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As the society is already permeated by data, a data-driven approach to inform design for sustainable behaviour can help to identify misbehaviours and target sustainable behaviours to achieve, as well as to select and implement the most suitable design strategies to promote a behavioural change and monitor their effectiveness. This work addresses the open challenge of providing designers with a model for Human-Machine Interactions (HMI) that helps to identify relevant data to collect for inferring user behaviour related to environmental sustainability during product use.We propose a systematic modelling framework that combines constructs from existing representation techniques to identify the most critical variables for resources consumption, which are the determinants of potential misbehaviours related to HMI. The analysis is represented as a Behaviour-Inefficiency Model that graphically supports the analyst/designer to link user behaviours with a quantitative representation of resources consumption.The paper describes the model through an example of the use of a kettle and an additional application of the same approach to a washing machine, in order to point out its versatility for modelling more complex interactions.

Dependability and performance analysis of modern systems is facing great challenges: their scale is growing, they are becoming massively distributed, interconnected, and evolving. Such complexity makes model-based assessment a difficult and time-consuming task. For the evaluation of large systems, reusable submodels are typically adopted as an effective way to address the complexity and to improve the maintainability of models. When using state-based models, a common approach is to define libraries of generic submodels, and then compose concrete instances by state sharing, following predefined “patterns” that depend on the class of systems being modeled. However, such composition patterns are rarely formalized, or not even documented at all. In this paper, we address this problem using a model-driven approach, which combines a language to specify reusable submodels and composition patterns, and an automated composition algorithm. Clearly defining libraries of reusable submodels, together with patterns for their composition, allows complex models to be automatically assembled, based on a high-level description of the scenario to be evaluated. This paper provides a solution to this problem focusing on: formally defining the concept of model templates, defining a specification language for model templates, defining an automated instantiation and composition algorithm, and applying the approach to a case study of a large-scale distributed system.

The SolarPACES Reflectance Guideline provides an essential tool to obtain comparable reflectance measurements, but because of the lack of adequate commercial instrumentation, till now the exhaustive characterization of reflectance behaviour versus incidence θi and (half) acceptance angle φ is unachieved. An expert group in Task III has been working to outline some practicable solutions. The Equivalent Model Algorithm (EMA) was found quite promising: with few input data, EMA allows to predict any reflectance feature by computation. The recent availability of reliable reflectance measurements at oblique incidence made possible the refinement of EMA by analysing a representative set of commercial solar mirrors. This paper describes the new EMA for solar mirror, named EMA4SM, and reports its validation1.

Often, either to expand the target market or to satisfy specific new requirements, software systems inside a company are cloned, refactored, and customized, generating new derived software systems. Although this is a practical solution, it is not effective in the long-term because of the high maintenance costs when maintaining each of these derived software systems. Software product lines (SPLs) were proposed to reduce these costs; however, the lack of integration between variability realization mechanisms and version control systems reduces its attractiveness in the software development industry, especially in small and medium software companies. In this paper we propose an approach to integrate the conditional compilation mechanism used to implement the SPL variabilities and the Git version control system used to manage software versions in order to increase the attractiveness of the SPLs in the industry. The proposed solution also could be seen as a method to manage software system families' evolution in space and time.

The optical behavior of devices based on thin films is determined by complex refractive index and thickness of each slab composing the stack; these important parameters are usually evaluated from photometric and/or ellipsometric spectral measurements, given a model of the stack, by means of dedicated software. In the case of complex multilayer devices, generally a number of simpler specimens (like single-film on substrate) must be preliminarily characterized. This paper introduces the reader to a new open source software for thin film characterization finally released after about 30 years of development. The software has already been used in various fields of physics, such as thin film optical filters, architectural glazing, detectors for high energy physics, solar energy, and, last but not least, photovoltaic devices. Code source files, user manual as well as a sample of working directories populated with assorted files can be freely downloaded from the kSEMAW GitHub repository.

The precise measurement of the reflected energy form a solar reflector to the Receiver is a challenging Task because of ist dependency on three different Parameters: the wavelength of the incident lieght, ist incidence angle and the acceptance angle of the Receiver. Up to today, the commonly empleyed measurement Equipment is not able to fully characterize the mirrors in the required range of Parameters which is relevant for CSP. Within this work, four reflectometers were upgraded to overcome the current drawbacks. A Round Robin test among the different characterization methods has been carried out on four reflector materials. The conclusions drawn from the comparison of the measurement results help to improve the current SolarPACES Reflectance Guideline.

