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Articles 241 - 270 of 666
Full-Text Articles in Computer Sciences
Building A Versatile Deduplication System, Zhichao Yan
Building A Versatile Deduplication System, Zhichao Yan
Computer Science and Engineering Dissertations - Archive
With the development of the Internet and information technology, a large amount of unstructured data is generated and stored in various storage systems. In particular, data reduction techniques such as compression and deduplication have become an effective way to address the combined challenges of explosive growth in data volume but lagging network bandwidth growth to increase the space and bandwidth efficiency of various storage systems. However, we have found that existing deduplication systems cannot effectively process compressed data and image data because existing deduplication systems only analyze the hash value of the bitstream to detect redundant data. At the same …
Defending Neural Networks Against Adversarial Examples, Armon Barton
Defending Neural Networks Against Adversarial Examples, Armon Barton
Computer Science and Engineering Dissertations - Archive
Deep learning is becoming a technology central to the safety of cars, the security of networks, and the correct functioning of many other types of systems. Unfortunately, attackers can create adversarial examples, small perturbations to inputs that trick deep neural networks into making a misclassification. Researchers have explored various defenses against this attack, but many of them have been broken. The most robust approaches are Adversarial Training and its extension, Adversarial Logit Pairing, but Adversarial Training requires generating and training on adversarial examples from any possible attack. This is not only expensive, but it is inherently vulnerable to novel attack …
Large-Scale Deep Learning With Application In Medical Imaging And Bio-Informatics, Zheng Xu
Large-Scale Deep Learning With Application In Medical Imaging And Bio-Informatics, Zheng Xu
Computer Science and Engineering Dissertations - Archive
With the recent advancement of the deep learning technology in the artificial intelligence area, nowadays people's lives have been drastically changed. However, the success of deep learning technology mostly relies on large-scale high-quality data-sets. The complexity of deeper model and larger scale datasets have brought us significant challenges. Inspired by this trend, in this dissertation, we focus on developing efficient and effective large-scale deep learning techniques in solving real-world problems, like cell detection in hyper-resolution medical image or drug screening from millions of compound candidates. With respect to the hyper-resolution medical imaging cell detection problem, the challenges are mainly the …
Malware Early-Stage Detection Using Machine Learning On Hardware Performance Counters, Anchal Raheja
Malware Early-Stage Detection Using Machine Learning On Hardware Performance Counters, Anchal Raheja
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’s 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 and financial damages are rising the researchers are finding new ways to tackle this threat. However, the usual approach is prone to high false positive rate or delayed detection rate. This research explores a dynamic approach for early-stage malware detection by modeling it’s behavior using hardware performance counters with low overhead. The …
Finding Representative Entities From Entity Graph By Using Neighborhood Based Entity Similarity, Ankit Anil Shingavi
Finding Representative Entities From Entity Graph By Using Neighborhood Based Entity Similarity, Ankit Anil Shingavi
Computer Science and Engineering Theses - Archive
Several applications deploy the use of large entity graphs. Given the entirety of its application scope, it is challenging to select a single entity graph for a particular need from numerous data sources. For a comprehensible overview of the entity graph, we may project a preview table for compact representation of an entity graph. Each preview table represents a single entity type in the dataset. We need to find the representative entities for a given entity type from the entity graph to show the coverage of a dataset. In this paper, we propose a method to find representative entities for …
Improving Time And Space Efficiency Of Trie Data Structure, Nirmik Milind Kale
Improving Time And Space Efficiency Of Trie Data Structure, Nirmik Milind Kale
Computer Science and Engineering Theses - Archive
Trie or prefix tree is a data structure that has been used widely in some applications such as prefix-matching, auto-complete suggestions, and IP routing tables for a long time. What makes tries even more interesting is that its time complexity is dependent on the length of the keys inserted or searched in the trie, instead of on the total number of keys in the data structure. Tries are also strong contenders to consider against hash tables in various applications due to two reasons - their almost deterministic time complexity based on average key length, especially when using large number of …
Hyper-Optimized Machine Learning And Deep Learning Methods For Geo-Spatial And Temporal Function Estimation, Neelabh Pant
Hyper-Optimized Machine Learning And Deep Learning Methods For Geo-Spatial And Temporal Function Estimation, Neelabh Pant
Computer Science and Engineering Dissertations - Archive
