检索结果-内蒙古大学图书馆

Journal of Computing sciences in Colleges 2024年第4期40卷

作者： Charles Moreno Carson Peterson BJ Klingenberg Bruce DeBruhl Computer Science and Software Engineering California Polytechnic State University San Luis Obispo CA USA

Over the previous 20 years the need for software engineering and computer security education in undergraduate computing curriculum has become apparent. At Cal Poly, we have adopted stand-alone courses in both of these domains but have identified the intersection of software engineering and cybersecurity as a domain with curricular opportunity. In this paper, we outline a secure software engineering course and share our experience with running this course. Our course is focused on practical hands-on education with three large projects covering producing a secure software product, threat modelling, and malware design. Lastly, we cover the ethical considerations of this course and potential pitfalls of similar secure software engineering courses.

关键词：

来源：评论

学校读者我要写书评

暂无评论

超算与机器学习辅助高渗透性海水反渗透膜系统优化设计

引用

science Bulletin 2023年第4期68卷 397-407,M0004页

作者：罗玖李明恒 Eric M.V.Hoek 衡益 School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China National Supercomputing Center in Guangzhou(NSCC-GZ) Guangzhou 510006China Guangdong Province Key Laboratory of Computational Science Guangzhou 510006China Department of Chemical and Materials Engineering California State Polytechnic UniversityPomona CA 91768USA Department of Civil&Environmental Engineering California NanoSystems Institute and Institute of the Environment&SustainabilityUniversity of CaliforniaLos Angeles(UCLA)Los Angeles CA 90095USA Energy Storage&Distributed Resources Division Lawrence Berkeley National LaboratoryBerkeley CA 94720USA

当前,高渗透性反渗透膜材料的研究引起了广泛的关注,然而高渗透导致的浓差极化与膜污染加剧等瓶颈问题限制了高性能膜材料的应用发展.本工作采用机器学习结合超级计算提出了针对先进反渗透膜材料的组件进水隔网(亚毫米级)与系统(米级)... 详细信息

当前,高渗透性反渗透膜材料的研究引起了广泛的关注,然而高渗透导致的浓差极化与膜污染加剧等瓶颈问题限制了高性能膜材料的应用发展.本工作采用机器学习结合超级计算提出了针对先进反渗透膜材料的组件进水隔网(亚毫米级)与系统(米级)的多尺度优化设计新方法.在进料含盐度35,000 ppm,回收率50%典型工况下,对标目前国际先进海水反渗透淡化工艺,本文提出的优化方案能使淡水制备比能耗(1.66 k Wh/m^(3))降低27.5%,所需膜面积减少约37.2%,系统最大浓差极化因子控制在工程允许范围以内(<1.20),可有效缓解高渗透膜系统中膜污染问题,为高性能膜材料精准设计提供理论依据、计算工具和大数据支撑,有重要的应用潜力.本文提出的机器学习结合超算的多尺度设计新研究范式,突破了基于“试错法”的传统单一尺度组件设计限制,高通量并行计算规模可扩展至93,120核以上,较串行算法计算效率提升3000倍以上,可大幅度缩短高性能膜组件的设计周期.

关键词： Reverse osmosis desalination High permeability membrane Multiscale design optimization Multilayer artificial neural networks Supercomputing Enhancement of mass transfer

来源：评论

学校读者我要写书评

暂无评论

Deep learning and artificial intelligence: Project collaboration across classes

Deep learning and artificial intelligence: Project collabora...

引用

2020 ASEE Virtual Annual Conference, ASEE 2020

作者： Kurfess, Franz J. Pantoja, Maria Humer, Irene Computer Science and Software Engineering Department California Polytechnic State University San Luis Obispo United States CalPoly San Luis Obispo Computer Engineering Computer Science and Software Engineering Office: 14-211 United States

Working in collaborative environments is an essential skill for computing professionals. In our program, students have significant team experience from previous classes;almost all of our classes in Cal Poly's computer science, software engineering and computer engineering programs have lab sections, and students start with team-based work early. However, they mostly work within small teams of about five members that are stable throughout a term. To stretch the students' collaborative skills and enhance their experience with more fluid team configurations, we experimented with creating teams across two related senior-level classes within computer science, but with different perspectives and approaches. For one particular project, shark spotting in drone video footage, the teams also collaborated with a group of Marine Biologists from the Shark Lab at CSU Long Beach, who provided the video material and acted as a customer for three teams across the two classes. This exposed our students to collaborators among different fields, with their own terminology, goals, work methods and practical approaches. Our paper reports on the initial experiment during the Fall 2019 term, involving two sections of an Artificial Intelligence class and one section of a Deep Learning class. We are planning to continue this collaboration in the future. © American Society for engineering Education 2020.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Tumbling to Succeed: A Predictive Analysis of User Success in Interactive Ontology Visualization 2020

Tumbling to Succeed: A Predictive Analysis of User Success i...

