The 47th Sonoma State University Computer Science Colloquium features lectures on topics such as creating computer-generated music, the challenges of animation at Pixar, and more. Lectures are Thursdays at noon in Salazar 2016. Admission is free, parking is $5-$8 on campus.
Cyber Security-Privacy: Are We All Living in Glass Houses? Can I Get Some Privacy, Please?
Levent Ertaul, California State University, East Bay
Cyber security, cyberwar, hacking, privacy, and governmental/personal data breaches are terms heard with increasing frequency. This creates cyber anxiety everywhere. On top of that, there is now knowledge that corporations and governments all around the world keep track of personal data. Mobile phones constantly provide information about a user's location to service providers; Google knows what users are thinking about from personal online searches; Facebook can see who their users' friends are; Yahoo knows the type of news users are interested in; online shopping patterns are recorded; governments are launching surveillance programs to collect personal data. As the list goes on, it is as if the public are all living in glass houses were there isn't any privacy or they can't keep any secrets anymore. Cyber security issues affect everyone, and that is why ignorance is not bliss in cyber security. Every day the public face new questions, new challenges on their rights and responsibilities as citizens of the cyber world to protect themselves, from new types of security threats. In this talk, Profesor Levent Ertaul will try to explain vulnerabilities and security issues in the cyber space along with what you can and cannot do to protect yourself.
Maya Ackerman, San Jose State University
Songwriting, the art of combining melodies and lyrics, poses new challenges to algorithmic composition. ALYSIA is a machine-learning system that learns the relationship between melodies and lyrics, and uses the resulting model to create new songs in the style of the corpus. While ALYSIA creates melodies for user-provided lyrics, another system, MABLE, creates computer-generated lyrics that convey a coherent story. Original works created with both systems will be shown.
Data Analytics: A Case Study in Healthcare
Mohammad Pourhomayoun, Cal State University, Los Angeles
The increasing cost of chronic disease management demands novel technological solutions that shift healthcare services from clinical and hospital settings to a remote and homebound scenario. Alternative and innovative technologies such as Remote Health Monitoring Systems, Big Data Analytics, and Wireless Health Technologies allow for collecting physiological and contextual data from patients, and providing unique opportunities for real-time data analytics to predict health conditions and prevent medically adverse events. The development of effective predictive models and big data analytics systems, however, faces several fundamental challenges regarding their robustness, scalability, and real-time processing of big heterogeneous data. These challenges necessitate the design and development of robust and scalable data processing techniques based on advanced machine learning algorithms that can efficiently extract the information from physiological data and allow for knowledge discovery and analysis. This talk presents a research methodology for data analytics in next-generation remote health management platforms.
Using Social Programming Environments to Improve Computing Education
Adam S. Carter, Humboldt State University
At only 46 percent, computing has one of the lowest baccalaureate retention rates. This statistic is especially distressing given the upward trend in demand for computing professionals. To address this problem, Professor Adam Carter employs social programming environments (SPEs) to explore the application and impact of social learning theory on students enrolled in computing courses. Unlike traditional integrated development environments, SPEs provide students with opportunities to form learning communities and to engage other classmates in both formal and informal discussions. Even though participation within a learning community is positively linked to retention, such communities are frequently absent in early computing courses.
Secure Computation and Its Applications
Mehrdad Aliasgari, California State University, Long Beach
Data is either stored, transmitted or used in computation. Information security aims to provide protection at all the stages of data. Traditional encryption algorithms help us achieve security for data at storage and transmission. However, in order to provide security while executing a function on private data, we need a completely new set of tools. In this talk, Professor Mehrdad Aliasgari will look at secure computation, its applications and challenges that lie ahead.
This Gaming Life
Jason Shankel, Roblox
In this talk Jason Shankel, a 25-year veteran of the gaming industry, will describe the changes the industry has gone through over the past three decades and what it takes to make it in the world of gaming today.
Natural Language Processing for Fake News Detection
William Wang, University of California, Santa Barbara
In the past election cycle for the 45th President of the United States, the world has witnessed a growing epidemic of fake news. The plague of fake news not only poses serious threats to the integrity of journalism, but has also created turmoil in the political and actual world. However, statistical approaches to combating fake news has been dramatically limited by the lack of labeled benchmark datasets. In this talk, Professor William Wang will describe LIAR, a new, publicly available dataset for fake news detection. Wang has collected a decade-long, 12.8K manually labeled short statements in various contexts from politifact.com, which provides detailed analysis report and links to source documents for each case. This dataset can be used for fact-checking research as well. Notably, this new dataset is an order of magnitude larger than previously largest public fake news datasets of similar type. Empirically, we investigate automatic fake news detection based on surface-level linguistic patterns. LIAR was designed a novel, hybrid convolutional neural network to integrate metadata with text. Wang will show that this hybrid approach can improve a text-only deep learning model outlining future directions, and discussing related technologies in natural language processing.
Simulating Just Enough Non-Linearity
Theodore Kim, Pixar
Most of the physical phenomena that are relevant to computer animation are inherently non-linear. These include the equations governing the flow of smoke and water, as well as the dynamics of skin and flesh. Which of these non-linearity’s are visually important, and which just introduce unnecessary trouble? In this talk, Theodore Kim will examine a few case studies.
FrameNet And Natural Language Processing
Miriam R. L. Petruck, International Computer Science Institute, UC Berkeley
This lecture presents an overview of FrameNet, a research project in corpus-based computational lexicography, based on the principles of Frame Semantics. FrameNet's initial goals included providing information about the valences, i.e., the semantic and syntactic combinatorial possibilities for the vocabulary of contemporary English, and documented by corpus findings.
Keys, Hollywood, And History: The Truth About ICANN, DNSSEC, And the Root Key
Richard Lamb, ICANN
For better or worse, Internet security has gained notoriety recently and with it greater interest in some humble key management operations. Specifically, much hay has been made by Hollywood of the role of those incorrectly referred to as the “7 key holders” of the Internet and ICANN. As the original architect of the DNSSEC root key management system, Lamb will describe the truth, unwritten or otherwise, behind this humble, trusted operation and how it came to be. Along the way he hopes to trick participants into learning about DNS, DNS Security Extensions, and how we might all benefit from innovation on this infrastructure.
The Maximum Community Partition Problem in Networks
Fay Zhong, California State University East Bay
Many network systems of interest are rising from real world networks, e.g. social networks and biological networks. One typical issue considered by researchers is how to find the community structures in those networks. Fay Zhong proposed a community structure detection problem, which aims to analyze the relationships among the data via the network topology. Collecting a series of unified definitions for community structures and formulate the community structure detection into a combinatorial optimization problem to identify as many communities as possible for a given network, and develop a heuristic algorithm based on greedy strategy. The experimental results on real networks show that the proposed algorithm is effective in terms of the number of valid communities and the modularity score.
Thanksgiving Break (No Colloquium)
Short presentations of research carried out by Sonoma State computer science students. Pizza will be provided during the presentations in Salazar 2016.
End of Semester Celebration & Awards
Awards presented to Sonoma State computer science majors. Pizza will be provided during the celebration in Salazar 2016.