The blockchain framework has increasingly moved from purely finance and the digital world to acquire a greater role as an indispensable diaphragm between the physical and the information processing parts of Cyber-Physical Systems. A significant need is the requirement for trustworthy contact between the two domains, which today can be handled with the Oracle concept and its inherent limitations. This proposal calls on the holonic paradigm to mitigate the Oracle problem, employing a constructivistic and second-order cybernetic stance that allows the trust model of the oracle to evolve and follow the dynamics of complex reality.

Blockchain technologies and paradigms can provide new ground for the management of complex socio-technical problems like supply chains. The contribution of this work is a work-in-progress experimental setting to demonstrate the effectiveness of a novel distributed business process management scheme that integrates the RMAS (Relational-model Multi Agent System) and the Blockchain frameworks. The research work is conducted in a food supply chain case study aiming to sustainability and social good through a purposeful organisation of the autonomous behaviour of the actors in the system.

Abstract BPMN choreography is a modeling language capable to describe scenarios where several independent participants have to collaborate in a climate of opposing interests and therefore are forced to trust each other. For this reason, in many contexts, a strong need for transparency, responsibility, and choreography compliance arise by the various participants. Blockchains and smart contracts, thanks to their characteristic of providing a decentralized and consensus‐based validation mechanism, seem to be able to meet these needs in an untrusted scenario. Nevertheless, most of the related work focused either on transparency, accountability, or compliance, but none on all three of them. Furthermore, such works do not take into account the nondeterministc nature of choreographies. This work aims at using blockchains and smart contracts in this scenario providing a formally well‐defined set of tools to match all three the aforementioned requirements. This work applies the proposed techniques to a case study from the construction industry, an economical relevant application domain where the demand for transparency, accountability, and compliance with procurement contracts (that can be modeled as choreographies) is very strong.

BACKGROUND - Automatic emotion recognition has powerful and interesting opportunities in the clinical field, but several critical aspects are still open, such as heterogeneity of methodologies or technologies tested mainly on healthy people. This systematic review aims to survey automatic emotion recognition systems applied in real clinical contexts (i.e., on a population of people with a pathology). METHODS - The literature review was conducted on the following scientific databases: IEEEXplore®, ScienceDirect®, Scopus®, PubMed®, ACM®. Inclusion criteria were the presence of an automatic emotion recognition algorithm and the enrollment of at least 2 patients in the experimental protocol. The review process followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Moreover, the works were analysed according to a reference model in the form of a class diagram, to highlight the most important clinical and technical aspects and relationships among them. RESULTS - 52 scientific papers passed the inclusion criteria. Based on our findings, most clinical applications involved neuro-developmental, neurological and psychiatric disorders with the aims of diagnosing, monitoring, or treating emotional symptoms. The study design seems to be mostly related to the aim of the study (it is generally observational for monitoring and diagnosis, interventional for treatment), the most adopted signals are video and audio, and supervised shallow learning emerged as most used approach for emotion recognition algorithm. DISCUSSION - Tiny samples, absence of a control group and of tests in real-life conditions emerged as important clinical limitations. Under a technical point of view, a great heterogeneity of performance metrics, datasets and algorithms challenges the comparability, robustness, reliability and reproducibility of results. Suggested guidelines are identified and discussed to help scientific community to overcome limitations and provide direction for future works.

Abstract Nowadays, the volume of the multimedia heterogeneous evidence presented for digital forensic analysis has significantly increased, thus requiring the application of big data technologies, cloud-based forensics services, as well as Machine Learning (ML) techniques. In digital forensics domain, ML algorithms have been applied for cybercrime investigation such as child abuse investigations, malware classification, and image forensics. This paper addresses this issues and deals with forensic analysis of digital images and videos. In particular, this work aims at proposing a multimedia classification tool with a parallel software architecture for a fast inspection, which is easy to use (to be used by officers during a search), requires limited hardware resources and it is built on an open-source software to limit its costs. Moreover, this tool must be able to quickly inspect multiple devices at a time. When positives are found in a device, such device will be seized for a deeper analysis later in the lab. It will not be seized otherwise, reducing the inconvenience for the suspect as well as the time required for the next analysis phase. As a case study, we focus on the identification of child pornography images. Experimental results show that the proposed architecture is capable of guaranteeing a high recall, a fast process and high performances in real scenarios.

Automated program repair has the potential to reduce the developers’ effort to fix errors in their code. In particular, modern programming languages, such as Java, C, and C#, represent time as integer variables that suffer from integer overflow, introducing subtle errors that are hard to discover and repair. Recent researches on automated program repair rely on test cases to discover failures to correct, making them suitable only for regression errors. We propose a new strategy to automatically repair programs that suffer from timestamp overflows that are manifested in comparison expressions. It unifies the benefits of static analysis and automatic program repair avoiding dependency on testing to identify and correct defected code. Our approach performs an abstract analysis over the time domain of a program using a Time Type System to identify the problematic comparison expressions. The repairing strategy rewrites the timestamp comparisons exploiting the binary representation of machine numbers to correct the code. We have validated the applicability of our approach with 20 open source Java projects. The results show that it is able to correctly repair all 246 identified errors. To further validate the reliability of our approach, we have proved the soundness of both, type system and repairing strategy. Furthermore, several patches for three open source projects have been acknowledged and accepted by their developers.