Owing to a high degree of freedom in human mobility, accurate modelling/estimation of human mobility function remains a challenge. Numerous work in the literature have tried to address the challenge using various traditional machine learning methods on spatio-temporal attributes of data. We compare the use of Varied-K Means clustering, Hidden Markov Model techniques, feed forward neural networks, recurrent neural networks (RNN) and Long Short Term Recurrent Neural Networks (LSTM) to predict a user's future movement based on the user's past historical data. Although several techniques were proposed to predict a user's movement, not many have concentrated on a user's location …
Supporting Efficient Large-Scale Key-Value Systems With An Optimized Storage Hierarchy, Xingbo Wu
Supporting Efficient Large-Scale Key-Value Systems With An Optimized Storage Hierarchy, Xingbo Wu
Computer Science and Engineering Dissertations - Archive
Driven by the growing demands from big-data applications, the focus of their data management has been largely shifted from traditional SQL databases to NoSQL (Not-only- SQL) databases, such as key-value (KV) stores, which provides essential functionalities and much higher performance for storing and retrieving data. Correspondingly new hardware technologies have been developed to support the fast data accesses, such as NVMe SSDs and Infiniband network. However, existing designs of NoSQL databases usually see sub- optimal performance on fast hardware. Traditionally the computing overhead of a database system is overshadowed by the slow storage and network. With the adoption of the …
Artificial Intelligence For Cognitive Behavior Assessment In Children, Srujana Gattupalli
Artificial Intelligence For Cognitive Behavior Assessment In Children, Srujana Gattupalli
Computer Science and Engineering Dissertations - Archive
Cognitive impairments in early childhood can lead to poor academic performance and require proper remedial intervention at the appropriate time. ADHD a?ects about 6-7% of children and is a psychiatric neurodevelopmental disorder that is very hard to diagnose or tell apart from other disorders. Cognitive insu?ciencies hinder the development of working memory and can a?ect school success and even have long term e?ects that can result in low self-esteem and self-acceptance. The main aim of this research is to investigate development of an automated and non-intrusive system for assessing physical exercises related to the treatment and diagnosis of Attention De?cit …
Algorithms For Exploratory Queries Over Web Database, Md Farhadur Rahman
Algorithms For Exploratory Queries Over Web Database, Md Farhadur Rahman
Computer Science and Engineering Dissertations - Archive
In recent years we have seen an increase in the popularity of many web applications. The functionality of these applications range from allowing users to interact using online social network, to assist users in their everyday activity such as selecting a hotel in an area, locating a nearby restaurant etc. Google Maps, WeChat, FourSquare, AirBnB, TripAdvisor, and Hotels.com are a few such examples. The backed database of these applications can be a rich source of information for the corresponding application domain. For example, using Google Maps a user can find the ratings, reviews, and price of a restaurant, using Zillow …
Improving Performance And Security In Anonymity Systems, Mohsen Imani
Improving Performance And Security In Anonymity Systems, Mohsen Imani
Computer Science and Engineering Dissertations - Archive
Tor is an anonymity network that provides online privacy for the Internet users. Tor hides the user's traffic among the others' traffic. The more users Tor attracts, the stronger anonymity it provides. Unfortunately, users of the Tor anonymity system suffer from less-than-ideal performance, in part because circuit building and selection processes are not tuned for speed. Moreover, there are some attacks like guard fingerprinting and website fingerprinting attacks that try to profile or de-anonymize the Tor users. In this dissertation, we propose methods to address both security and performance issues in Tor. We first examine the process of selecting among …
Face Detection And Recognition Using Moving Window Accumulator With Various Deep Learning Architecture, Anil Kumar Nayak
Face Detection And Recognition Using Moving Window Accumulator With Various Deep Learning Architecture, Anil Kumar Nayak
Computer Science and Engineering Theses - Archive
Recent advancement in the field of Computer Vision and Deep Learning is making object detection and recognition easier. Hence, growing research activities in the field of deep learning are enabling researchers to find new ideas in the area of face detection and recognition. Implementation of such systems has a number of challenges when it comes to the current approaches. In this paper, we have presented a system of Face Detection and Recognition with newly designed deep learning classification models like CNN, Inception and various state of art models like SVM and we also compared the result with FaceNet. Multiple approaches …
Design Of Haptically Enabled Wheelchair For Assistive Autonomy, Arjun Mani Gupta
Design Of Haptically Enabled Wheelchair For Assistive Autonomy, Arjun Mani Gupta
Computer Science and Engineering Theses - Archive
