March 18th, 2017
A Hands-on Introduction to TensorFlow and Machine Learning
Abhay Kashyap, UMBC ebiquity Lab
10:00-11:00am Tuesday, 28 March 2017, ITE346 ITE325b
As many of you know, TensorFlow is an open source machine learning library by Google which simplifies building and training deep neural networks that can take advantage of computers with GPUs. In this meeting, I will introduce some basic concepts of TensorFlow and machine learning in general. This will be a hands on tutorial where we will sit and code up some basic examples in TensorfFow. Specifically, we will use TensorFlow to implement linear regression, softmax classifiers and feed forward neural networks (MLP). You can find the Python notebooks here. If time permits, we will go over the implementation of the popular word2vec algorithm and introduce LSTMs to build language models.
What you need to know: Python and the basics of linear algebra and matrix operations. While it helps to know basics of machine learning, no prior knowledge will be assumed and there will be a gentle high level introduction to the algorithms we will implement.
What you need to bring: A laptop that has Python and pip installed. Having virtual environments set up on your computer is also a plus. (Warning: Windows-only users might be publicly shamed)
March 17th, 2017
The Semantics Toolkit
Paul Cuddihy and Justin McHugh
GE Global Research Center, Niskayuna, NY
10:00-11:00 Tuesday, 4 April 2017, ITE 346, UMBC
Paul Cuddihy is a senior computer scientist and software systems architect in AI and Learning Systems at the GE Global Research Center in Niskayuna, NY. He earned an M.S. in Computer Science from Rochester Institute of Technology. The focus of his twenty-year career at GE Research has ranged from machine learning for medical imaging equipment diagnostics, monitoring and diagnostic techniques for commercial aircraft engines, modeling techniques for monitoring seniors living independently in their own homes, to parallel execution of simulation and prediction tasks, and big data ontologies. He is one of the creators of the open source software “Semantics Toolkit” (SemTk) which provides a simplified interface to the semantic tech stack, opening its use to a broader set of users by providing features such as drag-and-drop query generation and data ingestion. Paul has holds over twenty U.S. patents.
Justin McHugh is computer scientist and software systems architect working in the AI and Learning Systems group at GE Global Research in Niskayuna, NY. Justin attended the State University of New York at Albany where he earned an M.S in computer science. He has worked as a systems architect and programmer for large scale reporting, before moving into the research sector. In the six years since, he has worked on complex system integration, Big Data systems and knowledge representation/querying systems. Justin is one of the architects and creators of SemTK (the Semantics Toolkit), a toolkit aimed at making the power of the semantic web stack available to programmers, automation and subject matter experts without their having to be deeply invested in the workings of the Semantic Web.
March 14th, 2017
Prajit Kumar Das, Anupam Joshi and Tim Finin, App behavioral analysis using system calls, MobiSec: Security, Privacy, and Digital Forensics of Mobile Systems and Networks, IEEE Conference on Computer Communications Workshops, May 2017.
System calls provide an interface to the services made available by an operating system. As a result, any functionality provided by a software application eventually reduces to a set of fixed system calls. Since system calls have been used in literature, to analyze program behavior we made an assumption that analyzing the patterns in calls made by a mobile application would provide us insight into its behavior. In this paper, we present our preliminary study conducted with 534 mobile applications and the system calls made by them. Due to a rising trend of mobile applications providing multiple functionalities, our study concluded, mapping system calls to functional behavior of a mobile application was not straightforward. We use Weka tool and manually annotated application behavior classes and system call features in our experiments to show that using such features achieves mediocre F1-measure at best, for app behavior classification. Thus leading to the conclusion that system calls were not sufficient features for app behavior classification.
March 4th, 2017
SADL – Semantic Application Design Language
Dr. Andrew W. Crapo
GE Global Research
10:00 Tuesday, 7 March 2017
The Web Ontology Language (OWL) has gained considerable acceptance over the past decade. Building on prior work in Description Logics, OWL has sufficient expressivity to be useful in many modeling applications. However, its various serializations do not seem intuitive to subject matter experts in many domains of interest to GE. Consequently, we have developed a controlled-English language and development environment that attempts to make OWL plus rules more accessible to those with knowledge to share but limited interest in studying formal representations. The result is the Semantic Application Design Language (SADL). This talk will review the foundational underpinnings of OWL and introduce the SADL constructs meant to capture, validate, and maintain semantic models over their lifecycle.
Dr. Crapo has been part of GE’s Global Research staff for over 35 years. As an Information Scientist he has built performance and diagnostic models of mechanical, chemical, and electrical systems, and has specialized in human-computer interfaces, decision support systems, machine reasoning and learning, and semantic representation and modeling. His work has included a graphical expert system language (GEN-X), a graphical environment for procedural programming (Fuselet Development Environment), and a semantic-model-driven user-interface for decision support systems (ACUITy). Most recently Andy has been active in developing the Semantic Application Design Language (SADL), enabling GE to leverage worldwide advances and emerging standards in semantic technology and bring them to bear on diverse problems from equipment maintenance optimization to information security.