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Retracted: Using Markov predictions for handling and allocating task to virtual machines in clouds data centres

IET Communications 2023年第13期17卷 1562-1576页

作者： Zavieh, Hadi Javadpour, Amir Li, Yuan Ja'fari, Forough Sangaiah, Arun Kumar Zhang, Weizhe Nasseri, Seyed Hadi School of Economics and Statistics Guangzhou University Guangzhou University Guangzhou China Harbin Institute of Technology Harbin China ADiT-Lab Electrotechnics and Telecommunications Department Instituto Politécnico de Viana do Castelo Porto Portugal International Graduate Institute of AI National Yunlin University of Science and Technology Douliu Taiwan Department of Computer Engineering Sharif University of Technology Tehran Iran Department of Mathematics University of Mazandaran Babolsar Iran

The above article from IET Communications, published online on 16 December 2022 in Wiley Online Library (***), has been retracted by agreement between the Interim Editor-in-Chief, Jian Ren, the Institution of engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract. © 2022 The Authors. IET Communications published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Virtual machine

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Analysis of auto cubic catalysis and nanoscale heat transport using the inclined magnetized Cross fluid past over the wedge

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Waves in Random and Complex Media 2023年

作者： Ayub, Assad Sabir, Zulqurnain Said, Salem Ben Baskonus, Haci Mehmet Núñez, Rafaél Artidoro Sandoval Sadat, R. Ali, Mohamed R. Department of Mathematics & Statistics Hazara University Manshera Pakistan Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Department of Mathematical Science College of Science United Arab Emirates University Al Ain United Arab Emirates Department of Mathematics Science Education Harran University Sanliurfa Turkey Department of General Studies National Autonomous University of Chota Chota Peru Department of Mathematics Faculty of Engineering Zagazig University Zagazig Egypt Faculty of Engineering and Technology Future University in Egypt New Cairo Egypt Basic Engineering Mathematics Department Benha Faculty of Engineering Benha University Benha Egypt

In this study, the chemical procedure is presented in the nanomaterial flow, which contains the astonishing topographies. Nanoparticles contain physical and chemical properties, which are continuously used in almost every field of nanotechnology and nanoscience. Therefore, the idea to implement of these investigations is related to the heat and mass transport based on the nanomaterial flow along with the Cross model, chemical process via autocatalysis and an inclined magnetic dipole over shrinking/stretching wedge. Moreover, heat transportation with non-uniform heat source/sink, thermophoretic diffusion impacts, and Brownian motion are also studied. The assumed problems in the mathematics generate the system of nonlinear partial differential equations using the basic form of momentum, energy, and mass. Non-dimensional variables provide the system of equations using the boundary conditions, which are further updated with the shooting method to reduce into the initial value problem. The solution of the nonlinear model is presented through the MATLAB built-in procedure Bvp4c to find the numerical solutions. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Brownian movement

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Real-Time Bitcoin Price Prediction Using Hybrid 2D-CNN LSTM Model

Real-Time Bitcoin Price Prediction Using Hybrid 2D-CNN LSTM ...

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International Conference on Web Research (ICWR)

作者： Saman Kazeminia Hedieh Sajedi Masoud Arjmand Department of Computer Engineering University of Tehran Kish Iran Department of Mathematics Statistics and Computer Science University of Tehran Tehran Iran

Due to the growing importance of the cryptocurrency market, as well as the diversity and expansion of online trading platforms, cryptocurrency technology has piqued the curiosity of a wide range of people, from market traders to researchers and analysts. Reliable price prediction is a necessity since investors face multiple challenges including market volatility, risk management, and market complexity. Therefore, numerous studies have been done using deep learning and machine learning algorithms to demonstrate their functionality and efficiency in this area. In this paper, we employed Bitcoin historical data to make predictions for the next day's closing price using a new hybrid 2D-CNNLSTM model with OPTUNA hyperparameter tuning. The dataset used to train the model was gathered using an automated web scraping technique. With the proposed model, the R2 error achieved 0.98166 and the MAPE was 0.034. Our proposed model is compared with three different models: CNN, LSTM, and GRU. The predicted results show that the proposed hybrid model is efficient for accurately predicting bitcoin prices and reliable for supporting investors to make their informed investment decisions. Additionally, the proposed model has outperformed other commonly used algorithms, namely CNN, LSTM, and GRU in terms of R2, and MAPE. This model is also capable of performing real-time forecasting.

