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Automation, Computing and Renewable systems (ICACRS), International Conference on

作者： A. Praveena N. Senthamilarasi T. S. Karthik Abirami S.K Vijayakrishna Rapaka E Shyamali Das Department of Computer Science and Engineering Jansons Institute of Technology Karumathampatti Coimbatore Tamilnadu India Department of Information Technology Panimalar Engineering College Chennai Tamilnadu India Department of Electronics and Communication Engineering Aditya College of Engineering and Technology Surampalem Andhrapradesh India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamilnadu India Data Science Indian Institute of Technology Madras Chennai Tamilnadu India Department of Computer Science CMR Group of Institutions (SOSS) Bangalore Karnataka India

ISBN: (纸本)9781665460859

Autism Spectrum Disorder (ASD) is a developing disorder if the symptoms develop obvious in the initial years of age but it could be present in some age groups. ASD is mental health problem that affects communicational, social, and non-verbal performances. It could not be cured entirely but is decreased when identified initially. The primary analysis was hampered by the difference and severity of ASD symptoms and containing symptoms usually realized in other mental health problems as well. With the application of machine learning (ML) for the predictive and recognition of several diseases with optimum accuracy, a ray of hope to initial recognition of ASD dependent upon many physiological and physical parameters is projected. This article designs an Equilibrium Optimizer with Deep Learning Model for Autism Spectral Disorder Classification (EODL-ASDC) technique. The presented EODL-ASDC technique mainly focuses on the identification and classification of ASD. To attain this, the presented EODL-ASDC technique exploits the deep belief network (DBN) system to perform the classification procedure. In addition, the EO algorithm is employed for the optimal hyperparameter tuning of the DBN approach. To demonstrate the enhanced ASD classification result of the EODL-ASDC approach, an extensive range of experimental evaluates was executed. The experimental results demonstrate the improvements of the EODL-ASDC technique over other approaches.

关键词： Deep learning Autism Renewable energy sources Automation Mental health Physiology Classification algorithms

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Weipeng Cao Xuyang Yao Zhiwu Xu Yinghui Pan Yixuan Sun Dachuan Li Bohua Qiu Muheng Wei Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen) Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States 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 ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model's capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods.

关键词： Visualization Adaptation models Semantics Zero shot learning Neural networks Termination of employment Transformers Market research Sun Optimization

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No Victim Left Behind: Developing a Dual-Use LEO Satellite System for Real-Time Population Mapping and Everyday Connectivity

No Victim Left Behind: Developing a Dual-Use LEO Satellite S...

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International Symposium on Communications and information Technologies (ISCIT)

作者： Takumi Ohashi Futo Noda Takashi Tomura Ampan Laosunthara Koudai Suzuki Yuno Tanaka Maki Kishimoto Rie Seto Hiraku Sakamoto Gia Khanh Tran Atsushi Shirane Department of Transdisciplinary Science and Engineering Tokyo Institute of Technology Tokyo Japan Disaster and Risk Management Information Systems Research Unit Chulalongkorn University Bangkok Thailand Department of Electrical and Electronic Engineering Tokyo Institute of Technology Tokyo Japan Department of Civil and Environmental Engineering Tokyo Institute of Technology Tokyo Japan Department of Architecture and Building Engineering Tokyo Institute of Technology Tokyo Japan Department of Observation & Data Assimilation Meteorological Research Institute Tsukuba Japan Department of Mechanical Engineering Tokyo Institute of Technology Tokyo Japan Institute of Innovative Research Tokyo Institute of Technology Tokyo Japan

ISBN: (数字)9798350353952

ISBN: (纸本)9798350353969

Disasters often disrupt communication infrastructure, impeding damage assessment and rescue operations. This paper presents a method for real-time population distribution mapping during disasters using the widespread prevalence of smartphones and Low Earth Orbit (LEO) satellites. The system detects signals emitted by smartphones using LEO satellites equipped with directional antennas. By analyzing the received signal strength indicators (RSSI), it generates high-resolution population distribution maps in affected areas, providing critical information for resource allocation and rescue efforts. The system's design allows for functionality during both normal and emergency conditions, offering connectivity and location-based services in daily life while supporting disaster response efforts. A proof-of-concept experiment demonstrates the detection of smartphone signals from tall buildings, confirming the system's ability to distinguish between the presence and absence of smartphones in the observed area based on the detected spectra. This preliminary result suggests the feasibility of estimating population distribution by detecting leaked signals from smartphones during disasters. Further research is needed to address challenges such as precise localization, indoor signal detection, and system scalability. This research explores the integration of satellite technology with mobile devices for effective disaster management, contributing to the development of resilient and efficient emergency response strategies.

