Information Sciences: an International Journal

Multi-view clustering has become one of the popular clustering branches with data accumulation from multiple domains. Unfortunately, a large heterogeneity gap exists due to cross-view discrepancy, resulting in inaccurate sample-wise similarity ...

• MC-TFM leverages inter-view feature-wise matching to bypass the inaccurate sample-wise similarity estimation.
• MC-TFM utilizes the feature matching tensor for sample assignment to exploit both intra-view and inter-view correlations.

In existing research on data streams, most problems are processed and studied based on single data streams. However, there exist multiple data streams and the reaching data may contain noise information which is considered uncertain in ...

In this work, we introduce a family of novel activation functions for deep neural networks that approximate n-ary, or n-argument, probabilistic logic. Logic has long been used to encode complex relationships between claims that are either true or ...

• A PF-WPA-CRITIC-MARCOS framework is reported to select the preferential sustainable food supplier for resilient cities.
• A hierarchical model of criteria for sustainable food supplier selection is constructed.
• The deviation between ...

Sustainable food supplier selection (SFSS) can be handled as an uncertain decision-making issue. The Pythagorean fuzzy set (PFS), a type of non-standard fuzzy set, offers an expanded description space for articulating fuzzy and uncertain data. ...

The model free fault-tolerant consensus tracking control problem is studied for the multiagent systems with actuator faults. To reduce the impact of fault, an adaptive estimation law is developed to estimate the fault information online, which ...

Due to limitations in data acquisition, data in real life often contains a wealth of uncertain information. Uncertainty measurement (UM) constructed within the framework of rough set theory (RST) is an important tool for processing uncertain ...

In this paper, we discuss in some detail how the quantum secret sharing schemes based on a class of irregular quantum network structures are designed, where these structures are consisting of hyperstar access structures with three hyper-edges. ...

Mobile Crowdsensing (MCs) is a widely applicable and inexpensive data-obtaining method that leverages mobile devices to sense and report data without deploying sensors. The rapid development of the Integration of IoT systems, as well as the ...

In the process of model training of the federated learning system, how to design an incentive mechanism to attract more high-quality worker nodes to join is a key issue. The existing researches on federated learning incentive mechanism only ...

In this paper, we propose a new distance measure between picture fuzzy sets (PFSs) to overcome the drawbacks of the existing distance measures between PFSs. We also propose a weight-determination method to determine attributes’ weights using the ...

Large-volume and high-dimensional big datasets are being generated quickly. They are expected to provide data-driven solutions for various pressing challenges such as cybersecurity, credit card fraud, network intrusion detection, etc. The visual ...

The incomplete multi-view clustering (IMVC) aims to explore the consensus and complementary information embedded in incomplete multi-view data, so as to accurately aggregate all samples into different clusters. Existing IMVC methods still have ...

• A novel method is proposed to handle large-scale incomplete multi-view clustering.
• View-specific anchors are learned to represent the original incomplete view data.
• Bipartite graph between all samples and anchors is constructed for ...

The complex-valued molecular model of mRNA and protein plays a crucial role in the regulatory mechanisms governing gene expression in the genetic regulatory network (GRN). In this study, we introduced a novel GRN utilizing the fractional-order ...

Fuzzy preference relation (FPR) models the preference information provided by decision-makers using pairwise comparison of alternatives. The extant consistency test of FPRs, as a premise of expert opinions aggregation, suffers from the rule ...

Robust supervisory control is an important issue for automated manufacturing systems (AMSs) with multiple unreliable resources. This study uses Petri nets to model failure-prone AMSs with multi-unit and multi-type resource acquisitions and ...

In the subjective evaluation process, the hesitant fuzzy set (HFS), as a convenient and robust presentation tool, cannot only suitably address the decision makers’ (DMs’) or experts’ hesitant and uncertain issues but also can arise the dimension ...

Situation awareness is a key technology in many decision systems, but its performance bottleneck in the third step, namely situation prediction, has not been overcome yet. This step involves time series prediction and commonly uses deep learning ...

For multi-agent systems with quantitative input, we propose a novel containment control scheme to guarantee that the agents can enter a convex hull generated by the leader's signals after starting from any initial position. The problem with ...

• A new flexible containment control scheme is designed with tunable reference signals for followers.
• Tunable predictive reference signals are designed and incorporated into containment controller design to avoid potential collisions.

Constrained multi-objective optimization problems (CMOPs) require a delicate balance between satisfying constraints and optimizing objectives. Existing constrained multi-objective evolutionary algorithms (CMOEAs) often struggle to balance ...

Temporal knowledge graphs have been widely used in artificial intelligence, but they are still incomplete. Therefore, the reasoning task is still a research hotspot. The existing temporal knowledge graph reasoning methods are mainly based on ...

• A novel comprehensive time series prediction framework comprising a decomposition-aggregation process and outputting information granules is proposed.
• Complexity is effectively used to guide modeling from two aspects.
• The ...

Complexity of time series has always been of significant interest to researchers; however, it is not yet effectively explored in assisting time series prediction modeling. In this study, we develop a decomposition-aggregation time series ...

Similarities play a pivotal role in diverse real-world scenarios, driving extensive research into methodologies for measuring entity similarity and expanding sets of entities with similar ones. Machines are nowadays adept at performing these ...

Federated learning (FL), as a safe distributed training mode, provides strong support for the edge intelligence of the Internet of Vehicles (IoV) to realize efficient collaborative control and safe data sharing. However, due to the resource ...

Action quality assessment (AQA) is a challenging vision task due to the complexity and variance of the scoring rules embedded in the videos. Recent approaches have reduced the prediction difficulty of AQA via learning action differences between ...

Cloud-edge architecture is an emerging technology that aims to meet the growing demands of intelligent applications. To address the issues of machine learning privacy leakages and benefiting from imbalanced data distribution, federated learning ...

• Design the CEFL framework to provide protection for distributed training while exploring its inherent vulnerabilities.
• Propose FlexibleFL defense method with the aim of evaluating the contributions of the participants.
• Introduce ...

Fake news often involves multimedia information such as text and image to mislead readers, proliferating and expanding its influence. Most existing fake news detection methods apply the co-attention mechanism to fuse multimodal features while ...