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arXiv 2023年

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

Forged content shared widely on social media platforms is a major social problem that requires increased regulation and poses new challenges to the research community. The recent proliferation of hyper-realistic deepfake videos has drawn attention to the threat of audio and visual forgeries. Most previous work on detecting AI-generated fake videos only utilizes visual modality or audio modality. While there are some methods in the literature that exploit audio and visual modalities to detect forged videos, they have not been comprehensively evaluated on multi-modal datasets of deepfake videos involving acoustic and visual manipulations. Moreover, these existing methods are mostly based on CNN and suffer from low detection accuracy. Inspired by the recent success of Transformer in various fields, to address the challenges posed by deepfake technology, in this paper, we propose an Audio-Visual Transformer-based Ensemble Network (AVTENet) framework that considers both acoustic manipulation and visual manipulation to achieve effective 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 multi-modal 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 our best model outperforms all existing methods and achieves state-of-the-art performance on Testset-I and Testset-II of the FakeAVCeleb dataset. © 2023, CC BY.

关键词： Statistical tests

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Development of a Method for Compliance Detection in Wearable Sensors

Development of a Method for Compliance Detection in Wearable...

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2023 IEEE International Conference on Electrical, computer and Energy Technologies, ICECET 2023

作者： Fard, Siavash Esfandiari Ghosh, Tonmoy Hossain, Delwar McCrory, Megan A Thomas, Graham Higgins, Janine Jia, Wenyan Baranowski, Tom Steiner-Asiedu, Matilda Anderson, Alex K. Sun, Mingui Frost, Gary Lo, Benny Sazonov, Edward University of Alabama Department of Electrical and Computer Engineering TuscaloosaAL35487 United States Boston University Department of Health Sciences BostonMA02215 United States Psychiatry and Human Behavior Warren Alpert Medical School of Brown University ProvidenceRI02903 United States University of Colorado Anschutz Medical Campus Division of Endocrinology Metabolism and Diabetes AuroraCO United States University of Pittsburgh Department of Neurological Surgery PittsburghPA15261 United States USDA/ARS Children's Nutrition Research Center Baylor College of Medicine Department of Pediatrics HoustonTX77030 United States University of Ghana Department of Nutrition and Food Science Legon Ghana University of Georgia Department of Nutritional Sciences AthensGA30602 United States Section for Nutrition Research Digestion and Reproduction University of Imperial College London Department of Metabolism LondonSW7 2BX United Kingdom Hamlyn Centre Imperial College London Department of Surgery and Cancer LondonSW7 2AZ United Kingdom

ISBN: (纸本)9798350327816

One of the crucial elements in studies relying on wearable sensors for quantification of human activities (like physical activity or food intake) is the assessment of wear time (compliance). In this paper, we propose a novel method based on the Automatic Ingestion Monitor v2 (AIM-2), deployed for measuring nutrient and energy intake. The proposed method was developed using data from a study of 30 participants for two days each (US dataset) and tested with an independent dataset (Ghana dataset) on 10 households (30 Participants, 3 days for each, a total of 90 days). The signals from the accelerometer sensor of the AIM-2 were used to extract features and train the gradient-boosting tree classifier. To reduce the error in the classification of non-compliance in situations where the sensor changes its position with respect to gravity, a two-stage classifier followed by post-processing was introduced. Previously, we developed an offline compliance classifier, and this work aimed to develop a classifier for a cloud-based feedback system. The accuracy and F1-score of the developed two-phase classifier based on K-fold validation for the training and validation dataset were 95.37% and 96.93%, and for the Ghana dataset, were 95.86% and 92.56%, respectively, showing satisfactory performance results. The trained classifier can be deployed to monitor compliance with device wear in realtime applications. Clinical Relevance- Food Intake and physical activity studies can contribute to detecting, controlling, and even improving eating or physical activity-related problems, like obesity, diabetes, and eating planning. To ensure effective monitoring, compliance with the wearing of the device is crucial. © 2023 IEEE.

