Category: Published

Call for Manuscripts:

Special Issue on Fault Tolerance in Cloud/Edge/Fog Computing in Future Internet, an international peer-reviewed open access monthly journal published by MDPI.

Informational Flyer

https://blogs.mtu.edu/icc/files/2021/04/ali-ebnenasir-call-for-papers-032521-sm.pdf

Deadline

April 20, 2021

Author Notification

June 10, 2021

Website

mdpi.com/journal/futureinternet/special_issues/FT_CEFC

Collection Editors

• Dr. Ali Ebnenasir, Michigan Technological University
• Dr. Sandeep S. Kulkarni, Michigan State University

Keywords

• Fault tolerance
• Cloud computing
• Edge computing
• Resource-constrained devices
• Distributed protocols
• State replication

Topics

Including, but not limited to:

• Faults and failures in cloud and edge computing.
• State replication on edge devices under the scarcity of resources.
• Fault tolerance mechanism on the edge and in the cloud.
• Models for the predication of service latency and costs in distributed fault-tolerant protocols on the edge and in the cloud.
• Fault-tolerant distributed protocols for resource management of edge devices.
• Fault-tolerant edge/cloud computing.
• Fault-tolerant computing on low-end devices.
• Load balancing (on the edge and in the cloud) in the presence of failures.
• Fault-tolerant data intensive applications on the edge and the cloud.
• Metrics and benchmarks for the evaluation of fault tolerance mechanisms in cloud/edge computing.

Background

The Internet of Things (IoT) has brought a new era of computing that permeates in almost every aspect of our lives. Low-end IoT devices (e.g., smart sensors) are almost everywhere, monitoring and controlling the private and public infrastructure (e.g., home appliances, urban transportation, water management system) of our modern life. Low-end IoT devices communicate enormous amount of data to the cloud computing centers through intermediate devices, a.k.a. edge devices, that benefit from stronger computational resources (e.g., memory, processing power).

To enhance the throughput and resiliency of such a three-tier architecture (i.e., low-end devices, edge devices and the cloud), it is desirable to perform some tasks (e.g., storing shared objects) on edge devices instead of delegating everything to the cloud. Moreover, any sort of failure in this three-tier architecture would undermine the quality of service and the reliability of services provided to the end users.

Scope

Theoretical and experimental methods that incorporate fault tolerance in cloud and edge computing, which have the potential to improve the overall robustness of services in three-tier architectures.

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website (https://www.mdpi.com/user/login/). Once you are registered, click here to go to the submission form (https://susy.mdpi.com/user/manuscripts/upload/?journal=futureinternet).

Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page.

Please visit the Instructions for Authors page before submitting a manuscript.

The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English.

Authors may use MDPI’s English editing service prior to publication or during author revisions.

eLife, a prestigious journal in cell biology, has published a paper co-written by Sangyoon Han, “Pre-complexation of talin and vinculin without tension is required for efficient nascent adhesion maturation.”

Dr. Han is an assistant professor in the Biomedical Engineering department, and a member of the Data Sciences research group of the Institute of Computing and Cybersystems (ICC).

View the paper here.

eLife is a non-profit organization created by funders and led by researchers. Their mission is to accelerate discovery by operating a platform for research communication that encourages and recognizes the most responsible behaviors.

A paper co-authored by Sidike Paheding, Applied Computing, has been published in the journal, IEEE Access. “Trends in Deep Learning for Medical Hyperspectral Image Analysis,” was available for early access on March 24, 2021.

The paper discusses the implementation of deep learning for medical hyperspectral imaging.

Co-authors of the paper are Uzair Khan, Colin Elkin, and Vijay Devabhaktuni, all with the Department of Electrical and Computer Engineering, Purdue University Northwest.

Abstract

Deep learning algorithms have seen acute growth of interest in their applications throughout several fields of interest in the last decade, with medical hyperspectral imaging being a particularly promising domain. So far, to the best of our knowledge, there is no review paper that discusses the implementation of deep learning for medical hyperspectral imaging, which is what this work aims to accomplish by examining publications that currently utilize deep learning to perform effective analysis of medical hyperspectral imagery.

