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Botnet is a malicious software that can perform malicious activities, such as (Distributed Denial of Services) DDoS, spamming, phishing, key logging, click fraud, steal personal information and important data, etc. Botnets can replicate themselves without user consent. Several systems of botnet detection have been done by using a machine learning method with feature selection approach. Currently,...
The recent, remarkable growth of machine learning has led to intense interest in the privacy of the data on which machine learning relies, and to new techniques for preserving privacy. However, older ideas about privacy may well remain valid and useful. This note reviews two recent works on privacy in the light of the wisdom of some of the early literature, in particular the principles distilled by...
A variety of applications (App) installed on mobile systems such as smartphones enrich our lives, but make it more difficult to the system management. For example, finding the specific Apps becomes more inconvenient due to more Apps installed on smartphones, and App response time could become longer because of the gap between more, larger Apps and limited memory capacity. Recent work has proposed...
Overhead depth map measurements capture sufficient amount of information to enable human experts to track pedestrians accurately. However, fully automating this process using image analysis algorithms can be challenging. Even though hand-crafted image analysis algorithms are successful in many common cases, they fail frequently when there are complex interactions of multiple objects in the image....
This study is motivated by the problem of evaluating reliable false alarm (FA) rates for sinusoid detection tests applied to unevenly sampled time series involving colored noise, when a (small) training data set of this noise is available. While analytical expressions for the FA rate are out of reach in this situation, we show that it is possible to combine specific periodogram standardization and...
Color is one of the attributes that play a role in identifying specific objects, color processing including the extraction of information about the spectral properties of the object's surface and look for the best similarity of a set of descriptions which have been known to do an introduction. Therefore, the classification is needed right fuji apples to obtain good quality fruit. Fuzzy model is one...
Distribution of bleach and chlorine rice makes some people uneasy because it is dangerous to health. The author conducted a simulation for implemantation the method of Principal Component Analysis to detect bleach and chlorine on rice with Matlab R2013a. This study focuses on the shape, texture, color, and position Pandanwangi, Rojolele, and Setra Ramos Rice intact. The result is an Euclidean value...
A large number of text data are regularly published in social networks and the media. Processing and analysis of such information is an highly required direction. This paper focuses on the way to use the entropy measure when dealing with big volumes of text data in classification. The used entropy measure stands for algorithm quality criteria when defining a class in a set of data. The work also features...
At present, machine learning is widely used for classification, such as automatic speech recognition, image identification, text classification and numbers of researches for fault diagnosis besides. Generally, most of the models used for fault diagnosis are based on the same data distribution, while the applications of the equipment in actual production and operation are mostly under unstable conditions,...
Collecting training data is not an easy task especially in situation involving robots that require tremendous physical effort. The ability to augment data through synthetic means is a convenient tool to solve this problem. Therefore it is important to evaluate the extent of the usefulness of augmented data. In this paper, we will explore data augmentation schemes in reverberant environment and investigate...
In recent years, Unmanned Aerial Vehicle (UAV) has been gaining more and more attention for military and civilian utilization. How to monitor its condition is a crucial problem. The accuracy of the sensing data is one of the basic requirements to achieve its correct condition. Thus, one kind of data anomaly detection approaches by Relevance Vector machine (RVM) is proposed in this article. By utilizing...
In this paper, we build a general summarization framework for both of edited video and raw video summarization. Overall, our work can be divided into three folds. 1) Four models are designed to capture the properties of video summaries, i.e., containing important people and objects (importance), representative to the video content (representativeness), no similar key-shots (diversity), and smoothness...
Radio tomographic imaging (RTI) is an emerging technique of device-free localization (DFL). The main challenge of RTI is the multipath interferences in RSS measurements, which could make the links become more unpredictable and finally lead to unsatisfactory DFL performance. For addressing this challenge, this paper presents a novel modeling method based on relevance vector machine (RVM), which can...
To make full use of the data information and improve the classification performance, a new evidential neural network classifier is proposed and a novel implementation of multiple classifier systems based on the new evidential neural network classifier is presented in this paper. The ambiguous data contained in the training data is considered as a new class — compound class and the training data is...
In this paper, we address the problem of estimating the total flow of a crowd of pedestrians from spatially limited observations. Our approach relies on identifying a dynamical system regime that characterizes the observed flow in a limited spatial domain by solving for the modes and eigenvalues of the corresponding Koopman operator. We develop a framework where we first approximate the Koopman operator...
We consider the problem of data augmentation, i.e., generating artificial samples to extend a given corpus of training data. Specifically, we propose attributed-guided augmentation (AGA) which learns a mapping that allows to synthesize data such that an attribute of a synthesized sample is at a desired value or strength. This is particularly interesting in situations where little data with no attribute...
In this paper we introduce a model of lifelong learning, based on a Network of Experts. New tasks / experts are learned and added to the model sequentially, building on what was learned before. To ensure scalability of this process, data from previous tasks cannot be stored and hence is not available when learning a new task. A critical issue in such context, not addressed in the literature so far,...
In many real-world scenarios, labeled data for a specific machine learning task is costly to obtain. Semi-supervised training methods make use of abundantly available unlabeled data and a smaller number of labeled examples. We propose a new framework for semi-supervised training of deep neural networks inspired by learning in humans. Associations are made from embeddings of labeled samples to those...
We propose a novel data-driven approach for automatically detecting and completing gaps in line drawings with a Convolutional Neural Network. In the case of existing inpainting approaches for natural images, masks indicating the missing regions are generally required as input. Here, we show that line drawings have enough structures that can be learned by the CNN to allow automatic detection and completion...
In this work, we present a monitoring system for Self-Organizing Industrial Systems (SOIS). It is based on an anomaly detection approach which evaluates the movement of objects within a factory by putting them together from sub-trajectories. By introducing two metrics — relative user frequency and pathlet occurence per user — the existing method is extended so that not only anomalous trajectories...
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