Tools for Behavioral Phenotyping of C. elegans

Animal behavior is critical to survival and provides a window into how the brain makes decisions and integrates sensory information. A simple model organism that allows researchers to more precisely interrogate the relationships between behavior and the brain is the nematode C. elegans. However, current phenotyping tools have technical limitations that make observing, intervening in, and quantifying behavior in diverse settings difficult. In this thesis, I develop enabling technological systems to resolve these challenges. To address scaling issues in observation and intervention in long-term behavior, I develop a platform for long-term continuous imaging, online behavior quantification, and online behavior-conditional intervention. I show that this tool is easy to build and use and can operate in an automated fashion for days at a time. I then use this platform to understand the consequences of quiescence deprivation to C. elegans health. To quantify complex animal postures, and plant and stem cell aggregate morphology, I develop an app to enable fast, versatile and quantitative annotation and demonstrate that it is both ~ 130-fold faster and in some cases less error-prone than state-of-the-art computational methods. This app is agnostic to image content and allows freehand annotation of curves and other complex and non-uniform shapes while also providing an automated way to distribute annotation tasks. This tool may be used to generate ground truth sets for testing or creating automated algorithms. Finally, I quantify C. elegans behavior using quantitative machine-learning analysis and map the worm’s behavioral repertoire across multiple physical environments that more closely mimic C. elegans’ natural environment. From this analysis, I identified subtle behaviors that are not easily distinguishable by eye and built a tool that allows others to explore our video dataset and behaviors in a facile way. I also use this analysis to examine the richness of C. elegans behavior across selected environments and find that behavior diversity is not uniform across environments. This has important implications for choice of media for behavioral phenotyping, as it suggests that the appropriate media choice may increase our ability to distinguish behavioral phenotypes in C. elegans. Together, these tools enable novel behavior experiments at a larger scale and with more nuanced phenotyping compared to currently available tools.Ph.D

Developing an Affect-Aware Rear-Projected Robotic Agent

Social (or Sociable) robots are designed to interact with people in a natural and interpersonal manner. They are becoming an integrated part of our daily lives and have achieved positive outcomes in several applications such as education, health care, quality of life, entertainment, etc. Despite significant progress towards the development of realistic social robotic agents, a number of problems remain to be solved. First, current social robots either lack enough ability to have deep social interaction with human, or they are very expensive to build and maintain. Second, current social robots have yet to reach the full emotional and social capabilities necessary for rich and robust interaction with human beings. To address these problems, this dissertation presents the development of a low-cost, flexible, affect-aware rear-projected robotic agent (called ExpressionBot), that is designed to support verbal and non-verbal communication between the robot and humans, with the goal of closely modeling the dynamics of natural face-to-face communication. The developed robotic platform uses state-of-the-art character animation technologies to create an animated human face (aka avatar) that is capable of showing facial expressions, realistic eye movement, and accurate visual speech, and then project this avatar onto a face-shaped translucent mask. The mask and the projector are then rigged onto a neck mechanism that can move like a human head. Since an animation is projected onto a mask, the robotic face is highly flexible research tool, mechanically simple, and low-cost to design, build and maintain compared with mechatronic and android faces. The results of our comprehensive Human-Robot Interaction (HRI) studies illustrate the benefits and values of the proposed rear-projected robotic platform over a virtual-agent with the same animation displayed on a 2D computer screen. The results indicate that ExpressionBot is well accepted by users, with some advantages in expressing facial expressions more accurately and perceiving mutual eye gaze contact. To improve social capabilities of the robot and create an expressive and empathic social agent (affect-aware) which is capable of interpreting users\u27 emotional facial expressions, we developed a new Deep Neural Networks (DNN) architecture for Facial Expression Recognition (FER). The proposed DNN was initially trained on seven well-known publicly available databases, and obtained significantly better than, or comparable to, traditional convolutional neural networks or other state-of-the-art methods in both accuracy and learning time. Since the performance of the automated FER system highly depends on its training data, and the eventual goal of the proposed robotic platform is to interact with users in an uncontrolled environment, a database of facial expressions in the wild (called AffectNet) was created by querying emotion-related keywords from different search engines. AffectNet contains more than 1M images with faces and 440,000 manually annotated images with facial expressions, valence, and arousal. Two DNNs were trained on AffectNet to classify the facial expression images and predict the value of valence and arousal. Various evaluation metrics show that our deep neural network approaches trained on AffectNet can perform better than conventional machine learning methods and available off-the-shelf FER systems. We then integrated this automated FER system into spoken dialog of our robotic platform to extend and enrich the capabilities of ExpressionBot beyond spoken dialog and create an affect-aware robotic agent that can measure and infer users\u27 affect and cognition. Three social/interaction aspects (task engagement, being empathic, and likability of the robot) are measured in an experiment with the affect-aware robotic agent. The results indicate that users rated our affect-aware agent as empathic and likable as a robot in which user\u27s affect is recognized by a human (WoZ). In summary, this dissertation presents the development and HRI studies of a perceptive, and expressive, conversational, rear-projected, life-like robotic agent (aka ExpressionBot or Ryan) that models natural face-to-face communication between human and emapthic agent. The results of our in-depth human-robot-interaction studies show that this robotic agent can serve as a model for creating the next generation of empathic social robots

