检索结果-内蒙古大学图书馆

Accelerating Space Commerce, Exploration, and New Discovery Conference, ASCEND 2023

作者： Ellis, Tim Hitaj, Briland Lindqvist, Ulf Shands, Deborah Tinnel, Laura DeBruhl, Bruce Computer Science Laboratory SRI International California Polytechnic State University United States Computer Science and Software Engineering Department California Polytechnic State University United States

ISBN: (数字)9781624107054

ISBN: (纸本)9781624107054

Space systems enable essential communications, navigation, imaging and sensing for a variety of domains, including agriculture, commerce, transportation, and emergency operations by first responders. Protecting the cybersecurity of these critical infrastructure systems is essential. While the space environment brings unique constraints to managing cybersecurity risks, lessons learned about risks and effective defenses in other critical infrastructure domains can help us to design effective defenses for space systems. In particular, discoveries regarding cybersecurity for industrial control systems (ICS) for energy, manufacturing, transportation, and the consumer and industrial Internet of Things (IoT) offer insights into cybersecurity for the space domain. This paper provides an overview of ICS and space system commonalities, lessons learned about cybersecurity for ICS that can be applied to space systems, and recommendations for future research and development to secure increasingly critical space systems. © 2023, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Cybersecurity

来源：评论

学校读者我要写书评

暂无评论

Improved Distributed Algorithms for Random Colorings 27

Improved Distributed Algorithms for Random Colorings

引用

27th International Conference on Principles of Distributed Systems, OPODIS 2023

作者： Carlson, Charlie Frishberg, Daniel Vigoda, Eric Department of Computer Science University of California Santa BarbaraCA United States Department of Computer Science and Software Engineering California Polytechnic State University San Luis ObispoCA United States

ISBN: (纸本)9783959773089

Markov Chain Monte Carlo (MCMC) algorithms are a widely-used algorithmic tool for sampling from high-dimensional distributions, a notable example is the equilibirum distribution of graphical models. The Glauber dynamics, also known as the Gibbs sampler, is the simplest example of an MCMC algorithm;the transitions of the chain update the configuration at a randomly chosen coordinate at each step. Several works have studied distributed versions of the Glauber dynamics and we extend these efforts to a more general family of Markov chains. An important combinatorial problem in the study of MCMC algorithms is random colorings. Given a graph G of maximum degree ∆ and an integer k ≥ ∆ + 1, the goal is to generate a random proper vertex k-coloring of G. Jerrum (1995) proved that the Glauber dynamics has O(n log n) mixing time when k > 2∆. Fischer and Ghaffari (2018), and independently Feng, Hayes, and Yin (2018), presented a parallel and distributed version of the Glauber dynamics which converges in O(log n) rounds for k > (2 + Ε)∆ for any Ε > 0. We improve this result to k > (11/6 − δ)∆ for a fixed δ > 0. This matches the state of the art for randomly sampling colorings of general graphs in the sequential setting. Whereas previous works focused on distributed variants of the Glauber dynamics, our work presents a parallel and distributed version of the more general flip dynamics presented by Vigoda (2000) (and refined by Chen, Delcourt, Moitra, Perarnau, and Postle (2019)), which recolors local maximal two-colored components in each step. © Charlie Carlson, Daniel Frishberg, and Eric Vigoda

关键词： Coloring

来源：评论

学校读者我要写书评

暂无评论

Wildfire Spread Prediction Using Attention Mechanisms in U-NET

Wildfire Spread Prediction Using Attention Mechanisms in U-N...

引用

2023 International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Shah, Kamen Pantoja, Maria California Polytechnic State University Dept. Computer Science and Software Engineering San Luis Obispo United States

ISBN: (纸本)9798350322972

Global warming has increased large-scale natural disasters at an alarmingly greater frequency. These natural disasters affect humans and the ecosystems that all species rely upon for food and shelter. A significant byproduct of global warming is the emergence of mega-fires, which are wildfires that burn more than 100,000 acres. Mega fires have exhibited the capacity to destroy entire ecosystems and urban areas. Researchers forecast a significant increase in wildfires during the next decade. This is a fundamentally growing problem that mandates modern solutions. Currently, simulation models are employed to predict wildfire propagation and aid firefighters in suppressing them. However, these methods are computationally expensive and often inaccurate. A recent advance in this area is the usage of neural networks (NN), which are systematically more accurate and computationally efficient. This paper focuses on predicting wildfire spread using NN with an attention mechanism to improve spatial recognition and overall performance in neural networks for wildfire spread prediction. We train different models on 12 distinct observational variables derived from the Google Earth Engine catalog. Evaluation is conducted with accuracy, Dice coefficient score, ROC-AUC, and F1-score. Results show that when augmenting segmentation models with attention mechanisms, the attention component improves feature suppression and recognition, improving overall performance. Furthermore, we use ensemble modeling to reduce bias and variation, leading to more consistent and accurate predictions. The architecture presented in this research achieved a ROC-AUC score of 86.2% and an accuracy of 82.1% when inferencing on wildfire propagation at 30-minute intervals. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Rapid Mixing for the Hardcore Glauber Dynamics and Other Markov Chains in Bounded-Treewidth Graphs 34

Rapid Mixing for the Hardcore Glauber Dynamics and Other Mar...

