检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Sun, Geng Ma, Weilong Li, Jiahui Sun, Zemin Wang, Jiacheng Niyato, Dusit Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore College of Software Jilin University Changchun130012 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

The low-altitude economy (LAE), driven by unmanned aerial vehicles (UAVs) and other aircraft, has revolutionized fields such as transportation, agriculture, and environmental monitoring. In the upcoming six-generation (6G) era, UAV-assisted mobile edge computing (MEC) is particularly crucial in challenging environments such as mountainous or disaster-stricken areas. The computation task offloading problem is one of the key issues in UAV-assisted MEC, primarily addressing the trade-off between minimizing the task delay and the energy consumption of the UAV. In this paper, we consider a UAV-assisted MEC system where the UAV carries the edge servers to facilitate task offloading for ground devices (GDs), and formulate a calculation delay and energy consumption multi-objective optimization problem (CDECMOP) to simultaneously improve the performance and reduce the cost of the system. Then, by modeling the formulated problem as a multi-objective Markov decision process (MOMDP), we propose a multi-objective deep reinforcement learning (DRL) algorithm within an evolutionary framework to dynamically adjust the weights and obtain non-dominated policies. Moreover, to ensure stable convergence and improve performance, we incorporate a target distribution learning (TDL) algorithm. Simulation results demonstrate that the proposed algorithm can better balance multiple optimization objectives and obtain superior non-dominated solutions compared to other methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

TSEML: A task-specific embedding-based method for few-shot classification of cancer molecular subtypes

TSEML: A task-specific embedding-based method for few-shot c...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ran Su Rui Shi Hui Cui Ping Xuan Chengyan Fang Xikang Feng Qiangguo Jin School of Computer Software College of Intelligence and Computing Tianjin University Tianjin China School of Software Northwestern Polytechnical University Shaanxi China Department of Computer Science and Information Technology La Trobe University Melbourne Australia Department of Computer Science School of Engineering Shantou University Guangdong China Yangtze River Delta Research Institute of Northwestern Polytechnical University Taicang China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Molecular subtyping of cancer is recognized as a critical and challenging upstream task for personalized therapy. Existing deep learning methods have achieved significant performance in this domain when abundant data samples are available. However, the acquisition of densely labeled samples for cancer molecular subtypes remains a significant challenge for conventional data-intensive deep learning approaches. In this work, we focus on the few-shot molecular subtype prediction problem in heterogeneous and small cancer datasets, aiming to enhance precise diagnosis and personalized treatment. We first construct a new few-shot dataset for cancer molecular subtype classification and auxiliary cancer classification, named TCGA Few-Shot, from existing publicly available datasets. To effectively leverage the relevant knowledge from both tasks, we introduce a task-specific embedding-based meta-learning framework (TSEML). TSEML leverages the synergistic strengths of a model-agnostic meta-learning (MAML) approach and a prototypical network (ProtoNet) to capture diverse and fine-grained features. Comparative experiments conducted on the TCGA FewShot dataset demonstrate that our TSEML framework achieves superior performance in addressing the problem of few-shot molecular subtype classification.

关键词： Metalearning Deep learning Knowledge engineering Medical treatment Benchmark testing Bioinformatics Cancer

来源：评论

学校读者我要写书评

暂无评论

A survey on computationally efficient neural architecture search

引用

Journal of Automation and Intelligence 2022年第1期1卷 8-22页

作者： Shiqing Liu Haoyu Zhang Yaochu Jin Chair of Nature Inspired Computing and Engineering Faculty of TechnologyBielefeld University33619 BielefeldGermany Department of Computer Science University of SurreyGuildfordGU27XHUnited Kingdom Engineering Research Center of Digitized Textile&Apparel Technology Ministry of EducationCollege of Information Science and TechnologyDonghua UniversityShanghai 201620China

Neural architecture search(NAS)has become increasingly popular in the deep learning community recently,mainly because it can provide an opportunity to allow interested users without rich expertise to benefit from the success of deep neural networks(DNNs).However,NAS is still laborious and time-consuming because a large number of performance estimations are required during the search process of NAS,and training DNNs is computationally *** solve this major limitation of NAS,improving the computational efficiency is essential in the design of ***,a systematic overview of computationally efficient NAS(CE-NAS)methods still *** fill this gap,we provide a comprehensive survey of the state-of-the-art on CE-NAS by categorizing the existing work into proxy-based and surrogate-assisted NAS methods,together with a thorough discussion of their design principles and a quantitative comparison of their performances and computational *** remaining challenges and open research questions are also discussed,and promising research topics in this emerging field are suggested.

