TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection Models

Driven by the proliferation of real-world application scenarios and scales, time series anomaly detection (TSAD) has attracted considerable scholarly and industrial interest. However, existing algorithms exhibit a gap in terms of training paradigm, online detection paradigm, and evaluation criteria when compared to the actual needs of real-world industrial systems. Firstly, current algorithms typically train a specific model for each individual time series. In a large-scale online system with tens of thousands of curves, maintaining such a multitude of models is impractical. The performance of using merely one single unified model to detect anomalies remains unknown. Secondly, most TSAD models are trained on the historical part of a time series and are tested on its future segment. In distributed systems, however, there are frequent system deployments and upgrades, with new, previously unseen time series emerging daily. The performance of testing newly incoming unseen time series on current TSAD algorithms remains unknown. Lastly, although some papers have conducted detailed surveys, the absence of an online evaluation platform prevents answering questions like "Who is the best at anomaly detection at the current stage?" In this paper, we propose TimeSeriesBench, an industrial-grade benchmark that we continuously maintain as a leaderboard. On this leaderboard, we assess the performance of existing algorithms across more than 168 evaluation settings combining different training and testing paradigms, evaluation metrics and datasets. Through our comprehensive analysis of the results, we provide recommendations for the future design of anomaly detection algorithms. To address known issues with existing public datasets, we release an industrial dataset to the public together with TimeSeriesBench. All code, data, and the online leaderboard have been made publicly available.

Customising General Large Language Models for Specialised Emotion Recognition Tasks

The advent of large language models (LLMs) has gained tremendous attention over the past year. Previous studies have shown the astonishing performance of LLMs not only in other tasks but also in emotion recognition in terms of accuracy, universality, explanation, robustness, few/zero-shot learning, and others. Leveraging the capability of LLMs inevitably becomes an essential solution for emotion recognition. To this end, we further comprehensively investigate how LLMs perform in linguistic emotion recognition if we concentrate on this specific task. Specifically, we exemplify a publicly available and widely used LLM -- Chat General Language Model, and customise it for our target by using two different modal adaptation techniques, i.e., deep prompt tuning and low-rank adaptation. The experimental results obtained on six widely used datasets present that the adapted LLM can easily outperform other state-of-the-art but specialised deep models. This indicates the strong transferability and feasibility of LLMs in the field of emotion recognition.

Refashioning Emotion Recognition Modelling: The Advent of Generalised Large Models

After the inception of emotion recognition or affective computing, it has increasingly become an active research topic due to its broad applications. Over the past couple of decades, emotion recognition models have gradually migrated from statistically shallow models to neural network-based deep models, which can significantly boost the performance of emotion recognition models and consistently achieve the best results on different benchmarks. Therefore, in recent years, deep models have always been considered the first option for emotion recognition. However, the debut of large language models (LLMs), such as ChatGPT, has remarkably astonished the world due to their emerged capabilities of zero/few-shot learning, in-context learning, chain-of-thought, and others that are never shown in previous deep models. In the present paper, we comprehensively investigate how the LLMs perform in emotion recognition in terms of diverse aspects, including in-context learning, few-short learning, accuracy, generalisation, and explanation. Moreover, we offer some insights and pose other potential challenges, hoping to ignite broader discussions about enhancing emotion recognition in the new era of advanced and generalised large models.

Cross-device Federated Learning for Mobile Health Diagnostics: A First Study on COVID-19 Detection

Federated learning (FL) aided health diagnostic models can incorporate data from a large number of personal edge devices (e.g., mobile phones) while keeping the data local to the originating devices, largely ensuring privacy. However, such a cross-device FL approach for health diagnostics still imposes many challenges due to both local data imbalance (as extreme as local data consists of a single disease class) and global data imbalance (the disease prevalence is generally low in a population). Since the federated server has no access to data distribution information, it is not trivial to solve the imbalance issue towards an unbiased model. In this paper, we propose FedLoss, a novel cross-device FL framework for health diagnostics. Here the federated server averages the models trained on edge devices according to the predictive loss on the local data, rather than using only the number of samples as weights. As the predictive loss better quantifies the data distribution at a device, FedLoss alleviates the impact of data imbalance. Through a real-world dataset on respiratory sound and symptom-based COVID-$19$ detection task, we validate the superiority of FedLoss. It achieves competitive COVID-$19$ detection performance compared to a centralised model with an AUC-ROC of $79\%$. It also outperforms the state-of-the-art FL baselines in sensitivity and convergence speed. Our work not only demonstrates the promise of federated COVID-$19$ detection but also paves the way to a plethora of mobile health model development in a privacy-preserving fashion.

