Task Performer Research Articles

HuBar: A Visual Analytics Tool to Explore Human Behavior based on fNIRS in AR Guidance Systems.

The concept of an intelligent augmented reality (AR) assistant has significant, wide-ranging applications, with potential uses in medicine, military, and mechanics domains. Such an assistant must be able to perceive the environment and actions, reason about the environment state in relation to a given task, and seamlessly interact with the task performer. These interactions typically involve an AR headset equipped with sensors which capture video, audio, and haptic feedback. Previous works have sought to facilitate the development of intelligent AR assistants by visualizing these sensor data streams in conjunction with the assistant's perception and reasoning model outputs. However, existing visual analytics systems do not focus on user modeling or include biometric data, and are only capable of visualizing a single task session for a single performer at a time. Moreover, they typically assume a task involves linear progression from one step to the next. We propose a visual analytics system that allows users to compare performance during multiple task sessions, focusing on non-linear tasks where different step sequences can lead to success. In particular, we design visualizations for understanding user behavior through functional near-infrared spectroscopy (fNIRS) data as a proxy for perception, attention, and memory as well as corresponding motion data (acceleration, angular velocity, and gaze). We distill these insights into embedding representations that allow users to easily select groups of sessions with similar behaviors. We provide two case studies that demonstrate how to use these visualizations to gain insights about task performance using data collected during helicopter copilot training tasks. Finally, we evaluate our approach by conducting an in-depth examination of a think-aloud experiment with five domain experts.

The Three Worlds of Task Complexity

Aim/Purpose: To provide a systematic approach to defining task complexity using a three worlds model previously introduced in informing science research. Background: The task complexity construct presents researchers with a quandary. While it appears useful on the surface, repeated attempts to define it rigorously have failed to gain traction in the broader research community. The level of inconsistency between definitions is shown to have changed little in the past 20 years. Methodology: Using a common framework that treats task complexity as a latent construct residing between sources and outcomes, moderated by both task familiarity and task discretion, separate models for each of the three worlds are developed. Contribution: Our paper proposes a potential path forward by showing how many issues in past task complexity research can be reconciled by framing the construct according to the three worlds model: the world we experience, the world of human artifacts, and the “real world.” Findings: The framework defines experienced complexity as occurring in the mind of the task performer while performing a single task instance, intrinsic complexity as a function of the internal characteristics of the problem space used to perform a bounded set of task instances, and extrinsic complexity as the ruggedness of the fitness landscape in which the task is performed. Recommendation for Researchers: It offers a path to convergence for definitions of task complexity. Future Research: The three worlds of task complexity can potentially be applied to many practical problems.

Talent identification and development in an English Premiership rugby union club: the perspectives of players and coaches.

The pathway towards senior professional status in sport is affected by a multitude of factors. An abductive examination of the talent identification and development processes at an English Premiership rugby union (RU) club was undertaken for the present study. Part one examined the perspectives on the selection and development processes of senior academy male players (n = 8), whereas part two explored the perceptions of male coaches (n = 7). A total of three focus groups were used. Three main themes were confirmed by players and coaches: (a) task constraints, (b) performer constraints, and (c) environmental constraints. Specifically, although athletes and coaches believed that performer constraints were highly impactful on players' career in RU, there were inconsistencies surrounding the task and environmental constraints. Despite an indication that three common themes impacted an players path, this preliminary study shows an imbalance in the understanding of some of the key factors perceived to be important for talent progression in the present rugby academy. More research using similar qualitative methods is recommended to better understand the differences in opinions between players and coaches. Meanwhile, practitioners should consider implementing objective and holistic strategies to improve the talent pathway in English RU academies.

Misconception and Legal Problems of Authority to Perform Medical Actions in Nursing Administrative Law

According to Article 29 paragraph (1) of Law Number 38 of 2014, nurses have several duties, namely as providers of nursing care, counselors and client counselors, managers of nursing services, nursing researchers, and task performers based on delegated authority and/or tasks in specific limited circumstances. One of the nurse's tasks that receives focused attention is "as a task performer based on delegated authority." According to Article 32 paragraph (2) of Law Number 38 of 2014, the implementation of nurse tasks based on delegated authority regulated in Article 29 paragraph (1) letter e of Law Number 38 of 2014 is carried out by nurses in a "delegative and mandate" manner. The aim of this research is to identify and analyze misconceptions and legal problems in performing medical actions in nursing administrative law. The research method used is a normative juridical research method. The research results are as follows: First, the term "delegated authority" is not accurately used in the context of nurse task implementation for performing medical actions. Second, the more appropriate term is "cooperation." Suggestions that can be put forward are as follows: First, Law Number 38 of 2014 concerning Nursing needs to be amended. Second, the term "delegated authority" in the legal relationship between medical personnel and nurses regulated in Article 29 paragraph (1) letter 3 of Law Number 38 of 2014 needs to be changed and replaced with the term "cooperation.

