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- research-articleApril 2024
Generalizing truth discovery by incorporating multi-truth features
Computing (CMPT), Volume 106, Issue 5May 2024, Pages 1557–1583https://doi.org/10.1007/s00607-024-01288-9AbstractTruth discovery is the fundamental technique for resolving the conflicts between the information provided by different data sources by detecting the true values. Traditional methods assume that each data item has only one true value and therefore ...
- research-articleApril 2024
Multi-view GCN for loan default risk prediction
Neural Computing and Applications (NCAA), Volume 36, Issue 20Jul 2024, Pages 12149–12162https://doi.org/10.1007/s00521-024-09695-xAbstractAs a significant application of machine learning in financial scenarios, loan default risk prediction aims to evaluate the client’s default probability. However, most existing deep learning solutions treat each application as an independent ...
- ArticleJuly 2024
MVis4LD: Multimodal Visual Interactive System for Lie Detection
- Md. Kowsar Hossain Sakib,
- Md Rafiqul Islam,
- Shanjita Akter Prome,
- Thanh Thao Lam Nguyen,
- David Asirvatham,
- Neethiahnanthan Ari Ragavan,
- Xianzhi Wang,
- Cesar Sanin
Intelligent Information and Database SystemsApr 2024, Pages 28–43https://doi.org/10.1007/978-981-97-4985-0_3AbstractLying is a prevalent concern impacting our daily lives and social interactions. The pattern to identify lying in text data can be improved by gaining a better understanding of individual behaviour. However, the study of lie detection (LD) is a ...
- research-articleJanuary 2024
A topic‐controllable keywords‐to‐text generator with knowledge base network
CAAI Transactions on Intelligence Technology (CIT2), Volume 9, Issue 3June 2024, Pages 585–594https://doi.org/10.1049/cit2.12280AbstractWith the introduction of more recent deep learning models such as encoder‐decoder, text generation frameworks have gained a lot of popularity. In Natural Language Generation (NLG), controlling the information and style of the output produced is ...
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- research-articleJanuary 2024
BehaviorNet: A Fine-grained Behavior-aware Network for Dynamic Link Prediction
ACM Transactions on the Web (TWEB), Volume 18, Issue 2Article No.: 25, Pages 1–26https://doi.org/10.1145/3580514Dynamic link prediction has become a trending research subject because of its wide applications in the web, sociology, transportation, and bioinformatics. Currently, the prevailing approach for dynamic link prediction is based on graph neural networks, in ...
- research-articleMarch 2024
Dyformer: A dynamic transformer-based architecture for multivariate time series classification
Information Sciences: an International Journal (ISCI), Volume 656, Issue CJan 2024https://doi.org/10.1016/j.ins.2023.119881AbstractMultivariate time series classification is a crucial task with applications in broad areas such as finance, medicine, and engineering. Transformer is promising for time series classification, but as a generic approach, they have limited ...
Highlights- We present a transformer-based dynamic architecture to achieve adaptive learning strategies for different frequency components of the time series data.
- We design a hierarchical pooling layer to decompose time series into subsequences ...
- research-articleFebruary 2024
Exploring explicit and implicit graph learning for multivariate time series imputation
Engineering Applications of Artificial Intelligence (EAAI), Volume 127, Issue PAJan 2024https://doi.org/10.1016/j.engappai.2023.107217AbstractMultivariate time series inherently contain missing values due to various issues, including incorrect data entry, broken equipment, and package loss during data transferring. The successful completion of time series data analysis tasks heavily ...
Highlights- EIGRN: a network combining GCN and RNN for time series imputation using explicit and implicit graphs.
- Our model outperforms in time series imputation on 4 datasets v.s. top baselines.
- research-articleOctober 2023
Generative Adversarial Reward Learning for Generalized Behavior Tendency Inference
IEEE Transactions on Knowledge and Data Engineering (IEEECS_TKDE), Volume 35, Issue 10Oct. 2023, Pages 9878–9889https://doi.org/10.1109/TKDE.2022.3186920Recent advances in reinforcement learning have inspired increasing interest in learning user modeling adaptively through dynamic interactions, e.g., in reinforcement learning based recommender systems. In most reinforcement learning applications, reward ...
- ArticleNovember 2023
MTSTI: A Multi-task Learning Framework for Spatiotemporal Imputation
Advanced Data Mining and ApplicationsAug 2023, Pages 180–194https://doi.org/10.1007/978-3-031-46677-9_13AbstractSpatiotemporal data analysis is crucial for various fields of applications, such as transportation, healthcare, and meteorology. Spatiotemporal data collected in the real world often contain missing values due to sensor failures or transmission ...
