UMBC ebiquity
UMBC eBiquity Blog

chmod 000 Freebase

Tim Finin, 8:22pm 2 May 2016

rip freebase

He’s dead, Jim.

Google recently shut down the query interface to Freebase. All that is left of this innovative service is the ability to download a few final data dumps.

Freebase was launched nine years ago by Metaweb as an online source of structured data collected from Wikipedia and many other sources, including individual, user-submitted uploads and edits. Metaweb was acquired by Google in July  2010 and Freebase subsequently grew to have more than 2.4 billion facts about 44 million subjects. In December 2014, Google announced that it was closing Freebase and four months later it became read-only. Sometime this week the query interface was shut down.

I’ve enjoyed using Freebase in various projects in the past two years and found that it complemented DBpedia in many ways. Although its native semantics differed from that of RDF and OWL, it was close enough to allow all of Freebase to be exported as RDF.  Its schema was larger than DBpedia’s and the data tended to be a bit cleaner.

Google generously  decided to donate the data to the Wikidata project, which began migrating Freebase’s data to Wikidata in 2015.  The Freebase data also lives on as part of Google’s Knowledge Graph.  Google recently allowed very limited querying of its knowledge graph and my limited experimenting with it suggests that has Freebase data at its core.


 

Representing and Reasoning with Temporal Properties/Relations in OWL/RDF

Tim Finin, 4:13pm 1 May 2016

Representing and Reasoning with Temporal
Properties/Relations in OWL/RDF

Clare Grasso

10:30-11:30 Monday, 2 May 2016, ITE346

OWL ontologies offer the means for modeling real-world domains by representing their high-level concepts, properties and interrelationships. These concepts and their properties are connected by means of binary relations. However, this assumes that the model of the domain is either a set of static objects and relationships that do not change over time, or a snapshot of these objects at a particular point in time. In general, relationships between objects that change over time (dynamic properties) are not binary relations, since they involve a temporal interval in addition to the object and the subject. Representing and querying information evolving in time requires careful consideration of how to use OWL constructs to model dynamic relationships and how the semantics and reasoning capabilities within that architecture are affected.


 

talk: A Hybrid Task Graph Scheduler API, Tim Blattner, UMBC

Tim Finin, 10:14pm 24 April 2016

A Hybrid Task Graph Scheduler API

Tim Blattner, UMBC

10:30am Monday, 25 April 2016, ITE 346

Scalability of applications is a key requirement to gaining performance in hybrid computing. Scheduling code to utilize the parallelism is difficult, particularly when dealing with dependencies, memory management, data motion, and processor occupancy. The Hybrid Task Graph Scheduler (HTGS) API increases programmer productivity to develop hybrid applications by creating a multiple-producer, multiple-consumer workflow system. HTGS improves upon existing task graph solutions with its design of execution pipelines that enables multi-GPU computation through data decomposition and task graph clustering that are bound to physical GPUs. The HTGS API is also capable of managing dependencies between tasks, represents CPU and GPU memories independently, overlaps disk I/O and memory transfers, and utilizes all available compute resources. We demonstrate the HTGS API by comparing a hybrid microscopy image stitching application with and without HTGS. By using HTGS in image stitching, code size is reduced by ~25% and shows favorable performance compared to image stitching without HTGS.


 

Context-Sensitive Policy Based Security in Internet of Things

Tim Finin, 11:13pm 18 April 2016

Prajit Kumar Das, Sandeep Nair, Nitin Kumar Sharma, Anupam Joshi, Karuna Pande Joshi, and Tim Finin, Context-Sensitive Policy Based Security in Internet of Things, 1st IEEE Workshop on Smart Service Systems, co-located with IEEE Int. Conf. on Smart Computing, St. Louis, 18 May 2016.

According to recent media reports, there has been a surge in the number of devices that are being connected to the Internet. The Internet of Things (IoT), also referred to as Cyber-Physical Systems, is a collection of physical entities with computational and communication capabilities. The storage and computing power of these devices is often limited and their designs currently focus on ensuring functionality and largely ignore other requirements, including security and privacy concerns. We present the design of a framework that allows IoT devices to capture, represent, reason with, and enforce information sharing policies. We use Semantic Web technologies to represent the policies, the information to be shared or protected, and the IoT device context. We discuss use-cases where our design will help in creating an “intelligent” IoT device and ensuring data security and privacy using context-sensitive information sharing policies.


