Blockchain Technology And Freight Forwarder Exploration Of Implications Focused On Practitioners In Shanghai,
2020
World Maritime University
Blockchain Technology And Freight Forwarder Exploration Of Implications Focused On Practitioners In Shanghai, Johannes Van Bohemen
World Maritime University Dissertations
No abstract provided.
Machine Learning And Deep Learning Based Entity Resolution Approaches For Unstructured References,
2020
University of Arkansas Little Rock
Machine Learning And Deep Learning Based Entity Resolution Approaches For Unstructured References, Xinming Li
Theses and Dissertations
As a fundamental task in data integration and data quality, Entity Resolution (ER) has been investigated for decades in various domains. The emerging volume of heterogeneously structured data, and even unstructured data, poses a challenge to traditional ER methods. This research is to explore machine learning and deep learning approach to address the challenge from unstructured references data. This research starts with pairwise matching, the core function of all ER tasks. Based on the similarity score vector derived from our designed similarity measurement tool, scoring matrix, machine leaning enhances the performance significantly compared to the manually threshold method. Without similarity …
Arlegislation: An R Package Of Arkansas Legislation Data And An Exploratory Use Case For Using Machine Learning To Identify Public Corruption,
2020
University of Arkansas Little Rock
Arlegislation: An R Package Of Arkansas Legislation Data And An Exploratory Use Case For Using Machine Learning To Identify Public Corruption, Nathan P. Chaney
Theses and Dissertations
This thesis describes the creation of a natural-language dataset from a corpus of legislation passed in the State of Arkansas between 2001 and 2019. The dataset also includes metadata about individual acts of legislation and the lawmakers who sponsored them. This thesis describes the creation of the dataset, including the transformation of raw textual input using various natural language processing techniques such as sentiment analysis and topic modeling. Finally, this thesis examines a use case for identifying corrupt lawmakers using machine learning tools trained on transformations of the dataset.
Multi‑View Clustering For Multi‑Omics Data Using Unifed Embedding,
2020
ADAPT Centre, Cork Institute of Technology, Cork, Ireland.
Multi‑View Clustering For Multi‑Omics Data Using Unifed Embedding, Mohammed Hasanuzzaman, Sayantan Mitra, Sriparna Saha
Department of Computer Science Publications
In real world applications, data sets are often comprised of multiple views, which provide consensus and complementary information to each other. Embedding learning is an effective strategy for nearest neighbour search and dimensionality reduction in large data sets. This paper attempts to learn a unified probability distribution of the points across different views and generates a unified embedding in a low-dimensional space to optimally preserve neighbourhood identity. Probability distributions generated for each point for each view are combined by conflation method to create a single unified distribution. The goal is to approximate this unified distribution as much as possible when …
Multi‑View Clustering For Multi‑Omics Data Using Unifed Embedding,
2020
ADAPT Centre, Cork Institute of Technology, Cork, Ireland.
Multi‑View Clustering For Multi‑Omics Data Using Unifed Embedding, Mohammed Hasanuzzaman, Sayantan Mitra, Sriparna Saha
Articles
In real world applications, data sets are often comprised of multiple views, which provide consensus and complementary information to each other. Embedding learning is an effective strategy for nearest neighbour search and dimensionality reduction in large data sets. This paper attempts to learn a unified probability distribution of the points across different views and generates a unified embedding in a low-dimensional space to optimally preserve neighbourhood identity. Probability distributions generated for each point for each view are combined by conflation method to create a single unified distribution. The goal is to approximate this unified distribution as much as possible when …
Snow-Albedo Feedback In Northern Alaska: How Vegetation Influences Snowmelt,
2020
CUNY Hunter College
Snow-Albedo Feedback In Northern Alaska: How Vegetation Influences Snowmelt, Lucas C. Reckhaus
Theses and Dissertations
This paper investigates how the snow-albedo feedback mechanism of the arctic is changing in response to rising climate temperatures. Specifically, the interplay of vegetation and snowmelt, and how these two variables can be correlated. This has the potential to refine climate modelling of the spring transition season. Research was conducted at the ecoregion scale in northern Alaska from 2000 to 2020. Each ecoregion is defined by distinct topographic and ecological conditions, allowing for meaningful contrast between the patterns of spring albedo transition across surface conditions and vegetation types. The five most northerly ecoregions of Alaska are chosen as they encompass …
Using High-Performance Computing Profilers To Understand The Performance Of Graph Algorithms,
2020
LSU New Orleans
Using High-Performance Computing Profilers To Understand The Performance Of Graph Algorithms, Costain Nachuma
LSU New Orleans Theses and Dissertations
An algorithm designer working with parallel computing systems should know how the characteristics of their implemented algorithm affects various performance aspects of their parallel program. It would be beneficial to these designers if each algorithm came with a specific set of standards that identified which algorithms worked better for a specified system. Therefore, the goal of this paper is to take implementations of four graphing algorithms, extract their features such as memory consumption, scalability using profilers (Vtunes /Tau) to determine which algorithms work to their fullest potential in one of the three systems: GPU, shared memory system, or distributed memory …
