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2017

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Articles 151 - 180 of 2767

Full-Text Articles in Computer Sciences

Accuracy Analysis Comparison Of Supervised Classification Methods For Anomaly Detection On Levees Using Sar Imagery, Ramakalavathi Marapareddy, James V. Aanstoos, Nicolas H. Younan Dec 2017

Accuracy Analysis Comparison Of Supervised Classification Methods For Anomaly Detection On Levees Using Sar Imagery, Ramakalavathi Marapareddy, James V. Aanstoos, Nicolas H. Younan

Faculty Publications

This paper analyzes the use of a synthetic aperture radar (SAR) imagery to support levee condition assessment by detecting potential slide areas in an efficient and cost-effective manner. Levees are prone to a failure in the form of internal erosion within the earthen structure and landslides (also called slough or slump slides). If not repaired, slough slides may lead to levee failures. In this paper, we compare the accuracy of the supervised classification methods minimum distance (MD) using Euclidean and Mahalanobis distance, support vector machine (SVM), and maximum likelihood (ML), using SAR technology to detect slough slides on earthen levees. …

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A Tidy Data Model For Natural Language Processing Using Cleannlp, Taylor B. Arnold Dec 2017

A Tidy Data Model For Natural Language Processing Using Cleannlp, Taylor B. Arnold

Department of Math & Statistics Faculty Publications

Recent advances in natural language processing have produced libraries that extract low level features from a collection of raw texts. These features, known as annotations, are usually stored internally in hierarchical, tree-based data structures. This paper proposes a data model to represent annotations as a collection of normalized relational data tables optimized for exploratory data analysis and predictive modeling. The R package cleanNLP, which calls one of two state of the art NLP libraries (CoreNLP or spaCy), is presented as an implementation of this data model. It takes raw text as an input and returns a list of normalized tables. …

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Authorship Identification Of Translation Algorithms., Keishin Nishiyama Dec 2017

Authorship Identification Of Translation Algorithms., Keishin Nishiyama

Electronic Theses and Dissertations

Authorship analysis is a process of identifying a true writer of a given document and has been studied for decades. However, only a handful of studies of authorship analysis of translators are available despite the fact that online translations are widely available and also popularly employed in automatic translations of posts in social networking services. The identification of translation algorithms has potential to contribute to the investigation of cybercrimes, involving translation of scam messages by algorithmic translations to reach speakers of foreign languages. This study tested bag of words (BOW) approach in authorship attribution and the existing approaches to translator …

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The Public’S Perception Of Humanlike Robots: Online Social Commentary Reflects An Appearance-Based Uncanny Valley, A General Fear Of A “Technology Takeover”, And The Unabashed Sexualization Of Female-Gendered Robots, Megan K. Strait, Cynthia Aguillon, Virginia Contreras, Noemi Garcia Dec 2017

The Public’S Perception Of Humanlike Robots: Online Social Commentary Reflects An Appearance-Based Uncanny Valley, A General Fear Of A “Technology Takeover”, And The Unabashed Sexualization Of Female-Gendered Robots, Megan K. Strait, Cynthia Aguillon, Virginia Contreras, Noemi Garcia

Computer Science Faculty Publications

Towards understanding the public’s perception of humanlike robots, we examined commentary on 24 YouTube videos depicting social robots ranging in human similarity – from Honda’s Asimo to Hiroshi Ishiguro’s Geminoids. In particular, we investigated how people have responded to the emergence of highly humanlike robots (e.g., Bina48) in contrast to those with more prototypically-“robotic” appearances (e.g., Asimo), coding the frequency at which the uncanny valley versus fears of replacement and/or a “technology takeover” arise in online discourse based on the robot’s appearance. Here we found that, consistent with Masahiro Mori’s theory of the uncanny valley, people’s commentary reflected an aversion …

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Transient Solution Of M[X1],M[X2]/G1,G2/1 With Priority Services, Modified Bernoulli Vacation, Bernoulli Feedback, Breakdown, Delaying Repair And Reneging, G. Ayyappan, J. Udayageetha Dec 2017