As customers, operations, and supply chains produce ever more volumes of data, firms must explore new ways of extracting valuable insights to improve efficiency and efficacy of decision-making processes at the same time as reducing noise and abnormalities in data streams (Sivarajah et al. 2017). Machine learning and artificial intelligence techniques can aid the creation and design of new products and services by producing highly sensitive analytics. These analytics can increase the speed of idea generation or “creative intensity” (Erevelles et al. 2016) by providing real-time assessments of multiple offering variations and predictions of their potential market success (Lehrer et al. 2018). Furthermore, these analytics can be a source of competitive advantage in highly competitive markets that require a continuous degree of product newness. Although several studies show that various organizational processes can be optimized and automated to some degree (Bradlow et al. 2017), relatively little is understood about the value that advanced data manipulation systems bring to the decision-making process at individual, cognitive, level, in particular when choices and judgments of product creativity or innovativeness are involved. Understanding how to capitalize on augmented and data-driven decision-making processes in relation to different creative alternatives becomes essential to generate competitive advantage in this area. Individual decision makers, with their limited mental capacities, are still required to frame problems, select which data to collect, assess the robustness of the data and their sources, and decide on analytical frameworks. Most importantly, decision makers must interpret the findings within the business context in which they operate and use insights to support strategic, tactical, and operational decisions leading to the development of new market offerings. Within this context, this study seeks to understand how managers use analytics to stimulate new product and service innovations and aid creativity decisions. In particular, the study seeks to contribute to the field of marketing decision-making by providing an assessment of how machine learning and artificial intelligence affects the generation and selection of new product ideas. It also contributes to the growing multidisciplinary literature on data analytics by assessing its value from a decision-making perspective.

The article stems from the main informative gaps of Design for Sustainable Behaviour and discusses the paramount role of a data-driven approach to inform design. The article stresses how quantitative data can address global sustainability, determine behaviours to modify, measure the impact of new learned sustainable behaviours as well as support the definition of behaviour change strategies, widening the spatial and temporal scales to communities and longitudinal studies and reducing unpredictable biases coming from tacit knowledge externalization and interpretation.

Knowledge-intensive Processes (KiPs) are processes characterized by high levels of unpredictability and dynamism. Their process structure may not be known before their execution. One way to cope with this uncertainty is to defer decisions regarding the process structure until run time. In this paper, we consider the definition of the process structure as a planning problem. Our approach uses automated planning techniques to generate plans that define process models according to the current context. The generated plan model relies on a metamodel called METAKIP that represents the basic elements of KiPs. Our solution explores Markov Decision Processes (MDP) to generate plan models. This technique allows uncertainty representation by defining state transition probabilities, which gives us more flexibility than traditional approaches. We construct an MDP model and solve it with the help of the PRISM model-checker. The solution is evaluated by means of a proof of concept in the medical domain which reveals the feasibility of our approach.

Mathematical models are an effective tool for studying the properties of complex systems. Constructing such models is a challenging task that often uses repeated patterns or templates. The Template Models Description Language (TMDL) has been developed to clearly define model templates that are used to generate model instances from the template specification. This paper describes the tool support that is being developed for applying the TDML approach with Stochastic Activity Networks (SANs) models. In particular, this paper details a graphical editor for SAN templates, which assists users in creating template-level models based on SANs. From these specifications, it will be possible to generate by model-transformation the subsequent instance-level models, which can be studied by simulation or analytical tools.

Encouraging physical activity and fitness is a key goal of many organizations and governments today. At the same time, the last decade has witnessed an unprecedented growth in ability to track behavior. The fact that anything and everything can now be tracked also means that anything can be turned into a game. Gamification, or the application of game design principles to non-game contexts (Robson et al. 2015), thus is on the rise. Health gamification is a potential means of motivating behavior changes (Robson et al. 2015, 2016) that support improved health and fitness. This work explores the point collection and redemption behavior of users in a large gamified fitness program. The results provide a framework to develop gamified experiences that are effective at generating improved sustained health through physical activity while also being cost effective.

Because of the substances they process and the conditions of operation, chemical plants are systems prone to the occurrence of undesirable and potentially dangerous events. Major accidents may occur when a triggering event produces a cascading accident that propagates to other units, a scenario known as domino effect. Assessing the probability of experiencing a domino effect and estimating the magnitude of its consequences is a complex task, as it depends on the nature of the substances being processed, the operating conditions, the failure proneness of equipment units, the execution of preventive maintenance activities, and of course the plant layout. In this work, we propose a stochastic modeling methodology to perform a probabilistic analysis of the likelihood of domino effects caused by propagating vapor cloud explosions. Our methodology combines mathematical models of the physical characteristics of the explosion, with stochastic state-based models representing the actual propagation among equipment units and the effect of maintenance activities. Altogether, the models allow predicting the likelihood of major events occurrence and the associated costs. A case study is analyzed, where various layouts of atmospheric gasoline tanks are assessed in terms of the predicted consequences of domino effects occurrence. The results of the analyses show that our approach can provide precious insights to support decision-making for safety and cost management.

Concentrating solar power plants use mirrors to redirect solar radiation toward receivers. Mirrors (facets) are suitably shaped to optimize the radiation capture ratio. Because deviations from ideal shape/slope degrade optical efficiency, the availability of reliable instruments for 3D shape measurement is essential. This paper deals with an innovative instrument, named VISproLF, specific for the case of facets for the primary reflector of linear Fresnel collectors. The instrument is simply composed of a LCD screen and a digital camera; to avoid artifacts, the camera must be equipped with a high quality lens and its characteristic matrix and distortion coefficients have to be accurately evaluated. The arrangement of the LCD screen and camera around the facet must follow the simple rule to view from the camera the facet surface entirely covered by the image of the screen. Then, the position and attitude of the camera (first) and LCD screen (second) are accurately evaluated by a simple procedure based on the pinhole camera model, which makes the instrument self-calibrating. The shape and slope of the facet surfaces are obtained by processing five binary images by means of a completely digital method of which a detailed description is given. In the experimental arrangement used to prepare the present paper, the slope accuracy is about 0.2 mrad. With respect to the most common instruments based on the Fringe reflection technology, VISproLF offers two great advantages: full-digital image processing and a self-calibration feature.

