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Performance testing aims at uncovering efficiency issues of software systems. In order to be both effective and practical, the design of a performance test must achieve a reasonable trade-off between result quality and testing time. This becomes ...

Recently, the issue of adversarial robustness in the time series domain has garnered significant attention. However, the available defense mechanisms remain limited, with adversarial training being the predominant approach, though it does not provide ...

Previous works for time series classification tend to assume that both the training and testing sets originate from the same distribution. This oversimplification deviates from the complexity of reality and makes it challenging to generalize methods to ...

In multivariate time series (MTS) classification, finding the important features (e.g., sensors) for model performance is crucial yet challenging due to the complex, high-dimensional nature of MTS data, intricate temporal dynamics, and the necessity for ...

Time series data has been demonstrated to be crucial in various research fields. The management of large quantities of time series data presents challenges in terms of deep learning tasks, particularly for training a deep neural network. Recently, a ...

Real-world environments are inherently non-stationary, frequently introducing new classes over time. This is especially common in time series classification, such as the emergence of new disease classification in healthcare or the addition of new ...

Clinical text in electronic health records (EHR) holds vital cues into a patient's journey, often absent in structured EHR data. Evidence-based healthcare decisions demand accurate extraction and modeling of these cues. The goal of our study is to ...

Time series data are ubiquitous in a variety of disciplines. Early classification of time series, which aims to predict the class label of a time series as early and accurately as possible, is a significant but challenging task in many time-sensitive ...

Time series classification is a supervised task in the field of temporal data mining. Time series naturally tend to be highly dimensional, requiring the use of reduction techniques such as discretization. eMODiTS is a data-driven method for ...${}_{}$

The growing availability of time series data has increased the usage of classifiers for this data type. Unfortunately, state-of-the-art time series classifiers are black-box models and, therefore, not usable in critical domains such as healthcare or ...

In this work, we present a lightweight attention network to perform Time Series Classification on Edge devices. We evaluate the merit of our system on a Human Activity Recognition dataset and show the demonstration with the help of a Wearable device (...

Current human activity recognition (HAR) techniques regard activity labels as integer class IDs without explicitly modeling the semantics of class labels. We observe that different activity names often have shared structures. For example, "open door" and ...

While one-dimensional convolutional neural networks (1D-CNNs) have been empirically proven effective in time series classification tasks, we find that there remain undesirable outcomes that could arise in their application, motivating us to further ...

On modern dairy farms technologies that are capable of measuring high frequency indicators (e.g. milk yield, milk flow-rates, and electrical conductivity) at every milking can play an important role in helping farmers manage animal health. The ...

Seismic monitoring systems sift through seismograms in real-time, searching for target events, such as underground explosions. In this monitoring system, a burst of aftershocks (minor earthquakes occur after a major earthquake over days or even years) ...

As a result of the digital transformation, the degree of automation in production environments is constantly increasing. The automation of logistics processes offers great potential for optimizing material flows within production. However, there ...

EXplainable Artificial Intelligence (XAI) methods are increasingly accepted as effective tools to trace complex machine learning models' decision-making processes. There are two underlying XAI paradigms: (1) traditional factual methods and (2) emerging ...

Breast cancer has become the leading cause of women cancer death worldwide. Despite the consensus that breast cancer early detection can significantly reduce treatment difficulty and cancer mortality, people still are reluctant to go to hospital for ...

Time-Series Classification (TSC) is an important problem in many fields across sciences. Many algorithms for TSC use symbolic representation to combat noise. In this paper we propose a framework, namely SCALE-BOSS, to build TSC algorithms that exploit ...

This article proposes TSCLAS, a time series classification strategy for the Internet of Things (IoT) data, based on the class separability analysis of their temporal dynamics. Given the large number and incompleteness of IoT data, the use of traditional ...