PAVING THE SUSTAINABILITY JOURNEY: FLEXIBLE PACKAGING BETWEEN CIRCULAR ECONOMY AND RESOURCE EFFICIENCY

Sustainability is the ability to continue a defined behavior indefinitely, therefore not hindering the future in any way, neither environmental, nor economic or social. The entire scientific community around the world recognizes and attributes the main cause of climate change and temperature anomaly to the significant increase of anthropogenic CO2 emissions, which in turn are the result of a linear economy of production, consumption and disposal. By 2030 the EU expects all packaging to be either recyclable or reusable. In this context, flexible packaging, always hailed as a sustainable and resource-efficient alternative to rigid packaging, is now being challenged, in terms of its recyclability and its preponderant role in pollution of seas and oceans. To avoid litter, leakage and marine pollution, flexibles must be collected within a recognized waste stream, and strictly kept out of general, undifferentiated garbage. GualapackGroup therefore promotes and advocates the collection of all packaging, and specifically the flexible plastic fraction of household waste. GualapackGroup is actively working to improve the recyclability of its packaging solutions and is at an advanced stage of prototyping and developing its first monomaterial, recyclable spouted pouch

Automatic Resource Allocation for High Availability Cloud Services

AbstractThis paper proposes an approach to support cloud brokers finding optimal configurations in the deployment of dependability and security sensitive cloud applications. The approach is based on model-driven principles and uses both UML and Bayesian Networks to capture, analyse and optimise cloud deployment configurations. While the paper is most focused on the initial allocation phase, the approach is extensible to the operational phases of the life-cycle. In such a way, a continuous improvement of cloud applications may be realised by monitoring, enforcing and re-negotiating cloud resources following detected anomalies and failures

Sensitivity of Machine Learning Approaches to Fake and Untrusted Data in Healthcare Domain

Machine Learning models are susceptible to attacks, such as noise, privacy invasion, replay, false data injection, and evasion attacks, which affect their reliability and trustworthiness. Evasion attacks performed to probe and identify potential ML-trained models’ vulnerabilities, and poisoning attacks, performed to obtain skewed models whose behavior could be driven when specific inputs are submitted, represent a severe and open issue to face in order to assure security and reliability to critical domains and systems that rely on ML-based or other AI solutions, such as healthcare and justice, for example. In this study, we aimed to perform a comprehensive analysis of the sensitivity of Artificial Intelligence approaches to corrupted data in order to evaluate their reliability and resilience. These systems need to be able to understand what is wrong, figure out how to overcome the resulting problems, and then leverage what they have learned to overcome those challenges and improve their robustness. The main research goal pursued was the evaluation of the sensitivity and responsiveness of Artificial Intelligence algorithms to poisoned signals by comparing several models solicited with both trusted and corrupted data. A case study from the healthcare domain was provided to support the pursued analyses. The results achieved with the experimental campaign were evaluated in terms of accuracy, specificity, sensitivity, F1-score, and ROC area

Introducing stateful conditional branching in Ciaramella

Conditional branching in Synchronous Data Flow (SDF) networks is a long-standing issue as it clashes with the underlying synchronicity model. For this reason, conditional update of state variables is rarely implemented in data flow programming environments, unlike simpler selection operators that do not execute code conditionally. We propose an extension to SDF theory to represent stateful conditional branching. We prove the effectiveness of such approach by adding conditional constructs to the Ciaramella programming language without compromising its modular declarative paradigm and maintaining domain-specific optimizations intact. This addition enables easy implementation of common DSP algorithms and helps in writing efficient complex programs

An approach for the automatic verification of blockchain protocols: the Tweetchain case study

