Kinds Of Tasks Research Articles

Flexible, Self-Determined… and Unhealthy? An Empirical Study on Somatic Health Among Crowdworkers.

Crowdwork is a new form of digitally enabled work in which organizations assign tasks to an anonymous group of workers via platform intermediaries. For crowdworkers, crowdwork offers both opportunities and risks. On the one side, crowdworkers enjoy high flexibility on when, where, and how much to work. On the other side, risks comparable to other forms of atypical employment arise: no labor regulation, unstable income, and uncertainty about whether enough tasks are available. Regulation of working hours lies within the crowdworkers’ own authority. Also, crowdwork in industrialized nations is often conducted during leisure times as a side-job to some other kind of employment. In accordance with Conservation of Resources Theory, we state that when leisure time gets used up with crowdwork, regeneration cannot occur and health declines. On a sample of N=748 German crowdworkers recruited from four different platform types, we analyzed whether participation in crowdwork is linked to increased somatic symptoms compared to regularly employed personnel. We found that crowdworkers show significantly increased somatic symptoms as compared to a German norm sample, that are stable across different kinds of tasks and platforms, gender, and age groups, and that is statistically due to the extent of participation in crowdwork. Specifically, we found that total work hours per week were not associated with an increase in somatic symptoms, but we did find associations with strain-based work–family conflict and the primary motivation to do crowdwork being to earn money. Consequences for research and labor regulations are discussed.

A Pyramid of Paperwork: Labors of Imagination and Interpretation in Late Twentieth Century Architectural Practice

A fundamental shift in employment patterns among architects in North America during the 1960s and 1970s impacted how particular kinds of tasks were either monopolized or delegated within firms. This article uses the archive of the U.S.-based architectural firm Gunnar Birkerts and Associates to show evidence of a growing gulf between executive architects and employee architects (particularly women assigned to work on interiors), as well as the persistence of chauvinistic ideals of practice under changed circumstances. The design for the Federal Reserve Bank of Minneapolis building (1967- 73) is shown to be illustrative of this gulf between imaginative and interpretive labor.

The experiences and needs of re-entering nurses during the COVID-19 pandemic: A qualitative study.

Background: During the COVID-19 outbreak in the Netherlands, thousands of former nurses have returned to nursing to support healthcare staff. After a period of absence and with little time to prepare, these former nurses re-entered during a challenging, uncertain and rapidly evolving pandemic. Little is known about the experiences and needs of these re-entering nurses.Objectives: Assessing the needs and experiences of re-entering nurses during the COVID-19 pandemic.Design: Qualitative study using a pragmatist approach within the interpretative paradigm.Settings: This study took place in the following settings within the Dutch healthcare system: Intensive care units, COVID and regular departments within hospitals, nursing home settings, a rehabilitation centre and newly established COVID-19 departments within nursing home settings.Participants: We purposively selected 20 nurses who had re-entered nursing during the first wave of the COVID-19 pandemic between March 2020 and June 2020 in the Netherlands. The first interview was conducted on the eighth of May 2020.Methods: We conducted 20 semi-structured interviews in Dutch. Interviews were transcribed verbatim and analysed via thematic content analysis in the coding program of MAXQDA2020. This study followed the SRQR and COREQ guidelines.Results: Seven main themes were identified. Clear job description: Participants mentioned that a lack of a clear job description led to lack of clarity about the kind of tasks that re-entering nurses were expected and allowed to perform. Training: the majority of the participants had received none or little training prior to their return. Training content: Re-entering nurses mentioned to wish for an easily accessible mentorship structure and an individualised and practical training program. Positive team dynamic: Re-entering nurses felt supported by a positive team dynamic, which was shaped by the sense of urgency and relevance of their work and helped them deal with stressful experiences. Mental health: Nearly all participants mentioned that re-entering during a pandemic did not lead to impairment of their mental health. mental health support: Most participants mentioned being able to cope with their mental health independently, sharing experiences with family and colleaguesConclusion: The results indicate that a rapid and safe return to nursing during a pandemic could be facilitated by: a clear description of roles and responsibilities; an individualised assessment determining the competences and knowledge disparities of re-entering nurses; practical training focussing on competencies needed during a pandemic; and a collaborative mentorship structure to guide re-entering nurses.Tweetable abstract: In-depth interviews with former nurses who returned to #nursing during the first wave of the #COVID19 #pandemic in the Netherlands

