Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)  

The COVID-19 pandemic has highlighted the need to better understand the dynamics of disease spread in cities in order to develop efficient and effective epidemiological strategies. In this study, we utilise fine-grained spatiotemporal population data obtained from mobile devices to identify areas and time of day that may contribute to COVID-19 spread, and investigate how they change throughout different waves of the pandemic. To evaluate the potential risk to city residents, we analyse the correlation between the effective reproduction number and population dynamics at locations regularly visited by these residents. Our case study of Tokyo identifies highly-correlated areas at a fine-grained level, revealing shifts in these areas within cities and across urban and suburban regions as the pandemic progresses. We also explore the characteristics of the potential areas of concern through the lenses of points of interest and population dynamics. Our findings have implications for comprehensively understanding the spatiotemporal dynamics of COVID-19 and offer insights into public health interventions for managing pandemics.

DOI： 10.1038/s41598-025-06658-7

Open Access

Web of Science

Scopus

PubMed

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Language：Japanese   Publisher：一般社団法人 人工知能学会  

DOI： 10.11517/jjsai.40.3_404

Open Access

CiNii Research

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1007/978-981-96-2074-6_24

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Other Link： https://dblp.uni-trier.de/db/conf/mmm/mmm2025-5.html#GanDZ25

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：ACM  

DOI： 10.1145/3733566.3734432

Open Access

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Other Link： https://dblp.uni-trier.de/db/conf/icdar2/icdar2025.html#NguyenZ25

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1145/3716553.3757087

Open Access

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Other Link： https://dblp.uni-trier.de/db/conf/icmi/icmi2025.html#GanDZ25

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/IJCNN64981.2025.11229149

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Other Link： https://dblp.uni-trier.de/db/conf/ijcnn/ijcnn2025.html#KhoaDZ25

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：ACM  

DOI： 10.1145/3733566.3734434

Open Access

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Other Link： https://dblp.uni-trier.de/db/conf/icdar2/icdar2025.html#ThanhZ25

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1145/3747327.3763041

Open Access

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Other Link： https://dblp.uni-trier.de/db/conf/icmi/icmi2025c.html#DaoNHZ25

Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1109/ACCESS.2025.3581769

Open Access

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Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1145/3737899.3768526

Open Access

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Other Link： https://dblp.org/db/conf/fededge/fededge2025.html#ThanhKDZ25

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/BigData66926.2025.11401673

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Other Link： https://dblp.org/db/conf/bigdataconf/bigdataconf2025.html#NguyenDHZ25

Publishing type：Research paper (scientific journal)  

DOI： 10.1007/s10489-023-05172-5

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Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/SMARTCOMP61445.2024.00039

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Other Link： https://dblp.uni-trier.de/db/conf/smartcomp/smartcomp2024.html#KhoaDNZ24

Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/BigData62323.2024.10825469

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Other Link： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2024.html#DaoZH24

Publishing type：Research paper (international conference proceedings)   Publisher：Association for Computational Linguistics  

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Other Link： https://dblp.uni-trier.de/rec/conf/emnlp/2024

Publishing type：Research paper (international conference proceedings)   Publisher：ACM  

DOI： 10.1145/3652583.3657623

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Other Link： https://dblp.uni-trier.de/db/conf/mir/icmr2024.html#DaoZ24

Publishing type：Research paper (international conference proceedings)   Publisher：ACM  

DOI： 10.1145/3643488.3660296

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Other Link： https://dblp.uni-trier.de/rec/conf/icdar2/2024

Publishing type：Research paper (international conference proceedings)   Publisher：Springer  

DOI： 10.1007/978-3-031-53308-2_7

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Other Link： https://dblp.uni-trier.de/db/conf/mmm/mmm2024-2.html#GanDZ24

Publishing type：Research paper (international conference proceedings)   Publisher：CEUR-WS.org  

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Other Link： https://dblp.uni-trier.de/rec/conf/ai4dt/2024

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Ceur Workshop Proceedings  

The emergence of digital twins as virtual replicas of physical assets has revolutionized various industries, offering unparalleled insights and opportunities for optimization. However, managing and coordinating interactions among digital twins pose significant challenges, necessitating the development of orchestrators. This paper tackles this issue by proposing an orchestrator framework designed to handle interconnected digital twins, examining its effectiveness through applied scenarios in smart city contexts. The framework comprises federation, translation, brokering, and synchronization components. To demonstrate its efficacy, digital twins of smart environments and smart driving were developed and collaborated on applications including hotspot prediction and eco-driving assistance within smart city services.

Scopus

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Emnlp 2024 2024 Conference on Empirical Methods in Natural Language Processing Proceedings of the Conference  

Large language models have emerged as a significant phenomenon due to their ability to produce natural text across various applications. However, the proliferation of generated text raises concerns regarding its potential misuse in fraudulent activities such as academic dishonesty, spam dissemination, and misinformation propagation. Prior studies have detected the generation of non-analogous text, which manifests numerous differences between original and generated text. We have observed that the similarity between the original text and its generation is notably higher than that between the generated text and its subsequent regeneration. To address this, we propose a novel approach named SimLLM, aimed at estimating the similarity between an input sentence and its generated counterpart to detect analogous machine-generated sentences that closely mimic human-written ones. Our empirical analysis demonstrates SimLLM's superior performance compared to existing methods.

