Multiple Types Of Tasks Research Articles

An Exploratory Study of the Impact of Task Selection Strategies on Worker Performance in Crowdsourcing Microtasks

In microtask crowdsourcing systems like Amazon Mechanical Turk (AMT) and Appen Figure-Eight, workers often employ task selection strategies, completing sequences of tasks to maximize earnings. While previous literature has explored the effects of sequential tasks with varying complexities of the same type, there is a lack of knowledge on the consequences when multiple types of tasks with similar levels of difficulty are performed. This study examines the impact of sequences of three frequently employed task types, namely image classification, text classification, and surveys, on workers' engagement, accuracy, and perceived workloads. In addition, we analyze the influence of workers' personality traits on their strategies for selecting tasks. Our study, which involved 558 participants using AMT, found that engaging in sequences of distinct task types had a detrimental effect on classification task engagement and accuracy. It also increases the perceived task load and the worker's frustration. Nevertheless, the precise order of tasks does not significantly impact these results. Moreover, we showed a slight association between personality traits and the workers' selection strategy for the tasks. The results offered valuable knowledge for designing an efficient and inclusive crowdsourcing platform.

Load-balanced offloading of multiple task types for mobile edge computing in IoT

The continuous development of mobile networks poses new challenges for end devices with limited computing power. Mobile or multi-access edge computing (MEC) has been proposed for providing the computing resources close to the end devices that need them. However, in real network environments, MEC servers also have limited computing resources that need to be shared among many devices and efficient resource allocation is critical to ensure that the limited resources are optimally used. In view of this, we propose the Balanced Offload for Multi-type Tasks (BOMT) algorithm. The tasks to be offloaded are first prioritized according to their type, size and maximum tolerable delay; then different offloading algorithms are executed for different priority tasks according to the level of the priority and the current load on the MEC server. Following which, the optimal offloading policy is determined iteratively. Simulation results show that BOMT can effectively reduce system delay, increase user coverage and offload task completion rates.

PREFER: Prompt Ensemble Learning via Feedback-Reflect-Refine

As an effective tool for eliciting the power of Large Language Models (LLMs), prompting has recently demonstrated unprecedented abilities across a variety of complex tasks. To further improve the performance, prompt ensemble has attracted substantial interest for tackling the hallucination and instability of LLMs. However, existing methods usually adopt a two-stage paradigm, which requires a pre-prepared set of prompts with substantial manual effort, and is unable to perform directed optimization for different weak learners. In this paper, we propose a simple, universal, and automatic method named PREFER (Prompt Ensemble learning via Feedback-Reflect-Refine) to address the stated limitations. Specifically, given the fact that weak learners are supposed to focus on hard examples during boosting, PREFER builds a feedback mechanism for reflecting on the inadequacies of existing weak learners. Based on this, the LLM is required to automatically synthesize new prompts for iterative refinement. Moreover, to enhance stability of the prompt effect evaluation, we propose a novel prompt bagging method involving forward and backward thinking, which is superior to majority voting and is beneficial for both feedback and weight calculation in boosting. Extensive experiments demonstrate that our PREFER achieves state-of-the-art performance in multiple types of tasks by a significant margin. We have made our code publicly available.

NASLMRP: Design of a Negotiation Aware Service Level Agreement Model for Resource Provisioning in Cloud Environments

Cloud resource provisioning requires examining tasks, dependencies, deadlines, and capacity distribution. Scalability is hindered by incomplete or complex models. Comprehensive models with low-to-moderate QoS are unsuitable for real-time scenarios. This research proposes a Negotiation Aware SLA Model for Resource Provisioning in cloud deployments to address these challenges. In the proposed model, a task-level SLA maximizes resource allocation fairness and incorporates task dependency for correlated task types. This process's new tasks are processed by an efficient hierarchical task clustering process. Priority is assigned to each task. For efficient provisioning, an Elephant Herding Optimization (EHO) model allocates resources to these clusters based on task deadline and make-span levels. The EHO Model suggests a fitness function that shortens the make-span and raises deadline awareness. Q-Learning is used in the VM-aware negotiation framework for capacity tuning and task-shifting to post-process allocated tasks for faster task execution with minimal overhead. Because of these operations, the proposed model outperforms state-of-the-art models in heterogeneous cloud configurations and across multiple task types. The proposed model outperformed existing models in terms of make-span, deadline hit ratio, 9.2% lower computational cycles, 4.9% lower energy consumption, and 5.4% lower computational complexity, making it suitable for large-scale, real-time task scheduling.

