Abstract
The optimisation of vineyards management requires efficient and automated methods able to identify individual plants. In the last few years, Unmanned Aerial Vehicles (UAVs) have become one of the main sources of remote sensing information for Precision Viticulture (PV) applications. In fact, high resolution UAV-based imagery offers a unique capability for modelling plant's structure making possible the recognition of significant geometrical features in photogrammetric point clouds. Despite the proliferation of innovative technologies in viticulture, the identification of individual grapevines relies on image-based segmentation techniques. In that way, grapevine and non-grapevine features are separated and individual plants are estimated usually considering a fixed distance between them. In this study, an automatic method for grapevine trunk detection, using 3D point cloud data, is presented. The proposed method focuses on the recognition of key geometrical parameters to ensure the existence of every plant in the 3D model. The method was tested in different commercial vineyards and to push it to its limit a vineyard characterised by several missing plants along the vine rows, irregular distances between plants and occluded trunks by dense vegetation in some areas, was also used. The proposed method represents a disruption in relation to the state of the art, and is able to identify individual trunks, posts and missing plants based on the interpretation and analysis of a 3D point cloud. Moreover, a validation process was carried out allowing concluding that the method has a high performance, especially when it is applied to 3D point clouds generated in phases in which the leaves are not yet very dense (January to May). However, if correct flight parametrizations are set, the method remains effective throughout the entire vegetative cycle.

Abstract
Scientific impact is commonly associated with the number of citations received. However, an author can easily boost his own citation count by (i) publishing articles that cite his own previous work (self-citations), (ii) having co-authors citing his work (co-author citations), or (iii) exchanging citations with authors from other research groups (reciprocated citations). Even though these friendly citations inflate an author's perceived scientific impact, author ranking algorithms do not normally address them. They, at most, remove self-citations. Here we present Friends-Only Citations AnalySer (FOCAS), a method that identifies friendly citations and reduces their negative effect in author ranking algorithms. FOCAS combines the author citation network with the co-authorship network in order to measure author proximity and penalises citations between friendly authors. FOCAS is general and can be regarded as an independent module applied while running (any) PageRank-like author ranking algorithm. FOCAS can be tuned to use three different criteria, namely authors' distance, citation frequency, and citation recency, or combinations of these. We evaluate and compare FOCAS against eight state-of-the-art author ranking algorithms. We compare their rankings with a ground-truth of best paper awards. We test our hypothesis on a citation and co-authorship network comprised of seven Information Retrieval top-conferences. We observed that FOCAS improved author rankings by 25% on average and, in one case, leads to a gain of 46%.

Abstract
High-maturity software development processes and development environments with automated data collection can generate significant amounts of data that can be periodically analyzed to identify performance problems, determine their root causes, and devise improvement actions. However, conducting the analysis manually is challenging because of the potentially large amount of data to analyze, the effort and expertise required, and the lack of benchmarks for comparison. In this article, we present ProcessPAIR, a novel method with tool support designed to help developers analyze their performance data with higher quality and less effort. Based on performance models structured manually by process experts and calibrated automatically from the performance data of many process users, it automatically identifies and ranks performance problems and potential root causes of individual subjects, so that subsequent manual analysis for the identification of deeper causes and improvement actions can be appropriately focused. We also show how ProcessPAIR was successfully instantiated and used in software engineering education and training, helping students analyze their performance data with higher satisfaction (by 25%), better quality of analysis outcomes (by 7%), and lower effort (by 4%), as compared to a traditional approach (with reduced tool support).

Abstract
Performance and power efficiency in edge and embedded systems can benefit from specialized hardware. To avoid the effort of manual hardware design, we explore the generation of accelerator circuits from binary instruction traces for several Instruction Set Architectures.

Abstract
This work studies Tor, an anonymous overlay network used to browse the Internet. Apart from its main purpose, this open-source project has gained popularity mainly because it does not hide its implementation. In this way, researchers and security experts can fully examine and confirm its security requirements. Its ease of use has attracted all kinds of people, including ordinary citizens who want to avoid being profiled for targeted advertisements or circumvent censorship, corporations who do not want to reveal information to their competitors, and government intelligence agencies who need to do operations on the Internet without being noticed. In opposition, an anonymous system like this represents a good testbed for attackers, because their actions are naturally untraceable. In this work, the characteristics of Tor traffic are studied in detail in order to devise an inspection methodology able to improve Tor detection. In particular, this methodology considers as new inputs the observer position in the network, the portion of traffic it can monitor, and particularities of the Tor browser for helping in the detection process. In addition, a set of Snort rules were developed as a proof-of-concept for the proposed Tor detection approach. © Springer Nature Switzerland AG 2020.

Abstract
With the increasing complexity of software systems, software developers would benefit from instant and continuous guidance about the system they are maintaining and evolving. Despite existing several solutions providing feedback and suggesting improvements, many tools require explicit invocation, leading to developers missing improvement opportunities, such as important refactorings, due to lost of train of thought. Therefore, to address these limitations, we propose an approach where developers receive instant and continuous feedback about their software systems. This guidance would include the detection of code smells and the suggestion of refactorings to improve the system, justified by relevant software quality metrics related to the recommendations. This research aims to improve the experience of developing and maintaining software systems by providing a live environment for continuous inspection and refactoring of software systems, that is informative, responsive, and tactically predictive, and thus helping developers to identify and solve quality problems in a quicker and better way.