Classifying corporate environmental, social, and governance (ESG) disclosures is challenging because they combine technical terminology with subtle, domain-specific distinctions. Pretrained Transformers such as ClimateBERT perform well but require ma...
Task-conditioned robotic manipulation requires grasps that are geometrically plausible and compatible with downstream function. Although vision–language models (VLMs) have shown promise for semantic reasoning in robotic manipulation, VLM-select...
Various deep learning applications benefit from multi-task learning with multiple regression and classification objectives by taking advantage of the similarities between individual tasks. This can result in improved learning efficiency and predictio...
Few-shot stressor extraction from temporally evolving social-media text remains challenging, primarily because of unstable span boundaries and the imperfect correspondence between structural confidence and task-semantic validity. We propose VFR-MISE,...
Background/Objectives: Recent theories of the neurocognitive architecture of semantic memory have included a distinction between semantic control in the left inferior frontal gyrus (LIFG) and semantic representation in the left anterior temporal lobe...
Driver workload classification requires a connection between observed multimodal responses and the cognitive demands of driving tasks. We propose ACT-R cognitive semantic lightweight fusion (ACTR-CSLF), which summarizes cognitive model states and pro...
With the rapid development of intelligent services, communication objectives are shifting from humans to multi-agent (MA) systems. This transition necessitates new communication paradigms capable of supporting real-time perception, decision-making, a...
Very-high-resolution (VHR) multispectral satellite imagery contains rich semantic information, yet its real-time transmission is constrained by limited satellite-to-ground bandwidth and dynamic channel impairments. Conventional communication schemes...
Generalized Zero-Shot Learning (GZSL) holds significant research importance as it enables the classification of samples from both seen and unseen classes. A prevailing approach for GZSL is learning transferable representations that can generalize wel...
Assessment of semantic processing capacities often relies on verbal tasks which are, however, sensitive to impairments at several language processing levels. Especially for persons with aphasia there is a strong need for a tool that measures semantic...
Advanced research in robotics has allowed robots to navigate diverse environments autonomously. However, conducting complex tasks while handling unpredictable circumstances is still challenging for robots. The robots should plan the task by understan...
Previous studies have established that insight enhances memory across a variety of tasks. However, it remains unclear whether memory performance differs between perceptual and semantic insights. To address this issue, this study employed a learning&n...
This paper analyzes the semantic rate–distortion problem motivated by task-oriented data compression with side information. The semantic information related to a task is not directly accessible to the encoder but implicitly impacts the observat...
Correct packet delivery does not guarantee correct semantic interpretation when endpoint meanings for the same learned codeword diverge. CoSMA DAI is proposed for mismatch detection, protected confirmation, task preservation and semantic repair. Chan...
In this paper, we propose the Semantic-Boundary-Conditioned Backbone (SBCB) framework, an effective approach to enhancing semantic segmentation performance, particularly around mask boundaries, while maintaining compatibility with various segmentatio...
This paper aims to solve the task of coloring a sketch image given a ready-colored exemplar image. Conventional exemplar-based colorization methods tend to transfer styles from reference images to grayscale images by employing image analogy technique...
In heterogeneous multi-robot systems, allocating tasks efficiently requires determining whether each robot can actually carry out a given task. In existing multi-robot task allocation research, however, such task feasibility has typically been handle...
In this work, a language-level Semantics-Conditioned framework for 3D Point cloud segmentation, called SeCondPoint, is proposed, where language-level semantics are introduced to condition the modeling of the point feature distribution, as well as the...
Humans tend to rely on heuristic strategies for intuitive judgment during decision-making. Existing research proposes an availability heuristic, suggesting that individuals are inclined to use highly available information as a basis for judgment. To...
Language-induced asymmetry to single word reading has been well investigated in past research. Less known are the complex processes and related asymmetries occurring when a word is compared with the previous one, according to specific tasks. To this...
Multi-unmanned aerial vehicle (UAV)-assisted communication is a critical technology for the low-altitude economy, supporting applications from logistics to emergency response. Semantic communication effectively enhances transmission efficiency and im...
Creativity is central to innovation and problem-solving, yet scalable training solutions remain limited. This study evaluates Creativeable, an AI-powered creativity training program that provides automated feedback and adjusts creative story writing...
Functional neuroimaging studies have highlighted the roles of three networks in processing language, all of which are typically left-lateralized: a ventral stream involved in semantics, a dorsal stream involved in phonology and speech production, and...
Individuals with attention deficit hyperactivity disorder (ADHD) struggle with impaired verbal fluency as an executive function. The left and right dorsolateral prefrontal cortex (dlPFC) and the right inferior frontal gurus (IFG), which show reduced...
Wireless Sensor Networks (WSNs) have emerged as an efficient solution for numerous real-time applications, attributable to their compactness, cost effectiveness, and ease of deployment. The rapid advancement of the Internet of Things (IoT), Artificia...
