Search Results (17)

The increasing use of instructional videos in educational settings has emphasized the need for a deeper understanding of their design requirements. This study investigates the impact of virtual backgrounds in educational videos on students' visual information processing and learning outcomes. Participants aged 14–17 (N = 47) were randomly assigned to one of three conditions: a video with a neutral, authentic, or off-topic background. Their prior knowledge and working memory capacity (WMC) were measured before watching the video, and eye tracking data was collected during the viewing. Learning outcomes and student experiences were assessed after viewing. The eye tracking data revealed that a neutral background was the least distracting, allowing students to pay better attention to relevant parts of the video. Students found the off-topic background most distracting, but the negative effect on learning outcomes was not statistically significant. In contrast to expectations, no positive effect was observed for the authentic background. Furthermore, WMC had a significant impact on visual information processing and learning outcomes. These findings suggest that educators should consider using neutral backgrounds in educational videos, particularly for learners with lower WMC. Consequently, this research underscores the significance of careful design considerations in the creation of instructional videos. Full article

The control of eye gaze is critical to the execution of many skills. The observation that task experts in many domains exhibit more efficient control of eye gaze than novices has led to the development of gaze training interventions that teach these behaviours. We aimed to extend this literature by i) examining the relative benefits of feed-forward (observing an expert’s eye movements) versus feed-back (observing your own eye movements) training, and ii) automating this training within virtual reality. Serving personnel from the British Army and Royal Navy were randomised to either feed-forward or feed-back training within a virtual reality simulation of a room search and clearance task. Eye movement metrics – including visual search, saccade direction, and entropy – were recorded to quantify the efficiency of visual search behaviours. Feed-forward and feed-back eye movement training produced distinct learning benefits, but both accelerated the development of efficient gaze behaviours. However, we found no evidence that these more efficient search behaviours transferred to better decision making in the room clearance task. Our results suggest integrating eye movement training principles within virtual reality training simulations may be effective, but further work is needed to understand the learning mechanisms. Full article

Good Clinical Judgment (GCJ) is associated with clinical excellence and accolades whereas poor clinical judgment is often associated with suboptimal care and the need for remediation. Although commonly referenced in practice, a shared definition for GCJ based on primary data is lacking. We interviewed 16 clinicians and surgeons across different specialties at one Canadian academic center to understand their conceptualization of GCJ. The data analysis led to the formulation of three pillars that were viewed by participants as core ingredients of GCJ. These included (1) a strong baseline knowledge and breadth of clinical experience, (2) the demonstration of curiosity, reflection, and wisdom, and (3) an ability to attend to contextual factors and understand the “bigger picture” when providing care to patients. Although there were inconsistent opinions regarding whether GCJ is innate or learned, participants reflected on strategies to support the development or improvement in clinical judgement for trainees. Full article

Banking malware are malicious programs that attempt to steal confidential information, such as banking authentication credentials, from users. Zeus is one of the most widespread banking malware variants ever discovered. Since the Zeus source code was leaked, many other variants of Zeus have emerged, and tools such as anti-malware programs exist that can detect Zeus; however, these have limitations. Anti-malware programs need to be regularly updated to recognise Zeus, and the signatures or patterns can only be made available when the malware has been seen. This limits the capability of these anti-malware products because they are unable to detect unseen malware variants, and furthermore, malicious users are developing malware that seeks to evade signature-based anti-malware programs. In this paper, a methodology is proposed for detecting Zeus malware network traffic flows by using machine learning (ML) binary classification algorithms. This research explores and compares several ML algorithms to determine the algorithm best suited for this problem and then uses these algorithms to conduct further experiments to determine the minimum number of features that could be used for detecting the Zeus malware. This research also explores the suitability of these features when used to detect both older and newer versions of Zeus as well as when used to detect additional variants of the Zeus malware. This will help researchers understand which network flow features could be used for detecting Zeus and whether these features will work across multiple versions and variants of the Zeus malware. Full article

Industries worldwide have faced continuous burdens since the beginning of the COVID-19 pandemic, while adjusting to rapidly changing rules and regulations. Industries need to be prepared to remain operational and productive in the face of current and emergent pathogens. While several businesses could remain functional through remote work, critical industries faced closings, worker shortages, and loss of productivity. Pharmaceutical industries were blessed with an increase in the stock market and creation of new jobs, but faced serious severe challenges due to shortage of medicines and drugs. Critical infrastructures such as healthcare, food and agriculture, manufacturing, construction, transportation, retail, waterworks, and waste management took a significant hit during the pandemic, and are still suffering from worker shortages to function optimally. Above all odds, companies were able to maintain the necessities by implementing strict safety protocols such as thorough and repeated cleaning, use of hand sanitizer/disinfectants, wearing face masks and personal protective equipment, and maintaining social distancing. This article addresses how COVID-19 disrupted normal operations on a large scale, and how essential businesses have learned to assess the impact, handle situations effectively, and become resilient for future crises. Best practices were tailored to each industry sector to prepare for and address the pandemic. Full article

