Women in Computing will be hosting its first meeting of the Spring semester at 6pm today, in Room 2277 at the Story Innovation Center! Women in Computing is open to all majors and students interesting in topics of computing technology, and diversity/inclusion within the tech industry. Everyone – all genders and majors is welcome! This meeting, we will be discussing the club, plans for this semester, and electing new members to the executive board.

If you are interest in joining, being on the executive board, and/or voting for our new members on the board please come by and see you soon! If you are unable to attend, but interested in the club, you can join our GroupMe: https://groupme.com/join_group/34681325/pIJInQ

Abstract

World models capture the dynamics of an environment and can be used to produce new, “imagined,” experiences. This can significantly reduce the number of real-world experiences required for an agent learn how to make decisions and can be combined with search for to allow an agent to plan before acting. However, in environments with a sub-symbolic representation, generating new experiences with a learned model over multiple timesteps can be difficult as small errors can accumulate over time. Furthermore, identifying previously encountered states during search can be difficult as the same state obtained by traversing different paths can result in slightly different representations. In this talk, I will discuss preliminary research on using discrete world models to address both issues. Using discrete world models, small errors can be corrected by simply rounding and identifying previously seen states is as simple as checking for equality between two arrays. Preliminary experiments with raw pixel representations of the Rubik’s cube and Sokoban show that a discrete world model can be learned using an offline dataset and can be unrolled over multiple timesteps without accumulating errors. Furthermore, after using the world model to learn a value function, combining the world model and value function with A* search solves 100% of test cases.

Bio

Forest Agostinelli is an assistant professor at the University of South Carolina. He received his B.S. from the Ohio State University, his M.S. from the University of Michigan, and his Ph.D. from the University of California, Irvine. His research group investigates how deep learning and reinforcement learning can be used to create agents that can solve complex problems and explain their solutions in a manner that humans can understand. His homepage is located at https://cse.sc.edu/~foresta/.

Location:

In person

Swearingen Engineering Center in Room 2A31

Virtual MS Teams

Friday, at the Seminar in Advances in Computing, Professor Peter Sadowski from the University of Hawai’i at Manoa will be giving a talk entitled “Deep Learning Applications in the Sciences”.

Abstract

Deep learning with artificial neural networks has enabled remarkable progress in traditional artificial intelligence applications including vision, natural language processing, and voice recognition. It also has myriad applications to science. I will review the paradigms for applying deep learning to scientific applications including inverse problems, surrogate models, and physics-informed machine learning.

Bio

Peter Sadowski is an assistant professor of Information and Computer Sciences at UH Manoa, where his lab works on a range of deep learning applications to astronomy, oceanography, and microbiome science. 

Location:

In person: Swearingen Engineering Center in Room 2A31

Virtual MS Teams

Develop collaborative assistants (chatbots) that offer innovative and ethical solutions to real-world problems !

Prizes

First Prize - $250 Second Prize - $150 Third Prize - $100

Problem ideas:

• Health: Which one or more medical specialties can treat my abdominal pain?

• Public safety: Which community is most unsafe for children?

• Water: Will there be a water problem if everyone starts washing their cars at home?

• Gardening: What happens to my water if we plant eucalyptus or cactus in SC?

• Information gathering: How is UofSC better than Clemson? Columbia v/s Charleston? based on crime statistics, hospitals, etc.

These are just suggestions to inspire. You are free to choose any problem that would help solve any problem in your community and preferably, South Carolina too.

See the event page for more information.

