掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1109/IJCNN64981.2025.11229149

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その他リンク： https://dblp.uni-trier.de/db/conf/ijcnn/ijcnn2025.html#KhoaDZ25

掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：ACM  

DOI： 10.1145/3733566.3734434

Open Access

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その他リンク： https://dblp.uni-trier.de/db/conf/icdar2/icdar2025.html#ThanhZ25

掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1145/3747327.3763041

Open Access

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その他リンク： https://dblp.uni-trier.de/db/conf/icmi/icmi2025c.html#DaoNHZ25

掲載種別：研究論文（学術雑誌）  

DOI： 10.1109/ACCESS.2025.3581769

Open Access

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出版者・発行元：Fledge AI 2025 Proceedings of the 2025 Federated Learning and Edge AI for Privacy and Mobility  

This paper introduces a virtual platform for federated split learning (FedSL) that supports on-device training using virtual Android smartphones as edge clients. To optimize model partitioning under both synchronous and asynchronous scenarios, we employ an adaptive offloading point (AOP) framework integrated with reinforcement learning and GMM clustering. The proposed virtualization framework enables diverse virtual Android instances to participate in training, enabling scalability beyond physical device limitations. Experimental results on both real (Google Pixel 8) and virtual devices validate the feasibility and effectiveness of AOP-based FedSL in both synchronous and asynchronous settings. Compared to traditional edge modules like Toradex Apalis, smartphones (both physical and virtual) demonstrate competitive performance for scalable, low-latency federated learning (FL) system. Furthermore, the virtual Android platform shows efficacy in supporting reproducible experiments using various FL algorithms.

DOI： 10.1145/3737899.3768526

Open Access

Web of Science

Scopus

掲載種別：研究論文（学術雑誌）  

DOI： 10.1007/s10489-023-05172-5

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掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：IEEE  

DOI： 10.1109/SMARTCOMP61445.2024.00039

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その他リンク： https://dblp.uni-trier.de/db/conf/smartcomp/smartcomp2024.html#KhoaDNZ24

掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1109/BigData62323.2024.10825469

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その他リンク： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2024.html#DaoZH24

掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：Association for Computational Linguistics  

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その他リンク： https://dblp.uni-trier.de/rec/conf/emnlp/2024

掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：ACM  

DOI： 10.1145/3652583.3657623

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その他リンク： https://dblp.uni-trier.de/db/conf/mir/icmr2024.html#DaoZ24

掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：ACM  

DOI： 10.1145/3643488.3660296

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その他リンク： https://dblp.uni-trier.de/rec/conf/icdar2/2024

掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：Springer  

DOI： 10.1007/978-3-031-53308-2_7

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その他リンク： https://dblp.uni-trier.de/db/conf/mmm/mmm2024-2.html#GanDZ24

掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：CEUR-WS.org  

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その他リンク： https://dblp.uni-trier.de/rec/conf/ai4dt/2024

掲載種別：研究論文（学術雑誌）  

DOI： 10.1007/s10489-023-04811-1

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掲載種別：研究論文（学術雑誌）   出版者・発行元：Applied Intelligence  

Spatial high-utility itemset (SHUI) mining is a significant big data analysis technique. It aims to locate all geographically interesting itemsets with high utility in a spatiotemporal database. An SHUI-Miner algorithm was presented in the literature to find the desired itemsets. Unfortunately, this algorithm suffered from performance issues when dealing with high-dimensional spatiotemporal databases. Based on this finding, this paper extends the state-of-the-art method by proposing a novel algorithm known as the high-dimensional SHUI-miner (HDSHUI-Miner). Our algorithm explores several novel pruning strategies to decrease the search space and computational cost required to find the desired itemsets. Experimental results obtained on seven real-world databases demonstrate that HDSHUI-Miner outperforms SHUI-Miner with respect to memory consumption, runtime, and scalability. Finally, we present two real-world case studies to illustrate the usefulness of the proposed algorithm.

