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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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consexpressionR: an R package for consensus differential gene expression analysis

arXiv

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arXiv 2025年

作者： Costa-Silva, Juliana Menotti, David Lopes, Fabricio M. Department of Informatics Federal University of Paraná Rua Coronel Francisco Heráclito dos Santos 100 Paraná81531-990 Brazil Department of Computer Science Bioinformatics Graduate Program Federal University of Technology - Paraná Av. Alberto Carazzai 1640 - Cornélio Procópio ParanáPostal code: 86300-000 Brazil

Motivation: Bulk RNA-Seq is a widely used method for studying gene expression across a variety of contexts. The significance of RNA-Seq studies has grown with the advent of high-throughput sequencing technologies. Computational methods have been developed for each stage of the identification of differentially expressed genes. Nevertheless, there are few studies exploring the association between different types of methods. In this study, we evaluated the impact of the association of methodologies in the results of differential expression analysis. By adopting two data sets with qPCR data (to gold-standard reference), seven methods were implemented and assessed in R packages (EBSeq, edgeR, DESeq2, limma, SAMseq, NOISeq, and Knowseq), which was performed and assessed separately and in association. The results were evaluated considering the adopted qPCR data. Results: Here, we introduce consexpressionR, an R package that automates differential expression analysis using consensus of at least seven methodologies, producing more assertive results with a significant reduction in false positives. Availability: consexpressionR is an R package available via Source code and support are available at GitHub (https://***/costasilvati/consexpressionR). © 2025, CC BY-NC-ND.

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Dimensionality reduction in 3D causal deep learning for neuroimage generation: an evaluation study

引用

Journal of Medical Imaging 2025年第2期12卷

作者： Ohara, Erik Y. Vigneshwaran, Vibujithan Souza, Raissa Vamosi, Finn G. Wilms, Matthias Forkert, Nils D. University of Calgary Biomedical Engineering Graduate Program CalgaryAB Canada University of Calgary Department of Radiology CalgaryAB Canada University of Calgary Hotchkiss Brain Institute CalgaryAB Canada University of Calgary Alberta Children's Hospital Research Institute CalgaryAB Canada University of Calgary Department of Computer Science CalgaryAB Canada University of Calgary Departments of Pediatrics and Community Health Sciences CalgaryAB Canada

Purpose: Causal deep learning (DL) using normalizing flows allows the generation of true counterfactual images, which is relevant for many medical applications such as explainability of decisions, image harmonization, and in-silico studies. However, such models are computationally expensive when applied directly to high-resolution 3D images and, therefore, require image dimensionality reduction (DR) to efficiently process the data. The goal of this work was to compare how different DR methods affect counterfactual neuroimage generation. Approach: Five DR techniques [2D principal component analysis (PCA), 2.5D PCA, 3D PCA, autoencoder, and Vector Quantised-Variational AutoEncoder] were applied to 23,692 3D brain images to create low-dimensional representations for causal DL model training. Convolutional neural networks were used to quantitatively evaluate age and sex changes on the counterfactual neuroimages. Age alterations were measured using the mean absolute error (MAE), whereas sex changes were assessed via classification accuracy. Results: The 2.5D PCA technique achieved the lowest MAE of 4.16 when changing the age variable of an original image. When sex was changed, the autoencoder embedding led to the highest classification accuracy of 97.84% while also significantly impacting the age variable predictions, increasing the MAE to 5.24 years. Overall, 3D PCA provided the best balance, with an age prediction MAE of 4.57 years while maintaining 94.01% sex classification accuracy when altering the age variable and 94.73% sex classification accuracy and the lowest age prediction MAE (3.84 years) when altering the sex variable. Conclusions: 3D PCA appears to be the best-suited DR method for causal neuroimage analysis. © 2025 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Principal component analysis

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Scientometric Analysis of Artificial Intelligence Research in Agriculture

Scientometric Analysis of Artificial Intelligence Research i...

引用

International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Fairuz Iqbal Maulana Andi Pramono Miftahul Hamim Sukeipah Yuli Prihatin Rahman Arifuddin Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Interior Design Department School of Design Bina Nusantara University Jakarta Indonesia Entrepreneurship Business Creation BINUS Business School Bina Nusantara University Jakarta Indonesia Informatics Study Program Politeknik Kota Malang Malang Indonesia Department of Electrical Engineering University of Merdeka Malang Malang Indonesia

ISBN: (纸本)9781665473286

Artificial Intelligence Research in Agriculture has grown so common, and the potential they provide is so revolutionary that it is considered essential for competitive growth. Until we conducted this research, only 1064 academic documents on Trends of Artificial Intelligence Research in Agriculture that we found from 2012 to 2021 were obtained by searching the Scopus database. This study uses bibliometric analysis methods to map scientific publications worldwide. We use an article selection process, the year limitation, then the database is exported to RIS and CSV format files. Researchers in the United States had the most published articles and were indexed by Scopus among the most prolific authors ( $\mathrm{N}=287$ ), followed by the United Kingdom at second ( $\mathrm{N}=139$ ) and Italy with 66 academic publications. Data analysis reveals an increasing trend in the number of Trends of Artificial Intelligence Research in Agriculture publications worldwide, as measured by the Scopus index.

