DISSERTATION DEFENSE Department of Computer Science and Engineering University of South Carolina Author : Xiaopeng Li Advisor : Dr. Lannon Luo Date : May 8, 2020 Time : 9:00 am Place : Virtual Defense Abstract Internet of Things (IoT) technologies have made our lives more convenient and better informed by sensing and monitoring our surroundings. Security applications, such as device pairing and user authentication, are the fundamentals for building a trustworthy smart environment. A secure and convenient pairing approach is critical to IoT enabled applications, as pairing is to establish a secure wireless communication channel for devices. Besides, since a smart environment usually has multiple people (e.g., kids and adults, patients and doctors), how to authenticate users operating on the densely deployed devices and sensitive objects (e.g., a cabinet storing medical records) is also an important problem. Existing security measures either rely on special hardware, have bad usability, or are vulnerable to attacks, and thus fail to protect resource-constrained IoT devices and dumb objects. This thesis aims at addressing the above shortcomings and implementing three security applications: (1) performing secure pairing for IoT devices that lack conventional user interfaces, such as keyboards and display; (2) providing secure and applicable authentication for IoT devices; (3) validating uses of sensitive dumb objects that have no user input interfaces. First, we propose a technique, Universal Operation Sensing, which allows an IoT device to sense the user’s physical operations on it without requiring inertial sensors. Based on this technique, a user carrying a smartphone or wearing a wristband can finish pairing in seconds by touching, in the form of some very simple operations, the target device. We design a pairing protocol based on fuzzy commitment, and build a prototype system named T2Pair. The comprehensive evaluation shows that it is secure and usable. Second, we design three usable authentication gestures by asking the user to ‘pet’ (in the form of some very simple touches for about 2 seconds) on the devices. We build a secure and intuitive authentication method that authenticates device users by comparing the petting operations sensed by devices and those captured by the user wristband. The authentication method is highly secure as physical operations are required, rather than based on proximity. It is also intuitive, adopting very simple authentication operations, e.g., clicking buttons, twisting rotary knobs, and swiping touchscreens. Unlike the state-of-the-art methods, our method does not require any hardware modifications of devices, and thus can be applied to commercial off-the-shelf (COTS) devices. Finally, We present the first implicit and accurate authentication approach for dumb objects, named MoMatch. (1) It provides implicit and continuous authentication. (2) It makes fast authentication decision based on a single object interaction, e.g., pushing a door. (3) It is accurate with average area under the curve (AUC) across 10 different objects =0.97. (4) It works with objects that have zero authentication interfaces. (5) It uses zero biometrics, so does not need per-user profiling. (6) Rigorous security studies are performed, showing that MoMatch is resilient to attacks. The approach is built on a solid causal relationship: an object has a motion typically because a human hand moves it. Thus, the object’s motion and the legitimate user’s hand movement must correlate to validate the use. The main challenge is how to calculate the correlation, as conventional approaches, such as Dynamic Time Warping (DTW) and SVM, all fail to work. We propose an Imagified Curve Comparison (ICC) technique that converts the motion-data correlation evaluation problem into an image comparison problem, and resolve it using neural networks successfully.

DISSERTATION DEFENSE Department of Computer Science and Engineering University of South Carolina Author : Asif Jamil Chowdhury Advisor : Dr. Gabriel A. Terejanu Date : Mar 18, 2020 Time : 11:30 am Place : Meeting Room 2265, Innovation Center Abstract Computational catalysis, in contrast to experimental catalysis, uses approximations such as density functional theory (DFT) to compute properties of reaction intermediates. But DFT calculations for a large number of surface species on variety of active site models are resource intensive. In this work, we are building a machine learning based predictive framework for adsorption energies of intermediate species, which can reduce the computational overhead significantly. Our work includes the study and development of appropriate machine learning models and effective fingerprints or descriptors to predict energies accurately for different scenarios. Furthermore, Bayesian inverse problem, that integrates experimental catalysis with its computational counterpart, uses Markov chain Monte Carlo (MCMC) methods to refine the uncertainties on the quantities-of-interest such as turnover frequency. However, large number of forward simulations required by MCMC can become a bottleneck, especially in computational catalysis, where the evaluation of likelihood functions involves finding the solution to microkinetic models. A novel and faster MCMC method is proposed to reduce the number of expensive target evaluations and to shorten the burn-in period by emulating the target along with using a better informed proposal distribution.

