Leveraging full-text article exploration for citation analysis

Scientific articles often include in-text citations quoting from external sources. When the cited source is an article, the citation context can be analyzed by exploring the article full-text. To quickly access the key information, researchers are often interested in identifying the sections of the cited article that are most pertinent to the text surrounding the citation in the citing article. This paper first performs a data-driven analysis of the correlation between the textual content of the sections of the cited article and the text snippet where the citation is placed. The results of the correlation analysis show that the title and abstract of the cited article are likely to include content highly similar to the citing snippet. However, the subsequent sections of the paper often include cited text snippets as well. Hence, there is a need to understand the extent to which an exploration of the full-text of the cited article would be beneficial to gain insights into the citing snippet, considering also the fact that the full-text access could be restricted. To this end, we then propose a classification approach to automatically predicting whether the cited snippets in the full-text of the paper contain a significant amount of new content beyond abstract and title. The proposed approach could support researchers in leveraging full-text article exploration for citation analysis. The experiments conducted on real scientific articles show promising results: the classifier has a 90% chance to correctly distinguish between the full-text exploration and only title and abstract cases

Machine learning supported next-maintenance prediction for industrial vehicles

Industrial and construction vehicles require tight periodic maintenance operations. Their schedule depends on vehicle characteristics and usage. The latter can be accurately monitored through various on-board devices, enabling the application of Machine Learning techniques to analyze vehicle usage patterns and design predictive analytics. This paper presents a data-driven application to automatically schedule the periodic maintenance operations of industrial vehicles. It aims to predict, for each vehicle and date, the actual remaining days until the next maintenance is due. Our Machine Learning solution is designed to address the following challenges: (i) the non-stationarity of the per-vehicle utilization time series, which limits the effectiveness of classic scheduling policies, and (ii) the potential lack of historical data for those vehicles that have recently been added to the fleet, which hinders the learning of accurate predictors from past data. Preliminary results collected in a real industrial scenario demonstrate the effectiveness of the proposed solution on heterogeneous vehicles. The system we propose here is currently under deployment, enabling further tests and tunings

A method to define the priority for maintenance and repair works of Italian motorway tunnels

The construction of motorways in Italy dates back to 1921 and still lasts today. Along them there is a large number of tunnels, many of which have been in service for more than 50 years and have experienced various levels of decay due to aging. An extensive assessment and inspection plan is taking place finalized to highlight situations where maintenance and repair works are needed to guarantee the continuation of service in safe conditions and functionality. Due to the number of tunnels, the need arises to classify them and define priorities for intervention on the basis of a first assessment and of a robust and scientific-based tool to orientate the investments. This paper describes the methodology that was developed by the Authors for this purpose, assessing the attention level of every tunnel. The method relies on a quantitative approach that allows quantifying the risk based on five risk factors composed of a number of relevant parameters. Their relative interaction, which guided the scores assigned to each parameter, was assessed by applying the Rock Engineering System [2]. A number of examples of existing tunnels are shown to illustrate the application of the method and to draw conclusions about its validity and reliability

A data-driven energy platform: from energy performance certificates to human-readable knowledge through dynamic high-resolution geospatial maps

The energy performance certificate (EPC) is a document that certifies the average annual energy consumption of a building in standard conditions and allows it to be classified within a so-called energy class. In a period such as this, when greenhouse gas emissions are of considerable importance and where the objective is to improve energy security and reduce energy costs in our cities, energy certification has a key role to play. The proposed work aims to model and characterize residential buildings’ energy efficiency by exploring heterogeneous, geo-referenced data with different spatial and temporal granularity. The paper presents TUCANA (TUrin Certificates ANAlysis), an innovative data mining engine able to cover the whole analytics workflow for the analysis of the energy performance certificates, including cluster analysis and a model generalization step based on a novel spatial constrained K-NN, able to automatically characterize a broad set of buildings distributed across a major city and predict different energy-related features for new unseen buildings. The energy certificates analyzed in this work have been issued by the Piedmont Region (a northwest region of Italy) through open data. The results obtained on a large dataset are displayed in novel, dynamic, and interactive geospatial maps that can be consulted on a web application integrated into the system. The visualization tool provides transparent and human-readable knowledge to various stakeholders, thus supporting the decision-making process

