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9th International Conference on Social computing, Behavioral-Cultural Modeling, and Prediction and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2016

作者： Seo, Kangwon Pan, Rong Panasyuk, Aleksey School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States Air Force Research Lab RomeNY United States

ISBN: (纸本)9783319399300

Controversial topics, particularly political topics, often provoke very different emotions among different communities. By detecting and analyzing communities formed around these controversial topics we can paint a picture of how polarized a country is and how these communities evolved over time. In this research, we made use of Internet data from Twitter, one of the most popular online social media sites, to identify a controversial topic of interest and the emotions expressed towards the topic. Communities were formed based on Twitter users’ sentiments towards the topic. In addition, the network structure of these communities was utilized to reveal those Twitter users that played important roles in their respective communities. © Springer International Publishing Switzerland 2016.

关键词： Sentiment analysis

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Unfolding the Mutual Relation Between Timeliness and Scalability in Cloud Monitoring

Unfolding the Mutual Relation Between Timeliness and Scalabi...

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IEEE Symposium on Computers and Communications

作者： Guilherme da Cunha Rodrigues Rodrigo N. Calheiros Glederson Lessa dos Santos Vinicius Tavares Guimaraes Lisandro Zambenedetti Granville Liane Tarouco Rajkumar Buyya Science and Technology Sul Rio-Grandense Federal Institute of Education Charqueadas Brazil School of Computing Engineering and Mathematics Western Sydney University Sydney Australia Computer Networks Group Institute of Informatics Federal University of Rio Grande do Sul Porto Alegre Brazil Cloud Computing and Distributed Systems (CLOUDS) Laboratory School of Computing and Information Systems The University of Melbourne Melbourne Australia

Cloud computing is a suitable solution for professionals, companies, and institutions that need to have access to computational resources on demand. Clouds rely on proper management to provide such computational resources with adequate quality of service, which is established by Service Level Agreements (SLAs), to customers. In this context, cloud monitoring is a critical function to achieve such proper management. Cloud monitoring systems have to accomplish requirements to perform its functions properly, and currently, there are plenty of requirements which includes: timeliness, adaptability, comprehensiveness, and scalability. However, such requirements usually have mutual influence, which is positive or negative, among them-selves, and it has prevented the development of complete cloud monitoring solutions. This paper presents a mathematical model to predict the mutual influence between timeliness and scalability, which is a step forward in cloud monitoring because it paves the way for the development of complete monitoring solutions. It complements our previous work that identified the monitoring parameters (e.g., frequency sampling, amount of monitoring data) that influence timeliness and scalability. Evaluations present the effectiveness of the mathematical model based on a comparison of the results provided by the mathematical model and the results obtained via simulation.

关键词： Monitoring Mathematical model Topology Scalability Cloud computing Network topology Time factors

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Instructional module development system (IMODS) 16

Instructional module development system (IMODS)

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2016 ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE 2016

作者： Bansal, Srividya K. Dalrymple, Odesma School of Computing Informatics Decision Systems Engineering Arizona State University MesaAZ85212 United States Shiley-Marcos School of Engineering University of San Diego San DiegoCA United States

ISBN: (纸本)9781450342315

To ensure that future generations of engineering, science, and other technological practitioners are equipped with the required knowledge and skills to continue to innovate solutions to solve societal challenges, effective courses or instructional modules that incorporate best pedagogical and assessment practices must be designed and delivered. Science, technology, engineering and mathematics (STEM) educators typically come from STEM backgrounds and have little or no formal STEM education training. Their approaches to learning, instruction, and assessment mimic the experiences they were exposed to as students and are not necessarily informed by scholarship in the area of how people learn. The road to effective STEM instruction starts with a wellconceived and constructed plan or curriculum that includes the tight alignment of content, pedagogical approaches and assessments, around the learning objectives, and draws upon bestpractices in each of these areas. An information technology (IT) tool that can guide STEM educators through the complex task of course design development, ensure tight alignment between various components of an instructional module, and provide relevant information about research-based pedagogical and assessment strategies will be of great value. This demonstration presents a Web-based software tool called the Instructional Module Development System (IMODS) that supports these ventures and broadens the impact and reach of professional development in the scholarship of teaching and learning, particularly to STEM faculty.

关键词： Curricula

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Sequentiality of product review information provision: An information foraging perspective

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MIS Quarterly: Management Information systems 2017年第3期41卷 867-892页

作者： Li, Mengxiang Tan, Chuan-Hoo Wei, Kwok-Kee Wang, Kanliang Department of Finance and Decision Sciences School of Business Hong Kong Baptist University 34 Renfrew Road Kowloon Tong Hong Kong Department of Information Systems School of Computing National University of Singapore COM1 13 Computing Drive Singapore117417 Singapore Department of Information Systems School of Computing COM1 13 Computing Drive Singapore117417 Singapore Department of Decision Sciences School of Business National University of Singapore COM1 13 Computing Drive Singapore117417 Singapore Department of Management Science and Engineering School of Business Renmin University of China No. 59 Zhongguancun Street Haidian District Beijing100872 China

