Generative Linguistics And Neural Networks At 60: Foundation, Friction, And Fusion, 2019 Selected Works
Generative Linguistics And Neural Networks At 60: Foundation, Friction, And Fusion, Joe Pater
Multi-Linear Algebraic Eigendecompositions And Their Application In Data Science, Randy Hoover, Kyle Caudle Dr., Karen Braman Dr.
SDSU Data Science Symposium
Multi-dimensional data analysis has seen increased interest in recent years. With more and more data arriving as 2-dimensional arrays (images) as opposed to 1-dimensioanl arrays (signals), new methods for dimensionality reduction, data analysis, and machine learning have been pursued. Most notably have been the Canonical Decompositions/Parallel Factors (commonly referred to as CP) and Tucker decompositions (commonly regarded as a high order SVD: HOSVD). In the current research we present an alternate method for computing singular value and eigenvalue decompositions on multi-way data through an algebra of circulants and illustrate their application to two well-known machine learning methods: Multi-Linear Principal ...
Cs04all: Machine Learning Module, 2019 CUNY John Jay College
Cs04all: Machine Learning Module, Hunter R. Johnson
Open Educational Resources
These are materials that may be used in a CS0 course as a light introduction to machine learning.
The materials are mostly Jupyter notebooks which contain a combination of labwork and lecture notes. There are notebooks on Classification, An Introduction to Numpy, and An Introduction to Pandas.
There are also two assessments that could be assigned to students. One is an essay assignment in which students are asked to read and respond to an article on machine bias. The other is a lab-like exercise in which students use pandas and numpy to extract useful information about subway ridership in NYC ...
Cs04all: Natural Language Processing Project, 2019 CUNY John Jay College
Cs04all: Natural Language Processing Project, Hunter R. Johnson
Open Educational Resources
In this archive there are two activities/assignments suitable for use in a CS0 or Intro course which uses Python.
In the first activity, students are asked to "fill in the code" in a series of short programs that compute a similarity metric (cosine similarity) for text documents. This involves string tokenization, and frequency counting using Python string methods and datatypes.
In the second activity (taken directly from Think Python 2e) students use a pronunciation dictionary to solve a riddle involving homophones.
Bots In Libraries: They're Coming For Your Jobs (Or Is It?), 2019 Singapore Management University
Bots In Libraries: They're Coming For Your Jobs (Or Is It?), Salihin Mohammed Ali
Research Collection Library
With advancements in Artificial Intelligence (AI) and Machine Learning (ML), we have seen a rise in the use of bots, specifically chatbots, to deliver information services. Motivated by the Smart Nation programme, these chatbots have sprung up in sectors as transport, healthcare, banking and education in Singapore. What are these chatbots? How do they work? Will they take our jobs?SMU Libraries tries to answer these questions by delving into the mechanics of creating chatbots. The proof-of-concept aims to find out and understand use cases where these bots can be useful to delivering library information services to its campus community.
Dish: Democracy In State Houses, 2019 California Polytechnic State University, San Luis Obispo
Dish: Democracy In State Houses, Nicholas A. Russo
Master's Theses and Project Reports
In our current political climate, state level legislators have become increasingly impor- tant. Due to cuts in funding and growing focus at the national level, public oversight for these legislators has drastically decreased. This makes it difficult for citizens and activists to understand the relationships and commonalities between legislators. This thesis provides three contributions to address this issue. First, we created a data set containing over 1200 features focused on a legislator’s activity on bills. Second, we created embeddings that represented a legislator’s level of activity and engagement for a given bill using a custom model called Democracy2Vec ...
Comparative Study Of Sentiment Analysis With Product Reviews Using Machine Learning And Lexicon-Based Approaches, 2019 Southern Methodist University
Comparative Study Of Sentiment Analysis With Product Reviews Using Machine Learning And Lexicon-Based Approaches, Heidi Nguyen, Aravind Veluchamy, Mamadou Diop, Rashed Iqbal
SMU Data Science Review
In this paper, we present a comparative study of text sentiment classification models using term frequency inverse document frequency vectorization in both supervised machine learning and lexicon-based techniques. There have been multiple promising machine learning and lexicon-based techniques, but the relative goodness of each approach on specific types of problems is not well understood. In order to offer researchers comprehensive insights, we compare a total of six algorithms to each other. The three machine learning algorithms are: Logistic Regression (LR), Support Vector Machine (SVM), and Gradient Boosting. The three lexicon-based algorithms are: Valence Aware Dictionary and Sentiment Reasoner (VADER), Pattern ...
