Examining the Effect of Social Media Tools on Virtual Team Conflicts: A Process Model

This research investigates how the use of social media tools affects virtual team conflicts. The novel concept of “feature richness”, which is understood as affordances of social media tools, is theorized. Feature richness distinguishes social media tools from other commonly used communication tools in virtual teams. The researchers propose a process model which suggests that operationally, feature richness is understood as the process nature of social media tools. The primary data was collected at corporate organizations in form of a Likert questionnaire. The research findings reveal that social media tools lead to effective communication, which encourages the development of trust, team cohesion and satisfaction in virtual teams. This further reflects in form of reduced virtual team conflicts

UNDERSTANDING THE MEDIATING ROLE OF SOCIAL MEDIA IN VIRTUAL TEAM CONFLICTS

Communication technology is recognized as an important component of a virtual team (VT). Communication technologies other than social media have been linked to VT conflicts by prior research. This research in progress explores using social media to see if any improvements can be made to conflicts in VTs. The researchers emphasize on the “feature richness” of social media which is understood as affordances of social media and it distinguishes social media from other commonly used communication technologies in a VT environment. The researchers theorize that “feature richness” rather than “media richness” of the communication technology can be more beneficial for a virtual team since it is hoped to simultaneously work towards reducing VT conflicts. The researchers propose a conceptual research model that contributes to understanding the mediating role that social media can play in virtual team conflicts

The Effect of the Social Media Tools on Virtual Team Performance: The Mediating Role of Transactive Memory System Mapping with the Feature Richness

The communication tool is an important component of a virtual team, and virtual teams are highly dependent upon the communication tools for accomplishing their tasks and fulfilling their needs effectively. This research in progress builds upon the existing literature and employs the concept of feature richness of social media tools and a Transactive Memory System (TMS) approach to develop a conceptual framework for understanding the impact of social media tools on virtual team performance. Thus, a conceptual research model which postulates that TMS mediates the relationship between social media tools and virtual team performance, is developed. This research tries to establish an appropriate component-level mapping between the components of TMS construct and the feature richness factors to provide a deeper understanding about the effect of social media tools on TMS and consequently, the impact on virtual team performance

Efficient Scopeformer: Towards Scalable and Rich Feature Extraction for Intracranial Hemorrhage Detection

The quality and richness of feature maps extracted by convolution neural networks (CNNs) and vision Transformers (ViTs) directly relate to the robust model performance. In medical computer vision, these information-rich features are crucial for detecting rare cases within large datasets. This work presents the "Scopeformer," a novel multi-CNN-ViT model for intracranial hemorrhage classification in computed tomography (CT) images. The Scopeformer architecture is scalable and modular, which allows utilizing various CNN architectures as the backbone with diversified output features and pre-training strategies. We propose effective feature projection methods to reduce redundancies among CNN-generated features and to control the input size of ViTs. Extensive experiments with various Scopeformer models show that the model performance is proportional to the number of convolutional blocks employed in the feature extractor. Using multiple strategies, including diversifying the pre-training paradigms for CNNs, different pre-training datasets, and style transfer techniques, we demonstrate an overall improvement in the model performance at various computational budgets. Later, we propose smaller compute-efficient Scopeformer versions with three different types of input and output ViT configurations. Efficient Scopeformers use four different pre-trained CNN architectures as feature extractors to increase feature richness. Our best Efficient Scopeformer model achieved an accuracy of 96.94\% and a weighted logarithmic loss of 0.083 with an eight times reduction in the number of trainable parameters compared to the base Scopeformer. Another version of the Efficient Scopeformer model further reduced the parameter space by almost 17 times with negligible performance reduction. Hybrid CNNs and ViTs might provide the desired feature richness for developing accurate medical computer vision model

Discriminative Link Prediction using Local Links, Node Features and Community Structure

A link prediction (LP) algorithm is given a graph, and has to rank, for each node, other nodes that are candidates for new linkage. LP is strongly motivated by social search and recommendation applications. LP techniques often focus on global properties (graph conductance, hitting or commute times, Katz score) or local properties (Adamic-Adar and many variations, or node feature vectors), but rarely combine these signals. Furthermore, neither of these extremes exploit link densities at the intermediate level of communities. In this paper we describe a discriminative LP algorithm that exploits two new signals. First, a co-clustering algorithm provides community level link density estimates, which are used to qualify observed links with a surprise value. Second, links in the immediate neighborhood of the link to be predicted are not interpreted at face value, but through a local model of node feature similarities. These signals are combined into a discriminative link predictor. We evaluate the new predictor using five diverse data sets that are standard in the literature. We report on significant accuracy boosts compared to standard LP methods (including Adamic-Adar and random walk). Apart from the new predictor, another contribution is a rigorous protocol for benchmarking and reporting LP algorithms, which reveals the regions of strengths and weaknesses of all the predictors studied here, and establishes the new proposal as the most robust.Comment: 10 pages, 5 figure

Locosim: an Open-Source Cross-Platform Robotics Framework

The architecture of a robotics software framework tremendously influences the effort and time it takes for end users to test new concepts in a simulation environment and to control real hardware. Many years of activity in the field allowed us to sort out crucial requirements for a framework tailored for robotics: modularity and extensibility, source code reusability, feature richness, and user-friendliness. We implemented these requirements and collected best practices in Locosim, a cross-platform framework for simulation and real hardware. In this paper, we describe the architecture of Locosim and illustrate some use cases that show its potential.Comment: 12 pages, 4 figures, 1 table, accepted to Clawar 2023, for associated video see https://youtu.be/ZwV1LEqK-L