Health literacy, i.e. the ability to read and understand medical text, is a relevant component of public health. Unfortunately, many medical texts are hard to grasp by the general population as they are targeted at highly-skilled professionals and use complex language and domain-specific terms. Here, automatic text simplification making text commonly understandable would be very beneficial. However, research and development into medical text simplification is hindered by the lack of openly available training and test corpora which contain complex medical sentences and their aligned simplified versions. In this paper, we introduce such a dataset to aid medical text simplification research. The dataset is created by filtering aligned health sentences using expert knowledge from an existing aligned corpus and a novel simple, language independent monolingual text alignment method. Furthermore, we use the dataset to train a state-of-the-art neural machine translation model, and compare it to a model trained on a general simplification dataset using an automatic evaluation, and an extensive human-expert evaluation. @en

Social media provides a timely yet challenging data source for adverse drug reaction (ADR) detection. Existing dictionary-based, semi-supervised learning approaches are intrinsically limited by the coverage and maintainability of laymen health vocabularies. In this paper, we introduce a data augmentation approach that leverages variational autoencoders to learn high-quality data distributions from a large unlabeled dataset, and subsequently, to automatically generate a large labeled training set from a small set of labeled samples. This allows for efficient social-media ADR detection with low training and re-training costs to adapt to the changes and emergence of informal medical laymen terms. An extensive evaluation performed on Twitter and Reddit data shows that our approach matches the performance of fully-supervised approaches while requiring only 25% of training data.@en

As crowdsourcing gains popularity, organisations seek ways to systematically and reliably involve their workforce with data processing pipelines. Mobile crowdsourcing allows for opportunistic task executions and thus, potentially, for higher throughput. However, how to engage and to retain employees in enterprise crowdsourcing campaigns is still an open research topic. .is paper discusses the results of a study performed in IBM Benelux. We surveyed 93 employees to discover the factors that might a.ect engagement in mobile enterprise crowdsourcing. .e survey informed the design of an experiment that aimed at investigating the e.ectiveness of di.erent task noti€cation strategies. We studied how factors such as time and context of noti€cation can a.ect the participation and retention of employees. Results show that break times are the most suitable for crowd work, and that "aggressive" noti€cation strategies act as deterrent for participation, while moderate yet regular nudges are the most likely to retain contributors.@en

In recent years, gamification, "the use of game design elements in non-game contexts", has drawn the attention of an increasing number of scientists. Although several studies highlighted the benefits of gamification in many applications, its potential in the enterprise environment still needs to be fully understood. This work contributes to the studies in enterprise gamification with an experiment performed at a large multinational company. The experiment involved 206 employees for a period of 2 months. We describe a modular and extensible framework for enterprise gamification, designed to seamlessly integrate with existing enterprise-classWeb systems. We studied how a gamified tool can help to foster employees' engagement with such systems, by making day-to-day tasks more stimulating. We show how different game mechanics can help to achieve two business needs, namely social interaction and learning. To this end, we exploited the gamification framework to develop a Q&A Web application combined with learning, news sharing, and social connections capabilities. Results provide strong evidence of how a gamified experience can foster learning and social behaviour in employees, and provide new insights about the effectiveness of several game mechanics in an enterprise context.

@en

The open data trend is considered to be a robust driver for innovation. The expectation is that open datasets will stimulate benefits across a variety of sectors (public, private, academia etc.), such as greater transparency, innovation and the rise of new business models (European Commission, 2011; Davies, 2010). Although they are one of the primary users of governmental open datasets, private sector companies themselves are not very active yet in opening the massive amount of data they produce (Bonina, 2013; Herzberg 2014). A variety of barriers hold back this potential, such as privacy issues, data security, proprietary interests and data protectionism (Verhulst 2014, Ponte 2015). Therefore our objective was to develop a decision support framework that offers an overview of the various steps required for such an action, taking the barriers and the benefits into account. We followed a design science approach in which we conducted a literature review on the concept of business open data, performed an in-depth analysis of seven empirical case studies of well-known examples such as Nike, Syngenta and IBM, and we conducted expert interviews. We thus developed a prototype of the decision support framework, based on the concept of open data ecosystems (Heimstädt et al, 2014; Ponte, 2015). The framework was evaluated by high-level experts in the field. We not only identified the main business drivers for private sector companies to open up datasets, such as community building, promotion, business innovation, and new revenue streams. We also addressed the key challenges encountered by the private organizations, which are: characteristics of the data itself, the process for opening-up the data and obstacles in using business open data (ie. the risks of misinterpretation). Our framework addresses the drivers as well as the barriers for private organizations to decide which datasets are eligible to be opened up, and supports them to consider all aspects that need to be taken into account during the decision making process. We claim that opening up data provides an opportunity to create a new business open data ecosystem, in which the private company supplying open data acts as a keystone by providing data as resource to the other members of the ecosystem. First movers can reap all the benefits that come with being a dominator in their business open data ecosystem.@en

Understanding the impact of corporate information publicly distributed on the Web is becoming more and more crucial. In this paper we report the result of a study that involved 130 IBM employees: we explored the correctness and extent of organisational information that can be observed from the online profiles of a company's employees. Our work con- Tributes new insights to the study of social networks by showing that, even by considering a small fraction of the available online data, it is possible to discover accurate infor- mation about an organisation, its structure, and the factors that characterise the social reach of their employees.@en