Poker is a family of card games that includes many varia- tions. We hypothesize that most poker games can be solved as a pattern matching problem, and propose creating a strong poker playing system based on a unified poker representa- tion. Our poker player learns through iterative self-play, and improves its understanding of the game by training on the results of its previous actions without sophisticated domain knowledge. We evaluate our system on three poker games: single player video poker, two-player Limit Texas Hold’em, and finally two-player 2-7 triple draw poker. We show that our model can quickly learn patterns in these very different poker games while it improves from zero knowledge to a competi- tive player against human experts. The contributions of this paper include: (1) a novel represen- tation for poker games, extendable to different poker vari- ations, (2) a Convolutional Neural Network (CNN) based learning model that can effectively learn the patterns in three different games, and (3) a self-trained system that signif- icantly beats the heuristic-based program on which it is trained, and our system is competitive against human expert players.

Public security events are occurring all over the world, bringing threat to personal and property safety, and homeland security. It is vital to construct an effective model to evaluate and predict the public security. In this work, we establish a Situation-Aware Public Security Evaluation (SAPE) platform. Based on conventional Recurrent Neural Networks (RNN), we develop a new variant of RNN to handle temporal contexts in public security event datasets. The proposed model can achieve better performance than the compared state-of-the-art methods. On SAPE, There are two parts of demonstrations, i.e., global public security evaluation and China public security evaluation. In the global part, based on Global Terrorism Database from UMD, for each country, SAPE can predict risk level and top-n potential terrorist organizations which might attack the country. The users can also view the actual attacking organizations and predicted results. For each province in China, SAPE can predict the risk level and the probability scores of different types of events in the next month. The users can also view the actual numbers of events and predicted risk levels of the past one year.

Convolutional neural networks (CNNs) have achieved impressive performance in a wide range of computer vision areas. However, the application on mobile devices remains intractable due to the high computation complexity. In this demo, we propose the Quantized CNN (Q-CNN), an efficient framework for CNN models, to fulfill efficient and accurate image classification on mobile devices. Our Q-CNN framework dramatically accelerates the computation and reduces the storage/memory consumption, so that mobile devices can independently run an ImageNet-scale CNN model. Experiments on the ILSVRC-12 dataset demonstrate 4~6x speed-up and 15~20x compression, with merely one percentage drop in the classification accuracy. Based on the Q-CNN framework, even mobile devices can accurately classify images within one second.

Agent-Oriented Software Engineering (AOSE) is an emerging software engineering paradigm that advocates the application of best practices in the development of Multi-Agent Systems (MAS) through the use of agents and organizations of agents. This paper outlines the MADE system, which provides an interactive platform for people who are not well-versed in AOSE to contribute to the rapid prototyping of MASs with ease.

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.

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 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.

Stephen Muggleton gave the invited talk "Meta-Interpretive Inductive Logic Programming (ILP) is a research area Learning: achievements and challenges". Meta-Interpretive formed at the intersection of Machine Learning and logicbased Learning (MIL) is an ILP technique aimed at supporting knowledge representation. ILP has originally used learning of recursive definitions, by automatically introducing logic programming as a uniform representation language sub-definitions that allow decomposition into a hierarchy for examples, background knowledge and hypotheses for of reusable parts (Muggleton et al. 2014; 2015). ILP has also explored several connections (or abducing) first-order clauses whose heads unify with with statistical learning and other probabilistic approaches, a given goal. MIL additionally fetches higher-order metarules expanding research horizons significantly. A recent survey whose heads unify with the goal and saves the resulting of ILP can be seen in (Muggleton et al. 2012).