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Combinatorial refinement on circulant graphs
The combinatorial refinement techniques have proven to be an efficient approach to isomorphism testing for particular classes of graphs. If the...
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Sublinear Algorithms in T-Interval Dynamic Networks
We consider standard T - interval dynamic networks , under the synchronous timing model and the broadcast CONGEST model. In a T - interval dynamic network ,...
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Evaluative Item-Contrastive Explanations in Rankings
The remarkable success of Artificial Intelligence in advancing automated decision-making is evident both in academia and industry. Within the...
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A deep learning based cognitive model to probe the relation between psychophysics and electrophysiology of flicker stimulus
The flicker stimulus is a visual stimulus of intermittent illumination. A flicker stimulus can appear flickering or steady to a human subject,...
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Improving Likert scale big data analysis in psychometric health economics: reliability of the new compositional data approach
Bipolar psychometric scales data are widely used in psychologic healthcare. Adequate psychological profiling benefits patients and saves time and...
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Multidimensional graph transformer networks for trajectory prediction in urban road intersections
With the rapid development of autonomous driving, accurately predicting the future movement trajectories of various agents in complex scenarios, such...
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Extract Implicit Semantic Friends and Their Influences from Bipartite Network for Social Recommendation
Social recommendation often incorporates trusted social links with user-item interactions to enhance rating prediction. Although methods that...
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Granular Syntax Processing with Multi-Task and Curriculum Learning
Syntactic processing techniques are the foundation of natural language processing (NLP), supporting many downstream NLP tasks. In this paper, we...
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Twin Bounded Support Vector Machine with Capped Pinball Loss
In order to obtain a more robust and sparse classifier, in this paper, we propose a novel classifier termed as twin bounded support vector machine...
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Prescribed-Time Sampled-Data Control for the Bipartite Consensus of Linear Multi-Agent Systems in Singed Networks
This article examines the prescribed-time sampled-data control problem for multi-agent systems in signed networks. A time-varying high gain-based...
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Pruning Deep Neural Networks for Green Energy-Efficient Models: A Survey
Over the past few years, larger and deeper neural network models, particularly convolutional neural networks (CNNs), have consistently advanced...
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The NP-hard problem of computing the maximal sample variance over interval data is solvable in almost linear time with a high probability
We consider the algorithm by Ferson et al. (Reliab Comput 11(3):207--233, 2005) designed for solving the NP-hard problem of computing the maximal...
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Stagnation Detection in Highly Multimodal Fitness Landscapes
Stagnation detection has been proposed as a mechanism for randomized search heuristics to escape from local optima by automatically increasing the...
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Unmasking GAN-Generated Faces with Optimal Deep Learning and Cognitive Computing-Based Cutting-Edge Detection System
The emergence of deep learning (DL) has improved the excellence of generated media. However, with the enlarged level of photorealism, synthetic media...
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A survey of multi-population optimization algorithms for tracking the moving optimum in dynamic environments
The solution spaces of many real-world optimization problems change over time. Such problems are called dynamic optimization problems (DOPs), which...
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Cognitive Intelligent Decisions for Big Data and Cloud Computing in Industrial Applications using Trifold Algorithms
In contemporary real-time applications, diminutive devices are increasingly employing a greater portion of the spectrum to transmit data despite the...
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Learning from Failure: Towards Develo** a Disease Diagnosis Assistant That Also Learns from Unsuccessful Diagnoses
In recent years, automatic disease diagnosis has gained immense popularity in research and industry communities. Humans learn a task through both...
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Multi-Modal Generative DeepFake Detection via Visual-Language Pretraining with Gate Fusion for Cognitive Computation
With the widespread adoption of deep learning, there has been a notable increase in the prevalence of multimodal deepfake content. These deepfakes...
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Multi-view Heterogeneous Graph Neural Networks for Node Classification
Recently, with graph neural networks (GNNs) becoming a powerful technique for graph representation, many excellent GNN-based models have been...
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Towards Long-Term Remembering in Federated Continual Learning
BackgroundFederated Continual Learning (FCL) involves learning from distributed data on edge devices with incremental knowledge. However, current FCL...
