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Articles 61 - 90 of 365
Full-Text Articles in Computer Sciences
Extractive Summarization And Simplification Of Scholarly Literature, Nilav Bharatkumar Vaghasiya
Extractive Summarization And Simplification Of Scholarly Literature, Nilav Bharatkumar Vaghasiya
Computer Science and Engineering Theses - Archive
Research papers and journals have always played a crucial role in the field of research and development. However, these research papers usually have a complex usage of language which limits the range of target readers. The language and the terms used in these literary works can make the concept or the topic tough to understand for a naive reader. The goal of this project is to simplify a complex piece of literature into something meaningful without sounding verbose. The idea is based upon Nobel Prize-winning physicist Richard Feynman’s learning technique known as the Feynman Technique that emphasizes the usage of …
Using Graph Convolutional Network And Message Passing Neural Networks For Solving Unit Commitment And Economic Dispatch In A Day Ahead Energy Trading Market Based On Ercot Nodal Model., Pradnya S. Gaikwad
Computer Science and Engineering Theses - Archive
Various machine learning applications will pre-process graphical representations into a vector of real values which in turn loses information regarding graph structure. Graph Neural Networks (GNNs) are a combination of an information diffusion mechanism and neural networks, which represent a set of transition functions and a set of output functions. Graph Convolution Network (GCN) is based on the optimized variant of CNN which operates on graph and is a scalable approach for semi-supervised learning on structured graph data. Message Passing Neural Networks (MPNNs) summaries the cohesions between many of the existing Neural Network models for structured graph data. This thesis …
Portable Electrochemical Sensing Platform: From Hardware To Software, Karthik Gangadhara
Portable Electrochemical Sensing Platform: From Hardware To Software, Karthik Gangadhara
Computer Science and Engineering Theses - Archive
Recent advances in the electrochemical biosensors is increasing the popularity of the point of care devices since the electrochemical sensor can provide low cost, portability, detectability, experimental simplicity, and capacity to provide real time monitoring. The point of care devices has been used in various biomedical applications such as blood glucose monitors, pregnancy tests, HIV tests, hemoglobin level tests etc. However, the existing portable devices are limited to a specific sensing mechanism due to the inability to include the various electrochemical sensing techniques into a compact formfactor. Thus, there is a need for miniaturized all-in-one electrochemical sensing platform for the …
Using Chebconv And B-Spline Gnn Models For Solving Unit Commitment And Economic Dispatch In A Day Ahead Energy Trading Market Based On Ercot Nodal Model, Yashodhan Kumthekar
Using Chebconv And B-Spline Gnn Models For Solving Unit Commitment And Economic Dispatch In A Day Ahead Energy Trading Market Based On Ercot Nodal Model, Yashodhan Kumthekar
Computer Science and Engineering Theses - Archive
Spectral Convolutions and B-Spline Graph Neural Network techniques have been used in past to learn embeddings in various complex, multidimensional structured knowledge graphs like genetics, social networks, geometric shapes and more. Spectral graphs provide a way to apply fast and localized filters on graph data. B-Spline kernels provides a way to keep the computation time independent by due to the local support property of B-spline basis functions. This thesis aims at using each of these models to test their viability for solving the Unit Commitment (UC) and Economic Dispatch (ED) problem for the energy market. There have been multiple attempts …
Big Data In Single Player Games, Mohammad Khaldoun Farhan Aldaboubi
Big Data In Single Player Games, Mohammad Khaldoun Farhan Aldaboubi
Computer Science and Engineering Theses - Archive
Improving video games can be exponentially more efficient by utilizing big data. Big data plays a big part in modern gaming, especially for multiplayer games like poker, first person shooter games. Utilizing the data gathered from video games can be used in ways that will improve the player experience massively and can be eye-opening to find issues, player pattern, and improve the game in ways that will be hard to pin point without gathering data of how the players are playing the game. However, while big data is being utilized in multiplayer games, it’s not utilized as much in single …
Parsing Code-Switched Taglish Language By Creating Constituents, Fadiah Qudah
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
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
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
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
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
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
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
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
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
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
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 …
Voice Controlled Accessibility And Testing Tool (Vcat), Nagendra Prasad Kasaghatta Ramachandra
Voice Controlled Accessibility And Testing Tool (Vcat), Nagendra Prasad Kasaghatta Ramachandra
Computer Science and Engineering Theses - Archive
