Patterns of Eye Movement Influenced by Multitasking Workload: A Clustering Analysis

Although multitasking has been studied in the past few decades, there has been a lack of investigation on how individuals’ multitasking performance can be predicted using eye movement data. To address this gap, this study proposed an exploratory approach to understand the manifestation of eye movement patterns that could provide diagnostic and predictive information of multitasking performance. Nineteen participants completed Multi-Attribute Task Battery (MATB-II) experiments under both low and high workloads and their eye movement and MATB-II task performance were collected. We applied a hierarchical clustering method that classified the participants into three clusters – clusters with small, medium, and large number of fixations. Then, we compared the MATB-II performance of the three clusters. The results s howed significant differences in average reaction time to stimuli and average error count among the three clusters. Our study showed that hierarchical clustering of eye fixations can effectively predict multitasking performance.

Multi-exponential error extrapolation and combining error mitigation techniques for NISQ applications

Noise in quantum hardware remains the biggest roadblock for the implementation of quantum computers. To fight the noise in the practical application of near-term quantum computers, instead of relying on quantum error correction which requires large qubit overhead, we turn to quantum error mitigation, in which we make use of extra measurements. Error extrapolation is an error mitigation technique that has been successfully implemented experimentally. Numerical simulation and heuristic arguments have indicated that exponential curves are effective for extrapolation in the large circuit limit with an expected circuit error count around unity. In this Article, we extend this to multi-exponential error extrapolation and provide more rigorous proof for its effectiveness under Pauli noise. This is further validated via our numerical simulations, showing orders of magnitude improvements in the estimation accuracy over single-exponential extrapolation. Moreover, we develop methods to combine error extrapolation with two other error mitigation techniques: quasi-probability and symmetry verification, through exploiting features of these individual techniques. As shown in our simulation, our combined method can achieve low estimation bias with a sampling cost multiple times smaller than quasi-probability while without needing to be able to adjust the hardware error rate as required in canonical error extrapolation.

A Distributed Approach to Speaker Count Problem in an Open-Set Scenario by Clustering Pitch Features

Counting the number of speakers in an audio sample can lead to innovative applications, such as a real-time ranking system. Researchers have studied advanced machine learning approaches for solving the speaker count problem. However, these solutions are not efficient in real-time environments, as it requires pre-processing of a finite set of data samples. Another approach for solving the problem is via unsupervised learning or by using audio processing techniques. The research in this category is limited and does not consider the large-scale open set environment. In this paper, we propose a distributed clustering approach to address the speaker count problem. The separability of the speaker is computed using statistical pitch parameters. The proposed solution uses multiple microphones available in smartphones in a large geographical area to capture and extract statistical pitch features from the audio samples. These features are shared between the nodes to estimate the number of speakers in the neighborhood. One of the major challenges is to reduce the error count that arises due to the proximity of the users and multiple microphones. We evaluate the algorithm’s performance using real smartphones in a multi-group arrangement by capturing parallel conversations between the users in both indoor and outdoor scenarios. The average error count distance is 1.667 in a multi-group scenario. The average error count distances in indoor environments are 16% which is better than in the outdoor environment.

Measuring software reliability under the influence of an infected patch

Patching service provides software firms an option to deal with the leftover bugs and is thereby helping them to keep a track of their product. More and more software firms are making use of this concept of prolonged testing. But this framework of releasing unprepared software in market involves a huge risk. The hastiness of vendors in releasing software patch at times can be dangerous as there are chances that firms release an infected patch. The infected patch (es) might lead to a hike in bug occurrence and error count and might make the software more vulnerable. The current work presents an understanding of such situation through mathematical modeling framework; wherein, the distinct behavior of testers (during in-house testing and field testing) and users is described. The proposed model has been validated on two software failure data sets of Tandem Computers and Brazilian Electronic Switching System, TROPICO R-1500.

The Effect of Holding an Object on Postural Stability and Suprapostural Task Performance

Holding an object has been found to reduce postural sway during quiet standing. However, people normally stand to accomplish suprapostural goals, such as fitting a key into a lock. Postural control should therefore be assessed by examining postural outcomes in the context of suprapostural task performance. This study assessed whether holding an object increased standing postural stability and improved the performance of a concurrent precision manual task. A total of 15 young adults performed a precision manual task with their dominant hand while holding or not holding an object in their nondominant hand. Postural stability was assessed using measures of postural sway and time to boundary. Suprapostural task performance was assessed as an error count. Holding did not influence postural sway or suprapostural task performance. Discrepancies among previous studies coupled with the present findings suggest that the effects of holding an object on standing posture are highly sensitive to the experimental context. The authors provide several explanations for their findings and discuss the limitations of previous suggestions that holding an object may have clinical relevance for balance-compromised populations.

Understanding freehand gestures: a study of freehand gestural interaction for immersive VR shopping applications

Unlike retail stores, in which the user is forced to be physically present and active during restricted opening hours, online shops may be more convenient, functional and efficient. However, traditional online shops often have a narrow bandwidth for product visualizations and interactive techniques and lack a compelling shopping context. In this paper, we report a study on eliciting user-defined gestures for shopping tasks in an immersive VR (virtual reality) environment. We made a methodological contribution by providing a varied practice for producing more usable freehand gestures than traditional elicitation studies. Using our method, we developed a gesture taxonomy and generated a user-defined gesture set. To validate the usability of the derived gesture set, we conducted a comparative study and answered questions related to the performance, error count, user preference and effort required from end-users to use freehand gestures compared with traditional immersive VR interaction techniques, such as the virtual handle controller and ray-casting techniques. Experimental results show that the freehand-gesture-based interaction technique was rated to be the best in terms of task load, user experience, and presence without the loss of performance (i.e., speed and error count). Based on our findings, we also developed several design guidelines for gestural interaction.