Analysing Temporal Evolution of Complex Data Using Similarity Queries

Regardless of the data domain, there are applications that must track the temporal evolution of data elements. Based on the instances present in the database, the goal is to estimate the state of a given element at a different time instant from those available in the database. This kind of task is common in many database application domains, such as medicine, meteorology, agriculture, financial, and others. In content-based retrieval with complex data (such as images, sounds and videos), data are usually represented in metric spaces, where only the distances between elements are available. Without dimensional coordinates, it is not possible simply to add a time dimension for trajectory estimation in these spaces, as is the case in multidimensional spaces. In this article we propose to map the metric data to a multidimensional space so that we can estimate the element’s status at a given time instant, based on known states of the same element. As it is not possible to create the complex data equivalent to its estimated position in mapped space, we propose to apply similarity queries using this position as query center. Then, we estimate how this element would be, retrieving the real data elements present in the database that are close to the estimate. In this article, in addition to the nearest neighbor query (k-NN), we propose to use two other queries: kAndRange and kAndRev. With both methods, we aim to prune non-relevant elements from the query results, retrieving only the elements that are really close to the estimates. We present experiments with different query scenarios, evaluating the effects of varying input parameters of the proposed queries.

Unique behavioral strategies in visuomotor learning: Hope for the non-learner

The existence of individual differences in motor learning capability is well known but the behaviors or strategies that contribute to this variability have been vastly understudied. What performance characteristics distinguish an expert level performer from individuals who experience little to no success, those labeled non-learners? We designed a rule-based visuomotor task which requires identification (discovery) and then exploitation of specific explicit and implicit task components that requires a specific movement pattern, the task rule, for goal achievement. When participants first attempt the task, they are informed about the goal, but are naïve to the task rule. Therefore, the purpose of this experiment is to determine how acquisition of both implicit and explicit task components, the inherent elements of the task rule, reveals differing strategies associated with performance and task success. We test the hypothesis that an examination of performance will reveal sub-groups with varying levels of success. Further, for each subgroup, we expect to find a unique relationship between visual Time-in-Target feedback (a measure of success) and subsequent updating of each task component. Out of 32 non-disabled adults, we identified three distinct sub-groups: (Low Performer/Non-Learner (LP, N=9), Moderate Performer (MP, N=12) and High Performer (HP, N=11)). A quantitative analysis of behavioral patterns reveals three findings: First, the LP sub-group demonstrated significantly lower task success which was associated with difficulty identifying the explicit component of the task. Second, the HP sub-group acquired the two task components in parallel over practice. Third, when both explicit and implicit component performance is plotted across sub-groups, a task component continuum emerges that seamlessly progresses from low to moderate to high performer groups. An exploratory analysis reveals that self-reported level of prior lifetime accumulation of video game and physical activity experience is a significant predictor of individual task performance (R2=0.50). In summary, what appears to be a key distinction between varying levels of human rule-based motor learning is the process by which feedback is used to update performance of inherent elements of the task rule. Evidence of a performance continuum and limited prior experience suggests that Low Performer/Non-Learners are generally inexperienced with these kinds of tasks, although the role of genetics and other innate learning capabilities in visuomotor learning is still largely unknown. These findings provoke new research directions toward probing the differential performance strategies associated with expertise and the development of interventions aimed to convert non-learners into learners.

Guaranteeing Timely Response to Changes of Monitored Objects by Assigning Deadlines and Periods to Tasks

Timely response to changes of monitored objects is the key to ensuring the safety and reliability of cyber-physical systems (CPSs). There are two kinds of tasks in CPSs: update tasks and control tasks. Update tasks are responsible for updating the data in the system based on the state of the objects they monitor. Control tasks are responsible for making decisions based on the data in the system. The response time of the system to the change of a monitored object consists of two parts: the time taken by update tasks to reflect the change to the system, and the time taken by control tasks to make decisions according to the data in the system. Deadlines and periods of update tasks and control tasks directly affect the response time. Reasonable deadline and period assignment is the key to ensuring timely response to the changes of monitored objects. In this paper, we study the deadline and period assignment in CPSs. To the best of our knowledge, all existing work only focuses on the deadline and period assignment for update tasks with the goal of ensuring the freshness of the data in CPSs, and this is the first study focusing on the deadline and period assignment for both update tasks and control tasks with the goal of ensuring timely response to the changes of monitored objects. A new problem about response time control and system workload control is defined in this paper. Two deadline and period assignment methods are proposed to solve the defined problem. All the proposed methods can be used in the CPSs adopting the earliest deadline first (EDF) scheduling method. Experiments with randomly generated tasks are conducted to evaluate the performance of the proposed methods in terms of acceptance ratio and execution efficiency.

