Data Science Commons^™

Node Classification On Relational Graphs Using Deep-Rgcns, Nagasai Chandra

Master's Theses

Knowledge Graphs are fascinating concepts in machine learning as they can hold usefully structured information in the form of entities and their relations. Despite the valuable applications of such graphs, most knowledge bases remain incomplete. This missing information harms downstream applications such as information retrieval and opens a window for research in statistical relational learning tasks such as node classification and link prediction. This work proposes a deep learning framework based on existing relational convolutional (R-GCN) layers to learn on highly multi-relational data characteristic of realistic knowledge graphs for node property classification tasks. We propose a deep and improved variant, …

Explainable Recommendation With Comparative Constraints On Product Aspects, Trung-Hoang Le, Hady W. Lauw

Research Collection School Of Computing and Information Systems

To aid users in choice-making, explainable recommendation models seek to provide not only accurate recommendations but also accompanying explanations that help to make sense of those recommendations. Most of the previous approaches rely on evaluative explanations, assessing the quality of an individual item along some aspects of interest to the user. In this work, we are interested in comparative explanations, the less studied problem of assessing a recommended item in comparison to another reference item.

In particular, we propose to anchor reference items on the previously adopted items in a user's history. Not only do we aim at providing comparative …

Bilateral Variational Autoencoder For Collaborative Filtering, Quoc Tuan Truong, Aghiles Salah, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Preference data is a form of dyadic data, with measurements associated with pairs of elements arising from two discrete sets of objects. These are users and items, as well as their interactions, e.g., ratings. We are interested in learning representations for both sets of objects, i.e., users and items, to predict unknown pairwise interactions. Motivated by the recent successes of deep latent variable models, we propose Bilateral Variational Autoencoder (BiVAE), which arises from a combination of a generative model of dyadic data with two inference models, user- and item-based, parameterized by neural networks. Interestingly, our model can take the form …

Contract Information Extraction Using Machine Learning, Zachary E. Butcher

Theses and Dissertations

The Air Force Sustainment Center assisted by the Data Analytics Resource Team and the Defense Logistics Agency collected four million contracts onto one of the Air Force Research Laboratory’s high power computers. This thesis focuses on the effort to determine if parts are available through those contracts. Some information is extracted using machine learning in combination with natural language processing. Where machine learning methods are unsuccessful or inappropriate, text mining techniques, such as pattern recognition and rules, are used. Upon completion, the information is combined into a Gantt chart for quick evaluation. Only 21% of the contracts have their information …

A Consent Framework For The Internet Of Things In The Gdpr Era, Gerald Chikukwa

Masters Theses & Doctoral Dissertations

The Internet of Things (IoT) is an environment of connected physical devices and objects that communicate amongst themselves over the internet. The IoT is based on the notion of always-connected customers, which allows businesses to collect large volumes of customer data to give them a competitive edge. Most of the data collected by these IoT devices include personal information, preferences, and behaviors. However, constant connectivity and sharing of data create security and privacy concerns. Laws and regulations like the General Data Protection Regulation (GDPR) of 2016 ensure that customers are protected by providing privacy and security guidelines to businesses. Data …

Jrevealpeg: A Semi-Blind Jpeg Steganalysis Tool Targeting Current Open-Source Embedding Programs, Charles A. Badami

Masters Theses & Doctoral Dissertations

Steganography in computer science refers to the hiding of messages or data within other messages or data; the detection of these hidden messages is called steganalysis. Digital steganography can be used to hide any type of file or data, including text, images, audio, and video inside other text, image, audio, or video data. While steganography can be used to legitimately hide data for non-malicious purposes, it is also frequently used in a malicious manner. This paper proposes JRevealPEG, a software tool written in Python that will aid in the detection of steganography in JPEG images with respect to identifying a …

Multi-Objective Database Queries In Combined Knapsack And Set Covering Problem Domains, Sean A. Mochocki, Gary B. Lamont, Robert C. Leishman, Kyle J. Kauffman

Faculty Publications

Database queries are one of the most important functions of a relational database. Users are interested in viewing a variety of data representations, and this may vary based on database purpose and the nature of the stored data. The Air Force Institute of Technology has approximately 100 data logs which will be converted to the standardized Scorpion Data Model format. A relational database is designed to house this data and its associated sensor and non-sensor metadata. Deterministic polynomial-time queries were used to test the performance of this schema against two other schemas, with databases of 100 and 1000 logs of …

Introduction To The Mathematical Analysis Of Data Ams 450, Harrison Dekker

Library Impact Statements

No abstract provided.