DOI： 10.1007/s10489-022-04436-w

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掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：Proceedings of the International Conference on Parallel and Distributed Systems ICPADS  

The study discusses challenges in deploying Artificial Intelligence (AI) on Internet-of-Things (IoT) devices and introduces a solution called Adaptive Offloading Point (AOP) for Federated Learning (FL) in Edge AI. AOP accelerates local training on resource-constrained devices and uses reinforcement learning-based clustering (RL) to determine which deep neural network (DNN) layers to offload to the server. Test results show that, in the VIT transformer model, AOP significantly reduces training time compared to classical FL and the baseline method called FedAdapt, making it a promising solution for Edge AI applications.

DOI： 10.1109/ICPADS60453.2023.00403

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その他リンク： https://dblp.uni-trier.de/db/conf/icpads/icpads2023.html#KhoaNDZ23

掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：Proceedings of the 4th ACM Workshop on Intelligent Cross Data Analysis and Retrieval ICDAR 2023 Joint with ACM International Conference on Multimedia Retrieval Icmr 2023  

Improving driver behaviors through driving education and coaching is well-recognized as being necessary and efficient for driving safely and reducing traffic accidents, as they promise to reduce human factors accounting for most of the crash involvements. Driver education programs are currently widely employed in many countries, ensuring that the necessary procedural driving skills and competencies are imparted during these processes, to make more skilled drivers. However, making people more skilled drivers does not make them safer ones, the effectiveness of driving education is greatly restricted by the limited amount of actual supervised driving involved and the absence of individualized feedback. To this aim, driving coaching emerges as a more practical alternative to develop safely driving by proactively providing coaching feedback to enhance skills and cultivate corrective behaviors, with the recent technological developments in intelligent vehicles and transportation. This paper presents a systematic review of the existing studies for examining the empirical evidences on the various coaching explorations for the development of drivers' procedural driving skills. In particular, we propose a taxonomy to classify existing driving coaching into four categories, and explore the answers to three questions: what types, when and how the different kinds of driving coaching are provided and delivered. Finally, the challenges and future directions are also presented from three aspects.

DOI： 10.1145/3592571.3592973

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その他リンク： https://dblp.uni-trier.de/db/conf/icdar2/icdar2023.html#GanDZ23

掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：Proceedings 2023 IEEE International Conference on Big Data Bigdata 2023  

This paper introduces MM-TrafficRisk, an innovative fleet management application that harnesses dashcam video data, environmental data, and physiological data to forecast, mitigate, and investigate traffic-risk events while uncovering traffic-risk patterns. To provide a comprehensive overview of this application, we outline its system architecture, encompassing a database, ETL processes, UI/UX components, and a fine-grained text-video search engine. Additionally, we present a groundbreaking two-stage near-miss accident prediction model designed to identify near-miss incidents within dashcam video databases, and the text-to-video search engine, facilitating rapid searches for traffic-risk events based on textual queries. These models and search engines are rigorously evaluated in a controlled laboratory environment to showcase their advantages. Moreover, we highlight several essential functions of the MM-TrafficRisk application through snapshots, emphasizing the collaborative efforts between a government agency and industrial companies to develop and deploy this application in practical settings. We also delve into our future endeavors, focusing on multi-modal deep learning event prediction and the adaptability of our application to Edge AI environments.

DOI： 10.1109/BigData59044.2023.10386866

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その他リンク： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2023.html#DaoPZ23

掲載種別：研究論文（学術雑誌）   出版者・発行元：IEEE Access  

Periodic-frequent patterns are a vital class of regularities in a temporal database. Most previous studies followed the approach of finding these patterns by storing the temporal occurrence information of a pattern in a list. While this approach facilitates the existing algorithms to be practicable on sparse databases, it also makes them impracticable (or computationally expensive) on dense databases due to increased list sizes. A renowned concept in set theory is that the larger the set, the smaller its complement will be. Based on this conceptual fact, this paper explores the complements, redefines the periodic-frequent pattern and proposes an efficient depth-first search algorithm that finds all periodic-frequent patterns by storing only non-occurrence information of a pattern in a database. Experimental results on several databases demonstrate that our algorithm is efficient.