Adapted Use of the TRIZ System Operator

The System Operator (SO), also known as Nine Boxes or Multiscreen, is classically used for analysing the undesired situation according to different perspectives. However, its logic can be suitably exploited also for other kinds of tasks. In such a context, this paper shows an alternative application of the SO. In particular, the new proposed SO approach allows us to formulate alternative problems, i.e., different from the central one, although referring to the same problem-solving task. By applying the proposed procedure to a real case study, this paper shows that the tool can be used for problem analysis and formulation purposes, aimed at better clarifying the problem-solving task. The case study concerns a preliminary set of experiments performed to evaluate the possibility of obtaining angular shapes by bending tiles made by a specific compostable composite material. The suggestions obtained using the proposed SO approach allowed us to find potentially valid solutions, thus paving the way for further validation tests. In particular, tests are still needed to validate the actual potentialities of the proposed SO. Additionally, further and more comprehensive tests are required to validate the solutions inferred in this study concerning the compostable tiles.

A strip steel surface defect detection method based on attention mechanism and multi-scale maxpooling

In industry, defect detection involves two kinds of tasks: defect classification and location, which make it difficult to ensure the accuracy of both, and also make the task still challenging in practical application. Based on the analysis of the advantages and disadvantages of the current defect detection method, this paper proposes a defect detection method based on attention mechanism and multi-scale maxpooling (MSMP). In order to effectively improve the detection accuracy of the model, we use Resnet50 as the pre-training network construct two-stage detection model which is used to be the baseline network, and introduce the attention mechanism and MSMP module on this basis. The attention mechanism can enhance the features of the feature map extracted in each stage of Resnet50, so that the network concentrates on the effective areas for the final detection results, and ignores the background areas that are invalid or even unfavorable for detection. The proposed MSMP can incrementally enhance the receptive field, distinguish the most significant context features, and effectively improve detection precision. The proposed method is used to train and test on the NEU-DET dataset. Compared with the baseline network without any improvement, the proposed method in this paper achieves 3.65% mAP performance improvement. Meanwhile, our method achieves a performance improvement of 3.65% mAP. In addition, compared with the feature fusion mechanism, our method improves 4.03% mAP. Moreover, compared with the attention mechanisms such as spatial attention and SE block, our method improves 1.51%/1.03% mAP. Furthermore, compared with the one-stage detection algorithm SSD/YOLO-V4, the proposed method improves 5.01%/4.92% mAP. In addition, the classification accuracy of our model is as high as 94.73%.

How participation in ecological restoration can foster a connection to nature

There have been strong claims made for ecological restoration's potential as a practice which is conductive to rethinking relationships with nature. The involvement of lay people in “hands‐on restoration” is thought to hold potential for enabling re‐examinations of human connection to nature. Restoration scholars suggest causal mechanisms present in restoration practice which may explain why it is so conductive to enabling connection to nature. This research used participatory observation and in‐depth interviewing to examine these largely untested ideas and gives insight into the casual mechanisms they present. Focusing on one case of ecological restoration in the Highlands of Scotland, the study found that exertion and achievement gained through restoration work created positive affect which people associated with their experience of nature, that laboring in nature created belonging and ownership, and that physical immersion enabled intimacy with nature. It found that learning about the legacies of human agency on landscape reduced reification and that the wider narrative of restoration gave people both a sense of being part of the unfolding history of the landscape and part of a redemptive future. It also found that focused attention created vivid memories and elevated the significance of the experience of being in nature and ritual created remarkable, memorable events. This study adds to previous work, finding that the emotional labor of leaders, the use of educational techniques, and the kinds of tasks in which participants engage are important in creating particular ideas of the relationship between humans and nature.

