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In this work, we propose a global planning strategy specifically designed for precision agriculture settings, where field activities may have different requirements ranging from a full orchard inspection to sparse targeted per-plant interventions. This global planning strategy is formulated as a novel Multi-Platform Steiner Traveling Salesman Problem (MP-STSP) where, in order to guarantee the exploitation of multiple moving platforms and the minimization of the overall operational time, the proposed formulation explicitly takes into account the time required to perform each task. By doing so, the computed itineraries attempt to balance the workload among the deployed platforms. Comparative simulations, inspired by the needs of the EU H2020 Project PANTHEON 1, are provided to numerically demonstrate the effectiveness of the proposed planning strategy for an orchard precision agriculture setting.

© 2020 IEEE. In this work, inspired by the needs of the H2020 European Project PANTHEON11http://www.project-pantheon.eu, we address the hazelnut sucker detection and canopy volume estimation problem on a per-plant basis. Sucker control is an essential but challenging practice in agriculture, given the fact that suckers, i.e., shoots that grow from the tree roots, compete with the tree itself for water and nutrients. This research is motivated by the observation that in current best-practice, sucker control is carried out by applying a non-calibrated amount of chemical inputs to each tree. Indeed, a proper sucker detection and estimation algorithm would represent the enabling technology for an environmentally friendly sucker control approach where the amount of herbicide could be properly calibrated according to the needs of each individual plant. In this work, we propose an end-to-end algorithm for detecting the presence of suckers and for estimating their canopy. First a sparse point cloud-based representation of the suckers is detected, then an approximated canopy estimation is achieved by means of a tailored meshing strategy that performs a leaf-based clustering and an iterative clusters connection. The volume is then estimated by the resulting mesh. Preliminary real-world experiments are provided to corroborate the effectiveness of the proposed canopy estimation strategy.

The massive adoption of social networks has made available an unprecedented amount of user-generated content, which may be analyzed in order to determine people's opinions and emotions on a large variety of topics. Research has made many efforts in defining accurate algorithms for the analysis of emotions conveyed by texts, however their performance often relies on the existence of large annotated datasets, whose current scarcity represents a major issue. The manual creation of such datasets represents a costly and time-consuming activity and hence there is an increasing demand for techniques for the automatic annotation of corpora. In this work we present a methodology for the automatic annotation of video subtitles on the basis of the analysis of facial expressions of people in videos, with the goal of creating annotated corpora that may be used to train emotion recognition algorithms. Facial expressions are analyzed through machine learning algorithms, on the basis of a set of manually -engineered facial features that are extracted from video frames. The soundness of the proposed methodology has been evaluated through an extensive experimentation aimed at determining the performance on real datasets of each methodological step.

Test automation has been an acknowledged software engineering best practice for years. However, the topic involves more than the repeated execution of test cases that often comes first to mind. Simply running test cases using a unit testing framework is no longer enough for test automation to keep up with the ever-shorter release cycles driven by continuous deployment and technological innovations such as microservices and DevOps pipelines. Now test automation needs to rise to the next level by going beyond mere test execution.

In testing, engineers want to run the most useful tests early (prioritization). When tests are run hundreds or thousands of times, minimizing a test set can result in significant savings (minimization). This paper proposes a new analysis technique to address both the minimal test set and the test case prioritization problems. This paper precisely defines the concept of mutant stubbornness, which is the basis for our analysis technique. We empirically compare our technique with other test case minimization and prioritization techniques in terms of the size of the minimized test sets and how quickly mutants are killed. We used seven C language subjects from the Siemens Repository, specifically the test sets and the killing matrices from a previous study. We used 30 different orders for each set and ran every technique 100 times over each set. Results show that our analysis technique performed significantly better than prior techniques for creating minimal test sets and was able to establish new bounds for all cases. Also, our analysis technique killed mutants as fast or faster than prior techniques. These results indicate that our mutant stubbornness technique constructs test sets that are both minimal in size, and prioritized effectively, as well or better than other techniques.

The performance of modern data-intensive applications is closely related to the speed of data access. However, a physical database optimization by design is often infeasible, due to the presence of large databases and time-varying workloads. In this paper we introduce a novel methodology for physical database optimization which allows for a quick and dynamic selection of indexes through the analysis of database logs. The application of the technique to cloud applications, which use a pay-per-use model, results in immediate cost savings, due to the presence of elastic resources. In order to demonstrate the effectiveness of the approach, we present the case study Nuvola, a SaaS multitenant application for schools that is characterized by heavy workloads. Experimental results show that the proposed technique leads to a 52.1% reduction of query execution time for a given workload. A comparative analysis of database performance before and after the optimization is also performed through a M/M/1 queue model and the results are discussed.

