8 February 2023
The definition of ‘soundscape’ emphasises the perceptual construct of sounds; thus, the mechanism of sound perceptions becomes vital for soundscape evaluations. Using a qualitative approach, this study explored the aspects and processes o...
Recently, Ji et al. established certain fixed-point results using Mann’s iterative scheme tailored to Gb-metric spaces. Stimulated by the notion of the F-contraction introduced by Wardoski, the contraction condition of Ji et al. was generalized...
It has been established that dilutions of a variety of substances, when exposed to vibration in the process of their preparation, acquire not only new structural characteristics in the form of nano-associates but also new physical properties, regardl...
This study proposes a cyber–physical framework for the integration of a heterogeneous swarm of robots, sensors, microrobots, and AR for structural health monitoring and confined space inspection based on the application’s unique challenge...
Cross-lingual document retrieval, which aims to take a query in one language to retrieve relevant documents in another, has attracted strong research interest in the last decades. Most studies on this task start with cross-lingual comparisons at the...
Building Information Modeling (BIM) is the process of structuring, capturing, creating, and managing a digital representation of physical and/or functional characteristics of a built space [1]. Current BIM has limited ability to represent dynamic sem...
Recommender systems help users filter items they may be interested in from massive multimedia content to alleviate information overload. Collaborative filtering-based models perform recommendation relying on users’ historical interactions, whic...
Knowledge base question answering (KBQA) aims to provide answers to natural language questions from information in the knowledge base. Although many methods perform well when dealing with simple questions, there are still two challenges for complex q...
Room segmentation is a basic task for the semantic enrichment of point clouds. Recent studies have mainly projected single-floor point clouds to binary images to realize two-dimensional room segmentation. However, these methods have difficulty solvin...
Extreme low-light image enhancement presents persistent challenges due to compounded degradations involving underexposure, sensor noise, and structural detail loss. Traditional low-light image enhancement methods predominantly employ global adjustmen...
Semantically rich indoor models are increasingly used throughout a facility’s life cycle for different applications. With the decreasing price of 3D sensors, it is convenient to acquire point cloud data from consumer-level scanners. However, mo...
Deep neural network-based deep clustering has achieved remarkable success by unifying representation learning and clustering. However, conventional representation modules are typically not tailored for clustering, resulting in conflicting objectives...
Few-shot knowledge graph completion (FKGC) tasks involve determining the authenticity of triple candidates using a small number of reference triples with a given relation. Intuitively, the expression of relation features contributes to the close corr...
This paper strives to improve the performance of video–text retrieval. To date, many algorithms have been proposed to facilitate the similarity measure of video–text retrieval from the single global semantic to multi-level semantics. Howe...
Current agentic AI frameworks such as LangGraph and AutoGen simulate autonomy via sequential prompt chaining but lack true multi-agent coordination architectures. These systems conflate semantic reasoning with orchestration, requiring LLMs at every c...
For ultra-high-resolution (UHR) image semantic segmentation, striking a balance between computational efficiency and storage space is a crucial research direction. This paper proposes a Feature Fusion Network (EFFNet) to improve UHR image semantic se...
Accurate and efficient path planning in indoor environments relies on high-quality navigation networks that faithfully represent the spatial and semantic structure of the environment. Three-dimensional semantic point clouds provide valuable spatial a...
We investigate whether representation-level, semantic diagnostics expose structure in electroencephalography (EEG) beyond conventional accuracy when contrasting perception and imagination and relating outcomes to self-reported imagery ability. Using...
This paper addresses the challenge of segmenting consumer behavior in contexts characterized by both numerical regularities and semantic variability. Traditional models, such as RFM-based segmentation, capture the transactional dimension but neglect...
Accurate detection of wildlife on the Tibetan Plateau is particularly challenging due to complex natural environments, significant scale variations, and the limited availability of annotated data. To address these issues, we propose a semantic-guided...
Mars has been a primary focus of planetary science, with significant advancements over the past two decades across disciplines including geological evolution, surface environment, and atmospheric and space science. However, the rapid growth of the re...
This paper describes a research work on Semantic Inference, which can be regarded as an extension of Grammar Inference. The main task of Grammar Inference is to induce a grammatical structure from a set of positive samples (programs), which can somet...
Semantic modeling of legal reasoning is an important research direction in the field of artificial intelligence and law (AI and law), aiming to enhance judicial transparency, fairness, and the consistency of legal applications through structured sema...
Emergency scenarios in the Internet of Vehicles (IoV) face significant challenges due to the stringent requirements for ultra-reliable and low-latency communication under high-mobility conditions. This paper proposes a cooperative transmission framew...