引用

34th International Symposium on Algorithms and Computation, ISAAC 2023

作者： Eppstein, David Frishberg, Daniel Department of Computer Science University of California IrvineCA United States Department of Computer Science and Software Engineering California Polytechnic State University San Luis ObispoCA United States

ISBN: (纸本)9783959772891

We give a new rapid mixing result for a natural random walk on the independent sets of a graph G. We show that when G has bounded treewidth, this random walk – known as the Glauber dynamics for the hardcore model – mixes rapidly for all fixed values of the standard parameter λ > 0, giving a simple alternative to existing sampling algorithms for these structures. We also show rapid mixing for analogous Markov chains on dominating sets, b-edge covers, b-matchings, maximal independent sets, and maximal b-matchings. (For b-matchings, maximal independent sets, and maximal b-matchings we also require bounded degree.) Our results imply simpler alternatives to known algorithms for the sampling and approximate counting problems in these graphs. We prove our results by applying a divide-and-conquer framework we developed in a previous paper, as an alternative to the projection-restriction technique introduced by Jerrum, Son, Tetali, and Vigoda. We extend this prior framework to handle chains for which the application of that framework is not straightforward, strengthening existing results by Dyer, Goldberg, and Jerrum and by Heinrich for the Glauber dynamics on q-colorings of graphs of bounded treewidth and bounded degree. © David Eppstein and Daniel Frishberg;licensed under Creative Commons License CC-BY 4.0.

关键词： Mixing

来源：评论

学校读者我要写书评

暂无评论

Exploring, Analyzing, and Benchmarking the Job Satisfaction Levels of Developers 8th

Exploring, Analyzing, and Benchmarking the Job Satisfaction ...

引用

8th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Hussain, Shahid Wang, Wen-Li Khan, Saif Ur Rahman Rahim, Muhammad Abdul Basit Ur Ibrahim, Naseem Zhao, Jie Department of Computer Science and Software Engineering Penn State University Erie United States Department of Software Engineering Shifa Tameer-e-Millat University Islamabad Pakistan Department of Computer Science California State University Long Beach United States

ISBN: (纸本)9789819757985

The long-term participation of trained and competent employees is required to improve the morale, productivity, safety, value, and autonomy of an organization. In small and medium-sized software industries, the long-term involvement of developers will not only boost the efficiency of software development but also help train their team members. Although software industries have managed to stimulate developers’ productivity and reduce their turnover with a better compensation plan, the incentive through money has caused contradictory arguments. Hence, there is a need to employ a more systematic way to explore, analyze, and calibrate the developer’s needs, so that the managerial roles can make informed decisions and incorporate influential motivators to increase a developer’s job satisfaction level. In this respect, we are conducting an empirical study using the basics of Maslow's theory, a motivational theory in psychology, to (1) explore the developer’s motivation level in software industries, (2) benchmark the developer’s needs for job satisfaction at each level, and (3) analyze the job-hunting factors for developers’ turnover. We conducted a questionnaire-based study to gather feedback from 128 developers in 10 software industries with four types of analyses being conducted. The results indicate that satisfaction levels can vary across software industries, but the management can anticipate developers’ high-level needs from their low-level needs to determine influencing and motivating factors for job satisfaction improvements. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Job satisfaction

来源：评论

学校读者我要写书评

暂无评论

Using Deep Reinforcement Learning and Formal Verification in Safety Critical Systems: Strategies and Challenges 23

Using Deep Reinforcement Learning and Formal Verification in...

引用

23rd IEEE International Conference on software Quality, Reliability, and Security Companion, QRS-C 2023

作者： Sharma, Satyam Rahim, Muhammad Abdul Basit Ur Hussain, Shahid Abid, Muhammad Rizwan Liu, Tairan California State University Computer Engineering and Computer Science Long BeachCA United States Computer Science and Software Engineering Penn State University Behrend PA United States Florida Polytechnic University Computer Science LakelandFL United States Mechanical and Aerospace Engineering California State University Long BeachCA United States

ISBN: (纸本)9798350359398

Deep Reinforcement Learning (DRL) is critical in modern Artificial Intelligence (AI), powering innovations from gaming to autonomous vehicles. As DRL continues its rapid ascent, ensuring its systems are both trustworthy and effective is crucial. This research focuses on different DRL techniques and the challenges faced in real-life scenarios. The paper also describes various formal verification techniques and the challenges related to their application. It sheds light on the different frameworks and tools that can enhance the credibility of systems. We performed an extensive literature survey to present the existing methodologies, tools, and frameworks. The analysis systematically reviews and categorizes various formal verification techniques and frameworks employed in DRL. The insights garnered from this study are anticipated to foster an enriched understanding of the processes and contribute to decision-making in Safety Critical Systems using DRL and verification. © 2023 IEEE.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Analysis and Comparison of Dockerized and Standalone Apache Spark Configurations for Efficient Distributed Data Processing

Analysis and Comparison of Dockerized and Standalone Apache ...

