About
113
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Introduction
Jinwei Liu is a Tenure-Track Assistant Professor in the CIS department at Florida A&M University. His research interests include cloud computing, AI/ML, big data, cybersecurity, wireless networks, social networks, HPC, IoT, health care, etc. His work has led to multiple top journal/conference publications (e.g., TON, TMC, TPDS, TC, NPJ Digital Medicine, etc.). He has served regularly on program chairs/committees of several international conferences/workshops and the EiC of journals.
Current institution
Additional affiliations
May 2012 - August 2016
August 2011 - May 2012
August 2011 - December 2015
Publications
Publications (113)
At Historically Black Colleges and Universities (HBCUs), biology is the most frequently chosen major among science disciplines and many STEM majors also require a general biology course. These courses often include traditional lectures and lab activities, which can lead to low success rates and diminished enthusiasm among students. This reliance on...
Non-Technical Abstract: Research Initiation Awards provide support for junior and mid-career faculty at Historically Black Colleges and Universities who are building new research programs or redirecting and rebuilding existing research programs. It is expected that the award helps to further the faculty member's research capability and effectivenes...
The use of digital twins (DTs) has proliferated across various fields and industries, with a recent surge in the healthcare sector. The concept of digital twin for health (DT4H) holds great promise to revolutionize the entire healthcare system, including management and delivery, disease treatment and prevention, and health well-being maintenance, u...
With the widespread adoption of electric vehicles (EVs), the demand for public charging services is steadily increasing. Consequently, the development of effective charging scheduling strategies, aimed at optimizing the utilization of limited charging infrastructure, has become a key problem. Considering the diversity of user demands, we propose a...
Time serves as the foundation of modern society and will continue to grow in value in the future world. Unlike previous research papers, authors delve into various time sources, ranging from atomic time and GPS time to quartz time. Specifically, we explore the time uncertainty associated with the four major Global Navigation Satellite Systems. In e...
The spread of COVID-19 misinformation (e.g., fake news) on social media poses a serious public health risk. It is critical to identify COVID-19 misinformation. In this paper, we propose SmartEye: a novel machine learning based (ML-based) approach for detecting COVID-19 misinformation on Twitter for mitigating public health risk. To test the approac...
Network intrusion detection is used to detect unauthorized activities on a digital network, with which the cybersecurity teams of organizations can then kick-start prevention protocols to protect the security of their networks and data. In real-life scenarios, due to the lack of high-quality attack instance data, building an in-depth network intrus...
Besides the frequentist methods, Bayesian approaches are applied to solve the non-parametric problems. This work focuses on comparing the univariate Kernel density estimation (KDE) with Bayesian density estimation using Dirichlet process mixtures (DPM) of Gaussians, which are non-parametric ways to estimate the probability density function. The res...
The integration of cyber-physical systems (CPS) has been extremely advantageous to society, it merges the attention of cybersecurity for vehicles as a timely concern as a matter of public and individual. The failure of any vehicle system could have a serious impact on vehicle control and cause undesired consequences. With the growing demand for sec...
Coronavirus disease 2019 (COVID-19) is a new disease caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It is a global pandemic that has claimed the death of 1,536,957 human beings worldwide including 287,842 deaths in the United States as of December 3, 2020. It has become a major threat to the medical community and...
Abstract: With social distance policies in place due to the outbreak of Coronavirus Disease (COVID-19) pandemic, virtual communication has become a critical source of misinformation. Social media remarkably facilitates the production and diffusion of COVID-19 misinformation. An increasing number of people heavily rely on social media platforms for...
Task scheduling in cloud computing can directly affect the resource usage and operational cost of a system. To improve the efficiency of task executions in a cloud, various metaheuristic algorithms, as well as their variations, have been proposed to optimize the scheduling. In this work, for the first time, we apply the latest metaheuristics WOA (t...
This research provides a thorough analysis of health disparities in the US based on multiple COVID-19 datasets. We examine the structural, social, and constructural determinants of health in the US to assist in ascertaining why disparities occur in infection and death rates due to COVID-19 pandemic. Extensive experimental results show the effective...
Data availability is one of the most important performance factors in cloud storage systems. To enhance data availability, replication is a common approach to handle the machine failures. However, previously proposed replication schemes cannot effectively handle both correlated and non-correlated machine failures, especially while increasing the da...
There is an emerging class of public health applications where non-health data from mobile apps, such as social media data, are used in subsequent models that identify threats to public health. On one hand, these models require accurate data, which would have an immense impact on public health. On the other hand, results from these models could com...
There is an emerging class of public health applications where non-health data from mobile apps, such as social media data, are used in subsequent models that identify threats to public health. On one hand, these models require accurate data, which would have an immense impact on public health. On the other hand, results from these models could com...
Conference Presentation--ISI'19
Social media are increasingly reflecting and influencing the behavior of human and financial market. Cognitive hacking leverages the influence of social media to spread deceptive information with an intent to gain abnormal profits illegally or to cause losses. Measuring the information content in financial social media can be useful for identifying...
