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- Concept drift (2)
- Deep learning (2)
- Incremental learning (2)
- Machine learning (2)
- ARTIFICIAL INTELLIGENCE (1)
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- Affine invariance (1)
- Alignment (1)
- Artificial neural network (1)
- BRAIN (1)
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- Causality (1)
- Classification (1)
- Clustering (1)
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- Congestion Control ; Active Queue Management ; Event-Driven Feedback Control (1)
- Convolutional Neural Network (1)
- Correlation (1)
- DATA (1)
- DEEP LEARNING (1)
- Data Mining (1)
- Dimension reduction (1)
- ELECTROENCEPHALOGRAM CLASSIFICATION (1)
- Face detection (1)
- Face matching (1)
- Feature extraction (1)
- Functional equivalence (1)
- GMTI (1)
- General Line Coordinates (1)
Articles 1 - 18 of 18
Full-Text Articles in Physical Sciences and Mathematics
Weighted Incremental–Decremental Support Vector Machines For Concept Drift With Shifting Window, Honorius Gâlmeanu, Răzvan Andonie
Weighted Incremental–Decremental Support Vector Machines For Concept Drift With Shifting Window, Honorius Gâlmeanu, Răzvan Andonie
Computer Science Faculty Scholarship
We study the problem of learning the data samples’ distribution as it changes in time. This change, known as concept drift, complicates the task of training a model, as the predictions become less and less accurate. It is known that Support Vector Machines (SVMs) can learn weighted input instances and that they can also be trained online (incremental–decremental learning). Combining these two SVM properties, the open problem is to define an online SVM concept drift model with shifting weighted window. The classic SVM model should be retrained from scratch after each window shift. We introduce the Weighted Incremental–Decremental SVM (WIDSVM), …
Information Bottleneck In Deep Learning - A Semiotic Approach, Bogdan Musat, Razvan Andonie
Information Bottleneck In Deep Learning - A Semiotic Approach, Bogdan Musat, Razvan Andonie
Computer Science Faculty Scholarship
The information bottleneck principle was recently proposed as a theory meant to explain some of the training dynamics of deep neural architectures. Via information plane analysis, patterns start to emerge in this framework, where two phases can be distinguished: fitting and compression. We take a step further and study the behaviour of the spatial entropy characterizing the layers of convolutional neural networks (CNNs), in relation to the information bottleneck theory. We observe pattern formations which resemble the information bottleneck fitting and compression phases. From the perspective of semiotics, also known as the study of signs and sign-using behavior, the saliency …
Electroencephalogram Classification Of Brain States Using Deep Learning Approach, Hrishitva Patel
Electroencephalogram Classification Of Brain States Using Deep Learning Approach, Hrishitva Patel
Computer Science Faculty Scholarship
The oldest diagnostic method in the field of neurology is electroencephalography (EEG). To grasp the information contained in EEG signals, numerous deep machine learning architectures have been developed recently. In brain computer interface (BCI) systems, classification is crucial. Many recent studies have effectively employed deep learning algorithms to learn features and classify various sorts of data. A systematic review of EEG classification using deep learning was conducted in this research, resulting in 90 studies being discovered from the Web of Science and PubMed databases. Researchers looked at a variety of factors in these studies, including the task type, EEG pre-processing …
Concept Drift Adaptation With Incremental–Decremental Svm, Honorius Gâlmeanu, Răzvan Andonie
Concept Drift Adaptation With Incremental–Decremental Svm, Honorius Gâlmeanu, Răzvan Andonie
Computer Science Faculty Scholarship
Data classification in streams where the underlying distribution changes over time is known to be difficult. This problem—known as concept drift detection—involves two aspects: (i) detecting the concept drift and (ii) adapting the classifier. Online training only considers the most recent samples; they form the so-called shifting window. Dynamic adaptation to concept drift is performed by varying the width of the window. Defining an online Support Vector Machine (SVM) classifier able to cope with concept drift by dynamically changing the window size and avoiding retraining from scratch is currently an open problem. We introduce the Adaptive Incremental–Decremental SVM (AIDSVM), a …
