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Benjamin C. M. Fung
School of Information Studies, McGill University, Montreal, QC H3A 1X1, Canada

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Journal article
Published: 10 August 2021 in Sensors
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Complementary metal-oxide-semiconductor (CMOS) image sensors can cause noise in images collected or transmitted in unfavorable environments, 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.

ACS Style

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. An Advanced Noise Reduction and Edge Enhancement Algorithm. Sensors 2021, 21, 5391 .

AMA Style

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. An Advanced Noise Reduction and Edge Enhancement Algorithm. Sensors. 2021; 21 (16):5391.

Chicago/Turabian Style

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. 2021. "An Advanced Noise Reduction and Edge Enhancement Algorithm." Sensors 21, no. 16: 5391.

Journal article
Published: 18 June 2021 in Computers & Security
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Malware currently presents a number of serious threats to computer users. Signature-based malware detection methods are limited in detecting new malware samples that are significantly different from known ones. Therefore, machine learning-based methods have been proposed, but there are two challenges these methods face. The first is to model the full semantics behind the assembly code of malware. The second challenge is to provide interpretable results while keeping excellent detection performance. In this paper, we propose an Interpretable MAlware Detector (I-MAD) that outperforms state-of-the-art static malware detection models regarding accuracy with excellent interpretability. To improve the detection performance, I-MAD incorporates a novel network component called the Galaxy Transformer network that can understand assembly code at the basic block, function, and executable levels. It also incorporates our proposed interpretable feed-forward neural network to provide interpretations for its detection results by quantifying the impact of each feature with respect to the prediction. Experiment results show that our model significantly outperforms existing state-of-the-art static malware detection models and presents meaningful interpretations.

ACS Style

Miles Q. Li; Benjamin C.M. Fung; Philippe Charland; Steven H.H. Ding. I-MAD: Interpretable Malware Detector Using Galaxy Transformer. Computers & Security 2021, 108, 102371 .

AMA Style

Miles Q. Li, Benjamin C.M. Fung, Philippe Charland, Steven H.H. Ding. I-MAD: Interpretable Malware Detector Using Galaxy Transformer. Computers & Security. 2021; 108 ():102371.

Chicago/Turabian Style

Miles Q. Li; Benjamin C.M. Fung; Philippe Charland; Steven H.H. Ding. 2021. "I-MAD: Interpretable Malware Detector Using Galaxy Transformer." Computers & Security 108, no. : 102371.

Journal article
Published: 26 April 2021 in Journal of Information Security and Applications
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Malware detection and classification are becoming more and more challenging, given the complexity of malware design and the recent advancement of communication and computing infrastructure. The existing malware classification approaches enable reverse engineers to better understand their patterns and categorizations, and to cope with their evolution. Moreover, new compositions analysis methods have been proposed to analyze malware samples with the goal of gaining deeper insight on their functionalities and behaviors. This, in turn, helps reverse engineers discern the intent of a malware sample and understand the attackers’ objectives. This survey classifies and compares the main findings in malware classification and composition analyses. We also discuss malware evasion techniques and feature extraction methods. Besides, we characterize each reviewed paper on the basis of both algorithms and features used, and highlight its strengths and limitations. We furthermore present issues, challenges, and future research directions related to malware analysis.

ACS Style

Adel Abusitta; Miles Q. Li; Benjamin C.M. Fung. Malware classification and composition analysis: A survey of recent developments. Journal of Information Security and Applications 2021, 59, 102828 .

AMA Style

Adel Abusitta, Miles Q. Li, Benjamin C.M. Fung. Malware classification and composition analysis: A survey of recent developments. Journal of Information Security and Applications. 2021; 59 ():102828.

Chicago/Turabian Style

Adel Abusitta; Miles Q. Li; Benjamin C.M. Fung. 2021. "Malware classification and composition analysis: A survey of recent developments." Journal of Information Security and Applications 59, no. : 102828.

Journal article
Published: 15 March 2021 in IEEE Access
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Haze removal techniques employed to increase the visibility level of an image play an important role in many vision-based systems. Several traditional dark channel prior-based methods have been proposed to remove haze formation and thereby enhance the robustness of these systems. However, when the captured images contain disproportionate haze distributions, these methods usually fail to attain effective restoration in the restored image. Specifically, disproportionate haze distribution in an image means that the background region possesses heavy haze density and the foreground region possesses little haze density. This phenomenon usually occurs in a hazy image with a deep depth of field. In response, a novel hybrid transmission map-based haze removal method that specifically targets this situation is proposed in this work to achieve clear visibility restoration and effective information maintenance. Experimental results via both qualitative and quantitative evaluations demonstrate that the proposed method is capable of performing with higher efficacy when compared with other state-of-the-art methods, in respect to both background regions and foreground regions of restored test images captured in real-world environments.

