Search

Paid crowdsourcing connects task requesters to a globalized, skilled workforce that is available 24/7. In doing so, this new labor model promises not only to complete work faster and more efficiently than any previous approach but also to harness the best of our collective capacities. Nevertheless, for almost a decade now, crowdsourcing has been limited to addressing rather straightforward and simple tasks. Large-scale innovation, creativity, and wicked problem-solving are still largely out of the crowd’s reach. In this opinion paper, we argue that existing crowdsourcing practices bear significant resemblance to the management paradigm of Taylorism. Although criticized and often abandoned by modern organizations, Taylorism principles are prevalent in many crowdsourcing platforms, which employ practices such as the forceful decomposition of all tasks regardless of their knowledge nature and the disallowing of worker interactions, which diminish worker motivation and performance. We argue that a shift toward post-Taylorism is necessary to enable the crowd address at scale the complex problems that form the backbone of today’s knowledge economy. Drawing from recent literature, we highlight four design rules that can help make this shift, namely, endorsing social crowd networks, encouraging teamwork, scaffolding ownership of one’s work within the crowd, and leveraging algorithm-guided worker self-coordination.

The e-tandem, as a means of distance learning in a hybrid system including presence learning and distance learning, makes it possible to develop oral proficiency in a foreign language and (inter)cultural competence. The tandem sessions are an extension of the oral exercises given as part of a Bac + 1 course in applied linguistics. Pairs of French-speaking (ULiège) and Dutch-speaking (KU Leuven) students contact each other every week, alternating languages. Research shows that free conversations do not de facto develop intercultural competence. It is therefore necessary to think about the types of language activities that lead to the development of oral and (inter)cultural skills. Two types of conversation scenarios are tested successively: first a macro-task (based on the pedagogy of the project) aimed at dismantling stereotypes (the project is carried out over several discussion sessions); second some conversations with specific and punctual subjects aimed at developing cultural awareness. Analysis of student perceptions shows that one-time conversations (punctual subjects) are more effective in developing targeted skills because, unlike macro-tasking, they are only carried out orally. When the task is too complex, students rather contact each other in writing. However, it seems that students prefer the script of free conversations.

The resourcefulness of crowdsourcing can be used to handle a wide range of complex macro-tasks, such as travel planning, translation, and software development. Multi-phase tasks are a type of macro-task that consists of several subtasks distributed across multiple sequential phases. Due to the recent work's disregard for the task's sequential correlation, it is difficult for them to handle multi-stage tasks effectively. This work bridges this gap. We call this novel approach Hint, which incorporates task design, pre hoc worker coordination, and post hoc crowd work coordination. Starting with the task interface design, Hint makes workers aware of the relationship between phases in order to improve their processing abilities. Second, pre hoc coordination of workers is to organize the workers to do the tasks to lower the monetary costs required to meet a specific quality standard. Third, post hoc coordination of crowd work is through a decision tree-based coordination strategy. Extensive tests are carried out on real-world datasets to validate the desirable qualities of the suggested mechanism. [ABSTRACT FROM AUTHOR]

Maintenance is an activity of growing interest, especially for critical systems. In particular, aircraft maintenance costs are becoming an important issue in the aeronautical industry. Managing an aircraft maintenance centre is a complex activity. One of the difficulties comes from the numerous uncertainties that affect the activity and disturb the plans in the short and medium term. Based on a helicopter maintenance planning and scheduling problem, we study in this paper the integration of uncertainties into tactical and operational multi-resource, multi-project planning (respectively Rough Cut Capacity Planning and the Resource Constraint Project Scheduling Problem). Our main contributions are in modelling the periodic workload on a tactical level considering uncertainties in macro-task work content, and modelling the continuous workload on the operational level considering uncertainties in task duration. We model uncertainties using a fuzzy/possibilistic approach instead of a stochastic approach since very limited data are available. We refer to the problems as the Fuzzy Rough Cut Capacity Problem (FRCCP) and the Fuzzy Resource Constraint Project Scheduling Problem (RCPSP). We apply our models to helicopter maintenance activity within the frame of the Helimaintenance project, an industrial project approved by the French Aerospace Valley cluster that aims at building a centre for civil helicopter maintenance. [ABSTRACT FROM PUBLISHER]

