Computer Sciences Commons^™

Factors Influencing Knowledge Transfer In Onshore Information Systems Outsourcing In Ethiopia, Solomon A. Nurye, Alem Molla, Temtim Assefa Desta

The African Journal of Information Systems

Knowledge transfer in onshore information systems (IS) outsourcing projects in Africa is an important but under-researched phenomenon. This study focuses on the client-vendor perspective and examines the factors that influence knowledge transfer in onshore information systems outsourcing in Ethiopia. Conceptually, knowledge-based perspectives of IS outsourcing is used to identify an initial set of factors to frame the empirical study. This is followed by semi-structured interviews with ten project managers. The findings indicate that five key factors, namely mutual absorptive capacity, mutual learning intent, mutual trust, mutual disseminative capacity and project staff turnover influence knowledge transfer in outsourced IS projects. The …

Csci49379: Intro To Blockchain (Syllabus), Wendy Mock, Nyc Tech-In-Residence Corps

Open Educational Resources

Syllabus for the course: CSCI 49397 - Blockchain. Delivered at Hunter College in Fall 2019 by Wendy Mock as part of the Tech-in-Residence Corps program.

Comprehending Test Code: An Empirical Study, Chak Shun Yu, Christoph Treude, Maurício Aniche

Research Collection School Of Computing and Information Systems

Developers spend a large portion of their time and effort on comprehending source code. While many studies have investigated how developers approach these comprehension tasks and what factors influence their success, less is known about how developers comprehend test code specifically, despite the undisputed importance of testing. In this paper, we report on the results of an empirical study with 44 developers to understand which factors influence developers when comprehending Java test code. We measured three dependent variables: the total time spent reading a test suite, the ability to identify the overall purpose of a test suite, and the ability …

Automatic Fashion Knowledge Extraction From Social Media, Yunshan Ma, Lizi Liao, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Fashion knowledge plays a pivotal role in helping people in their dressing. In this paper, we present a novel system to automatically harvest fashion knowledge from social media. It unifies three tasks of occasion, person and clothing discovery from multiple modalities of images, texts and metadata. A contextualized fashion concept learning model is applied to leverage the rich contextual information for improving the fashion concept learning performance. At the same time, to counter the label noise within training data, we employ a weak label modeling method to further boost the performance. We build a website to demonstrate the quality of …

Person Identification With Convolutional Neural Networks, Kang Zheng

Theses and Dissertations

Person identification aims at matching persons across images or videos captured by different cameras, without requiring the presence of persons’ faces. It is an important problem in computer vision community and has many important real-world applica- tions, such as person search, security surveillance, and no-checkout stores. However, this problem is very challenging due to various factors, such as illumination varia- tion, view changes, human pose deformation, and occlusion. Traditional approaches generally focus on hand-crafting features and/or learning distance metrics for match- ing to tackle these challenges. With Convolutional Neural Networks (CNNs), feature extraction and metric learning can be combined in …

Semantic Segmentation Considering Image Degradation, Global Context, And Data Balancing, Dazhou Guo

Theses and Dissertations

Recently, semantic segmentation – assigning a categorical label to each pixel in an im- age – plays an important role in image understanding applications, e.g., autonomous driving, human-machine interaction and medical imaging. Semantic segmentation has made progress by using the deep convolutional neural networks, which are sur- passing the traditional methods by a large margin. Despite the success of the deep convolutional neural networks (CNNs), there remain three major challenges.

The first challenge is how to segment the degraded images semantically, i.e., de- graded image semantic segmentation. In general, image degradations increase the difficulty of semantic segmentation, usually leading to …

Phylogenetic Reconstruction Analysis On Gene Order And Copy Number Variation, Ruofan Xia

Theses and Dissertations

Genome rearrangement is known as one of the main evolutionary mechanisms on the genomic level. Phylogenetic analysis based on rearrangement played a crucial role in biological research in the past decades, especially with the increasing avail- ability of fully sequenced genomes. In general, phylogenetic analysis aims to solve two problems: Small Parsimony Problem (SPP) and Big Parsimony Problem (BPP). Maximum parsimony is a popular approach for SPP and BPP which relies on itera- tively solving a NP-hard problem, the median problem. As a result, current median solvers and phylogenetic inference methods based on the median problem all face se- rious …

Stacked Modelling Framework, Kareem Abdelfatah

Theses and Dissertations

The thesis develops a predictive modeling framework based on stacked Gaussian processes and applies it to two main applications in environmental and chemical en- gineering. First, a network of independently trained Gaussian processes (StackedGP) is introduced to obtain analytical predictions of quantities of interest (model out- puts) with quantified uncertainties. StackedGP framework supports component- based modeling in different fields such as environmental and chemical science, en- hances predictions of quantities of interest through a cascade of intermediate predic- tions usually addressed by cokriging, and propagates uncertainties through emulated dynamical systems driven by uncertain forcing variables. By using analytical first and …