This paper discusses deep learning concepts that are relevant and applicable to medical hyperspectral imaging analysis, several of which have been implemented since the boom in deep learning. This will comprise of reviewing the use of deep learning for classification, segmentation, and detection in order to investigate the analysis of medical hyperspectral imaging. Lastly, we discuss the current and future challenges pertaining to this discipline and the possible efforts to overcome such trials.

DOI: 10.1109/ACCESS.2021.3068392

IEEE Access is a multidisciplinary, applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE’s fields of interest. Supported by article processing charges, its hallmarks are a rapid peer review and publication process with open access to all readers.

A scholarly paper co-authored by Assistant Professor Sidike Paheding, Applied Computing, has been published in the April 2021 issue of ISPRS Journal of Photogrammetry and Remote Sensing, published by Science Direct.

The title of the paper is, “Field-scale crop yield prediction using multi-temporal WorldView-3 and PlanetScope satellite data and deep learning.”

View the article abstract here.

Paheding is a member of the Institute of Computer and Cybersystems’s (ICC) Center for Data Sciences.

A publication by Associate Professor Yakov Nekrich, Computer Science, has been accepted to the 53rd Annual ACM Symposium on Theory of Computing (STOC).

The paper, “Optimal-Time Dynamic Planar Point Location in Connected Subdivisions,” describes an optimal-time solution for the dynamic point location problem and answers an open problem in computational geometry. 

The data structure described in the paper supports queries and updates in logarithmic time. This result is optimal in some models of computation.  Nekrich is the sole author of the publication.

The annual ACM Symposium on Theory of Computing (STOC), is the flagship
conference of SIGACT, the Special Interest Group on Algorithms and
Computation Theory, a special interest group of the Association for
Computing Machinery (ACM).

A scholarly paper co-authored by Assistant Professor Sidike Paheding, Applied Computing, is one of two papers to receive the 2020 Best Paper Award from the open-access journal Electronics, published by MDPI.

The paper presents a brief survey on the advances that have occurred in the area of Deep Learning.

Paheding is a member of the Institute of Computing and Cybersystems’ (ICC) Center for Data Sciences (DataS).

Co-authors of the article, “A State-of-the-Art Survey on Deep Learning Theory and Architectures,” are Md Zahangir Alom, Tarek M. Taha, Chris Yakopcic, Stefan Westberg, Mst Shamima Nasrin, Mahmudul Hasan, Brian C. Van Essen, Abdul A. S. Awwal, and Vijayan K. Asari. The paper was published March 5, 2019, appearing in volume 8, issue 3, page 292, of the journal.

View and download the paper here.

Papers were evaluated for originality and significance, citations, and downloads. The authors receive a monetary award , a certificate, and an opportunity to publish one paper free of charge before December 31, 2021, after the normal peer review procedure.

Electronics is an international peer-reviewed open access journal on the science of electronics and its applications. It is published online semimonthly by MDPI.

MDPI, a scholarly open access publishing venue founded in 1996, publishes 310 diverse, peer-reviewed, open access journals.

Paper Abstract

In recent years, deep learning has garnered tremendous success in a variety of application domains. This new field of machine learning has been growing rapidly and has been applied to most traditional application domains, as well as some new areas that present more opportunities. Different methods have been proposed based on different categories of learning, including supervised, semi-supervised, and un-supervised learning. Experimental results show state-of-the-art performance using deep learning when compared to traditional machine learning approaches in the fields of image processing, computer vision, speech recognition, machine translation, art, medical imaging, medical information processing, robotics and control, bioinformatics, natural language processing, cybersecurity, and many others.

This survey presents a brief survey on the advances that have occurred in the area of Deep Learning (DL), starting with the Deep Neural Network (DNN). The survey goes on to cover Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL). Additionally, we have discussed recent developments, such as advanced variant DL techniques based on these DL approaches. This work considers most of the papers published after 2012 from when the history of deep learning began.