关键词： Accelerometer Sensor Compliance Detection Embedded Systems Wearable Sensors

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Earthworm optimization algorithm based cascade LSTM-GRU model for android malware detection

Cyber Security and Applications

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Cyber Security and Applications 2025年 3卷

作者： Gupta, Brij B. Gaurav, Akshat Arya, Varsha Bansal, Shavi Attar, Razaz Waheeb Alhomoud, Ahmed Psannis, Konstantinos Department of Computer Science and Information Engineering Asia University Taiwan Taichung 413 China School of Cybersecurity Korea University Seoul South Korea Symbiosis Centre for Information Technology (SCIT) Symbiosis International University Pune India University of Economics and Human Science Warsaw Poland Ronin Institute Montclair NJ United States Hong Kong Metropolitan University (HKMU) Hong Kong Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India Insights2Techinfo India UCRD Chandigarh University Chandigarh India Management Department College of Business Administration Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Computer Sciences Faculty of Computing and Information Technology Northern Border University Rafha 91911 Saudi Arabia University of Macedonia Greece

The rise in mobile malware risks brought on by the explosion of Android smartphones required more efficient detection techniques. Inspired by a cascade of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, optimized using the Earthworm Optimization Algorithm (EOA), this study presents an android malware detection model. The paper used random forest model for feature selection. With a 99% accuracy and the lowest loss values, the proposed model performs better than conventional models including GRU, LSTM, RNN, Logistic Regression, and SVM. The findings highlight the possibility of proposed method in improving Android malware detection, thereby providing a strong answer in the changing scene of cybersecurity. © 2024

关键词： Android Earthworm optimization algorithm GRU LSTM Malware detection

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6D Movable Antenna Enhanced Interference Mitigation for Cellular-Connected UAV Communications

arXiv

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arXiv 2024年

作者： Ren, Tianshi Zhang, Xianchao Zhu, Lipeng Ma, Wenyan Gao, Xiaozheng Zhang, Rui School of Information and Electronics Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems Jiaxing University Jiaxing314001 China Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing University Jiaxing314001 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

Cellular-connected unmanned aerial vehicle (UAV) communications is an enabling technology to transmit control signaling or payload data for UAVs through cellular networks. Due to the line-of-sight (LoS) dominant air-to-ground channels, efficient interference mitigation is crucial to UAV communications, while the conventional fixed-position antenna (FPA) arrays have limited degrees of freedom (DoFs) to suppress the interference between the UAV and its non-associated co-channel base stations (BSs). To address this challenge, we propose in this letter a new approach by utilizing the six-dimensional movable antenna (6DMA) arrays to enhance the interference mitigation for the UAV. Specifically, we propose an efficient block coordinate descent (BCD) algorithm to iteratively optimize the antenna position vector (APV), array rotation vector (ARV), receive beamforming vector, and associated BS of the UAV to maximize its signal-to-interference-plus-noise ratio (SINR). Numerical results show that the proposed 6DMA enhanced cellular-connected UAV communication can significantly outperform that with the traditional FPA arrays and other benchmark schemes in terms of interference mitigation. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal to noise ratio

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LOCI: A Mobile QA System with Multimodal Motivation Scheme for Local Intent Questions in Dynamic Social Networks 91

LOCI: A Mobile QA System with Multimodal Motivation Scheme f...

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91st IEEE Vehicular Technology Conference, VTC Spring 2020

作者： Ali, Imad Chang, Ronald Y. Hsu, Cheng-Hsin Lee, Chi-Han Institute of Information Science Academia Sinica Taiwan International Graduate Program Social Networks and Human-Centered Computing Taipei Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan National Tsing Hua University Department of Computer Science Hsinchu Taiwan

ISBN: (纸本)9781728152073

Recent research has shown that people are frequently searching for information with local intent. The existing systems fail to answer this class of questions as they are not mainly designed to handle it. In this paper, we propose LOCI, a mobile question answer system with a multimodal motivation scheme for local intent questions where users provide timely and accurate answers in a dynamic social network. LOCI mines users' shared and contextual information to identify the most relevant users for each question. The proposed multimodal motivation scheme exploits users' social ties and monetary rewards to raise their basic motivation levels. LOCI assigns questions to the best answerers via our three proposed algorithms: (i) LOCI-RT, which immediately assigns newly arrived questions/users to available users/questions, (ii) LOCI-LO, which uses short-time batches for locally optimal questions-users assignment, and (iii) LOCI-AD, which assigns short- and long-deadline questions immediately and via batching, respectively. We conduct: (i) a survey to support our assumptions regarding the proposed multimodal motivation scheme, and (ii) trace-driven experiments to evaluate the performance of the three proposed LOCI's algorithms. © 2020 IEEE.