引用

10th International Conference on Web Intelligence, Mining and Semantics, WIMS 2020 and 5th International Conference on Real-time Intelligent Systems, RTIS 2020

作者： Fu, Bo Steichen, Ben McBride, Alexandra Computer Engineering and Computer Science California State University Long Beach United States Computer Science California State Polytechnic University Pomona United States

ISBN: (纸本)9781450375429

Ontology visualization is an important component in the support of human-ontology interaction, as it amplifies cognition and offloads cognitive efforts to the human perceptual system. While a significant amount of research efforts has focused on designing and developing various visual layouts and improve performance of large-scale visualizations, the differences in user preferences and cognitive abilities have been largely overlooked. This provides an opportunity to investigate ways to potentially provide more personalized visual support in human-ontology interaction. To this end, this paper demonstrates successful predictions on an individual user's likelihood to succeed in a given task, based on this person's gaze data collected during interaction. Specifically, we show several statistically significant predictions against a baseline classifier when inferring users' success before a given task is actually completed. Moreover, we present results showing that accurate predictions of user success can be achieved early on during user interaction, such as after just a few minutes in some cases. These findings suggest there are ample opportunities throughout various stages of human-ontology interaction where the underlying visual system may adapt in real time to the user's visual needs to provide the most appropriate visualization with the overall goal of possibly increasing user success in a given task. © 2020 ACM.

关键词： Eye tracking

来源：评论

学校读者我要写书评

暂无评论

DEVELOPING A RANKING PROBLEM LIBRARY (RPLIB) from A DATA-ORIENTED PERSPECTIVE

arXiv

引用

arXiv 2022年

作者： Anderson, Paul E. Tat, Brandon Ward, Charlie Langville, Amy N. Pedings-Behling, Kathryn E. Department of Computer Science and Software Engineering California Polytechnic State University San Luis ObispoCA United States Department of Mathematics College of Charleston CharlestonSC29401 United States

We present an improved library for the ranking problem called RPLIB. RPLIB includes the following data and features. (1) Real and artificial datasets of both pairwise data (i.e., information about the ranking of pairs of items) and feature data (i.e., a vector of features about each item to be ranked). These datasets range in size (e.g., from small n = 10 item datasets to large datasets with hundred of items), application (e.g., from sports to economic data), and source (e.g. real versus artificially generated to have particular structures). (2) RPLIB contains code for the most common ranking algorithms such as the linear ordering optimization method and the Massey method. (3) RPLIB also has the ability for users to contribute their own data, code, and algorithms. Each RPLIB dataset has an associated .JSON model card of additional information such as the number and set of optimal rankings, the optimal objective value, and corresponding figures. © 2022, CC BY.

关键词： Integer programming

来源：评论

学校读者我要写书评

暂无评论

Deep learning for agriculture

Deep learning for agriculture

引用

2020 ASEE Virtual Annual Conference, ASEE 2020

作者： Pantoja, Maria Kurfess, Franz J. Humer, Irene Cal Poly San Luis Obispo Computer Engineering Computer Science and Software Engineering Office: 14-211 United States Computer Science and Software Engineering Department California Polytechnic State University San Luis Obispo United States

The global population is estimated to reach 8 billion by 2023 [1]. To feed such an immense population in a sustainable way, while also enabling farmers to make a living, requires the modernization of production methods in agriculture. In recent years there has been a lot of excitement in academic research and industry about the application of modern computer technology to farming, making farming one of the favorites for investors. According to Forbes Magazine, the agricultural technology gold rush began in 2013, with Monsanto's purchase of the agricultural data company, The Climate Corporation, for $930 million [16]. The total investment in 2017 topped $1.5 billion, setting a new record. All economic indicators point to a huge increase in technology and in particular software used in the agriculture fields. The need for advanced technology in agriculture is clear. The technology is being developed and ready, but what is still lacking is the number of professionals that have both skills-agriculture and technological knowledge-in particular in advanced computing methods like computer vision and deep learning. The main goal of this paper is to report on our approach to close the gap between domain experts in agriculture and computer scientists by developing a practical, hands-on activity in the form of a workshop or tutorial specifically targeted at agricultural engineers and practitioners interested in applying computer vision techniques to solve agricultural problems. The tutorial consists of specific examples like detecting and counting bees, segmentation of fruit trees and automatic fruit classification. The examples for the tutorials are chosen because of their simplicity of implementation and because they are also easily expandable into more complex projects. For example, the segmentation tutorial can be used to estimate pruning weight which is useful to determine the baseline vigor levels of the fruit trees. It is one of the best ways to quantify areas of the or