Furthermore, DL approaches that have been explored and evaluated in different application domains are also included in this survey. We also included recently developed frameworks, SDKs, and benchmark datasets that are used for implementing and evaluating deep learning approaches. There are some surveys that have been published on DL using neural networks and a survey on Reinforcement Learning (RL). However, those papers have not discussed individual advanced techniques for training large-scale deep learning models and the recently developed method of generative models.

Assistant Professor Susanta Ghosh, ME-EM, has published the article, “Interpretable machine learning model for the deformation of multiwalled carbon nanotubes,” in the APS publication, Physical Review B.

Co-authors of the paper are Upendra Yadav and Shashank Pathrudkar. The article was published January 11, 2021.

Ghosh is a member of the Institute of Computing and Cybersystems’ Center for Data Sciences.

Article Abstract

In the paper, researchers present an interpretable machine learning model to predict accurately the complex rippling deformations of multiwalled carbon nanotubes made of millions of atoms. Atomistic-physics-based models are accurate but computationally prohibitive for such large systems. To overcome this bottleneck, we have developed a machine learning model. The proposed model accurately matches an atomistic-physics-based model whereas being orders of magnitude faster. It extracts universally dominant patterns of deformation in an unsupervised manner. These patterns are comprehensible and explain how the model predicts yielding interpretability. The proposed model can form a basis for an exploration of machine learning toward the mechanics of one- and two-dimensional materials.

APS Physics advances and diffuses the knowledge of physics for the benefit of humanity, promote physics, and serve the broader physics community.

Physical Review B (PRB) is the world’s largest dedicated physics journal, publishing approximately 100 new, high-quality papers each week. The most highly cited journal in condensed matter physics, PRB provides outstanding depth and breadth of coverage, combined with unrivaled context and background for ongoing research by scientists worldwide.

by University Marketing and Communications

If you would like to be highlighted in Michigan Tech’s next Research magazine, send University Marketing and Communications more info via this Google form about your book (or album, performance, or other longform scholarly work in mixed media).

Include a link, a cover image, and a way for us to contact you with questions. All submissions will be reviewed by University Marketing and Communications. Published projects must be research focused, published in 2020, and authored by members of the Michigan Tech community. Incomplete submissions will not be included.

A research paper by Assistant Professor Sidike Paheding, Applied Computing, is to be published in the November 2020 issue of the journal, Expert Systems and Applications.

An in-press version of the paper, “Binary Chemical Reaction Optimization based Feature Selection Techniques for Machine Learning Classification Problems,” is available online.

Highlights

• A chemical reaction optimization (CRO) based feature selection (FS) technique is proposed.
• The proposed CRO based FS technique is improvised using particle swarm optimization.
• Performance evaluation of proposed techniques on benchmark datasets gives promising results.

Paper Abstract

Feature selection is an important pre-processing technique for dimensionality reduction of high-dimensional data in machine learning (ML) field. In this paper, we propose a binary chemical reaction optimization (BCRO) and a hybrid binary chemical reaction optimization-binary particle swarm optimization (HBCRO-BPSO) based feature selection techniques to optimize the number of selected features and improve the classification accuracy.

Three objective functions have been used for the proposed feature selection techniques to compare their performances with a BPSO and advanced binary ant colony optimization (ABACO) along with an implemented GA based feature selection approach called as binary genetic algorithm (BGA). Five ML algorithms including K-nearest neighbor (KNN), logistic regression, Naïve Bayes, decision tree, and random forest are considered for classification tasks.

Experimental results tested on eleven benchmark datasets from UCI ML repository show that the proposed HBCRO-BPSO algorithm improves the average percentage of reduction in features (APRF) and average percentage of improvement in accuracy (APIA) by 5.01% and 3.83%, respectively over the existing BPSO based feature selection method; 4.58% and 3.12% over BGA; and 4.15% and 2.27% over ABACO when used with a KNN classifier.

Expert Systems With Applications, published by Science Direct/Elsevier, is a refereed international journal whose focus is on exchanging information relating to expert and intelligent systems applied in industry, government, and universities worldwide. The journal’s Impact factor is 5.4.