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This study presented established methods, along with new algorithmic developments, to automate point cloud processing in support of the Field Information Modeling (FIM)™ framework. More specifically, given a multi-dimensional (n-D) designed information model, and the point cloud’s spatial uncertainty, the problem of automatic assignment of point clouds to their corresponding model elements was considered. The methods addressed two classes of field conditions, namely (i) negligible construction errors and (ii) the existence of construction errors. Emphasis was given to defining the assumptions, potentials, and limitations of each method in practical settings. Considering the shortcomings of current frameworks, three generic algorithms were designed to address the point-cloud-to-model assignment. The algorithms include new developments for (i) point cloud vs. model comparison (negligible construction errors), (ii) robust point neighborhood definition, and (iii) Monte-Carlo-based point-cloud-to-model surface hypothesis testing (existence of construction errors). The effectiveness of the new methods was demonstrated in real-world point clouds, acquired from construction projects, with promising results. For the overall problem of point-cloud-to-model assignment, the proposed point cloud vs. model and point-cloud-to-model hypothesis testing methods achieved F-measures of 99.3% and 98.4%, respectively, on real-world datasets.

Building Information Modeling (BIM), also called n-D Modeling or Virtual Prototyping Technology, is a revolutionary development that is quickly reshaping the Architecture-Engineering-Construction (AEC) industry. BIM is both a technology and a process. The technology component of BIM helps project stakeholders to visualize what is to be built in a simulated environment to identify any potential design, construction or operational issues. The process component enables close collaboration and encourages integration of the roles of all stakeholders on a project. The paper presents an overview of BIM with focus on its core concepts, applications in the project life cycle and benefits for project stakeholders with the help of case studies. The paper also elaborates risks and barriers to BIM implementation and future trends.

Rigid registration of 3D indoor scenes is a fundamental yet vital task in various fields that include remote sensing (e.g., 3D reconstruction of indoor scenes), photogrammetry measurement, geometry modeling, etc. Nevertheless, state-of-the-art registration approaches still have defects when dealing with low-quality indoor scene point clouds derived from consumer-grade RGB-D sensors. The major challenge is accurately extracting correspondences between a pair of low-quality point clouds when they contain considerable noise, outliers, or weak texture features. To solve the problem, we present a point cloud registration framework in view of RGB-D information. First, we propose a point normal filter for effectively removing noise and simultaneously maintaining sharp geometric features and smooth transition regions. Second, we design a correspondence extraction scheme based on a novel descriptor encoding textural and geometry information, which can robustly establish dense correspondences between a pair of low-quality point clouds. Finally, we propose a point-to-plane registration technology via a nonconvex regularizer, which can further diminish the influence of those false correspondences and produce an exact rigid transformation between a pair of point clouds. Compared to existing state-of-the-art techniques, intensive experimental results demonstrate that our registration framework is excellent visually and numerically, especially for dealing with low-quality indoor scenes.

Humans can interact with several kinds of machine (motor vehicle, robots, among others) in different ways. One way is through his/her head pose. In this work, we propose a head pose estimation framework that combines 2D and 3D cues using the concept of key frames (KFs). KFs are a set of frames learned automatically offline that consist the following: 2D features, encoded through Speeded Up Robust Feature (SURF) descriptors; 3D information, captured by Fast Point Feature Histogram (FPFH) descriptors; and target’s head orientation (pose) in real-world coordinates, which is represented through a 3D facial model. Then, the KF information is re-enforced through a global optimization process that minimizes error in a way similar to bundle adjustment. The KF allows to formulate, in an online process, a hypothesis of the head pose in new images that is then refined through an optimization process, performed by the iterative closest point (ICP) algorithm. This KF-based framework can handle partial occlusions and extreme rotations even with noisy depth data, improving the accuracy of pose estimation and detection rate. We evaluate the proposal using two public benchmarks in the state of the art: (1) BIWI Kinect Head Pose Database and (2) ICT 3D HeadPose Database. In addition, we evaluate this framework with a small but challenging dataset of our own authorship where the targets perform more complex behaviors than those in the aforementioned public datasets. We show how our approach outperforms relevant state-of-the-art proposals on all these datasets.

We contribute to the ongoing debate in the psychological literature on the role of thin slices of observable information in predicting others' social behavior, and its generalizability to cross-cultural interactions. We experimentally assess the degree to which subjects, drawn from culturally dierent populations (France and Japan), are able to predict strangers' trustworthiness based on a set of visual stimuli (mugshot pictures, neutral videos, loaded videos, all recorded in an additional French sample) under varying cultural distance to the target agent in the recording. Our main nding is that cultural distance is not detrimental for predicting trustworthiness in strangers, but that it may aect the perception of dierent components of communication in social interactions.

Salient object detection is used as a pre-process in many computer vision tasks (such as salient object segmentation, video salient object detection, etc.). When performing salient object detection, depth information can provide clues to the location of target objects, so effective fusion of RGB and depth feature information is important. In this paper, we propose a new feature information aggregation approach, weighted group integration (WGI), to effectively integrate RGB and depth feature information. We use a dual-branch structure to slice the input RGB image and depth map separately and then merge the results separately by concatenation. As grouped features may lose global information about the target object, we also make use of the idea of residual learning, taking the features captured by the original fusion method as supplementary information to ensure both accuracy and completeness of the fused information. Experiments on five datasets show that our model performs better than typical existing approaches for four evaluation metrics.