An Analysis of Writing Tasks in “Pathway to English” Textbook for Twelfth Grade of SMAN 5 Kota Jambi

This research was aimed to find out whether the writing task in “Pathway to English” textbook appropriate with the criteria of writing task as suggested by Paul Nation, in Nation’s theory. There are four kinds of tasks that suggested. Each of tasks has the following type : (1) Experienced Task (linked skills, draw and write, ten perfect sentences, partial writing, setting your own questions), (2) Shared Task (reproduction exercise/dicto-gloss, blackboard composition, group-class composition, writing with a secretary), (3) Guided Task (translation, picture composition, delayed copying, writing with grammar help, answer the questions, correction, complete the sentences, back writing, put the words in order, follow the model, what is it?, change the sentences, join the sentences, writing by steps, marking guided writing), (4) Independent Task. The method of this research was descriptive qualitative. To analyzed the data, the researcher used qualitative content analysis method. Research findings there are 54 types of writing task: 2 Experience task (1 Partial Writing, 1 Setting your own question), 1 Shared task ( 5 group class composition), 8 Guided task (1 translation, 7 look and write, 1 picture composition, 7 writing with grammar help, 10 answer the question, 4 correction, 11 complete the sentences, 2 put the words in order, 1 Independent task (6 independent). The result of this research was that there are criteria are found that match with the criteria as suggested by Paul Nation, this proves that this book is appropriate with the criteria as suggested by Paul Nation.Key Words: Writing Task, English Textbook, Task Analysis

The impact of cognitive and noncognitive factors on odor discrimination performance

AbstractThis study aimed to investigate which components among a range of cognitive and noncognitive factors contribute to odor discrimination and to which extent. One hundred sixty participants were assessed for olfactory discrimination considering (a) the sequence of presentation of the detection target and (b) response time taken to perform the discrimination task. Participants were tested with several cognitive tests covering executive functioning, and semantic memory and noncognitive tests measuring optimism, mindset, general self‐efficacy, and self‐esteem. Hierarchical regression analyses conducted in classic and Bayesian ways demonstrate a strong impact of semantic memory and age on odor discrimination performance. Conversely, executive functions together with noncognitive factors and response time were rather weak predictors of odor discrimination performance. Additionally, the positioning of the target odor, when presented last, facilitated the correct choice in odor discrimination. In conclusion, odor discrimination is related to semantic memory, while noncognitive factors exert only minor influences on odor discrimination scores.Practical ApplicationsIn the present study, we found that the target odor, when presented as the last of the three pens, facilitated the correct choice in odor discrimination process. Even though, the influence of the target odor position was noticed only in case of several trials, it should be considered in the future studies. Moreover, the amount of time taken to respond when performing odor discrimination task did not contribute strongly to the final outcome. Thus, controlling for the response time in this kind of task appears not to be highly significant.

Cassini-Huygens mission images classification framework by deep learning advanced approach

Developing a deep learning (DL) model for image classification commonly demands a crucial architecture organization. Planetary expeditions produce a massive quantity of data and images. However, manually analyzing and classifying flight missions image databases with hundreds of thousands of images is ungainly and yield weak accuracy. In this paper, we speculate an essential topic related to the classification of remotely sensed images, in which the process of feature coding and extraction are decisive procedures. Diverse feature extraction techniques are intended to stimulate a discriminative image classifier. Features extraction is the primary engagement in raw data processing with the purpose of data classification; when it comes across the task of analysis of vast and varied data, these kinds of tasks are considered as time-consuming and hard to be treated with. Most of these classifiers are either, in principle, quite intricate or virtually unattainable to calculate for massive datasets. Stimulated by this perception, we put forward a straightforward, efficient classifier based on feature extraction by analyzing the cell of tensors via layered MapReduce framework beside meta-learning LSTM followed by a SoftMax classifier. Experiment results show that the provided model attains a classification accuracy of 96.7%, which makes the provided model quite valid for diverse image databases with varying sizes.