Evidence for a group-level performance optimum and performance-neutral adaptation in a procedural task

ABSTRACT We asked whether people performing different versions of a procedural task would adapt to the differences and achieve similarly high levels of performance. In Experiment 1 (N = 479), participants either did or did not receive a mnemonic for the sequence of procedural steps. Both groups had unlimited access to a help function giving the sequence. No-mnemonic participants used help more than did mnemonic-provided participants but performed similarly, suggesting that no-mnemonic participants adapted to lack of the mnemonic. In Experiment 2 (N = 538), access to help was either limited or unlimited. Limited-help participants used help less than did unlimited-help participants but again performed similarly, suggesting that limited-help participants adapted to the limit. The results are evidence for (a) a group-level performance optimum implicit in task environment and task performer characteristics, and not specified by an environmental payoff function; and (b) adaptive processes that people deploy to achieve this optimum.

Privacy-Preserving Task Distribution Mechanism with Cloud-Edge IoT for the Mobile Crowdsensing

Mobile crowdsensing under big data provides an efficient, win-win, and low-budget data collection solution for IoT applications such as the smart city. However, its open and all access scenarios raise the threat of data security and user privacy during task distribution of mobile crowdsensing. To eliminate the above threat, this paper first designs a privacy-preserving task distribution scheme (Scheme 1), which realizes fine-grained access control and the practical keyword search, as well as protects the access policy. But it incurs expensive computational and communication consumptions for the task performer side. In this regard, we construct Scheme 2 to attain a lightweight trapdoor generation and keyword search mechanism, and it enables the crowdsensing platform to predecrypt a ciphertext without revealing any information about the task and the performer’s privacy. Then, the resource-constrained device on the task performer side can recover the task with a few computational and communication overheads. The security of the scheme has been detailedly proved and analyzed, and theoretical comparisons and experiment demonstrate their practicability.

Face Mask Detection Using Machine Learning

Abstract: COVID-19 pandemic has rapidly affected ourday-to-day life disrupting the world trade and movements. Wearing a protective face mask hasbecome a new normal. In the near future, many public service providers will ask the customers to wear masks correctly to avail of their services. Therefore, face maskdetection has become a crucial task to help global society. This paper presents a simplified approach to achieve this purpose using some basic Machine Learning packages like TensorFlow, Keras and OpenCV. The application of “machine learning” and “artificial intelligence” has become popular within the last decade. Both terms are frequently used in science and media, sometimes interchangeably, sometimes with different meanings. In this work, we specify the contribution of machine learning to artificial intelligence. We review relevant literature and present a conceptual framework which clarifies the role of machine learning to build (artificial) intelligent agents.The proposed method detects the face from the image correctly and then identifies if it has a mask on it or not. As a surveillance task performer, it can also detect a face along with a mask in motion. The method attains accuracy up to 95.77% and 94.58% respectively on two different datasets. We explore optimized values of parameters using the mobileNetV2 which is a Convolutional Neural Network architecture to detect the presence of masks correctly without causing over- fitting.

Face Mask Detection using Open-Source Computer Vision Library and Scikit-Learn using Machine Learning

In this pandemic has rapidly affected our day to-day life disrupting the world trade and movements. Wearing a protective face mask has become anormal. In the near future, many public service providers will ask the customers to wear masks correctly to avail of their services. Therefore, face mask detection has become a crucial task to help global security. This project presents a simplified approach to achieve this purpose using some basic Machine Learning packages like OpenCV and Scikit-Learn. The proposed method detects the face from the image correctly and then identifies it has a mask on it or not. As a surveillance task performer, it can also detect a face along with the mask in motion. This method attains accuracy almost up to 90% on two data sets. We explore optimized values of parameters using the Viola Jones detection framework used to detect the presence of masks correctly without causing over-fitting.

