Open Access. Powered by Scholars. Published by Universities.®

Computer Sciences Commons™

Open Access. Powered by Scholars. Published by Universities.®

2020

Discipline
Institution
Keyword
Publication
Publication Type
File Type

Articles 2611 - 2640 of 4524

Full-Text Articles in Computer Sciences

What Do Undergraduate Engineering Students And Preservice Teachers Learn By Collaborating And Teaching Engineering And Coding Through Robotics?, Jennifer Jill Kidd, Krishnanand Kaipa, Samuel J. Jacks, Stacie I. Ringleb, Pilar Pazos, Kristie Gutierrez, Orlando M. Ayala, Lillian Maria De Souza Almeida Jun 2020

What Do Undergraduate Engineering Students And Preservice Teachers Learn By Collaborating And Teaching Engineering And Coding Through Robotics?, Jennifer Jill Kidd, Krishnanand Kaipa, Samuel J. Jacks, Stacie I. Ringleb, Pilar Pazos, Kristie Gutierrez, Orlando M. Ayala, Lillian Maria De Souza Almeida

Teaching & Learning Faculty Publications

This research paper presents preliminary results of an NSF-supported interdisciplinary collaboration between undergraduate engineering students and preservice teachers. The fields of engineering and elementary education share similar challenges when it comes to preparing undergraduate students for the new demands they will encounter in their profession. Engineering students need interprofessional skills that will help them value and negotiate the contributions of various disciplines while working on problems that require a multidisciplinary approach. Increasingly, the solutions to today's complex problems must integrate knowledge and practices from multiple disciplines and engineers must be able to recognize when expertise from outside their field can …

Go to article

Monte Carlo Tree Search Applied To A Modified Pursuit/Evasion Scotland Yard Game With Rendezvous Spaceflight Operation Applications, Joshua A. Daughtery Jun 2020

Monte Carlo Tree Search Applied To A Modified Pursuit/Evasion Scotland Yard Game With Rendezvous Spaceflight Operation Applications, Joshua A. Daughtery

Theses and Dissertations

This thesis takes the Scotland Yard board game and modifies its rules to mimic important aspects of space in order to facilitate the creation of artificial intelligence for space asset pursuit/evasion scenarios. Space has become a physical warfighting domain. To combat threats, an understanding of the tactics, techniques, and procedures must be captured and studied. Games and simulations are effective tools to capture data lacking historical context. Artificial intelligence and machine learning models can use simulations to develop proper defensive and offensive tactics, techniques, and procedures capable of protecting systems against potential threats. Monte Carlo Tree Search is a bandit-based …

Go to article

Neural Network Models For Nuclear Treaty Monitoring: Enhancing The Seismic Signal Pipeline With Deep Temporal Convolution, Joshua T. Dickey Jun 2020

Neural Network Models For Nuclear Treaty Monitoring: Enhancing The Seismic Signal Pipeline With Deep Temporal Convolution, Joshua T. Dickey

Theses and Dissertations

Seismic signal processing at the IDC is critical to global security, facilitating the detection and identification of covert nuclear tests in near-real time. This dissertation details three research studies providing substantial enhancements to this pipeline. Study 1 focuses on signal detection, employing a TCN architecture directly against raw real-time data streams and effecting a 4 dB increase in detector sensitivity over the latest operational methods. Study 2 focuses on both event association and source discrimination, utilizing a TCN-based triplet network to extract source-specific features from three-component seismograms, and providing both a complimentary validation measure for event association and a one-shot …

Go to article

Visual Attention Consistency Under Image Transforms For Multi-Label Image Classification, Hao Guo, Kang Zheng, Xiaochuan Fan, Hongkai Yu, Song Wang Jun 2020

Visual Attention Consistency Under Image Transforms For Multi-Label Image Classification, Hao Guo, Kang Zheng, Xiaochuan Fan, Hongkai Yu, Song Wang

