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Congress on Evolutionary Computation

作者： Parth C. Upadhyay G. N. DeSouza John A. Lory Department of Electrical Engineering and Computer Science (EECS) Vision-Guided and Intelligent Robotics (ViGIR) Laboratory Division of Plant Sciences University of Missouri Columbia MO

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Performing feature selection on a small number of instances with high-dimensional datasets poses a needed challenge in preventing over-fitting. To address this issue, this paper proposes a sequential transfer-learning approach combined with a multi-objective genetic algorithm (STMO-GA) for feature selection. Firstly, for the multi-objective component of our method, we employ a Non-dominated Sorting Genetic Algorithm (NSGA-II) to generate a Pareto front. Then, features are ranked based on their number of appearances in the same Pareto front. Next, during the sequential knowledge transfer process, the ranked features are iteratively selected until a predetermined $n$ number of features remains. This feature subspace is further refined by a k-fold cross-validation operation, starting from the rank-one feature, to determine the cut-off of the $n$ features that will remain. Comparative evaluations against both GA-based as well as traditional feature selection methods demonstrate that the proposed method achieves superior classification accuracy, while retaining the smallest number or a comparable number of features.

关键词： Accuracy Evolutionary computation Feature extraction Genetics Linear programming Robustness Optimization

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SMNDNet for Multiple Types of Deepfake Image Detection

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computers, Materials & Continua 2025年第6期83卷 4607-4621页

作者： Qin Wang Xiaofeng Wang Jianghua Li Ruidong Han Zinian Liu Mingtao Guo Department of Computer Science and Engineering Xi’an University of TechnologyXi’an710048China Department of Mathematics Xi’an University of TechnologyXi’an710054China The National Key Laboratory of Fundamental Science on Synthetic Vision Sichuan UniversityChengdu610065China

The majority of current deepfake detection methods are constrained to identifying one or two specific types of counterfeit images,which limits their ability to keep pace with the rapid advancements in deepfake ***,in this study,we propose a novel algorithm,StereoMixture Density Network(SMNDNet),which can detect multiple types of deepfake face manipulations using a single network *** is an end-to-end CNNbased network specially designed for detecting various manipulation types of deepfake face ***,we design a Subtle Distinguishable Feature Enhancement Module to emphasize the differentiation between authentic and forged ***,we introduce aMulti-Scale Forged Region AdaptiveModule that dynamically adapts to extract forged features from images of varying synthesis ***,we integrate a Nonlinear Expression Capability Enhancement Module to augment the model’s capacity for capturing intricate nonlinear patterns across various types of ***,these modules empower our model to efficiently extract forgery features fromdiverse manipulation types,ensuring a more satisfactory performance in multiple-types deepfake *** show that the proposed method outperforms alternative approaches in detection accuracy and AUC across all four types of deepfake *** also demonstrates strong generalization on cross-dataset and cross-type detection,along with robust performance against post-processing manipulations.

关键词： Convolutional neural network deepfake detection generative adversarial network feature enhancement

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Resilient Cooperative Control of Multiple DC Microgrids With Interconnection Networks Against Cyber Attacks

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical systems 2025年 3卷 116-126页

作者： Xiao, Feng Liu, Shiyu Wei, Bo Fang, Fang Qin, Jiahu North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Beijing102206 China North China Electric Power University School of Control and Computer Engineering Beijing102206 China North China Electric Power University New Energy Generation National Engineering Research Center Beijing102206 China University of Science and Technology of China Department of Automation Hefei230027 China

The two control objectives of voltage regulation and precise current sharing are conflicting in multi-bus DC microgrids. In this paper, a distributed control strategy is proposed, and it can achieve accurate current sharing and regulate the voltage of a critical node to the rated value. However, such a distributed control system is susceptible to potential cyber attacks that could deteriorate microgrids performances by corrupting communication links or actuators. To overcome these difficulties, we design the interconnected communication networks to establish a resilient cooperative system of multiple microgrids against unknown bounded injection attacks. The proposed resilient control strategy can achieve the control objectives of maintaining voltage regulation and current sharing within each microgrids under different operating conditions, and the interconnected system consisting of multiple microgrids cooperates to mitigate the adverse effect of cyber attacks. The dynamics and stability of the overall close-loop system of the DC microgrids are analyzed, in the present or absent of cyber attacks. Simulation results validate the effectiveness of the proposed approach in two 4-bus DC microgrids. © 2023 IEEE.