PAMI: Partition Input and Aggregate Outputs for Model Interpretation

There is an increasing demand for interpretation of model predictions especially in high-risk applications. Various visualization approaches have been proposed to estimate the part of input which is relevant to a specific model prediction. However, most approaches require model structure and parameter details in order to obtain the visualization results, and in general much effort is required to adapt each approach to multiple types of tasks particularly when model backbone and input format change over tasks. In this study, a simple yet effective visualization framework called PAMI is proposed based on the observation that deep learning models often aggregate features from local regions for model predictions. The basic idea is to mask majority of the input and use the corresponding model output as the relative contribution of the preserved input part to the original model prediction. For each input, since only a set of model outputs are collected and aggregated, PAMI does not require any model detail and can be applied to various prediction tasks with different model backbones and input formats. Extensive experiments on multiple tasks confirm the proposed method performs better than existing visualization approaches in more precisely finding class-specific input regions, and when applied to different model backbones and input formats. The source code is available at https://github.com/fuermowei/PAMI.

A 5-min paradigm to evoke robust emotional reactivity in neuroimaging studies.

The advent of the Research Domain Criteria (RDoC) approach to funding translational neuroscience has highlighted a need for research that includes measures across multiple task types. However, the duration of any given experiment is quite limited, particularly in neuroimaging contexts, and therefore robust estimates of multiple behavioral domains are often difficult to achieve. Here we offer a "turn-key" emotion-evoking paradigm suitable for neuroimaging experiments that demonstrates strong effect sizes across widespread cortical and subcortical structures. This short series could be easily added to existing fMRI protocols, and yield a reliable estimate of emotional reactivity to complement research in other behavioral domains. This experimental adjunct could be used to enable an initial comparison of emotional modulation with the primary behavioral focus of an investigator's work, and potentially identify new relationships between domains of behavior that have not previously been recognized.

Stable Strategy Formation for Mobile Users in Crowdsensing Using Co-Evolutionary Model

In crowdsensing, the diversity of the sensing tasks and an enhancement of the smart devices enable mobile users to accept multiple types of tasks simultaneously. In this study, we propose a new practical framework for dealing with the challenges of task assignment and user incentives posed by complex heterogeneous task scenarios in a crowdsensing market full of competition. First, based on the non-cooperative game property of mobile users, the problem is formulated into a Nash equilibrium problem. Then, to provide an efficient solution, a judgment method based on constraints (sensing time and sensing task dimension) is designed to decompose the problems into different situations according to the complexity. We propose a genetic-algorithm-based approach to find the combination of tasks that maximizes the utility of users and adopts a co-evolutionary model to formulate a stable sensing strategy that maintains the maximum utility of all users. Furthermore, we reveal the impact of competition between users and tasks on user strategies and use a cooperative weight to reflect it mathematically. Based on this, an infeasible solution repair method is designed in the genetic algorithm to reduce the search space, thus effectively accelerating the convergence speed. Extensive simulations demonstrate the effectiveness of the proposed method.

Solving a multiple-qualifications physician scheduling problem with multiple types of tasks by dynamic programming and variable neighborhood search

This article investigates a novel physician scheduling problem. Different types of tasks can be performed by physicians with certain qualifications. Tasks have different properties depending on their types, lengths, and starting times. Physicians performing tasks can yield different values of benefit and cost according to their qualifications and the task property. The objective is to maximise the sum of profit (i.e., benefit minus cost). For solving the studied problem, three layer-progressive processes are proposed and corresponding solution strategies are developed for them respectively. A Variable Neighbourhood Search is applied in the first-layer process to assign a certain qualification of physicians to each task property. The problem is then decomposed into scheduling physicians of single qualification as the second-layer process. On this layer, a heuristic incorporating a Dynamic Programming algorithm is developed to generate a task property list for each qualification of physicians to guarantee the optimum of the solutions. The Dynamic Programming algorithm is applied on the third-layer process to get the task property list for a physician. In the computational experiments, the proposed approach is compared with three meta-heuristic algorithms and Gurobi. The results show that the proposed approach outperforms other compared algorithms.

Histogram Publication over Numerical Values under Local Differential Privacy

Local differential privacy has been considered the standard measurement for privacy preservation in distributed data collection. Corresponding mechanisms have been designed for multiple types of tasks, like the frequency estimation for categorical values and the mean value estimation for numerical values. However, the histogram publication of numerical values, containing abundant and crucial clues for the whole dataset, has not been thoroughly considered under this measurement. To simply encode data into different intervals upon each query will soon exhaust the bandwidth and the privacy budgets, which is infeasible for real scenarios. Therefore, this paper proposes a highly efficient framework for differentially private histogram publication of numerical values in a distributed environment. The proposed algorithms can efficiently adopt the correlations among multiple queries and achieve an optimal resource consumption. We also conduct extensive experiments on real‐world data traces, and the results validate the improvement of proposed algorithms.

