arXiv subjects

Soraia Raupp Musse

Publications and source records attributed to Soraia Raupp Musse.

28 records · Page 2Linked to original sources

arXiv2022

Gender Representation in Brazilian Computer Science Conferences

This study presents an automated bibliometric analysis of 6569 research papers published in thirteen Brazilian Computer Science Society (SBC) conferences from 1999 to 2021. Our primary goal was to gather data to understand the gender representation in publications in the field of Computer Science. We applied a systematic assignment of gender to 23.573 listed papers authorships, finding that the gender gap for women is significant, with female authors being under-represented in all years of the study.

cs.DL↗

arXiv2022

Mitigating Bias in Facial Analysis Systems by Incorporating Label Diversity

Facial analysis models are increasingly applied in real-world applications that have significant impact on peoples' lives. However, as literature has shown, models that automatically classify facial attributes might exhibit algorithmic discrimination behavior with respect to protected groups, potentially posing negative impacts on individuals and society. It is therefore critical to develop techniques that can mitigate unintended biases in facial classifiers. Hence, in this work, we introduce a novel learning method that combines both subjective human-based labels and objective annotations based on mathematical definitions of facial traits. Specifically, we generate new objective annotations from two large-scale human-annotated dataset, each capturing a different perspective of the analyzed facial trait. We then propose an ensemble learning method, which combines individual models trained on different types of annotations. We provide an in-depth analysis of the annotation procedure as well as the datasets distribution. Moreover, we empirically demonstrate that, by incorporating label diversity, our method successfully mitigates unintended biases, while maintaining significant accuracy on the downstream tasks.

cs.CV↗

arXiv2021

Is my agent good enough? Evaluating Embodied Conversational Agents with Long and Short-term interactions

The use of digital resources has been increasing in every instance of todays society, being it in business or even ludic purposes. Despite such ever increasing use of technologies as interfaces, in all fields, it seems that it lacks the importance of users perception in this context. This work aims to present a case study about the evaluation of ECAs. We propose a Long-Term Interaction (LTI) to evaluate our conversational agent effectiveness through the user perception and compare it with Short-Term Interactions (STIs), performed by three users. Results show that many different aspects of users perception about the chosen ECA (i.e. Arthur) could be evaluated in our case study, in particular that LTI and STI are both important in order to have a better understanding of ECA impact in UX.

cs.HC↗

arXiv2021

Detecting Personality and Emotion Traits in Crowds from Video Sequences

This paper presents a methodology to detect personality and basic emotion characteristics of crowds in video sequences. Firstly, individuals are detected and tracked, then groups are recognized and characterized. Such information is then mapped to OCEAN dimensions, used to find out personality and emotion in videos, based on OCC emotion models. Although it is a clear challenge to validate our results with real life experiments, we evaluate our method with the available literature information regarding OCEAN values of different Countries and also emergent Personal distance among people. Hence, such analysis refer to cultural differences of each country too. Our results indicate that this model generates coherent information when compared to data provided in available literature, as shown in qualitative and quantitative results.

cs.CV↗

arXiv2021

Arthur: a new ECA that uses Memory to improve Communication

This article proposes an embodied conversational agent named Arthur. In addition to being able to talk to a person (using text and voice), he is also able to recognize the person he is talking to and detect his/her expressed emotion through facial expressions. Arthur uses these skills to improve communication with the user, also using his artificial memory, which stores and retrieves data about events and facts, based on a human memory model. We conducted some experiments to collect quantitative and qualitative information, which show that our model provides a consistent impact on users.

cs.HC↗

arXiv2020

CrowdEst: A Method for Estimating (and not Simulating) Crowd Evacuation Parameters in Generic Environments