关键词： Ethics Satellites Disasters Urban planning Low earth orbit satellites Disaster management Real-time systems Resource management Smart phones Signal detection

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Optimal kernel choice for score function-based causal discovery 24

Optimal kernel choice for score function-based causal discov...

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Proceedings of the 41st International Conference on Machine Learning

作者： Wenjie Wang Biwei Huang Feng Liu Xinge You Tongliang Liu Kun Zhang Mingming Gong School of Mathematics and Statistics The University of Melbourne Australia and Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Halicioğlu Data Science Institute (HDSI) University of California San Diego School of Computing and Information Systems The University of Melbourne Australia Huazhong University of Science and Technology China School of Computer Science Faculty of Engineering The University of Sydney Australia Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates and Department of Philosophy Carnegie Mellon University

Score-based methods have demonstrated their effectiveness in discovering causal relationships by scoring different causal structures based on their goodness of fit to the data. Recently, Huang et al. (2018) proposed a generalized score function that can handle general data distributions and causal relationships by modeling the relations in reproducing kernel Hilbert space (RKHS). The selection of an appropriate kernel within this score function is crucial for accurately characterizing causal relationships and ensuring precise causal discovery. However, the current method involves manual heuristic selection of kernel parameters, making the process tedious and less likely to ensure optimality. In this paper, we propose a kernel selection method within the generalized score function that automatically selects the optimal kernel that best fits the data. Specifically, we model the generative process of the variables involved in each step of the causal graph search procedure as a mixture of independent noise variables. Based on this model, we derive an automatic kernel selection method by maximizing the marginal likelihood of the variables involved in each search step. We conduct experiments on both synthetic data and real-world benchmarks, and the results demonstrate that our proposed method outperforms heuristic kernel selection methods.

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TEVA: Training-Efficient and Verifiable Aggregation for Federated Learning for Consumer Electronics in Industry 5.0

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IEEE Transactions on Consumer Electronics 2024年

作者： Xia, Yuanjun Liu, Yining Chen, Jingxue Liang, Yangfan Khan, Fazlullah Alturki, Ryan Wang, Xiaopei Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software School of Computer and Information Security Guilin541004 China Wenzhou University of Technology School of Data Science and Artificial Intelligence Wenzhou325027 China University of Technology School of Mathematical and Physical Sciences SydneyNSW2007 Australia Jiaxing University Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems The Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province The Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing314001 China University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Zhejiang Ningbo315104 China Umm AI-Qura University Makkah Department of Software Engineering College of Computing Saudi Arabia Riverside Department of Computer Science and Engineering University of California CA92521 United States

Federated learning (FL) has been widely used for privacy-preserving model updates in Industry 5.0, facilitated by 6G networks. Despite FL's privacy-preserving advantages, it remains vulnerable to attacks where adversaries can infer private data from local models or manipulate the central server (CS) to deliver falsified global models. Current privacy-preserving approaches, primarily based on the FedAvg algorithm, fail to optimize training efficiency for non-independent and identically distributed (non-IID) data. This article proposes training-efficient and verifiable aggregation (TEVA) for FL to resolve these issues. This scheme combines threshold Paillier homomorphic encryption (TPHE), verifiable aggregation, and an optimized double momentum update mechanism (OdMum). TEVA not only leverages TPHE to protect the privacy of local models but also ensures the integrity of the global model through a verifiable aggregation mechanism. Additionally, TEVA integrates the OdMum algorithm to effectively address the challenges posed by non-IID data, promoting rapid model convergence and significantly enhancing overall training efficiency. Security analysis indicates that TEVA meets the requirements for privacy protection. Extensive experimental results demonstrate that TEVA can accelerate model convergence while incurring lower computational and communication overheads. © 1975-2011 IEEE.