Hybrid flexibility strategy on personnel scheduling: Retail case study

This study evaluates the potential benefits of incorporating labor flexibility into personnel scheduling. The solution methodology develops, in an innovative way, a hybrid strategy of labor flexibility that combines the following two flexibility strategies: (i) flexible contracts, which allow to relax the duration of the shifts and the number of weekly hours employees must work; and (ii) multiskilled staff, employees trained to work on multiple task types. A mixed integer linear programming model is proposed to determine how many employees are required in each type of contract, and how many of them will be multiskilled employees and in which task types. Results using real data from a Chilean retail firm show the potential benefits of the proposed hybrid flexibility strategy in relation to experiments where flexibility is not considered (i.e., without flexible contracts and multiskilling), or even, when scenarios consider only one of the two flexibility strategies. The increases in the number of multiskilled employees are mainly produced by demand variability than by their respective size. Regarding the staff composition, empirical results show that on average the proportion of full-time employees with respect to part-time employees is approximately 1:3. It is also shown that the most attractive contracts are those that have short workdays.

Prevalence of Engagement in Single versus Multiple Types of Secondary Tasks: Results from the Naturalistic Engagement in Secondary Task (NEST) Dataset

We investigated engagement in single vs. multiple types of secondary tasks in distraction-affected, safety-critical events (SCEs), i.e., crashes/near-crashes, and baselines reported in the Naturalistic Engagement in Secondary Tasks (NEST) dataset. NEST was created from Second Strategic Highway Research Program (SHRP2) data for studying distractions in detail. Early descriptive analysis on NEST found that most distraction-affected SCE and baseline epochs (10 s long) include more than one type of secondary task, suggesting that a considerable number of drivers may be engaging in multiple secondary activities within a relatively short time frame, potentially being exposed to increased demands brought on by multi-tasking and task-switching. We conducted inferential statistics on NEST focusing on engagement in single vs. multiple types of tasks across SCEs and baselines. A logit model was built to compare the odds of engaging in single vs. multiple types of tasks with the following predictors: event type (SCE, baseline), environmental demand, GPS speed, and driver age. The last three predictors were included to capture the driving demands experienced, which may have impacted drivers’ task engagement behavior. Odds of engagement in multiple types of secondary tasks was higher in SCEs than baselines. Furthermore, with marginal statistical significance, drivers 65 years and over were less likely to engage in multiple types of secondary tasks than younger drivers. Overall, engagement in multiple secondary task types is more prevalent in SCEs. Most crash risk studies to date have reported the effects associated with one type of secondary task. However, it appears that these effects may be confounded by the presence of other secondary tasks.

Allocating Multiple Types of Tasks to Heterogeneous Agents Based on the Theory of Comparative Advantage

We present a method to allocate multiple tasks with uncertainty to heterogeneous robots using the theory of comparative advantage: an economic theory that maximizes the benefit of specialization. In real applications, robots often must execute various tasks with uncertainty and future multirobot system will have to work effectively with people as a team. As an example, it may be necessary to explore an unknown environment while executing a main task with people, such as carrying, rescue, military, or construction. The proposed task allocation method is expected to reduce the total makespan (total length of task-execution time) compared with conventional methods in robotic exploration missions. We expect that our method is also effective in terms of calculation time compared with the time-extended allocation method (based on the solution of job-shop scheduling problems). We simulated carrying tasks and exploratory tasks, which include uncertainty conditions such as unknown work environments (2 tasks and 2 robots, multiple tasks and 2 robots, 2 robots and multiple tasks, and multiple tasks and multiple robots). In addition, we compared our method with full searching and methods that maximize the sum of efficiency in these simulations by several conditions: first, 2 tasks (carrying and exploring) in the four uncertain conditions (later time, new objects appearing, disobedient robots, and shorter carrying time) and second, many types of tasks to many types of robots in the three uncertain conditions (unknown carrying time, new objects appearing, and some reasonable agents). The proposed method is also effective in three terms: the task-execution time with an increasing number of objects, uncertain increase in the number of tasks during task execution, and uncertainty agents who are disobedient to allocation orders compared to full searching and methods that maximize the sum of efficiency. Additionally, we performed two real-world experiments with uncertainty.

