Physical Sciences and Mathematics Commons^™

Detecting Fake News In Social Media: An Asia-Pacific Perspective, Meeyoung Cha, Wei Gao, Cheng-Te Li

Research Collection School Of Computing and Information Systems

In March 2011, the catastrophic accident known as "The Fukushima Daiichi nuclear disaster" took place, initiated by the Tohoku earthquake and tsunami in Japan. The only nuclear accident to receive a Level-7 classification on the International Nuclear Event Scale since the Chernobyl nuclear power plant disaster in 1986, the Fukushima event triggered global concerns and rumors regarding radiation leaks. Among the false rumors was an image, which had been described as a map of radioactive discharge emanating into the Pacific Ocean, as illustrated in the accompanying figure. In fact, this figure, depicting the wave height of the tsunami that followed, …

Network Traffic Analysis Framework For Cyber Threat Detection, Meshesha K. Cherie

Masters Theses & Doctoral Dissertations

The growing sophistication of attacks and newly emerging cyber threats requires advanced cyber threat detection systems. Although there are several cyber threat detection tools in use, cyber threats and data breaches continue to rise. This research is intended to improve the cyber threat detection approach by developing a cyber threat detection framework using two complementary technologies, search engine and machine learning, combining artificial intelligence and classical technologies.

In this design science research, several artifacts such as a custom search engine library, a machine learning-based engine and different algorithms have been developed to build a new cyber threat detection framework based …

Feature Agglomeration Networks For Single Stage Face Detection, Jialiang Zhang, Xiongwei Wu, Steven C. H. Hoi, Jianke Zhu

Research Collection School Of Computing and Information Systems

Recent years have witnessed promising results of exploring deep convolutional neural network for face detection. Despite making remarkable progress, face detection in the wild remains challenging especially when detecting faces at vastly different scales and characteristics. In this paper, we propose a novel simple yet effective framework of “Feature Agglomeration Networks” (FANet) to build a new single-stage face detector, which not only achieves state-of-the-art performance but also runs efficiently. As inspired by Feature Pyramid Networks (FPN) (Lin et al., 2017), the key idea of our framework is to exploit inherent multi-scale features of a single convolutional neural network by aggregating …

Using Reinforcement Learning To Minimize The Probability Of Delay Occurrence In Transportation, Zhiguang Cao, Hongliang Guo, Wen Song, Kaizhou Gao, Zhengghua Chen, Le Zhang, Xuexi Zhang

Research Collection School Of Computing and Information Systems

Reducing traffic delay is of crucial importance for the development of sustainable transportation systems, which is a challenging task in the studies of stochastic shortest path (SSP) problem. Existing methods based on the probability tail model to solve the SSP problem, seek for the path that minimizes the probability of delay occurrence, which is equal to maximizing the probability of reaching the destination before a deadline (i.e., arriving on time). However, they suffer from low accuracy or high computational cost. Therefore, we design a novel and practical Q-learning approach where the converged Q-values have the practical meaning as the actual …

Towards K-Vertex Connected Component Discovery From Large Networks, Li Yuan, Guoren Wang, Yuhai Zhao, Feida Zhu

Research Collection School Of Computing and Information Systems

In many real life network-based applications such as social relation analysis, Web analysis, collaborative network, road network and bioinformatics, the discovery of components with high connectivity is an important problem. In particular, k-edge connected component (k-ECC) has recently been extensively studied to discover disjoint components. Yet many real scenarios present more needs and challenges for overlapping components. In this paper, we propose a k-vertex connected component (k-VCC) model, which is much more cohesive, and thus supports overlapping between components very well. To discover k-VCCs, we propose three frameworks including top-down, bottom-up and hybrid …

The Search For Optimal Oxygen Saturation Targets In Critically Ill: Patients Observational Data From Large Icu Databases, Willem Van Den Boom, Michael Hoy, Jagadish Sankaran, Mengru Liu, Haroun Chahed, Mengling Feng, Kay Choong See