关键词： Deep learning

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

arXiv

引用

arXiv 2021年

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

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. © 2021, CC BY.

关键词： Mixing

来源：评论

学校读者我要写书评

暂无评论

DISTDET: a cost-effective distributed cyber threat detection system 23

DISTDET: a cost-effective distributed cyber threat detection...

引用

Proceedings of the 32nd USENIX Conference on Security Symposium

作者： Feng Dong Liu Wang Xu Nie Fei Shao Haoyu Wang Ding Li Xiapu Luo Xusheng Xiao School of Cyber Science and Engineering Huazhong University of Science and Technology and Sangfor Technologies Inc Beijing University of Posts and Telecommunications Case Western Reserve University School of Cyber Science and Engineering Huazhong University of Science and Technology Key Laboratory of High-Confidence Software Technologies (MOE) School of Computer Science Peking University The Hong Kong Polytechnic University Arizona State University

ISBN: (纸本)9781939133373

Building provenance graph that considers causal relationships among software behaviors can better provide contextual information of cyber attacks, especially for advanced attacks such as Advanced Persistent Threat (APT) attacks. Despite its promises in assisting attack investigation, existing approaches that use provenance graphs to perform attack detection suffer from two fundamental limitations. First, existing approaches adopt a centralized detection architecture that sends all system auditing logs to the server for processing, incurring intolerable costs of data transmission, data storage, and computation. Second, they adopt either rule-based techniques that cannot detect unknown threats, or anomaly-detection techniques that produce numerous false alarms, failing to achieve a balance of precision and recall in APT detection. To address these fundamental challenges, we propose DISTDET, a distributed detection system that detects APT attacks by (1) performing light weight detection based on the host model built in the client side, (2) filtering false alarms based on the semantics of the alarm proprieties, and (3) deriving global models to complement the local bias of the host models. Our experiments on a large-scale industrial environment (1,130 hosts, 14 days, ∼1.6 billion events) and the DARPA TC dataset show that DISTDET is as effective as sate-of-the-art techniques in detecting attacks, while dramatically reducing network bandwidth from 11.28Mb/s to 17.08Kb/S (676.5× reduction), memory usages from 364MB to 5.523MB (66× reduction), and storage from 1.47GB to 130.34MB (11.6× reduction). By the time of this writing, DISTDET has been deployed to 50+ industry customers with 22,000+ hosts for more than 6 months, and identified over 900 real-world attacks.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Addendum to NeBula: Toward Extending Team CoSTAR’s Solution to Larger Scale Environments