Properties, Learning Algorithms, And Applications Of Chain Graphs And Bayesian Hypergraphs, Mohammad Ali Javidian

Theses and Dissertations

Probabilistic graphical models (PGMs) use graphs, either undirected, directed, or mixed, to represent possible dependencies among the variables of a multivariate probability distri- bution. PGMs, such as Bayesian networks and Markov networks, are now widely accepted as a powerful and mature framework for reasoning and decision making under uncertainty in knowledge-based systems. With the increase of their popularity, the range of graphical models being investigated and used has also expanded. Several types of graphs with dif- ferent conditional independence interpretations - also known as Markov properties - have been proposed and used in graphical models.

The graphical structure of a …

Cybersecurity Issues In The Context Of Cryptographic Shuffling Algorithms And Concept Drift: Challenges And Solutions, Hatim Alsuwat

Theses and Dissertations

In this dissertation, we investigate and address two kinds of data integrity threats. We first study the limitations of secure cryptographic shuffling algorithms regarding preservation of data dependencies. We then study the limitations of machine learning models regarding concept drift detection. We propose solutions to address these threats.

Shuffling Algorithms have been used to protect the confidentiality of sensitive data. However, these algorithms may not preserve data dependencies, such as functional de- pendencies and data-driven associations. We present two solutions for addressing these shortcomings: (1) Functional dependencies preserving shuffle, and (2) Data-driven asso- ciations preserving shuffle. For preserving functional dependencies, …

Preface To Ieee Vast 2019 Conference Track And Vast Challenge, Remco Chang, Daniel Keim, Ross Maciejewski, Kristin Cook, R. Jordan Crouser

Computer Science: Faculty Publications

No abstract provided.

Mobile Software Development (Ksu), Yong Shi, Dan Lo, Selena He, Sarah North, Mingon Kang

Computer Science and Information Technology Grants Collections

This Grants Collection for Mobile Software Development was created under a Round Twelve ALG Textbook Transformation Grant.

Affordable Learning Georgia Grants Collections are intended to provide faculty with the frameworks to quickly implement or revise the same materials as a Textbook Transformation Grants team, along with the aims and lessons learned from project teams during the implementation process.

Documents are in .pdf format, with a separate .docx (Word) version available for download. Each collection contains the following materials:

• Linked Syllabus
• Initial Proposal
• Final Report

Cloud Computing Architecture For Elearning Systems In Secondary Schools In Tanzania, Kennedy F. Mwakisole, Mussa M. Kissaka Dr, Joel S. Mtebe Phd

The African Journal of Information Systems

In recent years, schools have been investing heavily on information and communications technology (ICT) infrastructure to implement eLearning systems to enhance the quality of education in secondary schools in Tanzania. The majority of these systems are implemented using a traditional web-based eLearning approach on school premises which is costly and limits usage due to lack of scalability and flexibility. Consequently, many schools have started adopting cloud computing as a solution. However, this adaption depends on well-defined cloud eLearning architecture. This study proposed cloud architecture for implementing an eLearning system in secondary schools in Tanzania by adopting various layers proposed in …

User Interface Engineering (Ksu), Yong Shi, Dan Lo, Selena He, Mingon Kang, Sarah North

Computer Science and Information Technology Grants Collections

This Grants Collection for User Interface Engineering was created under a Round Twelve ALG Textbook Transformation Grant.

Affordable Learning Georgia Grants Collections are intended to provide faculty with the frameworks to quickly implement or revise the same materials as a Textbook Transformation Grants team, along with the aims and lessons learned from project teams during the implementation process.

Documents are in .pdf format, with a separate .docx (Word) version available for download. Each collection contains the following materials:

• Linked Syllabus
• Initial Proposal
• Final Report

Computer Organization And Architecture (Ksu), Yong Shi, Dan Lo, Selena He, Mingon Kang, Sarah North

Computer Science and Information Technology Grants Collections

This Grants Collection for Computer Organization and Architecture was created under a Round Twelve ALG Textbook Transformation Grant.

Affordable Learning Georgia Grants Collections are intended to provide faculty with the frameworks to quickly implement or revise the same materials as a Textbook Transformation Grants team, along with the aims and lessons learned from project teams during the implementation process.

Documents are in .pdf format, with a separate .docx (Word) version available for download. Each collection contains the following materials:

• Linked Syllabus
• Initial Proposal
• Final Report

Advanced Web Development (Ksu), Meng Han, Jack Zheng

Computer Science and Information Technology Grants Collections

This Grants Collection for Advanced Web Development was created under a Round Twelve ALG Textbook Transformation Grant.

