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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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PseudoSC: A Binary Approximation to Stochastic Computing within Latent Operation-Space for Ultra-Lightweight on-Edge DNNs 23

PseudoSC: A Binary Approximation to Stochastic Computing wit...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Jihe Wang Zhaoqing Wang Danghui Wang School of Computer Science Northwestern Polytechnical University Xi'an China Engineering Research Center of Embedded System Integration Ministry of Education Northwestern Polytechnical University Xi'an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350323481

Recently, stochastic computing (SC) is increasingly popular in constructing MAC for on-edge DNNs benefiting from its outstanding energy-efficiency, including its adequate precision and gatelevel operation. However, current SC-DNN systems always include a lot of costly SNGs/APCs for inevitably switches between binary and stochastic domains, which mortgages incongruous resources to pay the "bill" of the domain-switches and impedes highly-concurrent deployments. In this work, PseudoSC, a binary approximation to low-discrepancy SC, is proposed to totally remove the domain-switch for SNG/APC-free SC-DNNs. Its basic idea is to virtually re-arrange a couple of stochastic operands into a 2-D latent op-space, in which, original Monte Carlo sampling can be partitioned into three sub-ops, i.e., two fixed binary-ops and a fractal recursion. In theory, the recursion forms an isomorphic partition of the sampling repeated in smaller scales until the binary base-case achieved, as a result, a SC-op is well approximated only with binary-ops. Based on above theory, a multi-lane micro-architecture is designed to unroll the recursion within a few cycles and its advantages on hardware saving is verified under popular DNNs. The evaluation shows that the DNN-models with our schemes achieve 98.7% accuracy of the fixed-point implementations, which significantly outperform other SOTA methods. In addition, its reduced structure improves the power efficiency by 3.67 times on average.

关键词： stochastic computing

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Optimal Trace Codes and Their Self-orthogonality

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IEEE Transactions on Information Theory 2025年

作者： Hyun, Jong Yoon Hu, Zhao Cheon, Eun Ju Wu, Yansheng Konkuk University Glocal Campus 268 Chungwon-daero Chungcheongbuk-do Chungju-si27478 Korea Republic of Hubei University School of Cyber Science and Technology Wuhan430062 China Hubei Provincial Engineering Research Center of Intelligent Connected Vehicle Network Security Wuhan430062 China Gyeongsang National University Department of Mathematics RINS Jinju52828 Korea Republic of Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China Jiangsu Cryptographic Technology Engineering Research Center Nanjing210023 China

The primary objective of this paper is the construction of optimal codes with self-orthogonality that can be used to construct quantum codes. Recently, Ding and Heng explored subfield codes, which can be viewed as trace codes. In this paper, we focus on investigating self-orthogonal optimal trace codes. First, we provide a novel description of trace codes by choosing suitable defining sets. Second, we determine the parameters of the codes and their trace codes whose defining sets are disjoint union of some affine subspaces in both non-projective cases and projective-cases. This result extends the main findings in (Hu, Li, Zeng, Wang, Tang, IEEE Trans. Inform. Theory, 68(7): 4408-4421, 2022). Third, we compute the parameters of trace codes for MacDonald codes, including the first order Reed-Muller codes and simplex codes as special cases. Finally, we examine their self-orthogonality and distance-optimality to find several classes of self-orthogonal Griesmer codes. Additionally, we resolve a problem proposed by Ding and Heng as a byproduct. © 1963-2012 IEEE.

关键词： Turbo codes

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VLC-enabled UAV Network for IoT with Co-channel Interference: Joint Spatial Deployment and Resource Allocation

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IEEE Internet of Things Journal 2025年

作者： Liu, Jiaji Yang, Fang An, Nan Song, Jian Han, Zhu State Key Laboratory of Widegap Semiconductor Optoelectronic Materials and Technologies Beijing100084 China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Beijing100084 China Research Institute of Tsinghua University Shenzhen518057 China Tsinghua University Shenzhen International Graduate School Shenzhen518055 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

