--T est-time adaptation (TT A) aims to boost the generalization capability of a trained model by conducting self- /unsupervised learning during the testing phase. While most existing TT A methods for video primarily utilize visual supervisory signals, they often overlook the potential contribution of inherent audio data. T o address this gap, we propose a novel approach that incorporates audio information into video TT A. Our method capitalizes on the rich semantic content of audio to generate audio-assisted pseudo-labels, a new concept in the context of video TT A. Specifically, we propose an audio-to-video label mapping method by first employing pre-trained audio models to classify audio signals extracted from videos and then mapping the audio-based predictions to video label spaces through large language models, thereby establishing a connection between the audio categories and video labels. T o effectively leverage the generated pseudo-labels, we present a flexible adaptation cycle that determines the optimal number of adaptation iterations for each sample, based on changes in loss and consistency across different views. This enables a customized adaptation process for each sample. Experimental results on two widely used datasets (UCF101-C and Kinetics-Sounds-C), as well as on two newly constructed audio-video TT A datasets (A VE-C and A VMIT -C) with various corruption types, demonstrate the superiority of our approach. EEP neural networks have achieved significant success in various video analysis tasks [1]-[4], but most methods assume that training and testing data come from the same distribution. This work was supported by the National Natural Science Foundation of China (NSFC) (Grant Nos. Qi Deng, Ronghao Zhang and Jian Chen are with School of Software Engineering, South China University of Technology, Guangzhou, 510000, China. Shuaicheng Niu is with College of Computing and Data Science, Nanyang Technological University, 639798, Singapore. Existing video test-time adaptation methods rely on visual supervision, overlooking the rich information inherent in audio. We propose a novel approach that involves extracting audio from videos and mapping the results of an open-source audio model to the video label space.

Recently, machine learning (ML) has become a popular approach to support self-adaptation. ML has been used to deal with several problems in self-adaptation, such as maintaining an up-to-date runtime model under uncertainty and scalable decision-making. Yet, exploiting ML comes with inherent challenges. In this paper, we focus on a particularly important challenge for learning-based self-adaptive systems: drift in adaptation spaces. With adaptation space we refer to the set of adaptation options a self-adaptive system can select from at a given time to adapt based on the estimated quality properties of the adaptation options. Drift of adaptation spaces originates from uncertainties, affecting the quality properties of the adaptation options. Such drift may imply that eventually no adaptation option can satisfy the initial set of the adaptation goals, deteriorating the quality of the system, or adaptation options may emerge that allow enhancing the adaptation goals. In ML, such shift corresponds to novel class appearance, a type of concept drift in target data that common ML techniques have problems dealing with. To tackle this problem, we present a novel approach to self-adaptation that enhances learning-based self-adaptive systems with a lifelong ML layer. We refer to this approach as lifelong self-adaptation. The lifelong ML layer tracks the system and its environment, associates this knowledge with the current tasks, identifies new tasks based on differences, and updates the learning models of the self-adaptive system accordingly. A human stakeholder may be involved to support the learning process and adjust the learning and goal models. We present a general architecture for lifelong self-adaptation and apply it to the case of drift of adaptation spaces that affects the decision-making in self-adaptation. We validate the approach for a series of scenarios using the DeltaIoT exemplar.

In the famous Google paper published by Sculley et al. in 2015 "Hidden Technical Debt in Machine Learning Systems", they highlight that in real-world Machine Learning (ML) systems, only a small fraction is comprised of actual ML code. There is a vast array of surrounding infrastructure and processes to support their evolution. They also discuss the many sources of technical debt that can accumulate in such systems, some of which are related to data dependencies, model complexity, reproducibility, testing, monitoring, and dealing with changes in the external world. Many of the same concerns are also present in traditional software systems, and Continuous Delivery has been the approach to bring automation, quality, and discipline to create a reliable and repeatable process to release software into production. "Continuous Delivery is the ability to get changes of all types -- including new features, configuration changes, bug fixes, and experiments -- into production, or into the hands of users, safely and quickly in a sustainable way".