Transient Solution Of M[X1],M[X2]/G1,G2/1 With Priority Services, Modified Bernoulli Vacation, Bernoulli Feedback, Breakdown, Delaying Repair And Reneging, G. Ayyappan, J. Udayageetha

Applications and Applied Mathematics: An International Journal (AAM)

This paper considers a queuing system which facilitates a single server that serves two classes of units: high priority and low priority units. These two classes of units arrive at the system in two independent compound Poisson processes. It aims to decipher average queue size and average waiting time of the units. Under the pre-emptive priority rule, the server provides a general service to these arriving units. It is further assumed the server may take a vacation after serving the last high priority unit present in the system or at the service completion of each low priority unit present in …

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The Fmx/Fm/1 Queue With Multiple Working Vacation, G. Kannadasan, N. Sathiyamoorthi Dec 2017

The Fmx/Fm/1 Queue With Multiple Working Vacation, G. Kannadasan, N. Sathiyamoorthi

Applications and Applied Mathematics: An International Journal (AAM)

This study investigates the batch arrival FMX/FM/1 queue with multiple working vacation. For this fuzzy queuing model, this research obtains some performance measure of interest such as mean system length, mean system sojourn time, mean busy period for the server and working vacation period. Finally, numerical results are presented to show the effects of system parameters.

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Towards Tightly Secure Deterministic Public Key Encryption, Daode Zhang, Bao Li, Yamin Liu, Haiyang Xue, Xianhui Lu, Dingding Jia Dec 2017

Towards Tightly Secure Deterministic Public Key Encryption, Daode Zhang, Bao Li, Yamin Liu, Haiyang Xue, Xianhui Lu, Dingding Jia

Research Collection School Of Computing and Information Systems

In this paper, we formally consider the construction of tightly secure deterministic public key encryption (D-PKE). Initially, we compare the security loss amongst the D-PKE schemes under the concrete assumptions and also analyze the tightness of generic D-PKE constructions. Furthermore, we prove that the CPA secure D-PKE scheme of Boldyreva et al. (Crypto’08) is tightly PRIV-IND-CPA secure for block-sources. Our security reduction improves the security loss of their scheme from O(nc∗) to O(1). Additionally, by upgrading the all-but-one trapdoor function (TDF) in the construction of Boldyreva et al. to all-but-n TDF defined by Hemenway et al. (Asiacrypt’11), we give general …

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On The Influence Of Spatio-Temporal Analysis On Clustering And Recommendation, Madhuri Debnath Dec 2017

On The Influence Of Spatio-Temporal Analysis On Clustering And Recommendation, Madhuri Debnath

Computer Science and Engineering Dissertations - Archive

In this dissertation, we propose efficient frameworks to analyze spatio-temporal data. In the first part of the dissertation, we use a clustering based method to mine useful information from trajectory data. Existing trajectory clustering algorithms have focused on geometric properties and spatial features of trajectories. In contrast to existing algorithms, we propose a new framework to cluster sub-trajectories based on a combination of spatial and non-spatial features. In the second part of dissertation, we propose a unified framework to build recommendation systems by analyzing human movement data. We propose recommendation frameworks to recommend POI locations and travel routes that use …

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A Framework For Clustering And Adaptive Topic Tracking On Evolving Text And Social Media Data Streams., Gopi Chand Nutakki Dec 2017

A Framework For Clustering And Adaptive Topic Tracking On Evolving Text And Social Media Data Streams., Gopi Chand Nutakki

Electronic Theses and Dissertations

Recent advances and widespread usage of online web services and social media platforms, coupled with ubiquitous low cost devices, mobile technologies, and increasing capacity of lower cost storage, has led to a proliferation of Big data, ranging from, news, e-commerce clickstreams, and online business transactions to continuous event logs and social media expressions. These large amounts of online data, often referred to as data streams, because they get generated at extremely high throughputs or velocity, can make conventional and classical data analytics methodologies obsolete. For these reasons, the issues of management and analysis of data streams have been researched extensively …

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A Coverage Metric To Aid In Testing Multi-Agent Systems, Jane Ostergar Linn Dec 2017