Most current browser-based web applications and software engineering tools, such as test generators and management tools, are not accessible to users who cannot use a traditional input device, such as a mouse and/or a keyboard. To address this shortcoming, this research leverages recent speech-recognition advances to create a chrome browser extension that interprets voice inputs as web browser commands and executes those commands within the browser. As a result, the Voice Controlled Accessibility and Testing tool (VCAT) leverages the Chrome browser to achieve higher accessibility, with the capability to perform webpage navigation using voice commands. The tool is also capable …
User Syndication Using Speech Rhythm, Faisal Z H Alnahhas
User Syndication Using Speech Rhythm, Faisal Z H Alnahhas
Computer Science and Engineering Theses - Archive
In recent years we have seen a variety of approaches to increase security on computers and mobile devices including fingerprint, and facial recognition. Such techniques while effective are very expensive. Voice biometrics, specifically speech rhythm, is a method that has been drawing attention and growing in recent years. Unlike other methods, it requires little to no additional hardware installed on a device for it to work accurately. Speech rhythm utilizes the device's built-in microphone, and analyzes speakers based on features of their speech. In this work we leverage the existing hardware and simply add an efficient layer of software to …
Data-Driven Modeling Of Heterogeneous Multilayer Networks For Computing Communities Using Bipartite Graphs, Kanthi Sannappa Komar
Data-Driven Modeling Of Heterogeneous Multilayer Networks For Computing Communities Using Bipartite Graphs, Kanthi Sannappa Komar
Computer Science and Engineering Theses - Archive
Today, more than ever, data modeling and analysis play a vital role for enterprises in terms of finding actionable business intelligence. Data is being collected on a large scale from multiple sources hoping they can be leveraged using big data analysis techniques. However, challenges associated with the analysis of such data are numerous and depends on the characteristics of the data being collected. In many real-world applications, data sets are becoming complex as they are characterised by multiple entity types and multiple features (termed relationships) between entities. There is a need for an elegant approach to not only model such …
A Dynamic Multi-Threaded Queuing Mechnism For Reducing The Inter-Process Communication Latency On Multi-Core Chips, Rohitshankar Vijay Shankar V Mishra
A Dynamic Multi-Threaded Queuing Mechnism For Reducing The Inter-Process Communication Latency On Multi-Core Chips, Rohitshankar Vijay Shankar V Mishra
Computer Science and Engineering Theses - Archive
Reducing latency in Inter-Process Communication (IPC) is one of the key challenges in multi-threaded applications in multi-core environments. High latencies can have serious impact on the performance of an application when many threads queue up for memory access. Often lower latencies are achieved by using lock-free algorithms that keep threads spinning but incur high CPU usage as a result. Blocking synchronization primitives such as mutual exclusion locks or semaphores achieve resource efficiency but yield lower performance. In this paper, we take a different approach of combining a lock-free algorithm with resource efficiency of blocking synchronization primitives. We propose a queueing …
Frameannotator - A Web-Based Frame Semantic Annotation Tool, Sarbajit Roy
Frameannotator - A Web-Based Frame Semantic Annotation Tool, Sarbajit Roy
Computer Science and Engineering Theses - Archive
News headlines around the world are alarming. They aim to trigger emotional responses from the consumer. In an era where news and social media posts spread so quickly, it is often difficult to distinguish between what is real and what is false. Human fact-checking fails to cope with the growth of such unprecedented information, thus increasing the demand and various steps of automatic fact-checking to judge the veracity of claims. An automatic fact-checking system is a statistical model that can help to detect misinformation. The current state-of-the-art frame semantic parsers suffer from lack of a large annotated data-set and there …
Effective Crypto Ransomware Detection Using Hardware Performance Counters, John Podolanko
Effective Crypto Ransomware Detection Using Hardware Performance Counters, John Podolanko
Computer Science and Engineering Theses - Archive
Systems affected by malware in the past 10 years has risen from 29 million to 780 million, which tells us it is a rapidly growing threat. Viruses, ransomware, worms, backdoors, botnets, etc. all come under malware. Ransomware alone is predicted to cost $11.5 billion in 2019. As the downtime, data loss, and financial damages are rising, researchers continue to look for new ways to mitigate this threat. However, the common approaches have shown to yield high false positive rates or delayed detection rates resulting in data loss. My research explores a dynamic approach for early-stage ransomware detection by modeling its …
Implementation And Analysis Of Xcache On Southwest Tier 2 Cloud Cluster For Large Hadron Collider Atlas Experiment, Priyam Banerjee
Implementation And Analysis Of Xcache On Southwest Tier 2 Cloud Cluster For Large Hadron Collider Atlas Experiment, Priyam Banerjee
Computer Science and Engineering Theses - Archive