 

Policies For Oblivious Cloud Storage Using Semantic Web Technologies

Tim Finin, 9:47am 3 April 2016

Policies For Oblivious Cloud Storage
Using Semantic Web Technologies

Vaishali Narkhede
10:30am, Monday, 4 April 2016, ITE 346, UMBC

Consumers want to ensure that their enterprise data is stored securely and obliviously on the cloud, such that the data objects or their access patterns are not revealed to anyone, including the cloud provider, in the public cloud environment. We have created a detailed ontology describing the oblivious cloud storage models and role based access controls that should be in place to manage this risk. We have also implemented the ObliviCloudManager application that allows users to manage their cloud data using oblivious data structures. This application uses role based access control model and collection based document management to store and retrieve data efficiently. Cloud consumers can use our system to define policies for storing data obliviously and manage storage on untrusted cloud platforms, even if they are not familiar with the underlying technology and concepts of the oblivious data structure.


 

Forum on Cybersecurity Concerns in Local Governments, Baltimore 4/15

Tim Finin, 8:41am 3 April 2016

The UMBC School of Public Policy, bwtech@UMBC Cyber Incubator, and UMBC Center for Cybersecurity are sponsoring a form on Cybersecurity Concerns in Local Governments from 8:30-11:00am on Friday, April 15, 2016 at the Columbus Center in Baltimore.

“Like their counterparts in the private sector, it is important for local government officials and managers to understand cybersecurity threats to their websites and information systems and to take actions to prevent cyber attacks. The purpose of this forum is to present research on cybersecurity initiatives in local governments in Maryland, and highlight the public policy implications of these initiatives.”

There is no charge to attend this forum, but registration is required. For questions or more information, contact policyforum@umbc.edu.

8:30 a.m. Coffee, light breakfast and networking

9:00 Welcome and Overview

Cybersecurity Challenges in American Local Government
Donald F. Norris, Professor and Director, UMBC School of Public Policy

Policy-driven Approaches to Security
Anupam Joshi, Professor and Director, UMBC Center for Cybersecurity

Perspectives from Maryland Local Governments
Rob O’Connor, Chief Technology Officer, Baltimore County
Jerome Mullen, Chief Technology Officer, City of Baltimore

10:15 Audience Q & A

11:00 Adjourn


 

Down the rabbit hole: An Android system call study, 10:30am Mon 3/28

Tim Finin, 8:47am 27 March 2016

Down the rabbit hole: An Android system call study

Prajit Kumar Das

10:30 am, Monday, March 28, 2016 ITE 346

App permissions and application sandboxing are the fundamental security mechanisms that protects user data on mobile platforms. We have worked on permission analytics before and come to a conclusion that just studying an app’s requested access rights (permissions) isn’t enough to understand potential data breaches. Techniques like privilege escalation have been previously used to gain further access to user and her data on mobile platforms like Android. Static code analysis and dynamic code execution may be studied to gather further insight into an app’s behavior. However, there is a need to study such a behavior at the lowest level of code execution and that is system calls. The system call is the fundamental interface between an application and the Linux kernel. In our current project, we are studying system calls made by apps for gathering a better understanding of their behavior.


 

Introduction to Microservices Architecture

Tim Finin, 11:37am 19 March 2016

Introduction to Microservices Architecture

Vladimir Korolev
10:30am 10:00-11:00am, Monday, March 21, 2016 ITE 346

Microservices is a new style of software architecture that relies on separately deployed loosely coupled components. Advantages of this architectural style are faster development cycles, better system resilience, smoother and easier scalability, and less friction with continuous deployment. In his talk Vlad Korolev will give overview of the architecture. Will show the way how to get started. And share personal experiences and gotchas encountered on several microservices based projects.