Research Collection School Of Computing and Information Systems

Background: Although low oxygen saturations are generally regarded as deleterious, recent studies in ICU patients have shown that a liberal oxygen strategy increases mortality. However, the optimal oxygen saturation target remains unclear. The goal of this study was to determine the optimal range by using real-world data. Methods: Replicate retrospective analyses were conducted of two electronic medical record databases: the eICU Collaborative Research Database (eICU-CRD) and the Medical Information Mart for Intensive Care III database (MIMIC). Only patients with at least 48 h of oxygen therapy were included. Nonlinear regression was used to analyze the association between median pulse oximetry-derived …

Algorithms To Profile Driver Behavior From Zero-Permission Embedded Sensors, Bharti Goel

USF Tampa Graduate Theses and Dissertations

In this dissertation, we design algorithms to profile driver behavior from zero-permission sensors embedded in modern smartphones and wearables. These sensors are typically the accelerometer, gyroscope, magnetometer, pressure sensor and a few more than are now available in most modern smartphones and wearables. In order to profile driving behavior, we devised algorithms for detecting distraction while driving due to the use of modern-day smartphones (e.g., calling, texting and reading while driving) in real-time.

To do so, we conduct an experiment with 16 subjects on a realistic driving simulator, where each subject, where each subject carries a smartphone and a wearable …

Scraping Bepress: Downloading Dissertations For Preservation, Stephen Zweibel

Copyright, Fair Use, Scholarly Communication, etc.

This article will describe our process developing a script to automate downloading of documents and secondary materials from our library’s BePress repository. Our objective was to collect the full archive of dissertations and associated files from our repository into a local disk for potential future applications and to build out a preservation system.

Unlike at some institutions, our students submit directly into BePress, so we did not have a separate repository of the files; and the backup of BePress content that we had access to was not in an ideal format (for example, it included “withdrawn” items and did not …

Establishing An Information System For Documenting Valuable Buildings By Using Gis In Egypt, Mona Mahrous Abdel Wahed

Emirates Journal for Engineering Research

Valuable heritage buildings are the history of nations, and history forms the identities of these nations. Many of these buildings are exposed to deterioration, destruction and distortion. Therefore, it is essential to protect and maintain these buildings to protect history. Effective documentation of valuable buildings is necessary to guide and assist stakeholders in making decisions regarding valuable buildings. Documentation requires robust and scientific methods. Therefore, it is important to utilize new technology in general and geographic information system GIS in particular in documenting valuable buildings. GIS has the potential to contribute and deal with valuable buildings at various stages and …

Image Enhanced Event Detection In News Articles, Meihan Tong, Shuai Wang, Yixin Cao, Bin Xu, Juaizi Li, Lei Hou, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Event detection is a crucial and challenging sub-task of event extraction, which suffers from a severe ambiguity issue of trigger words. Existing works mainly focus on using textual context information, while there naturally exist many images accompanied by news articles that are yet to be explored. We believe that images not only reflect the core events of the text, but are also helpful for the disambiguation of trigger words. In this paper, we first contribute an image dataset supplement to ED benchmarks (i.e., ACE2005) for training and evaluation. We then propose a novel Dual Recurrent Multimodal Model, DRMM, to conduct …

Mcdpc: Multi‐Center Density Peak Clustering, Yizhang Wang, Di Wang, Xiaofeng Zhang, Wei Pang, Chunyan Miao, Ah-Hwee Tan, You Zhou

Research Collection School Of Computing and Information Systems

Density peak clustering (DPC) is a recently developed density-based clustering algorithm that achieves competitive performance in a non-iterative manner. DPC is capable of effectively handling clusters with single density peak (single center), i.e., based on DPC’s hypothesis, one and only one data point is chosen as the center of any cluster. However, DPC may fail to identify clusters with multiple density peaks (multi-centers) and may not be able to identify natural clusters whose centers have relatively lower local density. To address these limitations, we propose a novel clustering algorithm based on a hierarchical approach, named multi-center density peak clustering (McDPC). …

Interpretable Rumor Detection In Microblogs By Attending To User Interactions, Ling Min Serena Khoo, Hai Leong Chieu, Zhong Qian, Jing Jiang

