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Multisource Data Fusion Using MLP for Human Activity Recognition

computers, Materials & Continua 2025年第2期82卷 2109-2136页

作者： Sujittra Sarakon Wansuree Massagram Kreangsak Tamee Department of Computer Science and Information Technology Faculty of ScienceNaresuan UniversityPhitsanulok65000Thailand Center of Excellence for Innovation and Technology for Detection and Advanced Materials(ITDAM) Faculty of ScienceNaresuan UniversityPhitsanulok65000Thailand Center of Excellence in Nonlinear Analysis and Optimizations Faculty of ScienceNaresuan UniversityPhitsanulok65000Thailand

This research investigates the application of multisource data fusion using a Multi-Layer Perceptron (MLP) for Human Activity Recognition (HAR). The study integrates four distinct open-source datasets—WISDM, DaLiAc, MotionSense, and PAMAP2—to develop a generalized MLP model for classifying six human activities. Performance analysis of the fused model for each dataset reveals accuracy rates of 95.83 for WISDM, 97 for DaLiAc, 94.65 for MotionSense, and 98.54 for PAMAP2. A comparative evaluation was conducted between the fused MLP model and the individual dataset models, with the latter tested on separate validation sets. The results indicate that the MLP model, trained on the fused dataset, exhibits superior performance relative to the models trained on individual datasets. This finding suggests that multisource data fusion significantly enhances the generalization and accuracy of HAR systems. The improved performance underscores the potential of integrating diverse data sources to create more robust and comprehensive models for activity recognition.

关键词： Multisource data fusion human activity recognition multi-layer perceptron(MLP) artificial intelligent

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Evaluating Text Summarization Techniques and Factual Consistency with Language Models

Evaluating Text Summarization Techniques and Factual Consist...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Islam, Md Moinul Muhammad, Usman Oussalah, Mourad University of Oulu Center for Machine Vision and Signal Analysis Finland Aalto University Department of Computer Science Finland

ISBN: (纸本)9798350362480

Standard evaluation of automated text summarization (ATS) methods relies on manually crafted golden summaries. With the advances in Large Language Models (LLMs), it is legitimate to question whether these models can now potentially complement or replace human-crafted summaries. This study examines the effectiveness of several language models (LMs) in specifically addressing the issue of preserving factual consistency. By conducting a thorough assessment of various conventional and state-of-the-art performance metrics, such as ROUGE, BLEU, BERTScore, FActScore, and LongDocFACTScore across diverse datasets, our findings highlight the important relationship between linguistic eloquence and factual accuracy. The findings suggest that whereas LLMs, such as GPT and LLaMA, demonstrate considerable competence in producing concise and contextually-aware summaries, there remain difficulties in ensuring factual accuracy, particularly in domain-specific situations. Moreover, this work enhances the existing knowledge on summarization dynamics and highlights the need of developing more reliable and tailored evaluation techniques that minimize the probability of factual errors in text generated by ATS. In particular, the findings advance the current domain by providing a rigorous assessment of the balance between linguistic fluency and factual correct- ness, highlighting the limitations of current ATS frameworks and metrics to enhance the factual reliability of LM-generated summaries. © 2024 IEEE.

关键词： Linguistics

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COMEM: In-Context Retrieval-Augmented Mass-Editing Memory in Large Language Models

COMEM: In-Context Retrieval-Augmented Mass-Editing Memory in...

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2024 Findings of the Association for Computational Linguistics: NAACL 2024

作者： Qiao, Shanbao Liu, Xuebing Na, Seung-Hoon Center for Advanced Image and Information Technology Department of Computer Science and Artificial Intelligence Jeonbuk National University Korea Republic of

ISBN: (纸本)9798891761193

Noting that world knowledge continuously evolves over time, large language models (LLMs) need to be properly adjusted by performing the "knowledge editing", which involves updating outdated information or correcting false information. To achieve reliable and "massive" editing capabilities in terms of generalization and specificity, this paper proposes a unified knowledge editing method called in-COntext retrieval-augmented Mass-Editing Memory (COMEM), which combines two types of editing approaches: parameter updating and in-context knowledge editing (IKE). In particular, COMEM incorporates retrieval-augmented IKE, a novel extension of IKE designed for massive editing tasks, based on an updating-aware demonstration construction. Experimental results on the zsRE and CounterFact datasets demonstrate that COMEM outperforms all existing methods, achieving state-of-the-art performance. Our code is available at https://***/JoveReCode/***. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Diffusion–Shock Inpainting 9th

Diffusion–Shock Inpainting

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9th International Conference on Scale Space and Variational Methods in computer Vision, SSVM 2023

作者： Schaefer, Kristina Weickert, Joachim Mathematical Image Analysis Group Department of Mathematics and Computer Science E1.7 Saarland University Saarbrücken66123 Germany

