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Crowdsourcing has become an efficient paradigm to utilize human intelligence to perform tasks that are challenging for machines. Many incentive mechanisms for crowdsourcing systems have been proposed. However, most of existing incentive mechanisms assume that there are sufficient participants to perform crowdsourcing tasks. In large-scale crowdsourcing scenarios, this assumption may be not applicable. To address this issue, we diffuse the crowdsourcing tasks in social network to increase the number of participants. To make the task diffusion more applicable to crowdsourcing system, we enhance the classic Independent Cascade model so the influence is strongly connected with both the types and topics of tasks. Based on the tailored task diffusion model, we formulate the Budget Feasible Task Diffusion ( BFTD ) problem for maximizing the value function of platform with constrained budget. We design a parameter estimation algorithm based on Expectation Maximization algorithm to estimate the parameters in proposed task diffusion model. Benefitting from the submodular property of the objective function, we apply the budget-feasible incentive mechanism, which satisfies desirable properties of computational efficiency, individual rationality, budget-feasible, truthfulness, and guaranteed approximation, to stimulate the task diffusers. The simulation results based on two real-world datasets show that our incentive mechanism can improve the number of active users and the task completion rate by 9.8% and 11%, on average.

With the advent of mobile crowd sourcing (MCS) systems and its applications, the selection of the right crowd is gaining utmost importance. The increasing variability in the context of MCS tasks makes the selection of not only the capable but also the willing workers crucial for a high task completion rate. Most of the existing MCS selection frameworks rely primarily on reputation-based feedback mechanisms to assess the level of commitment of potential workers. Such frameworks select workers having high reputation scores but without any contextual awareness of the workers, at the time of selection, or the task. This may lead to an unfair selection of workers who will not perform the task. Hence, reputation on its own only gives an approximation of workers’ behaviors since it assumes that workers always behave consistently regardless of the situational context. However, following the concept of cross-situational consistency, where people tend to show similar behavior in similar situations and behave differently in disparate ones, this work proposes a novel recruitment system in MCS based on behavioral profiling. The proposed approach uses machine learning to predict the probability of the workers performing a given task, based on their learned behavioral models. Subsequently, a group-based selection mechanism, based on the genetic algorithm, uses these behavioral models in complementation with a reputation-based model to recruit a group of workers that maximizes the quality of recruitment of the tasks. Simulations based on a real-life dataset show that considering human behavior in varying situations improves the quality of recruitment achieved by the tasks and their completion confidence when compared with a benchmark that relies solely on reputation.

In this article, we propose an approach to improve quality in crowdsourcing (CS) tasks using Task Completion Time (TCT) as a source of information about the reliability of workers in a game-theoretical competitive scenario. Our approach is based on the hypothesis that some workers are more risk-inclined and tend to gamble with their use of time when put to compete with other workers. This hypothesis is supported by our previous simulation study. We test our approach with 35 topics from experiments on the TREC-8 collection being assessed as relevant or non-relevant by crowdsourced workers both in a competitive (referred to as “Game”) and non-competitive (referred to as “Base”) scenario. We find that competition changes the distributions of TCT, making them sensitive to the quality (i.e., wrong or right) and outcome (i.e., relevant or non-relevant) of the assessments. We also test an optimal function of TCT as weights in a weighted majority voting scheme. From probabilistic considerations, we derive a theoretical upper bound for the weighted majority performance of cohorts of 2, 3, 4, and 5 workers, which we use as a criterion to evaluate the performance of our weighting scheme. We find our approach achieves a remarkable performance, significantly closing the gap between the accuracy of the obtained relevance judgements and the upper bound. Since our approach takes advantage of TCT, which is an available quantity in any CS tasks, we believe it is cost-effective and, therefore, can be applied for quality assurance in crowdsourcing for micro-tasks.

AbstractMobile instant messaging (MIM) has become the most popular means for young people to communicate. MIM apps typically come with a myriad of features that educators may leverage to increase student learning. However, it remains poorly understood to what extent and in what aspect MIM can facilitate student engagement. We address the gap by comparing the effects of using MIM and asynchronous online discussion (AOD) on student online engagement, using a quasi-experimental study involving a historical cohort control group. Understanding which communication mode can better promote student online engagement is particularly important during the current widespread use of online learning. Specifically, we examined engagement from the behavioral, emotional, and cognitive dimensions. The results showed that the MIM group was more behaviorally engaged in discussion activities, producing more messages, more words, and higher rates of participation, task completion, and interaction. Emotionally, no statistically significant difference was found in students’ affective evaluation of course interaction and satisfaction between the two groups. However, MIM appeared to help students with improved intimacy and interpersonal relationships. Cognitively, the MIM group was more engaged than the AOD group. In particular, MIM seemed to facilitate interactive idea exchange and thus contributing to more “creating” activities. We conclude by providing suggestions for future instructional practice and research directions.

