| 1. |
- Tran, Son N., et al.
(författare)
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On multi-resident activity recognition in ambient smart-homes
- 2020
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Ingår i: Artificial Intelligence Review. - : Springer. - 0269-2821 .- 1573-7462. ; 53:6, s. 3929-3945
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Tidskriftsartikel (refereegranskat)abstract
- Increasing attention to the research on activity monitoring in smart homes has motivated the employment of ambient intelligence to reduce the deployment cost and solve the privacy issue. Several approaches have been proposed for multi-resident activity recognition, however, there still lacks a comprehensive benchmark for future research and practical selection of models. In this paper, we study different methods for multi-resident activity recognition and evaluate them on the same sets of data. In particular, we explore the effectiveness and efficiency of temporal learning algorithms using sequential data and non-temporal learning algorithms using temporally-manipulated features. In the experiments we compare and analyse the results of the studied methods using datasets from three smart homes.
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| 2. |
- Vu, Xuan-Son, 1988-, et al.
(författare)
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Multimodal Review Generation with Privacy and Fairness Awareness
- 2020
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Ingår i: Proceedings of the 28th International Conference on Computational Linguistics (COLING), 2020. - Stroudsburg, PA, USA : International Committee on Computational LinguisticsInternational Committee on Computational Linguistics. ; , s. 414-425
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Konferensbidrag (refereegranskat)abstract
- Users express their opinions towards entities (e.g., restaurants) via online reviews which can be in diverse forms such as text, ratings, and images. Modeling reviews are advantageous for user behavior understanding which, in turn, supports various user-oriented tasks such as recommendation, sentiment analysis, and review generation. In this paper, we propose MG-PriFair, a multimodal neural-based framework, which generates personalized reviews with privacy and fairness awareness. Motivated by the fact that reviews might contain personal information and sentiment bias, we propose a novel differentially private (dp)-embedding model for training privacy guaranteed embeddings and an evaluation approach for sentiment fairness in the food-review domain. Experiments on our novel review dataset show that MG-PriFair is capable of generating plausibly long reviews while controlling the amount of exploited user data and using the least sentiment biased word embeddings. To the best of our knowledge, we are the first to bring user privacy and sentiment fairness into the review generation task. The dataset and source codes are available at https://github.com/ReML-AI/MG-PriFair.
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| 3. |
- Ait-Mlouk, Addi, et al.
(författare)
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WINFRA : A Web-Based Platform for Semantic Data Retrieval and Data Analytics
- 2020
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Ingår i: Mathematics. - : MDPI. - 2227-7390. ; 8:11
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Tidskriftsartikel (refereegranskat)abstract
- Given the huge amount of heterogeneous data stored in different locations, it needs to be federated and semantically interconnected for further use. This paper introduces WINFRA, a comprehensive open-access platform for semantic web data and advanced analytics based on natural language processing (NLP) and data mining techniques (e.g., association rules, clustering, classification based on associations). The system is designed to facilitate federated data analysis, knowledge discovery, information retrieval, and new techniques to deal with semantic web and knowledge graph representation. The processing step integrates data from multiple sources virtually by creating virtual databases. Afterwards, the developed RDF Generator is built to generate RDF files for different data sources, together with SPARQL queries, to support semantic data search and knowledge graph representation. Furthermore, some application cases are provided to demonstrate how it facilitates advanced data analytics over semantic data and showcase our proposed approach toward semantic association rules.
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| 4. |
- Vu, Xuan-Son, 1988-
(författare)
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Privacy-guardian : the vital need in machine learning with big data
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Social Network Sites (SNS) such as Facebook and Twitter, play a great role in our lives. On one hand, they help to connect people who would not otherwise be connected. Many recent breakthroughs in AI such as facial recognition [Kow+18], were achieved thanks to the amount of available data on the Internet via SNS (hereafter Big Data). On the other hand, many people have tried to avoid SNS to protect their privacy [Sti+13]. However, Machine Learning (ML), as the core of AI, was not designed with privacy in mind. For instance, one of the most popular supervised machine learning algorithms, Support Vector Machines (SVMs), try to solve a quadratic optimization problem in which the data of people involved in the training process is also published within the SVM models. Similarly, many other ML applications (e.g., ClearView) compromise the privacy of individuals presented in the data, especially when the big data era enhances the data federation. Thus, in the context of machine learning with big data, it is important to (1) protect sensitive information (privacy protection) while (2) preserving the quality of the output of algorithms (i.e., data utility). For the vital need of privacy in machine learning with big data, this thesis studies on: (1) how to construct information infrastructures for data federation with privacy guarantee in the big data era; (2) how to protect privacy while learning ML models with a good trade-off between data utility and privacy. To the first point, we proposed different frameworks empowered by privacy-aware algorithms. Regarding the second point, we proposed different neural architectures to capture the sensitivities of user data, from which, the algorithms themselves decide how much they should learn from user data to protect their privacy while achieving good performances for downstream tasks. The current outcomes of the thesis are: (a) privacy-guarantee data federation infrastructure for data analysis on sensitive data; (b) privacy utilities for privacy-concern analysis; and (c) privacy-aware algorithms for learning on personal data. For each outcome, extensive experimental studies were conducted on real-life social network datasets to evaluate aspects of the proposed approaches. Insights and outcomes from this thesis can be used by both academia and industry to provide privacy-guarantee data analysis and data learning in big data containing personal information. They also have the potential to facilitate relevant research in privacy-aware learning and its related evaluation methods.
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