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UTOPIA: Universally Trainable Optimal Prediction Intervals Aggregation

arXiv

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arXiv 2023年

作者： Fan, Jianqing Ge, Jiawei Mukherjee, Debarghya Department of Operations Research and Financial Engineering Princeton University United States Department of Mathematics and Statistics Boston University United States

Uncertainty quantification in prediction presents a compelling challenge with vast applications across various domains, including biomedical science, economics, and weather forecasting. There exists a wide array of methods for constructing prediction intervals, such as quantile regression and conformal prediction. However, practitioners often face the challenge of selecting the most suitable method for a specific real-world data problem. In response to this dilemma, we introduce a novel and universally applicable strategy called Universally Trainable Optimal Predictive Intervals Aggregation (UTOPIA). This technique excels in efficiently aggregating multiple prediction intervals while maintaining a small average width of the prediction band and ensuring coverage. UTOPIA is grounded in linear or convex programming, making it straightforward to train and implement. In the specific case where the prediction methods are elementary basis functions, as in kernel and spline bases, our method becomes the construction of a prediction band. Our proposed methodologies are supported by theoretical guarantees on the coverage probability and the average width of the aggregated prediction interval, which are detailed in this paper. The practicality and effectiveness of UTOPIA are further validated through its application to synthetic data and two real-world datasets in finance and macroeconomics. © 2023, CC BY.

关键词： Prediction models

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Lower Bound Methods for Sign-Rank and Their Limitations 25

Lower Bound Methods for Sign-Rank and Their Limitations

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25th International Conference on Approximation Algorithms for Combinatorial Optimization Problems and the 26th International Conference on Randomization and Computation, APPROX/RANDOM 2022

作者： Hatami, Hamed Hatami, Pooya Pires, William Tao, Ran Zhao, Rosie School of Computer Science McGill University Montreal Canada Department of Computer Science and Engineering The Ohio State University ColumbusOH United States Department of Mathematics and Statistics McGill University Montreal Canada

ISBN: (纸本)9783959772495

The sign-rank of a matrix A with ±1 entries is the smallest rank of a real matrix with the same sign pattern as A. To the best of our knowledge, there are only three known methods for proving lower bounds on the sign-rank of explicit matrices: (i) Sign-rank is at least the VC-dimension;(ii) Forster's method, which states that sign-rank is at least the inverse of the largest possible average margin among the representations of the matrix by points and half-spaces;(iii) Sign-rank is at least a logarithmic function of the density of the largest monochromatic rectangle. We prove several results regarding the limitations of these methods. We prove that, qualitatively, the monochromatic rectangle density is the strongest of these three lower bounds. If it fails to provide a super-constant lower bound for the sign-rank of a matrix, then the other two methods will fail as well. We show that there exist n × n matrices with sign-rank nΩ(1) for which none of these methods can provide a super-constant lower bound. We show that sign-rank is at most an exponential function of the deterministic communication complexity with access to an equality oracle. We combine this result with Green and Sanders' quantitative version of Cohen's idempotent theorem to show that for a large class of sign matrices (e.g., xor-lifts), sign-rank is at most an exponential function of the γ2 norm of the matrix. We conjecture that this holds for all sign matrices. Towards answering a question of Linial, Mendelson, Schechtman, and Shraibman regarding the relation between sign-rank and discrepancy, we conjecture that sign-ranks of the ±1 adjacency matrices of hypercube graphs can be arbitrarily large. We prove that none of the three lower bound techniques can resolve this conjecture in the affirmative. © Hamed Hatami, Pooya Hatami, William Pires, Ran Tao, and Rosie Zhao.

