检索结果-内蒙古大学图书馆

Energy Oriented Three-Tier Computation Offloading Scheme in Maritime Edge Computing Network

IEEE Transactions on Vehicular Technology 2025年第5期74卷 8126-8140页

作者： Zhang, Hongxia Xi, Shiyu Shang, Bodong Zhang, Peiying Wu, Sheng Jiang, Chunxiao Qingdao Institute of Software College of Computer Science and Technology Qingdao266580 China Eastern Institute of Technology Eastern Institute for Advanced Study Ningbo315200 China Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing100876 China Tsinghua University Beijing National Research Center for Information Science and Technology Beijing100084 China

With the development of maritime continuing to grow, large-scale maritime wireless devices (MWDs) are being deployed for various maritime applications. The rapid development of maritime Internet of Things (IoT) and growing demand for these applications have led to an increasing number of computation-intensive and resource-sensitive tasks. Considering the limited computing capacity and energy of MWDs, it is challenging to transmit and process massive maritime computing tasks in an energy efficient manner. In this paper, we propose a three-tier maritime edge computing system in which low earth orbit (LEO) satellite and offshore base station (OBS) support to provide communication and computing services for MWDs. The system optimizes association variable, task partitioning, transmission power, and computing resource allocation to reduce system energy consumption under latency constraints. The optimization problem is formulated as a mixed integer non-linear programming (MINLP) optimization problem. We decompose the original problem into four subproblems and design algorithms to solve them. Firstly, the association variable optimization subproblem is solved by slack variable method. Then, we adopt quadratic transformation and design a difference of convex algorithm to solve transmission power optimization subproblem. Further, task partitioning subproblem is solved by obtaining upper and lower bounds of offloading task size by strict deduction, and then solved by the standard convex method. Finally, Lagrangian dual method is utilized to deal with joint computing resource optimization subproblem of LEO satellite and OBS. In simulation results, the proposed algorithm saves 39.3% of system energy consumption compared to benchmark schemes. © 1967-2012 IEEE.

关键词： Computation offloading

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 15

Comparative Analysis of Differential Privacy Implementations...

引用

15th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2025

作者： Said, Huwida E. Mahmoud, Qusay H. Goyal, Mandeep Hashim, Faiza College of Technological Innovation Zayed University Department of Computing and Applied Technolgy Dubai United Arab Emirates Ontario Tech University Department of Electrical Computer and Software Engineering OshawaONL1G 0C6 Canada College of Information Technology Uae University Department of Computer Science and Software Engineering AI-Ain United Arab Emirates

ISBN: (纸本)9798331507695

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. © 2025 IEEE.

关键词： Data integrity

来源：评论

学校读者我要写书评

暂无评论

Building Modeling Tools Based on Metamodeling and Product Line Technologies

引用

Chinese Journal of Electronics 2025年第2期23卷 219-226页

作者： Zhiyi Ma Xiao He Software Institute School of Electronics Engineering and Computer Science Peking University Beijing China Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of Education Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

With the evolution of existing modeling languages and the emergence of more and more new modeling languages, it is necessary to rapidly build the corresponding software modeling tools with good quality. However, modeling tools for larger modeling languages are usually diversity in function and complexity in implementation technology. Taking building modeling tools as a domain, this paper presents an approach to building software modeling tools based on metamodeling and product line technologies. The paper provides the concept system of the approach and a feature model from diverse functions of modeling tools in order to specify the commonality and variability of the tools by deeply making the domain analysis, discusses the design and implementation of a general tool framework that provides the conveniences for reusing components and generating code for components, and specifies the mapping between the feature model and the components for modeling tools.

关键词： Analytical models Codes Buildings Metamodeling software Complexity theory Research and development

来源：评论

学校读者我要写书评

暂无评论

Strategy Masking: A Method for Guardrails in Value-based Reinforcement Learning Agents

arXiv

引用

arXiv 2025年

作者： Keane, Jonathan Keyser, Sam Kedziora, Jeremy Department of Computer Science and Software Engineering Milwaukee School of Engineering MilwaukeeWI United States

The use of reward functions to structure AI learning and decision making is core to the current reinforcement learning paradigm;however, without careful design of reward functions, agents can learn to solve problems in ways that may be considered "undesirable" or "unethical. Without thorough understanding of the incentives a reward function creates, it can be difficult to impose principled yet general control mechanisms over its behavior. In this paper, we study methods for constructing guardrails for AI agents that use reward functions to learn decision making. We introduce a novel approach, which we call strategy masking, to explicitly learn and then suppress undesirable AI agent behavior. We apply our method to study lying in AI agents and show that it can be used to effectively modify agent behavior by suppressing lying post-training without compromising agent ability to perform effectively. © 2025, CC BY.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Research on 2D Obstacle Avoidance Model Based on Field Rescue 24

Research on 2D Obstacle Avoidance Model Based on Field Rescu...

