Variability and Evolution in Systems of Systems

In this position paper (1) we discuss two particular aspects of Systems of Systems, i.e., variability and evolution. (2) We argue that concepts from Product Line Engineering and Software Evolution are relevant to Systems of Systems Engineering. (3) Conversely, concepts from Systems of Systems Engineering can be helpful in Product Line Engineering and Software Evolution. Hence, we argue that an exchange of concepts between the disciplines would be beneficial.Comment: In Proceedings AiSoS 2013, arXiv:1311.319

Fog paradigm for local energy management systems

Cloud Computing infrastructures have been extensively deployed to support energy computation within built environments. This has ranged from predicting potential energy demand for a building (or a group of buildings), undertaking heat profile/energy distribution simulations, to understanding the impact of climate and weather on building operation. Cloud computing usage in these scenarios have benefited from resource elasticity, where the number and types of resources can change based on the complexity of the simulation being considered. While there are numerous advantages of using a cloud based energy management system, there are also significant limitations. For instance, many such systems assume that the data has been pre-staged at a cloud platform prior to simulation, and do not take account of data transfer times from the building to the simulation platform. The need for supporting computation at edge resources, which can be hosted within the building itself or shared within a building complex, has become important over recent year. Additionally, network connectivity between the sensing infrastructure within a built environment and a data centre where analysis is to be carried out can be intermittent or may fail. There is therefore also a need to better understand how computation/analysis can be carried out closer to the data capture site to complement analysis that would be undertaken at the data centre. We describe how the Fog computing paradigm can be used to support some of these requirements, extending the capability of a data centre to support energy simulation within built environments

The Living and Working Together Perspective on Creativity in Organizations

Although creativity represents a cornerstone for organizations that want to keep up with competitors, customers, and the current socio-economic context, there is a dearth in the literature of systemic and comprehensive models focused on the complexity and addressing several dimensions and factors. In this context, we propose the perspective of “working and living together in organizations” to enrich the scientific dialogue with a proposition that aims to hold together different variables of interaction and relationship between different parts of the organization (Gozzoli, 2016a,b). In fact, according to our previous studies (Frascaroli et al., 2016; Gorli et al., 2016; Marta et al., 2016; Saita et al., 2016; Tamanza et al., 2016), a generative living and working together environment is itself directly linked to creativity and innovative processes. This is because in a generative living and working together environment relationality – that is, the possibility of exchange among workers mediated by the object of work – is enabled. With this study, we intend to provide a contribution to the creativity study field, applying our perspective to an extensive level of analysis. The model was tested using the Exploratory Structural Equation Modeling methodology with EQS-6.3. Our results found some interesting elements in support of the theory behind this study

Designing and Implementing an Advanced Algorithm to Measure the Trustworthiness Level of Federated Learning Models

Artificial intelligence (AI) has immersed our daily lives and assists in the decision process of critical sectors such as medicine and law. Therefore it is now more important than ever before that AI systems developed are reliable, ethical, and do not cause harm to humans. The High-Level Expert Group on AI (AI-HLEG) of the European Commission has laid the foundation by defining seven key requirements for trustworthy AI systems. To address concerns about privacy risks associated with centralized learning approaches federated learning (FL) has emerged as a promising and widely used alternative. FL allows multiple clients to collaboratively train machine learning models without the need for sharing private data. Because of the high adaption of FL systems, ensuring that they are trustworthy is crucial. Previous research efforts have proposed a trustworthy FL taxonomy with six pillars, each comprehensively defined with notions and metrics. This taxonomy covers six of the seven requirements defined by the AI-HLEG. However, one notable aspect that has been largely overlooked by research is the requirement for environmental well-being in trustworthy AI/FL. This leaves a significant gap between the expectations set by governing bodies and the guidelines applied and measured by researchers. This master thesis addresses this gap by introducing the sustainability pillar to the trustworthy FL taxonomy and thus presenting the first taxonomy that comprehensively addresses all the requirements defined by the AI-HLEG. The sustainability pillar focuses on assessing the environmental impact of FL systems and incorporates three main aspects: hardware efficiency, federation complexity, and the carbon intensity of the energy grid, each with well-defined metrics. As a second contribution, this master thesis extends an existing prototype to evaluate the trustworthiness of FL systems with the sustainability pillar. The prototype is then extensively evaluated in various scenarios, involving different federation configurations. The results shed light on the trustworthiness of different federation configurations in different settings with varying complexities, hardware, and energy grids used. Importantly, the sustainability pillar’s score corrects the overall trust score by considering the environmental impact of FL systems across seven key pillars. Thus, the proposed taxonomy and prototype are the first to comprehensively address all seven AI-HLEG requirements and lay the foundation for a more accurate trustworthiness assessment of FL systems

