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...
Personal semantic memory is a way of inducing subjectivity in intelligent agents. Personal semantic memory has knowledge related to personal beliefs, self-knowledge, preferences, and perspectives in humans. Modeling this cognitive feature in the inte...
Logistics transports of various resources such as production materials, foods, and products support the operation of smart cities. The ability to trace the states of logistics transports requires an efficient storage and retrieval of the states of lo...
A trajectory is a set of time-stamped locations of a moving object usually recorded by GPS sensors. Today, an abundance of these data is available. These large quantities of data need to be analyzed to determine patterns and associations of interest...
Address matching, which aims to match an input descriptive address with a standard address in an address database, is a key technology for achieving data spatialization. The construction of today’s smart cities depends heavily on the precise ma...
Geospatial data capture and handling of indoor spaces is increasing over the years and has had a varied history of data sources ranging from architectural and building drawings to indoor data acquisition approaches. While these have been more data fo...
Many of today’s data models for 3D applications, such as City Geography Markup Language (CityGML) or Industry Foundation Classes (IFC) encode rich semantic information in addition to the traditional geometry and materials representation. Howeve...
Large language models (LLMs) have recently demonstrated state-of-the-art performance in various natural language processing (NLP) tasks, achieving near-human levels in multiple language understanding challenges and aligning closely with the core prin...
To address the problem of poor semantic reasoning of models in multiple-choice Chinese machine reading comprehension (MRC), this paper proposes an MRC model incorporating multi-granularity semantic reasoning. In this work, we firstly encode articles,...
To solve the problem of text clustering according to semantic groups, we suggest using a model of a unified lexico-semantic bond between texts and a similarity matrix based on it. Using lexico-semantic analysis methods, we can create “term&ndas...
With the continuous deepening of Mars exploration missions, the Mars helicopter has become a key platform for acquiring high-resolution near-ground imagery. However, accurate semantic segmentation of the Martian surface remains challenging due to com...
The rapid development of natural language processing technology and improvements in computer performance in recent years have resulted in the wide-scale development and adoption of human–machine dialogue systems. In this study, the Icc_dialogue...
Robust segmentation in adverse weather conditions is crucial for autonomous driving. However, these scenes struggle with recognition and make annotations expensive, resulting in poor performance. As a result, the Segment Anything Model (SAM) was rece...
The rapid growth of new computing models that exploit the cloud continuum has a big impact on the adoption of microservices, especially in dynamic environments where the amount of workload varies over time or when Internet of Things (IoT) devices dyn...
Here we design a semantic trajectory model responding to specific needs expressed by tourism analyst experts. Thus, this model takes into account: (i) the description of sequences of imbricated semantic segments, (ii) the definition of enrichment dat...
Sketch-based 3D model retrieval has become an important research topic in many applications, such as computer graphics and computer-aided design. Although sketches and 3D models have huge interdomain visual perception discrepancies, and sketches of t...
In this paper, we introduce applications of Artificial Intelligence techniques, such as Decision Trees and Semantic Reasoning, for semi-automatic and semantic-model-based decision-making for archaeological feature comparisons. This paper uses the exa...
The process industries rely on various software systems and use a wide range of technologies. Predictive modeling techniques are often applied to data obtained from these systems to build the predictive functions used to optimize the production proce...
This study investigates the efficacy of an open vocabulary, multi-modal, foundation model for the semantic segmentation of images from complex urban street scenes. Unlike traditional models reliant on predefined category sets, Grounded SAM uses arbit...
The paper outlines a methodology for validating the accuracy of a control system’s runtime implementation. The runtime takes the form of a virtual machine executing portable code compliant with IEC 61131-3 standards. A formal model, comprising...
With the development of satellite remote sensing technology, a substantial quantity of remote sensing data can be obtained every day, but the ability to extract information from these data remains poor, especially regarding intelligent extraction mod...
Health problems in older adults lead to situations where communication with peers, family and caregivers becomes challenging for seniors; therefore, it is necessary to use alternative methods to facilitate communication. In this context, Augmentative...
Building information modeling (BIM) is undeniably the most important trend in the digitization of the construction sector in recent years. BIM models currently being built are extremely geometrically rich, that is, they are modeled at a high level of...
This study presents a knowledge graph construction scheme leveraging large language models (LLMs) for task-oriented semantic communication systems. The proposed methodology systematically addresses four critical stages: corpus collection, entity extr...
The distinctions between tumor areas and surrounding tissues are often subtle. Additionally, the morphology and size of tumors can vary significantly among different patients. These factors pose considerable challenges for the precise segmentation of...
