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Articles 1 - 30 of 4067

Full-Text Articles in Physical Sciences and Mathematics

Tcp Server And Client: Bookstore Enquiry, Fawaz Bukhowa Dec 2018

Tcp Server And Client: Bookstore Enquiry, Fawaz Bukhowa

Student Scholar Symposium Abstracts and Posters

An application called "Bookstore Enquiry", and it is implemented in Java using TCP client-server program. It contains two programs; one program is called "Server" and another one is called "Client". In this application, the 'server' maintains information about books and for each book it stores information like 'BookId', 'BookName', 'BookEdition', 'AvailableStock', 'UnitPrice', 'Discount'. This application works in such a way that, the server runs indefinitely and waits for client requests. The Client will accept the BookId & BookName from console and send it to server. If the server finds any books that matches with sent details, then it shows "BOOK FOUND ...


Phr: Patient Health Record, Quinn Nelson Dec 2018

Phr: Patient Health Record, Quinn Nelson

Theses/Capstones/Creative Projects

The rapid development of information technology systems has expanded into multiple disciplines and results in systems that are limited by initial design and implementation: the Healthcare Information Technology (HIT) space is no different. The introduction of the Electronic Health Record (EHR) system has changed the way healthcare operates. Initial designs of these systems were focused on serving the needs of insurance companies and healthcare billing departments. Research shows that the design of EHR systems negatively impact provider-patient interactions and the care they receive. This capstone project capitalizes on the collaboration efforts between UNO and UNMC – by joining a research group ...


Seasonal Warranty Prediction Based On Recurrent Event Data, Qianqian Shan, Yili Hong, William Q. Meeker Jr. Nov 2018

Seasonal Warranty Prediction Based On Recurrent Event Data, Qianqian Shan, Yili Hong, William Q. Meeker Jr.

Statistics Preprints

Warranty return data from repairable systems, such as vehicles, usually result in recurrent event data. The non-homogeneous Poisson process (NHPP) model is used widely to describe such data. Seasonality in the repair frequencies and other variabilities, however, complicate the modeling of recurrent event data. Not much work has been done to address the seasonality, and this paper provides a general approach for the application of NHPP models with dynamic covariates to predict seasonal warranty returns. A hierarchical clustering method is used to stratify the population into groups that are more homogeneous than the than the overall population. The stratification facilitates ...


Performance Indicators Analysis Inside A Call Center Using A Simulation Program, Ditila Ekmekçiu, Markela Muça, Adrian Naço Nov 2018

Performance Indicators Analysis Inside A Call Center Using A Simulation Program, Ditila Ekmekçiu, Markela Muça, Adrian Naço

International Journal of Business and Technology

This paper deals with and shows the results of different performance indicators analyses made utilizing the help of Simulation and concentrated on dimensioning problems of handling calls capacity in a call center. The goal is to measure the reactivity of the call center’s performance to potential changes of critical variables. The literature related to the employment of this kind of instrument in call centers is reviewed, and the method that this problem is treated momentarily is precisely described. The technique used to obtain this paper’s goal implicated a simulation model using Arena Contact Center software that worked as ...


Modelling Business And Management Systems Using Fuzzy Cognitive Maps: A Critical Overview, Peter P. Groumpos Nov 2018

Modelling Business And Management Systems Using Fuzzy Cognitive Maps: A Critical Overview, Peter P. Groumpos

International Journal of Business and Technology

A critical overview of modelling Business and Management (B&M) Systems using Fuzzy Cognitive Maps is presented. A limited but illustrative number of specific applications of Fuzzy Cognitive Maps in diverse B&M systems, such as e business, performance assessment, decision making, human resources management, planning and investment decision making processes is provided and briefly analyzed. The limited survey is given in a table with statics of using FCMs in B&M systems during the last 15 years. The limited survey shows that the applications of Fuzzy Cognitive Maps to today’s Business and Management studies has been steadily increased ...


