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arXiv 2023年

作者： Alharbi, Raed Chan-Olmsted, Sylvia Chen, Huan Thai, My T. Computer and Information Science and Engineering Department University of Florida GainesvilleFL32611 United States Department of Media Production Management and Technology University of Florida GainesvilleFL32611 United States Department of Advertising University of Florida GainesvilleFL32611 United States

Understanding the COVID-19 vaccine hesitancy, such as who and why, is very crucial since a large-scale vaccine adoption remains as one of the most efficient methods of controlling the pandemic. Such an understanding also provides insights into designing successful vaccination campaigns for future pandemics. Unfortunately, there are many factors involving in deciding whether to take the vaccine, especially from the cultural point of view. To obtain these goals, we design a novel culture-aware machine learning (ML) model, based on our new data collection, for predicting vaccination willingness. We further analyze the most important features which contribute to the ML model's predictions using advanced AI explainers such as the Probabilistic Graphical Model (PGM) and Shapley Additive Explanations (SHAP). These analyses reveal the key factors that most likely impact the vaccine adoption decisions. Our findings show that Hispanic and African American are most likely impacted by cultural characteristics such as religions and ethnic affiliation, whereas the vaccine trust and approval influence the Asian communities the most. Our results also show that cultural characteristics, rumors, and political affiliation are associated with increased vaccine rejection. Copyright © 2023, The Authors. All rights reserved.

关键词： COVID-19

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From Human Hand to Grasp Surface Detection, Tracking & Analysis

From Human Hand to Grasp Surface Detection, Tracking & Analy...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Diego Hidalgo-Carvajal Abdeldjallil Naceri Sami Haddadin Technical University of Munich Germany Department of Computer Engineering TUM School of Computation Information and Technology Chair of Robotics and Systems Intelligence (RSI) Munich Institute of Robotics and Machine Intelligence (MIRMI) Centre for Tactile Internet With Human-in-the-Loop (CeTI)

Hands are paramount for dexterous interactions that humans exhibit in daily life. Understanding the intricacies of human hand-object interactions is therefore necessary. Unfortunately, the limitations of state-of-the-art technologies make capturing the full hand-object complexity unfeasible, giving rise to the need for new technological means to achieve this aim. In this work, we propose an end-to-end framework in which individualized hand models are derived and used to capture quantitative personalized hand-object interaction information, precisely, hand shape, kinematics, and contact surfaces. The results of this study serve as a proof of concept that such a framework can significantly deepen personalized hand-object interaction analyses, providing, in perspective, insights for medical diagnoses and rehabilitation, among *** relevance— Our work showcases the need to incorporate bespoke human hand models in individualized hand function assessment technologies, as hand-object interaction information is subject-dependent.

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A system capable of verifiably and privately screening global DNA synthesis

arXiv

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arXiv 2024年

作者： Baum, Carsten Berlips, Jens Chen, Walther Cui, Hongrui Damgard, Ivan Dong, Jiangbin Esvelt, Kevin M. Foner, Leonard Gao, Mingyu Gretton, Dana Kysel, Martin Li, Juanru Li, Xiang Paneth, Omer Rivest, Ronald L. Sage-Ling, Francesca Shamir, Adi Shen, Yue Sun, Meicen Vaikuntanathan, Vinod Van Hauwe, Lynn Vogel, Theia Weinstein-Raun, Benjamin Wang, Yun Wichs, Daniel Wooster, Stephen Yao, Andrew C. Yu, Yu Zhang, Haoling Zhang, Kaiyi Department of Computer Science Aarhus University Denmark DTU Compute Technical University of Denmark Denmark SecureDNA Foundation Switzerland Department of Computer Science and Engineering Shanghai Jiao Tong University China Institute for Interdisciplinary Information Sciences Tsinghua University China Media Lab Massachusetts Institute of Technology United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States Department of Applied Mathematics Weizmann Institute of Science Israel Department of Computer Science Northeastern University United States China National GeneBank China Department of Political Science Massachusetts Institute of Technology United States Shanghai Qi Zhi Institute China

Printing custom DNA sequences is essential to scientific and biomedical research, but the technology can be used to manufacture plagues as well as cures. Just as ink printers recognize and reject attempts to counterfeit money, DNA synthesizers and assemblers should deny unauthorized requests to make viral DNA that could be used to ignite a pandemic. There are three complications. First, we don't need to quickly update printers to deal with newly discovered currencies, whereas we regularly learn of new viruses and other biological threats. Second, anti-counterfeiting specifications on a local printer can't be extracted and misused by malicious actors, unlike information on biological threats. Finally, any screening must keep the inspected DNA sequences private, as they may constitute valuable trade secrets. Here we describe SecureDNA, a free, privacy-preserving, and fully automated system capable of verifiably screening all DNA synthesis orders of 30+ base pairs against an up-to-date database of hazards, and its operational performance and specificity when applied to 67 million base pairs of DNA synthesized by providers in the United States, Europe, and China. Copyright © 2024, The Authors. All rights reserved.