A Coverage Metric To Aid In Testing Multi-Agent Systems, Jane Ostergar Linn

Theses and Dissertations

Models are frequently used to represent complex systems in order to test the systems before they are deployed. Some of the most complicated models are those that represent multi-agent systems (MAS), where there are multiple decision makers. Brahms is an agent-oriented language that models MAS. Three major qualities affect the behavior of these MAS models: workframes that change the state of the system, communication activities that coordinate information between agents, and the schedule of workframes. The primary method to test these models that exists is repeated simulation. Simulation is useful insofar as interesting test cases are used that enable the …

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Shadow Patching: Exemplar-Based Shadow Removal, Ryan Sears Hintze Dec 2017

Shadow Patching: Exemplar-Based Shadow Removal, Ryan Sears Hintze

Theses and Dissertations

Shadow removal is an important problem for both artists and algorithms. Previous methods handle some shadows well but, because they rely on the shadowed data, perform poorly in cases with severe degradation. Image-completion algorithms can completely replace severely degraded shadowed regions, and perform well with smaller-scale textures, but often fail to reproduce larger-scale macrostructure that may still be visible in the shadowed region. This paper provides a general framework that leverages degraded (e.g., shadowed) data to guide the image completion process by extending the objective function commonly used in current state-of-the-art image completion energy-minimization methods. This approach achieves realistic shadow …

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Recommender Systems For Family History Source Discovery, Derrick James Brinton Dec 2017

Recommender Systems For Family History Source Discovery, Derrick James Brinton

Theses and Dissertations

As interest in family history research increases, greater numbers of amateurs are participating in genealogy. However, finding sources that provide useful information on individuals in genealogical research is often an overwhelming task, even for experts. Many tools assist genealogists in their work, including many computer-based systems. Prior to this work, recommender systems had not yet been applied to genealogy, though their ability to navigate patterns in large amounts of data holds great promise for the genealogical domain. We create the Family History Source Recommender System to mimic human behavior in locating sources of genealogical information. The recommender system is seeded …

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Ethics And Bias In Machine Learning: A Technical Study Of What Makes Us “Good”, Ashley Nicole Shadowen Dec 2017

Ethics And Bias In Machine Learning: A Technical Study Of What Makes Us “Good”, Ashley Nicole Shadowen

Student Theses

The topic of machine ethics is growing in recognition and energy, but bias in machine learning algorithms outpaces it to date. Bias is a complicated term with good and bad connotations in the field of algorithmic prediction making. Especially in circumstances with legal and ethical consequences, we must study the results of these machines to ensure fairness. This paper attempts to address ethics at the algorithmic level of autonomous machines. There is no one solution to solving machine bias, it depends on the context of the given system and the most reasonable way to avoid biased decisions while maintaining the …

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Study Of Self-Similarity In Brain Data, Jennifer Holst Dec 2017

Study Of Self-Similarity In Brain Data, Jennifer Holst

Student Theses

In the area of computer science, past research has found that the concept of self-similarity is present in local and Internet-based network traffic. This study considers the possibility that data traveling through the neuronal network in the human brain is also self-similar. By analyzing publicly available raw EEG data and estimating its Hurst parameter, we find indications that brain data traffic may in fact be self-similar.

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3d Printing Concrete Structures And Verifying Integrity Of Their G-Code Instructions: Border Wall A Case Study, Jason Breland Dec 2017

3d Printing Concrete Structures And Verifying Integrity Of Their G-Code Instructions: Border Wall A Case Study, Jason Breland

Dissertations

Thanks to advances in Additive Manufacturing (AM) technology and continued research by academics and entrepreneurs alike, the ability to “3d print” permanent concrete structures such as homes or offices is now a reality. Generally, AM is the process that allows for a 3d model of an object to be converted into hardware instructions to generate that object layer by layer using a malleable medium such as a plastic. Specifically, large scale concrete AM can now generate a structure, such as a building, layer by layer more quickly and efficiently than traditional construction methods [6, 39]. This innovative, semi-autonomous process promises …

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Asynchronous 3d (Async3d): Design Methodology And Analysis Of 3d Asynchronous Circuits, Francis Corpuz Sabado Dec 2017

Asynchronous 3d (Async3d): Design Methodology And Analysis Of 3d Asynchronous Circuits, Francis Corpuz Sabado

