CEAI: CCM based Email Authorship Identification Model

In this paper we present a model for email authorship identification (EAI) by employing a Cluster-based Classification (CCM) technique. Traditionally, stylometric features have been successfully employed in various authorship analysis tasks; we extend the traditional feature-set to include some more interesting and effective features for email authorship identification (e.g. the last punctuation mark used in an email, the tendency of an author to use capitalization at the start of an email, or the punctuation after a greeting or farewell). We also included Info Gain feature selection based content features. It is observed that the use of such features in the authorship identification process has a positive impact on the accuracy of the authorship identification task. We performed experiments to justify our arguments and compared the results with other base line models. Experimental results reveal that the proposed CCM-based email authorship identification model, along with the proposed feature set, outperforms the state-of-the-art support vector machine (SVM)-based models, as well as the models proposed by Iqbal et al. [1, 2]. The proposed model attains an accuracy rate of 94% for 10 authors, 89% for 25 authors, and 81% for 50 authors, respectively on Enron dataset, while 89.5% accuracy has been achieved on authors' constructed real email dataset. The results on Enron dataset have been achieved on quite a large number of authors as compared to the models proposed by Iqbal et al. [1, 2]

From Public Outrage to the Burst of Public Violence: An Epidemic-Like Model

This study extends classical models of spreading epidemics to describe the phenomenon of contagious public outrage, which eventually leads to the spread of violence following a disclosure of some unpopular political decisions and/or activity. Accordingly, a mathematical model is proposed to simulate from the start, the internal dynamics by which an external event is turned into internal violence within a population. Five kinds of agents are considered: "Upset" (U), "Violent" (V), "Sensitive" (S), "Immune" (I), and "Relaxed" (R), leading to a set of ordinary differential equations, which in turn yield the dynamics of spreading of each type of agents among the population. The process is stopped with the deactivation of the associated issue. Conditions coinciding with a twofold spreading of public violence are singled out. The results shed a new light to understand terror activity and provides some hint on how to curb the spreading of violence within population globally sensitive to specific world issues. Recent world violent events are discussed.Comment: 22 pages, 9 figure

PSN: Portfolio Social Network

In this paper we present a web-based information system which is a portfolio social network (PSN) that provides solutions to recruiters and job seekers. The proposed system enables users to create portfolios so that he/she can add his specializations with piece of code, if any, specifically for software engineers, which is accessible online. The unique feature of the system is to enable the recruiters to quickly view the prominent skills of the users. A comparative analysis of the proposed system with the state of the art systems is presented. The comparative study reveals that the proposed system has advanced functionalities

Domain and culture-specific heuristic evaluation of the websites of universities of Pakistan

In this digital era, the website of a university is a doorway to its information and services. These websites reflect the actual university where the prospective students make plans to get admission, current students perform interaction daily, and other stakeholders also access it to get the required information. These users expect university websites to be designed professionally, in an organized manner, with a user-friendly interface, which helps them to search, navigate, and collect information effectively. Users pose cultural norms in their life unintentionally, which makes them expect the same preferences in their routine dealings. These preferences reflect a specific culture where the user resides and a specific domain where an application is being used. The heuristic evaluation methodology is widely applied for usability evaluation. It is an inspection-based, fast, and inexpensive approach for identifying usability problems in a system. These heuristics are well designed to pinpoint the general usability design faults however, they limit detecting potential domain and culture-specific usability problems. In this regard, the usability evaluation of the top ten universities of Pakistan is performed in this research using domain and culture-specific heuristic evaluation. The results of the evaluation are discussed that recognize the potential usability issues in the website design of the universities of Pakistan

Modeling Suspicious Email Detection using Enhanced Feature Selection

The paper presents a suspicious email detection model which incorporates enhanced feature selection. In the paper we proposed the use of feature selection strategies along with classification technique for terrorists email detection. The presented model focuses on the evaluation of machine learning algorithms such as decision tree (ID3), logistic regression, Na\"ive Bayes (NB), and Support Vector Machine (SVM) for detecting emails containing suspicious content. In the literature, various algorithms achieved good accuracy for the desired task. However, the results achieved by those algorithms can be further improved by using appropriate feature selection mechanisms. We have identified the use of a specific feature selection scheme that improves the performance of the existing algorithms