ISBN: (纸本)9783031319747

We propose diffusion–shock (DS) inpainting as a hitherto unexplored integrodifferential equation for filling in missing structures in images. It combines two carefully chosen components that have proven their usefulness in different applications: homogeneous diffusion inpainting and coherence-enhancing shock filtering. DS inpainting enjoys the complementary synergy of its building blocks: It offers a high degree of anisotropy along an eigendirection of the structure tensor. This enables it to connect interrupted structures over large distances. Moreover, it benefits from the sharp edge structure generated by the shock filter, and it exploits the efficient filling-in effect of homogeneous diffusion. The second order equation that underlies DS inpainting inherits a continuous maximum–minimum principle from its constituents. In contrast to other attractive second order inpainting equations such as edge-enhancing anisotropic diffusion, we can guarantee this property also for the proposed discrete algorithm. Our experiments show a performance that is comparable to or better than many linear or nonlinear, isotropic or anisotropic processes of second or fourth order. They include homogeneous diffusion, biharmonic interpolation, TV inpainting, edge-enhancing anisotropic diffusion, the methods of Tschumperlé and of Bornemann and März, Cahn–Hilliard inpainting, and Euler’s elastica. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Diffusion

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Learning Binary-Antithetical Information Bottleneck for Generalizable Face Anti-Spoofing

Learning Binary-Antithetical Information Bottleneck for Gene...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Yu, Hao Chen, Haoyu Zhao, Guoying Center for Machine Vision and Signal Analysis University of Oulu Finland Department of Computer Science Aalto University Finland

ISBN: (纸本)9798350368741

We investigate generalizable face anti-spoofing (FAS) using information bottleneck theory. As generalizable FAS aims to detect spoofing in unseen scenarios, it has recently gained significant attention. Existing methods often use adversarial strategies or auxiliary modules to learn domain-invariant features by mining data relationships from distinct source domains. However, their learned feature space may still shift for unseen data due to the spurious correlations overfitted from training domains. Our rationale is that the problem of generalized pattern learning in FAS can be framed as a unified binary-antithetical information transition process, grounded in information bottleneck theory. Specifically, we leverage mutual-information optimization to preserve the instance-level spoof-aware information while compressing domain-related information modeled from the antithetical identity distribution. This enables the model to dynamically identify domain-agnostic, minimal sufficient representations that consistently describe the live/spoof distributions while mitigating spurious correlations through cross-identity compression. In light of this, we propose a novel learning framework for FAS, named Binary-Antithetical Information Bottleneck (BIB)-FAS, which is proven to be effectively generalized to unseen scenarios without using auxiliary information (e.g., domain labels) for training. Extensive cross-domain evaluations show that BIB-FAS significantly outperforms state-of-the-art methods. The code is available at: ***/CV-AC/BIB-FAS. © 2025 IEEE.

关键词： Binary-antithetical transition Domain generalization Face anti-spoofing Information bottleneck

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Mitigating Data Sparsity in Point-of-Interest Recommendation Systems Using Multi-Modal User Profiles 10

Mitigating Data Sparsity in Point-of-Interest Recommendation...

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10th International Conference on Digital Arts, Media and Technology and 8th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering, ECTI DAMT and NCON 2025

作者： Arthan, Sudarat Tamee, Kreangsak Naresuan University Department of Computer Science and Information Technology Phitsanulok Thailand Naresuan University Research Center for Academic Excellence in Nonlinear Analysis and Optimization Naresuan University Department of Computer Science and Information Technology Phitsanulok Thailand

ISBN: (纸本)9798331543273

This study presents a solution to the data sparsity issue in POI recommendation systems by introducing a multi-modal user profile approach called Visual-Textual Collaborative Filtering (VTCF). VTCF integrates textual POI descriptions from Wikipedia with user-generated photos to improve the representation of user preferences. Using publicly available geo-tagged photo datasets, we compared VTCF with various baseline methods, including traditional user-based collaborative filtering and approaches based on visual and contextual preferences. The experimental results demonstrate that our approach successfully addresses the challenges of sparse check-in data, resulting in more accurate and relevant recommendations. The findings suggest that VTCF is a promising method for improving recommendation accuracy. Future work could explore additional data sources and techniques to further enhance POI recommendation systems. Moreover, computational trade-offs and scalability should be considered, as integrating textual and visual data adds system complexity. Strategies such as batch processing and parallel computation could help mitigate these issues for large-scale applications. © 2025 IEEE.

