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Articles 151 - 180 of 666
Full-Text Articles in Computer Sciences
Road Map Generation And Feature Extraction Algorithms From Gps Trajectories And Trajectories Data Warehousing, Tariq Alsahfi
Road Map Generation And Feature Extraction Algorithms From Gps Trajectories And Trajectories Data Warehousing, Tariq Alsahfi
Computer Science and Engineering Dissertations - Archive
Advanced technologies in location acquisition allow us to track the movement of moving objects (people, planes, vehicles, animals, ships, ..) in geographical space. These technologies generate a vast amount of trajectory data (TD). Several applica- tions in different fields can utilize such trajectory data, for example, traffic control management, social behavior analysis, wildlife migrations and movements, ship tra- jectories, shoppers behavior in a mall, facial nerve trajectory, location-based services (LBS) and many others. Fortunately, there are now many trajectory data sets avail- able that collected from moving objects such as cars with enabled GPS devices. Two main challenges arise when …
Adaptive Graph Convolutional Neural Network And Its Biomedical Applications, Ruoyu Li
Adaptive Graph Convolutional Neural Network And Its Biomedical Applications, Ruoyu Li
Computer Science and Engineering Dissertations - Archive
As the rise of graph neural networks, many deep learning frameworks have been extended to graph-structured data. The research in many diverse regimes have been tremendously reshaped, especially in areas like medical image understanding. When input data reach the scale of whole slides images (WSIs), the modeling becomes more challenging and we have to balance the trade-off between performance and efficiency. Furthermore, the theory of existing graph convolution has its own constraints which prevent learning robust graph representation on data that has diverse topological structure and are infeasible for graph sampling or coarsening. To tackle the problems we introduced a …
Evaluating The Accuracy Of Gaze Detection For Moving Character, Sanath Narasimhan
Evaluating The Accuracy Of Gaze Detection For Moving Character, Sanath Narasimhan
Computer Science and Engineering Theses - Archive
Joint attention, where a caregiver and an infant follow each other’s eye gaze plays an important role in the learning of language for new-born infants. To study joint attention, it is required to record and analyze the joint attention in a naturalistic environment. For this, we can use the head-mounted eye tracker. However, natural interaction involves the body movement which a?ects the accuracy of the measurement. In this work, I evaluated the accuracy of the eye-tracking system in the three di?erent scenarios: when the subject is sitting still in front of the target when the subject looks at the target …
"How Good Are They?" - A State Of The Effectiveness Of Anti-Phishing Tools On Twitter, Sayak Saha Roy
"How Good Are They?" - A State Of The Effectiveness Of Anti-Phishing Tools On Twitter, Sayak Saha Roy
Computer Science and Engineering Theses - Archive
Phishing websites are one of the most pervasive online attack vectors, with nearly 1.5 million such attacks created every month. Social media is the primary ground for phishing attacks, with 86% of these attacks originating from Twitter, Facebook, LinkedIn, etc. Prevalent approaches against these attacks includes URL scanners, anti-phishing blacklists and social media's own detection systems. In this work, we focus on Twitter, and through a combination of data-driven methods and emulations, we evaluate the verdicts provided by URL scanners, and Twitter’s detection system. We show that these sources provide a good amount of misinformation, which not only can lead …
In Situ Sensor Calibration Using Noise Consistency, Shriiesh Var Sharma
In Situ Sensor Calibration Using Noise Consistency, Shriiesh Var Sharma
Computer Science and Engineering Theses - Archive
Robots rely on sensors to map their surroundings. As a result, the accuracy of the map depends heavily on the sensor noise and in particular on accurate knowledge of it. The common way to minimize the impact of sensor noise is to use filtering algorithms. Accuracy of these filtering algorithms (like the Kalman filter) relies on the accuracy of the user supplied measurement noise model. Inaccurate noise models lead to higher residual noise in state estimates and errors in the estimate of the precision of the state estimate. It is therefore important to have precise noise models and thus accurately …
Classification Of Factual And Non-Factual Statements Using Adversarially Trained Lstm Networks, Daniel Obembe
Classification Of Factual And Non-Factual Statements Using Adversarially Trained Lstm Networks, Daniel Obembe
Computer Science and Engineering Theses - Archive
Being able to determine which statements are factual and therefore likely candidates for further verification is a key value-add in any automated fact-checking system. For this task, it has been shown that LSTMs outperform regular machine learning models, such as SVMs. However, the complexity of LSTMs can also result in over fitting (Gal and Ghahramani,1997), leading to poorer performance as models fail to generalize. To resolve this issue, we set out to utilize adversarial training as away to improve the performance of LSTMs for the task of classifying statements as factual or non-factual. In our experiment, we implement the adversarial …
Activity Recognition To Mimic Human Perception, Alankrit Gupta
Activity Recognition To Mimic Human Perception, Alankrit Gupta
Computer Science and Engineering Theses - Archive
