Architecture of a Web-based Predictive Editor for Controlled Natural Language Processing

In this paper, we describe the architecture of a web-based predictive text editor being developed for the controlled natural language PENG^{ASP). This controlled language can be used to write non-monotonic specifications that have the same expressive power as Answer Set Programs. In order to support the writing process of these specifications, the predictive text editor communicates asynchronously with the controlled natural language processor that generates lookahead categories and additional auxiliary information for the author of a specification text. The text editor can display multiple sets of lookahead categories simultaneously for different possible sentence completions, anaphoric expressions, and supports the addition of new content words to the lexicon

Learning from Unstructured Data to Monitor Human Health

The integration of mobile devices into our daily lives has created unique opportunities to improve human health and well-being. Many of these devices such as smartphones and smartwatches allow the users to enter unstructured data such as speech. This research is focused on utilizing such data for health monitoring through development of computational algorithms and optimization strategies that process unstructured data, compute health-related markers, and provide recommendations for improved health. The applications of this research include nutrition monitoring, dietary recommendation, personality assessment, and commonsense reasoning. Diet is known as an important lifestyle factor in self-management and prevention of chronic diseases. Although mobile and wearable sensors have been used to estimate eating context, accurate monitoring of dietary intake has remained a challenging problem. New approaches based on mobile devices have been proposed to facilitate the process of food intake recording. These technologies require individuals to use mobile devices such as smartphones to record nutrition intake by either entering text or taking images of the food. These technologies are prone to measurement errors related to challenges of human memory and bias. In order to address these limitations, we introduced development and validation of two nutrition monitoring frameworks, Speech2Health and EZNutriPal, which use unstructured data along with speech processing, natural language processing (NLP), and text mining techniques to facilitate dietary assessment.Implementing strategies that improve dietary intake is also very important. A general diet behavior change framework for joint nutrition monitoring and diet planning allows continuous diet recommendations for achieving a diet goal. This research introduces a diet planning framework, called iTell-uEat, to provide diet recommendations continuously based on user's diet habits. Two approaches are proposed including a reinforcement-learning-based and a greedy-based diet planning. An optimization algorithm is proposed to construct a meaningful action space for training reinforcement learning algorithms. Moreover, a linear optimization approach is developed forgreedy diet planning. To demonstrate the potential of utilizing unstructured data in applications beyond dietary assessment, a computational framework is proposed to analyze human personality traits based on expressed texts and to use these personalities for behavior and commonsense reasoning analysis

The representation of planning strategies

AbstractAn analysis of strategies, recognizable abstract patterns of planned behavior, highlights the difference between the assumptions that people make about their own planning processes and the representational commitments made in current automated planning systems. This article describes a project to collect and represent strategies on a large scale to identify the representational components of our commonsense understanding of intentional action. Three hundred and seventy-two strategies were collected from ten different planning domains. Each was represented in a pre-formal manner designed to reveal the assumptions that these strategies make concerning the human planning process. The contents of these representations, consisting of nearly one thousand unique concepts, were then collected and organized into forty-eight groups that outline the representational requirements of strategic planning systems

Mining Social Media and Structured Data in Urban Environmental Management to Develop Smart Cities

This research presented the deployment of data mining on social media and structured data in urban studies. We analyzed urban relocation, air quality and traffic parameters on multicity data as early work. We applied the data mining techniques of association rules, clustering and classification on urban legislative history. Results showed that data mining could produce meaningful knowledge to support urban management. We treated ordinances (local laws) and the tweets about them as indicators to assess urban policy and public opinion. Hence, we conducted ordinance and tweet mining including sentiment analysis of tweets. This part of the study focused on NYC with a goal of assessing how well it heads towards a smart city. We built domain-specific knowledge bases according to widely accepted smart city characteristics, incorporating commonsense knowledge sources for ordinance-tweet mapping. We developed decision support tools on multiple platforms using the knowledge discovered to guide urban management. Our research is a concrete step in harnessing the power of data mining in urban studies to enhance smart city development

A Pragmatic Reading of Friedman's Methodological Essay and What It Tells Us for the Discussion of ABMs

The issues of empirical calibration of parameter values and functional relationships describing the interactions between the various actors plays an important role in agent based modelling. Agent-based models range from purely theoretical exercises focussing on the patterns in the dynamics of interactions processes to modelling frameworks which are oriented closely at the replication of empirical cases. ABMs are classified in terms of their generality and their use of empirical data. In the literature the recommendation can be found to aim at maximizing both criteria by building so-called 'abductive models'. This is almost the direct opposite of Milton Friedman's famous and provocative methodological credo 'the more significant a theory, the more unrealistic the assumptions'. Most methodologists and philosophers of science have harshly criticised Friedman's essay as inconsistent, wrong and misleading. By presenting arguments for a pragmatic reinterpretation of Friedman's essay, we will show why most of the philosophical critique misses the point. We claim that good simulations have to rely on assumptions, which are adequate for the purpose in hand and those are not necessarily the descriptively accurate ones.Methodology, Agent-Based Modelling, Assumptions, Calibration