关键词： Cyber attacks

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Using Lock-Free Design for Throughput-Optimized Cache Eviction

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Proceedings of the ACM on Measurement and Analysis of Computing systems 2025年第2期9卷 1-28页

作者： Dong, Chao Wang, Fang Jiang, Hong Feng, Dan Huazhong University of Science and Technology Key Laboratory of Information Storage System Wuhan National Laboratory for Optoelectronics Engineering Research Center of data storage systems and Technology School of Computer Science and Technology Ministry of Education of China Hubei Wuhan China University of Texas at Arlington Department of Computer Science and Engineering ArlingtonTX United States

In large-scale information systems, storage device performance continues to improve while workloads expand in size and access characteristics. This growth puts tremendous pressure on caches and storage hierarchy in terms of concurrent throughput. However, existing cache eviction policies often struggle to provide adequate concurrent throughput due to their reliance on coarse-grained locking mechanisms and complex data structures. This paper presents a practical approach to cache eviction algorithm design, called Mobius, that optimizes the concurrent throughput of caches and reduces cache operation latency by utilizing lock-free data structures, while maintaining comparable hit ratios. Mobius includes two key designs. First, Mobius employs two lock-free FIFO queues to manage cache items, ensuring that all cache operations are executed efficiently in parallel. Second, Mobius integrates a consecutive detection mechanism that merges multiple modifications during eviction into a single operation, thereby reducing data races. Extensive evaluations using both synthetic and real-world workloads from high-concurrency clusters demonstrate that Mobius achieves a concurrent-throughput improvement ranging from 1.2× to 8.5× over state-of-the-art methods, while also maintaining lower latency and comparable cache hit ratios. The implementation of Mobius in CacheLib and RocksDB highlights its effectiveness in enhancing cache performance in practical scenarios. © 2025 ACM.

关键词： Keyways

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Super-resolution GANs for upscaling unplanned urban settlements from remote sensing satellite imagery - the case of Chinese urban village detection

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International Journal of Digital Earth 2023年第1期16卷 2623-2643页

作者： Alessandro Crivellari Hong Wei Chunzhu Wei Yuhui Shi Department of Computer Science and Engineering SouthernUniversity of Science and TechnologyShenzhenPeople’s Republic of China Ministry of Education Ecological Field Station for East Asian Migratory Birds Department of Earth System ScienceInstitute for Global Change StudiesTsinghua UniversityBeijingPeople’s Republic of China School of Geography and Planning Sun Yat-sen UniversityGuangzhouPeople’s Republic of China Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) ZhuhaiPeople’s Republic of China

The semantic segmentation of informal urban settlements represents an essential contribution towards renovation strategies and reconstruction *** this context,however,a big challenge remains unsolved when dealing with incomplete data acquisitions from multiple sensing devices,especially when study areas are depicted by images of different *** practice,traditional methodologies are directed to downgrade the higher-resolution data to the lowest-resolution measure,to define an overall homogeneous dataset,which is however ineffective in downstream segmentation activities of such crowded unplanned urban *** this purpose,we hereby tackle the problem in the opposite direction,namely upscaling the lower-resolution data to the highest-resolution measure,contributing to assess the use of cutting-edge super-resolution generative adversarial network(SR-GAN)*** experimental novelty targets the particular case involving the automatic detection of‘urban villages’,sign of the quick transformation of Chinese urban *** aligning image resolutions from two different data sources(Gaofen-2 and Sentinel-2 data),we evaluated the degree of improvement with regard to pixel-based landcover segmentation,achieving,on a 1 m resolution target,classification accuracies up to 83%,67%and 56%for 4x,8x,and 10x resolution upgrades respectively,disclosing the advantages of artificially-upscaled images for segmenting detailed characteristics of informal settlements.

关键词： SR-GAN resolution upscaling informal settlements urban villages cross-sensor analytics

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Hinge-Wasserstein: Estimating Multimodal Aleatoric Uncertainty in Regression Tasks

Hinge-Wasserstein: Estimating Multimodal Aleatoric Uncertain...