Unmanned aerial vehicles (UAVs) have gained significant attention due to their low cost and efficient deployment. Specifically, UAV-based communications hold promise to supplement the coverage limitations of terrestrial networks and achieve superior connectivity for the Internet of Things (IoT). Moreover, UAVs with visible light communication (VLC) capability do not interfere with existing radio frequency (RF) communication infrastructure, thereby providing flexible, high-throughput on-demand wireless communications. In this paper, considering the limited battery capacity of UAVs, we aim to maximize the energy efficiency (EE) of a multi-UAV downlink VLC system for IoT. To this end, the system model with co-channel interference is first established, followed by the formulation of the joint spatial deployment and resource allocation problem. Besides, the original problem is decomposed into three subproblems, focusing on spatial deployment, subchannel assignment, and power allocation. By employing the block coordinate descent method, the three subproblems are solved alternately. Moreover, simulation results demonstrate that the proposed method achieves superior improvements in EE compared to other baselines. In addition, the convergence and computational complexity of the proposed algorithm, as well as the impacts of key parameters on the overall system performance are also investigated. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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THE INCLUSION-EXCLUSION PRINCIPLE AND RECURRENCES FOR PARTITION NUMBERS

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2025年第1期87卷 97-106页

作者： Ballantine, Cristina Merca, Mircea Department of Mathematics and Computer Science College of The Holy Cross WorcesterMA01610 United States Department of Mathematical Methods and Models Fundamental Sciences Applied in Engineering Research Center National University of Science and Technology Politehnica Bucharest BucharestRO-060042 Romania Academy of Romanian Scientists BucharestRO-050044 Romania

The inclusion-exclusion principle together with Legendre type theorems for number of distinct restricted partitions weighted by the parity of their length are used to give several recurrence relations for restricted partition numbers as well as for overpartition numbers. In the case of overpartitions, POD partitions and 3-color partitions, we give combinatorial proofs for the Legendre type theorems and arrive to combinatorial proofs for the recurrence relations. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Combinatorial mathematics

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Revisiting One-stage Deep Uncalibrated Photometric Stereo via Fourier Embedding

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年

作者： Ju, Yakun Shi, Boxin Wen, Bihan Lam, Kin-Man Jiang, Xudong Kot, Alex C. University of Leicester School of Computing and Mathematical Sciences Leicester United Kingdom Nanyang Technological University School of Electrical and Electronic Engineering Singapore Peking University National Key Laboratory for Multimedia Information Processing National Engineering Research Center of Visual Technology School of Computer Science Beijing China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong Hong Kong

This paper introduces a one-stage deep uncalibrated photometric stereo (UPS) network, namely Fourier Uncalibrated Photometric Stereo Network (FUPS-Net), for non-Lambertian objects under unknown light directions. It departs from traditional two-stage methods that first explicitly learn lighting information and then estimate surface normals. Two-stage methods were deployed because the interplay of lighting with shading cues presents challenges for directly estimating surface normals without explicit lighting information. However, these two-stage networks are disjointed and separately trained so that the error in explicit light calibration will propagate to the second stage and cannot be eliminated. In contrast, the proposed FUPS-Net utilizes an embedded Fourier transform network to implicitly learn lighting features by decomposing inputs, rather than employing a disjointed light estimation network. Our approach is motivated from observations in the Fourier domain of photometric stereo images: lighting information is mainly encoded in amplitudes, while geometry information is mainly associated with phases. Leveraging this property, our method "decomposes" geometry and lighting in the Fourier domain as guidance, via the proposed Fourier Embedding Extraction (FEE) block and Fourier Embedding Aggregation (FEA) block, which generate lighting and geometry features for the FUPS-Net to implicitly resolve the geometry-lighting ambiguity. Furthermore, we propose a Frequency-Spatial Weighted (FSW) block that assigns weights to combine features extracted from the frequency domain and those from the spatial domain for enhancing surface reconstructions. FUPS-Net overcomes the limitations of two-stage UPS methods, offering better training stability, a concise end-to-end structure, and avoiding accumulated errors in disjointed networks. Experimental results on synthetic and real datasets demonstrate the superior performance of our approach, and its simpler training setup, potentially

关键词： Frequency domain analysis

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Stable Extended U-Net for Noise-Robust Speaker Verification

Stable Extended U-Net for Noise-Robust Speaker Verification

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

作者： Zonghui Wang Zhihua Fang Liang He School of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi China School of Intelligence Science and Technology Xinjiang University Urumqi China Department of Electronic Engineering Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