- ArticleNovember 2023
Exploring the Effectiveness of Positional Embedding on Transformer-Based Architectures for Multivariate Time Series Classification
Advanced Data Mining and ApplicationsAug 2023, Pages 34–47https://doi.org/10.1007/978-3-031-46661-8_3AbstractPositional embedding is an effective means of injecting position information into sequential data to make the vanilla Transformer position-sensitive. Current Transformer-based models routinely use positional embedding for their position-sensitive ...
- ArticleNovember 2023
From Time Series to Multi-modality: Classifying Multivariate Time Series via Both 1D and 2D Representations
Advanced Data Mining and ApplicationsAug 2023, Pages 19–33https://doi.org/10.1007/978-3-031-46661-8_2AbstractMultivariate time series classification is crucial for various applications such as activity recognition, disease diagnosis, and brain-computer interfaces. Deep learning methods have recently achieved promising performance thanks to their powerful ...
- research-articleAugust 2023
A novel uncertainty-aware deep learning technique with an application on skin cancer diagnosis
- Afshar Shamsi,
- Hamzeh Asgharnezhad,
- Ziba Bouchani,
- Khadijeh Jahanian,
- Morteza Saberi,
- Xianzhi Wang,
- Imran Razzak,
- Roohallah Alizadehsani,
- Arash Mohammadi,
- Hamid Alinejad-Rokny
Neural Computing and Applications (NCAA), Volume 35, Issue 30Oct 2023, Pages 22179–22188https://doi.org/10.1007/s00521-023-08930-1AbstractSkin cancer, primarily resulting from the abnormal growth of skin cells, is among the most common cancer types. In recent decades, the incidence of skin cancer cases worldwide has risen significantly (one in every three newly diagnosed cancer ...
- research-articleJuly 2023
Dual Contrastive Transformer for Hierarchical Preference Modeling in Sequential Recommendation
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2023, Pages 99–109https://doi.org/10.1145/3539618.3591672Sequential recommender systems (SRSs) aim to predict the subsequent items which may interest users via comprehensively modeling users' complex preference embedded in the sequence of user-item interactions. However, most of existing SRSs often model users'...
- research-articleApril 2023
Modeling Temporal Positive and Negative Excitation for Sequential Recommendation
WWW '23: Proceedings of the ACM Web Conference 2023April 2023, Pages 1252–1263https://doi.org/10.1145/3543507.3583463Sequential recommendation aims to predict the next item which interests users via modeling their interest in items over time. Most of the existing works on sequential recommendation model users’ dynamic interest in specific items while overlooking users’...
- research-articleMarch 2023
Deep reinforcement learning in recommender systems: A survey and new perspectives
Knowledge-Based Systems (KNBS), Volume 264, Issue CMar 2023https://doi.org/10.1016/j.knosys.2023.110335AbstractIn light of the emergence of deep reinforcement learning (DRL) in recommender systems research and several fruitful results in recent years, this survey aims to provide a timely and comprehensive overview of recent trends of deep ...
- research-articleFebruary 2023
Simplifying Graph-based Collaborative Filtering for Recommendation
WSDM '23: Proceedings of the Sixteenth ACM International Conference on Web Search and Data MiningFebruary 2023, Pages 60–68https://doi.org/10.1145/3539597.3570451Graph Convolutional Networks (GCNs) are a popular type of machine learning models that use multiple layers of convolutional aggregation operations and non-linear activations to represent data. Recent studies apply GCNs to Collaborative Filtering (CF)-...
- research-articleFebruary 2023
Exploiting Explicit and Implicit Item relationships for Session-based Recommendation
WSDM '23: Proceedings of the Sixteenth ACM International Conference on Web Search and Data MiningFebruary 2023, Pages 553–561https://doi.org/10.1145/3539597.3570432The session-based recommendation aims to predict users' immediate next actions based on their short-term behaviors reflected by past and ongoing sessions. Graph neural networks (GNNs) recently dominated the related studies, yet their performance heavily ...
- ArticleOctober 2022
Graph Neural Network with Self-attention and Multi-task Learning for Credit Default Risk Prediction
Web Information Systems Engineering – WISE 2022Oct 2022, Pages 616–629https://doi.org/10.1007/978-3-031-20891-1_44AbstractWe propose a graph neural network with self-attention and multi-task learning (SaM-GNN) to leverage the advantages of deep learning for credit default risk prediction. Our approach incorporates two parallel tasks based on shared intermediate ...
- ArticleOctober 2022
Mitigating Multi-class Unintended Demographic Bias in Text Classification with Adversarial Learning
Web Information Systems Engineering – WISE 2022Oct 2022, Pages 386–394https://doi.org/10.1007/978-3-031-20891-1_27AbstractText classification enables higher efficiency on text data queries in information retrieval. However, unintended demographic bias can impair text toxicity classification. Thus, we propose a novel debiasing framework utilizing Adversarial Learning ...