Bird's Eye View Research Articles

Capturing self‐regulated learning processes in virtual reality: Causal sequencing of multimodal data

AbstractThis study examines the embodied ways in which learners monitor their cognition while learning about exponential functions in an immersive virtual reality (VR) based game, Pandemic by Prisms of Reality. Traditionally, metacognitive monitoring has been assessed through behavioural traces and verbalised instances. When learning in VR, learners are fully immersed in the learning environment, actively manipulating it based on affordances designed to support learning, offering insights into the relationship between physical interaction and metacognition. The study collected multimodal data from 15 participants, including think‐aloud audio, bird's‐eye view video recordings and physiological data. Metacognitive monitoring was analysed through qualitative coding of the think‐aloud protocol, while movement was measured via optical flow analysis and cognitive load was assessed through heart rate variability analysis. The results revealed embodied metacognition by aligning the data to identify learners' physical states alongside their verbalised metacognition. The findings demonstrated a temporal interplay among cognitive load, metacognitive monitoring, and motion during VR‐based learning. Specifically, cognitive load, indicated by the low‐ and high‐frequency heart rate variability index, predicted instances of metacognitive monitoring, and monitoring predicted learners' motion while interacting with the VR environment. This study further provides future directions in understanding self‐regulated learning processes during VR learning by utilizing multimodal data to inform real‐time adaptive personalised support within these environments. Practitioner notesWhat is already known about this topic Immersive virtual reality (VR) environments have the potential to offer personalised support based on users' individual needs and characteristics. Self‐regulated learning (SRL) involves learners monitoring their progress and strategically regulating their learning when needed. Multimodal data captured during VR learning, such as birds‐eye‐view video, screen recordings, physiological changes and verbalisations, can provide insights into learners' SRL processes and support needs. What this paper adds Provides insights into the embodied aspects of learners' metacognitive monitoring during learning in an immersive VR environment. Demonstrates how SRL processes can be captured via the collection and analysis of multimodal data, including think‐aloud audio, bird's‐eye view video recordings and physiological data, to capture metacognitive monitoring and movement during VR‐based learning. Contributes to the understanding of the interplay between cognitive load, metacognitive monitoring, and motion in immersive VR learning. Implications for practice and/or policy Researchers and practitioners can use the causal relationships identified in this study to identify instances of SRL in an immersive VR setting. Educational technology developers can consider the integration of online measures, such as cognitive load and physiological arousal, into adaptive VR environments to enable real‐time personalised support for learners based on their self‐regulatory needs.

Non-Hermitian Floquet Topological Matter-A Review.

The past few years have witnessed a surge of interest in non-Hermitian Floquet topological matter due to its exotic properties resulting from the interplay between driving fields and non-Hermiticity. The present review sums up our studies on non-Hermitian Floquet topological matter in one and two spatial dimensions. We first give a bird's-eye view of the literature for clarifying the physical significance of non-Hermitian Floquet systems. We then introduce, in a pedagogical manner, a number of useful tools tailored for the study of non-Hermitian Floquet systems and their topological properties. With the aid of these tools, we present typical examples of non-Hermitian Floquet topological insulators, superconductors, and quasicrystals, with a focus on their topological invariants, bulk-edge correspondences, non-Hermitian skin effects, dynamical properties, and localization transitions. We conclude this review by summarizing our main findings and presenting our vision of future directions.

The Effects of Latency and In-Game Perspective on Player Performance and Game Experience

Previous work shows that high latency, a prolonged delay between player in- and system output, negatively affects player experience and performance. However, previous work also comes to contrary conclusions about how the in-game perspective alters the latency sensitivity of video games. Currently, it is unclear if the in-game perspective independently modulates latency's effects. To investigate how a game's in-game perspective interacts with latency, we developed a shooting game incorporating three perspectives (First-Person-, Third-Person-, and Bird's-Eye-View). In a study, participants (N = 36) played with two levels of latency (low and high) and the three perspectives. We show that latency reduces performance and experience, independent of the perspective. Moreover, Bayesian analysis suggests that the in-game perspective does not interact with latency and does not affects performance or experience. We conclude that more robust means to categorize latency sensitivity of video games than the in-game perspective are required.

