Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 20 for “"Corrupted Data"”.
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Sparse Support Matrix Machines for the Classification of Corrupted Data
… is fragile to the presence of outliers: even few corrupted data points can arbitrarily alter the quality of the approximation, What if a fraction of columns are corrupted? In real world, the data is noisy and most of the features may be redundant as well as may be useless, which in turn affect the …
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Missing values imputation and image registration for genetics applications
… thesis, we address several common scenarios of corrupted data in data and image processing pipelines. The first is in the setting of clustered data with missing values. We design an algorithm for imputing missing values using optimal recovery and derive an error bound for non-negative matrix …
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Distributed Inference and Learning with Byzantine Data
… faults and attacks, thus, providing</p> <p>corrupted data. Although the area of statistical inference has been an active area of research in the</p> <p>past, distributed learning and inference in a networked setup with potentially unreliable components</p> <p>has only gained attention …
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Serial digital multiplexing of transducer data for intrinsically safe applications
… uses the technique of allowing collisions of data to occur on the basis that they can be detected and the corrupted data can be subsequently ignored.
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Robust Bayesian Anomaly Detection Methods for Large Scale Sensor Systems
… monitoring to validate their quality, as corrupted data will increase both experimental downtime and budget and lead to inconclusive scientific and engineering results. One approach to validate sensor quality is monitoring individual sensor measurements' distribution. Although, in general …
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Manual and compiler assisted methods for generating fault-tolerant parallel programs
… Applications are modified to operate on encoded data and produce encoded results which may then be checked for correctness. An attractive feature of the scheme is that it requires little or no modification to the underlying hardware or system software. Previous algorithm-based methods for …
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A Supervised Low-Rank Matrix Decomposition for Matching
… typical of video surveillance, to robotics, metadata enrichment of social media content, and mobile applications. The most recent approaches rely on techniques such as sparse coding and low-rank matrix decomposition. Those build a generative representation of the data that on the one hand, …
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Machine Learning for Radio Frequency Interference Flagging
… (RFI) flagging involves the identification of corrupted data within radio astronomy measurements. This work explores the application of supervised machine learning algorithms for RFI flagging, trained on real measurement data and simulated data with simulated RFI. The goal of this work is to …
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Intrusion detection by random dispersion and voting on redundant Web server operations
… code could damage the system or cause it to send corrupted data back to the client. The goal of this thesis is to explore the question of whether voting, in conjunction with several key concepts from the study of fault-tolerant computing - namely masking, redundancy, and dispersion - can be …
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Applications of PMUSimulator in PDC Testing
… system, phasor measurement units and phasor data concentrators are essential for real time control of the system. PMUs are time synchronized throughout the power system and take sample measurements in very small windows of time. Phasor Data Concentrators accept PMU data and time align the …
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Parity-based Data Outsourcing: Extension, Implementation, and Evaluation
<p>Our research has developed a Parity-based Data Outsourcing (PDO) model. This model outsources a set of raw data by associating it with a set of parity data and then distributing both sets of data among a number of cloud servers that are managed independently by different service providers. Users …
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Uma metodologia para tratamento de dados de curvas de carga baseada em técnicas de inteligência artificial
Data quality is critical in the short-term load forecasting. Frequently, load data show aberrant values (outliers), discontinuities, and gaps (missing data) caused by the abnormal operation of the electrical system or failures and problems in the measurement system. The presence of corrupted data …
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Learning from Commerce Data: from Theory to Practice
… benefits from the vast potential of commerce data. However, deploying analytics platforms to extract value from such data poses a significant challenge for many organizations. One major obstacle lies in the ability to effectively learn from commerce data within an environment characterized by …
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Advanced Aeromagnetic Compensation Models for Airborne Magnetic Anomaly Navigation
… system collects real-time magnetic field data and uses predetermined magnetic anomaly maps of the earth to estimate location by aiding an inertial navigation system (INS), which continually drifts. MagNav has the benefits of being passive, globally available at all times and in all …
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Learning with Single View Co-training and Marginalized Dropout
… 1) a sufficiently large quantity of training data is available; 2) the training and testing data come from some common distribution. Although these assumptions are often met in practice, there are also many scenarios in which training data from the relevant distribution is insufficient. We …
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A neural relevance model for feature extraction from hyperspectral images, and its application in the wavelet domain
… sensed hyperspectral image. The higher the data dimension and/or larger the number of classes, the more advantage GRLVQI shows over GRLVQ. The improved performance of GRLVQI over GRLVQ is substantiated using several different methods discussed in the literature. We come to the important …
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Bayesian Estimators, Error Bounds, and Applications to Imaging
… fed into the front end of a channel and noisy or corrupted data are obtained at its back end, data from which one attempts to estimate the physical parameter. The error that accompanies such an estimate is usually characterized by mean squared error (MSE). In a Bayesian setting, the minimum value …
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Generative models meet similarity search: efficient, heuristic-free and robust retrieval
The rapid growth of digital data, especially visual and textual contents, brings many challenges to the problem of finding similar data. Exact similarity search, which aims to exhaustively find all relevant items through a linear scan in a dataset, is impractical due to its high computational …
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Analysis of bandwidth attacks in a bittorrent swarm
… Micro Transport Protocol. It is based on User Datagram Protocol with a novel congestion control called Low Extra Delay Background Transport. This protocol assumes that the receiver always provides correct feedback, otherwise this deteriorates throughput or yields to corrupted data. I show …
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Scalable Robust Models Under Adversarial Data Corruption
… presence of noise and corruption in real-world data can be inevitably caused by accidental outliers, transmission loss, or even adversarial data attacks. Unlike traditional random noise usually assume a specific distribution with low corruption ratio, the data collected from crowdsourcing or …