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arXiv 2024年

作者： Lei, Ming Pong, Ting Kei Sun, Shuqin Yue, Man-Chung College of Applied Mathematics Chengdu University of Information Technology Chengdu China Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong Key Laboratory of Optimization Theory and Applications China West Normal University of Sichuan Province School of Mathematics Education China West Normal University Nanchong China Musketeers Foundation Institute of Data Science Department of Data and Systems Engineering The University of Hong Kong Hong Kong

We study the class of subdifferentially polynomially bounded (SPB) functions, which is a rich class of locally Lipschitz functions that encompasses all Lipschitz functions, all gradient- or Hessian-Lipschitz functions, and even some non-smooth locally Lipschitz functions. We show that SPB functions are compatible with Gaussian smoothing (GS), in the sense that the GS of any SPB function is well-defined and satisfies a descent lemma akin to gradient-Lipschitz functions, with the Lipschitz constant replaced by a polynomial function. Leveraging this descent lemma, we propose GS-based zeroth-order optimization algorithms with an adaptive stepsize strategy for minimizing SPB functions, and analyze their convergence rates with respect to both relative and absolute stationarity measures. Finally, we also establish the iteration complexity for achieving a (δ, ϵ)-approximate stationary point, based on a novel quantification of Goldstein stationarity via the GS gradient that could be of independent interest. Copyright © 2024, The Authors. All rights reserved.

关键词： Gaussian distribution

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Decentralized Signaling Mechanisms

arXiv

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arXiv 2025年

作者： Boroujeni, Niloufar Mirzavand Iyer, Krishnamurthy Cooper, William L. Institute for Data Science in Oncology The University of Texas MD Anderson Cancer Center United States Department of Industrial and Systems Engineering University of Minnesota United States

We study a system composed of multiple distinct service locations that aims to convince customers to join the system by providing information to customers. We cast the system’s information design problem in the framework of Bayesian persuasion and describe centralized and decentralized signaling. We provide efficient methods for computing the system’s optimal centralized and decentralized signaling mechanisms and derive a performance guarantee for decentralized signaling when the locations’ states are independent. The guarantee states that the probability that a customer joins under optimal decentralized signaling is bounded below by the product of a strictly positive constant and the probability that a customer joins under optimal centralized signaling. The constant depends only on the number of service locations. We provide an example that shows that the constant cannot be improved. We consider an extension to more-general objectives for the system and establish that the same guarantee continues to hold. We also extend our analysis to systems where the locations’ states are correlated, and again derive a performance guarantee for decentralized signaling in that setting. For the correlated setting, we prove that the guarantee’s asymptotic dependence upon the number of locations cannot be substantially improved. A comparison of our guarantees for independent locations and for correlated locations reveals the influence of dependence on the performance of decentralized *** Codes 91A28, 90B99, 68W25 Copyright © 2025, The Authors. All rights reserved.

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C2AV-TSE: Context and Confidence-aware Audio Visual Target Speaker Extraction

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IEEE Journal on Selected Topics in Signal Processing 2025年

作者： Wu, Wenxuan Chen, Xueyuan Wang, Shuai Wang, Jiadong Meng, Lingwei Wu, Xixin Meng, Helen Li, Haizhou The Chinese University of Hong Kong Department of Systems Engineering and Engineering Management Hong Kong Nanjing University School of Intelligence Science and Technology Suzhou215163 China The Chinese University of Hon g Kong Shenzhen Research Institute of Big Data Shenzhen518172 China Technical University of Munich Germany The Chinese University of Hon g Kong Shenzhen Research Institute of Big Data School of Data Science Shenzhen China National University of Singapore Department of Electrical and Computer Engineering 119077 Singapore

Audio-Visual Target Speaker Extraction (AV-TSE) aims to mimic the human ability to enhance auditory perception using visual cues. Although numerous models have been proposed recently, most of them estimate target signals by primarily relying on local dependencies within acoustic features, underutilizing the human-like capacity to infer unclear parts of speech through contextual information. This limitation results in not only suboptimal performance but also inconsistent extraction quality across the utterance, with some segments exhibiting poor quality or inadequate suppression of interfering speakers. To close this gap, we propose a model-agnostic strategy called the Mask-And-Recover (MAR). It integrates both inter- and intra-modality contextual correlations to enable global inference within extraction modules. Additionally, to better target challenging parts within each sample, we introduce a Fine-grained Confidence Score (FCS) model to assess extraction quality and guide extraction modules to emphasize improvement on low-quality segments. To validate the effectiveness of our proposed model-agnostic training paradigm, six popular AV-TSE backbones were adopted for evaluation on the VoxCeleb2 dataset, demonstrating consistent performance improvements across various metrics. © 2025 IEEE.

