Change in person-job fit perceptions and job crafting behaviours: understanding their intertwined nature

Positive employee attitudes and behaviours arise when employees perceive that they are compatible with their jobs, known as person-job (PJ) fit (Kristof-Brown et al., 2005). Yet, to date, relatively few studies have examined the antecedents of PJ fit perceptions and their dynamic nature (e.g., Bayl-Smith & Griffin, 2018; De Cooman et al., 2019). In particular, little is known about the proactive role that employees may play in the development of these perceptions. Although scholars have proposed that a lack of PJ fit may trigger job crafting behaviours aimed at improving PJ fit (Tims & Bakker, 2010), more research is needed to test this proposition. Approaching PJ fit as both perceived needs-supplies (NS) fit and demands-abilities (DA) fit, the present research contributes to testing this proposition and develops theoretical arguments about the differential impact of these types of fit on job crafting behaviours, based on conservation of resources theory (Hobfoll, 1989). In so doing, approach and avoidance crafting behaviours are distinguished (Zhang & Parker, 2019). In addition, the interaction between perceived NS fit and DA fit in predicting job crafting behaviours is proposed and tested. Using a threemonth time-lag study design, hypotheses were tested using 492 participants from a heterogeneous group of US workers. Results from structural equation modelling showed that perceived DA fit negatively affected approach and avoidance crafting behaviours, while perceived NS fit facilitated approach crafting behaviours. Perceived NS fit strengthened the effect of perceived DA fit on job crafting behaviours. In addition, approach crafting behaviours facilitated a positive change in PJ fit perceptions, while avoidance crafting behaviours hindered this development. Overall, findings suggest that PJ fit perceptions might change through job crafting behaviours over time. The theoretical and practical implications of these findings are discussed and scholars are encouraged to carry out further research to assess their generalisability

Delivery Drones - Just a Hype? Towards Autonomous Air Mobility Services at Scale

While hype often arises around emerging technologies, delivery drones have received a significant share of attention in recent years. A variety of applications for drone networks formed, from delivering medical goods to drone-delivered pizza. Nevertheless, high expectations did not yet result in a widespread deployment of drones to improve logistic networks. We conducted semi-structured interviews with drone and aviation experts to derive a taxonomy of challenges for autonomous drone operations and gain practical insight into promising solution approaches that could transform the current hype into sound business models. Our findings comprise a multitude of operational, technical, social and legal issues that have not been identified in literature. Societal adaption and the development and interaction with AI-based systems pose a major challenge to provide autonomous air mobility services in the near future

Text revision in Scientific Writing Assistance: An Overview

Writing a scientific article is a challenging task as it is a highly codified genre. Good writing skills are essential to properly convey ideas and results of research work. Since the majority of scientific articles are currently written in English, this exercise is all the more difficult for non-native English speakers as they additionally have to face language issues. This article aims to provide an overview of text revision in writing assistance in the scientific domain. We will examine the specificities of scientific writing, including the format and conventions commonly used in research articles. Additionally, this overview will explore the various types of writing assistance tools available for text revision. Despite the evolution of the technology behind these tools through the years, from rule-based approaches to deep neural-based ones, challenges still exist (tools' accessibility, limited consideration of the context, inexplicit use of discursive information, etc.)Comment: Published at 13th International Workshop on Bibliometric-enhanced Information Retrieval 12 page

Inequalities in Oral Health for Children with Disabilities: A French National Survey in Special Schools

International audienceBackground: Despite wide recognition that children with disability often have poor oral health, few high quality, controlled results are available. Method: Twenty-four objective and subjective criteria covering feeding, autonomy, access to dental care, oral hygiene, oral disease, general health and behavior were evaluated in a observational cross-sectional study of 2,487 children with disability (DC group), 4,772 adolescents with disability (DA group) and 1,641 children without disability (NDC group). Five algorithms ranked the subjects according to clinical criteria in three original oral health indices: the Clinical Oral Health Index (COHI), indicating the level of oral health problems, the Clinical Oral Care Needs Index (COCNI) giving dental care need levels, and the Clinical Oral Prevention Index (COPI) determining possible needs in terms of dental education initiatives. Results: DC-group children presented poorer oral health and had greater needs in both treatment and preventive oral health actions than NDC-group children (OR = 3.97, 95% CI = 3.25-4.86 for COHI; OR = 2.01, 95% CI = 1.77-2.28 for COCNI; OR = 5.25, 95% CI = 4.55-6.02 for COPI). These conditions were worse again in the DA group comparing to the DC group (OR = 3.52, 95% CI = 2.7-4.6 for COHI; OR = 1.52, 95% CI = 1.38-1.69 for COCNI; OR = 1.53, 95% CI = 1.39-1.69 for COPI). Conclusion: Clinical indices generated by algorithmic association of various clinical indicators allow sensitive clinical measurement, and in this study demonstrated inequalities in oral health for children with disabilities schooling in institutions. Questions need now to be addressed as to the measures that could be taken to compensate for this situation