This study presents a systematic literature review to understand the applications, benefits, and challenges of digital assistants (DAs) in production and logistics tasks. Our conceptual framework covers three dimensions: information management, collaborative operations, and knowledge transfer. We evaluate human-DA collaborative tasks in the areas of product design, production, maintenance, quality management, and logistics. This allows us to expand upon different types of DAs, and reveal how they improve the speed and ease of production and logistic work, which was ignored in previous studies. Our results demonstrate that DAs improve the speed and ease of workers' interaction with machines/information systems in searching, processing, and demonstrating. Extant studies describe DAs with different levels of autonomy in decision-making; however, most DAs perform tasks as instructed or with workers' consent. Additionally, we observe that workers find it more intuitive to perform tasks and acquire knowledge when they receive multiple sensorial cues (e.g. auditory and visual cues). Consequently, future research can explore how DAs can be integrated with other technologies for robust multi-modal assistance such as eye tracking and augmented reality. This can provide customised DA support to workers with disabilities or conditions to facilitate more inclusive production and logistics. [ABSTRACT FROM AUTHOR]

Effective use of cache memory is getting more important with increasing gap between the processor speed and memory access speed. Also, use of multigrain parallelism is getting more important to improve effective performance beyond the limitation of loop iteration level parallelism. Considering these factors, this paper proposes a coarse grain task static scheduling scheme considering cache optimization. The proposed scheme schedules coarse grain tasks to threads so that shared data among coarse grain tasks can be passed via cache after task and data decomposition considering cache size at compile time. It is implemented on OSCAR Fortran multigrain parallelizing compiler and evaluated on Sun Ultra80 four-processor SMP workstation using Swim and Tomcatv from the SPEC fp 95. As the results, the proposed scheme gives us 4.56 times speedup for Swim and 2.37 times on 4 processors for Tomcatv respectively against the Sun Forte HPC Ver. 6 update 1 loop parallelizing compiler. [ABSTRACT FROM AUTHOR]

Copyright of Revista de Psicoterapia is the property of Revista de Psiquiatria y Psicologia Humanista, S.L. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In parallel and distributed applications, it is very likely that object-oriented languages, such as Java and Ruby, and large-scale semistructured data written in XML will be employed. However, because of their inherent dynamic memory management, parallel and distributed applications must sometimes suspend the execution of all tasks running on the processors. This adversely affects their execution on the parallel and distributed platform. In this paper, we propose a new task scheduling method called CP/MM (Critical Path/Memory Management) which can efficiently schedule tasks for applications requiring memory management. The underlying concept is to consider the cost due to memory management when the task scheduling system allocates ready (executable) coarse-grain tasks, or macro-tasks, to processors. We have developed three task scheduling modules, including CP/MM, for a task scheduling system which is implemented on a Java RMI (Remote Method Invocation) communication infrastructure. Our experimental results show that CP/MM can successfully prevent high-priority macro-tasks from being affected by the garbage collection arising from memory management, so that CP/MM can efficiently schedule distributed programs whose critical paths are relatively long. Copyright © 2005 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Cloud computing is a new technology that has adapted to the traditional way of service providing. Service providers are responsible for managing the allocation of resources. Selecting suitable containers and bandwidth for job scheduling has been a challenging task for the service providers. There are several existing systems that have introduced many algorithms for resource allocation. To overcome these challenges, the proposed system introduces an Optimized Task Scheduling Algorithm with Deep Learning (OTS-DL). When a job is assigned to a Cloud Service Provider (CSP), the containers are allocated automatically. The article segregates the containers as' Long-Term Container (LTC)' and 'Short-Term Container (STC)' for resource allocation. The system leverages an 'Optimized Task Scheduling Algorithm' to maximize the resource utilisation that initially inquires for micro-task and macro-task dependencies. The bottleneck task is chosen and acted upon accordingly. Further, the system initializes a 'Deep Learning' (DL) for implementing all the progressive steps of job scheduling in the cloud. Further, to overcome container attacks and errors, the system formulates a Container Convergence (Fault Tolerance) theory with high-level security. The results demonstrate that the used optimization algorithm is more effective for implementing a complete resource allocation and solving the large-scale optimization problem of resource allocation and security issues. [ABSTRACT FROM AUTHOR]

