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An Empirical Study on Deployment Faults of Deep Learning Based Mobile Applications [article]

Zhenpeng Chen and Huihan Yao and Yiling Lou and Yanbin Cao and Yuanqiang Liu and Haoyu Wang and Xuanzhe Liu
2021 arXiv   pre-print
Deep Learning (DL) is finding its way into a growing number of mobile software applications.  ...  In contrast, faults related to the deployment of DL models on mobile devices (named as deployment faults of mobile DL apps) have not been well studied.  ...  INTRODUCTION In recent years, deep learning (DL) has emerged as one of the most popular and promising techniques and has been widely adopted in various applications [1] - [5] .  ... 
arXiv:2101.04930v2 fatcat:hsy53lqbcreg3dbkpqtocnhjca

A Comprehensive Study on Challenges in Deploying Deep Learning Based Software [article]

Zhenpeng Chen and Yanbin Cao and Yuanqiang Liu and Haoyu Wang and Tao Xie and Xuanzhe Liu
2020 arXiv   pre-print
Deep learning (DL) becomes increasingly pervasive, being used in a wide range of software applications.  ...  Existing studies focus on the development of DL software and extensively analyze faults in DL programs. However, the deployment of DL software has not been comprehensively studied.  ...  INTRODUCTION Deep learning (DL) has been used in a wide range of software applications from different domains, including natural language processing [78] , speech recognition [92] , image processing  ... 
arXiv:2005.00760v4 fatcat:auxizifdrbd6ppahv6ji4cvxs4

Parallel machine learning algorithm using fine-grained-mode Spark on a Mesos big data cloud computing software framework for mobile robotic intelligent fault recognition

Guangming Xian
2020 IEEE Access  
An accurate and efficient intelligent fault diagnosis of mobile robotic roller bearings can significantly enhance the reliability and safety of mechanical systems.  ...  To improve the efficiency of intelligent fault classification of mobile robotic roller bearings, this paper proposes a parallel machine learning algorithm using fine-grained-mode Spark on a Mesos big data  ...  This study can serve as a theoretical basis for the automatic and rapid diagnosis of fault patterns, and has practical significance for promoting the combined application of parallel machine learning,  ... 
doi:10.1109/access.2020.3007499 fatcat:bchkaywhvzgpvhouzgaeavor6q

IEEE Access Special Section Editorial: Data Mining for Internet of Things

Chun-Wei Tsai, Mu-Yen Chen, Francesco Piccialli, Tie Qiu, Jason J. Jung, Patrick C. K. Hung, Sherali Zeadally
2021 IEEE Access  
In another study, ''Design and evaluation of a deep learning recommendation based augmented reality system for teaching programming and computational thinking,'' by Lin and Chen, a deep learning based  ...  In this study, Samy and his colleagues presented an attack detection framework on the fog layer to detect several IoT cyber-attacks by using a certain number of deep learning models.  ... 
doi:10.1109/access.2021.3090137 fatcat:cnleukmukfgexmkwx7tino2k5y

Research on Blending learning Design for Promoting Deep Learning

Huan Chenglin, College of computer science, Yangtze University, No.1 Nanhuan Road, Jingzhou, China., Chen Jianwei, Institute of jingzhou, No.85 Xueyuan Road, Jingzhou, China.
2021 Journal of Educational Research and Reviews  
Guided by the theory of deep learning, based on MOOC resource platform and teaching management platform, this study constructs a "3-stage-2 platform" blended learning mode for deep learning in three stages  ...  : before class, in class and after class, puts forward teaching strategies to promote deep learning, and based on this mode, takes the computer network course as an example to carry out the blended teaching  ...  and deployment application.  ... 
doi:10.33495/jerr_v9i7.21.137 fatcat:gkrbpjmcarfd5oqipiuvl4zzhu

AMBROSia: An Overview and Recent Results

Leana Golubchik, David Caron, Abhimanyu Das, Amit Dhariwal, Ramesh Govindan, David Kempe, Carl Oberg, Abhishek Sharma, Beth Stauffer, Gaurav Sukhatme, Bin Zhang
2011 Journal of Algorithms & Computational Technology  
In our AMBROSia project, we focus on a class of observing systems which are embedded into the environment, consist of stationary and mobile sensors, and react to collected observations by reconfiguring  ...  In this paper, we give an overview of AMBROSia. and the NSF EIA-0121141 grant.  ...  Finally, learning-based methods (based on Hidden Markov Models) are trained to statistically detect and identify classes of faults.  ... 
doi:10.1260/1748-3018.5.4.583 fatcat:45fneizwrjgunl3r5ts2qehfo4

Orchestrating the Development Lifecycle of Machine Learning-Based IoT Applications: A Taxonomy and Survey

Bin Qian, Jie Su, Zhenyu Wen, Devki Nandan Jha, Yinhao Li, Yu Guan, Deepak Puthal, Philip James, Renyu Yang, Albert Y. Zomaya, Omer Rana, Lizhe Wang (+2 others)
2020 ACM Computing Surveys  
Some of these achievements are based on the combination of DL and RL, i.e., Deep Reinforcement Learning.  ...  Function-based selection aims to choose an appropriate concept based on their functional difference.  ...  Finally, [32] delivers system-level implementation of FL based on previously mentioned techniques. It is able to train deep learning models with local data stored on mobile phones.  ... 
doi:10.1145/3398020 fatcat:zzgfcjxjxbhnhf53dmlo63rs3i

