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The Journal of Machine Learning Research 2024年第1期25卷 2398-2450页

作者： Nachuan Xiao Xiaoyin Hu Xin Liu Kim-Chuan Toh Institute of Operations Research and Analytics National University of Singapore Singapore School of Computer and Computing Science Hangzhou City University Hangzhou China State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Department of Mathematics and Institute of Operations Research and Analytics National University of Singapore Singapore

In this paper, we present a comprehensive study on the convergence properties of Adam-family methods for nonsmooth optimization, especially in the training of nonsmooth neural networks. We introduce a novel two-timescale framework that adopts a two-timescale updating scheme, and prove its convergence properties under mild assumptions. Our proposed framework encompasses various popular Adam-family methods, providing convergence guarantees for these methods in training nonsmooth neural networks. Furthermore, we develop stochastic subgradient methods that incorporate gradient clipping techniques for training nonsmooth neural networks with heavy-tailed noise. Through our framework, we show that our proposed methods converge even when the evaluation noises are only assumed to be integrable. Extensive numerical experiments demonstrate the high efficiency and robustness of our proposed methods.

关键词： nonsmooth optimization stochastic subgradient methods Adam nonconvex optimization gradient clipping

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A Survey on Recent Advances in Image Captioning

A Survey on Recent Advances in Image Captioning

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2021 International Conference on Electrical, Electronics and computing Technology, EECT 2021

作者： Chen, Feng Li, Xinyi Tang, Jintao Li, Shasha Wang, Ting Department of Computing Science College of Computer National University of Defense Technology China Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology China

Image captioning, an interdisciplinary research field of computer vision and natural language processing, has attracted extensive attention. Image captioning aims to produce reasonable and accurate natural language sentences to describe images. It requires the captioning model to recognize objects and describe their relationships accurately. Intuitively, it is difficult for a machine to have the general image understanding ability like human beings. However, deep learning provides the basis for intelligent exploration. In this review, we will focus on recent advanced deep methods for image captioning. We classify existing methods into different categories and discuss these categories respectively;meanwhile, we discuss the related datasets and evaluation metrics. We also prospect the future research directions. © Published under licence by IOP Publishing Ltd.

关键词： Deep learning

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Mobile Edge computing for Multi-Services Digital Twin-Enabled IoT Heterogeneous Networks

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IEEE Transactions on Cognitive Communications and Networking 2024年第3期11卷 1845-1853页

作者： Liu, Weiqi Hossain, Mohammad Arif Ansari, Nirwan Auburn University at Montgomery Department of Computer Science and Computer Information Systems MontgomeryAL36117 United States Middle Tennessee State University Department of Engineering Technology MurfreesboroTN37132 United States New Jersey Institute of Technology Advanced Networking Laboratory Department of Electrical and Computing Engineering NewarkNJ07102 United States

A scheme for edge computing-enabled offloading in a digital twin (DT) enabled heterogeneous network (HetNet) of multi-services IoT devices (IDs) is proposed. This scheme optimizes the association and handover of IDs, offloading ratio, and resource allocation considering the number of IDs, deadline requirements, and resource capacities. The objective is to enhance future generation networks by considering the ID movement, diverse ID requests, and network heterogeneity. We formulate the problem as Joint ID assOciatIon, offloadiNg ratio, Wireless bandwidth and computing reSource allocation, and digital twin placEment (JOINWISE), aiming to minimize the task completion time of all IDs while considering ID movement. Since JOINWISE is a mixed-integer nonlinear problem, we decompose it into two sub-problems: the ID Association (IDA) problem and the offloading Ratio, DT plAcement, bandwiDth and computing resource allOcation (RADIO) problem. IDA can be solved by mapping it to a multi-dimensional multiple knapsacks problem. Due to the non-convexity, high dimension of decision variables, and dynamic HetNet environment of RADIO, we propose a deep deterministic policy gradient (DDPG) based reinforcement learning method to iteratively solve the two sub-problems. Simulation results have confirmed the effectiveness of our proposed scheme in tackling the JOINWISE problem. © 2015 IEEE.

关键词： Mobile edge computing

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An Elite Archive-Assisted Multi-Objective evolutionary Algorithm for mRNA Design

An Elite Archive-Assisted Multi-Objective Evolutionary Algor...

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Congress on evolutionary Computation

作者： Wenjing Hong Cheng Chen Zexuan Zhu Ke Tang National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Messenger RNA (mRNA) vaccines have emerged as highly effective strategies in the prophylaxis and treatment of diseases. mRNA design, a key to the success of mRNA vaccines, in-volves finding optimal codons and increasing secondary structure stability to lengthen mRNA half-life, ultimately enhancing protein expression. Despite receiving widespread attention, most methods primarily rely on manual design, which is time-consuming and labor-intensive. While optimization approaches can alleviate this issue, existing methods still exhibit critical limitations caused by conflicts between codon usage and mRNA structural stability, compounded by the vast design space of mRNA resulting from the presence of synonymous codons. In this paper, a novel multi-objective evolutionary optimization-based mRNA design method is proposed. We first formulate the mRNA design problem as a multi-objective optimization problem and then develop an Elite Archive-Assisted Multi-Objective evolutionary algorithm for mRNA Design, namely EAA-MOED, by incorporating a novel elite archive-assisted method into a weighted optimization framework to improve search efficiency. Experimental studies, involving two state-of-the-art mRNA design methods and five well-known MOEAs, show the competitiveness of the proposed EAA-MOED in mRNA design.

