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IEEE International Conference on Big Data

作者： Yasith Jayawardana Alexander C. Nwala Gavindya Jayawardena Jian Wu Sampath Jayarathna Michael L. Nelson C. Lee Giles Old Dominion University Norfolk VA USA Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University Bloomington IN USA Information Sciences & Technology Pennsylvania State University PA USA

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

The vastness of the web imposes a prohibitive cost on building large-scale search engines with limited resources. Crawl frontiers thus need to be optimized to improve the coverage and freshness of crawled content. In this paper, we propose an approach for modeling the dynamics of change in the web using archived copies of webpages. To evaluate its utility, we conduct a preliminary study on the scholarly web using 19,977 seed URLs of authors' homepages obtained from their Google Scholar profiles. We first obtain archived copies of these webpages from the Internet Archive (IA), and estimate when their actual updates occurred. Next, we apply maximum likelihood to estimate their mean update frequency (λ) values. Our evaluation shows that λ values derived from a short history of archived data provide a good estimate for the true update frequency in the short-term, and that our method provides better estimations of updates at a fraction of resources compared to the baseline models. Based on this, we demonstrate the utility of archived data to optimize the crawling strategy of web crawlers, and uncover important challenges that inspire future research directions.

关键词： Big Data Search engines Portable document format Frequency estimation Data models Internet History

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TheHaloMod: An online calculator for the halo model

arXiv

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arXiv 2020年

作者： Murray, Steven G. Diemer, Benedikt Chen, Zhaoting Neuhold, Anton Glenn Schnapp, Alexander Peruzzi, Tia Blevins, Daniel Engelman, Trent School of Earth and Space Exploration Arizona State University TempeAZ United States Department of Astronomy University of Maryland College ParkMD20742 United States Jodrell Bank Centre for Astrophysics School of Physics and Astronomy University of Manchester ManchesterM13 9PL United Kingdom ASU School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States

The halo model is a successful framework for describing the distribution of matter in the Universe – from weak lensing observables to galaxy n-point correlation functions. We review the basic formulation of the halo model and several of its components in the context of galaxy 2-point statistics, developing a coherent framework for its application. We use this framework to motivate the presentation of a new python tool for simple and efficient calculation of halo model quantities, and their extension to galaxy statistics via a halo occupation distribution, called halomod. This tool is efficient, simple to use, comprehensive and importantly provides a great deal of flexibility in terms of custom extensions. This python tool is complemented by a new web-application at https://*** that supports the generation of many halo model quantities directly from the browser – useful for educators, students, theorists and observers. Copyright © 2020, The Authors. All rights reserved.

关键词： Python

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A boundary-enhanced supervoxel method for extraction of road edges in MLS point clouds 24

A boundary-enhanced supervoxel method for extraction of road...

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2020 24th ISPRS Congress - Technical Commission I

作者： Sha, Z. Chen, Y. Li, W. Wang, C. Nurunnabi, A. Li, J. Fujian Key Laboratory of Sensing and Computing for Smart Cities School of Informatics Xiamen University Xiamen Fujian361005 China Faculty of Science Technology and Communication University of Luxembourg Belval Campus 2 avenue de l'Université Esch-sur-AlzetteL-4365 Luxembourg Departments of Geography and Environmental Management and Systems Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Road extraction plays a significant role in production of high definition maps (HD maps). This paper presents a novel boundary-enhanced supervoxel segmentation method for extracting road edge contours from MLS point clouds. The proposed method first leverages normal feature judgment to obtain 3D point clouds global geometric information, then clusters points according to an existing method with global geometric information to enhance the boundaries. Finally, it utilizes the neighbor spatial distance metric to extract the contours and drop out existing outliers. The proposed method is tested on two datasets acquired by a RIEGL VMX-450 MLS system that contain the major point cloud scenes with different types of road boundaries. The experimental results demonstrate that the proposed method provides a promising solution for extracting contours efficiently and completely. Results show that the precision values are 1.5 times higher and approximately equal than the other two existing methods when the recall value is 0 for both tested two road datasets. © 2020 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives.