In the competitive hospitality sector, the adoption of Artificial Intelligence (AI) is essential for enhancing operational efficiency and improving customer experiences. This study explores how key entrepreneurial traits—Entrepreneurial Orientation (EO), Entrepreneurial Self-Efficacy (ESE), and Perceived Business Performance (PBP)—influence managers' attitudes toward adopting AI in small- and medium-sized enterprises (SMEs). Ts research utilizes data from 287 respondents, gathered through field research with a survey designed to measure the relationships among constructs, employing structural equation modeling (SEM) for analysis. Results reveal that PBP and certain ESE dimensions, such as Initiating Investor Relationships and Developing New Products, have only a modest positive effect on AI adoption. In contrast, EO—specifically Proactiveness and Innovativeness—exhibits a weak negative impact. Importantly, none of these factors directly affect managers' attitudes toward AI. Instead, this study highlights that managers' positive attitudes are the strongest predictors of AI adoption, aligning with the Technology Acceptance Model (TAM). The findings offer new insights into key entrepreneurial factors driving AI adoption and emphasize the need for targeted education and supportive policies to facilitate AI integration in hospitality SMEs. Fostering a positive perspective on AI is more important for adoption than overcoming skepticism, as negative attitudes do not influence AI adoption. [ABSTRACT FROM AUTHOR]

Occupational safety, reflecting the likelihood of work-related accidents, is crucial in work systems. A risk management model identifies, analyzes, and prioritizes risks, followed by the strategic application of resources to mitigate, monitor, and control the probability and impact of future events. Models integrating safety, ergonomics, and operational efficiency in risk management are non-existent, especially in the food retail sector. The proposed risk management model assigns the risk level to Safety using the Hazard Identification and Risk Assessment index (HIRA), an integral part of the Global Safety Index (GSI), both indices with five risk levels: 1 to 5 (acceptable to very critical). The organizational hierarchy of the evaluated company includes levels from microtask to insignia. The research aims to apply the HIRA index from the microtask to the area level. The HIRA application was conducted in a food retail company, starting with the identification and characterization of tasks in the "food" section and "fresh products" area (butchery, fishmonger, bakery, charcuterie/takeaway, and fruits and vegetables sections). The risk level of each microtask was assessed, then aggregated to higher organizational levels. Results showed that two new solutions reduced the safety risk in the mentioned sections proving the HIRA value as decision-making tool. [ABSTRACT FROM AUTHOR]

The increase in greenhouse gas emissions prompts the transport sector towards new technological perspectives on personal mobility. Addressing sustainable mobility through electric micromobility requires interdisciplinary design research methods and approaches. In the context of the LEONARDO project, funded under the Horizon 2020 framework, this paper addresses a critical literature review on the design thinking, design research models, tools, and mixed methods to be undertaken for driving product mobility innovation in a cross-disciplinary context. Following the "research through design" research strategy, the authors applied the Double-Diamond design thinking model to frame the design research process in four phases, aligning with three overarching objectives, four specific research objectives, and 24 research tasks, supported by a total of 71 mixed methods and tools. As a result, the transdisciplinary process provides a co-designed energy-efficient stand-alone microvehicle and a scalable interdisciplinary design model for urban transport product innovation. In conclusion, this case study suggests the value of the Double-Diamond design thinking model as a design research instrument capable of addressing sustainable mobility and guiding interdisciplinary design research, design practice, and education in the industrial engineering and design disciplinary sectors. [ABSTRACT FROM AUTHOR]

