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arXiv 2024年

作者： Li, Yicong Sun, Xiangguo Chen, Hongxu Zhang, Sixiao Yang, Yu Xu, Guandong Data Science and Machine Intelligence Lab Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW2007 Australia Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong999077 Hong Kong School of Information Technology and Electrical Engineering The University of Queensland BrisbaneQLD4072 Australia Department of Systems Engineering and Engineering Management Nanyang Technological University Singapore999077 Singapore Department of Computing The Hong Kong Polytechnic University Hong Kong999077 Hong Kong

Compared with only pursuing recommendation accuracy, the explainability of a recommendation model has drawn more attention in recent years. Many graph-based recommendations resort to informative paths with the attention mechanism for the explanation. Unfortunately, these attention weights are intentionally designed for model accuracy but not explainability. Recently, some researchers have started to question attention-based explainability because the attention weights are unstable for different reproductions, and they may not always align with human intuition. Inspired by the counterfactual reasoning from causality learning theory, we propose a novel explainable framework targeting path-based recommendations, wherein the explainable weights of paths are learned to replace attention weights. Specifically, we design two counterfactual reasoning algorithms from both path representation and path topological structure perspectives. Moreover, unlike traditional case studies, we also propose a package of explainability evaluation solutions with both qualitative and quantitative methods. We conduct extensive experiments on four real-world datasets, the results of which further demonstrate the effectiveness and reliability of our method. Copyright © 2024, The Authors. All rights reserved.

关键词： Graphic methods

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Mixture Gaussian Distribution-Based Collaborative Reinforcement Learning for 3D UAV Localization Optimization Against Jamming Attacks

Mixture Gaussian Distribution-Based Collaborative Reinforcem...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Yujiao Zhu Mingzhe Chen Sihua Wang Yuchen Liu Gaolei Li Changchuan Yin Tony Q.S. Quek Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Coral Gables FL USA Department of Computer Science North Carolina State University Raleigh NC USA School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In this paper, the optimization of unmanned aerial vehicle (UAV) localization under jamming attacks is studied. In the considered network, a base station (BS) collaborates with an active UAV to localize a target UAV. During this positioning process, a jamming UAV transmits discontinuous signals to passive UAVs to interfere the distance information measurement. To localize the target UAV under jamming attacks, the BS jointly use two localization methods: 1) generative adversarial network (GAN)-based positioning method and 2) time difference of arrival (TDOA)-based positioning method. Since GAN-based positioning method cannot defense in a strong jamming signal while TDOA-based positioning method may consume more energy and sacrifice localization accuracy, the BS must select an appropriate positioning method (GAN-based or TDOA-based methods) and four distance measurement information of passive UAVs to estimate the position of the target UAV. This problem is formulated as an optimization problem whose goal is to minimize the positioning error between the estimated and the ground truth positions of the target UAV while considering jamming attacks and the trajectory of passive UAVs. To solve this problem, we propose a mixture Gaussian distribution model-based collaborative reinforcement learning (RL) method which enables the active UAV to determine its transmit power and trajectory, and enables the BS to select the most appropriate subsets of distance measurement information and the optimal positioning method according to the movement of passive UAVs and the unknown jamming attack pattern of the jamming UAV. Simulation results show the proposed method can reduce the positioning error of the target UAV by up to 36.5% compared to the method that does not consider the GAN-based positioning method.

关键词： Location awareness Simulation Collaboration Reinforcement learning Gaussian distribution Autonomous aerial vehicles Distance measurement Trajectory Jamming Optimization

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Countering Eavesdroppers with Meta-learning-based Cooperative Ambient Backscatter Communications

arXiv

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arXiv 2023年

作者： Chu, Nam H. Van Huynh, Nguyen Nguyen, Diep N. Hoang, Dinh Thai Gong, Shimin Shu, Tao Dutkiewicz, Eryk Phan, Khoa T. School of Electrical and Data Engineering University of Technology Sydney Australia School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou China Department of Computer Science and Software Engineering Auburn University AuburnAL36849 United States School of Engineering and Mathematical Sciences Department of Computer Science and Information Technology La Trobe University Melbourne Australia