关键词： Extraction

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Model architecture-oriented combat system effectiveness simulation based on MDE

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Journal of systems engineering and Electronics 2017年第5期28卷 900-922页

作者： Yonglin Lei Ning Zhu Jian Yao Hessam Sarjoughian Weiping Wang School of Information Systems and Management National University of Defense Technology School of Computing Informatics and Decision Systems Engineering Arizona State University

Combat system effectiveness simulation (CSES) is a special type of complex system simulation. Three non-functional requirements (NFRs), i.e. model composability, domain specific modeling, and model evolvability, are gaining higher priority from CSES users when evaluating different modeling methodologies for CSES. Traditional CSES modeling methodologies are either domain-neutral (lack of domain characteristics consideration and limited support for model composability) or domain-oriented (lack of openness and evolvability) and fall short of the three NFRs. Inspired by the concept of architecture in systems engineering and software engineering fields, we extend it into a concept of model architecture for complex simulation systems, and propose a model architecture-oriented modeling methodology in which the model architecture plays a central role in achieving the three NFRs. Various model-driven engineering (MDE) approaches and technologies, including simulation modeling platform (SMP), unified modeling language (UML), domain specific modeling (DSM), eclipse modeling framework (EMF), graphical modeling framework (GMF), and so forth, are applied where possible in representing the CSES model architecture and its components' behaviors from physical and cognitive domain aspects. A prototype CSES system, called weapon effectiveness simulation system (WESS), and a non-trivial air-combat simulation example are presented to demonstrate the methodology.

关键词： combat system effectiveness simulation (CSES) model architecture model-driven engineering (MDE) simulation modeling platform (SMP) domain specific modeling (DSM) weapon effectiveness simulation system (WESS)

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BrainTracker: mHealth app for Remote Assessment of Pediatric Epilepsy and Comorbidities

BrainTracker: mHealth app for Remote Assessment of Pediatric...

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IEEE International Conference on Serious Games and Applications for Health (SeGAH)

作者： Kevin Gary Mason Cole Dipak Purbey Meng-Jung Lin Madison Berl Chintan Jarawala Sheetal Sohoni Krishnakantha Jayashree Chandrashekhar School of Computing Informatics & Decision Systems Engineering The Ira A. Fulton Schools of Engineering Arizona State University Mesa AZ USA Division of Pediatric Neuropsychology The Children's Research Intitute Children's National Hospital 111 Michigan Avenue NW Washington D.C. USA Cloud and AI Engineering Microsoft Seattle WA USA Graphic Information Technology The Polytechnic School The Ira A. Fulton Schools of Engineering Arizona State University Mesa AZ USA

ISBN: (数字)9781728190426

ISBN: (纸本)9781728190433

Children with chronic pediatric disorders are at increased risk for cognitive and psychosocial problems, but screening and monitoring for them is challenged by access to care and availability of sensitive psychosocial measures. We have developed a mobile health application (mHealth app) with tasks and surveys that target areas of functioning that are commonly at risk in children across medical disorders including attention, processing speed, socialization, anxiety, and depression. The design and implementation of this mHealth solution addresses several technical challenges in providing a usable platform for patients, parents and care providers. The end product is a point-of-care instrument that can quickly, easily, and reliably identify children that require additional assessment and/or intervention for the cognitive and psychosocial symptoms that they are at risk for, and rigorously quantify the diagnostic efficiency of mobile technology compared to standard clinical evaluation.

关键词： Task analysis Epilepsy Monitoring Portals Pediatrics Fingers Atmospheric measurements

