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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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Resilient Collaborative Caching for Multi-Edge Systems with Robust Federated Deep Learning

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IEEE/ACM Transactions on Networking 2024年

作者： Chen, Zheyi Liang, Jie Yu, Zhengxin Cheng, Hongju Min, Geyong Li, Jie Fuzhou University College of Computer and Data Science Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou350002 China Lancaster University School of Computing and Communications LancasterLA1 4YW United Kingdom University of Exeter Faculty of Environment Science and Economy Department of Computer Science ExeterEX4 4QF United Kingdom Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China

As a key technique for future networks, the performance of emerging multi-edge caching is often limited by inefficient collaboration among edge nodes and improper resource configuration. Meanwhile, achieving optimal cache hit rates poses substantive challenges without effectively capturing the potential relations between discrete user features and diverse content libraries. These challenges become further sophisticated when caching schemes are exposed to adversarial attacks that seriously impair cache performance. To address these challenges, we introduce RoCoCache, a resilient collaborative caching framework that uniquely integrates robust federated deep learning with proactive caching strategies, enhancing performance under adversarial conditions. First, we design a novel partitioning mechanism for multi-dimensional cache space, enabling precise content recommendations in user classification intervals. Next, we develop a new Discrete-Categorical Variational Auto-Encoder (DC-VAE) to accurately predict content popularity by overcoming posterior collapse. Finally, we create an original training mode and proactive cache replacement strategy based on robust federated deep learning. Notably, the residual-based detection for adversarial model updates and similarity-based federated aggregation are integrated to avoid the model destruction caused by adversarial updates, which enables the proactive cache replacement adapting to optimized cache resources and thus enhances cache performance. Using the real-world testbed and datasets, extensive experiments verify that the RoCoCache achieves higher cache hit rates and efficiency than state-of-the-art methods while ensuring better robustness. Moreover, we validate the effectiveness of the components designed in RoCoCache for improving cache performance via ablation studies. © 1993-2012 IEEE.

关键词： cache space partitioning content popularity prediction Multi-edge collaborative caching proactive cache replacement robust federated deep learning

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Boosting Meaningful Dependency Mining with Clustering and Covariance Analysis

Boosting Meaningful Dependency Mining with Clustering and Co...

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International Conference on data Engineering

作者： Xi Wang Ruochun Jin Wanrong Huang Yuhua Tang Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha China Department of Intelligent Data Science College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Functional dependencies (FDs) form a valuable ingredient for various data management tasks. However, existing methods can hardly discover practical and interpretable FDs, especially in large noisy real-life datasets. This paper studies the problem of discovering meaningful functional dependencies (FDms) that utilize support and error parameters to capture interesting dependencies in such datasets and proposes an efficient discovery algorithm called FDMε. In order to scale with large datasets, FDM ε employs an efficient sampling method with accuracy guarantees to capture the differences between tuple pairs and to quantify the connection between support/error of dependencies on samples and those on the entire dataset. Moreover, it adopts a clustering-based correlated attributes extraction to divide the exponentially large search space into multiple small sub-spaces and proposes an easy-first traversal strategy with covariance-based guidance that quickly detects candidate dependencies and validates them. Additionally, we prove a covariance lower bound as an additional pruning criterion to reduce the search space. Extensive experiments on real-life and synthetic datasets demonstrate that FDM ε is 14 times faster than existing discovery algorithms on average, up to 31 times, and scales to larger datasets with the least memory cost.

关键词： Accuracy Costs Clustering algorithms Search problems data engineering Noise measurement Frequency division multiplexing

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DOUBLY ROBUST PROXIMAL CAUSAL LEARNING FOR CONTINUOUS TREATMENTS 12

DOUBLY ROBUST PROXIMAL CAUSAL LEARNING FOR CONTINUOUS TREATM...

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12th International Conference on Learning Representations, ICLR 2024

作者： Wu, Yong Fu, Yanwei Wang, Shouyan Sun, Xinwei Institute of Science and Technology for Brain-Inspired Intelligence Fudan University China School of Data Science Fudan University China Zhangjiang Fudan International Innovation Center China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University China MOE Frontiers Center for Brain Science Fudan University China Shanghai Engineering Research Center of AI & Robotics Fudan University China Engineering Research Center of AI & Robotics Ministry of Education Fudan University China

Proximal causal learning is a powerful framework for identifying the causal effect under the existence of unmeasured confounders. Within this framework, the doubly robust (DR) estimator was derived and has shown its effectiveness in estimation, especially when the model assumption is violated. However, the current form of the DR estimator is restricted to binary treatments, while the treatments can be continuous in many real-world applications. The primary obstacle to continuous treatments resides in the delta function present in the original DR estimator, making it infeasible in causal effect estimation and introducing a heavy computational burden in nuisance function estimation. To address these challenges, we propose a kernel-based DR estimator that can well handle continuous treatments for proximal causal learning. Equipped with its smoothness, we show that its oracle form is a consistent approximation of the influence function. Further, we propose a new approach to efficiently solve the nuisance functions. We then provide a comprehensive convergence analysis in terms of the mean square error. We demonstrate the utility of our estimator on synthetic datasets and real-world applications. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Delta functions

