Machine learning for semi-automated scoping reviews

Scoping reviews are a type of research synthesis that aim to map the literature on a particular topic or research area. Though originally intended to provide a quick overview of a field of research, scoping review teams have been overwhelmed in recent years by a deluge of available research literature. This work presents the interdisciplinary development of a semi-automated scoping review methodology aimed at increasing the objectivity and speed of discovery in scoping reviews as well as the scalability of the scoping review process to datasets with tens of thousands of publications. To this end we leverage modern representation learning algorithms based on transformer models and established clustering methods to discover evidence maps, key themes within the data, knowledge gaps within the literature, and assess the feasibility of follow-on systematic reviews within a certain topic. To demonstrate the wide applicability of this methodology, we apply the here proposed semi-automated method to two separate datasets, a Virtual Human dataset with more than 30,000 peer-reviewed academic articles and a smaller Self-Avatar dataset with less than 500 peer-reviewed articles. To enable collaboration, we provide full access to analyzed datasets, keyword and author word clouds, as well as interactive evidence maps.</p

Virtual reality surgery simulation: A survey on patient specific solution

For surgeons, the precise anatomy structure and its dynamics are important in the surgery interaction, which is critical for generating the immersive experience in VR based surgical training applications. Presently, a normal therapeutic scheme might not be able to be straightforwardly applied to a specific patient, because the diagnostic results are based on averages, which result in a rough solution. Patient Specific Modeling (PSM), using patient-specific medical image data (e.g. CT, MRI, or Ultrasound), could deliver a computational anatomical model. It provides the potential for surgeons to practice the operation procedures for a particular patient, which will improve the accuracy of diagnosis and treatment, thus enhance the prophetic ability of VR simulation framework and raise the patient care. This paper presents a general review based on existing literature of patient specific surgical simulation on data acquisition, medical image segmentation, computational mesh generation, and soft tissue real time simulation

A Signal processing approach for preprocessing and 3d analysis of airborne small-footprint full waveform lidar data

The extraction of structural object metrics from a next generation remote sensing modality, namely waveform light detection and ranging (LiDAR), has garnered increasing interest from the remote sensing research community. However, a number of challenges need to be addressed before structural or 3D vegetation modeling can be accomplished. These include proper processing of complex, often off-nadir waveform signals, extraction of relevant waveform parameters that relate to vegetation structure, and from a quantitative modeling perspective, 3D rendering of a vegetation object from LiDAR waveforms. Three corresponding, broad research objectives therefore were addressed in this dissertation. Firstly, the raw incoming LiDAR waveform typically exhibits a stretched, misaligned, and relatively distorted character. A robust signal preprocessing chain for LiDAR waveform calibration, which includes noise reduction, deconvolution, waveform registration, and angular rectification is presented. This preprocessing chain was validated using both simulated waveform data of high fidelity 3D vegetation models, which were derived via the Digital Imaging and Remote Sensing Image Generation (DIRSIG) modeling environment and real small-footprint waveform LiDAR data, collected by the Carnegie Airborne Observatory (CAO) in a savanna region of South Africa. Results showed that the preprocessing approach significantly increased our ability to recover the temporal signal resolution, and resulted in improved waveform-based vegetation biomass estimation. Secondly, a model for savanna vegetation biomass was derived using the resultant processed waveform data and by decoding the waveform in terms of feature metrics for woody and herbaceous biomass estimation. The results confirmed that small-footprint waveform LiDAR data have significant potential in the case of this application. Finally, a 3D image clustering-based waveform LiDAR inversion model was developed for 1st order (principal branch level) 3D tree reconstruction in both leaf-off and leaf-on conditions. These outputs not only contribute to the visualization of complex tree structures, but also benefit efforts related to the quantification of vegetation structure for natural resource applications from waveform LiDAR data

Swarming Reconnaissance Using Unmanned Aerial Vehicles in a Parallel Discrete Event Simulation

Current military affairs indicate that future military warfare requires safer, more accurate, and more fault-tolerant weapons systems. Unmanned Aerial Vehicles (UAV) are one answer to this military requirement. Technology in the UAV arena is moving toward smaller and more capable systems and is becoming available at a fraction of the cost. Exploiting the advances in these miniaturized flying vehicles is the aim of this research. How are the UAVs employed for the future military? The concept of operations for a micro-UAV system is adopted from nature from the appearance of flocking birds, movement of a school of fish, and swarming bees among others. All of these natural phenomena have a common thread: a global action resulting from many small individual actions. This emergent behavior is the aggregate result of many simple interactions occurring within the flock, school, or swarm. In a similar manner, a more robust weapon system uses emergent behavior resulting in no weakest link because the system itself is made up of simple interactions by hundreds or thousands of homogeneous UAVs. The global system in this research is referred to as a swarm. Losing one or a few individual unmanned vehicles would not dramatically impact the swarms ability to complete the mission or cause harm to any human operator. Swarming reconnaissance is the emergent behavior of swarms to perform a reconnaissance operation. An in-depth look at the design of a reconnaissance swarming mission is studied. A taxonomy of passive reconnaissance applications is developed to address feasibility. Evaluation of algorithms for swarm movement, communication, sensor input/analysis, targeting, and network topology result in priorities of each model\u27s desired features. After a thorough selection process of available implementations, a subset of those models are integrated and built upon resulting in a simulation that explores the innovations of swarming UAVs

Multimodality for Passive Experience: Effects of Visual, Auditory, Vibration and Draught Stimuli on Sense of Presence

Adequate use of multimodal stimuli plays a crucial role in help forming the sense of presence within a virtual environment. While most of the presence research attempts to engage more sensory modalities to induce a higher sense of presence, this paper investigates the relevance of each sensory modality and different combinations on the subjective sense of presence using a specifically designed scenario of a passive experience. We chose a neutral test scenario of “waiting at a train station while a train is passing by” to avoid the potential influence of story narrative on mental presence and replicated realistic multimodal stimuli that are highly relevant to our test setting. All four stimuli -visual, auditory, vibration, and draught -with 16 possibilities of combinations were systematically evaluated with 24 participants. The evaluation was performed on one crucial aspect of presence –“realness” to reflect user presence in general. The perceived realism value was assessed using a scalometer. The findings of main effects indicate that the auditory stimuli had the most significant contribution in creating the sense of presence. The results of interaction effects suggest the impact of draught stimuli is significant in relation to other stimuli -visual and auditory. Also, the gender effects revealed that the sense of presence reported by female participants is influenced by more factors than merely adding more sensory modalities

Investigating the Transfer of Learning, Psychological, and Neural Effects in Immersive Virtual Reality

Achieving mastery or expertise requires a substantial amount of quality practice. Recent technological developments have introduced a novel approach to practice, virtual reality. Specifically, virtual reality offers a low-cost, customizable opportunity to practice while minimizing the risk of the individual. Given that some types of practice may not lead to the acquisition of a motor skill, or worse, lead to detriments of that skill, understanding the developing science of motor behavior in relation to virtual reality is imperative. The following literature review will begin with a brief historical account of the evolution of virtual reality. Next, some terms of virtual reality will be defined, and the technological characteristics will be introduced. Then, fundamental theories of transfer of learning and important variables which likely contribute to transfer of learning will be discussed. In the following section, the current understanding of virtual reality and motor learning will be explained. Research that has examined transfer of learning within immersive virtual reality will then be examined and discussions of the findings and limitations will be presented. Finally, to address the aforementioned shortcomings, the following project was a two-experimental study to investigate the transfer of learning effects of virtual reality motor skill practice