Heuristics For Sparsest Cut Approximations In Network Flow Applications,
2020
Southern Methodist University
Heuristics For Sparsest Cut Approximations In Network Flow Applications, Fernando Vilas
Computer Science and Engineering Theses and Dissertations
The Maximum Concurrent Flow Problem (MCFP) is a polynomially bounded problem that has been used over the years in a variety of applications. Sometimes it is used to attempt to find the Sparsest Cut, an NP-hard problem, and other times to find communities in Social Network Analysis (SNA) in its hierarchical formulation, the HMCFP. Though it is polynomially bounded, the MCFP quickly grows in space utilization, rendering it useful on only small problems. When it was defined, only a few hundred nodes could be solved, where a few decades later, graphs of one to two thousand nodes can still be …
Shakespeare In The Eighteenth Century: Algorithm For Quotation Identification,
2020
University of Arkansas, Fayetteville
Shakespeare In The Eighteenth Century: Algorithm For Quotation Identification, Marion Pauline Chiariglione
Graduate Theses and Dissertations
Quoting a borrowed excerpt of text within another literary work was infrequently done prior to the beginning of the eighteenth century. However, quoting other texts, particularly Shakespeare, became quite common after that. Our work develops automatic approaches to identify that trend. Initial work focuses on identifying exact and modified sections of texts taken from works of Shakespeare in novels spanning the eighteenth century. We then introduce a novel approach to identifying modified quotes by adapting the Edit Distance metric, which is character based, to a word based approach. This paper offers an introduction to previous uses of this metric within …
Robust Graph Learning From Noisy Data,
2020
Singapore Management University
Robust Graph Learning From Noisy Data, Zhao Kang, Haiqi Pan, Steven C. H. Hoi, Zenglin Xu
Research Collection School Of Computing and Information Systems
Learning graphs from data automatically have shown encouraging performance on clustering and semisupervised learning tasks. However, real data are often corrupted, which may cause the learned graph to be inexact or unreliable. In this paper, we propose a novel robust graph learning scheme to learn reliable graphs from the real-world noisy data by adaptively removing noise and errors in the raw data. We show that our proposed model can also be viewed as a robust version of manifold regularized robust principle component analysis (RPCA), where the quality of the graph plays a critical role. The proposed model is able to …
A Matheuristic Algorithm For Solving The Vehicle Routing Problem With Cross-Docking,
2020
Singapore Management University
A Matheuristic Algorithm For Solving The Vehicle Routing Problem With Cross-Docking, Aldy Gunawan, Audrey Tedja Widjaja, Pieter Vansteenwegen, Vincent F. Yu
Research Collection School Of Computing and Information Systems
This paper studies the integration of the vehicle routing problem with cross-docking, namely VRPCD. The aim is to find a set of routes to deliver single products from a set of suppliers to a set of customers through a cross-dock facility, such that the operational and transportation costs are minimized, without violating the vehicle capacity and time horizon constraints. A two-phase matheuristic approach that uses the routes of the local optima of an adaptive large neighborhood search (ALNS) as columns in a set-partitioning formulation of the VRPCD is designed. This matheuristic outperforms the state-of-the-art algorithms in solving a subset of …
Achieving Causal Fairness In Machine Learning,
2020
University of Arkansas, Fayetteville
Achieving Causal Fairness In Machine Learning, Yongkai Wu
Graduate Theses and Dissertations
Fairness is a social norm and a legal requirement in today's society. Many laws and regulations (e.g., the Equal Credit Opportunity Act of 1974) have been established to prohibit discrimination and enforce fairness on several grounds, such as gender, age, sexual orientation, race, and religion, referred to as sensitive attributes. Nowadays machine learning algorithms are extensively applied to make important decisions in many real-world applications, e.g., employment, admission, and loans. Traditional machine learning algorithms aim to maximize predictive performance, e.g., accuracy. Consequently, certain groups may get unfairly treated when those algorithms are applied for decision-making. Therefore, it is an imperative …
Advancing Performance Of Retail Recommendation Systems,
2020
Southern Methodist University
Advancing Performance Of Retail Recommendation Systems, Lisa Leininger, Johnny Gipson, Kito Patterson, Brad Blanchard
SMU Data Science Review
This paper presents two recommendation models, one traditional and one novel, for a retail men's clothing company. J. Hilburn is a custom-fit, menswear clothing company headquartered in Dallas, Texas. J. Hilburn employs stylists across the United States, who engage directly with customers to assist in selecting clothes that fit their size and style. J. Hilburn tasked the authors of this paper to leverage data science techniques to the given data set to provide stylists with more insight into clients’ purchase patterns and increase overall sales. This paper presents two recommendation systems which provide stylists with automatic predictions about possible clothing …
Improving Syntactic Relationships Between Language And Objects,
2020
Southern Methodist University
Improving Syntactic Relationships Between Language And Objects, Benjamin Wilke, Tej Tenmattam, Anand Rajan, Andrew Pollock, Joel Lindsey
SMU Data Science Review
