Proof of Location through a Blockchain Agnostic Smart Contract Language: Design and Evaluation over Algorand and Ethereum

Le applicazioni che offrono servizi sulla base della posizione degli utenti sono sempre più utilizzate, a partire dal navigatore fino ad arrivare ai sistemi di trasporto intelligenti (ITS) i quali permetteranno ai veicoli di comunicare tra loro. Alcune di questi servizi permettono perfino di ottenere qualche incentivo se l'utente visita o passa per determinate zone. Per esempio un negozio potrebbe offrire dei coupon alle persone che si trovano nei paraggi. Tuttavia, la posizione degli utenti è facilmente falsificabile, ed in quest'ultima tipologia di servizi, essi potrebbero ottenere gli incentivi in modo illecito, raggirando il sistema. Diviene quindi necessario implementare un'architettura in grado di impedire alle persone di falsificare la loro posizione. A tal fine, numerosi lavori sono stati proposti, i quali delegherebbero la realizzazione di "prove di luogo" a dei server centralizzati oppure collocherebbero degli access point in grado di rilasciare prove o certificati a quegli utenti che si trovano vicino. In questo lavoro di tesi abbiamo ideato un'architettura diversa da quelle dei lavori correlati, cercando di utilizzare le funzionalità offerte dalla tecnologia blockchain e dalla memorizzazione distribuita. In questo modo è stato possibile progettare una soluzione che fosse decentralizzata e trasparente, assicurando l'immutabilità dei dati mediante l'utilizzo della blockchain. Inoltre, verrà dettagliato un'idea di caso d'uso da realizzare utilizzando l'architettura da noi proposta, andando ad evidenziare i vantaggi che, potenzialmente, si potrebbero trarre da essa. Infine, abbiamo implementato parte del sistema in questione, misurando i tempi ed i costi richiesti dalle transazioni su alcune delle blockchain disponibili al giorno d'oggi, utilizzando le infrastrutture messe a disposizione da Ethereum, Polygon e Algorand.

Analysis and Evaluation of Tiny Machine Learning applications

The aim of TinyML is to bring the capability of Machine Learning to ultra-low-power devices, typically under a milliwatt, and with this it breaks the traditional power barrier that prevents the widely distributed machine intelligence. TinyML allows greater reactivity and privacy by conducting inference on the computer and near-sensor while avoiding the energy cost associated with wireless communication, which is far higher at this scale than that of computing. In addition, TinyML’s efficiency makes a class of smart, battery-powered, always-on applications that can revolutionize the collection and processing of data in real time. This emerging field, which is the end of a lot of innovation, is ready to speed up its growth in the coming years. In this thesis, we deploy three model on a microcontroller. For the model, datasets are retrieved from an online repository and are preprocessed as per our requirement. The model is then trained on the split of preprocessed data at its best to get the most accuracy out of it. Later the trained model is converted to C language to make it possible to deploy on the microcontroller. Finally, we take step towards incorporating the model into the microcontroller by implementing and evaluating an interface for the user to utilize the microcontroller’s sensors. In our thesis, we will have 4 chapters. The first will give us an introduction of TinyML. The second chapter will help setup the TinyML Environment. The third chapter will be about a major use of TinyML in Wake Word Detection. The final chapter will deal with Gesture Recognition in TinyML.

Comparison of different solutions for the hybridization of a motorcycle engine for Formula Student applications and evaluation of the effectiveness on the simulated lap time

Following the latest environmental concerns, the importance of minimising the detrimental effect of emissions of terrestrial vehicles has become a major goal for the whole automotive field. The key to achieve an emission-free long term future is the electrification of vehicle fleets; this huge step cannot be taken without intermediate technologies. In this context, hybrid vehicles are fundamental to reach this goal. Specifically, mild hybrid vehicles represent a trade-off between cost and emissions that could act now as a bridge towards electrification. Like the industry, also student engineering competitions are likely to take the same route: Combustion vehicles may well turn into hybrid vehicles. For this reason, a preliminary design overview is necessary to pinpoint the key performance indicators for the prototypes of the future.

Neural Machine Translation in the Field of Law. Analysis and Evaluation against Human Reference of NMT Raw Outputs of an Atto Costitutivo of S.r.l. from Italian into German.

Artificial Intelligence (AI) is gaining ever more ground in every sphere of human life, to the point that it is now even used to pass sentences in courts. The use of AI in the field of Law is however deemed quite controversial, as it could provide more objectivity yet entail an abuse of power as well, given that bias in algorithms behind AI may cause lack of accuracy. As a product of AI, machine translation is being increasingly used in the field of Law too in order to translate laws, judgements, contracts, etc. between different languages and different legal systems. In the legal setting of Company Law, accuracy of the content and suitability of terminology play a crucial role within a translation task, as any addition or omission of content or mistranslation of terms could entail legal consequences for companies. The purpose of the present study is to first assess which neural machine translation system between DeepL and ModernMT produces a more suitable translation from Italian into German of the atto costitutivo of an Italian s.r.l. in terms of accuracy of the content and correctness of terminology, and then to assess which translation proves to be closer to a human reference translation. In order to achieve the above-mentioned aims, two human and automatic evaluations are carried out based on the MQM taxonomy and the BLEU metric. Results of both evaluations show an overall better performance delivered by ModernMT in terms of content accuracy, suitability of terminology, and closeness to a human translation. As emerged from the MQM-based evaluation, its accuracy and terminology errors account for just 8.43% (as opposed to DeepL’s 9.22%), while it obtains an overall BLEU score of 29.14 (against DeepL’s 27.02). The overall performances however show that machines still face barriers in overcoming semantic complexity, tackling polysemy, and choosing domain-specific terminology, which suggests that the discrepancy with human translation may still be remarkable.