In this survey, the issues of urban routing are analyzed, and critical considerations for smart and cost-effective delivery services are highlighted. Smart cities require intelligent services and solutions to address their routing issues. This article gives a brief description of current services that either apply classical methods or services that employ machine learning approaches. Furthermore, a comparison of the most promising research options in regard to VRP is provided. Finally, an initial design of a holistic scheme that would optimally combine several tools and approaches to serve the needs of different users with regard to the VRP is presented. Full article

Eye-tracking can help decode the intricate control mechanism in human performance. In healthcare, physicians-in-training require extensive practice to improve their healthcare skills. When a trainee encounters any difficulty in the practice, they will need feedback from experts to improve their performance. Personal feedback is time-consuming and subjected to bias. In this study, we tracked the eye movements of trainees during their colonoscopic performance in simulation. We examined changes in eye movement behavior during the moments of navigation loss (MNL), a signature sign for task difficulty during colonoscopy, and tested whether deep learning algorithms can detect the MNL by feeding data from eye-tracking. Human eye gaze and pupil characteristics were learned and verified by the deep convolutional generative adversarial networks (DCGANs); the generated data were fed to the Long Short-Term Memory (LSTM) networks with three different data feeding strategies to classify MNLs from the entire colonoscopic procedure. Outputs from deep learning were compared to the expert’s judgment on the MNLs based on colonoscopic videos. The best classification outcome was achieved when we fed human eye data with 1000 synthesized eye data, where accuracy (91.80%), sensitivity (90.91%), and specificity (94.12%) were optimized. This study built an important foundation for our work of developing an education system for training healthcare skills using simulation. Full article

Most nitrogen (N) in organic fertilizers must be mineralized to become available to plants, a process in which microorganisms play crucial roles. Droughts may impact microorganisms associated with the N cycle, negatively affecting N mineralization and plant N supply. The effects of drought on N-related processes may further be shaped by the farming system. We buried ¹⁵N-enriched plant material and reduced precipitation in conventionally and organically (biodynamically) managed wheat fields. On two sampling dates, we evaluated the soil water content, plant parameters and the plants’ ¹⁵N isotope signature. We intended to study the microbial communities associated with the N cycle to link potential treatment effects on plant N provisioning with characteristics of the underlying microbial community. However, floods impaired the experiment after the first sampling date, and the molecular work on the microbial communities was not performed. Focusing on the pre-flooding sampling date, our data suggested that processes associated with N transformation are sensitive to drought, but the role of the farming system needs further investigation. Since the underlying research question, the set-up and the lessons learned from this study may guide future experiments, we presented improvements to the set-up and provided ideas for additional analyses, hoping to promote research on this topic. Full article

Wearable mobile eye trackers have great potential as they allow the measurement of eye movements during daily activities such as driving, navigating the world and doing groceries. Although mobile eye trackers have been around for some time, developing and operating these eye trackers was generally a highly technical affair. As such, mobile eye-tracking research was not feasible for most labs. Nowadays, many mobile eye trackers are available from eye-tracking manufacturers (e.g., Tobii, Pupil labs, SMI, Ergoneers) and various implementations in virtual/augmented reality have recently been released.The wide availability has caused the number of publications using a mobile eye tracker to increase quickly. Mobile eye tracking is now applied in vision science, educational science, developmental psychology, marketing research (using virtual and real supermarkets), clinical psychology, usability, architecture, medicine, and more. Yet, transitioning from lab-based studies where eye trackers are fixed to the world to studies where eye trackers are fixed to the head presents researchers with a number of problems. These problems range from the conceptual frameworks used in world-fixed and head-fixed eye tracking and how they relate to each other, to the lack of data quality comparisons and field tests of the different mobile eye trackers and how the gaze signal can be classified or mapped to the visual stimulus. Such problems need to be addressed in order to understand how world-fixed and head-fixed eye-tracking research can be compared and to understand the full potential and limits of what mobile eye-tracking can deliver. In this symposium, we bring together presenting researchers from five different institutions (Lund University, Utrecht University, Clemson University, Birkbeck University of London and Rochester Institute of Technology) addressing problems and innovative solutions across the entire breadth of mobile eye-tracking research. Hooge, presenting Hessels et al. paper, focus on the definitions of fixations and saccades held by researchers in the eyemovement field and argue how they need to be clarified in order to allow comparisons between world-fixed and head-fixed eye-tracking research.—Diaz et al. introduce machine-learning techniques for classifying the gaze signal in mobile eye-tracking contexts where head and body are unrestrained. Niehorster et al. compare data quality of mobile eye trackers during natural behavior and discuss the application range of these eye trackers. Duchowski et al. introduce a method for automatically mapping gaze to faces using computer vision techniques. Pelz et al. employ state-of-the-art techniques to map fixations to objects of interest in the scene video and align grasp and eye-movement data in the same reference frame to investigate the guidance of eye movements during manual interaction. Full article

