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In many cases, the information spread in an online network may not always be truthful or correct; such information corresponds to rumors. In recent years, signed networks have become increasingly popular because of their ability to represent diverse relationships such as friends, enemies, trust, and distrust. Signed networks are ideal for information flow in a network with varying beliefs (trust or...
Conflagration is one of the major disasters that threatens human life and property. If the proper action is not taken in detecting the symptom of conflagration events ahead of time, the number of such disasters will keep increasing. An effective solution in this context will alleviate many fire-related global problems to a great extent. Although fire detectors are not available in many places, WiFi...
The process of mining includes various methodologies and data classification is one of the advantageous methods involved in it. It not only eases the process of machine learning but also gives a platform for proper functioning of the process. There are cases wherein the data which is important or unidentified is missed during the process of classification. The process of mining is highly affected...
Occlusion handling is one of the most challenging issues for pedestrian detection, and no satisfactory achievement has been found in this issue yet. Using human body parts has been considered as a reasonable way to overcome such an issue. In this paper, we propose a brand new approach based on the fusion of Mid-level body part mining and Convolutional Neural Network (CNN) to solve this problem, named...
This paper proposes a modified selective search method that generates object proposals on RGB-D data in indoor scenes. The proposed method first applies color flattening to generate monotonous color variations in RGB image data. Then, from the color-flattened image and depth map data, cost function-based segment grouping and depth segmentation are applied to produce desirable segmentation results...
Run-time malware detection strategies have already attracted extensive attention due to its effectiveness and robustness. With regards to Windows operating system, the API (Application Program Interface) is chosen to analyze program behavior. However, the API call sequences can be manipulated by a crafty attacker to circumvent detection. In order to handle this problem, we present a novel run-time...
Scene text information extraction plays an important role in many computer vision applications. Unlike most existing text extraction algorithms for English texts, in this paper, we focus on Chinese texts, which are more complex in stroke and structure. To tackle this challenging problem, we propose a novel convolutional neural network (CNN) based text structure feature extractor for Chinese texts...
Proper feature selection for unsupervised outlier detection can improve detection performance but is very challenging due to complex feature interactions, the mixture of relevant features with noisy/redundant features in imbalanced data, and the unavailability of class labels. Little work has been done on this challenge. This paper proposes a novel Coupled Unsupervised Feature Selection framework...
One of the problems regarding graphics for blind individual is that we have few data on how he (she) acquires graphical information through touching. In this article, we propose a method of tracking the touch point of his (her) fingertip on graphics material using cameras (an analog of eye-tracking) for analyzing the image acquisition process. We exhibit the method on an edge-outlined tactile picture...
Identifying and detecting the unknown abnormal sparse signal has become an important issue for distributed networks. In this paper, we proposed a new detection scheme based on convex optimization for wireless sensor networks. Under the Neyman-Pearson testing framework, the detection scheme first estimates the unknown signal by employing the convex optimization at the fusion center. Then the sensor...
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...
Although security starts to be taken into account during software development, the tendency for source code to contain vulnerabilities persists. Open source static analysis tools provide a sensible approach to mitigate this problem. However, these tools are programmed to detect a specific set of vulnerabilities and they are often difficult to extend to detect new ones. WAP is a recent popular open...
To find opportunities for applying prefactoring, several techniques for detecting bad smells in source code have been proposed. Existing smell detectors are often unsuitable for developers who have a specific context because these detectors do not consider their current context and output the results that are mixed with both smells that are and are not related to such context. Consequently, the developers...
Even today, a large number of organizations collect data using paper forms. However, it can be difficult to aggregate, and analyze the data collected using paper forms. Better management and processing of forms and applications is indispensable to improving customer experience. But, typing the form data into a spreadsheet is time-consuming, mundane and may result in errors. Various attempts have been...
Data hiding is now becoming most important research area now a day. One efficient binary data hiding technique into a digital image has been proposed in this study. The proposed work focuses on the learning of different edge detection and data hiding techniques and analysis of their relative performances. Work focuses on finding the boundary edges of size of the length of the data to be hided. Sobel,...
Nowadays, people purchase a lot of products from online shopping sites. To support customers in decision making, some sites collect and provide user reviews on products. However, contents of the user reviews are too abundant for customers to analyze them in a short period of time. The automatic analysis of reviews is important to provide users with valuable information about goods of any category...
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...
An automatic calibration process for the LabPET II detector front-end module has been developed. By aiming at sub-millimetric spatial resolution, an unprecedented channel density is reached. The new detector front-end module is based on an application-specific integrated circuit (ASIC) implementing a Time-over-Threshold (ToT) scheme to extract both energy and time information. Consequently manual...
Mining massive data streams in real-time is one of the contemporary challenges for machine learning systems. Such a domain encompass many of difficulties hidden beneath the term of Big Data. We deal with massive, incoming information that must be processed on-the-fly, with lowest possible response delay. We are forced to take into account time, memory and quality constraints. Our models must be able...
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