Bang Wang
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View article: Large Language Models for Reranking: A Survey
Large Language Models for Reranking: A Survey Open
View article: Anchor-based Pairwise Comparison via Large Language Model for Recommendation Reranking
Anchor-based Pairwise Comparison via Large Language Model for Recommendation Reranking Open
View article: Association between estimated glucose disposal rate and the risk of MAFLD in American adults: a cross-sectional study
Association between estimated glucose disposal rate and the risk of MAFLD in American adults: a cross-sectional study Open
View article: A nonlinear dynamic hysteresis model and dynamic performance of the piezoelectric ring bending actuator
A nonlinear dynamic hysteresis model and dynamic performance of the piezoelectric ring bending actuator Open
A piezoelectric ring-bending actuator with a piezoelectric ring bender as the core component has the merits of fast response, high control accuracy, high driving frequency, and compact size. Therefore, it has broad application prospects. H…
View article: On Fusing Wireless Fingerprints with Pedestrian Dead Reckoning to Improve Indoor Localization Accuracy
On Fusing Wireless Fingerprints with Pedestrian Dead Reckoning to Improve Indoor Localization Accuracy Open
Accurate indoor positioning remains a critical challenge due to the limitations of single-source systems, such as signal instability and environmental obstructions. This study introduces a multi-source fusion positioning algorithm that int…
View article: Leveraging Retrieval-Augmented Generation for Swahili Language Conversation Systems
Leveraging Retrieval-Augmented Generation for Swahili Language Conversation Systems Open
A conversational system is an artificial intelligence application designed to interact with users in natural language, providing accurate and contextually relevant responses. Building such systems for low-resource languages like Swahili pr…
View article: Fsloc: Federated Learning Based Scalable Indoor Localization from Crowdsourced Samples
Fsloc: Federated Learning Based Scalable Indoor Localization from Crowdsourced Samples Open
View article: On Exploring and Exploiting Latent Group Information to Improve Implicit Collaborative Filtering
On Exploring and Exploiting Latent Group Information to Improve Implicit Collaborative Filtering Open
View article: Association between estimated glucose disposal rate and the risk of MAFLD in American adults: a cross-sectional study
Association between estimated glucose disposal rate and the risk of MAFLD in American adults: a cross-sectional study Open
The estimated glucose disposal rate (eGDR), as a novel metric of insulin resistance (IR), has been demonstrated to correlate with hepatic steatosis in patients with diabetes. Although IR is considered as a factor affecting lipid metabolism…
View article: Anatomic Surface Measurement of the Lumbar Laminae for Safely Resection During Percutaneous Endoscopic Surgery: A Computed Tomography-Based 3-Dimensional Morphometric Study
Anatomic Surface Measurement of the Lumbar Laminae for Safely Resection During Percutaneous Endoscopic Surgery: A Computed Tomography-Based 3-Dimensional Morphometric Study Open
This study has revealed significant anatomical diversity within the laminae of lumbar vertebral segments. These findings provide important anatomical information for surgeons performing precise laminar fenestration during endoscopic spinal…
View article: TaIncBC: Topic-aware In-context Prompt with Bias Calibration for Event Causality Identification
TaIncBC: Topic-aware In-context Prompt with Bias Calibration for Event Causality Identification Open
Event Causality Identification (ECI) is to determine whether there exists a causal relation between two event mentions. Recently, the prompt learning paradigm has been applied in the ECI task to leverage a large-scale pre-trained language …
View article: What Would Happen Next? Predicting Consequences from An Event Causality Graph
What Would Happen Next? Predicting Consequences from An Event Causality Graph Open
Existing script event prediction task forcasts the subsequent event based on an event script chain. However, the evolution of historical events are more complicated in real world scenarios and the limited information provided by the event …
View article: Identifying while Learning for Document Event Causality Identification
Identifying while Learning for Document Event Causality Identification Open
Event Causality Identification (ECI) aims to detect whether there exists a causal relation between two events in a document. Existing studies adopt a kind of identifying after learning paradigm, where events' representations are first lear…
View article: Encoding Hierarchical Schema via Concept Flow for Multifaceted Ideology Detection
Encoding Hierarchical Schema via Concept Flow for Multifaceted Ideology Detection Open
Multifaceted ideology detection (MID) aims to detect the ideological leanings of texts towards multiple facets. Previous studies on ideology detection mainly focus on one generic facet and ignore label semantics and explanatory description…
View article: In-context Contrastive Learning for Event Causality Identification
In-context Contrastive Learning for Event Causality Identification Open
Event Causality Identification (ECI) aims at determining the existence of a causal relation between two events. Although recent prompt learning-based approaches have shown promising improvements on the ECI task, their performance are often…