Computer Science Faculty Publications

Human visual perception shows good consistency for many multi-label image classification tasks under certain spatial transforms, such as scaling, rotation, flipping and translation. This has motivated the data augmentation strategy widely used in CNN classifier training -- transformed images are included for training by assuming the same class labels as their original images. In this paper, we further propose the assumption of perceptual consistency of visual attention regions for classification under such transforms, i.e., the attention region for a classification follows the same transform if the input image is spatially transformed. While the attention regions of CNN classifiers can be …

Go to article

A Virtualization Based System Infrastructure For Dynamic Program Analysis, Jiaqi Hong Jun 2020

A Virtualization Based System Infrastructure For Dynamic Program Analysis, Jiaqi Hong

Dissertations and Theses Collection (Open Access)

Dynamic malware analysis schemes either run the target program as is in an isolated environment assisted by additional hardware facilities or modify it with instrumentation code statically or dynamically. The hardware-assisted schemes usually trap the target during its execution to a more privileged environment based on the available hardware events. The more privileged environment is not accessible by the untrusted kernel, thus this approach is often applied for transparent and secure kernel analysis. Nevertheless, the isolated environment induces a virtual address gap between the analyzer and the target, which hinders effective and efficient memory introspection and undermines the correctness of …

Go to article

Cc2vec: Distributed Representations Of Code Changes, Thong Hoang, Hong Jin Kang, Julia Lawall, David Lo Jun 2020

Cc2vec: Distributed Representations Of Code Changes, Thong Hoang, Hong Jin Kang, Julia Lawall, David Lo

Research Collection School Of Computing and Information Systems

Existing work on software patches often use features specific to a single task. These works often rely on manually identified features, and human effort is required to identify these features for each task. In this work, we propose CC2Vec, a neural network model that learns a representation of code changes guided by their accompanying log messages, which represent the semantic intent of the code changes. CC2Vec models the hierarchical structure of a code change with the help of the attention mechanism and usesmultiple comparison functions to identify the differences between the removed and added code. To evaluate if CC2Vec can …

Go to article

Visual Commonsense Representation Learning Via Causal Inference, Tan Wang, Jianqiang Huang, Hanwang Zhang, Qianru Sun Jun 2020

Visual Commonsense Representation Learning Via Causal Inference, Tan Wang, Jianqiang Huang, Hanwang Zhang, Qianru Sun

Research Collection School Of Computing and Information Systems

We present a novel unsupervised feature representation learning method, Visual Commonsense Region-based Convolutional Neural Network (VC R-CNN), to serve as an improved visual region encoder for high-level tasks such as captioning and VQA. Given a set of detected object regions in an image (e.g., using Faster R-CNN), like any other unsupervised feature learning methods (e.g., word2vec), the proxy training objective of VC R-CNN is to predict the con-textual objects of a region. However, they are fundamentally different: the prediction of VC R-CNN is by using causal intervention: P(Y|do(X)), while others are by using the conventional likelihood: P(Y|X). We extensively apply …

Go to article

Transferring And Regularizing Prediction For Semantic Segmentation, Yiheng Zhang, Zhaofan Qiu, Ting Yao, Chong-Wah Ngo, Dong Liu, Tao Mei Jun 2020

Transferring And Regularizing Prediction For Semantic Segmentation, Yiheng Zhang, Zhaofan Qiu, Ting Yao, Chong-Wah Ngo, Dong Liu, Tao Mei

Research Collection School Of Computing and Information Systems

Semantic segmentation often requires a large set of images with pixel-level annotations. In the view of extremely expensive expert labeling, recent research has shown that the models trained on photo-realistic synthetic data (e.g., computer games) with computer-generated annotations can be adapted to real images. Despite this progress, without constraining the prediction on real images, the models will easily overfit on synthetic data due to severe domain mismatch. In this paper, we novelly exploit the intrinsic properties of semantic segmentation to alleviate such problem for model transfer. Specifically, we present a Regularizer of Prediction Transfer (RPT) that imposes the intrinsic properties …

Go to article

Secure Server-Aided Data Sharing Clique With Attestation, Yujue Wang, Hwee Hwa Pang, Robert H. Deng, Yong Ding, Qianhong Wu, Bo Qin, Kefeng Fan Jun 2020

