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IEEE International Conference on Big data

作者： Walter Gerych Kevin Hickey Thomas Hartvigsen Luke Buquicchio Abdulaziz Alajaji Kavin Chandrasekaran Hamid Mansoor Emmanuel Agu Elke Rundensteiner Department of Data Science Worcester Polytechnic Institute Worcester MA School of Data Science University of Virginia Charlottesville VA College of Computer and Informational Sciences King Saud University Riyadh Saudi Arabia Department of Computer Science Worcester Polytechnic Institute Worcester MA Department of Computer Science University of Victoria Victoria BC Canada

Generative modeling is a powerful technique that involves creating machine learning models capable of creating new data similar to the data it was trained on. Generative Adversarial Networks (GANs) are a leading approach for generative modeling. However, GAN training is known to be a notoriously difficult task. GAN convergence issues are largely caused by the supports of the real and generated distributions being disjoint. To tackle this open problem, we propose a novel GAN pre-training process that effectively aligns the supports of the generated and real data prior to applying traditional adversarial GAN training. The key component of our method, called AlignGAN, is learning a mapping between the input data distribution and a latent representation defined over a hypersphere, regularized by a One Class Classifier. This successfully encourages the generator to produce samples throughout the support of the real data, while not generating samples outside the support. We maintain support alignment through low-bandwidth noise convolutions and additional One Class regularization, leading to continued stable GAN training. We validate our approach against leading stabilization methods on three benchmark datasets, showing AlignGAN routinely produces the best results.

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DPP-HSS: Towards Fast and Scalable Hypervolume Subset Selection for Many-objective Optimization

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IEEE Transactions on Evolutionary Computation 2024年

作者： Gong, Cheng Nan, Yang Shang, Ke Guo, Ping Ishibuchi, Hisao Zhang, Qingfu City University of Hong Kong Department of Computer Science Hong Kong City University of Hong Kong Shenzhen Research Institute Shenzhen China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China

Hypervolume subset selection (HSS) has received significant attention since it has a strong connection with evolutionary multi-objective optimization (EMO), such as environment selection and post-processing to identify representative solutions for decision-makers. The goal of HSS is to find the optimal subset that maximizes the hypervolume indicator subject to a given cardinality constraint. However, existing HSS algorithms or related methods are not efficient in achieving good performance in high-dimensional objective spaces. This is primarily because HSS problems become NP-hard when the number of objectives exceeds two, and the calculation of hypervolume contribution is very time-consuming. To efficiently solve HSS problems while maintaining a good solution quality, we propose a fast and scalable hypervolume subset selection method for many-objective optimization based on the determinantal point process (DPP), named DPP-HSS, which is fully free of hypervolume contribution calculation. Specifically, DPP-HSS constructs a hypervolume kernel matrix by extracting the convergence and diversity representations of each solution for a given HSS problem. This matrix is then used to build a DPP model. Subsequently, the original HSS problem is reformulated as a new maximization optimization problem based on the constructed model. A greedy DPP-based hypervolume subset selection algorithm is implemented to solve this transformed problem. Extensive experiments show that the proposed DPP-HSS achieves significant speedup and good hypervolume performance in comparison with state-of-the-art HSS algorithms on benchmark problems. Furthermore, DPP-HSS demonstrates very good scalability with respect to the number of objectives. © 1997-2012 IEEE.

关键词： Optimization algorithms

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Set a Thief to Catch a Thief: Combating Label Noise through Noisy Meta Learning

arXiv

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arXiv 2025年

作者： Wang, Hanxuan Lu, Na Zhao, Xueying Yan, Yuxuan Ma, Kaipeng Keong, Kwoh Chee Carneiro, Gustavo Department of Automation Science and Engineering Xi’an Jiaotong University China School of Electrical Engineering Xi’an Jiaotong University China College of Computing and Data Science Nanyang Technological University Singapore Centre for Vision Speech and Signal Processing University of Surrey United Kingdom

