A user-centered approach for detecting emotions with low-cost sensors

AbstractDetecting 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

A mobile augmented reality application for supporting real-time skin lesion analysis based on deep learning

AbstractMelanoma is considered the deadliest skin cancer and when it is in an advanced state it is difficult to treat. Diagnoses are visually performed by dermatologists, by naked-eye observation. This paper proposes an augmented reality smartphone application for supporting the dermatologist in the real-time analysis of a skin lesion. The app augments the camera view with information related to the lesion features generally measured by the dermatologist for formulating the diagnosis. The lesion is also classified by a deep learning approach for identifying melanoma. The real-time process adopted for generating the augmented content is described. The real-time performances are also evaluated and a user study is also conducted. Results revealed that the real-time process may be entirely executed on the Smartphone and that the support provided is well judged by the target users

Entropy Based Template Analysis in Face Biometric Identification Systems

The accuracy of a biometric matching algorithm relies on its ability to better separate score distributions for genuine and impostor subjects. However, capture conditions (e.g. illumination or acquisition devices) as well as factors related to the subject at hand (e.g. pose or occlusions) may even take a generally accurate algorithm to provide incorrect answers. Techniques for face classification are still too sensitive to image distortion, and this limit hinders their use in large-scale commercial applications, which are typically run in uncontrolled settings. This paper will join the notion of quality with the further interesting concept of representativeness of a biometric sample, taking into account the case of more samples per subject. Though being of excellent quality, the gallery samples belonging to a certain subject might be very (too much) similar among them, so that even a moderately different sample of the same subject in input will cause an error. This seems to indicate that quality measures alone are not able to guarantee good performances. In practice, a subject gallery should include a sufficient amount of possible variations, in order to allow correct recognition in different situations. We call this gallery feature representativeness. A significant feature to consider together with quality is the sufficient representativeness of (each) subject’s gallery. A strategy to address this problem is to investigate the role of the entropy, which is computed over a set of samples of a same subject. The paper will present a number of applications of such a measure in handling the galleries of the different users who are registered in a system. The resulting criteria might also guide template updating, to assure gallery representativeness over time

Assessing the effectiveness of sequence diagrams in the comprehension of functional requirements: results from a family of five experiments

Modeling is a fundamental activity within the requirements engineering process and concerns the construction of abstract descriptions of requirements that are amenable to interpretation and validation. The choice of a modeling technique is critical whenever it is necessary to discuss the interpretation and validation of requirements. This is particularly true in the case of functional requirements and stakeholders with divergent goals and different backgrounds and experience. This paper presents the results of a family of experiments conducted with students and professionals to investigate whether the comprehension of functional requirements is influenced by the use of dynamic models that are represented by means of the UML sequence diagrams. The family contains five experiments performed in different locations and with 112 participants of different abilities and levels of experience with UML. The results show that sequence diagrams improve the comprehension of the modeled functional requirements in the case of high ability and more experienced participants.The authors wish to thank all the participants in the experiments. This research was partially supported by the MULTIPLE project (with ref. TIN2009-13838).Abrahao Gonzales, SM.; Gravino, .C.; Insfrán Pelozo, CE.; Scaniello, .G.; Tortora, .G. (2013). Assessing the effectiveness of sequence diagrams in the comprehension of functional requirements: results from a family of five experiments. IEEE Transactions on Software Engineering. 39(3):327-342. https://doi.org/10.1109/TSE.2012.27S32734239

comparing inspection methods using controlled experiments

Objective: In this paper we present an empirical study that was aimed at comparing three software inspection methods, in terms of needed time, precision, and recall values. The main objective of this study is to provide software engineers with some insight into choosing the inspection method to adopt. Method: We conducted a controlled experiment and a replication. These experiments involved 48 Master students in Computer Science at the University of Salerno. In the experiments, 6 academic researchers were also involved. The students had to discover defects within a software artefact using inspection methods that differ in terms of discipline and flexibility. In particular, we selected a disciplined but not flexible method (the Fagan's process), a disciplined and flexible method (a virtual inspection), and a flexible but not disciplined method (the pair inspection). Results: We observed a significant difference in favour of the Pair Inspection method for the time spent to perform the tasks. The data analysis also revealed a significant difference in favour of the Fagan's inspection process for precision. Finally, the effect of the inspection method on the recall is not significant. Conclusions: The empirical investigation showed that the discipline and flexibility of an inspection method affect both the time needed to identify defects and the precision of the inspection results. In particular, more flexible methods require less time to inspect a software artefact, while more disciplined methods enable the identification of a lower number of false defects

Automatic Event Geo-Location in Twitter

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