ResLearn-XR is a residual learning framework designed to predict extended reality (XR) network traffic and estimate Quality-of-Experience (QoE) risk. It uses a two‑stage temporal learning structure: a base sequence prediction model followed by task‑specific residual components that operate in value space for traffic forecasting and in logit space for probabilistic QoE risk estimation. The framework introduces a Data Descriptor Algorithm (DDA) to convert packet‑level observables into frame‑timing‑aware descriptors and is evaluated on a newly constructed XR Traffic‑QoE dataset, achieving significant reductions in SMAPE for both traffic prediction and QoE‑risk estimation compared to single‑stage baselines.
By Yoga Suhas Kuruba Manjunath, Jie Gao, Lian Zhao
QoNext is a new framework that applies Quality of Experience (QoE) principles from networking and multimedia to evaluate foundation models in a conversational setting. It identifies experiential factors that influence user experience, collects human ratings in controlled interaction scenarios, and builds a database and neural predictor to estimate user satisfaction from system parameters. The framework demonstrates the ability to decode the mechanisms of user satisfaction and predict human sentiment across varied service conditions.
By Yijin Guo, Farong Wen, Ye Shen, Junying Wang, Qi Jia, Xiaohong Liu, Zicheng Zhang, Guangtao Zhai
arXiv:2402.03694v3 Announce Type: replace-cross
Abstract: Network traffic analysis in cloud and internet service provider (ISP) networks increasingly relies on machine learning (ML) models, as encryp...
By Shinan Liu, Ted Shaowang, Gerry Wan, Jeewon Chae, Jonatas Marques, Sanjay Krishnan, Nick Feamster
arXiv:2510. 00481v2 Announce Type: replace-cross Abstract: In 2025, Large Language Model (LLM) services have launched a new feature -- AI video chat -- allowing users to interact with AI agents via real-time video communication (RTC), just like chatting with real people.
By Jiayang Xu, Xiangjie Huang, Zijie Li, Antariksh Verma, Zili Meng
The paper evaluates seven pre‑trained CNN architectures for detecting IoT DDoS attacks under resource constraints, using the CICDDoS2019 dataset and image‑based traffic representation. It combines performance metrics, reliability statistics, latency and training cost, and interpretability methods (Grad‑CAM and SHAP) to assess each model. DenseNet169 and MobileNetV3 emerge as top performers, offering strong reliability, interpretability, and a favorable latency‑accuracy trade‑off for fog‑level deployment.
By Nelly Elsayed
arXiv:2607. 22854v1 Announce Type: new Abstract: AI agents are enabling a new paradigm of agent-augmented real-time communication (RTC), where humans focus on high-level collaboration, while agents autonomously retrieve, analyze, and generate information in real time to support their interactions.
By Goodsol Lee, Juheon Yi, Jinglu Wang, Haowen Xu, Saewoong Bahk, Yan Lu