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Several works have shown that relationships between data points (i.e., context) in structured data can be exploited to obtain better recognition performance. In this paper, we explore a different, but related, problem: how can these inter-relationships be used to efficiently learn and continuously update a recognition model, with minimal human labeling effort. Towards this goal, we propose an active...
Feature selection is to select certain quantity of important features from large number of original features. In this paper, a new feature selection algorithm based on features unit (FU) is presented. The algorithm uses entropy of information to determine whether a feature should integrate with other features on the basis of its relevance to the class. As for the features which fit to integrate with...
The large, publicly accessible online product reviews have become a significant information resource for enterprise to discover preferences of the public and market trend. In this paper, we propose the text mining driven information gain model for identifying notable product features to enable enterprise understand what product features determine the customers' satisfaction about the given products...
In a variety of Network-based Intrusion Detection System (NIDS) applications, one desires to detect groups of unknown attack (e.g., botnet) packet-flows, with a group potentially manifesting its atypicality (relative to a known reference “normal”/null model) on a low-dimensional subset of the full measured set of features used by the IDS. What makes this anomaly detection problem quite challenging...
As a number of attacks such as Stuxnet and BlackEnergy targeting the control system of critical infrastructure have happened, the importance of security enhancement for the facilities such as industrial CPS (Cyber Physical System) has emerged. In this paper, by reflecting the characteristics of industrial CPS, we propose a packet diversity-based anomaly detection model which we can learn and conduct...
In machine learning, an information-theory optimal way to filter the best input features, without reference to any specific machine learning models, consists of maximizing the mutual information between the selected features and the model output, a choice which will minimize the uncertainty in the output to be predicted, given the feature values.
Forum has become one of the main platforms for people to express their personal point of view, with a lot of information surging in the forum everyday. How to detect automatically a forum topic among the massive information becomes an important and hard task. Though there are plenty of studies for topic detection, it is still a challenge to make it fast and accurately. This paper introduces the principle...
In order to realize a stable and effective gearbox fault diagnosis system, an uncertain information fusion method based on BP neural network and modified DS evidence theory is proposed. The common gearbox fault diagnosis system consists of two dominating parts: signal feature extraction and fusion decision. Actually, the main task of most papers is to extract the characteristics of gearbox fault with...
Wireless Physical Layer Identification (WPLI) system aims at identifying or classifying authorized devices based on the unique Radio Frequency Fingerprints (RFFs) extracted from their radio frequency signals at the physical layer. Current works of WPLI focus on demonstrating system feasibility based on experimental error performance of WPLI with a fixed number of users. While an important question...
This paper presents a work about palm print recognition using fuzzy entropy. A number of schemes have been proposed to combine the fuzzy set theory and its application to the entropy concept for modelling a palm print recognition system. The measure of uncertainty is adopted as a measure of information. Hence, the measures of fuzziness are known as fuzzy information measures. The measure of information...
In this paper the Authors present a new approach for image contrast enhancement based on fuzzy geometrical procedure in which statistics and fuzzy entropy work for getting the purpose and translating the problem as distances among points in fuzzy hyper-cubes. Interesting results have been carried out and they can be considered encouraging after comparison with established techniques.
In [1] we developed a new uncertainty measure which incorporates Rényi entropy instead of Shannon entropy. This new uncertainty measure was conjectured to be invariant to the Rényi order α > 0 for the case of the optimizer signals of Hirschman Uncertainty (Picket Fence functions whose lengths are a perfect square). In this paper, we prove this invariance, and test whether this invariance is predictive...
The description of information content in images is imprecise in nature. Quantification of uncertainty in images for pattern analysis has been addressed with the theories of probability and fuzzy sets. In this paper, an approach for modeling the spatial uncertainty of images is proposed in the setting of geostatistics and probability measure of fuzzy events. The proposed approach can be utilized to...
The mitochondrion is a membrane-bound organelle found in most eukaryotic cells. Mitochondria are considered as the powerhouse of the cell because they function as the platform for generating the production of chemical energy. The visual information of mitochondria revealed by the recent advanced technology in nanoimaging opens doors to life-science researchers to gain insights into its spatial structure...
Fusing the image information obtained by different sensors could make full use of all sensor information. D-S evidence theory is popular in fusion field. Aim to multi-sensor target detecting, we give the algorithm of mass function on D-S evidence theory, using the combination rule to combinate the three evidences of local variance offset, local variance contrast and local entropy of infrared and visible...
In the active learning challenge, we aim to improve the area under the learning curve (ALC), the global score in the challenge, by optimizing the classification methods and feature selection methods, and most importantly by refining the querying algorithm to select the most informative instances in the early iteration of active learning. For six different datasets in the development phase, we applied...
This paper presents an algorithm for Interactive Co-segmentation of a foreground object from a group of related images. While previous approaches focus on unsupervised co-segmentation, we use successful ideas from the interactive object-cutout literature. We develop an algorithm that allows users to decide what foreground is, and then guide the output of the co-segmentation algorithm towards it via...
In this paper we present a topological map building algorithm based on a Vocabulary Tree that is robust to features present in dynamic or similar environments. The algorithm recognises incorrect loop closures, not supported by the odometry, and uses this information to update the feature weights in the tree to suppress further associations from these features. Two methods of adjusting these feature...
Network security has become a major concern in recent years. In this research, we present an entropy-based network traffic profiling scheme for detecting security attacks. The proposed scheme consists of two stages. The purpose of the first stage is to systematically construct the probability distribution of relative uncertainty for normal network traffic behavior. In the second stage, we use the...
This paper presents two new approaches that enable the use of linear landmarks for planning paths with uncertainty in position in outdoor environments. The first approach uses a combination of forward simulation and entropy to reduce the dimensionality of the search space, while still preserving most of the information required to propagate a full covariance matrix. The second approach adds incremental...
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