Not all smokers are alike: the hidden cost of sustained attention during nicotine abstinence

Nicotine Withdrawal Syndrome (NWS)-associated cognitive deficits are notably heterogeneous, suggesting underlying endophenotypic variance. However, parsing this variance in smokers has remained challenging. In this study, we identified smoker subgroups based on response accuracy during a Parametric Flanker Task (PFT) and then characterized distinct neuroimaging endophenotypes using a nicotine state manipulation. Smokers completed the PFT in two fMRI sessions (nicotine sated, abstinent). Based on response accuracy in the stressful, high cognitive demand PFT condition, smokers split into high (HTP, n = 21) and low task performer (LTP, n = 24) subgroups. Behaviorally, HTPs showed greater response accuracy (88.68% ± 5.19 SD) vs. LTPs (51.04% ± 4.72 SD), independent of nicotine state, and greater vulnerability to abstinence-induced errors of omission (EOm, p = 0.01). Neurobiologically, HTPs showed greater BOLD responses in attentional control brain regions, including bilateral insula, dorsal ACC, and frontoparietal Cx for the [correct responses (–) errors of commission] PFT contrast in both states. A whole-brain functional connectivity (FC) analysis with these subgroup-derived regions as seeds identified two circuits: Precentral Cx↔Insula and Insula↔Occipital Cx, with abstinence-induced FC strength increases seen only in HTPs. Finally, abstinence-induced FC and behavior (EOm) differences were positively correlated for HTPs in a Precentral Cx↔Orbitofrontal cortical circuit. In sum, only the HTP subgroup demonstrated sustained attention deficits following 48-hr nicotine abstinence, a stressor in dependent smokers. Unpacking underlying smoker heterogeneity with this ‘dual (task and abstinence) stressor’ approach revealed discrete smoker subgroups with differential attentional deficits to withdrawal that could be novel pharmacological/behavioral targets for therapeutic interventions to improve cessation outcomes.

Business Improvement District Enabling Laws in the United States and Germany: A Comparative Analysis of Policy Learning

This study aims to answer the question of how political cultures of respective countries affect policy learning processes. Using historical and contextual information about the political cultures of the United States and Germany and with applications of the advocacy coalition framework and cultural theory, this study finds that the originally American business improvement district (BID) institutional form was adopted in Germany when its political culture was open to it. German policy makers adopted the provisions of the U.S. laws that were compatible with their secondary beliefs, which were related to where and how to deliver local services. They did not directly adopt the BID governing models in the U.S. laws, which were based on the American deep core and policy core beliefs. Instead, they adapted the American models to their own Hegelian deep core beliefs and the hierarchical political core beliefs, which resulted in the formulation of the “ Aufgabenträger” (task performer) model.

Designing optimizing procedures for task switching to ensure efficiency in the hospital laboratory

This study aims to improve the efficiency of task switching in hospital laboratories. In a laboratory, several medical technicians perform multiple tasks. Technicians are not aware of the marginal amount of time it takes to switch between tasks, and this accumulation of lost minutes can cause the technician to worry more about the remaining working time than work quality. They rush through their remaining tasks, thereby rendering their work less efficient. For time optimization, we identified work changeover times to help maintain the work quality in the laboratory while reducing the number of task switching instances. We used the turnaround time (TAT) compliance rate of emergency room samples as an indicator to evaluate laboratory performance and the number of task switching instances as an index of the task performer perspective (TPP). We experimented with a monitoring system that populates the time for sample classification according to the optimal time for task switching. Through the proposed methodology, we successfully reduced not only the instances of task switching by 10% but also the TAT non-compliance rate from 4.97 to 2.66%. Consequently, the introduction of new methodology has greatly increased work efficiency.

Face Mask Detection Using AI through Python

The corona virus COVID-19 pandemic is causing a global health crisis so the effective protection methods is wearing a face mask in public areas according to the World Health Organization (WHO). The COVID-19 pandemic forced governments across the world to impose lockdown to prevent virus transmissions. Reports indicate that wearing facemasks while at work clearly reduces the risk of transmission. An efficient and economic approach of using AI to create a safe environment in a manufacturing setup. A hybrid model using deep and classical machine learning for face mask detection will be presented. A face mask detection dataset consists of with mask and without mask images, we are going to use OpenCV to do real-time face detection from a live stream via our webcam. We will use the dataset to build a COVID-19 face mask detector with computer vision using Python, OpenCV, and Tensor Flow and Keras. Our goal is to identify whether the person on image/video stream is wearing a face mask or not with the help of computer vision and deep learning. As a surveillance task performer, it can also detect a face along with a mask in motion. The method attains accuracy up to 95.77% and 94.58% respectively on two different datasets. We explore optimized values of parameters using the Sequential Convolutional Neural Network of Deep Learning model to detect the presence of masks correctly without causing over-fitting.