DOI： 10.1109/ACCESS.2023.3326419

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掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：Proceedings 2023 IEEE International Conference on Multimedia and Expo Workshops Icmew 2023  

The proliferation of the internet and the availability of vast amounts of information have given rise to a critical and pressing issue of fake news. Among the various forms of fake news, cheapfakes are particularly prominent in deceiving people. Existing research on cheapfakes detection has primarily focused on analyzing the context and correlation between textual and visual information, but has largely overlooked the significance of external knowledge. As a result, most previous approaches, apart from the baseline of ICME'23 Grand Challenge on Detecting Cheapfakes, have heavily relied on evaluating the dataset itself to improve performance. However, despite achieving impressive results on public test datasets, these approaches often suffer from poor performance in real-world scenarios due to their overreliance on the given dataset. In this study, we propose a novel approach that utilizes knowledge graphs to address the issue of insufficient information from external knowledge. Unlike previous approaches, our proposal does not directly alter or participate in the public test dataset to enhance performance, which can potentially result in significant overfitting. Our proposed approach achieved an accuracy score of 83.52% on Task 1, surpassing the baseline by 1.7%, and an accuracy score of 84% on Task 2, outperforming the best result from the previous challenge by 8%.

DOI： 10.1109/ICMEW59549.2023.00024

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その他リンク： https://dblp.uni-trier.de/db/conf/icmcs/icmew2023.html#DaoZ23

掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：Proceedings 2023 IEEE International Conference on Big Data Bigdata 2023  

The paper aims to present a collaboration between the industry and government sectors, focusing on creating a benchmarking platform for predicting urban risk transportation through the utilization of multimodal data. In this collaboration, the industry partner contributes datasets and customer preference surveys obtained from its business operations. On the other hand, government partners curate open datasets sourced from non-profit organizations in both private and public domains. Furthermore, the government provides an accessible platform that allows individuals to conveniently access and leverage resources for the purpose of advancing application development and engaging in research endeavors. Throughout the collaborative effort, a variety of techniques have been under development for forecasting urban risk transportation through the analysis of weather patterns, congestion levels, and people flow data. The core objective of this partnership is to formulate two foundational prediction methods. These methods are intended to serve as benchmarks, offering future users a dependable means to assess the performance of their own approaches in terms of both time-series and datapoints analytics methodologies.

DOI： 10.1109/BigData59044.2023.10386827

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その他リンク： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2023.html#DaoUIWZ23

掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics  

An uncertain geo-referenced transactional database represents the probabilistic data produced by stationary spatial objects observing a particular phenomenon over time. Useful patterns that can empower the users to achieve socio-economic development lie hidden in this database. Finding these patterns is challenging as the existing frequent pattern mining studies ignore the spatial information of the items in a database. This paper proposes a generic model of Geo-referenced Frequent Patterns (GFPs) that may exist in an uncertain geo-referenced transactional database. This paper also introduces two new upper-bound constraints, namely “neighborhood-aware prefix item camp” and “neighborhood-aware expected support”, to effectively reduce the search space and the computational cost of finding the desired patterns. An efficient neighborhood-aware pattern-growth algorithm has also been presented in this paper to find all GFPs in a database. Experimental results demonstrate that our algorithm is efficient.

DOI： 10.1007/978-3-031-33380-4_3

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その他リンク： https://dblp.uni-trier.de/db/conf/pakdd/pakdd2023-3.html#LikhithaVKZ23

掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：IEEE International Conference on Fuzzy Systems  

Partial periodic patterns are an important class of regularities in multiple time series data. Most previous works focused on finding these patterns in a binary series by disregarding the quantities of objects. This paper explores the concept of 'fuzzy sets' and proposes a novel model of fuzzy partial periodic patterns (F3Ps) that may exist in a quantitative series. F3Ps have value because they represent regularities that are predictable in a series. Unfortunately, finding F3Ps is challenging due to its colossal search space. We introduce a novel pruning technique to reduce the search space and computational cost of finding these patterns. We also present an efficient depth-first search algorithm, F3P-Miner, to find all F3Ps in a series. We also describe a new pattern representation technique, temporal ordering and grouping with a wildcard character, to visualize long patterns easily. Experimental results demonstrate that the proposed algorithm is efficient. Finally, we present the real-world applicability of F3Ps by finding helpful information about polluted areas in the one year Japan's air pollution database.