Learning In Convolutional Neural Networks Accelerated By Transfer Entropy, Adrian Moldovan, Angel Caţaron, Răzvan Andonie
Learning In Convolutional Neural Networks Accelerated By Transfer Entropy, Adrian Moldovan, Angel Caţaron, Răzvan Andonie
Computer Science Faculty Scholarship
Recently, there is a growing interest in applying Transfer Entropy (TE) in quantifying the effective connectivity between artificial neurons. In a feedforward network, the TE can be used to quantify the relationships between neuron output pairs located in different layers. Our focus is on how to include the TE in the learning mechanisms of a Convolutional Neural Network (CNN) architecture. We introduce a novel training mechanism for CNN architectures which integrates the TE feedback connections. Adding the TE feedback parameter accelerates the training process, as fewer epochs are needed. On the flip side, it adds computational overhead to each epoch. …
Deep Learning Of 2-D Images Representing N-D Data In General Line Coordinates, Dmytro Dovhalets, Boris Kovalerchuk, Szilárd Vajda, Răzvan Andonie
Deep Learning Of 2-D Images Representing N-D Data In General Line Coordinates, Dmytro Dovhalets, Boris Kovalerchuk, Szilárd Vajda, Răzvan Andonie
Computer Science Faculty Scholarship
While knowledge discovery and n-D data visualization procedures are often efficient, the loss of information, occlusion, and clutter continue to be a challenge. General Line Coordinates (GLC) is a rather new technique to deal with such artifacts. GLC-Linear, which is one of the methods in GLC, allows transforming n-D numerical data to their visual representation as polylines losslessly. The method proposed in this paper uses these 2-D visual representations as input to Convolutional Neural Network (CNN) classifiers. The obtained classification accuracies are close to the ones obtained by other machine learning algorithms. The main benefit of the method is the …
Looking At Faces In The Wild, Eugene Borovikov, Szilárd Vajda, Michael Bonifant, Michael Gill
Looking At Faces In The Wild, Eugene Borovikov, Szilárd Vajda, Michael Bonifant, Michael Gill
Computer Science Faculty Scholarship
Recent advances in the face detection (FD) and recognition (FR) technology may give an impression that the problem of face matching is essentially solved, e.g. via deep learning models using thousands of samples per face for training and validation on the available benchmark data-sets. Human vision system seems to handle face localization and matching problem differently from the modern FR systems, since humans detect faces instantly even in most cluttered environments, and often require a single view of a face to reliably distinguish it from all others. This prompted us to take a biologically inspired look at building a cognitive …
Constructing Interactive Visual Classification, Clustering And Dimension Reduction Models For N-D Data, Boris Kovalerchuk, Dmytro Dovhalets
Constructing Interactive Visual Classification, Clustering And Dimension Reduction Models For N-D Data, Boris Kovalerchuk, Dmytro Dovhalets
Computer Science Faculty Scholarship
The exploration of multidimensional datasets of all possible sizes and dimensions is a long-standing challenge in knowledge discovery, machine learning, and visualization. While multiple efficient visualization methods for n-D data analysis exist, the loss of information, occlusion, and clutter continue to be a challenge. This paper proposes and explores a new interactive method for visual discovery of n-D relations for supervised learning. The method includes automatic, interactive, and combined algorithms for discovering linear relations, dimension reduction, and generalization for non-linear relations. This method is a special category of reversible General Line Coordinates (GLC). It produces graphs in 2-D that represent …
Correlation Of Partial Frames In Video Matching, Boris Kovalerchuk, Sergei Kovalerchuk
Correlation Of Partial Frames In Video Matching, Boris Kovalerchuk, Sergei Kovalerchuk
Computer Science Faculty Scholarship
Correlating and fusing video frames from distributed and moving sensors is important area of video matching. It is especially difficult for frames with objects at long distances that are visible as single pixels where the algorithms cannot exploit the structure of each object. The proposed algorithm correlates partial frames with such small objects using the algebraic structural approach that exploits structural relations between objects including ratios of areas. The algorithm is fully affine invariant, which includes any rotation, shift, and scaling.