Evacuation plans have been historically used as a safety measure for the construction of buildings. The existing crowd simulators require fully-modeled 3D environments and enough time to prepare and simulate scenarios, where the distribution and behavior of the crowd needs to be controlled. In addition, its population, routes or even doors and passages may change, so the 3D model and configurations have to be updated accordingly. This is a time-consuming task that commonly has to be addressed within the crowd simulators. With that in mind, we present a novel approach to estimate the resulting data of a given evacuation scenario without actually simulating it. For such, we divide the environment into smaller modular rooms with different configurations, in a divide-and-conquer fashion. Next, we train an artificial neural network to estimate all required data regarding the evacuation of a single room. After collecting the estimated data from each room, we develop a heuristic capable of aggregating per-room information so the full environment can be properly evaluated. Our method presents an average error of 5% when compared to evacuation time in a real-life environment. Our crowd estimator approach has several advantages, such as not requiring to model the 3D environment, nor learning how to use and configure a crowd simulator, which means any user can easily use it. Furthermore, the computational time to estimate evacuation data (inference time) is virtually zero, which is much better even when compared to the best-case scenario in a real-time crowd simulator.

cs.MA↗

arXiv2019

A Software to Detect OCC Emotion, Big-Five Personality and Hofstede Cultural Dimensions of Pedestrians from Video Sequences

This paper presents a video analysis application to detect personality, emotion and cultural aspects from pedestrians in video sequences, along with a visualizer of features. The proposed model considers a series of characteristics of the pedestrians and the crowd, such as number and size of groups, distances, speeds, among others, and performs the mapping of these characteristics in personalities, emotions and cultural aspects, considering the Cultural Dimensions of Hofstede (HCD), the Big-Five Personality Model (OCEAN) and the OCC Emotional Model. The main hypothesis is that there is a relationship between so-called intrinsic human variables (such as emotion) and the way people behave in space and time. The software was tested in a set of videos from different countries and results seem promising in order to identify these three different levels of psychological traits in the filmed sequences. In addition, the data of the people present in the videos can be seen in a crowd viewer.

cs.CV↗

arXiv2019

How much do you perceive this? An analysis on perceptions of geometric features, personalities and emotions in virtual humans (Extended Version)

This work aims to evaluate people's perception regarding geometric features, personalities and emotions characteristics in virtual humans. For this, we use as a basis, a dataset containing the tracking files of pedestrians captured from spontaneous videos and visualized them as identical virtual humans. The goal is to focus on their behavior and not being distracted by other features. In addition to tracking files containing their positions, the dataset also contains pedestrian emotions and personalities detected using Computer Vision and Pattern Recognition techniques. We proceed with our analysis in order to answer the question if subjects can perceive geometric features as distances/speeds as well as emotions and personalities in video sequences when pedestrians are represented by virtual humans. Regarding the participants, an amount of 73 people volunteered for the experiment. The analysis was divided in two parts: i) evaluation on perception of geometric characteristics, such as density, angular variation, distances and speeds, and ii) evaluation on personality and emotion perceptions. Results indicate that, even without explaining to the participants the concepts of each personality or emotion and how they were calculated (considering geometric characteristics), in most of the cases, participants perceived the personality and emotion expressed by the virtual agents, in accordance with the available ground truth.

cs.GR↗

arXiv2019

Predicting Future Pedestrian Motion in Video Sequences using Crowd Simulation

While human and group analysis have become an important area in last decades, some current and relevant applications involve to estimate future motion of pedestrians in real video sequences. This paper presents a method to provide motion estimation of real pedestrians in next seconds, using crowd simulation. Our method is based on Physics and heuristics and use BioCrowds as crowd simulation methodology to estimate future positions of people in video sequences. Results show that our method for estimation works well even for complex videos where events can happen. The maximum achieved average error is $2.72$cm when estimating the future motion of 32 pedestrians with more than 2 seconds in advance. This paper discusses this and other results.

cs.CV↗

arXiv2019

Using Big Five Personality Model to Detect Cultural Aspects in Crowds

The use of information technology in the study of human behavior is a subject of great scientific interest. Cultural and personality aspects are factors that influence how people interact with one another in a crowd. This paper presents a methodology to detect cultural characteristics of crowds in video sequences. Based on filmed sequences, pedestrians are detected, tracked and characterized. Such information is then used to find out cultural differences in those videos, based on the Big-five personality model. Regarding cultural differences of each country, results indicate that this model generates coherent information when compared to data provided in literature.

cs.CV↗