Affordable Learning Georgia Grants Collections are intended to provide faculty with the frameworks to quickly implement or revise the same materials as a Textbook Transformation Grants team, along with the aims and lessons learned from project teams during the implementation process.

Documents are in .pdf format, with a separate .docx (Word) version available for download. Each collection contains the following materials:

• Linked Syllabus
• Initial Proposal
• Final Report

Cloud Computing (Ksu), Yong Shi, Dan Lo, Selena He, Mingon Kang, Sarah North

Computer Science and Information Technology Grants Collections

This Grants Collection for Cloud Computing was created under a Round Twelve ALG Textbook Transformation Grant.

Affordable Learning Georgia Grants Collections are intended to provide faculty with the frameworks to quickly implement or revise the same materials as a Textbook Transformation Grants team, along with the aims and lessons learned from project teams during the implementation process.

Documents are in .pdf format, with a separate .docx (Word) version available for download. Each collection contains the following materials:

• Linked Syllabus
• Initial Proposal
• Final Report

Fractals As Basis For Design And Critique, John Charles Driscoll

Dissertations and Theses

The design profession is responding to the complex systems represented by architecture and planning by increasingly incorporating the power of computer technology into the design process. This represents a paradigm shift, and requires that designers rise to the challenge of both embracing modern technologies to perform increasingly sophisticated tasks without compromising their objective to create meaningful and environmentally sensitive architecture. This dissertation investigated computer-based fractal tools applied within a traditional architectural charette towards a design process with the potential to address the complex issues architects and planners face today. We developed and presented an algorithm that draws heavily from fractal …

Big-Data Talent Analytics In The Public Sector: A Promotion And Firing Model Of Employees At Federal Agencies, Rabih Neouchi

Operations Research and Engineering Management Theses and Dissertations

Talent analytics is a relatively new area of focus to researchers working in analytics and data science. Talent Analytics has the potential to help companies make many informed critical decisions around talent acquisition, promotion and retention. This work investigates data science to predict “shiny star” employees in the U.S. public sector, defined as top-notch performers over the years of a given time span. Its scope falls within talent analytics, also called people analytics, a relatively new research area.

We clean a data set made available by the U.S. Office of Personnel Management (OPM) and present two models to predict the …

Tracking The 6-Dof Flight Trajectory Of Windborne Debris Using Stereophotogrammetry, Chaman Sabharwal, Yanlin Guo

Computer Science Faculty Research & Creative Works

Numerous post-windstorm investigations have reported that windborne debris can cause costly damage to the envelope of buildings in urban areas under strong winds (e.g., during hurricanes or tornados). Thus, understanding the physics of debris flight is of critical importance. Previously developed numerical models describing debris flight physics have not been validated for the complex urban flow environment; such a validation requires experimentally measuring the debris flight trajectory in wind tunnel tests. In this context, this paper proposes a debris measurement algorithm using stereophotogrammetry. This algorithm aims to determine the six-degree-of-freedom (6-DOF) trajectory and velocity of flying debris, addressing the research …

The 2nd 3d Face Alignment In The Wild Challenge (3dfaw-Video): Dense Reconstruction From Video, Rohith Krishnan Pillai, Laszlo Attila Jeni, Huiyuan Yang, Zheng Zhang, Lijun Yin, Jeffrey F. Cohn

Computer Science Faculty Research & Creative Works

3D face alignment approaches have strong advantages over 2D with respect to representational power and robustness to illumination and pose. Over the past few years, a number of research groups have made rapid advances in dense 3D alignment from 2D video and obtained impressive results. How these various methods compare is relatively unknown. Previous benchmarks addressed sparse 3D alignment and single image 3D reconstruction. No commonly accepted evaluation protocol exists for dense 3D face reconstruction from video with which to compare them. The 2nd 3D Face Alignment in the Wild from Videos (3DFAW-Video) Challenge extends the previous 3DFAW 2016 competition …

Proximity Detection With Single-Antenna Iot Devices, Timothy J. Pierson, Travis Peters, Ronald Peterson, David Kotz

Dartmouth Scholarship

Providing secure communications between wireless devices that encounter each other on an ad-hoc basis is a challenge that has not yet been fully addressed. In these cases, close physical proximity among devices that have never shared a secret key is sometimes used as a basis of trust; devices in close proximity are deemed trustworthy while more distant devices are viewed as potential adversaries. Because radio waves are invisible, however, a user may believe a wireless device is communicating with a nearby device when in fact the user’s device is communicating with a distant adversary. Researchers have previously proposed methods for …

Nerf This: Copyright Highly Creative Video Game Streams As Sports Broadcasts, Madeleine A. Ball

William & Mary Law Review

Since the 1980s, video games have grown exponentially as an entertainment medium. Once relegated to the niche subcultures of nerds, video games are now decidedly mainstream, drawing over 200 million American consumers yearly. As a result, the industry has stepped up its game. No longer simply a diversion to be enjoyed individually, Americans are increasingly watching others play video games like they might watch television. This practice, where enthusiastic gamers broadcast their video game session online to crowds of viewers, is called “live streaming.”

