Collaborative Distance Research Articles

Stream Hub: Video Chat and Live Streaming Platform

Summary: With its emphasis on user-centric features, SyncMate is a trailblazing advancement in distant collaboration that improves digital teamwork. Focusing on smooth file sharing and high-quality video conferencing, SyncMate creates realistic online meetings where communication is easy. Its user-friendly UI and live chat feature make for a lively conversation that increases productivity and engagement. SyncMate's ability to accommodate a variety of communication requirements is demonstrated by its ability to facilitate both private messaging and group collaboration. Its immersive screen-sharing function also improves clarity and interaction, elevating presentations and group work sessions. SyncMate stands out as an innovator in a time when constant connectivity is essential, providing an extensive toolkit to revolutionize team communication and collaboration. This essay investigates the revolutionary effects of SyncMate on modern digital collaboration, highlighting its role in driving efficiency, creativity, and success in remote workenvironments

Federated Collaborative Graph Neural Networks for Few-shot Graph Classification

Federated Collaborative Graph Neural Networks for Few-shot Graph Classification

Constructing Closeness in Educational Collaboration in Extended Hours ECEC

AbstractAlthough quality early childhood education and care (ECEC) and strong collaborations between families and ECEC educators are important for all children, they may be particularly important for children and families who utilize nighttime or weekend ECEC. This study focuses on ECEC educators’ discourses about closeness versus distance in educational collaborations between parents with nonstandard schedules and professionals who work in extended hours ECEC. The data for this study were collected by interviewing ECEC teachers (n = 12) and nurses (n = 18) working in Finland. Research findings: Using the principles of discourse analysis, we found three tensional discourses: (a) the timing of ECEC and asynchrony; (b) a sense of time versus hurry; and (c) care versus criticism. Practice and policy: The findings revealed that extended hours ECEC provides both challenges and possibilities for creating close educational collaboration with parents. This has important implications for the kinds of structural supports and training educators in extended hours ECEC may need to foster quality collaboration with families.

DMHGNN: Double multi-view heterogeneous graph neural network framework for drug-target interaction prediction

Accurate identification of drug-target interactions (DTIs) plays a crucial role in drug discovery. Compared with traditional experimental methods that are labor-intensive and time-consuming, computational methods for drug-target interactions prediction are more popular in recent years. Conventional computational methods almost simply view heterogeneous network constructed by the drug-related and protein-related dataset instead of comprehensively exploring drug-protein pair (DPP) information. To address this limitation, we proposed a Double Multi-view Heterogeneous Graph Neural Network framework for drug-target interaction prediction (DMHGNN). In DMHGNN, one multi-view heterogeneous graph neural network is based on meta-paths and denoising autoencoder for protein-, drug-related heterogeneous network learning, and another multi-view heterogeneous graph neural network is based on multi-channel graph convolutional network for drug-protein pair similarity network learning. First, a meta-path-based graph encoder with the attention mechanism is used for substructure learning of complex relationships from heterogeneous network constructed by proteins, drugs, side-effects and diseases, obtaining key information that is easy to be ignored in global learning of heterogeneous networks, and multi-source neighbouring features for drugs and proteins are learned from heterogeneous network via denoising auto-encoder model. Then, multi-view graphs of drug-protein pairs (DPPs) including the topology graph, semantics graph and collaborative graph with shared weights are constructed, and the multi-channel graph convolutional network (GCN) is utilized to learn the deep representation of DPPs. Finally, a multi-layer fully connection network is trained to predict drug-target interactions. Experiments have demonstrated its effectiveness and better performance than state-of-the-art methods.

