UMBC ebiquity
2015 May

Archive for May, 2015

Initial impressions: Android M permissions

May 29th, 2015, by Prajit Kumar Das, posted in Google

Google I/O 2015 was a very important day for privacy researchers. For the first time Google acknowledged a need for better privacy control. Researchers and Developers working with Android for sometime probably know that their was a feature called AppOps. This feature was introduced in Android 4.3 and later removed in 4.4.2. The reasons stated for its inclusion and removal have been discussed extensively. However, the only conclusion we could clearly draw from all the discussion was that there was a demand for such a feature. Our friends from over at Apple have repeatedly mentioned how Apple has always cared for User Privacy more than Google. As a result of this, it was only a matter of time and a pleasant development for Android enthusiasts to see this new feature in Android.

We installed the new Android M OS on a Nexus 5. The first thing we wanted to see was the permissions feature. Listed below are our impressions of what we thought of this new feature from a Privacy researcher’s perspective.

The feature is not easy to find
We had to weed through the settings of our phone and we were not able to find it straightaway. There was no menu item for Privacy. How do you access it then? You will have to click on the phone’s setting and then click on “Apps” and then select a particular app whose permission access you wish to control. Following this you will have to click on “Permissions” for that app. At this point you get the menu which allows you to toggle the permissions.

The Permission control is essentially useless till your Apps upgrade
Now, Google stated yesterday that the behavior of apps which do not upgrade to the new API version will remain the same as before. Therefore, even with this feature present you cannot actually stop an app from accessing the restricted data. What you do see is a warning dialog stating the obvious.

Warning message for apps using pre Android M SDK

Warning message for apps using pre Android M SDK

Not all permissions shows up in the list
The granularity of permissions that will be available in this new feature is still uncertain. If you check the Facebook permission list in the Google Play Store, you will see that it requests a lot of permissions.

Permissions description

Permissions description

Permissions description

Permissions description

Permissions description

Permissions description

Permissions description

Permissions description

But when you check out the permission control menu, you will see just a few of these permissions here.

App permissions list

App permissions list

We can assume that Google is grouping the permissions into logical groups. However, that means that the primary issue that a lot of researchers have raised about granular access control is still not being addressed by Google. We have been doing research with fine-grained permission control for sometime now. In our work, we have created a system that is capable of controlling the access to data on a mobile device based on the context of the user. Such an intelligent system would not only know what data to give access to but also when to do so. That goal still remains to be completely realized.

Obviously, we must not forget that Something is always better than nothing! Google is taking steps to improve the means by which it protects a user’s privacy and provides security. It is an iterative process and it’s still far from the goal. It is getting closer to that goal though.

talk: Amit Sheth on Transforming Big data into Smart Data, 11a Tue 5/26

May 17th, 2015, by Tim Finin, posted in Big data, Semantic Web

Transforming big data into smart data:
deriving value via harnessing volume, variety
and velocity using semantics and semantic web

Professor Amit Sheth
Wright State University

11:00am Tuesday, 26 May 2015, ITE 325, UMBC

Big Data has captured a lot of interest in industry, with the emphasis on the challenges of the four Vs of Big Data: Volume, Variety, Velocity, and Veracity, and their applications to drive value for businesses. In this talk, I will describe Smart Data that is realized by extracting value from Big Data, to benefit not just large companies but each individual. If my child is an asthma patient, for all the data relevant to my child with the four V-challenges, what I care about is simply, "How is her current health, and what are the risk of having an asthma attack in her current situation (now and today), especially if that risk has changed?" As I will show, Smart Data that gives such personalized and actionable information will need to utilize multimodal data and their metadata, use domain specific knowledge, employ semantics and intelligent processing, and go beyond traditional reliance on Machine Learning and NLP. I will motivate the need for a synergistic combination of techniques similar to the close interworking of the top brain and the bottom brain in the cognitive models. I will present a couple of Smart Data applications in development at Kno.e.sis from the domains of personalized health, health informatics, social data for social good, energy, disaster response, and smart city.

Amit Sheth is an Educator, Researcher and Entrepreneur. He is the LexisNexis Ohio Eminent Scholar, an IEEE Fellow, and the executive director of Kno.e.sis – the Ohio Center of Excellence in Knowledge-enabled Computing a Wright State University. In World Wide Web (WWW), it is placed among the top ten universities in the world based on 10-year impact. Prof. Sheth is a well cited computer scientists (h-index = 87, >30,000 citations), and appears among top 1-3 authors in World Wide Web (Microsoft Academic Search). He has founded two companies, and several commercial products and deployed systems have resulted from his research. His students are exceptionally successful; ten out of 18 past PhD students have 1,000+ citations each.

Host: Yelena Yesha,

Clare Grasso: Information Extraction from Dirty Notes for Clinical Decision Support

May 11th, 2015, by Tim Finin, posted in Machine Learning, NLP, Ontologies, Semantic Web

Information Extraction from Dirty Notes
for Clinical Decision Support

Clare Grasso

10:00am Tuesday, 12 May 2015, ITE346

The term clinical decision support refers broadly to providing clinicians or patients with computer-generated clinical knowledge and patient-related information, intelligently filtered or presented at appropriate times, to enhance patient care. It is estimated that at least 50% of the clinical information describing a patient’s current condition and stage of therapy resides in the free-form text portions of the Electronic Health Record (EHR). Both linguistic and statistical natural language processing (NLP) models assume the presence of a formal underlying grammar in the text. Yet, clinical notes are often times filled with overloaded and nonstandard abbreviations, sentence fragments, and creative punctuation that make it difficult for grammar-based NLP systems to work effectively. This research focuses on investigating scalable machine learning and semantic techniques that do not rely on an underlying grammar to extract medical concepts in the text in order to apply them in CDS on commodity hardware and software systems. Additionally, by packaging the extracted data within a semantic knowledge representation, the facts can be combined with other semantically encoded facts and reasoned over to help to inform clinicians in their decision making.

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