Computer Sciences Commons^™

Local Radiance, Scott Peter Britell

Dissertations and Theses

Recent years have seen a proliferation of web applications based on content management systems (CMS). Using a CMS, non-technical content authors are able to define custom content types to support their needs. These content type names and the attribute names in each content type are typically domain-specific and meaningful to the content authors. The ability of a CMS to support a multitude of content types allows for endless creation and customization but also leads to a large amount of heterogeneity within a single application. While this meaningful heterogeneity is beneficial, it introduces the problem of how to write reusable functionality …

Analysis Of Batch Arrival Single And Bulk Service Queue With Multiple Vacation Closedown And Repair, T. Deepa, A. Azhagappan

Applications and Applied Mathematics: An International Journal (AAM)

In this paper, we analyze batch arrival single and bulk service queueing model with multiple vacation, closedown and repair. The single server provides single service if the queue size is ‘< a’ and bulk service if the queue size is ‘ ≥ a’. After completing the service (single or bulk), the server may breakdown with probability ξ and then it will be sent for repair. When the system becomes empty or the server is ready to serve after the repair but no one is waiting, the server resumes closedown and then goes for a multiple vacation of random length. Using supplementary variable technique, the steady-state probability generating function (PGF) of …

Employing Earth Observations And Artificial Intelligence To Address Key Global Environmental Challenges In Service Of The Sdgs, Wenzhao Li

Computational and Data Sciences (PhD) Dissertations

Earth Observation (EO) data provides the capability to integrate data from multiple sources and helps to produce more relevant, frequent, and accurate information about complex processes. EO, empowered by methodologies from Artificial Intelligence (AI), supports various aspects of the UN’s Sustainable Development Goals (SDGs). This dissertation presents author’s major studies using EO to fill in knowledge gaps and develop methodologies and cloud-based applications in selected SDGs, including SDG 6 (Clean Water and Sanitation), SDG 11 (Sustainable Cities and Communities), SDG 14 (Life below Water) and SDG 15 (Life on Land). For SDG 6, the study focuses on spatiotemporal water recharge …

Incorporating Word Order Explicitly In Glove Word Embedding, Brandon Cox

Computer Science and Computer Engineering Undergraduate Honors Theses

Word embedding is the process of representing words from a corpus of text as real number vectors. These vectors are often derived from frequency statistics from the source corpus. In the GloVe model as proposed by Pennington et al., these vectors are generated using a word-word cooccurrence matrix. However, the GloVe model fails to explicitly take into account the order in which words appear within the contexts of other words. In this paper, multiple methods of incorporating word order in GloVe word embeddings are proposed. The most successful method involves directly concatenating several word vector matrices for each position in …

Developing A Computational Framework For A Construction Scheduling Decision Support Web Based Expert System, Feroz Ahmed

Dissertations

Decision-making is one of the basic cognitive processes of human behaviors by which a preferred option or a course of action is chosen from among a set of alternatives based on certain criteria. Decision-making is the thought process of selecting a logical choice from the available options. When trying to make a good decision, all the positives and negatives of each option should be evaluated. This decision-making process is particularly challenging during the preparation of a construction schedule, where it is difficult for a human to analyze all possible outcomes of each and every situation because, construction of a project …

Why Spiking Neural Networks Are Efficient: A Theorem, Michael Beer, Julio Urenda, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Current artificial neural networks are very successful in many machine learning applications, but in some cases they still lag behind human abilities. To improve their performance, a natural idea is to simulate features of biological neurons which are not yet implemented in machine learning. One of such features is the fact that in biological neural networks, signals are represented by a train of spikes. Researchers have tried adding this spikiness to machine learning and indeed got very good results, especially when processing time series (and, more generally, spatio-temporal data). In this paper, we provide a theoretical explanation for this empirical …

Joule's 19th Century Energy Conservation Meta-Law And The 20th Century Physics (Quantum Mechanics And General Relativity): 21st Century Analysis, Vladik Kreinovich, Olga Kosheleva

Departmental Technical Reports (CS)

Joule's Energy Conservation Law was the first "meta-law": a general principle that all physical equations must satisfy. It has led to many important and useful physical discoveries. However, a recent analysis seems to indicate that this meta-law is inconsistent with other principles -- such as the existence of free will. We show that this conclusion about inconsistency is based on a seemingly reasonable -- but simplified -- analysis of the situation. We also show that a more detailed mathematical and physical analysis of the situation reveals that not only Joule's principle remains true -- it is actually strengthened: it is …

Why Gamma Distribution Of Seismic Inter-Event Times: A Theoretical Explanation, Laxman Bokati, Aaron A. Velasco, Vladik Kreinovich

Departmental Technical Reports (CS)

It is known that the distribution of seismic inter-event times is well described by the Gamma distribution. Recently, this fact has been used to successfully predict major seismic events. In this paper, we explain that the Gamma distribution of seismic inter-event times can be naturally derived from the first principles.

