Portrait de Benjamin Fung

Benjamin Fung

Membre académique associé
Professeur agrégé, McGill University, École des sciences de l'information
McGill University University
Sujets de recherche
Apprentissage automatique appliqué
Apprentissage de représentations
Apprentissage profond
Cybersécurité
Désinformation
Exploration des données
IA pour l'ingénierie logicielle
Recherche d'information
Vie privée
Site web
Google Scholar
LinkedIn
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Biographie

Benjamin Fung est titulaire d'une chaire de recherche du Canada en exploration de données pour la cybersécurité, professeur agrégé à l’École des sciences de l’information et membre agrégé de l’École d’informatique de l'Université McGill, rédacteur adjoint de IEEE Transactions of Knowledge and Data Engineering et rédacteur adjoint de Elsevier Sustainable Cities and Society (SCS). Il a obtenu un doctorat en informatique de l'Université Simon Fraser en 2007. Il a à son actif plus de 150 publications revues par un comité de lecture, et plus de 14 000 citations (h-index 57) qui couvrent les domaines de l'exploration de données, de l'apprentissage automatique, de la protection de la vie privée, de la cybersécurité et du génie du bâtiment. Ses travaux d'exploration de données dans les enquêtes criminelles et l'analyse de la paternité d’une œuvre ont été recensés par les médias du monde entier.

Publications

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
Benjamin Fung
2021-10-01
Energy (publié)
doi.org
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
Benjamin Fung
2021-10-01
Energy (published)
doi.org
A Data Mining Analysis of Cross-Regional Study of Apparel Consumption
Osmud Rahman
Benjamin Fung
2021-08-28
Comprehensible Science (publié)
doi.org
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
Benjamin Fung
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)
doi.org
VirtualGAN: Reducing Mode Collapse in Generative Adversarial Networks Using Virtual Mapping
Adel Abusitta
Omar Abdel Wahab
Benjamin Fung
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)
doi.org
A Data Mining Analysis of Cross-Regional Study of Apparel Consumption
Osmud Rahman
Benjamin Fung
2021-06-19
(published)
doi.org
ER-AE: Differentially Private Text Generation for Authorship Anonymization
Haohan Bo
Steven H. H. Ding
Benjamin Fung
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)
doi.org
arxiv.org
Differentially private data publishing for arbitrarily partitioned data
Rongli Wang
Benjamin Fung
Yan Zhu
Qiang Peng
2021-04-01
Information Sciences (publié)
doi.org
Factors influencing consumer choice: a study of apparel and sustainable cues from Canadian and Indian consumers’ perspectives
Osmud Rahman
Benjamin Fung
Devender Kharb
2021-03-19
International Journal of Fashion Design, Technology and Education (publié)
doi.org
Enabling Technologies for Energy Cloud
Thar Intisar Baker
Zehua Guo
Ali Ismail Ali Awad
Shangguang Wang
Benjamin Fung
2021-03-05
J. Parallel Distributed Comput. (publié)
doi.org
Enabling Secure Trustworthiness Assessment and Privacy Protection in Integrating Data for Trading Person-Specific Information
Rashid Hussain Khokhar
Farkhund Iqbal
Benjamin Fung
Jamal Bentahar
With increasing adoption of cloud services in the e-market, collaboration between stakeholders is easier than ever. Consumer stakeholders de… (voir plus)mand data from various sources to analyze trends and improve customer services. Data-as-a-service enables data integration to serve the demands of data consumers. However, the data must be of good quality and trustful for accurate analysis and effective decision making. In addition, a data custodian or provider must conform to privacy policies to avoid potential penalties for privacy breaches. To address these challenges, we propose a twofold solution: 1) we present the first information entropy-based trust computation algorithm, IEB_Trust, that allows a semitrusted arbitrator to detect the covert behavior of a dishonest data provider and chooses the qualified providers for a data mashup and 2) we incorporate the Vickrey–Clarke–Groves (VCG) auction mechanism for the valuation of data providers’ attributes into the data mashup process. Experiments on real-life data demonstrate the robustness of our approach in restricting dishonest providers from participation in the data mashup and improving the efficiency in comparison to provenance-based approaches. Furthermore, we derive the monetary shares for the chosen providers from their information utility and trust scores over the differentially private release of the integrated dataset under their joint privacy requirements.
2021-02-01
IEEE transactions on engineering management (publié)
doi.org
Automatic Fall Risk Detection based on Imbalanced Data
Yen-Hung Liu
Ye Liu
Patrick C. K. Hung
Farkhund Iqbal
Benjamin Fung
In recent years, the declining birthrate and aging population have gradually brought countries into an ageing society. Regarding accidents t… (voir plus)hat occur amongst the elderly, falls are an essential problem that quickly causes indirect physical loss. In this paper, we propose a pose estimation-based fall detection algorithm to detect fall risks. We use body ratio, acceleration and deflection as key features instead of using the body keypoints coordinates. Since fall data is rare in real-world situations, we train and evaluate our approach in a highly imbalanced data setting. We assess not only different imbalanced data handling methods but also different machine learning algorithms. After oversampling on our training data, the K-Nearest Neighbors (KNN) algorithm achieves the best performance. The F1 scores for three different classes, Normal, Fall, and Lying, are 1.00, 0.85 and 0.96, which is comparable to previous research. The experiment shows that our approach is more interpretable with the key feature from skeleton information. Moreover, it can apply in multi-people scenarios and has robustness on medium occlusion.
2021-01-01
IEEE Access (publié)
doi.org