Meeting Location:

Storey Innovation Center 1400

Live Meeting Link for the virtual audience

Talk Abstract:  About 15 years ago, as I was working on a graphical interface for scientific software in Matlab, I got frustrated by the clumsy code structure that Matlab required for GUI coding. Although I thought that the C++ Qt library would be a great alternative, I did not want to get my fast-prototyping process slowed by low-level coding. Since Python had bindings for Qt, I decided to translate all my code into Python. To my surprise, I was able to swiftly complete this process over the weekend. Since then, I have been working almost exclusively in Python, and I never regretted it a single day. In this talk, I will the main components of the Python stack for scientific programming, focusing on neuroscience and illustrating it by summarily analyzing EEG recordings (MNE-Python). I will discuss why Python has become a major player in this field and how limitations typical to interpreted languages (e.g., slow at runtime) have been tackled with libraries such as NumPy. I will also explain why Python is a strong environment for data wrangling by introducing libraries like Pandas – which offers data frame functionalities similar to R – and XArray. Finally, I will touch upon how libraries like Seaborn provide a high-level interface for quickly producing publication-quality figures with only a few (if not a single) lines of code.

Speaker's Bio: Christian O’Reilly received his B.Ing (elec eng; 2007), his M.Sc.A. (biomed eng; 2011), and his Ph.D. (biomed eng; 2012) from Polytechnique Montreal. He was a postdoc fellow at the CARSM (2012-2014) and then a NSERC postdoc fellow at McGill's Brain Imaging Center (2014-2015) where he worked on EEG sleep transients. He also worked at the EPFL (2015-2018) on modeling of the thalamocortical loop and at McGIll on brain connectivity (2020-2021). Since 2021, he is Assistant Professor at UofSC.

Meeting Location:

Storey Innovation Center 1400

Live Meeting Link for the virtual audience :

https://teams.microsoft.com/l/meetup-join/19%3ameeting_OWZkMTYwZDQtMDVmMy00NjA1LTgxNmEtZDExMDdiZTM2ZjYz%40thread.v2/0?context=%7b%22Tid%22%3a%224b2a4b19-d135-420e-8bb2-b1cd238998cc%22%2c%22Oid%22%3a%225fc2170a-7068-4a33-9021-df11b94ba696%22%7d

Talk Abstract: David’s talk will cover some of the exciting technologies the Devices, Circuits & Systems group at ARM is researching as well as what he sees in the general trend and future of process technology. And since it’s not possible to discuss CMOS scaling without commenting on Moore’s law, he will do that, too. 🙂

Speaker's Bio: David Pietromonaco has been in the semiconductor industry for almost 30 years at Hewlett-Packard, Sony, and most recently Artisan/Arm (for 20 of those). He works in Arm Research; in the Devices, Circuits & Systems group, specifically on the Technology Optimized Design team. That team tries to look 5-10 years ahead to understand future computing technologies and how to utilize them.

DISSERTATION DEFENSE

                                                                                  Department of Computer Science and Engineering

University of South Carolina 

Author : John Ravan

Advisor : Dr. Csilla Farkas

Date : November 1, 2021

Time : 1:30pm

Place : Virtual Defense

Join Zoom Meeting

https://citadelonline.zoom.us/j/5257755660?pwd=dzBwNW85RUdSRjVWdGp4RzRxbzE2UT09

Abstract

Concurrent database transactions within a web service environment can cause a variety of problems without the proper concurrency control mechanisms in place. A few of these problems involve data integrity issues, deadlock, and efficiency issues. Even with today's industry standard solutions to these problems, they have taken a reactive approach rather than proactively preventing these problems from happening. We deliver a twofold solution that presents a proactive prediction-based approach to ensure consistency while keeping execution time the same or faster than current industry solutions. The first part of this solution involves prototyping and formally proving a prediction-based scheduler. 

The prediction-based scheduler leverages a prediction-based metric that promotes transactions with reliable reputations based on the transaction's performance metric. This performance metric is based on the transaction's likelihood to commit and its efficiency within the system. We can then predict the outcome of the transaction based on the metric and apply customized lock behaviors to address consistency issues in current web service environments. We have formally proven that the solution will increase consistency among web service transactions without a performance degradation that is worse than industry standard 2PL. The simulation was developed using a multi-threaded approach to simulate concurrent transactions.  Experimentation results show that the solution works comparatively with industry solutions with the added benefit of ensured consistency in some cases and deadlock avoidance in others. This work has been published in IEEE Transactions on Services Computing. 