The recognition of activities from video is a capability that is important for a wide range of applications, ranging from basic scene understanding to the successful prediction of behavior in autonomous vehicle applications. At this time, human capabilities in this task by far outperform computer applications and thus the idea to mimic human perception should be promising. In this thesis we are proposing an architecture that processes videos to extract important action instances that describe the essential behaviors contained in any video and help us map the information from the video to a machine-understandable form. This is an important research …
Person Identification And Tinetti Score Assessment Using Balance Parameters To Determine Fall Risk, Varsha Rani Chawan
Person Identification And Tinetti Score Assessment Using Balance Parameters To Determine Fall Risk, Varsha Rani Chawan
Computer Science and Engineering Theses - Archive
This thesis is aimed at a substantial health problem among the elderly population that is “Fall”, a major cause of accidental home deaths. Studies show approximately one-third of community-dwelling people over 65 years of age will experience one or more falls each year. The balance and walking pattern are useful to determine the risk of fall in an individual and is highly influenced by several parameters and conditions. The deterioration in the balance and walking stability of an individual can occur because of the natural processes related to aging or as a result of various underlying health conditions, fatigue, muscle …
Learning Embeddings For Wearable-Based Human Activity Analysis, Taoran Sheng
Learning Embeddings For Wearable-Based Human Activity Analysis, Taoran Sheng
Computer Science and Engineering Dissertations - Archive
The embedded sensors in widely used smartphones, wearable devices and smart environments make the sensor data stream of human activity more accessible. With the development of deep neural networks, extensive studies have been conducted using deep learning methods to extract useful information from the sensor data to recognize the human activity, identify the person, or monitor the health condition of the person. However, applying deep neural networks to the sensor based human activity analysis task remains a challenging research problem in ubiquitous computing. Some of the reasons are: (i) The majority of the acquired data has no labels; (ii) Most …
Tail Latency Prediction For Fork-Join Structures, Sami Marzook Alesawi
Tail Latency Prediction For Fork-Join Structures, Sami Marzook Alesawi
Computer Science and Engineering Dissertations - Archive
The workflows of the predominant user-facing datacenter services, including web searching and social networking, are underlaid by various Fork-Join structures. Due to the lack of understanding the performance of Fork-Join structures in general, today’s datacenters often resort to resource overprovisioning, operating under low resource utilization, to meet stringent tail-latency service level objectives (SLOs) for such services. Hence, to achieve high resource utilization, while meeting stringent tail-latency SLOs, it is of paramount importance to be able to accurately predict the tail latency for a broad range of Fork-Join structures of practical interests. In this dissertation, we propose and conduct a comprehensive …
Efficient Construction And Explanation Of Machine Learning Models Through Database Techniques, Sona Hasani
Efficient Construction And Explanation Of Machine Learning Models Through Database Techniques, Sona Hasani
Computer Science and Engineering Dissertations - Archive
Machine learning (ML) has been widely adopted in the last few years and it has had an undeniable impact on the ways many organizations make decisions. While great advances have been made in developing new ML algorithms and applications, there is a major need for scalable ML solutions in order to meet the demands of the Big data era. In this dissertation, we focus on improving the efficiency of two main machine learning solutions through database techniques: i) efficient construction of machine learning models, and ii) efficient explanation of machine learning models for multiple predictions. First, we introduce application of …
Analysis Of Complex Data Sets Using Multilayer Networks: A Decoupling-Based Framework, Abhishek Santra
Analysis Of Complex Data Sets Using Multilayer Networks: A Decoupling-Based Framework, Abhishek Santra
Computer Science and Engineering Dissertations - Archive
We are on the cusp of analyzing a variety of data being collected in every walk of life - social, biological, health-care, corporate, climate, to name a few. The data sets are becoming diverse and complex in addition to increased size. Some of the complexity comes from interacting entities that arise in diverse disciplines, such as epidemiology, marketing strategy, social sciences, cybersecurity and drug design. Data sets becoming diverse and complex entails search for appropriate models and concomitant analytical techniques that are also efficient. Our ability to analyze large, complex, and disparate data for a broad set of analysis objectives …
Computer Vision Methods For Sign Language And Cognitive Evaluation Through Physical Tasks, Alex J. Dillhoff
Computer Vision Methods For Sign Language And Cognitive Evaluation Through Physical Tasks, Alex J. Dillhoff
Computer Science and Engineering Dissertations - Archive
