Article
Computer Science, Artificial Intelligence
Ai-Te Kuo, Haiquan Chen, Liang Tang, Wei-Shinn Ku, Xiao Qin
Summary: This paper presents a novel MapReduce-based framework, ProbSky, for fast parallel distributed evaluation of probabilistic skyline queries on large high-dimensional data. Extensive experiments show that ProbSky speeds up the evaluation of the exact p-skyline queries on large high dimensional data by at least one order of magnitude in most cases compared to the state-of-the-art methods.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Information Systems
Jong-Hyeok Choi, Fei Hao, Yoo-Sung Kim, Aziz Nasridinov
Summary: A new method for skyline point queries, utilizing decision tree construction to minimize unnecessary dominance tests and enhance query performance, was proposed. This method can be applied to improve various skyline query approaches, with experimental results showing performance improvements of up to 23.15 times.
Article
Computer Science, Information Systems
Yonis Gulzar, Ali A. Alwan
Summary: This paper proposes an efficient algorithm for processing skyline queries over partially complete databases in a cloud environment. Experimental results have shown that the algorithm outperforms existing algorithms in terms of processing time, domination tests, and data flow.
Article
Computer Science, Information Systems
Jiale Zhao, Yong Ma, Jiangtao Cui, Yanguo Peng, Kangshun Li, Tengyu Wang
Summary: The study focused on the security issues associated with skyline computation in the cloud server, proposing a novel framework called SecSky to address the problem. By combining improved B+ tree structure with symmetric encryption and applying pruning idea for efficient skyline query and dynamic update, the research aimed to ensure both security and efficiency of the data stored on the cloud server.
Article
Computer Science, Artificial Intelligence
Yifeng Zheng, Weibo Wang, Songlei Wang, Xiaohua Jia, Hejiao Huang, Cong Wang
Summary: The popularity of cloud computing has led to the outsourcing of database services, but this also brings the risk of data privacy violation. Existing research mostly focuses on secure keyword search in encrypted databases, ignoring the support for secure skyline query processing. This paper proposes SecSkyline, a system framework that uses lightweight cryptography to achieve fast privacy-preserving skyline queries over encrypted outsourced databases. Extensive experiments show that SecSkyline significantly outperforms the state-of-the-art in query latency, with up to 813x improvement.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Theory & Methods
Helan Liang, Bincheng Ding, Yanhua Du, Fanzhang Li
Summary: This paper proposes a novel approach for parallel optimization of QoS-aware big service processes with the discovery of skyline services. By introducing two algorithms to filter candidate services and find the best execution plan, the approach achieves efficient and accurate service process execution.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2021)
Article
Computer Science, Information Systems
Yonis Gulzar, Ali A. Alwan, Hamidah Ibrahim, Sherzod Turaev, Sharyar Wani, Arjumand Bano Soomo, Yasir Hamid
Summary: Skyline queries are widely used in contemporary database applications to retrieve non-dominated tuples. However, in dynamic and incomplete databases, challenges such as losing the transitivity property of the skyline technique and cyclic dominance testing between tuples may arise. This paper introduces the Incomplete Dynamic Skyline Algorithm (IDSA), incorporating pruning and selecting superior local skylines as optimization techniques to reduce the number of domination tests. Extensive experiments show that IDSA outperforms existing solutions in terms of both domination tests and processing time.
