Benjamin Fung

Jonathan Poon

Non-negative tensor factorization has been shown a practical solution to automatically discover phenotypes from the electronic health record… (voir plus)s (EHR) with minimal human supervision. Such methods generally require an input tensor describing the inter-modal interactions to be pre-established; however, the correspondence between different modalities (e.g., correspondence between medications and diagnoses) can often be missing in practice. Although heuristic methods can be applied to estimate them, they inevitably introduce errors, and leads to sub-optimal phenotype quality. This is particularly important for patients with complex health conditions (e.g., in critical care) as multiple diagnoses and medications are simultaneously present in the records. To alleviate this problem and discover phenotypes from EHR with unobserved inter-modal correspondence, we propose the collective hidden interaction tensor factorization (cHITF) to infer the correspondence between multiple modalities jointly with the phenotype discovery. We assume that the observed matrix for each modality is marginalization of the unobserved inter-modal correspondence, which are reconstructed by maximizing the likelihood of the observed matrices. Extensive experiments conducted on the real-world MIMIC-III dataset demonstrate that cHITF effectively infers clinically meaningful inter-modal correspondence, discovers phenotypes that are more clinically relevant and diverse, and achieves better predictive performance compared with a number of state-of-the-art computational phenotyping models.

2022-01-01

IEEE Transactions on Knowledge and Data Engineering (publié)

On the Effectiveness of Interpretable Feedforward Neural Network

Miles Q. Li

Adel Abusitta

Deep learning models have achieved state-of-the-art performance in many classification tasks. However, most of them cannot provide an explan… (voir plus)ation for their classification results. Machine learning models that are interpretable are usually linear or piecewise linear and yield inferior performance. Non-linear models achieve much better classification performance, but it is usually hard to explain their classification results. As a counter-example, an interpretable feedforward neural network (IFFNN) is proposed to achieve both high classification performance and interpretability for malware detection. If the IFFNN can perform well in a more flexible and general form for other classification tasks while providing meaningful explanations, it may be of great interest to the applied machine learning community. In this paper, we propose a way to generalize the interpretable feedforward neural network to multi-class classification scenarios and any type of feedforward neural networks, and evaluate its classification performance and interpretability on interpretable datasets. We conclude by finding that the generalized IFFNNs achieve comparable classification performance to their normal feedforward neural network counterparts and provide meaningful explanations. Thus, this kind of neural network architecture has great practical use.

2021-11-03

ArXiv (preprint)

The Topic Confusion Task: A Novel Evaluation Scenario for Authorship Attribution

Malik H. Altakrori

Jackie Cheung

2021-11-01

Findings of the Association for Computational Linguistics: EMNLP 2021 (publié)

Season-Based Occupancy Prediction in Residential Buildings Using Machine Learning Models

Bowen Yang

Fariborz Haghighat

Karthik Panchabikesan

2021-10-01

e-Prime (publié)

Trade-off Between Accuracy and Fairness of Data-driven Building and Indoor Environment Models: A Comparative Study of Pre-processing Methods

Ying Sun

Fariborz Haghighat

2021-10-01

Energy (publié)

Trade-off Between Accuracy and Fairness of Data-driven Building and Indoor Environment Models: A Comparative Study of Pre-processing Methods

Ying Sun

Fariborz Haghighat

2021-10-01

Energy (published)

A Data Mining Analysis of Cross-Regional Study of Apparel Consumption

Osmud Rahman

2021-08-28

Comprehensible Science (publié)

An Advanced Noise Reduction and Edge Enhancement Algorithm

Shih-Chia Huang

Quoc-Viet Hoang

Trung-Hieu Le

Yan-Tsung Peng

Ching-Chun Huang

Cheng Zhang

Kai-Han Cheng

Sha-Wo Huang

Complementary metal-oxide-semiconductor (CMOS) image sensors can cause noise in images collected or transmitted in unfavorable environments,… (voir plus) especially low-illumination scenarios. Numerous approaches have been developed to solve the problem of image noise removal. However, producing natural and high-quality denoised images remains a crucial challenge. To meet this challenge, we introduce a novel approach for image denoising with the following three main contributions. First, we devise a deep image prior-based module that can produce a noise-reduced image as well as a contrast-enhanced denoised one from a noisy input image. Second, the produced images are passed through a proposed image fusion (IF) module based on Laplacian pyramid decomposition to combine them and prevent noise amplification and color shift. Finally, we introduce a progressive refinement (PR) module, which adopts the summed-area tables to take advantage of spatially correlated information for edge and image quality enhancement. Qualitative and quantitative evaluations demonstrate the efficiency, superiority, and robustness of our proposed method.

2021-08-01

Italian National Conference on Sensors (publié)

VirtualGAN: Reducing Mode Collapse in Generative Adversarial Networks Using Virtual Mapping

Adel Abusitta

Omar Abdel Wahab

This paper introduces a new framework for reducing mode collapse in Generative adversarial networks (GANs). The problem occurs when the gene… (voir plus)rator learns to map several various input values (z) to the same output value, which makes the generator fail to capture all modes of the true data distribution. As a result, the diversity of synthetically produced data is lower than that of the real data. To address this problem, we propose a new and simple framework for training GANs based on the concept of virtual mapping. Our framework integrates two processes into GANs: merge and split. The merge process merges multiple data points (samples) into one before training the discriminator. In this way, the generator would be trained to capture the merged-data distribution rather than the (unmerged) data distribution. After the training, the split process is applied to the generator's output in order to split its contents and produce diverse modes. The proposed framework increases the chance of capturing diverse modes through enabling an indirect or virtual mapping between an input z value and multiple data points. This, in turn, enhances the chance of generating more diverse modes. Our results show the effectiveness of our framework compared to the existing approaches in terms of reducing the mode collapse problem.

2021-07-18

IEEE International Joint Conference on Neural Network (publié)

A Data Mining Analysis of Cross-Regional Study of Apparel Consumption

Osmud Rahman

2021-06-19

(published)

ER-AE: Differentially Private Text Generation for Authorship Anonymization

Haohan Bo

Steven H. H. Ding

Farkhund Iqbal

Most of privacy protection studies for textual data focus on removing explicit sensitive identifiers. However, personal writing style, as a … (voir plus)strong indicator of the authorship, is often neglected. Recent studies, such as SynTF, have shown promising results on privacy-preserving text mining. However, their anonymization algorithm can only output numeric term vectors which are difficult for the recipients to interpret. We propose a novel text generation model with a two-set exponential mechanism for authorship anonymization. By augmenting the semantic information through a REINFORCE training reward function, the model can generate differentially private text that has a close semantic and similar grammatical structure to the original text while removing personal traits of the writing style. It does not assume any conditioned labels or paralleled text data for training. We evaluate the performance of the proposed model on the real-life peer reviews dataset and the Yelp review dataset. The result suggests that our model outperforms the state-of-the-art on semantic preservation, authorship obfuscation, and stylometric transformation.

2021-06-01

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (publié)

Differentially private data publishing for arbitrarily partitioned data

Rongli Wang

Yan Zhu

Qiang Peng

2021-04-01

Information Sciences (publié)