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Missouri University of Science and Technology

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Articles 661 - 690 of 1938

Full-Text Articles in Computer Sciences

Learning Curve Analysis Using Intensive Longitudinal And Cluster-Correlated Data, Xiao Zhong, Zeyi Sun, Haoyi Xiong, Neil Heffernan, Md Monirul Islam Nov 2017

Learning Curve Analysis Using Intensive Longitudinal And Cluster-Correlated Data, Xiao Zhong, Zeyi Sun, Haoyi Xiong, Neil Heffernan, Md Monirul Islam

Engineering Management and Systems Engineering Faculty Research & Creative Works

Intensive longitudinal and cluster-correlated data (ILCCD) can be generated in any situation where numerical or categorical characteristics of multiple individuals or study units are observed and measured at tens, hundreds, or thousands of occasions. The spacing of measurements in time for each individual can be regular or irregular, fixed or random, and the number of characteristics measured at each occasion may be few or many. Such data can also arise in situations involving continuous-time measurements of recurrent events. Generalized linear models (GLMs) are usually considered for the analysis of correlated non-normal data, while multivariate analysis of variance (MANOVA) is another …

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Design The Capacity Of Onsite Generation System With Renewable Sources For Manufacturing Plant, Xiao Zhong, Md Monirul Islam, Haoyi Xiong, Zeyi Sun Nov 2017

Design The Capacity Of Onsite Generation System With Renewable Sources For Manufacturing Plant, Xiao Zhong, Md Monirul Islam, Haoyi Xiong, Zeyi Sun

Computer Science Faculty Research & Creative Works

The utilization of onsite generation system with renewable sources in manufacturing plants plays a critical role in improving the resilience, enhancing the sustainability, and bettering the cost effectiveness for manufacturers. When designing the capacity of onsite generation system, the manufacturing energy load needs to be met and the cost for building and operating such onsite system with renewable sources are two critical factors need to be carefully quantified. Due to the randomness of machine failures and the variation of local weather, it is challenging to determine the energy load and onsite generation supply at different time periods. In this paper, …

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The Interval Grey Numbers Ranking Based On Risk Preferences, Zhaobin Li, Zhuo Zhang, Jian Liu, Shuai Zhang Oct 2017

The Interval Grey Numbers Ranking Based On Risk Preferences, Zhaobin Li, Zhuo Zhang, Jian Liu, Shuai Zhang

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, a new method for ranking interval grey numbers to address the challenge in multi-criteria decision-making problems with interval grey numbers has been proposed. This new method involves the risk preferences of decision makers. First, we propose a new method to rank the interval grey numbers by comparing the possibility degree or whitened value. Second, we classify the decision makers into three different types according to their risk preferences then we establish the corresponding risk preference assumptions to solve the problem that different interval grey numbers with the same possibility degree or whitened value. Finally, we use a …

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Realistic Models For Characterizing The Performance Of Unmanned Aerial Vehicles, Ken Goss, Riccardo Musmeci, Simone Silvestri Sep 2017

Realistic Models For Characterizing The Performance Of Unmanned Aerial Vehicles, Ken Goss, Riccardo Musmeci, Simone Silvestri

Computer Science Faculty Research & Creative Works

Unmanned Aerial Vehicles (UAVs) are increasingly being adopted for military and civilian applications. UAVs available on the market are well known to be resource constrained, especially in terms of available energy. As a result, it is very challenging to predict the critical performance characteristics of a UAV, such as flight time or the ability of a UAV to complete a mission, given the system parameters. Nevertheless, such predictions would have several benefits, such as improving the effectiveness of mission planners and optimization algorithms in general, as well as enabling researchers to perform more realistic simulations. The goal of this paper …

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Minimal Coflow Routing And Scheduling In Openflow-Based Cloud Storage Area Networks, Chui Hui Chiu, Dipak Kumar Singh, Qingyang Wang, Kisung Lee, Seung Jong Park Sep 2017