IEEE Transactions on Field Robotics

引用

IEEE Transactions on Field Robotics 2024年 1卷 476-526页

作者： Benjamin Morrell Kyohei Otsu Ali Agha David D. Fan Sung-Kyun Kim Muhammad Fadhil Ginting Xianmei Lei Jeffrey Edlund Seyed Fakoorian Amanda Bouman Fernando Chavez Taeyeon Kim Gustavo J. Correa Maira Saboia Angel Santamaria-Navarro Brett Lopez Boseong Kim Chanyoung Jung Mamoru Sobue Oriana Claudia Peltzer Joshua Ott Robert Trybula Thomas Touma Marcel Kaufmann Tiago Stegun Vaquero Torkom Pailevanian Matteo Palieri Yun Chang Andrzej Reinke Matthew Anderson Frederik E. T. Schöller Patrick Spieler Lillian M. Clark Avak Archanian Kenny Chen Hovhannes Melikyan Anushri Dixit Harrison Delecki Daniel Pastor Barry Ridge Nicolas Marchal Jose Uribe Sharmita Dey Kamak Ebadi Kyle Coble Alexander Nikitas Dimopoulos Vivek Thangavelu Vivek S. Varadharajan Nicholas Palomo Antoni Rosinol Arghya Chatterjee Christoforos Kanellakis Bjorn Lindqvist Micah Corah Kyle Strickland Ryan Stonebraker Michael Milano Christopher E. Denniston Sami Sahnoune Thomas Claudet Seungwook Lee Gautam Salhotra Edward Terry Rithvik Musuku Robin Schmid Tony Tran Ara Kourchians Justin Schachter Hector Azpurua Levi Resende Arash Kalantari Jeremy Nash Josh Lee Christopher Patterson Jennifer G. Blank Kartik Patath Yuki Kubo Ryan Alimo Yasin Almalioglu Aaron Curtis Jacqueline Sly Tesla Wells Nhut T. Ho Mykel Kochenderfer Giovanni Beltrame George Nikolakopoulos David Shim Luca Carlone And Joel Burdick NASA Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA Department of Mechanical and Civil Engineering California Institute of Technology Pasadena CA USA Institut de Robòtica i Informàtica Industrial CSIC-UPC Barcelona Spain Department of Aerospace Engineering Korea Advanced Institute of Science and Technology Daejeon South Korea Department of Aeronautics and Astronautics Stanford University Stanford CA USA Department of Electrical And Information Engineering Polytechnic University of Bari Bari Italy Department of Aeronautics and Astronautics Massachusetts Institute of Technology Cambridge MA USA Department of Electrical and Computer Engineering Cornell University Ithaca NY USA Department of Computer Engineering and Software Engineering École Polytechnique de Montréal Montreal QC Canada Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden Department of Mechanical Engineering California State University at Northridge Northridge CA USA Department of Earth and Planetary Sciences McGill University Montreal QC Canada Blue Marble Space Institute of Science Seattle WA USA

This article presents an appendix to the original NeBula autonomy solution developed by the Team Collaborative SubTerranean Autonomous Robots (CoSTAR), participating in the DARPA Subterranean Challenge. Specifically, this article presents extensions to NeBula’s hardware, software, and algorithmic components that focus on increasing the range and scale of the exploration environment. From the algorithmic perspective, we discuss the following extensions to the original NeBula framework: 1) large-scale geometric and semantic environment mapping; 2) an adaptive positioning system; 3) probabilistic traversability analysis and local planning; 4) large-scale partially observable Markov decision process (POMDP)-based global motion planning and exploration behavior; 5) large-scale networking and decentralized reasoning; 6) communicationaware mission planning; and 7) multimodal ground–aerial exploration *** demonstrate the application and deployment of the presented systems and solutions in various large-scale underground environments, including limestone mine exploration scenarios as well as deployment in the DARPA Subterranean challenge.

关键词： Planning state estimation Semantics NASA Hardware Three-dimensional displays Systems architecture

来源：评论

学校读者我要写书评

暂无评论

Hidden high-risky states identification from routine urban traffic

arXiv

引用

arXiv 2024年

作者： Liu, Shiyan Bai, Mingyang Guo, Shengmin Gao, Jianxi Sun, Huijun Gao, Ziyou Li, Daqing School of Reliability and Systems Engineering Beihang University Beijing100191 China State Key Laboratory of Software Development Environment Beihang University Beijing100191 China Department of Computer Science Rensselaer Polytechnic Institute TroyNY12180 United States School of Systems Science Beijing Jiaotong University Beijing100044 China

One of the core risk management tasks is to identify hidden high-risky states that may lead to system breakdown, which can provide valuable early warning knowledge. However, due to high dimensionality and nonlinear interaction embedded in large-scale complex systems like urban traffic, it remains challenging to identify hidden high-risky states from huge system state space where over 99% of possible system states are not yet visited in empirical data. Based on maximum entropy model, we infer the underlying interaction network from complicated dynamical processes of urban traffic, and construct system energy landscape. In this way, we can locate hidden high-risky states that have never been observed from real data. These states can serve as risk signals with high probability of entering hazardous minima in energy landscape, which lead to huge recovery cost. Our finding might provide insights for complex system risk management. Significance statement Hidden high-risky states are lethal system states with catastrophic consequences for large-scale complex systems. Existing methods for identifying hidden high-risky states face challenges in routine urban traffic, where over 99% of possible system states are not yet visited in empirical data. Based on large-scale traffic-speed data, we build a least-structured network entropy model that captures intricate traffic interactions. This model can generate the entire state space and help the identification of hidden high-risky states that may lead to extreme events. Our results suggest a new way to provide reliable early warning signals for system breakdown based on critical risk scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Large scale systems

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：