The R Journal (December 2019) 11(2): Complete Issue, The R Foundation

The R Journal

Editorial, Michael J. Kane

Contributed Research Articles

Using Web Services to Work with Geodata in R, Jan-Philipp Kolb

orthoDr: Semiparametric Dimension Reduction via Orthogonality Constrained Optimization, Ruoqing Zhu, Jiyang Zhang, Ruilin Zhao, Peng Xu, Wenzhuo Zhou, and Xin Zhang

coxed: An R Package for Computing Duration-Based Quantities from the Cox Proportional Hazards Model, Jonathan Kropko and Jeffrey J. Harden

Modeling Regimes with Extremes: The Bayesdfa Package for Identifying and Forecasting Common Trends and Anomalies in Multivariate Time-Series Data, Eric J. Ward, Sean C. Anderson, Luis A. Damiano, Mary E. Hunsicker, and Michael A. Litzow

Fitting Tails by the Empirical Residual …

Finitely Generated Sets Of Fuzzy Values: If "And" Is Exact, Then "Or" Is Almost Always Approximate, And Vice Versa -- A Theorem, Julio Urenda, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In the traditional fuzzy logic, experts' degrees of confidence are described by numbers from the interval [0,1]. Clearly, not all the numbers from this interval are needed: in the whole history of the Universe, there will be only countably many statements and thus, only countably many possible degree, while the interval [0,1] is uncountable. It is therefore interesting to analyze what is the set S of actually used values. The answer depends on the choice of "and"-operations (t-norms) and "or"-operations (t-conorms). For the simplest pair of min and max, any finite set will do -- as long as it is …

Fuzzy Logic Explains The Usual Choice Of Logical Operations In 2-Valued Logic, Julio Urenda, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In the usual 2-valued logic, from the purely mathematical viewpoint, there are many possible binary operations. However, in commonsense reasoning, we only use a few of them: why? In this paper, we show that fuzzy logic can explain the usual choice of logical operations in 2-valued logic.

Which Distributions (Or Families Of Distributions) Best Represent Interval Uncertainty: Case Of Permutation-Invariant Criteria, Michael Beer, Julio Urenda, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In many practical situations, we only know the interval containing the quantity of interest, we have no information about the probability of different values within this interval. In contrast to the cases when we know the distributions and can thus use Monte-Carlo simulations, processing such interval uncertainty is difficult -- crudely speaking, because we need to try all possible distributions on this interval. Sometimes, the problem can be simplified: namely, it is possible to select a single distribution (or a small family of distributions) whose analysis provides a good understanding of the situation. The most known case is when we …

Analysis Of Two Stage M[X1],M[X2]/G1,G2/1 Retrial G-Queue With Discretionary Priority Services, Working Breakdown, Bernoulli Vacation, Preferred And Impatient Units, G. Ayyappan, B. Somasundaram

Applications and Applied Mathematics: An International Journal (AAM)

In this paper, we study M^[X₁] , M^[X2] /G₁ ,G₂ /1 retrial queueing system with discretionary priority services. There are two stages of service for the ordinary units. During the first stage of service of the ordinary unit, arriving priority units can have an option to interrupt the service, but, in the second stage of service it cannot interrupt. When ordinary units enter the system, they may get the service even if the server is busy with the first stage of service of an ordinary unit or may enter into the orbit or leave …

Parsing Code-Switched Taglish Language By Creating Constituents, Fadiah Qudah

Computer Science and Engineering Theses - Archive

When extracting meaning from language, a common first step is to break down language into constituents, or words that work together as a unit. This task, known as parsing, typically follows a specific grammar in order decompose the language into its underlying structure composed of constituents. Difficulties with this grammar-based parsing occur, however, with real-world natural language due to its unstructured nature. Code-switching, the phenomenon of alternating between languages while communicating, further complicates this task by requiring us to parse based on two (or more) languages instead of one. In this thesis, a data-driven method to parse code-switched language into …

Approxml: Efficient Approximate Ad-Hoc Ml Models Through Materialization And Reuse, Faezeh Ghaderi

Computer Science and Engineering Theses - Archive

Machine Learning (ML) has become an essential tool in answering complex predictive analytic queries. Model building for large scale datasets is one of the most time-consuming parts of the data science pipeline. Often data scientists are willing to sacrifice some accuracy in order to speed up this process during the exploratory phase. In this report, we aim to demonstrate ApproxML, a system that efficiently constructs approximate ML models for new queries from previously constructed ML models using the concepts of model materialization and reuse. ApproxML supports a wide variety of ML models such as generalized linear models for supervised learning …