 

Image description using deep neural networks

Tim Finin, 8:14am 27 February 2016

Image description using deep neural networks

Sunil Gandhi
10:30 am, Monday, February 29, 2016 ITE 346

With the explosion of image data on the internet, there has been a need for automatic generation of image descriptions. In this project we use deep neural networks for extracting vectors from images and we use them to generate text that describes the image. The model that we built makes use of the pre-trained VGGNET- a model for image classification and a recurrent neural network (RNN) for language modelling. The combination of the two neural networks provides a multimodal embedding between image vectors and word vectors. We trained the model on 8000 images from the Flickr8k dataset and we present our results on test images downloaded from the Internet. We provide a web-service for image description generation that takes the image URL as input and provides image description and image categories as output. Through our service, a user can correct the description automatically generated by the system so that we can improve our model using corrected description.

Sunil Gandhi is a Computer Science Ph.D. student at UMBC who is part of the  Cognition Robotics and Learning Lab (CORAL) research lab.


 

Detecting Botnets Using a Collaborative Situational-Aware IDPS

Tim Finin, 9:26am 17 February 2016

M. Lisa Mathews, Anupam Joshi and Tim Finin, Detecting Botnets Using a Collaborative Situational-Aware IDPS, 2nd Int. Conf. on Information Systems Security and Privacy, Rome, IT, February 2016

Botnet attacks turn susceptible victim computers into bots that perform various malicious activities while under the control of a botmaster. Some examples of the damage they cause include denial of service, click fraud, spamware, and phishing. These attacks can vary in the type of architecture and communication protocol used, which might be modified during the botnet lifespan. Intrusion detection and prevention systems are one way to safeguard the cyber-physical systems we use, but they have difficulty detecting new or modified attacks, including botnets. Only known attacks whose signatures have been identified and stored in some form can be discovered by most of these systems. Also, traditional IDPSs are point-based solutions incapable of utilizing information from multiple data sources and have difficulty discovering new or more complex attacks. To address these issues, we are developing a semantic approach to intrusion detection that uses a variety of sensors collaboratively. Leveraging information from these heterogeneous sources leads to a more robust, situational-aware IDPS that is better equipped to detect complicated attacks such as botnets.


 

Developmental Memetic Algorithms: A Fast and Efficient Approach for Optimization Applications

Tim Finin, 8:29am 15 February 2016

Developmental Memetic Algorithms: A Fast and
Efficient Approach for Optimization Applications

Ramin Ayanzadeh
10:30am, Monday, 22 February 2016, ITE 346

A Memetic algorithm, as a hybrid strategy, is an intelligent optimization method in problem solving. These algorithms are similar in nature to genetic algorithms as they follow evolutionary strategies, but they also incorporate a refinement phase during which they learn about the problem and search space. The efficiency of these algorithms depends on the nature and architecture of the imitation operator used. In this presentation, after a brief introduction, pros and cons of employing memetic algorithms would be discussed. Afterwards, developmental memetic algorithms will be proposed as an approach for subsiding the costs of using standard memetic algorithms. Developmental memetic algorithm is an adaptive memetic algorithm that has been developed in which the influence factor of environment on the learning abilities of each individual is set adaptively. This translates into a level of autonomous behavior, after a while that individuals gain some experience. Simulation results on benchmark function proved that this adaptive approach can increase the quality of the results and decrease the computation time simultaneously. The adaptive memetic algorithm also shows better stability when compared with the classic memetic algorithm.


 

Using Data Analytics to Detect Anomalous States in Vehicles

Tim Finin, 1:27pm 28 December 2015

 

Sandeep Nair, Sudip Mittal and Anupam Joshi, Using Data Analytics to Detect Anomalous States in Vehicles, Technical Report, December 2015.

Vehicles are becoming more and more connected, this opens up a larger attack surface which not only affects the passengers inside vehicles, but also people around them. These vulnerabilities exist because modern systems are built on the comparatively less secure and old CAN bus framework which lacks even basic authentication. Since a new protocol can only help future vehicles and not older vehicles, our approach tries to solve the issue as a data analytics problem and use machine learning techniques to secure cars. We develop a hidden markov model to detect anomalous states from real data collected from vehicles. Using this model, while a vehicle is in operation, we are able to detect and issue alerts. Our model could be integrated as a plug-n-play device in all new and old cars.