引用

Proceedings of the 2024 6th International Conference on Mechatronics Systems and Control engineering

作者： Ye Zhou Nengxin Mou College of Computer Science and Software Engineering Hohai University Nanjing Jiangsu China

ISBN: (纸本)9798400710346

This paper presents a novel 2D obstacle avoidance model tailored for field search and rescue robots. Leveraging advanced deep reinforcement learning techniques, the proposed model aims to enhance the robots’ navigation capabilities in dynamic and unpredictable environments. We evaluated four distinct reinforcement learning models—Deep Q-Learning (DQL), Proximal Policy Optimization (PPO), Advantage Actor-Critic (A2C), and Deep Q-Network (DQN)—to identify the most effective approach for real-time obstacle avoidance. Experimental results demon- strata that the DQL model outperforms the others in terms of success rate, average reward, and stability, making it particularly suitable for complex field operations. This research contributes to the development of robust and efficient navigation strategies for autonomous search and rescue missions, with potential implications for broader applications in robotics.

关键词： 2D Obstacle Avoidance

来源：评论

学校读者我要写书评

暂无评论

CLIMS++: Cross Language Image Matching with Automatic Context Discovery for Weakly Supervised Semantic Segmentation

引用

International Journal of computer Vision 2025年 1-20页

作者： Xie, Jinheng Deng, Songhe Hou, Xianxu Luo, Zhaochuan Shen, Linlin Huang, Yawen Zheng, Yefeng Shou, Mike Zheng Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Computer Science Wenzhou-Kean University Wenzhou325060 China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Show Lab National University of Singapore Singapore639798 Singapore School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou215400 China Jarvis Research Center Tencent YouTu Lab Shenzhen518703 China

While promising results have been achieved in weakly-supervised semantic segmentation (WSSS), limited supervision from image-level tags inevitably induces discriminative reliance and spurious relations between target classes and background regions. Thus, Class Activation Map (CAM) usually tends to activate discriminative object regions and falsely includes lots of class-related backgrounds. Without pixel-level supervisions, it could be very difficult to enlarge the foreground activation and suppress those false activation of background regions. In this paper, we propose a novel framework of Cross Language Image Matching with Automatic Context Discovery (CLIMS++), based on the recently introduced Contrastive Language-Image Pre-training (CLIP) model, for WSSS. The core idea of our framework is to introduce natural language supervision to activate more complete object regions and suppress class-related background regions in CAM. In particular, we design object, background region, and text label matching losses to guide the model to excite more reasonable object regions of each category. In addition, we propose to automatically find spurious relations between foreground categories and backgrounds, through which a background suppression loss is designed to suppress the activation of class-related backgrounds. The above designs enable the proposed CLIMS++ to generate a more complete and compact activation map for the target objects. Extensive experiments on PASCAL VOC 2012 and MS COCO 2014 datasets show that our CLIMS++ significantly outperforms the previous state-of-the-art methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Streamlining Classroom and Laboratory Management in University: Enhancing Problem Reporting Processes

Streamlining Classroom and Laboratory Management in Universi...

引用

Computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Qamar H. Naith Department of Software Engineering College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Managing the classroom and lab activities in the university and reporting process for the problems that occur during lectures is a hard process that needs more consideration. This study addresses challenges faced by technicians and academic staff in university classrooms, emphasizing the need for a streamlined automated issue reporting system. This paper proposes a comprehensive system aimed at improving communication, efficiency, and effectiveness in addressing classroom issues, benefiting technicians, students, and lecturers. Quantitative Analysis and evaluation of the system's user experience from instructors and technicians showed highly positive outcomes, indicating its high effectiveness in managing classroom issues and improving overall classroom management. Moreover, the statistical test revealed no statistically significant differences (α= 0.05) in the willingness to use the classroom issue management system between technicians and academic staff, indicating similar user perceptions and readiness for both groups to utilize the system.