Detect Traffic Signs From Large Street View Images With Deep Learning, Zhifei Deng

Computer Science and Engineering Theses - Archive

Autonomous driving is about to shaping the future of our life. Self-driving vehicles produced by Waymo or many other companies have demonstrated excellent driving capabilities on the road. However, accidents still happen. Correctly recognising the traffic signs, such as stop signs, is critical for a self-driving vehicle. Failing to recognise the traffic signs could lead to fatal accidents. Meanwhile, computer vision technology has made huge progress since the advent of deep learning, for example, image classification, object detection, and instance segmentation. Efforts have been made in developing faster and more accurate object detection methods. Faster R-CNN stands out as one …

Social Media Text Analysis Using Multi-Kernel Convolutional Neural Network, Anna Philips

Computer Science and Engineering Theses - Archive

Transportation planners and ride hailing platforms such as Uber and Lyft use their riders feedback to assess their services and monitor customer satisfaction. Social media websites such as Facebook, Instagram, LinkedIn and in particular Twitter provides a large dataset of micro-texts by users who regularly post to their social media accounts about their grievances with their ride experience. This data is often unorganized and intractable to process because of it’s extremely large size which is continuously increasing daily. In this project, we collected ride hailing service relevant text data from Twitter around New York and developed a novel Convolutional Neural …

Distributed Deep Neural Networks Training For Brain Imaging Applications, Sudheer Raja

Computer Science and Engineering Theses - Archive

Over the recent years, Deep Neural Networks (DNNs) have surpassed human-level intelligence in recognizing and interpreting complex patterns in data. Ever since the ImageNet competition in 2012, Deep Learning (DL) has become a promising approach for solving numerous problems in the field of Computer Science. However, the neuroscience community is not able to utilize the DL algorithms effectively because the brain imaging datasets are huge in terms of size, and the current sequential training techniques do not scale up well for such big datasets. Without the proper amount of training data, training DNN models to competitive accuracies is quite challenging. …

Comprehensive Study Of Generative Methods On Drug Discovery, Siyu Xiu

Computer Science and Engineering Theses - Archive

Observing the recent success of the deep learning (DL) technology in multiple life-changing application areas, e.g., autonomous driving, image/video search and discovery, natural language processing, etc., many new opportunities have presented themselves. One of the biggest ones lies in applying DL in accelerating the drug discovery, where millions of human lives could potentially be saved. However, applying DL into the drug discovery task turns out to be non-trivial. The most successful DL methods take fix-sized tensors/matrices, e.g., images, or sequences of tokens, e.g., sentences with variant numbers of words, as their inputs. However, none of these registers with the inputs …

Deduplication-Aware Page Cache In Linux Kernel For Improved Read Performance, Venkata Satya Ravi Kiran Boggavarapu

Computer Science and Engineering Theses - Archive

The amount of data being produced and consumed is increasing every day. As a result, there can be a large amount of redundant data in the storage system. Storing and accessing these duplicate data unnecessarily consumes disk space and I/O bandwidth. Deduplication techniques are widely deployed to remove the redundancy. In particular, the deduplication solutions that work at the block level are proven to be effective. These solutions aim to effectively use disk space and write bandwidth by avoiding duplicate data writes to the storage. However, such a design might not help in improving the read performance, which is critical …

Use Of Word Embedding To Generate Similar Words And Misspellings For Training Purpose In Chatbot Development, Sanjay Thapa

Computer Science and Engineering Theses - Archive

The advancement in the field of Natural Language Processing and Machine Learning has played a significant role in the huge improvement of conversational Artificial Intelligence (AI). The use of text-based conversation AI such as chatbots have increased significantly for the everyday purpose to communicate with real people for a variety of tasks. Chatbots are deployed in almost all popular messaging platforms and channels. The rise of chatbot development frameworks based on machine learning is helping to deploy chatbot easily and promptly. These chatbot development frameworks use machine learning and natural language understanding (NLU) to understand users' messages and intents and …

Hiding In Plain Sight? The Impact Of Face Recognition Services On Privacy, James Richard Ortega

Computer Science and Engineering Theses - Archive

The public at large is increasingly concerned with privacy online. While the focus is on the data privately collected by platforms, there are also privacy concerns in the realm of public data. Seemingly innocuous information shared in public, on online platforms, can be pieced together to detrimentally affect one's privacy in unexpected ways. On YouTube there exists a rich public dataset for adversaries to analyze for the purposes of breaching privacy; particularly due to the intersection of location and facial data. The goal of this work is to characterize the privacy risks that exists on YouTube, and explore the viability …