关键词： Recommender systems

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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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Ghost Module Based Residual Mixture of Self-Attention and Convolution for Online Signature Verification

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computers, Materials & Continua 2024年第4期79卷 695-712页

作者： Fangjun Luan Xuewen Mu Shuai Yuan Department of Computer Technology School of Computer Science and EngineeringShenyang Jianzhu UniversityShenyang110168China Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang Jianzhu UniversityShenyang110168China Shenyang Branch of National Special Computer Engineering Technology Research Center Shenyang Jianzhu UniversityShenyang110168China

Online Signature Verification (OSV), as a personal identification technology, is widely used in various ***, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. Toaddress these issues, we propose a novel approach for online signature verification, using a one-dimensionalGhost-ACmix Residual Network (1D-ACGRNet), which is a Ghost-ACmix Residual Network that combines convolutionwith a self-attention mechanism and performs improvement by using Ghost method. The Ghost-ACmix Residualstructure is introduced to leverage both self-attention and convolution mechanisms for capturing global featureinformation and extracting local information, effectively complementing whole and local signature features andmitigating the problem of insufficient feature extraction. Then, the Ghost-based Convolution and Self-Attention(ACG) block is proposed to simplify the common parts between convolution and self-attention using the Ghostmodule and employ feature transformation to obtain intermediate features, thus reducing computational ***, feature selection is performed using the random forestmethod, and the data is dimensionally reducedusing Principal Component analysis (PCA). Finally, tests are implemented on the MCYT-100 datasets and theSVC-2004 Task2 datasets, and the equal error rates (EERs) for small-sample training using five genuine andforged signatures are 3.07% and 4.17%, respectively. The EERs for training with ten genuine and forged signaturesare 0.91% and 2.12% on the respective datasets. The experimental results illustrate that the proposed approacheffectively enhances the accuracy of online signature verification.

关键词： Online signature verification feature selection ACG block ghost-ACmix residual structure

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Deep Ensemble Learning with Frame Skipping for Face Anti-Spoofing 12

Deep Ensemble Learning with Frame Skipping for Face Anti-Spo...

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12th International Conference on image Processing Theory, Tools and Applications, IPTA 2023

作者： Muhammad, Usman Hoque, Md Ziaul Oussalah, Mourad Laaksonen, Jorma University of Oulu Center for Machine Vision and Signal Analysis Finland Aalto University Department of Computer Science Finland

ISBN: (纸本)9798350325416

Face presentation attacks, also known as spoofing attacks, pose a substantial threat to biometric systems that rely on facial recognition systems, such as access control systems, mobile payments, and identity verification systems. To mitigate the spoofing risk, several video-based methods have been presented in the literature that analyze facial motion in successive video frames. However, estimating the motion between adjacent frames is a challenging task and requires high computational cost. In this paper, we rephrase the face anti-spoofing task as a motion prediction problem and introduce a deep ensemble learning model with a frame skipping mechanism. In particular, the proposed frame skipping adopts a uniform sampling approach by dividing the original video into video clips of fixed size. By doing so, every nth frame of the clip is selected to ensure that the temporal patterns can easily be perceived during the training of three different recurrent neural networks (RNNs). Motivated by the performance of individual RNNs, a meta-model is developed to improve the overall detection performance by combining the prediction of individual RNNs. Extensive experiments were performed on four datasets, and state-of-the-art performance is reported on MSU-MFSD (3.12%), Replay-Attack (11.19%), and OULU-NPU (12.23%) databases by using half total error rates (HTERs) in the most challenging cross-dataset testing scenario. © 2023 IEEE.

关键词： Face recognition

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ControlVideo: conditional control for one-shot text-driven video editing and beyond

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science China(Information sciences) 2025年第3期68卷 150-162页

作者： Min ZHAO Rongzhen WANG Fan BAO Chongxuan LI Jun ZHU Department of Computer Science and Technology Institute for AI Tsinghua-Bosch Joint ML CenterTsinghua Laboratory of Brain and Intelligence Lab Tsinghua University ShengShu Technology Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods Pazhou Laboratory (Huangpu)

This paper presents ControlVideo for text-driven video editing — generating a video that aligns with a given text while preserving the structure of the source video. Building on a pre-trained text-to-image diffusion model, ControlVideo enhances the fidelity and temporal consistency by incorporating additional conditions(such as edge maps), and fine-tuning the key-frame and temporal attention on the source video-text pair via an in-depth exploration of the design space. Extensive experimental results demonstrate that ControlVideo outperforms various competitive baselines by delivering videos that exhibit high fidelity w.r.t. the source content, and temporal consistency, all while aligning with the text. By incorporating low-rank adaptation layers into the model before training, ControlVideo is further empowered to generate videos that align seamlessly with reference images. More importantly, ControlVideo can be readily extended to the more challenging task of long video editing(e.g., with hundreds of frames), where maintaining long-range temporal consistency is crucial. To achieve this, we propose to construct a fused ControlVideo by applying basic ControlVideo to overlapping short video segments and key frame videos and then merging them by pre-defined weight functions. Empirical results validate its capability to create videos across 140 frames, which is approximately 5.83 to 17.5 times more than what previous studies achieved. The code is available at https://***/thu-ml/controlvideo.

关键词： diffusion models controllable generation text-driven editing video editing long video editing

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