Effective Visualization Approaches For Ultra-High Dimensional Datasets, Gurminder Kaur Oct 2018

Effective Visualization Approaches For Ultra-High Dimensional Datasets, Gurminder Kaur

LSU Doctoral Dissertations

Multivariate informational data, which are abstract as well as complex, are becoming increasingly common in many areas such as scientific, medical, social, business, and so on. Displaying and analyzing large amounts of multivariate data with more than three variables of different types is quite challenging. Visualization of such multivariate data suffers from a high degree of clutter when the numbers of dimensions/variables and data observations become too large. We propose multiple approaches to effectively visualize large datasets of ultrahigh number of dimensions by generalizing two standard multivariate visualization methods, namely star plot and parallel coordinates plot. We refine three ...


Learning To Love Data (Week): Creating Data Services Awareness On Campus, Katie Wissel, Lisa Deluca Sep 2018

Learning To Love Data (Week): Creating Data Services Awareness On Campus, Katie Wissel, Lisa Deluca

Lisa DeLuca, MLIS, MPA

No abstract provided.


Learning To Love Data (Week): Creating Data Services Awareness On Campus, Katie M. Wissel, Lisa Deluca Sep 2018

Learning To Love Data (Week): Creating Data Services Awareness On Campus, Katie M. Wissel, Lisa Deluca

Kathryn Wissel, MBA, MI

In May 2017, The Economist ran a cover story titled “The world’s most valuable resource is no longer oil, but data.” Given the continued growth in the sourcing, curating, and storing of data for academic research, it seems the academy would agree. In response to this growing need at Seton Hall, a midsized research university, the Seton Hall University (SHU) Libraries conducted an assessment of the current and emerging data requirements of the researchers and students on campus.


Programming For The Web: From Soup To Nuts: Implementing A Complete Gis Web Page Using Html5, Css, Javascript, Node.Js, Mongodb, And Open Layers., Charles W. Kann Iii Sep 2018

Programming For The Web: From Soup To Nuts: Implementing A Complete Gis Web Page Using Html5, Css, Javascript, Node.Js, Mongodb, And Open Layers., Charles W. Kann Iii

Gettysburg College Open Educational Resources

This book is designed to be used as a class text but should be easily accessible to programmers interested in Web Programming. It should even be accessible to an advanced hobbyist.

The original goal behind this text was to help students doing research with me in Web based mapping applications, generally using Open Layers. The idea was to provide persistent storage using REST and simple http request from JavaScript to store the data on a server.

When teaching this class, I became painfully aware of just how little students know about Web Programming. They did not know how to format ...


Question-Guided Hybrid Convolution For Visual Question Answering, Peng Gao, Pan Lu, Hongsheng Li, Shuang Li, Yikang Li, Steven C. H. Hoi, Xiaogang Wang Sep 2018

Question-Guided Hybrid Convolution For Visual Question Answering, Peng Gao, Pan Lu, Hongsheng Li, Shuang Li, Yikang Li, Steven C. H. Hoi, Xiaogang Wang

Research Collection School Of Information Systems

In this paper, we propose a novel Question-Guided Hybrid Convolution (QGHC)network for Visual Question Answering (VQA). Most state-of-the-art VQA methodsfuse the high-level textual and visual features from the neural network andabandon the visual spatial information when learning multi-modal features.Toaddress these problems, question-guided kernels generated from the inputquestion are designed to convolute with visual features for capturing thetextual and visual relationship in the early stage. The question-guidedconvolution can tightly couple the textual and visual information but alsointroduce more parameters when learning kernels. We apply the groupconvolution, which consists of question-independent kernels andquestion-dependent kernels, to reduce the parameter size and ...


The Influence Of Conversational Agent Embodiment And Conversational Relevance On Socially Desirable Responding, Ryan M. Schuetzler, Justin Scott Giboney, G. Mark Grimes, Jay F. Nunamaker Jr. Aug 2018

The Influence Of Conversational Agent Embodiment And Conversational Relevance On Socially Desirable Responding, Ryan M. Schuetzler, Justin Scott Giboney, G. Mark Grimes, Jay F. Nunamaker Jr.

