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ES-GP: An Ensemble Surrogate-Assisted Genetic Programming Approach to Image Classification

IEEE Transactions on Evolutionary Computation 2025年

作者： Fan, Qinglan Zhang, Yunfeng Yao, Xunxiang Bi, Ying Xue, Bing Zhang, Mengjie Shandong University of Finance and Economics School of Computing and Artificial Intelligence Shandong Key Laboratory of Lightweight Intelligent Computing and Visualization for Digital Economy Jinan250014 China Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China State Key Laboratory of Intelligent Agricultural Power Equipment Luoyang471000 China Victoria University of Wellington Center for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand

Genetic Programming (GP) is a promising evolutionary machine learning technique for image classification, known for its ability to evolve flexible, effective, and interpretable models. However, the high computational cost of fitness evaluations in evolutionary learning restricts its practical applications. While Surrogate models offer efficient approximations for costly fitness evaluations, their application in GP-based image classification remains in its early stages, facing challenges such as handing flexible tree-based representations with variable lengths, designing an effective surrogate, and the limited performance of a single surrogate across various image classification tasks. To address these issues, this paper proposes an ensemble surrogate-assisted GP approach to image classification. The new approach constructs one global surrogate model to explore broad areas and three local surrogate models within specific subspaces to exploit local regions, enabling more accurate predictions of GP individuals' fitness. Moreover, a dynamic weighting strategy is developed to assign different weights to the base surrogate models in the ensemble, improving prediction accuracy. Additionally, the proposed approach refines the surrogate training set1 construction method, previously limited to single-tree GP, enabling it to accelerate both single-tree and multi-tree GP 2 for image classification. Experimental results on five datasets of varying difficulty demonstrate that the new ensemble surrogate method significantly reduces the number of expensive fitness evaluations of both single-tree and multi-tree GP-based image classification methods while achieving competitive performance. The comparisons with other state-of-the-art methods also confirm its effectiveness. © 1997-2012 IEEE.

关键词： Genetic algorithms

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A Fragile Watermarking Algorithm for Speech Authentication by Modifying Least Significant Digits

A Fragile Watermarking Algorithm for Speech Authentication b...

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International Conference on Computers, Communications, and Systems (ICCCS)

作者： Qing Qian Yunhe Cui School of Information International Joint Research Center for Data Science and High-Performance Computing Guizhou University of Finance and Economics Guiyang China School of Computer Science and Technology Guizhou Provincial Key Laboratory of Public Big Data Guizhou University Guiyang China

ISBN: (数字)9781728161365

ISBN: (纸本)9781728161372

Speech content is often faced with malicious forgery and tampering in transit. Therefore, we introduce a novel fragile watermarking algorithm by modifying the least significant digits for speech content authentication and tampering recovery. In this letter, the proposed authentication scheme consists of two processes: the first one is watermark generation and embedding, the second one is content authentication and recovery. In the former process, the compressed speech signal, frame number and check information are generated based on speech content to form a watermark. Subsequently, the generated watermark is embedded into the least significant digits of speech sampling points using the proposed watermarking algorithm. In the latter process, the embedded watermark is utilized to locate the tampered area and recover that tampered content synchronously. Experimental results show that the proposed watermarking algorithm is fragile to common signal processing. Additionally, it has favorable inaudibility, accurate location ability and high recovery quality.

关键词： Watermarking Authentication Speech coding Signal to noise ratio Speech processing

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BPLF: A bi-parallel linear flow model for facial expression generation from emotion set images

arXiv

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arXiv 2021年

作者： Xu, Gao Long, Yuanpeng Liu, Siwei Yang, Lijia Xu, Shimei Yao, Xiaoming Shu, Kunxian School of Computer Science and Technology Chongqing Key Laboratory on Big Data for Bio Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China School of Economic Information Engineering Southwestern University of Finance and Economics Chengdu611130 China Hetie International Square Building 2 Room404 Sichuan Chengdu China

The flow-based generative model is a deep learning generative model, which obtains the ability to generate data by explicitly learning the data distribution. Theoretically its ability to restore data is stronger than other generative models. However, its implementation has many limitations, including limited model design, too many model parameters and tedious calculation. In this paper, a bi-parallel linear flow model for facial emotion generation from emotion set images is constructed, and a series of improvements have been made in terms of the expression ability of the model and the convergence speed in training. The model is mainly composed of several coupling layers superimposed to form a multi-scale structure, in which each coupling layer contains 1*1 reversible convolution and linear operation modules. Furthermore, this paper sorted out the current public data set of facial emotion images, made a new emotion data, and verified the model through this data set. The experimental results show that, under the traditional convolutional neural network, the 3-layer 3*3 convolution kernel is more conducive to extracte the features of the face images. The introduction of principal component decomposition can improve the convergence speed of the model. © 2021, CC BY.