In this paper we propose an effective vision-based navigation method that allows a multirotor vehicle to simultaneously reach a desired goal pose in the environment while constantly facing a target object or landmark. Standard techniques such as Position-Based Visual Servoing (PBVS) and Image-Based Visual Servoing (IBVS) in some cases (e.g., while the multirotor is performing fast maneuvers) do not allow to constantly maintain the line of sight with a target of interest. Instead, we compute the optimal trajectory by solving a non-linear optimization problem that minimizes the target re-projection error while meeting the UAV's dynamic constraints. The desired trajectory is then tracked by means of a real-time Non-linear Model Predictive Controller (NMPC): this implicitly allows the multirotor to satisfy both the required constraints. We successfully evaluate the proposed approach in many real and simulated experiments, making an exhaustive comparison with a standard approach., Conference paper at "European Conference on Mobile Robotics" (ECMR) 2017

Traditional lexicon-based approaches for sentiment analysis are usually not able to model negation, as they do not provide proper techniques to identify the right negation window. In this work we address the problem of the automatic determination of the scope of negation and we present a negation handling algorithm based on dependency-based parse trees. The proposal is based on the use of grammatical relations among words to model a sentence, and hence to determine words that are affected by negation. The proposed algorithm has been coupled with a semantic disambiguation technique to identify the sentiment of a sentence. Experiments on different datasets have proven that our proposal improves the accuracy of the sentiment analysis. The proposed algorithm has been implemented as part of a Social Information Discovery system, which allows for an integrated near-real-time analysis of discussions from multiple social networks.

In recent years, a multitude of assistive technologies have been devised to enhance people's capabilities by means of environments that are adaptive, sensitive and responsive to human needs. Following this approach, Ambient Assisted Living (AAL) technologies are developed to provide support especially to elderly people for prevention and recognition of medical threats, and improvement of well-being. Towards this direction, the aim of this paper is to introduce the principles of a goal-oriented methodology devoted to support system designers in the development of AAL environments. In the methodology, AAL requirements are elicited, analysed and then formally represented in an ontology, which serves as a collaboratively built knowledge base. Here, high-level goals are described in terms of subgoals and tasks, that are then linked to corresponding measures and devices. On its top, logic-based reasoning functionalities provide means to retrieve explicit and hidden knowledge, as we show in two typical applications of the methodology, namely the development from scratch of an AAL environment starting from a set of high-level user requirements and the redesign of an existing implementation according to changed requirements.

Enterprise information systems, while support daily activities, typically collect data on executed processes in event logs. These data describe the temporal sequence in which activities were carried out, hiding possible parallelism and other control flows. Representing the structure of each process execution in the form of an Instance Graph, enables managers to discover valuable knowledge on enterprise behaviors. In this work, we describe BIG4ProM, a tool which implements the Building Instance Graph (BIG) algorithm. BIG4ProM exploits filtering Process Discovery algorithms implemented in ProM in order to return the set of instance graphs related to the given event log. The plug-in is conceived to support both expert and standard users.

Process Mining (PM) encompasses a number of methodologies designed for extracting knowledge from event logs, typically recorded by operational information systems like ERPs, Workflow Management Systems or other process-aware enterprise systems. The structured nature of processes implemented in these systems has led to the development of effective techniques for conformance checking (check if a real execution trace conforms to a predefined process schema) or process discovery (synthesize a process schema from a set of real execution traces recorded in the trace log) [1]. However in many knowledge-intensive domains, like e.g. health care, emergency management, research and innovation development, processes are typically characterized by little or no structure, since the flow of activities strongly depends on context-dependent decisions that should rely on human knowledge. Consequently, classical process discovery techniques usually provide limited support in analyzing these processes. As a further issue, in these domains an integrated information system may not even exist, requiring to integrate a number of independent event logs.

Sentiment Analysis of microblog content calls for specific tools able to cope with the dynamic nature of information published in social networks, and the intrinsic complexity and ambiguity of human language. In this work we introduce a Word Sense Disambiguation (WSD) algorithm for polysemous word disambiguation which uses a dictionary-based approach to determine the most fitting meaning of a term, basing on nearby words in the sentence. The work is a part of a Business Intelligence system for the integration and discovery of social information from multiple social networks, namely Facebook and Twitter. In this work we also extend the number of sources taking into account LinkedIn, as it is typically used by professionals, and discussions thereof provide added benefits when a non-generic evaluation of the topic to be analyzed is required.

