Information Sciences: an International Journal

Multivariate time series classification is a crucial task with applications in broad areas such as finance, medicine, and engineering. Transformer is promising for time series classification, but as a generic approach, they have limited ...

• We present a transformer-based dynamic architecture to achieve adaptive learning strategies for different frequency components of the time series data.
• We design a hierarchical pooling layer to decompose time series into subsequences ...

Identifying causality between events is a crucial research task in natural language processing. However, existing methods either ignore background knowledge of events or do not consider interference of knowledge graph noise on event ...

• A graph contrast-based knowledge augmented network for event causality identification is proposed.
• The model aggregates descriptive and relational knowledge from knowledge graphs and alleviates labeled data scarcity issue.
• Graph ...

This paper investigates the finite-time tracking problem of a class of nonlinear interconnected systems, where each subsystem not only is subject to actuator faults but also has unknown system input powers. Based on Lyapunov stability theory and ...

In multi-view multi-label learning (MVML), the data is described by multiple feature views and annotated by a number of categorical labels. At present, most of the existing MVML methods are proposed based on subspace learning, neural networks and ...

In the realm of practical problem-solving, multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) are becoming increasingly prevalent. MOPRVIF involve determining the optimal decision variables ...

• Provide a unified solution for optimisation problems with redundant variables and indefinite objective functions.
• Propose a data-driven multi-objective optimisation algorithm.
• Designing an adaptive optimiser to enhance the ...

A variety of algorithms has been proposed to mitigate the overestimation bias of Q-learning. These algorithms reduce the estimation of maximum Q-value, i.e., homogeneous update. As a result, some of these algorithms such as Double Q-learning ...

Sequence recommendation is used to predict the user's next potentially interesting items and behaviors. It not only focuses on the user's independent interaction behavior, but also considers the user's historical behavior sequence. However, ...

The deep unsupervised time-series anomaly detector depends on the one-class representation, which is more effective by only formulating the normal samples. However, normal samples are always mixed with anomalies in the unlabeled training dataset. ...

As one of the main research tasks in software testing, automated test case generation based on path coverage (ATCG-PC) aims to achieve maximum path coverage with a minimized set of test cases. In ATCG-PC, the correlation among the dimensions of ...

Partial multi-label learning (PML) is designed to address the challenge of having both ground-truth labels and noisy labels in the label set of training instances. In real-world applications, there are often noisy features in addition to noisy ...

In this paper, we present an improved method for the state estimation of a labeled time Petri net (LTPN) system with unobservable transitions. Precisely, we provide a computationally efficient graph, called an observed modified state class graph (...

Linking digital accounts belonging to the same user has progressed from a research topic to a foundation for security, user satisfaction, and developing next-generation services. Still, few studies address account linkage in domains other than ...

The joint order batching and picker routing problem (JOBPRP) is a combinatorial optimization problem that occurs during in the order-picking operation of warehouse management. It consists of simultaneous assignment of customer orders to batches ...

The concept of bipolar-valued fuzzy set was introduced by Zhang. Can we discuss three-way decisions based on bipolar-valued fuzzy sets over three-way decision spaces? In response to this problem, firstly, the paper discusses partially orders on ...

Dynamic time warping (DTW) is the most widely used method to evaluate the similarity between time series. However, the DTW distance only takes into account the difference in amplitude, but does not reflect the time distortion information between ...

Stochastic multiplicative comparison matrices (SMCMs) are widely accepted due to their flexibility in measuring various types of decision-makers' uncertain preferences by treating the judgment as a random variable. However, few existing ...