This article introduces a novel lightweight framework using ambient backscattering communications to counter eavesdroppers. In particular, our framework divides an original message into two parts. The first part, i.e., the active-transmit message, is transmitted by the transmitter using conventional RF signals. Simultaneously, the second part, i.e., the backscatter message, is transmitted by an ambient backscatter tag that backscatters upon the active signals emitted by the transmitter. Notably, the backscatter tag does not generate its own signal, making it difficult for an eavesdropper to detect the backscattered signals unless they have prior knowledge of the system. Here, we assume that without decoding/knowing the backscatter message, the eavesdropper is unable to decode the original message. Even in scenarios where the eavesdropper can capture both messages, reconstructing the original message is a complex task without understanding the intricacies of the message-splitting mechanism. A challenge in our proposed framework is to effectively decode the backscattered signals at the receiver, often accomplished using the maximum likelihood (MLK) approach. However, such a method may require a complex mathematical model together with perfect channel state information (CSI). To address this issue, we develop a novel deep meta-learning-based signal detector that can not only effectively decode the weak backscattered signals without requiring perfect CSI but also quickly adapt to a new wireless environment with very little knowledge. Simulation results show that our proposed learning approach, without requiring perfect CSI and complex mathematical model, can achieve a bit error ratio close to that of the MLK-based approach. They also clearly show the efficiency of the proposed approach in dealing with eavesdropping attacks and the lack of training data for deep learning models in practical scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Backscattering

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Automated Defect Detection in Electronic Components using Convolutional Neural Networks

Automated Defect Detection in Electronic Components using Co...

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International Conference on science Technology Engineering and Management (ICONSTEM)

作者： A. Shobanadevi Sreekanth Kottu K R Senthil Kumar K. Amudha K. Praveena R. Venkatesh Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science & Engineering Mallareddy University Hyderabad Telangana India Department of Mechanical Engineering R.M.K. Engineering College Kavaraipettai Tamil Nadu India Department of Science and Humanities-Physics R.M.D. Engineering College (An Autonomous Institution) Kavaraipettai Tamil Nadu India Department of Electronics and Communication Engineering Mohan Babu University Erstwhile SreeVidyanikethan Engineering College Andhra Pradesh India Department of Physics PSNA College of Engineering and Technology (Autonomous) Dindigul Tamil Nadu India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

This exploration paper explores the operation of convolutional neural networks(CNNs) in automating the discovery of blights in electronic factors. With the rapid-fire advancement of technology, the demand for high- quality electronic factors has surged, emphasizing the need for effective disfigurement discovery styles. using the capabilities of CNNs, this study aims to develop a robust and accurate automated system able to relate colorful types of blights to electronic factors. By training the CNN model on a different dataset of disfigurement images, the system learns to fete subtle variations and anomalies reflective of faults, thereby enhancing examination delicacy and effectiveness. The proposed approach offers promising results in streamlining disfigurement discovery processes, reducing homemade intervention, and ameliorating overall quality control in electronics manufacturing. Results indicate the effectiveness and trustability of CNNs in automating disfigurement discovery, paving the way for enhanced productivity and product trustability in electronic diligence.

关键词： Training Productivity Process control Quality control Streaming media Mathematics Manufacturing

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Hollywood’s misrepresentation of death: A comparison of overall and by-gender mortality causes in film and the real world

arXiv

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arXiv 2024年

作者： Beauregard, Calla Danforth, Christopher M. Dodds, Peter Sheridan Computational Story Lab Vermont Advanced Computing Center University of Vermont BurlingtonVT05405 United States Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science University of Vermont BurlingtonVT05405 United States Department of Mathematics & Statistics University of Vermont BurlingtonVT05405 United States Department of Computer Science University of Vermont BurlingtonVT05405 United States Santa Fe Institute 1399 Hyde Park Rd Santa FeNM87501 United States