With the development of mobile Internet and the prevalence of sharing economy, spatial crowdsourcing (SC) is becoming more and more popular and attracts attention from both academia and industry. A fundamental issue in SC is assigning tasks to suitable workers to obtain different global objectives. Existing works often assume that the tasks in SC are micro and can be completed by any single worker. However, there also exist macro tasks which need a group of workers with different kinds of skills to complete collaboratively. Although there have been a few works on macro task assignment, they neglect the dynamics of SC and assume that the information of the tasks and workers can be known in advance. This is not practical as in reality tasks and workers appear dynamically and task assignment should be performed in real time according to partial information. In this paper, we study the multi-skill aware task assignment problem in real-time SC, whose offline version is proven to be NP-hard. To solve the problem effectively, we first propose the Online-Exact algorithm, which always computes the optimal assignment for the newly appearing tasks or workers. Because of Online-Exact's high time complexity which may limit its feasibility in real time, we propose the Online-Greedy algorithm, which iteratively tries to assign workers who can cover more skills with less cost to a task until the task can be completed. We finally demonstrate the effectiveness and efficiency of our solutions via experiments conducted on both synthetic and real datasets. [ABSTRACT FROM AUTHOR]

With the advancement of technology, connected and autonomous vehicles (CAVs) can be applied to complex tunnel networks in long tunnel construction to enhance vehicle operation safety and efficiency. This paper proposes an optimization method for CAVs' operation in long tunnel constructions. Firstly, a spatiotemporal coordinated optimization model with decentralized time and hierarchical networks is proposed to minimize the total working time for completing transportation services. The model integrates macro task allocation and micro node control and optimizes the vehicle-space-time relationships of CAVs to prevent conflicts and collisions. Secondly, a heuristic algorithm named Search-Adjustment Genetic Algorithm (SAGA) is developed to solve the problem considering the model's complexity and engineering characteristics. Thirdly, numerical experiments are designed to validate the feasibility and efficiency of the proposed model and algorithm. The results indicate that (1) the proposed model can effectively deconflict CAVs in the road network to ensure safety and obtain a low total working time to fulfill the transportation demand. (2) Compared to the commercial solver Gurobi, the proposed algorithm demonstrates significantly superior solution accuracy and efficiency within an acceptable time limit. (3) The solution ensures the safety and efficiency of CAVs and increases their utilization compared with engineering-oriented methods, resulting in a 50 % reduction in CAV acquisition costs, a 29 % and 85 % reduction in running time and delay respectively, and a reduction in fuel consumption. (4) As the number of transportation services and the complexity of the road network increases, the efficiency gains become more prominent and better adapted to the needs of the actual long tunnel construction project. To sum up, the proposed model and algorithm can ensure the safety and efficiency of providing transportation services in future long tunnel construction. Moreover, it can be adapted for controlling CAVs in road networks such as other construction scenarios and urban road networks. [ABSTRACT FROM AUTHOR]

In many fields including aerospace, automotive, and telecommunications, MathWorks' MATLAB/Simulink is contemporary standard for model-based design. The strengths of Simulink are rapid design and algorithm exploration. Models created with Simulink are just functional. Therefore, designers cannot effortlessly consider a Simulink model's architecture. As current architectures are optimized to run on multicore processors, software running on these processors needs to be parallelized in order to benefit from their natural performance. For instance, designers need to understand how a Simulink model could be parallelized and how an adequate multicore architecture is selected. This paper focuses on the dataflow-based parallelization of Simulink models and proposes a method based on dataflow to measure the performance of parallelized Simulink models running on multicore architectures. Throughout the parallelization process, the model is converted into a Hierarchical Synchronous DataFlow Graph (HSDFG) keeping its original semantics, and each composite node in the graph is flattened. Then, the graph is mapped and scheduled into a multicore architecture with the ultimate objective that minimizes the end-to-end latency. In the experiment of applying the proposed approach to a real Simulink model, latency of the parallelized model could be reduced successfully on a various multi-core architectures. [ABSTRACT FROM AUTHOR]