关键词： Surveys Productivity Bridges Statistical analysis User experience Stakeholders Usability Information technology

来源：评论

学校读者我要写书评

暂无评论

Multifaceted collaborative evolutionary constrained multimodal multiobjective optimization

引用

Swarm and Evolutionary Computation 2025年 96卷

作者： Wang, Zeyi Liu, Songbai Ma, Lijia Lin, Qiuzhen Chen, Jianyong College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

In addressing constrained multimodal multiobjective optimization problems (CMMOPs), this paper proposes a multifaceted collaborative evolutionary algorithm (MCEA) designed to balance feasibility, convergence, and diversity in both the objective and decision spaces. Existing approaches often focus solely on maintaining population diversity or feasibility, neglecting the intricacies of CMMOPs, which require simultaneous consideration of multiple conflicting goals. Our MCEA framework features a local–global collaborative search strategy that employs dynamic clustering for effective exploration and exploitation of diverse decision space regions. Additionally, a parent–offspring collaborative transfer strategy facilitates knowledge sharing between populations, enhancing convergence early in the evolutionary process and preserving diversity in later stages. Furthermore, we customize an objective-search space collaborative selection strategy that filters solutions based on population diversity across both spaces. Extensive experiments on thirty-one benchmark CMMOPs demonstrate that MCEA significantly outperforms state-of-the-art algorithms on more than half of the test problems, as measured by IGD, IGDX, RPSP, and HV performance indicators. Furthermore, MCEA effectively locates multiple Pareto subsets, showcasing its ability to balance convergence, diversity, and feasibility in solving CMMOPs. This work underscores the importance of a comprehensive approach to tackling the complexities of CMMOPs and provides valuable insights for future research in this domain. © 2025

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

Trauma-Informed Insights from Co-Design of Self-Disclosure Robots with Domestic Abuse Survivors 20

Trauma-Informed Insights from Co-Design of Self-Disclosure R...

引用

20th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2025

作者： Morris, Nyomi Carrine Williams, Tom Jelen, Ben Colorado School of Mines MIRRORLab GoldenCO United States Rose-Hulman Institute of Technology Department of Computer Science and Software Engineering Terre HauteIN United States

ISBN: (纸本)9798350378931

Survivors of domestic abuse face significant challenges securing recovery resources, such as housing, mental health care, and social connections. Accessing these resources requires survivors to disclose their status as survivors of domestic abuse;a process that can be traumatic and emotionally burdensome. In this work, we consider how social robots might help domestic abuse survivors to address challenges surrounding self-disclosure. To do so, we conducted a three-session co-design workshop involving 8 participants from a domestic abuse shelter. Our results provide three key contributions: (1) new insights into the benefits and barriers to self-disclosure;(2) new insights into the ways that social robots can help survivors to better achieve those benefits and overcome those barriers before, during, and after disclosure, including key design recommendations associated with each of these phases;and (3) observations into how a trauma-informed computing perspective may enable more effective design work in Human-Robot Interaction. © 2025 IEEE.

关键词： Social robots

来源：评论

学校读者我要写书评

暂无评论

Statistics-Fused Learning for Classification of Randomized Eeg Trials

SSRN

引用

SSRN 2025年

作者： Zhang, Wuxia Tian, Junchao Zhang, Xiaoyan Jiang, Jianmin College of Computer Science and Software Engineering Shenzhen University Shenzhen518000 China

Following the publication of EEG-ImageNet with 40 class labels by Spampinato et. al. [1], where natural images with multiple semantics are directly used to stimulate human brains in a deterministic order, extensive research has been attempted and widely reported in the literature over the topic of decoding human brains via EEG classifications, making people feel optimistic that mind reading via computational intelligence is emerging on the horizon [2, 3, 4, 5]. In 2020, however, Li et al. [6] published their investigation over EEG-ImageNet and reported that, when the order of brain stimulation is randomized, the classification rate in decoding brain perception of natural images drops to chances [7]. Ahmed et al. then updated EEG-ImageNet by increasing the number of samples to 10 times more and tested a range of classifiers, where the maximum classification rate achieved is 7% [8], presenting an extremely difficult challenge for this direction of *** this paper, we report our latest attempt to address this challenge with a new deep framework. Compared with the existing efforts, our proposed framework features in: (i) representational projection to transform decoding brain perception of natural images into a problem of image classification, paving the way for introducing ResNet as the primary backbone to exploit its success in image classification across the areas of computer vision and pattern recognition;(ii) replacing ReLU with ELU to increase its robustness to noises, and add a focused attention to improve its selectivity and intensiveness in learning;(iii) statistics-fused learning to coordinate four ResNet-based deep classifiers and maximize their collective efforts to produce the final decoding output. Extensive experiments and ablation studies are carried out, and the accuracy of classifying the randomized EEG trials has almost doubled the maximum rate reported so far in the literature. © 2025, The Authors. All rights reserved.

关键词： Image enhancement

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：