Total: 29
The current generation of senior citizens are enjoying unparalleled levels of good health than previous generations. The need for personal fulfilment after retirement has driven many of them to participate in productive aging activities such as volunteering. This paper outlines the Silver Productive (SP) mobile app, a system powered by the RTS-P intelligent personalized task sub-delegation approach with dynamic worker effort pricing functions. It provides an algorithmic crowdsourcing platform to enable seniors to contribute their effort through productive aging activities and help organizations efficiently utilize seniors' collective productivity.
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.
The conservation of key wildlife species such as tigers and elephants are threatened by poaching activities. In many conservation areas, foot patrols are conducted to prevent poaching but they may not be well-planned to make the best use of the limited patrolling resources. While prior work has introduced PAWS (Protection Assistant for Wildlife Security) as a game-theoretic decision aid to design effective foot patrol strategies to protect wildlife, the patrol routes generated by PAWS may be difficult to follow in areas with complex terrain. Subsequent research has worked on the significant evolution of PAWS, from an emerging application to a regularly deployed software. A key advance of the deployed version of PAWS is that it incorporates the complex terrain information and generates a strategy consisting of easy-to-follow routes. In this demonstration, we provide 1) a video introducing the PAWS system; 2) an interactive visualization of the patrol routes generated by PAWS in an example area with complex terrain; and 3) a machine-human competition in designing patrol strategy given complex terrain and animal distribution.
As many countries in the world start to experience population aging, there are an increasing number of people relying on medical insurance to access healthcare resources. Medical insurance frauds are causing billions of dollars in losses for public healthcare funds. The detection of medical insurance frauds is an important and difficult challenge for the artificial intelligence (AI) research community. This paper outlines HFDA, a hybrid AI approach to effectively and efficiently identify fraudulent medical insurance claims which has been tested in an online medical insurance claim system in China.
We present the EKNOT system that automatically discovers major events from online news articles, connects each event to its discussion in Twitter, and provides a comprehensive summary of the events from both news media and social media's point of view. EKNOT takes a time period as input and outputs a complete picture of the events within the given time range along with the public opinions. For each event, EKNOT provides multi-dimensional summaries: a) a summary from news for an objective description; b) a summary from tweets containing opinions/sentiments; c) an entity graph which illustrates the major players involved and their correlations; d) the time span of the event; and e) an opinion (sentiment) distribution. Also, if a user is interested in a particular event, he/she can zoom into this event to investigate its aspects (sub-events) summarized in the same manner. EKNOT is built on real-time crawled news articles and tweets, allowing users to explore the dynamics of major events with minimal delays.
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.
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.
With the growing online social media, rumors are spread fast and viewed by more and more people on the Internet. Rumors bring significant harm to daily life and public security. It is crucial to evaluate the credibility of information and detect the rumors on social media automatically. In this work, we establish a Network Information Credibility Evaluation (NICE) platform, which collects a database of rumors that have been verified on Sina Weibo and automatically evaluates the information generated by users on social media but has not been verified. Users can use a query to search related information. If the according information appears in our database, users can identify it is a rumor immediately. Otherwise, NICE will show users with real-time results crawled automatically from social media and can calculate credibility of a specific result with our algorithm. Our algorithm learns dynamic representations for information on social media based on behavior information, dynamic information, user information and comment information. Then, we use an ordinary logistic regression to classify information into rumors and non-rumors. Based on our algorithm, NICE system achieves satisfactory performance on evaluating information credibility and detecting rumors on social media.
This paper introduces the Interactive Relational Machine Learning (iRML) paradigm in which users interactively design relational models by specifying the various components, constraints, and relational data representation, as well as perform evaluation, analyze errors, and make adjustments and refinements in a closed-loop. iRML requires fast real-time learning and inference methods capable of interactive rates. Methods are investigated that enable direct manipulation of the various components of the RML method. Visual representation and interaction techniques are also developed for exploring the space of relational models and the trade-offs of the various components and design choices.
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.
Our aim with co-rank is to facilitate the grading of exams or assignments in massive open online courses (MOOCs).