Face Mask Detection using Convolutional Neural Networks

COVID-19 pandemic has rapidly affected our day-to-day life disrupting the world trade and movements. Wearing a protective face mask has become a new normal. Soon, many public service providers will ask the customers to wear masks correctly to avail of their services. Therefore, face mask detection has become a crucial task to help global society. This paper presents a simplified approach to achieve this purpose using some basic Machine Learning packages like Tensor Flow, Keras, Open CV and Scikit-Learn. The proposed method detects the face from the image correctly and then identifies if it has a mask on it or not. As a surveillance task performer, it can also detect a face along with a mask in motion. The method attains accuracy up to 92.75%. We explore optimized values of parameters using the Sequential Convolutional Neural Network model to detect the presence of masks correctly.

“Who Guards the Guards?”

The studies about panopticism have a highlighted spot on organizational researches. Recently, with the inclusion of information technology, it creates the digital panopticon, in which informational systems perform the control, including on the vigilantes themselves (managers). Thus, this study addresses hierarchy, power, and the insertion of information technology in banking organizations to understand the perception of managers about their power over the team on this new kind of materialization of power. For such, this qualitative exploratory research used interview and observation as data collection and triangulation techniques. The treatment of information performed was by speech analysis. The analysis of collected data revealed that the manager is not responsible for the conception of work anymore, because they became a task performer, showing their subjection to the system of the establishment and target control; after all, the power is in the system.

“A lot to think about”

AbstractThis paper takes a process-focused approach to investigating the dialogue interpreting task from the perspective of the task performer. It reports on a portion of a research study aimed at better understanding the cognitive and interactional processes involved in dialogue interpreting. Eight interpreters interpreted a simulated interaction and subsequently completed a retrospective process-tracing session. The results shed light on the complexities of dialogue interpreting performance as experienced by the task performer, specifically with regard to interpreters’ online monitoring of affect, behavior, cognition, and context.

Quality assurance program of a nuclear facility

Quality Assurance Program (QAP, Program) is a standard, which regulates and coordinates activity, as well as determines quality assurance policy regarding services rendered and construction works conducted on nuclear infrastructure facilities. The Program must comply with the requirements of federal rules and regulations in the field of nuclear power engineering. The present QAP is available to all organisation employees carrying out works and rendering services during construction, reconstruction and major repairs of nuclear facilities as well as to experts conducting works and rendering services on a contract basis. The QAP implementation analysis and the evaluation of its results is conducted through internal audits. The implementation of the quality assurance program is provided through the following principles: – the responsibility for quality assurance when conducting actual works and rendering services is imposed upon the task performer; – precise segregation of duties and responsibilities between all contractors; – regular control of compliance with regulations and developer’s requirements, as well as accurate documentation of the monitoring results; – systematic update tracking for all official regulations and norms; – the quality assurance methods incorporate the classification of equipment, systems and installation in terms of their impact on safety of nuclear facilities approved by official rules and regulations in the atomic energy sector.

A Knowledge Activation Approach to Testing the Circumvention-of-Limits Hypothesis

Abstract The circumvention-of-limits hypothesis holds that the more skilled and knowledgeable the task performer, the less it matters for task performance whether that person has limited general cognitive ability. We tested this hypothesis using a knowledge activation approach to manipulate knowledge experimentally. The criterion task, which we designed to capture a fundamental requirement of a broad class of real-world tasks, was a placekeeping task in which participants had to perform a sequence of operations in a specified order, applying 7 different 2-alternative decision rules to a series of randomly generated stimuli. The measures of interest in this task were response time and accuracy, as well as frequency of use of a help function to recall the correct sequence of steps. In the knowledge-activated condition, we gave participants a mnemonic in which the first letters of the decision rules spelled the English word unravel. In the knowledge-not-activated condition, no mnemonic was given, and the use and discovery of mnemonics was frustrated by reversal of the terms of some decision rules so that their first letters spelled the difficult to pronounce nonword unrbcel. The predictor tasks, which we used to predict performance in the placekeeping task, were standard tests of cognitive ability, which have been shown to predict performance differences in a wide range of complex tasks. Inconsistent with the circumvention-of-limits hypothesis, the positive effect of cognitive ability on placekeeping performance did not differ across conditions. This finding adds to previous evidence that it may not always be possible to overcome limitations on cognitive ability through extensive training.