Guidance In Feature Extraction To Resolve Uncertainty, Boris Kovalerchuk, Michael Kovalerchuk, Simon Streltsov, Matthew Best
Guidance In Feature Extraction To Resolve Uncertainty, Boris Kovalerchuk, Michael Kovalerchuk, Simon Streltsov, Matthew Best
Computer Science Faculty Scholarship
Automated Feature Extraction (AFE) plays a critical role in image understanding. Often the imagery analysts extract features better than AFE algorithms do, because analysts use additional information. The extraction and processing of this information can be more complex than the original AFE task, and that leads to the “complexity trap”. This can happen when the shadow from the buildings guides the extraction of buildings and roads. This work proposes an AFE algorithm to extract roads and trails by using the GMTI/GPS tracking information and older inaccurate maps of roads and trails as AFE guides.
Supporting Preemptive Task Executions And Memory Copies In Gpgpus, Kyoung-Don Kang, Can Basaran
Supporting Preemptive Task Executions And Memory Copies In Gpgpus, Kyoung-Don Kang, Can Basaran
Computer Science Faculty Scholarship
GPGPUs (General Purpose Graphic Processing Units) provide massive computational power. However, applying GPGPU technology to real-time computing is challenging due to the non-preemptive nature of GPGPUs. Especially, a job running in a GPGPU or a data copy between a GPGPU and CPU is non-preemptive. As a result, a high priority job arriving in the middle of a low priority job execution or memory copy suffers from priority inversion. To address the problem, we present a new lightweight approach to supporting preemptive memory copies and job executions in GPGPUs. Moreover, in our approach, a GPGPU job and memory copy between a …
A Federated Approach For Increasing The Timely Throughput Of Real-Time Data Services, Yan Zhou, Kyoung-Don Kang
A Federated Approach For Increasing The Timely Throughput Of Real-Time Data Services, Yan Zhou, Kyoung-Don Kang
Computer Science Faculty Scholarship
As the demand for real-time data services (e.g., ecommerce or online auctions) increases, it is desired for a real-time database to increase the timely throughput−the amount of data processed in a timely manner. As the timely throughput of a centralized real-time database is limited, it is desired to federate a set of real-time databases to increases the timely throughput. However, related work on distributed real-time databases is scarce. Most existing approaches are highly complex, incurring non-trivial overheads. Neither are they implemented in a real database system. To address the problem, we design a new system architecture for federated real-time data …
Functional Equivalence Of Spatial Images From Touch And Vision: Evidence From Spatial Updating In Blind And Sighted Individuals, Nicholas Giudice, M. R. Betty, J. M. Loomis
Functional Equivalence Of Spatial Images From Touch And Vision: Evidence From Spatial Updating In Blind And Sighted Individuals, Nicholas Giudice, M. R. Betty, J. M. Loomis
Computer Science Faculty Scholarship
This research examined whether visual and haptic map learning yield functionally equivalent spatial images in working memory, as evidenced by similar encoding bias and updating performance. In 3 experiments, participants learned 4-point routes either by seeing or feeling the maps. At test, blindfolded participants made spatial judgments about the maps from imagined perspectives that were either aligned or misaligned with the maps as represented in working memory. Results from Experiments 1 and 2 revealed a highly similar pattern of latencies and errors between visual and haptic conditions. These findings extend the well-known alignment biases for visual map learning to haptic …
Active Queue Management Via Event-Driven Feedback Control, Mehmet H. Suzer, Kyoung-Don Kang, Can Basaran
Active Queue Management Via Event-Driven Feedback Control, Mehmet H. Suzer, Kyoung-Don Kang, Can Basaran
Computer Science Faculty Scholarship