ACS Style

Shih-Chia Huang; Da-Wei Jaw; Wenli Li; Zhihui Lu; Sy-Yen Kuo; Benjamin C. M. Fung; Bo-Hao Chen; Thanisa Numnonda. Image Dehazing in Disproportionate Haze Distributions. IEEE Access 2021, 9, 44599 -44609.

AMA Style

Shih-Chia Huang, Da-Wei Jaw, Wenli Li, Zhihui Lu, Sy-Yen Kuo, Benjamin C. M. Fung, Bo-Hao Chen, Thanisa Numnonda. Image Dehazing in Disproportionate Haze Distributions. IEEE Access. 2021; 9 ():44599-44609.

Chicago/Turabian Style

Shih-Chia Huang; Da-Wei Jaw; Wenli Li; Zhihui Lu; Sy-Yen Kuo; Benjamin C. M. Fung; Bo-Hao Chen; Thanisa Numnonda. 2021. "Image Dehazing in Disproportionate Haze Distributions." IEEE Access 9, no. : 44599-44609.

Journal article
Published: 01 February 2021 in IEEE Transactions on Engineering Management
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ACS Style

Rashid Hussain Khokhar; Farkhund Iqbal; Benjamin C. M. Fung; Jamal Bentahar. Enabling Secure Trustworthiness Assessment and Privacy Protection in Integrating Data for Trading Person-Specific Information. IEEE Transactions on Engineering Management 2021, 68, 149 -169.

AMA Style

Rashid Hussain Khokhar, Farkhund Iqbal, Benjamin C. M. Fung, Jamal Bentahar. Enabling Secure Trustworthiness Assessment and Privacy Protection in Integrating Data for Trading Person-Specific Information. IEEE Transactions on Engineering Management. 2021; 68 (1):149-169.

Chicago/Turabian Style

Rashid Hussain Khokhar; Farkhund Iqbal; Benjamin C. M. Fung; Jamal Bentahar. 2021. "Enabling Secure Trustworthiness Assessment and Privacy Protection in Integrating Data for Trading Person-Specific Information." IEEE Transactions on Engineering Management 68, no. 1: 149-169.

Journal article
Published: 24 October 2020 in Information Sciences
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Many models have been proposed to preserve data privacy for different data publishing scenarios. Among these models, ∊-differential privacy is receiving increasing attention because it does not make assumptions about adversaries’ prior knowledge and can provide a rigorous privacy guarantee. Although there are numerous proposed approaches using ∊-differential privacy to publish centralized data of a single-party, differentially private data publishing for distributed data among multiple parties has not been studied extensively. The challenge in releasing distributed data is how to protect privacy and integrity during collaborative data integration and anonymization. In this paper, we present the first differentially private solution to anonymize data from two parties with arbitrarily partitioned data in a semi-honest model. We aim at satisfying two privacy requirements: (1) the collaborative anonymization should satisfy differential privacy; (2) one party cannot learn extra information about the other party’s data except for the final result and the information that can be inferred from the result. To meet these privacy requirements, we propose a distributed differentially private anonymization algorithm and guarantee that each step of the algorithm satisfies the definition of secure two-party computation. In addition to the security and cost analyses, we demonstrate the utility of our algorithm in classification analysis.

ACS Style

Rong Wang; Benjamin C.M. Fung; Yan Zhu; Qiang Peng. Differentially private data publishing for arbitrarily partitioned data. Information Sciences 2020, 553, 247 -265.

AMA Style

Rong Wang, Benjamin C.M. Fung, Yan Zhu, Qiang Peng. Differentially private data publishing for arbitrarily partitioned data. Information Sciences. 2020; 553 ():247-265.

Chicago/Turabian Style

Rong Wang; Benjamin C.M. Fung; Yan Zhu; Qiang Peng. 2020. "Differentially private data publishing for arbitrarily partitioned data." Information Sciences 553, no. : 247-265.

