Qualitative preferences over outcomes in a combinatorial domain (where many variables jointly describe the outcome) The CP-net visualizer presented is useful for researchers are useful in automated decision making and modeling human eliciting human preferences, building CP-nets for specific preferences in real world domains. Conditional Preference experiments, visualizing generated CP-nets, and for the general Networks (CP-nets), also known as Ceteris Paribus public learning more about preference modeling. It has Networks, are a compact graph-based mathematical formalism an interface consisting of three vertical panels. On the left is for representing such preferences (Boutilier et al. 2004).

We report on the ongoing development of EDDIE (Emotion Demonstration, Decoding, Interpretation, and Encoding), an interactive embodied AI to be deployed as an intervention system for children diagnosed with High-Functioning Autism Spectrum Disorders (HFASD). EDDIE presents the subject with interactive requests to decode facial expressions presented through an avatar, encode requested expressions, or do both in a single session. Facial tracking software interprets the subject’s response, and allows for immediate feedback. The system fills a need in research and intervention for children with HFASD by providing an engaging platform for presentation of exemplar expressions consistent with mechanical systems of facial action measurement integrated with an automatic system for interpreting and giving feedback to the subject’s expressions. Both live interaction with EDDIE and video recordings of human-EDDIE interaction will be demonstrated.

WordNets are useful resources for natural language processing. Various WordNets for different languages have been developed by different groups. Recently, World WordNet Database Structure (WWDS) was proposed by Redkar et. al (2015) as a common platform to store these different WordNets. However, it is underutilized due to lack of programming interface. In this paper, we present WWDS APIs, which are designed to address this shortcoming. These WWDS APIs, in conjunction with WWDS, act as a wrapper that enables developers to utilize WordNets without worrying about the underlying storage structure. The APIs are developed in PHP, Java, and Python, as they are the preferred programming languages of most developers and researchers working in language technologies. These APIs can help in various applications like machine translation, word sense disambiguation, multilingual information retrieval, etc.

AI is typically applied in video games in the creation of artificial opponents, in order to make them strong, realistic or even fallible (for the game to be "enjoyable" by human players). We offer a different perspective: we present the concept of "Art Games", a view that opens up many possibilities for AI research and applications. Conference participants will play Jikan to Kukan, an art game where the player dynamically creates the soundtrack with the AI system, while developing her experience in the unconscious world of a character.

Food safety is vital to the well-being of society; therefore, it is important to inspect food products to ensure minimal health risks are present. The presence of certain species of insects, especially storage beetles, is a reliable indicator of possible contamination during storage and food processing. However, the current approach of identifying species by visual examination of insect fragments is rather subjective and time-consuming. To aid this inspection process, we have developed in collaboration with FDA food analysts some image analysis-based machine intelligence to achieve species identification with up to 90% accuracy. The current project is a continuation of this development effort. Here we present an image analysis environment that allows practical deployment of the machine intelligence on computers with limited processing power and memory. Using this environment, users can prepare input sets by selecting images for analysis, and inspect these images through the integrated panning and zooming capabilities. After species analysis, the results panel allows the user to compare the analyzed images with reference images of the proposed species. Further additions to this environment should include a log of previously analyzed images, and eventually extend to interaction with a central cloud repository of images through a web-based interface.

Writing academic articles in English is a challenging task for non-native speakers, as more effort has to be spent to enhance their language expressions. This paper presents an academic writing assistant system called Write-righter, which can provide real-time hint and recommendation by analyzing the input context. To achieve this goal, some novel strategies, e.g., semantic extension based sentence retrieval and LDA based sentence structure identification have been proposed. Write-righter is expected to help people express their ideas correctly by recommending top N most possible expressions.

In this demo, we build a practical mobile application, Moodee, to help detect and release users' psychological stress by leveraging users' social media data in online social networks, and provide an interactive user interface to present users' and friends' psychological stress states in an visualized and intuitional way. Given users' online social media data as input, Moodee intelligently and automatically detects users' stress states. Moreover, Moodee would recommend users with different links to help release their stress. The main technology of this demo is a novel hybrid model - a factor graph model combined with Deep Neural Network, which can leverage social media content and social interaction information for stress detection. We think that Moodee can be helpful to people's mental health, which is a vital problem in

We demonstrate BBookX, a novel system that auto-matically builds in collaboration with a user online openbooks by searching open educational resources (OER).This system explores the use of retrieval technologies todynamically generate zero-cost materials such as text-books for personalized learning.

We consider the task of predicting various traits of a person given an image of their face. We aim to estimate traits such as gender, ethnicity and age, as well as more subjective traits as the emotion a person expresses or whether they are humorous or attractive. Due to the recent surge of research on Deep Convolutional Neural Networks (CNNs), we begin by using a CNN architecture, and corroborate that CNNs are promising for facial attribute prediction. To further improve performance, we propose a novel approach that incorporates facial landmark information for input images as an additional channel, helping the CNN learn face-specific features so that the landmarks across various training images hold correspondence. We empirically analyze the performance of our proposed method, showing consistent improvement over the baselines across traits. We demonstrate our system on a sizeable Face Attributes Dataset (FAD), comprising of roughly 200,000 labels, for 10 most sought-after traits, for over 10,000 facial images.

We propose a system for determining properties of mathematical functions given an image of their graph representation. We demonstrate our approach for two-dimensional graphs (curves of single variable functions) and three-dimensional graphs (surfaces of two variable functions), studying the properties of convexity and symmetry. Our method uses a Convolutional Neural Network which classifies functions according to these properties, without using any hand-crafted features. We propose algorithms for randomly constructing functions with convexity or symmetry properties, and use the images generated by these algorithms to train our network. Our system achieves a high accuracy on this task, even for functions where humans find it difficult to determine the function's properties from its image.