关键词： Convolution

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An optimized capsule-LSTM model for facial expression recognition with video sequences

arXiv

引用

arXiv 2021年

作者： Liu, Siwei Long, Yuanpeng Xu, Gao Yang, Lijia Xu, Shimei Yao, Xiaoming Shu, Kunxian School of Computer Science and Technology Chongqing Key Laboratory on Big Data for Bio Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China School of Economic Information Engineering Southwestern University of Finance and Economics Chengdu611130 China *** Hetie International Square Building 2 Room404 Sichuan Chengdu China

To overcome the limitations of convolutional neural network in the process of facial expression recognition, a facial expression recognition model Capsule-LSTM based on video frame sequence is proposed. This model is composed of three networks includingcapsule encoders, capsule decoders and LSTM network. The capsule encoder extracts the spatial information of facial expressions in video frames. Capsule decoder reconstructs the images to optimize the network. LSTM extracts the temporal information between video frames and analyzes the differences in expression changes between frames. The experimental results from the MMI dataset show that the Capsule-LSTM model proposed in this paper can effectively improve the accuracy of video expression recognition. © 2021, CC BY.

关键词： Long short-term memory

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A polynomial-time algorithm for simple undirected graph isomorphism

A polynomial-time algorithm for simple undirected graph isom...

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作者： He, Jing Chen, Jinjun Huang, Guangyan Cao, Jie Zhang, Zhiwang Zheng, Hui Zhang, Peng Zarei, Roozbeh Sansoto, Ferry Wang, Ruchuan Ji, Yimu Fan, Weibei Xie, Zhijun Wang, Xiancheng Guo, Mengjiao Chi, Chi-Hung de Souza, Paulo A. Zhang, Jiekui Li, Youtao Chen, Xiaojun Shi, Yong Green, David Kersi, Taraporewalla Van Zundert, André Institute of Information Technology Nanjing University of Finance and Economics Nanjing China Swinburne Data Science Research Institute Swinburne University of Technology Melbourne Australia School of Information Technology Deakin University Melbourne Australia Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing University of Posts and Telecommunications Nanjing China Department of Information Science and Engineering Ningbo University Ningbo China Ningbo Institute of Technology Zhejiang University Ningbo China CSIRO TAS Australia JingQi Smart Healthcare Pty Ltd Hefei China Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong Research Center on Fictitious Economy and Data Sciences Chinese Academy of Sciences Beijing China Faculty of Information Technology Monash University Melbourne Australia Royal Brisbane and Women's Hospital Herston Australia

The graph isomorphism problem is to determine two finite graphs that are isomorphic which is not known with a polynomial-time solution. This paper solves the simple undirected graph isomorphism problem with an algorithmic approach as NP=P and proposes a polynomial-time solution to check if two simple undirected graphs are isomorphic or not. Three new representation methods of a graph as vertex/edge adjacency matrix and triple tuple are proposed. A duality of edge and vertex and a reflexivity between vertex adjacency matrix and edge adjacency matrix were first introduced to present the core idea. Beyond this, the mathematical approval is based on an equivalence between permutation and bijection. Because only addition and multiplication operations satisfy the commutative law, we propose a permutation theorem to check fast whether one of two sets of arrays is a permutation of another or not. The permutation theorem was mathematically approved by Integer Factorization Theory, Pythagorean Triples Theorem, and Fundamental Theorem of Arithmetic. For each of two n-ary arrays, the linear and squared sums of elements were respectively calculated to produce the results. © 2019 John Wiley & Sons, Ltd.

关键词： Undirected graphs

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An autocovariance-based learning framework for high-dimensional functional time series

arXiv

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arXiv 2020年

作者： Chang, Jinyuan Chen, Cheng Qiao, Xinghao Yao, Qiwei Joint Laboratory of Data Science and Business Intelligence Southwestern University of Finance and Economics Sichuan Chengdu611130 China Department of Statistics London School of Economics LondonWC2A 2AE United Kingdom

Many scientific and economic applications involve the statistical learning of high-dimensional functional time series, where the number of functional variables is comparable to, or even greater than, the number of serially dependent functional observations. In this paper, we model observed functional time series, which are subject to errors in the sense that each functional datum arises as the sum of two uncorrelated components, one dynamic and one white noise. Motivated from the fact that the autocovariance function of observed functional time series automatically filters out the noise term, we propose a three-step framework by first performing autocovariance-based dimension reduction, then formulating a novel autocovariance-based block regularized minimum distance estimation to produce block sparse estimates, and based on which obtaining the final functional sparse estimates. We investigate theoretical properties of the proposed estimators, and illustrate the proposed estimation procedure with the corresponding convergence analysis via three sparse high-dimensional functional time series models. We demonstrate via both simulated and real datasets that our proposed estimators significantly outperform their competitors. Copyright © 2020, The Authors. All rights reserved.

关键词： White noise

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Cooperative Game Model of Delegation Computing: Verifier Separated from Calculators

Cooperative Game Model of Delegation Computing: Verifier Sep...