Evidence-based governance and e-democracy both rely on the capability to analyze aggregated and statistical data. Recent studies report that existing analysis tools were never fully embraced by managers mainly because of their complexity for many analytical use cases. This is even more true for citizens, that do not have full control over underlying data and analysis models. In the present work, we propose an innovative user-centered approach for data analytics, that facilitates the interaction of users with statistical and aggregated measures, i.e. indicators. We provide an overview of the framework, discussing its main components and functionalities. In particular we focus on an ontology representing both atomic and compound indicators, that are provided with a calculation formula. We show how such a logic-based representation of indicators allows the implementation of powerful, automatic reasoning services, capable to provide a valuable support to users for performing analysis tasks.

Software Product Lines (SPL) approach has been widely developed in academia and successfully applied in industry. Based on the selection of features, stakeholders can efficiently derive tailor-made programs satisfying different requirements. While SPL was very successful at building products based on identified features, achievements and preservation of many nonfunctional properties (NFPs) remain challenging. A knowledge how to deal with NFPs is still not fully obtained. In this paper, we present a systematic literature review of NFPs analysis for SPL products, focusing on runtime NFPs. The goal of the paper is twofold: (i) to present an holistic overview of SPL approaches that have been reported regarding the analysis of runtime NFPs, and (ii) to categorize NFPs treated in the scientific literature regarding development of SPLs. We analyzed 36 research papers, and identified that system performance attributes are typically the most considered. The results also aid future research studies in NFPs analysis by providing an unbiased view of the body of empirical evidence and by guiding future research directions.

Everyday millions of contents are generated and shared over the most popular social network. Enterprises have already realized the usefulness of social networks to enable marketing campaigns and communicate with their customers. However, only few enterprises use social network as an active source of information, interacting with the network's users in (near-)real time, e.g. for crowdsourcing and leveraging open-innovation. To encourage and facilitate this use of networks, we believe it is needed an information discovery system which elaborates simultaneously over more-than-one networks in an integrated scenario. Such a system has to be able to handle the speed at which the contents of social network are generated, the huge amount of available data and dynamism at which networks evolve and new kind of content are shared. Furthermore, the system has to ensure a quick response time. In this work we propose a methodology to design this kind of system and present the experience gained in the development of an information discovery system based on Exploratory Data Analysis and aimed at analyzing text contents from two social networks: Facebook and Twitter.

The analysis of data produced during collaborative activities allows organizations to improve collaboration management. Since people use several collaboration tools, these kind of data are difficult to obtain. Furthermore they are heterogeneous and require an important preprocessing step to be useful. In the present work we introduce a methodology aimed at obtaining a single log with all data related to team activities. To improve process analysis, such data log is semantically enriched by means of a multidimensional taxonomy capable of describing collaboration activities at various abstraction levels. We also introduce a case study to be used throughout the paper as an illustrative example.

Cardiac allograft vasculopathy (CAV) is the major cause limiting long term graft survival after heart transplantation (HT), and is characterized by changes in coronary artery geometry, such as intimal thickening and vessel remodeling. Given the limited strategies available to reduce its impact on outcome, early diagnosis of CAV - for which intravascular ultrasound (IVUS) is the gold standard - is crucial to appropriately modulate therapy and to reduce contributing risk factors. However, a highly reproducible image-analysis method is required to capture the complex mechanisms beyond CAV - related changes in coronary geometry.

Cloud computing is perceived as the next wave of ICT, and many real experiences are on the commercial scene. However this kind of architecture has open legal issues which makes it an endeavor for Public Administrations, despite its potential impact on the efficiency, effectiveness and transparency of administrative initiatives. In the present paper we present the experience made in the deployment of a Cloud solution in the Regione Marche Local Public Administration, which represents one of the pilot experiences at Nationallevel.