Minimal Coflow Routing And Scheduling In Openflow-Based Cloud Storage Area Networks, Chui Hui Chiu, Dipak Kumar Singh, Qingyang Wang, Kisung Lee, Seung Jong Park

Computer Science Faculty Research & Creative Works

Researches affirm that coflow scheduling/routing substantially shortens the average application inner communication time in data center networks (DCNs). The commonly desirable critical features of existing coflow scheduling/routing framework includes (1) coflow scheduling, (2) coflow routing, and (3) per-flow rate-limiting. However, to provide the 3 features, existing frameworks require customized computing frameworks, customized operating systems, or specific external commercial monitoring frameworks on software-defined networking (SDN) switches. These requirements defer or even prohibit the deployment of coflow scheduling/routing in production DCNs. In this paper, we design a coflow scheduling and routing framework, MinCOF which has minimal requirements on hosts and switches for …

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Augmenting Amdahl's Second Law: A Theoretical Model To Build Cost-Effective Balanced Hpc Infrastructure For Data-Driven Science, Arghya Kusum Das, Jaeki Hong, Sayan Goswami, Richard Platania, Kisung Lee, Wooseok Chang, Seung Jong Park, Ling Liu Sep 2017

Augmenting Amdahl's Second Law: A Theoretical Model To Build Cost-Effective Balanced Hpc Infrastructure For Data-Driven Science, Arghya Kusum Das, Jaeki Hong, Sayan Goswami, Richard Platania, Kisung Lee, Wooseok Chang, Seung Jong Park, Ling Liu

Computer Science Faculty Research & Creative Works

High-performance analysis of big data demands more computing resources, forcing similar growth in computation cost. So, the challenge to the HPC system designers is providing not only high performance but also high performance at lower cost. For high performance yet cost-effective cyberinfrastructure, we propose a new system model augmenting Amdahl's second law for balanced system to optimize price-performance-ratio. We express the optimal balance among CPU-speed, I/O-bandwidth and DRAM-size (i.e., Amdahl's I/O-and memory-number) in terms of application characteristics and hardware cost. Considering Xeon processor and recent hardware prices, we showed that a system needs almost 0.17GBPS I/O-bandwidth and 3GB DRAM per …

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Coflourish: An Sdn-Assisted Coflow Scheduling Framework For Clouds, Chui Hui Chiu, Dipak Kumar Singh, Qingyang Wang, Seung Jong Park Sep 2017

Coflourish: An Sdn-Assisted Coflow Scheduling Framework For Clouds, Chui Hui Chiu, Dipak Kumar Singh, Qingyang Wang, Seung Jong Park

Computer Science Faculty Research & Creative Works

Existing coflow scheduling frameworks effectively shorten communication time and completion time of cluster applications. However, existing frameworks only consider available bandwidth on hosts and overlook congestion in the network when making scheduling decisions. Through extensive simulations using the realistic workload probability distribution from Facebook, we observe the performance degradation of the state-of-the-art coflow scheduling framework, Varys, in the cloud environment on a shared data center network (DCN) because of the lack of network congestion information. We propose Coflourish, the first coflow scheduling framework that exploits the congestion feedback assistances from the software-defined-networking (SDN)-enabled switches in the networks for available bandwidth …

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Defining Consciousness, Adam Bateman Aug 2017

Defining Consciousness, Adam Bateman

Missouri S&T’s Peer to Peer

A researcher trying to develop a conscious artificial intelligence or examine consciousness in plants would be completely unable to do so without first obtaining a clear, concise, and global definition. This idea is what originally inspired my research. The main method of research to be used will be to thoroughly examine scholarly articles pertaining to consciousness and different theories of the mind. After gathering data and different ideas, I will create a definition that is plausible, and is optimized in terms of being useful to researchers. Currently, the issue is that there are an incredible amount of mental features that …

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Biosensors For Cancer Detection Applications, Shannon Griffin Aug 2017

