Information Sciences: an International Journal

• Equity value and debt maturity make this portfolio more complete by using KMV model.
• The objectives of the proposed portfolio include order moments and default distance.
• Improved NSGA-III with chaos map is developed to solve five-...

Portfolio management is a critical aspect of investment strategies, with the goal to balance the low-risk and high-return investments. Despite this, existing portfolios frequently overlook the integration of stock selection outcomes and ...

Recently, numerous unsupervised multi-view feature selection methods have been presented. However, these methods assess the significance of data features in each view individually or jointly evaluate the significance of data features across ...

The G-Skyline query aims to return the best groups that are not g-dominated by any other group of equal size; this approach plays an important role in many fields, such as multiobjective decision-making. The top-k G-Skyline query has important ...

Clustering approaches based on deep nonnegative matrix factorization have received significant attention because it can learn hierarchical semantics from data in a layer-wise manner. However, latent representation learning and clustering are two ...

Relation extraction (RE) is a crucial subtask of information extraction, which involves recognizing the relation between entity pairs in a sentence. Previous studies have extensively employed syntactic information, notably the shortest dependency ...

• Research highlights 1 (Leveraging syntactic info): Multi-task learning improves RE by predicting SDP token positions and capturing valuable semantic relationships.
• Research highlights 2 (Novel supervisory labels): Introducing SDP ...

Zero-shot learning (ZSL) aims to recognize unseen classes during training. Transductive methods have advanced in ZSL, however, often rely on pseudo labels based on confidence scores, leading to semantic misalignment between unseen-class image ...

With the enrichment of social platforms, large-scale group decision-making (LSGDM) in social networks has gradually taken shape. In practice, experts may show limited compromise behavior during consensus reaching process (CRP), which may lead to ...

The use of artificial intelligence for decision support and automation has shown tremendous potential in many areas. The ability to explain the decisions made by a machine learning algorithm is fundamental to facilitating the widespread use of ...

• COCOA provides actionable, plausible, sparse and diverse counterfactuals.
• Decision costs are taken into account when generating counterfactuals.
• The obtained counterfactuals improve cost savings.
• The method is presented for ...

With the advance of deep learning, adversarial attack and defense has becoming a hot research topic. However, existing defense methods rely on the prior knowledge of the adversarial attacks, and are also vulnerable to adaptive attacks. In this ...

• An adversarial defense framework by fusing downsampling and upsampling.
• Pairwise random downsampling to resist normal and adaptive adversarial attacks.
• A new deep learning architecture to perform speech super-resolution.

This paper is devoted to investigate the dynamical behaviors of malware attack on a familiar kind of complex heterogeneous networks, namely Wireless Sensor Network, and discuss an effective immunization treatment based on fractional ...

This article focuses on the problem of adaptive event-triggered anti-synchronization control for bidirectional associative memory neural networks subject to energy-limited denial of service attacks. First, a novel adaptive event-triggered scheme ...

This paper is interested in the design of full-order H ∞ memory filters for discrete-time nonlinear systems within finite-time domain. First, the investigated nonlinear systems are modeled by a Takagi–Sugeno fuzzy technique for the interval type-...

Least squares support vector machine (LSSVM) is a powerful classification tool based on hyperplanes. But the classical LSSVM does not perform well on small sample size data sets (SSS) because it lacks feature selection capability. To address this ...

In this paper, we consider the problem of approximating functions from a Korobov space on [ − 1 , 1 ] d by ReLU shallow neural networks and present a rate O ( m − 2 5 ( 1 + 2 d ) log ⁡ m ) of uniform approximation by networks of m hidden neurons. ...

This paper introduces a prediction model rooted in sparse representation and transfer learning, with the primary objective of predicting user behavior during rumor propagation. Users' behavior is dynamic, and rumor, rumor-refuting, and rumor-...

• A user behavior prediction model is proposed.
• Sparse representation can be used to express the rumor feature space.
• The complex influences that influence user behavior are quantified.

Explainability is crucial in domains where system decisions have significant implications for human trust in black-box models. Lack of understanding regarding how these decisions are made hinders the adoption of so-called clinical decision ...

Federated learning (FL) is a pivotal catalyst for enabling large-scale privacy-preserving distributed machine learning (ML). By eliminating the need for local raw dataset sharing, FL substantially reduces privacy concerns and alleviates the ...

• A novel model aggregating formula is proposed that eliminates the aggregating bias in decentralized FL.
• Selfish workers are introduced to the decentralized FL setting, which is simple and robust against backdoor attacks.
• A general ...

Medication recommendation (MR) focuses on generating a medication combination without adverse drug-drug interactions (DDIs) based on electronic health records (EHRs) in making prescriptions. However, how to capture the temporality in historical ...

• We model EHR sequential data for patient and medication representations.
• Both BiLSTM and self-attention mechanism are beneficial to patient representations.
• We deploy knowledge augmentation strategy for safe medication ...

Large-scale multiobjective optimization problems (LSMOPs), which optimize multiple conflicting objectives with hundreds or even thousands of decision variables, demand increasing computational resources to assure satisfactory performance as the ...

• This paper defines the scalability of large-scale multiobjective evolutionary algorithms and introduces a novel performance indicator to measure.
• We propose a scalable algorithm for LSMOPs to ensure consistent performance within ...

Distribution shift widely exists in graph representation learning and often reduces model performance. This work investigates how to improve the performance of a graph neural network (GNN) in a single graph by controlling distribution shift ...

Recent advances on low-rank representation have achieved promising performances for tensor completion in the area of information sciences. However, current low-rank tensor completion (LRTC) models merely model global low-rankness and lose sight ...

With the rise of deep learning, the U-Net network, based on a U-shaped architecture and skip connections, has found widespread application in various medical image segmentation tasks. However, the receptive field of the standard convolution ...

Hamming distance is a measure of the similarity between two strings of the same length. Privacy-preserving Hamming distance computation allows data users to obtain the Hamming distance between their data without disclosing their respective ...

Compared with other forms of cancer, gastric cancer has high mortality and incidence rates, making it a major cause of death worldwide. Accurate diagnosis is crucial in the treatment of stomach cancer. Researchers have used deep learning ...

Machine learning models often encounter significant difficulties when dealing with multiclass imbalanced data streams in nonstationary environments. These challenges can lead to biased and unreliable predictions, which ultimately impact the ...

In this paper, we are devoted to solving the problems of designing an estimator, and determining its estimator stability and estimation performance for a system with unknown measurement losses (UML). The solutions to these problems include three ...

• A detector-based estimator is designed for systems with unknown measurement losses (UML).
• A necessary and sufficient estimator stability condition is obtained for UML systems.
• The proposed estimator has almost surely the same ...

Sequential recommendations play a crucial role in recommender systems. Existing methods commonly focus on extracting sequential patterns within individual item sequences to make personalized recommendations. However, this paper argues that such a ...

• Two types of transition graphs are introduced to depict group behaviors and individual behaviors.
• Two information encoders are proposed to model collaborative and sequential information.
• A multi-source information fusion module is ...

Partial least squares (PLS) is a widely used multivariate statistical technique, which can be used in neuroimaging, process monitoring, economics, etc. Because the standard PLS is trained by nonlinear iterative partial least squares (NIPALS) ...

In this paper, we propose generalizations of the EI index to quantify the distinction between intragroup and intergroup connections in social networks. These generalizations enable the analysis of interactions between non-disjoint groups, as ...