关键词： Exponential functions

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Investigating Deep Neural Network Architecture and Feature Extraction Designs for Sensor-based Human Activity Recognition

arXiv

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arXiv 2023年

作者： Ahangarani, Danial Shirazi, Mohammad Ashraf, Navid Department of Computer Engineering Sharif University of Technology Iran Department of Statistics Mathematics and Computer Allameh Tabataba'i University Iran

The extensive ubiquitous availability of sensors in smart devices and the Internet of Things (IoT) has opened up the possibilities for implementing sensor-based activity recognition. As opposed to traditional sensor time-series processing and hand-engineered feature extraction, in light of deep learning's proven effectiveness across various domains, numerous deep methods have been explored to tackle the challenges in activity recognition, outperforming the traditional signal processing and traditional machine learning approaches. In this work, by performing extensive experimental studies on two human activity recognition datasets, we investigate the performance of common deep learning and machine learning approaches as well as different training mechanisms (such as contrastive learning), and various feature representations extracted from the sensor time-series data and measure their effectiveness for the human activity recognition task. © 2023, CC BY.

关键词： Feature extraction

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Joint Task Offloading and Resource Allocation in Heterogeneous Edge Environments

Joint Task Offloading and Resource Allocation in Heterogeneo...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Yu Liu Yingling Mao Zhenhua Liu Fan Ye Yuanyuan Yang Department of Electrical and Computer Engineering Stony Brook University USA Department of Applied Mathematics and Statistics Stony Brook University USA

Mobile edge computing is becoming one of the ubiquitous computing paradigms to support applications requiring low latency and high computing capability. FPGA-based reconfigurable accelerators have high energy efficiency and low latency compared to general-purpose servers. Therefore, it is natural to incorporate reconfigurable accelerators in mobile edge computing systems. This paper formulates and studies the problem of joint task offloading, access point selection, and resource allocation in heterogeneous edge environments for latency minimization. Due to the heterogeneity in edge computing devices and the coupling between offloading, access point selection, and resource allocation decisions, it is challenging to optimize over them simultaneously. We decomposed the proposed problem into two disjoint subproblems and developed algorithms for them. The first subproblem is to jointly determine offloading and computing resource allocation decisions and is NP-hard, where we developed an algorithm based on semidefinite relaxation. The second subproblem is to jointly determine access point selection and communication resource allocation decisions, where we proposed an algorithm with a provable approximation ratio of 2.62. We conducted extensive numerical simulations to evaluate the proposed algorithms. Results highlighted that the proposed algorithms outperformed baselines and were near-optimal over a wide range of settings.

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QUADRATURE METHODS FOR HIGHLY OSCILLATORY SINGULAR INTEGRALS

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Journal of Computational mathematics 2021年第2期39卷 227-260页

作者： Jing Gao Marissa Condon Arieh Iserles Benjamin Gilvey Jon Trevelyan School of Mathematics and Statistics Xi'an Jiaotong UniversityXi'an 710049China School of Electronic Engineering Dublin City UniversityIreland DAMTP Centre for Mathematical SciencesUniversity of CambridgeUK Department of Engineering Durham UniversityUK

We address the evaluation of highly oscillatory integrals,with power-law and logarithmic *** problems arise in numerical methods in ***,the evaluation of oscillatory integrals dominates the run-time for wave-enriched boundary integral formulations for wave scattering,and many of these exhibit *** show that the asymptotic behaviour of the integral depends on the integrand and its derivatives at the singular point of the integrand,the stationary points and the endpoints of the integral.A truncated asymptotic expansion achieves an error that decays faster for increasing *** on the asymptotic analysis,a Filon-type method is constructed to approximate the *** an asymptotic expansion,the Filon method achieves high accuracy for both small and large ***-valued quadrature involves interpolation at the zeros of polynomials orthogonal to a complex weight *** results indicate that the complex-valued Gaussian quadrature achieves the highest accuracy when the three methods are ***,while it achieves higher accuracy for the same number of function evaluations,it requires signi cant additional cost of computation of orthogonal polynomials and their zeros.