Optical and gravito-inertial contributions to the perception and control of height in a simulated Low-Altitude Flight context

Low-Altitude Flight (LAF) is a flight formation consisting of rapid close ground flight. Perception and control of self-motion, allowing for optimal information collection and rapid adaptation, are of fundamental importance during LAF, but remain largely unexplored. This study aimed to analyse the impact of visuo-vestibular stimuli on the monitoring of height in a motion-based simulated LAF context. Thirteen non-pilots were tested in different environmental conditions, in which optical and gravito-inertial (GI) information were manipulated. The visual environment, displayed with a VR headset, was a low-textured landscape with identical and equally spaced trees throughout the trials. The GI environment was designed thanks to a motion-based simulator. Results showed that participants had better performances in a visuo-vestibular environment than in a visual-only setting, indicating that multi-sensory information was picked-up faster than a mono-sensory structure. Additionally, we found differences in the contribution of vestibular inputs depending on the kind of task. Practitioner summary: Low-Altitude-Flight (LAF) manoeuvres require delicate aircraft control. Two experiments using a large flight simulator investigated how visual and vestibular stimulation contribute to LAF perception and control. Results suggest that both sources of stimulation need to be combined for accurate performance, with consequences for simulator-based training scenarios. Abbreviations: LAF: low altitude flight; GI: gravito-inertial; 1/2/3D: 1/2/3 dimensions; VR: virtual reality; Mvt: movement; GVE: good visual environment; DVE: degraded visual environment; SSQ: simulator motion sickness questionnary; RT: reaction time; DIMSS: dynamic interface modelling and simulation system metric; corrAcf: maximum correlation coefficient; corrLag: maximum correlation lag; DFT: deviation from target; StdJ: standard deviation of the joytick value; NCR: number of control reversal

Robust Rank-Constrained Sparse Learning: A Graph-Based Framework for Single View and Multiview Clustering.

Graph-based clustering aims to partition the data according to a similarity graph, which has shown impressive performance on various kinds of tasks. The quality of similarity graph largely determines the clustering results, but it is difficult to produce a high-quality one, especially when data contain noises and outliers. To solve this problem, we propose a robust rank constrained sparse learning (RRCSL) method in this article. The L2,1 -norm is adopted into the objective function of sparse representation to learn the optimal graph with robustness. To preserve the data structure, we construct an initial graph and search the graph within its neighborhood. By incorporating a rank constraint, the learned graph can be directly used as the cluster indicator, and the final results are obtained without additional postprocessing. In addition, the proposed method cannot only be applied to single-view clustering but also extended to multiview clustering. Plenty of experiments on synthetic and real-world datasets have demonstrated the superiority and robustness of the proposed framework.

FACSHuman, a software program for creating experimental material by modeling 3D facial expressions.

This paper presents the FACSHuman software program, a tool for creating facial expression materials (pictures and videos) based on the Facial Action Coding System (FACS) developed by Ekman et al. (2002). FACSHuman allows almost all the Action Units (AUs) described in the FACS Manual to be manipulated through a three-dimensional modeling software interface. Four experiments were conducted to evaluate facial expressions of emotion generated by the software and their theoretical efficiency regarding the FACS. The first study (a categorization task of facial emotions such as happiness, anger, etc.) showed that 85% of generated pictures of emotional expressions were correctly categorized. The second study showed that only 82% of the most-used AUs were correctly matched. In the third experiment, two independent FACS coders rated 47 AUs generated by FACSHuman using the standard methodology used in this kind of task (AU identification). Results showed good-to-excellent codification rates (64% and 85%). In the last experiment, 54 combinations of AU were evaluated by the same FACS coders. Results showed good-to-excellent codification rates (68-82%). Results suggested that FACSHuman could be used as experimental material for research into nonverbal communication and emotional expression.

Does attention solve the "apples-and-oranges" problems of judging task difficulty and task order?

How do we compare the difficulty of different kinds of tasks, and how we do sequence tasks of different kinds when the basis for the ordering is the tasks' difficulty levels? The ability to do these things requires a common currency, but the identity of that currency, if it exists, is unknown. We hypothesized that people may believe that the time that attention is paid to tasks enables people to compare and sequence tasks of different kinds. To evaluate this hypothesis, we tested three groups of participants. One group estimated the proportion of time that performance of a task requires attention-what we called attention time proportions or ATPs. We obtained ATPs for tasks that were "more intellectual" (counting) and others that were "more physical" (locomotion). Two additional groups made 2-alternative-forced-choice decisions about the relative ease and preferred sequencing of all possible pairs of tasks for which ATPs were independently obtained. We found that ATPs predicted judgments of task difficulty and preferred task order.