While streaming has become lucrative and popular, American copyright law currently nerfs this nascent industry. Streams …

The Internet Of Bodies, Andrea M. Matwyshyn

William & Mary Law Review

This Article introduces the ongoing progression of the Internet of Things (IoT) into the Internet of Bodies (IoB)—a network of human bodies whose integrity and functionality rely at least in part on the Internet and related technologies, such as artificial intelligence. IoB devices will evidence the same categories of legacy security flaws that have plagued IoT devices. However, unlike most IoT, IoB technologies will directly, physically harm human bodies—a set of harms courts, legislators, and regulators will deem worthy of legal redress. As such, IoB will herald the arrival of (some forms of) corporate software liability and a new legal …

Effective Capacity In Wireless Networks: A Comprehensive Survey, Muhammad Amjad, Mubashir Husain Rehmani, Leila Musavian

Publications

Low latency applications, such as multimedia communications, autonomous vehicles, and Tactile Internet are the emerging applications for next-generation wireless networks, such as 5th generation (5G) mobile networks. Existing physical layer channel models, however, do not explicitly consider quality of service (QoS) aware related parameters under specific delay constraints. To investigate the performance of low-latency applications in future networks, a new mathematical framework is needed. Effective capacity (EC), which is a link-layer channel model with QoS-awareness, can be used to investigate the performance of wireless networks under certain statistical delay constraints. In this paper, we provide a comprehensive survey on existing …

Insider’S Misuse Detection: From Hidden Markov Model To Deep Learning, Ahmed Saaudi

Theses and Dissertations

Malicious insiders increasingly affect organizations by leaking classified data to unautho- rized entities. Detecting insiders’ misuses in computer systems is a challenging problem. In this dissertation, we propose two approaches to detect such threats: a probabilistic graph- ical model-based approach and a deep learning-based approach. We investigate the logs of computer-based activities to discover patterns of misuse. We model user’s behaviors as sequences of computer-based events.

For our probabilistic graphical model-based approach, we propose an unsupervised model for insider’s misuse detection. That is, we develop Stochastic Gradient Descent method to learn Hidden Markov Models (SGD-HMM) with the goal of analyzing …

An Accelerated Hierarchical Approach For Object Shape Extraction And Recognition, Mahmoud K. Quweider, Bassam Arshad, Hansheng Lei, Liyu Zhang, Fitratullah Khan

Computer Science Faculty Publications

We present a novel automatic supervised object recognition algorithm based on a scale and rotation invariant Fourier descriptors algorithm. The algorithm is hierarchical in nature to capture the inherent intra-contour spatial relationships between the parent and child contours of an object. A set of distance metrics are introduced to go along with the hierarchical model. To test the algorithm, a diverse database of shapes is created and used to train standard classification algorithms, for shape-labeling. The implemented algorithm takes advantage of the multi-threaded architecture and GPU efficient image-processing functions present in OpenCV wherever possible, speeding up the running time and …

Designing Women: Essentializing Femininity In Ai Linguistics, Ellianie S. Vega

Student Publications

Since the eighties, feminists have considered technology a force capable of subverting sexism because of technology’s ability to produce unbiased logic. Most famously, Donna Haraway’s “A Cyborg Manifesto” posits that the cyborg has the inherent capability to transcend gender because of its removal from social construct and lack of loyalty to the natural world. But while humanoids and artificial intelligence have been imagined as inherently subversive to gender, current artificial intelligence perpetuates gender divides in labor and language as their programmers imbue them with traits considered “feminine.” A majority of 21st century AI and humanoids are programmed to fit female …

Python Practice Assignments For Computer Science I, Hyrum Carroll, Hillary Fleenor

Computer Science and Information Technology Ancillary Materials

This set of practice assignments for Computer Science 1 were created under a Round Twelve Mini-Grant for Ancillary Materials Creation and Revision.

The assignments use the Python coding language and the repl.it coding platform and cover the following topics:

• Modules;
• Functions;
• Selections;
• Loops;
• Strings;
• Lists;
• Files;
• Dictionaries.

Internet Of Things (Open Course), Rebecca Rutherfoord, Susan Vandeven, Guangzhi Zheng, Hossain Shahriar, Xin Tian

Computer Science and Information Technology Ancillary Materials

This open course for Internet of Things was created through a Round 13 Affordable Materials Grant.