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AVTENet: A Human-Cognition-Inspired Audio-Visual Transformer-Based Ensemble Network for Video Deepfake Detection

IEEE Transactions on Cognitive and Developmental systems 2025年

作者： Hashmi, Ammarah Shahzad, Sahibzada Adil Lin, Chia-Wen Tsao, Yu Wang, Hsin-Min Taiwan International Graduate Program Academia Sinica Social Networks and Human-Centered Computing Program Taipei11529 Taiwan National Tsing Hua University Institute of Information Systems and Applications Hsinchu30013 Taiwan National Chengchi University Department of Computer Science Taipei11605 Taiwan National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu300044 Taiwan Academia Sinica Research Center for Information Technology Innovation Taipei11529 Taiwan

The recent proliferation of hyper-realistic deepfake videos has drawn attention to the threat of audio and visual forgeries. Most previous studies on detecting artificial intelligence-generated fake videos only utilize visual modality or audio modality. While some methods exploit audio and visual modalities to detect forged videos, they have not been comprehensively evaluated on multimodal datasets of deepfake videos involving acoustic and visual manipulations, and are mostly based on convolutional neural networks with low detection accuracy. Considering that human cognition instinctively integrates multisensory information including audio and visual cues to perceive and interpret content and the success of transformer in various fields, this study introduces the audio-visual transformer-based ensemble network (AVTENet). This innovative framework tackles the complexities of deepfake technology by integrating both acoustic and visual manipulations to enhance the accuracy of video forgery detection. Specifically, the proposed model integrates several purely transformer-based variants that capture video, audio, and audio-visual salient cues to reach a consensus in prediction. For evaluation, we use the recently released benchmark multimodal audio-video FakeAVCeleb dataset. For a detailed analysis, we evaluate AVTENet, its variants, and several existing methods on multiple test sets of the FakeAVCeleb dataset. Experimental results show that the proposed model outperforms all existing methods and achieves state-of-the-art performance on Testset-I and Testset-II of the FakeAVCeleb dataset. We also compare AVTENet against humans in detecting video forgery. The results show that AVTENet significantly outperforms humans. © 2016 IEEE.

关键词： audio-visual AVTENet Deepfake detection

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Dynamic programming Algorithm using Furniture Industry 0/1 Knapsack Problem 5

Dynamic Programming Algorithm using Furniture Industry 0/1 K...

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5th International Conference on Cybernetics and Intelligent systems, ICORIS 2023

作者： Anditta, Mietha Amartya, Natashya Warnars, Laurens Spits Warnars, Harco Leslie Hendric Spits Ramadhan, Arief Siswanto, Teddy Mantoro, Teddy Noordin, Nurulhuda Bina Nusantara University School of Computer Science Computer Science Departement Jakarta11530 Indonesia Bina Nusantara University School of Information Systems Information Systems Departement Jakarta11530 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Departement Jakarta11530 Indonesia Telkom University Pjj Informatika Bandung40257 Indonesia Trisakti University Information Systems Department Jakarta11440 Indonesia Sampoerna University Faculty of Engineering and Technology Jakarta12760 Indonesia Universiti Teknologi Mara School of Computing Sciences College of Computing Informatics and Mathematics Selangor Malaysia

ISBN: (纸本)9798350369489

Dynamic programming is a fundamental algorithm that can be found in our daily lives easily. One of the dynamic programming algorithm implementations consists of solving the 0/1 knapsack problem. A 0/1 knapsack problem can be seen from industrial production cost. It is prevalent that a production cost has to be as efficient as possible, but the expectation is to get the proceeds of the products higher. Thus, the dynamic programming algorithm can be implemented to solve the diverse knapsack problem, one of which is the 0/1 knapsack problem, which would be the main focus of this paper. The implementation was implemented using C language. This paper was created as an early implementation algorithm using a Dynamic program algorithm applied to an Automatic Identification System (AIS) dataset. © 2023 IEEE.

