Abstract
In this paper we study the combined use of four different NLP toolkits-Stanford CoreNLP, GATE, OpenNLP and Twitter NLP tools-in the context of social media posts. Previous studies have shown performance comparisons between these tools, both on news and social media corporas. In this paper, we go further by trying to understand how differently these toolkits predict Named Entities, in terms of their precision and recall for three different entity types, and how they can complement each other in this task in order to achieve a combined performance superior to each individual one. Experiments on two publicly available datasets from the workshops WNUT-2015 and #MSM2013 show that using an ensemble of toolkits can improve the recognition of specific entity types - up to 10.62% for the entity type Person, 1.97% for the type Location and 1.31% for the type Organization, depending on the dataset and the criteria used for the voting. Our results also showed improvements of 3.76% and 1.69%, in each dataset respectively, on the average performance of the three entity types.

Abstract
Innovation is seen as a key element of an organization’s competitiveness. Along with the current imperative for innovation comes the need to adequately measure it. The purpose of this paper is to perform a literature review in the field of innovation performance models and metrics. The performed work aims to make an important contribution by facilitating the identification and categorization of innovation performance models and metrics formulated until the present time. A survey methodology was adopted to identify the most predominant contributions in the field. For that, the top three most popular bibliometric indexes (Web of Science, Scopus, and Google Scholar) were used. Then, the top ten most relevant studies on innovation performance models and metrics were compared to determine similarities and differences between each generation of models and metrics. Finally, the main innovation performance models and metrics were identified, classified and compared.

Abstract
Research data management is rapidly becoming a regular concern for researchers, and institutions need to provide them with platforms to support data organization and preparation for publication. Some institutions have adopted institutional repositories as the basis for data deposit, whereas others are experimenting with richer environments for data description, in spite of the diversity of existing workflows. This paper is a synthetic overview of current platforms that can be used for data management purposes. Adopting a pragmatic view on data management, the paper focuses on solutions that can be adopted in the long tail of science, where investments in tools and manpower are modest. First, a broad set of data management platforms is presented-some designed for institutional repositories and digital libraries-to select a short list of the more promising ones for data management. These platforms are compared considering their architecture, support for metadata, existing programming interfaces, as well as their search mechanisms and community acceptance. In this process, the stakeholders' requirements are also taken into account. The results show that there is still plenty of room for improvement, mainly regarding the specificity of data description in different domains, as well as the potential for integration of the data management platforms with existing research management tools. Nevertheless, depending on the context, some platforms can meet all or part of the stakeholders' requirements.

Abstract

Abstract
Identifying hand configuration is a critical feature of sign language translation. In this paper, we describe our approach to recognize hand configurations in real time with the purpose of providing accurate predictions to be used in automatic sign language translation. To capture the hand configuration we rely on data gloves with 14 sensors that measure finger joints bending. These inputs are sampled at a frequency of 100Hz and fed to a classifier that predicts the current hand configuration. The classification model is created from an annotated sample of hand configurations previously acquired. We expect this approach to be accurate and robust in the sense that the performance of the classification model should not vary significantly when the classifier is being used by one or another user. The results from our experimental evaluation show that there is a very high accuracy, meaning that data gloves are a good approach to capture the descriptive features of hand configurations. However, the robustness of such an approach is not as good as desirable since the accuracy of the classifier depends on the user, i.e., the accuracy is high when the classifier is used by a user who trained it but decreases in other cases.

Abstract
Concurrent C0 is an imperative programming language in the C family with session-typed messagepassing concurrency. The previously proposed semantics implements asynchronous (non-blocking) output; we extend it here with non-blocking input. A key idea is to postpone message reception as much as possible by interpreting receive commands as a request for a message. We implemented our ideas as a translation from a blocking intermediate language to a non-blocking language. Finally, we evaluated our techniques with several benchmark programs and show the results obtained. While the abstract measure of span always decreases (or remains unchanged), only a few of the examples reap a practical benefit.