Modeling the Visual Landscape: A Review on Approaches, Methods and Techniques.

Modeling the perception and evaluation of landscapes from the human perspective is a desirable goal for several scientific domains and applications. Human vision is the dominant sense, and human eyes are the sensors for apperceiving the environmental stimuli of our surroundings. Therefore, exploring the experimental recording and measurement of the visual landscape can reveal crucial aspects about human visual perception responses while viewing the natural or man-made landscapes. Landscape evaluation (or assessment) is another dimension that refers mainly to preferences of the visual landscape, involving human cognition as well, in ways that are often unpredictable. Yet, landscape can be approached by both egocentric (i.e., human view) and exocentric (i.e., bird's eye view) perspectives. The overarching approach of this review article lies in systematically presenting the different ways for modeling and quantifying the two 'modalities' of human perception and evaluation, under the two geometric perspectives, suggesting integrative approaches on these two 'diverging' dualities. To this end, several pertinent traditions/approaches, sensor-based experimental methods and techniques (e.g., eye tracking, fMRI, and EEG), and metrics are adduced and described. Essentially, this review article acts as a 'guide-map' for the delineation of the different activities related to landscape experience and/or management and to the valid or potentially suitable types of stimuli, sensors techniques, and metrics for each activity. Throughout our work, two main research directions are identified: (1) one that attempts to transfer the visual landscape experience/management from the one perspective to the other (and vice versa); (2) another one that aims to anticipate the visual perception of different landscapes and establish connections between perceptual processes and landscape preferences. As it appears, the research in the field is rapidly growing. In our opinion, it can be greatly advanced and enriched using integrative, interdisciplinary approaches in order to better understand the concepts and the mechanisms by which the visual landscape, as a complex set of stimuli, influences visual perception, potentially leading to more elaborate outcomes such as the anticipation of landscape preferences. As an effect, such approaches can support a rigorous, evidence-based, and socially just framework towards landscape management, protection, and decision making, based on a wide spectrum of well-suited and advanced sensor-based technologies.

Understanding Design Collaboration Between Designers and Artificial Intelligence: A Systematic Literature Review

Recent interest in design through the artificial intelligence (AI) lens is rapidly increasing. Designers, as a special user group interacting with AI, have received more attention in the Human-Computer Interaction community. Prior work has discussed emerging challenges that persist in designing for AI. However, few systematic reviews focus on AI for design to understand how designers and AI can augment each other's complementary strengths in design collaboration. In this work, we conducted a landscape analysis of AI for design, via a systematic literature review of 93 papers. The analysis first provides a bird's eye view of overall patterns in this area. The analysis also reveals three themes interpreted from the paper corpus associated with AI for design, including AI assisting designers, designers assisting AI, and characterizing designer-AI collaboration. We discuss the implications of our findings and suggested methodological proposals to guide HCI toward research and practices that center on collaborative creativity.

Semantic interpretation of raw survey vehicle sensory data for lane-level HD map generation

High-definition (HD) maps provide a complementary source of information for Advanced Driver Assistance Systems (ADAS), allowing them to better understand the vehicle’s surroundings and make more informed decisions. HD maps are also largely employed in virtual testing phases to evaluate the behavior of ADAS components under simulated conditions. With the advent of autonomous sensorized vehicles, raw machine-oriented data will be increasingly available. The proposed pipeline aims to provide a high-level semantic interpretation of raw vehicle sensory data to derive, in an automated fashion, lane-oriented HD maps of the environment. We first present RoadStarNet, a deep learning architecture designed to extract and classify road line markings from imagery data. We show how to obtain a semantic Bird’s-Eye View (BEV) mapping of the extracted road line markings by exploiting frame-by-frame localization information. Then, we present how to progress to a graph-based representation that allows modeling complex road line markings’ structures practically, as this representation can be leveraged to produce a Lanelet2 format HD map. Lastly, we experimentally evaluate the proposed approach in real-world scenarios in terms of accuracy and coverage performance.