Preservice teachers’ articulated noticing through pedagogies of practice

Elementary preservice teachers at six universities engaged in a task that provided them opportunities to articulate their professional noticing within video representations, written decompositions, and animated approximations of practice. The preservice teachers’ written accounts indicated that a majority attended to students or student thinking; however, when asked to illustrate their noticing through animation, focus shifted to the classroom teacher. Findings indicate the extent to which preservice teachers articulated specific mathematics concepts within and across pedagogies of practice and highlight the critical importance for selecting and utilizing multiple types of tasks to better understand preservice teacher noticing. Implications for eliciting and supporting preservice teacher noticing are discussed.

Mining intricate temporal rules for recognizing complex activities of daily living under uncertainty

Daily living activity recognition can be exploited to benefit mobile and ubiquitous computing applications. Techniques so far are mature to recognize simple actions. Due to the characteristics of diversity and uncertainty in daily living applications, most existing complex activity recognition approaches have notable limitations. First, graphical model-based approaches still lack sufficient expressive power to model rich temporal relations among activities. Second, it would be rather difficult for graphical model-based approaches to build a unified model for achieving multiple types of tasks. Third, current semantic-based approaches often fail to capture uncertainties. Fourth, formulae in these semantic-based approaches are often manually encoded. Meanwhile, it is impractical to handcraft each formula accurately in daily living scenarios where temporal relations among activities are intricate. To address these issues, we present a probabilistic semantic-based framework that combines Markov logic network with 15 temporal and hierarchical relations to explicitly perform diverse inference tasks of daily living in a unified manner. Advanced pattern mining techniques are introduced to automatically learn the propositional logic rules of intricate relations as well as their weights. Experimental results show that by logical reasoning with the mined temporal dependencies under uncertainty, the proposed model leads to an improved performance, particularly when recognizing complex activities involving the incomplete or incorrect observations of atomic actions.

Not As Smart As We Think: A Study of Collective Intelligence in Virtual Groups

Organizations increasingly use virtual groups for many types of work, yet little research has examined factors that make groups perform better across multiple different types of tasks. Previous research has proposed that groups, like individuals, have a general factor of collective intelligence, an ability to perform consistently across multiple types of tasks. We studied groups that used computer-mediated communication (CMC) to investigate whether collective intelligence is similar or different when groups work using CMC. A collective intelligence factor did not emerge among groups using CMC, suggesting that collective intelligence manifests itself differently depending on context. This is in contrast to previous findings. Our results surface a need for more research on boundary conditions of the construct of collective intelligence. Our findings also have practical implications: managers should take care when organizing virtual group work because groups that perform well on one type of task will not necessarily be the groups that do well on other tasks

Exploiting Thread-Level Parallelism on HEVC by Employing a Reference Dependency Graph

This paper presents an optimized parallel algorithm for the next-generation video codec High Efficiency Video Coding (HEVC). The proposed method provides maximized parallel scalability by exploiting two levels of parallelism: 1) frame level and 2) task level. Frame-level parallelism is exploited using a graph that efficiently provides a parallel coding order of the frames with complex reference dependencies. The proposed reference dependency graph is generated at runtime by a novel construction algorithm that dynamically analyzes the configuration of the HEVC codec. Task-level parallelism is exploited to provide further scalability to frame-level parallelization. A pipelined execution is allowed for independent tasks, which are defined by dividing and categorizing a single coding process into multiple types of tasks. The proposed parallel encoder and decoder do not suffer from loss in coding efficiency because neither constraints nor modification in coding options are required. The proposed parallel methods result in an average encoding speedup of 1.75 and the aggressive method that exploits additional frame-level parallelism achieved 6.52 speedup using eight physical cores.

An architecture for a continuous, user-driven, and data-driven application of clinical guidelines and its evaluation