关键词： Self-supervised learning

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Towards understanding structure–property relations in materials with interpretable deep learning

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npj Computational Materials 2023年第1期9卷 157-168页

作者： Tien-Sinh Vu Minh-Quyet Ha Duong-Nguyen Nguyen Viet-Cuong Nguyen Yukihiro Abe Truyen Tran Huan Tran Hiori Kino Takashi Miyake Koji Tsuda Hieu-Chi Dam Japan Advanced Institute of Science and Technology NomiIshikawaJapan HPC SYSTEMS Inc. MinatoTokyoJapan Applied Artificial Intelligence Institute Deakin UniversityGeelongVICAustralia School of Materials Science and Engineering Georgia Institute of TechnologyAtlantaGAUSA Research and Services Division of Materials Data and Integrated System National Institute for Materials ScienceTsukubaIbarakiJapan Research Center for Computational Design of Advanced Functional Materials National Institute of Advanced Industrial Science and TechnologyTsukubaIbarakiJapan Department of Computational Biology and Medical Sciences Graduate School of Frontier SciencesUniversity of TokyoKashiwa-shiChiba-kenJapan International Center for Synchrotron Radiation Innovation Smart(SRIS) Tohoku UniversityAoba-kuSendaiJapan

Deep learning(DL)models currently employed in materials research exhibit certain limitations in delivering meaningful information for interpreting predictions and comprehending the relationships between structure and material *** address these limitations,we propose an interpretable DL architecture that incorporates the attention mechanism to predict material properties and gain insights into their structure–property *** proposed architecture is evaluated using two well-known datasets(the QM9 and the Materials Project datasets),and three in-house-developed computational materials ***–test–split validations confirm that the models derived using the proposed DL architecture exhibit strong predictive capabilities,which are comparable to those of current state-of-the-art ***,comparative validations,based on first-principles calculations,indicate that the degree of attention of the atoms’local structures to the representation of the material structure is critical when interpreting structure–property relationships with respect to physical *** properties encompass molecular orbital energies and the formation energies of *** proposed architecture shows great potential in accelerating material design by predicting material properties and explicitly identifying crucial features within the corresponding structures.

关键词： properties property structure

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IoT-based Smart Home Automation systems for Energy Conservation 7

IoT-based Smart Home Automation Systems for Energy Conservat...

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Sudhakar, K.N. Devadarshini, P.T. Ramu, K.N. Deshveer Suryanarayana, N.V.S. Kanakala, V Raviteja Cmr Institute of Technology Department of Ise Karnataka Bengaluru560037 India Easwari Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India S.A. Engineering College Department of Mechanical Engineering Tamil Nadu Thiruverkadu India Ganga Institute of Technology and Management Department of Electrical Engineering Haryana Kablana India Central Tribal University of Andhra Pradesh Administrative Officer Vizianagaram India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350341485

This research study explores the numerous components of smart home automation systems, including actuators, sensors, and controllers, and the integration of these components with Internet of Things (IoT) technology. To demonstrate the system's ability to reduce energy consumption, this study involves an experimental analysis of a prototype system. In order to scientifically evaluate the system's performance, the test data must be statistically analysed. The results of this research study have the potential to contribute towards the enhancement of energy efficiency and sustainability of the proposed system by offering insights for the construction of sustainable residential settings. This study investigates the effects of the residential environments. The use of complex and innovative systems has the potential to significantly improve the efforts to save energy and promote a sustainable future. © 2023 IEEE.

