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A Comprehensive Survey on RIS-Enhanced Physical Layer Security in UAV-Assisted Networks

IEEE Internet of Things Journal 2025年

作者： Ahmed, Manzoor Soofi, Aized Amin Raza, Salman Li, Yongxiao Khan, Feroz Khan, Wali Ullah Asif, Muhammad Han, Zhu School of Computer and Information Science China Hubei Engineering University Institute for AI Industrial Technology Research Xiaogan City432000 China National University of Modern Languages Department of Computer Science Faisalabad38000 Pakistan National Textile University Department of Computer Science Faisalabad38000 Pakistan Beijing University of Posts and Telecommunications School of Electronic Engineering Beijing China Gachon University School of Computing Seongnam-si13120 Korea Republic of School of Computer Science Department of Electrical and Computer Engineering United States University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

This survey provides an in-depth examination of the role of reconfigurable intelligent surfaces (RIS) in enhancing physical layer security (PLS) within unmanned aerial vehicle (UAV)-assisted networks, which are essential for the secure and efficient operation of sixth-generation (6G) wireless communications. The study covers various types of RIS—passive, active, and hybrid—and their applications in both terrestrial and aerial environments to strengthen PLS. Key focus areas include advanced PLS techniques such as optimizing UAV trajectory, beamforming, and RIS phase-shift configurations, all aimed at improving secrecy rates (SRs) while mitigating the risks of eavesdropping and jamming. Moreover, the survey also addresses strategies for enhancing energy-efficient SRs and implementing anti-jamming mechanisms within UAV-assisted networks. Additionally, it explores the integration of RIS-UAV systems with emerging technologies such as non-orthogonal multiple access (NOMA), mobile edge computing (MEC), cognitive radio, and THz networks, demonstrating how security can be enhanced in such networks. Through detailed performance analysis, the paper highlights the transformative potential of RIS-equipped UAVs in overcoming the potential security challenges for future 6G networks. Finally, the survey presents lessons learned and identifies critical future research directions and open challenges, offering insights that will guide the development of robust and secure RIS-assisted UAV systems in next-generation wireless networks. © 2014 IEEE.

关键词： Drones

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A neural network component for knowledge-based semantic representations of text 12

A neural network component for knowledge-based semantic repr...

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12th International Conference on Recent Advances in Natural Language Processing, RANLP 2019

作者： Piad-Morffis, Alejandro Muñoz, Rafael Almeida-Cruz, Yudivián Gutiérrez, Yoan Estevez-Velarde, Suilan Montoyo, Andrés School of Math and Computer Science University of Havana Cuba Department of Languages and Computing Systems University of Alicante Spain University of Alicante Spain

ISBN: (纸本)9789544520557

This paper presents Semantic Neural Networks (SNNs), a knowledge-aware component based on deep learning. SNNs can be trained to encode explicit semantic knowledge from an arbitrary knowledge base, and can subsequently be combined with other deep learning architectures. At prediction time, SNNs provide a semantic encoding extracted from the input data, which can be exploited by other neural network components to build extended representation models that can face alternative problems. The SNN architecture is defined in terms of the concepts and relations present in a knowledge base. Based on this architecture, a training procedure is developed. Finally, an experimental setup is presented to illustrate the behaviour and performance of a SNN for a specific NLP problem, in this case, opinion mining for the classification of movie reviews. © 2019 Association for Computational Linguistics (ACL). All rights reserved.

关键词： Semantics

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Demo application for LETO: Learning engine through ontologies 12

Demo application for LETO: Learning engine through ontologie...

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12th International Conference on Recent Advances in Natural Language Processing, RANLP 2019

作者： Estevez-Velarde, Suilan Montoyo, Andrés Almeida-Cruz, Yudivián Gutiérrez, Yoan Piad-Morffis, Alejandro Muñoz, Rafael School of Math and Computer Science University of Havana Cuba Department of Languages and Computing Systems University of Alicante Spain University of Alicante Spain

ISBN: (纸本)9789544520557

The massive amount of multi-formatted information available on the Web necessitates the design of software systems that leverage this information to obtain knowledge that is valid and useful. The main challenge is to discover relevant information and continuously update, enrich and integrate knowledge from various sources of structured and unstructured data. This paper presents the Learning Engine Through Ontologies (LETO) framework, an architecture for the continuous and incremental discovery of knowledge from multiple sources of unstructured and structured data. We justify the main design decision behind LETO's architecture and evaluate the framework's feasibility using the Internet Movie Data Base (IMDB) and Twitter as a practical application. © 2019 Association for Computational Linguistics (ACL). All rights reserved.