Many Java programs encode temporal behaviors in their source code, typically mixing three features provided by the Java language: (1) pausing the execution for a limited amount of time, (2) waiting for an event that has to occur before a deadline expires, and (3) comparing timestamps. In this work, we show how to exploit modern SMT solvers together with static analysis in order to produce a network of timed automata approximating the temporal behavior of a set of Java threads. We also prove that the presented abstraction preserves the truth of MTL and ATCTL formulae, two well-known logics for expressing timed specifications. As far as we know, this is the first feasible approach enabling the user to automatically model check timed specifications of Java software directly from the source code.

From the dawn of parallelization, the diffusion of High-Performance Computing (HPC) has grown exponentially until nowadays, thereby leading to the birth and development of a huge research community in this field.During the last decades, the important research results have led to extend the domain of HPC into three different dimensions, namely Architectures, Research Topics, and Application Domains. ArchitecturesTraditional HPC architectures have been sided with several wide-area distributed paradigms such as Peer-to-Peer (P2P), Grid, Cloud, Fog and Pervasive Computing.[1][2][3] Each of them aims new specific objectives along with the original goal of improving performance and reliability.In this paper, the different declinations of the traditional HPC are grouped into three macro-categories, namely SIMD, Multi- * , and Cluster.• SIMD.It stands for "single instruction, multiple data" according to the Flynn taxonomy.4 This refers to computing systems where a single instruction stream operates on multiple data streams in order to perform operations that may be naturally parallelized (eg, array processors, GPUs, GPGPUs, etc).This category also includes many specialized processing units, as vector extensions of modern processors like Streaming SIMD Extensions (SSE) 5 or Advanced Vector Extensions (AVX). 6• Multi- * .It refers to many-core, multi-core, and multi-processor architectures.They are all architectures characterized by the presence of multiple instruction streams operating on multiple data streams.What distinguishes this type of architectures from other MIMD architectures ("multiple instructions, multiple data" always according to the Flynn taxonomy 4 ) is the sharing of the central memory.• Cluster.It refers to Cluster Computing, namely a group of cost-effective linked commercial off-the-shelf computers working together so closely that they act as a single machine.What distinguishes this type of architecture from other MIMD-like architectures is the absence of central memory sharing (each processing unit has its own private memory) but the presence of mass memory sharing.As a matter of fact, the current Top500 supercomputers 7 are mostly clusters.Regarding wide-area distributed paradigms, all of them are included in the MIMD category (according to the Flynn's taxonomy).What distinguishes this type of architecture from other MIMD architectures is the lack of sharing of both the central memory and the mass memory.This paper takes into consideration the main four distributed paradigms that arose from traditional HPC, namely Grid Computing, Ubiquitous Computing, Peer-to-Peer Computing, Cloud, and the emerging Fog Computing.In brief:• Grid Computing 8-10 denotes a distributed architecture that enables coordinated resource sharing for problem-solving within dynamic organizations consisting of individuals, institutions, and resources.• Ubiquitous Computing (also known as Pervasive Computing or UbiComp) 11,12 refers to making available to users computational resources, which provide information and services, anytime and everywhere throughout the physical environment.• Peer-to-peer (P2P) 13 is a distributed paradigm made by a network of nodes, where each node has the same capabilities of other nodes; the computation is carried out by the interactions between a node and its neighbors.P2P networks have been originally used for sharing contents.• Cloud and Fog Computing 14,15 refers to a computing model that enables ubiquitous and on-demand access to a shared set of resources spread over the Internet and delivered as services.Such resources are quickly provisioned and released at an infrastructure, platform, or application level.Cloud Computing 15 is often seen as an evolution of Grid Computing where the best-effort access to resources is substituted by a rapid provision of customized resources requiring a minimal management effort.Indeed, both Grid and Cloud computing trace their roots back to the notion of Utility Computing, ie, computing being delivered as a public utility.This notion was introduced by John McCarthy in a speech at the MIT Centennial in 1961.16 As in the Cloud, the computation is executed on remote machines; there could be non-negligible latency and security issues.This

Distributed storage systems and caching systems are becoming widespread, and this motivates the increasing interest on assessing their achievable performance in terms of reliability for legitimate users and security against malicious users. While the assessment of reliability takes benefit of the availability of well established metrics and tools, assessing security is more challenging. The classical cryptographic approach aims at estimating the computational effort for an attacker to break the system, and ensuring that it is far above any feasible amount. This has the limitation of depending on attack algorithms and advances in computing power. The information-theoretic approach instead exploits capacity measures to achieve unconditional security against attackers, but often does not provide practical recipes to reach such a condition. We propose a mixed cryptographic/information-theoretic approach with a twofold goal: estimating the levels of information-theoretic security and defining a practical scheme able to achieve them. In order to find optimal choices of the parameters of the proposed scheme, we exploit an effective probabilistic model checker, which allows us to overcome several limitations of more conventional methods.

