In human action classification task, a video must be classified into a pre-determined class. To cope with this problem, we propose a mid-level representation, in which information about quantization errors is embedded...
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In human action classification task, a video must be classified into a pre-determined class. To cope with this problem, we propose a mid-level representation, in which information about quantization errors is embedded together with the aggregated data on low level features. The main contributions of this article are twofold: (i) assembly of low-level features (dense trajectories) by a mid-level representation enriched with information about distances between descriptors and codewords; and (ii) a survey of the most common protocols for human action classification methods when applied to three different datasets. Regarding classification protocols, we have experimented the training and testing classification (called split), the leave-one-out cross-validation (LOOCV) and the leave-one-group-out cross-validation (25-fold CV). Experimental results demonstrated that our strategy either has improved the classification rates with respect to the state-of-the-art for KTH dataset, achieving 98%, or it is a competitive one, for UCF-11 with 90%, when compared with methods with no feature learning.
Uncertainty quantification (UQ) is a vital step in using mathematical models and simulations to take decisions. The field of cardiac simulation has begun to explore and adopt UQ methods to characterise uncertainty in ...
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Anomaly-based intrusion detection by the means of machine learning techniques is extensively studied in the literature mainly due to its promise to detect new attacks. However, despite the promising reported results, ...
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Anomaly-based intrusion detection by the means of machine learning techniques is extensively studied in the literature mainly due to its promise to detect new attacks. However, despite the promising reported results, it is hardly deployed to real world environments. The main challenge in its adoption is the discrepancy between the accuracy rates obtained during the classifier development process and the rates obtained during its use in production environments. Such a discrepancy is mainly caused by non-representative training databases and non-generalizable (scenario-specific) classifier's model. This paper presents a method to create intrusion databases, which aims at mimicking the production environments characteristics by using well-known tools. Moreover, we present and evaluate a new validation technique, which aims at ensuring the generalization capacity of the obtained models, reached using cross-validating with different intrusion databases. The evaluation tests showed the feasibility of the proposed method. The feature selection technique ensured the model generalization capacity, improving its accuracy rate by 13%, while testing in different intrusion databases. Finally, the proposed anomaly-based approach was compared with Snort, reaching an accuracy rate of 99% against 27% of Snort for detecting DoS attacks.
Nowadays, messaging technology in digital data form more often used and not less messages that confidentially wanted. Then it should be modified so that can be understood only by the sender and the intended recipients...
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Autism Spectrum Disorder is a pervasive developmental disorder that will affect children in terms of interpersonal communication, social interaction, and imaginative levels in play. Many therapies to help the motor ne...
Autism Spectrum Disorder is a pervasive developmental disorder that will affect children in terms of interpersonal communication, social interaction, and imaginative levels in play. Many therapies to help the motor neuron performance is one of them Pretend Play. Pretend Play is a therapy that invites children in playing to demonstrate something else and tell how to use objects that are considered in the child's imagination. However, in the era of highly developed technology, many fields have used the Augmented Reality method as a visualization of various aspects. With this method researchers will present the therapeutic visualization of the block to 3D transportation tool that is useful for strengthening motor nerves and visual strength of the child. The system can run well during marker detection, marker movement, and 3D object display with the accuracy of precision angle and distance between virtual world with real world reach 100% with angle 0 at distance 31 cm and the maximum distance from the marker is 46 cm and the maximum angle is 30˚.
This Research Paper presents the evaluation of an instrument to identify the impact of motivation and engagement factors in undergraduate students in computing. Although researches indicate a direct impact of motivati...
ISBN:
(纸本)9781538611753;9781538611746
This Research Paper presents the evaluation of an instrument to identify the impact of motivation and engagement factors in undergraduate students in computing. Although researches indicate a direct impact of motivation and engagement on student performance and retention, few studies have been found that address which factors are relevant in this process. The instrument is a questionnaire based on the compilation of several works of the literature containing 48 items divided into 6 groups: personal and demographic data, general perception about motivation, perception about the university, student behavior, perception about program and perception about classes/teachers. The questionnaire evaluation is based on a case study with 112 undergraduate students in Software Engineering. As a result, we found that the questionnaire can be considered reliable (Cronbach's alpha = .8904). Considering the validity of constructs, we found an acceptable degree of correlation between the most pair of items in each group (averaging 63%). We also found that the item-total correlation coefficient was only not adequate for one factor group, indicating satisfactory correlation for all other items. Finally, we found that the number of factors is coherent, but there are several items from different groups strongly correlated, indicating the need for a reorganization.
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