Graduate Theses and Dissertations

This dissertation focuses on the application of 3D integrated circuit (IC) technology on asynchronous logic paradigms, mainly NULL Convention Logic (NCL) and Multi-Threshold NCL (MTNCL). It presents the Async3D tool flow and library for NCL and MTNCL 3D ICs. It also analyzes NCL and MTNCL circuits in 3D IC. Several FIR filter designs were implement in NCL, MTNCL, and synchronous architecture to compare synchronous and asynchronous circuits in 2D and 3D ICs. The designs were normalized based on performance and several metrics were measured for comparison. Area, interconnect length, power consumption, and power density were compared among NCL, MTNCL, and …

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Secure Server-Aided Top-K Monitoring, Yujue Wang, Hwee Hwa Pang, Yanjiang Yang, Xuhua Ding Dec 2017

Secure Server-Aided Top-K Monitoring, Yujue Wang, Hwee Hwa Pang, Yanjiang Yang, Xuhua Ding

Research Collection School Of Computing and Information Systems

In a data streaming model, a data owner releases records or documents to a set of users with matching interests, in such a way that the match in interest can be calculated from the correlation between each pair of document and user query. For scalability and availability reasons, this calculation is delegated to third-party servers, which gives rise to the need to protect the integrity and privacy of the documents and user queries. In this paper, we propose a server-aided data stream monitoring scheme (DSM) to address the aforementioned integrity and privacy challenges, so that the users are able to …

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Extracting Implicit Suggestions From Students’ Comments: A Text Analytics Approach, Venky Shankararaman, Swapna Gottipati, Jeff Rongsheng Lin, Sandy Gan Dec 2017

Extracting Implicit Suggestions From Students’ Comments: A Text Analytics Approach, Venky Shankararaman, Swapna Gottipati, Jeff Rongsheng Lin, Sandy Gan

Research Collection School Of Computing and Information Systems

At the end of each course, students are required to give feedback on the course and instructor. This feedback includes quantitative rating using Likert scale and qualitative feedback as comments. Such qualitative feedback can provide valuable insights in helping the instructor enhance the course content and teaching delivery. However, the main challenge in analysing the qualitative feedback is the perceived increase in time and effort needed to manually process the textual comments. In this paper, we provide an automated solution for analysing comments, specifically extracting implicit suggestions from the students’ qualitative feedback comments. The implemented solution leverages existing text mining …

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Using Data Analytics For Discovering Library Resource Insights: Case From Singapore Management University, Ning Lu, Rui Song, Dina Li Gwek Heng, Swapna Gottipati, Aaron Tay Dec 2017

Using Data Analytics For Discovering Library Resource Insights: Case From Singapore Management University, Ning Lu, Rui Song, Dina Li Gwek Heng, Swapna Gottipati, Aaron Tay

Research Collection School Of Computing and Information Systems

Library resources are critical in supporting teaching, research and learning processes. Several universities have employed online platforms and infrastructure for enabling the online services to students, faculty and staff. To provide efficient services by understanding and predicting user needs libraries are looking into the area of data analytics. Library analytics in Singapore Management University is the project committed to provide an interface for data-intensive project collaboration, while supporting one of the library’s key pillars on its commitment to collaborate on initiatives with SMU Communities and external groups. In this paper, we study the transaction logs for user behavior analysis that …

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Home Health Care Delivery Problem, Aldy Gunawan, Hoong Chuin Lau, Kun Lu Dec 2017

Home Health Care Delivery Problem, Aldy Gunawan, Hoong Chuin Lau, Kun Lu

Research Collection School Of Computing and Information Systems

We address the Home Health Care Delivery Problem (HHCDP), which is concerned with staff scheduling in the home health care industry. The goal is to schedule health care providers to serve patients at their homes that maximizes the total collected preference scores from visited patients subject to several constraints, such as workload of the health care providers, time budget for each provider and so on. The complexity lies in the possibility of cancellation of patient bookings dynamically, and the generated schedule should attempt to patients’ preferred time windows. To cater to these requirements, we model the preference score as a …

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Using Teaching Cases For Achieving Bloom’S High-Order Cognitive Levels: An Application In Technically-Oriented Information Systems Course, Kar Way Tan Dec 2017