Journal article
Published: 20 May 2020 in Knowledge-Based Systems
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Many models have been proposed to preserve data privacy for different data publishing scenarios. Among these models, ϵ-differential privacy has drawn increasing attention in recent years due to its rigorous privacy guarantees. While many existing solutions using ϵ-differential privacy deal with relational data and set-valued data separately, most of the real-life data, such as electronic health records, are in heterogeneous form. Privacy protection on heterogeneous data has not been widely studied. Furthermore, many existing works in privacy protection consider preserving the utility for the tasks of frequent itemset mining or classification analysis, but few works have focused on data publication for cluster analysis. In this paper, we propose the first differentially-private solution to release heterogeneous data for cluster analysis. The challenge facing us is how to mask raw data without any explicit guidance. Our approach addresses this challenge by converting a clustering problem to a classification problem, in which class labels can be used to encode the cluster structure of the raw data and assist the masking process. The approach generalizes the raw data probabilistically and adds noise to them for satisfying ϵ-differential privacy. Through extensive experiments on real-life datasets, we validate the performance of our approach.

ACS Style

Rong Wang; Benjamin C.M. Fung; Yan Zhu. Heterogeneous data release for cluster analysis with differential privacy. Knowledge-Based Systems 2020, 201-202, 106047 .

AMA Style

Rong Wang, Benjamin C.M. Fung, Yan Zhu. Heterogeneous data release for cluster analysis with differential privacy. Knowledge-Based Systems. 2020; 201-202 ():106047.

Chicago/Turabian Style

Rong Wang; Benjamin C.M. Fung; Yan Zhu. 2020. "Heterogeneous data release for cluster analysis with differential privacy." Knowledge-Based Systems 201-202, no. : 106047.

Journal article
Published: 07 May 2020 in Sustainability
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Sustainability has received widespread attention in both academia and industry, but there is still a paucity of research investigating the relationships between gender, consumer innovativeness, and clothing, as well as how they may influence sustainable practices. The overarching objective of this study is to investigate clothing expenditure, product cues (intrinsic, extrinsic and sustainable), gender (men and women) and consumer innovativeness (fashion innovators and non-innovators) in China, in order to find out how these factors may influence consumers’ choices. To address the research objective, 10 intrinsic cues, three extrinsic cues, and seven sustainable cues were used to investigate apparel consumers’ choices and preferences. A self-administered online survey consisted of eight items on sustainable commitment and behaviour, six items of fashion innovativeness adapted from the Domain-Specific Innovativeness scale, 20 items concerning product cues, and numerous demographic and behaviour-related questions. In total, 1819 usable data were collected in China, including 614 males and 1196 females. The results revealed that four out of eleven hypotheses were supported, another four were partially supported, while the remainders were not. For example, both female consumers and fashion innovators relied more on style and colour to evaluate an apparel product than fashion non-innovators and male consumers. However, men tended to rely more on the brand name and country of origin to guide their product selection and purchases than women. In terms of the influence of sustainable cues, Chinese consumers are more concerned about the social/ethical cues than environmental cues. Interestingly, women were more concerned about “no animal skin use” in evaluating apparel products than men. All in all, the results of this study can provide valuable information and meaningful insight for fashion designers, product developers, and marketers to develop effective communication strategies to guide potential customers in understanding a plethora of apparel values, including functionality, aesthetics, finances, altruism, and sustainability.

ACS Style

Osmud Rahman; Benjamin C.M. Fung; Zhimin Chen. Young Chinese Consumers’ Choice between Product-Related and Sustainable Cues—The Effects of Gender Differences and Consumer Innovativeness. Sustainability 2020, 12, 3818 .

AMA Style

Osmud Rahman, Benjamin C.M. Fung, Zhimin Chen. Young Chinese Consumers’ Choice between Product-Related and Sustainable Cues—The Effects of Gender Differences and Consumer Innovativeness. Sustainability. 2020; 12 (9):3818.

Chicago/Turabian Style

Osmud Rahman; Benjamin C.M. Fung; Zhimin Chen. 2020. "Young Chinese Consumers’ Choice between Product-Related and Sustainable Cues—The Effects of Gender Differences and Consumer Innovativeness." Sustainability 12, no. 9: 3818.