Solar reflectance is one of the most important parameters characterizing the mirrors used to redirect the solar radiation toward a receiver in concentrating solar power plants. In Solar Power and Chemical Energy Systems (SolarPACES) Task III, an international group of experts is drafting the reflectance guidelines and debating the hard task of measuring off-normal near-specular solar-reflectance. Because of the lack of suitable commercial instruments, several research institutes are studying and setting up special equipment. Since 2012, the Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA) proposed an articulated strategy based on modeling the hemispherical spectral reflectance with the equivalent model algorithm and the ratio of near-specular to hemispherical reflectance with the total integral scattering equation; in the latter, the roughness is replaced by the heuristic parameter σφ, nonphysically dependent on the acceptance angle. The solar mirror qualification experimental setup was conceived for measuring σφ. Recently, SolarPACES approved a new project to support the working group for improving the proposed instruments. The paper summarizes the ENEA strategy and presents the latest version of the instrument, SMQ2, discussing its several important new features: self-calibration, setup simplification, straightforward applicability to high specular mirrors, measurement-time cutoff, and acceptance-angle range extended up to 50 mrad.Solar reflectance is one of the most important parameters characterizing the mirrors used to redirect the solar radiation toward a receiver in concentrating solar power plants. In Solar Power and Chemical Energy Systems (SolarPACES) Task III, an international group of experts is drafting the reflectance guidelines and debating the hard task of measuring off-normal near-specular solar-reflectance. Because of the lack of suitable commercial instruments, several research institutes are studying and setting up special equipment. Since 2012, the Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA) proposed an articulated strategy based on modeling the hemispherical spectral reflectance with the equivalent model algorithm and the ratio of near-specular to hemispherical reflectance with the total integral scattering equation; in the latter, the roughness is replaced by the heuristic parameter σφ, nonphysically dependent on the acceptance angle. The solar mirror qualifi...

Cloud services consumers deal with a major challenge in selecting services from several providers. Facilitating these choices has become critical, and an important factor is the service trustworthiness. To be trusted by users, cloud providers should explicitly communicate their capabilities to ensure important functional and non-functional requirements (such as security, privacy, dependability, fairness, among others). Thus, models and mechanisms are required to provide indicators that can be used to support clients on choosing high quality services. This paper presents a solution to support privacy measurement and analysis, which can help the computation of trustworthiness scores. The solution is composed of a reference model for trustworthiness, a privacy model instance, and a privacy monitoring and assessment component. Finally, we provide an implementation capable of monitoring privacy-related information and performing analysis based on privacy scores for eight different datasets.

Knowledge-intensive processes (KiPs) cannot be fully specified at design time because not all information about the process is available prior to its execution. At runtime, new information emerges reflecting environment changes or unexpected outcomes. The structure of this kind of processes varies from case to case and it is defined step-by-step based on knowledge worker's decisions made after analyzing the current situation. These decisions rely on the knowledge worker's experience and available information. Current process management approaches still need to adequately address the complex characteristics of knowledge-intensive processes, such as their unpredictability, emergency, non-repeatability, and dynamism. This paper proposes a metamodel for representing KiPs aiming to help knowledge workers during the decision-making process. Domain and organizational knowledge are modeled by objectives and tactics. The metamodel supports the definition of objectives, metrics, tactics, goals and strategies at runtime according to a specific situation. Also, it includes concepts related to context and environment elements, business artifacts, roles and rules. The feasibility of our model was evaluated via a proof of concept in the medical domain.

Service Oriented Architecture (SOA) is a common design pattern that allows building applications composed of several services. It promotes features as interoperability, scalability, and software reuse. Services composing a SOA system may evolve and change during runtime, often outside the control of the owner of the application, which makes the verification and validation processes complex. Among all the automated techniques to validate the behavior of an SOA application, is Model-Based Testing (MBT). MBT requires an accurate model of the application in order to generate suitable test cases. However, the intrinsic of a SOA application sets significant challenges to MBT effectiveness. In this paper we discuss the challenges in the testing of SOA applications, and we propose the use of Model-Driven Engineering (MDE) to improve the flexibility of testing tools. Finally, we outline our plan for realizing MDE-driven MBT within an existing online testing framework.

Coding conventions are a means to improve the reliability of software systems. They can be established for many reasons, ranging from improving the readability of code to avoiding the introduction of security flaws. However, coding conventions often come in the form of textual documents in natural language, which makes them hard to manage and to enforce. Following model-driven engineering principles, in this paper we propose an approach and language for specifying coding conventions using structured models. We ran a feasibility study, in which we applied our language for specifying 215 coding rules from two popular rulesets. The obtained results are promising and suggest that the proposed approach is feasible. However, they also highlight that many challenges still need to be overcome. We conclude with an overview on the ongoing work for generating automated checkers from such models, and we discuss directions for an objective evaluation of the methodology.