Hybrid automatic repeat request (HARQ) remains the backbone of link-layer reliability, yet it was designed for a setting in which every bit matters equally and the channel of the next slot is unknown. Neither assumption holds in semantic communicatio...
Semantic segmentation tasks encompass various applications, such as autonomous driving, medical imaging, and robotics. Achieving accurate semantic information retrieval under conditions of high dynamic range and rapid scene changes remains a signific...
Current mainstream deep learning methods for object detection are generally trained on high-quality datasets, which might have inferior performances under bad weather conditions. In the paper, a joint semantic deep learning algorithm is proposed to a...
Named entity recognition (NER) is one fundamental task in natural language processing, which is usually viewed as a sequence labeling problem and typically addressed by neural conditional random field (CRF) models, such as BiLSTM-CRF. Intuitively, th...
Semantic models are utilized to add context information to datasets and make data accessible and understandable in applications such as dataspaces. Since the creation of such models is a time-consuming task that has to be performed by a human expert,...
Sleep inertia (SI) refers to a complex psychophysiological phenomenon, observed after awakening, that can be described as the gradual recovery of waking-like status. The time course of cognitive performance dissipation in an everyday life condition i...
Most existing dialogue understanding methods rely on flat classification paradigms, failing to capture hierarchical semantic structures and cross-level dependencies. To address this limitation, we reformulate dialogue understanding as a hierarchical...
This study compares the breadth and temporal dynamics of positive and negative priming effects depending on the depth of stimulus processing. Two experiments were conducted using a Naming task (shallow processing) and a semantic Categorisation task i...
Music has been shown to increase arousal and attention and even facilitate processing during non-musical tasks, including those related to speech and language functions. Mind wandering has been studied in many sustained attention tasks. Here, we inve...
Communications in intelligent transportation systems (ITS) face explosive data growth from applications such as autonomous driving, remote diagnostics, and real-time monitoring, imposing severe challenges on limited spectrum, bandwidth, and latency....
The inherent redundancy in vehicle sensor data, coupled with constrained onboard resources and stringent latency requirements, renders traditional bit-oriented transmission paradigms inefficient for autonomous-driving perception tasks. Semantic commu...
Background and Objectives: The aim of the present study was to investigate the prognostic value of the qualitative components of verbal fluency (clustering, switching, intrusions, and perseverations) on the development of mild cognitive impairment (M...
Generative speech enhancement models often suffer from the “generative hallucination” issue, where artifacts are introduced or original speech characteristics are distorted during reconstruction due to insufficient prior constraints, seve...
Semantic features are very important for machine learning-based drug name recognition (DNR) systems. The semantic features used in most DNR systems are based on drug dictionaries manually constructed by experts. Building large-scale drug dictionaries...
This paper presents a semantic conceptual framework and definition of environmental monitoring and surveillance and demonstrates an ontology implementation of the framework. This framework is defined in a mathematical formulation and is built upon an...
Farmland ecosystems present complex pest–predator co-occurrence patterns, posing significant challenges for image-based multitarget recognition and ecological modeling in sensor-driven computer vision tasks. To address these issues, this study...
Autonomous driving systems heavily depend on perception tasks for optimal performance. However, the prevailing datasets are primarily focused on scenarios with clear visibility (i.e., sunny and daytime). This concentration poses challenges in trainin...
The perception of the surrounding environment is a key requirement for autonomous driving systems, yet the computation of an accurate semantic representation of the scene starting from RGB information alone is very challenging. In particular, the lac...
Underwater images often suffer from color distortion, low contrast, and structural blurring caused by wavelength-dependent absorption and scattering, which degrade both visual observation and downstream perception. Existing underwater image enhanceme...
Grounded in the Cognitive Theory of Multimedia Learning and Cognitive Load Theory, this study examined how cue-based instructional support relates to L2 Chinese vocabulary processing and immediate learning outcomes. Forty intermediate-to-advanced lea...
This paper contributes to our understanding of the grammatical architecture of heritage languages and, specifically, the role of lexical semantics, by examining the syntactic distribution of Spanish psych verbs. Object experiencer psych verbs in Span...
In distributed wireless systems enabled by multimodal sensing and semantic communication, heterogeneous modalities provide richer task-relevant information but simultaneously introduce asynchronous updates due to modality-dependent sensing, processin...
Environmental perception is an essential task for autonomous driving, which is typically based on LiDAR or camera sensors. In recent years, 4D mm-Wave radar, which acquires 3D point cloud together with point-wise Doppler velocities, has drawn substan...
Deep joint source–channel coding (Deep JSCC) provides an end-to-end alternative to separated image compression and channel coding, but reconstruction fidelity, channel use, and task utility are distinct design objectives. This study evaluates t...
Slot filling is a crucial component in task-oriented dialog systems that is used to parse (user) utterances into semantic concepts called slots. An ontology is defined by the collection of slots and the values that each slot can take. The most widely...
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