Location-based services in different applications push the research toward outdoor localization for users' equipment in Long Term Evolution (LTE) networks. Telecom operators can introduce valuable services to users based on their location, both in emergency and ordinary situations. This paper introduces DeepFeat: A deep-learning-based framework for outdoor localization using a rich feature set in LTE networks. DeepFeat works on the mobile operator side, and it leverages many mobile network features and other metrics to achieve high localization accuracy. In order to reduce computation and complexity, we introduce a feature selection module to choose the most appropriate features as inputs to the deep learning model. This module reduces the computation and complexity by around 20.6%, with enhancement in system accuracy. The feature selection module uses correlation and Chi-squared algorithms to reduce the feature set to 12 inputs only regardless of the area size, compared to a large number of cell towers in similar systems; such input increases exponentially with increasing the test area. In order to enhance the accuracy of DeepFeat, a One-to-Many augmenter is introduced to extend the dataset and improve the system's overall performance. The results show the impact of the proper features selection adopted by DeepFeat on the system performance. DeepFeat achieved median localization accuracy of 13.179m in an outdoor environment in a mid-scale area of 6.27Km 2 . In a large-scale area of 45Km 2 , the median localization accuracy is 13.7m. DeepFeat was compared to other state-of-the-art deep-learningbased localization systems that leverage a small number of features. We show that using the DeepFeat carefully selected feature set enhances the localization accuracy compared to the state-of-the-art systems by at least 286%.

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In the event of emergencies and disasters, massive amounts of web resources are generated and shared. Due to the rapidly changing nature of those resources, it is important to start archiving them as soon as a disaster occurs. This led us to develop a prototype system for constructing archives with minimum human intervention using the seed URLs extracted from tweet collections. We present the details of our prototype system. We applied it to five tweet collections that had been developed in advance, for evaluation. We also identify five categories of non-relevant files and conclude with a discussion of findings from the evaluation.

Problem/project Based Learning (PBL) is a highly effective student-centered teaching method, where student teams learn by solving problems. This paper describes an instance of PBL applied to digital library education. We show the design, implementation, results, and partial evaluation of a Computational Linguistics course that provides students an opportunity to engage in active learning about adding value to digital libraries with large collections of text, i.e., one aspect of "big data." Students are engaging in PBL with the semester long challenge of generating good English summaries of an event, given a large collection from our webpage archives. Six teams, each working with a different type of event, and applying three different summarization methods, learned how to generate good summaries; these have fair precision relative to the Wikipedia page that describes their event.

Many observers heralded the use of social media during recent political uprisings in the Middle East, even dubbing Iran’s post-election protests a “Twitter Revolution”. The authors seek to put into perspective the use of social media in Egypt during the mass political demonstrations in 2011. We draw on innovation diffusion theory to argue that these media could have had an impact beyond their low adoption rates due to other factors related to the essential role played by social networks in diffusion and the demographics of Internet and social media adoption in Egypt, Tunisia and (to a lesser extent) Iran. To illustrate the argument the authors draw on technology adoption, information use, discussion networks and demographics. They supplement the social media data analysis with survey data collected in June 2011 from an opportunity sample of Egyptian youth. The authors conclude that in addition to the contextual factors noted above, the individuals within Egypt who used Twitter during the uprising have the characteristics of opinion leaders, that is, a group of early adopters with influence throughout their social circles and beyond. These findings contribute to knowledge regarding the use and impact of social media during violent political demonstrations and their aftermath.