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Articles 121 - 150 of 666
Full-Text Articles in Computer Sciences
On The Efficacy Of Knowledge Graph Completion Methods, Accuracy Measures And Evaluation Protocols, Farahnaz Akrami
On The Efficacy Of Knowledge Graph Completion Methods, Accuracy Measures And Evaluation Protocols, Farahnaz Akrami
Computer Science and Engineering Dissertations - Archive
In the active research area of employing embedding models for knowledge graph completion, particularly for the task of link prediction, most prior studies used some specific benchmark datasets to evaluate such models. Most triples in those datasets belong to reverse and duplicate relations, which exhibit high data redundancy due to semantic duplication, correlation, or data incompleteness. This is a case of excessive data leakage—a model is trained using features that otherwise would not be available when the model needs to be applied for real prediction. There are also Cartesian product relations for which every triple formed by the Cartesian product …
Decoupling-Based Approach To Centrality Detection In Heterogeneous Multilayer Networks, Kiran Mukunda
Decoupling-Based Approach To Centrality Detection In Heterogeneous Multilayer Networks, Kiran Mukunda
Computer Science and Engineering Theses - Archive
Graph analysis is one of the techniques widely used for data analysis. It is used extensively on single graphs. Its ability to capture entities and relationships makes it an attractive data model. Search on graphs, such as finding triangles, cliques, shortest paths, etc., and aggregate analysis, such as communities, substructure, or centrality measures have well-defined algorithms for single graphs. The centrality measure, which is the focus of this thesis, identifies the most important nodes in a graph or network. While there are many centrality measures, the most commonly used ones are degree and betweenness centrality. Algorithms for analyzing these measures …
Machine Learning Methods To Improve Fairness And Prediction Accuracy On Largesocially Relevant Datasets, Bhanu Chaturvedi Jain
Machine Learning Methods To Improve Fairness And Prediction Accuracy On Largesocially Relevant Datasets, Bhanu Chaturvedi Jain
Computer Science and Engineering Dissertations - Archive
Machine learning-based decision support systems bring relief to the decision-makers in many domains such as loan application acceptance, dating, hiring, granting parole, insurance coverage, and medical diagnoses. These support systems facilitate processing tremendous amounts of data to decipher the embedded patterns. However,these decisions can also absorb and amplify bias embedded in the data. An increasing number of applications of machine learning-based decision sup-port systems in a growing number of domains has directed the attention of stake-holders to the accuracy, transparency, interpretability, cost effectiveness, and fairness encompassed in the ensuing decisions. In this dissertation, we have focused on fairness and accuracy …
Domain Adaptive Transfer Learning For Visual Classification, Ashiq Imran
Domain Adaptive Transfer Learning For Visual Classification, Ashiq Imran
Computer Science and Engineering Dissertations - Archive
Deep Neural Networks have made a significant impact on many computer vision applications with large-scale labeled datasets. However, in many applications, it is expensive and time-consuming to gather large-scale labeled data. With the limited availability of labeled data, it is challenging to obtain great performance. Moreover, in many real-world problems, transfer learning has been applied to cope with limited labeled training data. Transfer learning is a machine learning paradigm where pre-trained models on one task can be reused for another task. This dissertation investigates transfer learning and related machine learning techniques such as domain adaptation on visual categorization applications. At …
Modeling Factual Claims With Semantic Frames: Definitions, Datasets, Tools, And Fact-Checking Applications, Fatma Arslan
Modeling Factual Claims With Semantic Frames: Definitions, Datasets, Tools, And Fact-Checking Applications, Fatma Arslan
Computer Science and Engineering Dissertations - Archive
As social media sites have become major channels for the quick dissemination of news, misinformation has become a significant challenge for our society to tackle. Today fact-checking rests primarily on the shoulders of human fact-checkers who laboriously sift through various trustworthy sources, interview subject experts, and check references before reaching a verdict regarding the degree of truthfulness of a factual claim. Compounded with the speed and scale at which misinformation spreads, the demanding process may leave many harmful factual claims unchecked. In the fight to curb the spread of misinformation, researchers from various disciplines have come forward to assist fact-checkers …