The second part of the solution involves building the prediction-based metric mentioned previously. In the initial solution we assumed the prediction-based categorization coming into the solution in order to prove the feasibility and correctness of a prediction-based scheduler. 

Once that was established, we extended the four-category solution to a dynamic reputation score built upon transactional attributes. The attributes used in the reputation score are system abort ranking, user abort ranking, efficiency ranking, and commit ranking. With these four attributes we were able to establish a dynamic dominance structure that allowed for a transaction to promote or demote itself based on its performance within the system. This work has been submitted to ACM Transactions on Information Systems and awaiting review. 

Both phases provide a complete solution of prediction-based transaction scheduling that provides dynamic categorization no matter the transactional environment. 

Future work of this system would involve extending the prediction-based solution to a multi-level secure database with an added dimension. The dimension provides a security classification in addition to attributes for dynamic reputation that allows for transactions to establish dominance. The goal would be to prevent covert timing channels that occur in multi-level secure database systems due to the differing classifications. Our reputation score would provide a cover story for timing differences of transactions of different security levels to allow for a more robust scheduling algorithm. This would allow for high security transactions to gain priority over low security transactions without exposing a covert timing channel. 

Live Meeting Link for the virtual audience :

Talk Abstract: In the past 10 years, we have seen the rise of SAAS (Software As A Service). We have seen SAAS take over many of the existing businesses. Amazon, NetFlix, Expedia are household name SAAS-operated businesses that replaced traditional ones As Machine Learning (ML) and Artificial Intelligence (AI) rise, we are also seeing lots of jobs replaced and automated by machines. This is happening at a much faster pace than anticipated. AI is also replacing jobs that were once thought to be securely dominated by humans. These were jobs that require some form of human intellect We will be discussing what AI really is beyond what the media defines it to be. We will also discuss the implication of this automation on society & the labor market. We will try to show that AI, contrary to the latest media scare, is going to bring an era of unprecedented productivity gains and prosperity. Something comparable to what the industrial revolution brought a few centuries back. But that requires us to be prepared as a society

Speaker's Bio: 

Ahmad Abdulkader is a well-renowned industry expert, with over 50 publications and patents, in Machine Learning and Artificial Intelligence. 

https://scholar.google.com/citations?user=HZxrGFIAAAAJ&hl=en&fbclid=IwAR2DiNkhUYa-Z2NG04vTLsKZcL61qNUEelFlokIjkUfepy3D8F-fh79RzZE

Ahmad is currently a Distinguished Scientist at Facebook AI Applied Research. Ahmad invented DeepText, a Deep-Learning Text Understanding Platform that is widely used throughout FB and the open-source community.

https://engineering.fb.com/2016/06/01/core-data/introducing-deeptext-facebook-s-text-understanding-engine/

Prior to Facebook, Abdulkader was the co-founder & CTO of Voicea.ai which was acquired by Cisco in 2019. Voicea built a widely-used meetings platform that became part of Cisco's WebEx.

https://www.amazon.com/Attracting-technical-co-founders-corporate-fundraising/dp/B08KVDHRN8

Ahmad also worked for Google where he built the Optical Character Recognition and verification of Google's BookSearch. Ahmad is one of the main contributors to Tesseract: The most widely used open-source OCR Engine.

https://github.com/tesseract-ocr/tesseract/blob/master/AUTHORS

In addition, Ahmad was one of the pioneers of StreetView at Google. Ahmad was one of the main creators of StreetSmart; A Computer Vision platform for privacy protection and Scene Understanding in StreetView.

https://research.google/pubs/pub35481/

At Microsoft corporation, Ahmad was one of the pioneers of the Handwriting Recognition Technology that powers the Microsoft Surface devices. 

https://www.researchgate.net/publication/255619869_Personalization_of_an_Online_Handwriting_Recognition_System

Ahmad also is one of the earliest contributors to Arabic OCR & Handwriting Recognition. Ahmad is the co-inventor of the first Arabic OCR Engine (ICRA) in 1994

https://org.uib.no/smi/ksv/ArabOCR.html

Ahmad studied at Cairo University where he got his B.Sc. and M.Sc. in Electrical Engineering and at McMaster University & University of Washington where he got his M.Sc. & Ph.D. in Computer Science.

