Time series anomaly detection (TSAD) is an evolving area of research motivated by its critical applications, such as detecting seismic activity, sensor failures in industrial plants, predicting crashes in the stock market, and so on. Across domains, anomalies occur significantly less frequently than normal data, making the F1-score the most commonly adopted metric for anomaly detection. However, in the case of time series, it is not straightforward to use standard F1-score because of the dissociation between ‘time points’ and ‘time events’. To accommodate this, anomaly predictions are adjusted, called as point adjustment (PA), before the F1-score evaluation. However, these adjustments are heuristics-based, and biased towards true positive detection, resulting in over-estimated detector performance. In this work, we propose an alternative adjustment protocol called "Balanced point adjustment" (BA). It addresses the limitations of existing point adjustment methods and provides guarantees of fairness backed by axiomatic definitions of TSAD evaluation. Code and implementation details: https://github.com/summukhe/balanced_f1score.

The Second Diagnosis of COVID-19 using Acoustics (DiCOVA) Challenge aimed at accelerating the research in acoustics based detection of COVID-19, a topic at the intersection of acoustics, signal processing, machine learning, and healthcare. This paper presents the details of the challenge, which was an open call for researchers to analyze a dataset of audio recordings consisting of breathing, cough and speech signals. This data was collected from individuals with and without COVID-19 infection, and the task in the challenge was a two-class classification. The development set audio recordings were collected from 965 (172 COVID-19 positive) individuals, while the evaluation set contained data from 471 individuals (71 COVID-19 positive). The challenge featured four tracks, one associated with each sound category of cough, speech and breathing, and a fourth fusion track. A baseline system was also released to benchmark the participants. In this paper, we present an overview of the challenge, the rationale for the data collection and the baseline system. Further, a performance analysis for the systems submitted by the 21 participating teams in the leaderboard is also presented.

The COVID-19 outbreak resulted in multiple waves of infections that have been associated with different SARS-CoV-2 variants.Studies have reported differential impact of the variants on respiratory health of patients.We explore whether acoustic signals, collected from COVID-19 subjects, show computationally distinguishable acoustic patterns suggesting a possibility to predict the underlying virus variant.We analyze the Coswara dataset which is collected from three subject pools, namely, i) healthy, ii) COVID-19 subjects recorded during the delta variant dominant period, and iii) data from COVID-19 subjects recorded during the omicron surge.Our findings suggest that multiple sound categories, such as cough, breathing, and speech, indicate significant acoustic feature differences when comparing COVID-19 subjects with omicron and delta variants.The classification areas-under-the-curve are significantly above chance for differentiating subjects infected by omicron from those infected by delta.Using a score fusion from multiple sound categories, we obtained an area-under-the-curve of 89% and 52.4% sensitivity at 95% specificity.Additionally, a hierarchical three class approach was used to classify the acoustic data into healthy and COVID-19 positive, and further COVID-19 subjects into delta and omicron variants providing high level of 3-class classification accuracy.These results suggest new ways for designing sound based COVID-19 diagnosis approaches.

In this work, we propose an autonomous and onboard image-based agricultural land demarcation and path-planning system—IDeA ( I oT-Based Autonomous Aerial De marcation and Path Planning for Precision A griculture) with Unmanned Aerial Vehicles (UAVs)—using our advanced UAV-based aerial IoT platform. Our work successfully addresses the problem of onboard and autonomous path planning—which conventional UAV-based systems are not capable of—during stand-alone operations and without preloaded Global Positioning SYstem (GPS) markers for flight path waypoints. Our aerial system visually identifies non-electronically and singularly tagged agricultural plots and assesses the enclosing boundaries of the identified plot. Subsequently, an onboard path planning module autonomously generates GPS waypoints for traversing the identified plot with minimal overlaps and maximal coverage. Our proposed system exhibits an area coverage efficiency of 95.39%, performs pixel-to-GPS coordinate conversion with an accuracy of 90.35%, and has high agricultural potential in applications such as surveying crop health conditions and spraying pesticide/herbicides. The proposed system has massive applications in scenarios requiring aerial detection, demarcation, geographical tagging, and coverage of an area.