Testing Artificial Intelligence-Based Software Systems, Jaganmohan Chandrasekaran
Testing Artificial Intelligence-Based Software Systems, Jaganmohan Chandrasekaran
Computer Science and Engineering Dissertations - Archive
Artificial Intelligence (AI)-based software systems are increasingly used in high-stake and safety-critical domains, including recidivism prediction, medical diagnosis, and autonomous driving. There is an urgent need to ensure the reliability and correctness of AI-based systems. At the core of AI-based software systems is a machine learning (ML) model that is used to perform tasks such as classification and prediction. Unlike software programs, where a developer explicitly writes the decision logic, ML models learn the decision logic from a large training dataset. Furthermore, many ML models encode the decision logic in the form of mathematic functions that can be quite abstract …
Optimizing The Demand And Distribution Of Power In Smart Grids, Saifullah Khalid
Optimizing The Demand And Distribution Of Power In Smart Grids, Saifullah Khalid
Computer Science and Engineering Dissertations - Archive
The electricity is generated in bulk power plants and transported to the end-user through the transmission and distribution networks. The process incurs heavy losses adding to the operational costs. Secondly, fossil fuels dominate energy generation and are a major source of greenhouse gases. Thirdly, the power grid is vulnerable to natural disasters. The smart grid addresses these challenges by integrating distributed energy resources (DERs) in the distribution system closer to the load and with greater penetration of renewable energy. Renewable energy is key to cutting carbon emissions due to fossil fuel-based electricity generation and reducing operating costs. It can also …
Low-Dose Ct Image Denoising Using Deep Learning Methods, Zeheng Li
Low-Dose Ct Image Denoising Using Deep Learning Methods, Zeheng Li
Computer Science and Engineering Theses - Archive
Low-dose computed tomography (LDCT) has raised highly attention since the counterpart, full-dose computed tomography (FDCT), brings potential ionizing radiation influence to patients. However, LDCT still suffers from several issues such as relatively higher noise level, which limits its uses in practical applications. To improve LDCT image quality, conventional denoising methods, such as KSVD and BM3D, are first introduced to suppress noise in low-dose images. These methods, however, works under assumptions that are not robust to various data. In this paper, we conduct an extensive research on deep learning based denoising method in LDCT images. We mainly base on Generative-Adversarial Network …
Machine Learning With Graphs, Jianjin Deng
Machine Learning With Graphs, Jianjin Deng
Computer Science and Engineering Dissertations - Archive
In recent years, graph-based machine learning methods have attracted great attention because of their effectiveness and efficiency. Inspired by this trend, this thesis summarizes my research topics on machine learning techniques for the purpose of handling various kinds of problems on large graph data. Generally, this thesis contains two parts. The first part is devoted to graph embedding, which aims to encode graph structure into dense vectors (or embeddings). In particular, we will consider a low rank-matrix factorization based approach to learn embeddings of attributed graphs. By jointly preserving graph structure and attribute-level similarity, our approach can generate embeddings, whose …
End-User Framework For Robot Control, Kaustubh Kedar Rajpathak
End-User Framework For Robot Control, Kaustubh Kedar Rajpathak
Computer Science and Engineering Theses - Archive
This thesis describes in detail a developed end-user framework for a humanrobot collaborative system for common tasks, such as pick and place. The system is designed for semi-automated pick and place tasks as well as manual operation making it flexible for multiple use-case scenarios. The goal of the system is to make the robotic system multi-functional, easy to use with a graphical user interface and should perform common tasks with the help of a human teammate. Integration with object recognition neural networks (YOLOv3) and an RGB-Depth camera help automate pick and place tasks with a wide variety of objects.
