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Articles 211 - 240 of 666

Full-Text Articles in Computer Sciences

User Syndication Using Speech Rhythm, Faisal Z H Alnahhas Aug 2019

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 …

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Data-Driven Modeling Of Heterogeneous Multilayer Networks For Computing Communities Using Bipartite Graphs, Kanthi Sannappa Komar Aug 2019

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 …

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A Dynamic Multi-Threaded Queuing Mechnism For Reducing The Inter-Process Communication Latency On Multi-Core Chips, Rohitshankar Vijay Shankar V Mishra Aug 2019

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 …

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Learning For Clinical Outcome Prediction From Big Medical Data, Jiawen Yao Aug 2019

Learning For Clinical Outcome Prediction From Big Medical Data, Jiawen Yao

Computer Science and Engineering Dissertations - Archive

With the advance of recent technological innovations, nowadays scientists can easily capture and store tremendous amounts of different types of medical data such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), big pathological images and high dimensional cell profiling data. Developing deep learning and machine learning to analyze such large-scale medical data sets for patient health care is an interesting but challenging problem. Inspired by the trend, in this dissertation, we focus on solving real-world problems, like survival analysis on image-omics data and reducing uncertainty from undersampled MRI. Survival analysis is a crucial tool in the clinical study of cancer …

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Understanding And Optimizing Parallel Performance In Multi-Tenant Cloud, Yong Zhao Aug 2019

Understanding And Optimizing Parallel Performance In Multi-Tenant Cloud, Yong Zhao

Computer Science and Engineering Dissertations - Archive

As a critical component of resource management in multicore systems, fair schedulers in hypervisors and operating systems (OSes) must follow a simple invariant: guarantee that the computing resources such as CPU cycles are fairly allocated to each vCPU or thread. As simple as it may seem, we found this invariant is broken when parallel programs with blocking synchronization are colocated with CPU intensive programs in hypervisors such as Xen, KVM and OSes such as Linux CFS. On the other hand, schedulers in virtualized environment usually reside in two different layers: one is in the hypervisor which aims to schedule vCPU …

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Computational Approaches For Finding Disease Related Genes And Rnas, Negin Fraidouni Aug 2019

Computational Approaches For Finding Disease Related Genes And Rnas, Negin Fraidouni

Computer Science and Engineering Dissertations - Archive

Finding candidate genes that could cause specific diseases has been the subject of many studies. This is an important research task, however in the biological experimentation domain it can be very expensive and time consuming. So an alternative way is to find gene expression values from partial measurements and try to predict the rest. By using computational methods, we can statistically estimate these relationships faster and in a more efficient way, providing domain experts suggestions on what exploration of likely relationships they should be focusing. One common computational approach is to model the gene expression data as a matrix (where …

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Convex And Non-Convex Optimization Methods For Machine Learning, Fariba Zohrizadeh Aug 2019

Convex And Non-Convex Optimization Methods For Machine Learning, Fariba Zohrizadeh

Computer Science and Engineering Dissertations - Archive

This dissertation is concerned with modeling fundamental and challenging machine learning tasks as convex/non-convex optimization problems and designing a mechanism that could solve them in a cost and time-effective manner. Extensive theoretical and practical studies are carried out to give deeper insights into the robustness and effectiveness of the formulated problems. In what follows, we investigate some well-known tasks that frequently arise in machine learning applications. Image Segmentation: Image segmentation is a fundamental and challenging task in computer vision with diverse applications in various areas. One of the major challenges in image segmentation is to determine the optimal number of …

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Frameannotator - A Web-Based Frame Semantic Annotation Tool, Sarbajit Roy May 2019

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 …

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Effective Crypto Ransomware Detection Using Hardware Performance Counters, John Podolanko May 2019

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 …

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Implementation And Analysis Of Xcache On Southwest Tier 2 Cloud Cluster For Large Hadron Collider Atlas Experiment, Priyam Banerjee May 2019

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 …

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Experimental Evaluation Of N-Model Methodology, Mehrab Irani May 2019

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 …

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On The Feasibility Of Malware Unpacking With Hardware Performance Counters, Jay Mayank Patel May 2019

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 …

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Analysis And Categorization Of Drive-By Download Malware Using Sandboxing And Yara Ruleset, Mohit Singhal May 2019

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 …

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Blockchain: Resource Utilisation Analysis With A Game Theory Perspective, Vaibhav Soni May 2019

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.