Meeting Location:

Storey Innovation Center 1400

Live Meeting Link for the virtual audience

Speaker's Bio: Dr. Qiang Zeng is an Assistant Professor in the CSE department at the University of South Carolina. He received his Ph.D. in Computer Science and Engineering from Penn State University. His main interest is Computer Systems Security, with a focus on the Internet of Things and Mobile Computing. He is also interested in Adversarial Machine Learning. He publishes his work in CCS, USENIX Security, NDSS, MobiCom, MobiSys, PLDI, etc.

Talk Abstract: As IoT devices are integrated via automation and coupled with the physical environment, anomalies in an appified smart home, whether due to attacks or device malfunctions, may lead to severe consequences. Prior works that utilize data mining techniques to detect anomalies suffer from high false alarm rates and missing many real anomalies. Our observation is that data mining-based approaches miss a large chunk of information about automation programs (also called smart apps) and device relations. We propose Home Automation Watcher (HAWatcher), a semantics-aware anomaly detection system for appified smart homes. HAWatcher models a smart home’s normal behaviors based on both event logs and semantics. Given a home, HAWatcher generates hypothetical correlations according to semantic information, such as apps, device types, relations and installation locations, and verifies them with event logs. The mined correlations are refined using correlations extracted from the installed smart apps. The refined correlations are used by a Shadow Execution engine to simulate the smart home’s normal behaviors. During runtime, inconsistencies between devices’ real-world states and simulated states are reported as anomalies. We evaluate our prototype on the SmartThings platform in four real-world testbeds and test it against totally 62 different anomaly cases. The results show that HAWatcher achieves high accuracy, significantly outperforming prior approaches.

Two Lectures on AI Explainability 

As part of Trusted AI course in Fall 2021 by Prof. Biplav Srivastava https://sites.google.com/site/biplavsrivastava/research-1/trustedai  

Oct 19, Tuesday, 10:00-11:15 am - Talk 

Blackboard link: https://us.bbcollab.com/guest/f567247c101145cebc6eaa937af2cecd  

 Oct 21, Tuesday, 10:00-11:15 am – Talk and Working Session 

Blackboard link: https://us.bbcollab.com/guest/f567247c101145cebc6eaa937af2cecd  

On campus class at Seminar Room, AI Institute, 1112 Greene St, Columbia (5^th Floor; Science & Technology Building) 

Speakers: Dr. Diptikalyan Saha (Dipti) and Dr. Vijay Arya 

Speakers Bio: 

Dr. Diptikalyan Saha (Dipti) is a Senior Technical Staff Member and manager of Reliable AI team in Data&AI department of IBM Research at Bangalore. His research interest includes Artificial Intelligence, Natural Language Processing, Knowledge representation, Program Analysis, Security, Software Debugging, Testing, Verification, and Programming Languages. He received a  Ph.D. degree in Computer Science from the State University of New York at Stony Brook his B.E. degree in Computer Science and Engineering from Jadavpur University. His group’s work on Bias in AI Systems is available through AI OpenScale in IBM Cloud as well as through open-source AI Fairness 360.  

Vijay Arya is a senior researcher in IBM Research AI at the IBM India Research Lab where he works on problems related to Trusted AI. Vijay has 15 years of combined experience in research and software development. His research work spans Machine learning, Energy & smart grids, network measurements & modeling, wireless networks, algorithms, and optimization. His work has received outstanding technical achievement awards at IBM and has been deployed by power utilities in USA. Before joining IBM, Vijay worked as a researcher at National ICT Australia (NICTA) and received his PhD in Computer Science from INRIA, France, and a Masters from Indian Institute of Technology (IIT) Delhi. He has served on the program committees of IEEE, ACM, and IFIP conferences, he is a senior member of IEEE & ACM, and has more than 60 conference & journal publications and patents.