Article
Computer Science, Information Systems
Weiwei Cai, Fazhi He, Xiao Lv, Yuan Cheng
Summary: Multi-user collaborative editors are essential computer-aided tools for human-to-human collaboration, with selective undo being a critical utility. Operational transformation (OT) is commonly used in collaborative editors to address concurrency and consistency issues. However, designing an efficient and correct OT algorithm that can handle both normal do operations and user-initiated undo operations remains a significant challenge, as these two types of operations can interfere with each other in multiple ways. This paper introduces a semi-transparent selective undo algorithm that separates the processing of do operations from undo operations, with formal proofs demonstrating its effectiveness under established criteria. Theoretical analysis and experimental evaluation highlight the superior performance of the proposed algorithm compared to previous OT-based selective undo algorithms.
FRONTIERS OF COMPUTER SCIENCE
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Eric Brattli, Weihai Yu
Summary: Collaborative applications supporting eventual consistency may temporarily violate global invariants, and undo and redo are generic tools to restore global invariants. Replicated registers allow concurrent read and write at different sites. Currently, there is limited undo and redo support for eventually consistent replicated registers.
COOPERATIVE DESIGN, VISUALIZATION, AND ENGINEERING (CDVE 2021)
(2021)
Article
Computer Science, Software Engineering
Weiwei Cai, Fazhi He, Shangxu Yang, Xiao Lv, Yuan Cheng
Summary: Co-editors are collaborative systems that allow people to edit shared documents simultaneously over networks. This work proposes an optimized AST algorithm called ASTO to address the metadata overhead problem in using the AST technique.
SOFTWARE-PRACTICE & EXPERIENCE
(2022)
Proceedings Paper
Engineering, Electrical & Electronic
Ivan I. Figurin, Amir A. Kiamov
Summary: This article introduces a graphical application based on Python to address the complexity and errors encountered when working with documents. Emphasizing ease of use, the tool does not require special knowledge and relies on open-source work.
PROCEEDINGS OF THE 2021 IEEE CONFERENCE OF RUSSIAN YOUNG RESEARCHERS IN ELECTRICAL AND ELECTRONIC ENGINEERING (ELCONRUS)
(2021)
Proceedings Paper
Cheuk Yin Phipson Lee, Zhuohao Zhang, Jaylin Herskovitz, JooYoung Seo, Anhong Guo
Summary: Collaborative document editing tools are widely used, but blind users face challenges in gaining collaboration awareness. Through co-design sessions, we created CollabAlly, a system that assists blind users in accessing collaboration awareness. By centralizing information and simplifying operations, CollabAlly improves collaboration access.
PROCEEDINGS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI' 22)
(2022)
Proceedings Paper
Computer Science, Cybernetics
Matt-Heun Hong, Lauren A. Marsh, Jessica L. Feuston, Janet Ruppert, Jed R. Brubaker, Danielle Albers Szafr
Summary: Interpretive scholars generate knowledge from text corpora by manually sampling documents and applying codes. Machine learning can help scale data sampling and analysis, but there are concerns that algorithms may disrupt interpretive scholarship. In this research, a human-centered design approach is used to address these concerns and build a system called Scholastic, which incorporates a machine-assisted clustering algorithm for interpretive text analysis. The resulting coding schema serves as structured metadata to guide hierarchical document and word clusters inferred from the corpus.
