scholarly journals Tumor Detection in Brain MRIs by Computing Dissimilarities in the Latent Space of a Variational AutoEncoder

2020 ◽  
Vol 1 ◽  
pp. 6
Author(s):  
Alexandra Albu ◽  
Alina Enescu ◽  
Luigi Malagò
Keyword(s):  
Detection Method ◽  
Brain Mri ◽  
The Other ◽  
Detection Methods ◽  
Healthy Individuals ◽  
Latent Space ◽  
Variational Autoencoder ◽  
Mri Scans ◽  
Unsupervised Anomaly Detection ◽  
Aided Diagnosis

The ability to automatically detect anomalies in brain MRI scans is of great importance in computer-aided diagnosis. Unsupervised anomaly detection methods work primarily by learning the distribution of healthy images and identifying abnormal tissues as outliers. We propose a slice-wise detection method which first trains a pair of autoencoders on two different datasets, one with healthy individuals and the other one with images of normal and tumoral tissues. Next, it classifies slices based on the distance in the latent space between the enconding of the image and the encoding of the reconstructed image, obtained through the autoencoder trained on healthy images only. We validate our approach with a series of preliminary experiments on the HCP and BRATS-15 datasets.

scholarly journals egoDetect: Visual Detection and Exploration of Anomaly in Social Communication Network

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5895
Author(s):  
Jiansu Pu ◽  
Jingwen Zhang ◽  
Hui Shao ◽  
Tingting Zhang ◽  
Yunbo Rao
Keyword(s):  
Social Networks ◽  
Anomaly Detection ◽  
Communication Networks ◽  
Social Communication ◽  
Detection Method ◽  
Detection Methods ◽  
Visualization System ◽  
Egocentric Network ◽  
Unsupervised Anomaly Detection ◽  
The Relationship

The development of the Internet has made social communication increasingly important for maintaining relationships between people. However, advertising and fraud are also growing incredibly fast and seriously affect our daily life, e.g., leading to money and time losses, trash information, and privacy problems. Therefore, it is very important to detect anomalies in social networks. However, existing anomaly detection methods cannot guarantee the correct rate. Besides, due to the lack of labeled data, we also cannot use the detection results directly. In other words, we still need human analysts in the loop to provide enough judgment for decision making. To help experts analyze and explore the results of anomaly detection in social networks more objectively and effectively, we propose a novel visualization system, egoDetect, which can detect the anomalies in social communication networks efficiently. Based on the unsupervised anomaly detection method, the system can detect the anomaly without training and get the overview quickly. Then we explore an ego’s topology and the relationship between egos and alters by designing a novel glyph based on the egocentric network. Besides, it also provides rich interactions for experts to quickly navigate to the interested users for further exploration. We use an actual call dataset provided by an operator to evaluate our system. The result proves that our proposed system is effective in the anomaly detection of social networks.

ACCUMULATIVE INTERSECTION SPACE BASED CORNER DETECTION ALGORITHM

2008 ◽  
Vol 22 (08) ◽  
pp. 1559-1586 ◽  
Author(s):  
NA LU ◽  
ZUREN FENG
Keyword(s):  
Monte Carlo ◽  
Detection Method ◽  
Detection Algorithm ◽  
Search Method ◽  
The Other ◽  
Corner Detection ◽  
Detection Methods ◽  
Local Maxima ◽  
Edge Detectors ◽  
Theory And Experiments

There is no parametric formulation of corner, so the conventional Hough transform cannot be employed to detect corners directly. A random corner detection method is developed in this paper based on a new concept "accumulative intersection space" under Monte Carlo scheme. This method transforms the corner detection in the image space into local maxima localization in the accumulative intersection space where the intersections are accumulated by random computations. The proposed algorithm has been demonstrated by both theory and experiments. The proposed algorithm is isotropic, robust to image rotation, insensitive to noise and false corners on diagonal edges. Unlike the other existing contour based corner detection methods, our algorithm can effectively avoid the influence of the edge detectors, such as rounding corners or line interceptions. Extensive comparisons among our approach and the other detectors including Harris operator, Fei Shen and Han Wang detector, Han Wang and Brady detector, Foveated Visual Search method and SIFT feature, have shown the effectiveness of our method.

