Didactics and Self-Assessment: An Innovative Proposal for The University of Trento

The university institution is called today to face challenges concerning the ability to recognize and pursue new formative goals (Grion et al., 2018). In the light of this, the research wants to reflect on the reality of the University of Trento, so far, the only Italian university, among the 35 evaluated, to have obtained the highest rating assignable by the Anvur. The aim is to highlight both the primary nodes in which the University requires renewal and its hinges points, and report in detail the results of quantitative analysis, commissioned and drafted by the Joint Committee of the Department of Civil, Environmental and Mechanical Engineering (DICAM), which saw the need to further analyze the reality of students of the individual courses of the Department. The contribution links, in conclusion, the points emerged from the direct observation of the students to a consistent response to the emerging literature review. Specifically, reflecting the field of post-compulsory education paths, with a strong connection with self-assessment (SA). The results seem to show that self-assessment (SA) can be a new key to the promotion of an education capable of experimenting, through participatory and innovative teaching, knowledge, autonomy, responsibility and soft skills: fundamental elements that the University of Trento needs to improve to achieve European and international standards

Monitoring networked infrastructure with minimum data via sequential graph fourier transforms

Many urban infrastructures contain complex dynamics embedded in spatial networks. Monitoring using Internet-of-Things (IoT) sensors is essential for ensuring safe operations. An open challenge is given an existing sensor network, where best to collect the minimum amount of representative data. Here, we consider an urban underground water distribution network (WDN) and the problem of contamination detection. Existing topology-based approaches link complex network (e.g. Laplacian spectra) to optimal sensing selections, but neglects the underpinning fluid dynamics. Alternative data-driven approaches such as compressed sensing (CS) offer limited data reduction.In this work, we introduce a principal component analysis based Graph Fourier Transform (PCA-GFT) method, which can recover the full networked signal from a dynamic subset of sensors. Specifically, at each time step, we are able to predict which sensors are needed for the next time step. We do so, by exploiting the spatial-time correlations of the WDN dynamics, as well as predicting the sensor set needed using sparse coefficients in the transformed domain. As such, we are able to significantly reduce the number of samples compared with CS approaches. The drawback lies in the computational complexity of a data collection point (DCP) updating the PCA-GFT operator at each time-step. The experimental results show that, on average, with nearly 40% of the sensors reported, the proposed PCA-GFT method is able to fully recover the networked dynamics

Graph Input Representations for Machine Learning Applications in Urban Network Analysis

Understanding and learning the characteristics of network paths has been of particular interest for decades and has led to several successful applications. Such analysis becomes challenging for urban networks as their size and complexity are significantly higher compared to other networks. The state-of-the-art machine learning (ML) techniques allow us to detect hidden patterns and, thus, infer the features associated with them. However, very little is known about the impact on the performance of such predictive models by the use of different input representations. In this paper, we design and evaluate six different graph input representations (i.e., representations of the network paths), by considering the network's topological and temporal characteristics, for being used as inputs for machine learning models to learn the behavior of urban networks paths. The representations are validated and then tested with a real-world taxi journeys dataset predicting the tips using a road network of New York. Our results demonstrate that the input representations that use temporal information help the model to achieve the highest accuracy (RMSE of 1.42$)

Didactics and Self-Assessment: An Innovative Proposal for The University of Trento

The university institution is called today to face challenges concerning the ability to recognize and pursue new formative goals (Grion et al., 2018). In the light of this, the research wants to reflect on the reality of the University of Trento, so far, the only Italian university, among the 35 evaluated, to have obtained the highest rating assignable by the Anvur. The aim is to highlight both the primary nodes in which the University requires renewal and its hinges points, and report in detail the results of quantitative analysis, commissioned and drafted by the Joint Committee of the Department of Civil, Environmental and Mechanical Engineering (DICAM), which saw the need to further analyze the reality of students of the individual courses of the Department. The contribution links, in conclusion, the points emerged from the direct observation of the students to a consistent response to the emerging literature review. Specifically, reflecting the field of post-compulsory education paths, with a strong connection with self-assessment (SA). The results seem to show that self-assessment (SA) can be a new key to the promotion of an education capable of experimenting, through participatory and innovative teaching, knowledge, autonomy, responsibility and soft skills: fundamental elements that the University of Trento needs to improve to achieve European and international standards

Tunneling Trust Into the Blockchain: A Merkle Based Proof System for Structured Documents

The idea of Smart contracts foresees the possibility of automating contractual clauses using hardware and software tools and devices. One of the main perspectives of their implementation is the automation of interactions such as bets, collaterals, prediction markets, insurances. As blockchain platforms, such as Ethereum, offer very strong guarantees of untampered, deterministic execution, that can be exploited as smart contracts substrate, the problem of how to provide reliable information from the "outside world" into the contracts becomes central. In this article, we propose a system based on a Merkle tree representation of structured documents (such as all XML), with which it is possible to generate compact proofs on the content of web documents. The proofs can then be efficiently checked on-chain by a smart contract, to trigger contract action. We provide an end-to-end proof of concept, applying it to real use case scenarios, which allows us to give an estimate of the costs

Neural network approximation of graph Fourier transform for sparse sampling of networked dynamics

Infrastructure monitoring is critical for safe operations and sustainability. Like many networked systems, water distribution networks (WDNs) exhibit both graph topological structure and complex embedded flow dynamics. The resulting networked cascade dynamics are difficult to predict without extensive sensor data. However, ubiquitous sensor monitoring in underground situations is expensive, and a key challenge is to infer the contaminant dynamics from partial sparse monitoring data. Existing approaches use multi-objective optimization to find the minimum set of essential monitoring points but lack performance guarantees and a theoretical framework. Here, we first develop a novel Graph Fourier Transform (GFT) operator to compress networked contamination dynamics to identify the essential principal data collection points with inference performance guarantees. As such, the GFT approach provides the theoretical sampling bound. We then achieve under-sampling performance by building auto-encoder (AE) neural networks (NN) to generalize the GFT sampling process and under-sample further from the initial sampling set, allowing a very small set of data points to largely reconstruct the contamination dynamics over real and artificial WDNs. Various sources of the contamination are tested, and we obtain high accuracy reconstruction using around 5%–10% of the network nodes for known contaminant sources, and 50%–75% for unknown source cases, which although larger than that of the schemes for contaminant detection and source identifications, is smaller than the current sampling schemes for contaminant data recovery. This general approach of compression and under-sampled recovery via NN can be applied to a wide range of networked infrastructures to enable efficient data sampling for digital twins