关键词： Ontology

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Overview of the eHealth knowledge discovery challenge at IberLEF 2019

Overview of the eHealth knowledge discovery challenge at Ibe...

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2019 Iberian languages Evaluation Forum, IberLEF 2019

作者： Piad-Morffis, Alejandro Gutiérrez, Yoan Consuegra-Ayala, Juan Pablo Estevez-Velarde, Suilan Almeida-Cruz, Yudivián Muñoz, Rafael Montoyo, Andrés School of Math and Computer Science University of Havana Cuba Department of Languages and Computing Systems University of Alicante Spain University of Alicante Spain

The eHealth Knowledge Discovery Challenge, hosted at IberLEF 2019, proposes an evaluation task for the automatic identification of key phrases and the semantic relations between them in health-related documents in Spanish language. This paper describes the challenge design, evaluation metrics, participants and main results. The most promising approaches are analyzed and the significant challenges are highlighted and discussed. Analysis of the participant systems shows an overall trend of sequence-based deep learning architectures coupled with domain-specific or domain-agnostic unsupervised language representations. Successful approaches suggest that modeling the problem as an end-to-end learning task rather than separated in two subtasks improves performance. Interesting lines for future development were recognized, such as the option of increasing the corpus size with semi-automated approaches and designing more robust evaluation metrics. © 2019 for this paper by its authors.

关键词： Knowledge based systems

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Enforcing morphological information in fully convolutional networks to improve cell instance segmentation in fluorescence microscopy images

arXiv

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arXiv 2021年

作者： Zamora-Cárdenas, Willard Mendez, Mauro Calderon-Ramirez, Saul Vargas, Martin Monge, Gerardo Quiros, Steve Elizondo, David Torrents-Barrena, Jordina Molina-Cabello, Miguel A. Computing School Costa Rica Institute of Technology Costa Rica Department of Computer Technology De Montfort University United Kingdom Tropical Diseases Research Center Microbiology Faculty. University of Costa Rica Costa Rica Department of Computer Engineering and Mathematics Rovira i Virgili University Spain Department of Computer Languages and Computer Science University of Malaga Spain Instituto de Investigación Biomédica de Málaga – IBIMA Spain

Cell instance segmentation in fluorescence microscopy images is becoming essential for cancer dynamics and prognosis. Data extracted from cancer dynamics allows to understand and accurately model different metabolic processes such as proliferation. This enables customized and more precise cancer treatments. However, accurate cell instance segmentation, necessary for further cell tracking and behavior analysis, is still challenging in scenarios with high cell concentration and overlapping edges. Within this framework, we propose a novel cell instance segmentation approach based on the well-known U-Net architecture. To enforce the learning of morphological information per pixel, a deep distance transformer (DDT) acts as a back-bone model. The DDT output is subsequently used to train a top-model. The following top-models are considered: a three-class (e.g., foreground, background and cell border) U-net, and a watershed transform. The obtained results suggest a performance boost over traditional U-Net architectures. This opens an interesting research line around the idea of injecting morphological information into a fully convolutional model. © 2021, CC BY.

关键词： Convolutional neural networks

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Choosing between Long and Short Word Forms in Chinese 12

Choosing between Long and Short Word Forms in Chinese

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12th International Conference on Natural Language Generation, INLG 2019

作者： Li, Lin van Deemter, Kees Paperno, Denis Fan, Jinyu Zewangkuanzhuo Department of Information and Computing Sciences Utrecht University Netherlands Department of Computer Science Qinghai Normal University China CMLI Research Center Minzu University of China China Department of Computing Science University of Aberdeen United Kingdom Department of Languages Literature and Communication Utrecht University Netherlands Department of Physics and Information Qinghai Normal University China

ISBN: (纸本)9781950737949

Between 80% and 90% of all Chinese words have long and short form such as / (lao-hu/hu, tiger) (Duanmu, 2013). Consequently, the choice between long and short forms is a key problem for lexical choice across NLP and NLG in Chinese. Following on from earlier work on abbreviations in English (Mahowald et al., 2013), we bring a probabilistic perspective to word length choice, using both a behavioural and a corpus-based approach. Thus, we hypothesise that, in Chinese, short forms are likelier in supportive than in neutral contexts. Our corpus and behavioral study supported this hypothesis, but a closer analysis revealed striking differences between different types of Chinese words. © 2019 Association for Computational Linguistics

