Sharif Digital Repository

One of the most important obligations of privacy-enhancing technologies is to bring confidentiality and privacy to users' browsing activities on the Internet. The website fingerprinting attack enables a local passive eavesdropper to predict the target user's browsing activities even she uses anonymous technologies, such as VPNs, IPsec, and Tor. Recently, the growth of deep learning empowers adversaries to conduct the website fingerprinting attack with higher accuracy. In this paper, we propose a new defense against website fingerprinting attack using adversarial deep learning approaches called Adversarial Website Adaptation (AWA). AWA creates a transformer set in each run so that each... 

This paper introduces an incremental local learning algorithm inspired by learning in neurobiological systems. This algorithm has no training phase and learns the world during operation, in a lifetime manner. It is a semi-supervised algorithm which combines soft competitive learning in input space and linear regression with recursive update in output space. This method is also robust to negative interference and compromises bias-variance dilemma. These qualities make the learning method a good nonlinear function approximator having possible applications in neuro-robotic systems. Some simulations illustrate the effectiveness of the proposed algorithm in function approximation, time-series... 

Steady State Visual Evoked Potential (SSVEP) based Brain Computer Interfaces (BCI) can be great assistance for people suffering from physical disabilities due to their high accuracy, high speed, an acceptable number of possible targets, etc. Many researchers have managed to design such systems. Most of these BCIs utilize methods for frequency detection which cause the system to need a training phase for each new user, making the system a user-based one. That is why our goal was to design a BCI that not only has accuracy and speed comparable to similar systems, but also does not need any training phase and thus can be used by new users right away. Our final design got a mean accuracy of... 

In this paper, we introduce a novel method for human activity recognition that benefits from the structure and sequential properties of the test data as well as the training data. In the training phase, we obtain a fraction of data labels at constant time intervals and use them in a semi-supervised graph-based method for recognizing the user's activities. We use label propagation on a k-nearest neighbor graph to calculate the probability of association of the unlabeled data to each class in this phase. Then we use these probabilities to train an HMM in a way that each of its hidden states corresponds to one class of activity. These probabilities are used to learn the transition probabilities... 

Power and bandwidth constraints are two major challenges in wireless sensor networks. Since a considerable amount of energy in sensor networks is consumed for data transmission, compression techniques may prolong the life of such networks. Moreover, with fewer bits to transmit, the network can cope better with the problem of inadequate bandwidth. In this paper, we consider an image sensor network and propose a paradigm based on the principles of Distributed Source Coding (DSC) for efficient compression. Our method relies on high correlation between the sensor nodes. The algorithm consists of two phases: the Training Phase and the Main Phase. In the Training Phase an aggregation node or a... 

A combined texture- and color-based skin detection is proposed in this paper. Nonsubsampled contourlet transform is used to represent texture of the whole image. Local neighbor contourlet coefficients of a pixel are used as feature vectors to classify each pixel. Dimensionality reduction is addressed through principal component analysis (PCA) to remedy the curse of dimensionality in the training phase. Before texture classification, the pixel is tested to determine whether it is skin-colored. Therefore, the classifier is learned to discriminate skin and non-skin texture for skin colored regions. A multi-layer perceptron is then trained using the feature vectors in the PCA reduced space. The...