The ATLAS Experiment is one of the four major particle-detector experiments at the Large Hadron Collider at CERN (birthplace of the World Wide Web). The ATLAS was one of the LHC experiments that successfully demonstrated the discovery of the Higgs-Boson in July 2012. At the end of 2018, CERN data archiving on tape-based drives reached 330 PB. Through the Worldwide LHC Computing Grid (WLCG), a distributed computing infrastructure, the calibrated data out of the particle accelerator is split in chunks and distributed all around the world for analysis. The WLCG runs more than two million jobs per day. At peak …
Experimental Evaluation Of N-Model Methodology, Mehrab Irani
Experimental Evaluation Of N-Model Methodology, Mehrab Irani
Computer Science and Engineering Theses - Archive
Software maintenance is an essential part of the software development life cycle. Usually software engineers use ad hoc approaches to enhance legacy systems in the absence of a systematic methodology. However, there exists a methodology named "N- Model methodology" to enhance object-oriented legacy code. In this thesis, an experimental procedure is designed and applied to the N-Model methodology for enhancement of object-oriented software. A set of four categories of metrics; Process Metrics, Requirement Metrics, Design and Code Metrics and Test Metrics (total of 10 metrics) has been identified and applied. Additionally, a controlled experiment has been designed to compare the …
On The Feasibility Of Malware Unpacking With Hardware Performance Counters, Jay Mayank Patel
On The Feasibility Of Malware Unpacking With Hardware Performance Counters, Jay Mayank Patel
Computer Science and Engineering Theses - Archive
Most of the malware authors use Packers, to compress an executable file and attach a stub, to the file containing the code, to decompress it at runtime, which will turn a known piece of malware into something new, that known-malware scanners can't detect. The researchers are finding ways to unpack and find the original program from such packed binaries. However, the previous study of detection for unpacking in the packed malware using different approach won’t provide many promising results. This research explores a novel approach for the detection of the unpacking process using hardware performance counters. In this approach, the …
Analysis And Categorization Of Drive-By Download Malware Using Sandboxing And Yara Ruleset, Mohit Singhal
Analysis And Categorization Of Drive-By Download Malware Using Sandboxing And Yara Ruleset, Mohit Singhal
Computer Science and Engineering Theses - Archive
With the increase in the usage of websites as the main source of information gathering, malicious activity especially drive-by download has exponentially increased. A drive-by download refers to unintentional download of malicious code to a user computer that leaves the user open to a cyberattack. It has become the preferred distribution vector for many malware families. Malware is any software intentionally designed to cause damage to a user computer. The purpose of this research is to analyze the malware that were obtained from visiting approximately 100,000 malicious URLs and then running these binaries in sandboxes and then analyzing their runtime …
Blockchain: Resource Utilisation Analysis With A Game Theory Perspective, Vaibhav Soni
Blockchain: Resource Utilisation Analysis With A Game Theory Perspective, Vaibhav Soni
Computer Science and Engineering Theses - Archive
Major blockchain networks are using proof-of-work based consensus protocols to establish trust and decentralize resource management with different incentive mechanisms for the participants or nodes in the network. We formulate the computation resource management in the blockchain consensus process as a three stage Stackelberg game, where the profits of the miners, users and distributed app initiators are jointly optimized. Optimal decisions and strategies are devised in order to achieve the optimization through the Stackelberg equilibrium. Further, we study the interactions among these entities through a real experiment and the results are employed to justify our proposed model.
Deep Reinforcement Learning-Based Portfolio Management, Nitin Kanwar
Deep Reinforcement Learning-Based Portfolio Management, Nitin Kanwar
Computer Science and Engineering Theses - Archive
Machine Learning is at the forefront of every field today. The subfields of Machine Learning called Reinforcement Learning and Deep Learning, when combined have given rise to advanced algorithms which have been successful at reaching or surpassing the human-level performance at playing Atari games to defeating multiple times champion at Go. These successes of Machine Learning have attracted the interest of the financial community and have raised the question if these techniques could also be applied in detecting patterns in the financial markets. Until recently, mathematical formulations of dynamical systems in the context of Signal Processing and Control Theory have …
Ultra-Context: Maximizing The Context For Better Image Caption Generation, Ankit Khare
Ultra-Context: Maximizing The Context For Better Image Caption Generation, Ankit Khare
Computer Science and Engineering Theses - Archive
Several combinations of visual and semantic attention have been geared towards developing better image captioning architectures. In this work we introduce a novel combination of word-level semantic context with image feature-level visual context, which provides a more holistic overall context for image caption generation. This approach does not require training any explicit network structure, using any external resource for training semantic attributes, or supervision during any training step. The proposed architecture addresses the significance of learning to find context at three levels to achieve a better trade-off as well as a balance between the two lines of attentiveness (word-level and …