1,720,969 research outputs found
Deep learning approach for predicting university dropout: A case study at roma tre university
Full text linkBased on current trends in graduation rates, 39% of today young adults on average across OECD countries are expected to complete tertiary-type A (university level) education during their lifetime. In 2017, an average of 10.6% of young people (aged 1824) in the EU-28 were early leavers from education and training. Therefore the level of dropout in the scenery of European education is one of the major issue to be faced in a near future. The main aim of the research is to predict, as early as possible, which student will dropout in the Higher Education (HE) context. The accurate knowledge of this information would allow one to effectively carry out targeted actions in order to limit the incidence of the phenomenon. The recent breakthrough on Neural Networks with the use of Convolutional Neural Networks (CNN) architectures has become disruptive in AI. By stacking together tens or hundreds of convolutional neural layers, a “deep” network structure is obtained, which has been proved very effective in producing high accuracy models. In this research the administrative data of about 6000 students enrolled from 2009 in the Department of Education at Roma Tre University had been used to train a Convolutional Neural Network based. Then, the trained network provides a predictive model that predicts whether the student will dropout. Furthermore, we compared the results obtained using deep learning models to the ones using Bayesian networks. The accuracy of the obtained deep learning models ranged from 67.1% for the first-year students up to 94.3% for the third-year students
Auditing Sum Queries
No full textLecture Notes in Computer Science 2572 (G. Goos, J. Hartmanis, J. Van Leeuwen, eds.
Inferring Emotional State from Facial Micro-Expressions
No full textPersonalized systems are becoming more and more popular in everyday life. Their goal is to adapt the output to the characteristics (i.e., interests and preferences) of the active user. To achieve this purpose, a process of inferring these characteristics is needed. In this paper, we verify the existence of some significant correlation between the facial micro-expressions of individuals and their emotional state. If so, we could think of monitoring the user while enjoying a certain visual stimulus, to understand her emotional response. For example, we could comprehend whether a visitor of a museum or an exhibition likes or dislikes the object she is observing, thus deriving her interests and tastes, regardless of the reality from which she comes. It could foster the role of the museum/exhibition intended as a vehicle of aggregation between a broad range of users, thus favoring their cultural and social inclusion. It could also allow us to design and realize recommender systems for enhancing the experience of users with difficulty in explicitly expressing their interests, such as people belonging to vulnerable groups (e.g., elderly, children, disabled people) or different cultures. Although the sample analyzed is limited and concerns a specific context (i.e., music video clips), the experimental results have been encouraging, thus spurring us to carry on with our research activities
A Deep Learning-based Approach to Model Museum Visitors
No full textAlthough ubiquitous and fast access to the Internet allows us to admire objects and artworks exhibited worldwide from the comfort of our home, visiting a museum or an exhibition remains an essential experience today. Current technologies can help make that experience even more satisfying. For instance, they can assist the user during the visit, personalizing her experience by suggesting the artworks of her higher interest and providing her with related textual and multimedia content. To this aim, it is necessary to automatically acquire information relating to the active user. In this paper, we show how a deep neural network-based approach can allow us to obtain accurate information for understanding the behavior of the visitor alone or in a group. This information can also be used to identify users similar to the active one to suggest not only personalized itineraries but also possible visiting companions for promoting the museum as a vehicle for social and cultural inclusion
Exploiting Micro Facial Expressions for More Inclusive User Interfaces
No full textCurrent image/video acquisition and analysis techniques allow for not only the identification and classification of objects in a scene but also more sophisticated processing. For example, there are video cameras today able to capture micro facial expressions, namely, facial expressions that occur in a fraction of a second. Such micro expressions can provide useful information to define a person's emotional state. In this article, we propose to use these features to collect useful information for designing and implementing increasingly effective interactive technologies. In particular, facial micro expressions could be used to develop interfaces capable of fostering the social and cultural inclusion of users belonging to different realities and categories. The preliminary experimental results obtained by recording the reactions of individuals while observing artworks demonstrate the existence of correlations between the action units (i.e., single components of the muscular movement in which it is possible to break down facial expressions) and the emotional reactions of a sample of users, as well as correlations within some homogeneous groups of testers
Deep Learning Based Emotion Classification through EEG Spectrogram Images
Full text linkEmotion modeling for social robotics has the great potential to improve the life quality for the elderly and individuals with disabilities by making communication, care, and interactions more effective. It can help individuals with communication difficulties express their emotions. It can also be used to monitor the emotional well-being of elderly persons living alone and alert caregivers or family members if there are signs of distress. More broadly, emotion modeling is necessary to design robots closer and closer to human beings that can naturally interact with them by understanding their behavior and reactions. Here, we propose a deep learning technique for emotion classification using electroencephalogram (EEG) signals. We aim to recognize valence, arousal, dominance, and likability. Our technique uses the spectrogram from each of the 32 electrodes applied in the skull area. Then, we employ a Resnet101 convolutional neural network to learn a model capable of predicting several emotions. We built and tested our model on the DEAP dataset
The META4RS Proposal: Museum Emotion and Tracking Analysis For Recommender Systems
No full textIn this paper, we present the rationale and the ideas behind META4RS, a museum itinerary recommender system. The system leverages deep learning techniques to acquire data about the visitor’s position while ensuring her anonymity. Moreover, the visitor’s appraisal of the artwork she observes is inferred implicitly based on the emotional reactions she expresses while watching a given artwork. We are not aware of any such recommender system proposed in the research literature. However, this system should ensure several advantages: (i) it is non-intrusive since it makes use of simple badges and off-the-shelf cameras while ensuring the anonymity of the visitor; (ii) it is independent of the type of museum; (iii) it offers personalized itineraries to visitors based on their implicitly inferred interests and preferences. Specifically, we illustrate the background and describe the architecture of the proposed system, discussing the steps required for its implementation. We also provide details of what has already been done and what remains to be done, outlining the open problems
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
University dropout prediction through educational data mining techniques: A systematic review
No full textThe dropout rates in the European countries is one of the major issues to be faced in a near future as stated in the Europe 2020 strategy. In 2017, an average of 10.6% of young people (aged 18-24) in the EU-28 were early leavers from education and training according to Eurostat’s statistics. The main aim of this review is to identify studies which uses educational data mining techniques to predict university dropout in traditional courses. In Scopus and Web of Science (WoS) catalogues, we identified 241 studies related to this topic from which we selected 73, focusing on what data mining techniques are used for predicting university dropout. We identified 6 data mining classification techniques, 53 data mining algorithms and 14 data mining tools
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