关键词： Motivation

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A greedy perimeter stateless routing method based on a position prediction mechanism for flying ad hoc networks

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Journal of King Saud University - computer and Information sciences 2023年第8期35卷

作者： Hosseinzadeh, Mehdi Tanveer, Jawad Ionescu-Feleaga, Liliana Ionescu, Bogdan-Stefan Yousefpoor, Mohammad Sadegh Yousefpoor, Efat Ahmed, Omed Hassan Rahmani, Amir Masoud Mehmood, Asif Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Department of Computer Science and Engineering Sejong University Seoul 05006 South Korea Department of Accounting and Audit Bucharest University of Economic Studies Bucharest 010374 Romania Department of Management Information System Bucharest University of Economic Studies Bucharest 010374 Romania Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Information Technology University of Human Development Sulaymaniyah Iraq Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Biomedical Engineering Gachon University Gyeonggi-do Seongnam-si 13120 South Korea

Today, unmanned aerial vehicles (UAVs) are very popular in military and commercial applications as well as academic research. Flying ad hoc network (FANET) is a new type of ad hoc network that organizes small drones in an ad hoc form. The movement in 3D space, high mobility, frequent topological changes, restricted resources, and low density are the features of these networks. These features cause serious challenges in designing an appropriate routing approach for these networks. In this paper, a greedy perimeter stateless routing method based on a position prediction mechanism called GPSR+ is proposed for FANETs. This scheme employs a position forecast strategy to approximate the future position of UAVs and modifies the propagation period of hello messages based on this strategy to achieve better adaptability to the dynamic network. In addition, GPSR+ chooses a set of candidate UAVs using a new method called the spherical removal technique to decide on the next-hop UAVs in the routing process. Finally, the most stable UAV toward the destination will be chosen from the candidate set to act as the next-hop. The simulation results show that GPSR+ grows the packet delivery ratio in FANET and extends network longevity because it increases path stability and improves energy consumption. However, the delay in GPSR+ is high. © 2023 The Author(s)

关键词： Data transmission Drone Flying ad hoc network (FANET) Reliability Routing

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Integrative computational analyses implicate regulatory genomic elements contributing to spina bifida

Genetics in Medicine Open

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Genetics in Medicine Open 2024年 2卷 101894-101894页

作者： Wolujewicz, Paul Aguiar-Pulido, Vanessa Thareja, Gaurav Suhre, Karsten Elemento, Olivier Finnell, Richard H. Ross, M. Elizabeth Center for Neurogenetics Feil Family Brain and Mind Research Institute Weill Cornell Medicine New York NY United States Department of Computer Science University of Miami Coral Gables FL United States Weill Cornell Medicine-Qatar Doha Qatar Englander Institute for Precision Medicine Weill Cornell Medicine New York NY United States Center for Precision Environmental Health Departments of Molecular and Cellular Biology Molecular and Human Genetics and Medicine Baylor College of Medicine Houston TX United States

Purpose: Spina bifida (SB) arises from complex genetic interactions that converge to interfere with neural tube closure. Understanding the precise patterns conferring SB risk requires a deep exploration of the genomic networks and molecular pathways that govern neurulation. This study aims to delineate genome-wide regulatory signatures underlying SB pathophysiology, Methods: An untargeted, genome-wide approach was used to interrogate regulatory regions for rare single-nucleotide and copy-number variants (rSNVs and rCNVs, respectively) predicted to affect gene expression, comparing results from SB patients with healthy controls. Qualifying variants were subjected to a deep learning prioritization framework to identify the most functionally relevant variants, as well as the likely target genes affected by these rare regulatory variants. Results: This ensemble of computational tools identified rSNVs in specific transcription factor binding sites (TFBSs) that distinguish SB cases from controls. rSNV enrichment was found in specific TFBSs, especially CCCTC-binding factor binding sites. These TFBSs were subjected to a deep learning prioritization framework to identify the most functionally relevant variants, as well as the likely target genes affected by these rSNVs. The functional pathways or modules implicated by these regulated genes serve protein transport, cilia assembly, and central nervous system development. Moreover, the detected rare copy-number variants in SB cases are positioned to disrupt gene regulatory networks and alter 3-dimensional genomic architectures, including brain-specific enhancers and topologically associated domain boundaries of relevant cell types. Conclusion: Our study provides a resource for identifying and interpreting genomic regulatory DNA variant contributions to human SB genetic predisposition. © 2024 The Authors

关键词： Deep learning Intergenic variants Neural tube defects Topologically associating domains (TADs) Transcription factor binding sites (TFBS)

