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In this paper, we show that for a given pair of metrics, such as IGTE vs. IGFE, number of packets vs. number of network flows, etc., the functional relation between them may be complex and can not be described perfectly by linear equation. In order to capture this complex relationship, we make use of evidence function framework to automatically determine the optimal model for the metrics. Then we...
The recent success of representation learning is built upon the learning of relevant features, in particular from unlabelled data available in different domains. This raises the question of how to transfer and reuse such knowledge effectively so that the learning of a new task can be made easier or be improved. This poses a difficult challenge for the area of transfer learning where there is no label...
MSR Image Recognition Challenge (IRC) 2016 tries to deal with designing a dog breeds recognition system based on Clickture-Dog dataset. To address the task, we proposed an effective method with systematic strategies as follows. We presented a data cleaning method using faster-rcnn to learn a dog detector. Besides, we ensembeled a series of CNN models to enhance the robustness. A dense evaluation strategy...
In this paper, a statistical method of estimating the number of endmembers from hyperspectral images is proposed. A noncentra chi-squared distribution model is applied to calculate this number. Endmembers are important parameters in many hyperspectral detection algorithms based on Linear Mixture Model(LMM). The number of endmenbers is the only preset parameter and needed in some anomaly detectors...
The low-rank property of hyperspectral imagery is well exploited with low-rank decomposition methods recently. In our approach, a novel hyperspectral anomaly detector based on low-rank representation (LRR) and learned dictionary (LD) has been proposed. This method assumes that a two-dimensional matrix transformed from a three-dimensional hyperspectral imagery can be decomposed into two parts: a low...
This paper presents an automatic ship detection algorithm for polarimetric synthetic aperture radar (PolSAR) data. Based on the non-Gaussian K-Wishart distribution model for complex backscattering coefficients, the PolSAR image is clustered automatically by a modified expectation maximization algorithm. A goodness-of-fit test is incorporated to improve the model fitness of the cluster iteratively...
In this research we present a new framework and associated algorithms for mining high speed data streams that take advantage of concept recurrence. Different from previous work our approach detects volatility in a stream and then matches the learning paradigm to the degree of volatility. In high volatility stream segments a decision forest is used as the learning mechanism whereas in low volatility...
Online mining is a difficult task especially when such data streams evolve over time. Evolving data stream occurs when concepts drift or change completely, is becoming one of the core issues. A large portion of change detection research are carried out in the area of supervised learning, very little has been carried out for unlabeled data specifically in the area of transactional data streams. Overall...
The high levels of urban traffic is becoming a main concern in our societies, generating problems such as excessive fuel consumption, CO2 emissions. Recently, Intelligent Trans-portation Systems (ITS) have emerged as a way to mitigate these problems. However, traffic analysis, improvement typically rely on simulations, which should be as realistic as possible. Meeting this requirement can be complex...
Short-term traffic flow forecasting has been a crucial component in the area of intelligent transportation systems (ITS), which plays a significant role in operating traffic management systems and dynamic traffic assignment effectively as well as proactively. In this paper, a novel short-term traffic flow prediction method called Ensemble Real-time Sequential Extreme Learning Machine (ERS-ELM) with...
Recent high profile security breaches have highlighted the importance of insider threat detection systems for cybersecurity. However, issues such as high false positive rates and concerns over data privacy make it difficult to predict performance within an enterprise environment. These and other issues limit an organization's ability to effectively apply these tools. In this paper, we present an approach...
In this paper, the distributed detection problem of linear and nonlinear signals embedded in white Gaussian noise (WGN) is considered. First, the asymptotically optimal generalized likelihood ratio test (GLRT) detector is derived for both signal models. It is found that the GLRT detector requires the submission of all observed data to the central processor which is practically infeasible. Thus, several...
Leveraging Manhattan assumption we generate metrically rectified novel views from a single image, even for non-box scenarios. Our novel views enable the already trained classifiers to handle training data missing views (blind spots) without additional training. We demonstrate this on end-to-end scene text spotting under perspective. Additionally, utilizing our fronto-parallel views, we discover unsupendsed...
A novel method for localizing a mobile platform within a region of interest was previously conceived and modeled. The system relies on data received at the platform from two independent rotating laser beacons that encode their current rotational angle within the light beam. Using that data and trigonometric calculations, the location and orientation of the mobile platform can be derived in real time...
In this work, we extend the performance analysis of some single-pulse transmission signal processors that produce CFAR under the assumption of Pareto distributed clutter for more realistic approach where non coherent integrated pulses are transmitted. A logarithmic transformation approach, that enables true Gaussian CFAR processes to be translated for target detection in Pareto clutter scenario, is...
In this paper, we propose an adaptive algorithm to detect and localize a point-like target in heterogeneous environments. At the design stage we use the two-step GLRT-based design procedure: we first introduce the GLRT for known structure of the noise covariance matrix and unknown noise levels, possibly different from one cell to another; then, we make the detector fully adaptive by replacing the...
Tax fraud includes a large spectrum of methods to deny the facts and realities, claiming wrong information, and accomplishing financial businesses regardless of what the legal frameworks are. Nowadays, with the development tax systems and the large volume of the data stored in them, need is felt for a tool by which we can process the stored data and provide users with the information obtained from...
This paper is an empirical study of the application of visual detection and tracking methods to the problem of locating and tracking all AFL players during a game. While most person detection and tracking algorithms are designed for pedestrians, we show that with appropriate modifications, state of the art methods can be adapted to a more challenging domain where motion is significantly more varied...
In real-world applications, machine learning algorithms can be employed in spam detection, environmental monitoring, fraud detection, medical diagnoses, among others. Most of these problems present an environment which changes over time. The problem involving classification tasks in dynamic environments has become one of the major challenges in machine learning domain in the last decades. Currently...
We introduce the concept of Least Similar Nearest Neighbours (LeSiNN) and use LeSiNN to detect anomalies directly. Although there is an existing method which is a special case of LeSiNN, this paper is the first to clearly articulate the underlying concept, as far as we know. LeSiNN is the first ensemble method which works well with models trained using samples of one instance. LeSiNN has linear time...
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