With advancements in deep learning, speaker verification systems have significantly improved their performance in noisy environments. researchers typically demonstrate the effectiveness of their improved models by comparing performance on specific datasets, such as the VoxCeleb benchmark. However, in diverse real-world noise conditions, the out-of-domain generalization ability is also a crucial factor in evaluating a model’s performance improvement. research on stable learning indicates that eliminating the spurious correlation between training and testing data can enhance the generalization of the model. Building on this idea, we propose an improved speaker verification system with high generalization based on the extended U-Net (ExU-Net). It uses the sample reweighting method from stable learning to eliminate sample correlations and retains more effective speaker information through subpixel convolutions and coordinate attention mechanisms. We validate the effectiveness of this approach through extensive evaluations on VoxCeleb1, VOiCES, and other out-of-domain noise test sets, highlighting its generalization capability and model robustness.

关键词： Training Correlation Attention mechanisms Convolution Data models Stability analysis Robustness Noise measurement Speech processing Signal to noise ratio

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Correction: Materials laboratories of the future for alloys, amorphous, and composite materials (MRS Bulletin, (2025), 50, 2, (190-207), 10.1557/s43577-024-00846-y)

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MRS Bulletin 2025年 1-2页

作者： Banerjee, Sarbajit Meng, Y. Shirley Minor, Andrew M. Zhang, Minghao Zaluzec, Nestor J. Chan, Maria K.Y. Seidler, Gerald McComb, David W. Agar, Joshua Mukherjee, Partha P. Melot, Brent Chapman, Karena Guiton, Beth S. Klie, Robert F. McCue, Ian D. Voyles, Paul M. Robertson, Ian Li, Ling Chi, Miaofang Destino, Joel F. Devaraj, Arun Marquis, Emmanuelle A. Segre, Carlo U. Liu, Huinan H. Yang, Judith C. Momeni, Kasra Misra, Amit Abdolrahim, Niaz Medvedeva, Julia E. Cai, Wenjun Sehirlioglu, Alp Dizbay-Onat, Melike Mehta, Apurva Graham-Brady, Lori Maruyama, Benji Rajan, Krishna Warner, Jamie H. Taheri, Mitra L. Kalinin, Sergei V. Reeja-Jayan, B. Schwarz, Udo D. Simon, Sindee L. Brown, Craig M. Department of Chemistry and Department of Materials Science & Engineering Texas A & M University College Station United States Pritzker School of Molecular Engineering The University of Chicago Chicago United States Department of Materials Science & Engineering University of California Berkeley Berkeley United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory Berkeley United States Center for Nanoscale Materials Argonne National Laboratory Lemont United States Department of Physics University of Washington Seattle United States Department of Materials Science & Engineering The Ohio State University Columbus United States Department of Mechanical Engineering and Mechanics Drexel University Philadelphia United States School of Mechanical Engineering Purdue University West Lafayette United States Department of Chemistry University of Southern California Los Angeles United States Department of Chemistry Stony Brook University The State University of New York Stony Brook United States Department of Chemistry University of Kentucky Lexington United States Department of Physics University of Illinois at Chicago Chicago United States Department of Materials Science & Engineering Northwestern University Evanston Bangladesh Department of Materials Science & Engineering University of Wisconsin–Madison Madison United States Department of Materials Science and Engineering University of Pennsylvania Philadelphia United States Department of Mechanical Engineering and Materials Science Duke University Durham United States Department of Chemistry Creighton University Omaha United States Physical and Computational Sciences Directorate Pacific Northwest National Laboratory Richland United States Department of Materials Science & Engineering University of Michigan–Ann Arbor Ann Arbor United States Department of Physics Illinois Institute of Technology Chicago United States Department of Bioengineering and the Mater

This article was updated to correct Minghao Zhang’s affiliation from "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA" to "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA;Argonne national Laboratory, Lemont, USA." Argonne national Laboratory was left off the original publication. © The Author(s) 2025.

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Hybrid Approach to Optimize MPI Collectives by In-network-computation and Point-to-Point Messages 7

Hybrid Approach to Optimize MPI Collectives by In-network-co...