关键词： Actuators

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Multi-Modal Person Re-Identification Using Depth Estimation and Edge Detection

SSRN

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SSRN 2023年

作者： Rayani, Mohammad Saleh Keshavaz, Ahmad Ghimatgar, Hojat Mojtaba Mansorinejad, Mojtaba Computational Neuroscience Laboratory ICT Research Institute Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr*** Iran Department of Electrical Engineering Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr*** Iran Internet of Things Laboratory ICT Research Institute Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr*** Iran

Person re-identification is the task of identifying people across different non-overlapping cameras. Despite significant advancements in this field, several key issues remain, such as viewpoint variation, illumination variation, clothing variation and random occlusions. Notable efforts have been made to leverage the information existing in additional modalities, such as depth, infrared, thermal, skeleton some of which can be obtained by using RGB-D sensors. Notwithstanding the existence of these sensors, various limitations such as monetary or technical, may disincentivize some people from using them. Nevertheless, leveraging other modalities can help significantly in improving re-identification accuracy. In this paper we use depth estimation and edge detection algorithms to extract dept and edge maps and use the fusion in dissimilarity space framework for levering both depth and edge information. We train four models RGB, RGB-D, RGB-E, and RGB-DE with the corresponding modality as the extra input channel. Then for ensemble, we fuse the dissimilarity scores computed from each model to form a final ranking. We compare the performance of different combinations of models. Experimental results on the public iLIDS-VID and Market1501 datasets show that, using a simple ResNet50 backbone, for the iDILS-VID dataset our 3-modal ensemble achieves up to 12.9% and 16% improvement in mAP and Rank 1 metrics, and for the Market1501 dataset achieves up to 12.44% and 13.81% improvement. © 2023, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Cuttlefish Algorithm-Based Deep Learning Model to Predict the Missing data in Healthcare Application

Cuttlefish Algorithm-Based Deep Learning Model to Predict th...

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International Conference on data Analytics and Management, ICDAM 2023

作者： Sasi Kumar, A. Rajesh Kumar, T. Balamanigandan, R. Meganathan, R. Karwa, Roshan Mahaveerakannan, R. Inurture Education Solutions Pvt. Ltd. Bangalore India Department of Cloud Technology and Data Science Institute of Engineering and Technology Srinivas University Srinivas Nagar Mukka Surathkal Mangalore India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai602105 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Department of CSE Prof Ram Meghe Institute of Technology and Research Badnera-Amravati India

ISBN: (纸本)9789819965526

Countless sensors embedded in IoT devices produce an ocean of data. The quality of IoT services depends on this information;hence, its accuracy is critical. Unfortunately, noise, collision, unreliable network connectivity, failed equipment, and manual system shutdown are all common causes of missing and partial values in IoT data. The use of predictive analytics to EMRs, which include healthcare data, inspired this effort. Predictive models based on such data are inherently flawed because they include noise, missing data, and inconsistencies between classes of interest. We argue for the need to create specialised methods of data preprocessing and categorisation since typical data mining approaches frequently provide unsatisfactory performance measurements. To build the long-term predictor in the attendance of significant nonlinearity and noise in the missing data, in this study, each of us provides a bidirectional self-attentive encoding and decoding architecture (BEDA). Prior to any further processing, the raw data is subjected to a wavelet threshold filter for denoising purposes. The weights are optimised via a mapping-based cuttlefish optimisation technique, and the bidirectional long short-term-memory is chosen as the basic device for isolating temporal and serial features (MCFA). The prediction performance is then enhanced by adding the multi-head self-attention device into the encoder–decoder architecture. It demonstrates that the projected technique yields quick, accurate, and resilient classification consequences by comparing them to results from other models on public data sets with unbalanced classes and missing values in health-related applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： data mining

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Adversarial Attacks on Featureless Deep Learning Malicious URLs Detection

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Computers, Materials & Continua 2021年第7期68卷 921-939页

作者： Bader Rasheed Adil Khan S.M.Ahsan Kazmi Rasheed Hussain Md.Jalil Piran Doug Young Suh Institute of Data Science and Articial Intelligence Innopolis UniversityInnopolis420500Russia Institute of Information Security and Cyberphysical Systems Innopolis UniversityInnopolis420500Russia Department of Computer Science and Engineering Sejong UniversitySeoulKorea Department of Electronics Engineering Kyung Hee UniversityYonginKorea