引用

2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024

作者： Ndigande, Alain P. Ari, Ismail Ozer, Sedat Department of Computer Science Ozyegin University Istanbul Turkey Department of Electrical and Computer Engineering California State Polytechnic University Pomona Pomona United States

ISBN: (纸本)9798350379433

Apache Spark, a powerful distributed computing framework, has become a key to handling large-scale data processing tasks in many applications, including signal processing. However, there are different considerations for performance, cost, or ease of use during the development and deployment stages. This paper benchmarks Apache Spark on different local setups in terms of performance and elaborates on alternative cloud computing costs. Performances of different Spark master-slave configurations with Docker and no Docker are evaluated over different numbers of worker nodes, cores, memory, and executors. Spark WordCount benchmark is tested on Wikipedia datasets of sizes ranging from 1GB to 25GB. The results reveal that in local setups Docker creates additional parameter complexity as well as performance overheads, therefore a "no Docker" setup is a better choice. We also observe the dominance of I/O bottlenecks in local setups. These results can help practitioners choose optimal setups for different Development and Operations (DevOps) and big data processing scenarios. © 2024 IEEE.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

Personalized Health Assistant with Reinforcement Learning 1

Personalized Health Assistant with Reinforcement Learning

引用

1st IEEE International Conference on Artificial Intelligence for Medicine, Health and Care, AIMHC 2024

作者： Jin, Jennifer Kim, Mira Kim, Soo Dong Computer Science and Engineering California State University San BernardinoCA92407 United States Department of Computer Science California State University FullertonCA92831 United States School of Software Soongsil University Seoul Korea Republic of

ISBN: (纸本)9798350371987

Integration of medical IoT devices with machine learning presents significant advantages for personalized healthcare. This research is to develop a software solution which leverages smart scale devices to measure health indexes, recommend personalized health-promoting activities. We designed the system with Q-learning elements and tailored the Q-learning policy to keep learning the personal effectiveness of health-promoting activities through users’ episodes. The system was implemented in Python, and the experiments demonstrated a substantial average enhancement of 25% across health indexes and 53% in optimizing the performance of the recommender policy. ©2024 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Light field depth estimation:A comprehensive survey from principles to future

引用

High-Confidence Computing 2024年第1期4卷 92-107页

作者： Tun Wang Hao Sheng Rongshan Chen Da Yang Zhenglong Cui Sizhe Wang Ruixuan Cong Mingyuan Zhao State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Zhongfa Aviation Institute Beihang UniversityHangzhou 311115China Faculty of Applied Sciences Macao Polytechnic UniversityMacao 999078China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Light Field(LF)depth estimation is an important research direction in the area of computer vision and computational photography,which aims to infer the depth information of different objects in threedimensional scenes by capturing LF *** this new era of significance,this article introduces a survey of the key concepts,methods,novel applications,and future trends in this *** summarize the LF depth estimation methods,which are usually based on the interaction of radiance from rays in all directions of the LF data,such as epipolar-plane,multi-view geometry,focal stack,and deep *** analyze the many challenges facing each of these approaches,including complex algorithms,large amounts of computation,and speed *** addition,this survey summarizes most of the currently available methods,conducts some comparative experiments,discusses the results,and investigates the novel directions in LF depth estimation.

关键词： Light field Depth estimation Deep learning Sub-aperture image Epipolar-plane image

来源：评论

学校读者我要写书评

暂无评论

Modeling and Analysis for the Propagation of DDoS Attacks in Microgrid System

Modeling and Analysis for the Propagation of DDoS Attacks in...

引用

2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Xiong, Lei Xu, Qichao Fang, Dongfeng Zeng, Hui School of Mechatronic Engineering and Automation Shanghai University Shanghai China California Polytechnic State University Department of Computer Science and Software Engineering San Luis ObispoCA United States

ISBN: (纸本)9798350304602

With the development and progress of electric power technology and information technology, microgrid has become an important and indispensable part of smart grid. The cyber-security of microgrids has a significant impact on the stability and security of smart grids. Distributed denial-of-service (DDoS) attack as one of the common types of cyberattacks in microgrids has formed a considerable threat to the network security of microgrids. In a DDoS event, attackers first use malware to infect few nodes in the microgrid and the infected nodes can generate large-scale malware propagation in the system. These infection lays the foundation for the flood attacks. Therefore, this paper establishes a model to analyse the DDoS attack propagation in microgrid. Firstly, this paper establishs a dynamic model based on the epidemiological theory to describe the propagation dynamics of DDoS attack in microgrid. Then we go on to compute the attack threshold $(R_{0})$ of the model and prove the stability of equilibrium point. Finally we carry out the experimental simulation based on the model, and the experimental results show that the proposed model can reflect the propagation of DDoS attack in microgrid effectively. © 2023 IEEE.

关键词： Denial-of-service attack

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：