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Income Tax Fraud Deduction Using Machine Learning

Income Tax Fraud Deduction Using Machine Learning

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Advancement in Electronics & Communication Engineering (AECE), International Conference on

作者： Pradeep Sudhakaran Prudhvi Kolamudi Preetha M K Pragash Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Career Development Centre SRM Institute of Science and Technology Potheri Kattankulathur Tamil Nadu India Department of Artificial Intelligence and Data Science Sri Manakula Vinayagar Engineering College Madagadipet Puducherry India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

Specifically, the program is based on boosting methods in the context of the used machine learning, to reveal potential cases of evasion of income tax. The procedure consists of obtaining and using datasets of income tax for the learning and training purpose. The first and crucial goal is to enhance the accuracy of predicting fraud cases through learning boosting algorithms and graphical representation of the most significant factors leading to fraud detection. Depending on accuracy and sensitivity as well as specificity and precision for that matter, the said assessment will be conducted. The first goal entails a mechanization of the process of identifying foraging activities, eliminating possible errors, and improving efficiency of and security in income tax assessments with particular emphasis in distinguishing between probable real and potentially fraudulent tax returns [10]. The boosting techniques alongside other futuristic ways of identifying the suspicious transactions can help the tax authorities to improve the risk management processes of their organization. In other words, this approach of fraud detection makes it possible to proceed to more efficient work in terms of both accuracy and the reliability of certain results. Also, the <|reserved_special_token_265|> channels of tax avoidance can be closed by concentrating efforts on cases of high risk as the authorities are more likely to lose a lot of revenues. Machine learning technologies can easily address these requirements, giving the necessary flexibility and precision to prevent and identify fraudulent activities and strategies. These strategies are critical in protecting the Tax system a longer term game which can effectively counter financial fraudsters. The goal of the project are to gather information on the corporate taxation; to apply boosting algorithms for fraudulent activity detection, and to evaluate the efficiency of boosting algorithms. The main focus is primarily aimed at the increased app

关键词： Training Accuracy Machine learning algorithms Sensitivity Finance Boosting Prediction algorithms Fraud Stakeholders Security

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Intelligent New Energy Vehicle Battery Swap Scheme based on Blockchain Technology 23

Intelligent New Energy Vehicle Battery Swap Scheme based on ...

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Proceedings of the 2023 5th International Conference on Blockchain Technology

作者： Ya Han Jianbo Gao Zhi Guan Zhong Chen Lifeng Ren School of Computer Science Peking University China and Peking University Chongqing Research Institute of Big Data China National Engineering Research Center for Software Engineering Peking University China Peking University Chongqing Research Institute of Big Data China

ISBN: (纸本)9798400708930

With the development of electric vehicle technology, electric vehicles surpass fuel vehicles in terms of power performance and intelligence, and the cruising range has also increased to more than 400km with the increase of battery energy density and power consumption. However, the overall is still facing the problem of energy refueling anxiety, and the fuel vehicle refueling time is 5 minutes, and the current fast charge takes at least 60 minutes. The waiting time for charging during peak hours has also been further lengthened. Increasing the speed of energy replenishment has become an urgent need for new energy vehicle owners. The way electric vehicles are supplied with energy is fundamentally different from that of traditional cars. The battery swap mode naturally separates the vehicle and electricity, providing the possibility for novel business models (vehicle electricity separation, battery sharing). Let the battery return to its "energy carrier" use attribute, realize the sharing of batteries, create conditions for battery financialization, carry out full life cycle value management of batteries, implement battery gradient utilization, and provide a feasible path for future energy storage business. However, in battery swap mode, there are many divisions of responsibilities. For example, if the battery burns the car, should I find the main engine factory or the power exchange company/high-voltage electrical failure after the power exchange, especially when the responsibility is not clear, who will check and repair. This paper proposes a new energy vehicle monitoring platform based on blockchain technology, which can manage the whole process life cycle of new energy batteries through blockchain traceability technology. At the same time, according to the current responsibility division system, the transparent division of responsibilities is realized in combination with blockchain smart contracts.