Biosensors For Cancer Detection Applications, Shannon Griffin

Missouri S&T’s Peer to Peer

Cancer is one of the most deadly diseases, and current detection options are ineffective. Recently, a large amount of research has been conducted for the development of biosensors able to detect cancer biomarkers. Many biosensors have been created for cancer detecting purposes. I examined literature reviews outlining current biosensing methods. These reviews provided an overview of the sensing techniques that are currently in existence as well as evaluations of their effectiveness. I also read experimental reports that outline the construction of biosensors fabricated in laboratories and the results of their testings. These papers help to showcase the feasibility and effectiveness …

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Exploring Potential Flaws And Dangers Involving Machine Learning Technology, David Nicholas Skoff Aug 2017

Exploring Potential Flaws And Dangers Involving Machine Learning Technology, David Nicholas Skoff

Missouri S&T’s Peer to Peer

This paper seeks to explore the ways in which machine learning and AI may influence the world in the future and the potential for the technology to be misused or exploited. In 1959 Arthur Samuel defined machine learning as “the field of study that gives computers the ability to learn without being explicitly programmed” (Munoz). This paper will also seek to find out if there is merit to the current worry that robots will take over some jobs based in cognitive abilities. In the past, a human was required to perform these jobs, but with the rise of more complex …

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Network Security: Internet Protocol Version Six Security, Hannah Reinbolt Aug 2017

Network Security: Internet Protocol Version Six Security, Hannah Reinbolt

Missouri S&T’s Peer to Peer

It is no secret that the pool of public internet addresses available with Internet Protocol version Four (IPv4) is gone. (Morphy, 2011) Thus the migration to the more roomy Internet Protocol version Six (IPv6) has begun. This migration is a complex process including different security procedures and updates that require time and knowledge. This paper will dive into scientific writings, with databases like Scopus and IEEE, about various security risks in the IPv6 protocol such as tunneling practices, router issues and issues with Internet Protocol Security (IPsec). This paper will overview security practices to better clarify common vulnerabilities in IPv6. …

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Automated Breast Cancer Diagnosis Using Deep Learning And Region Of Interest Detection (Bc-Droid), Richard Platania, Jian Zhang, Shayan Shams, Kisung Lee, Seungwon Yang, Seung Jong Park Aug 2017

Automated Breast Cancer Diagnosis Using Deep Learning And Region Of Interest Detection (Bc-Droid), Richard Platania, Jian Zhang, Shayan Shams, Kisung Lee, Seungwon Yang, Seung Jong Park

Computer Science Faculty Research & Creative Works

Detection of suspicious regions in mammogram images and the subsequent diagnosis of these regions remains a challenging problem in the medical world. There still exists an alarming rate of misdiagnosis of breast cancer. This results in both over treatment through incorrect positive diagnosis of cancer and under treatment through overlooked cancerous masses. Convolutional neural networks have shown strong applicability to various image datasets, enabling detailed features to be learned from the data and, as a result, the ability to classify these images at extremely low error rates. In order to overcome the difficulty in diagnosing breast cancer from mammogram images, …

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Gaslight: A Comprehensive Fuzzing Architecture For Memory Forensics Frameworks, Andrew Case, Arghya Kusum Das, Seung Jong Park, J. (Ram) Ramanujam, Golden G. Richard Aug 2017

Gaslight: A Comprehensive Fuzzing Architecture For Memory Forensics Frameworks, Andrew Case, Arghya Kusum Das, Seung Jong Park, J. (Ram) Ramanujam, Golden G. Richard

Computer Science Faculty Research & Creative Works

Memory forensics is now a standard component of digital forensic investigations and incident response handling, since memory forensic techniques are quite effective in uncovering artifacts that might be missed by traditional storage forensics or live analysis techniques. Because of the crucial role that memory forensics plays in investigations and because of the increasing use of automation of memory forensics techniques, it is imperative that these tools be resilient to memory smear and deliberate tampering. Without robust algorithms, malware may go undetected, frameworks may crash when attempting to process memory samples, and automation of memory forensics techniques is difficult. In this …