Information Systems and Quantitative Analysis Faculty Publications

Conversational agents (CAs) are becoming an increasingly common component in a wide range of information systems. A great deal of research to date has focused on enhancing traits that make CAs more humanlike. However, few studies have examined the influence such traits have on information disclosure. This research builds on self-disclosure, social desirability, and social presence theories to explain how CA anthropomorphism affects disclosure of personally sensitive information. Taken together, these theories suggest that as CAs become more humanlike, the social desirability of user responses will increase. In this study, we use a laboratory experiment to examine the influence of ...


A Linked Coptic Dictionary Online, Frank Feder, Maxim Kupreyev, Emma Manning, Caroline T. Schroeder, Amir Zeldes Aug 2018

A Linked Coptic Dictionary Online, Frank Feder, Maxim Kupreyev, Emma Manning, Caroline T. Schroeder, Amir Zeldes

College of the Pacific Faculty Presentations

We describe a new project publishing a freely available online dictionary for Coptic. The dictionary encompasses comprehensive cross-referencing mechanisms, including linking entries to an online scanned edition of Crum’s Coptic Dictionary, internal cross-references and etymological information, translated searchable definitions in English, French and German, and linked corpus data which provides frequencies and corpus look-up for headwords and multiword expressions. Headwords are available for linking in external projects using a REST API. We describe the challenges in encoding our dictionary using TEI XML and implementing linking mechanisms to construct a Web interface querying frequency information, which draw on NLP tools ...


Sort Vs. Hash Join On Knights Landing Architecture, Victor L. Pan, Felix Lin Aug 2018

Sort Vs. Hash Join On Knights Landing Architecture, Victor L. Pan, Felix Lin

The Summer Undergraduate Research Fellowship (SURF) Symposium

With the increasing amount of information stored, there is a need for efficient database algorithms. One of the most important database operations is “join”. This involves combining columns from two tables and grouping common values in the same row in order to minimize redundant data. The two main algorithms used are hash join and sort merge join. Hash join builds a hash table to allow for faster searching. Sort merge join first sorts the two tables to make it more efficient when comparing values. There has been a lot of debate over which approach is superior. At first, hash join ...


Expected Length Of The Longest Chain In Linear Hashing, Pongthip Srivarangkul, Hemanta K. Maji Aug 2018

Expected Length Of The Longest Chain In Linear Hashing, Pongthip Srivarangkul, Hemanta K. Maji

The Summer Undergraduate Research Fellowship (SURF) Symposium

Hash table with chaining is a data structure that chains objects with identical hash values together with an entry or a memory address. It works by calculating a hash value from an input then placing the input in the hash table entry. When we place two inputs in the same entry, they chain together in a linear linked list. We are interested in the expected length of the longest chain in linear hashing and methods to reduce the length because the worst-case look-up time is directly proportional to it.

The linear hash function used to calculate hash value is defined ...


Predict The Failure Of Hydraulic Pumps By Different Machine Learning Algorithms, Yifei Zhou, Monika Ivantysynova, Nathan Keller Aug 2018

Predict The Failure Of Hydraulic Pumps By Different Machine Learning Algorithms, Yifei Zhou, Monika Ivantysynova, Nathan Keller

The Summer Undergraduate Research Fellowship (SURF) Symposium

Pump failure is a general concerned problem in the hydraulic field. Once happening, it will cause a huge property loss and even the life loss. The common methods to prevent the occurrence of pump failure is by preventative maintenance and breakdown maintenance, however, both of them have significant drawbacks. This research focuses on the axial piston pump and provides a new solution by the prognostic of pump failure using the classification of machine learning. Different kinds of sensors (temperature, acceleration and etc.) were installed into a good condition pump and three different kinds of damaged pumps to measure 10 of ...