Analyzing human motion is vital for a multitude of tasks including human-computer interaction, sign language recognition, and the assessment of cognitive disorders. Providing automatic assessments for cognitive disorders increases the accessibility and affordability of life-changing tests and treatments. For sign language recognition, automated translation systems bridge the gap between native and non-native signers. Additionally, dictionary look-up systems are helpful for native signers learning a new language. Common to both of these tasks is the reliance of fine motor function in the hands. Hand Pose Estimation methods are used to drive applications that rely on hand shape. These tasks present unique …
Learning Health Information From Floor Sensor Data Within A Pervasive Smart Home Environment, Nicholas Brent Burns
Learning Health Information From Floor Sensor Data Within A Pervasive Smart Home Environment, Nicholas Brent Burns
Computer Science and Engineering Dissertations - Archive
Spatial and temporal gait analysis can provide useful measures for determining a person’s state of health while also identifying deviations in day-to-day activity. The SmartCare project is a multi-discipline health technologies project that aims to provide an unobtrusive and pervasive system that provides in-home health monitoring for the elderly. This research work focuses on the pressure-sensitive smart floor of the SmartCare project by using an experimental floor to develop methods for future use on a floor deployed within a home. This work presents a procedure to automatically calibrate a smart floor’s pressure sensors without specialized physical effort. The calibration algorithm …
Hand-Over-Face Segmentation, Sakher Ghanem
Hand-Over-Face Segmentation, Sakher Ghanem
Computer Science and Engineering Dissertations - Archive
Accurate hand segmentation is vital in many applications in which the hands play a central role, such as sign language recognition, action recognition, and gesture recognition. A relatively unexplored obstacle to correct hand segmentation is when the hand overlaps the face. The shortage of a dataset for this research area has been one motivation for this work. However, this dissertation investigates and proposes improvements for the hand-over-face segmentation task. Toward an in-depth study of the hand segmentation problem, the work presented in this dissertation will yield several contributions. First, it introduces a survey on sign language recognition systems using mobile …
Das Page Replacement Algorithm, Ramya Danappa
Das Page Replacement Algorithm, Ramya Danappa
Computer Science and Engineering Theses - Archive
There are different page replacement algorithms and yet there seems to be some drawbacks in them, making no page replacement algorithm ideal. To be one step closer to achieving the ideal algorithm it is vital to have a maximum cache hit ratio and strong consistent across different workload. In this paper we shall explore a new cache management policy called “Dynamic and Stable page replacement algorithm” uses frequency and recency importance dynamically”. The proposed page replacement algorithm has overcome the drawbacks of the LRU (Least Recently Used) algorithm in many scenarios and also overcome the drawbacks of LFU (Least Recently …
Semi-Supervised Learning Using Triple-Siamese Network, Debapriya Banerjee
Semi-Supervised Learning Using Triple-Siamese Network, Debapriya Banerjee
Computer Science and Engineering Theses - Archive
Missing data problem is inevitable in mostly all research areas including Artificial Intelligence, Machine Learning and Computer Vision where we have modicum knowledge about the complete dataset. One of the key reasons of missing data in AI is insufficiency of accurately labeled data. To solve a classification problem using ML or training a Deep Neural Network model, we need a huge amount of labeled data. It is difficult to get labeled data but unlabeled data is inexpensive and available easily. It is usual that we get no more than a single element per class to train our models due to …
Development Of Text Analytics For Debriefing Reflection Essays, Md Shadekur Rahman
Development Of Text Analytics For Debriefing Reflection Essays, Md Shadekur Rahman
Computer Science and Engineering Theses - Archive
Evaluating and providing feedback to hundreds of free text assignments in an online environment is a challenging task for an instructor where he has to scan through essays to identify perspectives that are expected to appear in those essays. Reading large number of essays and then finding themes and providing customized feedback are time-consuming process. We have proposed a text analytics system named EssayIQ that aids course instructor in identifying assignment themes, providing theme presence statistics and giving feedback to learners. To the best of our knowledge, this is the first system that analyzes free text assignments in line with …
Tapin: A Two-Factor User Authentication Scheme For Smartwatches Through Secret Finger Tapping, Akash Lohani
Tapin: A Two-Factor User Authentication Scheme For Smartwatches Through Secret Finger Tapping, Akash Lohani
Computer Science and Engineering Theses - Archive
Nowadays, smartwatches have become one of the most common wearable gadgets as they are small and portable. As more and more personal information is managed and processed inside smartwatches, it is important to have a secure user authentication scheme in place. There have been many successful authentication schemes for a smartphones such as Password/PIN, bio-metric approach(e.g. fingerprint, face recognition), etc directly used on smartwatches. However, these approaches are not quite suitable for smartwatches due to its constraints in size and limited computation power. To address this issue, we propose TaPIN that allows users to authenticate themselves by playing out the …