Journal article
Published: 27 December 2019 in Energy
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Many technical solutions have been developed to reduce buildings’ energy consumption, but limited efforts have been made to adequately address the role or action of building occupants in this process. Our earlier investigations have shown that occupants play a significant role in buildings’ energy consumption: It was shown that savings of up to 20% could be achieved by modifying occupant behavior thorough direct feedback and recommendations. Studying the role of occupants in building energy consumption requires an understanding of the interrelationships between climatic conditions; building characteristics; and building services and operation. This paper describes the development of a systematic procedure to provide building occupants with direct feedback and recommendations to help them take appropriate action to reduce building energy consumption. The procedure is geared toward developing a Reference Building (RB) (an energy-efficient building) for a specific given building. The RB is then compared against its given building to inform the occupants of the given building how they are using end-use loads and how they can improve them. The RB is generated using a data-mining approach, which involves clustering analysis and neural networks. The framework is based on clustering similar buildings by effects unrelated to occupant behavior. The buildings are then grouped based on their energy consumption, and those with lower consumption are combined to generate the RB. Performance evaluation is determined by comparison of a given building with an RB. This comparison provides feedback that can lead occupants to take appropriate measures (e.g., turning off unnecessary lights or heating, ventilation, and air conditioning (HVAC), etc.) to improve building energy performance. More accurate, scalable, and realistic results are achiveable through current methodology which is shown through comparison with existing literature.

ACS Style

Milad Ashouri; Benjamin C.M. Fung; Fariborz Haghighat; Hiroshi Yoshino. Systematic approach to provide building occupants with feedback to reduce energy consumption. Energy 2019, 194, 116813 .

AMA Style

Milad Ashouri, Benjamin C.M. Fung, Fariborz Haghighat, Hiroshi Yoshino. Systematic approach to provide building occupants with feedback to reduce energy consumption. Energy. 2019; 194 ():116813.

Chicago/Turabian Style

Milad Ashouri; Benjamin C.M. Fung; Fariborz Haghighat; Hiroshi Yoshino. 2019. "Systematic approach to provide building occupants with feedback to reduce energy consumption." Energy 194, no. : 116813.

Journal article
Published: 28 May 2019 in IEEE Access
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Mining high utility patterns in dynamic databases is an important data mining task. While a naive approach is to mine a newly updated database in its entirety, the state-of-the-art mining algorithms all take an incremental approach. However, the existing incremental algorithms either take a two-phase paradigm that generates a large number of candidates that causes scalability issues, or employ a vertical data structure that incurs a large number of join operations that leads to efficiency issues. To address the challenges with the existing incremental algorithms, this paper proposes a new algorithm Id2HUP+ (Incremental Direct Discovery of High Utility Patterns). Id2HUP+ adapts a one-phase paradigm by improving the relevance-based pruning and upper-bound-based pruning, proposes a novel data structure for quick update of dynamic databases, and proposes the absence-based pruning and legacy-based pruning dedicated to incremental mining. Extensive experiments show that our algorithm is up to 1 to 3 orders of magnitude more efficient than the state-of-the-art algorithms, and is the most scalable algorithm.

ACS Style

Junqiang Liu; Xinyi Ju; Xingxing Zhang; Benjamin C. M. Fung; Xiangcai Yang; Changhong Yu. Incremental Mining of High Utility Patterns in One Phase by Absence and Legacy-Based Pruning. IEEE Access 2019, 7, 74168 -74180.

AMA Style

Junqiang Liu, Xinyi Ju, Xingxing Zhang, Benjamin C. M. Fung, Xiangcai Yang, Changhong Yu. Incremental Mining of High Utility Patterns in One Phase by Absence and Legacy-Based Pruning. IEEE Access. 2019; 7 (99):74168-74180.

Chicago/Turabian Style

Junqiang Liu; Xinyi Ju; Xingxing Zhang; Benjamin C. M. Fung; Xiangcai Yang; Changhong Yu. 2019. "Incremental Mining of High Utility Patterns in One Phase by Absence and Legacy-Based Pruning." IEEE Access 7, no. 99: 74168-74180.

Journal article
Published: 28 February 2019 in Information Processing & Management
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Users of social media websites tend to rapidly spread breaking news and trending stories without considering their truthfulness. This facilitates the spread of rumors through social networks. A rumor is a story or statement for which truthfulness has not been verified. Efficiently detecting and acting upon rumors throughout social networks is of high importance to minimizing their harmful effect. However, detecting them is not a trivial task. They belong to unseen topics or events that are not covered in the training dataset. In this paper, we study the problem of detecting breaking news rumors, instead of long-lasting rumors, that spread in social media. We propose a new approach that jointly learns word embeddings and trains a recurrent neural network with two different objectives to automatically identify rumors. The proposed strategy is simple but effective to mitigate the topic shift issues. Emerging rumors do not have to be false at the time of the detection. They can be deemed later to be true or false. However, most previous studies on rumor detection focus on long-standing rumors and assume that rumors are always false. In contrast, our experiment simulates a cross-topic emerging rumor detection scenario with a real-life rumor dataset. Experimental results suggest that our proposed model outperforms state-of-the-art methods in terms of precision, recall, and F1.

