检索结果-内蒙古大学图书馆

5th IEEE Research and Applications of Photonics in Defense Conference, RAPID 2022

作者： Gieser, Anna Vitola, Sabina Enochson, Avery Wolf, Henry Striker, Ryan Cleveland, Jerika Braaten, Benjamin D. Ewert, Daniel Mitra, Dipankar Allen, Jeffery Allen, Monica North Dakota State University Department of Electrical and Computer Engineering Fargo United States Minnesota State University Bell Program Department of Integrated Engineering MankatoMN United States Program of Minnesota State University Department of Integrated Engineering at Iron Range Engineering MankatoMN United States University of Wisconsin - la Crosse Department of Computer Science and Computer Engineering La CrosseWI United States Munitions Directorate Air Force Research Laboratory Eglin Air Force Base FL32542 United States

ISBN: (数字)9781665402231

ISBN: (纸本)9781665402231

Human cognitive processes remain an area of strong interest and ongoing research. One tool to gain greater insight into this process is neuronal modeling. The following features are desirable in a neuronal modeling tool: a library of known parameters for different neurons and species, the ability to select the neuron and species of interest, the ability to quickly simulate neuronal behavior, and the ability to calculate metabolic requirements and efficiencies for various neuronal activity. Many of these can be found in software today, but not all elements can be found in one entity. The goal of this work is to develop a neuronal modeling tool that incorporates these features. © 2022 IEEE.

关键词： Neurons

来源：评论

学校读者我要写书评

暂无评论

laSE: A Forward-Looking Item Selection Strategy for CD-CAT

LASE: A Forward-Looking Item Selection Strategy for CD-CAT

引用

Machine Intelligence Theory and Applications (MiTA), International Conference on

作者： Xi Cao Lijun Yin Ying Lin Jun Zhang Department of Computer Science and Information Technology La Trobe University Melbourne Australia Department of Psychology Sun Yat-sen University Guangzhou China Hanyang University Seoul South Korea Nankai University Tianjin China

ISBN: (数字)9798350385670

ISBN: (纸本)9798350385687

Cognitive diagnostic computerized adaptive testing (CD-CAT) is a cutting-edge testing approach in educational environments. It combines the merits of computerized adaptive testing (CAT) and cognitive diagnosis models (CDMs), allowing for adaptive testing methods to be applied to fine-grained diagnosis. Two crucial factors in CD-CAT item selection are diagnostic accuracy and item bank utilization. Previous research focused either solely on enhancing diagnostic performance or on achieving a balance between bank utilization and diagnostic accuracy, often at the significant expense of the latter. To strike a more effective equilibrium between diagnostic performance and bank utilization, the current study proposes a forwardlooking item selection strategy for CD-CAT, named lookingahead Shannon entropy (laSE). laSE shifts the focus from the immediate next item to a broader perspective, considering sequences of multiple items ahead, thereby offering a longer-term view in item selection. This strategy involves a branch searching scheme to find appropriate multi-item sequences and introduces the laSE metric for evaluating these sequences. The parameter within the laSE method are investigated. Simulation studies based on two item banks are conducted to demonstrate laSE’s effectiveness compared to existing methods. The results show that laSE offers a more balanced item selection for CD-CAT in terms of diagnostic accuracy and bank utilization.

关键词： Measurement Adaptation models Accuracy Computational modeling Entropy Machine intelligence Testing

来源：评论

学校读者我要写书评

暂无评论

Cryptanalysis of TRUTH: Fixing a Protocol to Make it 5G-Compliant

Cryptanalysis of TRUTH: Fixing a Protocol to Make it 5G-Comp...