DOI： 10.1109/FUZZ52849.2023.10309773

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その他リンク： https://dblp.uni-trier.de/db/conf/fuzzIEEE/fuzzIEEE2023.html#VeenaLKLFZ23

掲載種別：研究論文（学術雑誌）  

DOI： 10.48550/arXiv.2301.02726

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出版者・発行元：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

掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1007/978-3-031-00123-9_45

Web of Science

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その他リンク： https://dblp.uni-trier.de/db/conf/dasfaa/dasfaa2022-1.html#KiranCCRDZ22

掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1007/978-981-99-1642-9_16

Web of Science

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その他リンク： https://dblp.uni-trier.de/db/conf/iconip/iconip2022-5.html#LikhithaVKZTF22

掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：ACM  

DOI： 10.1145/3512731.3534211

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その他リンク： https://dblp.uni-trier.de/rec/conf/mir/2022icdar

掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics  

Periodic-frequent pattern mining involves finding all periodically occurring patterns in a temporal database. Most previous studies found these patterns by storing the temporal occurrence information of an item in a list structure. Unfortunately, this approach makes pattern mining computationally expensive on dense databases due to increased list sizes. With this motivation, this paper explores the concept of complements, and proposes an efficient algorithm that records non-occurrence information of an item to find all desired patterns in a dense database. Experimental results demonstrate that our algorithm is efficient.

DOI： 10.1007/978-3-031-12426-6_16

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その他リンク： https://dblp.uni-trier.de/db/conf/dexa/dexa2022-2.html#VeenaTKDZWZ22

掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics  

Finding partial periodic 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 partial periodic patterns in columnar temporal databases. One cannot ignore the importance of the knowledge that exists in very large columnar temporal databases. It is because real-world big data is widely stored in columnar temporal databases. With this motivation, this paper proposes an efficient algorithm, Partial Periodic Pattern-Equivalence Class Transformation (3P-ECLAT), to find desired patterns in a columnar temporal database. Experimental results on synthetic and real-world databases demonstrate that 3P-ECLAT is not only memory and runtime efficient but also highly scalable. Finally, we present the usefulness of 3P-ECLAT with a case study on air pollution analytics.

DOI： 10.1007/978-3-031-21967-2_12

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その他リンク： https://dblp.uni-trier.de/db/conf/aciids/aciids2022-2.html#RavikumarRLKWIZ22

掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：Proceedings 2022 IEEE International Conference on Big Data Big Data 2022  

Split learning (SL) is a popular distributed machine learning (ML) method used to enable ML. It divides a neural network based model into subnetworks. Then, it separately trains the subnetworks on distributed parties (e.g., client and server). In distributed ML, data are generated and collected on the client-side. In contrast, the collected data are processed using an application deployed on the server side. However, when applied in practice using Internet of things systems and clients, numerous obstacles occur because of limited configuration and resources. Dividing neural networks in the SL is the biggest problem and an open question in numerous studies. This study introduces splitDyn, which is a new dynamic SL solution to solve the aforementioned problems. This method provides a solution for eliminating their inherent drawbacks. The main idea is to apply a Round-Robin schedule to select the client for the training process. Then, the next idea is to use the Hungarian optimization algorithm to assign a layer to a client and enhance the accuracy. The proposed method reasonably achieved better accuracy and reduced processing time than the other learning models. Furthermore, it applies the incident datasets to predict the incident event and in edge computing for edge artificial intelligence (AI) applications.