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On the importance of AI research beyond disciplines

arXiv

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arXiv 2023年

作者： Dignum, Virginia Casey, Donal Cerratto-Pargman, Teresa Dignum, Frank Fantasia, Valentina Formark, Bodil Hammarfelt, Björn Holmberg, Gunnar Holzapfel, André Larsson, Stefan Lagerkvist, Amanda Lakemond, Nicolette Lindgren, Helena Lorig, Fabian Marusic, Ana Rahm, Lina Razmetaeva, Yulia Sikström, Sverker Tatar, Kıvanç Tucker, Jason Department of Computing Science Umeå University Sweden Department of Philosophy Lund University Sweden Department of Business Studies Uppsala University Sweden Dept. of Computer and Systems Sciences Stockholm University Sweden Umeå University Sweden Swedish School of Library and Information Science University of Borås Sweden Department of Management and Engineering Linköping University Sweden Division of Media Technology and Interaction Design KTH Sweden Department of Technology and Society Lund University Sweden Department of Informatics and Media Uppsala University Sweden Department of Computer Science and Media Technology Malmö University Sweden University of Split School of Medicine Split Croatia Department of Philosophy and History KTH Sweden Department of Law Uppsala University Sweden Department of Psychology Lund University Sweden Chalmers University of Technology Sweden Department of Global Political Studies Malmö University Sweden

As the impact of AI on various scientific fields is increasing, it is crucial to embrace interdisciplinary knowledge to understand the impact of technology on society. The goal is to foster a research environment beyond disciplines that values diversity and creates, critiques and develops new conceptual and theoretical frameworks. Even though research beyond disciplines is essential for understanding complex societal issues and creating positive impact it is notoriously difficult to evaluate and is often not recognized by current academic career progression. The motivation for this paper is to engage in broad discussion across disciplines and identify guiding principles fir AI research beyond disciplines in a structured and inclusive way, revealing new perspectives and contributing to societal and human wellbeing and sustainability. © 2023, CC BY.

关键词： Sustainable development

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Key principles for workforce upskilling via online learning: A learning analytics study of a professional course in additive manufacturing

arXiv

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arXiv 2020年

作者： Peppler, Kylie Huang, Joey Richey, Michael C. Ginda, Michael Börner, Katy Quinlan, Haden Hart, A. John Department of Informatics University of California IrvineCA United States Associate Technical Fellow Boeing Company United States Department of Intelligent Systems Engineering Luddy School of Informatics Computing and Engineering Indiana University Bloomington United States Department of Mechanical Engineering and Center for Additive and Digital Advanced Production Technologies Massachusetts Institute of Technology CambridgeMA United States

Effective adoption of online platforms for teaching, learning, and skill development is essential to both academic institutions and workplaces. Adoption of online learning has been abruptly accelerated by COVID-19 pandemic, drawing attention to research on pedagogy and practice for effective online instruction. Moreover, online learning requires a multitude of skills and resources spanning from learning management platforms to interactive assessment tools, combined with multimedia content, presenting challenges to many instructors and organizations. This study focuses on ways that learning sciences and visual learning analytics can be used to design, and to improve, online workforce training in advanced manufacturing. University faculty and industry experts, educational researchers, and specialists in data analysis and visualization collaborated to study the performance of a cohort of 900 professionals enrolled in an online training course focused on additive manufacturing The course was offered through MIT xPRO, MIT Open Learning’s professional learning organization, and hosted in a dedicated instance of the massive open online course (MOOC) edX platform. This study combines learning objective analysis and visual learning analytics to examine the relationships among learning trajectories, engagement, and performance. The results demonstrate how visual learning analytics can be used for targeted course modification, and interpretation of learner engagement and performance, such as by more direct mapping of assessments to learning objectives, and to expected and actual time needed to complete each segment of the course. The study also emphasizes broader strategies for course designers and instructors to align course assignments, learning objectives, and assessment measures with learner needs and interests, and argues for a synchronized data infrastructure to facilitate effective just-in-time learning and continuous improvement of online courses. Copyright © 2020, The

关键词： 3D printers

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Using operative features to identify surgical complexity: a case in breast surgery practice

Using operative features to identify surgical complexity: a ...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Derya Kilinc Narges Shahraki Esma S. Gel Amy C. Degnim Tanya Hoskin Mustafa Y. Sir Kalyan S. Pasupathy Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery Mayo Clinic Rochester MN USA School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ USA Department of Surgery Mayo Clinic Rochester MN USA Department of Health Science and Research Mayo Clinic Rochester MN USA