关键词： DNA sequences

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Domain-Invariant Feature Learning for Domain Adaptation

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International Journal of Pattern Recognition and Artificial Intelligence 2023年第3期37卷

作者： Tu, Ching-Ting Lin, Hsiau-Wen Lin, Hwei Jen Tokuyama, Yoshimasa Chu, Chia-Hung Department of Applied Mathematics National Chung Hsing University Taichung401 Taiwan Department of Information Management Chihlee University of Technology Banqiao Dist. New Taipei City220305 Taiwan Department of Computer Science and Information Engineering Tamkang University Tamsui Dist. New Taipei City251 Taiwan Department of Media and Image Technology Faculty of Engineering Tokyo Polytechnic University Tokyo164-8678 Japan

Unsupervised domain adaptation (UDA) mainly explores how to learn domain-invariant features from the source domain when the target domain label is unknown. To learn domain-invariant features requires aligning the distribution of samples from two domains in the feature space, which can be achieved by minimizing the maximum mean discrepancy (MMD) of samples from the two domains. However, there is still no effective way to find the best parameter values of MMD. Such a problem is addressed in the MMD with deep kernels (MMD-D), whose optimal parameters can be obtained through training. This study proposes a method of domain-invariant feature learning for UDA, whose architecture, named MMDDCDA, comprises an MMD-D module and a cross domain adaptation (CDA) module. MMDDCDA performs alternating training similar to adversarial training to alternately boost the power of the two modules. To our knowledge, this is the first UDA method that performs such alternating training on a UDA architecture using MMD with deep kernels. Experimental validation showed that the proposed method yields state-of-the-art results among UDA methods using other MMD variants and some UDA benchmarks. © 2023 World Scientific Publishing Company.

关键词： Generative adversarial networks

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Unveiling the Post-Covid Economic Impact Using NLP Techniques 15

Unveiling the Post-Covid Economic Impact Using NLP Technique...

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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Barhanpurkar, Kanishk Mandlik, Nikita Rajawat, Anand Singh Goyal, S.B. Mihaltan, Traian Candin Verma, Chaman Raboaca, Maria Simona Thomas J. Watson College of Engineering and Applied Science Binghamton University United States School of Computer Science & Engineering Sandip University Nashik India City University Faculty of Information Technology Petaling Jaya Malaysia Technical University of Cluj-Napoca Faculty of Building Services Cluj-Napoca40033 Romania Eötvös Loránd University Faculty of Informatics Department of Media and Educational Informatics Budapest1053 Hungary Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department National Ramnicu Valcea Romania

ISBN: (纸本)9798350321388

The research paper presents a novel analysis of textual data based on Natural Language Processing (NLP) techniques to analyse New York Times articles from January 2019 to May 2023. The purpose of this paper is to gain an understanding of the economic impact that follows Covid-19 disease. New York Times (NYT), it was started in 1884 is one of the most prominent newspapers in the world. Additionally, we have used the New York Times Archive API to collect the data for the given timeframe. By analysing sentiment analysis, topic modelling, entity recognition, and keyword extraction, valuable insights can be gathered into market trends, industry shifts, and policy interventions. The authors have created a data science pipeline using Amazon Web Services (AWS), which enables data collection, storage, and visualization. It contributes to a better understanding of the pandemic's short-term and long-term economic effects. The results of this study demonstrate Natural Language Processing techniques' potential as a tool for financial analytics, assisting policymakers, economists, and businesses in formulating recovery strategies. © 2023 IEEE.

关键词： Economic and social effects

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STAR-RIS Enhanced Covert Communication with Delay Constraint

STAR-RIS Enhanced Covert Communication with Delay Constraint

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IEEE International Conference on Communications in China (ICCC)

作者： Beixi Cheng Jinsong Hu Youjia Chen Feng Shu Zhizhang David Chen College of Physics and Information Engineering Fuzhou University Fuzhou Fujian Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information Fuzhou China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing China School of Information and Communication Engineering Hainan University Haikou China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

This work confirms the performance gain achieved by deploying a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) in covert communication. To maximize the system covert rate, we jointly optimize the power allocation coefficients at the transmitter and the transmission and reflection coefficients at the STAR-RIS, subject to the delay-constrained and power-constrained. Furthermore, we propose a two-stage alternating optimization algorithm based on the semidefinite relaxation (SDR) method to derive the optimal solutions to the above non-convex optimization problem. The effectiveness and superiority of the proposed algorithm and STAR-RIS-assisted covert scheme are demonstrated by the numerical results.