Big Data: Ethics, Resources, And Potential Collaboration, Matthew Zook

Geography Presentations

This presentation goes over 10 simple rules for responsible big data research.

Branched Water Resources Management Models, Toshtemir Khojakulov, Rashid Oteniyazov, Fazliddin Kholmuminov

Bulletin of TUIT: Management and Communication Technologies

This paper describes the distribution of water resources in Uzbekistan, the elimination of water problems. Aral Sea water resources and water resources distribution models have been introduced in the country.

Unsupervised Data Mining Technique For Clustering Library In Indonesia, Robbi Rahim, Joseph Teguh Santoso, Sri Jumini, Gita Widi Bhawika, Daniel Susilo, Danny Wibowo

Library Philosophy and Practice (e-journal)

Organizing school libraries not only keeps library materials, but helps students and teachers in completing tasks in the teaching process so that national development goals are in order to improve community welfare by producing quality and competitive human resources. The purpose of this study is to analyze the Unsupervised Learning technique in conducting cluster mapping of the number of libraries at education levels in Indonesia. The data source was obtained from the Ministry of Education and Culture which was processed by the Central Statistics Agency (abbreviated as BPS) with url: bps.go.id/. The data consisted of 34 records where the attribute …

Development Of Algorithm Of Priority Estimation Of Parameters For Building Models., Toshtemir Khojakulov

Bulletin of TUIT: Management and Communication Technologies

The article reviewed the development of the priority assessment algorithm, the operation of the control object, the quality and reliability of the measuring path, the quality and reliability of computer equipment and communication channels, problem solving, etc. With a solution to the problem offers an algorithm. Considering the above estimates as some generalized coordinates, we obtain an effective mathematical description of complex production processes

A New Feature Selection Method Based On Class Association Rule, Sami A. Al-Dhaheri

Dissertations, Theses, and Capstone Projects

Feature selection is a key process for supervised learning algorithms. It involves discarding irrelevant attributes from the training dataset from which the models are derived. One of the vital feature selection approaches is Filtering, which often uses mathematical models to compute the relevance for each feature in the training dataset and then sorts the features into descending order based on their computed scores. However, most Filtering methods face several challenges including, but not limited to, merely considering feature-class correlation when defining a feature’s relevance; additionally, not recommending which subset of features to retain. Leaving this decision to the end-user may …

Exploring Media Portrayals Of People With Mental Disorders Using Nlp, Swapna Gottipati, Mark Chong, Andrew Wei Kiat Lim, Benny Haryanto Kawidiredjo

Research Collection School Of Computing and Information Systems

Media plays an important role in creating an impact in society. Several studies show that news media and entertainment channels, at times may create overwhelming images of the mental illness that emphasize criminality and dangerousness. The consequences of such negative impact may impact the audience with stigma and on the other hand, they impair the self-esteem and help-seeking behavior of the people with mental disorders. This is the first study to examine the Singapore media’s portrayal of persons with mental disorders (MDs) using text analytics and natural language processing. To date, most studies on media portrayal of people with MDs …

Deep Learning For Multi-Tissue Cancer Classification Of Gene Expressions, Tarek Khorshed

Theses and Dissertations

We contribute in saving the lives of cancer patients through early detection and diagnosis, since one of the major challenges in cancer treatment is that patients are diagnosed at very late stages when appropriate medical interventions become less effective and full curative treatment is no longer achievable. Cancer classification using gene expressions is extremely challenging given the complexity and high dimensionality of the data. Current classification methods typically rely on samples collected from a single tissue type and perform a prerequisite of gene feature selection to avoid processing the full set of genes. These methods fall short in taking advantage …

Delineating Knowledge Domains In Scientific Domains In Scientific Literature Using Machine Learning (Ml), Abhay Maurya, Smarajit Paul Choudhury Mr., Kshitij Jaiswal Mr.

