of citations per publication is 1.44. Most publications were published in Proceedings International Conference on Software engineering and ACM International Conference Proceeding Series, with China and the United States emerging as the leading countries. It was discovered that international collaboration on the issue was inadequate. The most often used keywords in the publications were "software design," "code (symbols)," and "code generation." Following the content analysis, three themes emerged: 1) Integration of LLM into software engineering education, 2) application of LLM in software engineering, and 3) potential and limitation of LLM in software engineering. The results of this study are expected to provide researchers and academics with insights into the current state of LLM in software engineering research, allowing them to develop future conclusions. © (2025), (Science and Information Organization). All Rights Res

关键词： Software design

来源：评论

学校读者我要写书评

暂无评论

Heterogeneous Resource Allocation with Multi-task Learning for Wireless Networks

arXiv

引用

arXiv 2025年

作者： Mitsiou, Nikos A. Bouzinis, Pavlos S. Sarigiannidis, Panagiotis G. Karagiannidis, George K. Group Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki54124 Greece Kozani 50100 Greece Department of Informatics and Telecommunications Engineering University of Western Macedonia Kozani50100 Greece

The optimal solution to an optimization problem depends on the problem’s objective function, constraints, and size. While deep neural networks (DNNs) have proven effective in solving optimization problems, changes in the problem’s size, objectives, or constraints often require adjustments to the DNN architecture to maintain effectiveness, or even retraining a new DNN from scratch. Given the dynamic nature of wireless networks, which involve multiple and diverse objectives that can have conflicting requirements and constraints, we propose a multi-task learning (MTL) framework to enable a single DNN to jointly solve a range of diverse optimization problems. In this framework, optimization problems with varying dimensionality values, objectives, and constraints are treated as distinct tasks. To jointly address these tasks, we propose a conditional computation-based MTL approach with routing. The multi-task DNN consists of two components, the base DNN (bDNN), which is the single DNN used to extract the solutions for all considered optimization problems, and the routing DNN (rDNN), which manages which nodes and layers of the bDNN to be used during the forward propagation of each task. The output of the rDNN is a binary vector which is multiplied with all bDNN’s weights during the forward propagation, creating a unique computational path through the bDNN for each task. This setup allows the tasks to either share parameters or use independent ones, with the decision controlled by the rDNN. The proposed framework supports both supervised and unsupervised learning scenarios. Numerical results demonstrate the efficiency of the proposed MTL approach in solving diverse optimization problems. In contrast, benchmark DNNs lacking the rDNN mechanism were unable to achieve similar levels of performance, highlighting the effectiveness of the proposed architecture. © 2025, CC BY.

关键词： Multi-task learning

来源：评论

学校读者我要写书评

暂无评论

ACDV: Adaptive Content Delivery for Vehicular Digital Twin Networks

引用

IEEE Transactions on Vehicular Technology 2025年第5期74卷 7084-7098页

作者： Zheng, Jinkai Luan, Tom H. Li, Guanjie Yin, Zhisheng Wu, Yuan Dong, Mianxiong Dongguan University of Technology School of Electrical Engineering and Intelligentization Dongguan523808 China Xi'an Jiaotong University School of Cyber Science and Engineering Xi'an710049 China Xidian University School of Cyber Engineering Xi'an710071 China Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore Xidian University State Key Laboratory of Isn Xi'an710071 China University of Macau State Key Laboratory of Internet of Things for Smart City 999078 China University of Macau Department of Computer Information Science 999078 China Muroran Institute of Technology Department of Sciences and Informatics Muroran05-08585 Japan

