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arXiv 2020年

作者： Brazil, Garrick Pons-Moll, Gerard Liu, Xiaoming Schiele, Bernt Michigan State University Computer Science & Engineering United States Max Planck Institute for Informatics Saarland Informatics Campus Germany

Perceiving the physical world in 3D is fundamental for self-driving applications. Although temporal motion is an invaluable resource to human vision for detection, tracking, and depth perception, such features have not been thoroughly utilized in modern 3D object detectors. In this work, we propose a novel method for monocular video-based 3D object detection which leverages kinematic motion to extract scene dynamics and improve localization accuracy. We first propose a novel decomposition of object orientation and a self-balancing 3D confidence. We show that both components are critical to enable our kinematic model to work effectively. Collectively, using only a single model, we efficiently leverage 3D kinematics from monocular videos to improve the overall localization precision in 3D object detection while also producing useful by-products of scene dynamics (ego-motion and per-object velocity). We achieve state-of-the-art performance on monocular 3D object detection and the Bird’s Eye View tasks within the KITTI self-driving dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Object recognition

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Decision Support System on Potential Land Palm Oil Cultivation using Promethee with Geographical Visualization 3

Decision Support System on Potential Land Palm Oil Cultivati...

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3rd International Conference on science, ICOS 2019

作者： Widians, J.A. Taruk, M. Fauziah, Y. Setyadi, H.J. Informatics Engineering Department Faculty of Computer Science and Information Technology Mulawarman University Samarinda Indonesia

Preparation of oil palm cultivation land is the initial physical activity of the planting area. Prior to oil palm cultivation it is advisable to study potential land suitability, to assess the land as appropriate or not to oil palm growth and support crop productivity. Potential land evaluation includes an example of a consideration where it requires the right decision to make a potential land-cultivation way to support crop productivity more effectively. Therefore, system with the PROMETHEE (Preference Ranking Organization for Enrichment Evaluation) method is built because of the ability to handle multiple comparisons, indicating priorities and preferences for each criterion by focusing on values. The criteria needed are: Rainfall, Dry Moon, Topography, Above Sea Level, Soil Acidity, Slope Tilt and Peat Density. The results achieved in this research is decision support system that yields recommendation of potential land of oil palm cultivation, which can be considered by decision maker in determination of potential land of oil palm cultivation. © Published under licence by IOP Publishing Ltd.

关键词： Decision support systems

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State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review

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JMIR Medical informatics 2022年第8期10卷 e38454页

作者： Petmezas, Georgios Stefanopoulos, Leandros Kilintzis, Vassilis Tzavelis, Andreas Rogers, John A. Katsaggelos, Aggelos K. Maglaveras, Nicos Lab of Computing Medical Informatics and Biomedical-Imaging Technologies The Medical School Aristotle University of Thessaloniki Thessaloniki Greece Department of Biomedical Engineering Northwestern University Evanston IL United States Department of Material Science Northwestern University Evanston IL United States Department of Electrical and Computer Engineering Northwestern University Evanston IL United States

Background: Electrocardiogram (ECG) is one of the most common noninvasive diagnostic tools that can provide useful information regarding a patient’s health status. Deep learning (DL) is an area of intense exploration that leads the way in most attempts to create powerful diagnostic models based on physiological signals. Objective: This study aimed to provide a systematic review of DL methods applied to ECG data for various clinical applications. Methods: The PubMed search engine was systematically searched by combining “deep learning” and keywords such as “ecg,” “ekg,” “electrocardiogram,” “electrocardiography,” and “electrocardiology.” Irrelevant articles were excluded from the study after screening titles and abstracts, and the remaining articles were further reviewed. The reasons for article exclusion were manuscripts written in any language other than English, absence of ECG data or DL methods involved in the study, and absence of a quantitative evaluation of the proposed approaches. Results: We identified 230 relevant articles published between January 2020 and December 2021 and grouped them into 6 distinct medical applications, namely, blood pressure estimation, cardiovascular disease diagnosis, ECG analysis, biometric recognition, sleep analysis, and other clinical analyses. We provide a complete account of the state-of-the-art DL strategies per the field of application, as well as major ECG data sources. We also present open research problems, such as the lack of attempts to address the issue of blood pressure variability in training data sets, and point out potential gaps in the design and implementation of DL models. Conclusions: We expect that this review will provide insights into state-of-the-art DL methods applied to ECG data and point to future directions for research on DL to create robust models that can assist medical experts in clinical decision-making. ©Georgios Petmezas, Leandros Stefanopoulos, Vassilis Kilintzis, Andreas Tzavelis, John A Rogers