关键词： Dynamic programming

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Gait proficiency index in children aged 3.5 to 9.5 years old

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Research on Biomedical engineering 2021年第2期37卷 429-437页

作者： das Neves, Ana Maria Bender Seidenfuss de Souza, Leandro Daniel Aires, Andréia Gomes Souza, Thalita Borges Giacomazzi, Leandro Peserico Dachi, Édison Pereira Baptista, Rafael Reimann School of Technology Graduate Program in Electrical Engineering PUCRS Porto Alegre Brazil School of Health and Life Sciences Undergraduate Program in Physical Education PUCRS Porto Alegre Brazil School of Technology Undergraduate Program in Electrical Engineering PUCRS Porto Alegre Brazil School of Technology Associate Degree Program in Industrial Automation and Embedded Systems SENAI Porto Alegre Brazil School of Technology and School of Health and Life Sciences Pontifical Catholic University of Rio Grande do Sul Av. Ipiranga 6681 Partenon Porto Alegre90619-900 Brazil

Introduction: The purpose of this study is to present a novel approach for gait assessment called gait proficiency index (GPI) using only ground reaction forces (GRF)-derived parameters. Material and methods: Barefoot gait from 51 healthy children was evaluated with the GPI in this study. The participants walked at their self-selected speed along a 6-m walkway with 8 force platforms placed in pairs. The force-time analysis resulted in a dynamic symmetry assessment (DSA) that is the result of vertical GRF component distribution for all gait cycles both the left and right foot. DSA and impulse calculation from GRF led to the calculation of the GPI, which is the relationship between the impulse of load response and mid-stance and impulse of terminal stance and pre-swing of both right and left feet multiplied by the DSA index. The children were divided into three groups with 17 participants each and classified according to age (mean age ± standard deviation): G1 (4.39 ± 0.511), G2 (6.33 ± 0.639), G3 (8.49 ± 0.636). Results: The results show that as children mature, the GPI seems to increase from G1 to G2 and decreases from G2 to G3 and that both the standard deviations and coefficient of variation decrease with the increase of age. Conclusions: This can be an indicative that the index can assess improvements in gait quality through age. However, there was no statistical significance between the groups in this variable. Also, the correlation between either DSA and age or GPI and age found is weak to make other conclusions but that the study of adults should be included to either validate or refute the use of GPI. © 2021, Sociedade Brasileira de Engenharia Biomedica.

关键词： Biophysics

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Improvement of Spatial Ambiguity in Multi-Channel Speech Separation Using Channel Attention

Improvement of Spatial Ambiguity in Multi-Channel Speech Sep...

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2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021

作者： Hong, Qian-Bei Wu, Chung-Hsien Nguyen, Thanh Binh Wang, Hsin-Min National Cheng Kung University and Academia Sinica Graduate Program of Multimedia Systems and Intelligent Computing Tainan Taiwan National Cheng Kung University Department of Computer Science and Information Engineering Tainan Taiwan

ISBN: (纸本)9789881476890

Multi-channel speech separation has been successfully applied in a complex real-world environment such as the far-field condition. The common solution to deal with the far-field condition is using a multi-channel signal captured by a structured microphone array and leveraging the inner difference between channels to enhance the speech separation performance. The spatial feature has been widely used in recent speech separation research. This feature appears to be insufficient when the location information becomes ambiguous. This is known as the spatial ambiguity problem. In order to deal with the spatial ambiguity problem, this study proposes an attention mechanism for the Temporal-Spatial Neural Filter (TSNF), in which the channel attention on merged features and the feature map of 1D convolution block in the temporal convolution network is proposed. The proposed method is evaluated on the multi-channel reverberant dataset which is built based on the WSJ0-2mix dataset. The dataset is simulated in the real-environment room by using the Room Impulse Response generator. In the experimental results, the proposed methods produced the SI -SNR improvement of about 1.2dB in close speakers' case, while a small decrease of 0.1dB in other cases. © 2021 APSIPA.

关键词： Convolution

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Exploring current trends in agricultural commodities forecasting methods through text mining: Developments in statistical and artificial intelligence methods

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Heliyon 2024年第23期10卷 e40568页

作者： Guindani, Luana Gonçalves Oliveirai, Gilson Adamczuk Ribeiro, Matheus Henrique Dal Molin Gonzalez, Gabriel Villarrubia de Lima, José Donizetti Industrial & Systems Engineering Graduate Program (PPGEPS) Federal University of Technology - Parana (UTFPR) Via Do Conhecimento KM 01 - Fraron PR Pato Branco 85503–390 Brazil Expert Systems and Applications Lab Faculty of Science University of Salamanca Salamanca 37008 Spain

