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Events for January
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PhD Defense - Sunghyun Park
Fri, Jan 08, 2016 @ 10:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Computational Modeling of Human Behavior in Negotiation and Persuasion: The Challenges of Micro-Level Behavior Annotations and Multimodal Modeling
PhD Candidate: Sunghyun Park
Date / Time: Jan. 8th (Friday), 10 a.m. ~ noon
Place: SAL322
Committee:
Prof. Louis-Philippe Morency (Chair)
Prof. Jonathan Gratch
Prof. Aiichiro Nakano
Prof. Shrikanth Narayanan (external)
Abstract
Having a deeper understanding of human communication and modeling it computationally has substantial implications for our lives due to its potential synergistic impact with ever advancing technologies. It is an important step for a technology to be accepted as having effective artificial intelligence. However, human communication is a complicated phenomenon that can take an in-depth multimodal analysis of human behavior to understand, in all of the verbal, vocal, and visual channels. The challenge of multimodality is further complicated by many behavioral cues that are subtle and ambiguous.
The work described in this thesis primarily revolves around computational modeling of human behavior, approaching it largely from the affective and social perspectives. This thesis explores computational behavior analysis and modeling in terms of two important contexts of human communication, one in face-to-face interaction and the other in online telemediated interaction. Firstly, this thesis explores human communication in the context of face-to-face dyadic negotiation to better understand and model interpersonal dynamics that occur during close negotiation interaction. Secondly, this thesis explores human communication in the context of online persuasion, to obtain a deeper understanding of persuasive behavior and explore its computational models with online social multimedia content.
In studying human communication in these two contexts of face-to-face negotiation and online persuasion, this thesis addresses four significant research challenges: large-scale annotations, behavior representations, temporal modeling, and multimodal fusion. Firstly, this thesis addresses the challenge of obtaining annotations of human behavior on a large scale, which provide the basis from which computational models can be built. Secondly, this thesis addresses the challenge of making computational representations of multimodal human behavior, in terms of individual behavior and also interpersonal behavior for capturing the dynamics during face-to-face interaction. Thirdly, this thesis addresses the challenge of modeling human behavior with a temporal aspect, specifically for the purpose of making real-time analysis and prediction. Lastly, this thesis explores multimodal fusion techniques in building computational models of human behavior.
Location: Henry Salvatori Computer Science Center (SAL) - 322
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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PhD Defense - Yi Chang
Mon, Jan 11, 2016 @ 01:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Time-Sensitive Social Media Mining and its Applications
PhD Candidate: Yi Chang
Date / Time: Jan. 11th (Monday), 1:00~3:00 PM
Place: SAL 213
Committee:
Prof. Yan Liu (Chair)
Prof. Cyrus Shahabi
Prof. Lian Jian (external)
Abstract
Social media is growing at an explosive rate and it becomes increasingly difficult for users to consume and digest useful information from massive and high-velocity data. To overcome this information overload problem, in this thesis, we have studied several key challenges, which could effectively re-structure and re-organize massive information on social media sites.
First, it is critical to effectively detect and model a burst of topics on social media, which is reflected by extremely frequent mentions of certain keywords in a short time interval. We propose a novel time-series modeling approach which captures the rise and fade temporal patterns via life cycle model, then invent a probabilistic graphical model to automatically discover inherent temporal patterns within a collection of buzz time-series. Second, as each individual tweet is short and lacks sufficient context information, users cannot effectively understand or consume information on Twitter, which can either make users less engaged or even detached from using Twitter. In order to provide informative context to a Twitter user, we initiate the task of Twitter cascade summarization, and propose a supervised learning framework with a set of novel features to generates a succinct summary from a large but noisy Twitter context cascade. Third, we address the challenge of timeline detection from social media, which is to detect a chain of spiking events in chronological order, and it can help social media users not only rediscover the most important historical events about entities but also understand the order and trends of those events. In order to capture the life circle patterns of events in timelines and combine temporal shapes with content information, we propose a novel probabilistic framework to effectively detect timelines of entities in social media. Finally, we address the challenge of timeline abstracting from social media, which is to detect a list of timeline events, and abstract each events with its representative social media post. In order to automatically identify the number of timeline events, we propose a non-parametric framework to effectively and efficiently detect and abstract timeline events. Gibbs sampling is employed to infer the model parameters, and a fast burn-in strategy based on temporal bursts is further introduced to speed up the model inference.
