The COVID-19 pandemic has forced workers around the world to switch their working paradigms from on-site to video-mediated communication. Despite the advantages of videoconferencing, diverse circumstances have prevented people from focusing on their work. One of the most typical problems they face is that various surrounding factors distract them during their meetings. This study focuses on conditions in which remote workers are distracted by factors that disturb, interrupt, or restrict them during their meetings. We aim to explore the various problem situations and user needs. To understand users’ pain points and needs, focus group interviews and participatory design workshops were conducted to learn about participants’ troubled working experiences over the past two years and the solutions they expected. Our study provides a unified framework of distracting factors by which to understand causes of poor user experience and reveals valuable implications to improve videoconferencing experiences.

A major challenge for autonomous vehicles (AVs) today is driving in a complex urban environment where various traffic participants, infrastructures, and events are mixed. A growing body of research is studying the interaction between AVs and human road users (HRUs) to mitigate this challenge. Although traffic is complicated, research has focused on limited situations, such as pedestrian crossings. This study aims to explore scenarios that have a high possibility of causing problems in mixed traffic situations. We devised a design workshop method using miniatures and small cameras, thus allowing participants to experience scenarios from the HRU perspective and easily creating new road situations to uncover various problems. By analyzing 133 scenarios found through the workshop, we defined five factors and 51 elements of AV–HRU problem scenarios. Through qualitative analysis of the factors found through experiments and comparison with existing studies, we identified research gaps and discussed future design challenges.

M-LWWF/M-LWDF scheduling schemes have attracted much interest due to their ability to both stabilize queues whenever possible and control delay through parameter selection. However, a good implementation of these schedulers would require a mechanism to minimize the required fraction of the bandwidth while satisfying its stability and delay requirements. To the best of our knowledge, previous works on these scheduling policies did not address the problem of minimizing the bandwidth utilization while satisfying delay constraints. In this paper, we explore the solution of this problem using a joint bandwidth and weight adaptation approach. We characterize the problem solution space for M-LWWF and M-LWDF scheduling, assuming time-varying traffic. We also show that, starting from any point in the solution space, simple dynamic bandwidth and weight updates can surely lead to the convergence to the optimal operation point in this space. Based on these characteristics, we propose a dynamic parameter adaptation algorithm that is able to track the time-varying optimal operation points for dynamic traffic and channel conditions. Simulation results show the efficiency of our proposed algorithm in tracking the optimal operation points in dynamic traffic and channel settings.

We consider a bandwidth optimization problem in a Generalized Processor Sharing servers with multiple class queues to minimize the total bandwidth such that QoS requirements for each class queue are satisfied. In our previous paper [13], since it is difficult to obtain the performance such as the delay distribution mathematically with self-similar input traffic in a GPS server, we obtained an optimized bandwidth vector using a simulation-based optimization technique. However, the previous optimization algorithm requires rather long simulation time to solve the problem by using exterior penalty function methods. We propose a new bandwidth optimization algorithm based on bandwidth ratio adjustment without complex calculations. In numerical results, the required time to find the optimal bandwidth allocation in GPS servers is greatly reduced.