Improving Vix Futures Forecasts Using Machine Learning Methods, 2019 Southern Methodist University
Improving Vix Futures Forecasts Using Machine Learning Methods, James Hosker, Slobodan Djurdjevic, Hieu Nguyen, Robert Slater
SMU Data Science Review
The problem of forecasting market volatility is a difficult task for most fund managers. Volatility forecasts are used for risk management, alpha (risk) trading, and the reduction of trading friction. Improving the forecasts of future market volatility assists fund managers in adding or reducing risk in their portfolios as well as in increasing hedges to protect their portfolios in anticipation of a market sell-off event. Our analysis compares three existing financial models that forecast future market volatility using the Chicago Board Options Exchange Volatility Index (VIX) to six machine/deep learning supervised regression methods. This analysis determines which models provide ...
Improving Gas Well Economics With Intelligent Plunger Lift Optimization Techniques, 2019 Southern Methodist University
Improving Gas Well Economics With Intelligent Plunger Lift Optimization Techniques, Atsu Atakpa, Emmanuel Farrugia, Ryan Tyree, Daniel W. Engels, Charles Sparks
SMU Data Science Review
In this paper, we present an approach to reducing bottom hole plunger dwell time for artificial lift systems. Lift systems are used in a process to remove contaminants from a natural gas well. A plunger is a mechanical device used to deliquefy natural gas wells by removing contaminants in the form of water, oil, wax, and sand from the wellbore. These contaminants decrease bottom-hole pressure which in turn hampers gas production by forming a physical barrier within the well tubing. As the plunger descends through the well it emits sounds which are recorded at the surface by an echo-meter that ...
Analyzing Neuronal Dendritic Trees With Convolutional Neural Networks, 2019 Yale University
Analyzing Neuronal Dendritic Trees With Convolutional Neural Networks, Olivier Trottier, Jonathon Howard
Yale Day of Data
In the biological sciences, image analysis software are used to detect, segment or classify a variety of features encountered in living matter. However, the algorithms that accomplish these tasks are often designed for a specific dataset, making them hardly portable to accomplish the same tasks on images of different biological structures. Recently, convolutional neural networks have been used to perform complex image analysis on a multitude of datasets. While applications of these networks abound in the technology industry and computer science, use cases are not as common in the academic sciences. Motivated by the generalizability of neural networks, we aim ...
A Comparative Evaluation Of Recommender Systems For Hotel Reviews, 2019 Southern Methodist University
A Comparative Evaluation Of Recommender Systems For Hotel Reviews, Ryan Khaleghi, Kevin Cannon, Raghuram Srinivas
SMU Data Science Review
There has been increasing growth in deployment of recommender systems across Internet sites, with various models being used. These systems have been particularly valuable for review sites, as they seek to add value to the user experience to gain market share and to create new revenue streams through deals. Hotels are a prime target for this effort, as there is a large number for most destinations and a lot of differentiation between them. In this paper, we present an evaluation of two of the most popular methods for hotel review recommender systems: collaborative filtering and matrix factorization. The accuracy of ...
The Benefits Of Artificial Intelligence In Cybersecurity, 2019 La Salle University
The Benefits Of Artificial Intelligence In Cybersecurity, Ricardo Calderon
Economic Crime Forensics Capstones
Cyberthreats have increased extensively during the last decade. Cybercriminals have become more sophisticated. Current security controls are not enough to defend networks from the number of highly skilled cybercriminals. Cybercriminals have learned how to evade the most sophisticated tools, such as Intrusion Detection and Prevention Systems (IDPS), and botnets are almost invisible to current tools. Fortunately, the application of Artificial Intelligence (AI) may increase the detection rate of IDPS systems, and Machine Learning (ML) techniques are able to mine data to detect botnets’ sources. However, the implementation of AI may bring other risks, and cybersecurity experts need to find a ...
An Evaluation Of Learning Employing Natural Language Processing And Cognitive Load Assessment, 2019 Dublin Institute of Technology
An Evaluation Of Learning Employing Natural Language Processing And Cognitive Load Assessment, Mrunal Tipari
One of the key goals of Pedagogy is to assess learning. Various paradigms exist and one of this is Cognitivism. It essentially sees a human learner as an information processor and the mind as a black box with limited capacity that should be understood and studied. With respect to this, an approach is to employ the construct of cognitive load to assess a learner's experience and in turn design instructions better aligned to the human mind. However, cognitive load assessment is not an easy activity, especially in a traditional classroom setting. This research proposes a novel method for evaluating ...
Facial Re-Enactment, Speech Synthesis And The Rise Of The Deepfake, 2019 Edith Cowan University
Facial Re-Enactment, Speech Synthesis And The Rise Of The Deepfake, Nicholas Gardiner
Theses : Honours
Emergent technologies in the fields of audio speech synthesis and video facial manipulation have the potential to drastically impact our societal patterns of multimedia consumption. At a time when social media and internet culture is plagued by misinformation, propaganda and “fake news”, their latent misuse represents a possible looming threat to fragile systems of information sharing and social democratic discourse. It has thus become increasingly recognised in both academic and mainstream journalism that the ramifications of these tools must be examined to determine what they are and how their widespread availability can be managed.