Large-scale data stores are an increasingly important component of cloud datacenter services. However, cloud storage system usually experiences data loss, hindering data durability. Three-way random replication is commonly used to lead better data durability in cloud storage systems. However, three-way random replication cannot effectively handle c...
![Fig. 1: Diverse dependency relations among tasks [6].](profile/Jinwei-Liu-2/publication/326257912/figure/fig1/AS:741294120919041@1553749699620/Diverse-dependency-relations-among-tasks-6_Q320.jpg)
Task scheduling and preemption are two important functions in data-parallel clusters. Though directed acyclic graph (DAG) task dependencies are common in data-parallel clusters, previous task scheduling and preemption methods do not fully utilize such task dependency to increase throughput since they simply schedule precedent tasks prior to their d...
The prevalent use of social media facilitates the spreading of malicious messages that may inject misleading information to divert normal market operations. However, identifying these threats from noisy social media poses a challenge due to the rarity of confirmed positive threat cases and severe imbalance of ordinary datasets. It is critical to re...
Mobile crowdsensing serves as a critical building block for emerging Internet of Things (IoT) applications. However, the sensing devices continuously generate a large amount of data, which consumes much resources (e.g., bandwidth, energy, and storage) and may sacrifice the Quality-of-Service (QoS) of applications. Prior work has demonstrated that t...
As a popular routing protocol in wireless sensor networks (WSNs), greedy routing has received great attention. The previous works characterize its data deliverability in WSNs by the probability of all nodes successfully sending their data to the base station. Their analysis, however, neither provides the information of the quantitative relation bet...
Software-defined networks are constantly evolving due to the updates such as network function (NF) state updates, VM migrations. Network functions virtualization (NFV) with software-defined networking (SDN) has the capability of accurately monitoring and manipulating network traffic, and reducing operating cost. However, it cannot effectively handl...
Replication is a common approach to enhance data availability in cloud storage systems. Previously proposed replication schemes cannot effectively handle both correlated and non-correlated machine failures while increasing the data availability with the limited resource. The schemes for correlated machine failures must create a constant number of r...
Cloud storage system usually experiences data loss, hindering data durability. Three-way random replication is commonly used to prevent data loss in cloud storage systems. However, it cannot effectively handle correlated machine failures. Although Copyset Replication and Tiered Replication can reduce data loss in correlated and independent failures...
In cloud systems, efficient resource provisioning is needed to maximize the resource utilization while reducing the Service Level Objective (SLO) violation rate, which is important to cloud providers for high profit. Several methods have been proposed to provide efficient provisioning. However, the previous methods do not consider leveraging the co...
Questions
Questions (4)
Call for Papers
The first IEEE World Forum on Public Safety Technology (IEEE WF-PST 2024) aims to foster engagement and dialog among researchers and practitioners on addressing current and future needs and improving current and emerging technologies for public safety applications. Consideration will be given to identifying the importance and value for new technologies that could be used for Public Safety applications. The track (Track 2: Public Safety in Transportation) seeks original ideas and submissions and will follow the policies as per the main WF-PST conference policies.
The track topics may include the following but are not limited to the topics listed below.
Track Topics:
● V2V, V2X vehicular networking for public safety applications
● Intelligent transportation systems
● Intelligent transport monitoring systems
● Intelligent traffic management for traffic safety
● Risk assessment of autonomous transport systems
● Networking alarm systems for vehicle anti-theft
Important Dates:
Paper Submission Deadline: 18 December 2023 (extended, firm)
Notification of Acceptance: 29 January 2024
Camera-ready Version: 25 March 2024
Paper Publication Guidelines
Conference papers that are accepted and presented at the conference will be published in IEEE Xplore. WF-PST requests that all submissions follow the IEEE Manuscript Templates for Conference Proceedings template to aid with reviews and publishing. Papers are requested to be 5-6 pages in length, in English. Papers of length up to 8 pages will be accepted for an additional fee.
To be published in the 2024 WF-PST Conference Proceedings and to be eligible for publication in IEEE Xplore, an author of an accepted paper is required to register for the conference, and the paper must be presented by an author of that paper at the conference unless granted permission for a substitute presenter arranged in advance and who is qualified both to present and answer questions. Other options, such as on-demand recordings or virtual presentations for those who are not able to travel, are being decided on a case-by-case basis.
The use of artificial intelligence (AI)–generated text in an article shall be disclosed in the acknowledgements section of any paper submitted to an IEEE Conference or Periodical. The sections of the paper that use AI-generated text shall have a citation to the AI system used to generate the text.
This and other submission policies can be found in the IEEE Author Center Conference Author Submission Policies page.
IMPORTANT IEEE POLICY ANNOUNCEMENT: The IEEE reserves the right to exclude a paper from distribution after the conference (including its removal from IEEE Explore) if the paper is not presented at the conference.