The common phrase ‘representation matters’ asserts that media has a measurable and important impact on civic society’s perception of self and others. The representation of health in media, in particular, may reflect and perpetuate a society’s disease burden. Here, for the top 10 major causes of death in the United States, we examine how cinematic representation of overall and by-gender mortality diverges from reality. Using crowd-sourced data on film deaths from Cinemorgue Wiki, we employ natural language processing (NLP) techniques to analyze shifts in representation of deaths in movies versus the 2021 National Vital Statistic Survey (NVSS) top ten mortality causes. Overall, movies strongly overrepresent suicide and, to a lesser degree, accidents. In terms of gender, movies overrepresent men and underrepresent women for nearly every major mortality cause, including heart disease and cerebrovascular disease. The two exceptions for which women are overrepresented are suicide and accidents. We discuss the implications of under- and over-representing causes of death overall and by gender, as well as areas of future research. © 2024, CC BY-SA.

关键词： Natural language processing systems

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Body fluid biomarkers and psychosis risk in The Accelerating Medicines Partnership® Schizophrenia Program: design considerations

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Schizophrenia 2025年第1期11卷 1-11页

作者： Diana O. Perkins Jenna Barbee Rachel S. Bleggi Clark D. Jeffries Scott R. Clark Catherine Toben Rachel Upthegrove Cassandra M. J. Wannan G. Paul Amminger Christina Phassouliotis Patrick D. McGorry Barnaby Nelson Naomi R. Wray Qingqin S. Li Kim Q. Do Ines Khadimallah Paul Klauser Elaine Walker Alan Anticevic Angela R. Nunez Scott W. Woods David Cotter David Mongan Lauren M. Ellman Sharin Roth Tashrif Billah Sylvain Bouix Ofer Pasternak Martha E. Shenton Cheryl Corcoran Inge Winter-van Rossum Rene S. Kahn Monica E. Calkins Daniel H. Wolf Felecia Cerrato Alicia R. Martin Jai L. Shah John M. Kane Carrie E. Bearden Department of Psychiatry University of North Carolina at Chapel Hill Chapel Hill NC USA Rennaisance Computing Institute University of North Carolina at Chapel Hill Chapel Hill NC USA Discipline of Psychiatry University of Adelaide Adelaide SA Australia Basil Hetzel Institute Woodville SA Australia Institute for Mental Health University of Birmingham Birmingham UK Birmingham Womens and Childrens NHS Foundation Trust Birmingham UK Centre for Youth Mental Health The University of Melbourne Parkville VIC Australia Orygen Parkville VIC Australia Department of Psychiatry University of Oxford Oxford UK Institute for Molecular Biosciences University of Queensland Queensland Australia JRD Data Science Janssen Research & Development LLC Titusville NJ USA Department of Psychiatry Center for Psychiatric Neuroscience Lausanne University Hospital and University of Lausanne Lausanne Switzerland Department of Psychological Medicine Institute of Psychiatry Psychology and Neuroscience King’s College London London UK Department of Psychiatry Service of Child and Adolescent Psychiatry Lausanne University Hospital and University of Lausanne Lausanne Switzerland Departments of Psychology and Psychiatry Emory University Atlanta GA United States of America Department of Psychiatry Yale University School of Medicine New Haven CT USA Connecticut Mental Health Center New Haven CT USA Department of Psychiatry Royal College of Surgeons in Ireland Dublin Ireland School of Computing and Information Systems The University of Melbourne Parkville VIC Australia Centre for Public Health Queen’s University Belfast Belfast United Kingdom Department of Psychology and Neuroscience Temple University Philadelphia PA USA Genomics and Biomarker Research Otsuka Pharmaceutical Development & Commercialization Inc Rockville MD USA Department of Psychiatry Brigham and Women’s Hospital and Harvard Medical School Boston MA USA Department of Radiology Brigham and Women’s Hospital