关键词： Measurement Design methodology evolutionary computation Search problems Linear programming Stability analysis Vaccines

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Copula Guided Parallel Gibbs Sampling for Nonparametric and Coherent Topic Discovery (Extended Abstract) 39

Copula Guided Parallel Gibbs Sampling for Nonparametric and ...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Lin, Lihui Rao, Yanghui Xie, Haoran Lau, Raymond Y. K. Yin, Jian Wang, Fu Lee Li, Qing Sun Yat-sen University School of Computer Science and Engineering Guangzhou China Lingnan University Department of Computing and Decision Sciences New Territories Hong Kong City University of Hong Kong Department of Information Systems Kowloon Hong Kong Sun Yat-sen University Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Hong Kong Metropolitan University School of Science and Technology Kowloon Hong Kong The Hong Kong Polytechnic University Department of Computing Kowloon Hong Kong

ISBN: (纸本)9798350322279

In terms of the generative process, the Gamma-Gamma-Poisson Process (G2PP) is equivalent to the nonparametric topic model of Hierarchical Dirichlet Process (HDP). Considering the high computational cost of estimating parameters in HDP, a parallel G2PP was developed to generate topics efficiently via multi-threading. Unfortunately, the above model needs to predefine the number of topics. To address this issue, we first propose a Topic Self-Adaptive Model (TSAM) for nonparametric and parallel topic discovery. In TSAM, a monitor-executor mechanism is developed to manage the global topic information using a hierarchical structure of threads. Based on the apparatus of copulas, we further extend our TSAM to TSAMcop for coherent topic modeling by exploiting a copula guided parallel Gibbs sampling algorithm. Extensive experiments validate the effectiveness of both TSAM and TSAMcop. © 2023 IEEE.

关键词： Information management

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Ciphertext-only fault analysis of GIFT lightweight cryptosystem

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science China(Information sciences) 2022年第3期65卷 263-265页

作者： Wei LI Shan CAO Dawu GU Jiayao LI Tianpei CAI Menglin WANG Li SUN Zhiqiang LIU Ya LIU School of Computer Science and Technology Donghua University Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai Key Laboratory of Computing and Systems Shanghai Jiao Tong University Shanghai Key Laboratory of Integrate Administration Technologies for Information Security Shanghai Jiao Tong University Department of Computer Science and Engineering University of Shanghai for Science and Technology

Dear editor,The GIFT cryptosystem was proposed by Banik et al. [1]in CHES 2017. It can be widely applied to protect RFID tags and other low-resource devices. It has an SPN structure with a fixed 128-bit key size and two flexible variants of 64-bit and 128-bit block sizes. In simulations, GIFT achieves good performance and surpasses both SIMON and SKINNY [1]. In 2013, Fuhr et al. [2] proposed a ciphertextonly fault analysis(CFA) of AES for three types of faults:zero-byte fault, zero-nibble fault,

关键词： lightweight cryptosystem GIFT ciphertext-only fault analysis cryptanalysis fault analysis

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Utilizing Web Scraping and Machine Learning for Improving Accuracy of Smartphone Price Prediction

Utilizing Web Scraping and Machine Learning for Improving Ac...

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Advances in Data engineering and Intelligent computing systems (ADICS), International Conference on

作者： V. Vasanth Kumar C. TamizhSelvan L. Manoharan R. Vishnu Sekhar V. Vinoth Kumar Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology India Department of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India Department of Biotechnology Integrated Bioprocessing Laboratory School of Bioengineering SRM Institute of Science and Technology India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

Mobile prices play a pivotal role in determining their popularity amongst consumers and their competitive standing within the market. As customers consider their budget while evaluating a mobile phone's specifications and design, estimating the price beforehand becomes essential to cater to their needs and expectations. Hence, accurately forecasting mobile phone prices is a vital step in the product launch process to remain competitive and assess the market dynamics and competitors. The primary aim of this research is to determine the true market value of mobile phones. In this research, the dataset was extracted by means of utilizing a web scraping process from the flipkart website. The main objective of this research is to fetch the most recent dataset based on the most recent features. The extracted dataset is fitted into regression models such as linear regression, decision tree regressor, random forest regressor, KNN regressor, and gradient boosting regressor and performs the comparative analysis. The models are evaluated based on the mean absolute error metric.