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On Critical Service Recovery After Massive Network Failures, Novella Bartolini, Stefano Ciavarella, Thomas F. La Porta, Simone Silvestri Aug 2017

On Critical Service Recovery After Massive Network Failures, Novella Bartolini, Stefano Ciavarella, Thomas F. La Porta, Simone Silvestri

Computer Science Faculty Research & Creative Works

This paper addresses the problem of efficiently restoring sufficient resources in a communications network to support the demand of mission critical services after a large-scale disruption. We give a formulation of the problem as a mixed integer linear programming and show that it is NP-hard. We propose a polynomial time heuristic, called iterative split and prune (ISP) that decomposes the original problem recursively into smaller problems, until it determines the set of network components to be restored. ISP's decisions are guided by the use of a new notion of demand-based centrality of nodes. We performed extensive simulations by varying the …

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Evaluation Of Deep Learning Frameworks Over Different Hpc Architectures, Shayan Shams, Richard Platania, Kisung Lee, Seung Jong Park Jul 2017

Evaluation Of Deep Learning Frameworks Over Different Hpc Architectures, Shayan Shams, Richard Platania, Kisung Lee, Seung Jong Park

Computer Science Faculty Research & Creative Works

Recent advances in deep learning have enabled researchers across many disciplines to uncover new insights about large datasets. Deep neural networks have shown applicability to image, time-series, textual, and other data, all of which are available in a plethora of research fields. However, their computational complexity and large memory overhead requires advanced software and hardware technologies to train neural networks in a reasonable amount of time. To make this possible, there has been an influx in development of deep learning software that aim to leverage advanced hardware resources. In order to better understand the performance implications of deep learning frameworks …

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Cnn Based 3d Facial Expression Recognition Using Masking And Landmark Features, Huiyuan Yang, Lijun Yin Jul 2017

Cnn Based 3d Facial Expression Recognition Using Masking And Landmark Features, Huiyuan Yang, Lijun Yin

Computer Science Faculty Research & Creative Works

Automatically recognizing facial expression is an important part for human-machine interaction. In this paper, we first review the previous studies on both 2D and 3D facial expression recognition, and then summarize the key research questions to solve in the future. Finally, we propose a 3D facial expression recognition (FER) algorithm using convolutional neural networks (CNNs) and landmark features/masks, which is invariant to pose and illumination variations due to the solely use of 3D geometric facial models without any texture information. The proposed method has been tested on two public 3D facial expression databases: BU-4DFE and BU-3DFE. The results show that …

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Homogenization Of Plastic Deformation In Heterogeneous Lamella Structures, Rui Yuan, Irene J. Beyerlein, Caizhi Zhou Jul 2017

Homogenization Of Plastic Deformation In Heterogeneous Lamella Structures, Rui Yuan, Irene J. Beyerlein, Caizhi Zhou

Materials Science and Engineering Faculty Research & Creative Works

It has been shown that unlike its constituent nanocrystalline (NC) phase, a heterogeneous lamella (HL) composite comprising NC and coarse-grain layers exhibits greatly improved ductility. To understand the origin of this enhancement, we present a 3D discrete dislocation, crystal plasticity finite element model to study the development of strains across this microstructure. Here we show that the HL structure homogenizes the plastic strains in the NC layer, weakening the effect of strain concentrations. These findings can provide valuable insight into the effects of material length scales on material instabilities, which is needed to design heterogeneous structures with superior properties.

