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arXiv 2023年

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

Multimodal manipulations (also known as audiovisual deepfakes) make it difficult for unimodal deepfake detectors to detect forgeries in multimedia content. To avoid the spread of false propaganda and fake news, timely detection is crucial. The damage to either modality (i.e., visual or audio) can only be discovered through multi-modal models that can exploit both pieces of information simultaneously. Previous methods mainly adopt uni-modal video forensics and use supervised pre-training for forgery detection. This study proposes a new method based on a multi-modal self-supervised-learning (SSL) feature extractor to exploit inconsistency between audio and visual modalities for multi-modal video forgery detection. We use the transformer-based SSL pre-trained Audio-Visual HuBERT (AV-HuBERT) model as a visual and acoustic feature extractor and a multi-scale temporal convolutional neural network to capture the temporal correlation between the audio and visual modalities. Since AV-HuBERT only extracts visual features from the lip region, we also adopt another transformer-based video model to exploit facial features and capture spatial and temporal artifacts caused during the deepfake generation process. Experimental results show that our model outperforms all existing models and achieves new state-of-the-art performance on the FakeAVCeleb and DeepfakeTIMIT datasets. © 2023, CC BY.

关键词： Audio acoustics

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CapST: An Enhanced and Lightweight Method for Deepfake Video Classification

arXiv

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arXiv 2023年

作者： Ahmad, Wasim Peng, Yan-Tsung Chang, Yuan-Hao Ganfure, Gaddisa Olani Khan, Sarwar Shahzad, Sahibzada Adil Institute of Information Science Academia Sinica Taipei11529 Taiwan Department of Computer Science National Chengchi University Taipei116 Taiwan Social Networks Human-Centered Computing Taiwan International Graduate Program Academia Sinica Taipei11529 Taiwan Dire Dawa University Ethiopia Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan

The proliferation of deepfake videos, synthetic media produced through advanced Artificial Intelligence techniques, has raised significant concerns across various sectors, encompassing realms such as politics, entertainment, and security. In response, this research introduces an innovative and streamlined model designed to classify deepfake videos generated by five distinct encoders adeptly. Our approach not only achieves state-of-the-art performance but also optimizes computational resources. At its core, our solution employs part of a VGG19_bn as a backbone to efficiently extract features, a strategy proven effective in image-related tasks. We integrate a Capsule Network coupled with a Spatial-Temporal attention mechanism to bolster the model’s classification capabilities while conserving resources. This combination captures intricate hierarchies among features, facilitating robust identification of deepfake attributes. Delving into the intricacies of our innovation, we introduce an existing video-level fusion technique that artfully capitalizes on temporal attention mechanisms. This mechanism serves to handle concatenated feature vectors, capitalizing on the intrinsic temporal dependencies embedded within deepfake videos. By aggregating insights across frames, our model gains a holistic comprehension of video content, resulting in more precise predictions. Experimental results on an extensive benchmark dataset of deepfake videos (DFDM) showcase the efficacy of our proposed method. Notably, our approach achieves up to a 4% improvement in accurately categorizing deepfake videos compared to baseline models, all while demanding fewer computational resources. Copyright © 2023, The Authors. All rights reserved.

关键词： Routing algorithms

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Detection of Service Provider Hardware Over-commitment in Container Orchestration Environments

Detection of Service Provider Hardware Over-commitment in Co...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Pedro Horchulhack Eduardo K. Viegas Altair O. Santin Pontifícia Universidade Católica do Paraná (PUCPR) — Graduate Program in Computer Science (PPGIa) Curitiba Paraná Brazil Secure Systems Research Center Technology Innovation Institute Abu Dhabi United Arab Emirates

ISBN: (纸本)9781665435413

The deployment of container-based services continues to increase as time passes, mainly due to its fast provision time and lower allocation overheads. Yet, the literature still neglects the performance degradation in containers due to multi-tenancy and service provider hardware over-commitment. This paper proposes a new hardware over-commitment detection for container orchestration environments, implemented twofold. First, the containerized hardware usage of deployed containers is continuously monitored in a non-intrusive manner, leveraging the container engine resource management interface. Second, collected features are used by a recurrent neural network model for detecting both container and service level hardware over-commitment, following a time-series rationale. Experiments run on a containerized Apache Spark distribution have shown that multi-tenancy and hardware over-commitment significantly affect its performance. In addition, our proposed model is able to detect hardware over-commitment with up to 91% of true-positive at the container level, and up to 93% true-positive at the service level.