The network is the pervasive organizational image of the new millennium. This book examines one particular kind of network - the 'knowledge network' - whose primary mandate is to create and disseminate knowledge based on multidisciplinary research that is informed by problem-solving as well as theoretical agendas. In their examination of five knowledge networks based in Canadian universities, and in most cases working closely with researchers in developing countries, the authors demonstrate the ability of networks to cross disciplinary boundaries, to blend the operational with the theoretical, and to respond to broad social processes. Operating through networks, rather than through formal, hierarchical structures, diverse communities of researchers create different kinds of knowledge and disseminate their results effectively across disciplinary, sectoral, and spatial boundaries. Analysis of networks in health, environment, urban, and educational fields suggests that old categories of 'north' and 'south' are becoming blurred, and that the new structures of knowledge creation and dissemination help to sustain collaborative research.

The computed tomography (CT) chest is a tool for diagnostic tests and the early evaluation of lung infections, pulmonary interstitial damage, and complications caused by common pneumonia and COVID-19. Additionally, computer-aided diagnostic systems and methods based on entropy, fractality, and deep learning have been implemented to analyse lung CT images. This article aims to introduce an Entropy-based Measure of Complexity (EMC). In addition, derived from EMC, a Lung Damage Measure (LDM) is introduced to show a medical application. CT scans of 486 healthy subjects, 263 diagnosed with COVID-19, and 329 with pneumonia were analysed using the LDM. The statistical analysis shows a significant difference in LDM between healthy subjects and those suffering from COVID-19 and common pneumonia. The LDM of common pneumonia was the highest, followed by COVID-19 and healthy subjects. Furthermore, LDM increased as much as clinical classification and CO-RADS scores. Thus, LDM is a measure that could be used to determine or confirm the scored severity. On the other hand, the d-summable information model best fits the information obtained by the covering of the CT; thus, it can be the cornerstone for formulating a fractional LDM.

This study built its premise upon the notion that digital technologies have created new bridges for communication between tourists and marketers. The study aims to determine the influence of domestic tourists’ perceptions on the intention to use digital marketing tools and platforms. Based on the Technology Acceptance Model and the updated DeLone and McLean Information Systems, regression analyses were used to test the hypotheses based on 401 surveys conducted with tourists using self-administered questionnaires. Surveys were selected following a non-probability, convenience procedure and stratified proportional sampling technique. The study findings highlight that perceived usefulness, information quality, system quality, service quality and user satisfaction are significantly related to the intention to use digital marketing tools and platforms. The success of digital marketing strategies employed by tourism marketers depends on tourists’ use and adoption of tourism digital marketing tools and platforms. The study findings have policy and practical implications for policymakers, managers and marketers in developing effective and efficient digital marketing strategies that meet tourists’ needs and expectations.

Aim/Purpose: This research investigates the success variables affecting the adoption of social networking sites (SNS), namely Facebook, and learning management systems (LMS), specifically Moodle, in developing countries. Background: In contemporary education, universities invest heavily in the integration of LMS with traditional classrooms. Conversely, such technologies face a high rate of failure and not all learners are satisfied with LMS services. In turn, this leads to the exploitation of SNS interactive features and services, which are subsequently included in the process of teaching and learning. However, the success of both SNS and LMS has rarely been studied in the context of developing nations. Methodology: In this study, a cross-sectional survey was used to collect the research data. It targeted a population sampled from amongst state-sector university undergraduates in Iraq (N=143). The study was based on an extension of DeLone and McLean’s Information Systems Success (D&M ISS) model to include four antecedent variables: system quality, information quality, technology experience, and Internet experience as direct determinants of technology use and user satisfaction, both of which affect the net benefits of Facebook and Moodle. The collected data were analyzed with SmartPLS, using a partial least squares-structural equation model (PLS-SEM). Contribution: This research extends previous literature on the critical success factors (CSF) of SNS and LMS in the case of developing countries. The study guides the way in which the acceptance of SNS and LMS in higher education can be organized in the developing world in general, especially in the Middle East, thereby bridging this research gap and extending previous literature. Findings: The research results support the influence of quality and experience antecedents on technology use and learner satisfaction. The extended model also provides full support for the association between technology use and learner satisfaction, concerning the net benefits of Facebook and Moodle. The proposed model achieved a good fit and explained 61.4% and 68.1% of the variance of LMS and SNS success, respectively. Recommendations for Practitioners: The significant influence of the constructs investigated in this research could shape strategies and approaches to be adopted for the enhancement of SNS and LMS implementation in educational institutions. More specifically, this study is aimed at guiding SNS and LMS acceptance in developing countries, especially in Middle Eastern higher education. Recommendation for Researchers: This work offers a theoretical understanding of the body of knowledge on SNS and LMS application in institutes of higher education. It further supports the usefulness of the D&M ISS model for predicting the success of social networks and e-learning systems. Future Research: As with most empirical literature, this research makes a number of recommendations for further work. Future research could investigate other constructs that potentially influence technology success in education such as facilitating conditions, perceived privacy, and security. Moreover, researchers from different contexts are invited to apply this extended model and conduct a mixed methods (quantitative and qualitative) analysis to deepen the current understanding of the effect of SNS on teaching and learning, while also comparing it with the impact of LMS in this digital era.