1,720,983 research outputs found
FOPA-MC: fuzzy multi-criteria group decision making for peer assessment
No full textMassive Open Online Courses are gaining popularity with millions of students enrolled, thousands of courses available and hundreds of learning institutions involved. Due to the high number of students and the relatively small number of tutors, student assessment, especially for complex tasks, is a typical issue of such courses. Thus, peer assessment is becoming increasingly popular to solve such a problem and several approaches have been proposed so far to improve the reliability of its outcomes. Among the most promising, there is fuzzy ordinal peer assessment (FOPA) that adopts models coming from fuzzy set theory and group decision Making. In this paper we propose an extension of FOPA supporting multi-criteria assessment based on rubrics. Students are asked to rank a small number of peer submissions against specified criteria, then provided rankings are transformed in fuzzy preference relations, expanded to obtain missing values and aggregated to estimate final grades. Results obtained are promising if compared to other peer assessment techniques both in the reconstruction of the correct ranking and on the estimation of students’ grades
Cooperating Experts for Soundtrack Analysis of MPEG Movies
No full textIn this paper, we propose a multi-expert classification system (MES) for the audio classification of MPEG movies. The system has been designed according to an hybrid architecture which is made of three cascaded stages and constitutes an ensemble of different classifiers, each one implemented by means of a multi-expert architecture.
Classification of the audio tracks exploits four pure classes (music, speech, silence and noise) plus three hybrid classes associated to complex patterns resulting from the overlap of different components (e.g., speech overlapped with music or noise).
The soundtracks of 30 movies selected from various genres have been used for building a wide database of samples and for the successive assessment of system performance. A significant amount of experimental results obtained by the proposed MES, by other classification systems using a single classifier, and by another MES using a parallel fusion scheme, are reported in the paper together with comments and comparative analyses.
In addition, the paper demonstrates the application of the knowledge arising from an analysis of intermediate classification results in order to obtain indications about the definition of the MES architecture. The results achieved by using our system are extremely encouraging when compared with those obtained by the other MES
A deep learning based system for handwashing procedure evaluation
No full textHand washing preparation can be considered as one of the main strategies for reducing the risk of surgical site contamination and thus the infections risks. Within this context, in this paper we propose an embedded system able to automatically analyze, in real-time, the sequence of images acquired by a depth camera to evaluate the quality of the handwashing procedure. In particular, the designed system runs on an NVIDIA Jetson NanoTM computing platform. We adopt a convolutional neural network, followed by a majority voting scheme, to classify the movement of the worker according to one of the ten gestures defined by the World Health Organization. To test the proposed system, we collect a dataset built by 74 different video sequences. The results achieved on this dataset confirm the effectiveness of the proposed approach
Optimizing Crowd Counting in Dense Environments Through Curriculum Learning Training Strategy
No full textCounting individuals in highly crowded environments, characterized by thousands of people, has garnered significant attention in recent years, due to the high number of vertical markets wherein such algorithms can prove beneficial, ranging from smart city and transportation to retail sectors, among others. Within this context, in this paper we introduce a novel training methodology tailored for estimating the number of people, ensuring precise counting accuracy in both moderately and highly crowded scenarios. The proposed approach exploits a formulation of the problem based on point detection, where each point represents an individual’s head. Our innovative contributions center around the designing of a novel training strategy employing Curriculum Learning (CL), which aims to replicate the gradual learning process observed in human cognition, training on simpler tasks at the beginning and tackling more complex tasks as the training evolves. In order to evaluate the complexity of each sample image, we propose a novel indicator taking into account both the number of people and their distribution within the image. The experimentation phase encompassed 18 publicly available datasets; the obtained results validate the effectiveness of the proposed approach, surpassing the baseline state-of-the-art point detection by 71% and 70% in terms of Mean Absolute Error (MAE) and Mean Squared Error (MSE), respectively
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