scholarly journals You Only Look Once, But Compute Twice: Service Function Chaining for Low-Latency Object Detection in Softwarized Networks

2021 ◽  
Vol 11 (5) ◽  
pp. 2177
Author(s):  
Zuo Xiang ◽  
Patrick Seeling ◽  
Frank H. P. Fitzek
Keyword(s):  
Machine Learning ◽  
Object Detection ◽  
Low Latency ◽  
Connected Vehicles ◽  
Network Nodes ◽  
Machine Learning Model ◽  
Fold Reduction ◽  
Broad Variety ◽  
Computational Resources ◽  
Service Latency

With increasing numbers of computer vision and object detection application scenarios, those requiring ultra-low service latency times have become increasingly prominent; e.g., those for autonomous and connected vehicles or smart city applications. The incorporation of machine learning through the applications of trained models in these scenarios can pose a computational challenge. The softwarization of networks provides opportunities to incorporate computing into the network, increasing flexibility by distributing workloads through offloading from client and edge nodes over in-network nodes to servers. In this article, we present an example for splitting the inference component of the YOLOv2 trained machine learning model between client, network, and service side processing to reduce the overall service latency. Assuming a client has 20% of the server computational resources, we observe a more than 12-fold reduction of service latency when incorporating our service split compared to on-client processing and and an increase in speed of more than 25% compared to performing everything on the server. Our approach is not only applicable to object detection, but can also be applied in a broad variety of machine learning-based applications and services.

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Au Nanoparticles ◽  
Learning Model ◽  
Energy Calculations ◽  
Atomic Distribution ◽  
Machine Learning Model ◽  
Fold Reduction ◽  
Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

scholarly journals Augmented Reality and Machine Learning Incorporation Using YOLOv3 and ARKit

2021 ◽  
Vol 11 (13) ◽  
pp. 6006
Author(s):  
Huy Le ◽  
Minh Nguyen ◽  
Wei Qi Yan ◽  
Hoa Nguyen
Keyword(s):  
Machine Learning ◽  
Augmented Reality ◽  
Object Detection ◽  
Feature Detection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Data Annotation ◽  
Machine Learning Model ◽  
Potential Benefits ◽  
Feature Detection And Tracking

Augmented reality is one of the fastest growing fields, receiving increased funding for the last few years as people realise the potential benefits of rendering virtual information in the real world. Most of today’s augmented reality marker-based applications use local feature detection and tracking techniques. The disadvantage of applying these techniques is that the markers must be modified to match the unique classified algorithms or they suffer from low detection accuracy. Machine learning is an ideal solution to overcome the current drawbacks of image processing in augmented reality applications. However, traditional data annotation requires extensive time and labour, as it is usually done manually. This study incorporates machine learning to detect and track augmented reality marker targets in an application using deep neural networks. We firstly implement the auto-generated dataset tool, which is used for the machine learning dataset preparation. The final iOS prototype application incorporates object detection, object tracking and augmented reality. The machine learning model is trained to recognise the differences between targets using one of YOLO’s most well-known object detection methods. The final product makes use of a valuable toolkit for developing augmented reality applications called ARKit.

scholarly journals Object detection and used car price predicting analysis system (ucpas) using machine learning technique

2021 ◽  
Vol 5 (S2) ◽  
Author(s):  
Anu Yadav ◽  
Ela Kumar ◽  
Piyush Kumar Yadav
Keyword(s):  
Machine Learning ◽  
Object Detection ◽  
Research Area ◽  
Machine Learning Technique ◽  
Car Detection ◽  
Machine Learning Model ◽  
Implementation Techniques ◽  
Learning Technique ◽  
Used Car ◽  
Analysis System

The highly interesting research area that noticed in the last few years is object detection and find out the prediction based on the features that can be benefited to consumers and the industry. In this paper, we understand the concept of object detection like the car detection, to look into the price of a second-hand car using automatic machine learning methods. We also understand the concept of object detection categories. Nowadays, the most challenging task is to determine what is the listed price of a used car on the market, Possibility of various factors that can drive a used car price. The main objective of this paper is to develop machine learning models which make it possible to accurately predict the price of a second-hand car according to its parameter or characteristics. In this paper, implementation techniques and evaluation methods are used on a Car dataset consisting of the selling prices of various models of  car across different cities of India. The outcome of this experiment shows that clustering with linear regression and Random Forest model yield the best accuracy outcome. The machine learning model produces a satisfactory result within a short duration of time compared to the aforementioned self.