关键词： Neural architecture search(NAS) One-shot NAS Surrogate model Bayesian optimization Performance predictor

来源：评论

学校读者我要写书评

暂无评论

HEET: A Heterogeneity Measure to Quantify the Difference across Distributed computing Systems

arXiv

引用

arXiv 2023年

作者： Mokhtari, Ali Ghafouri, Saeid Jamshidi, Pooyan Salehi, Mohsen Amini The School of Computing and Informatics University of Louisiana LafayetteLA United States Queen Mary University of London London United Kingdom The Computer Science and Engineering Department University of South Carolina South Carolina United States

Although system heterogeneity has been extensively studied in the past, there is yet to be a study on measuring the impact of heterogeneity on system performance. For this purpose, we propose a heterogeneity measure that can characterize the impact of the heterogeneity of a system on its performance behavior in terms of throughput or makespan. We develop a mathematical model to characterize a heterogeneous system in terms of its task and machine heterogeneity dimensions and then reduce it to a single value, called Homogeneous Equivalent Execution Time (HEET), which represents the execution time behavior of the entire system. We used AWS EC2 instances to implement a real-world machine learning inference system. Performance evaluation of the HEET score across different heterogeneous system configurations demonstrates that HEET can accurately characterize the performance behavior of these systems. In particular, the results show that our proposed method is capable of predicting the true makespan of heterogeneous systems without online evaluations with an average precision of 84%. This heterogeneity measure is instrumental for solution architects to configure their systems proactively to be sufficiently heterogeneous to meet their desired performance objectives. Copyright © 2023, The Authors. All rights reserved.

关键词： Online systems

来源：评论

学校读者我要写书评

暂无评论

Security Protection of Information Utilizing Halfway Speculation 1

Security Protection of Information Utilizing Halfway Specula...

引用

1st International Conference on Advances in Computational science and Engineering, ICACSE 2020

作者： Ishwarya, M.V. Suganya, J. Nalini, M. Kumaresan, A. Suresh Anand, M. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamilnadu Chennai India Department of Electronics and Communication Engineering Mailam Engineering College Tamilnadu Mailam India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Tamilnadu Chennai India Department of Information Technology School of Computing Sciences HITS Tamilnadu Chennai India Department of Computer Science and Engineering Sri Sairam Engineering College Anna University Tamilnadu Chennai India

Securing privacy in information-digging (data mining) has become indispensable for buying and selling labelled statistics in statistical evaluation, validation, and guide. In such cases, records are either released in published form for re(use) through others for research functions. Here, privacy takes an extensive role to secure the records from ever-escalating net phishing and different probable attackers. Significant upgrades for strong privateness upkeep and protection have emerged as mandatory. In this paper, we endorse the approach of partial speculation. This technique is essential to mask the information in this sort of manner that different facts mining strategies can be without difficulty applied to it. On the alternative hand, it makes the information okay-nameless for this reason, information cannot be re-identified at the same time as records remain almost useful. In partial speculation, simplest identifiers that are vital are speculated first. If this step, safeguards the records from external information linkage, then the technique no longer speculates besides, preventing extra speculation than important with minimal facts loss. © Published under licence by IOP Publishing Ltd.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