关键词： Battery swap mode Blockchain New energy Responsibility division

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Linking Multi-Scale Brain Connectivity with Vigilance, Working Memory, and Behavior in Adolescents 22

Linking Multi-Scale Brain Connectivity with Vigilance, Worki...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Bajracharya, Prerana Ballem, Ram Chen, Jiayu Andrés-Camazón, Pablo Khasayeva, Nigar Calhoun, Vince Liu, Jingyu Iraji, Armin Tri-Institutional Center for Translational Research in Neuroimaging and Data Science Atlanta United States Georgia State University Department of Computer Science Atlanta United States Institute of Psychiatry and Mental Health Hospital General Universitario Gregorio Marañón IiSGM Cibersam Isciii School of Medicine Universidad Complutense Madrid Spain

ISBN: (纸本)9798331520526

This study examines how multi-scale intrinsic connectivity networks (ICNs) relate to cognitive and behavioral functions in adolescents, focusing on attention/vigilance, working memory, and behavioral regulation. Leveraging the NeuroMark 2.2 multi-scale ICN template obtained from over 100,000 subjects, we obtained multi-scale ICNs from baseline resting-state fMRI data from the ABCD Study. For this study, we are interested in 'the fronto-thalamo-cerebellar (FTC) circuitry' and choose the subdomains of Neuromark 2.2 that cover it: Cerebellar (CB), Subcortical - Extended Thalamic (SC-ET), Higher Cognition - Insular Temporal (HC-IT), and Higher Cognition - Frontal (HC-FR), previously identified as relevant to cognitive and behavioral functions. Employing a multivariate approach combining principal component analysis (PCA) and canonical correlation analysis (CCA), we examined associations between these multi-scale ICNs and cognitive-behavioral outcomes. Our findings revealed significant associations, particularly for one of the estimated canonical components, linking multi-scale ICNs to cognitive and behavioral measures across both discovery and replication sets. This connectivity pattern may serve as a potential marker for attention, working memory, and behavioral regulation, offering new insights into a wide spectrum of neurodevelopmental disorders including Attention-Deficit/Hyperactivity Disorder (ADHD). © 2025 IEEE.

关键词： Behavioral research

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Wait, wasn’t that code here before? Detecting Outdated Software Documentation

Wait, wasn’t that code here before? Detecting Outdated Soft...

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International Conference on Software Maintenance (ICSM)

作者： Wen Siang Tan Markus Wagner Christoph Treude School of Computer Science University of Adelaide Adelaide SA Australia Department of Data Science & AI Monash University Melbourne VIC Australia School of Computing and Information Systems The University of Melbourne Melbourne VIC Australia

Encountering outdated documentation is not a rare occurrence for developers and users in the software engineering community. To ensure that software documentation is up-to-date, developers often have to manually check whether the documentation needs to be updated whenever changes are made to the source code. In our previous work, we proposed an approach to automatically detect outdated code element references in software repositories and found that more than a quarter of the 1000 most popular projects on GitHub contained at least one outdated reference. In this paper, we present a GitHub Actions tool that builds on our previous work’s approach that GitHub developers can configure to automatically scan for outdated code element references in their GitHub project’s documentation whenever a pull request is submitted.

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UAV Visual Tracking Algorithm Based on Feature Fusion of the Attention Mechanism 5

UAV Visual Tracking Algorithm Based on Feature Fusion of the...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Ma, Sugang Zhang, Zixian Zhao, Zhixian Yang, Xiaobao Hou, Zhiqiang School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Shaanxi Xi'an China Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an University of Posts and Telecommunications Shaanxi Xi'an China

ISBN: (纸本)9781450396899

To enhance the expression ability of deep features and improve the tracking performance of the fully convolutional siamese network (SiamFC) in the UAV scene, we propose a UAV visual tracking algorithm based on feature fusion of the attention mechanism. By designing the local perception attention module and the global perception attention module to enhance the features extracted from the backbone network, a set of complementary local enhanced features and global enhanced features are obtained. And then, the tracking response map fused with the two features is then located, which effectively improves the tracking robustness of SiamFC in the UAV scene. The algorithm and nine other related algorithms such as SiamFC are tested on the DTB70 dataset. The experiments show that the algorithm has a good tracking performance and can adapt to the visual object tracking task in the UAV scene. © 2022 ACM.

关键词： Unmanned aerial vehicles (UAV)

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An overview of diffusion models for generative artificial intelligence

arXiv

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arXiv 2024年

作者： Gallon, Davide Jentzen, Arnulf von Wurstemberger, Philippe Applied Mathematics: Institute for Analysis and Numerics University of Münster Germany School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen [CUHK-Shenzhen China Risklab Department of Mathematics ETH Zurich Switzerland

This article provides a mathematically rigorous introduction to denoising diffusion probabilistic models (DDPMs), sometimes also referred to as diffusion probabilistic models or diffusion models, for generative artificial intelligence. We provide a detailed basic mathematical framework for DDPMs and explain the main ideas behind training and generation procedures. In this overview article we also review selected extensions and improvements of the basic framework from the literature such as improved DDPMs, denoising diffusion implicit models, classifier-free diffusion guidance models, and latent diffusion models. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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