引用

International Symposium on Networks, computers and Communications (ISNCC)

作者： J. Munilla M. Burmester P. Caballero-Gil Dpt. of Telecommunication Engineering University of Málaga Málaga Spain Department of Computer Science Florida State Unviersity Tallahassee Florida USA Dept. of Computer Science and AI University of La Laguna La Laguna Tenerife Spain

ISBN: (数字)9798350364910

ISBN: (纸本)9798350364927

Trust and security are critical deployment require-ments for Industrial Internet of Things (IIoT) networks. A recent protocol, called TRUTH, integrates security mechanisms for authentication and privacy alongside a Dempster-Shafer based trust model, to assess the trustworthiness of collected data. We analyze this protocol and show that it does not comply with the implementation requirements of the 5G standard, and that flaws in the identification of involved parties hinder its adoption by 5G devices. We show how to fix this protocol and tailor it for the specific 5G implementation constraints, by enhancing its computational and communication performance. Finally, we show how to prove the security of this fixed protocol (TRUTH +) using the Tamarin automated verification tool.

关键词： Fault diagnosis Performance evaluation Data privacy Protocols 5G mobile communication Data models Synchronization Object recognition Standards Industrial Internet of Things

来源：评论

学校读者我要写书评

暂无评论

A First Order Meta Stackelberg Method for Robust Federated Learning

arXiv

引用

arXiv 2023年

作者： Pan, Yunian Li, Tao Li, Henger Xu, Tianyi Zheng, Zizhan Zhu, Quanyan Department of Electrical and Computer Engineering New YorkNY United States Department of Computer Science Tulane University New OrleansLA United States

Previous research has shown that federated learning (FL) systems are exposed to an array of security risks. Despite the proposal of several defensive strategies, they tend to be non-adaptive and specific to certain types of attacks, rendering them ineffective against unpredictable or adaptive threats. This work models adversarial federated learning as a Bayesian Stackelberg Markov game (BSMG) to capture the defender's incomplete information of various attack types. We propose meta-Stackelberg learning (meta-SL), a provably efficient meta-learning algorithm, to solve the equilibrium strategy in BSMG, leading to an adaptable FL defense. We demonstrate that meta-SL converges to the first-order Ε-equilibrium point in O(Ε−2) gradient iterations, with O(Ε−4) samples needed per iteration, matching the state of the art. Empirical evidence indicates that our meta-Stackelberg framework performs exceptionally well against potent model poisoning and backdoor attacks of an uncertain nature. © 2023, CC BY.

关键词： Learning algorithms

来源：评论

学校读者我要写书评

暂无评论

MDDE: multitasking distributed differential evolution for privacy-preserving database fragmentation

MDDE: multitasking distributed differential evolution for pr...

引用

作者： Ge, Yong-Feng Orlowska, Maria Cao, Jinli Wang, Hua Zhang, Yanchun Department of Computer Science and Information Technology La Trobe University Melbourne Australia Faculty of Information Technology Polish-Japanese Academy of Information Technology Warsaw Poland Institute for Sustainable Industries and Liveable Cities Victoria University Footscray Australia

Database fragmentation has been used as a protection mechanism of database’s privacy by allocating attributes with sensitive associations into separate data fragments. A typical relational database consists of multiple relations. Thus, fragmentation process is applied to each relation separately in a sequential manner. In other words, the existing database fragmentation approaches regard each relation fragmentation problem as an independent task. When solving a sequence of fragmentation problems, redundant computational resources are consumed when extracting the same fragmentation information and limit the performance of those algorithms. In this paper, a multitasking database fragmentation problem for privacy preservation requirements is formally defined. A multitasking distributed differential evolution algorithm is introduced, including a multitasking distributed framework enriched by two new operators. The introduced framework can help exchange generic and effective allocation information among different database fragmentation problems. A similarity-based alignment operator is proposed to adjust the fragment orders in different database fragmentation solutions. A perturbation-based mutation operator with adaptive mutation strategy selection is designed to sufficiently exchange evolutionary information in the solutions. Experimental results show that the proposed algorithm can outperform other competitors in terms of solution accuracy, convergence speed, and scalability. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Multitasking

来源：评论

学校读者我要写书评

暂无评论

Optimizing Privacy and Utility Tradeoffs for Group Interests Through Harmonization

arXiv

引用

arXiv 2024年

作者： Mandal, Bishwas Amariucai, George Wei, Shuangqing Department of Computer Science Kansas State University ManhattanKS66506 United States Division of Electrical & Computer Engineering Louisiana State University Baton RougeLA70803 United States

