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We study a universal outlying sequence detection problem, in which there are M sequences of samples out of which a small subset of outliers need to be detected. A sequence is considered as an outlier if the observations therein are generated by a distribution different from those generating the observations in the majority of the sequences. In the universal setting, the goal is to identify all the...
We describe an analytical process to determine how much UAS traffic is feasible. The process is a simulator and data processing tools. The two are applied to the US San Francisco Bay Area and Norrköping, Sweden. The amount of UAS traffic is measured in flights per day and simulated up to 200,000 flights. A UAS traffic volume is feasible if specified metrics meet operational requirements with high...
With the increasing penetration of renewable energy sources in the modern electric grid, it becomes more technically difficult and costly for system operators to balance generation and demand as traditional providers of flexibility (i.e., flexible generation) become uneconomic. Therefore new sources of flexibility are needed to maintain reliable operation. Flexible demand, including from electric...
The community structure of complex networks reveals hidden relationships in the organization of their constituent nodes. Indeed, many practical problems stemming from different fields of knowledge such as Biology, Sociology, Chemistry and Computer Science can be modeled as a graph. Therefore, graph analysis and community detection have become a key component for understanding the inherent relational...
This work presents a computationally efficient real-time adaptive clustering algorithm that recognizes and adapts to dynamic changes observed in neural recordings. The algorithm consists of an off-line training phase that determines initial cluster positions and an on-line operation phase that continuously tracks drifts in clusters and periodically verifies acute changes in cluster composition. Analysis...
Semi-supervised clustering has been widely explored in the last years. In this paper, we present HCAC-ML (Hierarchical Confidence-based Active Clustering with Metric Learning), an innovative approach for this task which employs distance metric learning through cluster-level constraints. HCAC-ML is based on the HCAC algorithm, an state-of-the-art algorithm for hierarchical semi-supervised clustering...
Trajectory reversing is a method commonly used for estimating the region of attraction of stabilized equilibria. Using a discrete set of points obtained by trajectory reversing, this paper presents an algorithm for estimation and mathematical representation of the region of attraction using convex hulls. Several two-dimensional examples are presented to illustrate the usefulness of the algorithm....
In practice, there are a variety of real-world datasets that have an imbalanced nature where one of two classes dominates the data. These datasets are generally difficult to classify using machine learning algorithms as the skewed nature of the data has a significant impact on the training process. In order to combat this difficulty, many methods of under sampling and over sampling have been proposed...
The Service Oriented Computing (SOC) paradigm promotes building new applications by discovering and then invoking services, i.e., software components accessible through the Internet. Discovering services means inspecting registries where textual descriptions of services functional capabilities are stored. To automate this, existing approaches index descriptions and associate users' queries to relevant...
Credit scoring plays an important role in financial institutions and debt based crowdfunding platforms as well as peer to peer lending platforms. In the last few years, adopting ensemble methods for credit scoring has become much more popular. However, the performance of ensemble methods is easily affected by the parameter settings and the number of base classifiers. Ensemble classification based...
Analyzing social iterations in a scientific environment will assist researchers in expanding their collaborative networks. Scientific social networks represent the researchers' social iterations in an academic environment. The analysis of these networks requires a detailed study of their structure and it is important the use of visual resources in order to a better understanding of how the social...
Text mining discover and extract useful information from documents, whenever increase the size and number documents leads to redouble features. The huge features for the documents adds challenge to text mining called high dimension. The aim of this proposed study is minimize the high dimension of the documents, and improve Arabic text mining using clustering. In order to achieve this goal, we propose...
In order to evaluate and increase modularity this paper combines a method for visualizing and measuring software architectures and two algorithms for decoupling. The combination is tested on a software system at Ericsson. Our analysis show that the system has one large cluster of components (18% of the system, a Core), all interacting with each other. By employing cluster and dominator analysis we...
Image segmentation from noisy CT image has a number of applications in different clinical diagnosis. In order to automatically perform this task from an under-sampled noisy CT image, we have merged compressed sensing reconstruction technique and hierarchical clustering algorithm together in this paper. Denoising based approximate message passing (D-AMP) algorithm is used as a compressed sensing reconstruction...
Similarity search is arguably the most important primitive in time series data mining. Recent research has made significant progress on fast algorithms for time series similarity search under Dynamic Time Warping (DTW) and Uniform Scaling (US) distance measures. However, the current state-ofthe-art algorithms cannot support greater amounts of rescaling in many practical applications. In this paper,...
While accurate tumor delineation in FDG-PET is a vital task, noisy and blurring imaging system makes it a challenging work. In this paper, we propose to address this issue using the theory of belief functions, a powerful tool for modeling and reasoning with uncertain and/or imprecise information. An automatic segmentation method based on clustering is developed in 3-D, where, different from available...
Packing and placement are two crucial stages for FPGA realization. In the design flow, the basic logic units, such as look-up-tables (LUTs) and flip-flops (FFs), have to be merged into configurable logic blocks (CLBs) before placement. How the basic logic blocks are clustered in the packing stage has a great impact on the placement quality. This work presents an analytical placement framework for...
The paper presents the researches to determine the effectiveness of different criteria to estimate the complex biology objects clustering quality. The gene expression sequences of cancer patients were used as experimental data. The degree of the studied objects similarity was estimated by the comparison of the gene expression sequences profile using different metrics to estimate the objects proximity...
The problem of checking a logged event trace against a temporal logic specification arises in many practical cases. Unfortunately, known algorithms for an expressive logic like MTL (Metric Temporal Logic) do not scale with respect to two crucial dimensions: the length of the trace and the size of the time interval of the formula to be checked. The former issue can be addressed by distributed and parallel...
Superpixel segmentation targets at grouping pixels in an image into atomic regions that align well with the natural object boundaries. In this paper, we propose a novel superpixel segmentation method based on an iterative and adaptive clustering algorithm that embraces color, contour, texture, and spatial features together. The algorithm adjusts the weights of different features automatically in a...
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