Abstract. Smart phones are now being used to store users' identities and sensitive information/data. Therefore, it is important to authenticate legitimate users of a smart phone and to block imposters. In this paper, we demonstrate that keystroke dynamics of a smart phone user can be translated into a viable feature set for accurate user identification. To this end, we collect and analyze keystroke data of 25 diverse smart phone users. Based on this analysis, we select six distinguishing keystroke features that can be used for user identification. We show that these keystroke features for different users are diffused and therefore a fuzzy classifier is well-suited to cluster and classify them. We then optimize the front-end fuzzy classifier using Particle Swarm Optimizer (PSO) and Genetic Algorithm (GA) as back-end dynamic optimizers to adapt to variations in usage patterns. Finally, we provide a novel keystroke dynamics based PIN verification mode to ensure information security on smart phones. The results of our experiments show that the proposed user identification system has an average error rate of 2% after the detection mode and the error rate of rejecting legitimate users is dropped to zero after the PIN verification mode. We also compare error rates (in terms of detecting both legitimate users and imposters) of our proposed classifier with 5 existing state-of-the-art techniques for user identification on desktop computers. Our results show that the proposed technique consistently and considerably outperforms existing schemes.

The major contribution of this paper is a hybrid GA-PSO fuzzy user identification system, UGuard, for smart phones. Our system gets 3 phone usage features as input to identify a user or an imposter. We show that these phone usage features for different users are diffused; therefore, we justify the need of a front end fuzzy classifier for them. We further show that the fuzzy classifier must be optimized using a back end online dynamic optimizer. The dynamic optimizer is a hybrid of Particle Swarm Optimizer (PSO) and Genetic Algorithm (GA). We have collected phone usage data of 10 real users having Symbian smart phones for 8 days. We evaluate our UGuard system on this dataset. The results of our experiments show that UGuard provides on the average an error rate of 2% or less. We also compared our system with four classical classifiers -Naïve Bayes, Back Propagation Neural Networks, J48 Decision Tree, and Fuzzy System -and three evolutionary schemes -fuzzy system optimized by ACO, PSO, and GA. To the best of our knowledge, the current work is the first system that has achieved such a small error rate. Moreover, the system is simple and efficient; therefore, it can be deployed on real world smart phones.

Abstract-Nature-inspired routing protocols are becoming an active area of research. Researchers in the community follow a well known engineering philosophy: inspire, abstract, design, develop and validate. As a consequence, the protocols are designed on the basis of heuristics and then their performance is evaluated in a network simulator. To the best of our knowledge, virtually no attention has been paid in developing a formal framework that provides an analytical insight into the behavior and performance of such algorithms. The lack of formal treatment of Nature-inspired routing protocols is often criticized in the networking community. In this paper we propose a formal framework that helps in analyzing a Nature-inspired routing protocol, BeeHive. We have verified the correctness of our model by comparing its estimated values with the results obtained from extensive network simulations. An important outcome of the work is that the estimated and measured values only differ by a small deviation. We believe that this work will be instrumental for Nature-inspired Telecommunications.

Real time object tracking finds its applications in diverse fields. An online embedded system for image tracking should be fast, accurate, robust and efficient. This paper presents a hardware based architecture and implementation of an Image Coprocessor on FPGA using Verilog Hardware Description Language. The core concept is to ensure a fast and memory efficient dedicated hardware which could increase the efficiency manifolds and enhance the overall output, thereby making real time detection and tracking possible at much higher rates than achievable using C++. The design was simulated using Xilinx ISE Simulator and ModelSim. An embedded system was later on developed for complete implementation of Image Coprocessor on FPGA, using Xilinx Embedded Development Kit. Six modules including Edge Image, Bhattacharya Coefficient, Histograms, Epanechnikov Kernel, Translation and Rotation have been implemented on FPGA, and a comparsion has been made with their implementation in C++. The results demonstrate upto 66 times improved performance of these individual modules. Thus, an efficient Image Coprocessor has been developed.

Languages are dynamic in nature and Urdu language is no exception. This study aims to probe semantic change in Urdu lexis and focuses on the meaning of the word "mashkoor" (thanked). For this study, Urdu dictionaries, a corpus of 25 million Urdu words and a questionnaire have been used. Our analysis determines that "mashkoor" has shifted meanings from being "thanked" to "thankful". The results depict that the grammarians, lexicographers or the teachers are not the authority to decide correct usage in a language but it is the prerogative of users as well. The present study strengthens the idea that Urdu language has changed with the passage of time. It also proposes that Urdu dictionaries should be corpus based and include the current usage by the masses to incorporate the latest changes. This study will serve for other researchers as a springboard to further explore the other aspects of Urdu language.