Abstract-Dialect recognition is one of the hottest topics in the speech analysis area. In this study, a system for dialect and language recognition is developed using phonetic and a style-based features. The study suggests a new set of feature using one-dimensional local binary pattern (LBP). The results show that the proposed LBP set of the feature is useful to improve dialect and language recognition accuracy. The acquired data involved in this study are three Kurdish dialects (Sorani, Badini, and Hawrami) with three neighbor languages (Arabic, Persian, and Turkish). The study proposed a new method to interpret the closeness of the Kurdish dialects and their neighbor languages using confusion matrix and a non-metric multi-dimensional visualization technique. The result shows that the Kurdish dialects can be clustered and linearly separated from the neighbor languages.

Facebook occupies an important part of people's time and life due to the recent developments in the field of communication technologies (CT). This paper aims at investigating the Facebook impact (FI) on undergraduate students at Charmo University. Moreover, Facebook also has a great impact on students' life studies at universities. However, in addition to its benefits, Facebook also has some side effects on students' life study. In regard to data collection, an online survey was used in which 15 questions were answered by 100 participants from different faculties at Charmo University. The data collection process was conducted from First of March to 30 of April 2016. The program of SPSS was used to analyze the results of the survey. The results of the current study show that students spend a significant amount of their times on using Facebook.

An apparently interminable scope of techniques and methodologies has developed to misuse the capability of innovation. The issue has not been a shortage of research. Actually a great many examinations identified with computer and learning has been distributed amid the recent decades. The issue has been one of comprehending the tremendous, and developing, group of accessible research. There is developing enthusiasm for utilizing the ability of new innovation in a productive and successful approach to meet the instructional and research needs of faculty and students. The utilization of data innovation is one of numerous means to accomplishing magnificence. However the recharged enthusiasm for promoting brilliance in advanced education through Information Technology has carried with it a more prominent accentuation on the requirement for high-bore administration in this limit. Capability, combination of learning, joint effort with industry, collaboration among grounds at different levels, helpful procurement endeavors, what's more, transferability of gaining starting with one organization then onto the next are immeasurably imperative components in the administration way to deal with IT in higher Education. In addition to an extensive literature review, this examination contemplate utilized a trial also, quantitative, self-planned and controlled overview instrument to decide the mentalities and qualified respondents, containing IT leaders, IT staff, also, executives in higher instructive at open and private colleges in Kurdistan Region Government.

Over the last twenty years face recognition has made immense progress based on statistical learning or subspace discriminant analysis. This paper investigates a technique to reduce features necessary for face recognition based on local binary pattern, which is constructed by applying wavelet transform into local binary pattern. The approach is evaluated in two ways: wavelet transform applied to the LBP features and wavelet transform applied twice on the original image and LBP features. The resultant data are compared to the results obtained without applying wavelet transform, revealing that the reduction base one wavelet achieves the same or sometimes improved accuracy. The proposed algorithm is experimented on the Cambridge ORL Face database.