PROCEEDINGS OF THE 35TH ANNUAL ACM SYMPOSIUM ON USER INTERFACE SOFTWARE AND TECHNOLOGY, UIST 2022
(2022)
Article
Computer Science, Information Systems
Jing Bai, Zhiwen Zeng, Tian Wang, Shaobo Zhang, Neal N. Xiong, Anfeng Liu
Summary: In this article, a trust-based active notice task offloading (TANTO) scheme is proposed to provide trust and low-delay task offloading for resource-limited IoT devices in areas with no available communication infrastructure. The main innovations of TANTO include a novel task offloading mechanism, a trust calculation and reasoning method, and an online UAV trajectory optimization algorithm. Experimental results show that TANTO outperforms previous studies in terms of task completion rate, tasks' average completion time, and UAV's flight cost.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Weiwei Fang, Wenyuan Xu, Chongchong Yu, Neal N. Xiong
Summary: The advent of Deep Neural Networks (DNNs) has empowered numerous computer-vision applications, but it is challenging to efficiently deploy and execute DNNs in industrial scenarios due to computational intensity and resource constraints. This article presents EdgeDI, a framework for executing DNN inference in a partitioned, distributed manner on a cluster of Industrial Internet-of-Things (IIoT) devices. EdgeDI achieves high inference performance through model compression based on deep architecture design and distributed inference based on adaptive workload partitioning.
ACM TRANSACTIONS ON INTERNET TECHNOLOGY
(2023)
Article
Chemistry, Analytical
Chunzhi Wang, Shaowen Xing, Rong Gao, Lingyu Yan, Naixue Xiong, Ruoxi Wang
Summary: In this paper, a novel disentangled dynamic deviation transformer network ((DTN)-T-3) is proposed for anomaly detection of multivariate time series. The network utilizes multiscale dynamic inter-sensor dependencies and long-term temporal dependencies to improve the accuracy of the multivariate time series prediction.
Article
Computer Science, Theory & Methods
Jiayi Yu, Zeyuan Li, Naixue Xiong, Shaobo Zhang, Anfeng Liu, Athanasios V. Vasilakos
Summary: Mobile Crowd Sensing (MCS) platform is a promising computing paradigm that recruits participants to sense and report data for building and providing services to consumers. However, malicious attacks based on false data and uncertain behaviors of participants can harm cybersecurity and reduce the platform's benefit. This paper proposes a Time, Reliability and Truth-aware Online Auction (TRT-OA) mechanism that considers these properties to ensure cybersecurity and maximize platform benefit. The TRT-OA mechanism achieves computational efficiency, budget feasibility, truthfulness, individual rationality, and strategy-proofness, resulting in a 44.06% increase in benefit compared to OMG and TDMC.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2023)
Article
Engineering, Electrical & Electronic
Shiwen Zhang, Wang Hu, Wei Liang, Changjian Lei, Neal N. Xiong
Summary: In this study, an efficient intelligent control algorithm based on the back-propagation neural network (BP) is proposed. The STM32F103 is used as the central controller and multiple sensors are utilized to collect environmental information. The algorithm integrates remote control, voice keywords, and buttons for intelligent control. It also includes a motor drive intelligence control algorithm based on BP neural network (MCBP) to improve control accuracy. An application (APP) is developed to display environmental data and enable various intelligent control functions. Extensive experiments confirm the accuracy and efficiency of the proposed algorithm compared to normal control and programmable logic controller control methods.
IET COMMUNICATIONS
(2023)
Article
Computer Science, Information Systems
Xin Liu, Xiaomeng Liu, Neal Xiong, Dan Luo, Gang Xu, Xiubo Chen
Summary: With the development of cloud computing and other modern technologies, collaborative computing between data is increasing, which brings more attention to privacy protection and secure multi-party computation. This paper proposes a secure search protocol based on graph shape, using the cut-choose method and zero-knowledge proof. The proposed protocol achieves secure graph search and matching while resisting malicious attacks. Experimental simulations demonstrate high execution efficiency.
Article
Computer Science, Information Systems
Xin Liu, Jianwei Kong, Dan Luo, Neal Xiong, Gang Xu, Xiubo Chen
Summary: This paper proposes an Intelligent Semi-Honest System (ISHS) for secret matching against malicious adversaries. It designs a secure computation protocol based on the semi-honest model for the secret matching of text strings using a new digital encoding method and an ECC encryption algorithm. It also presents a text string matching protocol under the malicious model that utilizes the cut-and-choose method and zero-knowledge proof to resist malicious behaviors. The correctness and security of the protocol are analyzed, and it is shown to be more efficient and practical compared to existing algorithms. Secure text matching has important engineering applications.
