| 1. |
- Vu, Xuan-Son, 1988-, et al.
(author)
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dpUGC : learn differentially private representation for user generated contents
- 2023
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In: Computational linguistics and intelligent text processing. - Cham : Springer. - 9783031243363 - 9783031243370 ; , s. 316-331
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Conference paper (peer-reviewed)abstract
- This paper firstly proposes a simple yet efficient generalized approach to apply differential privacy to text representation (i.e., word embedding). Based on it, we propose a user-level approach to learn personalized differentially private word embedding model on user generated contents (UGC). To our best knowledge, this is the first work of learning user-level differentially private word embedding model from text for sharing. The proposed approaches protect the privacy of the individual from re-identification, especially provide better trade-off of privacy and data utility on UGC data for sharing. The experimental results show that the trained embedding models are applicable for the classic text analysis tasks (e.g., regression). Moreover, the proposed approaches of learning differentially private embedding models are both framework- and dataindependent, which facilitates the deployment and sharing. The source code is available at https://github.com/sonvx/dpText.
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| 2. |
- Hatefi, Arezoo, 1990-, et al.
(author)
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ADCluster: Adaptive Deep Clustering for unsupervised learning from unlabeled documents
- 2023
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In: Proceedings of the 6th International Conference on Natural Language and Speech Processing (ICNLSP 2023). - : Association for Computational Linguistics. ; , s. 68-77
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Conference paper (peer-reviewed)abstract
- We introduce ADCluster, a deep document clustering approach based on language models that is trained to adapt to the clustering task. This adaptability is achieved through an iterative process where K-Means clustering is applied to the dataset, followed by iteratively training a deep classifier with generated pseudo-labels – an approach referred to as inner adaptation. The model is also able to adapt to changes in the data as new documents are added to the document collection. The latter type of adaptation, outer adaptation, is obtained by resuming the inner adaptation when a new chunk of documents has arrived. We explore two outer adaptation strategies, namely accumulative adaptation (training is resumed on the accumulated set of all documents) and non-accumulative adaptation (training is resumed using only the new chunk of data). We show that ADCluster outperforms established document clustering techniques on medium and long-text documents by a large margin. Additionally, our approach outperforms well-established baseline methods under both the accumulative and non-accumulative outer adaptation scenarios.
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| 3. |
- Kristan, Matej, et al.
(author)
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The first visual object tracking segmentation VOTS2023 challenge results
- 2023
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In: 2023 IEEE/CVF International conference on computer vision workshops (ICCVW). - : Institute of Electrical and Electronics Engineers Inc.. - 9798350307443 - 9798350307450 ; , s. 1788-1810
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Conference paper (peer-reviewed)abstract
- The Visual Object Tracking Segmentation VOTS2023 challenge is the eleventh annual tracker benchmarking activity of the VOT initiative. This challenge is the first to merge short-term and long-term as well as single-target and multiple-target tracking with segmentation masks as the only target location specification. A new dataset was created; the ground truth has been withheld to prevent overfitting. New performance measures and evaluation protocols have been created along with a new toolkit and an evaluation server. Results of the presented 47 trackers indicate that modern tracking frameworks are well-suited to deal with convergence of short-term and long-term tracking and that multiple and single target tracking can be considered a single problem. A leaderboard, with participating trackers details, the source code, the datasets, and the evaluation kit are publicly available at the challenge website1
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| 4. |
- Nguyen, Minh-Thuan, et al.
(author)
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ViGPTQA - state-of-the-art LLMs for Vietnamese question answering : system overview, core models training, and evaluations
- 2023
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In: Proceedings of the 2023 conference on empirical methods in natural language processing. - : Association for Computational Linguistics (ACL). ; , s. 754-764
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Conference paper (peer-reviewed)abstract
- Large language models (LLMs) and their applications in low-resource languages (such as in Vietnamese) are limited due to lack of training data and benchmarking datasets. This paper introduces a practical real-world implementation of a question answering system for Vietnamese, called ViGPTQA, leveraging the power of LLM. Since there is no effective LLM in Vietnamese to date, we also propose, evaluate, and open-source an instruction-tuned LLM for Vietnamese, named ViGPT. ViGPT demonstrates exceptional performances, especially on real-world scenarios. We curate a new set of benchmark datasets that encompass both AI- and human-generated data, providing a comprehensive evaluation framework for Vietnamese LLMs. By achieving state-of-the-art results and approaching other multilingual LLMs, our instruction-tuned LLM underscores the need for dedicated Vietnamese-specific LLMs. Our open-source model supports customized and privacy-fulfilled Vietnamese language processing systems.
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| 5. |
- Nguyen, Tuan Minh, et al.
(author)
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Privacy and trust in IoT ecosystems with big data : a survey of perspectives and challenges
- 2023
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In: Proceedings - IEEE 9th International Conference on Big Data Computing Service and Applications, BigDataService 2023. - : IEEE. - 9798350333794 - 9798350335347 ; , s. 215-222
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Conference paper (peer-reviewed)abstract
- The Internet of Things (IoT) has become a vital part of our daily lives, enabling interconnectedness between various devices and systems. As the amount of data generated by IoT devices and systems continues to increase immensely, privacy and security concerns have emerged as a significant challenge for researchers and enterprises. Although we are aware of how much data IoT devices will generate per day, there is a lack of knowledge of how the collected data will be used. The privacy risks associated with data collection raise individual concerns in the IoT ecosystem. For instance, when sensitive personal information is exposed due to weak security practices, it can result in identity theft, financial fraud, or other types of cybercrime. The misuse of IoT devices also puts someone susceptible to physical risks, such as a compromised medical device leading to health complications. In this paper, we introduce the definition of the next-gen IoT Ecosystem and its relations to Big Data as well as investigate privacy and security risks associated with IoT ecosystems, identify the gaps in current privacy and security practices, and present technical solutions to tackle these problems. We aim to identify challenges and raise awareness about developing secure and privacy-preserving IoT systems in the era of Big Data.