You are invited at the CSCE Colloquium on Wednesday 03/11/2020 at 10:15. Abstract: Combination puzzles, such as the Rubik’s cube, pose unique challenges for artificial intelligence. Furthermore, solutions to such puzzles are directly linked to problems in the natural sciences. In this talk, I will present DeepCubeA, a deep reinforcement learning and search algorithm that can solve the Rubik’s cube, and six other puzzles, without domain specific knowledge. Next, I will discuss how solving combination puzzles opens up new possibilities for solving problems in the natural sciences. In particular, I will describe how we are using DeepCubeA to tackle problems in chemistry. Finally, I will show how problems we encounter in the natural sciences motivate future research directions. A demonstration of our work can be seen at http://deepcube.igb.uci.edu/. Bio: Forest Agostinelli is a postdoctoral researcher at UC, Irvine. He received his B.S. from the Ohio State University, his M.S. from the University of Michigan, and his Ph.D. from UC, Irvine. His research interests include deep learning, reinforcement learning, search, bioinformatics, neuroscience, and chemistry. His homepage is located at https://www.ics.uci.edu/~fagostin/.

Abstract: As human-robot collaboration in both industry and household environments becomes more prevalent, several aspects need to be further developed to allow for natural and safe collaboration. First, a generalized task learning framework must be developed in order to allow robots to perform various manipulation and assembly tasks. Second, better communication between agents is necessary to allow humans and robots to work together effectively and to teach the robot to perform tasks. This instruction can be accomplished in several ways, including verbal instruction and human demonstration. Combining these two methods into a single multimodal system will provide a more seamless interaction between two or more agents. Additionally, natural language can provide a communication channel between the human and robot which would allow the robot to inform the human of issues during learning and ask for assistance in resolving them. Finally, the human’s attention needs to be monitored and considered during decision making and task execution in order to allow humans and robots to work alongside each other safely and reliably. This type of interaction will be the basis for seamless human-robot collaboration for both industrial and household tasks. In household environments, this research would allow users to utilize robots for activities of daily living, which they are unable to perform themselves. In an industry setting, it would allow employees to train robots for new tasks themselves while ensuring they can work alongside the robots safely and reliably. Bio: Janelle is a Ph.D. Candidate and Graduate Research Fellow in Computer Science at the University of Nevada, Reno. She will be finishing her Ph.D. in May 2020. Previously, she completed her M.S. in Computer Science at the University of Nevada, Reno and her B.S. in Applied Mathematics at the University of Nevada, Reno. She was awarded a fellowship from the Nevada Space Grant Consortium in 2016. Her recent work was a Best Paper Finalist at the International Conference on Social Robotics (ICSR) in 2019. Her research interests are generalized task learning, natural language processing, and machine learning for robotics applications. These interests are motivated by the desire for creating a seamless workflow for collaborative muti-robot/human-robot teams in both industrial and household environments. You are invited at the CSCE Colloquium on Monday 03/09/2020 a t 10:15.

Abstract: Intelligent decision making is at the heart of Artificial Intelligence (AI). A large number of real-world domains, such as autonomous vehicles, delivery robots, cyber security, and so many others, involve multiple AI decision makers, or agents, that cooperate with collective efforts in a distributed manner, where each agent's decisions are based on its local information, with often limited communication with others. This distributed nature makes it challenging to design efficient and reliable multiagency. Issues like failure to coordinate, unsafe interactions, and resource misallocation can easily arise. A promising approach to tackling these challenges is to explicitly build dependencies among cooperating agents, where one agent can be trusted to facilitate the execution of another. In this talk, I will present a framework that achieves trustworthy dependency by borrowing the notion of social commitments. Intuitively, a commitment regularizes an agent’s behavior so that it can be well anticipated and exploited by another. This talk will build up a formalism of this intuition, and discuss how multiagency commitments can be efficiently identified and faithfully fulfilled. Finally, this talk will conclude with my future agenda, covering topics of verification of trustworthy multiagency, discovery of safety-related dependency, and interpretability-performance tradeoff in multiagency. Bio: Qi Zhang is a final year Ph.D. student at the University of Michigan, advised by Edmund Durfee and Satinder Singh. His research interest is in artificial intelligence, with focuses on planning under uncertainty, reinforcement learning, and multiagent coordination. His long-term goal is to build safe, reliable, and trustworthy AI systems that retain their power and flexibility to handle complex, diverse contexts. Friday, March 6, Storey Innovation Center (Room 2277) from 10:15 am - 11:15 am.

