Liming Ge

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Liming Ge (12)
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CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management (1)
CIKM '24: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (1)
ICPP '22: Proceedings of the 51st International Conference on Parallel Processing (1)
IJCAI '23: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (1)
MobiCom '22: Proceedings of the 28th Annual International Conference on Mobile Computing And Networking (1)
MSWIM '19: Proceedings of the 22nd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (1)
Q2SWinet '23: Proceedings of the 19th ACM International Symposium on QoS and Security for Wireless and Mobile Networks (1)
SEC '19: Proceedings of the 4th ACM/IEEE Symposium on Edge Computing (1)

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research-article
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Published By ACM
Published By ACM
Robust Federated Unlearning
- Xinyi Sheng
  School of Computer Science, The University of Sydney, Sydney, NSW, Australia
  ,
- Wei Bao
  School of Computer Science, The University of Sydney, Sydney, NSW, Australia
  ,
- Liming Ge
  School of Computer Science, The University of Sydney, Sydney, NSW, Australia
CIKM '24: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management•October 2024, pp 2034-2044• https://doi.org/10.1145/3627673.3679817

Federated unlearning (FU) algorithms offer participants in federated learning (FL) the "right to be forgotten'' for their individual data and its impact on a collaboratively trained model. Existing FU algorithms primarily focus on accelerating the ...
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Article
Real-EVE: Real-Time Edge-Assist Video Enhancement for Joint Denoising and Super-Resolution
- Liming Ge
  https://ror.org/0384j8v12Faculty of Engineering, The University of Sydney, Sydney, Australia
  ,
- Wei Bao
  https://ror.org/0384j8v12Faculty of Engineering, The University of Sydney, Sydney, Australia
  ,
- Dong Yuan
  https://ror.org/0384j8v12Faculty of Engineering, The University of Sydney, Sydney, Australia
  ,
- Bing Bing Zhou
  https://ror.org/0384j8v12Faculty of Engineering, The University of Sydney, Sydney, Australia
Algorithms and Architectures for Parallel Processing•October 2023, pp 320-339• https://doi.org/10.1007/978-981-97-0834-5_19
Abstract
Real-time video applications have received much attention in recent years. However, the perceived quality of real-time videos in many situations is far from ideal due to two major obstacles: noise in the video frames caused by limited camera ...
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Article
3RE-Net: Joint Loss-REcovery and Super-REsolution Neural Network for REal-Time Video
- Liming Ge
  https://ror.org/0384j8v12Faculty of Engineering, The University of Sydney, Sydney, Australia
  ,
- David Zhaochen Jiang
  https://ror.org/02grkyz14Department of Electrical and Computer Engineering, University of Western Ontario, London, ON, Canada
  ,
- Wei Bao
  https://ror.org/0384j8v12Faculty of Engineering, The University of Sydney, Sydney, Australia
AI 2023: Advances in Artificial Intelligence•November 2023, pp 165-177• https://doi.org/10.1007/978-981-99-8388-9_14
Abstract
Real-time video over the Internet suffers from packet loss and low network bandwidth. The receiving side may receive down-sampled video with damaged frames. In this work, we are motivated to enhance the quality of video by joint loss recovery and ...
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Published By ACM
Published By ACM
ASRSR: Adaptive Sending Resolution and Super-resolution for Real-time Video Streaming
- Ruoyu Wu
  The University of Sydney, Sydney, NSW, Australia
  ,
- Wei Bao
  The University of Sydney, Sydney, NSW, Australia
  ,
- Liming Ge
  The University of Sydney, Sydney, NSW, Australia
  ,
- Bing Bing Zhou
  The University of Sydney, Sydney, NSW, Australia
Q2SWinet '23: Proceedings of the 19th ACM International Symposium on QoS and Security for Wireless and Mobile Networks•October 2023, pp 61-68• https://doi.org/10.1145/3616391.3622763