关键词： Data analysis Databases Multimedia systems Bibliometrics Market research Agriculture Indexes

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Platform for Evanescent Wave Sensor Fabrication Based on 3D-Printed Fiber Optic Holder

Platform for Evanescent Wave Sensor Fabrication Based on 3D-...

引用

Optical Fiber Sensors (LAWOFS), Latin American Workshop on

作者： Afonso Henrique Henschel Baptista Pedro Bertemes-Filho Aleksander Paterno Raquel Bettega Jose Pedro Mansueto Serbena Nathalia Prediger Ricardo Kamikawachi Department of Electrical Engineering Center for Science and Technology - Santa Catarina State University – CCT-UDESC Joinville Brazil Group of Organic Optoelectronic Devices (GOOD) Federal University of Parana – UFPR Curitiba Brazil Graduate Program in Electrical and Computer Engineering Federal University of Technology - Parana – UTFPR Curitiba Brazil

ISBN: (数字)9786589532026

ISBN: (纸本)9798350362725

This paper describes the low-cost manufacturing process of an evanescent wave fiber sensor platform that allows the etching of the fiber in hydrofluoric acid with the proposed 3D-printed fiber holder in an acid resistant material. The fiber holder is practical for allowing several processing procedures during its use and sensor fabrication, allowing sensor optical and electrical operation. In this work, some aspects of its characterization are presented in the optical domain showing the excitation of surface plasmons in the visible range, and also indication of its electrical operation of the thin-film as electrode.

关键词： Optical fiber polarization Optical fiber sensors Gold Optical polarization Biomedical optical imaging Optical device fabrication Optical films

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A Large Reservoir Computing Forecasting Method Based on Randomized Fuzzy Cognitive Maps

A Large Reservoir Computing Forecasting Method Based on Rand...

引用

IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)

作者： Omid Orang Fabricio J. Erazo-Costa Petrônio C. L. Silva Guilherme de Alencar Barreto Frederico Gadelha Guimarães Department of Computer Science Universidade Federal de Minas Gerais Belo Horizonte Brazil Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte Brazil Federal Institute of Northern Minas Gerais Januária Campus Brazil Department of Teleinformatics Engineering Universidade Federal do Ceará Fortaleza Brazil

ISBN: (数字)9798350366235

ISBN: (纸本)9798350366242

This paper introduces a novel forecastings technique based on randomized fuzzy cognitive maps (FCM), called LRHFCM (or large reservoir of randomized high-order FCM) for predicting univariate time series. LR-HFCM is a hybrid method combining fuzzy time series (FTS), FCMs, and reservoir computing. It is a type of echo state network (ESN) consisting of the input layer, intermediate (or large reservoir) layer, and output layer, where LASSO regression is applied to train the output layer. The novelty of this approach is that the internal layer includes a very large reservoir, considering different combinations from the sets of concepts and order using a certain number of sub-reservoirs to capture different dynamics of input time series. It is important to highlight that the weights within each sub-reservoir are chosen randomly and remain constant throughout the training process. The validity of the LR-HFCM approach is evaluated across 15 different time series datasets. The results highlight the outperformance of the LR-HFCM technique in comparison to various baseline models.

关键词： Training Adaptation models Adaptive systems Computational modeling Time series analysis Fuzzy cognitive maps Reservoirs

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Learning Pairwise Preferences from Movie Ratings 7

Learning Pairwise Preferences from Movie Ratings

引用

7th International Conference on ICT for Smart Society, ICISS 2020

作者： Qomariyah, Nunung Nurul Fajar, Ahmad Nurul Bina Nusantara University Faculty of Computing and Media Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program-Master of Information Systems Management Information Systems Management Department Jakarta11480 Indonesia

ISBN: (纸本)9781665404228

Preference Elicitation is now considered a crucial stage in recommender system. That is the stage where we collect and query preferences of the users as part of interactive decision support system. Preference elicitation can be performed using many ways, such as asking the user explicitly to give ratings or feedbacks for some products, or implicitly observing their behaviour. Once this preference has been collected by the system, it can then be used to generate item recommendation. Using explicit feedback itself, such as rating only, has several drawbacks. Using ratings is very subjective. Two users with similar taste might give different rating to the same item. A user also may not be consistent in giving number to express their liking. In other case, a user may like an item more than the other items, but feels difficult to give lower rating on the other item, because it means that the particular item is just as bad as the one he/she does not like. By showing two different items as a pair, it will be easier for the users to pick which one is the best. In this paper, we show the result of our experiment in predicting user pairwise preferences by using their movie ratings data. We evaluated five different machine learning algorithms to predict the data in pairwise preference format. The result shows that K-Nearest Neighbour and Random Forest outperformed the other algorithms. © 2020 IEEE.