The literature highlights the importance of product review information to a consumer, but limited knowledge exists on the provision of such information in a shopping website. This research uses information foraging theory (IFT) to determine how to provide product reviews. Product reviews differ according to whether the content is mainly on product attributes (i.e., attribute-oriented product reviews) or usage experience (i.e., usage-oriented product reviews). Two empirical studies were conducted. Study 1 examined consumer information diet for product reviews using the think-aloud approach. Results showed that consumers use two different genres of product review information sequentially (1) to follow product attribute-oriented review information during the screening phase and (2) to forage for product usage-oriented review information during the evaluation phase. The findings were extended to Study 2 through a field experiment, in which different patches of product reviews were purposefully and sequentially given in accordance with the consumer information diet for product reviews. The results revealed that an online shopping website could offer varying genres of product reviews at different junctures to enhance consumers' decision-making performance. This research presents empirical evidence on the effectiveness of embedding product review information on a shopping website. This work further contributes to IFT, which is traditionally descriptive and conceptual in nature, by theorizing information provision and information-foraging behaviors of online consumers.

关键词： decision making

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The good, the bad, and the ugly: uncovering novel research opportunities in social media mining

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International Journal of Data Science and Analytics 2016年第3-4期1卷 137-143页

作者： Liu, Huan Morstatter, Fred Tang, Jiliang Zafarani, Reza School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85287-8809 United States Computer Science and Engineering Michigan State University East LansingMI48824 United States Electrical Engineering and Computer Science Syracuse University SyracuseNY13244 United States

Big data is ubiquitous and can only become bigger, which challenges traditional data mining and machine learning methods. Social media is a new source of data that is significantly different from conventional ones. Social media data are mostly user-generated, and are big, linked, and heterogeneous. We present the good, the bad and the ugly associated with the multi-faceted social media data and exemplify the importance of some original problems with real-world examples. We discuss bias in social media data, evaluation dilemma, data reduction, inferring invisible information, and big-data paradox. We illuminate new opportunities of developing novel algorithms and tools for data science. In our endeavor of employing the good to tame the bad with the help of the ugly, we deepen the understanding of ever growing and continuously evolving data and create innovative solutions with interdisciplinary and collaborative research of data science. © 2016, Springer International Publishing Switzerland.

关键词： Big data

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Tenant-centric Sub-Tenancy Architecture in Software-as-a-Service

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CAAI Transactions on Intelligence Technology 2016年第2期1卷 150-161页

作者： Wei-Tek Tsai Peide Zhong Yinong Chen School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ 85287 USA

Multi-tenancy architecture （MTA） is often used in Software-as-a-Service （SaaS） and the central idea is that multiple tenant applications can be developed using components stored in the SaaS infrastructure. Recently, MTA has been extended to allow a tenant application to have its own sub-tenants, where the tenant application acts like a SaaS infrastructure. In other words, MTA is extended to STA （Sub-Tenancy Architecture）. In STA, each tenant application needs not only to develop its own functionalities, but also to prepare an infrastructure to allow its sub-tenants to develop customized applications. This paper applies Crowdsourcing as the core to STA component in the development life cycle. In addition, to discovering adequate fit tenant developers or components to help build and compose new components, dynamic and static ranking models are proposed. Furthermore, rank computation architecture is presented to deal with the case when the number of tenants and components becomes huge. Finally, experiments are performed to demonstrate that the ranking models and the rank computation architecture work as design.

关键词： SaaS MTA STA Tenant Sub-tenant Crowdsourcing Ranking

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Empowering cortical thickness measures in clinical diagnosis of Alzheimer's disease with spherical sparse coding

Empowering cortical thickness measures in clinical diagnosis...

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IEEE International Symposium on Biomedical Imaging

作者： Jie Zhang Yonghui Fan Qingyang Li Paul M. Thompson Jieping Ye Yalin Wang School of Computing Informatics and Decision Systems Engineering Arizona State Univ.z Tempe AZ Imaging Genetics Center Institute for Neuroimaging and Informatics Univ.of Southern California Mari na del Rey CA Dept. of Computational Medicine and Bioinformatics Univ.of Michigan Ann Arbor MI

ISBN: (纸本)9781509011735

Cortical thickness estimation performed in vivo via magnetic resonance imaging (MRI) is an important technique for the diagnosis and understanding of the progression of Alzheimer's disease (AD). Directly using raw cortical thickness measures as features with Support Vector Machine (SVM) for clinical group classification only yields modest results since brain areas are not equally atrophied during AD progression. Therefore, feature reduction is generally required to retain only the most relevant features for the final classification. In this paper, a spherical sparse coding and dictionary learning method is proposed and it achieves relatively high classification results on publicly available data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) 2 dataset (N = 201) which contains structural MRI data of four clinical groups: cognitive unimpaired (CU), early mild cognitive impairment (EMCI), later MCI (LMCI) and AD. The proposed framework takes the estimated cortical thickness and the spherical parameterization computed by FreeSurfer as inputs and constructs weighted patches in the spherical parameter domain of the cortical surface. Then sparse coding is applied to the resulting surface patch features, followed by max-pooling to extract the final feature sets. Finally, SVM is employed for binary group classifications. The results show the superiority of the proposed method over other cortical morphometry systems and offer a different way to study the early identification and prevention of AD.