ACS Style

Sarah A. Alkhodair; Steven H.H. Ding; Benjamin C.M. Fung; Junqiang Liu. Detecting breaking news rumors of emerging topics in social media. Information Processing & Management 2019, 57, 102018 .

AMA Style

Sarah A. Alkhodair, Steven H.H. Ding, Benjamin C.M. Fung, Junqiang Liu. Detecting breaking news rumors of emerging topics in social media. Information Processing & Management. 2019; 57 (2):102018.

Chicago/Turabian Style

Sarah A. Alkhodair; Steven H.H. Ding; Benjamin C.M. Fung; Junqiang Liu. 2019. "Detecting breaking news rumors of emerging topics in social media." Information Processing & Management 57, no. 2: 102018.

Journal article
Published: 23 January 2019 in IEEE Access
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Given the limits of human memory, clinicians have trouble recalling therapeutic recommendations, even when the clinician previously judged that information relevant for the care of a specific patient. To tackle this problem, we present a knowledge-based recommender system prototype that links electronic patient records to clinical information previously delivered to the target physician and judged to be potentially beneficial. We developed this prototype within the context of RxTx, a Canadian continuing medical education program. We apply a constraint-based recommendation strategy as follows: (1) clinical experts (taggers) map a set of therapeutic recommendations (called Highlights) to a requirement statement built from standard clinical codes and supplementary demographic information, when applicable; (2) a matching system identifies patient-Highlights recommendation pairs through requirement satisfaction; (3) given a patient record being examined, the recommended Highlights can be retrieved online at the point of care. We tested this prototype using electronic medical records from the Canadian Primary Care Sentinel Surveillance Network and 87 therapeutic Highlights from the RxTx collection, evaluating the system’s performance against a gold standard consisting of a two-expert consolidated patient-Highlight matching set for 150 patient records. The requirement-based recommendation system exhibits very high precision (mode: 1.0, 89% of the time; average precision: 0.95) and moderate recall (mode 1.0, 48.7% of the time; average recall: 0.61). The near-perfect precision minimizes the possibility of generating alert fatigue in physicians using the system. We note that more than half of the false negatives result from the information being available in the text of the electronic medical records (EMRs) but unavailable as a clinical code. The near-perfect precision over the tested patient set suggests that the system has the potential to deliver high-quality recommendations of clinical information at the point of care while being easily integrated within a continuing medical education program and the clinician’s workflow.

ACS Style

Manuel Gil; Reem El Sherif; Manon Pluye; Benjamin C. M. Fung; Roland Grad; Pierre Pluye. Towards a Knowledge-Based Recommender System for Linking Electronic Patient Records With Continuing Medical Education Information at the Point of Care. IEEE Access 2019, 7, 15955 -15966.

AMA Style

Manuel Gil, Reem El Sherif, Manon Pluye, Benjamin C. M. Fung, Roland Grad, Pierre Pluye. Towards a Knowledge-Based Recommender System for Linking Electronic Patient Records With Continuing Medical Education Information at the Point of Care. IEEE Access. 2019; 7 (99):15955-15966.

Chicago/Turabian Style

Manuel Gil; Reem El Sherif; Manon Pluye; Benjamin C. M. Fung; Roland Grad; Pierre Pluye. 2019. "Towards a Knowledge-Based Recommender System for Linking Electronic Patient Records With Continuing Medical Education Information at the Point of Care." IEEE Access 7, no. 99: 15955-15966.

Journal article
Published: 21 January 2019 in IEEE Access
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Due to the rapid development of Internet technologies and social media, sentiment analysis has become an important opinion mining technique. Recent research work has described the effectiveness of different sentiment classification techniques ranging from simple rule-based and lexicon-based approaches to more complex machine learning algorithms. While lexicon-based approaches have suffered from the lack of dictionaries and labeled data, machine learning approaches have fallen short in terms of accuracy. This paper proposes an integrated framework which bridges the gap between lexicon-based and machine learning approaches to achieve better accuracy and scalability. To solve the scalability issue that arises as the featureset grows, a novel Genetic Algorithm (GA) based feature reduction technique is proposed. By using this hybrid approach, we are able to reduce the feature-set size by up to 42% without compromising the accuracy. The comparison of our feature reduction technique with more widely used Principal Component Analysis (PCA) and Latent Semantic Analysis (LSA) based feature reduction techniques have shown up to 15.4% increased accuracy over PCA and up to 40.2% increased accuracy over LSA. Furthermore, we also evaluate our sentiment analysis framework on other metrics including precision, recall, F-measure, and feature size. In order to demonstrate the efficacy of GA based designs, we also propose a novel cross-disciplinary area of geopolitics as a case study application for our sentiment analysis framework. The experiment results have shown to accurately measure public sentiments and views regarding various topics such as terrorism, global conflicts, social issues etc. We envisage the applicability of our proposed work in various areas including security and surveillance, law-and-order, and public administration