The Benefits of Computer Conferences in Distance Education: An Assessment

This study examined the positive influence of computer conferencing on distance education, particularly its capacity to foster a sense of community, collaboration, and interaction among learners. The study employed semi-structured interviews, focus groups, observations, and document analysis to investigate the experiences of students, instructors, and educational technologists of Delar College of Education through a qualitative research design. The primary findings indicated that asynchronous communication provides convenience and flexibility, whereas synchronous communication enables immediate engagement and dynamic discussions. Additionally, computer conferencing facilitates the interchange of information at a rapid pace, reduces feelings of isolation, and facilitates the development of diverse learning environments by facilitating detailed discussions through sub-conferences. The research identified substantial advantages of computer conferencing, but it also emphasized potential constraints, including the necessity for effective facilitation and technological dependence. The research emphasizes the transformative potential of computer conferencing in improving the distance education experience. Keywords: Educational Technology, Computer Conferencing, Interaction, Collaboration Distance Education, Online Learning, Qualitative Study

Collaborative graph neural networks for augmented graphs: A local-to-global perspective

In the field of graph neural networks (GNNs) for representation learning, a noteworthy highlight is the potential of embedding fusion architectures for augmented graphs. However, prevalent GNN embedding fusion architectures mainly focus on handling graph combinations from a global perspective, often ignoring their collaboration with the information of local graph combinations. This inherent limitation constrains the ability of the constructed models to handle multiple input graphs, particularly when dealing with noisy input graphs collected from error-prone sources or those resulting from deficiencies in graph augmentation methods. In this paper, we propose an effective and robust embedding fusion architecture from a local-to-global perspective termed collaborative graph neural networks for augmented graphs (LoGo-GNN). Essentially, LoGo-GNN leverages a pairwise graph combination scheme to generate local perspective inputs. Together with the global graph combination, this serves as the basis to generate a local-to-global perspective. Specifically, LoGo-GNN employs a perturbation augmentation strategy to generate multiple augmentation graphs, thereby facilitating collaboration and embedding fusion from a local-to-global perspective through the use of graph combinations. In addition, LoGo-GNN incorporates a novel loss function for learning complementary information between different perspectives. We also conduct theoretical analysis to assess its expressive power under ideal conditions, demonstrating the effectiveness of LoGo-GNN. Our experiments, focusing on node classification and clustering tasks, highlight the superior performance of LoGo-GNN compared to state-of-the-art methods. Additionally, robustness analysis further confirms its effectiveness in addressing uncertainty challenges.

A Collaboration Graph of the Lilavati Prize Winners with Certain Domination Parameters

A Collaboration Graph of the Lilavati Prize Winners with Certain Domination Parameters

Review and Analysis of RGBT Single Object Tracking Methods: A Fusion Perspective

Visual tracking is a fundamental task in computer vision with significant practical applications in various domains, including surveillance, security, robotics, and human-computer interaction. However, it may face limitations in visible light data, such as low-light environments, occlusion, and camouflage, which can significantly reduce its accuracy. To cope with these challenges, researchers have explored the potential of combining the visible and infrared modalities to improve tracking performance. By leveraging the complementary strengths of visible and infrared data, RGB-infrared fusion tracking has emerged as a promising approach to address these limitations and improve tracking accuracy in challenging scenarios. In this article, we present a review on RGB-infrared fusion tracking. Specifically, we categorize existing RGBT tracking methods into four categories based on their underlying architectures, feature representations, and fusion strategies, namely feature decoupling based method, feature selecting based method, collaborative graph tracking method, and traditional fusion method. Furthermore, we provide a critical analysis of their strengths, limitations, representative methods, and future research directions. To further demonstrate the advantages and disadvantages of these methods, we present a review of publicly available RGBT tracking datasets and analyze the main results on public datasets. Moreover, we discuss some limitations in RGBT tracking at present and provide some opportunities and future directions for RGBT visual tracking, such as dataset diversity, unsupervised and weakly supervised applications. In conclusion, our survey aims to serve as a useful resource for researchers and practitioners interested in the emerging field of RGBT tracking, and to promote further progress and innovation in this area.