The Effect of Movement Speed on Mental Workload During a Simple Visually Guided Task

Mental workload (MW) is a subjective measure of the cognitive effort required to complete a task. Factors such as task speed and the use of feedback can affect the perceived MW and ability to correctly perform an activity. PURPOSE: 1) Determine the effect of task speed on perceived MW during a wrist movement pattern guided by visual feedback (VF); and 2) Determine if MW is different for those who correctly perform the task (Performer: P) compared to those who do not (Non-performer: NP). METHODS: Twelve healthy young subjects were recruited and asked to flex and extend their right wrist to match two different speeds of a target sine wave: 0.7 Hz (FAST) or 1.0 Hz (SLOW). VF of wrist movement and the target pattern were provided concurrently on a screen. Five 20-second trials with two-minute rest intervals were completed at each speed. During rest periods the Raw NASA-TLX (RTLX), a tool used to assess perceived workload, was administered. RTLX categories representing purely MW include: mental demand (MD), temporal demand (TD), and frustration (FR). Cross-correlation analysis of task performance was computed for each subject’s 5th trial at both speed conditions in order to categorize individuals as either P (r > 0.8) or NP (r < 0.8). 2x2 mixed model ANOVAs compared the change in perceived MD, TD, and FR from trial one to five between speed conditions (FAST vs. SLOW) and the two groups (P vs. NP). Post hoc Tukey HSD tests were used to compare differences. Positive changes in MD, TD, and FR denote improvements in MW. RESULTS: Significant speed x group interactions were observed for MD (p=0.008) and TD (p=0.005). No significant interaction or main effects were found for FR (p=0.204). Post hoc tests revealed significant differences in changes in MW between FAST and SLOW speeds among P (MD FAST: 31.4, SLOW: 11.4, p=0.027; TD FAST: 30.0, SLOW: -0.71, p=0.022) and between P and NP during the FAST speed (MD P: 31.4, NP: -2.0, p=0.044; TD P: 30.0, NP: -8.0, p=0.031). CONCLUSIONS:P and NP showed similar changes in MW from trial one to five during the SLOW speed, whereas only P experienced improved MW during the FAST speed. These results suggest MW is affected by movement speed in accordance with subject performance.

Approaches to Measuring the Difficulty of Games in Dynamic Difficulty Adjustment Systems

ABSTRACTIn this article, three approaches are proposed for measuring difficulty that can be useful in developing Dynamic Difficulty Adjustment (DDA) systems in different game genres. Our analysis of the existing DDA systems shows that there are three ways to measure the difficulty of the game: using the formal model of gameplay, using the features of the game, and direct examination of the player. These approaches are described in this article and supplemented by appropriate examples of DDA implementations. In addition, the article describes the distinction between task complexity and task difficulty in DDA systems. Separating task complexity (especially the structural one) is suggested, which is an objective characteristic of the task, and task difficulty, which is related to the interaction between the task and the task performer.

Self-regulated learning processes of medical students during an academic learning task.

This study was designed to identify the self-regulated learning (SRL) processes of medical students during a biomedical science learning task and to examine the associations of the SRL processes with previous performance in biomedical science examinations and subsequent performance on a learning task. A sample of 76Year1 medical students were recruited based on their performance in biomedical science examinations and stratified into previous high and low performers. Participants were asked to complete a biomedical science learning task. Participants' SRL processes were assessed before (self-efficacy, goal setting and strategic planning), during (metacognitive monitoring) and after (causal attributions and adaptive inferences) their completion of the task using an SRL microanalytic interview. Descriptive statistics were used to analyse the means and frequencies of SRL processes. Univariate and multiple logistic regression analyses were conducted to examine the associations of SRL processes with previous examination performance and the learning task performance. Most participants (from 88.2% to 43.4%) reported task-specific processes for SRL measures. Students who exhibited higher self-efficacy (odds ratio [OR] 1.44, 95% confidence interval [CI] 1.09-1.90) and reported task-specific processes for metacognitive monitoring (OR 6.61, 95% CI 1.68-25.93) and causal attributions (OR 6.75, 95% CI 2.05-22.25) measures were more likely to be high previous performers. Multiple analysis revealed that similar SRL measures were associated with previous performance. The use of task-specific processes for causal attributions (OR 23.00, 95% CI 4.57-115.76) and adaptive inferences (OR 27.00, 95% CI 3.39-214.95) measures were associated with being a high learning task performer. In multiple analysis, only the causal attributions measure was associated with high learning task performance. Self-efficacy, metacognitive monitoring and causal attributions measures were associated positively with previous performance. Causal attributions and adaptive inferences measures were associated positively with learning task performance. These findings may inform remediation interventions in the early years of medical school training.