Maia And Admonita: Mandatory Integrity Control Language And Dynamic Trust Framework For Arbitrary Structured Data,
2020
Western Michigan University
Maia And Admonita: Mandatory Integrity Control Language And Dynamic Trust Framework For Arbitrary Structured Data, Wassnaa Al-Mawee
Dissertations
The expansion of attacks against information systems of companies that operate nuclear power stations and other energy facilities in the United States and other countries, are noticeable with potential catastrophic real-world implications. Data integrity is a fundamental component of information security. It refers to the accuracy and the trustworthiness of data or resources. Data integrity within information systems becomes an important factor of security protection as the data becomes more integrated and crucial to decision-making. The security threats brought by human errors whether, malicious or unintentional, such as viruses, hacking, and many other cybersecurity threats, are dangerous and require mandatory …
Adaptive Task Sampling For Meta-Learning,
2020
Singapore Management University
Adaptive Task Sampling For Meta-Learning, Chenghao Liu, Zhihao Wang, Doyen Sahoo, Yuan Fang, Kun Zhang, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Meta-learning methods have been extensively studied and applied in computer vision, especially for few-shot classification tasks. The key idea of meta-learning for few-shot classification is to mimic the few-shot situations faced at test time by randomly sampling classes in meta-training data to construct fewshot tasks for episodic training. While a rich line of work focuses solely on how to extract meta-knowledge across tasks, we exploit the complementary problem on how to generate informative tasks. We argue that the randomly sampled tasks could be sub-optimal and uninformative (e.g., the task of classifying “dog” from “laptop” is often trivial) to the meta-learner. …
Colleague To Banner Migration: Data Conversion Guide For Institutional Research,
2020
Dakota State University
Colleague To Banner Migration: Data Conversion Guide For Institutional Research, Laura Osborn
Masters Theses & Doctoral Dissertations
When the SDBOR decided to migrate their current student information system into a shared system with HR and Finance, adjustments needed to be made to accommodate for current Banner settings and work around tables that were already populated with HRFIS data. The change in data type of the student identifier from that of a 7-digit numeric field to a 9- digit alpha-numeric field poses problems for running aggregate data calculations. Additional complications include having some information such as first-generation status that was not migrated between the systems, and cases such as college coding where tables that were designed for student …
A Generalised Bound For The Wiener Attack On Rsa,
2020
Singapore Management University
A Generalised Bound For The Wiener Attack On Rsa, Willy Susilo, Joseph Tonien, Guomin Yang
Research Collection School Of Computing and Information Systems
Since Wiener pointed out that the RSA can be broken if the private exponent d is relatively small compared to the modulus N, it has been a general belief that the Wiener attack works for d
Diffusion Of Falsehoods On Social Media,
2020
The University of Texas Rio Grande Valley
Diffusion Of Falsehoods On Social Media, Kelvin Kizito King
Theses and Dissertations
Misinformation has captured the interest of academia in recent years with several studies looking at the topic broadly. However, these studies mostly focused on rumors which are social in nature and can be either classified as false or real. In this research, we attempt to bridge the gap in the literature by examining the impacts of user characteristics and feature contents on the diffusion of (mis)information using verified true and false information. We apply a topic allocation model augmented by both supervised and unsupervised machine learning algorithms to identify tweets on novel topics. We find that retweet count is higher …
Creativity And Engagement In Ideas Crowdsourcing: A Situation Awareness Perspective,
2020
The University of Texas Rio Grande Valley
Creativity And Engagement In Ideas Crowdsourcing: A Situation Awareness Perspective, James Gitau Wairimu
Theses and Dissertations
This dissertation investigates the influence of performance feedback in user motivation and creativity development in idea crowdsourcing engagement. Creativity occurs when users of idea crowdsourcing communities engage in direct and indirect interactions that expose them to a pool of knowledge that enhances their cognitive development leading to the contribution of novel ideas for innovation in organizations. Additionally, participant motivation to engage in ideas crowdsourcing is increased through rewards and conditions that make the ideation process more inclusive and enjoyable. An idea network design is developed by applying social network analysis principles. The idea network design consists of mechanisms for motivating …
Bootstrapping Web Archive Collections From Micro-Collections In Social Media,
2020
Old Dominion University
Bootstrapping Web Archive Collections From Micro-Collections In Social Media, Alexander C. Nwala
Computer Science Theses & Dissertations