This research project seeks to examine ...
Speech Interfaces And Pilot Performance: A Meta-Analysis, 2019 Embry-Riddle Aeronautical University
Speech Interfaces And Pilot Performance: A Meta-Analysis, Kenneth A. Ward
International Journal of Aviation, Aeronautics, and Aerospace
As the aviation industry modernizes, new technology and interfaces must support growing aircraft complexity without increasing pilot workload. Natural language processing presents just such a simple and intuitive interface, yet the performance implications for use by pilots remain unknown. A meta-analysis was conducted to understand performance effects of using speech and voice interfaces in a series of pilot task analogs. The inclusion criteria selected studies that involved participants performing a demanding primary task, such as driving, while interacting with a vehicle system to enter numbers, dial radios, or enter a navigation destination. Compared to manual system interfaces, voice interfaces reduced ...
Routing And Scheduling For A Last-Mile Transportation System, 2019 Singapore Management University
Routing And Scheduling For A Last-Mile Transportation System, Hai Wang
Research Collection School Of Information Systems
The last-mile problem concerns the provision of travel services from the nearest public transportation node to a passenger’s home or other destination. We study the operation of an emerging last-mile transportation system (LMTS) with batch demands that result from the arrival of groups of passengers who desire last-mile service at urban metro stations or bus stops. Routes and schedules are determined for a multivehicle fleet of delivery vehicles, with the objective of minimizing passenger waiting time and riding time. An exact mixed-integer programming (MIP) model for LMTS operations is presented first, which is difficult to solve optimally within acceptable ...
Machine Learning Methods For Personalized Health Monitoring Using Wearable Sensors, 2019 University of Massachusetts Amherst
Machine Learning Methods For Personalized Health Monitoring Using Wearable Sensors, Annamalai Natarajan
Mobile health is an emerging field that allows for real-time monitoring of individuals between routine clinical visits. Among others it makes it possible to remotely gather health signals, track disease progression and provide just-in-time interventions. Consumer grade wearable sensors can remotely gather health signals and other time series data. While wearable sensors can be readily deployed on individuals, there are significant challenges in converting raw sensor data into actionable insights. In this dissertation, we develop machine learning methods and models for personalized health monitoring using wearables. Specifically, we address three challenges that arise in these settings. First, data gathered from ...
Anomaly Detection In Bacnet/Ip Managed Building Automation Systems, 2019 Edith Cowan University
Anomaly Detection In Bacnet/Ip Managed Building Automation Systems, Matthew Peacock
Theses: Doctorates and Masters
Building Automation Systems (BAS) are a collection of devices and software which manage the operation of building services. The BAS market is expected to be a $19.25 billion USD industry by 2023, as a core feature of both the Internet of Things and Smart City technologies. However, securing these systems from cyber security threats is an emerging research area. Since initial deployment, BAS have evolved from isolated standalone networks to heterogeneous, interconnected networks allowing external connectivity through the Internet. The most prominent BAS protocol is BACnet/IP, which is estimated to hold 54.6% of world market share. BACnet ...
Learning To Grasp In Unstructured Environments With Deep Convolutional Neural Networks Using A Baxter Research Robot, 2019 Edith Cowan University
Learning To Grasp In Unstructured Environments With Deep Convolutional Neural Networks Using A Baxter Research Robot, Shehan Caldera
Theses: Doctorates and Masters
Recent advancements in Deep Learning have accelerated the capabilities of robotic systems in terms of visual perception, object manipulation, automated navigation, and human-robot collaboration. The capability of a robotic system to manipulate objects in unstructured environments is becoming an increasingly necessary skill. Due to the dynamic nature of these environments, traditional methods, that require expert human knowledge, fail to adapt automatically. After reviewing the relevant literature a method was proposed to utilise deep transfer learning techniques to detect object grasps from coloured depth images. A grasp describes how a robotic end-effector can be arranged to securely grasp an object and ...
Learning With Aggregate Data, 2019 University of Massachusetts Amherst
Learning With Aggregate Data, Tao Sun
Various real-world applications involve directly dealing with aggregate data. In this work, we study Learning with Aggregate Data from several perspectives and try to address their combinatorial challenges.
At first, we study the problem of learning in Collective Graphical Models (CGMs), where only noisy aggregate observations are available. Inference in CGMs is NP- hard and we proposed an approximate inference algorithm. By solving the inference problems, we are empowered to build large-scale bird migration models, and models for human mobility under the differential privacy setting.
Secondly, we consider problems given bags of instances and bag-level aggregate supervisions. Specifically, we study ...