关键词： Measurement Computational modeling Linear regression Predictive models Feature extraction Boosting Data models

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Addressing Non-stationarity with Stochastic Trend in the Context of Limited Time Series Data: An Experimental Survey

Research Square

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Research Square 2025年

作者： Batoure Bamana, Apollinaire Sokdou Bila Lamou, Yannick Fotsa-Mbogne, David Jaures Shafiee Kamalabad, Mahdi Department of Computer Engineering University Institute of Technology The University of Ngaoundere Cameroon Department of Robotic and Industrial Computing School of Chemical Engineering and Mineral Industries The University of Ngaoundere Cameroon Department of Mathematics and Computer Science Faculty of Science The University of Ngaoundere Cameroon Laboratory of Mathematics Computer Science and Applications Department of Mathematics and Computer Science National Higher School of Agro-Industrial Sciences The University of Ngaoundéré Cameroon Laboratory of Mathematics and Computer Science Department of Mathematics Statistics and Computer Science Faculty of Science The University of Bertoua Cameroon Department of Methodology and Statistics Faculty of Social and Behavioural Sciences Utrecht University Utrecht Netherlands Centre for Complex Systems Studies Utrecht University Utrecht Netherlands

Stationarity in time series is a key property for practical data analysis, inferences, and predictions particularly in biosciences. Stationarity can be either deterministic or stochastic. If a time series data is not stationary, it can be rendered stationary through detrending or differentiation techniques. Differentiation can be performed in either an integer or fractional form. This paper investigates non-stationarity in time series data, focusing specifically on stochastic trends characterised by a unit root. It hypothesises that fractional differentiation can enhance forecast accuracy for datasets with limited volume. For experiments, 24 series corresponding to weekly Malaria and Typhoid Fever from Adamawa Region (Cameroon) are analysed and forecasted. Each series comprises 156 observations covering January 2021 to December 2023. After collecting, checking and classifying the data, four forecasting models, Auto Regressive Integrated Moving Average (ARIMA), Fractional ARIMA (ARFIMA), Long Short-Term Memory (LSTM), and the proposed Fractional Differencing and LSTM (FD-LSTM), are implemented. The models' performances are evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of determination (R2) metrics, which inform further analysis and recommendations. Results reveal that ARFIMA outperformed the ARIMA by 93% in training and 100% in testing. Similarly, FD-LSTM achieved a 100% improvement over LSTM in training and testing. These findings underscore the value of achieving stationarity through fractional differentiation, enhancing model performance and providing a robust framework for forecasting in datasets with limited observations. © 2025, CC BY.

关键词： Time series

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Road Signs Recognition by Using YOLOv8 Model

Road Signs Recognition by Using YOLOv8 Model

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International Conference on Intelligent systems: Theories and Applications (SITA)

作者： Abdellah Nabou Atheer L. Salih Ibtissam Ben Rabbaa El-Hassan Abdelwahed Mouaad Ait Ahmed Ouhamou Computer Science Dept Computing Systems Engineering Laboratory (LISI) Cadi Ayyad University Marrakesh Morocco Electrical & Communications Engineering Department DHBW University - Campus Friedrichshafen Germany Computer Science Dept Cadi Ayyad University Marrakesh Morocco

Efficient Road sign recognition is key to improving road safety, navigation and to enhance driver assistance for an intelligent transportation system (ITS). However, achieving efficient road sign and traffic recognition is accompanied by many challenges, in our last few years, several deep learning algorithms have been deployed for responding to these limitations. With the exponential increase in the number of vehicles in Morocco, the need for accurate and real-time detection and classification of road signs has become essential. In this paper, we propose an advanced approach for recognizing Moroccan road signs utilizing the YOLOv8 (You Only Look Once) model that very known for its efficiency and effectiveness in object detection tasks. the model is trained and evaluated under a comprehensive dataset that comprising diverse Moroccan road signs. The test of YOLOv8 model give an encouraging result in different performance metrics such as accuracy, recall, precision and mAP which have respectively an average of 0.95, 0.94, 0.96 and 0.97.

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Fast Heterogeneous Multi-Problem Surrogates for Transfer evolutionary Multiobjective Optimization

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IEEE Transactions on evolutionary Computation 2024年 1-1页

作者： Li, Hao Xiong, Pu Gong, Maoguo Qin, A.K. Wu, Yue Xing, Lining Key Laboratory of Collaborative Intelligence Systems Ministry of Education School of Electronic Engineering Xidian University Xi’an China Department of Computing Technologies Swinburne University of Technology Hawthorn Victoria Australia School of Computer Science and Technology Xidian University Xi’an China

Transfer evolutionary multiobjective optimization leverages the relevant knowledge from other source problems (distinct but possibly related) to assist the optimization of the target problem of interest. Multi-problem surrogates stack multiple source surrogates to reduce the number of function evaluations of the target expensive problem. The current multi-problem surrogates only considers several source problems and the source and target problems are assumed to be homogeneous. In order to address the above issues, this paper proposes fast heterogeneous multi-problem surrogates for transfer evolutionary multiobjective optimization with a large number of surrogates. First, an iterative surrogate selection strategy is designed to select the highly relevant surrogates from the large-scale surrogate pool to avoid negative transfer. Second, heterogeneous multi-problem surrogates are established to align the features of the source and target models. Finally, an adaptive k-fold cross-validation method is proposed to obtain the predicted values of the target model with low computational costs. Experiments on the multiobjective optimization benchmark problems and multiobjective neural architecture search problems have demonstrated that the proposed method is able to avoid negative transfer in the large-scale scenarios and reduce the computational costs. IEEE

关键词： Multiobjective optimization

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