Using Teaching Cases For Achieving Bloom’S High-Order Cognitive Levels: An Application In Technically-Oriented Information Systems Course, Kar Way Tan

Research Collection School Of Computing and Information Systems

Case-teaching has been an attractive pedagogy method for bringing in real-world examples into the classroom. However, it is challenging to introduce cases to address high-order cognitive skills such as analyzing and creating new IT solutions in technically-oriented computing course. In this research, we present our experience in introducing three types of case studies -- Story-Telling case, Design-and-Problem-Solving case, and Create-Design-Implement case to a course in an undergraduate Information Systems programme. For each case study, we plan and map the learning objectives to address various cognitive levels in the revised Bloom’s Taxonomy. Using surveys conducted over two academic years, we show …

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Disease Gene Classification With Metagraph Representations, Sezin Kircali Ata, Yuan Fang, Min Wu, Xiao-Li Li, Xiaokui Xiao Dec 2017

Disease Gene Classification With Metagraph Representations, Sezin Kircali Ata, Yuan Fang, Min Wu, Xiao-Li Li, Xiaokui Xiao

Research Collection School Of Computing and Information Systems

Protein-protein interaction (PPI) networks play an important role in studying the functional roles of proteins, including their association with diseases. However, protein interaction networks are not sufficient without the support of additional biological knowledge for proteins such as their molecular functions and biological processes. To complement and enrich PPI networks, we propose to exploit biological properties of individual proteins. More specifically, we integrate keywords describing protein properties into the PPI network, and construct a novel PPI-Keywords (PPIK) network consisting of both proteins and keywords as two different types of nodes. As disease proteins tend to have a similar topological characteristics …

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Compact Hierarchical Ibe From Lattices In The Standard Model, Daode Zhang, Fuyang Fang, Bao Li, Haiyang Xue, Bei Liang Dec 2017

Compact Hierarchical Ibe From Lattices In The Standard Model, Daode Zhang, Fuyang Fang, Bao Li, Haiyang Xue, Bei Liang

Research Collection School Of Computing and Information Systems

At Crypto’10, Agrawal et al. proposed a lattice-based selectively secure Hierarchical Identity-based Encryption (HIBE) scheme (ABB10b) with small ciphertext on the condition that (the length of identity at each level) is small in the standard model. In this paper, we present another lattice-based selectively secure HIBE scheme with depth d, using a gadget matrix with enough large to replace the matrix in the HIBE scheme proposed by Agrawal et al. at Eurocrypt’10. In our HIBE scheme, not only the size of ciphertext at level is larger than the size in ABB10b and at least smaller than the sizes in the …

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A Novel Density Peak Clustering Algorithm Based On Squared Residual Error, Milan Parmar, Di Wang, Ah-Hwee Tan, Chunyan Miao, Jianhua Jiang, You Zhou Dec 2017

A Novel Density Peak Clustering Algorithm Based On Squared Residual Error, Milan Parmar, Di Wang, Ah-Hwee Tan, Chunyan Miao, Jianhua Jiang, You Zhou

Research Collection School Of Computing and Information Systems

The density peak clustering (DPC) algorithm is designed to quickly identify intricate-shaped clusters with high dimensionality by finding high-density peaks in a non-iterative manner and using only one threshold parameter. However, DPC has certain limitations in processing low-density data points because it only takes the global data density distribution into account. As such, DPC may confine in forming low-density data clusters, or in other words, DPC may fail in detecting anomalies and borderline points. In this paper, we analyze the limitations of DPC and propose a novel density peak clustering algorithm to better handle low-density clustering tasks. Specifically, our algorithm …

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On Modeling Sense Relatedness In Multi-Prototype Word Embedding, Yixin Cao, Juanzi Li, Jiaxin Shi, Zhiyuan Liu, Chengjiang Li Dec 2017

On Modeling Sense Relatedness In Multi-Prototype Word Embedding, Yixin Cao, Juanzi Li, Jiaxin Shi, Zhiyuan Liu, Chengjiang Li