ObjectivesDesign, implement, and evaluate a new architecture for realistic continuous guideline (GL)-based decision support, based on a series of requirements that we have identified, such as support for continuous care, for multiple task types, and for data-driven and user-driven modes. MethodsWe designed and implemented a new continuous GL-based support architecture, PICARD, which accesses a temporal reasoning engine, and provides several different types of application interfaces. We present the new architecture in detail in the current paper. To evaluate the architecture, we first performed a technical evaluation of the PICARD architecture, using 19 simulated scenarios in the preeclampsia/toxemia domain. We then performed a functional evaluation with the help of two domain experts, by generating patient records that simulate 60 decision points from six clinical guideline-based scenarios, lasting from two days to four weeks. Finally, 36 clinicians made manual decisions in half of the scenarios, and had access to the automated GL-based support in the other half. The measures used in all three experiments were correctness and completeness of the decisions relative to the GL. ResultsMean correctness and completeness in the technical evaluation were 1±0.0 and 0.96±0.03 respectively. The functional evaluation produced only several minor comments from the two experts, mostly regarding the output’s style; otherwise the system’s recommendations were validated. In the clinically oriented evaluation, the 36 clinicians applied manually approximately 41% of the GL’s recommended actions. Completeness increased to approximately 93% when using PICARD. Manual correctness was approximately 94.5%, and remained similar when using PICARD; but while 68% of the manual decisions included correct but redundant actions, only 3% of the actions included in decisions made when using PICARD were redundant. ConclusionsThe PICARD architecture is technically feasible and is functionally valid, and addresses the realistic continuous GL-based application requirements that we have defined; in particular, the requirement for care over significant time frames. The use of the PICARD architecture in the domain we examined resulted in enhanced completeness and in reduction of redundancies, and is potentially beneficial for general GL-based management of chronic patients.

An Exploration of Three-Dimensional Integrated Assessment for Computational Thinking

Computational thinking (CT) is a fundamental skill for students, and assessment is a critical factor in education. However, there is a lack of effective approaches to CT assessment. Therefore, we designed the Three-Dimensional Integrated Assessment (TDIA) framework in this article. The TDIA has two aims: one was to integrate three dimensions (directionality, openness, and process) into the design of effective assessment tasks; and the other was to assess comprehensively the three dimensions of CT including computational concepts, practices, and perspectives. Guided by the TDIA framework, we designed three pairs of tasks: closed forward tasks and closed reverse tasks, semiopen forward tasks and semiopen reverse tasks, and open tasks with a creative design report and open tasks without a creative design report. To further confirm each task’s applicability and its advantages and disadvantages, we conducted a test experiment at the end of the autumn semester in 2014 in a primary school for 3 weeks. The results indicated that (a) the reverse tasks were not more superior than the forward tasks; (b) the semiopen tasks and the open tasks were more effective than the closed tasks, and the semiopen tasks had higher difficulty and discrimination than the others; (c) the self-reports provided a helpful function for learning diagnosis and guidance; (d) the scores had no significant difference between the schoolboys and the schoolgirls in all six tasks; and (e) the six tasks’ difficulty and discrimination were all acceptable, and the semiopen tasks had higher difficulty and discrimination than the others. To effectively apply them, the following suggestions for teachers to design computational tasks are proposed: motivating students’ interest and enthusiasm, incorporating semifinished artifacts, involving learning diagnosis and guidance, and including multiple types of tasks in an assessment.

Workload Change Over Time for Nuclear Power Plant Operation Tasks

Nuclear Power Plant (NPP) operation requires multiple task types to complete necessary day-to-day activities and operating procedures. The accurate completion of these tasks is necessary for safety-critical domains, as errors and omissions can potentially result in severe consequences. As a result, it is important to determine the cause of human error within various task types. One factor that contributes to human performance is workload. The present experiment is a follow-up, further investigating levels of workload, within a NPP simulated environment, with experienced participants. EEG, ECG, and the NASA-TLX have been shown to be sensitive to changes in workload across domains and are thus utilized in the present experiment to record workload differences. Specifically, the present experiment investigated these participants across 27 sessions for three different NPP task types –checking, detection, and response implementation.

Linkages between informal and formal care-givers in home-care networks of frail older adults

ABSTRACTIn ageing societies, policy makers aim for more contact between informal and formal care-givers as it may enhance the quality of care. So far, the linkage between formal and informal care-givers is generally studied from a one-sided or a single dyadic perspective, without taking into account that care networks of community-dwelling older adults often exist of multiple informal and formal care-givers. The current study examines discussion of care between all potential informal–formal care-giver dyads in a care network, and relates this to characteristics of the older care recipient, the care network and the care-givers. Seventy-four Dutch older care recipients provided information on all care-givers who helped with five different types of tasks; 410 care-givers reported on the contact between all care-givers identified. Multi-level logistic regression was conducted in 2,150 informal–formal care-giver dyads and revealed that in 26 per cent of all these dyads discussion on care occurred. This was more likely when both care-givers performed multiple types of tasks, the informal care-giver was residing with the care recipient, and contact within the formal and the informal sub-network was higher. To enhance discussion of care between informal and formal care-givers in care networks where no discussion occurs at all, home-care organisations may need to allocate formal care-givers who form a bridge with an extra-residential care-giver of care recipients living alone.