Wiki-induced Cognitive Elaboration in Project Teams: An Empirical Study

Researchers have exerted increasing efforts to understand how wikis can be used to improve team performance. Previous studies have mainly focused on the effect of the quantity of wiki use on performance in wiki-based communities; however, only inconclusive results have been obtained. Our study focuses on the quality of wiki use in a team context. We develop a construct of wiki-induced cognitive elaboration, and explore its nomological network in the team context. Integrating the literatures on wiki and distributed cognition, we propose that wiki-induced cognitive elaboration influences team performance through knowledge integration among team members. We also identify its team-based antecedents, including task involvement, critical norm, task reflexivity, time pressure and process accountability, by drawing on the motivated information processing literature. The research model is empirically tested using multiple-source survey data collected from 46 wiki-based student project teams. The theoretical and practical implications of our findings are also discussed

РОЗРАХУНОК ТА ПРОЕКТУВАННЯ ОПТИМАЛЬНОГО ВЕРТОДРОМНОГО ПОКРИТТЯ

The article considers the calculation of rigid helipad coverings, singlelayer covering modeling in PC LIRA CAD, FEAFAA; calculation of cement-concrete covering, temporary coverings of metal plates, the differential equation of the membrane curved surface. A great contribution to this work was made by the master of construction T.V. Blyznyuk.У статті розглянуто розрахунок жорстких гелікоптерних покриттів, моделювання одношарового покриття в ПК ЛІРА САПР, FEAFAA, розрахунок цементобетонного покриття, тимчасового покриття з металевих пластин, диференціальне рівняння криволінійної поверхні мембрани. Дана робота виконана майстром будівництва Близнюк Т.В

EPISTEMIC MOTIVATION AND KNOWLEDGE CONTRIBUTION BEHAVIORS IN WIKI TEAMS: A CROSS-LEVEL MODERATION MODEL

Prior research on how to facilitate individuals’ participation in wiki knowledge contribution generally pays little attention to the differentiation of knowledge contributions and the embeddedness of individual team members in team context. This paper examines how an individual’s epistemic motivation and team task reflexivity interact to jointly influence adding, deleting and revising behaviors in distinct ways. Empirical data of 166 university students in 51 teams support our hypotheses. Individuals’ adding, deleting and revising behaviors on wikis are influenced differently by the interactive effect of individual epistemic motivation and team task reflexivity. First, the positive relationship between epistemic motivation and adding behaviors is stronger when the team’s task reflexivity is high. Second, the epistemic motivation stimulates deleting behaviors only when team task reflexivity is high. Third, epistemic motivation is significantly associated with more revising behaviors no matter the level of task reflexivity is high or low

HELIPORT PAVEMENT DESIGNING FEATURES

In recent years, there has been a significant progress in helicopter traffic, its volume increasing every year. To solve the transport problem of megacities, first of all heliports should be built in the largest cities, with the corporate sector as the customers, and the possibility of excursion transport flights not excluded. UTair-Ukraine (the world’s largest airline in terms of helicopter fleet operating more than 300 helicopters of various models) plans to actively develop the helicopter business in Ukraine. To date, only one modern helipad has been built in the capital of Ukraine – Dnipro-1. Kyiv needs at least 19 helicopter pads located in such a way as to reduce the traffic load on roads and provide an opportunity to evacuate the population in case of an emergency. The article considers the history of the development of designing hard surface airfields and heliports design, the main heliport parts, determination of the helipad geometric parameters, helicopters classification by their impact on the covering.In recent years, there has been a significant progress in helicopter traffic, its volume increasing every year. To solve the transport problem of megacities, first of all heliports should be built in the largest cities, with the corporate sector as the customers, and the possibility of excursion transport flights not excluded. UTair-Ukraine (the world’s largest airline in terms of helicopter fleet operating more than 300 helicopters of various models) plans to actively develop the helicopter business in Ukraine. To date, only one modern helipad has been built in the capital of Ukraine – Dnipro-1. Kyiv needs at least 19 helicopter pads located in such a way as to reduce the traffic load on roads and provide an opportunity to evacuate the population in case of an emergency. The article considers the history of the development of designing hard surface airfields and heliports design, the main heliport parts, determination of the helipad geometric parameters, helicopters classification by their impact on the covering

USD: Unknown Sensitive Detector Empowered by Decoupled Objectness and Segment Anything Model

Open World Object Detection (OWOD) is a novel and challenging computer vision task that enables object detection with the ability to detect unknown objects. Existing methods typically estimate the object likelihood with an additional objectness branch, but ignore the conflict in learning objectness and classification boundaries, which oppose each other on the semantic manifold and training objective. To address this issue, we propose a simple yet effective learning strategy, namely Decoupled Objectness Learning (DOL), which divides the learning of these two boundaries into suitable decoder layers. Moreover, detecting unknown objects comprehensively requires a large amount of annotations, but labeling all unknown objects is both difficult and expensive. Therefore, we propose to take advantage of the recent Large Vision Model (LVM), specifically the Segment Anything Model (SAM), to enhance the detection of unknown objects. Nevertheless, the output results of SAM contain noise, including backgrounds and fragments, so we introduce an Auxiliary Supervision Framework (ASF) that uses a pseudo-labeling and a soft-weighting strategies to alleviate the negative impact of noise. Extensive experiments on popular benchmarks, including Pascal VOC and MS COCO, demonstrate the effectiveness of our approach. Our proposed Unknown Sensitive Detector (USD) outperforms the recent state-of-the-art methods in terms of Unknown Recall, achieving significant improvements of 14.3\%, 15.5\%, and 8.9\% on the M-OWODB, and 27.1\%, 29.1\%, and 25.1\% on the S-OWODB

Reconstruction-Aware Prior Distillation for Semi-supervised Point Cloud Completion

Point clouds scanned by real-world sensors are always incomplete, irregular, and noisy, making the point cloud completion task become increasingly more important. Though many point cloud completion methods have been proposed, most of them require a large number of paired complete-incomplete point clouds for training, which is labor exhausted. In contrast, this paper proposes a novel Reconstruction-Aware Prior Distillation semi-supervised point cloud completion method named RaPD, which takes advantage of a two-stage training scheme to reduce the dependence on a large-scale paired dataset. In training stage 1, the so-called deep semantic prior is learned from both unpaired complete and unpaired incomplete point clouds using a reconstruction-aware pretraining process. While in training stage 2, we introduce a semi-supervised prior distillation process, where an encoder-decoder-based completion network is trained by distilling the prior into the network utilizing only a small number of paired training samples. A self-supervised completion module is further introduced, excavating the value of a large number of unpaired incomplete point clouds, leading to an increase in the network's performance. Extensive experiments on several widely used datasets demonstrate that RaPD, the first semi-supervised point cloud completion method, achieves superior performance to previous methods on both homologous and heterologous scenarios