Digital Twins (DTs), serving personalized cloud-based digital assistants, hold great promise for supporting infotainment applications of the Internet of Vehicles (IoVs), in which personalized content such as high-definition maps, system updates, and social video streaming can be delivered from individualized DTs to vehicular users (VUEs) to enhance the intelligence of IoV services and improve their driving experience. This paper proposes a novel content caching framework tailored to DT-enabled IoVs, where DTs selectively cache on-demand contents on edge devices (i.e., cellular base stations and roadside units) managed by the edge service manager (ESM) along the VUE's trip to avoid backbone congestion yet save download time. However, ESMs are inherently selfish and reluctant to contribute resources to cache contents without benefits. Furthermore, considering the diverse trajectories of vehicles, it is inefficient to cache data along a single path. To address these challenges, we propose ACDV, which first models the interactions between the ESMs and DTs as a Stackelberg game to incentivize ESMs to actively participate in the content caching process. We then deploy a Markov model to predict the VUE distribution, which enables DTs to cache contents with a focus on predicted trajectories. To save storage costs, we derive an upper bound of content sizes that the VUE can download within a limited network connection time. Considering the lack of network information and users' private utility model in practical scenarios, we further develop a learning-based algorithm to find the optimal pricing scheme and content size strategies of ESMs and DTs. Through extensive simulations, we show that our proposal can effectively find the optimal strategy and achieve a fast convergence speed and high-level performance compared to the baselines. © 1967-2012 IEEE.

关键词： Markov processes

来源：评论

学校读者我要写书评

暂无评论

Clustering Single-Cell RNA-seq Data: Impact of Data Binarization on Algorithmic Performance

Clustering Single-Cell RNA-seq Data: Impact of Data Binariza...

引用

18th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Widzisz, Karolina Kania, Mateusz Zyla, Joanna Polański, Andrzej Department of Computer Graphics Vision and Digital Systems Silesian University of Technology Akademicka 16 Gliwice Poland Department of Applied Informatics Silesian University of Technology Akademicka 16 Gliwice Poland Department of Data Science and Engineering Silesian University of Technology Akademicka 16 Gliwice Poland

The primary objective of this study was to test the hypothesis that the binary information on the presence or absence of gene expression can sufficiently capture the inherent heterogeneity within single-cell RNA se quencing (scRNA-seq) data. This hypothesis posits that even without detailed expression levels, valuable insights about cellular diversity can be obtained. Utilizing this method can be particularly advantageous when analyzing large datasets, a common scenario in the field of scRNA-seq. In this paper, we evaluate clustering performance and cluster separability of a variety of model-based algorithms and distance-based methods to an alyze both expression level data and threshold-encoded binarized data. We examined the performance of the Bernoulli-mixture model and Gaussian-mixture model. These were compared against traditional clustering techniques such as hierarchical clustering, K-means, and the Louvain algorithm on a range of scRNA-seq datasets. Our findings reveal that mixture models exhibit a lower dependence on the specific dataset compared to distance-based methods. Mixture models, particularly, demonstrate greater efficacy in accurately estimat ing the number of clusters present within the data. Among analyzed algorithms, the Bernoulli-mixture model stands out, outperforming distance-based approaches in several key aspects. Binary data, presence/absence of gene expression, seem to be indeed adequate to capture the heterogeneity of scRNA-seq data when clus tering with methods specifically designed for binary datasets. The implications of this finding are significant, as it opens up new possibilities for simplifying data analysis in scRNA-seq studies without compromising the accuracy of the results. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： Data accuracy

来源：评论

学校读者我要写书评

暂无评论

A Wideband 8×8 Slot Array Antenna Using Gap Waveguide MLW Coaxial Line Technology for mmWave Applications

引用

IEEE Antennas and Wireless Propagation Letters 2025年

作者： Farahbakhsh, Ali Zarifi, Davood Vosoogh, Abbas Bencivenni, Carlo Mrozowski, Michal Gdansk University of Technology Department of Microwave and Antenna Engineering Faculty of Electronics Telecommunications and Informatics Gdansk80-233 Poland Graduate University of Advanced Technology Department of Electrical and computer engineering Kerman Iran Gapwaves Ab Gothenburg Sweden

The paper presents a wideband 8×8-element slot antenna array operating in the E-band. The proposed design employs a low-loss coaxial waveguide transmission line filled with air implemented using multilayer waveguide (MLW) technology by vertically stacking thin, unconnected metal plates to meet the typical radiation pattern and bandwidth requirements of a slot antenna array. To validate the concept, the simulation and measurement results of an 8×8 slot antenna array are presented. The proposed antenna array exhibits |S11| © 2025 IEEE.

