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2022 ◽  
Vol 136 ◽  
pp. 103590
Mara Fuchs ◽  
Florian Beckert ◽  
Jörn Biedermann ◽  
Björn Nagel

2022 ◽  
Vol 74 ◽  
pp. 102281
Qiushi Cao ◽  
Cecilia Zanni-Merk ◽  
Ahmed Samet ◽  
Christoph Reich ◽  
François de Bertrand de Beuvron ◽  

2022 ◽  
Vol 30 (4) ◽  
pp. 1-23
Vincent Cho ◽  
Lara C. Roll ◽  
C. H. Wu ◽  
Valerie Tang

Virtual teams play a crucial role in today’s knowledge-based organisation for overcoming challenges in our dynamic world, especially in the current situation of the COVID-19 pandemic. Teams play a key role in today’s knowledge-based organization for overcoming challenges in our dynamic world. Drawing on social information processing theory, this study explores the effect of members’ humility and team environment within a leaderless team mainly based on virtual platforms. Their impacts on shared leadership, relationship conflict and team and individual performance were investigated. Surveying 219 students forming 61 virtual leaderless teams, our findings showed that a high level of humility and a positive team environment can help to improve shared leadership within a team, which contributes to team performance. Moreover, both humility and team environment have a negative relationship with relationship conflict, which depressed both team and individual performance. Our analysis also indicated that humility positively interacts with team environment on shared leadership.

2022 ◽  
Vol 140 ◽  
pp. 85-94
Qiuqin He ◽  
Maria Guijarro-Garcia ◽  
Juan Costa-Climent

2022 ◽  
Vol 73 ◽  
pp. 102242
Chen Zheng ◽  
Jiajian Xing ◽  
Zhanxi Wang ◽  
Xiansheng Qin ◽  
Benoît Eynard ◽  

2022 ◽  
Vol 40 (1) ◽  
pp. 1-33
Yang Deng ◽  
Yuexiang Xie ◽  
Yaliang Li ◽  
Min Yang ◽  
Wai Lam ◽  

Answer selection, which is involved in many natural language processing applications, such as dialog systems and question answering (QA), is an important yet challenging task in practice, since conventional methods typically suffer from the issues of ignoring diverse real-world background knowledge. In this article, we extensively investigate approaches to enhancing the answer selection model with external knowledge from knowledge graph (KG). First, we present a context-knowledge interaction learning framework, Knowledge-aware Neural Network, which learns the QA sentence representations by considering a tight interaction with the external knowledge from KG and the textual information. Then, we develop two kinds of knowledge-aware attention mechanism to summarize both the context-based and knowledge-based interactions between questions and answers. To handle the diversity and complexity of KG information, we further propose a Contextualized Knowledge-aware Attentive Neural Network, which improves the knowledge representation learning with structure information via a customized Graph Convolutional Network and comprehensively learns context-based and knowledge-based sentence representation via the multi-view knowledge-aware attention mechanism. We evaluate our method on four widely used benchmark QA datasets, including WikiQA, TREC QA, InsuranceQA, and Yahoo QA. Results verify the benefits of incorporating external knowledge from KG and show the robust superiority and extensive applicability of our method.

2022 ◽  
Vol 9 ◽  
Zackary Falls ◽  
Jonathan Fine ◽  
Gaurav Chopra ◽  
Ram Samudrala

The human immunodeficiency virus 1 (HIV-1) protease is an important target for treating HIV infection. Our goal was to benchmark a novel molecular docking protocol and determine its effectiveness as a therapeutic repurposing tool by predicting inhibitor potency to this target. To accomplish this, we predicted the relative binding scores of various inhibitors of the protease using CANDOCK, a hierarchical fragment-based docking protocol with a knowledge-based scoring function. We first used a set of 30 HIV-1 protease complexes as an initial benchmark to optimize the parameters for CANDOCK. We then compared the results from CANDOCK to two other popular molecular docking protocols Autodock Vina and Smina. Our results showed that CANDOCK is superior to both of these protocols in terms of correlating predicted binding scores to experimental binding affinities with a Pearson coefficient of 0.62 compared to 0.48 and 0.49 for Vina and Smina, respectively. We further leveraged the Database of Useful Decoys: Enhanced (DUD-E) HIV protease set to ascertain the effectiveness of each protocol in discriminating active versus decoy ligands for proteases. CANDOCK again displayed better efficacy over the other commonly used molecular docking protocols with area under the receiver operating characteristic curve (AUROC) of 0.94 compared to 0.71 and 0.74 for Vina and Smina. These findings support the utility of CANDOCK to help discover novel therapeutics that effectively inhibit HIV-1 and possibly other retroviral proteases.

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