Location: Henry Salvatori Computer Science Center (SAL) - 213
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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PhD Defense - Alok Gautam Kumbhare
Wed, Jan 13, 2016 @ 10:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
University Calendar
**Ph.D. Defense Announcement**
"Adaptive and Resilient Stream Processing on Clouds"
Ph.D. candidate: Alok Gautam Kumbhare
Wednesday, January 13, 2016
10:00AM -“ 12:00PM
EEB 248
Abstract:
Ubiquitous deployment of physical and virtual sensors, coupled with tremendous increase in the number of connected devices has led to the explosion of data, not only in terms of the volume and but also the velocity at which it is being generated. Scalable stream processing systems are necessary to process these high velocity data streams and to derive useful insights in real-time. However, unlike high volume batch processing, stream processing applications need to run continuously with minimum downtime and hence are susceptible to the three dimensions of dynamism: domain dynamism -“ variations in domain requirements; infrastructure dynamism -“ temporal and spatial variations in infrastructure performance and system failures; and data dynamism -“ changes in input data rates over time, all of which adversely affect the application QoS and value achieved from the application.
We posit that applications that are aware of this dynamism and can adapt to the changing conditions are critical in achieving the desired QoS in a cost-efficient manner. We thus propose a novel Dynamic Dataflow Application model and execution framework that inherently supports flexible stream processing applications and allow seamless runtime recomposition. The model provides an extremely powerful tool to develop, deploy and execute long-running dynamic applications with minimum overhead and promotes the notion of value-driven execution through continuous adaptations to achieve the best value from the application.
We also propose several scheduling and resource mapping heuristics for deployment of these dynamic dataflows on public clouds that take advantage of the pay-as-you-go cost model to achieve the desired quality of service (QoS) and provide a balance between the application value, and resource cost. Finally, to ensure uninterrupted execution in the presence of infrastructure failures, we propose novel integrated approach for efficient runtime elasticity, fault-tolerance and load balancing that enables seamless scaling and provide high fault-tolerance with sub-second recovery latency and hence offer strong resilience and service guarantees.
Bio:
Alok Gautam Kumbhare is a PhD candidate at the department of Computer Science, University of Southern California under the guidance of Prof. Viktor K. Prasanna and Prof. Yogesh Simmhan (IISc Bangalore). His primary research focus is on developing adaptive scheduling algorithms and large-scale distributed systems for processing high-velocity and highly variable data streams on Cloud computing environments. His research interests also include big data analytics and large scale machine learning, resource management, and graph analytics. He completed his B.Tech from Indian Institute of Technology Guwahati in 2008.
Defense Committee: Prof. Aiichiro Nakano, Prof. Viktor K. Prasanna (chair), Prof. Cauligi Raghavendra
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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W.V.T. Rusch Engineering Honors Program Colloquium
Fri, Jan 15, 2016 @ 01:00 PM - 01:50 PM
USC Viterbi School of Engineering, Viterbi School of Engineering Student Affairs
University Calendar
Join us for a presentation by Dr. Carmen Boening, from the Jet Propulsion Laboratory, titled "Measuring Climate Change with Gravity."
Location: Seeley G. Mudd Building (SGM) - 101
Audiences: Everyone Is Invited
Contact: Ramon Borunda/Academic Services
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PhD Defense - Niloofar Montazeri
Tue, Jan 19, 2016 @ 04:45 AM - 06:45 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Building a Knowledgebase for Deep Lexical Semantics
PhD Candidate: Niloofar Montazeri
Date / Time: Jan 19th (Tuesday), 4:45-6:45 pm
Place: SAL 213
Abstract:
Words describe the world, so if we are going to draw the appropriate inferences in understanding a text, we must have a prior explication of how we view the world (world knowledge) and how words and phrases map to this view (lexical semantics knowledge).
Existing world knowledge and lexical semantics knowledge resources are not particularly suitable for deep reasoning, either due to lack of connection between their elements or due to their simple knowledge representation method (binary relations between natural language phrases).
To enable deep understanding and reasoning over natural language, (Hobbs 2008) has proposed the idea of "Deep Lexical Semantics". In Deep Lexical Semantics, principal and abstract domains of commonsense knowledge are encoded into "core theories" and words are linked to these theories through axioms that use predicates from these theories. This research is concerned with the second task: Axiomatizing words in terms of predicates in core theories.
