Towards A Sentiment Analyzing Discussion-Board
Brian Thoms, California State University, Channel Islands
The design and evaluation of a sentiment analyzing discussion board was used to support learning and interaction within an existing online social networking (OSN) system. More specifically, this research introduces an innovative extension to learning management software (LMS) that combines real-time sentiment analysis with the goal of fostering student engagement and course community. In this research we perform data mining to extract sentiment on over 6,000 historical discussion board posts. This initial data was analyzed for sentiment and interaction patterns and used as the guiding design principle for redesigning an existing asynchronous online discussion board (AOD). The redesign incorporates a sentiment analyzer, which allows users to analyze the sentiment of their individual contributions before submitting. Through a controlled experiment the software was measured using content analysis, social network analysis and survey data.
Let Them Eat Robots
Jonathan Bachrach, University of California, Berkeley
We are in the midst of a new industrial revolution creating more powerful products with less human labor. How do we reap the benefits of this increased productivity? Unfortunately, automation is rapidly replacing jobs. How do we suppress mass riots and feed starving people? Guaranteed minimum income has been proposed as at least a back stop, but there are many problems with this, not the least of which is paying for it. This talk introduces the rough idea of a robot given to each person upon birth to supply his/her needs and to supplement any guaranteed income. I go into what this entails and discuss the many open challenges.
Correctness And Control For Human-Cyber-Physical Systems
Dorsa Sadigh, University of California, Berkeley
Cyber-physical systems deployed in societal-scale applications almost always interact with humans, e.g. semi-autonomous vehicles interacting with drivers in the car or on the road, semi-autonomous aerial vehicles interacting with human operators, or medical robots interacting with doctors. Due to the safety-critical nature of these human-cyber-physical systems (h-CPS), we, as designers, need to be able to provide guarantees about their safety and performance. My work focuses on creating a new formal design methodology for control and verification of h-CPS closely interfacing with data-driven models in order to ensure provable guarantees.
Big Data On A Network: Massive Integration Of Domain Knowledge For Drug Repurposing
Sergio E. Baranzini, University of California, San Francisco
The time and resources spent in drug development are exorbitant. In parallel, the probabilities that a given lead compound makes it to the clinic are minuscule. Even with the discovery of a few repurposing strategies, the search for a potential repositioning example is still very much trial and error. A paradigm shift is needed if safer, more effective therapeutics are to be developed at a pace that matches the societal demands for treating an ever-increasing segment of the population affected by chronic illnesses, including multiple sclerosis. We have developed a framework to integrate millions of experimental and clinical results in the form of a heterogeneous network, in which multiple entities (drugs, diseases, genes, etc) are connected through known relationships by mining a vast space of the entire domain knowledge in a computationally effective manner. Next machine learning approaches were used to compute the probability that any given drug would interfere with the pathogenic mechanisms of a disease of interest (as a proxy for a potential therapeutic). Our results show that a large proportion of the top predictions correspond to existing indications. However, a number of high-level predictions are not yet known indications, thus providing a compelling rationale to further explore their potential for development. The architecture of this hetnet and the initial results as well as future plans will be discussed during this presentation.
Cooperative Mobile Robots
Oscar Morales Ponce, California State University, Long Beach
Cooperative mobile robots are autonomous entities capable of self-coordinate their actions to solve common problems. For example, a set of mobile robots can be used to patrol a protected area more efficient than using only one robot. Other examples are intelligent vehicles. In this scenario, vehicles can self-coordinate the maneuvers such as crossing uncontrolled junctions with minimum time or safely changing lanes. I describe some challenges and solutions that arise with the use of cooperative mobile robots.