In the real world, many data are naturally represented as graph data such as social networks. Deep learning methods have been very successful in various fields such as computer vision and natural language processing. However, developing deep learning methods on graph data is challenging due to the lack of locality information. In this talk, I will present my work on developing deep learning methods on graph data. My work addresses this challenge and significantly advances feature learning and structure learning on graphs in both accuracy and efficiency. Specifically, I will introduce our proposed learnable graph convolution layer and hard graph attention layer, which enables fully learnable convolution and hard attention operations on graph data while saving computational resources. Then I will discuss our developed efficient and effective graph pooling operators that significantly advance state-of-the-art performance. Besides layer-wise methods, I will talk about the first encoder-decoder network architecture on graph data. This series of research works result in a series of publications in top-tier journals and conferences. Bio: Hongyang Gao is a Ph.D. Candidate in the Department of Computer Science & Engineering at Texas A&M University in College Station, Texas. His primary research interests are machine learning and artificial intelligence with a special focus on deep learning. In particular, he mainly pays attention to the performance and efficiency of deep learning methods with applications to various data types like graphs. His research work has been recognized with a series of publications in top-tier journals and conferences. Before his Ph.D. work, Hongyang received his M.S. in Computer Science from Tsinghua University in 2012 and his B.S. from Peking University in 2009. Monday 3/2/20 at 10:15am

ABSTRACT: Historically, Artificial Intelligence has taken a symbolic route for representing and reasoning about objects at a higher-level or a statistical route for learning complex models from large data. To achieve true AI, it is necessary to make these different paths meet and enable seamless human interaction. First, I will introduce for learning from rich, structured, complex and noisy data. One of the key attractive properties of the learned models is that they use a rich representation for modeling the domain that potentially allows for seam-less human interaction. I will present the recent progress that allows for more reasonable human interaction where the human input is taken as “advice” and the learning algorithm combines this advice with data. Finally, I will discuss more recent work on “closing-the-loop” where information is solicited from humans as needed that allows for seamless interactions with the human expert. I will discuss these methods in the context of supervised learning, planning, reinforcement learning and inverse reinforcement learning. BIO: Dr. Sriraam Natarajan is an Associate Professor and the Director for Center for ML at the Department of Computer Science at University of Texas Dallas. He was previously an Associate Professor and earlier an Assistant Professor at Indiana University, Wake Forest School of Medicine, a post-doctoral research associate at University of Wisconsin-Madison and had graduated with his PhD from Oregon State University. His research interests lie in the field of Artificial Intelligence, with emphasis on Machine Learning, Statistical Relational Learning and AI, Reinforcement Learning, Graphical Models and Biomedical Applications. He has received the Young Investigator award from US Army Research Office, Amazon Faculty Research Award, Intel Faculty Award, XEROX Faculty Award, Verisk Faculty Award and the IU trustees Teaching Award from Indiana University. He is the program co-chair of SDM 2020 and ACM CoDS-COMAD 2020 conferences. He is the chief editor of Frontiers in ML and AI journal, an editorial board member of MLJ, JAIR and DAMI journals and is the electronics publishing editor of JAIR.

Speaker: Dr. Saeedeh Shekarpour Affiliation: University of Dayton Location: Innovation Center, Room 2277 Time: Friday 02/21/2020 (10:15 - 11:15am) Question Answering (QA) systems are becoming an inspiring model for the future of search engines. While, recently, datasets underlying QA systems have been promoted from unstructured datasets to structured datasets with semantically highly enriched metadata, question answering systems are still facing serious challenges and are therefore not meeting users’ expectations. Especially, question answering over interlinked data sources is raising new challenges due to two inherent characteristics. First, different datasets employ heterogeneous schemas and each one may only contain a part of the answer to a certain question. Second, constructing a federated formal query across different datasets requires exploiting links between them on both the schema and instance levels. This talk will be conducted in the three following directions:

1. SINA strategies for addressing the salient challenges of QA systems [1,2,3]
2. Re-engineering Question Answering Systems [4,5,6]
3. A look to the future

Minor Part (20 minutes) This part covers the other research directions which I am involving, such as the challenges related to misinformation, information extraction, social media analytics. Saeedeh Shekarpour is an assistant professor at the University of Dayton, Ohio. She accomplished her Ph.D. at the University of Bonn in Germany. Saeedeh also spent one year as a postdoctoral researcher in the EIS research group at the Bonn University and two years as a postdoctoral researcher at Knoesis research center. She is passionate to conduct advanced research in the following fields: (i) Knowledge representation in AI technologies, such as Question Answering, chatbot technology (ii) Information Disorder (fake news, harassing language) (iii) Ontology Development, (iv) Text and knowledge Analytics. She successfully published her research results in the most renowned conferences and journals of her field including the World Wide Web, AAAI, NAACL, Web Intelligence conference, Web science, Journal of Web Semantics, Semantic Web Journal, and PLOS ONE.