Research Collection School Of Computing and Information Systems

We address rumor detection by learning to differentiate between the community’s response to real and fake claims in microblogs. Existing state-of-the-art models are based on tree models that model conversational trees. However, in social media, a user posting a reply might be replying to the entire thread rather than to a specific user. We propose a post-level attention model (PLAN) to model long distance interactions between tweets with the multi-head attention mechanism in a transformer network. We investigated variants of this model: (1) a structure aware self-attention model (StA-PLAN) that incorporates tree structure information in the transformer network, and (2) …

Topic Modeling On Document Networks With Adjacent-Encoder, Ce Zhang, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Oftentimes documents are linked to one another in a network structure,e.g., academic papers cite other papers, Web pages link to other pages. In this paper we propose a holistic topic model to learn meaningful and unified low-dimensional representations for networked documents that seek to preserve both textual content and network structure. On the basis of reconstructing not only the input document but also its adjacent neighbors, we develop two neural encoder architectures. Adjacent-Encoder, or AdjEnc, induces competition among documents for topic propagation, and reconstruction among neighbors for semantic capture. Adjacent-Encoder-X, or AdjEnc-X, extends this to also encode the network structure …

Stochastically Robust Personalized Ranking For Lsh Recommendation Retrieval, Dung D. Le, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Locality Sensitive Hashing (LSH) has become one of the most commonly used approximate nearest neighbor search techniques to avoid the prohibitive cost of scanning through all data points. For recommender systems, LSH achieves efficient recommendation retrieval by encoding user and item vectors into binary hash codes, reducing the cost of exhaustively examining all the item vectors to identify the topk items. However, conventional matrix factorization models may suffer from performance degeneration caused by randomly-drawn LSH hash functions, directly affecting the ultimate quality of the recommendations. In this paper, we propose a framework named SRPR, which factors in the stochasticity of …

Joint Learning Of Answer Selection And Answer Summary Generation In Community Question Answering, Yang Deng, Wai Lam, Yuexiang Xie, Daoyuan Chen, Yaliang Li, Min Yang, Ying Shen

Research Collection School Of Computing and Information Systems

Community question answering (CQA) gains increasing popularity in both academy and industry recently. However, the redundancy and lengthiness issues of crowdsourced answers limit the performance of answer selection and lead to reading difficulties and misunderstandings for community users. To solve these problems, we tackle the tasks of answer selection and answer summary generation in CQA with a novel joint learning model. Specifically, we design a question-driven pointer-generator network, which exploits the correlation information between question-Answer pairs to aid in attending the essential information when generating answer summaries. Meanwhile, we leverage the answer summaries to alleviate noise in original lengthy answers …

Deepdualmapper: A Gated Fusion Network For Automatic Map Extraction Using Aerial Images And Trajectories, Hao Wu, Hanyuan Zhang, Xinyu Zhang, Weiwei Sun, Baihua Zheng, Yuning Jiang

Research Collection School Of Computing and Information Systems

Automatic map extraction is of great importance to urban computing and location-based services. Aerial image and GPS trajectory data refer to two different data sources that could be leveraged to generate the map, although they carry different types of information. Most previous works on data fusion between aerial images and data from auxiliary sensors do not fully utilize the information of both modalities and hence suffer from the issue of information loss. We propose a deep convolutional neural network called DeepDualMapper which fuses the aerial image and trajectory data in a more seamless manner to extract the digital map. We …

Multi-Level Head-Wise Match And Aggregation In Transformer For Textual Sequence Matching, Shuohang Wang, Yunshi Lan, Yi Tay, Jing Jiang, Jingjing Liu

Research Collection School Of Computing and Information Systems

Transformer has been successfully applied to many natural language processing tasks. However, for textual sequence matching, simple matching between the representation of a pair of sequences might bring in unnecessary noise. In this paper, we propose a new approach to sequence pair matching with Transformer, by learning head-wise matching representations on multiple levels. Experiments show that our proposed approach can achieve new state-of-the-art performance on multiple tasks that rely only on pre-computed sequence-vectorrepresentation, such as SNLI, MNLI-match, MNLI-mismatch, QQP, and SQuAD-binary

A Visual Analytics System For Making Sense Of Real-Time Twitter Streams, Amir Haghighatimaleki