A bird's eye view of civic engagement and its facets: Canonical correlation analysis across 34 countries

ABSTRACT Civic engagement is a multi-faceted concept that is integral to the proper functioning of a democratic society. The present study investigates the effect that a wide range of individual-level demographic, educational, cognitive, and health-related predictors have on civic engagement and its facets, providing an overview of 34 countries around the world. The use of canonical correlation analysis allows us to quantify both the shared and unique contribution of the predictors to two facets of civic engagement, voluntary work and political efficacy. The common scale of the analysis provides an intuitive visualization of barriers and pathways to one's engagement in the community and society at large. We further ground our findings in country-level socio-demographic data, providing external validation to our method.

A Photo Score For Aesthetic Outcome In Sagittal Synostosis: An ERN CRANIO Collaboration.

European Reference Network (ERN) CRANIO is focused on optimizing care for patients with rare or complex craniofacial anomalies, including craniosynostosis and/or rare ear, nose, and throat disorders. The main goal of ERN CRANIO is to collect uniform data on treatment outcomes for multicenter comparison. We aimed to develop a reproducible and reliable suture-specific photo score that can be used for cross-center comparison of phenotypical severity of sagittal synostosis and aesthetic outcome of treatment. We conducted a retrospective study among nonsyndromic sagittal synostosis patients aged <19 years. We included preoperative and postoperative photo sets from 6 ERN CRANIO centers. Photo sets included bird's eye, lateral, and anterior-posterior views. The sagittal synostosis photo score was discussed in the working group, and consensus was obtained on its contents. Interrater agreement was assessed with weighted Fleiss' Kappa and intraclass correlation coefficients.The photo score consisted of frontal bossing, elongated skull, biparietal narrowness, temporal hollowing, vertex line depression, occipital bullet, and overall phenotype. Each item was scored as normal, mild, moderate, or severe. Results from 36 scaphocephaly patients scored by 20 raters showed kappa values ranging from 0.38 [95% bootstrap CI: 0.31, 0.45] for biparietal narrowness to 0.56 [95% bootstrap CI: 0.47, 0.64] for frontal bossing. Agreement was highest for the sum score of individual items [intraclass correlation coefficients agreement 0.69 [95% CI: 0.57, 0.82]. This is the first large-scale multicenter study in which experts investigated a photo score to assess the severity of sagittal synostosis phenotypical characteristics. Agreement on phenotypical characteristics was suboptimal (fair-moderate agreement) and highest for the summed score of individual photo score items (substantial agreement), indicating that although experts interpret phenotypical characteristics differently, there is consensus on overall phenotypical severity.

Generating evidential BEV maps in continuous driving space

Safety is critical for autonomous driving, and one aspect of improving safety is to accurately capture the uncertainties of the perception system, especially knowing the unknown. Different from only providing deterministic or probabilistic results, e.g., probabilistic object detection, that only provide partial information for the perception scenario, we propose a complete probabilistic model named GevBEV. It interprets the 2D driving space as a probabilistic Bird’s Eye View (BEV) map with point-based spatial Gaussian distributions, from which one can draw evidence as the parameters for the categorical Dirichlet distribution of any new sample point in the continuous driving space. The experimental results show that GevBEV not only provides more reliable uncertainty quantification but also outperforms the previous works on the benchmarks OPV2V and V2V4Real of BEV map interpretation for cooperative perception in simulated and real-world driving scenarios, respectively. A critical factor in cooperative perception is the data transmission size through the communication channels. GevBEV helps reduce communication overhead by selecting only the most important information to share from the learned uncertainty, reducing the average information communicated by 87% with only a slight performance drop. Our code is published at https://github.com/YuanYunshuang/GevBEV.

Bird’s-Eye View Semantic Segmentation for Autonomous Driving through the Large Kernel Attention Encoder and Bilinear-Attention Transform Module

Building an autonomous driving system requires a detailed and unified semantic representation from multiple cameras. The bird’s eye view (BEV) has demonstrated remarkable potential as a comprehensive and unified perspective. However, most current research focuses on innovating the view transform module, ignoring whether the crucial image encoder can construct long-range feature relationships. Hence, we redesign an image encoder with a large kernel attention mechanism to encode image features. Considering the performance gains obtained by the complex view transform module are insignificant, we propose a simple and effective Bilinear-Attention Transform module to lift the dimension completely. Finally, we redesign a BEV encoder with a CNN block of a larger kernel size to reduce the distortion of BEV features away from the ego vehicle. The results on the nuScenes dataset confirm that our model outperforms other models with equivalent training settings on the segmentation task and approaches state-of-the-art performance.

