College students often struggle to balance their work with personal wellness. In part, this occurs because students work when they are unable to focus. We hypothesize that we can adapt the Experience Sampling Method (ESM) to build a model of users’ efficacy and predict when they will be most likely to experience flow, a state of motivation and immersion. We also hypothesize that we can present this information effectively to users, allowing them to understand when they are most likely to achieve flow. In order to test these hypotheses, we introduce the Productivity and Wellness Pal (PaWPal), a smartphone-based application that seeks to make users aware of their efficacy at various tasks as well as which courses of action are likely to lead to immersive experiences.

This paper describes a learning-based approach for automatic derivation of word variant forms bythe suffixation process. We employ the sequence labeling technique, which entails learning when to preserve, delete, substitute, or add a letter to form a new word from a given word. The features used by the learner are based on characters, phonetics, and hyphenation positions of the given word. To ensure that our system is robust to word variants that can arise from different forms of a root word, we generate multiple variant hypothesis for each word based on the sequence labeler's prediction. We then filter out ill-formed predictions, and create clusters of word variants by merging together a word and its predicted variants with other words and their predicted variants provided the groups share a word in common. Our results show that this learning-based approach is feasible for the task and warrants further exploration.

We propose a simple multi-pass sieve framework that applies tiers of deterministic normalization modules one at a time from highest to lowest precision for the task of normalizing names. While a sieve based architecture has been shown effective in coreference resolution, it has not yet been applied to the normalization task. We find that even in this task, the approach retains its characteristic features of being simple, and highly modular. In addition, it also proves robust when evaluated on two different kinds of data: clinical notes and biomedical text, by demonstrating high accuracy in normalizing disorder names found in both datasets.

The NO paradigm can model many operations. Examples When agents dwell inside an organization, they form patterns are systems of river dam control, factory cells, electrical of interactions that we call paradigms. There are many power grids, and traffic control on land, sea, and space. As existing paradigms to describe organizations, which affect a paradigm, it does not functionally alter the operations to its performance features. These paradigms include hierarchies, which it is applied. The paradigm can be understood in terms holarchies, coalitions, teams, congregations, societies, of the ways it permits command and control regimes. Invariably, federations, markets and matrix organizations (Horling and NO relies on a network on which it dwells.

In recent years, there has been renewed interest in the NLP community in genuine language understanding and dialogue. Thus the long-standing issue of how the semantic content of language should be represented is reentering the communal discussion. This paper provides a brief "opinionated survey" of broad-coverage semantic representation (SR). It suggests multiple desiderata for such representations, and then outlines more than a dozen approaches to SR — some long-standing, and some more recent, providing quick characterizations, pros, cons, and some comments on implementations.

Most real-world games and many recreational games are games of incomplete information. Over the last dozen years, abstraction has emerged as a key enabler for solving large incomplete-information games. First, the game is abstracted to generate a smaller, abstract game that is strategically similar to the original game. Second, an approximate equilibrium is computed in the abstract game. Third, the strategy from the abstract game is mapped back to the original game. In this paper, I will review key developments in the field. I present reasons for abstracting games, and point out the issue of abstraction pathology. I then review the practical algorithms for information abstraction and action abstraction. I then cover recent theoretical breakthroughs that beget bounds on the quality of the strategy from the abstract game, when measured in the original game. I then discuss how to reverse map the opponent's action into the abstraction if the opponent makes a move that is not in the abstraction. Finally, I discuss other topics of current and future research.

Cyber-Physical Production Systems (CPPSs) are in the focus of research, industry and politics: By applying new IT and new computer science solutions, production systems will become more adaptable, more resource ef- ficient and more user friendly. The analysis and diagnosis of such systems is a major part of this trend: Plants should detect automatically wear, faults and suboptimal configurations. This paper reflects the current state-of- the-art in diagnosis against the requirements of CPPSs, identifies three main gaps and gives application scenarios to outline first ideas for potential solutions to close these gaps.

This paper summarizes an ongoing multi-year project aiming to uncover knowledge and techniques for devising intelligent environments for user-adaptive visualizations. We ran three studies designed to investigate the impact of user and task characteristics on user performance and satisfaction in different visualization contexts. Eye-tracking data collected in each study was analyzed to uncover possible interactions between user/task characteristics and gaze behavior during visualization processing. Finally, we investigated user models that can assess user characteristics relevant for adaptation from eye tracking data.

In this paper, I summarize the results of a decade-plus of research and development driven by the vision that human knowledge can be grounded in a small number of prototypical components that can be extended through composition and analogy. These ideas have been embodied in a system called AURA, which has been used to engineer an expressive knowledge base for an intelligent biology textbook. The focus of the current paper is to abstract away from the specifics and, to instead describe the core ideas in such a manner that they can be transferred and applied in different contexts, and to relate those ideas to the ongoing research by others.

I draw the reader's attention to machine teaching, the problem of finding an optimal training set given a machine learning algorithm and a target model. In addition to generating fascinating mathematical questions for computer scientists to ponder, machine teaching holds the promise of enhancing education and personnel training. The Socratic dialogue style aims to stimulate critical thinking.