Paper Submission link: https://edas.info/newPaper.php?c=31286&track=119434
Detailed Information:
2024 IEEE World Forum on Public Safety Technology (WF-PST)
14-15 May 2024 | Washington, DC, USA
Call for Posters
Scope and Motivation
The 2024 IEEE World Forum on Public Safety Technology (WF-PST) welcomes extended abstracts for presentation as Posters (https://ieee-wfpst.org/call-for-papers/call-for-posters/). We seek provocative ideas that challenge existing approaches to Public Safety technologies and applications. We invite contributions from researchers and practitioners from industry and academia.
Submission Guidelines
Posters must be submitted via EDAS. Submitted posters should be written in the English language, with a maximum page limit of 3 printed pages, including all the figures, references and appendices, and not published or under review elsewhere. The 3-page extended abstract describing the Poster will be evaluated on significance, presentation, and interest to the conference attendees. Posters longer than 3 pages will not be reviewed.
Use the standard IEEE Conference templates for Microsoft Word or LaTeX formats found at: https://www.ieee.org/conferences/publishing/templates.html.
Regardless of the source of your Poster formatting, you must submit your Poster in the PDF format. The Poster must be printed clearly and legibly, including all the figures, on standard black-and-white printers. Reviewers are not required to read your submission in color.
SUBMIT HERE
Important Dates
Poster Submission Deadline: 18 December 2023 (anywhere on Earth)
Notification of Acceptance: 22 January 2024 (anywhere on Earth)
Camera-ready Version: 25 March 2024 (anywhere on Earth)
On behalf of the organizing committee, thank you for your participation at the 6th ACM/IEEE Conference on Internet of
Things Design and Implementation (IoTDI) which was held from May 18 – 21, 2021. We greatly appreciate your participation, and we hope you enjoyed the conference.
To view the session recordings, please visit https://www.youtube.com/watch?v=VYUvPBivLQI&list=PLTXOFWg43hoVPVO4z9VzLE38Q3oYrcpIS.
Sincerely,
Jinwei Liu
IoTDI 2021 Publicity Chair
We are organizing a workshop on Mobile Crowdsensing (MCSSA 2019) collocated with IEEE MASS 2019 (https://sites.google.com/view/mass2019/home). The First Mobile Crowdsensing workshop was collocated with ACM SenSys 2017 (http://sensys.acm.org/2017/workshops/). The extended paper submission deadline is September 27, 2019 (Please refer to the website of the workshop for more details). Submission of original research is welcomed from both academic and commercial communities. Please feel free to distribute it to those who might be interested.
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MCSSA 2019 WORKSHOP
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Call for Papers:
With the rapid development of wireless communication and sensor technology, ubiquitous mobile devices equipped with increasingly rich sensors have more powerful computing and sensing abilities. Mobile crowdsensing (MCS), as a special form of crowdsourcing where communities contribute sensing information and human intelligence using mobile devices to form a body of knowledge, has received extensive attentions from both academia and industry. Various MCS applications come forth, such as indoor positioning, environment monitoring, and transportation, etc. MCS spans a wide spectrum of user involvement, from collecting sensor measurements with no user intervention to requiring active participation of mobile users. Despite the growing interest and some commercial success in MCS, MCS still faces significant challenges such as motivation and incentives, low data quality (incomplete data, noisy data, redundant data, etc.), privacy, security and data integrity, localized analytics, resource limitations, context-awareness, sensing resource management, aggregate analytics, and public safety, etc. This workshop aims to bring together researchers in the field of mobile crowdsensing to exchange ideas and advance the research frontier. We invite submissions in the following non-exclusive list of topics:
Topics of Interest (but not limited to):
§ Mobile crowdsensing frameworks, platforms and systems
§ Mobile crowdsensing applications and case study
§ Mobile crowdsensing, participatory and opportunistic sensing and data collection
§ Design and analysis of sensing scheduling algorithms in mobile crowdsensing
§ Sensing resource management in mobile crowdsensing
§ Mobile crowdsensing data communication and sharing
§ Incentive mechanism and economic systems for mobile crowdsensing
§ Distributed and parallel algorithms for processing big crowd sensed data in mobile crowdsensing
§ Big crowd sensed data processing, storage and mining
§ Machine learning and data mining algorithms and applications for sensed data in mobile crowdsensing
§ Architecture and framework for management of crowd sensed data in mobile crowdsensing
§ Human centric data management and analytics models in mobile crowdsensing
§ Big data spatial-temporal analysis in mobile crowdsensing
§ Data quality and pricing in mobile crowdsensing
§ Security, data privacy preservation, and trust management in mobile crowdsensing
§ Location and mobility prediction and inference
§ Social networks in mobile crowdsensing
§ Mobile crowdsensing with IoT for smart cities and ecology
§ Novel mobile crowdsensing and human centric data management applications
Important Dates:
Paper Submission Deadline: September 27, 2019 (Anywhere on Earth)
Notification of Acceptance: October 8, 2019
Camera-ready Version: October 15, 2019 (Firm Deadline)
Many thanks for your help!
Best regards,
Jinwei Liu