Mln-Subdue: Decoupling Approach-Based Substructure Discovery In Multilayer Networks (Mlns), Anish Rai
Mln-Subdue: Decoupling Approach-Based Substructure Discovery In Multilayer Networks (Mlns), Anish Rai
Computer Science and Engineering Theses - Archive
Substructure discovery is well-researched for single graphs (both simple and attribute) as it is an important component of knowledge discovery for many applications such as finding the core substructure in a protein, important concept in a large graph, etc. However, multilayer networks or MLNs (instead of attribute graphs) have been shown to be better for modeling complex data sets that have multiple entity and feature types. This model provides more clarity on semantics of data sets as well as the ability to use an arbitrary subset of layers for analysis. However, the challenge is that many algorithms such as community …
Randomized And Evolutionary Approaches To Dataset Characterization, Feature Weighting, And Sampling In K-Nearest Neighbors, Suryoday Basak
Randomized And Evolutionary Approaches To Dataset Characterization, Feature Weighting, And Sampling In K-Nearest Neighbors, Suryoday Basak
Computer Science and Engineering Theses - Archive
K-Nearest Neighbors (KNN) has remained one of the most popular methods for supervised machine learning tasks. However, its performance often depends on the characteristics of the dataset and on appropriate feature scaling. In this thesis, characteristics of a dataset that make it suitable for being used within KNN are explored. As part of this, two new measures for dataset dispersion, called mean neighborhood target variance (MNTV), and mean neighborhood target entropy (MNTE) are developed to help determine the performance we expect while using KNN regressors and classifiers, respectively. It is empirically demonstrated that these measures of dispersion can be indicative …
3d Skeleton Construction From Multiple Camera Views For Quantifying Gait Parameters, Saket Gupta
3d Skeleton Construction From Multiple Camera Views For Quantifying Gait Parameters, Saket Gupta
Computer Science and Engineering Theses - Archive
Research has shown that human gait characteristics permit inference with respect to different personal and health characteristics and can thus be used as a diagnostic tool. To do this automatically it is important to be able to extract them from senor information. This thesis work is aimed at doing this from multiple camera views and for this focuses on construction of a 3D body skeleton from multiple viewpoint video (MVV) and then quantifying a number of gait characteristics such as swing time, step time, cadence, stride length, single support, or double support. The method introduced here uses a marker-less approach …
Intrinsic Curiosity In Reinforcement Learning By Improving Next State Prediction, Paul Lewis Lobo
Intrinsic Curiosity In Reinforcement Learning By Improving Next State Prediction, Paul Lewis Lobo
Computer Science and Engineering Theses - Archive
In Reinforcement Learning, an agent receives feedback from the environment in the form of an extrinsic reward. It learns to take actions that maximize this extrinsic reward. However, to start learning, the agent needs to be able to get feedback from the environment by using random actions. This works in environments with frequent rewards, however, in environments where the rewards are sparse the probability of reaching any reward even once becomes very low. One way to explore an environment efficiently is for the agent to generate its own intrinsic reward by using the prediction error from a model that is …
Azul - Using Multimodal Sensors From Mobile And Wearable Devices For Assessment And Monitoring Of Depression Symptoms, Manu Srivastava
Azul - Using Multimodal Sensors From Mobile And Wearable Devices For Assessment And Monitoring Of Depression Symptoms, Manu Srivastava
Computer Science and Engineering Theses - Archive
With the ubiquitous presence of mobile phones, there is too much data that can be collected for different purposes which can be correlated to monitor multiple more user information. If used responsibly, this data can be used to assess and monitor various mental health conditions. For example, fitness sensors in wearable devices collect step counts, sleep-related data and heart rate; GPS sensors in smartphones continually collect device location, and Android on-device services collect smartphone usage habits of the user. All the data from the previous example relate to exhibited depression symptoms in users. This data, when used in conjugation, has …
Mr_Qp: A Scalable Approach To Query Processing On Arbitrary-Size Graphs Using The Map/Reduce Framework, Harshit Ashokkumar Modi
Mr_Qp: A Scalable Approach To Query Processing On Arbitrary-Size Graphs Using The Map/Reduce Framework, Harshit Ashokkumar Modi
Computer Science and Engineering Theses - Archive
The utility and widespread use of Relational Database Management Systems(RDBMSs) comes not only from its simple, easy-to-understand data model (a relation or a set) but mainly from the ability to write non-procedural queries and their optimization by the system. Queries produce exact answers that match the contents of the database. Query processing of RDBMSs has been researched for more than 4 decades and includes extensions to more complex analysis on data warehouses. In contrast, search has not been addressed by RDBMSs. As the use of other other data types (key-value store, column-store, and graphs to name a few) are becoming …
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 …