68 Result(s)
within
**qiang Cai
Computer Science
English
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Article
End-to-End Video Text Spotting with Transformer
Recent video text spotting methods usually require the three-staged pipeline, i.e., detecting text in individual images, recognizing localized text, tracking text streams with post-processing to generate final re...
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Article
Exploring highly concise and accurate text matching model with tiny weights
In this paper, we propose a simple and general lightweight approach named AL-RE2 for text matching models, and conduct experiments on three well-studied benchmark datasets across tasks of natural language inf...
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Chapter and Conference Paper
Machine Anomalous Sound Detection Based on Feature Fusion and Gaussian Mixture Model
Anomalous sound detection (ASD) is a technique used for audio monitoring of machines in factories, aiming to identify machine failures by analyzing the sound produced during machine operation. However, it is c...
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Chapter and Conference Paper
Impact of Conversational Agent Language and Text Structure on Student Language
This study examines how conversational agents’ language (formal vs. informal) and text structures (comparison vs. causation) impact student language in written summaries using an intelligent tutoring system (I...
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Article
Fractional core-based collapse mechanism and structural optimization in complex systems
Catastrophic and major disasters in real-world systems ranging from financial markets and ecosystems, often show generic early-warning signals that may indicate a collapse. Hence, understanding the collapse me...
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Chapter and Conference Paper
Negative Reversion: Toward Intelligent Co-raters for Coding Qualitative Data in Quantitative Ethnography
Artificial intelligence has been applied to simulate many human activities in Quantitative Ethnography(QE). This paper evaluates the creation of an intelligent co-rater for coding qualitative (text) data in QE...
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Chapter and Conference Paper
Does Active Learning Reduce Human Coding?: A Systematic Comparison of Neural Network with nCoder
In quantitative ethnography (QE) studies which often involve large datasets that cannot be entirely hand-coded by human raters, researchers have used supervised machine learning approaches to develop automated...
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Chapter and Conference Paper
Multiclass Rotations in Epistemic Network Analysis
The task of succinctly and insightfully discussing themes in the differences between several (three or more) groups in naturalistic, ethnographic research faces a number of constraints. The number of all possi...
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Chapter and Conference Paper
DSE-Net: Deep Semantic Enhanced Network for Mobile Tongue Image Segmentation
Tongue diagnosis plays an important role in traditional Chinese medicine (TCM) because of noninvasive for health assessment. Taking advantage of the portability of mobile devices to develop a tongue diagnosis ...
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Chapter and Conference Paper
LSTM Neural Network Assisted Regex Development for Qualitative Coding
Regular expression (regex) based automated qualitative coding helps reduce researchers’ effort in manually coding text data, without sacrificing transparency of the coding process. However, researchers using r...
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Chapter and Conference Paper
A Lightweight Interactive Regular Expression Generator for Qualitative Coding in Quantitative Ethnography
Quantitative ethnography approaches are often used to analyze large scale qualitative data. Manually coding such data is expensive and time consuming, if not impractical or impossible. In contrast, machine lea...
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Chapter and Conference Paper
Improving Generalization of Multi-agent Reinforcement Learning Through Domain-Invariant Feature Extraction
The limited generalization ability of reinforcement learning constrains its potential applications, particularly in complex scenarios such as multi-agent systems. To overcome this limitation and enhance the ge...
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Chapter and Conference Paper
Thin Data, Thick Description: Modeling Socio-Environmental Problem-Solving Trajectories in Localized Land-Use Simulations
Many learning technologies are now able to support both user-customization of the content and automated personalization of the experience based on user activities. However, there is a tradeoff between customiz...
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Chapter and Conference Paper
Impact of Agent Language on Student Language in the Structures of Language Connections
This study explores the impact of conversational agent language (formal vs. informal) on student language in an intelligent tutoring system (ITS). Unlike previous studies that analyzed language features in iso...
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Chapter and Conference Paper
Ordered Network Analysis
Collaborative Problem Solving (CPS) is a socio-cognitive process that is interactive, interdependent, and temporal. As individuals interact with each other, information is added to the common ground, or the curre...
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Article
Open AccessPatterns of Adults with Low Literacy Skills Interacting with an Intelligent Tutoring System
A common goal of Intelligent Tutoring Systems (ITS) is to provide learning environments that adapt to the varying abilities and characteristics of users. This type of adaptivity is possible only if the ITS has...
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Article
Attentive frequency learning network for super-resolution
Benefiting from the strong capability of capturing long-range dependencies, a series of self-attention based single image super-resolution (SISR) methods have achieved promising performance. However, the exist...
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Chapter and Conference Paper
Zero Re-centered Projection: An Alternative Proposal for Modeling Empty Networks in ENA
This paper examines the impact of having empty networks in an Epistemic Network Analysis model, that is, units whose networks contain no connections in a given model. These empty networks, also known as zero p...
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Chapter and Conference Paper
Learn to Rectify Label Through Kernel Extreme Learning Machine
Recent studies attempt to construct complicated and redundant Convolutional Neural Networks (CNNs) to improve image classification performance. In this paper, instead of painstakingly designing a CNN’s archite...
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Chapter and Conference Paper
Using Topic Modeling for Code Discovery in Large Scale Text Data
When text datasets are very large, manually coding line by line becomes impractical. As a result, researchers sometimes try to use machine learning algorithms to automatically code text data. One of the most p...