The first records of wheeled seats being used for transporting disabled people date to 8th century in China, however the wheelchair has evolved tremendously since its inception. An electric-powered wheelchair, commonly called a "powerchair" is a wheelchair which incorporates batteries and electric motors into the frame, and so it can be controlled by either the user or an attendant. This control is most commonly done via a small joystick mounted on the armrest, or on the upper rear of the frame. For users who cannot manage a manual joystick, head-switches, chin-operated joysticks, sip-and-puff controllers or other custom controls may allow …
Mavvstream: Expressing And Processing Situations On Videos Using The Stream Processing Paradigm, Mayur Arora
Mavvstream: Expressing And Processing Situations On Videos Using The Stream Processing Paradigm, Mayur Arora
Computer Science and Engineering Theses - Archive
Image and Video Analysis (IVA) has been ongoing for several decades and has come up with impressive techniques for object identification, re-identification, activity detection etc. A large number of techniques have been developed and used for processing video frames to detect objects and situations from videos. Camera angles, lighting effect, color differences, and attire make it difficult to analyze videos. Several approaches for searching, and querying videos and images have been developed using indexing and other techniques. This thesis takes a novel approach by converting a video (through extraction of its contents) into a representation over which queries can be …
From Text Classification To Image Clustering, Problems Less Optimized, Amirhossein Herandi
From Text Classification To Image Clustering, Problems Less Optimized, Amirhossein Herandi
Computer Science and Engineering Theses - Archive
Machine Learning is thriving. Every industry is using its techniques in some way to improve their efficiency and revenue. However, the focus on research is not divided equally between all of the different areas and problems that this field can tackle and analyze. Currently, Computer Vision is the one area that is being focused very extensively by researchers and companies alike, and as a result has seen an amazing boost in the recent years. This ranges from the well-known problems of classification that use discriminative models all the way to more novel problems that use generative models such as style …
Learning To Generate Individual Data Sequence From Population Statistics Using Dynamic Bayesian Networks, Mohammed Azmat Qureshi
Learning To Generate Individual Data Sequence From Population Statistics Using Dynamic Bayesian Networks, Mohammed Azmat Qureshi
Computer Science and Engineering Theses - Archive
Data collection rose exponentially with the dawn of the 21st Century, However the most important data to humans, individual health data, is difficult to get approved for public research, as medical history is very sensitive to be distributed. The only available public data which can be retrieved from institutions like the Centre for Disease Control (CDC), World Health Organization (WHO), National Health Interview Survey (NHIS), etc. largely only contain population statistics for different attributes of a person.What we propose here is a generative model which would learn to create data sequences for a population, each sequence mimicking an individual person’s …
Jsspe: A Symbolic Partial Evaluator For Javascript, Sumeyye Suslu
Jsspe: A Symbolic Partial Evaluator For Javascript, Sumeyye Suslu
Computer Science and Engineering Theses - Archive
Currently, JavaScript is one of the mostly used programming languages for Web and Mobile platforms. This brings a large demand for optimization and smarter resource allocation of the applications written in JavaScript. Partial evaluation is a program transformation technique which rewrites a program by evaluating it with respect to its known variables. Recently, Facebook proposed Prepack: A partial evaluator for JavaScript which will make original program shorter and faster by performing both concrete and symbolic evaluation (concolic evaluation). Although it is proposed as a planned improvement, symbolic evaluation engine currently does not implement an SMT solver. In this work, a …
Enabling Third Party Services Over Deep Web Databases And Location Based Services, Yeshwanth Durairaj Gunasekaran
Enabling Third Party Services Over Deep Web Databases And Location Based Services, Yeshwanth Durairaj Gunasekaran
Computer Science and Engineering Theses - Archive
Deep web databases are pillars of today’s internet services hidden behind HTML forms and Top-K search interfaces. While Top-K search interfaces provide a good way to retrieve information, it still lacks in addressing the diverse preferences of the users. Due to query rate limit constraint - i.e., maximum number of k-Nearest Neighbors queries a user/IP address can issue over a specific period of time, it is often impossible to access all the tuples in backed database. With the query rate limit constraint in mind, our motivation is twofold (i) Enable users to obtain individual records from these databases and rank …
Deep Learning For Recognition Of Objects, Activities, Faces, And Spatio-Temporal Patterns, Amir Ghaderi
Deep Learning For Recognition Of Objects, Activities, Faces, And Spatio-Temporal Patterns, Amir Ghaderi
Computer Science and Engineering Dissertations - Archive