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IEEE computer Society Conference on computer vision and Pattern Recognition Workshops (CVPRW)

作者： Ziliang Xiong Arvi Jonnarth Abdelrahman Eldesokey Joakim Johnander Bastian Wandt Per-Erik Forssén Department of Electrical Engineering Computer Vision Laboratory LiU Sweden Husqvarna Group Huskvarna Sweden Visual Computing Center KAUST Saudi Arabia Zenseact Sweden

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

computer vision systems that are deployed in safety-critical applications need to quantify their output uncertainty. We study regression from images to parameter values and here it is common to detect uncertainty by predicting probability distributions. In this context, we investigate the regression-by-classification paradigm which can represent multimodal distributions, without a prior assumption on the number of modes. Through experiments on a specifically designed synthetic dataset, we demonstrate that traditional loss functions lead to poor probability distribution estimates and severe overconfidence, in the absence of full ground truth distributions. In order to alleviate these issues, we propose hinge-Wasserstein – a simple improvement of the Wasserstein loss that reduces the penalty for weak secondary modes during training. This enables prediction of complex distributions with multiple modes, and allows training on datasets where full ground truth distributions are not available. In extensive experiments, we show that the proposed loss leads to substantially better uncertainty estimation on two challenging computer vision tasks: horizon line detection and stereo disparity estimation.

关键词： Training computer vision Uncertainty Conferences Estimation Probability distribution Pattern recognition

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Tera-MIND: Tera-scale mouse brain simulation via spatial mRNA-guided diffusion

arXiv

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arXiv 2025年

作者： Wu, Jiqing Berg, Ingrid Li, Yawei Konukoglu, Ender Koelzer, Viktor H. Department of Biomedical Engineering University of Basel Switzerland Department of Pathology and Molecular Pathology University Hospital University of Zurich Switzerland Computer Vision Lab ETH Zurich Switzerland Integrated System Laboratory ETH Zurich Switzerland Institute of Medical Genetics and Pathology University Hospital Basel Switzerland

Holistic 3D modeling of molecularly defined brain structures is crucial for understanding complex brain functions. Emerging tissue profiling technologies enable the construction of a comprehensive atlas of the mammalian brain with sub-cellular resolution and spatially resolved gene expression data. However, such tera-scale volumetric datasets present significant computational challenges in understanding complex brain functions within their native 3D spatial context. Here, we propose the novel generative approach Tera-MIND, which can simulate Tera-scale Mouse braINs in 3D using a patch-based and boundary-aware Diffusion model. Taking spatial transcriptomic data as the conditional input, we generate virtual mouse brains with comprehensive cellular morphological detail at teravoxel scale. Through the lens of 3D gene-gene self-attention, we identify spatial molecular interactions for key transcriptomic pathways in the murine brain, exemplified by glutamatergic and dopaminergic neuronal systems. Importantly, these in-silico biological findings are consistent and reproducible across three tera-scale virtual mouse brains. Therefore, Tera-MIND showcases a promising path toward efficient and generative simulations of whole organ systems for biomedical research. Project website: https://***/*** © 2025, CC BY.

关键词： Mammals

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An Autoencoder-Like Nonnegative Matrix Co-Factorization for Improved Student Cognitive Modeling 38

An Autoencoder-Like Nonnegative Matrix Co-Factorization for ...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Yu, Shenbao Pan, Yinghui Zeng, Yifeng Doshi, Prashant Liu, Guoquan Poh, Kim-Leng Lin, Mingwei College of Computer and Cyber Security Fujian Normal University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer and Information Sciences Northumbria University United Kingdom Intelligent Thought and Action Lab School of Computing University of Georgia United States Financial Technology Research Institute Fudan University China College of Design and Engineering National University of Singapore Singapore

Student cognitive modeling (SCM) is a fundamental task in intelligent education, with applications ranging from personalized learning to educational resource allocation. By exploiting students' response logs, SCM aims to predict their exercise performance as well as estimate knowledge proficiency in a subject. Data mining approaches such as matrix factorization can obtain high accuracy in predicting student performance on exercises, but the knowledge proficiency is unknown or poorly estimated. The situation is further exacerbated if only sparse interactions exist between exercises and students (or knowledge concepts). To solve this dilemma, we root monotonicity (a fundamental psychometric theory on educational assessments) in a co-factorization framework and present an autoencoder-like nonnegative matrix co-factorization (AE-NMCF), which improves the accuracy of estimating the student's knowledge proficiency via an encoder-decoder learning pipeline. The resulting estimation problem is nonconvex with nonnegative constraints. We introduce a projected gradient method based on block coordinate descent with Lipschitz constants and guarantee the method's theoretical convergence. Experiments on several real-world data sets demonstrate the efficacy of our approach in terms of both performance prediction accuracy and knowledge estimation ability, when compared with existing student cognitive models. © 2024 Neural information processing systems foundation. All rights reserved.