Detecting malicious Uniform Resource Locators(URLs)is crucially important to prevent attackers from committing *** researches have investigated the role of machine learning(ML)models to detect malicious *** using ML algorithms,rst,the features of URLs are extracted,and then different ML models are *** limitation of this approach is that it requires manual feature engineering and it does not consider the sequential patterns in the ***,deep learning(DL)models are used to solve these issues since they are able to perform featureless ***,DL models give better accuracy and generalization to newly designed URLs;however,the results of our study show that these models,such as any other DL models,can be susceptible to adversarial *** this paper,we examine the robustness of these models and demonstrate the importance of considering this susceptibility before applying such detection systems in real-world *** propose and demonstrate a black-box attack based on scoring functions with greedy search for the minimum number of perturbations leading to a *** attack is examined against different types of convolutional neural networks(CNN)-based URL classiers and it causes a tangible decrease in the accuracy with more than 56%reduction in the accuracy of the best classier(among the selected classiers for this work).Moreover,adversarial training shows promising results in reducing the inuence of the attack on the robustness of the model to less than 7%on average.

关键词： Malicious URLs detection deep learning adversarial attack web security

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Personalized Federated Learning with Collaborative Aggregation Networks for Multi-Site Brain Disorder Diagnosis 4

Personalized Federated Learning with Collaborative Aggregati...

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4th International Conference on Industrial Automation, Robotics and Control engineering, IARCE 2024

作者： Si, Qian Li, Yang School of Cyber Science and Technology Beihang University Beijing China Department of Automation Science and Electrical Engineering The Beijing Advanced Innovation Center for Big Data and Brain Computing State Key Laboratory of Virtual Reality Technology and Systems Beijing China Advanced Institute of Information Technology Peking University Beijing China Beihang University Beijing China

ISBN: (纸本)9798350380323

In multi-site brain disease diagnosis studies, traditional centralized training methods necessitate sharing medical data, posing significant privacy risks. Federated learning (FL) offers a privacy-preserving solution by enabling global model training through aggregating locally trained models from multiple data centers without sharing raw data. However, current FL approaches rely on a server-based network topology, where central server failure disrupts training. Additionally, data heterogeneity across sites often slows convergence and reduces accuracy. To overcome these issues, we introduce a decentralized personalized federated learning collaborative aggregation network (pFedCAN). This framework has two core components: (1) separating local models into shared and personalized layers, and (2) forming a collaborative aggregation network via similarity detection in the shared layers. Specifically, each center trains its local model, then separates it into shared and personalized layers. The shared layer is exchanged with other centers, while the personalized layer remains local. data centers analyze similarities in received shared layers to build a collaborative network, where shared layers from similar centers are aggregated to refine the model. This approach flexibly adapts to varying levels of data heterogeneity, enhancing model training efficiency. Validation on public datasets, ABIDE I and ADHD, shows that the proposed method outperforms current leading techniques. © 2024 IEEE.

关键词： Federated learning

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Wavelength-division-multiplexed Transmission and Dispersion Compensation Enabled by Soliton Microcombs and Cascaded Microrings

Wavelength-division-multiplexed Transmission and Dispersion ...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Liu, Yuanbin Zhang, Hongyi Liu, Jiacheng Lu, Liangjun Du, Jiangbing Li, Yu He, Zuyuan Chen, Jianping Zhou, Linjie Poon, Andrew W. State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab of Navigation and Location Services Shanghai Institute for Advanced Communication and Data Science Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China Photonic Device Laboratory Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong

ISBN: (纸本)9781957171395

We demonstrate wavelength-division-multiplexed data transmission and dispersion compensation of 25 Gb/s × 9 on-off-keying signals over a 20-km singlemode fiber using an integrated single-soliton microcomb and a cascaded microring resonator-based reconfigurable dispersion compensator. © Optica Publishing Group 2024, © 2024 The Author(s)

关键词： Optical fibers

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