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Software Architecture for ML-based Systems: What Exists and What Lies Ahead [article]

Henry Muccini, Karthik Vaidhyanathan
2021 arXiv   pre-print
The increasing usage of machine learning (ML) coupled with the software architectural challenges of the modern era has resulted in two broad research areas: i) software architecture for ML-based systems  ...  , which focuses on developing architectural techniques for better developing ML-based software systems, and ii) ML for software architectures, which focuses on developing ML techniques to better architect  ...  their constant efforts during the design, implementation, and deployment of the system.  ... 
arXiv:2103.07950v2 fatcat:dtybnizkavge7cv6wif3qe7p4m

Using Digital Twins and Intelligent Cognitive Agencies to Build Platforms for Automated CxO Future of Work [article]

John-Thones Amenyo
2018 arXiv   pre-print
Building platforms for CxO automation is challenging.  ...  AI, Algorithms and Machine based automation of executive functions in enterprises and institutions is an important niche in the current considerations about the impact of digitalization on the future of  ...  engineering, game, gamification systems); self-* automonomics, computational heuristics (G.  ... 
arXiv:1808.07627v1 fatcat:s74bfjresbc3lipkgcrkzsrobe

Hardware-assisted Machine Learning in Resource-constrained IoT Environments for Security: Review and Future Prospective

Georgios Kornaros
2022 IEEE Access  
This work investigates into machine learning (ML) and deep learning (DL) methodologies for IoT device security and examine the benefits, drawbacks, and potential.  ...  Machine learning (ML) based intrusion and anomaly detection has lately gained traction due to its capacity to cope with encrypted and rapidly developing threat techniques.  ...  Second, for assault detection, sensor patterns are used in a self-learning, simplified method.  ... 
doi:10.1109/access.2022.3179047 fatcat:damwrncpzzbxzamtghwlmrg6v4

Evaluation of Software Architectures under Uncertainty

Dalia Sobhy, Rami Bahsoon, Leandro Minku, Rick Kazman
2021 ACM Transactions on Software Engineering and Methodology  
evaluation as multiple evaluations of the software architecture that begins at the early stages of the development and is periodically and repeatedly performed throughout the lifetime of the software system  ...  To remedy this lack, we present a set of necessary requirements for continuous evaluation and describe some examples.  ...  Therefore, another important element when developing a continuous architecture evaluation framework is leveraging machine learning techniques.  ... 
doi:10.1145/3464305 fatcat:5y2ixmfq55crdgqiocp7v3vq2i

D3.1 – State-of-the-Art and Market Analysis Report

ASSIST-IoT Consortium
2021 Zenodo  
service, within a reset range without external control, • Self-learning: using machine learning techniques such as unsupervised learning which does not require external control, • Self-awareness (also  ...  The self-* dynamic grid systems addressed p2p techniques for self-management/adaptability/dynamicity, on-demand resource allocation, heterogeneity, and fault tolerance.  ... 
doi:10.5281/zenodo.6705158 fatcat:xote6pjzubcvxo4aqxxbraooxi

Internet of Things 2.0: Concepts, Applications, and Future Directions

Ian Zhou, Imran Makhdoom, Negin Shariati, Muhammad Ahmad Raza, Rasool Keshavarz, Justin Lipman, Mehran Abolhasan, Abbas Jamalipour
2021 IEEE Access  
SONs provide machine learning-driven self-configuration, self-optimization, and self-healing functionalities [111] .  ...  However, machine learning and deep learning based IoT systems are susceptible to "Butterfly Effect."  ... 
doi:10.1109/access.2021.3078549 fatcat:g5jkc5p6tngpfonbhtsbcjipai

Mentor's Musings on Artificial Computational Intelligence and the Internet of Everything

N. Kishor Narang
2020 IEEE Internet of Things Magazine  
There are two types of machine intelligence: the artificial type based on hard computing techniques, and the computational type, based on soft computing methods, which enable adaptation to many situations  ...  The need of the hour is to teach how to design any electronics products and systems by teaching them how to leverage their theoretical learning to design practical solutions.  ... 
doi:10.1109/miot.2020.9319622 fatcat:tmun7rdel5a4rfcsznenubasmy

Guest Editorial: Empowering Sustainable Energy Infrastructures via AI-Assisted Wireless Communications

Moayad Aloqaily, Salil Kanhere, Yang Xiao, Ismaeel Al Ridhawi, Wael Guibene
2021 IEEE wireless communications  
This Special Issue considers the adaptation of intelligent learning techniques within wireless networks to reduce power consumption for both wireless communication and wireless nodes.  ...  The DBFL architec- ture has been evaluated on multiple scenarios for proving its efficacy concerning the centralized federated learning approach. • Using AI in energy systems enables machines to learn  ... 
doi:10.1109/mwc.2021.9690482 fatcat:y6qnd7iyofb2fdwu4tdwjyqtmi