Multi-View Incremental Learning with Structured Hebbian Plasticity for Enhanced Fusion Efficiency

arXiv

引用

arXiv 2024年

作者： Chen, Yuhong Song, Ailin Yin, Huifeng Zhong, Shuai Chen, Fuhai Xu, Qi Wang, Shiping Xu, Mingkun College of Computer and Data Science Fuzhou University Fuzhou China Guangdong Institute of Intelligence Science and Technology Hengqin Zhuhai China University of Electronic Science and Technology of China Chengdu China Center for Brain Inspired Computing Research Department of Precision Instrument Tsinghua University Beijing China School of Computer Science and Technology Dalian University of Technology Dalian China

The rapid evolution of multimedia technology has revolutionized human perception, paving the way for multi-view learning. However, traditional multi-view learning approaches are tailored for scenarios with fixed data views, falling short of emulating the intricate cognitive procedures of the human brain processing signals sequentially. Our cerebral architecture seamlessly integrates sequential data through intricate feed-forward and feedback mechanisms. In stark contrast, traditional methods struggle to generalize effectively when confronted with data spanning diverse domains, highlighting the need for innovative strategies that can mimic the brain’s adaptability and dynamic integration capabilities. In this paper, we propose a bio-neurologically inspired multiview incremental framework named MVIL aimed at emulating the brain’s fine-grained fusion of sequentially arriving views. MVIL lies two fundamental modules: structured Hebbian plasticity and synaptic partition learning. The structured Hebbian plasticity reshapes the structure of weights to express the high correlation between view representations, facilitating a fine-grained fusion of view representations. Moreover, synaptic partition learning is efficient in alleviating drastic changes in weights and also retaining old knowledge by inhibiting partial synapses. These modules bionically play a central role in reinforcing crucial associations between newly acquired information and existing knowledge repositories, thereby enhancing the network’s capacity for generalization. Experimental results on six benchmark datasets show MVIL’s effectiveness over state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Optimized Flame Detection Using a YOLOv5 Neural Network

Optimized Flame Detection Using a YOLOv5 Neural Network

引用

computing, Robotics and System sciences (ICRSS), International Conference on

作者： Shahid Karim Muhammad Ibrar Anas Bilal Akeel Qadir Irfana Bibi School of Information Engineering Xi'an Eurasia University Xi'an China Software College Shenyang Normal University Shenyang China College of Information Science and Technology Hainan Normal University Haikou China Department of Computer Science Faculty of Computing and Information Technology University of Punjab Lahore Punjab Pakistan

ISBN: (数字)9798331511296

ISBN: (纸本)9798331511302

With the rising prevalence of smart homes, there's an increasing demand for comprehensive automation solutions to mitigate fire risks, especially when homeowners are absent or in homes with elderly residents. This study focuses on enhancing flame detection techniques, particularly for smaller, hard-to-detect fire sources. A customized Yolov5 neural network was developed to improve detection precision. This enhancement was facilitated using a dataset of over 2,000 images from lab camera observations and online platforms. These images were then manually labeled and clustered for anchor dimensions using the K-means++ algorithm. The CIOU and diou _ nms techniques were integrated to optimize the network's performance. Upon training, the refined model was tested on the Jet Nano, revealing its high efficacy in flame detection.

关键词： YOLO Training Accuracy Sensitivity Service robots Neural networks Fires Clustering algorithms Smart homes Safety

来源：评论

学校读者我要写书评

暂无评论

Conflict-Free Genetic Algorithm with Nash Equilibrium Seeking for Game-Based Battery Swapping Station Recommendation

