At the 2025 IEEE 7th International Conference on Applied Computational Intelligence in Information Systems (ACIIS), held October 20-22 at Universiti Teknologi Brunei in Brunei Darussalam, Prof. Weber received the Best Paper Award for his work on a robust aggregate scoring system for evaluating digital services. The winning paper, “Relatively Robust QoS and QoE Score Aggregation,” is forthcoming on IEEE Xplore as part of the conference proceedings.
ACIIS 2025, technically sponsored by the IEEE Computational Intelligence Society, centered on the theme “Intelligent and Resilient Digital Innovations for Sustainable Living.” Focus areas included Big Data, Internet of Things (IoT), Machine Learning, Business Intelligence, Predictive Analytics, Digital Reality, Blockchain, SMAC (Social, Mobile, Analytics, Cloud), product and design technology, smart products, user experience (UX), human-centered design (HCD), IR 4.0, information security, computer networks, and cyber technologies.
We present a robust methodology for aggregating Quality of Service (QoS) and Quality of Experience (QoE) scores across multiple criteria in settings where the relative importance of individual metrics (i.e., weights) is unknown or ambiguous. Building on recent advances in relatively robust multicriteria decision theory, we define a Robust Aggregate Score (RAS) based on the worst-case performance ratio with respect to all admissible weight configurations. The method is weight-agnostic, invariant to rescaling of individual metrics, and accommodates criteria with both positive and negative preference direction. We illustrate the approach in the context of video-streaming service evaluation and demonstrate how it enables interpretable and defensible rankings across heterogeneous quality indicators. The proposed framework is broadly applicable to quality assessment tasks that require reliability under weight uncertainty.
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