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Events for May 03, 2023
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PhD Thesis Defense - Yu-Chuan Yen
Wed, May 03, 2023 @ 08:30 AM - 10:30 AM
Thomas Lord Department of Computer Science
University Calendar
PhD Thesis Defense - Yu-Chuan Yen
Title: Constructing an unambiguous user-and-machine-friendly, natural-language protocol specification system
Committee Members: Barath Raghavan, Ramesh Govindan, Murali Annavaram
Abstract: Protocol specification has existed for decades to deliver the design and implementation of numerous protocols.
As the guideline and foundation of diverse advanced systems, the methods to process and compose protocol specification have not changed much despite emerging advanced techniques.
The production of specifications remains labor-intensive and involves rigorous discussion to avoid miscommunication via natural language media. A key reason behind these facts is the existence of ambiguities in natural language articles. Ambiguities could represent an unreasonable sentence, a multiple-meaning sentence, or any under-specified behaviors. However, identification of ambiguities is challenging to be applied in domain specific context. In addition, lack of studies applying advanced natural language processing techniques limits our understanding and practices of improving specification production. Motivated by the above observations, this thesis makes the first steps in introducing and building a prototype system that is user-and-machine-friendly and able to process natural language protocol specification while guaranteeing the ambiguous level of the specification. The contributions are four-fold. Firstly, it applies advanced natural language processing techniques called Combinatory Categorial Grammar to analyze protocol specification texts and identifies ambiguous sentences that could result in buggy implementations. Secondly, it parses unambiguous English specification and generates corresponding executable protocol codes that can interoperate with well-known third party code. Thirdly, it defines protocol behaviors with a math definition and introduces unambiguous configurations. The specification configuration is easy for authors to design and easy to automatically generate corresponding English specification and executable code. Lastly, it categorizes a set of verification rules that are able to assist in filtering unreasonable configurations which could not be turned into pieces of English paragraphs or code blocks
Audiences: Everyone Is Invited
Contact: Melissa Ochoa
Event Link: https://usc.zoom.us/j/2553045376
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Mangan Power Distribution Trojan Talk (Virtual)
Wed, May 03, 2023 @ 12:00 PM - 02:00 PM
Viterbi School of Engineering Career Connections
Workshops & Infosessions
Mangan Power Distribution Group
Date: Wednesday, May 3rd, 2023
Time: 12:00 p.m. - 2:00 p.m.
Location: Zoom RSVP HERE
HIRING NOW!
Mangan Power Distribution is a division of Mangan Inc., a nationally recognized Specialty Engineering, Automation, and Integration company. Our engineers excel in providing state-of-the-art electrical engineering services to clients throughout the United States.
Mangan PDG is looking to hire Graduate and Undergraduate Electrical Engineers with Power Systems background. Students on CPT/OPT are welcome.Location: Zoom, please see below for details on how to RSVP
Audiences: Everyone Is Invited
Contact: RTH 218 Viterbi Career Connections
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PhD Thesis Proposal - Arvin Hekmati
Wed, May 03, 2023 @ 03:00 PM - 04:30 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Thesis Proposal - Arvin Hekmati
Title: Correlation-Aware Neural Networks for DDoS Attack Detection In IoT Systems
Committee Members: Bhaskar Krishnamachari (Chair), Cyrus Shahabi, Aiichiro Nakano, Mohammad Rostami, Cauligi Raghavendra
Abstract: We present a comprehensive study on applying machine learning to detect distributed Denial of service (DDoS) attacks using large-scale Internet of Things (IoT) systems. While prior works and existing DDoS attacks have largely focused on individual nodes transmitting packets at a high volume, we investigate more sophisticated futuristic attacks that use large numbers of IoT devices and camouflage their attack by having each node transmit at a volume typical of benign traffic. We introduce new correlation-aware architectures that take into account the correlation of traffic across IoT nodes, and we also compare the effectiveness of centralized and distributed detection models. We extensively analyze the proposed architectures by evaluating five different neural network models trained on a dataset derived from a 4060-node real-world IoT system. We observe that long short-term memory (LSTM) and a transformer-based model, in conjunction with the architectures that use correlation information of the IoT nodes, provide higher performance (in terms of F1 score and binary accuracy) than the other models and architectures, especially when the attacker camouflages itself by following benign traffic distribution on each transmitting node. For instance, by using the LSTM model, the distributed correlation-aware architecture gives 81 percent F1 score for the attacker that camouflages their attack with benign traffic as compared to 35 percent for the architecture that does not use correlation information. We also investigate the performance of heuristics for selecting a subset of nodes to share their data for correlation-aware architectures to meet resource constraints.
Audiences: Everyone Is Invited
Contact: Melissa Ochoa
Event Link: https://usc.zoom.us/j/92583528716?pwd=S01uOUlYQXU5Z0xudXZXbzgwOE0wQT09
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PhD Thesis Defense - Leili Tavabi
Wed, May 03, 2023 @ 03:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Thesis Defense - Leili Tavabi
Committee Members: Mohammad Soleymani (Chair), Maja Mataric, Shrikanth Narayanan, Stefan Scherer
Title: Computational Modeling of Mental Health Therapy Sessions
Abstract: Despite the growing prevalence of mental health disorders, there is a large gap between the needs and available resources for diagnosis and treatment. The recent advancements in machine learning and deep learning provide an opportunity for developing AI assisted assessment of therapy sessions through automated behavior analysis. In this dissertation, I present multiple approaches for modeling and analyzing client therapist dialogue from real world Motivational Interviews toward efficient and systematic assessment of the sessions. I present models for automatic recognition of client intent on a local utterance level, and quality metrics like therapist empathy at the global session level. I further explore the association of in session behaviors with subsequent outcomes, and provide interpretable insights on psychologically relevant features associated with the modeled constructs
Audiences: Everyone Is Invited
Contact: Melissa Ochoa
Event Link: https://usc.zoom.us/j/96609451060?pwd=YnhUOWxjY0ZCaWFadkR4S2srNmZKZz09