Indoor simultaneous localization and mapping based on fringe projection profilometry

Simultaneous Localization and Mapping (SLAM) plays an important role in outdoor and indoor applications ranging from autonomous driving to indoor robotics. Outdoor SLAM has been widely used with the assistance of LiDAR or GPS. For indoor applications, the LiDAR technique does not satisfy the accuracy requirement and the GPS signals will be lost. An accurate and efficient scene sensing technique is required for indoor SLAM. As the most promising 3D sensing technique, the opportunities for indoor SLAM with fringe projection profilometry (FPP) systems are obvious, but methods to date have not fully leveraged the accuracy and speed of sensing that such systems offer. In this paper, we propose a novel FPP-based indoor SLAM method based on the coordinate transformation relationship of FPP, where the 2D-to-3D descriptor-assisted is used for mapping and localization. The correspondences generated by matching descriptors are used for fast and accurate mapping, and the transform estimation between the 2D and 3D descriptors is used to localize the sensor. The provided experimental results demonstrate that the proposed indoor SLAM can achieve the localization and mapping accuracy around one millimeter.

A Summary of the ComParE COVID-19 Challenges

The COVID-19 pandemic has caused massive humanitarian and economic damage. Teams of scientists from a broad range of disciplines have searched for methods to help governments and communities combat the disease. One avenue from the machine learning field which has been explored is the prospect of a digital mass test which can detect COVID-19 from infected individuals' respiratory sounds. We present a summary of the results from the INTERSPEECH 2021 Computational Paralinguistics Challenges: COVID-19 Cough, (CCS) and COVID-19 Speech, (CSS).

Benchmarking Uncertainty Quantification on Biosignal Classification Tasks under Dataset Shift

A biosignal is a signal that can be continuously measured from human bodies, such as respiratory sounds, heart activity (ECG), brain waves (EEG), etc, based on which, machine learning models have been developed with very promising performance for automatic disease detection and health status monitoring. However, dataset shift, i.e., data distribution of inference varies from the distribution of the training, is not uncommon for real biosignal-based applications. To improve the robustness, probabilistic models with uncertainty quantification are adapted to capture how reliable a prediction is. Yet, assessing the quality of the estimated uncertainty remains a challenge. In this work, we propose a framework to evaluate the capability of the estimated uncertainty in capturing different types of biosignal dataset shifts with various degrees. In particular, we use three classification tasks based on respiratory sounds and electrocardiography signals to benchmark five representative uncertainty quantification methods. Extensive experiments show that, although Ensemble and Bayesian models could provide relatively better uncertainty estimations under dataset shifts, all tested models fail to meet the promise in trustworthy prediction and model calibration. Our work paves the way for a comprehensive evaluation for any newly developed biosignal classifiers.

COVID-19 Disease Progression Prediction via Audio Signals: A Longitudinal Study

Recent work has shown the potential of the use of audio data in screening for COVID-19. However, very little exploration has been done of monitoring disease progression, especially recovery in COVID-19 through audio. Tracking disease progression characteristics and patterns of recovery could lead to tremendous insights and more timely treatment or treatment adjustment, as well as better resources management in health care systems. The primary objective of this study is to explore the potential of longitudinal audio dynamics for COVID-19 monitoring using sequential deep learning techniques, focusing on prediction of disease progression and, especially, recovery trend prediction. We analysed crowdsourced respiratory audio data from 212 individuals over 5 days to 385 days, alongside their self-reported COVID-19 test results. We first explore the benefits of capturing longitudinal dynamics of audio biomarkers for COVID-19 detection. The strong performance, yielding an AUC-ROC of 0.79, sensitivity of 0.75 and specificity of 0.70, supports the effectiveness of the approach compared to methods that do not leverage longitudinal dynamics. We further examine the predicted disease progression trajectory, which displays high consistency with the longitudinal test results with a correlation of 0.76 in the test cohort, and 0.86 in a subset of the test cohort with 12 participants who report disease recovery. Our findings suggest that monitoring COVID-19 progression via longitudinal audio data has enormous potential in the tracking of individuals' disease progression and recovery.

Benchmarking Uncertainty Qualification on Biosignal Classification Tasks under Dataset Shift

A biosignal is a signal that can be continuously measured from human bodies, such as respiratory sounds, heart activity (ECG), brain waves (EEG), etc, based on which, machine learning models have been developed with very promising performance for automatic disease detection and health status monitoring. However, dataset shift, i.e., data distribution of inference varies from the distribution of the training, is not uncommon for real biosignal-based applications. To improve the robustness, probabilistic models with uncertainty qualification are adapted to capture how reliable a prediction is. Yet, assessing the quality of the estimated uncertainty remains a challenge. In this work, we propose a framework to evaluate the capability of the estimated uncertainty in capturing different types of biosignal dataset shifts with various degrees. In particular, we use three classification tasks based on respiratory sounds and electrocardiography signals to benchmark five representative uncertainty qualification methods. Extensive experiments show that, although Ensemble and Bayesian models could provide relatively better uncertainty estimations under dataset shifts, all tested models fail to meet the promise in trustworthy prediction and model calibration. Our work paves the way for a comprehensive evaluation for any newly developed biosignal classifiers.