Secure Server-Aided Data Sharing Clique With Attestation, Yujue Wang, Hwee Hwa Pang, Robert H. Deng, Yong Ding, Qianhong Wu, Bo Qin, Kefeng Fan

Research Collection School Of Computing and Information Systems

In this paper, we consider the security issues in data sharing cliques via remote server. We present a public key re-encryption scheme with delegated equality test on ciphertexts (PRE-DET). The scheme allows users to share outsourced data on the server without performing decryption-then-encryption procedures, allows new users to dynamically join the clique, allows clique users to attest the message underlying a ciphertext, and enables the server to partition outsourced user data without any further help of users after being delegated. We introduce the PRE-DET framework, propose a concrete construction and formally prove its security against five types of adversaries regarding …

Go to article

Towards Distributed Node Similarity Search On Graphs, Tianming Zhang, Yunjun Gao, Baihua Zheng, Lu Chen, Shiting Wen, Wei Guo Jun 2020

Towards Distributed Node Similarity Search On Graphs, Tianming Zhang, Yunjun Gao, Baihua Zheng, Lu Chen, Shiting Wen, Wei Guo

Research Collection School Of Computing and Information Systems

Node similarity search on graphs has wide applications in recommendation, link prediction, to name just a few. However, existing studies are insufficient due to two reasons: (i) the scale of the real-world graph is growing rapidly, and (ii) vertices are always associated with complex attributes. In this paper, we propose an efficiently distributed framework to support node similarity search on massive graphs, which considers both graph structure correlation and node attribute similarity in metric spaces. The framework consists of preprocessing stage and query stage. In the preprocessing stage, a parallel KD-tree construction (KDC) algorithm is developed to form a newly …

Go to article

Goods Consumed During Transit In Split Delivery Vehicle Routing Problems: Modeling And Solution, Wenzhe Yang, Di Wang, Wei Pang, Ah-Hwee Tan, You Zhou Jun 2020

Goods Consumed During Transit In Split Delivery Vehicle Routing Problems: Modeling And Solution, Wenzhe Yang, Di Wang, Wei Pang, Ah-Hwee Tan, You Zhou

Research Collection School Of Computing and Information Systems

This article presents the modeling and solution of an extended type of split delivery vehicle routing problem (SDVRP). In SDVRP, the demands of customers need to be met by efficiently routing a given number of capacitated vehicles, wherein each customer may be served multiple times by more than one vehicle. Furthermore, in many real-world scenarios, consumption of vehicles en route is the same as the goods being delivered to customers, such as food, water and fuel in rescue or replenishment missions in harsh environments. Moreover, the consumption may also be in virtual forms, such as time spent in constrained tasks. …

Go to article

Hyperbolic Visual Embedding Learning For Zero-Shot Recognition, Shaoteng Liu, Jingjing Chen, Liangming Pan, Chong-Wah Ngo, Tat-Seng Chua, Yu-Gang Jiang Jun 2020

Hyperbolic Visual Embedding Learning For Zero-Shot Recognition, Shaoteng Liu, Jingjing Chen, Liangming Pan, Chong-Wah Ngo, Tat-Seng Chua, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

This paper proposes a Hyperbolic Visual Embedding Learning Network for zero-shot recognition. The network learns image embeddings in hyperbolic space, which is capable of preserving the hierarchical structure of semantic classes in low dimensions. Comparing with existing zeroshot learning approaches, the network is more robust because the embedding feature in hyperbolic space better represents class hierarchy and thereby avoid misleading resulted from unrelated siblings. Our network outperforms exiting baselines under hierarchical evaluation with an extremely challenging setting, i.e., learning only from 1,000 categories to recognize 20,841 unseen categories. While under flat evaluation, it has competitive performance as state-of-the-art methods but …

Go to article

Maximum A Posteriori Estimation For Information Source Detection, Biao Chang, Enhong Chen, Feida Zhu, Qi Liu, Tong Xu Jun 2020

Maximum A Posteriori Estimation For Information Source Detection, Biao Chang, Enhong Chen, Feida Zhu, Qi Liu, Tong Xu