Real-time video streaming application has been adopted for a wide range of services in recent years. A major challenge for real-time video streaming is the low resolution and high latency caused by limited and unstable network bandwidth. A ...
1Citation
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research-article
Online task assignment with controllable processing time
- Ruoyu Wu
  School of Computer Science, The University of Sydney
  ,
- Wei Bao
  School of Computer Science, The University of Sydney
  ,
- Liming Ge
  School of Computer Science, The University of Sydney
IJCAI '23: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence•August 2023, Article No.: 607, pp 5466-5474• https://doi.org/10.24963/ijcai.2023/607

We study a new online assignment problem, called the Online Task Assignment with Controllable Processing Time. In a bipartite graph, a set of online vertices (tasks) should be assigned to a set of offline vertices (machines) under the known adversarial ...
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research-article
Published By ACM
Published By ACM
Semi-Online Multi-Machine with Restart Scheduling for Integrated Edge and Cloud Computing Systems
- Liming Ge
  The University of Sydney, Australia
  ,
- Zizhao Wang
  The University of Sydney, Australia
  ,
- Wei Bao
  The University of Sydney, Australia
  ,
- Dong Yuan
  The University of Sydney, Australia
  ,
- Nguyen H. Tran
  The University of Sydney, Australia
  ,
- Bing B. Zhou
  The University of Sydney, Australia
  ,
- Albert Y. Zomaya
  The University of Sydney, Australia
ICPP '22: Proceedings of the 51st International Conference on Parallel Processing•August 2022, Article No.: 70, pp 1-13• https://doi.org/10.1145/3545008.3545059

We study the multi-machine task scheduling problem in an integrated serverless edge and cloud computing system, where tasks can be scheduled locally on edge processors or offloaded to cloud servers, with the objective of minimizing the makespan, i.e., ...
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demonstration
Published By ACM
Published By ACM
Edge-assisted deep video denoising and super-resolution for real-time surveillance at night
- Liming Ge
  The University of Sydney, Australia
  ,
- Wei Bao
  The University of Sydney, Australia
  ,
- Dong Yuan
  The University of Sydney, Australia
  ,
- Bing B. Zhou
  The University of Sydney, Australia
MobiCom '22: Proceedings of the 28th Annual International Conference on Mobile Computing And Networking•October 2022, pp 783-785• https://doi.org/10.1145/3495243.3558754

Video surveillance cameras have been extensively deployed over the last few years. In case of incidents such as natural disaster rescue, it provides vital guidance in real-time. However, due to the limited camera hardware and network bandwidth, noise ...
1Citation
311
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research-article
Dynamic path learning in decision trees using contextual bandits
- Weiyu Ju
  Faculty of Engineering, The University of Sydney, 2006, Sydney, NSW, Australia
  ,
- Dong Yuan
  Faculty of Engineering, The University of Sydney, 2006, Sydney, NSW, Australia
  ,
- Wei Bao
  Faculty of Engineering, The University of Sydney, 2006, Sydney, NSW, Australia
  ,
- Liming Ge
  Faculty of Engineering, The University of Sydney, 2006, Sydney, NSW, Australia
  ,
- Bing Bing Zhou
  Faculty of Engineering, The University of Sydney, 2006, Sydney, NSW, Australia
World Wide Web, Volume 26, Issue 1•Jan 2023, pp 271-296 • https://doi.org/10.1007/s11280-022-01043-0
Abstract
We present a novel online decision-making solution, where the optimal path of a given decision tree is dynamically found based on the contextual bandits analysis. At each round, the learner finds a path in the decision tree by making a sequence of ...
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Published By ACM
Published By ACM
Dynamic Early Exit Scheduling for Deep Neural Network Inference through Contextual Bandits
- Weiyu Ju
  The University of Sydney, Sydney, NSW, Australia
  ,
- Wei Bao
  The University of Sydney, Sydney, NSW, Australia
  ,
- Liming Ge
  The University of Sydney, Sydney, NSW, Australia
  ,
- Dong Yuan
  The University of Sydney, Sydney, NSW, Australia
CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management•October 2021, pp 823-832• https://doi.org/10.1145/3459637.3482335