Opportunity to Leverage Information-as-an-Asset in the IoT -- The Road Ahead

Sylvain Kubler, Min-Jung Yoo, Cyril Cassagnes, Kary Framling, Dimitris Kiritsis, Mark Skilton
2015 2015 3rd International Conference on Future Internet of Things and Cloud  
In this respect, this paper introduces the major "laws of information" and discusses how these laws can be leveraged to their full extend thanks to the IoT possibilities.  ...  (including subscriptions and apps, new analytics for cognitive capabilities. . . ).  ...  As previously stated, one promising branch of the Artificial Intelligence is "Context-Awareness" that offers huge innovation potential to leverage system decision-making and self-adaptation capabilities  ... 
doi:10.1109/ficloud.2015.63 dblp:conf/ficloud/KublerYCFKS15 fatcat:x56k65awdzb6hfdnabrny2axty

Leveraging Digital Twin Technology in Model-Based Systems Engineering

Azad Madni, Carla Madni, Scott Lucero
2019 Systems  
The paper discusses the benefits of integrating digital twins with system simulation and Internet of Things (IoT) in support of MBSE and provides specific examples of the use and benefits of digital twin  ...  This paper presents an overall vision and rationale for incorporating digital twin technology into MBSE.  ...  In addition, it has unsupervised machine learning capability to discern Adaptive Digital Twin Level 3 is the Adaptive Digital Twin.  ... 
doi:10.3390/systems7010007 fatcat:2zax2p44vnbczjcptrsscs7wzu

Data Science and Artificial Intelligence

Irena Atov, Kwang-Cheng Chen, Ahmed E. Kamal, Malamati Louta
2020 IEEE Communications Magazine  
properties necessitated for AI-based systems include robustness and efficiency, AI goal alignment, active learning and explainable AI techniques.  ...  and Joerg Widmer, proposes a general machine learning-based framework that leverages AI and ML tools to efficiently manage and optimize the performance of highly dynamic wireless networks.  ...  properties necessitated for AI-based systems include robustness and efficiency, AI goal alignment, active learning and explainable AI techniques.  ... 
doi:10.1109/mcom.2020.9141187 fatcat:kszk7ystfbdo3bphkjunwr4ns4

Tutorial: Open-Source EDA and Machine Learning for IC Design: A Live Update

Abdelrahman Hosny, Andrew B. Kahng
2020 2020 33rd International Conference on VLSI Design and 2020 19th International Conference on Embedded Systems (VLSID)  
Towards that goal, he investigates machine learning techniques (specifically, reinforcement learning) for optimizing EDA flows with no human in the loop. Abdelrahman received a  ...  Importantly, open-source EDA and the goal of "self-driving", no-humans IC design puts a spotlight on machine learning that reduces design effort and schedule.  ...  Designing Hardware Accelerators for Deep Neural Networks (By Manish Pandey) As machine learning is used in increasingly diverse applications, ranging from IoT edge devices to self-driving vehicles, specialized  ... 
doi:10.1109/vlsid49098.2020.00016 dblp:conf/vlsid/HosnyK20 fatcat:gsvvnrgbr5dpdjwkkx63jnf2f4

Iot and Big Data Framework for Paddy Cultivation

2019 International journal of recent technology and engineering  
At the end the paper would develop a framework for approaching the IoT and Big Data in paddy cultivation.  ...  The framework would outline the architecture components, protocols, communication interfaces which could be leveraged for paddy cultivation.  ...  This would also enable integration capability with other services like Streaming, Machine Learning and Deep Learning Capabilities.  ... 
doi:10.35940/ijrte.b3611.078219 fatcat:w2cuymwdnvclzdkbcoe46eyvr4

From 5G to 6G Technology: Meets Energy, Internet-of-Things and Machine Learning: A Survey

Mohammed Najah Mahdi, Abdul Rahim Ahmad, Qais Saif Qassim, Hayder Natiq, Mohammed Ahmed Subhi, Moamin Mahmoud
2021 Applied Sciences  
In this work, we have considered the applications of three of the highly demanding domains, namely: energy, Internet-of-Things (IoT) and machine learning.  ...  This work presents a thorough review of 370 papers on the application of energy, IoT and machine learning in 5G and 6G from three major libraries: Web of Science, ACM Digital Library, and IEEE Explore.  ...  Machine learning approaches that are both intelligent and adaptive are employed to enable 6G networks to be self-sufficient while also capturing insights and comprehension about their surrounding environment  ... 
doi:10.3390/app11178117 fatcat:4vtzn5cae5eqtnzobtvzysi6mm

Emergent Behaviors in the Internet of Things: The Ultimate Ultra-Large-Scale System

Damian Roca, Daniel Nemirovsky, Mario Nemirovsky, Rodolfo Milito, Mateo Valero
2016 IEEE Micro  
Doing so leads to the building of Ultra-Large Scale Systems (ULSS) in several verticals, including Autonomous Vehicles, Smart Cities, and Smart Grids.  ...  The Internet of Things (IoT) promises a plethora of new services and applications.  ...  Rodolfo Milito, a senior Technical Leader with the Chief Technology and Architecture Office of Cisco Systems, is currently engaged in IoT, Big Data and Machine Learning, and working on a distributed compute  ... 
doi:10.1109/mm.2016.102 fatcat:ognrp2ly3jhufedk4sox5ctkpe
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