Using Sentiment And Emotion Analysis Of News Articles To Analyze The Effects Of Leader’S Statements On Covid-19 Spread, Poojitha Thota
Using Sentiment And Emotion Analysis Of News Articles To Analyze The Effects Of Leader’S Statements On Covid-19 Spread, Poojitha Thota
Computer Science and Engineering Theses - Archive
Leaders generally include government officials, politicians, etc. Their statements can highly affect people’s decisions in many ways. Currently, in the pandemic situation, many statements were being passed every hour and day, which showed an impact on the spread of corona virus cases at certain location. So, this paper proposes a supervised model to analyze the variations of COVID-19 data based upon the leader’s statements passed at certain time and location. The proposed methodology consists of sentiment and emotion analysis for the leader’s statements to determine the true intentions of the leader. The leader’s statements are a collection of data obtained …
Learning Hierarchical Traversability Representation For Efficient Multi-Resolution Path Planning, Reza Etemadi Idgahi
Learning Hierarchical Traversability Representation For Efficient Multi-Resolution Path Planning, Reza Etemadi Idgahi
Computer Science and Engineering Theses - Archive
Path finding on grid-based obstacle maps is an important and much studied problem with applications in robotics and autonomy. Traditionally, in the AI community, heuristic search methods (e.g. based on Dijkstra and A*, or based on random trees) are used to solve this problem. This search, however incurs significant computational cost that grows with the size and resolution of the obstacle grid and has to be mitigated with effective heuristics in order to allow path finding in real time. In this work we introduce a learning framework using deep neural networks with a stackable convolution kernel to establish a hierarchy …
Structure Aware Human Pose Estimation Using Adversarial Learning, Suryam Sharma
Structure Aware Human Pose Estimation Using Adversarial Learning, Suryam Sharma
Computer Science and Engineering Theses - Archive
Pose estimation using Deep Neural Networks (DNNs) has shown outstanding performance in recent years, due to the availability of powerful GPUs and larger training datasets. However, there are still many challenges due to the large variability of human body appearances, lighting conditions, complex background, occlusions and postures. Among all these peculiarities, partial occlusions, and overlapping body poses often result in deviated pose predictions. These circumstances can result in wrong and sometimes unrealistic results. The human mind can predict such poses because of the underlying structural awareness of the geometry, of a human body. In this thesis, we discuss an efficient …
Glaze Epochs: Externalizing Material Knowledge Through Tangible Data Records In A Ceramics Studio, Hedieh Moradi
Glaze Epochs: Externalizing Material Knowledge Through Tangible Data Records In A Ceramics Studio, Hedieh Moradi
Computer Science and Engineering Theses - Archive
The "material turn" in HCI has placed a renewed focus on informing design from the relationships found in material-based interactions. While several ethnographic works provide insight into how practitioners converse with materials, it is less understood how these conversations transform into a skilled practitioner's mental model. I examine the material practice of glazing that gives ceramics its decorative and functional characteristics and involves fusing mixtures of silica, alumina, and flux onto a clay body through kiln firing. This practice evolves over decades, developing from multiple trajectories, including theoretical foundations, systematic experimentation, and happy accidents. This work describes virtual site visits …
Predict Behavioural Scores In Sleep Apnea Patients From Resting State Near-Infrared Spectroscopy (Fnirs), Amnah Abdelrahman
Predict Behavioural Scores In Sleep Apnea Patients From Resting State Near-Infrared Spectroscopy (Fnirs), Amnah Abdelrahman
Computer Science and Engineering Theses - Archive
Sleep disorders are common among adults and children; it has serious consequences on their heath, cognitive development and quality of life. However, some sleep disorders are challenging to diagnose and more challenging to treat. Practitioners often rely on AIH for OSA patients’ classification task, where considering one measurement could raise a risk of oversimplification. Studies show the correlation between sleep disorders, specifically OSA, and mental health. On the other side there are an increasing number of studies suggested evidence of a relationship between the dynamic properties of functional brain structure with the behaviors and cognition attributes. This novel work objective …