The evolution of innovation management in last decades was strongly influenced and led by the theory of the “Open Innovation” introduced by Chesbrough [1], and has become one of the hottest topic in business Literature. In the current economical scenario an increasingly number of organizations decide to adopt a more open approach in their innovation policy, trying to establish more or less strong relations with external partners, directly involving them in innovative projects. Consequently the collaborative work is gaining a growing importance in innovation practices of organizations, since the success or failure of innovative projects is often strictly related to results of collaborative tasks. Therefore, to support innovation processes of an organization one can investigate and improve its collaboration practices, with the aim to discover the best ones, i.e. those that maximize the success probability of organizations innovative projects. However, this kind of analysis is often prevented by the lack of real world data, mainly due to the limited diffusion of innovation management systems capable to collect innovation activities traces. Nevertheless, the daily activities of an enterprise, both internal and external, are almost completely performed by software systems. Both explicitly and implicitly, these systems keep track of users activities, e.g. ERP logs, versioning systems, list of emails, file timestamps, and so forth. In the present work we propose a methodology aimed to discover relevant collaboration patterns based on real data daily collected by enterprises, with the aim of providing business users with a better understanding of the dynamics of the interactions among members of collaborating groups. Our idea is firstly to collect any kind of data produced during the collaborative development of an innovation project, then to integrate them into a unique knowledge base storing traces of enterprise activities. Through preprocessing analysis, such traces are translated into process schemas, that can be considered as a representation of collaborative innovation processes in the organization, on which we can perform pattern discovery. To this aim we consider hierarchical clustering, which is capable to extracts frequent subprocesses representing common collaboration patterns and to arrange them in a hierarchy with different level of abstractions. The rest of this work is organized in two sections, the former aimed to describe the main ideas of the methodology, the latter to sketch out future extensions we plan to conduct.

Innovation management and promotion has become one of the most important topics in the Literature about business and executive decision support. In particular, the relationship between innovation and collaboration, both intra- and inter-organization, is gaining an increasing attention in many works, for example in the Open Innovation research field [2]. Innovation activities, especially those that involve collaboration, are typically not structured; they don't follow a predefined scheme or procedure and are influenced by multiple factors, for instance the individual behaviour, that makes it difficult to apply classical methods of process analysis. In this paper we describe a methodology to discover significant and recurrent patterns in innovation activities, that can be used to support and improve such kind of processes. To evaluate our approach we conducted a set of experiments on a synthetic dataset, which contains a set of traces of innovation activities generated from some abstract templates, drew with the aim to model the typical ways in which innovation is carried on.

The analysis of non-functional properties of Service-Based Systems (SBSs) is a complex task, mostly because it requires models that encompass the composition of service properties into architectural properties. For example, the reliability of a SBS is given by the composition of service and interconnection reliabilities. Although several approaches have been introduced in the last few years to address these issues, the tradeoff analysis among non-functional properties of software services has not yet been studied enough. The goal of this paper is to introduce a set of optimization models that allow quantifying the costs of service failure repair/mitigation actions aimed at keeping the whole SBS reliability over a certain threshold. On the basis of our previous work in this area, we first introduce an optimization model aimed at selecting either in-house built or provided services with the goal of minimizing the SBS cost while guaranteeing a certain level of reliability. Thereafter we strengthen the reliability constraints, and we build two different optimization models that aim to solve the same problem under new constraints, where one model starts from the solution obtained in the original model and tries to improve it, while the other one looks for an optimal solution in the whole search space. Finally, we introduce a fourth model, based on stochastic optimization, with the goal of rather searching for solutions that explicitly take into account the stochastic nature of the problem and search for new repair/mitigation actions cheaper than the ones identified by the other models. Each optimization model has been experimented on about 300 variations of a nominal model. The experimental results show the efficacy of our optimization models to quantify the costs of different failure repairing/mitigation actions in different contexts.

In service-oriented computing, software applications are dynamically built by assembling existing, loosely-coupled, distributed, and heterogeneous services. Predicting their reliability is important to appropriately drive the selection and assembly of services. This paper presents an approach to predict the reliability of a service component architecture. We adopt a lightweight formal component model, SCA-ASM, as core modeling technique for both architecture and behavior, supported by a run-time platform. This component model is based on the OASIS standard Service Component Architecture for heterogeneous service assembly and on the formal method Abstract State Machines for modeling service behavior, interactions, and orchestration in an abstract but executable way. The proposed reliability prediction method exploits ideas from architecture-based and path-based reliability models.

Team building plays a crucial role in many collaborative projects. The use of semantic technologies and tools, like ontologies and metadata management proved to be a powerful approach to organize people competencies and support the formation of teams. Although Knowledge Discovery in Databases (KDD) has inherent collaborative characteristics, team building in this domain has not been the subject of extensive work yet. In this paper we start filling this gap by presenting TeamOnto, an ontology for the representation of project teams. TeamOnto is part of a wide, modular and integrated Knowledge Base about KDD projects, of which we briefly illustrate some modules, showing their use to support team building in the KDD domain.

Knowledge Discovery in Databases (KDD) is a complex and computationally intensive process aimed at extracting knowledge from large amounts of data. To provide effective support to users, especially non-experts, in this work we propose a knowledge-centric platform specifically aimed at supporting collaborative design of KDD processes in distributed environments.