We propose a novel problem formulation to address the privacy-utility tradeoff, specifically when dealing with two distinct user groups characterized by unique sets of private and utility attributes. Unlike previous studies that primarily focus on scenarios where all users share identical private and utility attributes and often rely on auxiliary datasets or manual annotations, we introduce a collaborative data-sharing mechanism between two user groups through a trusted third party. This third party uses adversarial privacy techniques with our proposed data-sharing mechanism to internally sanitize data for both groups and eliminates the need for manual annotation or auxiliary datasets. Our methodology ensures that private attributes cannot be accurately inferred while enabling highly accurate predictions of utility features. Importantly, even if analysts or adversaries possess auxiliary datasets containing raw data, they are unable to accurately deduce private features. Additionally, our data-sharing mechanism is compatible with various existing adversarially trained privacy techniques. We empirically demonstrate the effectiveness of our approach using synthetic and real-world datasets, showcasing its ability to balance the conflicting goals of privacy and utility. © 2024, CC BY.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Initial Exploration of Zero-Shot Privacy Utility Tradeoffs in Tabular Data Using GPT-4

arXiv

引用

arXiv 2024年

We investigate the application of large language models (LLMs), specifically GPT-4, to scenarios involving the tradeoff between privacy and utility in tabular data. Our approach entails prompting GPT-4 by transforming tabular data points into textual format, followed by the inclusion of precise sanitization instructions in a zero-shot manner. The primary objective is to sanitize the tabular data in such a way that it hinders existing machine learning models from accurately inferring private features while allowing models to accurately infer utility-related attributes. We explore various sanitization instructions. Notably, we discover that this relatively simple approach yields performance comparable to more complex adversarial optimization methods used for managing privacy-utility tradeoffs. Furthermore, while the prompts successfully obscure private features from the detection capabilities of existing machine learning models, we observe that this obscuration alone does not necessarily meet a range of fairness metrics. Nevertheless, our research indicates the potential effectiveness of LLMs in adhering to these fairness metrics, with some of our experimental results aligning with those achieved by well-established adversarial optimization techniques. © 2024, CC BY.

关键词： Metadata

来源：评论

学校读者我要写书评

暂无评论

Deadline-aware workload scheduling for edge-enhanced iot devices: A blockchain-enabled approach to incentive-based computing

引用

Peer-to-Peer Networking and Applications 2025年第4期18卷

作者： Chaudhry, Muhammad Tayyab Yousafzai, Abdullah Zia, Ali Abid, Shahbaz Akhtar Ahmad, Farooq Department of Computer Science COMSATS University Islamabad Punjab Lahore Pakistan Artificial Intelligence and Cyber Futures Institute Charles Sturt University BathurstNSW2795 Australia Australian National University CanberraACT Australia School of Computing Engineering and Mathematical Sciences La Trobe University MelbourneNSW Australia

This paper explore Blockchain-enabled incentive-driven workload scheduling for deadline-sensitive computational tasks over edge devices in mission critical applications such as healthcare, autonomous vehicles, and smart cities. Leveraging the power of volunteer edge devices, our study addresses the critical challenge of meeting job deadlines to optimize revenue and minimize penalties. We introduce a novel deadline-aware scheduling approach, augmented by classical algorithms like First Come First Serve (FCFS), First Fit, and Best Fit, as well as state-of-the-art counterparts, for robust comparative analysis. By integrating bin packing techniques and computational resource boosting on volunteer edge devices, the proposed method ensures effective deadline adherence. Additionally, we propose innovative mechanisms for volunteer edge device ranking and penalty-reward calculations to further enhance system efficiency. To ensure transparency and trust, the proposed system utilizes blockchain to securely log work activities and facilitate incentive payments. The proposed framework is comprehensively evaluated across ten key metrics such as completion time, deadline violations, penalties, device income, and device rankings using the Bitbrains workload traces. The results underscore the significant advantages of our deadline-aware algorithm, showcasing improvements ranging from 4% to 44% across all metrics, including enhanced income for volunteer devices, improved time efficiency, balanced workload distribution, and reduced deadline violations. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Edge computing

来源：评论

学校读者我要写书评

暂无评论

Distributed Signal Strength Prediction using Satellite Map empowered by Deep Vision Transformer

Distributed Signal Strength Prediction using Satellite Map e...