A popular method in machine learning is Convolutional Neural Network (CNN). CNN had was of high interest to the research community in the 1990s, but after that its popularity receded compared to the Support Vector Machine Support Vector Machine (SVM)[1]. One of the reasons was the relatively lower computational demands of SVM. Training CNNs requires significantly more computational power, time, and data than training SVM. One of the important issues in showing the power of the CNN is the availability of the huge amount of data and introducing big datasets. With increased availability of powerful GPU processing, using several improvements …
Interactive Learning And Adaptation For Personalized Robot-Assisted Training, Konstantinos Tsiakas
Interactive Learning And Adaptation For Personalized Robot-Assisted Training, Konstantinos Tsiakas
Computer Science and Engineering Dissertations - Archive
Robot-Assisted Training (RAT) is a growing body of research in Human-Robot Interaction (HRI) that studies how robots can assist humans during a physical or cognitive training task. Robot-Assisted Training systems have a wide range of applications, varying from physical and/or social assistance in post-stroke rehabilitation to intervention and therapy for children with Autism Spectrum Disorders. The main goal of such systems is to provide a personalized and tailored session that matches user abilities and needs, by adjusting task-related parameters (e.g., task difficulty, robot behavior), in order to enhance the effects of the training session. Moreover, such systems need to adapt …
Indexing, Querying, Prediction, And Integration For Network-Constrained Moving Objects Databases, Mohammadhani Fouladgar
Indexing, Querying, Prediction, And Integration For Network-Constrained Moving Objects Databases, Mohammadhani Fouladgar
Computer Science and Engineering Dissertations - Archive
The emergence and presence of satellites and GPS devices have led to the creation of a huge amounts of spatial and spatio-temporal data, which had significant effects on creating new applications to analyze and mine these data. In this regard, a lot of research has been done on moving objects databases as a part of spatial and spatio-temporal databases. In this dissertation, we focus on those moving objects that are not allowed to move in all directions freely, but they (almost) always are restricted to travel on a specific network. One of the most popular example of these moving objects …
Applications Of Deep Learning In Large-Scale Object Detection And Semantic Segmentation, Wei Xiang
Applications Of Deep Learning In Large-Scale Object Detection And Semantic Segmentation, Wei Xiang
Computer Science and Engineering Dissertations - Archive
With the massive storage of multimedia data and increasing computational power of mobile devices, developing scalable computer vision applications has become the primary motivation for both research and industrial community. Among these applications, object detection and semantic segmentation are two of the most popular topics which, in addition, serve as the fundamental features for many computer vision systems under platforms like mobile, healthcare, autonomous driving, etc. Inspired by the current and foreseeable trend, this thesis focuses on developing both effective and efficient object detection and semantic segmentation models, with the large-scale, publicly available data sets sourced for various applications. In …
Efficient Evaluation Of Contextual And Reverse Pareto-Optimality Queries, Afroza Sultana
Efficient Evaluation Of Contextual And Reverse Pareto-Optimality Queries, Afroza Sultana
Computer Science and Engineering Dissertations - Archive
Many real-world applications analyze data to find objects that ``stand out'' with regard to various contexts and ways of valuing the objects. Examples of such application scenarios include vendors recommending products to potential customers, social networks improving content selection for users, and Google Scholar notifying newly published articles based on profiles. Besides, journalists identify conditions to substantiate the significance of an event or the interestingness of an object. Interesting events can be retrieved from stock data, weather data, and criminal records. Apart from journalists, those events convey significant information for financial analysts, scientist, and citizens. The aforementioned application needs can …
Cyber-Physical Systems: From Specification Inference To Design Analysis, Luan Viet Nguyen
Cyber-Physical Systems: From Specification Inference To Design Analysis, Luan Viet Nguyen
Computer Science and Engineering Dissertations - Archive
Due to the high degree of uncertainty and complexity, design and analysis of cyber-physical systems (CPS) are very challenging tasks. The challenge arises immediately in the early CPS development cycle, which is the stage of developing the requirements to capture the desirable behaviors of a system. Missing or incomplete requirements make the CPS design untestable or unverifiable, resulting in CPS failures. Hence, there is an urgent need for the development of specification language as well as specification inference techniques that can efficiently determine formal specifications, well-formulated requirements for CPS. This dissertation presents three different methodologies to determine formal specifications and …
Hierarchical Representation Learning With Connectionist Models, De Wang
Hierarchical Representation Learning With Connectionist Models, De Wang