Research Collection School Of Computing and Information Systems

Information source detection is to identify nodes initiating the diffusion process in a network, which has a wide range of applications including epidemic outbreak prevention, Internet virus source identification, and rumor source tracing in social networks. Although it has attracted ever-increasing attention from research community in recent years, existing solutions still suffer from high time complexity and inadequate effectiveness, due to high dynamics of information diffusion and observing just a snapshot of the whole process. To this end, we present a comprehensive study for single information source detection in weighted graphs. Specifically, we first propose a maximum a posteriori (MAP) …

Go to article

An Extended Framework Of Privacy-Preserving Computation With Flexible Access Control, Wenxiu Ding, Rui Hu, Zheng Yan, Xinren Qian, Robert H. Deng, Laurence T. Yang, Mianxiong Dong Jun 2020

An Extended Framework Of Privacy-Preserving Computation With Flexible Access Control, Wenxiu Ding, Rui Hu, Zheng Yan, Xinren Qian, Robert H. Deng, Laurence T. Yang, Mianxiong Dong

Research Collection School Of Computing and Information Systems

Cloud computing offers various services based on outsourced data by utilizing its huge volume of resources and great computation capability. However, it also makes users lose full control over their data. To avoid the leakage of user data privacy, encrypted data are preferred to be uploaded and stored in the cloud, which unfortunately complicates data analysis and access control. In particular, few existing works consider the fine-grained access control over the computational results from ciphertexts. Though our previous work proposed a framework to support several basic computations (such as addition, multiplication and comparison) with flexible access control, privacy-preserving division calculations …

Go to article

A New Framework For Privacy-Preserving Biometric-Based Remote User Authentication, Yangguang Tian, Yingjiu Li, Robert H. Deng, Nan Li, Pengfei Wu, Anyi Liu Jun 2020

A New Framework For Privacy-Preserving Biometric-Based Remote User Authentication, Yangguang Tian, Yingjiu Li, Robert H. Deng, Nan Li, Pengfei Wu, Anyi Liu

Research Collection School Of Computing and Information Systems

In this paper, we introduce the first general framework for strong privacy-preserving biometric-based remote user authentication based on oblivious RAM (ORAM) protocol and computational fuzzy extractors. We define formal security models for the general framework, and we prove that it can achieve user authenticity and strong privacy. In particular, the general framework ensures that: (1) a strong privacy and a log-linear time-complexity are achieved by using a new tree-based ORAM protocol; (2) a constant bandwidth cost is achieved by exploiting computational fuzzy extractors in the challenge-response phase of remote user authentications.

Go to article

Optimal Control Of A Rumor Propagation Model With Different Propagation Degrees In Social Network, Yingfeng Tang Jun 2020

Optimal Control Of A Rumor Propagation Model With Different Propagation Degrees In Social Network, Yingfeng Tang

Student Works (2020-2029)

Rumor is a social interaction of information, and its development is of great significance to human beings. In this paper, by studying the D K model and a rumor model spreading with rumor latent period, deduces the rumor model with differe nt propagation degrees of the spreaders. The two equilibrium points in the system are found through derivation. In real life, enterprises often ignore the reasonable planning of the cost of rumor control. By means of public education and media technology u sing by the authorities to debunk rumors, an optimal control problem i s established. The Pontryagin’s maximum principle …

Go to article

Integrated In Silico Techniques For Mechanism Studies Of Antiviral And Antidiabetic Compounds, Mohd Isa Diyana Jun 2020

Integrated In Silico Techniques For Mechanism Studies Of Antiviral And Antidiabetic Compounds, Mohd Isa Diyana

Student Works (2020-2029)

Discovery and development of drug is an iterative process that involves identification of target and leads discovery, lead optimization and pre-clinical of biological profile and is continued until the drugs are eligible to be tested in the clinical phase. Computer-aided drug design (CADD) is an efficient way to overcome the demands of cost, time consumption and drug validation. CADD utilizes in silico technique approaches such as molecular docking to understand their binding pattern and affinity with the target protein and molecular dynamic (MD) simulations to provide an atomic view of the dynamic behavior and stability of bioactive compound inside the …