Advances in proteomic assay methodologies and genomics have significantly improved our understanding of the blood proteome. Schizophrenia and psychosis risk are linked to polygenic scores for schizophrenia and other mental disorders, as well as to altered blood and saliva levels of biomarkers involved in hormonal signaling, redox balance, and chronic systemic inflammation. The Accelerating Medicines Partnership® Schizophrenia (AMP®SCZ) aims to ascertain biomarkers that both predict clinical outcomes and provide insights into the biological processes driving clinical outcomes in persons meeting CHR criteria. AMP®SCZ will follow almost 2000 CHR and 640 community study participants for two years, assessing biomarkers at baseline and two-month follow-up including the collection of blood and saliva samples. The following provides the rationale and methods for plans to utilize polygenic risk scores for schizophrenia and other disorders, salivary cortisol levels, and a discovery-based proteomic platform for plasma analyses. We also provide details about the standardized methods used to collect and store these biological samples, as well as the study participant metadata and quality control measures related to preanalytical factors that could influence the values of the biomarkers. Finally, we discuss our plans for analyzing the results of blood- and saliva-based biomarkers. Watch Dr. Perkins discuss their work and this article:https://***/1062879582?share=copy#t=0.

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Heterogeneous message passing for heterogeneous networks

arXiv

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arXiv 2023年

作者： Cantwell, George T. Kirkley, Alec Radicchi, Filippo Santa Fe Institute 1399 Hyde Park Road Santa FeNM87501 United States Institute of Data Science University of Hong Kong Hong Kong Department of Urban Planning and Design University of Hong Kong Hong Kong Urban Systems Institute University of Hong Kong Hong Kong Center for Complex Networks and Systems Research School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States

Message passing (MP) is a computational technique used to find approximate solutions to a variety of problems defined on networks. MP approximations are generally accurate in locally tree-like networks but require corrections to maintain their accuracy level in networks rich with short cycles. However, MP may already be computationally challenging on very large networks and additional costs incurred by correcting for cycles could be prohibitive. We show how the issue can be addressed. By allowing each node in the network to have its own level of approximation, one can focus on improving the accuracy of MP approaches in a targeted manner. We perform a systematic analysis of 109 real-world networks and show that our node-based MP approximation is able to increase both the accuracy and speed of traditional MP approaches. We find that, compared to conventional MP, a heterogeneous approach based on a simple heuristic is more accurate in 81% of tested networks, faster in 64% of cases, and both more accurate and faster in 49% of cases. Copyright © 2023, The Authors. All rights reserved.

关键词： Message passing

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An assessment of measuring local levels of homelessness through proxy social media signals

arXiv

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arXiv 2023年

作者： Bird, Yoshi Meke Grobe, Sarah E. Arnold, Michael V. Rogers, Sean P. Fudolig, Mikaela I. Zimmerman, Julia Witte Danforth, Christopher M. Dodds, Peter Sheridan Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science Vermont Advanced Computing Center University of Vermont BurlingtonVT05405 United States University of Vermont BurlingtonVT05405 United States Department of Mathematics and Statistics University of Vermont BurlingtonVT05405 United States Department of Computer Science University of Vermont BurlingtonVT05405 United States Santa Fe Institute 1399 Hyde Park Rd Santa FeNM87501 United States