Deep Learning Software Engineering: State of Research and Future Directions [article]

Prem Devanbu, Matthew Dwyer, Sebastian Elbaum, Michael Lowry, Kevin Moran, Denys Poshyvanyk, Baishakhi Ray, Rishabh Singh, Xiangyu Zhang
2020 arXiv   pre-print
Given the current transformative potential of research that sits at the intersection of Deep Learning (DL) and Software Engineering (SE), an NSF-sponsored community workshop was conducted in co-location  ...  with the 34th IEEE/ACM International Conference on Automated Software Engineering (ASE'19) in San Diego, California.  ...  Acknowledgement of Sponsorship This material is based upon work supported by the NSF under Grants No. CCF-1927679 and  ... 
arXiv:2009.08525v1 fatcat:w3lt3j6iavdx5df6xvwyvz6xsm

Configurations and Diagnosis for Ultra-Dense Heterogeneous Networks: From Empirical Measurements to Technical Solutions [article]

Wei Wang, Lin Yang, Qian Zhang, Tao Jiang
2018 arXiv   pre-print
Based on these observations from our measurements, we investigate possible models and corresponding challenges, and propose a heterogeneity-aware scheme that takes into account the disparity of user mobility  ...  In this article, we investigate the fine-grained traffic patterns of mobile users by analyzing the network data containing millions of subscribers and covering thousands of cells in a large metropolitan  ...  To this end, we conduct an empirical study on large-scale anonymized IP flow traces from a tier-1 cellular provider in China, and envision the impact of heterogeneity in future ultra-dense HetNets.  ... 
arXiv:1804.10505v1 fatcat:s7akzwdxxzfcddqcpfnruvitm4

Deep learning models for human centered computing in fog and mobile edge networks

B. B. Gupta, Dharma P. Agrawal, Shingo Yamaguchi
2018 Journal of Ambient Intelligence and Humanized Computing  
concepts and applicationsDeep learning algorithms for learning the behavior analysis in human centered computing in fog and mobile edge networks • Deep learning for dynamic processes in human centered  ...  • Deep learning for information revelation and privacy in human centered computing in fog and mobile edge networks • Deep learning for industrial system in fog and mobile edge networks • Deep learning  ... 
doi:10.1007/s12652-018-0919-8 fatcat:pnpq3apvqzbpzktwjvpur75ygq

Editorial for June 2020 Issue

S. V. Raghavan, Navakanta Bhat, Krishna Sivalingam, Sanjiva Prasad
2020 CSI Transactions on ICT  
Of these the research related to Electronic System Design and Manufacturing concentrates on Materials and & S. V. Raghavan sv.raghavan@gov.in 1 CSI Transactions on ICT,  ...  The program itself is titled after Sir Visvesvaraya, about whom I had the opportunity to write in one of our earlier issues.  ...  Of these the research related to Electronic System Design and Manufacturing concentrates on Materials and  ... 
doi:10.1007/s40012-020-00310-7 fatcat:h7u3aejaa5gsfk44uqthuc2s6q

Deep Learning in Mobile and Wireless Networking: A Survey

Chaoyun Zhang, Paul Patras, Hamed Haddadi
2019 IEEE Communications Surveys and Tutorials  
Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains.  ...  We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems.  ...  This system enables deployments of complex deep learning applications over large-scale mobile networks.  ... 
doi:10.1109/comst.2019.2904897 fatcat:xmmrndjbsfdetpa5ef5e3v4xda

Orchestrating the Development Lifecycle of Machine Learning-Based IoT Applications: A Taxonomy and Survey [article]

Bin Qian, Jie Su, Zhenyu Wen, Devki Nandan Jha, Yinhao Li, Yu Guan, Deepak Puthal, Philip James, Renyu Yang, Albert Y. Zomaya, Omer Rana, Lizhe Wang (+2 others)
2020 arXiv   pre-print
This paper provides a comprehensive and systematic survey on the development lifecycle of ML-based IoT application.  ...  Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML techniques unlock complete potentials of IoT with intelligence, and IoT applications increasingly feed data collected by  ...  Finally, [32] delivers system-level implementation of FL based on previously mentioned techniques. It is able to train deep learning models with local data stored on mobile phones.  ... 
arXiv:1910.05433v5 fatcat:ffvjipmylve6feuzdbav2syxfu

Deep Learning in Mobile and Wireless Networking: A Survey [article]

Chaoyun Zhang, Paul Patras, Hamed Haddadi
2019 arXiv   pre-print
Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains.  ...  We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems.  ...  This system enables deployments of complex deep learning applications over large-scale mobile networks.  ... 
arXiv:1803.04311v3 fatcat:awuvyviarvbr5kd5ilqndpfsde

Editorial: Second Quarter 2021 IEEE Communications Surveys and Tutorials

Dusit Tao Niyato
2021 IEEE Communications Surveys and Tutorials  
Therefore, recent years have seen a spike of Deep Learning based RF sensing applications.  ...  a taxonomy of the current state-of-the art of deep learning applications in RF based sensing supported by a comprehensive review.  ... 
doi:10.1109/comst.2021.3078013 fatcat:5sqcayv4njgyhe6sqeryw4h5na
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