Transaction Cost Optimization For Online Portfolio Selection, Bin Li, Jialei Wang, Dingjiang Huang, Steven C. H. Hoi Aug 2018

Transaction Cost Optimization For Online Portfolio Selection, Bin Li, Jialei Wang, Dingjiang Huang, Steven C. H. Hoi

Research Collection School Of Information Systems

To improve existing online portfolio selection strategies in the case of non-zero transaction costs, we propose a novel framework named Transaction Cost Optimization (TCO). The TCO framework incorporates the L1 norm of the difference between two consecutive allocations together with the principles of maximizing expected log return. We further solve the formulation via convex optimization, and obtain two closed-form portfolio update formulas, which follow the same principle as Proportional Portfolio Rebalancing (PPR) in industry. We empirically evaluate the proposed framework using four commonly used data-sets. Although these data-sets do not consider delisted firms and are thus subject to survival bias ...


Exact Processing Of Uncertain Top-K Queries In Multi-Criteria Settings, Kyriakos Mouratidis, Bo Tang Aug 2018

Exact Processing Of Uncertain Top-K Queries In Multi-Criteria Settings, Kyriakos Mouratidis, Bo Tang

Research Collection School Of Information Systems

Traditional rank-aware processing assumes a dataset that contains available options to cover a specific need (e.g., restaurants, hotels, etc) and users who browse that dataset via top-k queries with linear scoring functions, i.e., by ranking the options according to the weighted sum of their attributes, for a set of given weights. In practice, however, user preferences (weights) may only be estimated with bounded accuracy, or may be inherently uncertain due to the inability of a human user to specify exact weight values with absolute accuracy. Motivated by this, we introduce the uncertain top-k query (UTK). Given uncertain preferences ...


A Fine-Grained Attribute Based Data Retrieval With Proxy Re-Encryption Scheme For Data Outsourcing Systems, Hanshu Hong, Ximeng Liu, Zhixin Sun Aug 2018

A Fine-Grained Attribute Based Data Retrieval With Proxy Re-Encryption Scheme For Data Outsourcing Systems, Hanshu Hong, Ximeng Liu, Zhixin Sun

Research Collection School Of Information Systems

Attribute based encryption is suitable for data protection in data outsourcing systems such as cloud computing. However, the leveraging of encryption technique may retrain some routine operations over the encrypted data, particularly in the field of data retrieval. This paper presents an attribute based date retrieval with proxy re-encryption (ABDR-PRE) to provide both fine-grained access control and retrieval over the ciphertexts. The proposed scheme achieves fine-grained data access management by adopting KP-ABE mechanism, a delegator can generate the re-encryption key and search indexes for the ciphertexts to be shared over the target delegatee’s attributes. Throughout the process of data ...


Trajectory-Driven Influential Billboard Placement, Ping Zhang, Zhifeng Bao, Yuchen Li, Guoliang Li, Yipeng Zhang, Zhiyong Peng Aug 2018

Trajectory-Driven Influential Billboard Placement, Ping Zhang, Zhifeng Bao, Yuchen Li, Guoliang Li, Yipeng Zhang, Zhiyong Peng

Research Collection School Of Information Systems

In this paper we propose and study the problem of trajectory-driven influential billboard placement: given a set of billboards U (each with a location and a cost), a database of trajectories T and a budget L, find a set of billboards within the budget to influence the largest number of trajectories. One core challenge is to identify and reduce the overlap of the influence from different billboards to the same trajectories, while keeping the budget constraint into consideration. We show that this problem is NP-hard and present an enumeration based algorithm with (1−1/e) approximation ratio. However, the enumeration ...