Article
Computer Science, Artificial Intelligence
Xu Chen, Brett Wujek
Summary: In this paper, we propose a novel unified framework called AutoDAL for automated distributed active learning to address multiple challenging problems in active learning. The framework is able to handle limited labeled data, imbalanced datasets, automatic hyperparameter selection, and scalability to big data. Experimental results show that the proposed AutoDAL algorithm achieves significantly better performance compared to several state-of-the-art AutoML approaches and active learning algorithms.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Computer Science, Information Systems
Zhaoman Liu, Lei Wu, Weizhi Meng, Hao Wang, Wei Wang
Summary: With the maturity of Internet-of-Things technology, location-based service (LBS) is rapidly developing in intelligent terminal devices, but the large amount of spatial data generated poses a burden on providers; outsourcing spatial data to cloud server has become a new trend, but faces issues of data disclosure and query disclosure; the proposed ARQ scheme allows efficient range query while protecting data and user privacy, applicable to various forms of spatial data, and has practical significance.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Information Systems
Mohamed Yassin, Hakima Ould-Slimane, Chamseddine Talhi, Hanifa Boucheneb
Summary: IT service providers are transitioning towards cloud computing, with SaaS referring to cloud service-oriented web applications. The shift towards computation outsourcing and multi-tenancy introduces new security challenges, requiring providers to integrate IDS and secure tenants individually.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2022)
Article
Computer Science, Information Systems
Romaric Duvignau, Bastian Havers, Vincenzo Gulisano, Marina Papatriantafilou
Summary: The study aims to efficiently localize relevant data in a vehicular fleet for analysis applications by leveraging the edge processing paradigm. The algorithms developed by the team outperform baseline algorithms in terms of efficiency and configurability, providing significant speedups and reduced computational overhead.
Article
Engineering, Biomedical
Supriya Supriya, Siuly Siuly, Hua Wang, Yanchun Zhang
Summary: Epilepsy, a chronic brain disorder, poses challenges in diagnosis and treatment. Graph-theory based automated epilepsy detection methods have emerged as a promising approach to analyze the complex nature of EEG signals and understand brain activity. This paper provides a comprehensive review of such methods, aiming to assist neurologists and researchers in improving epilepsy diagnosis and developing intelligent systems.
IEEE REVIEWS IN BIOMEDICAL ENGINEERING
(2023)
Article
Computer Science, Information Systems
Le Sun, Jin Wu, Yang Xu, Yanchun Zhang
Summary: A key challenge in physiological signal processing for the Internet of Medical Things is the dynamic distribution changes of physiological signals, and patients' data vulnerability to disclosures. To address these problems, a meta continual learning method called MetaCL is proposed, which includes a shared feature extractor, a knowledge base, a micro classifier module, and a blockchain module. Experimental results show that MetaCL achieves a refined classification performance of 98.35% and outperforms other state-of-the-art works. It also demonstrates effective backward transfer and privacy protection in the Internet of Medical Things through a blockchain-based engineering application.
INFORMATION SCIENCES
(2023)
Article
Health Care Sciences & Services
Shangzhi Xiong, Wei Jiang, Ruilin Meng, Chi Hu, Hui Liao, Yongchen Wang, Chang Cai, Xinyi Zhang, Pengpeng Ye, Yanqiuzi Ma, Tingzhuo Liu, Dandan Peng, Jiajuan Yang, Li Gong, Qiujun Wang, David Peiris, Limin Mao, Maoyi Tian
Summary: The study assessed China's primary health care (PHC) system to understand the factors influencing the uptake of the National Essential Public Health Service Package (NEPHSP) for hypertension and type-2 diabetes (T2DM) management. The results showed that despite the government's efforts to strengthen the PHC system, there are still many barriers, including insufficient and under-qualified PHC personnel, gaps in medicines and equipment, fragmented health information systems, low trust and utilization of PHC among residents, challenges in coordinated and continuous care, and lack of cross-sectorial collaborations.