DOI： 10.1109/BigData55660.2022.10020803

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その他リンク： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2022.html#KhoaNDZ22

掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：Proceedings 2022 IEEE International Conference on Big Data Big Data 2022  

Sleep plays a vital role in our physical, cognitive, and psychological well-being. Despite its importance, long-term monitoring of personalized sleep quality (SQ) in real-world contexts is still challenging. Many sleep researches are still developing clinically and far from accessible to the general public. Fortunately, wearables and IoT devices provide the potential to explore the sleep insights from multimodal data, and have been used in some SQ researches. However, most of these studies analyze the sleep related data and present the results in a delayed manner (i.e., today's SQ obtained from last night's data), it is sill difficult for individuals to know how their sleep will be before they go to bed and how they can proactively improve it. To this end, this paper proposes a computational framework to monitor the individual SQ based on both the objective and subjective data from multiple sources, and moves a step further towards providing the personalized feedback to improve the SQ in a data-driven manner. The feedback is implemented by referring the insights from the PMData dataset based on the discovered patterns between life events and different levels of SQ. The deep learning based personal SQ model (PerSQ), using the long-term heterogeneous data and considering the carry-over effect, achieves higher prediction performance compared with baseline models. A case study also shows reasonable results for an individual to monitor and improve the SQ in the future.

DOI： 10.1109/BigData55660.2022.10020829

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その他リンク： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2022.html#GanDZ22a

掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics  

Understanding the associations between different traffic factors (e.g., time, vehicles, trees, and people) and the air pollution in a particular region is a challenging problem of great concern in Intelligent Transportation Systems. Most previous works primarily focused on efficient prediction of air pollution levels the given traffic imagery data. To the best of our knowledge, there exists no study that tries to discover hidden associations (or correlation) that exist between the traffic factors and the air pollution towards predicting PM2.5 levels within a certain period of time. With this motivation, this paper proposes a novel framework that aims to discover hidden associations that exist between the traffic factors and the air pollution towards predicting air pollution level in short- and medium-term time. Our framework has the following six steps: (i) Extract features from the traffic images using any machine learning algorithm, (ii) generate a new dataset by joining the extracted features dataset and air pollution dataset using time, (iii) transform this new dataset into an uncertain temporal database using fuzzy rules, (iv) apply uncertain periodic-frequent pattern mining techniques to discover hidden associations between various traffic factors and air pollution, (v) estimate air pollution level from a given image using transfer learning on a pre-trained model, and (vi) predict air pollution level using estimated air pollution level and mined patterns dataset. Experimental results show that our method can estimate and predict air pollution level with high accuracy (from 77% to 98%).

DOI： 10.1007/978-3-031-08530-7_50

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その他リンク： https://dblp.uni-trier.de/db/conf/ieaaie/ieaaie2022.html#TejimaDZ22

掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：ICDAR 2022 Proceedings of the 3rd ACM Workshop on Intelligent Cross Data Analysis and Retrieval  

IoT-based multimodal learning analytics promises to obtain an in-depth understanding of the learning process. It provides the insights for not only the explicit learning indicators but also the implicit attributes of learners, based on which further potential learning support can be timely provided in both physical and cyber world accordingly. In this paper, we present a systematic review of the existing studies for examining the empirical evidences on the usage of IoT data in education and the capabilities of multimodal analysis to provide useful insights for smarter education. In particular, we classify the multimodal data into four categories based on the data sources (data from digital, physical, physiological and environmental spaces). Moreover, we propose a concept framework for better understanding the current state of the filed and summarize the insights into six main themes (learner behavior understanding, learner affection computing, smart learning environment, learning performance prediction, group collaboration modeling and intelligent feedback) based on the objectives for intelligent learning. The associations between different combinations of data modalities and various learning indicators are comprehensively discussed. Finally, the challenges and future directions are also presented from three aspects.

DOI： 10.1145/3512731.3534208

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その他リンク： https://dblp.uni-trier.de/db/conf/mir/icdar2022.html#GanDZS22

掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：Proceedings 2022 IEEE International Conference on Big Data Big Data 2022  

Federated learning (FL) has been used to protect data contributors' privacy by allowing training at clients and then feeding back machine learning models to servers for aggregation. Conventional methods of FL aggregation methods average model weights to produce a fused global model. However, in real-world applications in cyber-space systems, which often have heterogeneous Internet of Things data configuration and collection, FL encounters obstacles with non-independent and identically distributed (Non-IID) data. The main problem is the aggregated global models deviating from the optimal model trained on centralized servers. According to recent research, most Non-IID FL aggregation methods attempt to direct the movement of gradients to the optimal one using differentiation from trained models. In this paper, we propose a framework for using feature probability distribution in aggregation calculation. The proposed aggregation algorithm shows robustness on different Non-IID datasets and outperforms state-of-the-art methods in various FL experiments.