Learning from noisy labels (LNL) aims to train high-performance deep models using noisy datasets. Meta learning based label correction methods have demonstrated remarkable performance in LNL by designing various meta label rectification tasks. However, extra clean validation set is a prerequisite for these methods to perform label correction, requiring extra labor and greatly limiting their practicality. To tackle this issue, we propose a novel noisy meta label correction framework STCT, which counterintuitively uses noisy data to correct label noise, borrowing the spirit in the saying "Set a Thief to Catch a Thief". The core idea of STCT is to leverage noisy data which is i.i.d. with the training data as a validation set to evaluate model performance and perform label correction in a meta learning framework, eliminating the need for extra clean data. By decoupling the complex bi-level optimization in meta learning into representation learning and label correction, STCT is solved through an alternating training strategy between noisy meta correction and semi-supervised representation learning. Extensive experiments on synthetic and real-world datasets demonstrate the outstanding performance of STCT, particularly in high noise rate scenarios. STCT achieves 96.9% label correction and 95.2% classification performance on CIFAR-10 with 80% symmetric noise, significantly surpassing the current state-of-the-art. © 2025, CC BY.

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SpatioTemporal Learning for Human Pose Estimation in Sparsely-Labeled Videos 39

SpatioTemporal Learning for Human Pose Estimation in Sparsel...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Jiao, Yingying Wang, Zhigang Wu, Sifan Fan, Shaojing Liu, Zhenguang Xu, Zhuoyue Wu, Zheqi College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China College of Computer Science and Technology Zhejiang Gongshang University China School of Computing National University of Singapore Singapore The State Key Laboratory of Blockchain and Data Security Zhejiang University China Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security China

ISBN: (纸本)157735897X

Human pose estimation in videos remains a challenge, largely due to the reliance on extensive manual annotation of large datasets, which is expensive and labor-intensive. Furthermore, existing approaches often struggle to capture long-range temporal dependencies and overlook the complementary relationship between temporal pose heatmaps and visual features. To address these limitations, we introduce STDPose, a novel framework that enhances human pose estimation by learning spatiotemporal dynamics in sparsely-labeled videos. STDPose incorporates two key innovations: 1) A novel Dynamic-Aware Mask to capture long-range motion context, allowing for a nuanced understanding of pose changes. 2) A system for encoding and aggregating spatiotemporal representations and motion dynamics to effectively model spatiotemporal relationships, improving the accuracy and robustness of pose estimation. STDPose establishes a new performance benchmark for both video pose propagation (i.e., propagating pose annotations from labeled frames to unlabeled frames) and pose estimation tasks, across three large-scale evaluation datasets. Additionally, utilizing pseudo-labels generated by pose propagation, STDPose achieves competitive performance with only 26.7% labeled data. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Causal Augmentation for Causal Sentence Classification 1

Causal Augmentation for Causal Sentence Classification

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1st Workshop on Causal Inference and Natural Language Processing, CI+NLP 2021

作者： Tan, Fiona Anting Hazarika, Devamanyu Ng, See-Kiong Poria, Soujanya Zimmermann, Roger Institute of Data Science National University of Singapore Singapore School of Computing National University of Singapore Singapore Information Systems Technology and Design Singapore University of Technology and Design Singapore

ISBN: (纸本)9781954085893

Scarcity of annotated causal texts leads to poor robustness when training state-of-the-art language models for causal sentence classification. In particular, we found that models misclassify on augmented sentences that have been negated or strengthened with respect to its causal meaning. This is worrying since minor linguistic differences in causal sentences can have disparate meanings. Therefore, we propose the generation of counterfactual causal sentences by creating contrast sets (Gardner et al., 2020) to be included during model training. We experimented on two model architectures and predicted on two out-of-domain corpora. While our strengthening schemes proved useful in improving model performance, for negation, regular edits were insufficient. Thus, we also introduce heuristics like shortening or multiplying root words of a sentence. By including a mixture of edits when training, we achieved performance improvements beyond the baseline across both models, and within and out of corpus' domain, suggesting that our proposed augmentation can also help models generalize. © 2021 Association for Computational Linguistics.

关键词： Classification (of information)

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Exposure-NeuS: Multi-View Head Reconstruction Using Exposure-Distorted data

Exposure-NeuS: Multi-View Head Reconstruction Using Exposure...