Library Philosophy and Practice (e-journal)

The recent years have witnessed an upsurge in the number of published documents. Organizations are showing an increased interest in text classification for effective use of the information. Manual procedures for text classification can be fruitful for a handful of documents, but the same lack in credibility when the number of documents increases besides being laborious and time-consuming. Text mining techniques facilitate assigning text strings to categories rendering the process of classification fast, accurate, and hence reliable. This paper classifies chemistry documents using machine learning and statistical methods. The procedure of text classification has been described in chronological order like …

A Two-Warehouse Inventory Model With Rework Process And Time-Varying Demand, Nurnadiah Nurhasril

Student Works (2020-2029)

It is commonly assumed that in a classical inventory model, all items produced during a production cycle are of perfect items. However, contradictory to reality, production processes are not perfect at all times, hence resulting in the production of defectives. In order to minimise the cost incurred and reduce wastage in a production process, we might consider a rework process to be implemented on the defective items. Besides that, most classical inventory models also consider the assumption that the available storage facility has an unlimited capacity. Conversely, in reality an additional storage space commonly known as a rented warehouse (RW) …

Exchanges In A Virtual Environment For Diabetes Self-Management Education And Support: Social Network Analysis, Carlos A Pérez-Aldana, Allison A Lewinski, Constance M Johnson, Allison A Vorderstrasse, Sahiti Myneni

Faculty, Staff and Student Publications

BACKGROUND: Diabetes remains a major health problem in the United States, affecting an estimated 10.5% of the population. Diabetes self-management interventions improve diabetes knowledge, self-management behaviors, and clinical outcomes. Widespread internet connectivity facilitates the use of eHealth interventions, which positively impacts knowledge, social support, and clinical and behavioral outcomes. In particular, diabetes interventions based on virtual environments have the potential to improve diabetes self-efficacy and support, while being highly feasible and usable. However, little is known about the patterns of social interactions and support taking place within type 2 diabetes-specific virtual communities.

OBJECTIVE: The objective of this study was to …

Knowledge Network Embedding Of Transcriptomic Data From Spaceflown Mice Uncovers Signs And Symptoms Associated With Terrestrial Diseases, Amber M. Paul, Charlotte A. Nelson, Ana Uriarte Acuna, Ryan T. Scott, Atul J. Butte, Egle Cekanaviciute, Sergio E. Baranzini

Publications

There has long been an interest in understanding how the hazards from spaceflight may trigger or exacerbate human diseases. With the goal of advancing our knowledge on physiological changes during space travel, NASA GeneLab provides an open-source repository of multi-omics data from real and simulated spaceflight studies. Alone, this data enables identification of biological changes during spaceflight, but cannot infer how that may impact an astronaut at the phenotypic level. To bridge this gap, Scalable Precision Medicine Oriented Knowledge Engine (SPOKE), a heterogeneous knowledge graph connecting biological and clinical data from over 30 databases, was used in combination with GeneLab …

Flow-Based And Packet-Based Intrusion Detection Using Blstm, Brook Andreas, Jayaweera Dilruksha, Eric Mccandless

SMU Data Science Review

Abstract. Networks are always under the threat of malicious intrusions. Deep learning models are used to help identify and mitigate intrusions before damage can occur. Various types of deep learning models have been researched, built, and tested with the goal of improving intrusion detection and efficiencies. In this paper, a two-phase deep learning approach called a Hybrid Intrusion Detection System (HIDS) is proposed that uses Bi-Directional Long Short-Term Memory Neural Network (BLSTM) to assess both flow-based network data and packet-based data. This approach is unique because BLSTM is employed rather than a traditional Deep Neural Network (DNN) and two models …