Forecasts announce that the number of connected objects will exceed 20 billion by 2025. Objects, such as sensors, drones or autonomous cars participate in pervasive applications of various domains ranging from smart cities, quality of life, transport...
The compaction and structuring of system logs facilitate and expedite anomaly and cyberattack detection processes using machine-learning techniques, while simultaneously reducing alert fatigue caused by false positives. In this work, we implemented a...
Timely identification of crop conditions is relevant for informed decision-making in precision agriculture. The initial step in determining the conditions that crops require involves isolating the components that constitute them, including the leaves...
Recognition and understanding of ship motion patterns have excellent application value for ship navigation and maritime supervision, i.e., route planning and maritime risk assessment. This paper proposes a semantic recognition method for ship motion...
Recent advances in laser scanning systems have enabled the acquisition of 3D point cloud representations of scenes, revolutionizing the fields of Architecture, Engineering, and Construction (AEC). This paper presents a novel pipeline for the automati...
In cybersecurity, constructing an accurate knowledge graph is vital for discovering key entities and relationships in security incidents buried in vast unstructured threat reports. Traditional knowledge-graph construction pipelines based on handcraft...
Underwater vision research is the foundation of marine-related disciplines. The target contour extraction is significant for target tracking and visual information mining. Aiming to resolve the problem that conventional active contour models cannot e...
The individualization of information processes based on artificial intelligence (AI), especially in the context of industrial tasks, requires new, hybrid approaches to process modeling that take into account the novel methods and technologies both in...
Accurate spatial information of cropland is crucial for precision agricultural management and ensuring national food security. High-resolution remote sensing imagery combined with deep learning algorithms provides a promising approach for extracting...
Road scene segmentation is an integral part of the Intelligent Transport System (ITS) for precise interpretation of the environment and safer vehicle navigation. Traditional segmentation methods have faced difficulties in meeting the requirements of...
Numerous methods have been proposed for semantic segmentation and the state-of-the-art part is likely to be incorporated by deep learning-based methods which show a salient performance. This study addresses the challenge of semantic segmentation in l...
Currently, Convolutional Neural Networks (CNN) are widely used for processing and analyzing image or video data, and an essential part of state-of-the-art studies rely on training different CNN architectures. They have broad applications, such as ima...
Semantic segmentation of urban meshes plays an increasingly crucial role in the analysis and understanding of 3D environments. Most existing large-scale urban mesh semantic segmentation methods focus on integrating multi-scale local features but stru...
Instance segmentation in remote sensing (RS) imagery aims to predict the locations of instances and represent them with pixel-level masks. Thanks to the more accurate pixel-level information for each instance, instance segmentation has enormous poten...
Petrochemical port cities in China face the challenge of promoting industrial development and improving environmental quality. In this situation, this paper constructs a semantic ontology-based data model from the perspective of the overall classific...
A new text matching model based on dynamic gated sparse attention feature distillation with a faithful semantic preservation strategy is proposed to address the fact that text matching models are susceptible to interference from weakly relevant infor...
Robust and highly accurate localization using a camera is a challenging task when appearance varies significantly. In indoor environments, changes in illumination and object occlusion can have a significant impact on visual localization. In this pape...
Over the past few years, there has been an increasing need for semantic information in automatic city modelling. However, due to the complexity of building structure, the semantic reconstruction of buildings is still a challenging task because it is...
Extensive collections of data of linguistic, historical and socio-cultural importance are stored in libraries, museums and national archives with enormous potential to support research. However, a sizable portion of the data remains underutilised bec...
Ontology-based data management and knowledge graphs have emerged in recent years as efficient approaches for managing and utilizing diverse and large data sets. In this regard, research on algorithms for automatic semantic labeling and modeling as a...
Real-time analysis of UAV low-altitude remote sensing images at airborne terminals facilitates the timely monitoring of weeds in the farmland. Aiming at the real-time identification of rice weeds by UAV low-altitude remote sensing, two improved ident...
Ship behavior is the semantic expression of corresponding trajectory in spatial-temporal space. The intelligent identification of ship behavior is critical for safety supervision in the waterborne transport. In particular, the complicated behavior re...
Humans have an innate ability of environment modeling, perception, and planning while simultaneously performing tasks. However, it is still a challenging problem in the study of robotic cognition. We address this issue by proposing a neuro-inspired c...
The widespread deployment of large language models (LLMs) has raised urgent demands for verifiable content attribution and misuse mitigation. Existing text watermarking techniques often struggle in black-box or sampling-based scenarios due to limitat...
The paper explores two fortified systems, as case studies, to evaluate different modelling approaches, the parameterisation of architectural components, and semantic interpretation, to define a repeatable methodology for classifying, accessing, and s...
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