Edge Intelligence : Empowering Intelligence to the Edge of Network

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe

Edge Intelligence : Empowering Intelligence to the Edge of Network

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe

A theory of information processing for machine visual perception: inspiration from psychology, formal analysis and applications

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura : 20-09-201

Online Participation and Dissent in Turkey: From the Gezi Protests to the 15 July Coup Attempt

How do people use smartphones and online social networks to participate in social movements? The role of online social networks in political participation has previously been made light of with concepts like ‘clicktivism’ and ‘slacktivism’ which emphasise the authenticity of all offline activity over online practices. This thesis challenges the concepts and terminologies that reflect the dichotomous understandings of the online vs. offline worlds by questioning the scientific validity of the exaltation of street over online participation. The project proposes a more integrated understanding of participation in politics through an overlapping of online and offline actions and consequences with the help of two case studies concerning the recent political history of Turkey, namely, the 2013 Gezi protests and the 2016 attempted coup. Video posts created by participants in each case have been analysed to understand videocapturing and posting strategies and how different content has been created in anti-and pro-government political situations. The findings from these studies are interpreted with reference to the legal trajectory of Internet technologies and online social networks in Turkey and reveal how this changing online landscape acts both as a perpetrator and a product of certain political participation strategies. By proposing the concept of ‘meta-activism’ as key to understanding the inevitable role of online actions in political participation in the contemporary world, this thesis aims to demystify the purist notion of activism as a street-based practice. This thesis also contributes to discussions about online vs. offline participation under unusual political circumstances by presenting a specifically non-Eurocentric perspective on the practices and consequences of online participation in politics as well as bringing together findings from two very distinct cases of political upheaval in Turkey’s recent history

The Tiger Vol. 103 Issue 1 2009-01-16

https://tigerprints.clemson.edu/tiger_newspaper/2376/thumbnail.jp

B!SON: A Tool for Open Access Journal Recommendation

Finding a suitable open access journal to publish scientific work is a complex task: Researchers have to navigate a constantly growing number of journals, institutional agreements with publishers, funders’ conditions and the risk of Predatory Publishers. To help with these challenges, we introduce a web-based journal recommendation system called B!SON. It is developed based on a systematic requirements analysis, built on open data, gives publisher-independent recommendations and works across domains. It suggests open access journals based on title, abstract and references provided by the user. The recommendation quality has been evaluated using a large test set of 10,000 articles. Development by two German scientific libraries ensures the longevity of the project

One rhizome, two unstoppable blossoms: environmental communication and ecological rhetoric

This dissertation will contribute to the project of explaining what environmental communication studies may offer theoretically to an understanding of communication and rhetoric in general, investigate a complex problem in rhetoric from a variety of methodological approaches, offer examples of rhetorical criticism relevant to environmental communication researchers, and delve into certain salient aspects of ecological rhetoric (defined herein as rhetoric from an ecological perspective). The hypothesis of this doctoral thesis is twofold: 1) studying environmental communication can illuminate much about communication and rhetoric in general, and 2) ecological rhetoric has considerable persuasive potential in itself for reasons that can be demonstrated through criticism and ascertained through theoretical reflection. This dissertation is a critical and conceptual project that might also shed light on some practical questions about environmental advocacy. Case studies in historically significant instances of environmental communication from Garrett Hardin and Al Gore, as well as reflections upon the Interstate and the erosion of the Louisiana coastline, analyzed by means of rhetorical criticism, media ecology, semiotics, and quantitative methods, should highlight the narrative, material, and indexical quality of ecological rhetoric. Finally, a survey of concepts from network science will connect ecological rhetoric with recent debates in rhetorical theory