Go to article

Vision And Sensor-Based Signer-Independent Framework For Arabic Sign Language Recognition, Al-Shamayleh Ahmad Sami Abd Alkareem Jun 2020

Vision And Sensor-Based Signer-Independent Framework For Arabic Sign Language Recognition, Al-Shamayleh Ahmad Sami Abd Alkareem

Student Works (2020-2029)

Hearing and speech-impairment disability is widespread throughout the world. At present, 15 million people have this disability in the Arab world, and about 86% of them come from low- and middle-income countries. Meanwhile, sign language (SL) can be classified into standard Arabic sign language (ArSL) and local Arabic sign language (LArSL). ArSL is the formal standard and is the more acceptable SL in the Arab world; it is also considered as the medium of instructions for schools and universities as well as television news, shows and programmes. With the absence of usable ArSL recognition (ArSLR) platforms, hearing- and speech-impaired people …

Go to article

Empirical Evaluation Of Three Common Assumptions In Building Political Media Bias Datasets, Soumen Ganguly, Juhi Kulshrestha, Jisun An, Haewoon Kwak Jun 2020

Empirical Evaluation Of Three Common Assumptions In Building Political Media Bias Datasets, Soumen Ganguly, Juhi Kulshrestha, Jisun An, Haewoon Kwak

Research Collection School Of Computing and Information Systems

In this work, we empirically validate three common assumptions in building political media bias datasets, which are (i) labelers' political leanings do not affect labeling tasks, (ii) news articles follow their source outlet's political leaning, and (iii) political leaning of a news outlet is stable across different topics. We build a ground-truth dataset of manually annotated article-level political leaning and validate the three assumptions. Our findings warn that the three assumptions could be invalid even for a small dataset. We hope that our work calls attention to the (in)validity of common assumptions in building political media bias datasets.

Go to article

On The Relationship Between Developer Experience And Refactoring: An Exploratory Study And Preliminary Results, Eman Abdullah Alomar, Anthony Peruma, Christian D. Newman, Mohamed Wiem Mkaouer, Ali Ouni Jun 2020

On The Relationship Between Developer Experience And Refactoring: An Exploratory Study And Preliminary Results, Eman Abdullah Alomar, Anthony Peruma, Christian D. Newman, Mohamed Wiem Mkaouer, Ali Ouni

Articles

Refactoring is one of the means of managing technical debt and maintaining a healthy software structure through enforcing best design practices, or coping with design defects. Previous refactoring surveys have shown that these code restructurings are mainly executed by developers who have sufficient knowledge of the system’s design, and disposing of leadership roles in their development teams. However, these surveys were mainly limited to specific projects and companies. In this paper, we explore the generalizability of the previous results though analyzing 800 open-source projects. We mine their refactoring activities, and we identify their corresponding contributors. Then, we associate an expertise …

Go to article

Work-In-Progress: Augmented Reality System For Vehicle Health Diagnostics And Maintenance, Yuzhong Shen, Anthony W. Dean, Rafael Landaeta Jun 2020

Work-In-Progress: Augmented Reality System For Vehicle Health Diagnostics And Maintenance, Yuzhong Shen, Anthony W. Dean, Rafael Landaeta

Electrical & Computer Engineering Faculty Publications

This paper discusses undergraduate research to develop an augmented reality (AR) system for diagnostics and maintenance of the Joint Light Tactical Vehicle (JLTV) employed by U.S. Army and U.S. Marine Corps. The JLTV’s diagnostic information will be accessed by attaching a Bluetooth adaptor (Ford Reference Vehicle Interface) to JLTV’s On-board diagnostics (OBD) system. The proposed AR system will be developed for mobile devices (Android and iOS tablets and phones) and it communicates with the JLTV’s OBD via Bluetooth. The AR application will contain a simplistic user interface that reads diagnostic data from the JLTV, shows vehicle sensors, and allows users …