Most research into Swarm Intelligence explores swarms of autonomous robots or simulated agents. Little work, however, has been done on swarms of networked humans. This paper introduces UNU, an online platform that enables networked users to assemble in real-time swarms and tackle problems as an Artificial Swarm Intelligence (ASI). Modeled after biological swarms, UNU enables large groups of networked users to work together in real-time synchrony, forging a unified dynamic system that can quickly answer questions and make decisions. Early testing suggests that human swarming has significant potential for harnessing the Collective Intelligence (CI) of online groups, often exceeding the natural abilities of individual participants.
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.
Internet user behavior models characterize user browsing dynamics or the transitions among web pages. The models help Internet companies improve their services by accurately targeting customers and providing them the information they want. For instance, specific web pages can be customized and prefetched for individuals based on sequences of web pages they have visited. Existing user behavior models abstracted as time-homogeneous Markov models cannot efficiently model user behavior variation through time. This demo presents DECT, a scalable time-variant variable-order Markov model. DECT digests terabytes of user session data and yields user behavior patterns through time. We realize DECT using Apache Spark and deploy it on top of Yahoo! infrastructure. We demonstrate the benefits of DECT with anomaly detection and ad click rate prediction applications. DECT enables the detection of higher-order path anomalies and provides deep insights into ad click rates with respect to user visiting paths.
We present SVVAMP, a Python package dedicated to the study of voting systems with an emphasis on manipulation analysis.
In this demo, we build a practical mobile application, Moodee,to help detect and release users’ psychological stress byleveraging 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 andintuitional way.Given users’ online social media data as input, Moodee intelligentlyand automatically detects users’ stress states. Moreover,Moodee would recommend users with different linksto help release their stress. The main technology of this demois a novel hybrid model - a factor graph model combinedwith Deep Neural Network, which can leverage social mediacontent and social interaction information for stress detection.We think that Moodee can be helpful to people’s mentalhealth, which is a vital problem in modern world.
In this demo we present a semantic framework for innovation and patent analytics powered by Mined Semantic Analysis (MSA). Our framework provides cognitive assistance to its users through a Web-based visual and interactive interface. First, we describe building a conceptual knowledge graph by mining user-generated encyclopedic textual corpus for semantic associations. Then, we demonstrate applying the acquired knowledge to support many cognition and knowledge based use cases for innovation analysis including technology exploration and landscaping, competitive analysis, literature and prior art search and others.
Voice is a critical user interface on smart devices (wearables, phones, speakers, televisions) to access applications (or services) available on them. Unfortunately, only a few native applications (provided by the OS developer) are typically voice enabled in devices of today. Since, the utility of a smart device is determined more by the strength of external applications developed for the device, voice enabling non-native applications in a scalable, seamless manner within the device is a critical use case and is the focus of our work. We have developed a Natural Language Understanding (NLU) framework that uses templates supported by the application (as determined by the application developer). This framework can be employed in any mobile OS (Android, iOS, Tizen, Android wear) for a wide range of devices. To aid this demonstration, we have implemented the framework as a service in Android OS. When the user issues a voice command, the natural language query is obtained by this service (using one of local, cloud based or hybrid speech recognizers). The service then executes our NLU framework to identify the relevant application and particular action details. In this demonstration, we will showcase this NLU framework implemented as an Android service on a set of applications that will be installed on the fly. Specifically, we will show how the voice queries are understood and necessary services are launched on android smart wearables and phones.
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.
Many papers describe the use of Fuzzy Cognitive Maps as a modeling/representation technique for real-life scenarios’ simulation or prediction. However, not many real software implementations are described neither found. In this proposal the authors describe a modeling and experimentation framework where realistic problems can be recreated using Fuzzy Cognitive Maps as a knowledge representation form. Design elements, and descriptions of the algorithms that have been incorporated into the software, and hybridized with Fuzzy Cognitive Maps, are presented in this paper. Case studies were conducted and are illustrated with the intention of demonstrating the success and practical value of the general approach together with the implementation tool.
We demonstrate a system for predicting gaming related properties from Twitter accounts. Our system predicts various traits of users based on the tweets publicly available in their profiles. Such inferred traits include degrees of tech-savviness and knowledge on computer games, actual gaming performance, preferred platform, degree of originality, humor and influence on others. Our system is based on machine learning models trained on crowd-sourced data. It allows people to select Twitter accounts of their fellow gamers, examine the trait predictions made by our system, and the main drivers of these predictions. We present empirical results on the performance of our system based on its accuracy on our crowd-sourced dataset.
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.
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 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.
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.