This paper presents the integration of natural language processing and computer vision to improve the syntax of the language generated when describing objects in images. The goal was to not only understand the objects in an image, but the interactions and activities occurring between the objects. We implemented a multi-modal neural network combining convolutional and recurrent neural network architectures to create a model that can maximize the likelihood of word combinations given a training image. The outcome was an image captioning model that leveraged transfer learning techniques for architecture components. Our novelty was to quantify the effectiveness of transfer learning …
Data-Driven Investment Decisions In P2p Lending: Strategies Of Integrating Credit Scoring And Profit Scoring,
2020
Kennesaw State University
Data-Driven Investment Decisions In P2p Lending: Strategies Of Integrating Credit Scoring And Profit Scoring, Yan Wang
Doctor of Data Science and Analytics Dissertations
In this dissertation, we develop and discuss several loan evaluation methods to guide the investment decisions for peer-to-peer (P2P) lending. In evaluating loans, credit scoring and profit scoring are the two widely utilized approaches. Credit scoring aims at minimizing the risk while profit scoring aims at maximizing the profit. This dissertation addresses the strengths and weaknesses of each scoring method by integrating them in various ways in order to provide the optimal investment suggestions for different investors. Before developing the methods for loan evaluation at the individual level, we applied the state-of-the-art method called the Long Short Term Memory (LSTM) …
Philosophical Perspectives,
2020
CUNY Hunter College
Philosophical Perspectives, Jochen Albrecht
Publications and Research
This entry follows in the footsteps of Anselin’s famous 1989 NCGIA working paper entitled “What is special about spatial?” (a report that is very timely again in an age when non-spatial data scientists are ignorant of the special characteristics of spatial data), where he outlines three unrelated but fundamental characteristics of spatial data. In a similar vein, I am going to discuss some philosophical perspectives that are internally unrelated to each other and could warrant individual entries in this Body of Knowledge. The first one is the notions of space and time and how they have evolved in …
Fast Clustering Using A Grid-Based Underlying Density Function Approximation,
2020
Kennesaw State University
Fast Clustering Using A Grid-Based Underlying Density Function Approximation, Daniel Brown
Master of Science in Computer Science Theses
Clustering is an unsupervised machine learning task that seeks to partition a set of data into smaller groupings, referred to as “clusters”, where items within the same cluster are somehow alike, while differing from those in other clusters. There are many different algorithms for clustering, but many of them are overly complex and scale poorly with larger data sets. In this paper, a new algorithm for clustering is proposed to solve some of these issues. Density-based clustering algorithms use a concept called the “underlying density function”, which is a conceptual higher-dimension function that describes the possible results from the continuous …
Nonlinear Least Squares 3-D Geolocation Solutions Using Time Differences Of Arrival,
2020
University of New Mexico
Nonlinear Least Squares 3-D Geolocation Solutions Using Time Differences Of Arrival, Michael V. Bredemann
Mathematics & Statistics ETDs
This thesis uses a geometric approach to derive and solve nonlinear least squares minimization problems to geolocate a signal source in three dimensions using time differences of arrival at multiple sensor locations. There is no restriction on the maximum number of sensors used. Residual errors reach the numerical limits of machine precision. Symmetric sensor orientations are found that prevent closed form solutions of source locations lying within the null space. Maximum uncertainties in relative sensor positions and time difference of arrivals, required to locate a source within a maximum specified error, are found from these results. Examples illustrate potential requirements …
Achieving Obfuscation Through Self-Modifying Code: A Theoretical Model,
2020
Liberty University
Achieving Obfuscation Through Self-Modifying Code: A Theoretical Model, Heidi Waddell
Senior Honors Theses
With the extreme amount of data and software available on networks, the protection of online information is one of the most important tasks of this technological age. There is no such thing as safe computing, and it is inevitable that security breaches will occur. Thus, security professionals and practices focus on two areas: security, preventing a breach from occurring, and resiliency, minimizing the damages once a breach has occurred. One of the most important practices for adding resiliency to source code is through obfuscation, a method of re-writing the code to a form that is virtually unreadable. …
Storage Management Strategy In Mobile Phones For Photo Crowdsensing,
2020
Jilin University
Storage Management Strategy In Mobile Phones For Photo Crowdsensing, En Wang, Zhengdao Qu, Xinyao Liang, Xiangyu Meng, Yongjian Yang, Dawei Li, Weibin Meng
Department of Computer Science Faculty Scholarship and Creative Works
In mobile crowdsensing, some users jointly finish a sensing task through the sensors equipped in their intelligent terminals. In particular, the photo crowdsensing based on Mobile Edge Computing (MEC) collects pictures for some specific targets or events and uploads them to nearby edge servers, which leads to richer data content and more efficient data storage compared with the common mobile crowdsensing; hence, it has attracted an important amount of attention recently. However, the mobile users prefer uploading the photos through Wifi APs (PoIs) rather than cellular networks. Therefore, photos stored in mobile phones are exchanged among users, in order to …
Feature Extraction And Analysis Of Binaries For Classification,
2020
Dakota State University
Feature Extraction And Analysis Of Binaries For Classification, Micah Flack
Annual Research Symposium