Cooperative Perception With V2V Communication for Autonomous Vehicles

Occlusion is a critical problem in the Autonomous Driving System. Solving this problem requires robust collaboration among autonomous vehicles traveling on the same roads. However, transferring the entirety of raw sensors' data among autonomous vehicles is expensive and can cause a delay in communication. This paper proposes a method called Realtime Collaborative Vehicular Communication based on Bird's-Eye-View (BEV) map. The BEV map holds the accurate depth information from the point cloud image while its 2D representation enables the method to use a novel and well-trained image-based backbone network. Most importantly, we encode the object detection results into the BEV representation to reduce the volume of data transmission and make real-time collaboration between autonomous vehicles possible. The output of this process, the BEV map, can also be used as direct input to most route planning modules. Numerical results show that this novel method can increase the accuracy of object detection by cross-verifying the results from multiple points of view. Thus, in the process, this new method also reduces the object detection challenges that stem from occlusion and partial occlusion. Additionally, different from many existing methods, this new method significantly reduces the data needed for transfer between vehicles, achieving a speed of 21.92 Hz for both the object detection process and the data transmission process, which is sufficiently fast for a real-time system.

The Software Design Overview by Processing The Recording From Bird's-Eye View Images to Determine The Crop Detection and Functionality of The Various Land Types

To meet the demand for food, humanity has begun to use the latest technologies such as artificial intelligence, the internet of things, and drones, in addition to advanced tractors, seeds, and crop planting methods. Precision agriculture has been achieved by using the latest technology in this field, especially in recent years, the use of drones for agricultural land spraying has gained great interest. In this study, a Python programming language was used to process the video footage of a certain height taken from agricultural land with the help of a drone, using individual photo frames obtained from the footage. Each pixel was separated into different color contrast values, and certain colors were distinguished by counting. The proportional distribution of different types of surfaces on the land was determined. The software enabled the determination of various geophysical properties such as the productivity of crops, crop development status, and identification of areas where the crop does not grow.

Constitutional Government in the Time of Covid: The Scottish Experience

The author provides a bird's eye view from his time as an MSP of the Scottish Government's legislative and executive response to the Covid-19 pandemic. He concentrates on the efforts of Scottish Parliament and the Scottish courts to hold them to account, and ultimately finds them lacking.

RS-Aug: Improve 3D Object Detection on LiDAR With Realistic Simulator Based Data Augmentation

Light detection and ranging (LiDAR) is an essential sensor for three dimensional (3D) object detection via generating 3D point cloud of the surroundings, and it has been widely used in the various visual applications, especially autonomous driving. However, limited numbers of labeled LiDAR datasets brutally restrain the development of 3D object detector, and this situation breeds an urgent demand on data augmentation in this field. By far, most of the traditional methods reuse the labeled samples, while those unlabeled are hastily untaken. Motivated by this, we propose a Realistic Simulator based data augmentation (RS-Aug). It aims to construct augmented real scenes to enrich the diversity of training dataset. To train 3D object detector in a supervised learning way, the first step of RS-Aug is auto-annotation. Time-continuous LiDAR frames are used to construct the dense scene, which is beneficial to annotation and the subsequent rendering augmentation. However, 3D points with incorrect semantic labels are naturally gathered during multi-view reconstruction, causing the negative effect on auto-annotation. We propose an algorithm of cluster guided <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> -nearest neighbor (c- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> NN). It emphasizes on de-nosing semantic labels of clustered points using distance and intensity constraints. Then, the next step of RS-Aug is rendering augmentation on the real scene. To enhance the rendering quality using collision and distance constraints with the less computation complexity, we propose a scheme of heuristic search (HS) based object insertion. It estimates the proper position of the inserted object from 2D bird’s eye view (BEV). Experiments demonstrate the de-noising accuracy of c- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> NN, rendering quality of HS based object insertion, and improvement of RS-Aug on object detection.