Agriculture stands as one of the major economic pillars worldwide, with food production contributing significantly to income growth. However, agricultural activities also entail risks associated with uncontrollable factors along the supply chain. To address these challenges, mathematical models have been developed for forecasting crucial variables in managing agribusiness activities. In this context, this article employs a combination of systematic bibliometric analysis and the Latent Dirichlet Allocation (LDA) method, a semi-automated approach. The main objective of this study was to automate the identification of relevant topics and construct a bibliographic portfolio (BP) covering the period 2015–2022, focusing on methodologies used in articles and other bibliometric analyses. The 30 articles included in the BP address issues related to methodologies applied in the temporal analysis of agricultural commodities. These articles were categorized based on the nature of the prediction models used, classified as (i) machine learning (ML), (ii) machine learning and artificial neural networks (ML-NN), (iii) machine learning and ensemble (ML-Ensemble), (iv) machine learning and hybrid (ML-hybrid), and (v) statistical. Regarding the results, the topic that stood out the most was termed “Forecasting Methods Applied to Agribusiness Time Series.” The most utilized classes were ML-hybrid (41.95 %) and statistical (29.31 %), followed by ML-NN (14.94 %), ML (9.20 %), and ML-Ensemble (4.60 %) types. The theoretical contribution of this study lies in identifying literary gaps concerning forecasting methods applied to agribusiness, while its practical implication is to identify forecasting methodologies to support decision-making. © 2024 The Authors

关键词： Agribusiness Agricultural commodities Forecasting Latent dirichlet allocation Machine learning Text mining

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Glucose Sensing Utilizing Complex-Valued Neural Networks 23

Glucose Sensing Utilizing Complex-Valued Neural Networks

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4th International Conference on Computing, Networks and Internet of Things, CNIOT 2023

作者： Lv, Yiqi Meng, Xiang Luo, Yong Pei, Yan The Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai University China and Jt. International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai University Shanghai China Doctoral Program Grad. Sch. of Comp. Science and Engineering Graduate Department of Computer and Information Systems University of Aizu Aizuwakamatsu Japan Computer Science Division University of Aizu Aizuwakamatsu Japan

ISBN: (纸本)9798400700705

This paper proposes a Complex-Valued Neural Network (CVNN) for glucose sensing in milli-meter wave (mmWave). Based on the propagation characteristics of millimeter wave in glucose medium, we obtain the S21 parameter of glucose with the concentration range of 0-300mg/dL in the 60-80 GHz frequency band by High Frequency Structure Simulator (HFSS) simulations. Then we combine the sensing model with a neural network to detect and predict the glucose concentration relying on the learning ability of the neural network. In the prediction of the concentration of unknown samples, the absolute error between the predicted value and the true value is within 5mg/dL, which confirms the ability of the proposed CVNN model. © 2023 ACM.

关键词： Millimeter waves

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An Investigation of Heuristic Control Strategies for Multi-Electrolyzer Wind-Hydrogen systems Considering Degradation

An Investigation of Heuristic Control Strategies for Multi-E...

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Control Technology and Applications (CCTA),

作者： Zachary Tully Genevieve Starke Kathryn Johnson Jennifer King Advanced Energy Systems Graduate Program Colorado School of Mines Golden CO USA National Renewable Energy Laboratory Golden CO USA Electrical Engineering Department Colorado School of Mines Golden CO USA

With the growing demand for renewable-energy-powered hydrogen generation and the corresponding increase in plant capacity, individually controlling many electrolyzer stacks will be critical for increasing the plant’s lifetime and efficiency. This paper introduces a rule-based controller framework targeting electrolyzer degradation to explore the opportunity space in multi-stack hydrogen plant control. A novel control-oriented degradation model is also presented in this work, which quantifies the impact of steady power input, fluctuating power input, and the number of startup/shutdown cycles on electrolyzer degradation. Using a wind power input signal, 13 controller configurations are tested on 5 MW hydrogen plants consisting of 2, 5, 10, and 25 stacks. These configurations are evaluated on their capability to mitigate electrolyzer degradation and efficiently produce hydrogen from the time-varying input power. The results show that multi-stack control for degradation can extend the plant’s lifetime by more than a factor of 3 with minimal impact on instantaneous hydrogen production.