Article
Computer Science, Information Systems
Fengze Cai, Qiang Hu, Renjie Zhou, Neal Xiong
Summary: Relation extraction is a crucial technology for intelligent information extraction, benefiting intelligent communication systems. Existing approaches suffer from the limitation of one-sided sentence embedding in their final prediction vector. This paper proposes an innovative model, REEGAT, which integrates enhanced word embedding from graph neural networks to improve relation extraction performance.
Article
Computer Science, Information Systems
Xikun Jiang, Neal N. Xiong, Xudong Wang, Chenhao Ying, Fan Wu, Yuan Luo
Summary: This paper proposes an efficient and flexible online service platform architecture for data classification and investigates pricing design to maximize revenue. It addresses three significant challenges and proposes DIVINE, a query-based data classification service with an online pricing mechanism. Experimental results demonstrate that DIVINE outperforms existing pricing mechanisms in terms of revenue.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Shiming He, Meng Guo, Zhuozhou Li, Ying Lei, Siyuan Zhou, Kun Xie, Neal N. Xiong
Summary: KPI clustering is important for AIOps when dealing with a large number of KPIs. This approach divides KPIs into classes and applies the same model to detect anomalies or predict outcomes for the KPIs in each class, reducing computational overhead. However, irregular KPIs caused by varying sampling strategies have not been fully addressed. This study proposes an iterative clustering scheme based on matrix factorization to solve the problem of clustering irregular KPIs and achieves higher NMI compared to non-iterative clustering.
INFORMATION SCIENCES
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Kaiyin Zhu, Neal N. Xiong, Mingming Lu
Summary: Weakly-supervised image semantic segmentation is a popular technology in computer vision and deep learning today. It aims to train a model with only coarse or sparse annotations, assigning labels to pixels through refinement or propagation. It has a wide range of applications, but fully-supervised semantic segmentation is expensive, leading to a focus on weakly-supervised methods. This paper reviews the state-of-the-art research, introduces datasets and models, and analyzes existing problems and future directions in weakly-supervised semantic segmentation.
2023 IEEE 9TH INTL CONFERENCE ON BIG DATA SECURITY ON CLOUD, BIGDATASECURITY, IEEE INTL CONFERENCE ON HIGH PERFORMANCE AND SMART COMPUTING, HPSC AND IEEE INTL CONFERENCE ON INTELLIGENT DATA AND SECURITY, IDS
(2023)
Article
Computer Science, Information Systems
Ziqing Xia, Zhangyang Gao, Anfeng Liu, Neal N. Xiong
Summary: In this paper, an asymmetric quorum-based neighbor discovery (AQND) protocol is proposed to reduce delay, improve energy utilization and lifetime, and outperform previous strategies in main performance indicators.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiaohuan Liu, Anfeng Liu, Shaobo Zhang, Tian Wang, Neal N. Xiong
Summary: This paper proposes a delay differentiated services routing (DDSR) scheme to reduce the deployment costs for wireless sensor networks (WSNs) with wake-up radio (WuR) functionality, while meeting the delay requirement of forwarding urgent data and maintaining a long lifetime.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Artificial Intelligence
Qinghua Gu, Yan Wang, Peipei Wang, Xuexian Li, Lu Chen, Neal N. Xiong, Di Liu
Summary: This paper proposes a new ensemble clustering method that combines the influence of cluster level and the base clustering level in a unified framework. The method inserts a global weighting strategy into a local ensemble cluster learning framework, improving the robustness and stability of clustering.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Engineering, Civil
Run Liu, Anfeng Liu, Zhenzhe Qu, Neal N. Xiong
Summary: This paper proposes a UAV-enabled Computing-Communications Intelligent Offloading (UAV-CCIO) scheme to achieve energy-efficient task offloading in a UAV-enabled edge network. The scheme selects task gathering nodes and utilizes an optimization strategy to reduce the energy consumption of the UAV. Experimental simulations demonstrate that the performance of the UAV-CCIO scheme is superior to existing schemes.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