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Machine Culture

arXiv

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arXiv 2023年

作者： Brinkmann, Levin Baumann, Fabian Bonnefon, Jean-François Derex, Maxime Müller, Thomas F. Nussberger, Anne-Marie Czaplicka, Agnieszka Acerbi, Alberto Griffiths, Thomas L. Henrich, Joseph Leibo, Joel Z. McElreath, Richard Oudeyer, Pierre-Yves Stray, Jonathan Rahwan, Iyad Max Planck Institute for Human Development Center for Humans & Machines Lentzeallee 94 Berlin14195 Germany Toulouse School of Economics Esplanade de l'Université Toulouse31080 Cedex 06 France Department of Sociology and Social Research University of Trento Italy Institute for Advanced Study in Toulouse Esplanade de l’Université Toulouse31080 Cedex 06 France Department of Psychology Department of Computer Science Princeton University PrincetonNJ08540 United States Department of Human Evolutionary Biology Harvard University 11 Divinity Ave CambridgeMA02138 United States DeepMind Technologies Ltd London United Kingdom Max Planck Institute for Evolutionary Anthropology Leipzig Germany Inria Flowers team Université de Bordeaux France Center for Human-Compatible Artificial Intelligence University of California Berkeley United States

The ability of humans to create and disseminate culture is often credited as the single most important factor of our success as a species. In this Perspective, we explore the notion of 'machine culture,' culture mediated or generated by machines. We argue that intelligent machines simultaneously transform the cultural evolutionary processes of variation, transmission, and selection. Recommender algorithms are altering social learning dynamics. Chatbots are forming a new mode of cultural transmission, serving as cultural models. Furthermore, intelligent machines are evolving as contributors in generating cultural traits–from game strategies and visual art to scientific results. We provide a conceptual framework for studying the present and anticipated future impact of machines on cultural evolution, and present a research agenda for the study of machine culture. © 2023, CC BY-NC-ND.

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Cardiovascular disease identification using a hybrid CNN-LSTM model with explainable AI

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Informatics in Medicine Unlocked 2023年 42卷

作者： Hossain, Md Maruf Ali, Md Shahin Ahmed, Md Mahfuz Rakib, Md Rakibul Hasan Kona, Moutushi Akter Afrin, Sadia Islam, Md Khairul Ahsan, Md Manjurul Raj, Sheikh Md Razibul Hasan Rahman, Md Habibur Department of Biomedical Engineering Islamic University Kushtia 7003 Bangladesh Department of Food and Nutrition Government College of Applied Human Science Azimpur Dhaka 1205 Bangladesh Department of Radiology Northwestern University Machine and Hybrid Intelligence Lab Chicago 60611 IL United States Industrial & Production Engineering Shahjalal University of Science and Technology Sylhet 3114 Bangladesh Department of Information and Communication Technology Islamic University Kushtia 7003 Bangladesh Department of Computer Science and Engineering Islamic University Kushtia 7003 Bangladesh Center for Advanced Bioinformatics and Artificial Intelligent Research Islamic University Kushtia 7003 Bangladesh

Cardiovascular disease (CVD) is a leading cause of death worldwide, with millions dying each year. The identification and early diagnosis of CVD are critical in preventing adverse health outcomes. Hence, this study proposes a hybrid deep learning (DL) model that combines a convolutional neural network (CNN) and long short-term memory (LSTM) to identify CVD from the clinical data. This study utilizes CNN to extract the relevant features from the input data and the LSTM network to process sequential data and capture dependencies and patterns over time. This study provides insights into the potential of a hybrid DL model combined with feature engineering and explainable AI to improve the accuracy and interpretability of CVD prediction. We evaluated our model on a publicly available dataset where the proposed CNN-LSTM achieved a high accuracy of 73.52% and 74.15% with and without feature engineering, respectively, in identifying individuals with CVD, which is the best result compared to the current state-of-the-art model. The results of this study demonstrate the potential of DL models for the early diagnosis of CVD. Our proposed CNN-LSTM model also incorporates explainable AI to identify the top features responsible for CVD. They could be used to develop more eﬀective screening tools in clinical practice. © 2023 The Authors

关键词： Cardiovascular disease CNN-LSTM Deep learning Explainable AI Feature engineering