Chemical process plants are subject to risk caused by the handling and storage of hazardous substances. Major accidents may occur, particularly in those unfortunate circumstances when a triggering event produces a cascading accident that propagates to other units, a failure propagation scenario known as domino effect. An important aspect of designing such industrial plants is to properly arrange hazardous equipment such that, in the event of failures, cascading effects are minimized. In this work, we present a modeling approach to perform a probabilistic analysis of the likelihood of domino effects caused by propagating vapor cloud explosions. The approach combines the modeling of accident propagation based on physical properties of gas clouds, such as released mass and explosion distance, with the probabilistic modeling of cascading effects based on Stochastic Petri Nets. The proposed methodology is subsequently applied to a case study where different layouts of atmospheric gasoline tanks are analyzed, in order to evaluate the likelihood of domino effect occurrence.

Dynamic features offered by programming languages provide greater flexibility to the programmer (e.g., dynamic constructions of classes and methods) and reduction of duplicate code snippets. However, the unnecessary use of dynamic features may detract from the code in many ways, such as readability, comprehension, and maintainability of software. Therefore, this paper proposes 20 refactorings that replace dynamic instructions with static ones. In an evaluation on 28 open-source Ruby systems, we could refactor 743 of 1,651 dynamic statements (45%).

CHESS is an open source methodology and toolset for the development of safety-critical systems. More specifically, CHESS is a model-based methodology, which supports the design, dependability analysis, and code generation for critical systems. Despite its rather mature level in terms of technology readiness, systematic guidance needs to be developed to promote its usage for certification purposes. In this paper, we present a systematic process to guide designers and analysts in the usage of the CHESS toolset for model-based dependability analysis of safety-critical systems in compliance with ISO 26262 Parts 3 and 4, SAE ARP 4754A safety process, and DO-331 model-based development principles. We also have applied our process to a real world automotive hybrid braking system. The proposed process can be used to guide analysts in using CHESS methodology to support both system design and dependability analysis. Finally, we draw our conclusion and sketch future work.

With the expansion of autonomous robotics and its applications (e.g. medical, competition, military), the biggest hurdle in developing mobile robots lies in endowing them with the ability to interact with the environment and to make correct decisions so that their tasks can be executed successfully. However, as the complexity of robotic systems grows, the need to organize and modularize software for their correct functioning also becomes a challenge, making the development of software for controlling robots a complex and intricate task. In the robotics domain, there is a lack of reference software architectures and, although most robot architectures available in the literature facilitate the creation process with their modularity, existing solutions do not provide development guidance on reusing existing modules. Based on the well- known IBM Autonomic Computing reference architecture (known as MAPE-K), this work defines a refined architecture following the Robotics perspective. To explore the capabilities of the proposed refinement, we implemented the RoCS (Robotics and Cognitive Systems) framework for autonomous robots. We successfully tested the framework under simulated robotics scenarios that mimic typical robotics tasks and evidence the framework reuse capability. Finally, we understand the proposed framework needs further experimental evaluation, particularly, assessments on real-world scenarios.

Current ICT infrastructures are characterized by increasing requirements of reliability, security, performance, availability, adaptability. A relevant issue is represented by the scalability of the system with respect to the increasing number of users and applications, thus requiring a careful dimensioning of resources. Furthermore, new security issues to be faced arise from exposing applications and data to the Internet, thus requiring an attentive analysis of potential threats and the identification of stronger security mechanisms to be implemented, which may produce a negative impact on system performance and scalability properties. The paper presents a model-based evaluation of scalability and security tradeoffs of a multi-service web-based platform, by evaluating how the introduction of security mechanisms may lead to a degradation of performance properties. The evaluation focuses on the OPENNESS platform, a web-based platform providing different kind of services, to different categories of users. The evaluation aims at identifying the bottlenecks of the system, under different configurations, and assess the impact of security countermeasures which were identified by a thorough threat analysis activity previously carried out on the target system. The modeling activity has been carried out using the Stochastic Activity Networks (SANs) formalism, making full use of its characteristics of modularity and reusability. The analysis model is realized through the composition of a set of predefined template models, which facilitates the construction of the overall system model, and the evaluation of different configuration by composing them in different ways.

The shape of Critical Infrastructures (CIs) has changed drastically in recent years, leading them to become interconnected systems with complex interactions. This will be especially true for future power grids, known as "Smart Grids". In such complex systems, one of the main challenges consists in understanding the possible impact of failures on the overall system, and avoiding cascading or escalading effects. In this paper we present an extensible framework for the analysis of failures propagation in power networks, based on model templates developed using the Stochastic Activity Networks formalism. The approach is applied to a case study of a power network, derived from a real system. The obtained results demonstrate the effectiveness of the approach in assessing the impact of failures on the network, both when considering random failures and when performing what-if analyses on specific nodes.

Socio-technical systems are characterized by the interplay of heterogeneous entities i.e., humans, organizations, and technologies. Application domains such as petroleum, e-health, and many others rely on solutions based on safety-critical socio-technical systems. To ensure a safe operation of these interacting heterogeneous entities, multifaceted and integrated modeling and analysis capabilities are needed. Currently, such capabilities are not at disposal. To contribute to the provision of such capabilities, in this paper we propose SafeConcert, a metamodel that offers constructs to model socio-technical entities and their safety-related properties. SafeConcert also represents a unified and harmonized language that supports the integrated application of qualitative as well as quantitative safety analyses techniques. To support our claims we briefly report about the evaluation that was conducted and documented in the context of the EU CONCERTO project.