Time course analysis of eye movements during free exploration of real-world scenes often reveals an increase in fixation durations together with a decrease in saccade amplitudes, which has been explained within the two visual systems approach, i.e., a transition from ambient to focal. Short fixations and long saccades during early viewing periods are classified as ambient mode of vision, which is concerned with spatial orientation and is related to simple visual properties such as motion, contrast, and location. Longer fixations and shorter saccades during later viewing periods are classified as focal mode of vision, which is concentrated in the foveal projection and is capable of object identification and its semantic categorization. While these findings are mainly obtained in the context of image exploration, the present study endeavors to investigate whether the same pattern of interplay between ambient and focal visual attention is deployed when people work on complex real-world tasks--and if so, when? Based on a re-analysis of existing data that integrates concurrent think aloud and eye tracking protocols, the present study correlated participants' internal thinking models to the parameters of their eye movements when they planned solutions to an open-ended design problem in a real-world setting. We hypothesize that switching between ambient and focal attentional processing is useful when solvers encounter difficulty compelling them to shift their conceptual direction to adjust the solution path. Individuals may prefer different attentional strategies for information-seeking behavior, such as ambient-to-focal or focal-to-ambient. The observed increase in fixation durations and decrease in saccade amplitudes during the periods around shifts in conceptual direction lends support to the postulation of the ambient-to-focal processing; however, focal-to-ambient processing is not evident. Furthermore, our data demonstrate that the beginning of a shift in conceptual direction is observable in eye movement behavior with a significant prolongation of fixation. Our findings add to the conclusions drawn from laboratory settings by providing preliminary evidence for ambient and focal processing characteristics in realworld problem-solving. [ABSTRACT FROM AUTHOR]

Background: The field of implementation science was developed to address the significant time delay between establishing an evidence-based practice and its widespread use. Although implementation science has contributed much toward bridging this gap, the evidence-to-practice chasm remains a challenge. There are some key aspects of implementation science in which advances are needed, including speed and assessing causality and mechanisms. The increasing availability of artificial intelligence applications offers opportunities to help address specific issues faced by the field of implementation science and expand its methods. Main text: This paper discusses the many ways artificial intelligence can address key challenges in applying implementation science methods while also considering potential pitfalls to the use of artificial intelligence. We answer the questions of "why" the field of implementation science should consider artificial intelligence, for "what" (the purpose and methods), and the "what" (consequences and challenges). We describe specific ways artificial intelligence can address implementation science challenges related to (1) speed, (2) sustainability, (3) equity, (4) generalizability, (5) assessing context and context-outcome relationships, and (6) assessing causality and mechanisms. Examples are provided from global health systems, public health, and precision health that illustrate both potential advantages and hazards of integrating artificial intelligence applications into implementation science methods. We conclude by providing recommendations and resources for implementation researchers and practitioners to leverage artificial intelligence in their work responsibly. Conclusions: Artificial intelligence holds promise to advance implementation science methods ("why") and accelerate its goals of closing the evidence-to-practice gap ("purpose"). However, evaluation of artificial intelligence's potential unintended consequences must be considered and proactively monitored. Given the technical nature of artificial intelligence applications as well as their potential impact on the field, transdisciplinary collaboration is needed and may suggest the need for a subset of implementation scientists cross-trained in both fields to ensure artificial intelligence is used optimally and ethically. [ABSTRACT FROM AUTHOR]

Group work is a commonly used method of working, and the performance of a group can vary depending on the type and structure of the task at hand. Research suggests that groups can exhibit "collective intelligence"—the ability to perform well across tasks—under certain conditions, making group performance somewhat predictable. However, predictability of task performance becomes difficult when a task relies heavily on coordination among group members or is ill-defined. To address this issue, we propose a technical solution in the form of a chatbot providing advice to facilitate group work for more predictable performance. Specifically, we target well-defined, high-coordination tasks. Through experiments with 64 virtual groups performing various tasks and communicating via text-based chat, we found a relationship between the average intelligence of group members and their group performance in such tasks, making performance more predictable. The practical implications of this research are significant, as the assembly of consistently performing groups is an important organizational activity. [ABSTRACT FROM AUTHOR]