关键词： Recommender systems

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Deep learning-based channel estimation for RIS-assisted UAV-enabled wireless communications system

Deep learning-based channel estimation for RIS-assisted UAV-...

引用

International Conference on Innovation, Communication and Engineering (ICICE 2023)

作者： Belayneh A Tesfaw Hsin-piao Lin Getaneh B Tarekegn Rong-Terng Juang Shiann-Shiun Jeng Department of International Graduate Program in Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Institute of Aerospace and System Engineering National Taipei University of Technology Taipei 10608 Taiwan Department of Electrical and Computer Engineering National Yang-Ming Chiao Tung University Hsinchu 30010 Taiwan Department of Electrical Engineering National Dong Hwa University Hualien Taiwan

ISBN: (数字)9781839539954

Wireless communication via unmanned aerial vehicles (UAVs) has drawn a great deal of attention due to its flexibility in establishing line-of-sight (LoS) communications. However, in complex urban and dynamic environments, the movement of UAVs can be blocked by trees and high-rise buildings that obstruct directional paths. This problem can be effectively addressed by employing reconfigurable intelligent surfaces (RIS). To achieve this goal, accurate channel estimation in RIS-assisted UAV-enabled wireless communications is crucial. This paper proposes an accurate channel estimation model using long short-term memory (LSTM) for a multi-user RIS-assisted UAV-enabled wireless communications system. Simulation results showed that LSTM can effectively improve the channel estimation performance of RIS-assisted UAV-enabled wireless communications.

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Interpretable ECG Analysis for Myocardial Infarction Detection through Counterfactuals

arXiv

引用

arXiv 2023年

作者： Tanyel, Toygar Atmaca, Sezgin Gökçe, Kaan Balık, M. Yiğit Güler, Arda Aslanger, Emre Öksüz, İlkay Biomedical Engineering Graduate Program Istanbul Technical University Istanbul Turkey Mehmet Akif Ersoy Thoracic and Cardiovascular Surgery Training and Research Hospital Istanbul Turkey Department of Computer Science Aalto University Espoo Finland Department of Cardiology Başakşehir Pine and Sakura City Hospital Istanbul Turkey Department of Computer Engineering Istanbul Technical University Istanbul Turkey

In the evolving landscape of ECG signal analysis, the challenge of limited transparency in machine learning models remains a significant barrier to their effective integration into clinical practice. This study addresses this issue by investigating the use of counterfactual explanations to improve model interpretability for clinicians, particularly in differentiating healthy subjects from myocardial infarction patients. Utilizing the PTB-XL dataset, we developed a methodology for systematic feature extraction and refinement to prepare for counterfactual analysis. This led to the creation of the Visualizing Counterfactual Clues on Electrocardiograms (VCCE) method, designed to improve the practicality of counterfactual explanations in a clinical setting. The validity of our approach was assessed using custom metrics that reflect the diagnostic relevance of counterfactuals, evaluated with the help of two cardiologists. Our findings suggest that this approach could support future efforts in using ECGs to predict patient outcomes for cardiac conditions, achieving interpretation validity scores of 23.29 ± 1.04 and 20.28 ± 0.99 out of 25 for high and moderate-quality interpretations, respectively. Clinical alignment scores of 0.83 ± 0.12 for high-quality and 0.57 ± 0.10 for moderate-quality interpretations underscore the potential clinical applicability of our method. The methodology and findings of this study contribute to the ongoing discussion on enhancing the interpretability of machine learning models in cardiology, offering a concept that bridges the gap between advanced data analysis techniques and clinical decision-making. The source code for this study is available at https://***/tanyelai/vcce. © 2023, CC BY-NC-SA.

关键词： Contrastive Learning

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Application for sugarcane plantation: Manpower and transportation planning

引用

AIP Conference Proceedings 2023年第1期2594卷

作者： H. G. Mawandha D. Sudigyo T. Suparyanto B. Pardamean Department of Agrotechnology Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Sugarcane plantations in Indonesia are still managed in a labor-intensive manner, especially plantation-owned by farmers. Labor intensive means that every stage of work in sugarcane plantation management still uses manpower. One of the jobs in sugarcane plantations that requires a lot of labor is sugar cane harvesting. Trend changing in the workforce gender would affect the quantity and quality of jobs, which is determined by the workforce's capacity and the workload of the work performed. The result of this research was a sugarcane agricultural calculator that made it simple for farmers to measure manpower and transportation requirements in estimating harvest costs. Manpower and transportation of harvested sugarcane commodities were estimated through the workload approach, namely, crop production and manpower's ability to carry a load. These two components helped sugarcane farmers to determine manpower and transportation during the harvest. The final yield produced by the projected harvest labor cost would be known immediately.

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