Spam detection using semantic web in mail services

2018 ◽  
Vol 7 (2.7) ◽  
pp. 44
Author(s):  
S Bhavika ◽  
B Prema Sindhuri ◽  
G Bhavana
Keyword(s):  
Semantic Web ◽  
Detection Method ◽  
The Other ◽  
Detection Methods ◽  
Spam Detection ◽  
Daily Lives ◽  
Do So

Electronic mails have become a part of our daily lives to exchange different type of information and messages. They provide a great medium to communicate with large number of people in a single stretch. This made so many marketing groups to think that email is a great platform for publicizing their goods or products. Not only are these marketers there so many other types of users who wants to make use of these emails for their own needs. As the time prolongs, this had become a problem for the other users because of the continuous undesired electronic messages sent by different marketing and some other unauthorized users. These messages are termed as spam messages. These spam mails have become a serious issue and there is a need to clear away all these junk mails. To do so different spam detection methodologies are developed and employed for providing an effective mailing service to the users. In this paper, we present various spam detection methods that are existing and also finding the accurate, effective and reliable spam detection method.

scholarly journals Line-Detection Based on the Sum of Gradient Angle Differences

2019 ◽  
Vol 10 (1) ◽  
pp. 254 ◽  
Author(s):  
Suyoung Seo
Keyword(s):  
Root Mean Square Error ◽  
Detection Method ◽  
The Other ◽  
Natural Images ◽  
Detection Methods ◽  
Line Detection ◽  
Mean Square ◽  
Simulation Experiments ◽  
Line Model ◽  
Second Derivatives

This paper presents a method to detect line pixels based on the sum of gradient angle differences (SGAD). The gradient angle differences are calculated by comparing the four pairs of gradients arising from eight neighboring pixels. In addition, a method to classify line pixels into ridges and valleys is proposed. Furthermore, a simple line model is defined for simulation experiments. Experiments are conducted with simulation images generated using the simple line model for three line-detection methods: second-derivatives (SD)-based method, extremity-count (EC)-based method, and proposed method. The results of the simulation experiments show that the proposed method produces more accurate line-detection results than the other methods in terms of the root mean square error when the line width is relatively large. In addition, the experiments conducted with natural images show that the SD- and EC-based methods suffer from bifurcation, fragmentation, and missing pixels. By contrast, for the original and the noise-contaminated versions of the natural images, the proposed SGAD-based line-detection method is affected by such problems to a considerably smaller extent than the other two methods.

scholarly journals Semi-supervised Adversarial Variational Autoencoder

2020 ◽  
Author(s):  
Ryad Zemouri
Keyword(s):  
Image Processing ◽  
Learning Process ◽  
Original Data ◽  
Experimental Results ◽  
The Other ◽  
Training Process ◽  
Adversarial Learning ◽  
Latent Space ◽  
Variational Autoencoder ◽  
Label Information

We present a method to improve the reconstruction and generation performance of variational autoencoder (VAE) by injecting an adversarial learning. On the other hand, instead of comparing the reconstructed with the original data to calculate the reconstruction loss, we use a consistency principle for deep features. The training process of the VAE is then divided into two steps, training the encoder and then training the decoder. By using this two-step learning process, our method can be more widely used in applications other than image processing. While training the encoder, the label information is integrated to better structure the latent space in a supervised way. The adversarial constraints allow the decoder to generate data with better authenticity and more realistic than the conventional VAE. We present experimental results to show that our method gives better performance than the original VAE.

scholarly journals Image Generation using Variational Autoencoders

2020 ◽  
Vol 9 (5) ◽  
pp. 517-520
Keyword(s):  
Probability Distribution ◽  
Multivariate Normal Distribution ◽  
Input Image ◽  
The Other ◽  
Image Generation ◽  
Multivariate Normal ◽  
High Quality ◽  
Latent Space ◽  
Variational Autoencoder ◽  
Primary Advantage

The proposed system generates new images from the existing images using variational autoencoders. The autoencoder aims to map the input image to a multivariate normal distribution in the latent space. Variational autoencoder transforms input image into a remarkable output by reducing the reconstruction and KL divergence losses. The primary advantage of implementing variational autoencoder over the other autoencoders is that it follows a specific probability distribution called Gaussian distribution and results in generating high quality images.

