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The Effect Of Conversational Agent Skill On User Behavior During Deception, Ryan M. Schuetzler, G. Mark Grimes, Justin Scott Giboney 2019 University of Nebraska at Omaha

The Effect Of Conversational Agent Skill On User Behavior During Deception, Ryan M. Schuetzler, G. Mark Grimes, Justin Scott Giboney

Information Systems and Quantitative Analysis Faculty Publications

Conversational agents (CAs) are an integral component of many personal and business interactions. Many recent advancements in CA technology have attempted to make these interactions more natural and human-like. However, it is currently unclear how human-like traits in a CA impact the way users respond to questions from the CA. In some applications where CAs may be used, detecting deception is important. Design elements that make CA interactions more human-like may induce undesired strategic behaviors from human deceivers to mask their deception. To better understand this interaction, this research investigates the effect of conversational skill—that is, the ability of ...


Securing Messaging Services Through Efficient Signcryption With Designated Equality Test, Yujue WANG, Hwee Hwa PANG, Robert H. DENG, Yong DING, Qianhong WU, Bo QIN 2019 Guilin University of Electronic Technology

Securing Messaging Services Through Efficient Signcryption With Designated Equality Test, Yujue Wang, Hwee Hwa Pang, Robert H. Deng, Yong Ding, Qianhong Wu, Bo Qin

Research Collection School Of Information Systems

To address security and privacy issues in messaging services, we present a public key signcryption scheme with designated equality test on ciphertexts (PKS-DET) in this paper. The scheme enables a sender to simultaneously encrypt and sign (signcrypt) messages, and to designate a tester to perform equality test on ciphertexts, i.e., to determine whether two ciphertexts signcrypt the same underlying plaintext message. We introduce the PKS-DET framework, present a concrete construction and formally prove its security against three types of adversaries, representing two security requirements on message confidentiality against outsiders and the designated tester, respectively, and a requirement on message ...


A Hidden Markov Model For Matching Spatial Networks, Benoit Costes, Julien Perret 2019 The University of Maine

A Hidden Markov Model For Matching Spatial Networks, Benoit Costes, Julien Perret

Journal of Spatial Information Science

Datasets of the same geographic space at different scales and temporalities are increasingly abundant, paving the way for new scientific research. These datasets require data integration, which implies linking homologous entities in a process called data matching that remains a challenging task, despite a quite substantial literature, because of data imperfections and heterogeneities. In this paper, we present an approach for matching spatial networks based on a hidden Markov model (HMM) that takes full benefit of the underlying topology of networks. The approach is assessed using four heterogeneous datasets (streets, roads, railway, and hydrographic networks), showing that the HMM algorithm ...


Evaluating Existing Manually Constructed Natural Landscape Classification With A Machine Learning-Based Approach, Rok Ciglic, Erik Strumbelj, Rok Cesnovar, Mauro Hrvatin, Drago Perko 2019 University of Ljubljana

Evaluating Existing Manually Constructed Natural Landscape Classification With A Machine Learning-Based Approach, Rok Ciglic, Erik Strumbelj, Rok Cesnovar, Mauro Hrvatin, Drago Perko

Journal of Spatial Information Science

Some landscape classifications officially determine financial obligations; thus, they must be objective and precise. We presume it is possible to quantitatively evaluate existing manually constructed classifications and correct them if necessary. One option for achieving this goal is a machine learning method. With (re)modeling of the landscape classification and an explanation of its structure, we can add quantitative proof to its original (qualitative) description. The main objectives of the paper are to evaluate the consistency of the existing manually constructed natural landscape classification with a machine learning-based approach and to test the newly developed general black-box explanation method in ...


Discovery Of Topological Constraints On Spatial Object Classes Using A Refined Topological Model, Ivan Majic, Elham Naghizade, Stephan Winter, Martin Tomko 2019 The University of Melbourne

Discovery Of Topological Constraints On Spatial Object Classes Using A Refined Topological Model, Ivan Majic, Elham Naghizade, Stephan Winter, Martin Tomko

Journal of Spatial Information Science

In a typical data collection process, a surveyed spatial object is annotated upon creation, and is classified based on its attributes. This annotation can also be guided by textual definitions of objects. However, interpretations of such definitions may differ among people, and thus result in subjective and inconsistent classification of objects. This problem becomes even more pronounced if the cultural and linguistic differences are considered. As a solution, this paper investigates the role of topology as the defining characteristic of a class of spatial objects. We propose a data mining approach based on frequent itemset mining to learn patterns in ...


