The Effects of JPEG and JPEG2000 Compression on Attacks using Adversarial Examples

Adversarial examples are known to have a negative effect on the performance of classifiers which have otherwise good performance on undisturbed images. These examples are generated by adding non-random noise to the testing samples in order to make classifier misclassify the given data. Adversarial attacks use these intentionally generated examples and they pose a security risk to the machine learning based systems. To be immune to such attacks, it is desirable to have a pre-processing mechanism which removes these effects causing misclassification while keeping the content of the image. JPEG and JPEG2000 are well-known image compression techniques which suppress the high-frequency content taking the human visual system into account. JPEG has been also shown to be an effective method for reducing adversarial noise. In this paper, we propose applying JPEG2000 compression as an alternative and systematically compare the classification performance of adversarial images compressed using JPEG and JPEG2000 at different target PSNR values and maximum compression levels. Our experiments show that JPEG2000 is more effective in reducing adversarial noise as it allows higher compression rates with less distortion and it does not introduce blocking artifacts

Configuration of neural networks to model seasonal and cyclic time series.

Time series often exhibit periodical patterns that can be analysed by conventional statistical techniques. These techniques rely upon an appropriate choice of model parameters that are often difficult to determine. Whilst neural networks also require an appropriate parameter configuration, they offer a way in which non-linear patterns may be modelled. However, evidence from a limited number of experiments has been used to argue that periodical patterns cannot be modelled using such networks. Researchers have argued that combining models for forecasting gives better estimates than single time series models particularly for seasonal and cyclic series. For example, a hybrid architecture comprising an autoregressive integrated moving average model (ARIMA) and a neural network is a well-known technique that has recently been shown to give better forecasts by taking advantage of each model's capabilities. However, this assumption carries the danger of underestimating the relationship between the model's linear and non-linear components, particularly by assuming that individual forecasting techniques are appropriate, say, for modelling the residuals. In this thesis, we show that such combinations do not necessarily outperform individual forecasts. On the contrary, we show that the combined forecast can underperform significantly compared to its constituents'. We also present a method to overcome the perceived limitations of neural networks by determining the configuration parameters of a time delayed neural network from the seasonal data it is being used to model. The motivation of our method is that Occam's razor should guide us in selecting a simpler solution compared to a complex solution. Our method uses a fast Fourier transform to calculate the number of input tapped delays, with results demonstrating improved performance as compared to that of other linear and hybrid seasonal modelling techniques on twelve benchmark time series. Keywords: neural networks, time series, cycles, ARIMA-NN hybrids, Fourier, TDNN

An unsupervised method for anomaly detection from crowd videos

Anomaly detection from crowd videos is an issue that is becoming more important due to the difficulties in maintaining the public security in crowded places. Surveillance videos has a significant role for enabling the real time analysis of the captured events occurring in crowded places. This paper presents a method that detects anomalies in crowd in real-time using computer vision and machine learning techniques. The proposed method consists of extracting the crowd behavior properties (velocity, direction) by tracking scale invariant feature transform (SIFT) feature points and fitting the extracted behavior properties into a Gaussian Model. In this paper, only the global anomalies which occur on the overall video frame are handled. According to the test results, the method gives comparable results with the state-of-art methods and also can run in real-time. In addition, it is less complex than the compared state-of-art methods and works unsupervised

A Comparative Study of Autoregressive Neural Network Hybrids

Many researchers have argued that combining many models for forecasting gives better estimates than single time series models. For example, a hybrid architecture comprising an autoregressive integrated moving average model (ARIMA) and a neural network is a well-known technique that has recently been shown to give better forecasts by taking advantage of each model's capabilities. However, this assumption carries the danger of underestimating the relationship between the model's linear and non-linear components, particularly by assuming that individual forecasting techniques are appropriate, say, for modeling the residuals. In this paper, we show that such combinations do not necessarily outperform individual forecasts. On the contrary, we show that the combined forecast can underperform significantly compared to its constituents' performances. We demonstrate this using nine data sets, autoregressive linear and time-delay neural network models

The Impact of Motivation and Personality on Academic Performance in Online and Blended Learning Environments

This study investigates the impact of students' motivation and personality traits on their academic performance in online and blended learning environments. It was conducted with students attending a mandatory introductory information technology course given in a university in Turkey. The Big Five Inventory and Motivated Strategies for Learning Questionnaire were completed by a total of 316 students. A learning management system (LMS) was used for online collaboration and accessing course materials. At the end of the course, information on the accessibility of LMS and students' academic performance including their exam results was obtained. The Bayesian Structural Equation Modeling was used to examine academic performance in terms of its relationship with motivation and personality. In the online learning environment, the results showed that the conscientiousness trait was significantly related to LMS use whereas in blended learning, there were no significant relations between personality traits and LMS use. Self-efficacy was found to be the predictor of LMS use in the online environment while task value and test anxiety were the predictors in the blended learning environment. Conscientiousness and LMS use were significantly related to course grades in both learning environments. Finally, self-efficacy for learning performance was also associated with course grades in the online learning environment