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Complex Systems and Intelligent science (CSIS-IAC), International Annual Conference on

作者： Zhanliang Cheng Bo Peng Ning An School of Data Science University of Science and Technology of China Hefei China School of Mathematical Science University of Science and Technology of China Hefei China Research Institute of Mine Artificial Intelligence China Coal Research Institute Beijing China State Key Laboratory of Intelligent Coal Mining and Strata Control Beijing China

ISBN: (数字)9798331504755

ISBN: (纸本)9798331504762

We propose Exposure-NeuS, a high fidelity human head reconstruction method from multi-view images, even from exposure-distorted images. Previous methods either did not reconstruct with sufficient accuracy, such as failing to reconstruct hair, or resulted in artifacts or holes in the reconstructed results due to exposure in the input data. To solve these challenging problems, we improve the original fine sampling of the model by using piecewise power function sampling to obtain sampling points closer to the real surface. And we develop a prior latent code to implicitly model factors such as environment and exposure. Experiments show that we eliminate or reduce the erroneous facial depressions and hair holes that occur in the baseline method during reconstruction. Released code is provided as follows:https://***/zhanliangOO/***

关键词： Hair Surface reconstruction Head Codes Reconstruction algorithms Rendering (computer graphics) Depression Digital humans Complex systems Image reconstruction

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WMAJL: Watcher-Mediated Attention Joint Learning Model for Multimodal Relation Extraction

WMAJL: Watcher-Mediated Attention Joint Learning Model for M...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yunrui Dong Guiduo Duan Tianxi Huang Yunhao Li School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of China Chengdu China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu China Chengdu Textile College College of Humanities and General Education Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the domain of Multimodal Relation Extraction (MRE), we present the $\color{Red}{\text{W}}$atcher-$\color{Red}{\text{M}}$ediated $\color{Red}{\text{A}}$ttention $\color{Red}{\text{J}}$oint $\color{Red}{\text{L}}$earning Model ($\color{Red}{\text{WMAJL}}$), a novel approach addressing the challenges of modality alignment noise, cross-modal fusion disparity, preservation of textual relative position information, and the distinctiveness of classification labels. WMAJL employs an integrative framework leveraging contrastive learning and variational autoencoder constraints to mitigate modality alignment noise by prioritizing relevant semantic data and effectively reducing extraneous noise that does not contribute to the task. The model’s innovative architecture includes a mediator watcher, which facilitates enhanced cross-modal fusion by enabling nuanced information exchange between textual and visual modalities while preserving the unique characteristics of each modality. Additionally, the design of auxiliary tasks, such as Named Entity Recognition (NER), and output supervision constructs loss functions that preserve relative position information, ensuring a precise depiction of entity relationships throughout the multilayer encoding processes. A key differentiator of WMAJL is its label-centric self-information loss technique, inspired by InfoNCE, which trains the model to cluster similar relation labels in semantically coherent areas, thereby optimizing classification label uniqueness by discerning subtle differences among relation types. The synergistic application of these strategies has led to a significant enhancement of WMAJL’s performance, as evidenced by its state-of-the-art F1 score of $\color{Red}{84.93\%}$ on the MNRE dataset. This achievement surpasses existing benchmarks and sets a new standard for multimodal knowledge extraction, underscoring WMAJL’s potential to revolutionize the MRE landscape.

关键词： Resistance Visualization Technological innovation Noise Autoencoders Semantics Contrastive learning Nonhomogeneous media Speech processing Standards

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Flow-TSVAD: Target-Speaker Voice Activity Detection via Latent Flow Matching for Speaker Diarization

Flow-TSVAD: Target-Speaker Voice Activity Detection via Late...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhengyang Chen Bing Han Shuai Wang Yidi Jiang Yanmin Qian Department of Computer Science and Engineering Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China Shenzhen Research Institute of Big Data Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China National University of Singapore Singapore

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Speaker diarization is typically considered as a discriminative task, using discriminative approaches to produce fixed diarization results. In this paper, we explore for the first time the use of neural network-based generative methods for speaker diarization. We implement a Flow-Matching (FM) based generative algorithm within the sequenceto-sequence target speaker voice activity detection (Seq2Seq-TSVAD) diarization system. Our experiments reveal that applying the generative method directly to the original binary label sequence space of the TS-VAD output is ineffective. To address this issue, we propose mapping the binary label sequence into a dense latent space before applying the generative algorithm, and our proposed Flow-TSVAD method can significantly outperform the traditional Seq2Seq-TSVAD system. Additionally, we observe that the FM algorithm converges rapidly during the inference stage, only requiring two inference steps to achieve promising results. Moreover, as a generative model, Flow-TSVAD allows for sampling different diarization results by running the model multiple times, so the ensemble system combining the results from various sampling instances can further boost the diarization performance.