High-performance computing: to boldly go where no human has gone beforeAnyone can build a fast CPU.The trick is to build a fast system.

Summary Cloud federation can be described through the concept of collaboration, where each organization has its own cloud(s) that deals with a different and independent domain but needs to work together with other organizations in order to fulfill a specific shared objective. According to this perspective, the federation is a collection of interacting clouds that collaborate with one another through the instantiation and management of shared subsets of resources (computation and storage resources as well as sensors and actuators). This idea could be profitably used in those scenarios in which different organizations have to share several resources (e.g., emergency response or disaster management scenario). On the other hand, when different independent organizations share their resources, several issues arise. One of them is related to interoperability problems. As a consequence, this work also introduces a framework for an ontology‐based resource life cycle management and provisioning in a federated cloud infrastructure. Therefore, The main contributions of this work consists of redesigning a cloud infrastructure architecture from the ground up, leveraging Semantic Web and Semantic Web Service Technologies, and natively supporting a federated provisioning of any kind of resource. This paper exploits, as a motivating scenario, a flood emergency response system. Copyright © 2014 John Wiley & Sons, Ltd.

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Model checking is a formal verification method widely accepted in the web service world because of its capability to reason about service behavior at process level. It has been used as a basic tool in several scenarios such as service selection, service validation, and service composition. The importance of semantics is also widely recognized. Indeed, there are several solutions to the problem of providing semantics to web services, most of them relying on some form of Description Logic. This paper presents an integration of model checking and semantic reasoning technologies in an efficient way. This can be considered the first step toward the use of semantic model checking in problems of selection, validation, and composition. The approach relies on a representation of services at process level that is based on semantically annotated state transition systems (asts) and a representation of specifications based on a semantically annotated version of computation tree logic (anctl). This paper proves that the semantic model checking algorithm is sound and complete and can be accomplished in polynomial time. This approach has been evaluated with several experiments.

We developed XAL, a framework that, in our opinion, allows to build Web-oriented applications and services in a more productive way. The core of the framework is a programming language based upon timed-automata. We believe this formalism reflects the nature of many web-oriented applications, each page being a state, and each link being a transition toward another state. Once the programmer defined the set of states that characterize the application, she/he can provide a behavior to each single state, binding the state to a small program written in its favorite programming language. Furthermore, we realized that often companies require an application to behave differently depending on some conditions over real-time. Our language, being a modified version of the timed-automata, allows the programmer to specify constraints over real-time in a declarative way, rather than mix them within the logic of the application.

Several approaches have been proposed to tackle the selection of distributed processes described as semantic Web services. However, their practical applicability in real composition scenarios is still an open question. Addressing this problem requires on the one hand to deal with services described as stateful business processes and, on the other hand, to consider complex selection requirements concerning both the service interface and its behavior. In fact, in most existing approaches the selection is performed on the basis of the ldquofunctionalrdquo description of a service, i.e. in terms of its inputs, outputs, preconditions and effects. In this paper, we present our approach for the process-level service selection and evaluate it on a real world scenario that entails a high level of complexity: the eBay Web Services,the Amazon E-Commerce Services and the e-payment service offered by PayPal. The approach is based on a representation of services at the process level that is based on BPEL and WSDL specifications and that extends these standard specifications with minimal semantic annotations that permit to perform an efficient and yet useful, semantic reasoning for the process-level selection of Web services.

This paper is a first attempt towards a theory for reactive planning systems, i.e. systems able to plan and control execution of plans in a partially known and unpredictable environment. We start from an experimental real-world application developed at IRST, discuss some of the fundamental requirements and propose a formal theory based on these requirements. The theory takes into account the following facts: (1) actions may fail, since they correspond to complex programs controlling sensors and actuators which have to work in an unpredictable environment; (2) actions need to acquire information from the real world by activating sensors and actuators; (3) actions need to generate and execute plans of actions, since the planner needs to activate different special-purpose planners and to execute the resulting plans.

Recent research in planning is more and more focusing on planning systems working in ‘real world’ domains. These systems need to act in sense and represent the real world. Furthermore, no action, even if apparently simple, is guaranteed to succeed and, therefore, no planning can be ‘sound’ (with respect to the real world) without taking into account possible failures. This is mainly due to the intrinsic complexity of reality. A planning language is therefore required to represent explicitly failures, sensing tasks, planning tasks, and task combinations. In this paper, we propose a planning language (called ℒ) which addresses the above features. ℒ allows representing the basic planning activities, the control structures and the basic operations to deal with failures. As a consequence, a uniform representation is used to describe both acting/sensing in the external world and basic planning activities. In this paper, we give the syntax and the semantics of ℒ. Furthermore we also give some examples from an application (the project MAIA) which uses ℒ as planning language.