This paper focuses on the use of controlled vocabularies (CVs) and ontologies especially in the area of proteomics, primarily related to the work of the Proteomics Standards Initiative (PSI). It describes the relevant proteomics standard formats and the ontologies used within them. Software and tools for working with these ontology files are also discussed. The article also examines the “mapping files” used to ensure correct controlled vocabulary terms that are placed within PSI standards and the fulfillment of the MIAPE (Minimum Information about a Proteomics Experiment) requirements. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan., Highlights ► The semantic annotation using ontologies is a prerequisite for the semantic web. ► The HUPO-PSI defined a set of XML-based standard formats for proteomics. ► These standard formats allow the referencing of CV terms defined in obo files. ► The CV terms can be used to enforce MIAPE compliance of the data files. ► The mass spectrometry CV is constantly maintained in a community process.

Solar dishes allow us to obtain highly concentrated solar fluxes used to produce electricity or feed thermal processes/storage. For practical reasons, the reflecting surface is composed by a number of facets. After the dish assembly, facet-canting is an important task for improving the concentration of solar radiation around the focus-point, as well as the capture ratio at the receiver placed there. Finally, flux profile should be measured or evaluated to verify the concentration quality. All these tasks can be achieved by the new tool we developed at ENEA, named VISdish. The instrument is based on the visual inspection system (VIS) approach and can work in two functionalities: canting and shape-measurement. The shape data are entered in a simulation software for evaluating the flux profile and concentration quality. With respect to prior methods, VISdish offers several advantages: (i) simpler data processing, because light point-source and its reflections are univocally related, (ii) higher accuracy. The instrument functionality is illustrated through the preliminary experimental results obtained on the dish recently installed in ENEA-Casaccia in the framework of the E.U. project OMSoP. © 2017 Author(s).

Several formalisms and techniques have been introduced in the literature for the purpose of modeling and evaluation of complex systems. Each of them has its strengths and weaknesses, which also depend on the purpose of the evaluation. In this paper we propose the integration of two different formalisms in a single framework for the modeling, validation, and optimization of production-supply problems. In particular, the proposed framework combines Process Graphs (P-Graphs) as the modeling formalism, and Stochastic Activity Networks (SAN) for the analysis and optimization. The integration proposed in this paper extends the capabilities of the P-Graph formalism to include performance and dependability metrics in the optimization process, without hampering the modeling convenience of P-Graphs. The proposed approach is applied to a case study of the optimization of a power supply network.

Agile software development methodologies use an iterative and incremental development in order to handle evolving systems. Consolidated techniques in the field of testing have been applied to these techniques with the main purpose of aiding in the test creation stage. An example is Model-Based Test Driven Development (MBTDD) which joins the concepts of Model-Based Testing (MBT) and Test Driven Development (TDD). However, when iterative and incremental processes are used, problems appear as the consequence of the evolution of the system, such as: how to reuse the test artefacts, and how to select the relevant tests for implementing the new version of the system. In this context, this work proposes a process called D-MBTDD in which the agile development of a system is guided by model-based tests, focusing on helping with the reuse of test artefacts and on the process of identifying tests relevant to development. The information about the modifications between two versions of the test model are used in this approach, which was compared to the Regenerate-All approach, which regenerates test cases along the iterations and does not reuse any of them.

Accidents on petroleum installations can have huge consequences; to mitigate the risk, a number of safety barriers are devised. Faults and unexpected events may cause barriers to temporarily deviate from their nominal state. For safety reasons, a work permit process is in place: decision makers accept or reject work permits based on the current state of barriers. However, this is difficult to estimate, as it depends on a multitude of physical, technical and human factors. Information obtained from different sources needs to be aggregated by humans, typically within a limited amount of time. In this paper we propose an approach to provide an automated decision support to the work permit system, which consists in the evaluation of quantitative measures of the risk associated with the execution of work. The approach relies on state-based stochastic models, which can be automatically composed based on the work permit to be examined.

With the advent of recent technological advances, more demanding tele-immersive applications have started to emerge. In the World Opera application, artists from different opera houses across the globe can participate in a single united performance, and interact almost as if they were co-located. One of the main design challenges in this application domain is to assess to what extent the inevitable failures of some of the numerous and complex hardware, software, and network components affect the quality of experience for the user. This challenge cannot be addressed by traditional system-centric methods for dependability evaluation, which do not take personalized user perspective into account when considering meaningful and acceptable degradation of services. In this paper, we propose a novel method to assess the quality of experience in presence of failures, based on a new metric called perceived reliability . The method takes the human perspective into account and allows considering factors such as human perception of video and audio, characteristics of the audience, as well as performance elements and artistic content. This method can help system designers and engineers compare architectural variants and determine the dependability budget. We show the feasibility of our method by applying it to a World Opera performance. To this end, we construct a SAN-based model and run simulations in the Mobius framework. The obtained results provide useful guidelines for system engineers towards improving the quality of experience of World Opera performances despite the presence of failures.

Personal information is continuously gathered and processed by modern web applications. Due to regulation laws and to protect the privacy of users, customers, and business partners, such information must be kept private. A recurring problem in constructing web applications and services that protect privacy is the insufficient resources for documenting them. As web applications must be developed consistently with the statements of the privacy policy in order to enforce them, a structured documentation is necessary to model privacy protection during application design. To contribute with solutions to this problem, in this paper we propose a UML profile for privacy-aware applications. This profile helps building UML models that specify and structure particular concepts of privacy and, consequently, improve privacy definition and enforcement. After introducing the main privacy concepts, we describe how they are represented in the UML language. The profile's ability to model statements of realistic privacy policies is then demonstrated on a case study.

