1,721,085 research outputs found
Recent Findings and Open Issues concerning the Seismic Behaviour of Masonry Infill Walls in RC Buildings
Full text linkThe extension of the damages observed after the last major earthquakes shows that the seismic risk mitigation of infilled reinforced concrete structures is a paramount topic in seismic prone regions. In the assessment of existing structures and the design of new ones, the infill walls are considered as nonstructural elements by most of the seismic codes and, generally, comprehensive provisions for practitioners are missing. However, nowadays, it is well recognized by the community the importance of the infills in the seismic behaviour of the reinforced concrete structures. Accurate modelling strategies and appropriate seismic assessment methodologies are crucial to understand the behaviour of existing buildings and to develop efficient and appropriate mitigation measures to prevent high level of damages, casualties, and economic losses. The development of effective strengthening solutions to improve the infill seismic behaviour and proper analytical formulations that could help design engineers are still open issues, among others, on this topic. The main aim of this paper is to provide a state-of-the-art review concerning the typologies of damages observed in the last earthquakes where the causes and possible solutions are discussed. After that, a review of in-plane and out-of-plane testing campaigns from the literature on infilled reinforced concrete frames are presented as well as their relevant findings. The most common strengthening solutions to improve the seismic behaviour are presented, and some examples are discussed. Finally, a brief summary of the modelling strategies available in the literature is presented
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
Using the normalized levenshtein distance to analyze relationship between faults and local variables with confusing names: A further investigation
No full textThis paper exploits further uses of NLD (Normalized Levenshtein Distance), proposed in a recent study, to quantify the level of confusion of variables with the aim of verifying if they can provide indications about the presence of faults. We provide further evidence that fault prediction models based on the considered NLD measures can provide accurate estimations
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
Associazione Italiana di Sociologia- Sezione sociologia della salute e della medicina. Tavolo 1 Mente, cervello e comportamento tra neuroscienze e scienze sociali, Coordinatori: Boria, S., Giarelli G.. Conferenza AiS Salute : I nuovi orizzonti della salute in prospettiva interdisciplinare: cervello, tecnologie, ambiente e trasformazioni sociali .
No full textA graph-based approach to detect unreachable methods in Java software
No full textIn this paper, we have defined a static approach named DUM (Detecting Unreachable Methods) that works on Java byte-code and detects unreachable methods by traversing a graph-based representation of the software to be analyzed. To assess the validity of our approach, we have implemented it in a prototype software system. Both our approach and prototype have been validated on four open-source software. Results have shown the correctness, the completeness, and the accuracy of the methods that our solution detected as unreachable. We have also compared our solution with: JTombstone and Google CodePro AnalytiX. This comparison suggested that DUM outperforms baselines
Clustering and lexical information support for the recovery of design pattern in source code
No full textWe propose an approach that leverages lexical information and fuzzy clustering to reduce the number of the design pattern instances that existing approaches based on structural information (i.e., navigating the dependencies among software elements) erroneously recover in source code. To assess the effectiveness of the techniques, we present the results of a case study conducted on four open source software systems implemented in java. The data analysis indicates that the use of lexical information and fuzzy clustering improves the correctness of the results achieved by existing design pattern recovery approaches based on structural information, while preserving the number of design pattern instances correctly identified. © 2011 IEEE
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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