Continuous American Sign Language Translation With English Speech Synthesis Using Encoder-Decoder Approach, Preetham Ganesh
Continuous American Sign Language Translation With English Speech Synthesis Using Encoder-Decoder Approach, Preetham Ganesh
Computer Science and Engineering Theses - Archive
Interaction between human beings brings about improvements in science and technology. However, the interaction is limited for people who are deaf or hard-of-hearing, as they can only communicate with others who also know their sign language. With the help of recent technologies, such as Deep Learning, the gap can be bridged by converting Sentence-based Sign Language videos into English language speech. The methods discussed in this thesis are taking a step closer to solve that problem. There are four steps involved in converting ASL (American Sign Language) videos to English language speech. Step 1 is to recognize the phrases performed …
Extend The Sensing Boundary Of Mobile Systems: Security And New Applications, Wenqiang Jin
Extend The Sensing Boundary Of Mobile Systems: Security And New Applications, Wenqiang Jin
Computer Science and Engineering Dissertations - Archive
The exploding growth of mobile devices like smartphones and wearables has envisioned various applications, which are developed to collect a wide spectrum of data using on-board device sensors and process them to serve peoples' life in all kinds of scenarios. Noticing the sensing capabilities of current mobile device are limited to its on-board sensors' default functionalities. We study the mechanism designs that extend the mobile device's sensing capabilities to perform new sensing tasks other than its defaults. In this thesis, we investigate the mobile systems' security issues and develop new applications by exploring the device's sensing capabilities. Our contributions are …
Advanced Algorithms For Combinatorial And Sequential Test Generation With Constraints, Feng Duan
Advanced Algorithms For Combinatorial And Sequential Test Generation With Constraints, Feng Duan
Computer Science and Engineering Dissertations - Archive
Combinatorial and sequential testing are software testing strategies that have attracted significant interests from both academic and industrial communities. This dissertation addresses the problem of how to efficiently generate tests for both combinatorial and sequential testing. The dissertation makes two major contributions. For combinatorial testing, we present several optimizations on an existing t-way test generation algorithm called IPOG. These optimizations are designed to reduce the number of tests generated by IPOG. For sequential testing, we develop a notion for expressing commonly used sequencing constraints and present a t-way test sequence generation algorithm that support constraints expressed using notation. We demonstrate …
Deep Learning Methods For Image Restoration And Reconstruction, Zahra Anvari
Deep Learning Methods For Image Restoration And Reconstruction, Zahra Anvari
Computer Science and Engineering Dissertations - Archive
The problem of image reconstruction and restoration refers to recovering the clean images from corrupted ones. Corruption or degradation can occur due to atmospheric conditions such as rain, fog, mist, snow, dust, and air pollution or technical drawbacks of imaging devices such as motion blurriness, compression noise, low-resolution, etc. Image reconstruction algorithms aim at reducing these artifacts and degradation and generate clear images. Scenes captured under bad weather conditions such as rain, fog, mist, and haze suffer from visibility issues thus introduce obstacles for computer vision applications, e.g. object detection, recognition, tracking, and segmentation. In this dissertation, we focus on …
An Intelligent Framework To Assess Embodied Cognition From Physical Activities In Children, Ashwin Ramesh Babu
An Intelligent Framework To Assess Embodied Cognition From Physical Activities In Children, Ashwin Ramesh Babu
Computer Science and Engineering Dissertations - Archive
Cognition refers to "The mental actions or process of acquiring knowledge and understanding through thought, experience, and the senses". It encompasses many aspects of intellectual functions and processes such as attention, working memory, response inhibition, motor functions, and more. Humans start to develop these cognitive skills right from their childhood and become fully developed through their adulthood. Impairments in these cognitive functions, specifically in Executive Functions (Higher-order cognitive functions), disrupt their everyday life leading to a troubled childhood and lifelong difficulties in family, employment, and community functioning leading to socio-economic repercussions. Identifying such impairments at the right age (early childhood) …