Background: Navigating large hospitals can be very challenging due to the functional complexity as well as the evolving changes and expansions of such facilities. Hospital wayfinding issues could lead to stress, negative mood, and poor healthcare experience among patients, staff, and family members. Objectives: A survey-embedded experiment was conducted using immersive virtual environment (IVE) techniques to explore people’s wayfinding performance and their mood and spatial experience in hospital circulation spaces with or without visible greenspaces. Methods: Seventy-four participants were randomly assigned to either group to complete wayfinding tasks in a timed session. Participants’ wayfinding performances were interpreted using several indicators, including task completion, duration, walking distance, stop, sign-viewing, and route selection. Participants’ mood states and perceived environmental attractiveness and atmosphere were surveyed; their perceived levels of presence in the IVE hospitals were also reported. Results: The results revealed that participants performed better on high complexity wayfinding tasks in the IVE hospital with visible greenspaces, as indicated by less time consumed and shorter walking distance to find the correct destination, less frequent stops and sign viewing, and more efficient route selection. Participants also experienced enhanced mood states and favorable spatial experience and perceived aesthetics in the IVE hospital with visible greenspaces than the same environment without window views. IVE techniques could be an efficient tool to supplement environment-behavior studies with certain conditions noted. Conclusions: Hospital greenspaces located at key decision points could serve as landmarks that positively attract people’s attention, aid wayfinding, and improve their navigational experience.

Virtual reality devices are used for several application domains, such as medicine, entertainment, marketing and training. A handheld controller is the common interaction method for direct object manipulation in virtual reality environments. Using hands would be a straightforward way to directly manipulate objects in the virtual environment if hand-tracking technology were reliable enough. In recent comparison studies, hand-based systems compared unfavorably against the handheld controllers in task completion times and accuracy. In our controlled study, we compare these two interaction techniques with a new hybrid interaction technique which combines the controller tracking with hand gestures for a rigid object manipulation task. The results demonstrate that the hybrid interaction technique is the most preferred because it is intuitive, easy to use, fast, reliable and it provides haptic feedback resembling the real-world object grab. This suggests that there is a trade-off between naturalness, task accuracy and task completion time when using these direct manipulation interaction techniques, and participants prefer to use interaction techniques that provide a balance between these three factors.

Eye-tracking based usability testing and User Experience (UX) research are widespread in the development processes of various types of software; however, there exist specific difficulties during usability tests of three-dimensional (3D) software. Analysing the screen records with gaze plots, heatmaps of fixations, and statistics of Areas of Interests (AOI), methodological problems occur when the participant wants to rotate, zoom, or move the 3D space. The data gained regarded the menu bar is mainly interpretable; however, the data regarded the 3D environment is hardly so, or not at all. Our research tested four software applications with the aforementioned problem in mind: ViveLab and Jack Digital Human Modelling (DHM) and ArchiCAD and CATIA Computer Aided Design (CAD) software. Our original goal was twofold. Firstly, with these usability tests, we aimed to identify issues in the software. Secondly, we tested the utility of a new methodology which was included in the tests. This paper summarizes the results on the methodology based on individual experiments with different software applications. One of the main ideas behind the methodology adopted is to tell the participants (during certain subtasks of the tests) not to move the 3D space while they perform the given tasks at a certain point in the usability test. During the experiments, we applied a Tobii eye-tracking device, and after the task completion, each participant was interviewed. Based on these experiences, the methodology appears to be both useful and applicable, and its visualisation techniques for one or more participants are interpretable.

In the Industrial Internet of Things (IIoT), various tasks are created dynamically because of the small quantity batch production. Hence, it is difficult to execute tasks only with devices that have limited battery lives and computation capabilities. To solve this problem, we adopted the mobile edge computing (MEC) paradigm. However, if there are numerous tasks to be processed on the MEC server (MECS), it may not be suitable to deal with all tasks in the server within a delay constraint owing to the limited computational capability and high network overhead. Therefore, among cooperative computing techniques, we focus on task offloading to nearby devices using device-to-device (D2D) communication. Consequently, we propose a method that determines the optimal offloading strategy in an MEC environment with D2D communication. We aim to minimize the energy consumption of the devices and task execution delay under certain delay constraints. To solve this problem, we adopt a Q-learning algorithm that is part of reinforcement learning (RL). However, if one learning agent determines whether to offload tasks from all devices, the computing complexity of that agent increases tremendously. Thus, we cluster the nearby devices that comprise the job shop, where each cluster’s head determines the optimal offloading strategy for the tasks that occur within its cluster. Simulation results show that the proposed algorithm outperforms the compared methods in terms of device energy consumption, task completion rate, task blocking rate, and throughput.

The purpose of this study was assessing user experience while working with two popular CMS systems: WordPress and PrestaShop. The evaluation was done using a questionnaire and an eye tracking technique. Average task completion time, the number of fixations, the percentage of correctly completed tasks and the SUS index were used for comparisons. On the basis of the obtained results which, were collected during and after the users' interaction with a given system, it is difficult to clearly state which CMS proved to be better.

This paper concerns the study of user experience and focuses on two aspects i.e. usability and user satisfaction. Two mobile applications for monitoring human activity, Mi Fit and Google Fit, were tested. Both applications work with sports armbands. Two methods were used for the study: a questionnaire and eye tracking. The comparison of the applications was made on the basis of the collected results from questionnaires, measurements of task completion times and the number and type of errors detected. Nine respondents participated in the study. The Google Fit application received a higher average score for user satisfaction, fewer errors and shorter task completion times.