ACS Style

Farkhund Iqbal; Jahanzeb Maqbool Hashmi; Benjamin C. M. Fung; Rabia Batool; Asad Masood Khattak; Saiqa Aleem; Patrick C. K. Hung. A Hybrid Framework for Sentiment Analysis Using Genetic Algorithm Based Feature Reduction. IEEE Access 2019, 7, 14637 -14652.

AMA Style

Farkhund Iqbal, Jahanzeb Maqbool Hashmi, Benjamin C. M. Fung, Rabia Batool, Asad Masood Khattak, Saiqa Aleem, Patrick C. K. Hung. A Hybrid Framework for Sentiment Analysis Using Genetic Algorithm Based Feature Reduction. IEEE Access. 2019; 7 (99):14637-14652.

Chicago/Turabian Style

Farkhund Iqbal; Jahanzeb Maqbool Hashmi; Benjamin C. M. Fung; Rabia Batool; Asad Masood Khattak; Saiqa Aleem; Patrick C. K. Hung. 2019. "A Hybrid Framework for Sentiment Analysis Using Genetic Algorithm Based Feature Reduction." IEEE Access 7, no. 99: 14637-14652.

Journal article
Published: 09 January 2019 in IEEE Access
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ACS Style

Farkhund Iqbal; Benjamin C. M. Fung; Mourad Debbabi; Rabia Batool; Andrew Marrington. Wordnet-Based Criminal Networks Mining for Cybercrime Investigation. IEEE Access 2019, 7, 22740 -22755.

AMA Style

Farkhund Iqbal, Benjamin C. M. Fung, Mourad Debbabi, Rabia Batool, Andrew Marrington. Wordnet-Based Criminal Networks Mining for Cybercrime Investigation. IEEE Access. 2019; 7 ():22740-22755.

Chicago/Turabian Style

Farkhund Iqbal; Benjamin C. M. Fung; Mourad Debbabi; Rabia Batool; Andrew Marrington. 2019. "Wordnet-Based Criminal Networks Mining for Cybercrime Investigation." IEEE Access 7, no. : 22740-22755.

Journal article
Published: 08 January 2019 in ACM Transactions on Asian and Low-Resource Language Information Processing
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Law enforcement faces problems in tracing the true identity of offenders in cybercrime investigations. Most offenders mask their true identity, impersonate people of high authority, or use identity deception and obfuscation tactics to avoid detection and traceability. To address the problem of anonymity, authorship analysis is used to identify individuals by their writing styles without knowing their actual identities. Most authorship studies are dedicated to English due to its widespread use over the Internet, but recent cyber-attacks such as the distribution of Stuxnet indicate that Internet crimes are not limited to a certain community, language, culture, ideology, or ethnicity. To effectively investigate cybercrime and to address the problem of anonymity in online communication, there is a pressing need to study authorship analysis of languages such as Arabic, Chinese, Turkish, and so on. Arabic, the focus of this study, is the fourth most widely used language on the Internet. This study investigates authorship of Arabic discourse/text, especially tiny text, Twitter posts. We benchmark the performance of a profile-based approach that uses n -grams as features and compare it with state-of-the-art instance-based classification techniques. Then we adapt an event-visualization tool that is developed for English to accommodate both Arabic and English languages and visualize the result of the attribution evidence. In addition, we investigate the relative effect of the training set, the length of tweets, and the number of authors on authorship classification accuracy. Finally, we show that diacritics have an insignificant effect on the attribution process and part-of-speech tags are less effective than character-level and word-level n -grams.

ACS Style

Malik H. Altakrori; Farkhund Iqbal; Benjamin C. M. Fung; Steven H. H. Ding; Abdallah Tubaishat. Arabic Authorship Attribution. ACM Transactions on Asian and Low-Resource Language Information Processing 2019, 18, 1 -51.

AMA Style

Malik H. Altakrori, Farkhund Iqbal, Benjamin C. M. Fung, Steven H. H. Ding, Abdallah Tubaishat. Arabic Authorship Attribution. ACM Transactions on Asian and Low-Resource Language Information Processing. 2019; 18 (1):1-51.

