Papers Component Classification
“Component Classification” 태그가 달린 논문 27편 · 필터 해제
When More Modalities Hurt: Modality Dropout for Heavy-Duty Vehicle Engine Diagnostics
Heavy-duty vehicle diagnostics generate three disconnected data modalities: unstructured multi- lingual service complaints, high-dimensional sensor telemetry with over 80% missing values, and Diagnostic Trouble Codes (DT…
Component ClassificationFrom Argument Components to Graphs: A Multi-Agent Debate with Confidence Gating for Argument Relations
Large Language Models (LLMs) are increasingly assessed and utilized in the field of Argument Mining (AM), thanks to their strong general reasoning capabilities. However, standard training-free models often miss sophistic…
Component ClassificationArgument MiningEnhancing Science Classroom Discourse Analysis through Joint Multi-Task Learning for Reasoning-Component Classification
Analyzing the reasoning patterns of students in science classrooms is critical for understanding knowledge construction mechanism and improving instructional practice to maximize cognitive engagement, yet manual coding o…
Component ClassificationMulti-Task LearningData AugmentationMulti-Agent Dialectical Refinement for Enhanced Argument Classification
Argument Mining (AM) is a foundational technology for automated writing evaluation, yet traditional supervised approaches rely heavily on expensive, domain-specific fine-tuning. While Large Language Models (LLMs) offer a…
Component ClassificationArgument MiningArgument Mining as a Text-to-Text Generation Task
Argument Mining(AM) aims to uncover the argumentative structures within a text. Previous methods require several subtasks, such as span identification, component classification, and relation classification. Consequently,…
Component ClassificationRelation ClassificationText GenerationArgument MiningWrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL
Learning effective netlist representations is fundamentally constrained by the scarcity of labeled datasets, as real designs are protected by Intellectual Property (IP) and costly to annotate. Existing work therefore foc…
Component ClassificationRepresentation LearningData AugmentationCode GenerationCompact Prompting in Instruction-tuned LLMs for Joint Argumentative Component Detection
Argumentative component detection (ACD) is a core subtask of Argument(ation) Mining (AM) and one of its most challenging aspects, as it requires jointly delimiting argumentative spans and classifying them into components…
Component ClassificationAutocleanEEG ICVision: Automated ICA Artifact Classification Using Vision-Language AI
We introduce EEG Autoclean Vision Language AI (ICVision) a first-of-its-kind system that emulates expert-level EEG ICA component classification through AI-agent vision and natural language reasoning. Unlike conventional …
Component ClassificationExploration of Deep Learning Based Recognition for Urdu Text
Urdu is a cursive script language and has similarities with Arabic and many other South Asian languages. Urdu is difficult to classify due to its complex geometrical and morphological structure. Character classification …
Component ClassificationSynthetic generation of 2D data records based on Autoencoders
Gas Chromatography coupled with Ion Mobility Spectrometry (GC-IMS) is a dual-separation analytical technique widely used for identifying components in gaseous samples by separating and analysing the arrival times of thei…
Component ClassificationLocalization of Seizure Onset Zone based on Spatio-Temporal Independent Component Analysis on fMRI
Localizing the seizure onset zone (SOZ) as a step of presurgical planning leads to higher efficiency in surgical and stimulation treatments. However, the clinical localization including structural, ictal, and invasive da…
Component ClassificationAutomatic EEG Independent Component Classification Using ICLabel in Python
ICLabel is an important plug-in function in EEGLAB, the most widely used software for EEG data processing. A powerful approach to automated processing of EEG data involves decomposing the data by Independent Component An…
Component ClassificationEEGART: Artifact Removal Transformer for Reconstructing Noise-Free Multichannel Electroencephalographic Signals
Artifact removal in electroencephalography (EEG) is a longstanding challenge that significantly impacts neuroscientific analysis and brain-computer interface (BCI) performance. Tackling this problem demands advanced algo…
Brain Computer InterfaceComponent ClassificationDenoisingEEG+1Modeling of spatially embedded networks via regional spatial graph convolutional networks
Efficient representation of complex infrastructure systems is crucial for system-level management tasks, such as edge prediction, component classification, and decision-making. However, the complex interactions between t…
Component ClassificationDecision MakingMultimodal Deep LearningRepresentation LearningVoltaVision: A Transfer Learning model for electronic component classification
In this paper, we analyze the effectiveness of transfer learning on classifying electronic components. Transfer learning reuses pre-trained models to save time and resources in building a robust classifier rather than le…
ClassificationComponent ClassificationTransfer LearningPrincipal Component Classification
We propose to directly compute classification estimates by learning features encoded with their class scores using PCA. Our resulting model has a encoder-decoder structure suitable for supervised learning, it is computat…
ClassificationComponent ClassificationDecoderAmmunition Component Classification Using Deep Learning
Ammunition scrap inspection is an essential step in the process of recycling ammunition metal scrap. Most ammunition is composed of a number of components, including case, primer, powder, and projectile. Ammo scrap conta…
ClassificationComponent ClassificationDeep Learningobject-detection+1Doc-GCN: Heterogeneous Graph Convolutional Networks for Document Layout Analysis
Recognizing the layout of unstructured digital documents is crucial when parsing the documents into the structured, machine-readable format for downstream applications. Recent studies in Document Layout Analysis usually …
Component ClassificationDocument Layout AnalysisAutomatic code generation from sketches of mobile applications in end-user development using Deep Learning
A common need for mobile application development by end-users or in computing education is to transform a sketch of a user interface into wireframe code using App Inventor, a popular block-based programming environment. …
Code GenerationComponent ClassificationMulti-Task Attentive Residual Networks for Argument Mining
We explore the use of residual networks and neural attention for multiple argument mining tasks. We propose a residual architecture that exploits attention, multi-task learning, and makes use of ensemble, without any ass…
Argument MiningComponent ClassificationLink PredictionMulti-Task Learning+1