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QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge

arXiv

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arXiv 2024年

作者： Li, Hongwei Bran Navarro, Fernando Ezhov, Ivan Bayat, Amirhossein Das, Dhritiman Kofler, Florian Shit, Suprosanna Waldmannstetter, Diana Paetzold, Johannes C. Hu, Xiaobin Wiestler, Benedikt Zimmer, Lucas Amiranashvili, Tamaz Prabhakar, Chinmay Berger, Christoph Weidner, Jonas Alonso-Basanta, Michelle Rashid, Arif Baid, Ujjwal Adel, Wesam Alis, Deniz Baheti, Bhakti Bai, Yingbin Bhat, Ishaan Cetindag, Sabri Can Chen, Wenting Cheng, Li Dutande, Prasad Dular, Lara Elattar, Mustafa A. Feng, Ming Gao, Shengbo Huisman, Henkjan Hu, Weifeng Innani, Shubham Ji, Wei Karimi, Davood Kuijf, Hugo J. Kwak, Jin Tae Le, Hoang Long Li, Xiang Lin, Huiyan Liu, Tongliang Ma, Jun Ma, Kai Ma, Ting Oksuz, Ilkay Holland, Robbie Oliveira, Arlindo L. Pal, Jimut Bahan Pei, Xuan Qiao, Maoying Saha, Anindo Selvan, Raghavendra Shen, Linlin Silva, Joao Lourenco Spiclin, Ziga Talbar, Sanjay Wang, Dadong Wang, Wei Wang, Xiong Wang, Yin Xi, Ruiling Xu, Kele Yang, Yanwu Yergin, Mert Yu, Shuang Zeng, Lingxi Zhang, YingLin Zhao, Jiachen Zheng, Yefeng Zukovec, Martin Do, Richard Becker, Anton Simpson, Amber Konukoglu, Ender Jakab, Andras Bakas, Spyridon Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School United States Department of Quantitative Biomedicine University of Zurich Switzerland University Children’s Hospital Zurich University of Zurich Switzerland Department of Radioncology and Radiation Theraphy Klinikum rechts der Isar Technical University of Munich Germany Department of Information Technology and Electrical Engineering ETH-Zurich Switzerland Department of Radiology Memorial Sloan Kettering Cancer Center New York City United States Department of Biomedical and Molecular Sciences Queen’s University Canada TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany McGovern Institute Massachusetts Institute of Technology United States Institute for Diagnostic and Interventional Radiology Unveristy Zurich Hospital Switzerland BioMedIA Imperial College London United Kingdom Department of Radiation Oncology University of Pennsylvania PA United States University of Pennsylvania PA United States Department of Radiation Oncology Winship Cancer Institute of Emory University Georgia United States Nile University Cairo Egypt Department of Medical Sciences Acibadem University Istanbul Turkey Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Trustworthy Machine Learning Lab University of Sydney Australia Image Sciences Institute University Medical Center Utrecht Netherlands Computer Engineering Department Istanbul Technical University Istanbul Turkey School of Computer Science Shenzhen University Shenzhen China University of Alberta United States University of Ljubljana Faculty of Electrical Engineering Ljubljana Slovenia Tongji University Shanghai China OPPO Research Institute Shanghai China School of Biological and Medical Engineering Beihang University Beijing China Harvard Medical School Boston

Uncertainty in medical image segmentation tasks, especially inter-rater variability, arising from differences in interpretations and annotations by various experts, presents a significant challenge in achieving consistent and reliable image segmentation. This variability not only reflects the inherent complexity and subjective nature of medical image interpretation but also directly impacts the development and evaluation of automated segmentation algorithms. Accurately modeling and quantifying this variability is essential for enhancing the robustness and clinical applicability of these algorithms. We report the set-up and summarize the benchmark results of the Quantification of Uncertainties in Biomedical Image Quantification Challenge (QUBIQ), which was organized in conjunction with International Conferences on Medical Image Computing and computer-Assisted Intervention (MICCAI) 2020 and 2021. The challenge focuses on the uncertainty quantification of medical image segmentation which considers the omnipresence of inter-rater variability in imaging datasets. The large collection of images with multi-rater annotations features various modalities such as MRI and CT;various organs such as the brain, prostate, kidney, and pancreas;and different image dimensions 2D-vs-3D. A total of 24 teams submitted different solutions to the problem, combining various baseline models, Bayesian neural networks, and ensemble model techniques. The obtained results indicate the importance of the ensemble models, as well as the need for further research to develop efficient 3D methods for uncertainty quantification methods in 3D segmentation tasks. © 2024, CC BY-NC-ND.

关键词： Image segmentation

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