Virtual reality classrooms vs. video conferencing platform, initial design and evaluation study for collaborative distance learning

Virtual reality classrooms vs. video conferencing platform, initial design and evaluation study for collaborative distance learning

CHALLENGES AND OPPORTUNITIES TO COLLABORATIVE DISTANCE LEARNING IN ESP INSTRUCTION

In post pandemic instruction fueled by digital media and technology, distance learning emerges as a powerful opportunity for higher education collaboration. The Directorate of Students’ Learning, Ministry of Education and Culture of Indonesia opens opportunities for universities across Indonesia to have collaborative online learning with those students based on underprivileged regions in Indonesia. This research investigates a case of collaborative online learning project between two universities in East Java and East Nusa Tenggara province with two ESP courses designed collaboratively and implemented with shared classes in online mode. Through this case study, two things are explored during the program: challenges and opportunities in the stage of course development and course implementation. Two course developers, four university instructors, and thirteen selected students from both universities were involved in this study. Data collection combined interviews, virtual observations, and instructional documents. Analysis employed thematic layering and triangulation, revealing challenges in four areas: course development, technology readiness, teaching methods, and student support. However, rooms of improvement are there for more prepared future collaboration. Despite the challenges, collaborative distance learning conducted by both universities can be a lesson to learn for a better online instruction in the future amidst the pluralistic backgrounds of the students and problematic technology support.

Collaborative graph contrastive learning for recommendation

Recently, Graph Neural Networks (GNNs) using aggregating neighborhood collaborative information have shown effectiveness in recommendation. However, GNNs-based models suffer from over-smoothing and data sparsity problems. Due to its self-supervised nature, contrastive learning has gained considerable attention in the field of recommendation, aiming at alleviating highly sparse data. Graph contrastive learning models are widely used to learn the consistency of representations by constructing different graph augmentation views. Most current graph augmentation with random perturbation destroy the original graph structure information, which mislead embeddings learning. In this paper, an effective graph contrastive learning paradigm CollaGCL is proposed, which constructs graph augmentation by using singular value decomposition to preserve crucial structure information. CollaGCL enables perturbed views to effectively capture global collaborative information, mitigating the negative impact of graph structural perturbations. To optimize the contrastive learning task, the extracted meta-knowledge was propagate throughout the original graph to learn reliable embedding representations. The self-information learning between views enhances the semantic information of nodes, thus alleviating the problem of over-smoothing. Experimental results on three real-world datasets demonstrate the significant improvement of CollaGCL over state-of-the-art methods.

Online Multi-Agent Forecasting With Interpretable Collaborative Graph Neural Networks.

This article considers predicting future statuses of multiple agents in an online fashion by exploiting dynamic interactions in the system. We propose a novel collaborative prediction unit (CoPU), which aggregates the predictions from multiple collaborative predictors according to a collaborative graph. Each collaborative predictor is trained to predict the agent status by integrating the impact of another agent. The edge weights of the collaborative graph reflect the importance of each predictor. The collaborative graph is adjusted online by multiplicative update, which can be motivated by minimizing an explicit objective. With this objective, we also conduct regret analysis to indicate that, along with training, our CoPU achieves similar performance with the best individual collaborative predictor in hindsight. This theoretical interpretability distinguishes our method from many other graph networks. To progressively refine predictions, multiple CoPUs are stacked to form a collaborative graph neural network. Extensive experiments are conducted on three tasks: online simulated trajectory prediction, online human motion prediction, and online traffic speed prediction, and our methods outperform state-of-the-art works on the three tasks by 28.6%, 17.4%, and 21.0% on average, respectively; in addition, the proposed CoGNNs have lower average time costs in one online training/testing iteration than most previous methods.

Kolmogorov, stochastics in Bulgaria, and probabilistic problems with unexpected solutions

In this talk, I am going to share with the readers my memories of personal meetings with Andrey Nikolaevich Kolmogorov (1903–1987), the content of our conversations, and the fruitful consequences. The reader is familiar with, or could read, the timely prepared recent comprehensive paper [34], presented by N.M. Yanev at the 52nd Spring Conference of the UBM. Included here are several new details about Andrey Nikolaevich and the great influence of the Moscow Probability School on the development of Stochastics in Bulgaria. I have used MathSciNet and introduced the ‘Kolmogorov number’, a ‘collaboration distance’ between a mathematician and Kolmogorov, with a focus on Bulgarian stochasticians. I am writing about Kolmogorov’s approach to doing mathematics in general and the role of counterexamples. Discussed are two specific probabilistic problems which, according to him, are with most unusual solutions, ‘Skitovich-Darmois theorem’ and ‘Plackett problem’. Several related short stories and not well-known facts are presented.