Recent advances in Deep Neural Networks (DNNs) have dramatically improved the accuracy of DNN inference, but also introduce larger latency. In this paper, we investigate how to utilize early exit, a novel method that allows inference to exit at earlier ...
11Citation
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research-article
Published By ACM
Published By ACM
eDeepSave: Saving DNN Inference using Early Exit During Handovers in Mobile Edge Environment
- Weiyu Ju
  Faculty of Engineering, The University of Sydney, Sydney, NSW
  ,
- Dong Yuan
  Faculty of Engineering, The University of Sydney, Sydney, NSW
  ,
- Wei Bao
  Faculty of Engineering, The University of Sydney, Sydney, NSW
  ,
- Liming Ge
  Faculty of Engineering, The University of Sydney, Sydney, NSW
  ,
- Bing Bing Zhou
  Faculty of Engineering, The University of Sydney, Sydney, NSW
ACM Transactions on Sensor Networks, Volume 17, Issue 3•August 2021, Article No.: 30, pp 1-28 • https://doi.org/10.1145/3447267
Recent advances in deep neural networks (DNNs) have substantially improved the accuracy of intelligent applications. One effective scheme known as DNN partition further improves the speed of the inference by partitioning the DNN to a mobile device and its ...
6Citation
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Published By ACM
SEE: Scheduling Early Exit for Mobile DNN Inference during Service Outage
- Zizhao Wang
  University of Sydney, Sydney, Australia
  ,
- Wei Bao
  University of Sydney, Sydney, Australia
  ,
- Dong Yuan
  University of Sydney, Sydney, Australia
  ,
- Liming Ge
  University of Sydney, Sydney, Australia
  ,
- Nguyen H. Tran
  University of Sydney, Sydney, Australia
  ,
- Albert Y. Zomaya
  University of Sydney, Sydney, Australia
MSWIM '19: Proceedings of the 22nd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems•November 2019, pp 279-288• https://doi.org/10.1145/3345768.3355917

In recent years, the rapid development of edge computing enables us to process a wide variety of intelligent applications at the edge, such as real-time video analytics. However, edge computing could suffer from service outage caused by the fluctuated ...
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research-article
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Published By ACM
DeepSave: saving DNN inference during handovers on the edge
- Weiyu Ju
  The University of Sydney, Sydney, NSW, Australia
  ,
- Dong Yuan
  The University of Sydney, Sydney, NSW, Australia
  ,
- Wei Bao
  The University of Sydney, Sydney, NSW, Australia
  ,
- Liming Ge
  The University of Sydney, Sydney, NSW, Australia
  ,
- Bing Bing Zhou
  The University of Sydney, Sydney, NSW, Australia
SEC '19: Proceedings of the 4th ACM/IEEE Symposium on Edge Computing•November 2019, pp 166-178• https://doi.org/10.1145/3318216.3363301

Recent advances in deep neural networks (DNNs) have substantially improved the accuracy and speed of a variety of intelligent applications, for example, real-time video classification. However, one of the challenges is how to maintain the quality of ...
7Citation
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Last 12 Months18
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Author Profile Pages