Research and Implementation of Scheduling Strategy in Kubernetes for Computer Science Laboratory in Universities

How to design efficient scheduling strategy for different environments is a hot topic in cloud computing. In the private cloud of computer science labs in universities, there are several kinds of tasks with different resource requirements, constraints, and lifecycles such as IT infrastructure tasks, course design tasks submitted by undergraduate students, deep learning tasks and and so forth. Taking the actual needs of our laboratory as an instance, these tasks are analyzed, and scheduled respectively by different scheduling strategies. The Batch Scheduler is designed to process tasks in rush time to improve system throughput. Dynamic scheduling algorithm is proposed to tackle long-term lifecycle tasks such as deep learning tasks which are hungry for GPU resources and have dynamically changing priorities. Experiments show that the scheduling strategies proposed in this paper improve resource utilization and efficiency.

Algorithm appreciation or aversion? Comparing in-service and pre-service teachers’ acceptance of computerized expert models

Although computerized expert models (i.e., algorithms) could improve educational decisions and judgments, initial research has demonstrated that teachers, like other professional groups, tend to be “algorithm averse.” In the current study, we use behavioral and questionnaire data to examine the extent to which in-service and pre-service (i.e., students in training to become) teachers accept advice from expert models and investigate how teachers' acceptance of expert models could be improved. Although it is often presumed that younger generations are less algorithm averse, we demonstrate that both in-service and pre-service teachers prefer advice from a human source (school counselor) than from an expert model, to a similar extent. Furthermore, we find that advice acceptance depends on the difficulty of the decision task, but we find no evidence that pre-service teachers’ acceptance of computerized advice depends on their numeracy or the Big Five traits of openness and neuroticism. Finally, we find that in-service teachers lacked knowledge of computerized expert models but indicated that advice from expert models would be superior to human advice in certain kinds of tasks. Our results indicate that both in- and pre-service teachers could profit from training about the definition and value of computerized expert models, and we provide suggestions for training and future research.

Task MRI-Based Functional Brain Network of Major Depression.

This chapter will focus on task magnetic resonance imaging (MRI) to understand the biological mechanisms and pathophysiology of brain in major depressive disorder (MDD), which would have minor alterations in the brain function. Therefore, the functional study, such as task MRI functional connectivity, would play a crucial role to explore the brain function in MDD. Different kinds of tasks would determine the alterations in functional connectivity in task MRI studies of MDD. The emotion-related tasks are linked with alterations in anterior cingulate cortex, insula, and default mode network. The emotional memory task is linked with amygdala-hippocampus alterations. The reward-related task would be related to the reward circuit alterations, such as fronto-straital. The cognitive-related tasks would be associated with frontal-related functional connectivity alterations, such as the dorsolateral prefrontal cortex, anterior cingulate cortex, and other frontal regions. The visuo-sensory characteristics of tasks might be associated with the parieto-occipital alterations. The frontolimbic regions might be major components of task MRI-based functional connectivity in MDD. However, different scenarios and tasks would influence the representations of results.

Evaluation of Writing Efficiency When Using Tablet Computers at Inclines

At present, owing to the convenience of stylus pen-based input devices and touch-sensitive screens, users often use tablet computers to draw, take notes, and annotate documents. The aim of the present research was to investigate, for tablet computer users, the writing speed, tracing accuracy, comfort perception, and satisfaction in terms of five angles formed by the use of an external peripheral. In two kinds of tasks (writing and tracing lines), a control experimental design with various tilt angles (5°, 15°, 25°, 35° and 45° from horizontal) was adopted. The efficiency of writing tasks and the accuracy of line tracing were calculated following each incline. Using the five-point Likert Scale, the tablet computer users’ perceived comfort and ease of use were also evaluated. The results reveal that when tablet personal computers (Tablet PCs) were mounted at a high tilt angle (35°), the writing speed was high (P = 0.028). Meanwhile, when the tablet computer was mounted at a low tilt angle (5°), user comfort and satisfaction were high (P < 0.001). The findings of the present article suggest that users who need to write or draw for short periods increase the angle (approximate 35°) of their Tablet PC to improve their writing speed. Conversely, users who need long periods of use could position the Tablet PC at a lower tilt angle (approximate 5°) to reduce the risk of any musculoskeletal disorder.