Cluster-based active learning for compact image classification

In this paper, we consider active sampling to label pixels grouped with hierarchical clustering. The objective of the method is to match the data relationships discovered by the clustering algorithm with the user's desired class semantics. The first is represented as a complete tree to be pruned and the second is iteratively provided by the user. The active learning algorithm proposed searches the pruning of the tree that best matches the labels of the sampled points. By choosing the part of the tree to sample from according to current pruning's uncertainty, sampling is focused on most uncertain clusters. This way, large clusters for which the class membership is already fixed are no longer queried and sampling is focused on division of clusters showing mixed labels. The model is tested on a VHR image in a multiclass classification setting. The method clearly outperforms random sampling in a transductive setting, but cannot generalize to unseen data, since it aims at optimizing the classification of a given cluster structure.

Aspirin and statin use and the subsequent development of depression in men and women: Results from a longitudinal population-based study.

OBJECTIVE: Low-grade chronic inflammation is one potential mechanism underlying the well-established association between major depressive disorder (MDD) and increased cardiovascular morbidity. Both aspirin and statins have anti-inflammatory properties, which may contribute to their preventive effect on cardiovascular diseases. Previous studies on the potentially preventive effect of these drugs on depression have provided inconsistent results. The aim of the present paper was to assess the prospective association between regular aspirin or statin use and the incidence of MDD. METHOD: This prospective cohort study included 1631 subjects (43.6% women, mean age 51.7 years), randomly selected from the general population of an urban area. Subjects underwent a thorough physical evaluation as well as semi-structured interviews investigating DSM-IV mental disorders at baseline and follow-up (mean duration 5.2 years). Analyses were adjusted for a wide array of potential confounders. RESULTS: Our main finding was that regular aspirin or statin use at baseline did not reduce the incidence of MDD during follow-up, regardless of sex or age (hazard ratios, aspirin: 1.19; 95%CI, 0.68-2.08; and statins: 1.25; 95%CI, 0.73-2.14; respectively). LIMITATIONS: Our study is not a randomized clinical trial and could not adjust for all potential confounding factors, information on aspirin or statin use was collected only for the 6 months prior to the evaluations, and the sample was restricted to subjects between 35 and 66 years of age. CONCLUSION: Our data do not support a large scale preventive treatment of depression using aspirin or statins in subjects aged from 35 to 66 years from the community.

Privacy-friendly Business Models for Location-Based Mobile Services

This paper presents a theoretical model to analyze the privacy issues around location based mobile business models. We report the results of an exploratory field experiment in Switzerland that assessed the factors driving user payoff in mobile business. We found that (1) the personal data disclosed has a negative effect on user payoff; (2) the amount of personalization available has a direct and positive effect, as well as a moderating effect on user payoff; (3) the amount of control over user's personal data has a direct and positive effect, as well as a moderating effect on user payoff. The results suggest that privacy protection could be the main value proposition in the B2C mobile market. From our theoretical model we derive a set of guidelines to design a privacy-friendly business model pattern for third-party services. We discuss four examples to show the mobile platform can play a key role in the implementation of these new business models.

Business Model Considerations for Privacy Protection in a Mobile Location Based Context

In this paper we discuss the main privacy issues around mobile business models and we envision new solutions having privacy protection as a main value proposition. We construct a framework to help analyze the situation and assume that a third party is necessary to warrant transactions between mobile users and m-commerce providers. We then use the business model canvas to describe a generic business model pattern for privacy third party services. This pattern is then illustrated in two different variations of a privacy business model, which we call privacy broker and privacy management software. We conclude by giving examples for each business model and by suggesting further directions of investigation

Hire me: Computational inference of hirability in employment interviews based on nonverbal behavior

Understanding the basis on which recruiters form hirability impressions for a job applicant is a key issue in organizational psychology and can be addressed as a social computing problem. We approach the problem from a face-to-face, nonverbal perspective where behavioral feature extraction and inference are automated. This paper presents a computational framework for the automatic prediction of hirability. To this end, we collected an audio-visual dataset of real job interviews where candidates were applying for a marketing job. We automatically extracted audio and visual behavioral cues related to both the applicant and the interviewer. We then evaluated several regression methods for the prediction of hirability scores and showed the feasibility of conducting such a task, with ridge regression explaining 36.2% of the variance. Feature groups were analyzed, and two main groups of behavioral cues were predictive of hirability: applicant audio features and interviewer visual cues, showing the predictive validity of cues related not only to the applicant, but also to the interviewer. As a last step, we analyzed the predictive validity of psychometric questionnaires often used in the personnel selection process, and found that these questionnaires were unable to predict hirability, suggesting that hirability impressions were formed based on the interaction during the interview rather than on questionnaire data.

Prototype of a web-based participative decision support platform in natural hazards and risk management

This paper presents the current state and development of a prototype web-GIS (Geographic Information System) decision support platform intended for application in natural hazards and risk management, mainly for floods and landslides. This web platform uses open-source geospatial software and technologies, particularly the Boundless (formerly OpenGeo) framework and its client side software development kit (SDK). The main purpose of the platform is to assist the experts and stakeholders in the decision-making process for evaluation and selection of different risk management strategies through an interactive participation approach, integrating web-GIS interface with decision support tool based on a compromise programming approach. The access rights and functionality of the platform are varied depending on the roles and responsibilities of stakeholders in managing the risk. The application of the prototype platform is demonstrated based on an example case study site: Malborghetto Valbruna municipality of North-Eastern Italy where flash floods and landslides are frequent with major events having occurred in 2003. The preliminary feedback collected from the stakeholders in the region is discussed to understand the perspectives of stakeholders on the proposed prototype platform.

Change points detection in crime-related time series: an on-line fuzzy approach based on a shape space representation

The extension of traditional data mining methods to time series has been effectively applied to a wide range of domains such as finance, econometrics, biology, security, and medicine. Many existing mining methods deal with the task of change points detection, but very few provide a flexible approach. Querying specific change points with linguistic variables is particularly useful in crime analysis, where intuitive, understandable, and appropriate detection of changes can significantly improve the allocation of resources for timely and concise operations. In this paper, we propose an on-line method for detecting and querying change points in crime-related time series with the use of a meaningful representation and a fuzzy inference system. Change points detection is based on a shape space representation, and linguistic terms describing geometric properties of the change points are used to express queries, offering the advantage of intuitiveness and flexibility. An empirical evaluation is first conducted on a crime data set to confirm the validity of the proposed method and then on a financial data set to test its general applicability. A comparison to a similar change-point detection algorithm and a sensitivity analysis are also conducted. Results show that the method is able to accurately detect change points at very low computational costs. More broadly, the detection of specific change points within time series of virtually any domain is made more intuitive and more understandable, even for experts not related to data mining.