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| 6. |
- Nguyen, Thuy Trinh, et al.
(author)
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Multimodal machine learning for mental disorder detection : a scoping review
- 2023
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In: 27th international conference on knowledge based and intelligent information and engineering sytems (KES 2023). - : Elsevier. ; , s. 1458-1467
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Conference paper (peer-reviewed)abstract
- Recent advancements in machine learning and multimedia technologies have paved new ways for automatic medical diagnosis. In mental health, multimodal inputs such as visual and audible sensing data are promising to investigate the underlying mechanisms of many conditions, such as depression and bipolar disorders. With the increasing burden on healthcare systems, timely diagnosis of mental diseases using multiple modalities might benefit millions of people worldwide. This scoping review provides an exploratory overview of recent multimodal machine learning approaches for mental disorder screening. We also discuss a generalised end-to-end multimodal machine learning pipeline for future research and development of multimodal disease detection.
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| 7. |
- Tran, Khanh-Tung, et al.
(author)
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Personalization for robust voice pathology detection in sound waves
- 2023
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In: Proceedings of the annual conference of the international speech communication association, INTERSPEECH. - : International Speech Communication Association. ; , s. 1708-1712
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Conference paper (peer-reviewed)abstract
- Automatic voice pathology detection is promising for noninvasive screening and early intervention using sound signals. Nevertheless, existing methods are susceptible to covariate shifts due to background noises, human voice variations, and data selection biases leading to severe performance degradation in real-world scenarios. Hence, we propose a non-invasive framework that contrastively learns personalization from sound waves as a pre-train and predicts latent-spaced profile features through semi-supervised learning. It allows all subjects from various distributions (e.g., regionality, gender, age) to benefit from personalized predictions for robust voice pathology in a privacy-fulfilled manner. We extensively evaluate the framework on four real-world respiratory illnesses datasets, including Coswara, COUGHVID, ICBHI, and our private dataset - ASound under multiple covariate shift settings (i.e., cross-dataset), improving up to 4.12% in overall performance.
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| 8. |
- Volodina, Elena, 1973, et al.
(author)
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Grandma Karl is 27 years old – research agenda for pseudonymization of research data
- 2023
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In: 2023 IEEE Ninth International Conference on Big Data Computing Service and Applications (BigDataService), Athens, Greece, 2023. - Los Alamitos : IEEE Computer Society. - 9798350333794 - 9798350335347
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Conference paper (peer-reviewed)abstract
- Accessibility of research data is critical for advances in many research fields, but textual data often cannot be shared due to the personal and sensitive information which it con- tains, e.g names or political opinions. General Data Protection Regulation (GDPR) suggests pseudonymization as a solution to secure open access to research data, but we need to learn more about pseudonymization as an approach before adopting it for manipulation of research data. This paper outlines a research agenda within pseudonymization, namely need of studies into the effects of pseudonymization on unstructured data in relation to e.g. readability and language assessment, as well as the effectiveness of pseudonymization as a way of protecting writer identity, while also exploring different ways of developing context-sensitive algorithms for detection, labelling and replacement of personal information in unstructured data. The recently granted project on pseudonymization ‘Grandma Karl is 27 years old’1 addresses exactly those challenges.
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| 9. |
- Vu, Xuan-Son, 1988-, et al.
(author)
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MetaVSID : a robust meta-reinforced learning approach for VSI-DDoS detection on the edge
- 2023
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In: IEEE Transactions on Network and Service Management. - : IEEE. - 1932-4537 .- 1932-4537. ; 20:2, s. 1625-1643
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Journal article (peer-reviewed)abstract
- The explosive growth of end devices that generate massive amounts of data requires close-proximity computing resources for processing at the network’s edge. Having geographic distributions and limited resources of edge nodes or servers opens several doors for attackers to exploit them primarily to the detriment of deployed services; one of the recent attacks is Very Short Intermittent Distributed Denial of Services (VSI-DDoS). Deep learning-based models have been developed to detect and mitigate such attacks but cause the degrading quality of models due to covariate shifts when deployed in real-world environments. Therefore, we propose a new approach, called MetaVSID, to detect VSI-DDoS attacks in edge clouds using meta-reinforcement learning followed by ensemble learning to increase the robustness of the model in detecting VSI-DDoS attacks early. The proposed model can capture dynamic patterns of VSI-DDoS attacks, from which it identifies manipulated services and increase service availability when covariate shifts at deployment time. We carry out extensive experiments to validate the MetaVSID using both testbed and benchmark datasets. Via the meta-reinforced downsampling process, the proposed method improves sample efficiency, leading to cost-effective policies. Moreover, the optimized policies are generalized to adapt to dynamic changes in the training distribution. Our experimental results demonstrate that MetaVSID stably achieves better performance in multiple evaluation settings with the difference from baseline models from 1.5% to 7.5% in terms of AUC for both VSI-DDoS and DDoS detection, especially under covariate shift settings.
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