Optimisation of geothermal resources in urban areas

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Self-Learning Classifier for Internet traffic

Network visibility is a critical part of traffic engineering, network management, and security. Recently, unsupervised algorithms have been envisioned as a viable alternative to automatically identify classes of traffic. However, the accuracy achieved so far does not allow to use them for traffic classification in practical scenario. In this paper, we propose SeLeCT, a Self-Learning Classifier for Internet traffic. It uses unsupervised algorithms along with an adaptive learning approach to automatically let classes of traffic emerge, being identified and (easily) labeled. SeLeCT automatically groups flows into pure (or homogeneous) clusters using alternating simple clustering and filtering phases to remove outliers. SeLeCT uses an adaptive learning approach to boost its ability to spot new protocols and applications. Finally, SeLeCT also simplifies label assignment (which is still based on some manual intervention) so that proper class labels can be easily discovered. We evaluate the performance of SeLeCT using traffic traces collected in different years from various ISPs located in 3 different continents. Our experiments show that SeLeCT achieves overall accuracy close to 98%. Unlike state-of-art classifiers, the biggest advantage of SeLeCT is its ability to help discovering new protocols and applications in an almost automated fashio

The views of primary education teachers on the verification of multiplication

Learning and using the four mathematical operations -addition, subtraction, multiplication and division- are very important in the primary school syllabus curriculum. The verifications for the correctness of the operations are simple since they can be justified with the use of basic mathematical properties. However, this is not the case for one of the verifications of multiplication which seems to be preferred by most of the elementary school teachers in their practice. With this verification, the control of multiplication’s correctness is only a necessary but not sufficient condition and it is based on the Numbers’ Theory. In this paper we present the findings of a study on the views of agroup consisted of twenty four elementary school teachers using activities related to the operation of the multiplication and its verification

Bring Your Own Data to X-PLAIN

Exploring and understanding the motivations behind black-box model predictions is becoming essential in many different applications. X-PLAIN is an interactive tool that allows human-in-the-loop inspection of the reasons behind model predictions. Its support for the local analysis of individual predictions enables users to inspect the local behavior of different classifiers and compare the knowledge different classifiers are exploiting for their prediction. The interactive exploration of prediction explanation provides actionable insights for both trusting and validating model predictions and, in case of unexpected behaviors, for debugging and improving the model itself

Indeterminate Problems in Greek Primary Education

Indeterminate problems are problems that can be written with κ equations with more than κ unknowns and have been used since ancient times from many civilizations.Problem solving constitutes a critical part of Mathematics Educations, in which emphasis is given on the Curricula of Mathematics. Open-ended problems may have several correct answers or differed ways of finding the correct answer.In the present study the way students of the 5th grade manage an open-ended problem is examined and also elements of the way they solve it are presented

NetCluster: a Clustering-Based Framework for Internet Tomography

Abstract — In this paper, Internet data collected via passive measurement are analyzed to obtain localization information on nodes by clustering (i.e., grouping together) nodes that exhibit similar network path properties. Since traditional clustering algorithms fail to correctly identify clusters of homogeneous nodes, we propose a novel framework, named “NetCluster”, suited to analyze Internet measurement datasets. We show that the proposed framework correctly analyzes synthetically generated traces. Finally, we apply it to real traces collected at the access link of our campus LAN and discuss the network characteristics as seen at the vantage point. I. INTRODUCTION AND MOTIVATIONS The Internet is a complex distributed system which continues to grow and evolve. The unregulated and heterogeneous structure of the current Internet makes it challenging to obtai

Development and testing of a novel geothermal wall system

Shallow geothermal energy systems have the potential to contribute to the decarbonization of heating and cooling demands of buildings. These systems typically present drawbacks as high initial investments and occupancy of wide areas. In this study, a novel energy wall system is proposed to overcome the limitations of conventional geothermal applications in urban areas. The system is characterized by ease of installation, low initial costs and applicability to existing buildings undergoing energy retrofitting. The paper illustrates the implementation of the prototype of such a system to an existing structure in Torino (Italy). An overview of the components is given together with the interpretation of an illustrative test carried out in heating mode. The data from both heating and cooling experimental campaigns allow us to highlight the potential of the proposed technology. The results suggest that an average thermal power of about 17 W per unit area can be exchanged with the ground in heating mode, while an average of 68 W per unit area is exchanged in cooling operations. The negligible impact on the stress–strain state of the wall and the surrounding soil thermal and hygrometric regime is also testified by the results collected. These aspects are associated with a reduced probability of interferences with other installations in highly urbanized areas, easiness of installation and affordable cost

RECLAIM: Reverse Engineering Classification Metrics

Being able to compare machine learning models in terms of performance is a fundamental part of improving the state of the art in a field. However, there is a risk of getting locked into only using a few -- possibly not ideal -- performance metrics, only for comparability with earlier works. In this work, we explore the possibility of reconstructing new classification metrics starting from what little information may be available in existing works. We propose three approaches to reconstruct confusion matrices and, as a consequence, other classification metrics. We empirically verify the quality of the reconstructions, drawing conclusions on the usefulness that various classification metrics have for the reconstruction task