DOI： 10.1109/BigData55660.2022.10020923

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その他リンク： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2022.html#NguyenTZ22

掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：ICDAR 2022 Proceedings of the 3rd ACM Workshop on Intelligent Cross Data Analysis and Retrieval  

"Good night"is the most common saying everyone uses every day. That shows sleep plays a vital role in human life, and about a third of a lifetime is spent sleeping. Having a good sleep means having good health, spirit, and intellect to work. Many studies have analyzed predicted sleep quality using machine learning (ML). However, no studies have federated learning (FL) to analyze and predict sleep quality predictions. Our study operated federated multiple convolutional neural networks (FedMCRNN) and multi-modal data collected from wearables for sleep quality prediction. We measure the performance of the FedMCRNN in many-To-one and many-To-many cases using a variety of metrics and compare it with traditional machine learning models. The results show that FedMCRNN predicts quality intention reliably, with 96.774% and 68.721% accuracies for the two cases, many-To-one and many-To-many, respectively. Besides, other metrics have better value than methods. The results also show that FedMCRNN performs better than previous most advanced methods for predicting sleep quality and clearly shows which features influence sleep quality. Our findings have implications for the development of Artificial Intelligence (AI) doctors.

DOI： 10.1145/3512731.3534207

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その他リンク： https://dblp.uni-trier.de/db/conf/mir/icdar2022.html#KhoaNTZ22

掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1109/DSAA54385.2022.10032391

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その他リンク： https://dblp.uni-trier.de/db/conf/dsaa/dsaa2022.html#RavikumarKLCWZ22

掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1109/FUZZ-IEEE55066.2022.9882785

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その他リンク： https://dblp.uni-trier.de/db/conf/fuzzIEEE/fuzzIEEE2022.html#VeenaRKKGWZ22

掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：Proceedings 2022 IEEE International Conference on Big Data Big Data 2022  

Driver reaction is of vital importance in risk scenarios. Drivers can take correct evasive maneuver at proper cushion time to avoid the potential traffic crashes, but this reaction process is highly experience-dependent and requires various levels of driving skills. To improve driving safety and avoid the traffic accidents, it is necessary to provide all road drivers with on-board driving assistance. This study explores the plausibility of case-based reasoning (CBR) as the inference paradigm underlying the choice of personalized crash evasive maneuvers and the cushion time, by leveraging the wealthy of human driving experience from the steady stream of traffic cases, which have been rarely explored in previous studies. To this end, in this paper, we propose an open evolving framework for generating personalized on-board driving assistance. In particular, we present the FFMTE model with high performance to model the traffic events and build the case database; A tailored CBR-based method is then proposed to retrieve, reuse and revise the existing cases to generate the assistance. We take the 100-Car Naturalistic Driving Study dataset as an example to build and test our framework; the experiments show reasonable results, providing the drivers with valuable evasive information to avoid the potential crashes in different scenarios.

DOI： 10.1109/BigData55660.2022.10020284

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その他リンク： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2022.html#GanDZ22

出版者・発行元：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

Web of Science

記述言語：日本語   出版者・発行元：一般社団法人 人工知能学会  

<p>感染拡大が続く新型コロナウイルス感染症（COVID-19）の流行制御と経済活動を両立するために，集中的に感染が発生している地区に対象を限定した介入政策が求められている．しかし，多くの陽性者について感染経路の特定が出来ておらず，感染源となっている場所を特定することは難しい．専門家による人手調査の負荷を軽減するために，本研究では，携帯電話人口統計データと市区町村単位の陽性者数時系列を用いて，感染源となっている可能性がある場所，すなわち感染リスク地区を抽出する手法を提案する．提案手法は，感染リスク地区の人口の増減が新規陽性者数の増減に反映される，及び人口変動と感染症の実効再生産数の変動の関係が近似的に線形である，という2つの仮定の下で，人口変動と実効再生産数の相関が大きいメッシュを感染リスク地区として抽出する．実際の時系列には非線形な摂動が含まれるため，単純な相関係数の代わりに，動的時間伸縮法に基づく方法で相関を求める．東京都渋谷区を対象にした実験の結果，夜の繁華街の人口と区の実効再生産数の相関が大きいことが確認された．</p>