关键词： Millimeter waves

来源：评论

学校读者我要写书评

暂无评论

Modelling parking occupancy using algorithm of 3D visibility network

引用

IEEE Access 2025年

作者： Babic, Matej Ster, Branko Hluchy, Ladislav Baiamonte, Giuliana Cali, Michele Faculty of Information Studies Ljubljanska cesta 31a Novo MestoSI-8000 Slovenia University of Ljubljana Faculty of Computer and Informatics LjubljanaSI-1000 Slovenia Institute of Informatics Slovak Academy of Sciences Dúbravská cesta 9 45 Bratislava845 07 Slovakia University of Catania Electric Electronics and Computer Engineering Department Catania95125 Italy

The modernization of the information communication infrastructure of the regional data transmission network has advanced in order to increase the maximum transmission speed of existing transport routes, ensuring the quality of the service and its reliability. In the present research work, a new approach for the 3D visibility network algorithm has been developed, showing how graph theory applied to wireless sensor networks (WSNs) enables technological development for new solutions in areas such as public infrastructure. The possibility of determining in such networks whether an area of interest is sufficiently covered by a given set of sensors by means of the Voronoi diagram is discussed. The parking dynamics and parking system were modeled with cellular neural networks (CNNs) based on weather conditions, and magnetic parking sensors were replaced with pillar sensors. The proposed method has proven its effectiveness in determining the position of the minimum sensors covering the area of interest, in order to find a solution in the occupation of parking spaces in the presence of different weather conditions. The proposed approach and experimental results offer potential applications in various fields such as lighting and rendering, motion planning, pattern recognition, computer graphics and computational geometry, in order to conduct studies on problems and perspectives of pillar sensor technology while reducing costs compared to magnetic ones. © 2013 IEEE.

关键词： Rendering (computer graphics)

来源：评论

学校读者我要写书评

暂无评论

Design of an Ultra-thin Metamaterial Absorber with Machine Learning-Assisted Absorption Prediction

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2025年

作者： Hossain, Md. Jakir Mubashsara, Nafisa Khan, Riasat Matin, Mohammad Abdul Sarigiannidis, Panagiotis Goudos, Sotirios K. North South University Department of Electrical and Computer Engineering Dhaka Bangladesh University of Western Macedonia Department of Informatics and Telecommunications Engineering Kozani Greece Aristotle University of Thessaloniki ELEDIA@AUTH School of Physics Thessaloniki541 24 Greece

An absorber is a block of material employed to absorb a portion of the energy of an incident particle. Research on absorbers is rapidly expanding as a result of their prospective applications in several fields, including wireless communications and military purposes. However, conventional absorbers have dense size and design complexity that make them unsuitable for a multitude of practical applications. Metamaterial-based absorbers (MMAs) have garnered significant attention with respect to their low density, narrow layers, and high absorption capacity. In this paper, we introduce a new ultra-thin trilayered Ni–MgF2–Ni metamaterial absorber structure, which is designed using machine learning. It has a balanced and symmetrical layout with a metal-dielectric-metal arrangement and a high-temperature endurance. Its resonator design is defined by mathematical deduction from the Conference matrix. The performance of MMA has been evaluated at wavelengths ranging from 250 nm to 1150 nm, as well as in various modes-TM, TE, and TEM. The results demonstrate that the proposed MMA has exceptional absorption capacities, with an overall absorption of 96.18% within the test range. It offers excellent mean absorption percentages of 99% in the optical region (350 to 750 nm), 95.23% in the ultraviolet (UV) range, and 94.38% in the near-infrared (NIR) range. Furthermore, MMA exhibits an absorption efficiency of 99.99% at a particular wavelength of 424.93 nm. In this work, various machine learning techniques have been applied to predict the absorption of the proposed MMA design. The XGBoost meta-learner-based Stacking ensemble machine learning technique achieves the highest prediction efficiency. Finally, the explainable AI technique with the LIME framework has been used to analyze the predictions of applied machine learning models. © 2020 IEEE.

关键词： Magnesium compounds

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：