View article: One Backpropagation in Two Tower Recommendation Models
One Backpropagation in Two Tower Recommendation Models Open
Recent years have witnessed extensive researches on developing two tower recommendation models for relieving information overload. Four building modules can be identified in such models, namely, user-item encoding, negative sampling, loss …
View article: Dual-Side Adversarial Learning Based Fair Recommendation for Sensitive Attribute Filtering
Dual-Side Adversarial Learning Based Fair Recommendation for Sensitive Attribute Filtering Open
With the development of recommendation algorithms, researchers are paying increasing attention to fairness issues such as user discrimination in recommendations. To address these issues, existing works often filter users’ sensitive informa…
View article: Mitigating Popularity Bias in Recommendation Systems Via Negative Sample Mixing
Mitigating Popularity Bias in Recommendation Systems Via Negative Sample Mixing Open
View article: Category-Integrated Dual-Task Graph Neural Networks for Session-Based Recommendation
Category-Integrated Dual-Task Graph Neural Networks for Session-Based Recommendation Open
View article: Modeling Document Causal Structure with a Hypergraph for Event Causality Identification
Modeling Document Causal Structure with a Hypergraph for Event Causality Identification Open
View article: Mining User–Item Interactions via Knowledge Graph for Recommendation
Mining User–Item Interactions via Knowledge Graph for Recommendation Open
Introducing a Knowledge Graph (KG) to facilitate a recommender system has become a tendency in recent years. Many existing methods leverage KGs to obtain side information of items to promote item representation learning for enhancing recom…
View article: A barley SS2a single base mutation at the splicing site leads to obvious changes in starch
A barley SS2a single base mutation at the splicing site leads to obvious changes in starch Open
Starch biosynthesis is a complex process that relies on the coordinated action of multiple enzymes. Resistant starch is not digested in the small intestine, thus preventing the rapid rise of the glycemic index. Starch synthase 2a (SS2a), a…
View article: Adaptive Prompt Learning with Distilled Connective Knowledge for Implicit Discourse Relation Recognition
Adaptive Prompt Learning with Distilled Connective Knowledge for Implicit Discourse Relation Recognition Open
Implicit discourse relation recognition (IDRR) aims at recognizing the discourse relation between two text segments without an explicit connective. Recently, the prompt learning has just been applied to the IDRR task with great performance…
View article: Debiased Pairwise Learning from Positive-Unlabeled Implicit Feedback
Debiased Pairwise Learning from Positive-Unlabeled Implicit Feedback Open
Learning contrastive representations from pairwise comparisons has achieved remarkable success in various fields, such as natural language processing, computer vision, and information retrieval. Collaborative filtering algorithms based on …
View article: DAPrompt: Deterministic Assumption Prompt Learning for Event Causality Identification
DAPrompt: Deterministic Assumption Prompt Learning for Event Causality Identification Open
Event Causality Identification (ECI) aims at determining whether there is a causal relation between two event mentions. Conventional prompt learning designs a prompt template to first predict an answer word and then maps it to the final de…
View article: Prompt Learning for News Recommendation
Prompt Learning for News Recommendation Open
Some recent \\textit{news recommendation} (NR) methods introduce a Pre-trained\nLanguage Model (PLM) to encode news representation by following the vanilla\npre-train and fine-tune paradigm with carefully-designed\nrecommendation-specific …
View article: Reducing Popularity Bias in Recommender Systems through AUC-Optimal Negative Sampling
Reducing Popularity Bias in Recommender Systems through AUC-Optimal Negative Sampling Open
Popularity bias is a persistent issue associated with recommendation systems, posing challenges to both fairness and efficiency. Existing literature widely acknowledges that reducing popularity bias often requires sacrificing recommendatio…
View article: TEPrompt: Task Enlightenment Prompt Learning for Implicit Discourse Relation Recognition
TEPrompt: Task Enlightenment Prompt Learning for Implicit Discourse Relation Recognition Open
Implicit Discourse Relation Recognition (IDRR) aims at classifying the relation sense between two arguments without an explicit connective. Recently, the ConnPrompt~\cite{Wei.X:et.al:2022:COLING} has leveraged the powerful prompt learning …
View article: RadarPDR: Radar-Assisted Indoor Pedestrian Dead Reckoning
RadarPDR: Radar-Assisted Indoor Pedestrian Dead Reckoning Open
Pedestrian dead reckoning (PDR) is the critical component in indoor pedestrian tracking and navigation services. While most of the recent PDR solutions exploit in-built inertial sensors in smartphones for next step estimation, due to measu…
View article: Encoding Node Diffusion Competence and Role Significance for Network Dismantling
Encoding Node Diffusion Competence and Role Significance for Network Dismantling Open
Percolation theory shows that removing a small fraction of critical nodes can lead to the disintegration of a large network into many disconnected tiny subnetworks. The network dismantling task focuses on how to efficiently select the leas…