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Automatic Privacy Prediction To Accelerate Social Image Sharing, Zhenzhong Kuang, Zongmin Li, Dan Lin, Jianping Fan Jun 2017

Automatic Privacy Prediction To Accelerate Social Image Sharing, Zhenzhong Kuang, Zongmin Li, Dan Lin, Jianping Fan

Computer Science Faculty Research & Creative Works

The manual process for privacy setting could be very time-consuming and challenging for common users. By assuming that there are hidden correlations between the visual properties of images (i.e., visual features) or object classes and the privacy settings for image sharing, an effective algorithm is developed in this paper to achieve automatic prediction of image privacy, so that the best-matching privacy setting can be recommended automatically for each single image being shared. Our algorithm for automatic image privacy prediction contains two approaches: (a) feature-based approach by learning more representative deep features and discriminative classifier for assigning each single image being …

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Energy-Efficient Multi-Core Scheduling For Real-Time Dag Tasks, Zhishan Guo, Ashikahmed Bhuiyan, Abusayeed Saifullah, Nan Guan, Haoyi Xiong Jun 2017

Energy-Efficient Multi-Core Scheduling For Real-Time Dag Tasks, Zhishan Guo, Ashikahmed Bhuiyan, Abusayeed Saifullah, Nan Guan, Haoyi Xiong

Computer Science Faculty Research & Creative Works

In this work, we study energy-aware real-time scheduling of a set of sporadic Directed Acyclic Graph (DAG) tasks with implicit deadlines. While meeting all real-time constraints, we try to identify the best task allocation and execution pattern such that the average power consumption of the whole platform is minimized. To the best of our knowledge, this is the first work that addresses the power consumption issue in scheduling multiple DAG tasks on multi-cores and allows intra-task processor sharing. We first adapt the decomposition-based framework for federated scheduling and propose an energy-sub-optimal scheduler. Then we derive an approximation algorithm to identify …

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Autonomous Mobile Sensor Placement In Complex Environments, Novella Bartolini, Tiziana Calamoneri, Stefano Ciavarella, Thomas La Porta, Simone Silvestri May 2017

Autonomous Mobile Sensor Placement In Complex Environments, Novella Bartolini, Tiziana Calamoneri, Stefano Ciavarella, Thomas La Porta, Simone Silvestri

Computer Science Faculty Research & Creative Works

In this article, we address the problem of autonomously deploying mobile sensors in an unknown complex environment. In such a scenario, mobile sensors may encounter obstacles or environmental sources of noise, so that movement and sensing capabilities can be significantly altered and become anisotropic. Any reduction of device capabilities cannot be known prior to their actual deployment, nor can it be predicted. We propose a new algorithm for autonomous sensor movements and positioning, called DOMINO (DeplOyment of Mobile Networks with Obstacles). Unlike traditional approaches, DOMINO explicitly addresses these issues by realizing a grid-based deployment throughout the Area of Interest (AoI) …

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A Deep Learning Framework For Automated Vesicle Fusion Detection, Haohan Li, Zhaozheng Yin, Yingke Xu Apr 2017

A Deep Learning Framework For Automated Vesicle Fusion Detection, Haohan Li, Zhaozheng Yin, Yingke Xu

Computer Science Faculty Research & Creative Works

Quantitative analysis of vesicle-plasma membrane fusion events in the fluorescence microscopy, has been proven to be important in the vesicle exocytosis study. In this paper, we present a framework to automatically detect fusion events. First, an iterative searching algorithm is developed to extract image patch sequences containing potential events. Then, we propose an event image to integrate the critical image patches of a candidate event into a single-image joint representation as the input to Convolutional Neural Networks (CNNs). According to the duration of candidate events, we design three CNN architectures to automatically learn features for the fusion event classification. Compared …

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Early Detection Of Diseases Using Electronic Health Records Data And Covariance-Regularized Linear Discriminant Analysis, Jiang Bian, Laura E. Barnes, Guanling Chen, Haoyi Xiong Apr 2017

Early Detection Of Diseases Using Electronic Health Records Data And Covariance-Regularized Linear Discriminant Analysis, Jiang Bian, Laura E. Barnes, Guanling Chen, Haoyi Xiong