Conflict-Free Genetic Algorithm with Nash Equilibrium Seekin...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Chang-Long Sun Xin-Xin Xu Chun-Hua Chen Jun Hong Zhenan He Dengxiu Yu Sam Kwong Zhi-Hui Zhan School of Computer Science and Engineering South China University of Technology Guangzhou China School of Computer Science and Technology Ocean University of China Qingdao China School of Software Engineering South China University of Technology Guangzhou China College of Computer Science Sichuan University Chengdu China School of Artificial Intelligence Northwestern Polytechnical University Xi'an China Department of Computing and Decision Science Lingnan University Hong Kong SAR College of Artificial Intelligence Nankai University Tianjin China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The rapid growth of electric vehicles (EVs) has led to significant challenges in providing efficient and sustainable charging solutions. This paper addresses the battery swapping station (BSS) recommendation problem by proposing a novel conflict-free genetic algorithm (CFGA) integrated with a Nash equilibrium seeking (NES) approach to identify optimal Nash equilibrium (ONE) solutions to such a non-cooperative optimization problem. The CFGA employs specialized crossover and mutation operators to generate offspring that satisfy the constraints of the problem, ensuring that each EV decides a unique battery swap strategy without conflict. Firstly, an order crossover operator is proposed to preserve the order of genes in the chromosomes. Secondly, a replacement and exchange mutation operator is proposed to enhance mutation diversity. The resulting optimal solution is then used as the initial strategy for the NES, which iteratively converges to the ONE. The proposed CFGA with NES algorithm is evaluated under both small-scale and large-scale cases, demonstrating its effectiveness in achieving a balance between costs for EVs and utilization for BSSs. The study's findings have practical implications for the smart grid and EV integration, offering a robust method for optimizing EV infrastructure and operations.

关键词： Costs Scalability Ecosystems Nash equilibrium Electric vehicles Batteries Smart grids Optimization Cybernetics Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

An Efficient Cyclic Entailment Procedure in a Fragment of Separation Logic

arXiv

引用

arXiv 2022年

作者： Le, Quang Loc Le, Xuan-Bach D. Department of Computer Science University College London United Kingdom School of Computing and Information Systems The University of Melbourne Australia

An efficient entailment proof system is essential to compositional verification using separation logic. Unfortunately, existing decision procedures are either inexpressive or inefficient. For example, Smallfoot is an efficient procedure but only works with hardwired lists and trees. Other procedures that can support general inductive predicates run exponentially in time as their proof search requires back-tracking to deal with disjunction in the consequent. In this paper, we present a decision procedure that can derive cyclic entailment proofs for general inductive predicates in polynomial time. Our procedure is efficient and does not require back-tracking;it uses normalisation rules that help avoid the introduction of disjunction in the consequent. Moreover, our decidable fragment is sufficiently expressive: It is based on compositional predicates and can capture a wide range of data structures, including sorted and nested list segments, skip lists with fast forward pointers, and binary search trees. We have implemented the proposal in a prototype tool and evaluated it over challenging problems taken from a recent separation logic competition. The experimental results confirm the efficiency of the proposed system. © 2022, CC BY.

关键词： Separation

来源：评论

学校读者我要写书评

暂无评论

Guarding Voices, Protecting Homes: A Comprehensive Case Study on Voice Assistant Security in Smart Living

Guarding Voices, Protecting Homes: A Comprehensive Case Stud...

引用

International Symposium on Digital Forensic and Security (ISDFS)

作者： Fazalur Rehman Joudat Hashmi Moiz Abdullah Hafsah Zaman School of Electrical Engineering and Computer Science National University of Sciences and Technology Islamabad Pakistan Department of Analytics College of Engineering Georgia Tech University USA Department of Cyber Security Faculty of Computing and Artificial Intelligence Air University Islamabad Pakistan

ISBN: (数字)9798350330366

ISBN: (纸本)9798350330373

The evolution of technologies in the last decades has transformed the whole human interaction experience with the digital world. Integrated into users' home voice assistant devices has rationalized personal interaction with the technology however convenience of intelligent voice assistant presents security and privacy risks. This paper investigates the security vulnerabilities of smart voice assistant devices specifically focusing on Google Home Mini. Through a series of simulated attacks and comprehensive analysis, this study uncovers potential risks ranging from unauthorized access to user data privacy compromise within home networks. The objective is to raise awareness about these vulnerabilities and their implication urging the need for robust security measures and providing valuable observation for future research. As voice devices are becoming an integral part of daily life addressing these challenges is paramount to ensuring user privacy data integrity and overall security of the smart home ecosystem.

关键词： Data privacy Privacy Ecosystems Personal voice assistants Focusing Smart homes Distance measurement

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：