Neural combinatorial optimization (NCO) has gained significant attention due to the potential of deep learning to efficiently solve combinatorial optimization problems. NCO has been widely applied to job shop scheduling problems (JSPs) with the current focus predominantly on deterministic problems. In this paper, we propose a novel attention-based scenario processing module (SPM) to extend NCO methods for solving stochastic JSPs. Our approach explicitly incorporates stochastic information by an attention mechanism that captures the embedding of sampled scenarios (i.e., an approximation of stochasticity). Fed with the embedding, the base neural network is intervened by the attended scenarios, which accordingly learns an effective policy under stochasticity. We also propose a training paradigm that works harmoniously with either the expected makespan or Value-at-Risk objective. Results demonstrate that our approach outperforms existing learning and non-learning methods for the flexible JSP problem with stochastic processing times on a variety of instances. In addition, our approach holds significant generalizability to varied numbers of scenarios and disparate distributions.

Interactive collaborative environments built upon high quality video applications require minimum capture-to-display latency to maintain feeling of natural communication. In practice, end users often encounter interruptions due to network congestion, which they could not avoid without knowledge of physical network infrastructure. We solve the problem of application level multipoint routing without assuming accurate knowledge of network link capacities, but rather relying on their estimates by network inference and bandwidth measurement tools. Our approach can handle uncertainties caused by both unreliable bandwidth measurements and unexpected capacity sharing. Support for on-the-fly transcoding of data streams on multicast agents allows per-recipient adaptation of video quality and bandwidth. We have implemented an ant colony optimization algorithm in the Co Universe middleware for orchestration of collaborative environments. Experiments show very good quality of results on most practical use cases and ability to solve significantly larger instances than optimal approaches based on mixed integer programming.

High-quality video, both uncompressed and compressed with relatively low compression ratio, is used in interactive environments, where minimum end-to-end latency is required. The media streams planning solves multi-point data routing for such applications even when bit rate of streams is comparable to capacity of network links. Recent advances in acceleration of high-throughput low-latency video processing enable on-the-fly video transcoding even for 4K and 8K video streams, thus allowing individual adaptation of data stream for each recipient without affecting the others. Stimulated by these advances, we present a novel media streams planning with transcoding on network nodes, and a mixed integer programming approach to solve the problem optimally. Our approach also enhances traditional end-to-end network model of the whole system with known topology of certain network regions. Our solution was implemented in the Co Universe, which is middleware for orchestration of collaborative environments. Measurements show that performance of the new solver is sufficient for interactive solving of most practical use-cases, thus enabling self-organization of high-performance collaborative environments.

We present a videoconferencing tool, GColl, which aims to support collaboration among remote groups of participants. GColl supports mutual gaze as well as partial gaze awareness for all participants, while still retaining a modest technical requirements: a camera and an echo-canceling microphone at

Videoconferencing has emerged as a popular method of remote communication. The design of a videoconferencing environment strongly influences the communication efficiency and user satisfaction. Various studies on perceived effects of videoconferencing environments and their parameters have been done, however, they primarily focused on dyadic conversation. This paper describes an exploratory study on the effects on remote groups communication. Beginning with the standard environment, a videoconferencing design has been developed iteratively according to information obtained from the experimental group. Two new videoconferencing environment designs are introduced to suit the group conversation needs better and bring the group videoconference closer to face-to-face communication.