We propose an architecture for a system that will 'watch and listen to' an instructional video of a human performing a task and translate the audio and video information into a task for a robot to perform. This enables the use of readily available instructional videos from the Internet to train robots to perform tasks instead of programming them. We implemented an operational prototype based on the architecture and showed it could 'watch and listen to' two instructional videos on how to clean golf clubs and translate the audio and video information from the instructional video into tasks for a robot to perform. The key contributions of this architecture are: integration of multiple modalities using trees and pruning with filters; task decomposition into macro-tasks composed of parameterized task-primitives and other macro-tasks, where the task-primitive parameters are an action (e.g., dip, clean, dry) taken on an object (e.g., golf club) using a tool (e.g., pail of water, brush, towel); and context, for determining missing and implied task-primitive parameter values, as a set of canonical task-primitive parameter values with a confidence score based on the number of times the parameter value was detected in the video and audio information and how long ago it was detected. [ABSTRACT FROM AUTHOR]

This work aims to generalize and formalize the problem of macro task allocation to aggregated production resources in the virtual enterprise context. Firstly, the product/process constraints are identified without any consideration of the resources. Then, the current availability of the aggregated resources within the virtual enterprise is taken into account. This paper does not address the allocation problem as such but provides a generic formalization of the feasibility domain as a support for human decision-makers in a dynamic production network. [ABSTRACT FROM AUTHOR]

Edge–fog–cloud computing system can be divided into edge or IoT layer (tier 1), fog layer (tier 2) and cloud layer (tier 3). The devices at the edge layer generate different types of tasks which may be computation-intensive or communication intensive or having a combination of these properties. Depending on the characteristics of tasks, those may be scheduled to run at the edge or fog or cloud layers. There are many advantages of offloading some of the computationally intensive workloads, which includes improved response time, satisfying the deadlines of delay-sensitive tasks and overall reduced make span of the workloads. In this context, there is a need for designing a scheduling algorithm with the goal to minimize the overall execution time while satisfying the deadlines of the tasks and maximizing the resource utilization at fog layer. In this paper, we are proposing a task offloading and scheduling algorithm based on the osmotic approach. In the osmotic approach, the devices and tasks are classified, and the tasks are assigned to the most suitable devices based on their dynamically available capacity. The proposed scheduling algorithm is compared with traditional random task offloading and round robin task offloading algorithms using synthetic data sets and found that the proposed algorithm performance is significantly better than other algorithms. [ABSTRACT FROM AUTHOR]

Crowdsourcing arose as a problem-solving strategy that uses a large number of workers to achieve tasks and solve specific problems. Although there are many studies that explore crowdsourcing platforms and systems, little attention has been paid to define what a crowd-powered project is. To address this issue, this article introduces a general-purpose conceptual model that represents the essential elements involved in this kind of project and how they relate to each other. We consider that the workflow in crowdsourcing projects is context-oriented and should represent the planning and coordination by the crowdsourcer in the project, instead of only facilitating decomposing a complex task into subtask sets. Since structural models are limited to cannot properly represent the execution flow, we also introduce the use of behavioural conceptual models, specifically Unified Modeling Language (UML) activity diagrams, to represent the user, tasks, assets, control activities and products involved in a specific project. [ABSTRACT FROM AUTHOR]

To examine the ways that 6‐ to 11‐year‐old children's conversation with their parents support their developing understandings of evolution, 49 parent–child dyads participated in a study with two elicited discussion tasks: origins of species and potential species change. Conversational data were transcribed, coded, and qualitatively and quantitatively analyzed to compare the appearance of reasoning in each type of task. Quantitative analyses revealed correlations between tasks in informed naturalistic reasoning as well as differences in the way reasoning was expressed in each task. In addition, parent–child dyads with older children were more likely to use informed naturalistic reasoning than parent–child dyads with younger children. A subset of the data was analyzed qualitatively and showed that irrespective of how much evolution reference was present in the conversation, parents supported their children's learning through scaffolding. However, greater amounts of nonscientific reasoning appeared in the groups with less evolution talk. This study demonstrates that family talk about evolution varies with context both within and between families. [ABSTRACT FROM AUTHOR]