LANCET REGIONAL HEALTH-WESTERN PACIFIC
(2023)
Article
Medicine, Research & Experimental
Dandan Peng, Tingmei Zhao, Weiqi Hong, Minyang Fu, Cai He, Li Chen, Wenyan Ren, Hong Lei, Jingyun Yang, Aqu Alu, Yanghong Ni, Jian Liu, Jiong Li, Wei Wang, Guobo Shen, Zhiwei Zhao, Li Yang, Jinliang Yang, Zhenling Wang, Yoshimasa Tanaka, Guangwen Lu, Xiangrong Song, Xiawei Wei
Summary: BA.4 and BA.5 (BA.4/5), the subvariants of Omicron, are more transmissible and have stronger immune evasion capability compared to BA.1. Hence, a third booster vaccination is urgently needed for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) due to these variants. Heterologous boosters, especially with a third heterologous protein subunit booster, may provide more effective immunity. In this study, a Delta full-length spike protein sequence-based mRNA vaccine was used as the priming shot, and a recombinant trimeric receptor-binding domain (RBD) protein vaccine (RBD-HR/trimer) was developed as a third heterologous booster. The heterologous group showed higher neutralizing antibody titers and stronger cellular immune response against BA.4/5-included SARS-CoV-2 variants compared to the homologous mRNA group. The RBD-HR/trimer vaccine is a suitable candidate for booster immune injection.
Article
Biology
Yinyao Dai, Yaguan Qian, Fang Lu, Bin Wang, Zhaoquan Gu, Wei Wang, Jian Wan, Yanchun Zhang
Summary: Recent studies show that medical images are susceptible to adversarial attacks due to the complexity and high resolution of lesion features. To address this issue, a simple and effective method called global attention noise injection (GATN) is proposed, which enhances lesion features and locally smooths the model to defend against attacks. GATN demonstrates improved robustness and outperforms existing defense methods on various datasets, achieving high accuracy under different attack scenarios.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Engineering, Electrical & Electronic
Le Sun, Chenyang Li, Bo Liu, Yanchun Zhang
Summary: This paper presents a learning framework called C-DGAM for multi-label classification of mHealth data in DTMN. C-DGAM captures complex class relationships and improves classification performance through a dynamic graph attention module. Experimental results demonstrate that C-DGAM achieves leading performance on multiple datasets.
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
(2023)
Article
Engineering, Civil
Le Sun, Kai Liu
Summary: This paper proposes an end-to-end two-stage text spotting model named Center TextSpotter, which does not involve region proposal networks and recurrent neural networks. It improves the performance of text recognition through weakly supervised training and feature fusion methods.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xi Chen, Xiangmin Zhou, Jeffrey Chan, Lei Chen, Timos Sellis, Yanchun Zhang
Summary: Complex social event summarization is a significant problem with practical applications in crisis management, rumor control, and government policy tracking. This paper proposes an online approach called SOMA, which comprehensively summarizes complex social events by considering multiple attributes simultaneously. By utilizing deep learning, a summary generator, and a location estimation method, our proposed approach outperforms existing solutions in terms of effectiveness and efficiency.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Biology
Lucas W. H. Sun, Halida Thanveer Asana Marican, Lih Khiang Beh, Hongyuan Shen
Summary: The secA5 transgenic zebrafish model enables direct visualization of radiation-induced apoptosis and possesses the structural complexity of a vertebrate brain, offering a precise and effective method for investigating normal tissue radioprotectants in vivo.
INTERNATIONAL JOURNAL OF RADIATION BIOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Le Sun, Yuhang Li, Min Zheng, Zhaoyi Zhong, Yanchun Zhang
Summary: In the fields of civil and military, remote sensing image fusion is widely used to improve the quality of images. Multimodal image fusion, which combines information from different wavebands, is commonly employed in remote sensing image fusion to obtain synthetic images with rich information. However, current fusion networks often lack spatial or texture features. To address this issue, a new method called MCnet is proposed, which focuses on multiscale fusion and also incorporates objective functions to enhance fusion quality.
Article
Engineering, Civil
Le Sun, Jiancong Liang, Chunjiong Zhang, Di Wu, Yanchun Zhang
Summary: This paper proposes a deep learning-based time series classification method for intelligent transportation systems supported by 6G. The difficulties of labeling time series data in 6G-supported ITS are discussed, and a pre-training strategy called Meta-transfer metric Learning using Scale function (MLS) is introduced to improve the convergence rate and reduce the computational cost of pre-training.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