The grand goal of Machine Learning is to develop software which can learn from previous experience—similar to how we humans do. Ultimately, to reach a level of usable intelligence, we need (1) to learn from prior data, (2) to extract knowledge, (3) to generalize—i.e., guessing where probability function mass/density concentrates, (4) to fight the curse of dimensionality, and (5) to disentangle underlying explanatory factors of the data—i.e., to make sense of the data in the context of an application domain. To address these challenges and to ensure successful machine learning applications in various domains an integrated machine learning approach is important. This requires a concerted international effort without boundaries, supporting collaborative, cross-domain, interdisciplinary and transdisciplinary work of experts from seven sections, ranging from data pre-processing to data visualization, i.e., to map results found in arbitrarily high dimensional spaces into the lower dimensions to make it accessible, usable and useful to the end user. An integrated machine learning approach needs also to consider issues of privacy, data protection, safety, security, user acceptance and social implications. This paper is the inaugural introduction to the new journal of MAchine Learning & Knowledge Extraction (MAKE). The goal is to provide an incomplete, personally biased, but consistent introduction into the concepts of MAKE and a brief overview of some selected topics to stimulate future research in the international research community. Full article

Recent years have witnessed a remarkable growth in the way mathematics, informatics, and computer science can process data. In disciplines such as machine learning, pattern recognition, computer vision, computational neurology, molecular biology, information retrieval, etc., many new methods have been developed to cope with the ever increasing amount and complexity of the data. These new methods offer interesting possibilities for processing, classifying and interpreting eye-tracking data. The present paper exemplifies the application of topological arguments to improve the evaluation of eye-tracking data. The task of classifying raw eye-tracking data into saccades and fixations, with a single, simple as well as intuitive argument, described as coherence of spacetime, is discussed, and the hierarchical ordering of the fixations into dwells is shown. The method, namely identification by topological characteristics (ITop), is parameter-free and needs no pre-processing and post-processing of the raw data. The general and robust topological argument is easy to expand into complex settings of higher visual tasks, making it possible to identify visual strategies. Full article

Eye tracking is increasingly being used in Educational Science and so has the interest of the eye tracking community grown in this topic. In this paper we briefly introduce the discipline of Educational Science and why it might be interesting to couple it with eye tracking research. We then introduce three major research areas in Educational Science that have already successfully used eye tracking: First, eye tracking has been used to improve the instructional design of computer-based learning and testing environments, often using hyper- or multimedia. Second, eye tracking has shed light on expertise and its development in visual domains, such as chess or medicine. Third, eye tracking has recently been also used to promote visual expertise by means of eye movement modeling examples. We outline the main educational theories for these research areas and indicate where further eye tracking research is needed to expand them. Full article

This short paper describes an update of A Simple Tool For Examining Fixations (ASTEF) developed for facilitating the examination of eye-tracking data and for computing a spatial statistics algorithm that has been validated as a measure of mental workload (namely, the Nearest Neighbor Index: NNI). The code is based on Matlab® 2013a and is currently distributed on the web as an open-source project. This implementation of ASTEF got rid of many functionalities included in the previous version that are not needed anymore considering the large availability of commercial and open-source software solutions for eye-tracking. That makes it very easy to compute the NNI on eye-tracking data without the hassle of learning complicated tools. The software also features an export function for creating the time series of the NNI values computed on each minute of the recording. This feature is crucial given that the spatial distribution of fixations must be used to test hy-potheses about the time course of mental load. Full article

Every day, people from different professions and disciplines need to use information to make decisions, plan courses of action, discover patterns in big data, solve problems, analyze situations, make sense of phenomena, learn new concepts, make forecasts about future trends, and so on. People whose professions involve the frequent or continual performance of such activities include scientists, healthcare specialists, medical researchers, librarians, journalists, engineers, stock brokers, archeologists, educators, social scientists, and others—i.e., the so-called knowledge workers. As the amount and complexity of information is on the rise, it is becoming more important to understand how humans use and interact with information to support their everyday tasks and activities. [...] Full article

Outreach by scientific researchers in school classrooms often results in widespread benefit for learners, classroom teachers and researchers. This paper presents a consideration of these benefits using two case studies in the Geography, Earth and Environmental Sciences (GEES). In each case, different school classroom-based activities were designed by scientists, but were improved by input from educational professionals, which helped to maximize the mutual learning experiences and to ensure the quality of the content and its delivery. Each case study suggests an improvement in scientist’s working knowledge of best practices for classroom-based outreach activities, which can translate to improved practices for University-level teaching, among other tangible career-relevant benefits. Despite these benefits, these projects highlight the well-established need for improved training for researchers in effective outreach practices, increased value on programme evaluation, and the growing need for meaningful professional recognition for researchers involved in these important, and ever-growing, outreach activities. Full article