Abstract
Vineyard parcels delimitation is a preliminary but important task to support zoning activities, which can be burdensome and time-consuming when manually performed. In spite of being desirable to overcome such issue, the implementation of a semi-/fully automatic delimitation approach can meet serious development challenges when dealing with vineyards like the ones that prevail in Douro Region (north-east of Portugal), mainly due to the great diversity of parcel/row formats and several factors that can hamper detection as, for example, interrupted rows and inter-row vegetation. Thereby, with the aim of addressing vineyard parcels detection and delimitation in Douro Region, a preliminary method based on segmentation and morphological operations upon high-resolution aerial imagery is proposed. This method was tested in a data set collected from vineyards located at the University of Trás-os-Montes and Alto Douro(Vila Real, Portugal). The presence of some of the previously mentioned challenging conditions - namely interrupted rows and inter-row grassing - in a few parcels contributed to lower the overall detection accuracy, pointing out the need for future improvements. Notwithstanding, encouraging preliminary results were achieved. © 2018 Association for Computing Machinery.

Abstract
Graphs with a large number of nodes and edges are difficult to visualize. Semantic graphs add to the challenge since their nodes and edges have types and this information must be mirrored in the visualization. A common approach to cope with this difficulty is to omit certain nodes and edges, displaying sub-graphs of smaller size. However, other transformations can be used to abstract semantic graphs and this research explores a particular one, both to reduce the graph’s size and to focus on its path patterns. Antigraphs are a novel kind of graph designed to highlight path patterns using this kind of abstraction. They are composed of antinodes connected by antiedges, and these reflect respectively edges and nodes of the semantic graph. The prefix “anti” refers to this inversion of the nature of the main graph constituents. Antigraphs trade the visualization of nodes and edges by the visualization of graph path patterns involving typed edges. Thus, they are targeted to users that require a deep understanding of the semantic graph it represents, in particular of its path patterns, rather than to users wanting to browse the semantic graph’s content. Antigraphs help programmers querying the semantic graph or designers of semantic measures interested in using it as a semantic proxy. Hence, antigraphs are not expected to compete with other forms of semantic graph visualization but rather to be used a complementary tool. This paper provides a precise definition both of antigraphs and of the mapping of semantic graphs into antigraphs. Their visualization is obtained with antigraphs diagrams. A web application to visualize and interact with these diagrams was implemented to validate the proposed approach. Diagrams of well-known semantic graphs are also presented and discussed. © José Paulo Leal.

Abstract
Linking an expert to his knowledge areas is still a challenging research problem. The task is usually divided into two steps: identifying the knowledge areas/topics in the text corpus and assign them to the experts. Common approaches for the expert profiling task are based on the Latent Dirichlet Allocation (LDA) algorithm. As a result, they require pre-defining the number of topics to be identified which is not ideal in most cases. Furthermore, LDA generates a list of independent topics without any kind of relationship between them. Expert profiles created using this kind of flat topic lists have been reported as highly redundant and many times either too specific or too general. In this paper we propose a methodology that addresses these limitations by creating hierarchical expert profiles, where the knowledge areas of a researcher are mapped along different granularity levels, from broad areas to more specific ones. For the purpose, we explore the rich structure and semantics of Heterogeneous Information Networks (HINs). Our strategy is divided into two parts. First, we introduce a novel algorithm that can fully use the rich content of an HIN to create a topical hierarchy, by discovering overlapping communities and ranking the nodes inside each community. We then present a strategy to map the knowledge areas of an expert along all the levels of the hierarchy, exploiting the information we have about the expert to obtain an hierarchical profile of topics. To test our proposed methodology, we used a computer science bibliographical dataset to create a star-schema HIN containing publications as star-nodes and authors, keywords and ISI fields as attribute-nodes. We use heterogeneous pointwise mutual information to demonstrate the quality and coherence of our created hierarchies. Furthermore, we use manually labelled data to serve as ground truth to evaluate our hierarchical expert profiles, showcasing how our strategy is capable of building accurate profiles. © 2018, Springer Nature Switzerland AG.

Abstract
Immersion is a state of altered consciousness-not the prim suspension of disbelief, but its joyous capsizing. Since approximately 2012, a new ecosystem of immersive virtual reality technologies and experiments has emerged. In this emerging ecosystem, journalism is still a minor component. Nevertheless, media outlets such as The New York Times, BBC or ABC News have been producing virtual reality news stories. This led to the advent of immersive journalism, not only as a media phenomenon, but also as an academic concept. Drawing on some notions and concepts like the definitions of immersive journalism, immersion and presence, as well as some examples of the relation between journalism practices and visual media, we analyse, reflect and provide a general overview about the main concepts, uses, opportunities and limits of immersive journalism. Thus, the main goal of this article is to provide a theoretical and conceptual gateway that serves as a starting point for immersive journalism future academic and industry endeavours.

Abstract
Quantitative assessment of mitral valve (MV) morphology is important for diagnosing MV pathology and for planning of reparative procedures. Although this is typically done using 3D transesophageal echocardiography (TEE), recent advances in the spatiotemporal resolution of 3D transthoracic echocardiography (TTE) have enabled the use of this more patient friendly modality. However, manual data analysis is time consuming and operator dependent. In this study, a fully automatic method for MV segmentation and tracking in 3D TTE is proposed and validated. The proposed framework takes advantage of a previously proposed left ventricle (LV) segmentation framework to localize the MV and performs segmentation based on the B-spline Explicit Active Surfaces (BEAS) framework. The orientation of the MV is obtained and the MV surface is cropped to the mitral annulus (MA) and divided into posterior and anterior leaflets. The segmented MV at end diastole (ED) is propagated to end systole (ES) using localized anatomical affine optical flow (lAAOF). Because the orientation and leaflet division is known, relevant clinical parameters can then be extracted from the mesh at any time point. The proposed framework shows excellent segmentation results with a mean absolute distance (MAD) and Hausdorff distance (HD) of 1.19 +/- 0.25 mm and 5.79 +/- 1.25 mm at ED and 1.39 +/- 0.32 mm and 6.70 +/- 1.97 mm at ES against manual analysis. In conclusion, an automatic method for MV segmentation is proposed which could provide valuable clinical information in a more patient-friendly manner.

Abstract
Precision agriculture is gaining an increasing interest in the current farming paradigm. This new production concept relies on the use of information technology (IT) to provide a control and supervising structure that can lead to better management policies. In this framework, imaging techniques that provide visual information over the farming area play an important role in production status monitoring. As such, accurate representation of the gathered production images is amajor concern, especially if those images are used in detection and classification tasks. Real scenes, observed in natural environment, present high dynamic ranges that cannot be represented by the common LDR (Low Dynamic Range) devices. However, this issue can be handled by High Dynamic Range (HDR) images since they have the ability to store luminance information similarly to the human visual system. In order to prove their advantage in image processing, a comparative analysis between LDR and HDR images, for fruits detection and counting, was carried out. The obtained results show that the use of HDR images improves the detection performance to more than 30% when compared to LDR.