引用

2021 IEEE Globecom Workshops, GC Wkshps 2021

作者： Yu, Haiyao Hou, Zhanwei Gu, Yifan Cheng, Peng Ouyang, Wanli Li, Yonghui Vucetic, Branka University of Sydney School of Electrical and Information Engineering SydneyNSW Australia Computer Science & Information Technology La Trobe University MelbourneVIC Australia

ISBN: (纸本)9781665423908

The accurate prediction of received signal strength (RSS) is the key to coverage optimization and interference management in network planning, as well as proactive resource allocation and anticipated network management. Traditional methods for RSS prediction are based on ray tracing or stochastic radio propagation model. The former requires the detailed 3D geometry and dielectric properties of the reflectors, which may not be available practically. The latter roughly classify the environment as either urban, suburban and rural scenarios and does not make full use of the environment information. In this paper, by leveraging accessible satellite maps to capture the features of radio environment, a distributed federated learning (FL) RSS prediction framework is proposed to fully exploit the user generated real-time data while preserving the users' privacy. To further improve the prediction accuracy, the deep vision transformer (DeepVIT) is utilized to process the images of the satellite map, because it is capable of learning to "pay attention to"important parts of an image such as reflection surfaces and blockages. The proposed method is evaluated by the real-world data set including around 60, 000 individual measurements. Simulations results verified that the prediction accuracy of the proposed method outperforms baseline methods including ray tracing, Urban Macro (UMa) model and convolutional neural network (CNN) based method. Moreover, the computational time is reduced five times compared with CNN based method. © 2021 IEEE.

关键词： Satellites

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-based Wildfire Smoke Detection using Uncrewed Aircraft System Imagery

Deep Learning-based Wildfire Smoke Detection using Uncrewed ...

引用

International Conference on Ubiquitous Robots (UR)

作者： Khan Raqib Mahmud Lingxiao Wang Xiyuan Liu L. Jiahao Sunzid Hassan Computer Science Department Louisiana Tech University Ruston LA USA Electrical Engineering Department Louisiana Tech University Ruston LA USA Mathematics and Statistics Department Louisiana Tech University Ruston LA USA

ISBN: (数字)9798350361070

ISBN: (纸本)9798350361087

Recent years have seen notable advancements in wildfire smoke detection, particularly in Uncrewed Aircraft Systems (UAS)-based detection employing diverse deep learning (DL) approaches. Despite the promise exhibited by these approaches, the task of detecting smoke from UAS imagery remains challenging due to difficulties in differentiating smoke from similar phenomena such as clouds and water. This work introduces a novel DL-based method for smoke detection from UAS visual observations. The core idea involves segregating forest areas from non-forest regions, such as sky and lake, and exclusively applying smoke detection to forested areas, thus eliminating the chance of misidentifying clouds and water as smoke. Specifically, we utilized a Mask Region-Based Convolutional Neural Network (Mask R-CNN) for semantic segmentation to remove non-forest regions (e.g., sky and lake): Subsequently, a customized You Only Look Once-version 7 (YOLOv7) model was trained to detect smoke within the forest areas. The proposed method was validated on an image dataset collected from our previous prescribed burn experiment, where we extracted 246 images to train both MASK R-CNN and YOLOv7 models. Additionally, we extract another 128 images to validate and confirm the efficacy of our enhanced wildfire smoke detection approach. The test results demonstrate that our proposed approach, employing MASK R-CNN and YOLOv7 models, outperforms the YOLOv7-only model by 25.3% in precision, 18.7% in recall, and 45% in mean Average Precision (mAP). The datasets are available at: https://***/khanRmahmud/wildfire-smoke-detection.

关键词： Visualization Wildfires Accuracy Atmospheric modeling Semantic segmentation Forestry lakes

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：