DOI： 10.11517/pjsai.jsai2021.0_1j3gs10e03

CiNii Research

掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1109/ICDMW53433.2021.00080

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その他リンク： https://dblp.uni-trier.de/db/conf/incdm/incdm2021w.html#VeenaNLKWZ21

出版者・発行元：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

Web of Science

Scopus

掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：Proceedings 2021 IEEE International Conference on Big Data Big Data 2021  

Air pollution prediction for smart city applications has been attracted in artificial intelligence research to overcome problems to the health of citizen. Conventionally, environmental IoT data is gathered from monitoring station sensors then is sent to servers for centralized predictive model training at a whole region. That causes latency issues in data transmission from IoT devices to cloud servers. This paper describes federated learning paradigm approach for air pollution prediction model training on environmental monitoring sensor data. In the research, we design distributed learning framework that assists cooperative training among participants from different spatial areas such as cities and prefectures. At each area, Convolutional Recurrent Neural Networks (CRNN) are trained locally aiming to predict local Oxidant warning level while aggregated global model enhances distilled knowledge from all areas of a region. The research illustrates that designed common parts of CRNN can be fused globally meanwhile adaptive structure at predictive part of the deep neural network model can capture different environmental monitoring stations configuration at local areas. Some experiment results also hint methods to keep balance between federated learning synchronous training rounds and local deep neural network training epochs to maximize accuracy of the whole federated learning system. The results also prove that new participating areas can train and quickly obtain optimized local models by using transferred common global model.

DOI： 10.1109/BigData52589.2021.9671336

Web of Science

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その他リンク： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2021.html#NguyenZ21

掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：ICDAR 2021 Proceedings of the 2021 Workshop on Intelligent Cross Data Analysis and Retrieval  

The development of remote/wearable sensors enables more research in the health care area. Based on these kinds of sensors, the information of human's active level, health parameters can be collected to predict one's health status. Sleeping quality is an important factor to make a person feel healthy. In this work, we summarize the current models to predict sleeping quality. Inputs of those models could be environmental factors, activities, or time-series data from wearable sensors. The characteristic of the input data may lead to the choice of prediction models. The domain of data that was used to forecast sleeping quality will be considered carefully in parallel with the prediction model. Challenges and future work for this research direction will be discussed in this paper.

DOI： 10.1145/3463944.3469268

Web of Science

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その他リンク： https://dblp.uni-trier.de/db/conf/mir/icdar2021.html#NguyenNZ21

掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：IEEE  

DOI： 10.1109/BigData52589.2021.9671724

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その他リンク： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2021.html#DaoPNNZ21

掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：ICDAR 2021 Proceedings of the 2021 Workshop on Intelligent Cross Data Analysis and Retrieval  

Privacy protection technology has become a crucial part of almost every existing cross-data analysis application. The privacy-preserving technique allows sharing sensitive personal information and preserves the users' privacy. This new trend influences data collection results by improving the analytical accuracy, increasing the number of participants, and better understand the participants' environments. Herein, collecting these personal data is significant to many advantageous applications such as health monitoring. Nevertheless, these applications encounter real privacy threats and concerns about handling personal information. This paper aims to determine privacy-preserving personal data mining technologies and analyze these technologies' advantages and shortcomings. Our purpose is to provide an in-depth understanding of personal data privacy and highlight important viewpoints, existing challenges, and future research directions.