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7th International Conference on computer and Communication Systems, ICCCS 2022

作者： Chen, Shuping He, Wangquan Qi, Fengbin Zheng, Yan Yu, Kang National Research Center of Parallel Computer Engineering and Technology Bejing China

ISBN: (数字)9781665450607

ISBN: (纸本)9781665450607

Using in-network-computation capabilities of the network devices (also called hardware collectives) to optimize MPI collectives has become popular in high-performance computing, and shows significant performance advantages. However, the hardware collectives are not flawless in practical use scenarios. One of the problems is that it is difficult to use. In order to obtain the performance advantage of hardware collectives, the network management software need to create dedicated aggregate tree for each MPI communicator, which is a complicated task. One solution is to make MPI communicators sharing the global imprecise aggregate trees created by the management software when initiating networks, but it leads to heavy interference between MPI communicators and causes significant performance degradation. So we have to make tradeoff between performance and ease of use. We propose a hybrid approach to optimize MPI collectives by in-network-computation and point-to-point messages. On the one hand, we use the pre-created aggregate trees in each super-node, rather than sending requests to the network management software to create dedicated aggregate trees. On the other hand, the hardware collectives are transferred only in the local super-node, so it cannot disturb the jobs running on other super-nodes. We provide a cost model to evaluate the overhead of the hybrid collective algorithms. We also test its performance in the new generation Sunway supercomputer. The results show that our approach reduces the median latency by 18%~74% compared to collectives implemented by point-to-point messages, although the performance decrease slightly compared to the original hardware collectives. In addition, the tail latency of our approach is significantly lower than that of the original hardware collectives in the presence of heavy interference. © 2022 IEEE.

关键词： Aggregates

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Evaluating the Machine Learning Models in Predicting Intensive Care Unit Discharge for Neurosurgical Patients Undergoing Craniotomy: A Big Data Analysis

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Neurocritical Care 2025年 1-18页

作者： Khaniyev, Taghi Cekic, Efecan Koc, Muhammet Abdullah Dogan, Ilke Hanalioglu, Sahin Faculty of Engineering Department of Industrial Engineering Bilkent University Ankara Turkey National Magnetic Resonance Research Center Bilkent University Ankara Turkey MIT Sloan School of Management Massachusetts Institute of Technology Cambridge MA United States Faculty of Medicine Department of Neurosurgery Hacettepe University Ankara Turkey Faculty of Engineering Department of Computer Engineering Bilkent University Ankara Turkey Department of Neurosurgery Brigham and Women’s Hospital Harvard Medical School Boston MA United States

Background: Predicting intensive care unit (ICU) discharge for neurosurgical patients is crucial for optimizing bed sources, reducing costs, and improving outcomes. Our study aims to develop and validate machine learning (ML) models to predict ICU discharge within 24 h for patients undergoing craniotomy. Methods: The 2,742 patients undergoing craniotomy were identified from Medical Information Mart for Intensive Care dataset using diagnosis-related group and International Classification of Diseases codes. Demographic, clinical, laboratory, and radiological data were collected and preprocessed. Textual clinical examinations were converted into numerical scales. Data were split into training (70%), validation (15%), and test (15%) sets. Four ML models, logistic regression (LR), decision tree, random forest, and neural network (NN), were trained and evaluated. Model performance was assessed using area under the receiver operating characteristic curve (AUC), average precision (AP), accuracy, and F1 scores. Shapley Additive Explanations (SHAP) were used to analyze importance of features. Statistical analyses were performed using R (version 4.2.1) and ML analyses with Python (version 3.8), using scikit-learn, tensorflow, and shap packages. Results: Cohort included 2,742 patients (mean age 58.2 years;first and third quartiles 47–70 years), with 53.4% being male (n = 1,464). Total ICU stay was 15,645 bed days (mean length of stay 4.7 days), and total hospital stay was 32,008 bed days (mean length of stay 10.8 days). Random forest demonstrated highest performance (AUC 0.831, AP 0.561, accuracy 0.827, F1-score 0.339) on test set. NN achieved an AUC of 0.824, with an AP, accuracy, and F1-score of 0.558, 0.830, and 0.383, respectively. LR achieved an AUC of 0.821 and an accuracy of 0.829. The decision tree model showed lowest performance (AUC 0.813, accuracy 0.822). Key predictors of SHAP analysis included Glasgow Coma Scale, respiratory-related parameters (i.e., tidal volume,

关键词： Craniotomy Discharge Intensive care unit Machine learning Neurosurgery

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