Computer Science Faculty Research & Creative Works

The availability of Electronic Health Records (EHR) in health care settings provides terrific opportunities for early detection of patients' potential diseases. While many data mining tools have been adopted for EHR-based disease early detection, Linear Discriminant Analysis (LDA) is one of the most widely used statistical prediction methods. To improve the performance of LDA for early detection of diseases, we proposed to leverage CRDA - Covariance-Regularized LDA classifiers on top of diagnosis-frequency vector data representation. Specifically, CRDA employs a sparse precision matrix estimator derived based on graphical lasso to boost the accuracy of LDA classifiers. Algorithm analysis demonstrates that the …

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Inelastic Rate Coefficients For Collisions Of C₆Hˉ With H₂ And He, Kyle M. Walker, François Lique, Fabien Dumouchel, Richard Dawes Apr 2017

Inelastic Rate Coefficients For Collisions Of C₆Hˉ With H₂ And He, Kyle M. Walker, François Lique, Fabien Dumouchel, Richard Dawes

Chemistry Faculty Research & Creative Works

The recent detection of anions in the interstellar medium has shown that they exist in a variety of astrophysical environments -- circumstellar envelopes, cold dense molecular clouds and star-forming regions. Both radiative and collisional processes contribute to molecular excitation and de-excitation in these regions so that the ‘local thermodynamic equilibrium’ approximation, where collisions cause the gas to behave thermally, is not generally valid. Therefore, along with radiative coefficients, collisional excitation rate coefficients are needed to accurately model the anionic emission from these environments. We focus on the calculation of state-to-state rate coefficients of the C6H- molecule in …

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Using Mobile Sensing To Test Clinical Models Of Depression, Social Anxiety, State Affect, And Social Isolation Among College Students, Philip I. Chow, Karl Fua, Yu Huang, Wesley Bonelli, Haoyi Xiong, Laura E. Barnes, Bethany A. Teachman Mar 2017

Using Mobile Sensing To Test Clinical Models Of Depression, Social Anxiety, State Affect, And Social Isolation Among College Students, Philip I. Chow, Karl Fua, Yu Huang, Wesley Bonelli, Haoyi Xiong, Laura E. Barnes, Bethany A. Teachman

Computer Science Faculty Research & Creative Works

Background: Research in psychology demonstrates a strong link between state affect (moment-to-moment experiences of positive or negative emotionality) and trait affect (e.g., relatively enduring depression and social anxiety symptoms), and a tendency to withdraw (e.g., spending time at home). However, existing work is based almost exclusively on static, self-reported descriptions of emotions and behavior that limit generalizability. Despite adoption of increasingly sophisticated research designs and technology (e.g., mobile sensing using a global positioning system [GPS]), little research has integrated these seemingly disparate forms of data to improve understanding of how emotional experiences in everyday life are associated with time spent …

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Hadoop-Based Replica Exchange Over Heterogeneous Distributed Cyberinfrastructures, Richard Platania, Shayan Shams, Chui Hui Chiu, Nayong Kim, Joohyun Kim, Seung Jong Park Feb 2017

Hadoop-Based Replica Exchange Over Heterogeneous Distributed Cyberinfrastructures, Richard Platania, Shayan Shams, Chui Hui Chiu, Nayong Kim, Joohyun Kim, Seung Jong Park

Computer Science Faculty Research & Creative Works

We present Hadoop-based replica exchange (HaRE), a Hadoop-based implementation of the replica exchange scheme developed primarily for replica exchange statistical temperature molecular dynamics, an example of a large-scale, advanced sampling molecular dynamics simulation. By using Hadoop as a framework and the MapReduce model for driving replica exchange, an efficient task-level parallelism is introduced to replica exchange statistical temperature molecular dynamics simulations. In order to demonstrate this, we investigate the performance of our application over various distributed cyberinfrastructures (DCI), including several high-performance computing systems, our cyberinfrastructure for reconfigurable optical networks testbed, the global environment for network innovations testbed, and the CloudLab …