Using Property-Based Testing, Weighted Grammar-Based Generators, And A Consensus Oracle To Test Browser Rendering Engines And To Reproduce Minimized Versions Of Existing Test Cases, Joel David Martin

Computer Science and Engineering Theses - Archive

Verifying that a web browser rendering engine correctly renders all valid web pages is challenging due to the size of the input space (valid web pages), the difficulty of determining correct rendering for any given web page (the test oracle problem), and the degree to which normal variation in browser rendering behavior can obscure other differences (fonts, bor- ders, input controls, etc). These challenges lead to manual human involvement during the testing process. We propose a new Property-Based Testing (PBT) approach that addresses these challenges in order to enable automated web browser render testing. Our approach is composed of the …

The Impact Of Toxic Replies On Twitter Conversations, Nazanin Salehabadi

Computer Science and Engineering Theses - Archive

Social media has become an empowering agent for individual voices and freedom of expression. Yet, it can also serve as a breeding ground for hate speech. According to a Pew Research Center study, 41% of Americans have been personally subjected to harassing behavior online, 66% have witnessed these behaviors directed at others, and 18% have been subjected to particularly severe forms of harassment online, such as physical threats, harassment over a sustained period, sexual harassment, or stalking. Recently, many research studies have tried to understand online hate speech and its implications, focusing on detecting and characterizing hate speech. One limitation …

Performance Modeling And Resource Provisioning For Data-Intensive Applications, Zhongwei Li

Computer Science and Engineering Theses - Archive

Performance evaluation and resource provisioning are two most critical factors to be considered for designers of distributed systems at modern warehouse data centers. The ever-increasing volumes of data in recent years have pushed many businesses to move their computing tasks to the Cloud, which offers many benefits including the low system management and maintenance costs and better scalability. As a result, most recent prominently emerging workloads are data-intensive, calling for scaling out the workload to a large number of servers for parallel processing. Questions can be asked as what factors impact the system scaling performance, and how to efficiently schedule …

Social Media Sentiment Analysis With A Deep Neural Network: An Enhanced Approach Using User Behavioral Information, Ahmed Sulaiman M. Alharbi

Dissertations

Sentiment analysis on social media such as Twitter has become a very important and challenging task. Due to the characteristics of such data (including tweet length, spelling errors, abbreviations, and special characters), the sentiment analysis task in such an environment requires a non-traditional approach. Moreover, social media sentiment analysis constitutes a fundamental problem with many interesting applications, such as for Business Intelligence, Medical Monitoring, and National Security. Most current social media sentiment classification methods judge the sentiment polarity primarily according to textual content and neglect other information on these platforms. In this research, we propose deep learning based frameworks that …

Learning Robot Manipulation Tasks Via Observation, Michail Theofanidis

Computer Science and Engineering Dissertations - Archive

The coexistence of humans and robots has been the aspiration of many scientific endeavors in the past century. Most anthropomorphic or industrial robots are highly articulated and complex machines, which are designed to carry out tasks that often involve the manipulation of physical objects. Traditionally, robots learn how to perform such tasks with the aid of a human programmer or operator. In this regard, the human acts as a teacher who provides a demonstration of a task. From the data of the demonstration, the robot must learn a state-action mapping that accomplishes the task. This state-action mapping is often addressed …

Feature Extraction In Noise-Diverse Environments For Human Activities Recognition Using Wi-Fi, Sheheryar Arshad

Computer Science and Engineering Dissertations - Archive

With the rapid development of 802.11 standard and Internet of Things (IoT) applications, Wi-Fi (IEEE 802.11) has emerged as the most widely used wireless communication technology. Wi-Fi based sensing has found widespread use cases involving activity recognition, indoor localization, design of smart spaces and in healthcare applications. This dissertation presents the study of human activities’ sensing and recognition using channel state information (CSI) of Wi-Fi. We highlight the limitations of existing methods and consequently design the frameworks for collecting stable CSI and monitoring different indoor and outdoor environments for human activities. Specifically, this dissertation provide means to define and extract …

Using Property-Based Testing, Weighted Grammar-Based Generators, And A Consensus Oracle To Test Browser Rendering Engines And To Reproduce Minimized Versions Of Existing Test Cases, Joel David Martin

Computer Science and Engineering Dissertations - Archive

Verifying that a web browser rendering engine correctly renders all valid web pages is challenging due to the size of the input space (valid web pages), the difficulty of determining correct rendering for any given web page (the test oracle problem), and the degree to which normal variation in browser rendering behavior can obscure other differences (fonts, bor- ders, input controls, etc). These challenges lead to manual human involvement during the testing process. We propose a new Property-Based Testing (PBT) approach that addresses these challenges in order to enable automated web browser render testing. Our approach is composed of the …

R Foundation News, Torsten Hothorn

The R Journal

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