Feature Detection and Tracking for Extraction of Crowd Dynamics

Extraction of crowd dynamics from video is the fundamental step for automatic detection of abnormal events. However, it is difficult to obtain sufficient performance with object tracking due to occlusions and insufficient resolution of the objects in the scene. As a result, optical flow or feature tracking methods are preferred in crowd videos. These applications also require algorithms to work in real-time. In this work, we investigated the applicability and performance of feature detection and tracking algorithms in crowd videos. The algorithms that were tested in this paper include Scale Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF) as well as relatively newer approaches Binary Robust Independent Elementary Features (BRIEF) and Oriented Fast and Rotated Brief (ORB). These algorithms have been tested with videos having different crowd densities and comparative results of their accuracy and computational performance have been reported. The results show that BRIEF is computationally faster than the others, allowing real-time operation, and comparable with other algorithms regarding matching accuracy

On Inference of Sense of Place from Geo-Social Networks

The aim of the study is to investigate whether individuals report the places they are attached to in location-based services, and whether there is a relationship between the attachment scores of these places and their corresponding check-in frequency information. A survey is conducted to measure the degree of place attachment of individuals based on self-reported locations. Then their Foursquare log data is collected which includes their check-in and venue information. Our results show that the majority of the participants check in to locations that they are attached to. Attachment score is shown to be related to the check-in frequency. The tips left for venues include terms and phrases, suggesting place attachment

Density aware anomaly detection in crowded scenes

Coherent nature of crowd movement allows representing the crowd motion using sparse features. However, surveillance videos recorded at different periods of time are likely to have different crowd densities and motion characteristics. These varying scene properties necessitate use of different models for an effective representation of behaviour at different periods. In this study, a density aware approach is proposed to detect motion-based anomalies for scenes having varying crowd densities. In the training, the sparse features are modelled using separate hidden Markov models, each of which becomes an expert for specific scene characteristics. These models are then used for anomaly detection. The proposed method automatically adapts to the changing scene dynamics by switching to the most representative model at each frame. The authors demonstrate the effectiveness and real-time performance of the proposed method on real-life datasets as well as on simulated crowd videos that they generated and made publicly available to download

Real-time multi-camera video analytics system on GPU

In this article, parallel implementation of a real-time intelligent video surveillance system on Graphics Processing Unit (GPU) is described. The system is based on background subtraction and composed of motion detection, camera sabotage detection (moved camera, out-of-focus camera and covered camera detection), abandoned object detection, and object-tracking algorithms. As the algorithms have different characteristics, their GPU implementations have different speed-up rates. Test results show that when all the algorithms run concurrently, parallelization in GPU makes the system up to 21.88 times faster than the central processing unit counterpart, enabling real-time analysis of higher number of cameras

UBDroid: Kullanici davranis analizi için akilli telefon uygulamalari kullanim izleme arac

Son yıllarda mobil teknolojiler günlük rutin işleri gerçekleştirmek üzere sıkça kullanılmaya başlanmıştır. Mobil teknolojilerin bu denli hızla yaygınlaşması ise birçok sektör açısından ve akademik çalışmalar için kullanıcı verisi elde etmek üzere güvenilir bir kaynak haline gelmiştir. Mobil telefonlardan elde edilen veriler ile kullanıcıların alışkanlıklarını belirlemek, uygun anlarını bulmak, kişiye özgü tavsiyelerde bulunmak mümkündür. Bu çalışmada da kullanıcı davranış analizi yapabilmek üzere Android işletim sistemi üzerinde çalışan UBDroid adında bir mobil uygulama geliştirilmiştir. Uygulama, mobil telefonda bulunan uygulamaların başlatılma zamanı ve uygulamaların çalışma süresi ile algılayıcı (sensör) verilerini kaydeder; kişilerin Google Takvim bilgilerini anonim (gizli) olarak kullanarak, kişilerin hangi zaman dilimlerinde boş, hangi zaman dilimlerinde müsait olduğu bilgisinin çıkarılması için veri toplar. Sensör verisi olarak; konum, aktivite, telefon ses modu ve kablosuz ağ bilgileri kullanılmıştır. Mobil cihaz üzerinde toplanan veriler, daha sonra uzak sunucuya belirli aralıklarla aktarılmaktadır. Sunucu uygulaması da Google Play’den mobil cihazlarda kullanılan uygulama puan ve kategori gibi bilgileri edinmekte ve bu toplanan veriyi işlemektedir. Çalışmada UBDroid uygulamasının nasıl geliştirildiği ve performans değerlendirilmesi sunulmaktadır. Yapılan testler sonucunda, oluşturulan sistemin (UBDroid) kullanıcı davranışı analizi için kullanım bilgisi toplayan enerji verimli bir sistem olduğu görülmüştür