Guest Editorial: Graph-powered machine learning in future-generation computing systems

2021 ◽  
Author(s):  
Shirui Pan ◽  
Shaoxiong Ji ◽  
Di Jin ◽  
Feng Xia ◽  
Philip S. Yu
Keyword(s):  
Machine Learning ◽  
Guest Editorial ◽  
Future Generation ◽  
Computing Systems ◽  
Generation Computing

scholarly journals Application of Reservoir Computing for the Modeling of Nano-Contact Vortex Oscillator

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1315 ◽  
Author(s):  
Ali Rida Ismail ◽  
Slavisa Jovanovic ◽  
Sébastien Petit-Watelot ◽  
Hassan Rabah
Keyword(s):  
Spectral Analysis ◽  
Power Spectral Analysis ◽  
Future Generation ◽  
Realistic Model ◽  
Reservoir Computing ◽  
Computing Systems ◽  
Power Spectral ◽  
Generation Computing ◽  
Highly Nonlinear ◽  
Dc Current

The Nano-Contact Vortex Oscillator (NCVO) is a highly nonlinear spintronic device that can depict chaotic and nonchaotic behaviors according to the current flowing through it. The potential use of such a device in the future-generation computing systems requires the knowledge of a realistic model capable of describing its exact dynamics. In this paper, we firstly investigate the behavior of NCVO based on the power spectral analysis. Furthermore, we propose and demonstrate two efficient approaches of reservoir computing for the modeling of such a device. The performances of the proposed models are addressed in two ways. First, the generated time-varying signals are compared with the simulated magnetizations of the NCVO at different operating currents. Then, the power spectral analysis of one of the two models is carried out to examine its overall behavior over the complete DC current operating range and its ability to diagnose chaotic and non-chaotic regimes. The proposed models show quite promising results that can be counted on for further research.

scholarly journals A Survey on Machine-Learning Based Security Design for Cyber-Physical Systems

2021 ◽  
Vol 11 (12) ◽  
pp. 5458
Author(s):  
Sangjun Kim ◽  
Kyung-Joon Park
Keyword(s):  
Machine Learning ◽  
Physical System ◽  
Large Scale ◽  
Computing System ◽  
Future Research ◽  
Physical Security ◽  
Computing Systems ◽  
Physical Systems ◽  
Security Research ◽  
Simultaneous Control

A cyber-physical system (CPS) is the integration of a physical system into the real world and control applications in a computing system, interacting through a communications network. Network technology connecting physical systems and computing systems enables the simultaneous control of many physical systems and provides intelligent applications for them. However, enhancing connectivity leads to extended attack vectors in which attackers can trespass on the network and launch cyber-physical attacks, remotely disrupting the CPS. Therefore, extensive studies into cyber-physical security are being conducted in various domains, such as physical, network, and computing systems. Moreover, large-scale and complex CPSs make it difficult to analyze and detect cyber-physical attacks, and thus, machine learning (ML) techniques have recently been adopted for cyber-physical security. In this survey, we provide an extensive review of the threats and ML-based security designs for CPSs. First, we present a CPS structure that classifies the functions of the CPS into three layers: the physical system, the network, and software applications. Then, we discuss the taxonomy of cyber-physical attacks on each layer, and in particular, we analyze attacks based on the dynamics of the physical system. We review existing studies on detecting cyber-physical attacks with various ML techniques from the perspectives of the physical system, the network, and the computing system. Furthermore, we discuss future research directions for ML-based cyber-physical security research in the context of real-time constraints, resiliency, and dataset generation to learn about the possible attacks.

scholarly journals Guest Editorial: Special Issue on Data Analytics and Machine Learning for Network and Service Management—Part II

2021 ◽  
Vol 18 (1) ◽  
pp. 775-779
Author(s):  
Nur Zincir-Heywood ◽  
Giuliano Casale ◽  
David Carrera ◽  
Lydia Y. Chen ◽  
Amogh Dhamdhere ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analytics ◽  
Service Management ◽  
Guest Editorial ◽  
Special Issue ◽  
Editorial Special Issue

scholarly journals Security of new generation computing systems

2013 ◽  
Vol 26 (8) ◽  
pp. 1475-1476 ◽  
Author(s):  
Li Xu ◽  
Elisa Bertino ◽  
Yi Mu
Keyword(s):  
Computing Systems ◽  
Generation Computing ◽  
New Generation

scholarly journals Machine Learning at the Network Edge: A Survey

2022 ◽  
Vol 54 (8) ◽  
pp. 1-37
Author(s):  
M. G. Sarwar Murshed ◽  
Christopher Murphy ◽  
Daqing Hou ◽  
Nazar Khan ◽  
Ganesh Ananthanarayanan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Systems ◽  
Major Research ◽  
Sensors And Actuators ◽  
Computing Systems ◽  
Privacy Concerns ◽  
Iot Devices ◽  
Cloud Servers ◽  
Typical Solution ◽  
Operational Aspects

Resource-constrained IoT devices, such as sensors and actuators, have become ubiquitous in recent years. This has led to the generation of large quantities of data in real-time, which is an appealing target for AI systems. However, deploying machine learning models on such end-devices is nearly impossible. A typical solution involves offloading data to external computing systems (such as cloud servers) for further processing but this worsens latency, leads to increased communication costs, and adds to privacy concerns. To address this issue, efforts have been made to place additional computing devices at the edge of the network, i.e., close to the IoT devices where the data is generated. Deploying machine learning systems on such edge computing devices alleviates the above issues by allowing computations to be performed close to the data sources. This survey describes major research efforts where machine learning systems have been deployed at the edge of computer networks, focusing on the operational aspects including compression techniques, tools, frameworks, and hardware used in successful applications of intelligent edge systems.

scholarly journals Guest Editorial Special Issue on Multimedia Services Provision Over Future Mobile Computing Systems

2018 ◽  
Vol 12 (1) ◽  
pp. 12-15 ◽  
Author(s):  
George Mastorakis ◽  
Evangelos Pallis ◽  
Constandinos X. Mavromoustakis ◽  
Lei Shu ◽  
Joel J. P. C. Rodrigues
Keyword(s):  
Mobile Computing ◽  
Guest Editorial ◽  
Special Issue ◽  
Multimedia Services ◽  
Computing Systems ◽  
Editorial Special Issue

scholarly journals Thermodynamic Computing: An Intellectual and Technological Frontier

Proceedings ◽  
2020 ◽  
Vol 47 (1) ◽  
pp. 23
Author(s):  
Todd Hylton
Keyword(s):  
Machine Learning ◽  
Device Fabrication ◽  
Power Supplies ◽  
Self Organization ◽  
Technological Systems ◽  
Computing Systems ◽  
Cooling Systems ◽  
Computing Paradigm ◽  
Transport Losses ◽  
State Changes

Concepts from thermodynamics are ubiquitous in computing systems today—e.g., in power supplies and cooling systems, in signal transport losses, in device fabrication, in state changes, and in the methods of machine learning. Here we propose that thermodynamics should be the central, unifying concept in future computing systems. In particular, we suppose that future computing technologies will thermodynamically evolve in response to electrical and information potential in their environment and, therefore, address the central challenges of energy efficiency and self-organization in technological systems. In this article, we summarize the motivation for a new computing paradigm grounded in thermodynamics and articulate a vision for such future systems.

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