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Generalization Bounds for Contextual Stochastic Optimization using Kernel Regression

arXiv

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arXiv 2024年

作者： Wang, Yijie Hanasusanto, Grani A. Ho, Chin Pang Graduate Program in Operations Research and Industrial Engineering The University of Texas at Austin AustinTX78712-1591 United States Department of Industrial and Enterprise Systems Engineering University of Illinois Urbana-Champaign UrbanaIL61801 United States School of Data Science City University of Hong Kong Hong Kong

In this paper, we consider contextual stochastic optimization using Nadaraya-Watson kernel regression, which is one of the most common approaches in nonparametric regression. Recent studies have explored the asymptotic convergence behavior of using Nadaraya-Watson kernel regression in contextual stochastic optimization;however, the performance guarantee under finite samples remains an open question. This paper derives a finite-sample generalization bound of the Nadaraya-Watson estimator with a spherical kernel under a generic loss function. Based on the generalization bound, we further establish a suboptimality bound for the solution of the Nadaraya-Watson approximation problem relative to the optimal solution. Finally, we derive the optimal kernel bandwidth and provide a sample complexity analysis of the Nadaraya-Watson approximation problem. © 2024, CC BY.

关键词： Optimization

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PPM-HC: A method for helping project portfolio management based on topic hierarchy learnings

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International Journal of Business Intelligence and Data Mining 2021年第3期18卷 364-382页

作者： Pinto, Ricardo A.M. Bernardini, Flavia Marcacini, Ricardo M. Graduate Program in Industrial Engineering and Computing Systems Fluminense Federal University Jardim Bela Vista Rio das OstrasRJ Brazil Computing Institute Fluminense Federal University São Domingos NiteróiRJ Brazil Information Systems Federal University of Mato Grosso do Sul P.O. Box 210 Três LagoasMS Brazil

The projects categorisation is a crucial step in the project portfolio management (PPM). Categorising projects allows the organisation to identify categories with a lack or excess of projects, according to its strategic objectives. In this work, we present a new method for project portfolio management based on hierarchical clustering (PPM-HC) to organise the projects at several levels of abstraction. In the PPM-HC, similar projects are allocated to the same clusters and subclusters. PPM-HC automatically learns an understandable topic hierarchy from the project portfolio dataset, thereby facilitating the (human) task of exploring, analysing and prioritising the projects of the organisation. We also proposed a card sorting-based technique which allows the evaluation of the projects categorisation using an intuitive visual map. We carried out an experimental evaluation based on a benchmark dataset and we also presented a real-world case study. The results show that the proposed PPM-HC method is promising. © 2021 Inderscience Enterprises Ltd.

关键词： Investments

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The Fluorescent Effect of Chitosan Magnetitic Nanoparticles for Near-Infrared Imaging 3

The Fluorescent Effect of Chitosan Magnetitic Nanoparticles ...

引用

3rd Novel Intelligent and Leading Emerging Sciences Conference, NILES 2021

作者： Abuelmakarem, Hala S. Ahmed, Wafaa A. Mofed, Dina Wael, Mohammed Sami, Sherif A. The Higher Institute of Engineering Elshrouk Academy Biomedical and Systems Engineering Department Egypt Biochemistry and Molecular Biology Unit National Cancer Institute Cairo University Cancer Biology Department Giza Egypt Cairo University Zoology graduate program Department of zoology Faculty of science Giza Egypt Cairo University Faculty of Engineering Department of Biomedical Engineering and Systems Giza Egypt

ISBN: (纸本)9781665421577

Early cancer detection increases the healing rate according to the cancer stage. Nanotechnology is an advanced technique in cancer management due to the permeability and retention effect. Nano contrast agents are chief in early cancer detection because they diffuse in the cancerous tissue due to permeability and retention. This study aims to synthesize Chitosan Magnetic Nanoparticles (CM NPS) to evaluate them as a contrast agent for cells imaging, in vitro, under near-infrared irradiation. CM NPs were synthesized using the wetted chemical method. They were characterized using Dynamic Light Scattering, High-resolution Transmission Electronic Microscopy (HR- TEM), Fourier Transform Infrared (FTIR) Spectroscopy, and X-ray diffraction (XRD). The shining effect of the CM NPs was evaluated under the inverted microscopy over through a human lung epithelial cell culture in vitro. The characterization results ensured the successful synthesis of CM NPs. The photomicrographs appeared shining light of CM NPs on the cells photographed under the inverted microscope. © 2021 IEEE.

关键词： Chitosan

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