Related papers: CSCR:Computer Supported Collaborative Research
It is suggested that a new area of CSCR (Computer Supported Collaborative Research) is distinguished from CSCW (Computer Supported Collaborative Work) and CSCL (Computer Supported Collaborative Learning) and that the demarcation between the…
In a previous paper the CSCR domain was defined. Here this is taken to the next stage where the design of a particular Collaborative Research Environment to support Students and Supervisors (CRESS) is considered. Following the CSCR…
In a previous paper the CSCR domain was defined. Here this is taken to the next stage where we consider the design of a particular Collaborative Research Environment to support Students and Supervisors CRESS. Following the CSCR structure a…
Unsupervised clustering aims at discovering the semantic categories of data according to some distance measured in the representation space. However, different categories often overlap with each other in the representation space at the…
Computer-supported collaborative learning (CSCL) has been a steady topic of research since the early 1990s, and the trend has continued to this date. The basic benefits of CSCL in the classroom have been established in many fields of…
Determining coreference of concept mentions across multiple documents is a fundamental task in natural language understanding. Previous work on cross-document coreference resolution (CDCR) typically considers mentions of events in the news,…
Computer-Supported Cooperative Work, or simply CSCW, is the research area that studies the design and use of socio-technical technology for supporting group work. CSCW has a long tradition in interdisciplinary work exploring technical,…
Concept-cognitive learning (CCL) is a hot topic in recent years, and it has attracted much attention from the communities of formal concept analysis, granular computing and cognitive computing. However, the relationship among cognitive…
Cross-domain recommendation (CDR) is a task that aims to improve the recommendation performance in a target domain by leveraging the information from source domains. Contrastive learning methods have been widely adopted among intra-domain…
Pseudo supervision is regarded as the core idea in semi-supervised learning for semantic segmentation, and there is always a tradeoff between utilizing only the high-quality pseudo labels and leveraging all the pseudo labels. Addressing…
This thesis is in the area called computational social choice which is an intersection area of algorithms and social choice theory.
Citizen science changes the way scientific research is pursued. It opens up data collection and analysis to the general public, to the wisdom of crowds. In this emerging area, there is much research to be done to better understand how we…
Collaborative filtering is a rapidly advancing research area. Every year several new techniques are proposed and yet it is not clear which of the techniques work best and under what conditions. In this paper we conduct a study comparing…
We discuss the connection between computational social choice (comsoc) and computational complexity. We stress the work so far on, and urge continued focus on, two less-recognized aspects of this connection. Firstly, this is very much a…
Recommendation Systems (SR) suggest items exploring user preferences, helping them with the information overload problem. Two approaches to SR have received more prominence, Collaborative Filtering, and Content-Based Filtering. Moreover,…
This paper outlines ongoing dissertation research located in the intersection of science fiction, human-computer interaction and computer science. Through an interdisciplinary perspective, drawing from fields such as human-computer…
Continuum robots (CRs), owing to their compact structure, inherent compliance, and flexible deformation, have been widely applied in various fields. By coordinating multiple CRs to form collaborative continuum robots (CCRs), task…
New methods and technologies for engaging future users and other stakeholders in participatory (design) processes are being developed and proposed. Increasingly, researchers refer to co-creation in order to capture such approaches. However,…
Clustering is a well-known unsupervised machine learning approach capable of automatically grouping discrete sets of instances with similar characteristics. Constrained clustering is a semi-supervised extension to this process that can be…
Statistical machine learning algorithms have achieved state-of-the-art results on benchmark datasets, outperforming humans in many tasks. However, the out-of-distribution data and confounder, which have an unpredictable causal relationship,…