关键词： clinical decision CNN convolutional neural networks decision support deep learning diagnostic tools ECG ECG databases electrocardiogram long short-term memory LSTM residual neural network ResNet

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Prediction of football matches results: Decision forest against neural networks 18

Prediction of football matches results: Decision forest agai...

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18th International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON 2021

作者： Azeman, Azrel Aiman Mustapha, Aida Razali, Nazim Nanthaamomphong, Aziz Abd Wahab, Mohd Helmy Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor Malaysia Faculty of Applied Sciences and Technology Universiti Tun Hussein Onn Malaysia Johor Malaysia Andaman Intelligent Tourism and Service Informatics Center College of Computing Prince of Songkla University Phuket Thailand Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Johor Malaysia

ISBN: (纸本)9780738111278

Modelling football match outcomes is a popular prediction strategy based on either statistical approaches, machine learning approaches or rating system approaches. This paper is set to study football match outcomes prediction (win, lose and draw) for English Premier League Season 2005-2006 consisting of 20 football teams which played 380 matches per season in term of machine learning approaches. Two prediction algorithms are proposed, which are the Multiclass Neural Network and the Multiclass Decision Forest. Past research showed that both decision tree and neural network have capabilities to predict football matches outcomes. Although decision forest is a extended version of decision tree, there are few comparative study between neural network and decision tree in football matches outcomes prediction. The comparative experiments were carried using the Azure Machine Learning Studio and the results showed that the Multiclass Decision Forest performed the best as compared to Multiclass Neural Networks. © 2021 IEEE.

关键词： Forecasting

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Towards a rigorous evaluation of explainability for multivariate time series

arXiv

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arXiv 2021年

作者： Saluja, Rohit Malhi, Avleen Knapič, Samanta Främling, Kary Cavdar, Cicek Department of Computer Science Aalto University Konemiehentie 2 Espoo02150 Finland Department of Computing Science Umeå University Mit-huset Umeå90187 Sweden Department of Computing and Informatics Bournemouth United Kingdom Department of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

Machine learning-based systems are rapidly gaining popularity and in-line with that there has been a huge research surge in the field of explainability to ensure that machine learning models are reliable, fair, and can be held liable for their decision-making process. Explainable Artificial Intelligence (XAI) methods are typically deployed to debug black-box machine learning models but in comparison to tabular, text, and image data, explainability in time series is still relatively unexplored. The aim of this study was to achieve and evaluate model agnostic explainability in a time series forecasting problem. This work focused on proving a solution for a digital consultancy company aiming to find a data-driven approach in order to understand the effect of their sales related activities on the sales deals closed. The solution involved framing the problem as a time series forecasting problem to predict the sales deals and the explainability was achieved using two novel model agnostic explainability techniques, Local explainable model- agnostic explanations (LIME) and Shapley additive explanations (SHAP) which were evaluated using human evaluation of explainability. The results clearly indicate that the explanations produced by LIME and SHAP greatly helped lay humans in understanding the predictions made by the machine learning model. The presented work can easily be extended to any time series forecasting or classification scenario for achieving explainability and evaluating the produced explanations. © 2021, CC BY-SA.