Description: The Author Profile Page initially collects all the professional information known about authors from the publications record as known by the ACM bibliographic database, the Guide. Coverage of ACM publications is comprehensive from the 1950's. Coverage of other publishers generally starts in the mid 1980's. The Author Profile Page supplies a quick snapshot of an author's contribution to the field and some rudimentary measures of influence upon it. Over time, the contents of the Author Profile page may expand at the direction of the community.
Please see the following 2007 Turing Award winners' profiles as examples:
History: Disambiguation of author names is of course required for precise identification of all the works, and only those works, by a unique individual. Of equal importance to ACM, author name normalization is also one critical prerequisite to building accurate citation and download statistics. For the past several years, ACM has worked to normalize author names, expand reference capture, and gather detailed usage statistics, all intended to provide the community with a robust set of publication metrics. The Author Profile Pages reveal the first result of these efforts.
Normalization: ACM uses normalization algorithms to weigh several types of evidence for merging and splitting names.
These include:
- co-authors: if we have two names and cannot disambiguate them based on name alone, then we see if they have a co-author in common. If so, this weighs towards the two names being the same person.
- affiliations: names in common with same affiliation weighs toward the two names being the same person.
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The more conservative the merging algorithms, the more bits of evidence are required before a merge is made, resulting in greater precision but lower recall of works for a given Author Profile. Many bibliographic records have only author initials. Many names lack affiliations. With very common family names, typical in Asia, more liberal algorithms result in mistaken merges.

Automatic normalization of author names is not exact. Hence it is clear that manual intervention based on human knowledge is required to perfect algorithmic results. ACM is meeting this challenge, continuing to work to improve the automated merges by tweaking the weighting of the evidence in light of experience.
Bibliometrics: In 1926, Alfred Lotka formulated his power law (known as Lotka's Law) describing the frequency of publication by authors in a given field. According to this bibliometric law of scientific productivity, only a very small percentage (~6%) of authors in a field will produce more than 10 articles while the majority (perhaps 60%) will have but a single article published. With ACM's first cut at author name normalization in place, the distribution of our authors with 1, 2, 3..n publications does not match Lotka's Law precisely, but neither is the distribution curve far off. For a definition of ACM's first set of publication statistics, see Bibliometrics
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ACM will expand this edit facility to accommodate more types of data and facilitate ease of community participation with appropriate safeguards. In particular, authors or members of the community will be able to indicate works in their profile that do not belong there and merge others that do belong but are currently missing.

A direct search interface for Author Profiles will be built.

An institutional view of works emerging from their faculty and researchers will be provided along with a relevant set of metrics.

It is possible, too, that the Author Profile page may evolve to allow interested authors to upload unpublished professional materials to an area available for search and free educational use, but distinct from the ACM Digital Library proper. It is hard to predict what shape such an area for user-generated content may take, but it carries interesting potential for input from the community.

Bibliometrics

The ACM DL is a comprehensive repository of publications from the entire field of computing.

It is ACM's intention to make the derivation of any publication statistics it generates clear to the user.

Average citations per article = The total Citation Count divided by the total Publication Count.
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Publication Count = all works of any genre within the universe of ACM's bibliographic database of computing literature of which this person was an author. Works where the person has role as editor, advisor, chair, etc. are listed on the page but are not part of the Publication Count.
Publication Years = the span from the earliest year of publication on a work by this author to the most recent year of publication of a work by this author captured within the ACM bibliographic database of computing literature (The ACM Guide to Computing Literature, also known as "the Guide".
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Downloads (12 months) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server over the last 12-month period for which statistics are available. The counts displayed are usually 1-2 weeks behind the current date. (12-month download counts for individual works are displayed with the individual record.)
Downloads (6 weeks) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server over the last 6-week period for which statistics are available. The counts displayed are usually 1-2 weeks behind the current date. (6-week download counts for individual works are displayed with the individual record.)

ACM Author-Izer Service

Summary Description

ACM Author-Izer is a unique service that enables ACM authors to generate and post links on both their homepage and institutional repository for visitors to download the definitive version of their articles from the ACM Digital Library at no charge.

Downloads from these sites are captured in official ACM statistics, improving the accuracy of usage and impact measurements. Consistently linking to definitive version of ACM articles should reduce user confusion over article versioning.

ACM Author-Izer also extends ACM’s reputation as an innovative “Green Path” publisher, making ACM one of the first publishers of scholarly works to offer this model to its authors.