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Deep Reinforcement Learning-Based Portfolio Management, Nitin Kanwar May 2019

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 …

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Ultra-Context: Maximizing The Context For Better Image Caption Generation, Ankit Khare May 2019

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 …

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Claimportal: Building A Social Media Analytics System For Assisting Fact-Checking, Sarthak Majithia May 2019

Claimportal: Building A Social Media Analytics System For Assisting Fact-Checking, Sarthak Majithia

Computer Science and Engineering Theses - Archive

We are in a digital era where claims made by people can attract attention and spread like wildfire. Misinformation and disinformation about important social and political issues can be intentional and motive can be malicious. Thus, we built a Twitter monitoring platform, namely, ClaimPortal. It assists its users by searching, checking, and providing analytics of factual claims made by politicians and influential people on Twitter. ClaimPortal empowers users with a search API which enables filtering conditions such as date range, tweets from/mentioning specific users, keyword based search, hashtags, check-worthiness scores, and types of claims. We explain the architecture of ClaimPortal …

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From Body To Brain: Using Artificial Intelligence To Identify User Skills & Intentions In Interactive Scenarios, Michalis Papakostas May 2019

From Body To Brain: Using Artificial Intelligence To Identify User Skills & Intentions In Interactive Scenarios, Michalis Papakostas

Computer Science and Engineering Dissertations - Archive

Artificial Intelligence has probably been the most rapidly evolving field of science during the last decade. Its numerous real-life applications have radically altered the way we experience daily-living with great impact in some of the most basic aspects of human lives including but not limited to health and well-being, communication and interaction, education, driving, daily, and entertainment. Human-Computer Interaction (HCI) is the field of Computer Science lying in the epicenter of this evolution and is responsible for transforming rudimentary research findings and theoretical principles into intuitive tools, responsible for enhancing human performance, increasing productivity and ensuring safety. Two of the …

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Designing Highly-Efficient Deduplication Systems With Optimized Computation And I/O Operations, Fan Ni May 2019

Designing Highly-Efficient Deduplication Systems With Optimized Computation And I/O Operations, Fan Ni

Computer Science and Engineering Dissertations - Archive

Data deduplication has been widely used in various storage systems for saving storage space, I/O bandwidth, and network traffic. However, existing deduplication techniques are inadequate as they introduce significant computation and I/O cost. First, to detect duplicates the input data (files) are usually partitioned into small chunks in the chunking process. It can be very time consuming if the content-defined chunking (CDC) method is adopted, where the chunk boundaries are determined by checking the data content byte-by-byte, for detecting duplicates among modified files. Second, for each chunk generated in the chunking process, we need to apply a collision resistant hash …

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Learning Representations Using Reinforcement Learning, Sourabh Bose May 2019

Learning Representations Using Reinforcement Learning, Sourabh Bose

Computer Science and Engineering Dissertations - Archive

The framework of reinforcement learning is a powerful suite of algorithms that can learn generalized solutions to complex decision making problems. However, the applications of reinforcement learning algorithms to traditional machine learning problems such as clustering, classification and representation learning, have rarely been explored. With the advent of large amounts of data, robust models are required which can extract meaningful representations from the data that can potentially be applied to new unseen tasks. The presented work investigates the applications of reinforcement learning algorithms in the perspective of transfer learning by applying algorithms in the framework of reinforcement learning to address …

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N-Model Methodology For Enhancement Of Object-Oriented Software, Anam Sahoo May 2019

N-Model Methodology For Enhancement Of Object-Oriented Software, Anam Sahoo

Computer Science and Engineering Dissertations - Archive

Software maintenance typically consumes an average of 60\% of software life costs, of which more than 60\% are spent on enhancements. These are a challenge for the software community, in which hundreds of millions of lines of legacy code need to be modified during enhancement maintenance. Unfortunately, our extensive literature survey and industrial experiences show that there is a lack of a systematic methodology for software reengineering and for enhancement. As a consequence, software engineers use ad hoc approaches to enhance a legacy system. This dissertation presents an agile process, called the N-model process and methodology, for enhancing object-oriented legacy …

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Deepsign: A Deep-Learning Architecture For Sign Language, Jai Amrish Shah Dec 2018

Deepsign: A Deep-Learning Architecture For Sign Language, Jai Amrish Shah

Computer Science and Engineering Theses - Archive

Sign languages are used by deaf people for communication. In sign languages, humans use hand gestures, body, facial expressions and movements to convey meaning. Humans can easily learn and understand sign languages, but automatic sign language recognition for machines is a challenging task. Using recent advances in the field of deep-learning, we introduce a fully automated deep-learning architecture for isolated sign language recognition. Our architecture tries to address three problems: 1) Satisfactory accuracy with limited data samples 2) Reducing chances of over-fitting when the data is limited 3) Automating recognition of isolated signs. Our architecture uses deep convolutional encoder-decoder architecture …

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Classification Of Clinical Narratives Using Convolutional Neural Network, Nikit Rajiv Lonari Dec 2018

Classification Of Clinical Narratives Using Convolutional Neural Network, Nikit Rajiv Lonari

Computer Science and Engineering Theses - Archive

Patient safety is a key aspect for good consumer care. When an individual is hospitalized or receives medication the family wants the patient safety to be above all factors. For instance, a drug can do both either cure the disease or perhaps, give rise to an adverse event. A drug administered for an indicated condition has substantial power to reduce or cure a disease and further to prevent it from happening again in the future but at the risk of side effects. At present, there are several methods in patient safety and in particular in the area of signal detection …

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Towards End-To-End Semi-Supervised Deep Learning For Drug Discovery, Xiaoyu Zhang Dec 2018

Towards End-To-End Semi-Supervised Deep Learning For Drug Discovery, Xiaoyu Zhang