关键词： Forecasting

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Generative Adversarial Networks Based Cognitive Feedback Analytics System for Integrated Cyber-Physical System and Industrial Iot Networks

Research Square

引用

Research Square 2021年

作者： Upreti, Kamal Syed, Mohammad Haider Alam, Mohammad Shabbir Alhudhaif, Adi Shuaib, Mohammed Sharma, Arvind K. Department of Information & Technology Inderprastha Engineering College Ghaziabad India College of Computing and Informatics Saudi Electronic University Saudi Arabia Department of Computer Science College of computer science and Information Technology Jazan University Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences in Al-kharj Prince Sattam bin Abdulaziz University P.O. Box 151 al-Kharj11942 Saudi Arabia College of Computer Science & IT Jazan University Saudi Arabia Dept. of CSI University of Kota Rajasthan India

In the modern era of technologies, the internet grows in the advancement of our day-to-day life like automation devices. The devices to set up industries with integrated cyber-physical systems and industrial IoT applications. Generative adversarial networks (GAN) can generate Cognitive feedback analysis with various data for both generator and discriminator in a supervised model. Neural networks are used for artificial intelligence algorithms, but in adversarial networks, feedback analytics is analyzed with the significance of data. The modern age of intelligent manufacturing will indeed be ushered in by Cyber-Physical Production Systems (CPPS). However, because of the connections between the virtual and physical worlds, CPPS would be subject to cross-domain assaults. Against Denial-of-Service (DoS) threats, this paper concentrates on complex performance feedback management of Cyber-Physical Systems (CPS). To begin, a swapping system modelling approach for the complex response feedback CPS is provided by analyzing the distinct effects of DoS assaults on the sensor-controller (SC) and controller-to-actuator (C-A) channels, accordingly. Given the difference in bandwidth between the dual channels and the accused's energy cap, it is reasonable to conclude that an offender can only jam a single communication stream at a point and also that the possible number of successive DoS attacks is limited. Second, using a packet-based transfer scheme, a nested switching paradigm is built on the foundation of the switching mechanism, considering both the spatial heterogeneity and the temporal durability of DoS attacks. The probability of discriminator gets analyzed feedback data to check whether actual data or fake data is sampled, and it is generated. Cognitive feedback supports genetic algorithms to sample the feedback data in a system for advanced technologies. © 2021, CC BY.

关键词： Cyber Physical System

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Augmented reality for interactive promotion media at Faculty of computer science and Information Technology Mulawarman University 3

Augmented reality for interactive promotion media at Faculty...

引用

3rd International Conference on science, ICOS 2019

作者： Firdaus, M.B. Widians, J.A. Padant, J.Y. Informatics Engineering Department Faculty of Computer Science and Information Technology Mulawarman University Samarinda Indonesia

Showing the design and layout of the building using a model, making this model requires time and accuracy because the model is built on a scale that has been adjusted to the building to be made later, a problem that often occurs in modeling is the suitability of the scale and detail of the model. Augmented Reality as a variation of the virtual environment. Users of AR technology can see the real world, with virtual objects added to the real world. So, users see virtual objects and real objects in the same place. Submission of information by utilizing the three-dimensional visualization technique is very interesting because it can display the 3D model of an object before it is built in real terms. The Faculty of computer science and Information Technology Mulawarman University Samarinda has an exterior building design of a new building, but there are no interactive media that can display the exterior of the building as a whole and in detail. This application is able to be used to apply the 3D model of the building as a whole and can be seen from various viewpoints. © Published under licence by IOP Publishing Ltd.

关键词： Augmented reality

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Backpropagation neural network prediction for cryptocurrency bitcoin prices 1

Backpropagation neural network prediction for cryptocurrency...