To access ACM Author-Izer, authors need to establish a free ACM web account. Should authors change institutions or sites, they can utilize the new ACM service to disable old links and re-authorize new links for free downloads from a different site.

How ACM Author-Izer Works

Authors may post ACM Author-Izer links in their own bibliographies maintained on their website and their own institution’s repository. The links take visitors to your page directly to the definitive version of individual articles inside the ACM Digital Library to download these articles for free.

The Service can be applied to all the articles you have ever published with ACM.

Depending on your previous activities within the ACM DL, you may need to take up to three steps to use ACM Author-Izer.

For authors who do not have a free ACM Web Account:

Go to the ACM DL http://dl.acm.org/ and click SIGN UP. Once your account is established, proceed to next step.

For authors who have an ACM web account, but have not edited their ACM Author Profile page:

Sign in to your ACM web account and go to your Author Profile page. Click "Add personal information" and add photograph, homepage address, etc. Click ADD AUTHOR INFORMATION to submit change. Once you receive email notification that your changes were accepted, you may utilize ACM Author-izer.

For authors who have an account and have already edited their Profile Page:

Sign in to your ACM web account, go to your Author Profile page in the Digital Library, look for the ACM Author-izer link below each ACM published article, and begin the authorization process. If you have published many ACM articles, you may find a batch Authorization process useful. It is labeled: "Export as: ACM Author-Izer Service"

ACM Author-Izer also provides code snippets for authors to display download and citation statistics for each “authorized” article on their personal pages. Downloads from these pages are captured in official ACM statistics, improving the accuracy of usage and impact measurements. Consistently linking to the definitive version of ACM articles should reduce user confusion over article versioning.

Note: You still retain the right to post your author-prepared preprint versions on your home pages and in your institutional repositories with DOI pointers to the definitive version permanently maintained in the ACM Digital Library. But any download of your preprint versions will not be counted in ACM usage statistics. If you use these AUTHOR-IZER links instead, usage by visitors to your page will be recorded in the ACM Digital Library and displayed on your page.

FAQ

Q. What is ACM Author-Izer?
A. ACM Author-Izer is a unique, link-based, self-archiving service that enables ACM authors to generate and post links on either their home page or institutional repository for visitors to download the definitive version of their articles for free.
Q. What articles are eligible for ACM Author-Izer?
A. ACM Author-Izer can be applied to all the articles authors have ever published with ACM. It is also available to authors who will have articles published in ACM publications in the future.

Q. Are there any restrictions on authors to use this service?
A. No. An author does not need to subscribe to the ACM Digital Library nor even be a member of ACM.

Q. What are the requirements to use this service?
A. To access ACM Author-Izer, authors need to have a free ACM web account, must have an ACM Author Profile page in the Digital Library, and must take ownership of their Author Profile page.

Q. What is an ACM Author Profile Page?
A. The Author Profile Page initially collects all the professional information known about authors from the publications record as known by the ACM Digital Library. The Author Profile Page supplies a quick snapshot of an author's contribution to the field and some rudimentary measures of influence upon it. Over time, the contents of the Author Profile page may expand at the direction of the community. Please visit the ACM Author Profile documentation page for more background information on these pages.

Q. How do I find my Author Profile page and take ownership?
A. You will need to take the following steps:
- Create a free ACM Web Account
- Sign-In to the ACM Digital Library
- Find your Author Profile Page by searching the ACM Digital Library for your name
- Find the result you authored (where your author name is a clickable link)
- Click on your name to go to the Author Profile Page
- Click the "Add Personal Information" link on the Author Profile Page
- Wait for ACM review and approval; generally less than 24 hours

Q. Why does my photo not appear?
A. Make sure that the image you submit is in .jpg or .gif format and that the file name does not contain special characters

Q. What if I cannot find the Add Personal Information function on my author page?
A. The ACM account linked to your profile page is different than the one you are logged into. Please logout and login to the account associated with your Author Profile Page.