The research project, Feature Extraction and, Analysis of Binaries for Classification, provides an in-depth examination of the features shared by unlabeled binary samples, for classification into the categories of benign or malicious software using several different methods. Because of the time it takes to manually analyze or reverse engineer binaries to determine their function, the ability to gather features and then instantly classify samples without explicitly programming the solution is incredibly valuable. It is possible to use an online service; however, this is not always viable depending on the sensitivity of the binary. With Python3 and the Pefile library, we …
Cylindrical Similarity Measurement For Helices In Medium-Resolution Cryo-Electron Microscopy Density Maps,
2020
Old Dominion University
Cylindrical Similarity Measurement For Helices In Medium-Resolution Cryo-Electron Microscopy Density Maps, Salim Sazzed, Peter Scheible, Maytha Alshammari, Willy Wriggers, Jing He
College of Sciences Posters
Cryo-electron microscopy (cryo-EM) density maps at medium resolution (5-10 Å) reveal secondary structural features such as α-helices and β-sheets, but they lack the side chains details that would enable a direct structure determination. Among the more than 800 entries in the Electron Microscopy Data Bank (EMDB) of medium-resolution density maps that are associated with atomic models, a wide variety of similarities can be observed between maps and models. To validate such atomic models and to classify structural features, a local similarity criterion, the F1 score, is proposed and evaluated in this study. The F1 score is theoretically normalized to a …
Atmospheric Contrail Detection With A Deep Learning Algorithm,
2020
University of Minnesota - Morris
Atmospheric Contrail Detection With A Deep Learning Algorithm, Nasir Siddiqui
Student Research, Papers, and Creative Works
Aircraft contrail emission is widely believed to be a contributing factor to global climate change. We have used machine learning techniques on images containing contrails in hopes of being able to identify those which contain contrails and those that do not. The developed algorithm processes data on contrail characteristics as captured by long-term image records. Images collected by the United States Deparment of Energy’s Atmospheric Radiation Management user facility(ARM) were used to train a deep convolutional neural network for the purpose of this contrail classification. The neural network model was trained with 1600 images taken by the Total Sky Imager(TSI) …
A Semi-Automatic Integrated Framework For Non-English Sentiment Lexicons,
2020
Universiti Malaya
A Semi-Automatic Integrated Framework For Non-English Sentiment Lexicons, Abdullah Kaity Mohammed Salem
Student Works (2020-2029)
There has been significant growth in social media networks in the last few years. Posting opinions and messages on social networking websites has become a popular activity on the Internet. The data sources are necessary for business intelligence and market analytics, as human opinions form a major indicator of human desires and behaviour. This has resulted in the development of a new study field called sentiment analysis. This includes the analysis, evaluation and interpretation of the opinions with the help of text mining and Natural Language Processing (NLP) processes, for identifying the text polarity, as positive, neutral or negative. It …
Neural Network Pruning For Ecg Arrhythmia Classification,
2020
California Polytechnic State University, San Luis Obispo
Neural Network Pruning For Ecg Arrhythmia Classification, Isaac E. Labarge
Master's Theses
Convolutional Neural Networks (CNNs) are a widely accepted means of solving complex classification and detection problems in imaging and speech. However, problem complexity often leads to considerable increases in computation and parameter storage costs. Many successful attempts have been made in effectively reducing these overheads by pruning and compressing large CNNs with only a slight decline in model accuracy. In this study, two pruning methods are implemented and compared on the CIFAR-10 database and an ECG arrhythmia classification task. Each pruning method employs a pruning phase interleaved with a finetuning phase. It is shown that when performing the scale-factor pruning …
Randomized Algorithms And How Society Uses Them Everyday,
2020
Otterbein University
Randomized Algorithms And How Society Uses Them Everyday, Rosaley Milano
Undergraduate Honors Thesis Projects
Randomness is an interesting and very beneficial phenomenon. In computer science randomness facilitates great advances in efficiency but topics like randomized algorithms aren’t taught until someone enters graduate school. This paper provides undergraduates as well as people unacquainted with computer science an opportunity to explore the topic of randomness by guiding them from essential topics all the way through the graduate level topic of randomized algorithms. Topics like what an algorithm is, how they are represented and the history that brought them into existence bring the reader up to speed before diving deeper into randomized algorithms. A discussion of complexity …
Multi-Tier Classification Based On Sentiment, Type, Emotion And Purpose For Online Diabetes Community,
2020
Universiti Malaya
Multi-Tier Classification Based On Sentiment, Type, Emotion And Purpose For Online Diabetes Community, Ratan Singh Wandeep Kaur
Student Works (2020-2029)
The evolution of social media platforms has created a niche for users to increasingly turn to such sites in order to share and exchange health related information. Facebook being one of the largest social networking sites has only encouraged such exchange thus mounting to a sheer amount of data that is hidden within unstructured text. The aim of this research is to propose a multi-tier classification based on sentiment, type, emotion and purpose (STEP) to classify data collected from diabetes community within Facebook. There are three tiers within the proposed STEP framework namely type, purpose and sentiment (and emotion within …