We show that a large scale lexical semantics knowledgebase for a given domain can be developed by dividing the authoring task using the optimum mix of manual and automatic methods. We use concept relations in existing lexical semantics resources to systematically identify the optimum set of concepts that need to be axiomatized manually and axiomatize a large number of relevant concepts automatically. We have used this method to axiomatize concepts related to the domain of composite entities and evaluated the quality of the resulting axioms. Furthermore, we have evaluated the usefulness of these axioms on the well-studied task of extracting part-of relations from text.
Committee:
Prof. Jerry R. Hobbs (Chair)
Prof. Kevin Knight
Prof. Andrew Gordon
Prof. Elsi Kaiser (External Member)
Location: Henry Salvatori Computer Science Center (SAL) - 213
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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PhD- Defense - Gholamreza Safi
Tue, Jan 19, 2016 @ 11:00 AM - 01:00 PM
Thomas Lord Department of Computer Science
University Calendar
Date and Location: Tuesday, January 19th, 11:00 am at SAL 322.
Title: Detecting Anomalies in Event-Based Systems Through Static Analysis
PhD Candidate: Gholamreza Safi
Committee: Nenad Medvidovic (chair), William GJ Halfond, Sandeep Gupta(outside member)
The event-based paradigm allows developers to design and build systems that are highly flexible and can be easily adapted. There are two main complications that can occur in the systems that are based on this paradigm. The first complication concerns inter-component interactions. Events that are used by components for this purpose are sent, received, and processed nondeterministically, due to the systems' reliance on implicit invocation and implicit concurrency. This nondeterminism can lead to event anomalies, which occur when an event-based system receives multiple events that lead to the write of a shared field or memory location. Event anomalies can lead to unreliable, error-prone, and hard-to-debug behavior in an event-based system. The second complication concerns intra-component interactions that usually occur through method calls. Each sequence of method calls introduces an execution path to the system. It is possible that there exist multiple execution paths that are not accessing the same memory locations or sharing data but are unnecessarily synchronized with each other despite the fact that they can be executed concurrently. I call these situation synchronization anomalies.
To detect event anomalies, this dissertation presents a new static analysis technique, DEvA, for automatically Detecting Event Anomalies. DEvA has been evaluated on a set of open-source event-based systems against a state-of-the-art technique for detecting data races in multi-threaded systems and a recent technique for solving a similar problem with event processing in Android applications. DEvA exhibited high precision with respect to manually constructed ground truths and was able to locate event anomalies that had not been detected by the existing solutions.
Also, this dissertation presents a new static analysis technique, DSA, for automatically Detecting Synchronization Anomalies. I have evaluated DSA both empirically and analytically. My empirical evaluation shows that synchronization anomalies are a common problem and can occur in any randomly chosen system. Also, DSA is efficient and scalable while exhibiting high precision, meaning that there were no false positives in its results after being applied to fourteen subject systems. The analytical evaluation of DSA provides guidelines for the situations where removing a synchronization anomaly can be more beneficial. By removing just one synchronization anomaly from two of our subject systems based on the provided guidelines, there was an enhancement of 10% in the performance of those systems.
Location: Henry Salvatori Computer Science Center (SAL) - 322
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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W.V.T. Rusch Engineering Honors Program Colloquium
Fri, Jan 22, 2016 @ 01:00 PM - 01:50 PM
USC Viterbi School of Engineering, Viterbi School of Engineering Student Affairs
University Calendar
Join us for a presentation by Prof. Greg Autry, from the University of Southern California, titled "Entrepreneurship in the New Space Industry."
Location: Seeley G. Mudd Building (SGM) - 101
Audiences: Everyone Is Invited
Contact: Ramon Borunda/Academic Services
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W.V.T. Rusch Engineering Honors Program Colloquium
Fri, Jan 29, 2016 @ 01:00 PM - 01:50 PM
USC Viterbi School of Engineering, Viterbi School of Engineering Student Affairs
University Calendar
Join us for a presentation by Michael Hiltzik, from the Los Angeles Times, titled "Big Science: Ernest Lawrence and the Invention that Launched the Military-Industrial Complex."
Location: Seeley G. Mudd Building (SGM) - 123
Audiences: Everyone Is Invited
Contact: Ramon Borunda/Academic Services