Research Collection School Of Computing and Information Systems

To enhance the expression ability of distributional word representation learning model, many researchers tend to induce word senses through clustering, and learn multiple embedding vectors for each word, namely multi-prototype word embedding model. However, most related work ignores the relatedness among word senses which actually plays an important role. In this paper, we propose a novel approach to capture word sense relatedness in multi-prototype word embedding model. Particularly, we differentiate the original sense and extended senses of a word by introducing their global occurrence information and model their relatedness through the local textual context information. Based on the idea of …

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Proactive Sequential Resource (Re)Distribution For Improving Efficiency In Urban Environments, Supriyo Ghosh Dec 2017

Proactive Sequential Resource (Re)Distribution For Improving Efficiency In Urban Environments, Supriyo Ghosh

Dissertations and Theses Collection (Open Access)

Due to the increasing population and lack of coordination, there is a mismatch in supply and demand of common resources (e.g., shared bikes, ambulances, taxis) in urban environments, which has deteriorated a wide variety of quality of life metrics such as success rate in issuing shared bikes, response times for emergency needs, waiting times in queues etc. Thus, in my thesis, I propose efficient algorithms that optimise the quality of life metrics by proactively redistributing the resources using intelligent operational (day-to-day) and strategic (long-term) decisions in the context of urban transportation and health & safety. For urban transportation, Bike Sharing …

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Online Learning With Nonlinear Models, Doyen Sahoo Dec 2017

Online Learning With Nonlinear Models, Doyen Sahoo

Dissertations and Theses Collection (Open Access)

Recent years have witnessed the success of two broad categories of machine learning algorithms: (i) Online Learning; and (ii) Learning with nonlinear models. Typical machine learning algorithms assume that the entire data is available prior to the training task. This is often not the case in the real world, where data often arrives sequentially in a stream, or is too large to be stored in memory. To address these challenges, Online Learning techniques evolved as a promising solution to having highly scalable and efficient learning methodologies which could learn from data arriving sequentially. Next, as the real world data exhibited …

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Policy Analytics For Environmental Sustainability: Household Hazardous Waste And Water Impacts Of Carbon Pollution Standards, Kustini Dec 2017

Policy Analytics For Environmental Sustainability: Household Hazardous Waste And Water Impacts Of Carbon Pollution Standards, Kustini

Dissertations and Theses Collection (Open Access)

Policy analytics are essential in supporting more informed policy-making in environmental management. This dissertation employs a fusion of machine methods and explanatory empiricism that involves data analytics, math programming, optimization, econometrics, geospatial and spatiotemporal analysis, and other approaches for assessing and evaluating current and future environmental policies.
Essay 1 discusses household informedness and its impact on the collection and recycling of household hazardous waste (HHW). Household informedness is the degree to which households have the necessary information to make utility-maximizing decisions about the handling of their waste. Such informedness seems to be influenced by HHW public education and environmental quality …

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Privacy-Preserving Trajectory Data Publishing Via Differential Privacy, Ishita Dwivedi Dec 2017

Privacy-Preserving Trajectory Data Publishing Via Differential Privacy, Ishita Dwivedi

Boise State University Theses and Dissertations

Over the past decade, the collection of data by individuals, businesses and government agencies has increased tremendously. Due to the widespread of mobile computing and the advances in location-acquisition techniques, an immense amount of data concerning the mobility of moving objects have been generated. The movement data of an object (e.g. individual) might include specific information about the locations it visited, the time those locations were visited, or both. While it is beneficial to share data for the purpose of mining and analysis, data sharing might risk the privacy of the individuals involved in the data. Privacy-Preserving Data Publishing (PPDP) …

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Uncovering New Links Through Interaction Duration, Laxmi Amulya Gundala Dec 2017

Uncovering New Links Through Interaction Duration, Laxmi Amulya Gundala

Boise State University Theses and Dissertations

Link Prediction is the problem of inferring new relationships among nodes in a network that can occur in the near future. Classical approaches mainly consider neighborhood structure similarity when linking nodes. However, we may also want to take into account whether the two nodes we are going to link will benefit from that by having an active interaction over time. For instance, it is better to link two nodes � and � if we know that these two nodes will interact in the social network in the future, rather than suggesting �, who may never interact with �. Thus, the …

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