Thought and language disturbance in bipolar disorder quantified via process-oriented verbal fluency measures

Bipolar disorder (BD) is characterized by speech abnormalities, reflected by symptoms such as pressure of speech in mania and poverty of speech in depression. Here we aimed at investigating speech abnormalities in different episodes of BD, including mixed episodes, via process-oriented measures of verbal fluency performance – i.e., word and error count, semantic and phonological clustering measures, and number of switches–, and their relation to neurocognitive mechanisms and clinical symptoms. 93 patients with BD – i.e., 25 manic, 12 mixed manic, 19 mixed depression, 17 depressed, and 20 euthymic–and 31 healthy controls were administered three verbal fluency tasks – free, letter, semantic–and a clinical and neuropsychological assessment. Compared to depression and euthymia, switching and clustering abnormalities were found in manic and mixed states, mimicking symptoms like flight of ideas. Moreover, the neuropsychological results, as well as the fact that error count did not increase whereas phonological associations did, showed that impaired inhibition abilities and distractibility could not account for the results in patients with manic symptoms. Rather, semantic overactivation in patients with manic symptoms, including mixed depression, may compensate for trait-like deficient semantic retrieval/access found in euthymia. “For those who are manic, or those who have a history of mania, words move about in all directions possible, in a three-dimensional ‘soup’, making retrieval more fluid, less predictable.” Kay Redfield Jamison (2017, p. 279).

Novel box trainer for taTME – prospective evaluation among medical students

BackgroundTransanal total mesorectal excision (taTME) has been subject to extensive research and increasing clinical application. It allows further reduction of trauma by accessing via a natural orifice. Manifold platforms and instruments have been introduced and heterogeneity in surgical techniques exists. Because of the technique’s complexity there is a persistent need for dedicated training devices and concepts.Materials and methodsThe key steps of taTME were analyzed and a box trainer with three modules resembling these steps was designed and manufactured. Twenty-one surgically inexperienced medical students performed five repetitions of the three tasks with the new box trainer. Time and error count were analyzed for assessment of a learning curve.ResultsA significant reduction of processing time could be demonstrated for tasks 1–3 (p < 0.001; p < 0.001; p = 0.001). The effect size was high for comparison of repetition 1 and 5 and decreased over the course (task 1: r = 0.88 vs. r = 0.21; task 2: r = 0.86 vs. r = 0.23; task 3: r = 0.74 vs. r = 0.44). Also, a significant reduction of errors was demonstrated for tasks 1 and 2. The decrease of effect size was analogously demonstrated.ConclusionsThe trainer might help to reduce the use of animal models for testing of platforms and instruments as well as gaining first-hand experience in transanal rectal resection.

Instance-Based Cost-Sensitive Boosting

Many classification algorithms aim to minimize just their training error count; however, it is often desirable to minimize a more general cost metric, where distinct instances have different costs. In this paper, an instance-based cost-sensitive Bayesian consistent version of exponential loss function is proposed. Using the modified loss function, the derivation of instance-based cost-sensitive extensions of AdaBoost, RealBoost and GentleBoost are developed which are termed as ICSAdaBoost, ICSRealBoost and ICSGentleBoost, respectively. In this research, a new instance-based cost generation method is proposed instead of doing this expensive process by experts. Thus, each sample takes two cost values; a class cost and a sample cost. The first cost is equally assigned to all samples of each class while the second cost is generated according to the probability of each sample within its class probability density function. Experimental results of the proposed schemes imply 12% enhancement in terms of [Formula: see text]-measure and 13% on cost-per-sample over a variety of UCI datasets, compared to the state-of-the-art methods. The significant priority of the proposed method is supported by applying the pair of [Formula: see text]-tests to the results.

Longitudinal decline of driving safety in Parkinson disease

Objective:To longitudinally assess and predict on-road driving safety in Parkinson disease (PD).Methods:Drivers with PD (n = 67) and healthy controls (n = 110) drove a standardized route in an instrumented vehicle and were invited to return 2 years later. A professional driving expert reviewed drive data and videos to score safety errors.Results:At baseline, drivers with PD performed worse on visual, cognitive, and motor tests, and committed more road safety errors compared to controls (median PD 38.0 vs controls 30.5; p < 0.001). A smaller proportion of drivers with PD returned for repeat testing (42.8% vs 62.7%; p < 0.01). At baseline, returnees with PD made fewer errors than nonreturnees with PD (median 34.5 vs 40.0; p < 0.05) and performed similar to control returnees (median 33). Baseline global cognitive performance of returnees with PD was better than that of nonreturnees with PD, but worse than for control returnees (p < 0.05). After 2 years, returnees with PD showed greater cognitive decline and larger increase in error counts than control returnees (median increase PD 13.5 vs controls 3.0; p < 0.001). Driving error count increase in the returnees with PD was predicted by greater error count and worse visual acuity at baseline, and by greater interval worsening of global cognition, Unified Parkinson's Disease Rating Scale activities of daily living score, executive functions, visual processing speed, and attention.Conclusions:Despite drop out of the more impaired drivers within the PD cohort, returning drivers with PD, who drove like controls without PD at baseline, showed many more driving safety errors than controls after 2 years. Driving decline in PD was predicted by baseline driving performance and deterioration of cognitive, visual, and functional abnormalities on follow-up.