Active queue management (AQM) is investigated to avoid incipient congestion in gateways to complement congestion control run by the transport layer protocol such as the TCP. Most existing work on AQM can be categorized as (1) ad-hoc event-driven control and (2) time-driven feedback control approaches based on control theory. Ad hoc event-driven approaches for congestion control, such as RED (random early detection), lack a mathematical model. Thus, it is hard to analyze their dynamics and tune the parameters. Time-driven control theoretic approaches based on solid mathematical models have drawbacks too. As they sample the queue length and run AQM algorithm …
Robust Fuzzy Cpu Utilization Control For Dynamic Workloads, Can Basaran, Mehmet H. Suzer, Kyoung-Don Kang, Xue Liu
Robust Fuzzy Cpu Utilization Control For Dynamic Workloads, Can Basaran, Mehmet H. Suzer, Kyoung-Don Kang, Xue Liu
Computer Science Faculty Scholarship
In a number of real-time applications such as target tracking, precise workloads are unknown a priori but may dynamically vary, for example, based on the changing number of targets to track. It is important to manage the CPU utilization, via feedback control, to avoid severe overload or underutilization even in the presence of dynamic workloads. However, it is challenge to model a real-time system for feedback control, as computer systems cannot be modeled via physics laws. In this paper, we present a novel closed-loop approach for utilization control based on formal fuzzy logic control theory, which is very effective to …
Visual Discovery In Multivariate Binary Data, Boris Kovalerchuk, Florian Delizy, Logan Riggs, Evgenii Vityaev
Visual Discovery In Multivariate Binary Data, Boris Kovalerchuk, Florian Delizy, Logan Riggs, Evgenii Vityaev
Computer Science Faculty Scholarship
This paper presents the concept of Monotone Boolean Function Visual Analytics (MBFVA) and its application to the medical domain. The medical application is concerned with discovering breast cancer diagnostic rules (i) interactively with a radiologist, (ii) analytically with data mining algorithms, and (iii) visually. The coordinated visualization of these rules opens an opportunity to coordinate the rules, and to come up with rules that are meaningful for the expert in the field, and are confirmed with the database. This paper shows how to represent and visualize binary multivariate data in 2-D and 3-D. This representation preserves the structural relations that …
Towards Context-Aware Real-Time Information Dissemination, Kyoung-Don Kang, Greg Vert
Towards Context-Aware Real-Time Information Dissemination, Kyoung-Don Kang, Greg Vert
Computer Science Faculty Scholarship
Real-time information dissemination is essential for the success of key applications such as transportation management and battlefield monitoring. In these applications, relevant information should be disseminated to interested users in a timely fashion. However, it is challenging to support timely information dissemination due to the limited and even time-varying network bandwidth. Thus, a naive approach disseminating every data with no consideration of the context that describes where and when the data is acquired and how it can satisfy users may only provide poor performance and user perceived quality of service (QoS). To address the problem, we design a novel context-aware …
Automated Vector-To-Raster Image Registration, Boris Kovalerchuk, Peter Doucette, Gamal Seedahmed, Robert Brigantic, Michael Kovalerchuk, Brian Graff
Automated Vector-To-Raster Image Registration, Boris Kovalerchuk, Peter Doucette, Gamal Seedahmed, Robert Brigantic, Michael Kovalerchuk, Brian Graff
Computer Science Faculty Scholarship
The variability of panchromatic and multispectral images, vector data (maps) and DEM models is growing. Accordingly, the requests and challenges are growing to correlate, match, co-register, and fuse them. Data to be integrated may have inaccurate and contradictory geo-references or not have them at all. Alignment of vector (feature) and raster (image) geospatial data is a difficult and time-consuming process when transformational relationships between the two are nonlinear. The robust solutions and commercial software products that address current challenges do not yet exist. In the proposed approach for Vector-to-Raster Registration (VRR) the candidate features are auto-extracted from imagery, vectorized, and …