The advent of sequencing and structural genomics projects has provided a dramatic boost in the number of protein structures and sequences. Due to the high-throughput nature of these projects, many of the molecules are uncharacterised and their functions unknown. This, in turn, has led to the need for a greater number and diversity of tools and databases providing annotation through transfer based on homology and prediction methods. Though many such tools to annotate protein sequence and structure exist, they are spread throughout the world, often with dedicated individual web pages. This situation does not provide a consensus view of the data and hinders comparison between methods. Integration of these methods is needed. So far this has not been possible since there was no common vocabulary available that could be used as a standard language. A variety of terms could be used to describe any particular feature ranging from different spellings to completely different terms. The Protein Feature Ontology (http://www.ebi.ac.uk/ontology-lookup/browse.do?ontName=BS) is a structured controlled vocabulary for features of a protein sequence or structure. It provides a common language for tools and methods to use, so that integration and comparison of their annotations is possible. The Protein Feature Ontology comprises approximately 100 positional terms (located in a particular region of the sequence), which have been integrated into the Sequence Ontology (SO). 40 non-positional terms which describe general protein properties have also been defined and, in addition, post-translational modifications are described by using an already existing ontology, the Protein Modification Ontology (MOD). The Protein Feature Ontology has been used by the BioSapiens Network of Excellence, a consortium comprising 19 partner sites in 14 European countries generating over 150 distinct annotation types for protein sequences and structures.

An optical profilometer has been developed based on the idea that the large panels that compose solar-power concentrators and a tolerance threshold for slope-errors to some milliradians, can allow the use of a geometric-optics framework and the investigation of ray-paths by means of a laser beam. The instrument scheme is discussed in detail together with the related problems and the solutions adopted. Data analysis also makes use of an innovative method, based on the iterative application of a criterion about the intersection between the tangents of adjacent points, which allows a realistic profile of the reflector-surface to be drawn. The methodology adopted allows us to call our instrument a profilometer rather than an instrument that gives only information about the tangent of the profile. The instrument accuracy on profile and arctangent deviations from the ideal parabola is 20 μm and 15 μrad, respectively. The results obtained for a reflecting panel are also reported.

Safety is a fundamental property for a wide class of systems, which can be assessed through safety analysis. Recent standards, as the ISO26262 for the automotive domain, recommend safety analysis processes to be performed at system, hardware, and software levels. While Failure Modes and Effects Analysis (FMEA) is a well-known technique for safety assessment at system level, its application at software level is still an open problem, especially concerning its integration into certification processes. Fault injection has been envisioned as a viable approach for performing Software-FMEA (SW-FMEA), but it typically requires an advanced development stage where code is available. The approach we propose in this paper, aims to perform software fault injection at model-level, namely onfUML-ALF models obtained from a component-based UML description through transformations proposed in a previous work. Model-level fault injection allows SW-FMEA to assess the effectiveness of safety mechanisms from the early stages of system design. The work in this paper focuses on how the software fault injection is implemented, and on the study of fault propagation through appropriate points of observation to highlight possible violations of requirements, with the identification critical paths.

For several years, the vulnerability of Critical Infrastructures (CIs) to cyber-threats has been limited, since they were mostly isolated systems, using proprietary protocols. Nowadays, CIs are increasingly threatened by external attacks: the use of off-the-shelf components is common, they have become interconnected, and sometimes also connected to the Internet. This problem is exacerbated by the recent trend towards the adoption of wireless connectivity and mobile devices, which is gaining interest also in this domain. One of the main challenges is to quantify the impact that external attacks may have on the infrastructure, and ensure that its dependability and safety requirements can still be fulfilled. In this paper we focus on the ALARP system, which protects workers on the railway infrastructure using distributed mobile terminals, and evaluate the impact of two attacks to the communication infrastructure. In performing such analysis, we experiment with a new method, which combines a stochastic model of the system with a model of the attacker, and quantifies the impact of specific attacks on precise safety and availability metrics.

Safety analysis is increasingly important for a wide class of systems. In the automotive field, the recent ISO26262 standard foresees safety analysis to be performed at system, hardware, and software levels. Failure Modes and Effects Analysis (FMEA) is an important step in any safety analysis process, and its application at hardware and system levels has been extensively addressed in the literature. Conversely, its application to software architectures is still to a large extent an open problem, especially concerning its integration into a general certification process. The approach we propose in this paper aims at performing semi-automated FMEA on component-based software architectures described in UML. The foundations of our approach are model-execution and fault-injection at model-level, which allows us to compare the nominal and faulty system behaviors and thus assess the effectiveness of safety countermeasures. Besides introducing the detailed workflow for SW FMEA, the work in this paper focuses on the process for obtaining an executable model from a component-based software architecture specified in UML.