This paper proposes a model-driven approach for the security modelling and analysis of blockchain based protocols. The modelling is built upon the definition of a UML profile, which is able to capture transaction-oriented information. The analysis is based on existing formal analysis tools. In particular, the paper considers the Tweetchain protocol, a recent proposal that leverages online social networks, i.e., Twitter, for extending blockchain to domains with small-value transactions, such as IoT. A specialized textual notation is added to the UML profile to capture features of this protocol. Furthermore, a model transformation is defined to generate a Tamarin model, from the UML models, via an intermediate well-known notation, i.e., the Alice &Bob notation. Finally, Tamarin Prover is used to verify the model of the protocol against some security properties. This work extends a previous one, where the Tamarin formal models were generated by hand. A comparison on the analysis results, both under the functional and non-functional aspects, is reported here too

Towards Automatic Model Completion: from Requirements to SysML State Machines

Even if model-driven techniques have been enabled the centrality of the models in automated development processes, the majority of the industrial settings does not embrace such a paradigm due to the procedural complexity of managing model life cycle. This paper proposes a semi-automatic approach for the completion of high-level models of critical systems. The proposal suggests a specification guidelines that starts from a partial SysML (Systems Modeling Language) model of a system and on a set of requirements, expressed in the well-known Behaviour-Driven Design paradigm. On the base of such requirements, the approach enables the automatic generation of SysML state machines fragments. Once completed, the approach also enables the modeller to check the results improving the quality of the model and avoiding errors both coming from the mis-interpretation of the tool and from the modeller himself/herself. An example taken from the railway domain shows the approach.Comment: Editor: Ib\'eria Medeiros. 18th European Dependable Computing Conference (EDCC 2022), September 12-15, 2022, Zaragoza, Spain. Student Forum Proceedings - EDCC 202

MOSTO: A toolkit to facilitate security auditing of ICS devices using Modbus/TCP

The integration of the Internet into industrial plants has connected Industrial Control Systems (ICS) worldwide, resulting in an increase in the number of attack surfaces and the exposure of software and devices not originally intended for networking. In addition, the heterogeneity and technical obsolescence of ICS architectures, legacy hardware, and outdated software pose significant challenges. Since these systems control essential infrastructure such as power grids, water treatment plants, and transportation networks, security is of the utmost importance. Unfortunately, current methods for evaluating the security of ICS are often ad-hoc and difficult to formalize into a systematic evaluation methodology with predictable results. In this paper, we propose a practical method supported by a concrete toolkit for performing penetration testing in an industrial setting. The primary focus is on the Modbus/TCP protocol as the field control protocol. Our approach relies on a toolkit, named MOSTO, which is licensed under GNU GPL and enables auditors to assess the security of existing industrial control settings without interfering with ICS workflows. Furthermore, we present a model-driven framework that combines formal methods, testing techniques, and simulation to (formally) test security properties in ICS networks

A Multimodal Knowledge-Based Deep Learning Approach for MGMT Promoter Methylation Identification

Glioblastoma Multiforme (GBM) is considered one of the most aggressive malignant tumors, characterized by a tremendously low survival rate. Despite alkylating chemotherapy being typically adopted to fight this tumor, it is known that O(6)-methylguanine-DNA methyltransferase (MGMT) enzyme repair abilities can antagonize the cytotoxic effects of alkylating agents, strongly limiting tumor cell destruction. However, it has been observed that MGMT promoter regions may be subject to methylation, a biological process preventing MGMT enzymes from removing the alkyl agents. As a consequence, the presence of the methylation process in GBM patients can be considered a predictive biomarker of response to therapy and a prognosis factor. Unfortunately, identifying signs of methylation is a non-trivial matter, often requiring expensive, time-consuming, and invasive procedures. In this work, we propose to face MGMT promoter methylation identification analyzing Magnetic Resonance Imaging (MRI) data using a Deep Learning (DL) based approach. In particular, we propose a Convolutional Neural Network (CNN) operating on suspicious regions on the FLAIR series, pre-selected through an unsupervised Knowledge-Based filter leveraging both FLAIR and T1-weighted series. The experiments, run on two different publicly available datasets, show that the proposed approach can obtain results comparable to (and in some cases better than) the considered competitor approach while consisting of less than 0.29% of its parameters. Finally, we perform an eXplainable AI (XAI) analysis to take a little step further toward the clinical usability of a DL-based approach for MGMT promoter detection in brain MRI