Knowledge-Based Systems

Recently, heterogeneous graph attention network (HGAT) has been widely applied to various machine learning tasks and achieved remarkable results with sufficient labeled data. However, it is noteworthy that in many tasks, labeled data is scarce ...

• To alleviate the influence of limited labels on the performance of HGAT, we utilize active semi-supervised learning to empower heterogeneous graph attention network.
• Under the uncertainty-aware mechanism, we smoothly fuse uncertainty ...

In long-tailed recognition tasks, the knowledge distillation technology is widely adopted for improving performance of deep neural networks. These methods distill the knowledge from the pretrained teacher model to the student model, which enables ...

As a cognitive process, concept-cognitive learning (CCL) emphasizes the structured expression of data through systematic cognition and understanding, to obtain valuable information in the data. Although concept-cognitive learning has achieved ...

Existing representation-based methods usually conduct industrial anomaly detection in two stages: obtain feature representations with a pre-trained model and perform distance measures for anomaly detection. Among them, K-nearest neighbor (KNN) ...

Symbolic data analysis data has provided several advances in regression models concerning the type of symbolic variable. Due to the advantages of using symbolic polygonal data, this paper introduces a linear regression approach for polygonal data ...

• An approach based on Generalized Linear Models for symbolic polygonal data is proposed.
• Polygonal residuals are defined for evaluating the adequacy of the fitted model.
• The prediction quality is measured by a metric based on ...

Nitrogen dioxide (NO₂) is a prevalent air pollutant, particularly abundant in densely populated urban regions. Given its harmful impact on health and the environment, precise real-time monitoring of NO₂ concentration is crucial, particularly for ...

Multiobjective Multitasking Optimization (MO-MTO) has become a hot research spot in the field of evolutionary computing. The fundamental problem of MO-MTO is to inhibit the negative transfer phenomenon. Mining the relationship among multiple ...

In this paper, we propose a novel model for a malware classification system based on Application Programming Interface (API) calls and opcodes, to improve classification accuracy. This system uses a novel design of combined Convolutional Neural ...

Complementary Labels Learning (CLL) arises in many real-world tasks such as private questions classification and online learning, which aims to alleviate the annotation cost compared with standard supervised learning. Unfortunately, most previous ...

Feature selection is an essential process in machine learning in selecting the features that contribute the most to the prediction target to build more interpretable and robust models. However, most feature selection algorithms must exploit ...

Dialogue State Tracking (DST) is critical for comprehensively interpreting user and system utterances, thereby forming the cornerstone of efficient dialogue systems. Despite past research efforts focused on enhancing DST performance through ...

Data availability and quality are crucial for the development of semantic segmentation techniques. However, creating high-quality fire scene datasets in a safe and efficient manner remains an unsolved challenge. To fill this gap, we introduce ...

• Low-Cost Fire Diffusion Model FireDM for Endless Generation of Segmentation Datasets.
• LLM generates textual descriptions to prompt generation of multi-scenario fire datasets.
• Dual diffusion architecture (SD2.1 and SDXL 1.0) to ...

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Neural Radiance Fields (NeRF) has gained prominence in the domain of 3D reconstruction. Despite its popularity, NeRF algorithm typically require clear, static images to function effectively, leading to reduced performance when dealing with real-...

Swarm intelligence optimization algorithms (SIOAs) are widely used to address complex problems but often trapped in local optima. To overcome this, we propose a novel multi-population SIOA based on cellular coordination mechanisms in immune ...

Cross-domain collaborative filtering (CDCF) is an effective solution to alleviate the data sparsity problem. Most of existing CDCF methods rely on overlapping data, such as users, items or both. But in some realistic scenes, detection and ...

Owing to the variable characteristics of system structures, traditional grey prediction models with fixed parameters often struggle to accurately capture real-world dynamic changes. In this paper, we propose a novel time-varying polynomial grey ...

Graph neural network (GNN)-based methods have demonstrated remarkable performance in various knowledge graph (KG) tasks. However, most existing approaches rely on observing all entities during training, posing a challenge in real-world knowledge ...

• A controller to prevent secondary crashes after an initial rear-end collision is proposed.
• Rule-based switching control is embedded into reinforcement learning.
• Pre-collision control and post-collision control are combined.
• ...

Autonomous navigation in dense traffic scenarios, such as on-ramp forced merging, still poses significant challenges for autonomous vehicles to prevent accidents and alleviate traffic congestion. This paper introduces a novel motion planning ...

In this article, we propose a novel model of a robust fuzzy query engine that addresses vagueness in data and users’ requirements. It aims to assist users in recommending similar products or services by retrieving the most suitable entities when ...

• Added monotonicity axiom in proximity relation for similarity among categorical data.
• The proximity relation and conformance function are extended by triangular fuzzy sets.
• The quantified aggregation by strict quantifiers of ...

The Intelligent Transport System (ITS) is an emerging paradigm that offers numerous services at the infrastructure level for vehicle applications. Vehicle-to-infrastructure (V2I) is an advanced form of ITS where diverse vehicle services are ...

In recent years, the fashion industry has developed rapidly, leading to an increasing demand for personalized clothing matching. Although noteworthy progress has been made by existing work with advanced graph neural networks, there still remain ...

The graph clustering optimization problems are ubiquitous in both scientific and engineering fields, for example the graph partitioning problem and facility location problem. Since most of these problems are NP-hard, it is challenging to design ...

• Developed a novel self-supervised model for graph clustering optimization problems
• Adopted a pre-training and fine-tuning framework assisted with contrastive learning
• Introduced two encoding mechanisms to extract graph structural ...

As time passes, the data within information systems may continuously evolve, causing the target concept to drift. To ensure the effectiveness of data-driven decision making, it is crucial to detect drift in a timely manner and gather relevant ...

The primary objective of multivariate time-series anomaly detection is to spot deviations from regular patterns in time-series data compiled concurrently from various sensors and systems. This method finds application across diverse industries, ...

Reinforcement learning (RL) is an effective approach to solve autonomous obstacle avoidance and target tracking for Unmanned Aerial Vehicle (UAV). However, due to communication interruptions or delays, transmission information loss often occurs ...

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• Considering the issue of target information loss and tracking failure caused by communication interruption or delay in UAV tracking, it is more practical in engineering.
• Using historical observations, UAV compensates for lost target ...

A temporal knowledge graph (TKG) is a set of facts associated with different timestamps. TKG completion (TKGC) is the task of inferring unknown facts based on known facts, where mining and understanding the expressional characterization of ...

• We address the problems of type-diversity and dependence-variability.
• For type-diversity, we unify the processing in encoding the factor matrix.
• For dependence-variability, we present the temporal-position method.
• Developing a ...