Collaborative Graph Neural Networks for Attributed Network Embedding

Collaborative Graph Neural Networks for Attributed Network Embedding

Computation of certain domination numbers of collaboration graphs

Computation of certain domination numbers of collaboration graphs

Predicting disease-gene associations through self-supervised mutual infomax graph convolution network

Illuminating associations between diseases and genes can help reveal the pathogenesis of syndromes and contribute to treatments, but a large number of associations remained unexplored. To identify novel disease-gene associations, many computational methods have been developed using disease and gene-related prior knowledge. However, these methods remain of relatively inferior performance due to the limited external data sources and the inevitable noise among the prior knowledge. In this study, we have developed a new method, Self-Supervised Mutual Infomax Graph Convolution Network (MiGCN), to predict disease-gene associations under the guidance of external disease-disease and gene-gene collaborative graphs. The noises within the collaborative graphs were eliminated by maximizing the mutual information between nodes and neighbors through a graphical mutual infomax layer. In parallel, the node interactions were strengthened by a novel informative message passing layer to improve the learning ability of graph neural network. The extensive experiments showed that our model achieved performance improvement over the state-of-art method by more than 8 % on AUC. The datasets, source codes and trained models of MiGCN are available at https://github.com/biomed-AI/MiGCN.

DL-SLOT: Tightly-Coupled Dynamic LiDAR SLAM and 3D Object Tracking Based on Collaborative Graph Optimization

DL-SLOT: Tightly-Coupled Dynamic LiDAR SLAM and 3D Object Tracking Based on Collaborative Graph Optimization

MVCAR: Multi-View Collaborative Graph Network for Private Car Carbon Emission Prediction

MVCAR: Multi-View Collaborative Graph Network for Private Car Carbon Emission Prediction

TLNet: Temporal Span Localization Network With Collaborative Graph Reasoning for Video Question Answering

TLNet: Temporal Span Localization Network With Collaborative Graph Reasoning for Video Question Answering

Computation of structural parameters of a collaboration graph from social networks

Mathematicians and Sociologists are analyzing a social network from the perspective of both quality and quantity. To identify a network structure, it is pertinent to know not only every individual in the network, but also his/her role. In such networks, we fix one individual and probe all his contacts with reference to a specific relation. Here, we take up for our investigation, one such social network called Rolf Nevanlinna Prize Winners academic research collaboration network.Follow the procedure given in Appendix-1 to peruse the data available in Mathematical Reviews-MR of American Mathematical Society-AMS website and find the Erdos Numbers-ENs of Rolf Nevanlinna Prize Winners-RNPWs and their links to Erdos through various levels as per their Erdos Numbers to generate the RNPWs Collaboration Graph-RNPWCG. (Erdos is at Level 0; Direct co-authors of Erdos are at Level 1; Co-authors to the direct co-authors of Erdos are at Level 2 and so on). Investigate balanced graphs and devise procedures for obtaining various structural parameters like, tuner sets, positive influence dominating sets, obtain general results concerning 2-dominating sets for any graph G, computation of various domination numbers of RNPWCG.We have (a) derived bounds for parameters such as top, gap for balanced and non-balanced sets, (b) exact values of domination parameters given above and (c) the Tuner sets for the Rolf Nevanlinna Prize Winners collaboration graph for the period 1982–2006 for illustrative purposes. Further, in addition to recording the motivational factors for this work we also list a number of open problems for further study.We are able to determine the role, position and contribution of every individual in the social interaction academic research collaboration network of mathematicians related to the Rolf Nevanlinna Prize winners.