Q. What happens if an author changes the location of his bibliography or moves to a new institution?
A. Should authors change institutions or sites, they can utilize ACM Author-Izer to disable old links and re-authorize new links for free downloads from a new location.

Q. What happens if an author provides a URL that redirects to the author’s personal bibliography page?
A. The service will not provide a free download from the ACM Digital Library. Instead the person who uses that link will simply go to the Citation Page for that article in the ACM Digital Library where the article may be accessed under the usual subscription rules.

However, if the author provides the target page URL, any link that redirects to that target page will enable a free download from the Service.

Q. What happens if the author’s bibliography lives on a page with several aliases?
A. Only one alias will work, whichever one is registered as the page containing the author’s bibliography. ACM has no technical solution to this problem at this time.

Q. Why should authors use ACM Author-Izer?
A. ACM Author-Izer lets visitors to authors’ personal home pages download articles for no charge from the ACM Digital Library. It allows authors to dynamically display real-time download and citation statistics for each “authorized” article on their personal site.

Q. Does ACM Author-Izer provide benefits for authors?
A. Downloads of definitive articles via Author-Izer links on the authors’ personal web page are captured in official ACM statistics to more accurately reflect usage and impact measurements.

Authors who do not use ACM Author-Izer links will not have downloads from their local, personal bibliographies counted. They do, however, retain the existing right to post author-prepared preprint versions on their home pages or institutional repositories with DOI pointers to the definitive version permanently maintained in the ACM Digital Library.

Q. How does ACM Author-Izer benefit the computing community?
A. ACM Author-Izer expands the visibility and dissemination of the definitive version of ACM articles. It is based on ACM’s strong belief that the computing community should have the widest possible access to the definitive versions of scholarly literature. By linking authors’ personal bibliography with the ACM Digital Library, user confusion over article versioning should be reduced over time.

In making ACM Author-Izer a free service to both authors and visitors to their websites, ACM is emphasizing its continuing commitment to the interests of its authors and to the computing community in ways that are consistent with its existing subscription-based access model.

Q. How do authors access ACM Author-Izer?
A. See How to Use

Q. Why can’t I find my most recent publication in my ACM Author Profile Page?
A. There is a time delay between publication and the process which associates that publication with an Author Profile Page. Right now, that process usually takes 4-8 weeks.

Q. How does ACM Author-Izer expand ACM’s “Green Path” Access Policies?
A. ACM Author-Izer extends the rights and permissions that authors retain even after copyright transfer to ACM, which has been among the “greenest” publishers. ACM enables its author community to retain a wide range of rights related to copyright and reuse of materials. They include:
- Posting rights that ensure free access to their work outside the ACM Digital Library and print publications
- Rights to reuse any portion of their work in new works that they may create
- Copyright to artistic images in ACM’s graphics-oriented publications that authors may want to exploit in commercial contexts
- All patent rights, which remain with the original owner

Liming Ge

Liming Ge

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Robust Federated Unlearning

Real-EVE: Real-Time Edge-Assist Video Enhancement for Joint Denoising and Super-Resolution

3RE-Net: Joint Loss-REcovery and Super-REsolution Neural Network for REal-Time Video

ASRSR: Adaptive Sending Resolution and Super-resolution for Real-time Video Streaming

Online task assignment with controllable processing time

Semi-Online Multi-Machine with Restart Scheduling for Integrated Edge and Cloud Computing Systems

Edge-assisted deep video denoising and super-resolution for real-time surveillance at night

Dynamic path learning in decision trees using contextual bandits

Dynamic Early Exit Scheduling for Deep Neural Network Inference through Contextual Bandits

eDeepSave: Saving DNN Inference using Early Exit During Handovers in Mobile Edge Environment

SEE: Scheduling Early Exit for Mobile DNN Inference during Service Outage

DeepSave: saving DNN inference during handovers on the edge