关键词： Differential privacy

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A hybrid method to select morphometric features using tensor completion and F-score rank for gifted children identification

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science China(Technological sciences) 2021年第9期64卷 1863-1871页

作者： ZHANG Jin FENG Fan HAN TianYi DUAN Feng SUN Zhe CAIAFA Cesar F. SOLÉ-CASALS Jordi College of Computer Science Nankai UniversityTianjin 300350China Department of Artificial Intelligence Nankai UniversityTianjin 300350China Computational Engineering Applications Unit Head Office for Information Systems and CybersecurityRIKEN351-0198 SaitamaJapan Instituto Argentino de Radioastronomía-CCT La Plata CONICET/CIC-PBA/UNLP1894 V.ElisaArgentina Department of Psychiatry University of CambridgeCambridge CB20SZUnited Kingdom Data and Signal Processing Group University of Vic-Central University of Catalonia08500 VicCataloniaSpain

Gifted children are able to learn in a more advanced way than others, probably due to neurophysiological differences in the communication efficiency in neural pathways. Topological features contribute to understanding the correlation between the brain structure and intelligence. Despite decades of neuroscience research using MRI, methods based on brain region connectivity patterns are limited by MRI artifacts, which therefore leads to revisiting MRI morphometric features, with the aim of using them to directly identify gifted children instead of using brain connectivity. However, the small, high-dimensional morphometric feature dataset with outliers makes the task of finding good classification models challenging. To this end, a hybrid method is proposed that combines tensor completion and feature selection methods to handle outliers and then select the discriminative features. The proposed method can achieve a classification accuracy of 93.1%, higher than other existing algorithms, which is thus suitable for the small MRI datasets with outliers in supervised classification scenarios.

关键词： gifted children identification morphometric features tensor completion feature selection

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A Thousand-Hand Bodhisattva: Emergent Abilities of Artificial General Intelligence Via Single- Objective to Multi-Objective Optimization

SSRN

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SSRN 2024年

作者： Xu, Wendi Ming, Zhang Shengchun, Zheng National Frontier Sciences Center for Industrial Intelligence and Systems Optimization Ministry of Education China College of Information Science and Engineering Northeastern University Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education China Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education China Xinjiang Astronomical Observatory Chinese Academy of Sciences China Key Laboratory for Radio Astronomy Chinese Academy of Sciences Nanjing China University of Chinese Academy of Sciences China Fujian Coastal Environmental Monitoring Station China

Towards artificial general intelligence, emergent abilities of large language models (LLMs) are observed wildly especially for well-known GPTs, which are due to scaling up primarily along three factors: training computation, model parameters, and dataset size. Scaling up makes emergence. Inspired by the insights for LLMs case, we scale up the number of auxiliary tasks (from 2 to many) to boost the core task for the model of single-objective to multi-objective optimization (SMO) in evolutionary transfer optimization (ETO), therefore achieve a new version of SMO in the open benchmarks of vehicle routing problems, permutation flow shop scheduling problem and travel salesman problem. We name the new SMO armed with transfer core as a "Thousand-Hand Bodhisattva" with many arms or hands and analyze its emergent abilities. © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Drones

arXiv

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arXiv 2022年

作者： Zhang, Zhe Ma, Shiyao Yang, Zhaohui Xiong, Zehui Kang, Jiawen Wu, Yi Zhang, Kejia Niyato, Dusit The School of Data Science and Technology Heilongjiang University China The College of Information and Communication Engineering Dalian Minzu University China The Department of Electronic and Electrical Engineering University College London LondonWC1E 6BT United Kingdom The Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore The Automation School Guangdong University of Technology China The School of Mathematical Science Heilongjiang University China School of Computer Science and Engineering Nanyang Technological University Singapore

