CyberGuarder: a virtualization security assurance architecture for green cloud computing

Cloud Computing, Green Computing, Virtualization, Virtual Security Appliance, Security Isolation

Substitutional reality:using the physical environment to design virtual reality experiences

Experiencing Virtual Reality in domestic and other uncontrolled settings is challenging due to the presence of physical objects and furniture that are not usually defined in the Virtual Environment. To address this challenge, we explore the concept of Substitutional Reality in the context of Virtual Reality: a class of Virtual Environments where every physical object surrounding a user is paired, with some degree of discrepancy, to a virtual counterpart. We present a model of potential substitutions and validate it in two user studies. In the first study we investigated factors that affect participants' suspension of disbelief and ease of use. We systematically altered the virtual representation of a physical object and recorded responses from 20 participants. The second study investigated users' levels of engagement as the physical proxy for a virtual object varied. From the results, we derive a set of guidelines for the design of future Substitutional Reality experiences

MetaSpace II: Object and full-body tracking for interaction and navigation in social VR

MetaSpace II (MS2) is a social Virtual Reality (VR) system where multiple users can not only see and hear but also interact with each other, grasp and manipulate objects, walk around in space, and get tactile feedback. MS2 allows walking in physical space by tracking each user's skeleton in real-time and allows users to feel by employing passive haptics i.e., when users touch or manipulate an object in the virtual world, they simultaneously also touch or manipulate a corresponding object in the physical world. To enable these elements in VR, MS2 creates a correspondence in spatial layout and object placement by building the virtual world on top of a 3D scan of the real world. Through the association between the real and virtual world, users are able to walk freely while wearing a head-mounted device, avoid obstacles like walls and furniture, and interact with people and objects. Most current virtual reality (VR) environments are designed for a single user experience where interactions with virtual objects are mediated by hand-held input devices or hand gestures. Additionally, users are only shown a representation of their hands in VR floating in front of the camera as seen from a first person perspective. We believe, representing each user as a full-body avatar that is controlled by natural movements of the person in the real world (see Figure 1d), can greatly enhance believability and a user's sense immersion in VR.Comment: 10 pages, 9 figures. Video: http://living.media.mit.edu/projects/metaspace-ii

Automated Bridge Inspection for Concrete Surface Defect Detection Using Deep Neural Network Based on LiDAR Scanning

Structural inspection and maintenance of bridges are essential to improve the safety and sustainability of the infrastructure systems. Visual inspection using non-equipped eyes is the principal method of detecting surface defects of bridges, which is time-consuming, unsafe, and encounters inspectors falling risks. Therefore, there is a need for automated bridge inspection. Recently, Light Detection and Ranging (LiDAR) scanners are used for detecting surface defects. LiDAR scanners can collect high-quality 3D point cloud datasets. In order to automate the process of structural inspection, it is important to collect proper datasets and use an efficient approach to analyze them and find the defects. Deep Neural Networks (DNNs) have been recently used for detecting 3D objects within 3D point clouds. PointNet and PointNet++ are deep neural networks for classification, part segmentation, and semantic segmentation of point clouds that are modified and adapted in this work to detect surface concrete defects. The research contributions are: (1) Designing a LiDAR-equipped UAV platform for structural inspection using an affordable 2D scanner for data collection, and (2) Proposing a method for detecting concrete surface defects using deep neural networks based on LiDAR generated point clouds. Training and testing datasets are collected from four concrete bridges in Montréal and annotated manually. The point cloud dataset prepared in five areas, which contain more than 51 million points and 2,572 annotated defects in four classes of crack, light spalling, medium spalling, and severe spalling. The accuracies of 75% (adapted PointNet) and 79% (adapted PointNet++) in detecting defects are achieved in binary semantic segmentation

Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.Comment: 29 pages, 5 figure

Railway bridge geometry assessment supported by cutting-edge reality capture technologies and 3D as-designed models

Documentation of structural visual inspections is necessary for its monitoring, maintenance, and decision about its rehabilitation, and structural strengthening. In recent times, close-range photogrammetry (CRP) based on unmanned aerial vehicles (UAVs) and terrestrial laser scanners (TLS) have greatly improved the survey phase. These technologies can be used independently or in combination to provide a 3D as-is image-based model of the railway bridge. In this study, TLS captured the side and bottom sections of the deck, while the CRP-based UAV captured the side and top sections of the deck, and the track. The combination of post-processing techniques enabled the merging of TLS and CRP models, resulting in the creation of an accurate 3D representation of the complete railway bridge deck. Additionally, a 3D as-designed model was developed based on the design plans of the bridge. The as-designed model is compared to the as-is model through a 3D digital registration. The comparison allows the detection of dimensional deviation and surface alignments. The results reveal slight deviations in the structural dimension with a global average value of 9 mm.The authors would like to thank the financial support from: Base Funding—UIDB/04708/ 2020 and Programmatic Funding—UIDP/04708/2020 of the CONSTRUCT—“Instituto de I&D em Estruturas e Construções, as well as ISISE (UIDB/04029/2020) and ARISE (LA/P/0112/2020)”—funded by national funds through the FCT/MCTES (PIDDAC). Additionally, the support by the doctoral grant UI/BD/150970/2021 (to Rafael Cabral)—Portuguese Science Foundation, FCT/MCTES. Furthermore, this work is framed within the project “Intelligent structural condition assessment of existing steel railway bridges” financed by the bilateral agreement FCT-NAWA (2022-23), as well as project “FERROVIA 4.0”, with reference to POCI-01-0247-FEDER-046111, co-funded by the European Regional Development Fund (ERDF), through the Operational Program for Competitiveness and Internationalization (COMPETE 2020) and the Lisbon Regional Operational Program (LISBOA 2020), under the PORTUGAL 2020 Partnership Agreement, as well as “NEXUS: Innovation Pact Digital and Green Transition—Transports, Logistics and Mobility”, nr. C645112083-00000059, investment project nr. 53, financed by the Recovery and Resilience Plan (PRR) and by European Union—NextGeneration EU