Electronic Thesis and Dissertation Repository

Through social media platforms, massive amounts of data are being produced. Twitter, as one such platform, enables users to post “tweets” on an unprecedented scale. Once analyzed by machine learning (ML) techniques and in aggregate, Twitter data can be an invaluable resource for gaining insight. However, when applied to real-time data streams, due to covariate shifts in the data (i.e., changes in the distributions of the inputs of ML algorithms), existing ML approaches result in different types of biases and provide uncertain outputs. This thesis describes a visual analytics system (i.e., a tool that combines data visualization, human-data interaction, and …

Umaine System Data Governance Annual Report 2019, University Of Maine System Data Advisory Committee

General University of Maine Publications

The University of Maine System Data Governance program serves the missions of all seven state university campuses by integrating academic and administrative data practices to become a more effective and responsive organization. In order to meet the needs of Maine's local, statewide, and national communities, UMS must be able to utilize data to make optimal decisions in support of students, faculty, and staff. This cooperative work helps provide a common understanding of data codes and processes across campuses. It also builds a stronger framework for unifying services and focusing resources on the most innovative and relevant investments in instruction, research, …

Migrating From Monoliths To Cloud-Based Microservices: A Banking Industry Example, Alan Megargel, Venky Shankararaman, David K. Walker

Research Collection School Of Computing and Information Systems

As more organizations are placing cloud computing at the heart of their digital transformation strategy, it is important that they adopt appropriate architectures and development methodologies to leverage the full benefits of the cloud. A mere “lift and move” approach, where traditional monolith applications are moved to the cloud will not support the demands of digital services. While, monolithic applications may be easier to develop and control, they are inflexible to change and lack the scalability needed for cloud environments. Microservices architecture, which adopts some of the concepts and principles from service-oriented architecture, provides a number of benefits when developing …

The Future Of Work Now: Medical Coding With Ai, Thomas H. Davenport, Steven M. Miller

Research Collection School Of Computing and Information Systems

The coding of medical diagnosis and treatment has always been a challenging issue. Translating a patient’s complex symptoms, and a clinician’s efforts to address them, into a clear and unambiguous classification code was difficult even in simpler times. Now, however, hospitals and health insurance companies want very detailed information on what was wrong with a patient and the steps taken to treat them— for clinical record-keeping, for hospital operations review and planning, and perhaps most importantly, for financial reimbursement purposes.

A Systematic Literature Survey Of Unmanned Aerial Vehicle Based Structural Health Monitoring, Sreehari Sreenath

Theses, Dissertations and Capstones

Unmanned Aerial Vehicles (UAVs) are being employed in a multitude of civil applications owing to their ease of use, low maintenance, affordability, high-mobility, and ability to hover. UAVs are being utilized for real-time monitoring of road traffic, providing wireless coverage, remote sensing, search and rescue operations, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection. They are the next big revolution in technology and civil infrastructure, and it is expected to dominate more than $45 billion market value. The thesis surveys the UAV assisted Structural Health Monitoring or SHM literature over the last decade and categorize UAVs …

Mind The Gap: Understanding Stakeholder Reactions To Different Types Of Data Security, Audra Diers-Lawson, Amelia Symons

International Crisis and Risk Communication Conference

Data security breaches are an increasingly common problem for organizations, yet there are critical gaps in our understanding of how different stakeholders understand and evaluate organizations that have experienced these kinds of security breaches. While organizations have developed relatively standard approaches to responding to security breaches that: (1) acknowledge the situation; (2) highlight how much they value their stakeholders’ privacy and private information; and (3) focus on correcting and preventing the problem in the future, the effectiveness of this response strategy and factors influencing it have not been adequately explored. This experiment focuses on a 2 (type of organization) x …

Exploring Strategies For Recruiting And Retaining Diverse Cybersecurity Professionals, Vivian Lyon

Walden Dissertations and Doctoral Studies

The cyber threat landscape has led some cybersecurity leaders to focus on a holistic approach encompassing people, processes, and technology to make their government agencies and organizations more responsive to a more diverse and inclusive cyber workforce to protect critical infrastructure from hackers or cybercriminals intent on causing harm. This qualitative multiple case study used Schein’s organizational culture theory to explore strategies used by cybersecurity leaders to attract, recruit, and retain diverse cybersecurity professionals to effectively and efficiently protect sensitive systems from rising cyber threats. The study's population consisted of cybersecurity leaders from 3 government agencies and 9 organizations in …