Parallel Streaming Random Sampling, Kanat Tangwongsan, Srikanta Tirthapura 2019 Mahidol University International College

Parallel Streaming Random Sampling, Kanat Tangwongsan, Srikanta Tirthapura

Srikanta Tirthapura

This paper investigates parallel random sampling from a potentially-unending data stream whose elements are revealed in a series of element sequences (minibatches). While sampling from a stream was extensively studied sequentially, not much has been explored in the parallel context, with prior parallel random-sampling algorithms focusing on the static batch model. We present parallel algorithms for minibatch-stream sampling in two settings: (1) sliding window, which draws samples from a prespecified number of most-recently observed elements, and (2) infinite window, which draws samples from all the elements received. Our algorithms are computationally and memory efficient: their work matches the fastest sequential ...


Data Mining And Machine Learning To Improve Northern Florida’S Foster Care System, Daniel Oldham, Nathan Foster, Mihhail Berezovski 2019 Embry-Riddle Aeronautical University, Daytona Beach

Data Mining And Machine Learning To Improve Northern Florida’S Foster Care System, Daniel Oldham, Nathan Foster, Mihhail Berezovski

Beyond: Undergraduate Research Journal

The purpose of this research project is to use statistical analysis, data mining, and machine learning techniques to determine identifiable factors in child welfare service records that could lead to a child entering the foster care system multiple times. This would allow us the capability of accurately predicting a case’s outcome based on these factors. We were provided with eight years of data in the form of multiple spreadsheets from Partnership for Strong Families (PSF), a child welfare services organization based in Gainesville, Florida, who is contracted by the Florida Department for Children and Families (DCF). This data contained ...


Encoding Invariances In Deep Generative Models, Viraj Shah, Ameya Joshi, Sambuddha Ghosal, Balaji Pokuri, Soumik Sarkar, Baskar Ganapathysubramanian, Chinmay Hegde 2019 Iowa State University

Encoding Invariances In Deep Generative Models, Viraj Shah, Ameya Joshi, Sambuddha Ghosal, Balaji Pokuri, Soumik Sarkar, Baskar Ganapathysubramanian, Chinmay Hegde

Baskar Ganapathysubramanian

Reliable training of generative adversarial networks (GANs) typically require massive datasets in order to model complicated distributions. However, in several applications, training samples obey invariances that are \textit{a priori} known; for example, in complex physics simulations, the training data obey universal laws encoded as well-defined mathematical equations. In this paper, we propose a new generative modeling approach, InvNet, that can efficiently model data spaces with known invariances. We devise an adversarial training algorithm to encode them into data distribution. We validate our framework in three experimental settings: generating images with fixed motifs; solving nonlinear partial differential equations (PDEs); and ...


Parallel Streaming Random Sampling, Kanat Tangwongsan, Srikanta Tirthapura 2019 Mahidol University International College

Parallel Streaming Random Sampling, Kanat Tangwongsan, Srikanta Tirthapura

Electrical and Computer Engineering Publications

This paper investigates parallel random sampling from a potentially-unending data stream whose elements are revealed in a series of element sequences (minibatches). While sampling from a stream was extensively studied sequentially, not much has been explored in the parallel context, with prior parallel random-sampling algorithms focusing on the static batch model. We present parallel algorithms for minibatch-stream sampling in two settings: (1) sliding window, which draws samples from a prespecified number of most-recently observed elements, and (2) infinite window, which draws samples from all the elements received. Our algorithms are computationally and memory efficient: their work matches the fastest sequential ...


Healthcare It In Skilled Nursing And Post-Acute Care Facilities: Reducing Hospital Admissions And Re-Admissions, Improving Reimbursement And Improving Clinical Operations, Scott L. Hopes 2019 University of South Florida

Healthcare It In Skilled Nursing And Post-Acute Care Facilities: Reducing Hospital Admissions And Re-Admissions, Improving Reimbursement And Improving Clinical Operations, Scott L. Hopes

Scott Hopes

Health information technology (HIT), which includes electronic health record (EHR) systems and clinical data analytics, has become a major component of all health care delivery and care management. The adoption of HIT by physicians, hospitals, post-acute care organizations, pharmacies and other health care providers has been accepted as a necessary (and recently, a government required) step toward improved quality, care coordination and reduced costs: “Better coordination of care provides a path to improving communication, improving quality of care, and reducing unnecessary emergency room use and hospital readmissions. LTPAC providers play a critical role in achieving these goals” (HealthIT.gov, 2013 ...


