1,721,120 research outputs found
Indagine geomorfologica e geofisica nell’area di Ca’ Zennare sull’entroterra meridionale della laguna di Venezia.
No full textSupporting autism spectrum disorder screening and intervention with machine learning and wearables: a systematic literature review
No full textThe number of autism spectrum disorder individuals is dramatically increasing. For them, it is difficult to get an early diagnosis or to intervene for preventing challenging behaviors, which may be the cause of social isolation and economic loss for all their family. This SLR aims at understanding and summarizing the current research work on this topic and analyze the limitations and open challenges to address future work. We consider papers published between 2015 and the beginning of 2021. The initial selection included about 2140 papers. 11 of them respected our selection criteria. The papers have been analyzed by mainly considering: (1) the kind of action taken on the autistic individual, (2) the considered wearables, (3) the machine learning approaches, and (4) the evaluation strategies. Results revealed that the topic is very relevant, but there are many limitations in the considered studies, such as reduced number of participants, absence of datasets and experimentation in real contexts, need for considering privacy issues, and the adoption of appropriate validation approaches. The issues highlighted in this analysis may be useful for improving machine learning techniques and highlighting areas of interest in which experimenting with the use of different noninvasive sensors
Towards a global geophysical approach to image earthen levees
No full textThe recent occurrence of floods in Northern and Central Italy as well as in the entire Europe caused important damages to agriculture, industry and infrastructures, to residential buildings and in some cases were also reported several casualties. Among these the November 2010 event, mostly occurred in the plain area of the Veneto Region (North-Eastern Italy), was particularly severe reaching a maximum rainfall intensity of more than 200 mm in 10 hours and causing the failure of 15 river embankments. The structure and the stratigraphy of earthen levees and of their geological basement represent a vital information and the eventuality of a collapse, also of minor embankments, frequently involves unaffordable social cost. The standard approach of monitoring the levees using sparse geotechnical tests and visual analysis is no longer satisfactory as the recent changes in the flood regime increased the stress on these structures and the associated risk of failures. An high resolution geophysical imaging procedure (namely EMAR), based on electromagnetic induction (FDEM) and multichannel radar (GPR), has been developed, tested and validated on a large scale survey of the embankments of a major river in North-eastern Italy. Using this procedure it was possible to investigate more than 100 km of embankments in 5 working days showing great potentials to cost-effectively monitoring earthen levees
A user-centered approach for detecting emotions with low-cost sensors
No full textDetecting emotions is very useful in many fields, from health-care to human-computer interaction. In this paper, we propose an iterative user-centered methodology for supporting the development of an emotion detection system based on low-cost sensors. Artificial Intelligence techniques have been adopted for emotion classification. Different kind of Machine Learning classifiers have been experimentally trained on the users’ biometrics data, such as hearth rate, movement and audio. The system has been developed in two iterations and, at the end of each of them, the performance of classifiers (MLP, CNN, LSTM, Bidirectional-LSTM and Decision Tree) has been compared. After the experiment, the SAM questionnaire is proposed to evaluate the user’s affective state when using the system. In the first experiment we gathered data from 47 participants, in the second one an improved version of the system has been trained and validated by 107 people. The emotional analysis conducted at the end of each iteration suggests that reducing the device invasiveness may affect the user perceptions and also improve the classification performance
Are IoBT services accessible to everyone?
No full textBiometric recognition aims at identifying a person by using their physiological or behavioral characteristics. When adopted for improving the security in the Internet of Things (IoT) field, it is commonly named Internet of Biometric Things (IoBT). However, despite its advantages there are further considerations on security and different ethical and legal issues, such as the possibility of exclusion of individuals due to pathologies, injuries, disabilities, or genetic defects. Indeed, these specific physical condition would lead to not satisfy the requirements commonly used for biometric recognition. As a consequence, the limitations of current biometric systems can exclude a person from the use of IoBT services. In this paper, we focus on the difficulty of iris recognition when it is affected by Coloboma, a congenital abnormality of membranes of the eye. We show how this pathological state impacts on the performance of the Daugman and Canny edge detection algorithms, which represent the most widespread methods used for the iris localization step in eye-based biometric. Results of an experimentation revealed that they correctly detected only 15.79% and 47.37% of Coloboma iris, respectively. In order to avoid the use of these inaccurate algorithms in case of Coloboma eye, we designed and experimented a Residual Neural Network classifier able to detect the presence of this disease with 99.79% of accuracy. This classifier may be a first step towards a more sophisticated “diversity-aware” biometric system which represents an alternative to actual IoBT authentication method for people with special physical condition
Active Landslide Portions Contribute to Surface Water Concentration: Insights from GIS Analysis and Field Data in the Northern Apennines
No full textThe distribution of small water bodies associated to landslides in a part of the Northern Apennines (Italy) has been explored, for the first time, using GIS analysis, field surveys and geophysical imaging. The analysis on the whole investigated area was performed using the Topographic Wetness Index (TWI), a proxy for surface soil moisture patterns based on topographic characteristics. The case studies correspond to two large landslides deep-rooted in the bedrock. The Sauna landslide in the Parma torrent basin and the Berceto landslide in the Taro river basin, have been investigated through field work, geophysics, boreholes and radiocarbon dating for the time constraints of the water bodies. The TWI analysis carried out both at the regional and the case-study scale has shown that low values of this index (drier areas) are more associated with inactive landslides portions, whereas higher values (wetter areas) are more associated with active portions. The analyses on the case studies highlighted that the condition characterized by wet soil and/or the presence of small water bodies are spatially persistent across time in correspondence of the same portion of the landslide that preserves landforms able to maintain these waters. As highlighted by geophysics, these landforms are in connection with deep shapes of the sliding/rupture surface of the landslide that mimic those at the surface
The fortified Venice: the line of fortifications around Venice and its relation with the geomorphology of its lagoon and the mainland.
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