A Roadmap for HEP Software and Computing R&D for the 2020s

Particle physics has an ambitious and broad experimental programme for the coming decades. This programme requires large investments in detector hardware, either to build new facilities and experiments, or to upgrade existing ones. Similarly, it requires commensurate investment in the R&D of software to acquire, manage, process, and analyse the shear amounts of data to be recorded. In planning for the HL-LHC in particular, it is critical that all of the collaborating stakeholders agree on the software goals and priorities, and that the efforts complement each other. In this spirit, this white paper describes the R&D activities required to prepare for this software upgrade.Peer reviewe

Federated Survival Forests

Survival analysis is a subfield of statistics concerned with modeling the occurrence time of a particular event of interest for a population. Survival analysis found widespread applications in healthcare, engineering, and social sciences. However, real-world applications involve survival datasets that are distributed, incomplete, censored, and confidential. In this context, federated learning can tremendously improve the performance of survival analysis applications. Federated learning provides a set of privacy-preserving techniques to jointly train machine learning models on multiple datasets without compromising user privacy, leading to a better generalization performance. However, despite the widespread development of federated learning in recent AI research, few studies focus on federated survival analysis. In this work, we present a novel federated algorithm for survival analysis based on one of the most successful survival models, the random survival forest. We call the proposed method Federated Survival Forest (FedSurF). With a single communication round, FedSurF obtains a discriminative power comparable to deep-learning-based federated models trained over hundreds of federated iterations. Moreover, FedSurF retains all the advantages of random forests, namely low computational cost and natural handling of missing values and incomplete datasets. These advantages are especially desirable in real-world federated environments with multiple small datasets stored on devices with low computational capabilities. Numerical experiments compare FedSurF with state-of-the-art survival models in federated networks, showing how FedSurF outperforms deep-learning-based federated algorithms in realistic environments with non-identically distributed data

Strategic Position and Trade Union Power: An Analysis of Trade Union Strategies in the Automotive, Chemical and Edible Oil Sectors in Argentina, 2003-2015)

The aim of this paper is to analyse empirically the importance of union strategies as a moderating variable between unions’ structural and associational power and their bargaining and mobilisation power. To this end, we selected three unions, one from each of three strategic industrial sectors in the Argentinian national economy – automotive, chemical and edible oils – and analysed their dynamics of collective bargaining and labour conflict in the 2003–2015 period. The research is based on a review of secondary sources (collective bargaining agreements and conflict databases) as well as primary sources (semi-structured interviews with managers, trade union leaders, worker representatives and activists). Whereas workers in each of these sectors have a similarly high degree of structural power, we observed differences among the sectors in working, wage and organisational conditions. These differences are associated with three different union strategies for building union power, which we identify as partnership, confrontational and combative.             KEYWORDS: strategic position; trade union power; union strategies; sectoral analysis; Argentin

The Future of Digital Health with Federated Learning

Data-driven Machine Learning has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems. Existing medical data is not fully exploited by ML primarily because it sits in data silos and privacy concerns restrict access to this data. However, without access to sufficient data, ML will be prevented from reaching its full potential and, ultimately, from making the transition from research to clinical practice. This paper considers key factors contributing to this issue, explores how Federated Learning (FL) may provide a solution for the future of digital health and highlights the challenges and considerations that need to be addressed.Comment: This is a pre-print version of https://www.nature.com/articles/s41746-020-00323-

SGDE: Secure Generative Data Exchange for Cross-Silo Federated Learning

Privacy regulation laws, such as GDPR, impose transparency and security as design pillars for data processing algorithms. In this context, federated learning is one of the most influential frameworks for privacy-preserving distributed machine learning, achieving astounding results in many natural language processing and computer vision tasks. Several federated learning frameworks employ differential privacy to prevent private data leakage to unauthorized parties and malicious attackers. Many studies, however, highlight the vulnerabilities of standard federated learning to poisoning and inference, thus raising concerns about potential risks for sensitive data. To address this issue, we present SGDE, a generative data exchange protocol that improves user security and machine learning performance in a cross-silo federation. The core of SGDE is to share data generators with strong differential privacy guarantees trained on private data instead of communicating explicit gradient information. These generators synthesize an arbitrarily large amount of data that retain the distinctive features of private samples but differ substantially. In this work, SGDE is tested in a cross-silo federated network on images and tabular datasets, exploiting beta-variational autoencoders as data generators. From the results, the inclusion of SGDE turns out to improve task accuracy and fairness, as well as resilience to the most influential attacks on federated learning