This paper introduces a patent searching framework to assess the state of the art of a product or a technology and to support technology transfer activities. It combines several dimensions (IPC, Object & Behaviour/Function keywords) together with an integrated abstraction methodology based on WordNet/Multi-screen in order to systemize and facilitate FBOS (Function/Behaviour–Oriented Search). The core of the method is the abstraction of behaviour (based on a semantic approach) resulting in keywords at different abstraction levels, and a patent search based on these keywords and on a preassembled classification of Physical Effects. Key patents and space opportunities are mapped in a suitable graph, based on a revision of the classical Gero's FBS theory. An exemplary application in the lens sterilization domain shows the functioning of the patent-pending software.

Session management in distributed Internet services is traditionally based on username and password, explicit logouts and mechanisms of user session expiration using classic timeouts. Emerging biometric solutions allow substituting username and password with biometric data during session establishment, but in such an approach still a single verification is deemed sufficient, and the identity of a user is considered immutable during the entire session. Additionally, the length of the session timeout may impact on the usability of the service and consequent client satisfaction. This paper explores promising alternatives offered by applying biometrics in the management of sessions. A secure protocol is defined for perpetual authentication through continuous user verification. The protocol determines adaptive timeouts based on the quality, frequency and type of biometric data transparently acquired from the user. The functional behavior of the protocol is illustrated through Matlab simulations, while model-based quantitative analysis is carried out to assess the ability of the protocol to contrast security attacks exercised by different kinds of attackers. Finally, the current prototype for PCs and Android smartphones is discussed.

Design, management and control of a logistic distribution system are very critical issues in supply chain management. They involve a large number of interdependent decisions, such as the determination of the best location and capacity of a distribution center (DC), a production plant, a wholesaler etc., the allocation of customer demand to suppliers, e.g. regional DC (RDC), the adoption of a transportation mode, e.g. rail and truck, the vehicles routing adopting/not adopting a groupage strategy. This chapter presents an original and automatic supporting decisions platform for the integration of strategic (long-term), tactical (mid-term) and operational (short-term) decisions in the design, management and control of a logistic network including up to four operating levels: sources (production plants), central distribution centers (CDCs), RDCs, and customers. A case study is illustrated and obtained results discussed in presence of different problem settings and operating hypotheses.

The Proteomics Standards Initiative (PSI) aims to define community standards for data representation in proteomics and to facilitate data comparison, exchange and verification. Rapid progress has been made in the development of common standards for data exchange in the fields of both mass spectrometry and protein–protein interactions since the first PSI meeting [1]. Both hardware and software manufacturers have agreed to work to ensure that a proteomics-specific extension is created for the emerging ASTM mass spectrometry standard and the data model for a proteomics experiment has advanced significantly. The Protein–Protein Interactions (PPI) group expects to publish the Level 1 PSI data exchange format for protein–protein interactions by early summer this year, and discussion as to the additional content of Level 2 has been initiated.

Dependability and performance analysis of modern systems is facing great challenges: their scale is growing, they are becoming massively distributed, interconnected, and evolving. Such complexity makes model-based assessment a difficult and time-consuming task. For the evaluation of large systems, reusable sub models are typically adopted as an effective way to address the complexity and improve the maintanability of models. Approaches based on Stochastic Petri Nets often compose sub models by state-sharing, following predefined "patterns", depending on the scenario of interest. However, such composition patterns are typically not formalized. Clearly defining libraries of reusable sub models, together with valid patterns for their composition, would allow complex models to be automatically assembled, based on a high-level description of the scenario to be evaluated. The contribution of this paper to this problem is twofold: on one hand we describe our workflow for the automated generation of large per formability models, on the other hand we introduce the TMDL language, a DSL to concretely support the workflow. After introducing the approach and the language, we detail their implementation within the Eclipse modeling platform, and briefly show its usage through an example.

Model-transformation techniques have increasingly gained attention in the design and evaluation of high-integrity systems, with the purpose to provide (semi-)automatic tools for non-functional analysis. Analysis models are automatically derived from an architectural description of the system in a UML-like language. One of the main challenges is designing tools which can be reused: the modeling language, the analysis tools, and possibly the analysis method itself are going to evolve over time (e.g., due to different domains, new software versions, updates to standards). In this paper we describe the design and implementation of the toolchain for state-based dependability analysis developed within the CHESS project. The toolchain, which also provides back-annotation facilities, has been designed to be adapted to different modeling languages and analysis tools. The tool has been implemented as a plugin for the Eclipse platform, and it is publicly available on the CHESS website.

Highly distributed, autonomous and self-powered systems operating in harsh, outdoors environments face several threats in terms of dependability, timeliness and security, due to the challenging operating conditions determined by the environment. Despite such difficulties, there is an increasing demand to deploy these systems to support critical services, thus calling for severe timeliness, safety, and security requirements. Several challenges need to be faced and overcome. First, the designed architecture must be able to cope with the environmental challenges and satisfy dependability, timeliness and security requirements. Second, the assessment of the system must be carried on despite potentially incomplete field-data, and complex cascading effects that small modifications in system properties and operating conditions may have on the targeted metrics. In this paper we present our experience from the EU-funded project ALARP (A railway automatic track warning system based on distributed personal mobile terminals), which aims to build and validate a distributed, real-time, safety-critical system that detects trains approaching a railway worksite and notifies their arrivals to railway trackside workers. The paper describes the challenges we faced, and the solutions we adopted, when architecting and evaluating the ALARP system.