Chaff From The Wheat: Characterizing And Determining Valid Bug Reports, Yuanrui Fan, Xin Xia, David Lo, Ahmed E. Hassan Aug 2018

Chaff From The Wheat: Characterizing And Determining Valid Bug Reports, Yuanrui Fan, Xin Xia, David Lo, Ahmed E. Hassan

Research Collection School Of Information Systems

Developers use bug reports to triage and fix bugs. When triaging a bug report, developers must decide whether the bug report is valid (i.e., a real bug). A large amount of bug reports are submitted every day, with many of them end up being invalid reports. Manually determining valid bug report is a difficult and tedious task. Thus, an approach that can automatically analyze the validity of a bug report and determine whether a report is valid can help developers prioritize their triaging tasks and avoid wasting time and effort on invalid bug reports.


Deep Learning For Practical Image Recognition: Case Study On Kaggle Competitions, Xulei Yang, Zeng Zeng, Sin G. Teo, Li Wang, Vijay Chandrasekar, Steven C. H. Hoi Aug 2018

Deep Learning For Practical Image Recognition: Case Study On Kaggle Competitions, Xulei Yang, Zeng Zeng, Sin G. Teo, Li Wang, Vijay Chandrasekar, Steven C. H. Hoi

Research Collection School Of Information Systems

In past years, deep convolutional neural networks (DCNN) have achieved big successes in image classification and object detection, as demonstrated on ImageNet in academic field. However, There are some unique practical challenges remain for real-world image recognition applications, e.g., small size of the objects, imbalanced data distributions, limited labeled data samples, etc. In this work, we are making efforts to deal with these challenges through a computational framework by incorporating latest developments in deep learning. In terms of two-stage detection scheme, pseudo labeling, data augmentation, cross-validation and ensemble learning, the proposed framework aims to achieve better performances for practical ...


A Characterization Of The Medical-Legal Partnership (Mlp) Of Nebraska Medicine, Jordan Pieper Aug 2018

A Characterization Of The Medical-Legal Partnership (Mlp) Of Nebraska Medicine, Jordan Pieper

Service Learning/Capstone Experience

This research study was completed at Legal Aid of Nebraska’s Health, Education, and Law Project through the partnership it has formed working with Nebraska Medicine and Iowa Legal Aid. Traditionally, health and disease have always been viewed exclusively as "healthcare" issues. But with healthcare consistently growing towards holistic approaches to help patients, we now know there are deeper, structural conditions of society that can act as strong driving forces of a person's poor daily living conditions that can negatively impact health. The importance of a Medical-Legal Partnership is that it considers a patient's social determinants of health ...


International Data Sources & Data Literacy, Lisa Deluca Jul 2018

International Data Sources & Data Literacy, Lisa Deluca

Lisa DeLuca, MLIS, MPA

No abstract provided.


Cryptovisor: A Cryptocurrency Advisor Tool, Matthew Baldree, Paul Widhalm, Brandon Hill, Matteo Ortisi Jul 2018

Cryptovisor: A Cryptocurrency Advisor Tool, Matthew Baldree, Paul Widhalm, Brandon Hill, Matteo Ortisi

SMU Data Science Review

In this paper, we present a tool that provides trading recommendations for cryptocurrency using a stochastic gradient boost classifier trained from a model labeled by technical indicators. The cryptocurrency market is volatile due to its infancy and limited size making it difficult for investors to know when to enter, exit, or stay in the market. Therefore, a tool is needed to provide investment recommendations for investors. We developed such a tool to support one cryptocurrency, Bitcoin, based on its historical price and volume data to recommend a trading decision for today or past days. This tool is 95.50% accurate ...


Machine Learning To Predict College Course Success, Anthony R.Y. Dalton, Justin Beer, Sriharshasai Kommanapalli, James S. Lanich Ph.D. Jul 2018

Machine Learning To Predict College Course Success, Anthony R.Y. Dalton, Justin Beer, Sriharshasai Kommanapalli, James S. Lanich Ph.D.

SMU Data Science Review

In this paper, we present an analysis of the predictive ability of machine learning on the success of students in college courses in a California Community College. The California Legislature passed assembly bill 705 in order to place students in non-remedial coursework, based on high school transcripts, to increase college completion. We utilize machine learning methods on de-identified student high school transcript data to create predictive algorithms on whether or not the student will be successful in college-level English and Mathematics coursework. To satisfy the bill’s requirements, we first use exploratory data analysis on applicable transcript variables. Then we ...