关键词： Degradation Recurrent neural networks Cluster computing Containers Feature extraction Hardware Resource management

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Role of Wireless Communications in Digital Economy in the Present Context 6

Role of Wireless Communications in Digital Economy in the Pr...

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6th International Conference on Trends in Electronics and Informatics, ICOEI 2022

作者： Revathi, S. Shrivastava, Arpan Yussupova, Ardak Hidayatulloh, A. Nururrochman Saltanat, Daurbekova Mishra, Arpana Sri Ramakrishna Institute of Technology Department of Computer Science and Engineering Coimbatore India Management Devi Ahilya Vishva Vidyalaya Madhya Pradesh India Kazakh National University of Arts Department of Art Studies Nur-Sultan Kazakhstan Universitas Gadjah Mada Leadership and Policy Innovation Program Indonesia International Education Corporation Department of Construction Almaty050042 Kazakhstan Sharda University Electrical Electronics and Communication Engineering Greater Noida India

ISBN: (纸本)9781665483285

Wireless communication has connected millions of people across the world to internet to enable them to reap benefits of digital economy in present scenario. Almost all the sectors of economy including healthcare, agriculture and transportation nowadays rely largely upon wireless communications. In the present context, the sudden outbreak of Covid-19 has caused governments to impose restrictions on various activities and employees have been directed to work from their homes to ensure safety. In such a situation, wireless communication has made it possible to connect with each other via the internet. Apart from this, Smart healthcare-system has been developed through utilisation of wireless communication systems. During this pandemic situation, delivery of healthcare services in remote areas has gained huge momentum and wireless communication plays a vital role in this regard. Aim of current critical assessment is to provide an in-depth analysis of the role played by wireless communication in establishment of a well-structured digital economy in present scenario. In literature-review, different applications of wireless technology via ICT in various sectors and aspects of life have been discussed through a conceptual and a theoretical framework. The Method section of current study has provided an in-depth insight into the ways required information have been collected in this study. As found in the results and discussion section, a positive relation exists between wireless-communication and establishment of digital economy in current situation. This study has concluded that there are certain challenges associated with wireless communication;however, it has significant influence in making processes smarter and faster as compared to earlier. © 2022 IEEE.

关键词： Internet of things

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Temperature Dependent Anomalous Threshold Voltage Modulation of a-IGZO TFT by Incorporating Variant Gate Stresses

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ECS Journal of Solid State science and Technology 2024年第6期13卷 065008-065008页

作者： Aslam, Muhammad Chang, Shu-Wei Chen, Yi-Ho Lee, Yao-Jen Li, Yiming Lee, Wen-Hsi Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Hsinchu 300093 Taiwan Department of Electrical Engineering National Cheng Kung University Tainan 701401 Taiwan Graduate Degree Program of College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu 300093 Taiwan Institute of Pioneer Semiconductor Innovation Industry Academic Innovation School National Yang Ming Chiao Tung University Hsinchu 300093 Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu 300093 Taiwan