Towards Efficient Testing And Debugging Of Emerging Software Applications, Huadong Feng
Towards Efficient Testing And Debugging Of Emerging Software Applications, Huadong Feng
Computer Science and Engineering Dissertations - Archive
Big Data and Smart Contract are among the top emerging technologies tipped to revolutionize the way businesses and organizations are run. Testing and debugging are the most important tasks during the development of any software application. Big data and smart contract applications possess unique characteristics. There is an urgent need to develop efficient techniques for testing and debugging these applications. The first part of the dissertation addresses the problem of how to debug big data applications. When a failure occurs in big data applications, debugging at the system-level can be expensive due to the large amount of data being processed. …
Human Factors Analysis And Monitoring To Enhance Human-Robot Collaboration, Akilesh Rajavenkatanarayanan
Human Factors Analysis And Monitoring To Enhance Human-Robot Collaboration, Akilesh Rajavenkatanarayanan
Computer Science and Engineering Dissertations - Archive
Human-Machine Interaction (HMI) can be defined as a way for us to communicate with machines through user interfaces. User interfaces have evolved from complicated punch cards and levers in the first analog computers to a more natural way of interaction using speech or gestures in today's digital assistants. Technological advancements in computing devices have paved the way for smart, powerful computers to be part of our everyday lives. There is also an increasing trend of using smart computing devices and robots in manufacturing lines, medical procedures, rehabilitation, and personal care. The umbrella of HMI typically covers several areas like Human-Robot …
Kopos: A Framework To Study And Detect Physical And Cognitive Fatigue Concurrently, Varun Ajay Kanal
Kopos: A Framework To Study And Detect Physical And Cognitive Fatigue Concurrently, Varun Ajay Kanal
Computer Science and Engineering Dissertations - Archive
Fatigue is one of the most prevalent phenomena in human beings, and yet its detection is highly subjective and poorly understood. The phenomenon of fatigue has a huge impact on performance, the ability to execute tasks safely and correctly, and the ability to retain or secure a job. Fatigue can be classified into two types: physical and cognitive fatigue. Physical fatigue may occur due to excessive physical exertion, while cognitive fatigue may occur due to excessive mental exertion. Historically, these two types of fatigue have been studied independently. However, in the real world, although these often occur at the same …
Generating Adversarial Examples For Recruitment Ranking Algorithms, Anahita Samadi
Generating Adversarial Examples For Recruitment Ranking Algorithms, Anahita Samadi
Computer Science and Engineering Theses - Archive
There is no doubt that recruitment process plays an important role for both employers and applicants. Based on huge number of job candidates and open vacancies, recruitment process is expensive, time consuming and stressful for both applicants and companies. In today’s world so many recruitment processes are based on machine learning techniques. Therefore, it is very important to ensure security of these algorithms. Adversarial examples are proposed to examine vulnerability of machine leaning algorithms. Many research studies have been done on evaluating the resistance of artificial intelligence-based systems, in computer vision and text classification, against adversarial examples. However, to the …
Semi-Automatic Hand Pose Estimation Using A Single Depth Camera, Giffy Jerald Chris
Semi-Automatic Hand Pose Estimation Using A Single Depth Camera, Giffy Jerald Chris
Computer Science and Engineering Theses - Archive
This paper addresses the problem of 3D hand pose annotations using a single depth camera. Although hand pose estimation methods rely critically on accurate 3D training data, creating such reliable training data is challenging and labor intensive. We propose a semi-automatic method for efficiently and accurately labeling the 3D hand key-points in a hand depth video. The process starts by selecting a subset of frames that are representative of all the frames in the dataset and the annotator only provides an estimate of the 2D hand key-points in these selected frames. We use this information to infer the 3D location …