Cityscapes 3D: Dataset and Benchmark for 9 DoF Vehicle Detection

Detecting vehicles and representing their position and orientation in the three dimensional space is a key technology for autonomous driving. Recently, methods for 3D vehicle detection solely based on monocular RGB images gained popularity. In order to facilitate this task as well as to compare and drive state-of-the-art methods, several new datasets and benchmarks have been published. Ground truth annotations of vehicles are usually obtained using lidar point clouds, which often induces errors due to imperfect calibration or synchronization between both sensors. To this end, we propose Cityscapes 3D, extending the original Cityscapes dataset with 3D bounding box annotations for all types of vehicles. In contrast to existing datasets, our 3D annotations were labeled using stereo RGB images only and capture all nine degrees of freedom. This leads to a pixel-accurate reprojection in the RGB image and a higher range of annotations compared to lidar-based approaches. In order to ease multitask learning, we provide a pairing of 2D instance segments with 3D bounding boxes. In addition, we complement the Cityscapes benchmark suite with 3D vehicle detection based on the new annotations as well as metrics presented in this work. Dataset and benchmark are available online.Comment: 2020 "Scalability in Autonomous Driving" CVPR Worksho

Knowledge Graphs for Data And Knowledge Management in Cyber-Physical Production Systems

Cyber-physical production systems are constituted of various sub-systems in a production environment, from machines to logistics networks, that are connected and exchange data in real-time. Every sub-system consumes and generates data. This data has the potential to support decision making and optimization of production processes. To extract valuable information from this data, however, different data sources must be consolidated and analyzed. A Knowledge Graph (KG), also known as a semantic network, represents a net of real-world entities, i.e., machines, sensors, processes, or concepts, and illustrates their relationship. KG allows us to encode the knowledge and data context into a human interpretable form and is amenable to automated analysis and inference. This paper presents the potential of KG in manufacturing and proposes a framework for its implementation. The proposed framework should assist practitioners in integrating raw data from multiple data sources in production, developing a suitable data model, creating the knowledge graph, and using it in a graph application. Although the framework is applicable for different purposes, this work illustrates its use for supporting the quality assessment of products in a discrete manufacturing production line

On The Reliability Of Machine Learning Applications In Manufacturing Environments

The increasing deployment of advanced digital technologies such as Internet of Things (IoT) devices and Cyber-Physical Systems (CPS) in industrial environments is enabling the productive use of machine learning (ML) algorithms in the manufacturing domain. As ML applications transcend from research to productive use in real-world industrial environments, the question of reliability arises. Since the majority of ML models are trained and evaluated on static datasets, continuous online monitoring of their performance is required to build reliable systems. Furthermore, concept and sensor drift can lead to degrading accuracy of the algorithm over time, thus compromising safety, acceptance and economics if undetected and not properly addressed. In this work, we exemplarily highlight the severity of the issue on a publicly available industrial dataset which was recorded over the course of 36 months and explain possible sources of drift. We assess the robustness of ML algorithms commonly used in manufacturing and show, that the accuracy strongly declines with increasing drift for all tested algorithms. We further investigate how uncertainty estimation may be leveraged for online performance estimation as well as drift detection as a first step towards continually learning applications. The results indicate, that ensemble algorithms like random forests show the least decay of confidence calibration under drift.publishedVersio

Frameworks for data-driven quality management in cyber-physical systems for manufacturing: A systematic review

Recent advances in the manufacturing industry have enabled the deployment of Cyber-Physical Systems (CPS) at scale. By utilizing advanced analytics, data from production can be analyzed and used to monitor and improve the process and product quality. Many frameworks for implementing CPS have been developed to structure the relationship between the digital and the physical worlds. However, there is no systematic review of the existing frameworks related to quality management in manufacturing CPS. Thus, our study aims at determining and comparing the existing frameworks. The systematic review yielded 38 frameworks analyzed regarding their characteristics, use of data science and Machine Learning (ML), and shortcomings and open research issues. The identified issues mainly relate to limitations in cross-industry/cross-process applicability, the use of ML, big data handling, and data security.publishedVersio

Virtual sensors for erroneous data repair in manufacturing a machine learning pipeline

Manufacturing converts raw materials into finished products using machine tools for controlled material removal or deposition. It can be observed using sensors installed within and around machine tools. These sensors measure quantities, such as vibrations, cutting forces, temperature, currents, power consumption, and acoustic emission, to diagnose defects and enable zero-defect manufacturing as part of the Industry 4.0 vision. The continuity of high-quality sensor data streams is fundamental to predicting phenomena, such as geometric deformations, surface roughness, excessive coolant use, and imminent tool wear with adequate accuracy and appropriate timing. However, in practice, data acquired by some sensors can be of poor quality and unsuitable for prediction due to sensor faults stemming from environmental factors. In this paper, we answer if we can repair erroneous data in a faulty sensor based on data simultaneously available in redundant sensors that observe the same process. We present a machine learning pipeline to synthesize virtual sensors that can step in for faulty sensors to maintain reasonable quality and continuity in sensor data streams. We have validated the synthesized virtual sensors in four industrial case studies.publishedVersio