Amorphous indium gallium zinc oxide (a-IGZO) has recently made significant advancement as a key material for electronic component design owing to its compatibility with complementary metal oxide semiconductor technologies. A comprehensive analysis of reliability-related issues is required to determine the true potential of a-IGZO-based devices for next-generation electronics applications. To address this objective, we electrically characterize scaled-channel a-IGZO thin film transistors (TFTs) under positive bias (temperature) stress (PB(T)S). Both PBS and PBTS are characterized by positive and negative V_th shift, respectively, during the various gate stresses. In particular, the negative V_th shift is explained by the generation of donor-like traps stimulated by ionization of oxygen vacancy/hydrogen at elevated temperature. The TFTs exhibit relatively decent stability during the PBS operation. The analysis of devices with variant channel dimensions implies that long-channel devices exhibit relatively higher stability and performance compared to the short-channel ones. We also observe that the V_th can be controllably adjusted by employing the top gate (TG) with bottom gate sweep. Moreover, the stress-induced partial recovery mechanism is experimentally observed owing to detrapping of charges. Generally, the reported results infer a perceptive understanding of scaled-channel a-IGZO-TFTs which helps with shaping performance-enhancement strategies. © 2024 The Author(s). Published on behalf of The Electrochemical Society by IOP Publishing Limited

关键词： amorphous IGZO-TFT electrical instabilities hydrogen-impact scaling impact on reliability

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LMCG-Net: Integrating LLMs and ECG for LVEF-Based Classification for Pacing Patients

LMCG-Net: Integrating LLMs and ECG for LVEF-Based Classifica...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wonkyeong Shim Namjun Park Donggeun Ko Hyebin Gwag Youngjun Park Seungjung Park Jaekwang Kim Dept. of Computer Science and Engineering Sungkyunkwan University Suwon South Korea Aim Future AI Seoul South Korea Samsung Changwon Hospital Changwon South Korea Wonju Severance Christian Hospital Kangwon-do South-Korea Samsung Medical Center Seoul South Korea Convergence Program for Social Innovation School of Convergence Sungkyunkwan University Seoul South Korea

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Detecting left ventricular systolic dysfunction (LVSD) traditionally relies on expensive and specialized echocardiography, limiting accessibility for many patients. To address this, researchers explore the potential of electrocardiography (ECG), a more affordable and widely available alternative, despite its historically limited performance in cardiac dysfunction detection. In this study, we present a novel approach that integrates a 1D convolutional neural networks (CNNs) with a large-scale language model (LLM) to simultaneously analyze sequential ECG data and non-sequential clinical metadata. To validate our model’s effectiveness, we conducted rigorous comparative experiments on both specially collected clinical data and public datasets, achieving an impressive AUROC of 0.97 across both. Our findings underscore the capability of ECG-based AI to accurately predict LVSD including pacemaker patients, offering a rapid and cost-effective alternative to traditional echocardiography. This innovative approach could significantly enhance early diagnosis and management of cardiac dysfunction.

关键词： Convolution Echocardiography Pacemakers Electrocardiography Metadata Feature extraction Vectors Data mining Convolutional neural networks Long short term memory

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Gamification of Donation App: What Affect Donators’ Decision?

Gamification of Donation App: What Affect Donators’ Decisio...

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International Conference on Information Technology (INCIT)

作者： Thidawan Chaichana Peetikorn Preechachot Surapong Uttama Computer Science and Innovation Program School of Information Technology Mae Fah Luang University Chiang Rai Thailand Center of Excellence in Artificial Intelligence and Emerging Technologies School of Information Technology Mae Fah Luang University Chiang Rai Thailand

This paper proposes a donation mobile app gamified to monitor players’ donation decision. Three main scenarios were simulated to observe which factors affect the donation. First, five categories of donation project were available. Second, an expiring donation project was set and was alerted to players. Last, auctions were added to some donation projects to draw more players’ attention. Testing with twelve young players revealed that the most influenced donation factor is the project’s type. Health-related project was the most attentive. Also, the expiring donation project triggered the players’ interest. Unfortunately, auctions rarely increased the amount of donation which may be due to the uninteresting bidding products.