scholarly journals Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Au Nanoparticles ◽  
Learning Model ◽  
Energy Calculations ◽  
Atomic Distribution ◽  
Machine Learning Model ◽  
Fold Reduction ◽  
Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

scholarly journals Population-Based Screening for Endometrial Cancer: Human vs. Machine Intelligence

2020 ◽  
Vol 3 ◽  
Author(s):  
Gregory R. Hart ◽  
Vanessa Yan ◽  
Gloria S. Huang ◽  
Ying Liang ◽  
Bradley J. Nartowt ◽  
...  
Keyword(s):  
Machine Learning ◽  
Endometrial Cancer ◽  
False Positive Rate ◽  
Population Based ◽  
Personal Health ◽  
Average Risk ◽  
Endometrial Cancer Risk ◽  
Machine Learning Model ◽  
Fold Reduction ◽  
Positive Rate

Incidence and mortality rates of endometrial cancer are increasing, leading to increased interest in endometrial cancer risk prediction and stratification to help in screening and prevention. Previous risk models have had moderate success with the area under the curve (AUC) ranging from 0.68 to 0.77. Here we demonstrate a population-based machine learning model for endometrial cancer screening that achieves a testing AUC of 0.96.We train seven machine learning algorithms based solely on personal health data, without any genomic, imaging, biomarkers, or invasive procedures. The data come from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO). We further compare our machine learning model with 15 gynecologic oncologists and primary care physicians in the stratification of endometrial cancer risk for 100 women.We find a random forest model that achieves a testing AUC of 0.96 and a neural network model that achieves a testing AUC of 0.91. We test both models in risk stratification against 15 practicing physicians. Our random forest model is 2.5 times better at identifying above-average risk women with a 2-fold reduction in the false positive rate. Our neural network model is 2 times better at identifying above-average risk women with a 3-fold reduction in the false positive rate.Our machine learning models provide a non-invasive and cost-effective way to identify high-risk sub-populations who may benefit from early screening of endometrial cancer, prior to disease onset. Through statistical biopsy of personal health data, we have identified a new and effective approach for early cancer detection and prevention for individual patients.

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Au Nanoparticles ◽  
Learning Model ◽  
Energy Calculations ◽  
Atomic Distribution ◽  
Machine Learning Model ◽  
Fold Reduction ◽  
Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

scholarly journals Continual Learning Strategy in One-Stage Object Detection Framework Based on Experience Replay for Autonomous Driving Vehicle

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6777
Author(s):  
Jeng-Lun Shieh ◽  
Qazi Mazhar ul Haq ◽  
Muhamad Amirul Haq ◽  
Said Karam ◽  
Peter Chondro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Object Detection ◽  
Learning Strategy ◽  
Autonomous Driving ◽  
Learning Method ◽  
Object Class ◽  
Prior Learning ◽  
Machine Learning Model ◽  
Experience Replay ◽  
Continual Learning

Object detection is an important aspect for autonomous driving vehicles (ADV), which may comprise of a machine learning model that detects a range of classes. As the deployment of ADV widens globally, the variety of objects to be detected may increase beyond the designated range of classes. Continual learning for object detection essentially ensure a robust adaptation of a model to detect additional classes on the fly. This study proposes a novel continual learning method for object detection that learns new object class(es) along with cumulative memory of classes from prior learning rounds to avoid any catastrophic forgetting. The results of PASCAL VOC 2007 have suggested that the proposed ER method obtains 4.3% of mAP drop compared against the all-classes learning, which is the lowest amongst other prior arts.

Declarative data serving

2021 ◽  
Vol 14 (11) ◽  
pp. 2555-2562
Author(s):  
Ted Shaowang ◽  
Nilesh Jain ◽  
Dennis D. Matthews ◽  
Sanjay Krishnan
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Computer Architecture ◽  
Edge Computing ◽  
Low Latency ◽  
Complex Task ◽  
Machine Learning Model ◽  
Data Flows ◽  
The Right ◽  
High Level

Recent advances in computer architecture and networking have ushered in a new age of edge computing, where computation is placed close to the point of data collection to facilitate low-latency decision making. As the complexity of such deployments grow into networks of interconnected edge devices, getting the necessary data to be in "the right place at the right time" can become a challenge. We envision a future of edge analytics where data flows between edge nodes are declaratively configured through high-level constraints. Using machine learning model-serving as a prototypical task, we illustrate how the heterogeneity and specialization of edge devices can lead to complex, task-specific communication patterns even in relatively simple situations. Without a declarative framework, managing this complexity will be challenging for developers and will lead to brittle systems. We conclude with a research vision for database community that brings our perspective to the emergent area of edge computing.

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