scholarly journals Highly-available and consistent group collaboration at the edge with colony

2021 ◽  
Author(s):  
Ilyas Toumlilt ◽  
Pierre Sutra ◽  
Marc Shapiro
2014 ◽  
Vol 13 (7) ◽  
pp. 4625-4632
Author(s):  
Jyh-Shyan Lin ◽  
Kuo-Hsiung Liao ◽  
Chao-Hsing Hsu

Cloud computing and cloud data storage have become important applications on the Internet. An important trend in cloud computing and cloud data storage is group collaboration since it is a great inducement for an entity to use a cloud service, especially for an international enterprise. In this paper we propose a cloud data storage scheme with some protocols to support group collaboration. A group of users can operate on a set of data collaboratively with dynamic data update supported. Every member of the group can access, update and verify the data independently. The verification can also be authorized to a third-party auditor for convenience.


2015 ◽  
Vol 18 (2) ◽  
pp. 22-35 ◽  
Author(s):  
Chwen Jen Chen ◽  
Kee Man Chuah ◽  
Jimmy Tho ◽  
Chee Siong Teh

Abstract Wikis, being one of the popular Web 2.0 tools, have impacted students’ engagement and performance particularly in the aspects of second and foreign language learning. While an increasing number of studies have focused on the effectiveness of wiki in improving students’ writing skills, this study was conducted to examine the attitudinal factors that influence English as a Second Language (ESL) students’ group collaboration in using wikis for a writing task that was divided into three phases: pre-writing, individual-construction, joint-constructions. Data collected from these students after completing this task was analyzed based on three attitudinal aspects: motivation, perceived usefulness, and perceived ease of use. The findings reveal high mean scores for all aspects. Further multiple regression analysis reveals that motivation is the most important factor associated with group collaboration, indicating the need to boost students’ motivation to encourage effective collaboration in completing wiki writing tasks.


2021 ◽  
Vol 3 ◽  
Author(s):  
Anirudh Som ◽  
Sujeong Kim ◽  
Bladimir Lopez-Prado ◽  
Svati Dhamija ◽  
Nonye Alozie ◽  
...  

Early development of specific skills can help students succeed in fields like Science, Technology, Engineering and Mathematics. Different education standards consider “Collaboration” as a required and necessary skill that can help students excel in these fields. Instruction-based methods is the most common approach, adopted by teachers to instill collaborative skills. However, it is difficult for a single teacher to observe multiple student groups and provide constructive feedback to each student. With growing student population and limited teaching staff, this problem seems unlikely to go away. Development of machine-learning-based automated systems for student group collaboration assessment and feedback can help address this problem. Building upon our previous work, in this paper, we propose simple CNN deep-learning models that take in spatio-temporal representations of individual student roles and behavior annotations as input for group collaboration assessment. The trained classification models are further used to develop an automated recommendation system to provide individual-level or group-level feedback. The recommendation system suggests different roles each student in the group could have assumed that would facilitate better overall group collaboration. To the best of our knowledge, we are the first to develop such a feedback system. We also list the different challenges faced when working with the annotation data and describe the approaches we used to address those challenges.


2021 ◽  
Author(s):  
Yang Bai ◽  
Caiyun He ◽  
Junjiajia Long ◽  
Xuefei Li ◽  
Xiongfei Fu

AbstractCoordination of individuals with diversity often requires sophisticated communications and high-order computational abilities. Microbial populations can exhibit diverse individualistic behaviors and yet can engage in collective migratory patterns with a spatially sorted arrangement of phenotypes following a self-generated attractant gradient. However, it’s unclear how individual bacteria without complex computational abilities can achieve the consistent group performance and determine their positions in the group while facing spatiotemporally dynamic stimuli. Here, we investigate the statistics of bacterial run-and-tumble trajectories during group migration. We discover that, despite of the constant migrating speed as a group, the individual drift velocity exhibits a spatially dependent structure that decreases from the back to the front of the group. The spatial modulation of individual stochastic behaviors constrains cells in the group, ensuring the coherent population movement with ordered patterns of phenotypes. These results reveal a simple computational principle for emergent collective behaviors from heterogeneous individuals.


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