Go to article

Real-Time Tracking And Mining Of Users’ Actions Over Social Media, Ejub Kajan, Noura Faci, Zakaria Maamar, Mohamed Sellami, Emir Ugljanin, Hamamache Kheddouci, Dragan H. Stojanović, Djamal Benslimane Jun 2020

Real-Time Tracking And Mining Of Users’ Actions Over Social Media, Ejub Kajan, Noura Faci, Zakaria Maamar, Mohamed Sellami, Emir Ugljanin, Hamamache Kheddouci, Dragan H. Stojanović, Djamal Benslimane

All Works

© 2020, ComSIS Consortium. All rights reserved. With the advent of Web 2.0 technologies and social media, companies are actively looking for ways to know and understand what users think and say about their products and services. Indeed, it has become the practice that users go online using social media like Facebook to raise concerns, make comments, and share recommendations. All these actions can be tracked in real-time and then mined using advanced techniques like data analytics and sentiment analysis. This paper discusses such tracking and mining through a system called Social Miner that allows companies to make decisions about …

Go to article

Learning Context-Aware Outfit Recommendation, Ahed Abugabah, Xiaochun Cheng, Jianfeng Wang Jun 2020

Learning Context-Aware Outfit Recommendation, Ahed Abugabah, Xiaochun Cheng, Jianfeng Wang

All Works

© 2020 by the authors. With the rapid development and increasing popularity of online shopping for fashion products, fashion recommendation plays an important role in daily online shopping scenes. Fashion is not only a commodity that is bought and sold but is also a visual language of sign, a nonverbal communication medium that exists between the wearers and viewers in a community. The key to fashion recommendation is to capture the semantics behind customers' fit feedback as well as fashion visual style. Existing methods have been developed with the item similarity demonstrated by user interactions like ratings and purchases. By …

Go to article

Is Using Deep Learning Frameworks Free?: Characterizing Technical Debt In Deep Learning Frameworks, Jiakun Liu, Qiao Huang, Xin Xia, Emad Shihab, David Lo, Shanping Li Jun 2020

Is Using Deep Learning Frameworks Free?: Characterizing Technical Debt In Deep Learning Frameworks, Jiakun Liu, Qiao Huang, Xin Xia, Emad Shihab, David Lo, Shanping Li

Research Collection School Of Computing and Information Systems

Developers of deep learning applications (shortened as application developers) commonly use deep learning frameworks in their projects. However, due to time pressure, market competition, and cost reduction, developers of deep learning frameworks (shortened as framework developers) often have to sacrifice software quality to satisfy a shorter completion time. This practice leads to technical debt in deep learning frameworks, which results in the increasing burden to both the application developers and the framework developers in future development.In this paper, we analyze the comments indicating technical debt (self-admitted technical debt) in 7 of the most popular open-source deep learning frameworks. Although framework …

Go to article

Gpu-Accelerated Subgraph Enumeration On Partitioned Graphs, Wentian Guo, Yuchen Li, Mo Sha, Bingsheng He, Xiaokui Xiao, Kian-Lee Tan Jun 2020

Gpu-Accelerated Subgraph Enumeration On Partitioned Graphs, Wentian Guo, Yuchen Li, Mo Sha, Bingsheng He, Xiaokui Xiao, Kian-Lee Tan

Research Collection School Of Computing and Information Systems

Subgraph enumeration is important for many applications such as network motif discovery and community detection. Recent works utilize graphics processing units (GPUs) to parallelize subgraph enumeration, but they can only handle graphs that fit into the GPU memory. In this paper, we propose a new approach for GPU-accelerated subgraph enumeration that can efficiently scale to large graphs beyond the GPU memory. Our approach divides the graph into partitions, each of which fits into the GPU memory. The GPU processes one partition at a time and searches the matched subgraphs of a given pattern (i.e., instances) within the partition as in …

Go to article

Greenwatch-Shing: Using Ai To Detect Greenwashing, Theodor Florian Cojoianu, Andreas G. F. Hoepner, Georgiana Ifrim, Yanan Lin Jun 2020