Computer Science and Engineering Theses - Archive

Observing the recent progress in Deep Learning, the employment of AI is surging to accelerate drug discovery and cut R&D costs in the last few years. However, the success of deep learning is attributed to large-scale clean high-quality labeled data, which is generally unavailable in drug discovery practices. In this thesis, we address this issue by proposing an end-to-end deep learning framework in a semi supervised learning fashion. That is said, the proposed deep learning approach can utilize both labeled and unlabeled data. While labeled data is of very limited availability, the amount of available unlabeled data is generally huge. …

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Dwrelu : Double Weighted Rectifier Linear Unit An Activation Function With Trainable Scaling Parameter, Bhaskar Chandra Trivedi Dec 2018

Dwrelu : Double Weighted Rectifier Linear Unit An Activation Function With Trainable Scaling Parameter, Bhaskar Chandra Trivedi

Computer Science and Engineering Theses - Archive

Deep Neural Network have become very popular for computer vision application in recent years. At the same time, it remains important to understand the different implementation choices that need to be made when designing a neural network and to thoroughly investigate existing and novel alternatives for those choices. One of those choices is the activation function. The ReLU activation function is a widely used activation function. It discards all the values below zero and keeps the ones greater than zero. Variations such as Leaky ReLU and Parametric ReLU do not discard values, so that gradiants are nonzero for the entire …

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Health Monitoring Of Atlas Data Center Clusters And Failure Analysis, Meenakshi Balasubramanian Dec 2018

Health Monitoring Of Atlas Data Center Clusters And Failure Analysis, Meenakshi Balasubramanian

Computer Science and Engineering Theses - Archive

Monitoring the health of data center clusters is an integral part of any industrial facility. ATLAS is one of the High Energy Physics experiments at the Large Hadron Collider (LHC) at CERN. ATLAS DDM (Distributed Data Management) is a system that manages data transfer, staging, deletions and experimental data on the LHC grid. Currently, the DDM system relies on Rucio software, with Cloud based object storage and No-SQL solutions. It is a cumbersome process in the current system, to fetch and analyze the transfer, staging and deletion metrics of a specific site for any regional center. In this thesis, a …

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Monitoring Of Swt2 Data Clusters For The Atlas Experiment, Antara Ray Dec 2018

Monitoring Of Swt2 Data Clusters For The Atlas Experiment, Antara Ray

Computer Science and Engineering Theses - Archive

Monitoring of the South West Tier 2 RSEs is done by CERN with the help of Rucio. The challenge faced by the team monitoring the servers at the University of Texas site was that the monitoring data is pictorially represented and provided to them in GIF format. In this work we focus on creating an interactive site that will not only monitor the data at the local RSEs but also create a platform to analyze the data storage systems. It turn it will also create alerts whenever during monitoring an aberration from expected behavior is noticed either in the storage …

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Topological And Feature Based Identification Of Hole Boundaries In Point Cloud Data And Differentiation Between Surface And Physical Holes, Aaqif Muhtasim Dec 2018

Topological And Feature Based Identification Of Hole Boundaries In Point Cloud Data And Differentiation Between Surface And Physical Holes, Aaqif Muhtasim

Computer Science and Engineering Theses - Archive

With the advent of autonomous agents becoming prominent in everyday lives, the importance of processing the surroundings into understandable features becomes more and more important. 3D point clouds play a major role in the perception of such agents and thus having the ability to correctly decipher features from point clouds is crucial to the planning of actions that the agent would need to undertake. This thesis analyzes holes found in point clouds. Based on two approaches that center around topological data analysis and local point set features respectively. It studies how each of the methods works and how a combination …

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Generating An Adaptive Path Using Rrt Sampling And Potential Functions With Directional Nearest Neighbors, Sandeep Chahal Dec 2018

Generating An Adaptive Path Using Rrt Sampling And Potential Functions With Directional Nearest Neighbors, Sandeep Chahal

Computer Science and Engineering Theses - Archive

Planning algorithms have attained omnipresent successes in several fields including robotics, animation, manufacturing, drug design, computational biology and aerospace applications. Path Planning is an essential component for autonomous robots. The problem involves searching the configuration space and constructing a desired collision-free path that connects two states (the start and the goal) for a robot to gradually navigate from one state to another. In global path planners, the complete path is computed prior to the robot set off. Sampling based planning like Rapidly Expanding Random Trees (RRT) and Probabilistic Road Maps (PRM) used for single or multi-query planning has gained popularity …

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Text Mining On Twitter Data To Evaluate Sentiment, Srijanee Niyogi Dec 2018

Text Mining On Twitter Data To Evaluate Sentiment, Srijanee Niyogi

Computer Science and Engineering Theses - Archive

Social media platforms have been a major part of our daily lives. But with the freedom of expression there is no way one can check whether the posts/tweets/expressions are classified on which polarity. Since Twitter is one of the biggest social platforms for microblogging, hence the experiment was done on this platform. There are several topics that are popular over the internet like sports, politics, finance, technology are chosen as the source of the experiment. These tweets were collected over a span of time for more than 2 months via a cron job. Every tweet can be divided into three …

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