Natural outdoor conditions pose unique obstacles for researchers, above and beyond those inherent to all mobile eye-tracking research. During analyses of a large set of eye-tracking data collected on geologists examining outdoor scenes, we have found that the nature of calibration, pupil identification, fixation detection, and gaze analysis all require procedures different from those typically used for indoor studies. Here, we discuss each of these challenges and present solutions, which together define a general method useful for investigations relying on outdoor eye-tracking data. We also discuss recommendations for improving the tools that are available, to further increase the accuracy and utility of outdoor eye-tracking data.

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Collecting and Analyzing Eye-Tracking Data in Outdoor Environments

Off-line recognition of text play a significant role in several application such as the automatic sorting of postal mail or editing old documents. It is the ability of the computer to distinguish characters and words. Automatic off-line recognition of text can be divided into the recognition of printed and handwritten characters. Off-line Arabic handwriting recognition still faces great challenges. This paper provides a survey of Arabic character recognition systems which are classified into the character recognition categories: printed and handwritten. Also, it examines the literature on the most significant work in handwritten text recognition without segmentation and discusses algorithms which split the words into characters.

A Survey on Arabic Character Recognition

Nowadays, we see more and more cyber-attacks on major Internet sites and enterprise networks. Intrusion Detection System (IDS) is a critical component of such infrastructure defense mechanism. IDS monitors and analyzes networks' activities for potential intrusions and security attacks. Machine-learning (ML) models have been well accepted for signature-based IDSs due to their learn ability and flexibility. However, the performance of existing IDSs does not seem to be satisfactory due to the rapid evolution of sophisticated cyber threats in recent decades. Moreover, the volumes of data to be analyzed are beyond the ability of commonly used computer software and hardware tools. They are not only large in scale but fast in/out in terms of velocity. In big data IDS, the one must find an efficient way to reduce the size of data dimensions and volumes. In this paper, we propose novel feature selection methods, namely, RF-FSR (Random Forest-Forward Selection Ranking) and RF-BER (Random Forest-Backward Elimination Ranking). The features selected by the proposed methods were tested and compared with three of the most well-known feature sets in the IDS literature. The experimental results showed that the selected features by the proposed methods effectively improved their detection rate and false-positive rate, achieving 99.8% and 0.001% on well-known KDD-99 dataset, respectively.

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Machine-Learning-Based Feature Selection Techniques for Large-Scale Network Intrusion Detection

MediaQ is a novel online media management system to collect, organize, share, and search mobile multimedia contents using automatically tagged geospatial metadata. User-generated-videos can be uploaded to the MediaQ from users' smartphones, iPhone and Android, and displayed accurately on a map interface according to their automatically sensed geospatial and other metadata. The MediaQ system provides the following distinct features. First, individual frames of videos (or any meaningful video segments) are automatically annotated by objective metadata which capture four dimensions in the real world: the capture time (when), the camera location and viewing direction (where), several key-words (what) and people (who). We term this data W4-metadata and they are obtained by utilizing camera sensors, geospatial and computer vision techniques. Second, a new approach of collecting multimedia data from the public has been implemented using spatial crowdsourcing, which allows media content to be collected in a coordinated manner for a specific purpose. Lastly, flexible video search features are implemented using W4 metadata, such as directional queries for selecting multimedia with a specific viewing direction.This paper is to present the design of a comprehensive mobile multimedia management system, MediaQ, and to share our experience in its implementation. Our extensive real world experimental case studies demonstrate that MediaQ can be an effective and comprehensive solution for various mobile multimedia applications.

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MediaQ: mobile multimedia management system

The growth of internet attacks is a major problem for today's computer networks. Hence, implementing security methods to prevent such attacks is crucial for any computer network. With the help of Machine Learning and Data Mining techniques, Intrusion Detection Systems (IDS) are able to diagnose attacks and system anomalies more effectively. Though, most of the studied methods in this field, including Rule-based expert systems, are not able to successfully identify the attacks which have different patterns from expected ones. By using Artificial Neural Networks (ANNs), it is possible to identify the attacks and classify the data, even when the dataset is nonlinear, limited, or incomplete. In this paper, a method based on the combination of Decision Tree (DT) algorithm and Multi-Layer Perceptron (MLP) ANN is proposed which is able to identify attacks with high accuracy and reliability.

Intrusion detection system based on Multi-Layer Perceptron Neural Networks and Decision Tree

� Abstract— Optical Character Recognition (OCR) is a very old and of great interest in pattern recognition field. In this paper we introduce a very powerful approach to recognize Persian text. We have used morphological operators, especially Hit/Miss operator to descript each sub-word and by using a template matching approach we have tried to classify generated description. We used just one font in two different sizes to verify our approach. We achieved a very good rate, up to 99.9%.

A Persian OCR System using Morphological Operators

The quality of features directly affects the performance of classification. Many feature selection methods introduced to remove redundant and irrelevant features, because raw features may reduce accuracy or robustness of classification. In this paper we proposed a new method for feature selection based on Decision Dependent Correlation (DDC). We have used SVM classifier and the results on DARPA KDD99 benchmark dataset indicate that the proposed method outperforms Principal Component Analysis (PCA).

Comparison of Two Feature Selection Methods in Intrusion Detection Systems

A Persian OCR System Using Morphological Operators.

A video sequence consists of several hundred frames, and as a result, creating a panoramic image from these frames is a very time-consuming process. Consecutive frames have large overlap areas that do not provide much information. Therefore, some key frames must be extracted for better performance. There are a number of methods for key-frame selection that match all frames in a video sequence. We present a novel and efficient method to select key frames from video for creating a large panoramic mosaic without matching all frames. Consecutive frames are transformed and projected onto the common mosaic surface and the position of each corner of the next frame is predicted with a distinct Kalman filter on this surface. The overlap area between each predicted frame and its previous key frame is used as the criterion to select the next key frame. This method uses video information to reduce features and align frames with repeated structures more accurately. We show that this approach is an efficient preprocessing step and substantially reduces the time required to construct panorama from a video sequence.

Efficient key frames selection for panorama generation from video

A video sequence consists of several hundred frames and as a result creating a panoramic image from these frames is a very time consuming process. Consecutive frames have large overlap areas which do not provide much information. Therefore, some key frames must be extracted for better performance. There are a number of methods for key frame selection, which match all frames in a video sequence. In this paper, we present a novel and efficient method to select key frames from video for creating a large panoramic mosaic without matching all frames. A Kalman filter is used to predict the overlap area between each frame and its previous key frame. It predicts the trajectory of each frame corner to estimate its position in a common mosaic surface. It is shown that this approach substantially reduces the time required to construct panorama from a video sequence.

Mohammad Javad Fadaeieslam

Papers

A Persian OCR System using Morphological Operators

Comparison of Two Feature Selection Methods in Intrusion Detection Systems

A Persian OCR System Using Morphological Operators.

Efficient key frames selection for panorama generation from video

Key frames selection into panoramic mosaics