Recent technological advances have made it possible to design bandwidth demanding distributed interactive multimedia applications such as the World Opera application. In this application artists from different opera houses across the globe, can participate in a single united performance and interact almost as if they were co-located.One of the main design challenges in this application domain is to determine the composition of system components necessary to satisfy the desired quality of service in presence of failures and budget constraints. This challenge is exacerbated by the fact that quality of service depends on a multitude of factors such as human perception of video and audio, the type of audience, performance elements, etc. These factors cannot be captured by traditional approaches for dependability evaluation such as reliability, i.e., continuous delivery of correct service. This calls for developing a more comprehensive "Quality of Experience" concept.In this paper, we propose a novel method to assess the quality of experience in presence of failures, based on a new metric called perceived reliability. This method can help the system designers and engineers compare architectural variants and to determine the dependability budget. We show the feasibility of our method by applying it to a World Opera performance. Our experimental results provide useful guidelines for system engineers towards improving the quality of experience of World Opera performances despite presence of failures.

The key factor in decision-making is the quality of information collected and processed in the problem analysis. In most cases, patents represent a very important source of information. The main problem is how to extract such information from the huge corpus of documents with a high recall and precision, and in a short time. This article demonstrates a patent search and classification method, called Knowledge Organizing Module, which consists of creating, almost automatically, a pool of patents based on polysemy expansion and homonymy disambiguation. Since the pool is done, an automatic patent technology landscaping is provided for fixing the state of the art of our product, and exploring competing alternative treatments and/or possible technological opportunities. An exemplary case study is provided, it deals with a patent analysis in the field of verruca treatments.

International patent corpus is a gigantic source containing today about 80million of documents. Every patent is manually analyzed by patent officers and then classified by a specific code called Patent Class (PC). Cooperative Patent Classification CPC is the new classification system introduced since January 2013 in order to standardize the classification systems of all major patent offices. Like keywords for papers, PCs point to the core of the invention, describing concisely what they contain inside. Most of patents strategies are based on PC as filter for results therefore the selection of relevant PCs is often a primary and crucial activity. This task is considered particularly challenging and only few tools have been specially developed for this purpose. The most efficient tools are provided by patent offices of EPO and WIPO. This paper analyzes their PCs search strategy (mainly based on keyword-based engines) in order to identify main limitations in terms of missing relevant PCs (recall) and non-relevant results (precision). Patents have been processed by KOM, a semantic patent search tool developed by the authors. Unlike all other PC search tools, KOM uses semantic parser and many knowledge bases for carrying out a conceptual patent search. Its functioning is described step by step through a detailed analysis pointing out the benefits of a concept-based search vis-a-vis a keyword-based search. An exemplary case is proposed dealing with CPCs describing the sterilization of contact lenses. Comparison could be likewise conducted on other PCs such as International (IPC), European (ECLA) or United States (USPC) patent classification codes.

In this paper we perform a model-based analysis of the Timed Reliable Communication (TRC) protocol, which is being used within the EU funded ALARP project for railway worksite com-munication. TRC is a group communication protocol based on IEEE 802.11 networks, targeting safety-critical applications with limited bandwidth requirements. The paper contains an in-depth analysis of the performance and reliability characteristics of the protocol using a Stochastic Activity Networks model. The results are first compared with available experimental measurements for the sake of model validation. The validated model is then used for a thorough analysis of a set of key metrics under different envi-ronment and network conditions. The obtained results allow: i) to assess that the protocol allows to satisfy the ALARP targeted performance and reliability requirements, and ii) to evaluate the existing tradeoffs and help in choosing parameter values for the final implementation.

Modelling and simulation are well-suited approaches to analyse critical infrastructures (CIs), providing useful insights into how components’ failures might propagate along interconnected infrastructures, possibly leading to cascading or escalating failures, and to quantitatively assess the impact of these failures on the service delivered to users. This chapter focuses on the usage of graphical formalisms for modelling and simulation of CIs. It fiidentifi es and motivates the main requirements that a modelling and simulation framework for CI analysis should have. Then, it provides an overview of the available graphical formalisms, discussing how they have been used in the literature for CI analysis and assessing the extent to which they actually meet the identifi ed modelling and simulation requirements. The second part of the chapter investigates how a subset of the identifi ed requirements are actually met adopting a specifi c graphical modelling formalism, the Stochastic Activity Networks formalism, which has been extensively used by the authors of this chapter in past European FP6 projects dealing with CI analysis.

This chapter provides an overview of the state of knowledge related to stochastic model-based assessment approaches, which are most commonly used for resiliency evaluation of current computing systems. The chapter first introduces a set of representative surveys developed in recent European projects, and then it provides a deeper description of common techniques used in model-based assessment of resilient systems. The most widely used modelling formalisms are reviewed, with a particular focus on state-based formalisms like Stochastic Petri Nets and its extensions. Techniques used in model construction and solution are also discussed, as well as the different classes of analysis tools and frameworks. The techniques analyzed in the chapter span from largeness avoidance and largeness tolerance techniques to more comprehensive modelling approaches that are integrated in the system’s development and assessment process. Some of these techniques try to cope with system’s complexity by automatically deriving the analysis models from engineering models like UML or AADL. Other approaches attack the complexity issue combining different evaluation methods, exploiting their possible complementarities and synergies. A discussion on the open research challenges in model-based resilience assessment is finally provided in the last part of the chapter, based on the reviewed techniques and on the activities carried out within the AMBER Coordination Action.