关键词： Numerical quadrature Singular highly oscillatory integrals Asymptotic analysis Boundary Element Method Plane wave enrichment Partition of Unity

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Transformer-Based Multivariate Time Series Anomaly Localization

Transformer-Based Multivariate Time Series Anomaly Localizat...

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Computational Intelligence on engineering/Cyber Physical Systems (CIES), IEEE Symposium on

作者： Charalampos Shimillas Kleanthis Malialis Konstantinos Fokianos Marios M. Polycarpou KIOS Research and Innovation Center of Excellence Nicosia Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Department of Mathematics and Statistics University of Cyprus Nicosia Cyprus

ISBN: (数字)9798331508272

ISBN: (纸本)9798331508289

With the growing complexity of Cyber-Physical Systems (CPS) and the integration of Internet of Things (IoT), the use of sensors for online monitoring generates large volume of multivariate time series (MTS) data. Consequently, the need for robust anomaly diagnosis in MTS is paramount to maintaining system reliability and safety. While significant advancements have been made in anomaly detection, localization remains a largely underexplored area, though crucial for intelligent decisionmaking. This paper introduces a novel transformer-based model for unsupervised anomaly diagnosis in MTS, with a focus on improving localization performance, through an in-depth analysis of the self-attention mechanism's learning behavior under both normal and anomalous conditions. We formulate the anomaly localization problem as a three-stage process: time-step, window, and segment-based. This leads to the development of the SpaceTime Anomaly Score (STAS), a new metric inspired by the connection between transformer latent representations and spacetime statistical models. STAS is designed to capture individual anomaly behaviors and inter-series dependencies, delivering enhanced localization performance. Additionally, the Statistical Feature Anomaly Score (SFAS) complements STAS by analyzing statistical features around anomalies, with their combination helping to reduce false alarms. Experiments on real world and synthetic datasets illustrate the model's superiority over state-of-the-art methods in both detection and localization tasks.

关键词： Location awareness Measurement Time series analysis Transformers Sensor systems Safety Internet of Things Reliability Monitoring Synthetic data

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Air Particulate Matters Auto-Rule-Based Labeling to Support Long-Distance Run Environment Data Classification 13

Air Particulate Matters Auto-Rule-Based Labeling to Support ...

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13th IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2023

作者： Wandy, Wandy Adi, Kusworo Ayu, Media Anugerah Diponegoro University Doctoral Program of Information Systems School of Postgraduate Studies Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Physics Semarang Indonesia Sampoerna University Faculty of Engineering and Technology Department of Computer Science South Jakarta Indonesia

ISBN: (纸本)9798350347319

Sports Science is an interdisciplinary and multidisciplinary science that strives to increase athletic performance and endurance. Sport Science recognizes and prevents injuries. Sensors and statistics formalize Sports Science. Runners need coaches and teams to support them before, during, and after the run race. Coaches generate running training plans to boost performance. Running race performances may be impacted by air pollution exposure while training, so coaches should consider limiting air pollution exposure when training. One of the external factors is Particulate Matter (PM2.5 and PM10). Sensors connecting to the Internet can record external factors and produce csv data. The foundation of supervised machine learning is the labeling process. Labeling a set of data is one of the laborious and time-consuming phases in every machine-learning application because it requires verifying the accuracy of the labels and making any necessary revisions. This research aimed to find a solution to automatically label numerous air particulate matter raw data using a rule based on parameters to reduce manual work, human errors and faster processes. This labeled data will later be used for supervised machine learning classification to support the coach in generating training programs for the runners in a Sports Information System. Based on Indonesia Air Quality index rule-based approach, labeled text data in csv has been generated and tested with PM2.5 and PM10 parameters in three scenarios with a 100% success rate. It was possible to automate the labeling process, and it explained how automation results in fast and accurate results. © 2023 IEEE.

关键词： Classification (of information)

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