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Can A Society of Generative Agents Simulate Human Behavior and Inform Public Health Policy? — A Case Study on Vaccine Hesitancy

arXiv

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arXiv 2025年

作者： Hou, Abe Bohan Du, Hongru Wang, Yichen Zhang, Jingyu Wang, Zixiao Liang, Paul Pu Khashabi, Daniel Gardner, Lauren He, Tianxing Department of Computer Science Johns Hopkins University United States Civil & Systems Engineering Johns Hopkins University United States Department of Computer Science University of Chicago United States Department of Epidemiology Harvard University United States MIT Media Lab Department of EECS Massachusetts Institute of Technology United States Institute of Interdisciplinary Information Sciences Tsinghua University China Shanghai Qi Zhi Institute China

Can we simulate a sandbox society with generative agents to model human behavior, thereby reducing the over-reliance on real human trials for assessing public policies? In this work, we investigate the feasibility of simulating health-related decision-making, using vaccine hesitancy, defined as the delay in acceptance or refusal of vaccines despite the availability of vaccination services (MacDonald, 2015), as a case study. To this end, we introduce the VACSIM1 framework with 100 generative agents powered by Large Language Models (LLMs). VACSIM simulates vaccine policy outcomes with the following steps: 1) instantiate a population of agents with demographics based on census data;2) connect the agents via a social network and model vaccine attitudes as a function of social dynamics and disease-related information;3) design and evaluate various public health interventions aimed at mitigating vaccine hesitancy. To align with real-world results, we also introduce simulation warmup and attitude modulation to adjust agents’ attitudes. We propose a series of evaluations to assess the reliability of various LLM simulations. Experiments indicate that models like Llama and Qwen can simulate aspects of human behavior but also highlight real-world alignment challenges, such as inconsistent responses with demographic profiles. This early exploration of LLM-driven simulations is not meant to serve as definitive policy guidance;instead, it serves as a call for action to examine LLM-based social simulation for policy development. Copyright © 2025, The Authors. All rights reserved.

关键词： Population dynamics

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Using Free Space Optics for Beamsteering of 5G Metasurface Antenna

Using Free Space Optics for Beamsteering of 5G Metasurface A...

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IEEE Photonics Conference (IPC)

作者： A. Bilal A. Quddious M. A. Antoniades A. Kanno P.T. Dat K. Inagaki T. Kawanishi S. Iezekiel Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus EMPHASIS Research Centre University of Cyprus Nicosia Cyprus Chair for RF and Photonics Engineering Technische Universität Dresden Germany Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto Canada National Institute of Information and Communications Technology Tokyo Japan Graduate School of Engineering Nagoya Institute of Technology Aichi Japan Faculty of Science and Engineering Waseda University Tokyo Japan

Beamsteering of a sub-6 GHz 5G metasurface antenna is achieved via free space optical control. A scanning angle of 66 ° is demonstrated at 3.25 GHz. The antenna consists of varactor-based phase shifters, whose vo...

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Fixed-Length Arbitrary-Phase Tunable NRI-TL Metamaterial Phase Shifter for Antenna Beam-Steering Applications

Fixed-Length Arbitrary-Phase Tunable NRI-TL Metamaterial Pha...

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European Conference on Antennas and Propagation, EuCAP

作者： Asif Bilal Abdul Quddious Atsushi Kanno Tetsuya Kawanishi Marco A. Antoniades Stavros Iezekiel EMPHASIS Research Centre University of Cyprus Nicosia Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Chair for RF and Photonics Engineering Technische Universitat Dresden Dresden Germany National Institute of Information and Communications Technology Tokyo Japan Japan Graduate School of Engineering Nagoya Institute of Technology Aichi Japan School of Fundamental Science and Engineering Waseda University Tokyo Japan Department of Electrical Computer & Biomedical Engineering Ryerson University Toronto ON Canada

A fixed-length arbitrary-phase tunable phase shifter, based on the concept of negative-refractive-index transmission-line (NRI-TL) metamaterials for sub-6 GHz antenna beam steering applications is proposed. The design consists of a fixed microstrip transmission line which is reconfigured by loading varactor diodes in both the series and shunt branches. A continuously tunable phase of 210° at 3.2 GHz for the transmitted signal is achieved at the output by varying the biasing voltage from 0 – 7 V for the varactor diodes. The insertion loss varies from 0.9 – 2.5 dB for the design in the frequency range of 3.2 – 3.6 GHz. The total length of the tunable NRI-TL metamaterial phase shifter is λg/4.

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Artificial Intelligence's Effects on Corporate Decision-Making Processes

Artificial Intelligence's Effects on Corporate Decision-Maki...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Neeraj Kumari Kalpana Gangwar Vibhor Gaur K. Ranjith Singh S. Arul Antran Vijay Jayashree Tamkhade CCSIT Teerthankar Mahaveer University Moradabad India Department of Computer Applications Invertis University Bareilly India School of Media Film & Television Studies IIMT University Meerut India Department of Computer Science Karpagam Academy of Higher Education Coimbatore India Department of Computer Science Engineering Karpagam College of Engineering Coimbatore India E & TC Department Vishwakarma Institute of Information Technology Pune India

With the introduction of artificial intellectual ability life choice machine learning, how has decision-changed? The predictability of the judgement state space, communication resources of the life choice procedure and result, size of the unconventional set, life choice rate, and representativeness are five key plans in place factors in this blog entry as the peculiarities of human and Machine learning strategic planning. The paper develops a novel paradigm describing why both modalities of judgement may be merged to best benefit the level of company judgement call upon an examination of individual and Digital taking decisions or something along those parameters. Three major categories-full human to AI outsourcing, mixed mortal and Cognitive technologies sequence strategic planning, and merged decision-making between humans and AI- are presented by the structure as ways in which party members' choices can be coupled with AI-based judgments.

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