A Survey On Ddos Attacks In Edge Servers, Iftakhar Ahmad
A Survey On Ddos Attacks In Edge Servers, Iftakhar Ahmad
Computer Science and Engineering Theses - Archive
In modern times, the need for latency sensitive applications is growing rapidly. Cloud computing infrastructure is unable to provide support to such delay sensitive applications. Therefore, a new paradigm called edge computing has emerged. In edge computing various paradigms like Fog, Cloudlet, Mobile Edge Computing, etc. provide real-time, location aware services to users. As a result number of requests are generated for processing in the edge servers. If these edge servers for some reason become unavailable for providing service, users will not be able to perform their delay sensitive or location aware operations. Like other servers in the network, edge …
Incomplete Time Series Forecasting Using Generative Neural Networks, Harshit Tarun Shah
Incomplete Time Series Forecasting Using Generative Neural Networks, Harshit Tarun Shah
Computer Science and Engineering Theses - Archive
Dealing with missing data is a long pervading problem and it becomes more challenging when forecasting time series data because of the complex relationships between data and time, which is why incomplete data can lead to unreliable results. While some general-purpose methods like mean, zero, or median imputation can be employed to alleviate the problem, they might disrupt the inherent structure and the underlying data distributions. Another problem associated with conventional time series forecasting methods whose goal is to predict mean values is that they might sometimes overlook the variance or fluctuations in the input data and eventually lead to …
Link Prediction Based Face Clustering Using Variational Attentional Graph Autoencoder, Harish Deepak Verlekar
Link Prediction Based Face Clustering Using Variational Attentional Graph Autoencoder, Harish Deepak Verlekar
Computer Science and Engineering Theses - Archive
In this work, we address the problem of clustering faces according to their individual identities present inherently in the dataset.The current clustering frameworks are either based on some heuristic method or require labelled data for training the models,also some of them make assumptions on data distribution or shape of the clusters.We have framed the problem of forming clusters to that of link prediction on graphs and learn how to do that in a completely unsupervised way by proposing to use Variational Graph Autoencoders and use Graph Attentional Network as the Encoder. We call this network as Variational Attentional Graph Autoencoder(VAGAE).Our …
Early Detection Of Glaucoma Using Modified Residual U-Net Convolutional Neural Network, Balasubramaniam Theetharappan
Early Detection Of Glaucoma Using Modified Residual U-Net Convolutional Neural Network, Balasubramaniam Theetharappan
Computer Science and Engineering Theses - Archive
Glaucoma is the second leading cause of blindness all over the world, with apparently 75 million cases reported worldwide in 2018. If it’s not diagnosed at an early stage, glaucoma may cause irreversible damage to the optic nerve which results in blindness. The Optic head examination is the widely used structured diagnosis approach in the current medical field for Glaucoma detection which involves measuring the Optic Cup-to-Disc ratio from the fundus image. Estimation of Optic Cup-to-Disc requires accurate segmentation of the Optic Cup and Optic Disc from the fundus which is a tedious and time-consuming task even for the experienced …
Generalized Algorithmic Frameworks For Optimizing Distance Calls In Generalized Metric Space Proximity Problems And Methods For Realizing Efficient Signal Reconstruction, Jees Augustine
Computer Science and Engineering Dissertations - Archive
The exponential rise in data, along with its heterogeneity and complexity, helped individuals, businesses, hospitals, enterprises and even governments to thrive on data-driven decision making. However, as the size and complexity of the data surged, challenges in searching for similar objects within databases (proximity search) compounded. As is well known, proximity search is the key and successful method used in Information Retrieval (IR) in vast databases including, Genomics Databases, Image Databases, Video Databases, Text databases, etc. The objective is to retrieve contents from the database, similar to a given object in the database. This dissertation revisits a suite of popular …