A matter of words: NLP for quality evaluation of Wikipedia medical articles

Automatic quality evaluation of Web information is a task with many fields of applications and of great relevance, especially in critical domains like the medical one. We move from the intuition that the quality of content of medical Web documents is affected by features related with the specific domain. First, the usage of a specific vocabulary (Domain Informativeness); then, the adoption of specific codes (like those used in the infoboxes of Wikipedia articles) and the type of document (e.g., historical and technical ones). In this paper, we propose to leverage specific domain features to improve the results of the evaluation of Wikipedia medical articles. In particular, we evaluate the articles adopting an "actionable" model, whose features are related to the content of the articles, so that the model can also directly suggest strategies for improving a given article quality. We rely on Natural Language Processing (NLP) and dictionaries-based techniques in order to extract the bio-medical concepts in a text. We prove the effectiveness of our approach by classifying the medical articles of the Wikipedia Medicine Portal, which have been previously manually labeled by the Wiki Project team. The results of our experiments confirm that, by considering domain-oriented features, it is possible to obtain sensible improvements with respect to existing solutions, mainly for those articles that other approaches have less correctly classified. Other than being interesting by their own, the results call for further research in the area of domain specific features suitable for Web data quality assessment

How and When Socially Entrepreneurial Nonprofit Organizations Benefit From Adopting Social Alliance Management Routines to Manage Social Alliances?

Social alliance is defined as the collaboration between for-profit and nonprofit organizations. Building on the insights derived from the resource-based theory, we develop a conceptual framework to explain how socially entrepreneurial nonprofit organizations (SENPOs) can improve their social alliance performance by adopting strategic alliance management routines. We test our framework using the data collected from 203 UK-based SENPOs in the context of cause-related marketing campaign-derived social alliances. Our results confirm a positive relationship between social alliance management routines and social alliance performance. We also find that relational mechanisms, such as mutual trust, relational embeddedness, and relational commitment, mediate the relationship between social alliance management routines and social alliance performance. Moreover, our findings suggest that different types of social alliance motivation can influence the impact of social alliance management routines on different types of the relational mechanisms. In general, we demonstrate that SENPOs can benefit from adopting social alliance management routines and, in addition, highlight how and when the social alliance management routines–social alliance performance relationship might be shaped. Our study offers important academic and managerial implications, and points out future research directions

GP-Localize: Persistent Mobile Robot Localization using Online Sparse Gaussian Process Observation Model

Central to robot exploration and mapping is the task of persistent localization in environmental fields characterized by spatially correlated measurements. This paper presents a Gaussian process localization (GP-Localize) algorithm that, in contrast to existing works, can exploit the spatially correlated field measurements taken during a robot's exploration (instead of relying on prior training data) for efficiently and scalably learning the GP observation model online through our proposed novel online sparse GP. As a result, GP-Localize is capable of achieving constant time and memory (i.e., independent of the size of the data) per filtering step, which demonstrates the practical feasibility of using GPs for persistent robot localization and autonomy. Empirical evaluation via simulated experiments with real-world datasets and a real robot experiment shows that GP-Localize outperforms existing GP localization algorithms.Comment: 28th AAAI Conference on Artificial Intelligence (AAAI 2014), Extended version with proofs, 10 page

Take another little piece of my heart: a note on bridging cognition and emotions

Science urges philosophy to be more empirical and philosophy urges science to be more reflective. This markedly occurred along the “discovery of the artificial” (CORDESCHI 2002): in the early days of Cybernetics and Artificial Intelligence (AI) researchers aimed at making machines more cognizant while setting up a framework to better understand human intelligence. By and large, those genuine goals still hold today, whereas AI has become more concerned with specific aspects of intelligence, such as (machine) learning, reasoning, vision, and action. As a matter of fact, the field suffers from a chasm between two formerly integrated aspects. One is the engineering endeavour involving the development of tools, e.g., autonomous systems for driving cars as well as software for semantic information retrieval. The other is the philosophical debate that tries to answer questions concerning the nature of intelligence. Bridging these two levels can indeed be crucial in developing a deeper understanding of minds. An opportunity might be offered by the cogent theme of emotions. Traditionally, computer science, psychological and philosophical research have been compelled to investigate mental processes that do not involve mood, emotions and feelings, in spite of Simon’s early caveat (SIMON 1967) that a general theory of cognition must incorporate the influences of emotion. Given recent neurobiological findings and technological advances, the time is ripe to seriously weigh this promising, albeit controversial, opportunity