Multi-source spatio-temporal data analysis is an important task in the development of smart cities. However, traditional data analysis methods cannot adapt to the growth rate of massive multi-source spatio-temporal data and explain the practical sign...
With the widespread application of location-based services, the appropriate representation of indoor spaces and efficient indoor 3D reconstruction have become essential tasks. Due to the complexity and closeness of indoor spaces, it is difficult to d...
People’s actions and behaviours contribute to the diversity and personality of a space, transforming it into a vibrant and thriving living environment. The main goal of this research is to present a GIS-based framework for assessing places. The...
Network embedding (NE) is an important method to learn the representations of a network via a low-dimensional space. Conventional NE models focus on capturing the structural information and semantic information of vertices while neglecting such infor...
The perception of 3D space by mobile robots is rapidly moving from flat metric grid representations to hybrid metric-semantic graphs built from human-interpretable concepts. While most approaches first build metric maps and then add semantic layers,...
The knowledge graph link prediction task currently faces challenges such as insufficient semantic fusion of structured knowledge and unstructured text, limited representation learning of long-tailed entities, and insufficient interpretability of the...
The objective of this research was to design a method to assign topics to claims debunked by fact-checking agencies. During the fact-checking process, access to more structured knowledge is necessary; therefore, we aim to describe topics with semanti...
Heterogeneous information network (HIN) embedding transforms multi-type nodes into low-dimensional vectors to preserve structural and semantic information for downstream tasks. However, it struggles with multiplex networks where nodes connect via div...
Semantic scene completion through AI-driven content generation is a rapidly evolving field with crucial applications in 3D reconstruction and scene understanding. This task presents considerable challenges, arising from the intrinsic data sparsity an...
With the proliferation of mobile devices, identifying previously unseen mobile applications has become a critical challenge in network security. Traditional application identification approaches rely heavily on fixed training categories and limited t...
With the rapid growth of software scale, binary code similarity detection is of great significance in security analysis tasks, such as malicious code detection and vulnerability mining. However, due to differences in instruction sets and inconsistent...
Monocular depth estimation (MDE) has emerged as a powerful technique for extracting scene depth from a single image, particularly in the context of computational imaging. Conventional MDE methods based on RGB images often degrade under varying illumi...
From physics to the social sciences, information is now seen as a fundamental component of reality. However, a form of information seems still underestimated, perhaps precisely because it is so pervasive that we take it for granted: the information e...
With the significant and rapid growth in the number of remote-sensing images, deep hash methods have become a research topic. The main work of deep hash method is to build a discriminate embedding space through the similarity relation between sample...
The purpose of knowledge representation learning is to learn the vector representation of research objects projected by a matrix in low-dimensional vector space and explore the relationship between embedded objects in low-dimensional space. However,...
To semantically understand remote sensing images, it is not only necessary to detect the objects in them but also to recognize the semantic relationships between the instances. Scene graph generation aims to represent the image as a semantic structur...
The urban low-altitude airspace is an integral part of urban space. As low-altitude utilization activities are being performed closer to the land surface, the management of the low-altitude space has become a focus of land administration. The managem...
This paper describes a working example of semantically modelling cultural heritage information and data from the National Gallery collection in London. The paper discusses the process of semantically representing and enriching the available cultural...
This study examines whether Drawmetrics (DM), an expressive–semantic personality system, can be linked with the Five-Factor Model (Big Five) through an embedding-based mapping approach and network psychometric methods. A total of 185 participan...
In the field of remote sensing image segmentation, achieving high accuracy and efficiency in diverse and complex environments remains a challenge. Additionally, there is a notable imbalance between the underlying features and the high-level semantic...
Data-driven text classification technology is crucial for understanding and managing a large number of entrepreneurial policy-related texts, yet it is hindered by two primary challenges. First, the intricate, multi-faceted nature of policy documents...
Under the growing demand for processing multimodal and cross-lingual information, traditional retrieval systems have encountered substantial limitations when handling heterogeneous inputs such as images, textual layouts, and multilingual language exp...
Knowing the GIS expression of geological phenomena is an important basis for the combination of geology and GIS. Regional geological structures include folds, faults, strata, rocks, and other typical geological phenomena and are the focus of geologic...
In this paper, we introduce a multi-label lazy learning approach to deal with automatic semantic indexing in large document collections in the presence of complex and structured label vocabularies with high inter-label correlation. The proposed metho...
Finding similar code snippets is a fundamental task in the field of software engineering. Several approaches have been proposed for this task by using statistical language model which focuses on syntax and structure of codes rather than deep semantic...
Entity alignment is used to determine whether entities from different sources refer to the same object in the real world. It is one of the key technologies for constructing large-scale knowledge graphs and is widely used in the fields of knowledge gr...
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