Examining Medline Search Query Reproducibility And Resulting Variation In Search Results, C. Sean Burns, Robert M. Shapiro II, Tyler Nix, Jeffrey T. Huber 2019 University of Kentucky

Examining Medline Search Query Reproducibility And Resulting Variation In Search Results, C. Sean Burns, Robert M. Shapiro Ii, Tyler Nix, Jeffrey T. Huber

C. Sean Burns

The MEDLINE database is publicly available through the National Library of Medicine’s PubMed but the data file itself is also licensed to a number of vendors, who may offer their versions to institutional and other parties as part of a database platform. These vendors provide their own interface to the MEDLINE file and offer other technologies that attempt to make their version useful to subscribers. However, little is known about how vendor platforms ingest and interact with MEDLINE data files, nor how these changes influence the construction of search queries and the results they produce. This poster presents a ...


Encoding Invariances In Deep Generative Models, Viraj Shah, Ameya Joshi, Sambuddha Ghosal, Balaji Pokuri, Soumik Sarkar, Baskar Ganapathysubramanian, Chinmay Hegde 2019 Iowa State University

Encoding Invariances In Deep Generative Models, Viraj Shah, Ameya Joshi, Sambuddha Ghosal, Balaji Pokuri, Soumik Sarkar, Baskar Ganapathysubramanian, Chinmay Hegde

Mechanical Engineering Publications

Reliable training of generative adversarial networks (GANs) typically require massive datasets in order to model complicated distributions. However, in several applications, training samples obey invariances that are \textit{a priori} known; for example, in complex physics simulations, the training data obey universal laws encoded as well-defined mathematical equations. In this paper, we propose a new generative modeling approach, InvNet, that can efficiently model data spaces with known invariances. We devise an adversarial training algorithm to encode them into data distribution. We validate our framework in three experimental settings: generating images with fixed motifs; solving nonlinear partial differential equations (PDEs); and ...


Radish: A Cross Platform Meal Prepping App For Beginner Weightlifters, Spoorthy S. Vemula, Tanay Gottigundala, Cory Baxes 2019 California Polytechnic State University, San Luis Obispo

Radish: A Cross Platform Meal Prepping App For Beginner Weightlifters, Spoorthy S. Vemula, Tanay Gottigundala, Cory Baxes

Computer Science and Software Engineering

With the increasing ease of access and decreasing price of most food, obesity rates in the developing world have risen dramatically in recent years. As of March 23rd, 2019, obesity rates had reached 39.6%, a 6% increase in just 8 years. Research has shown that people with obesity have a significantly increased risk of heart disease, stroke, type 2 diabetes, and certain cancers, among other life-threatening diseases. In addition, 42% of people who begin weightlifting quit because it’s too difficult to follow a diet or workout regimen.

We created Radish in an attempt to tackle these problems. Radish ...


Reach - A Community Service Application, Samuel Noel Magana 2019 California Polytechnic State University, San Luis Obispo

Reach - A Community Service Application, Samuel Noel Magana

Computer Engineering

Communities are familiar threads that unite people through several shared attributes and interests. These commonalities are the core elements that link and bond us together. Many of us are part of multiple communities, moving in and out of them depending on our needs. These common threads allow us to support and advocate for each other when facing a common threat or difficult situation. Healthy and vibrant communities are fundamental to the operation of our society. These interactions within our communities define the way we as individuals interact with each other, and society at large. Being part of a community helps ...


Schema Migration From Relational Databases To Nosql Databases With Graph Transformation And Selective Denormalization, Krishna Chaitanya Mullapudi 2019 San Jose State University

Schema Migration From Relational Databases To Nosql Databases With Graph Transformation And Selective Denormalization, Krishna Chaitanya Mullapudi

Master's Projects

We witnessed a dramatic increase in the volume, variety and velocity of data leading to the era of big data. The structure of data has become highly flexible leading to the development of many storage systems that are different from the traditional structured relational databases where data is stored in “tables,” with columns representing the lowest granularity of data. Although relational databases are still predominant in the industry, there has been a major drift towards alternative database systems that support unstructured data with better scalability leading to the popularity of “Not Only SQL.”