Greenwatch-Shing: Using Ai To Detect Greenwashing, Theodor Florian Cojoianu, Andreas G. F. Hoepner, Georgiana Ifrim, Yanan Lin

Research Collection College of Integrative Studies

The rise of fake news has resulted in a wide discrepancy between the attention given to the scientific understanding of an issue versus misinformed and sometimes purposefully disinformed claims (false information that is purposely spread to deceive people) coming from outside the scientific community. One such example relates to climate change, where a nature study shows that climate change contrarians are featured in 49% more media articles than scientists, despite the overwhelming consensus in the scientific community over the significance of anthropogenic climate change. In addition, many companies worldwide make inaccurate and often misleading claims about their environmental and social …

Go to article

Exploring Category-Agnostic Clusters For Open-Set Domain Adaptation, Yingwei Pan, Ting Yao, Yehao Li, Chong-Wah Ngo, Tao Mei Jun 2020

Exploring Category-Agnostic Clusters For Open-Set Domain Adaptation, Yingwei Pan, Ting Yao, Yehao Li, Chong-Wah Ngo, Tao Mei

Research Collection School Of Computing and Information Systems

Unsupervised domain adaptation has received significant attention in recent years. Most of existing works tackle the closed-set scenario, assuming that the source and target domains share the exactly same categories. In practice, nevertheless, a target domain often contains samples of classes unseen in source domain (i.e., unknown class). The extension of domain adaptation from closedset to such open-set situation is not trivial since the target samples in unknown class are not expected to align with the source. In this paper, we address this problem by augmenting the state-of-the-art domain adaptation technique, Self-Ensembling, with category-agnostic clusters in target domain. Specifically, we …

Go to article

Mining User-Generated Content Of Mobile Patient Portal: Dimensions Of User Experience, Mohammad Al-Ramahi, Cherie Noteboom Jun 2020

Mining User-Generated Content Of Mobile Patient Portal: Dimensions Of User Experience, Mohammad Al-Ramahi, Cherie Noteboom

Research & Publications

Patient portals are positioned as a central component of patient engagement through the potential to change the physician-patient relationship and enable chronic disease self-management. The incorporation of patient portals provides the promise to deliver excellent quality, at optimized costs, while improving the health of the population. This study extends the existing literature by extracting dimensions related to the Mobile Patient Portal Use. We use a topic modeling approach to systematically analyze users’ feedback from the actual use of a common mobile patient portal, Epic’s MyChart. Comparing results of Latent Dirichlet Allocation analysis with those of human analysis validated the extracted …

Go to article

Check Your Tech – Considering The Provenance Of Data Used To Build Digital Products And Services: Case Studies And An Ethical Checksheet, Dympna O'Sullivan, Damian Gordon Jun 2020

Check Your Tech – Considering The Provenance Of Data Used To Build Digital Products And Services: Case Studies And An Ethical Checksheet, Dympna O'Sullivan, Damian Gordon

Conference papers

Digital products and services are producing unprecedented amounts of data worldwide. These products and services have broad reach and include many users and consumers in the developing world. Once data is collected it is often used to create large and valuable datasets. A lack of data protection regulation in the developing world has led to concerns about digital colonization and a lack of control of their data on the part of citizens in the developing world. The authors of this paper are developing a new digital ethics curriculum for the instruction of computer science students. In this paper we present …

Go to article

The Accumulation Of It Capability And Its Long-Term Effect On Financial Performance, Jin Ho Kim Jun 2020

The Accumulation Of It Capability And Its Long-Term Effect On Financial Performance, Jin Ho Kim

Theses and Dissertations in Business Administration

Many scholars have been studying information technology (IT) capability and its impact on business performance. However, it has been debated whether the IT capability influences firm performance because prior literature shows mixed results. To understand this phenomenon, I performed two studies in this research, placed in two parts. First, by deploying the new concept of accumulation of IT capability, I attempted to reinvestigate the relationship between IT capability and business performance. Next, I examined what factors influence the accumulation of IT capability. In the first part, I suggested two novel constructs that measure the extent of the accumulation of IT …

Go to article

First Prev Next Last