COMPARATIVE EVALUATION OF KEYPOINT DETECTORS FOR 3D DIGITAL AVATAR RECONSTRUCTION

Three-dimensional personalized human avatars have been successfully utilized in shopping, entertainment, education, and health applications. However, it is still a challenging task to obtain both a complete and highly detailed avatar automatically. One approach is to use general-purpose, photogrammetry-based algorithms on a series of overlapping images of the person. We argue that the quality of avatar reconstruction can be increased by modifying parts of the photogrammetry-based algorithm pipeline to be more specifically tailored to the human body shape. In this context, we perform an extensive, standalone evaluation of eleven algorithms for keypoint detection, which is the first phase of the photogrammetry-based reconstruction pipeline. We include well established, patented Distinctive image features from scale-invariant keypoints (SIFT) and Speeded up robust features (SURF) detection algorithms as a baseline since they are widely incorporated into photogrammetry-based software. All experiments are conducted on a dataset of 378 images of human body captured in a controlled, multi-view stereo setup. Our findings are that binary detectors highly outperform commonly used SIFT-like detectors in the avatar reconstruction task, both in terms of detection speed and in number of detected keypoints

The impact of multimodal collaborative virtual environments on learning: A gamified online debate

Online learning platforms are integrated systems designed to provide students and teachers with information, tools and resources to facilitate and enhance the delivery and management of learning. In recent years platform designers have introduced gamification and multimodal interaction as ways to make online courses more engaging and immersive. Current Web-based platforms provide a limited degree of immersion in learning experiences, thereby diminishing potential learning impact. To improve immersion, it is necessary to stimulate some or all the human senses by engaging users in an environment that perceptually surrounds them and allows intuitive and rich interaction with other users and its content. Learning in these collaborative virtual environments (CVEs) can be aided by increasing motivation and engagement through the gamification of the educational task. This rich interaction that combines multimodal stimulation and gamification of the learning experience has the potential to draw students into the learning experience and improve learning outcomes. This paper presents the results of an experimental study designed to evaluate the impact of multimodal real-time interaction on user experience and learning of gamified educational tasks completed in a CVE. Secondary school teachers and students participated in the study. The multimodal CVE is an accurate reconstruction of the European Parliament in Brussels, developed using the REVERIE (Real and Virtual Engagement In Realistic Immersive Environment) framework. In the study, we compared the impact of the VR parliament to a non-multimodal control (an educational platform called Edu-Simulation) for the same educational tasks. Our results show that the multimodal CVE improves student learning performance and aspects of subjective experience when compared to the non-multimodal control. More specifically it resulted in a more positive effect on the ability of the students to generate ideas compared to a non-multimodal control. It also facilitated a sense of presence (strong emotional and a degree of spatial) for students in the VE. The paper concludes with a discussion of future work that focusses on combining the best features of both systems in a hybrid system to increase its educational impact and evaluate the prototype in real-world educational scenarios

Securing cloud-hosted applications using active defense with rule-based adaptations

Security cloud-based applications is a dynamic problem since modern attacks are always evolving in their sophistication and disruption impact. Active defense is a state-of-the-art paradigm where proactive or reactive cybersecurity strategies are used to augment passive defense policies (e.g., firewalls). It involves using knowledge of the adversary to create of dynamic policy measures to secure resources and outsmart adversaries to make cyber-attacks difficult to execute. Using intelligent threat detection systems based on machine learning and active defense solutions implemented via cloud resource adaptations, we can slowdown attacks and derail attackers at an early stage so that they cannot proceed with their plots, while also increasing the probability that they will expose their presence or reveal their attack vectors. In this MS Thesis, we demonstrate the concept and benefits of active defense in securing cloud-based applications through rule-based adaptations on distributed resources. Specifically, we propose two novel active defense strategies to mitigate impact of security anomaly events within: (a) social virtual reality learning environment (VRLE), and (b) healthcare data sharing environment (HDSE). Our first strategy involves a "rule-based 3QS-adaptation framework" that performs risk and cost aware trade-off analysis to control cybersickness due to performance/security anomaly events during a VRLE session. VRLEs provide immersive experience to users with increased accessibility to remote learning, thus a breach of security in critical VRLE application domains (e.g., healthcare, military training, manufacturing) can disrupt functionality and induce cybersickness. Our framework implementation in a real-world social VRLE viz., vSocial monitors performance/security anomaly events in network data. In the event of an anomaly, the framework features rule-based adaptations that are triggered by using various decision metrics. Based on our experimental results, we demonstrate the effectiveness of our rulebased 3QS-adaptation framework in reducing cybersickness levels, while maintaining application functionality. Our second strategy involves a "defense by pretense methodology" that uses real-time attack detection and creates cyber deception for HDSE applications. Healthcare data consumers (e.g., clinicians and researchers) require access to massive, protected datasets, thus loss of assurance/auditability of critical data such as Electronic Health Records (EHR) can severely impact loss of privacy of patient's data and the reputation of the healthcare organizations. Our cyber deception utilizes elastic capacity provisioning via use of rule-based adaptation to provision Quarantine Virtual Machines (QVMs) that handle redirected attacker's traffic and increase threat intelligence collection. We evaluate our defense by pretense design by creating an experimental Amazon Web Services (AWS) testbed hosting a real-world OHDSI setup for protected health data analytics/sharing with electronic health record data (SynPUF) and publications data (CORD-19) related to COVID-19. Our experiment results show how we can successfully detect targeted attacks such as e.g., DDoS and create redirection of attack sources to QVMs.Includes bibliographical references