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Fun as Moderate Divergence: Evaluating Experience-Driven PCG via RL

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IEEE Transactions on Games 2024年第2期17卷 360-373页

作者： Wang, Ziqi Li, Yuchen Du, Haocheng Liu, Jialin Yannakakis, Georgios N. Research Institute of Trustworthy Autonomous System Shenzhen518055 China Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China Southern University of Science and Technology Shenzhen518055 China Tsinghua Shenzhen International Graduate School Shenzhen518055 China University of Malta Institute of Digital Games Msida2080 Malta

The computational modelling of player experience is key to the generation of personalised game content. The notion of fun, as one of the most peculiar and core aspects of game experience, has often been modelled and quantified for the purpose of content generation with varying success. Recently, measures of a player's fun have been ad-hoc designed to model moderate levels of in-game divergence in platformer games, inspired by Koster's theory of fun. Such measures have shaped the reward functions of game content generative methods following the experience-driven procedural content generation via reinforcement learning (EDRL) paradigm in Super Mario Bros. In this paper, we present a comprehensive user study involving over 90 participants with a dual purpose: to evaluate the ad-hoc fun metrics introduced in the literature and test the effectiveness of the EDRL framework to generate personalised fun Super Mario Bros experiences in an online fashion. Our key findings suggest that moderate degrees of game level and gameplay divergence are highly consistent with the perceived notion of fun of our participants, cross-verifying the ad-hoc designed fun metrics. On the other hand, it appears that EDRL generators manage to match the preferred (i.e., fun) game experiences of each persona, only in part and for some players. Our findings suggest that the use of multi-faceted in-game data, such as events and actions, will likely enable the modelling of more nuanced gameplay behaviours. Additionally, the verification of player persona modelling and the enhancement of player engagement through dynamic experience modelling are suggested as potential future directions. © 2024 IEEE.

关键词： Digital elevation model

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A Practical Approach to Constructing a Geological Knowledge Graph:A Case Study of Mineral Exploration Data

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Journal of Earth Science 2023年第5期34卷 1374-1389页

作者： Qinjun Qiu Bin Wang Kai Ma Hairong Lü Liufeng Tao Zhong Xie Collaborative Innovation Center of Geo-Information Technology for Smart Central Plains Zhengzhou 450046China Key Laboratory of Spatiotemporal Perception and Intelligent Processing Ministry of Natural ResourcesZhengzhou 450046China Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang 443002China College of Computer and Information Technology China Three Gorges UniversityYichang 443002China Department of Automation Tsinghua UniversityBeijing 100084China

Open data initiatives have promoted governmental agencies and scientific organizations to publish data online for *** of geoscience focuses on processing georeferenced quantitative data(e.g.,rock parameters,geochemical tests,geophysical surveys and satellite imagery)for discovering new *** knowledge is the cognitive result of human knowledge of the spatial distribution,evolution and interaction patterns of geological objects or *** graphs(KGs)can formalize unstructured knowledge into structured form and have been used in supporting decision-making *** this paper,we propose a novel framework that can extract the geological knowledge graph(GKG)from public reports relating to a modelling *** on the analysis of basic questions answered by geology,we summarize and abstract geological knowledge elements and then explore a geological knowledge representation model with three levels of“geological conceptsgeological entities-geological relations”to describe semantic units of geological knowledge and their logic ***,based on the characteristics of mineral resource reports,the geological knowledge representation model oriented to“object relationships”and the hierarchical geological knowledge representation model oriented to“process relationships”are proposed with reference to the commonly used geological knowledge graph *** research in this paper can provide some implications for the formalization and structured representation of geological knowledge graphs.

关键词： mineral resource report geological knowledge knowledge graph ontology hierarchical knowledge representation model

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