Although nearly 600,000 people experience homelessness in the United States every year, efforts to address this public health crisis are limited by the underperformance of standard methods to estimate localized and nationwide homelessness. Recent studies suggest social media activity can function as a proxy for measures of state-level public health, detectable through straightforward applications of natural language processing. We present results of our efforts to apply this approach to estimate homelessness at the state level throughout the US during the period 2010-2019 and 2022 using a dataset of roughly 1 million geotagged tweets containing the substring "homeless." Correlations between homelessness-related tweet counts and ranked per capita homelessness volume, but not general-population densities, suggest a relationship between the likelihood of Twitter users to personally encounter or observe homelessness in their everyday lives and their likelihood to communicate about it online. An increase to the log-odds of the word "homeless" appearing in an English-language tweet, as well as an acceleration in the increase in average tweet sentiment, suggest that tweets about homelessness are also affected by trends at the nation-scale. Additionally, changes to the lexical content of tweets over time suggest that reversals to the polarity of national or state-level trends may be detectable through an increase in political or service-sector language over the semantics of charity or direct appeals. Although tweet sentiment does not correlate to changes in homelessness volume, an analysis of user account type undertaken to explain nationwide sentiment dynamics revealed changes to Twitter-use patterns by accounts authored by individuals versus entities that may provide an additional signal to confirm changes to homelessness density in a given jurisdiction. While a computational approach to social media analysis may provide a low-cost, real-time dataset rich with information

关键词： Public health

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Quantifying knowledge synchronisation in the 21st century

arXiv

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arXiv 2022年

作者： Yoon, Jisung Park, Jinseo Yun, Jinhyuk Jung, Woo-Sung Department Of Industrial And Management Engineering Pohang University Of Science And Technology Pohang37673 Korea Republic of Center For Complex Networks And Systems Research Luddy School Of Informatics Computing And Engineering Indiana University BloomingtonIN47408 United States Center For Global R&D Data Analysis Korea Institute Of Science And Technology Information Seoul02456 Korea Republic of School Of AI Convergence Soongsil University Seoul06978 Korea Republic of Department Of Physics Pohang University Of Science And Technology Pohang37673 Korea Republic of

Humans acquire and accumulate knowledge through language usage and eagerly exchange their knowledge for advancement. Although geographical barriers had previously limited communication, the emergence of information technology has opened new avenues for knowledge exchange. However, it is unclear which communication pathway is dominant in the 21st century. Here, we explore the dominant path of knowledge diffusion in the 21st century using Wikipedia, the largest communal dataset. We evaluate the similarity of shared knowledge between population groups, distinguished based on their language usage. When population groups are more engaged with each other, their knowledge structure is more similar, where engagement is indicated by socioeconomic connections, such as cultural, linguistic, and historical features. Moreover, geographical proximity is no longer a critical requirement for knowledge dissemination. Furthermore, we integrate our data into a mechanistic model to better understand the underlying mechanism and suggest that the knowledge "Silk Road" of the 21st century is based online. © 2022, CC BY.

关键词： Knowledge management

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Hyperbolic feature augmentation via distribution estimation and infinite sampling on manifolds 22

Hyperbolic feature augmentation via distribution estimation ...

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Proceedings of the 36th International Conference on Neural Information Processing systems

作者： Zhi Gao Yuwei Wu Yunde Jia Mehrtash Harandi Beijing Lab of Intellegent Information Technology School of Computer Science Beijing Institute of Technology China Beijing Lab of Intellegent Information Technology School of Computer Science Beijing Institute of Technology China and Guangdong Lab of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China Guangdong Lab of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China and Beijing Lab of Intellegent Information Technology School of Computer Science Beijing Institute of Technology China Department of Electrical and Computer Systems Eng. Monash University and Data61 Australia

ISBN: (纸本)9781713871088

Learning in hyperbolic spaces has attracted growing attention recently, owing to their capabilities in capturing hierarchical structures of data. However, existing learning algorithms in the hyperbolic space tend to overfit when limited data is given. In this paper, we propose a hyperbolic feature augmentation method that generates diverse and discriminative features in the hyperbolic space to combat overfitting. We employ a wrapped hyperbolic normal distribution to model augmented features, and use a neural ordinary differential equation module that benefits from meta-learning to estimate the distribution. This is to reduce the bias of estimation caused by the scarcity of data. We also derive an upper bound of the augmentation loss, which enables us to train a hyperbolic model by using an infinite number of augmentations. Experiments on few-shot learning and continual learning tasks show that our method significantly improves the performance of hyperbolic algorithms in scarce data regimes.

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