In a Web plagued by disappearing resources, Web archive collections provide a valuable means of preserving Web resources important to the study of past events. These archived collections start with seed URIs (Uniform Resource Identifiers) hand-selected by curators. Curators produce high quality seeds by removing non-relevant URIs and adding URIs from credible and authoritative sources, but this ability comes at a cost: it is time consuming to collect these seeds. The result of this is a shortage of curators, a lack of Web archive collections for various important news events, and a need for an automatic system for generating seeds. …
A Gis-Based Method For Archival And Visualization Of Microstructural Data From Drill Core Samples.,
2020
University of Louisville
A Gis-Based Method For Archival And Visualization Of Microstructural Data From Drill Core Samples., Elliott Holmes
Electronic Theses and Dissertations
Core samples obtained from scientific drilling could provide large volumes of direct microstructural and compositional data, but generating results via the traditional treatment of such data is often time-consuming and inefficient. Unifying microstructural data within a spatially referenced Geographic Information System (GIS) environment provides an opportunity to readily locate, visualize, correlate, and explore the available microstructural data. Using 26 core billet samples from the San Andreas Fault Observatory at Depth (SAFOD), this study developed procedures for: 1. A GIS-based approach for spatially referenced visualization and storage of microstructural data from drill core billet samples; and 2. Producing 3D models of …
Dual-Dropout Graph Convolutional Network For Predicting Synthetic Lethality In Human Cancers,
2020
Guangdong University of Technology
Dual-Dropout Graph Convolutional Network For Predicting Synthetic Lethality In Human Cancers, Ruichu Cai, Xuexin Chen, Yuan Fang, Min Wu, Yuexing Hao
Research Collection School Of Computing and Information Systems
Motivation: Synthetic lethality (SL) is a promising form of gene interaction for cancer therapy, as it is able to identify specific genes to target at cancer cells without disrupting normal cells. As high-throughput wet-lab settings are often costly and face various challenges, computational approaches have become a practical complement. In particular, predicting SLs can be formulated as a link prediction task on a graph of interacting genes. Although matrix factorization techniques have been widely adopted in link prediction, they focus on mapping genes to latent representations in isolation, without aggregating information from neighboring genes. Graph convolutional networks (GCN) can capture …
A Systematic Density-Based Clustering Method Using Anchor Points,
2020
Jilin University
A Systematic Density-Based Clustering Method Using Anchor Points, Yizhang Wang, Di Wang, Wei Pang, Ah-Hwee Tan, You Zhou
Research Collection School Of Computing and Information Systems
Clustering is an important unsupervised learning method in machine learning and data mining. Many existing clustering methods may still face the challenge in self-identifying clusters with varying shapes, sizes and densities. To devise a more generic clustering method that considers all the aforementioned properties of the natural clusters, we propose a novel clustering algorithm named Anchor Points based Clustering (APC). The anchor points in APC are characterized by having a relatively large distance from data points with higher densities. We take anchor points as centers to obtain intermediate clusters, which can divide the whole dataset more appropriately so as to …
Rethinking Pruning For Accelerating Deep Inference At The Edge,
2020
Beijing University of Aeronautics and Astronautics (Beihang University)
Rethinking Pruning For Accelerating Deep Inference At The Edge, Dawei Gao, Xiaoxi He, Zimu Zhou, Yongxin Tong, Ke Xu, Lothar Thiele
Research Collection School Of Computing and Information Systems
There is a growing trend to deploy deep neural networks at the edge for high-accuracy, real-time data mining and user interaction. Applications such as speech recognition and language understanding often apply a deep neural network to encode an input sequence and then use a decoder to generate the output sequence. A promising technique to accelerate these applications on resource-constrained devices is network pruning, which compresses the size of the deep neural network without severe drop in inference accuracy. However, we observe that although existing network pruning algorithms prove effective to speed up the prior deep neural network, they lead to …
Mining User-Generated Content Of Mobile Patient Portal: Dimensions Of User Experience,
2020
Texas A&M University-San Antonio
Mining User-Generated Content Of Mobile Patient Portal: Dimensions Of User Experience, Mohammad A. Al-Ramahi, Cherie Noteboom
Computer Information Systems Faculty Publications (Archived)
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 …
An Attention-Based Rumor Detection Model With Tree-Structured Recursive Neural Networks,
2020
Hong Kong Baptist University
An Attention-Based Rumor Detection Model With Tree-Structured Recursive Neural Networks, Jing Ma, Wei Gao, Shafiq Joty, Kam-Fai Wong
Research Collection School Of Computing and Information Systems
Rumor spread in social media severely jeopardizes the credibility of online content. Thus, automatic debunking of rumors is of great importance to keep social media a healthy environment. While facing a dubious claim, people often dispute its truthfulness sporadically in their posts containing various cues, which can form useful evidence with long-distance dependencies. In this work, we propose to learn discriminative features from microblog posts by following their non-sequential propagation structure and generate more powerful representations for identifying rumors. For modeling non-sequential structure, we first represent the diffusion of microblog posts with propagation trees, which provide valuable clues on how …