Migration from relational databases to NoSQL databases ...


Image Retrieval Using Image Captioning, Nivetha Vijayaraju 2019 San Jose State University

Image Retrieval Using Image Captioning, Nivetha Vijayaraju

Master's Projects

The rapid growth in the availability of the Internet and smartphones have resulted in the increase in usage of social media in recent years. This increased usage has thereby resulted in the exponential growth of digital images which are available. Therefore, image retrieval systems play a major role in fetching images relevant to the query provided by the users. These systems should also be able to handle the massive growth of data and take advantage of the emerging technologies, like deep learning and image captioning. This report aims at understanding the purpose of image retrieval and various research held in ...


Detection Of Bleeding Events In Electronic Health Record Notes Using Convolutional Neural Network Models Enhanced With Recurrent Neural Network Autoencoders: Deep Learning Approach, Rumeng Li, Baotian Hu, Feifan Liu, Weisong Liu, Francesca Cunningham, David D. McManus, Hong Yu 2019 University of Massachusetts Amherst

Detection Of Bleeding Events In Electronic Health Record Notes Using Convolutional Neural Network Models Enhanced With Recurrent Neural Network Autoencoders: Deep Learning Approach, Rumeng Li, Baotian Hu, Feifan Liu, Weisong Liu, Francesca Cunningham, David D. Mcmanus, Hong Yu

David D. McManus

BACKGROUND: Bleeding events are common and critical and may cause significant morbidity and mortality. High incidences of bleeding events are associated with cardiovascular disease in patients on anticoagulant therapy. Prompt and accurate detection of bleeding events is essential to prevent serious consequences. As bleeding events are often described in clinical notes, automatic detection of bleeding events from electronic health record (EHR) notes may improve drug-safety surveillance and pharmacovigilance.

OBJECTIVE: We aimed to develop a natural language processing (NLP) system to automatically classify whether an EHR note sentence contains a bleeding event.

METHODS: We expert annotated 878 EHR notes (76,577 ...


Predictive Analysis For Cloud Infrastructure Metrics, Paridhi Agrawal 2019 San Jose State University

Predictive Analysis For Cloud Infrastructure Metrics, Paridhi Agrawal

Master's Projects

In a cloud computing environment, enterprises have the flexibility to request resources according to their application demands. This elastic feature of cloud computing makes it an attractive option for enterprises to host their applications on the cloud. Cloud providers usually exploit this elasticity by auto-scaling the application resources for quality assurance. However, there is a setup-time delay that may take minutes between the demand for a new resource and it being prepared for utilization. This causes the static resource provisioning techniques, which request allocation of a new resource only when the application breaches a specific threshold, to be slow and ...


Mapping In The Humanities: Gis Lessons For Poets, Historians, And Scientists, Emily W. Fairey 2019 CUNY Brooklyn College

Mapping In The Humanities: Gis Lessons For Poets, Historians, And Scientists, Emily W. Fairey

Open Educational Resources

User-friendly Geographic Information Systems (GIS) is the common thread of this collection of presentations, and activities with full lesson plans. The first section of the site contains an overview of cartography, the art of creating maps, and then looks at historical mapping platforms like Hypercities and Donald Rumsey Historical Mapping Project. In the next section Google Earth Desktop Pro is introduced, with lessons and activities on the basics of GE such as pins, paths, and kml files, as well as a more complex activity on "georeferencing" an historic map over Google Earth imagery. The final section deals with ARCGIS Online ...


Mapping Manuscript Migrations: Digging Into Data For The History And Provenance Of Medieval And Renaissance Manuscripts, Toby Burrows, Eero Hyvönen, Lynn Ransom, Hanno Wijsman 2019 University of Western Australia

Mapping Manuscript Migrations: Digging Into Data For The History And Provenance Of Medieval And Renaissance Manuscripts, Toby Burrows, Eero Hyvönen, Lynn Ransom, Hanno Wijsman

Manuscript Studies

Mapping Manuscript Migrations is a new two-year project funded by the Trans-Atlantic Platform in the fourth round of its Digging into Data Challenge. The project is a collaboration between four international partners: the University of Oxford, the University of Pennsylvania, the Institut de recherche et d’histoire des textes (IRHT) in Paris, and Aalto University in Helsinki.

The project aims to combine data from various different sources to enable the large-scale analysis of the history and provenance of medieval and Renaissance manuscripts.


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