Chicago/Turabian Style

Malik H. Altakrori; Farkhund Iqbal; Benjamin C. M. Fung; Steven H. H. Ding; Abdallah Tubaishat. 2019. "Arabic Authorship Attribution." ACM Transactions on Asian and Low-Resource Language Information Processing 18, no. 1: 1-51.

Journal article
Published: 01 December 2018 in Energy and Buildings
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Identifying the impacts of occupants on building energy consumption has become an important issue in recent years. This is due to the interrelationship of influencing factors such as urban climate, building characteristics, occupant behavior, and building services and operation, which makes it challenging to identify the role of occupants in energy consumption. The research problem in this study lies in the fact that the occupants of a building may not be cautious regarding energy savings, and there exists no ground to assess their energy consumption behavior. One solution is the development of a systematic comparison procedure between similar buildings. This paper introduces a new procedure for comparison between occupants of several buildings to show the rank of each building among others and suggest occupants on reducing their energy consumption and improving their rank. The proposed framework is developed based on multiple data-mining methods, including clustering, association rules mining, and neural networks. The proposed methodology is composed of two levels. The first considers the amount of energy usage by occupants after filtering effects unrelated to the occupant behavior. The second ranks the buildings in terms of achieved and potential savings during the time under investigation. To demonstrate the application, the methodology was applied on a set of monitored residential buildings in Japan. Results suggest that the proposed method enhances the evaluation of buildings’ energy-saving potential by revealing the occupants’ contribution. It also provides diverse and prioritized strategies to help occupants manage their energy consumption by revealing the building energy end-use patterns.

ACS Style

Milad Ashouri; Fariborz Haghighat; Benjamin C.M. Fung; Hiroshi Yoshino. Development of a ranking procedure for energy performance evaluation of buildings based on occupant behavior. Energy and Buildings 2018, 183, 659 -671.

AMA Style

Milad Ashouri, Fariborz Haghighat, Benjamin C.M. Fung, Hiroshi Yoshino. Development of a ranking procedure for energy performance evaluation of buildings based on occupant behavior. Energy and Buildings. 2018; 183 ():659-671.

Chicago/Turabian Style

Milad Ashouri; Fariborz Haghighat; Benjamin C.M. Fung; Hiroshi Yoshino. 2018. "Development of a ranking procedure for energy performance evaluation of buildings based on occupant behavior." Energy and Buildings 183, no. : 659-671.

Journal article
Published: 01 August 2018 in Energy and Buildings
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ACS Style

Milad Ashouri; Fariborz Haghighat; Benjamin C.M. Fung; Amine Lazrak; Hiroshi Yoshino. Development of building energy saving advisory: A data mining approach. Energy and Buildings 2018, 172, 139 -151.

AMA Style

Milad Ashouri, Fariborz Haghighat, Benjamin C.M. Fung, Amine Lazrak, Hiroshi Yoshino. Development of building energy saving advisory: A data mining approach. Energy and Buildings. 2018; 172 ():139-151.

Chicago/Turabian Style

Milad Ashouri; Fariborz Haghighat; Benjamin C.M. Fung; Amine Lazrak; Hiroshi Yoshino. 2018. "Development of building energy saving advisory: A data mining approach." Energy and Buildings 172, no. : 139-151.

Journal article
Published: 07 July 2018 in Computer Networks
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In recent years, the collection of spatio-temporal data that captures human movements has increased tremendously due to the advancements in hardware and software systems capable of collecting person-specific data. The bulk of the data collected by these systems has numerous applications, or it can simply be used for general data analysis. Therefore, publishing such big data is greatly beneficial for data recipients. However, in its raw form, the collected data contains sensitive information pertaining to the individuals from which it was collected and must be anonymized before publication. In this paper, we study the problem of privacy-preserving passenger trajectories publishing and propose a solution under the rigorous differential privacy model. Unlike sequential data, which describes sequentiality between data items, handling spatio-temporal data is a challenging task due to the fact that introducing a temporal dimension results in extreme sparseness. Our proposed solution introduces an efficient algorithm, called SafePath, that models trajectories as a noisy prefix tree and publishes ϵ-differentially-private trajectories while minimizing the impact on data utility. Experimental evaluation on real-life transit data in Montreal suggests that SafePath significantly improves efficiency and scalability with respect to large and sparse datasets, while achieving comparable results to existing solutions in terms of the utility of the sanitized data.