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Increasing workload is one of the main problems that surgical practices face. This increase is not only due to the increasing demand volume but also due to increasing case complexity. This raises the question on how to measure and predict the complexity to address this issue. Predicting surgical duration is critical to parametrize surgical complexity, improve surgeon satisfaction by avoiding unexpected overtime, and improve operation room utilization. Our objective is to utilize the historical data on surgical operations to obtain complexity groups and use this groups to improve *** study first leverages expert opinion on the surgical complexity to identify surgical groups. Then, we use a tree-based method on a large retrospective dataset to identify similar complexity groups by utilizing the surgical features and using surgical duration as a response variable. After obtaining the surgical groups by using two methods, we statistically compare expert-based grouping with the data-based grouping. This comparison shows that a tree-based method can provide complexity groups similar to the ones generated by an expert by using features that are available at the time of surgical listing. These results suggest that one can take advantage of available data to provide surgical duration predictions that are data-driven, evidence-based, and practically relevant.

关键词： Surgery Complexity theory Breast Manganese Regression tree analysis Cancer Planning

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Multivariate Metrics of Normal and Anomalous Network Behaviors

Multivariate Metrics of Normal and Anomalous Network Behavio...

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IFIP/IEEE International Symposium on Integrated Network Management

作者： Nong Ye Douglas Montgomery Kevin Mills Mark Carson School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe Arizona USA ITL/Advanced Network Technologies Division National Institute of Standards and Technology Gaithersburg Maryland USA

ISBN: (纸本)9781728106182;9783903176157

Detecting network anomalies is a fundamental part of day to day operations for Internet Service Providers and enterprises to maintain the efficiency and reliability of computer networks. Network anomaly detection is based on data characteristics of normal and anomalous network behaviors. Although many existing studies report univariate data characteristics of normal and anomalous network behaviors, there are few studies on multivariate data characteristics of normal and anomalous network behaviors. The goal of this study is to investigate multivariate data characteristics of normal and anomalous network behaviors using the Partial-Value Association Discovery (PVAD) algorithm. This paper illustrates the use of the PVAD algorithm to analyze network flow data of a medium size enterprise under the normal condition and an anomalous condition and reveal multivariate data characteristics of the normal and anomalous network flows in the form of multivariate data associations.

关键词： Security Anomaly detection IP networks Standards Internet of Things

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Modulated Predictive Voltage Control of a Four-Leg Inverter with Fixed Switching Frequency

Modulated Predictive Voltage Control of a Four-Leg Inverter ...

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Power Electronic and Drive systems and Technologies Conference (PEDSTC)

作者： Venkata Yaramasu Alexander Dahlmann Apparao Dekka Marco Rivera Tomislav Dragicevic Jose Rodriguez School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff USA Lakehead University Thunder Bay Canada Faculty of Engineering Universidad de Talca Curico Chile Aalborg University Aalborg Denmark Faculty of Engineering Universidad Andres Bello Santiago Chile

ISBN: (数字)9781728158495

ISBN: (纸本)9781728158501

In this paper, a novel modulated model predictive voltage control method is proposed for a two-level four-leg inverter with an output LC filter. To decouple the dynamics of state variables in an output LC filter and to improve the load voltage quality, the capacitor voltages and inductor currents are controlled simultaneously. The proposed controller predicts the state variables using the discrete-time model of LC-filtered four-leg inverter for 16 voltage vectors and evaluates them by a dual-objective cost function. The active and zero voltage vectors corresponding to the lowest cost value are realized by the three-dimensional space vector modulation, resulting in fast transient response, low steady-state error, and harmonic components centered around the fixed switching frequency. The proposed method's feasibility is confirmed through MATLAB simulation studies under balanced/unbalanced reference voltages and nonlinear loads. The performance comparison of proposed controller with the classical predictive voltage control is presented considering the transient and steady-state operating conditions.

关键词： discrete time systems electric current control load regulation Matlab nonlinear control systems power capacitors power harmonic filters predictive control PWM invertors switching convertors transient response voltage control Voltage regulation Power harmonic filters load control Power capacitors Pulse width modulation inverters electric current control discrete time systems switching converters Nonlinear control systems TRANSIENT RESPONSE MATLAB Predictive control LC FILTERS Fixed State variable

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