DOI： 10.18653/v1/2024.emnlp-main.1246

Open Access

Scopus

Publishing type：Research paper (scientific journal)  

DOI： 10.1007/s10489-023-04811-1

Web of Science

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Publishing type：Research paper (scientific journal)  

DOI： 10.1007/s10489-022-04436-w

Web of Science

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Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/ICPADS60453.2023.00403

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Other Link： https://dblp.uni-trier.de/db/conf/icpads/icpads2023.html#KhoaNDZ23

Publishing type：Research paper (scientific journal)  

DOI： 10.48550/arXiv.2301.02726

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Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/FUZZ52849.2023.10309773

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Other Link： https://dblp.uni-trier.de/db/conf/fuzzIEEE/fuzzIEEE2023.html#VeenaLKLFZ23

Publishing type：Research paper (international conference proceedings)   Publisher：ACM  

DOI： 10.1145/3592571.3592973

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Other Link： https://dblp.uni-trier.de/db/conf/icdar2/icdar2023.html#GanDZ23

Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/BigData59044.2023.10386866

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Other Link： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2023.html#DaoPZ23

Publishing type：Research paper (scientific journal)  

DOI： 10.1109/ACCESS.2023.3326419

Open Access

Web of Science

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Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/ICMEW59549.2023.00024

Scopus

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Other Link： https://dblp.uni-trier.de/db/conf/icmcs/icmew2023.html#DaoZ23

Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/BigData59044.2023.10386827

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Other Link： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2023.html#DaoUIWZ23

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Springer  

DOI： 10.1007/978-3-031-33380-4_3

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Other Link： https://dblp.uni-trier.de/db/conf/pakdd/pakdd2023-3.html#LikhithaVKZ23

Publisher：Electronics Switzerland  

Periodic pattern mining is an emerging technique for knowledge discovery. Most previous approaches have aimed to find only those patterns that exhibit full (or perfect) periodic behavior in databases. Consequently, the existing approaches miss interesting patterns that exhibit partial periodic behavior in a database. With this motivation, this paper proposes a novel model for finding partial periodic patterns that may exist in temporal databases. An efficient pattern-growth algorithm, called Partial Periodic Pattern-growth (3P-growth), is also presented, which can effectively find all desired patterns within a database. Substantial experiments on both real-world and synthetic databases showed that our algorithm is not only efficient in terms of memory and runtime, but is also highly scalable. Finally, the effectiveness of our patterns is demonstrated using two case studies. In the first case study, our model was employed to identify the highly polluted areas in Japan. In the second case study, our model was employed to identify the road segments on which people regularly face traffic congestion.

DOI： 10.3390/electronics11101523

Open Access

Web of Science

Scopus

Publishing type：Research paper (international conference proceedings)   Publisher：Springer  

DOI： 10.1007/978-3-031-00123-9_45

Web of Science

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Other Link： https://dblp.uni-trier.de/db/conf/dasfaa/dasfaa2022-1.html#KiranCCRDZ22

Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/FUZZ-IEEE55066.2022.9882785

Web of Science

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Other Link： https://dblp.uni-trier.de/db/conf/fuzzIEEE/fuzzIEEE2022.html#VeenaRKKGWZ22

Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/BigData55660.2022.10020284

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Other Link： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2022.html#GanDZ22

Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/DSAA54385.2022.10032391

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Other Link： https://dblp.uni-trier.de/db/conf/dsaa/dsaa2022.html#RavikumarKLCWZ22

Publishing type：Research paper (international conference proceedings)   Publisher：Springer  

DOI： 10.1007/978-981-99-1642-9_16

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Other Link： https://dblp.uni-trier.de/db/conf/iconip/iconip2022-5.html#LikhithaVKZTF22

Publishing type：Research paper (international conference proceedings)   Publisher：ACM  

DOI： 10.1145/3512731.3534211

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Other Link： https://dblp.uni-trier.de/rec/conf/mir/2022icdar

Publishing type：Research paper (international conference proceedings)   Publisher：Springer  

DOI： 10.1007/978-3-031-12426-6_16

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Other Link： https://dblp.uni-trier.de/db/conf/dexa/dexa2022-2.html#VeenaTKDZWZ22

Publishing type：Research paper (international conference proceedings)   Publisher：Springer  

DOI： 10.1007/978-3-031-21967-2_12

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Other Link： https://dblp.uni-trier.de/db/conf/aciids/aciids2022-2.html#RavikumarRLKWIZ22

Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/BigData55660.2022.10020803

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Other Link： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2022.html#KhoaNDZ22

Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/BigData55660.2022.10020829

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Other Link： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2022.html#GanDZ22a

Publishing type：Research paper (international conference proceedings)   Publisher：Springer  

DOI： 10.1007/978-3-031-08530-7_50

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Other Link： https://dblp.uni-trier.de/db/conf/ieaaie/ieaaie2022.html#TejimaDZ22