The Dark Tetrad

2015 ◽  
Vol 36 (4) ◽  
pp. 228-236 ◽  
Author(s):  
Janko Međedović ◽  
Boban Petrović
Keyword(s):  
Personality Traits ◽  
Antisocial Behavior ◽  
Second Order ◽  
The Other ◽  
Negative Pole ◽  
Latent Space ◽  
Order Factor

Abstract. Machiavellianism, narcissism, and psychopathy are personality traits understood to be dispositions toward amoral and antisocial behavior. Recent research has suggested that sadism should also be added to this set of traits. In the present study, we tested a hypothesis proposing that these four traits are expressions of one superordinate construct: The Dark Tetrad. Exploration of the latent space of four “dark” traits suggested that the singular second-order factor which represents the Dark Tetrad can be extracted. Analysis has shown that Dark Tetrad traits can be located in the space of basic personality traits, especially on the negative pole of the Honesty-Humility, Agreeableness, Conscientiousness, and Emotionality dimensions. We conclude that sadism behaves in a similar manner as the other dark traits, but it cannot be reduced to them. The results support the concept of “Dark Tetrad.”

scholarly journals PRATD: A Phased Remote Access Trojan Detection Method with Double-Sided Features

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1894
Author(s):  
Chun Guo ◽  
Zihua Song ◽  
Yuan Ping ◽  
Guowei Shen ◽  
Yuhei Cui ◽  
...  
Keyword(s):  
False Positive ◽  
Detection Method ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Remote Access ◽  
Detection Methods ◽  
Security Threats ◽  
True Positive ◽  
Trojan Detection ◽  
Positive Rate

Remote Access Trojan (RAT) is one of the most terrible security threats that organizations face today. At present, two major RAT detection methods are host-based and network-based detection methods. To complement one another’s strengths, this article proposes a phased RATs detection method by combining double-side features (PRATD). In PRATD, both host-side and network-side features are combined to build detection models, which is conducive to distinguishing the RATs from benign programs because that the RATs not only generate traffic on the network but also leave traces on the host at run time. Besides, PRATD trains two different detection models for the two runtime states of RATs for improving the True Positive Rate (TPR). The experiments on the network and host records collected from five kinds of benign programs and 20 famous RATs show that PRATD can effectively detect RATs, it can achieve a TPR as high as 93.609% with a False Positive Rate (FPR) as low as 0.407% for the known RATs, a TPR 81.928% and FPR 0.185% for the unknown RATs, which suggests it is a competitive candidate for RAT detection.

scholarly journals Detection of Movement Events of Long-Track Speed Skating Using Wearable Inertial Sensors

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3649
Author(s):  
Yosuke Tomita ◽  
Tomoki Iizuka ◽  
Koichi Irisawa ◽  
Shigeyuki Imura
Keyword(s):  
Gold Standard ◽  
Detection Method ◽  
Standard Measure ◽  
Detection Methods ◽  
Phase Identification ◽  
Foot Pressure ◽  
Pressure System ◽  
Speed Skating ◽  
Stance Time ◽  
Integrated Detection

Inertial measurement units (IMUs) have been used increasingly to characterize long-track speed skating. We aimed to estimate the accuracy of IMUs for use in phase identification of long-track speed skating. Twelve healthy competitive athletes on a university long-track speed skating team participated in this study. Foot pressure, acceleration and knee joint angle were recorded during a 1000-m speed skating trial using the foot pressure system and IMUs. The foot contact and foot-off timing were identified using three methods (kinetic, acceleration and integrated detection) and the stance time was also calculated. Kinetic detection was used as the gold standard measure. Repeated analysis of variance, intra-class coefficients (ICCs) and Bland-Altman plots were used to estimate the extent of agreement between the detection methods. The stance time computed using the acceleration and integrated detection methods did not differ by more than 3.6% from the gold standard measure. The ICCs ranged between 0.657 and 0.927 for the acceleration detection method and 0.700 and 0.948 for the integrated detection method. The limits of agreement were between 90.1% and 96.1% for the average stance time. Phase identification using acceleration and integrated detection methods is valid for evaluating the kinematic characteristics during long-track speed skating.

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