关键词： Voice activity detection Frequency modulation Computational modeling Signal processing algorithms Signal processing Inference algorithms Acoustics Computational efficiency

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A Blockchain-assisted Authentication Protocol for RFID-enabled Supply Chain Management System

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IEEE Transactions on Network science and Engineering 2025年

作者： Tariq, Tayyaba Obaidat, Mohammad S. Kuo, Wen-Chung Mahmood, Khalid Ayub, Muhammad Faizan Alenazi, Mohammed J.F National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Douliu64002 Taiwan The University of Jordan King Abdullah II School of Information Technology Amman11942 Jordan University of Science and Technology Beijing School of Communication and Computing Engineering Beijing100083 China Amity University Amity School of Engineering and Technology Noida201301 India National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Riyadh11451 Saudi Arabia

In the dynamic domain of supply chain management, integrating Radio-Frequency Identification (RFID) technology with blockchain technology represents a significant leap forward. This integration has created a blockchain-assisted RFID-enabled Supply Chain Management System (SCMS). With its ability to utilize electromagnetic fields to identify and track tags attached to objects, RFID technology has revolutionized product management and tracking within supply chains. (SCMS) transmits tracking and product management information through public communication channels. However, (SCMS)'s communication on these channels is vulnerable to various security attacks and privacy challenges. Numerous authentication protocols have recently been proposed to tackle these security and privacy challenges. Unfortunately, most protocols are susceptible to different security attacks, such as tag or reader impersonation, denial of service, physical cloning, desynchronization attacks, etc. Therefore, we have proposed an authentication protocol for an RFID-enabled (SCMS) that also leverages blockchain technology. The integration of blockchain technology ensures data integrity, immutability and transparency across each department involved in (SCMS). In the proposed protocol, we also employ a Physically Unclonable Function (PUF) to secure (SCMS) against physical cloning attacks. We validate the security of the proposed protocol through both informal and formal analysis. The informal analysis confirms that our protocol significantly enhances security and efficiency. Moreover, a performance analysis of the proposed protocol against various competing existing protocols shows its superior performance. Notably, the proposed protocol substantially reduces computational and communication costs by 24.38% and 8.03%, respectively, which underscores its enhanced performance and resource efficiency. © 2013 IEEE.

关键词： Efficiency

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Channel Gain Map Estimation for Wireless Networks Based on Scatterer Model

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IEEE Transactions on Wireless Communications 2025年

作者： Sun, He Zhu, Lipeng Zhang, Rui National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore The Chinese University of Hong Kong School of Science and Engineering Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China

Channel gain map (CGM) contains crucial large-scale fading information regarding wireless channels at specific frequency bands in wireless networks. Traditional CGM construction methods require either detailed propagation environment information or numerous channel measurements, rendering them practically cumbersome to implement. To overcome such difficulties, we propose in this paper a novel scatterer-based CGM construction framework to characterize the large-scale channel fading in a spatial region by estimating the scatterer responses from a few channel gain measurements at designated locations in the region. In particular, the region is divided into equally-spaced grids so as to smooth out the small-scale fading by averaging the channel power gain within each grid. As the average channel power gain within each grid can be expressed as a function of large-scale multi-path channel parameters such as the scatterer response coefficients and path-loss coefficients, an iterative algorithm is proposed to estimate these parameters based on only a sufficient number of channel gain measurements taken at random locations around each scatterer. Specifically, the proposed algorithm decomposes the parameter estimation problem into a set of univariate or linear estimation subproblems, which can be efficiently solved by one-dimensional (1D) line search and least-squares methods. Based on the estimated channel parameters, the CGM over the whole region can be estimated by calculating the average channel power gain for each grid. Simulation results under both the two-dimensional (2D) multipath channel model and the practical three-dimensional (3D) ray-tracing channel model verify that the proposed scatterer-based channel gain characterization can accurately capture the large-scale channel gain spatial distribution within a region, and the proposed CGM estimation framework outperforms other channel-measurement-based benchmarks in terms of estimation accuracy, while requiring a sign

关键词： Least squares approximations

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