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Social loafing in knowledge contribution (namely, knowledge contribution loafing [KCL]) usually happens in group context, especially in the mobile collaboration tasks. KCL shows dynamic features over time. However, most previous studies are based on static assumption, that is, KCL will not change over time. This paper aims to reveal the dynamics of KCL in mobile collaboration and analyze how network centrality influences KCL states considering the current loafing state.

This study is based on empirical study design. Real mobile collaboration behavioral data related to knowledge contribution were collected to investigate the dynamic relationship between network centrality and KCL. In total, 4,127 chat contents were collected through Slack (a mobile collaboration APP). The text data were first analyzed using the text analysis method and then analyzed by a machine learning method called hidden Markov model.

First, the results reveal the inner structure of KCL, showing that it has three states (low, medium and high). Second, it is found that network centrality positively influences individuals involved in medium and high loafing state, while it has a negative influence on individuals with low loafing state.

The limitations are related to the single machine learning method and no subdivision of social network. First, this paper only uses one kind of text classification model (TF-IDF) to divide chat contents, which may not be superior to other classification models. This paper considers the eigenvector centrality, and not further divides the social network into advice network and expressive network.

This study helps companies infer tendency of different KCL and dynamically re-organize a mobile collaborative team for better knowledge contribution.

First, previous studies based on static assumptions regarding KCL as static and the relationship between loafing reducing mechanisms and team members KCL does not change over time. This study relaxes static assumptions and allows KCL to change during the process of collaboration. Second, this study allows the impact of network centrality to be different when members are in different KCL states.

Feedback-seeking behavior is an important way for individuals to actively seek information feedback to achieve individuals' goals. In the environment driven by contactless digital technologies, the way of individual feedback-seeking behavior through monitoring indirectly becomes obvious, especially for people who complete the work online in digital collaboration. However, previous empirical research on feedback-seeking behavior mainly focused on direct inquiry. The purpose of this paper is to verify the impact of individual learning goal orientation and the digital feedback environment on individuals' feedback-seeking behaviors through inquiry and monitoring approaches. And the moderating effect of time pressure on these relationships was also investigated.

Based on socio-technical system theory, this study proposes a model to describe the formation of the two approaches of feedback-seeking behaviors (inquiry and monitoring). The hypotheses were examined with the structural equation model method and data were collected from 152 graduate students who completed online surveys.

The results show that both the digital feedback environment and learning goal orientation can promote individual inquiry and monitoring approaches of feedback-seeking. Furthermore, time pressure moderates the relationship between the digital feedback environment and feedback monitoring negatively.

This study establishes an antecedent model that influences the choice of feedback-seeking approaches in digital environments from the perspective of a socio-technical system. The empirical results supplement the explanation of the influence of both technical and social factors on individual feedback-seeking behavior in digital environments.

Conventional learning-based visual odometry (VO) systems usually use convolutional neural networks (CNN) to extract features, where some important context-related and attention-holding global features might be ignored. Without essential global features, VO system will be sensitive to various environmental perturbations. The purpose of this paper is to design a novel learning-based framework that aims to improve accuracy of learning-based VO without decreasing the generalization ability.

Instead of CNN, a context-gated convolution is adopted to build an end-to-end learning framework, which enables convolutional layers that dynamically capture representative local patterns and composes local features of interest under the guidance of global context. In addition, an attention mechanism module is introduced to further improve learning ability and enhance robustness and generalization ability of the VO system.

The proposed system is evaluated on the public data set KITTI and the self-collected data sets of our college building, where it shows competitive performance compared with some classical and state-of-the-art learning-based methods. Quantitative experimental results on the public data set KITTI show that compared with CNN-based VO methods, the average translational error and rotational error of all the test sequences are reduced by 45.63% and 37.22%, respectively.

The main contribution of this paper is that an end-to-end deep context gate convolutional VO system based on lightweight attention mechanism is proposed, which effectively improves the accuracy compared with other learning-based methods.

The purpose of this paper is to review and examine the necessities and potentials for transition of a library from paper‐based paradigm to all media paradigm in the new changing age; and to recount the innovation efforts made by Shanghai Library aimed at actualising its development strategy.

This paper reviews the major impacts that had fallen on libraries of the world, and the dilemma in particular that Shanghai Library was confronted with in its attempt to optimise the library services. Examples are given with facts and figures to illustrate how the library has been conducting innovation practices to fulfil its paradigm shift.

The paper concludes that today's libraries need fundamental transformation through innovation practices in various aspects and dimensions to keep up with the times.

The paper offers practical information and advice to those considering paradigm shift as a strategic organisational imperative.

This paper demonstrates how the Shanghai Library has achieved transcendence through transition of its business priority from the paper collection to the various formats that keeps in alliance with the digital and, in further foresight, the omnimedia age.