In campo. Il ruolo pubblico dell'antropologia

La tesi riflette sulla necessità di un ripensamento delle scienze antropologiche nel senso di un loro uso pubblico e del loro riconoscimento al di fuori dell’accademia. Viene introdotto il dibattito sulla dimensione applicata dell’antropologia a partire dalle posizioni in campo nel panorama internazionale. Negli Stati Uniti la riflessione si sviluppa dalla proposta della public anthropology, l’antropologo pubblico si discosta dalla tradizionale figura europea di intellettuale pubblico. Alla luce delle varie posizioni in merito, la questione dell’applicazione è esaminata dal punto di vista etico, metodologico ed epistemologico. Inizialmente vengono prese in considerazione le diverse metodologie elaborate dalla tradizione dell’applied anthropology a partire dalle prime proposte risalenti al secondo dopoguerra. Successivamente viene trattata la questione del rapporto tra antropologia, potere coloniale e forze armate, fino al recente caso degli antropologi embedded nello Human Terrain System. Come contraltare vengono presentate le diverse forme di engagement antropologico che vedono ricercatori assumere diversi ruoli fino a casi estremi che li vedono divenire attivisti delle cause degli interlocutori. La questione del ruolo giocato dal ricercatore, e di quello che gli viene attribuito sul campo, viene approfondita attraverso la categoria di implication elaborata in contesto francese. Attraverso alcune esperienze di campo vengono presentate forme di intervento concreto nel panorama italiano che vogliono mettere in luce l’azione dell’antropologo nella società. Infine viene affrontato il dibattito, in corso in Italia, alla luce della crisi che sta vivendo la disciplina e del lavoro per la costituzione dell’associazione nazionale di antropologia professionale.

Monitoring and Prediction of Thermal Emergencies in High Performance Computing Systems

Modern scientific discoveries are driven by an unsatisfiable demand for computational resources. High-Performance Computing (HPC) systems are an aggregation of computing power to deliver considerably higher performance than one typical desktop computer can provide, to solve large problems in science, engineering, or business. An HPC room in the datacenter is a complex controlled environment that hosts thousands of computing nodes that consume electrical power in the range of megawatts, which gets completely transformed into heat. Although a datacenter contains sophisticated cooling systems, our studies indicate quantitative evidence of thermal bottlenecks in real-life production workload, showing the presence of significant spatial and temporal thermal and power heterogeneity. Therefore minor thermal issues/anomalies can potentially start a chain of events that leads to an unbalance between the amount of heat generated by the computing nodes and the heat removed by the cooling system originating thermal hazards. Although thermal anomalies are rare events, anomaly detection/prediction in time is vital to avoid IT and facility equipment damage and outage of the datacenter, with severe societal and business losses. For this reason, automated approaches to detect thermal anomalies in datacenters have considerable potential. This thesis analyzed and characterized the power and thermal characteristics of a Tier0 datacenter (CINECA) during production and under abnormal thermal conditions. Then, a Deep Learning (DL)-powered thermal hazard prediction framework is proposed. The proposed models are validated against real thermal hazard events reported for the studied HPC cluster while in production. This thesis is the first empirical study of thermal anomaly detection and prediction techniques of a real large-scale HPC system to the best of my knowledge. For this thesis, I used a large-scale dataset, monitoring data of tens of thousands of sensors for around 24 months with a data collection rate of around 20 seconds.

Distributed argumentation technology: advancing risk analysis and regulatory compliance of distributed ledger technologies for transaction and management of securities

Distributed argumentation technology is a computational approach incorporating argumentation reasoning mechanisms within multi-agent systems. For the formal foundations of distributed argumentation technology, in this thesis we conduct a principle-based analysis of structured argumentation as well as abstract multi-agent and abstract bipolar argumentation. The results of the principle-based approach of these theories provide an overview and guideline for further applications of the theories. Moreover, in this thesis we explore distributed argumentation technology using distributed ledgers. We envision an Intelligent Human-input-based Blockchain Oracle (IHiBO), an artificial intelligence tool for storing argumentation reasoning. We propose a decentralized and secure architecture for conducting decision-making, addressing key concerns of trust, transparency, and immutability. We model fund management with agent argumentation in IHiBO and analyze its compliance with European fund management legal frameworks. We illustrate how bipolar argumentation balances pros and cons in legal reasoning in a legal divorce case, and how the strength of arguments in natural language can be represented in structured arguments. Finally, we discuss how distributed argumentation technology can be used to advance risk management, regulatory compliance of distributed ledgers for financial securities, and dialogue techniques.