Publishing type：Research paper (international conference proceedings)   Publisher：ACM  

DOI： 10.1145/3512731.3534208

Scopus

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Other Link： https://dblp.uni-trier.de/db/conf/mir/icdar2022.html#GanDZS22

Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/BigData55660.2022.10020923

Scopus

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Other Link： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2022.html#NguyenTZ22

Publishing type：Research paper (international conference proceedings)   Publisher：ACM  

DOI： 10.1145/3512731.3534207

Scopus

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Other Link： https://dblp.uni-trier.de/db/conf/mir/icdar2022.html#KhoaNTZ22

Publisher：Aip Conference Proceedings  

We have conducted a fundamental study on an information provision method for visualized traffic risks in situations where a driver recognizes the risks and makes a route-selection decision. Experiments were performed to compare the effects of the standard map expression with a simplified-route expression of risk data based on situational recognition. We found that simplification did not affect the correctness of the selection task but did influence the accuracy of situation recognition. This result suggests that to reduce unintended communication errors in the vehicle, it is necessary to condense the information in advance.

DOI： 10.1063/5.0068764

Scopus

DOI： 10.3390/electronics10121478

Open Access

Web of Science

Language：Japanese   Publisher：The Japanese Society for Artificial Intelligence  

<p>We propose a method to extract areas potentially spreading COVID-19 by focusing on correlation between mobile phone population statistics and the number of new positive cases. Our experiment showed that our method can successfully extract areas that are consistent with the government's views on infection sources.</p>

DOI： 10.11517/pjsai.jsai2021.0_1j3gs10e03

Open Access

CiNii Research

Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/ICDMW53433.2021.00080

Web of Science

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Other Link： https://dblp.uni-trier.de/db/conf/incdm/incdm2021w.html#VeenaNLKWZ21

Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/FUZZ45933.2021.9494594

Web of Science

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Other Link： https://dblp.uni-trier.de/db/conf/fuzzIEEE/fuzzIEEE2021.html#VeenaBKAZ21

Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/BigData52589.2021.9671556

Web of Science

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Other Link： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2021.html#LikithaVKWZ21

Publisher：Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics  

Finding periodic-frequent patterns in temporal databases is a challenging problem of great importance in many real-world applications. Most previous studies focused on finding these patterns in row temporal databases. To the best of our knowledge, there exists no study that aims to find periodic-frequent patterns in columnar temporal databases. One cannot ignore the importance of the knowledge that exists in very large columnar temporal databases. It is because the real-world big data is widely stored in columnar temporal databases. With this motivation, this paper proposes an efficient algorithm, Periodic Frequent-Equivalence CLass Transformation (PF-ECLAT), to find periodic-frequent patterns in a columnar temporal database. Experimental results on sparse and dense real-world databases demonstrate that PF-ECLAT is not only memory and runtime efficient but also highly scalable. Finally, we present the usefulness of PF-ECLAT with a case study on air pollution analytics.

DOI： 10.1007/978-3-030-79457-6_3

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Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/BigData52589.2021.9671336

Web of Science

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Other Link： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2021.html#NguyenZ21

Publishing type：Research paper (international conference proceedings)   Publisher：ACM  

DOI： 10.1145/3463944.3469268

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Other Link： https://dblp.uni-trier.de/db/conf/mir/icdar2021.html#NguyenNZ21

Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/BigData52589.2021.9671724

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Other Link： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2021.html#DaoPNNZ21

Publishing type：Research paper (international conference proceedings)   Publisher：ACM  

DOI： 10.1145/3463944.3469267

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Other Link： https://dblp.uni-trier.de/db/conf/mir/icdar2021.html#HamzaZ21

Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/BigData52589.2021.9671403

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Other Link： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2021.html#LaDTKZ21

Publishing type：Research paper (international conference proceedings)   Publisher：Springer  

DOI： 10.1007/978-3-030-79463-7_28

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Other Link： https://dblp.uni-trier.de/db/conf/ieaaie/ieaaie2021-2.html#DaoZK21

Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/BigData52589.2021.9671536

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Other Link： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2021.html#KhoaNDZ21

Publishing type：Research paper (international conference proceedings)   Publisher：Springer  

DOI： 10.1007/978-3-030-86472-9_20

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Other Link： https://dblp.uni-trier.de/db/conf/dexa/dexa2021-1.html#NakamuraKPRWDZT21

Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/BigData52589.2021.9671912

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Other Link： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2021.html#PallikilaVKAIZR21

Publishing type：Research paper (international conference proceedings)   Publisher：Springer  

DOI： 10.1007/978-3-030-79457-6_5

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Other Link： https://dblp.uni-trier.de/db/conf/ieaaie/ieaaie2021-1.html#BommisettyRKDZ21

Publishing type：Research paper (international conference proceedings)   Publisher：Springer  

DOI： 10.1007/978-3-030-92307-5_83

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Other Link： https://dblp.uni-trier.de/db/conf/iconip/iconip2021-5.html#KiranLDZZ21