Mining Temporal Activity Patterns On Social Media, Nikan Chavoshi Jul 2018

Mining Temporal Activity Patterns On Social Media, Nikan Chavoshi

Computer Science ETDs

Social media provide communication networks for their users to easily create and share content. Automated accounts, called bots, abuse these platforms by engaging in suspicious and/or illegal activities. Bots push spam content and participate in sponsored activities to expand their audience. The prevalence of bot accounts in social media can harm the usability of these platforms, and decrease the level of trustworthiness in them. The main goal of this dissertation is to show that temporal analysis facilitates detecting bots in social media. I introduce new bot detection techniques which exploit temporal information. Since automated accounts are controlled by computer ...


Creating Real-Time Dynamic Knowledge Graphs, Swati Padhee, Sarasi Lalithsena, Amit P. Sheth Jul 2018

Creating Real-Time Dynamic Knowledge Graphs, Swati Padhee, Sarasi Lalithsena, Amit P. Sheth

Kno.e.sis Publications

No abstract provided.


Modeling Contemporaneous Basket Sequences With Twin Networks For Next-Item Recommendation, Duc Trong Le, Hady Wirawan Lauw, Yuan Fang Jul 2018

Modeling Contemporaneous Basket Sequences With Twin Networks For Next-Item Recommendation, Duc Trong Le, Hady Wirawan Lauw, Yuan Fang

Research Collection School Of Information Systems

Our interactions with an application frequently leave a heterogeneous and contemporaneous trail of actions and adoptions (e.g., clicks, bookmarks, purchases). Given a sequence of a particular type (e.g., purchases)-- referred to as the target sequence, we seek to predict the next item expected to appear beyond this sequence. This task is known as next-item recommendation. We hypothesize two means for improvement. First, within each time step, a user may interact with multiple items (a basket), with potential latent associations among them. Second, predicting the next item in the target sequence may be helped by also learning from another ...


Pacela: A Neural Framework For User Visitation In Location-Based Social Networks, Thanh-Nam Doan, Ee-Peng Lim Jul 2018

Pacela: A Neural Framework For User Visitation In Location-Based Social Networks, Thanh-Nam Doan, Ee-Peng Lim

Research Collection School Of Information Systems

Check-in prediction using location-based social network data is an important research problem for both academia and industry since an accurate check-in predictive model is useful to many applications, e.g. urban planning, venue recommendation, route suggestion, and context-aware advertising. Intuitively, when considering venues to visit, users may rely on their past observed visit histories as well as some latent attributes associated with the venues. In this paper, we therefore propose a check-in prediction model based on a neural framework called Preference and Context Embeddings with Latent Attributes (PACELA). PACELA learns the embeddings space for the user and venue data as ...


Low-Rank Sparse Subspace For Spectral Clustering, Xiaofeng Zhu, Shichao Zhang, Yonggang Li, Jilian Zhang, Lifeng Yang, Yue Fang Jul 2018

Low-Rank Sparse Subspace For Spectral Clustering, Xiaofeng Zhu, Shichao Zhang, Yonggang Li, Jilian Zhang, Lifeng Yang, Yue Fang

Research Collection School Of Information Systems

The current two-step clustering methods separately learn the similarity matrix and conduct k means clustering. Moreover, the similarity matrix is learnt from the original data, which usually contain noise. As a consequence, these clustering methods cannot achieve good clustering results. To address these issues, this paper proposes a new graph clustering methods (namely Low-rank Sparse Subspace clustering (LSS)) to simultaneously learn the similarity matrix and conduct the clustering from the low-dimensional feature space of the original data. Specifically, the proposed LSS integrates the learning of similarity matrix of the original feature space, the learning of similarity matrix of the low-dimensional ...