Heterogeneous industrial vehicle usage predictions: A real case

Predicting future vehicle usage based on the analysis of CAN bus data is a popular data mining application. Many of the usage indicators, like the utilization hours, are non-stationary time series. To predict their values, recent approaches based on Machine Learning combine multiple data features describing engine status, travels, and roads. While most of the proposed solutions address cars and trucks usage prediction, a smaller body of work has been devoted to industrial and construction vehicles, which are usually characterized by more complex and heterogeneous usage patterns. This paper describes a real case study performed on a 4-year CAN bus dataset collecting usage data about 2 250 construction vehicles of various types and models. We apply a statistics-based approach to select the most discriminating data features. Separately for each vehicle, we train regression algorithms on historical data enriched with contextual information. The achieved results demonstrate the effectiveness of the proposed solution

The BaR-SPOrt Experiment

BaR-SPOrt (Balloon-borne Radiometers for Sky Polarisation Observations) is an experiment to measure the linearly polarized emission of sky patches at 32 and 90 GHz with sub-degree angular resolution. It is equipped with high sensitivity correlation polarimeters for simultaneous detection of both the U and Q stokes parameters of the incident radiation. On-axis telescope is used to observe angular scales where the expected polarization of the Cosmic Microwave Background (CMBP) peaks. This project shares most of the know-how and sophisticated technology developed for the SPOrt experiment onboard the International Space Station. The payload is designed to flight onboard long duration stratospheric balloons both in the Northern and Southern hemispheres where low foreground emission sky patches are accessible. Due to the weakness of the expected CMBP signal (in the range of microK), much care has been spent to optimize the instrument design with respect to the systematics generation, observing time efficiency and long term stability. In this contribution we present the instrument design, and first tests on some components of the 32 GHz radiometer.Comment: 12 pages, 10 figures, Astronomical Telescopes and Instrumentation (Polaimetry in Astronomy) Hawaii August 2002 SPIE Meetin

On the Thermal Activation of Turin Metro Line 2 Tunnels

The Turin metro Line 2 will extend for nearly 28 km and include 26 stations. It will connect the SW suburbs of the city to the NE ones. The excavation will be performed by means of TBM and Cut & Cover techniques and, once concluded, will host a fully automated driverless light metro. This paper will describe the feasibility study carried out to assess the energy potential of the thermal activation of the line by using an innovative tunnel lining segment (ENERTUN) recently patented and tested in real operating conditions. A novel methodology was adopted, involving thermo-hydraulic 3D FE numerical anal- yses to identify the geothermal potential for the different sections of the line. A study of the possible collectors for the thermal energy produced was also performed considering the planned stations, the existing buildings and the future urban developments

CAS-MINE: Providing personalized services in context-aware applications by means of generalized rules

Context-aware systems acquire and exploit information on the user context to tailor services to a particular user, place, time, and/or event. Hence, they allowservice providers to adapt their services to actual user needs, by offering personalized services depending on the current user context. Service providers are usually interested in profiling users both to increase client satisfaction and to broaden the set of offered services. Novel and efficient techniques are needed to tailor service supply to the user (or the user category) and to the situation inwhich he/she is involved. This paper presents the CAS-Mine framework to efficiently discover relevant relationships between user context data and currently asked services for both user and service profiling. CAS-Mine efficiently extracts generalized association rules, which provide a high-level abstraction of both user habits and service characteristics depending on the context. A lazy (analyst-provided) taxonomy evaluation performed on different attributes (e.g., a geographic hierarchy on spatial coordinates, a classification of provided services) drives the rule generalization process. Extracted rules are classified into groups according to their semantic meaning and ranked by means of quality indices, thus allowing a domain expert to focus on the most relevant patterns. Experiments performed on three context-aware datasets, obtained by logging user requests and context information for three real applications, show the effectiveness and the efficiency of the CAS-Mine framework in mining different valuable types of correlations between user habits, context information, and provided services