Information Sciences: an International Journal

This paper presents a method called Interval Incremental Learning (IIL) to capture spatial and temporal patterns in uncertain data streams. The patterns are represented by information granules and a granular rule base with the purpose ...

In this paper, a new two-dimensional gaussian hierarchical priority fuzzy system (TGHPFS) is proposed to handle interval-valued data. TGHPFS first performs hierarchical clustering of the average value of interval-valued data in each ...

N4-methylcytosine (4mC), as a DNA modification, plays a crucial role in epigenetic regulation. However, the existing experimentation methods for accurately identifying 4mC sites are inefficient and highly consumable, making them ...

• Prediction of N4-methylcytosine sites via kernelized higher-order fuzzy inference system and deep multiple kernel learning.

This paper investigates the algorithm design problem of recursive state estimation (RSE) for a class of complex networks (CNs) subject to quantized coupled parameter, missing measurements (MMs) and amplify-and-forward (AF) relay. In ...

• The recursive state estimation issue is addressed for coupled complex networks under amplify-and-forward relay communication.

We study synchronization phenomena in complex networks in terms of machine learning oscillators without conventional dynamical equations. Specifically, we adopt an effective machine learning technique known as reservoir computing for ...

• We study synchronization phenomena in complex networks in terms of machine learning oscillators.

Many real-world applications can be categorized as expensive multimodal optimization problems which not only have multiple global optima, but also their objective functions are time-consuming. To address them, the article proposed a ...

• A novel neighborhood evolutionary sampling framework is proposed.
• Two ...

The existing single dictionary learning algorithms are applied to face recognition and achieve satisfactory results. However, their performance is poor when dealing with noisy images and images involving complex variations such as ...

Extracting various types of event relations in financial texts can benefit many downstream applications supporting financial analysis. This paper addresses the multi-type event relation extraction problem in the finance domain focusing ...

• We define six event relation types based on practical needs in finance.
• We ...

Dynamic online learning has been given great concerns as real-time and non-stationary systems develop and it can be used for solving many practical sequential decision problems like dynamic recommendation systems for online ...

Although federated learning can provide privacy protection for individual raw data, some studies have shown that the shared parameters or gradients under federated learning may still reveal user privacy. Differential privacy is a ...

This paper studies the centralized and distributed state estimation problems for nonlinear multi-sensor systems with unknown probability of measurement loss. Based on the variational Bayesian (VB) method and the cubature information ...

Recently, hyperbolic embedding has successfully demonstrated its superiority over Euclidean analogues in representing hierarchical data. As the scale-free network that usually exhibits rich hierarchical structures, knowledge graphs ...

• A hyperbolic embedding model for KG reasoning is proposed.
• A hyperbolic graph ...

Unsupervised temporal action localization in untrimmed videos is a challenging and open issue. Existing works focus on the “clustering + localization” framework for unsupervised temporal action localization. However, it heavily relies ...

• We propose a new framework (APSL) for unsupervised temporal action localization.

GCN is a widely-used representation learning method for capturing hidden features in graph data. However, traditional GCNs suffer from the over-smoothing problem, hindering their ability to extract high-order information and obtain ...

We propose a novel model that integrates low- and high-order information from node neighborhoods. First, we perform feature mapping and propagation to create a feature tensor that stores diverse-order node representations. ...

• Propose a high-order graph attention model to alleviate the over-smoothing problem.

With the ever-growing amount of online information, recommender systems (RSs) act as information filtering tools and are widely used in various e-commerce platforms. Recommender methods generally adopt only one type of behavior data ...

Due to their numerous applications such as in decision making, information fusion, game theory, and data mining, Choquet integrals have recently attracted much attention. In this study, two generalization types of Choquet integrals are ...

The broad learning system (BLS) based on the random vector functional link neural network is a new versatile non-iterative neural network for rapidly selecting models. One of its fascinating features is the broad expansion, a dynamic ...

One of the significant problems in data stream classification is the concept drift phenomenon, which consists of the change in probabilistic characteristics of the classification task. Such changes in posterior ...

• The proposal of a new chunk-base classifier ensemble for non-stationary data streams.

Modular supervisory control may lead to conflicts among the modular supervisors for large-scale discrete event systems. The existing methods for ensuring nonblocking control of modular supervisors either exploit favorable structures in ...

In this note, we point at a flaw in the process of applying SMOTE in [A Comprehensive Analysis of Synthetic Minority Oversampling Technique (SMOTE) for handling class imbalance, Information Sciences, 505 (2019) 32–64]. We present the ...

In this study, we deal with the problem of judging the credibility of movie reviews. The problem is challenging because even experts cannot clearly and efficiently judge the credibility of a movie review and the number of movie reviews ...

Display Omitted

• Micro-expressions are swift and often occur when humans suppress genuine emotions.

Facial micro-expressions play a significant role in revealing concealed emotions. However, the recognition of micro-expressions is challenging due to their fleeting nature. Moreover, the visual features of the face and the visual ...

Micro-videos have gained popularity on various social media platforms because they provide a great medium for real-time storytelling. Although micro-videos can be naturally characterized by several modalities, for situations with ...

Facial expression recognition (FER) with disturbances is challenging because of contamination of global features and inappropriate patch cropping of local features. Although methods based on global and local features have been combined,...

Rumors have been widespread with the development of self-media, which has brought new challenges to government departments in dealing with rumor management. Considering the combined effect of the external refutation of media reports ...

• This paper presents a lightweight and efficient spatial–temporal transformer network to learn collaboratively and efficiently to detect epileptic seizures.

Epilepsy is a predominant paroxysmal neurological disturbance that is usually recognized as the incidence of impulsive seizures rarely seen in medicine. Automatic detection of epileptic seizures from electroencephalogram (EEG) signals ...

Professional social networks (PSNs) usually involve a large amount of valuable information for the business world. A heterogeneous network is constructed based on the structural characteristics of several communities from a PSN. Then, ...

Existing models for classifying pest images require a wide range of labeled training images, which is typically labour-intensive to obtain in real applications. To overcome this limitation, many existing studies rely on unsupervised ...

• An unsupervised deep metric learning method guided by multi-modal pseudo-information is proposed.

Evolutionary algorithms and swarm intelligence ones are commonly used to solve many complex optimization problems in different fields. Yet, some of them have limited performance when dealing with high-dimensional complex problems ...

• A self-adaptive algorithm named STORA is proposed to solve high-dimensional problems.