关键词： Mobile applications Information technology Monitoring Testing

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Behavioral Office Syndrome with IoT Devices

Behavioral Office Syndrome with IoT Devices

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International Conference on Information Technology (INCIT)

作者： Pun Kongsomboon Surapong Uttama Computer Science and Innovation Program School of Information Technology Mae Fah Luang University Chiang Rai Thailand Center of Excellence in Artificial Intelligence and Emerging Technologies School of Information Technology Mae Fah Luang University Chiang Rai Thailand

This paper presents a collection of computer use behavior prone to office syndrome by using pressure sensors in conjunction with IoT devices. IoT device was attached with a chair, and sensors were installed on the seat and the backrest. Another IoT set was linked with sensors installed under the mouse pad. The collected data of seven testers instructed to use computer and move freely revealed different behaviors. Some were sitting constantly with or without using backrest or touching mouse. Also a unique character like standing while using computer was found. To link with office syndrome estimation, we proposed a set of rules including thresholds for pressure values, time criteria and risk scoring. Using these criteria, the testers were found to have low and moderate risks of office syndrome.

关键词： Pressure sensors Estimation Prototypes Medical services Mice Behavioral sciences Internet of Things

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PacECG-Net: A Multi-modal Approach Integrating LLMs and ECG for LVSD Classification in Pacemaker Patients 25

PacECG-Net: A Multi-modal Approach Integrating LLMs and ECG ...

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Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing

作者： Wonkyeong Shim Namjun Park Donggeun Ko San Kim Hye Bin Gwag Young Jun Park Seung-Jung Park Jaekwang Kim Department of Computer Science and Engineering Sungkyunkwan University Suwon Republic of Korea Aim Future AI Seoul Republic of Korea Sungkyunkwan University Suwon Republic of Korea Samsung Changwon Hospital Changwon Republic of Korea Wonju Severance Christian Hospital Wonju Republic of Korea Samsung Medical Center Seoul Republic of Korea School of Convergence / Convergence Program for Social Innovation Sungkyunkwan University Seoul Republic of Korea

ISBN: (纸本)9798400706295

This study presents an AI-based model using ECG signals to predict left ventricular systolic dysfunction (LVSD) in pacemaker patients. A 1D convolutional neural network (CNN) combined with large language models processed both sequential ECG data and nonsequential clinical metadata. The model achieved an AUROC of 0.97 on both general and pacemaker-specific datasets, demonstrating its high accuracy. This approach offers a fast, cost-effective alternative to traditional echocardiography, improving LVSD detection in patients with pacemakers.

关键词： pacemaker

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Influence of Crystallographic Orientation on the Deformation of Ag Nanoparticles During High-Speed Impact

SSRN

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SSRN 2022年

作者： Becker, Michael F. Kovar, Desiderio Chitrakar, T.V. The University of Texas at Austin AustinTX78712 United States Materials Science and Engineering Program Texas Materials Institute United States Department of Electrical and Computer Engineering United States Department of Mechanical Engineering Center for Additive Manufacturing and Design Innovation

Several related aerosol processes utilize high velocity impact of solid nanoparticles to produce nanograined films including the aerosol deposition method, jet molding, vacuum kinetic spraying, and micro cold spray. Although the influence of important variables such as particle size and particle impact velocity, on particle deformation and film formation have been previously explored, other variables have not been systematically explored. Because particles impact with a full range of orientations, an understanding of the effect of particle orientation is required to predict film microstructures. Molecular dynamics simulations were conducted to determine the influence of particle crystallographic orientation on the particle deformation upon impact and the resulting microstructure of the deposit. We show that the orientations that produce the largest overall particle deformation are not correlated to orientations where initiation of plastic deformation is easiest. Rather, the deformation is heterogenous and depends on the mechanisms responsible for deformation. Two deformation mechanisms are identified: 1) dislocation plasticity and 2) disordering followed by viscous flow. The fraction of the atoms in the impacting particle that experience permanent deformation by each mechanism is quantified as a function of particle orientation. The implications of the effects of particle crystallographic orientation on film microstructures are also discussed. © 2022, The Authors. All rights reserved.

关键词： Molecular dynamics

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