If you aren't familiar, ACM is one of Computer Science's oldest and most established professional organization. Our university ACM chapter primarily hosts weekly student talks by undergrads and graduate students in CSE. This semester these will usually be held on Thursday nights at 7 in the CSE Student Lounge (SWGN 2a17) We also do a lot else, so consider joining our mailing list (email kennethj@email.sc.edu) or checking out our website: http://acm.cse.sc.edu While most students that come to our meetings are in CSE, we welcome students from across the university to attend. There is no expectation of your time, so come as often or as little as you wish. Furthermore, while some talks will certainly be more advanced than others, we believe there is something of interest in every presentation for all levels of technical experience. Only an interest in computing is required to receive your free pizza! This week we will be returning to our normal cadence with a presentation by undergraduate Josh Nelson. There will be FREE PIZZA from 7-7:15 before Josh's talk begins. The details: --------------------------------------------------- "Getting Started in Open Source" Thursday, February 6th, 2020 7:00-8:15pm Swearingen Engineering Center, room 2A17 FREE PIZZA All majors and backgrounds, both technical or otherwise, welcome! ------------------------------------------------------- Josh will cover the key components of getting started - contributing- to open source projects. This includes: - finding a project - deciding what to work on - communicating with the maintainers - making your first pull request (PR) Josh will use his real PRs and projects as examples of open source best practices (and not-so-best practices). There will be a discussion of how to use common open-source tools like git, markdown, continuous integration (CI) providers, and unit tests as they pertain to contributing to a project. If time permits, we can help attendees find projects they'd be interested in contributing to. While this talk caters to beginners, there is surely something of interest for programmers of all skill levels. Come with questions (and bring a friend)! Information about the talk can be found on our website at https://acm.cse.sc.edu/events/2020-02-06.html. In other news... Be on the lookout for more information about our Spring 2020 ACM Code-a-Thon! Please note that information is still being finalized at this time. I'll send a standalone email out as an announcement with all the details as soon as we divine them ourselves. Details will be posted to the website as well: https://acm.cse.sc.edu/events/2020-code-a-thon.html. As always, please feel free to reach out to me with any questions! (Just respond to this email to get in touch.) Best, Kenneth Johnson ACM Communications Chair

Author : Mingzhe Du Advisor : Dr. Jose Vidal Date : Feb 5, 2020 Time : 3 pm Place : Meeting Room 2267, Innovation Center Abstract In social sciences, theories are used to explain and predict observed phenomena in the natural world. Theory construction is the research process of building testable scientific theories to explain and predict observed phenomena in the natural world. The conceptual new ideas and meanings of theories are conveyed through carefully chosen definitions and terms. The principle of parsimony, an important criterion for evaluating the quality of theories (e.g., as exemplified by Occam's Razor) mandates that we minimize the number of definitions (terms) used in a given theory. Conventional methods for theory construction and parsimony analysis are based on the heuristic approaches. However, it is not always easy for young researchers to fully understand the theoretical work in a given area because of the problem of ``tacit knowledge'', which often makes results lack coherence and logical integrity. In this research, we propose to help with this problem in three parts. In particular, for the first part of this study, we present Wikitheoria, a generic knowledge aggregation framework to facilitate the parsimonious approach of theory construction with a cloud-based theory modularization platform and semantic-based algorithms to minimize the number of definitions. The presented approach is demonstrated and evaluated using the modularized theories from the database and sociological definitions retrieved from the system lexicon and sociological literature. This study proves the effectiveness of using the cloud-based knowledge aggregation system and semantic analysis models for promoting the parsimonious sociology theory construction. In the second part, our study is focused on semantic-based parsimony analysis. We introduce an embedding-based approach using machine learning models to reduce the semantically similar sociological definitions, where definitions are encoded with word embeddings and sentence embeddings. Given several types of embeddings exist, we compare the definition's encodings with the goal of understanding what embeddings are more suitable for knowledge representation, and what classifiers are more capable for capturing semantic similarity in the task of parsimonious theory construction. In the final part of this study, we propose SOREC, a novel semantic content-based recommendation system with supervised machine learning model for theoretical parsimony evaluation by checking the semantic consistency of definitions while constructing theories. Specifically, we evaluate the XGBoost tree-based classifier with the combination of low-level features and high-level features on our dataset. The proposed CBRS substantially outperforms conventional matrix factorization-based CBRS in suggesting semantically related sociological definitions. In this study, we provide a solid baseline for future studies in the research area of sociological definition semantic similarity computation. Moreover, theory construction is a common research process in a lot of human science-related disciplines such as psychology, criminology, and other social sciences. The results of this study can be further applied to the theory construction in these disciplines.