ACS Style

Khalil Al-Hussaeni; Benjamin C.M. Fung; Farkhund Iqbal; Gaby G. Dagher; Eun G. Park. SafePath: Differentially-private publishing of passenger trajectories in transportation systems. Computer Networks 2018, 143, 126 -139.

AMA Style

Khalil Al-Hussaeni, Benjamin C.M. Fung, Farkhund Iqbal, Gaby G. Dagher, Eun G. Park. SafePath: Differentially-private publishing of passenger trajectories in transportation systems. Computer Networks. 2018; 143 ():126-139.

Chicago/Turabian Style

Khalil Al-Hussaeni; Benjamin C.M. Fung; Farkhund Iqbal; Gaby G. Dagher; Eun G. Park. 2018. "SafePath: Differentially-private publishing of passenger trajectories in transportation systems." Computer Networks 143, no. : 126-139.

Conference paper
Published: 01 July 2018 in Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
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Non-negative tensor factorization has been shown effective for discovering phenotypes from the EHR data with minimal human supervision. In most cases, an interaction tensor of the elements in the EHR (e.g., diagnoses and medications) has to be first established before the factorization can be applied. Such correspondence information however is often missing. While different heuristics can be used to estimate the missing correspondence, any errors introduced will in turn cause inaccuracy for the subsequent phenotype discovery task. This is especially true for patients with multiple diseases diagnosed (e.g., under critical care). To alleviate this limitation, we propose the hidden interaction tensor factorization (HITF) where the diagnosis-medication correspondence and the underlying phenotypes are inferred simultaneously. We formulate it under a Poisson non-negative tensor factorization framework and learn the HITF model via maximum likelihood estimation. For performance evaluation, we applied HITF to the MIMIC III dataset. Our empirical results show that both the phenotypes and the correspondence inferred are clinically meaningful. In addition, the inferred HITF model outperforms a number of state-of-the-art methods for mortality prediction.

ACS Style

Kejing Yin; William K. Cheung; Yang Liu; Benjamin C. M. Fung; Jonathan Poon. Joint Learning of Phenotypes and Diagnosis-Medication Correspondence via Hidden Interaction Tensor Factorization. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence 2018, 3627 -3633.

AMA Style

Kejing Yin, William K. Cheung, Yang Liu, Benjamin C. M. Fung, Jonathan Poon. Joint Learning of Phenotypes and Diagnosis-Medication Correspondence via Hidden Interaction Tensor Factorization. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. 2018; ():3627-3633.

Chicago/Turabian Style

Kejing Yin; William K. Cheung; Yang Liu; Benjamin C. M. Fung; Jonathan Poon. 2018. "Joint Learning of Phenotypes and Diagnosis-Medication Correspondence via Hidden Interaction Tensor Factorization." Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence , no. : 3627-3633.

Journal article
Published: 01 May 2018 in Information Sciences
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Mining high utility patterns is an important data mining problem that is formulated as finding patterns whose utilities are no less than a threshold. As the mining results are very sensitive to such a threshold, it is difficult for users to specify an appropriate one. An alternative formulation of the problem is to find the top-n high utility patterns. However, the second formulation is more challenging because the corresponding threshold is unknown in advance and the solution search space becomes even larger. When there are very long patterns prior algorithms simply cannot work to mine top-n high utility patterns even for very small n. This paper proposes a novel algorithm for mining top-n high utility patterns that are long. The proposed algorithm adopts an opportunistic pattern growth approach and proposes five opportunistic strategies for scalably maintaining shortlisted patterns, for efficiently computing utilities, and for estimating tight upper bounds to prune search space. Extensive experiments show that the proposed algorithm is 1 to 3 orders of magnitude more efficient than the state-of-the-art top-n high utility pattern mining algorithms, and it is even up to 2 orders of magnitude faster than high utility pattern mining algorithms that are tuned with an optimal threshold.

ACS Style

Junqiang Liu; Xingxing Zhang; Benjamin C.M. Fung; Jiuyong Li; Farkhund Iqbal. Opportunistic mining of top-n high utility patterns. Information Sciences 2018, 441, 171 -186.

AMA Style

Junqiang Liu, Xingxing Zhang, Benjamin C.M. Fung, Jiuyong Li, Farkhund Iqbal. Opportunistic mining of top-n high utility patterns. Information Sciences. 2018; 441 ():171-186.

Chicago/Turabian Style

Junqiang Liu; Xingxing Zhang; Benjamin C.M. Fung; Jiuyong Li; Farkhund Iqbal. 2018. "Opportunistic mining of top-n high utility patterns." Information Sciences 441, no. : 171-186.