Bird's Eye View Semantic Segmentation based on Improved Transformer for Automatic Annotation

Bird's Eye View Semantic Segmentation based on Improved Transformer for Automatic Annotation

Bird's eye view of augmented reality and applications for education and training: A survey of surveys and reviews

AbstractAugmented reality (AR) is a field of knowledge being developed since the middle of the last century. Its use has been spreading because of its usefulness, but more recently because of mobile platforms being widespread and accessible. AR has been applied in several fields of activity, and also in the field of Education and Training, because AR has several advantages over other teaching methods. In this paper, we search and analyze surveys and reviews of AR to present a brief history and its definition. We also present a classification of our sample under a scheme we developed in past work, and present also examples of technologies and applications of AR in each field. Finally, we do a deeper analysis over the publications of Education and Training, advantages and issues of AR in this field, and some research trends.

Global Christian higher education from 1950 to 2020: An updated analysis

A recent global reconnaissance of Christian higher education found a number of key themes that shaped current developments, such as the pressing challenges of secularization and nationalization but also the advantages of privatization and massification. This article provides an update to this older analysis by taking a birds-eye view of trends within the past 70 years of global Christian higher education. It reveals that privatization and massification trends do not predict new developments in countries and outliers always exist. Furthermore, it highlights some findings missed in previous analyses related to the importance of indigenous Christian groups and Christian general education.

AN ANALYSIS OF PUBLIC INVESTMENT ON HIGHER EDUCATION IN INDIA

Higher education is the integral part of social infrastructure which determines the destiny of any nation. Public policy on education is one of the most significant areas of contemporary development strategy and in a developing country like India, investment on education assumes most important place in such a policy. The higher education sector in India has witnessed a paradigm shift in recent years, in order to shape the future social and economic progress it is necessary to expand, restructure and raising the quality of education in the country. Development of human resources is one of the main purposes of Higher Education, which will play a critical role in preparing the workforce to be constructively utilized. Therefore, India needs to show more concern over higher education in terms of quantity and quality. In this background, the paper presents a birds eye view of the public expenditure by the centre and the state on higher education along with sector wise allocation in the country. An attempt has been made to analyze the growth and trends in public investment with the help of statistical tools like correlation and regression analysis. KEY WORDS: Higher Education, Public Investment, Sector-wise Allocation, Gross Domestic Product.

Bird’s Eye View feature selection for high-dimensional data

In machine learning, an informative dataset is crucial for accurate predictions. However, high dimensional data often contains irrelevant features, outliers, and noise, which can negatively impact model performance and consume computational resources. To tackle this challenge, the Bird’s Eye View (BEV) feature selection technique is introduced. This approach is inspired by the natural world, where a bird searches for important features in a sparse dataset, similar to how a bird search for sustenance in a sprawling jungle. BEV incorporates elements of Evolutionary Algorithms with a Genetic Algorithm to maintain a population of top-performing agents, Dynamic Markov Chain to steer the movement of agents in the search space, and Reinforcement Learning to reward and penalize agents based on their progress. The proposed strategy in this paper leads to improved classification performance and a reduced number of features compared to conventional methods, as demonstrated by outperforming state-of-the-art feature selection techniques across multiple benchmark datasets.

SBcoyote: An extensible Python-based reaction editor and viewer

SBcoyote is an open-source cross-platform biochemical reaction viewer and editor released under the liberal MIT license. It is written in Python and uses wxPython to implement the GUI and the drawing canvas. It supports the visualization and editing of compartments, species, and reactions. It includes many options to stylize each of these components. For instance, species can be in different colors and shapes. Other core features include the ability to create alias nodes, alignment of groups of nodes, network zooming, as well as an interactive bird-eye view of the network to allow easy navigation on large networks. A unique feature of the tool is the extensive Python plugin API, where third-party developers can include new functionality. To assist third-party plugin developers, we provide a variety of sample plugins, including, random network generation, a simple auto layout tool, export to Antimony, export SBML, import SBML, etc. Of particular interest are the export and import SBML plugins since these support the SBML level 3 layout and render standard, which is exchangeable with other software packages. Plugins are stored in a GitHub repository, and an included plugin manager can retrieve and install new plugins from the repository on demand. Plugins have version metadata associated with them to make it install plugin updates. Availability: https://github.com/sys-bio/SBcoyote.