引用

1st International Conference computer science and engineering, IC2SE 2019

作者： Sovia, Rini Yanto, Musli Budiman, Arif Mayola, Liga Saputra, Dio Department Informatics Engineering Faculty of Computer Science University Putra Indonesia YPTK Padang Indonesia

The value of bitcoin currency is very volatile, hard to guess for every hour, so many of the bitcoin traders suffer losses because they are wrong in managing their bitcoin assets. Changes in the price of bitcoin itself are influenced by many things such as the closing of the bitcoin market in a country, the occurrence of hacker attacks on the bitcoin blockchain and the emergence of new coins that use technology similar to bitcoin. But when a stable market situation changes the price of bitcoin is purely influenced by market forces. By implementing an artificial neural network using backpropagation method, it will be able to predict the price of bitcoin by giving a form of predictive results that are strengthened with a fairly good value of accuracy. This research begins by determining prediction variables with target values that can be determined based on previous bitcoin prices. This artificial neural network process is able to conduct training and testing of data based on network patterns that have been formed, then the results of training and testing of the network will be analysed again, so that at the last stage the best network patterns will be used in the prediction process. © Published under licence by IOP Publishing Ltd.

关键词： Forecasting

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Evolution Stage Identification of Haze Pollution Episodes Over the North China Plain Using Constrained Dynamic Time Warping and Multiway Principal Component Analysis

SSRN

引用

SSRN 2022年

作者： Zhang, Hao Ran, Jinye Yu, Junyi Zhu, Morui Jia, Zihan Wang, Ying Li, Hengqi Jiao, Yongli School of Chemistry and Chemical Engineering Southwest University Chongqing400715 China College of Computer and Information Science Southwest University Chongqing400715 China Dalian National Laboratory for Clean Energy Dalian Institute of Chemical Physics Chinese Academy of Sciences Dalian116023 China Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest1111 Hungary Science in Business Administration Bryant University RI02917 United States Shanghai201204 China

To divide haze pollution episodes into different types, dynamic time warping (DTW) distance is employed to calculate the dissimilarities of different episodes. Then, a multiway principal component analysis (MPCA) method based on the constrained DTW, which is used to adjust episodes of different duration to the same length, is carried out to identify different evolution stage of each episode automatically according to the change rate of the first principal component in MPCA model. Contribution rates of meteorological factors and other air pollutant to the fluctuation of PM2.5 concentrations in different evolution stages are also calculated using the contributions to the principal component subspace. Results show that each episode consists of an early formational stage, an accumulation stage, a steady stage, and a clearing stage. © 2022, The Authors. All rights reserved.

关键词： Principal component analysis

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Federated Learning for Inference at Anytime and Anywhere

arXiv

引用

arXiv 2022年

作者： Liu, Zicheng Li, Da Fernandez-Marques, Javier Laskaridis, Stefanos Gao, Yan Dudziak, Lukasz Li, Stan Z. Hu, Shell Xu Hospedales, Timothy School of Information Science & Electronic Engineering Zhejiang University Hangzhou China Machine Learning & Data Intelligence Samsung AI Center Cambridge United Kingdom Automated AI Samsung AI Center Cambridge United Kingdom Distributed AI Samsung AI Center Cambridge United Kingdom Department of Computer Science and Technology The University of Cambridge Cambridge United Kingdom School of Engineering Westlake University Hangzhou China School of Informatics The University of Edinburgh Edinburgh United Kingdom

Federated learning has been predominantly concerned with collaborative training of deep networks from scratch, and especially the many challenges that arise, such as communication cost, robustness to heterogeneous data, and support for diverse device capabilities. However, there is no unified framework that addresses all these problems together. This paper studies the challenges and opportunities of exploiting pre-trained Transformer models in FL. In particular, we propose to efficiently adapt such pre-trained models by injecting a novel attention-based adapter module at each transformer block that both modulates the forward pass and makes an early prediction. Training only the lightweight adapter by FL leads to fast and communication-efficient learning even in the presence of heterogeneous data and devices. Extensive experiments on standard FL benchmarks, including CIFAR-100, FEMNIST and SpeechCommandsv2 demonstrate that this simple framework provides fast and accurate FL while supporting heterogenous device capabilities, efficient personalization, and scalable-cost anytime inference. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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