关键词： Dictionaries Encoding Support vector machines Harmonic analysis Thickness measurement Alzheimer's disease Image coding

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Dimension reconstruction for visual exploration of subspace clusters in high-dimensional data 9

Dimension reconstruction for visual exploration of subspace ...

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9th IEEE Pacific Visualization Symposium, PacificVis 2016

作者： Zhou, Fangfang Li, Juncai Huang, Wei Zhao, Ying Yuan, Xiaoru Liang, Xing Shi, Yang School of Information Science and Engineering Central South University China School of Software Central South University China School of EECS Peking University China School of Computing Informatics and Decision Systems Engineering Arizona State University United States

ISBN: (纸本)9781509014514

Subspace-based analysis has increasingly become the preferred method for clustering high-dimensional data. A visually interactive exploration of subspaces and clusters is a cyclic process. Every meaningful discovery will motivate users to re-search subspaces that can provide improved clustering results and reveal the relationships among clusters that can hardly coexist in the original subspaces. However, the combination of dimensions from the original subspaces is not always effective in finding the expected subspaces. In this study, we present an approach that enables users to reconstruct new dimensions from the data projections of subspaces to preserve interesting cluster information. The reconstructed dimensions are included into an analytical workflow with the original dimensions to help users construct target-oriented subspaces which clearly display informative cluster structures. We also provide a visualization tool that assists users in the exploration of subspace clusters by utilizing dimension reconstruction. Several case studies on synthetic and real-world data sets have been performed to prove the effectiveness of our approach. Lastly, further evaluation of the approach has been conducted via expert reviews. © 2016 IEEE.

关键词： Visualization

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Shape Analysis with Hyperbolic Wasserstein Distance

Shape Analysis with Hyperbolic Wasserstein Distance

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IEEE Conference on Computer Vision and Pattern Recognition

作者： Jie Shi Wen Zhang Yalin Wang School of Computing Informatics and Decision Systems Engineering Arizona State University

ISBN: (纸本)9781467388528

Shape space is an active research field in computer vision study. The shape distance defined in a shape space may provide a simple and refined index to represent a unique shape. Wasserstein distance defines a Riemannian metric for the Wasserstein space. It intrinsically measures the similarities between shapes and is robust to image noise. Thus it has the potential for the 3D shape indexing and classification research. While the algorithms for computing Wasserstein distance have been extensively studied, most of them only work for genus-0 surfaces. This paper proposes a novel framework to compute Wasserstein distance between general topological surfaces with hyperbolic metric. The computational algorithms are based on Ricciflow, hyperbolic harmonic map, and hyperbolic power Voronoi diagram and the method is general and robust. We apply our method to study human facial expression, longitudinal brain cortical morphometry with normal aging, and cortical shape classification in Alzheimer's disease (AD). Experimental results demonstrate that our method may be used as an effective shape index, which outperforms some other standard shape measures in our AD versus healthy control classification study.

关键词： shape analysis Hyperbolic Alzheimer Disease image noise Shape images computer vision AD

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Modeling and Testing Solar Power for Globally Migrating Submarine systems

Modeling and Testing Solar Power for Globally Migrating Subm...

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Oceans (Conference)

作者： Lauren Adoram-Kershner Taylor Bruce Colt Morris Rachel Holser Daniel Costa Paul G. Flikkema Michael W. Shafer Department of Mechanical Engineering Northern Arizona University School of Informatics Computing and Cyber Systems Northern Arizona University Costa Lab University of California Santa Cruz

ISBN: (纸本)9781509064298;9780692946909

Terrestrial sensor systems and platforms regularly rely on photovoltaic cells as their primary power source. With a rechargeable battery backup, solar modules provide these remote sensor systems with reliable, simple, and predictable power. For these terrestrial systems, power predictions are well established and engineers developing power budgets have multiple avenues for estimating daily average energy production. The same is not true for sensor systems in the marine environment. The lack of predictive methods for solar power is a major contributing factor to the nearly exclusive reliance on fixed charge batteries as the power source for submarine sensor systems and their plat-forms. The inability to harvest energy during a deployment limits deployment durations and data collection for these marine systems. Our group has worked to develop a method of predicting photovoltaic power production in the marine environment and recently conducted a deployment of a custom solar measurement module in order to validate and refine the model. In April of 2017 the module was deployed on a female northern elephant seal (Mirounga angustirostris) as it swam from Monterey, California, USA to Ano Nuevo, California, USA. Current-voltage solar cell characterization curves were recorded throughout the deployment. Then, based on animal telemetry records, the solar assessment model was used to predict cell performance and the results were compared here to the cell performance recorded by the datalogging unit. Agreement was found between the modeled and measured data, with variations due to assumptions concerning model inputs.

关键词： Marine Telemetry Solar Power Modeling Submerged Irradiance Biologging Tag Wildlife Energy Harvesting

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