Computer Science and Engineering Dissertations - Archive
To unleash the power of big data, efficient algorithms which are scalable to millions of data are desired. Deep learning is one area that benefits from big data enormously. Deep learning uses neural networks to mimic human brains, this approach is termed connectionist in AI community. In this dissertation, we propose several novel learning strategies to improve the performance of connectionist models. Evaluation of a large neural network during inference phase requires a lot of GPU memory and computation, which will degrade user experience due to response latency. Model distillation is one way to distill the knowledge contained in one …
Algorithms For Building Compact Representatives And Processing Ranking Queries, Abolfazl Asudeh Naee
Algorithms For Building Compact Representatives And Processing Ranking Queries, Abolfazl Asudeh Naee
Computer Science and Engineering Dissertations - Archive
Ranked retrieval model has rapidly replaced the traditional Boolean retrieval model as the de facto way for query processing when a large portion of (big) data matches a given query. Returning all the query results in these cases is not efficient nor informative. Unlike the Boolean retrieval model, the ranked retrieval model orders the matching tuples according to an often proprietary ranking function and returns the top-k of them. In this dissertation, we study ranked retrieval model and propose exact and approximate algorithms for (i) building representatives for fast query processing, and (ii) online processing of ranking queries. We study …
Crypto Ransomware Analysis And Detection Using Process Monitor, Ashwini Balkrushna Kardile
Crypto Ransomware Analysis And Detection Using Process Monitor, Ashwini Balkrushna Kardile
Computer Science and Engineering Theses - Archive
Ransomware is a faster growing threat that encrypts user’s files and locks the computer and holds the key required to decrypt the files for ransom. Over the past few years, the impact of ransomware has increased exponentially. There have been several reported high profile ransomware attacks, such as CryptoLocker, CryptoWall, WannaCry, Petya and Bad Rabbit which have collectively cost individuals and companies well over a billion dollars according to FBI. As the threat of ransomware has become more prevalent, security companies and researchers have begun proposing new approaches for detection and prevention of ransomware. However, these approaches generally lack dynamicity …
Scalable Conversion Of Textual Unstructured Data To Nosql Graph Representation Using Berkeley Db Key-Value Store For Efficient Querying, Jasmine Manoj Varghese
Scalable Conversion Of Textual Unstructured Data To Nosql Graph Representation Using Berkeley Db Key-Value Store For Efficient Querying, Jasmine Manoj Varghese
Computer Science and Engineering Theses - Archive
Graph database is a popular choice for representing data with relationships. It facilitates easy modifications to the relational information without the need for structural redefinition, as in case of relational databases. Exponentially growing graph sizes demand efficient querying, memory limitations notwithstanding. Use of indexes, to speed up query processing, is integral to databases. Existing works have used in-memory approaches that were limited by the main memory size. This thesis proposes a way to use graph representation, indexing technique and secondary memory to efficiently answer queries. Textual unstructured data is parsed to identify entities and assign unique identification. The entities and …
Portable Wireless Antenna Sensor For Simultaneous Shear And Pressure Monitoring, Farnaz Farahanipad
Portable Wireless Antenna Sensor For Simultaneous Shear And Pressure Monitoring, Farnaz Farahanipad
Computer Science and Engineering Theses - Archive
Microstrip antenna-sensor has received considerable interests in recent years due to its simple configuration, compact size, and multi-modality sensitivity. Having a simple and conformal planar configuration, antenna-sensor can be easily attached on the structure surface for Structure Health Monitoring (SHM). As a promising sensor, the resonant frequency of the antenna-sensor is sensitive to different structure properties: such as planar stress, temperature, moisture, pressure and shear. As a passive antenna, antenna-sensor’s resonant frequency can be wirelessly interrogated at a middle range distance without using an on-board battery. However, a major challenge of antenna-sensor’s wireless interrogation is to isolate the antenna backscattering …
Social Coding Standards On Touchdevelop: An Empirical Study, Shivangi Kulshrestha
Social Coding Standards On Touchdevelop: An Empirical Study, Shivangi Kulshrestha
Computer Science and Engineering Theses - Archive
This study compares and contrasts the application development pattern on Microsoft’s mobile application development platform with leading version control and social coding sites like Github. TouchDevelop is an in-browser editor for developing mobile applications with the main aim to concentrate on ‘touch’ as the only input. Apart from being the first of it’s kind platform, TouchDevelop also allows users to upload their script directly to cloud. This is what makes this study interesting, since the API data of the app has never been studied before to follow social coding standards or version control techniques. Till today, all major IDEs, e.g …