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Daehr: A Discriminant Analysis Framework For Electronic Health Record Data And An Application To Early Detection Of Mental Health Disorders, Haoyi Xiong, Jinghe Zhang, Yu Huang, Kevin Leach, Laura E. Barnes Feb 2017

Daehr: A Discriminant Analysis Framework For Electronic Health Record Data And An Application To Early Detection Of Mental Health Disorders, Haoyi Xiong, Jinghe Zhang, Yu Huang, Kevin Leach, Laura E. Barnes

Computer Science Faculty Research & Creative Works

Electronic health records (EHR) provide a rich source of temporal data that present a unique opportunity to characterize disease patterns and risk of imminent disease. While many data-mining tools have been adopted for EHR-based disease early detection, linear discriminant analysis (LDA) is one of the most commonly used statistical methods. However, it is difficult to train an accurate LDA model for early disease diagnosis when too few patients are known to have the target disease. Furthermore, EHR data are heterogeneous with significant noise. In such cases, the covariance matrices used in LDA are usually singular and estimated with a large …

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Network Connectivity Assessment And Improvement Through Relay Node Deployment, Maggie X. Cheng, Yi Ling, Brian M. Sadler Jan 2017

Network Connectivity Assessment And Improvement Through Relay Node Deployment, Maggie X. Cheng, Yi Ling, Brian M. Sadler

Computer Science Faculty Research & Creative Works

In wireless ad hoc networks, maintaining network connectivity is very important as high-level network functions all depend on it. However, how to measure network connectivity remains a fundamental challenge. For example, a network can have good overall k-connectivity and yet still have a communication bottleneck. In this paper, we address how to locate bottlenecks and relieve them. A new connectivity measure based on the Cheeger's Constant is used for bottleneck discovery, and a partition algorithm that divides the network at the bottleneck is developed. After the network is partitioned, we consider deploying a relay node to increase the conductance of …

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Privacy Setting Recommendation For Image Sharing, Jun Yu, Zhenzhong Kuang, Zhou Yu, Dan Lin, Jianping Fan Jan 2017

Privacy Setting Recommendation For Image Sharing, Jun Yu, Zhenzhong Kuang, Zhou Yu, Dan Lin, Jianping Fan

Computer Science Faculty Research & Creative Works

This paper aims to simultaneously consider two inseparable issues for privacy setting recommendation: (1) sensitiveness of visual content of the images being shared; and (2) trustworthiness of users being granted. First, an object-based approach is developed for image content sensitiveness (privacy) representation. Secondly, the users on a social network are clustered into a set of representative social groups to generate a discriminative dictionary for user trustworthiness characterization. Finally, a tree classifier is trained hierarchically to recommend appropriate privacy settings for image sharing.

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Application Of Nearly Linear Solvers To Electric Power System Computation, Lisa L. Grant Jan 2017

Application Of Nearly Linear Solvers To Electric Power System Computation, Lisa L. Grant

Doctoral Dissertations

"To meet the future needs of the electric power system, improvements need to be made in the areas of power system algorithms, simulation, and modeling, specifically to achieve a time frame that is useful to industry. If power system time-domain simulations could run in real-time, then system operators would have situational awareness to implement and avoid cascading failures, significantly improving power system reliability. Several power system applications rely on the solution of a very large linear system. As the demands on power systems continue to grow, there is a greater computational complexity involved in solving these large linear systems within …

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Ieee Access Special Section Editorial: Emergent Topics For Mobile And Ubiquitous Systems In Smartphone, Iot, And Cloud Computing Era, Takahiro Hara, Stephan Sigg, Lei Shu, Francesco De Pellegrini, Chiara Petrioli, Sanjay Kumar Madria Jan 2017

Ieee Access Special Section Editorial: Emergent Topics For Mobile And Ubiquitous Systems In Smartphone, Iot, And Cloud Computing Era, Takahiro Hara, Stephan Sigg, Lei Shu, Francesco De Pellegrini, Chiara Petrioli, Sanjay Kumar Madria

Computer Science Faculty Research & Creative Works

No abstract provided.

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