Information Systems Strategies For Small And Medium Size Enterprise Sustainability, Oluseyi Solomon Awotayo

Walden Dissertations and Doctoral Studies

Small and medium size business owners who do not use information systems effectively degrade business models, reduce customer value, and diminish the prospects for business stability, profitability, and growth. Grounded in the resource based view framework, the purpose of this qualitative multiple-case study was to explore the information systems strategies small business owners used to sustain their business beyond 5 years. A purposeful sample of 5 owners of 5 different small and medium sized businesses in the state of Texas participated in the study. Data were collected via semistructured, face-to-face interviews, company documents, and member checking. Data were analyzed using …

Exploring Cybersecurity Awareness And Training Strategies To Protect Information Systems And Data, Michael Hanna

Walden Dissertations and Doctoral Studies

Ineffective security education, training, and awareness (SETA) programs contribute to compromises of organizational information systems and data. Inappropriate actions from users due to ineffective SETA programs may result in legal consequences, fines, reputational damage, adverse impacts on national security, and criminal acts. Grounded in social cognitive theory, the purpose of this qualitative multiple case study was to explore strategies hospitality organizational information technology (IT) leaders utilized to implement SETA successfully. The participants were organizational IT leaders from four organizations in Hampton Roads, Virginia. Data collection was performed using telephone and video teleconference interviews with organizational IT leaders (n = 6) …

Strategies For Automating Pharmacovigilance Adverse Event Case Processing, Mythily Easwar

Walden Dissertations and Doctoral Studies

Business leaders who fail to implement innovative technology solutions in their companies face economic distress in these organizations. Guided by the task technology fit model as the conceptual framework, the purpose of this qualitative single case study was to explore strategies used by pharmacovigilance (PV) systems leaders to implement innovative technology solutions. The participants were 4 PV systems managers working in a pharmaceutical company in the Boston area of Massachusetts, United States, who used successful strategies to implement innovative technology solutions to automate adverse events case processing. Data were collected using semistructured interviews and company documents. The collected data were …

Information Technology Disaster Recovery Planning By Florida Nonprofit Organizations, Derek Keith Erfourth

Walden Dissertations and Doctoral Studies

Inadequate information technology (IT) disaster recovery planning (DRP) by nonprofit organizations could lead to organizational failure post-large-scale natural disasters. Without proper funding and planning, organizations may not be able to withstand the effects of a natural disaster resulting in the closure and the community losing a critical need service. Grounded in resilience theory, the purpose of this qualitative multiple case study was to explore strategies utilized by Florida-based nonprofit organization technology managers to adopt and implement an IT DRP to aid in post-natural disaster recovery efforts. The data collection included interviews with 5 IT managers and reviews of 4 business …

Implementing Cloud-Based Enterprise Resource Planning Solutions In Small And Medium Enterprises, Ali Hamdar

Walden Dissertations and Doctoral Studies

Lacking strategies to implement a cloud-based enterprise resource planning (ERP) solution in small and medium-sized enterprises (SMEs) can lead to a failed implementation. SME owners can improve company performance by integrating company processes by successfully implementing a cloud-based ERP solution. Grounded in the diffusion of innovation theory augmented with business process management design for Six Sigma, the purpose of this qualitative multiple case study was to explore strategies SME owners use to implement cloud-based ERP solutions. The participants consisted of 4 SME owners in Lebanon who successfully implemented a cloud-based ERP solution and improved company performance and growth. Data were …

Strategies In Software Development Effort Estimation, Kevin R. Roark

Walden Dissertations and Doctoral Studies

Software development effort estimating has notoriously been the Achilles heel of the software planning process. Accurately evaluating the effort required to accomplish a software change continues to be problematic, especially in Agile software development. IT organizations and project managers depend on estimation accuracy for planning software deliveries and cost determination. The purpose of this multiple case qualitative study was to identify strategies used by software development professionals in providing accurate effort estimations to stakeholders. The planning fallacy served as the study’s conceptual framework. The participants were 10 software development professionals who were actively engaged in delivering estimates of effort on …