Air access networks have been recognized as a significant driver of various Internet of Things (IoT) services and applications. In particular, the aerial computing network infrastructure centered on the Internet of Drones has set off a new revolution in automatic image recognition. This emerging technology relies on sharing ground truth labeled data between Unmanned Aerial Vehicle (UAV) swarms to train a high-quality automatic image recognition model. However, such an approach will bring data privacy and data availability challenges. To address these issues, we first present a Semi-supervised Federated Learning (SSFL) framework for privacy-preserving UAV image recognition. Specifically, we propose model parameters mixing strategy to improve the naive combination of FL and semi-supervised learning methods under two realistic scenarios (labels-at-client and labels-at-server), which is referred to as Federated Mixing (FedMix). Furthermore, there are significant differences in the number, features, and distribution of local data collected by UAVs using different camera modules in different environments, i.e., statistical heterogeneity. To alleviate the statistical heterogeneity problem, we propose an aggregation rule based on the frequency of the client’s participation in training, namely the FedFreq aggregation rule, which can adjust the weight of the corresponding local model according to its frequency. Numerical results demonstrate that the performance of our proposed method is significantly better than those of the current baseline and is robust to different non-IID levels of client data. Copyright © 2022, The Authors. All rights reserved.

关键词： Drones

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MF-CLIP: Leveraging CLIP as Surrogate Models for No-box Adversarial Attacks

arXiv

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arXiv 2023年

作者： Zhang, Jiaming Qiu, Lingyu Yi, Qi Li, Yige Sang, Jitao Xu, Changsheng Yeung, Dit-Yan Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Department of Mathematics and Applications University of Naples Federico II Naples Italy School of Computing and Information Systems Singapore Management University Singapore School of Computer and Information Technology Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China National Lab of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing100190 China

The vulnerability of Deep Neural Networks (DNNs) to adversarial attacks poses a significant challenge to their deployment in safety-critical applications. While extensive research has addressed various attack scenarios, the no-box attack setting—where adversaries have no prior knowledge, including access to training data of the target model—remains relatively underexplored despite its practical relevance. This work presents a systematic investigation into leveraging large-scale Vision-Language Models (VLMs), particularly CLIP, as surrogate models for executing no-box attacks. Our theoretical and empirical analyses reveal a key limitation in the execution of no-box attacks stemming from insufficient discriminative capabilities for direct application of vanilla CLIP as a surrogate model . To address this limitation, we propose MF-CLIP (Margin-based Fine-tuned CLIP), a novel framework that enhances CLIP’s effectiveness as a surrogate model through margin-aware feature space optimization. Comprehensive evaluations across diverse architectures and datasets demonstrate that MF-CLIP substantially advances the state-of-the-art in no-box attacks, surpassing existing baselines by 15.23% on standard models and achieving a 9.52% improvement on adversarially trained models. Our code (attached in the supplement) will be made publicly available to facilitate reproducibility and future research in this direction. Copyright © 2023, The Authors. All rights reserved.

关键词： Visual languages

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Nutmeg: a MIP and CP Hybrid Solver Using Branch-and-Check

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Operations Research Forum 2020年第3期1卷 1-27页

作者： Lam, Edward Gange, Graeme Stuckey, Peter J. Van Hentenryck, Pascal Dekker, Jip J. Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University Melbourne VIC Australia H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta GA United States

This paper describes the implementation of Nutmeg, a solver that hybridizes mixed integer linear programming and constraint programming using the branch-and-cut style of logic-based Benders decomposition known as branch-and-check. Given a high-level constraint programming model, Nutmeg automatically derives a mixed integer programming master problem that omits global constraints with weak linear relaxations, and a constraint programming subproblem identical to the original model. At every node in the branch-and-bound search tree, the linear relaxation computes dual bounds and proposes solutions, which are checked for feasibility of the omitted constraints in the constraint programming subproblem. In the case of infeasibility, conflict analysis generates Benders cuts, which are appended to the linear relaxation to cut off the candidate solution. Experimental results show that Nutmeg’s automatic decomposition outperforms pure constraint programming and pure mixed integer programming on problems known to have successful implementations of logic-based Benders decomposition, but performs poorly on general problems, which lack specific decomposable structure. Nonetheless, Nutmeg outperforms the standalone approaches on one problem with no known decomposable structure, providing preliminary indications that a hand-tailored decomposition for this problem could be worthwhile. On the whole, Nutmeg serves as a valuable tool for novice modelers to try hybrid solving and for expert modelers to quickly compare different logic-based Benders decompositions of their problems. © 2020, Springer Nature Switzerland AG.

关键词： Branch-and-check Conflict analysis Constraint programming Dual bound Hybridization Lazy clause generation Linear constraints Logic-based Benders decomposition Mixed integer programming Nogood

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