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IEEE Conference on Computer Communications Workshops, INFOCOM Wksps

作者： Duo Zhang Youliang Tian Linjie Wang Sheng Gao Jianfeng Ma State Key Laboratory of Public Big Data College of Computer Science and Technology College of Mathematics and Statistics Guizhou University Guiyang China State Key Laboratory of Public Big Data College of Computer Science and Technology Institute of Cryptography and Date Security Guizhou University Guiyang China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China School of Information Central University of Finance and Economics Beijing China School of Cyber Engineering College of Computer Science and Technology Xi'dian University Xi'an China

ISBN: (数字)9781728186955

ISBN: (纸本)9781728186962

In cloud computing, the delegation computation service is required because end users usually are resource-constrained, and at the same time the correctness of computing results needs to be verified. However, all the existing and available technologies have a high cost to verify which is the main problem faced by the delegation computing under the cloud platform. In order to address this issue, based on the game theory and a smart contract, we construct three protocols. The client and the calculator sign the Prisoner's Protocol to incentivize correct computation by asking the calculator to pay a deposit upfront; The client and the verifier sign the Long-acting Mechanism Protocol to ensure the validity of the verification results; The calculator and the verifier sign the Collusion Protocol to make collusion the most profitable strategy for all colluding parties. Through the combination of these protocols, the work is realized by separation the computing task and the verification task, and the heavy verification task is avoided. Finally, the performance analysis of the results shows that the combination of all the protocols not only solves the problem of verification complexity in the traditional delegation computing, but also guarantees the benefit of the honest player.

关键词： Calculators Contracts Protocols Task analysis Computational modeling Cloud computing Security

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LNAD: Towards Lightweight Network Anomaly Detection in Software-Defined Networking

LNAD: Towards Lightweight Network Anomaly Detection in Softw...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Yunhe Cui Qing Qian Huanlai Xing Saifei Li State Key Laboratory of Public Big Data School of Computer Science and Technology Guizhou University Guiyang China School of Information Guizhou University of Finance and Economics Guiyang China School of Information Science and Technology Southwest Jiaotong University Chengdu China

ISBN: (纸本)9781728176505

As an emerging architecture, Software-Defined Networking (SDN) is suffering from a lot of security issues. Network anomaly detection technology plays a crucial role in addressing SDN security issues. In SDN, the network status can be denoted by the packets or flow entries. Hence, the existing network anomaly detection methods are commonly designed based on inspecting packets or flow entries. Subsequently, these anomaly detection methods have to collect packets or flow entries from the underlying switches. More seriously, some anomaly detection methods even need to add extra modules to the switches. Unlike these existing network anomaly detection methods, this work proposes a lightweight network anomaly detection method. The main design principle of the proposed method is mining the inherent OpenFlow messages in SDN to represent the network status and further detect the network anomaly. The proposed method does not need to collect any additional messages from the underlying switches or add any additional modules to the underlying switches. The evaluation results demonstrate that the proposed network anomaly detection method can afford high detection accuracy and reduce the overhead of SDN controller.

关键词： High performance computing Design methodology Conferences Security Communication networks Anomaly detection

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Robust Spatial Cell-Type Deconvolution with Qualitative Reference for Spatial Transcriptomics (Small Methods 5/2025)

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Small Methods 2025年第5期9卷

作者： Qishi Dong Yi Yang Ziye Luo Haipeng Shen Xingjie Shi Jin Liu College of Big Data and Internet Shenzhen Technology University Shenzhen 518118 China The Key Laboratory of Developmental Genes and Human Disease School of Life Science and Technology Southeast University Nanjing 210018 China Department of Biometrics Astrazeneca Global R&D (China) CO. Ltd. Shanghai 200085 China Faculty of Business and Economics Hong Kong University Pokfulam Hong Kong SAR China KLATASDS-MOE Academy of Statistics and Interdisciplinary Sciences School of Statistics East China Normal University Shanghai 200062 China School of Data Science The Chinese University of Hong Kong-Shenzhen Shenzhen 518172 China

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data-driven optimization algorithm for economic operation of microgrid based on dynamic Kriging model and two-stage search strategy

Research Square

引用

Research Square 2021年

作者： Fan, Chaodong Hou, Bo Qu, Xilong Xiao, Leyi Cheng, Fanyong Hunan Provincial Key Laboratory of Finance & Economics Big Data Science and Technology School of Information Technology and Management Hunan University of Finance and Economics Changsha410205 China Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment Ministry of Education Anhui Polytechnic University Wuhu241000 China Foshan Green Intelligent Manufacturing Research Institute Xiangtan University Guangdong 528311 China School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore School of Automation and Electronic Information Xiangtan University Xiangtan411105 China

The economic operation optimization of microgrid is an important research topic in the power system. Therefore, this paper proposes a surrogate model particle swarm optimization algorithm based on the global-local search mechanism. Firstly, aiming at the problem that the statistical information of Kriging model is difficult to guarantee the prediction accuracy, the dynamic transformation is carried out to enhance the robustness of the model;secondly, the global-local search mechanism is introduced to make the algorithm fully explore the fitness landscape near the Kriging model after quickly locating the current optimal particle position, so as to achieve the balance of convergence quality and convergence efficiency. The proposed method has been tested on numerous benchmark test functions from two test suites, and the results show that the proposed algorithm has more advantages than other comparison algorithms in optimization accuracy. Finally, simulations are carried out in two operating modes of microgrid islanding and grid-connected, which has verified the effectiveness of the proposed method. © 2021, CC BY.

关键词： Particle swarm optimization (PSO)

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