DOI： 10.1145/3463944.3469267

Web of Science

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その他リンク： https://dblp.uni-trier.de/db/conf/mir/icdar2021.html#HamzaZ21

掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1109/BigData52589.2021.9671403

Web of Science

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その他リンク： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2021.html#LaDTKZ21

掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1007/978-3-030-79463-7_28

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その他リンク： https://dblp.uni-trier.de/db/conf/ieaaie/ieaaie2021-2.html#DaoZK21

掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：Proceedings 2021 IEEE International Conference on Big Data Big Data 2021  

Due to the rapid recent development of cloud-edge networks, smart devices can facilitate rapid access to patients' health information. Success has been achieved in the healthcare sector with the training of a federated learning (FL) model on large amounts of the personal data of users. However, some challenges remain that other FL models have not yet addressed. Firstly, FL models with computational parameters are very complex, which results in a high communication cost in the cloud-edge network. Furthermore, trained models in the cloud are not personalized. If personalization is present, the models do not provide practical solutions to fine-tune parameters in order to accurately predict performance in health monitoring. To address the above challenges, this paper presents the Fed xData framework for contextual health monitoring in cloud-edge networks. The Fed xData framework introduces a continuous data balancing supplemented structure using the RandomOverSample method, which solves all data classes. The FL model is an encode depth convolutional network (EDCN) model designed for both server and client. It solves various problems, for instance by using the fine-tuning model to increase personalization and solving not independent and identically (Non-IID) distribution problems regarding user health. Test results based on human activity recognition indicate that Fed xData is far superior to others for use in general centralized learning models and FL models.

DOI： 10.1109/BigData52589.2021.9671536

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その他リンク： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2021.html#KhoaNDZ21

掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics  

Partial periodic-frequent pattern mining is an important knowledge discovery technique in data mining. It involves identifying all frequent patterns that have exhibited partial periodic behavior in a temporal database. The following two limitations have hindered the successful industrial application of this technique: (i) there exists no algorithm to find the desired patterns in columnar temporal databases, and (ii) existing algorithms are computationally expensive both in terms of runtime and memory consumption. This paper tackles these two challenging problems by proposing a novel algorithm known as partial periodic-frequent depth-first search (PPF-DFS). The proposed algorithm compresses a given row or columnar temporal database into a unified dictionary structure and mines this structure recursively to find all desired patterns. Experimental results demonstrate that PPF-DFS is 2 to 8.8 times faster and 5 to 31 times more memory efficient than the state-of-the-art algorithm.

DOI： 10.1007/978-3-030-86472-9_20

Web of Science

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その他リンク： https://dblp.uni-trier.de/db/conf/dexa/dexa2021-1.html#NakamuraKPRWDZT21

掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1109/BigData52589.2021.9671912

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その他リンク： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2021.html#PallikilaVKAIZR21

掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics  

Finding spatial high utility itemsets (SHUIs) in a quantitative spatiotemporal database is a challenging problem of great importance in many real-world applications. Most previous studies have focused on the efficient discovery of SHUIs in a low-dimensional database. This paper advances the state-of-the-art by introducing an efficient algorithm to find SHUIs in a high-dimensional spatiotemporal database. Two novel concepts, ‘spatially closed utilities of an itemset’ and ‘spatially non-closed utilities of an itemset,’ were explored to tackle the predicament caused by the violation of downward closure property of SHUIs. The performance study shows that our algorithm is efficient and is about an order of magnitude faster than the state-of-the-art algorithm.

DOI： 10.1007/978-3-030-79457-6_5

Web of Science

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その他リンク： https://dblp.uni-trier.de/db/conf/ieaaie/ieaaie2021-1.html#BommisettyRKDZ21

掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1007/978-3-030-92307-5_83

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その他リンク： https://dblp.uni-trier.de/db/conf/iconip/iconip2021-5.html#KiranLDZZ21

掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1109/BigData52589.2021.9671556

Web of Science

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その他リンク： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2021.html#LikithaVKWZ21

掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1109/FUZZ45933.2021.9494594

Web of Science

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その他リンク： https://dblp.uni-trier.de/db/conf/fuzzIEEE/fuzzIEEE2021.html#VeenaBKAZ21