关键词： Computational linguistics

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Human-centered artificial intelligence in education: Seeing the invisible through the visible

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computers and Education: Artificial Intelligence 2021年 2卷

作者： Yang, Stephen J.H. Ogata, Hiroaki Matsui, Tatsunori Chen, Nian-Shing Department of Computer Science and Information Engineering National Central University No. 300 Zhongda Rd. Zhongli District Taoyuan City32001 Taiwan Academic Center for Computing and Media Studies Kyoto University Yoshidahonmachi Sakyo Ward Kyoto606-8501 Japan School of Human Sciences Waseda University 1-104 Totsukamachi Shinjuku-ku Tokyo169-8050 Japan Department of Applied Foreign Languages National Yunlin University of Science and Technology 123 University Road Section 3 Douliou CityYunlin64002 Taiwan

The inevitable rise and development of artificial intelligence (AI) was not a sudden occurrence. The greater the effect that AI has on humans, the more pressing the need is for us to understand it. This paper addresses research on the use of AI to evaluate new design methods and tools that can be leveraged to advance AI research, education, policy, and practice to improve the human condition. AI has the potential to educate, train, and improve the performance of humans, making them better at their tasks and activities. The use of AI can enhance human welfare in numerous respects, such as through improving the productivity of food, health, water, education, and energy services. However, the misuse of AI due to algorithm bias and a lack of governance could inhibit human rights and result in employment, gender, and racial inequality. We envision that AI can evolve into human-centered AI (HAI), which refers to approaching AI from a human perspective by considering human conditions and contexts. Most current discussions on AI technology focus on how AI can enable human performance. However, we explore AI can also inhibit the human condition and advocate for an in-depth dialog between technology- and humanity-based researchers to improve understanding of HAI from various perspectives. © 2021 The Author(s)

关键词： Artificial intelligence

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Innovation, Research and Development in Engineering Education. Activities of the Spanish Chapter of the IEEE Education Society: Directive Board of the IEEE Education Society Spanish Chapter 2020-2021

Innovation, Research and Development in Engineering Educatio...

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Technologies Applied to Electronics Teaching (TAEE)

作者： Ó scar Martí nez Bonastre Cristina Ferná ndez Lluí s Vicent Francisco Mur Manuel Castro Gabriel Diaz Jose Carpio Elio Sancristobal Sergio Martin Jose Sá nchez Francisco Javier Arcega Solsona Juan Suardiaz Muro Manuel Caeiro-Rodrí guez Martí n Llamas Inmaculada Plaza Baltasar Ferná ndez Manjó n Pedro Muñ oz-Merino Edmundo Tovar Telmo Zarraonandia Ayo Javier Garcia Zubia José Antonio Delgado Pení n Department of Statistics Mathematics and Computing Science Operations Research Centre Universidad Miguel Hern&#x00E1 ndez Elche Spain Department of Electronic Technology Universidad Carlos III de Madrid Madrid Spain UPF Barcelona School of Management Barcelona Spain Department of Electrical Electronic and Control Engineering UNED Madrid Spain Department of Electrical Engineering Universidad de Zaragoza Zaragoza Spain Department of Automatics Electrical Engineering and Electronic Technology Technical University of Cartagena Cartagena Spain atlanTTic Research Center University of Vigo Vigo Spain Department of Electronics and Communication Universidad de Zaragoza Zaragoza Spain Department of Software Engineering and Artificial Intelligence Complutense University of Madrid Madrid Spain Department of Telematic Engineering Universidad Carlos III de Madrid Madrid Spain Department of Computer Languages and Systems and Software Engineering Universidad Polit&#x00E9 cnica de Madrid Madrid Spain Department of Computer Science and Engineering Universidad Carlos III de Madrid Madrid Spain Department of Electronic Engineering Universidad de Deusto Bilbao Spain Universitat Polit&#x00E8 cnica de Catalunya Barcelona Spain

ISBN: (数字)9781665421614

ISBN: (纸本)9781665421621

The Technical Committee of the CESEI (Spanish Chapter of the IEEE Education Society) is focused on the Innovation, Research and Development (I+R+D) of the education in engineering, mainly Electric and computer Engineering. As in other interdisciplinary fields, the I+R+D involves several sources of information and different stakeholders, each one with its own focus and purpose. As a result, sometimes it may be complex for practitioners and researchers to identify the more appropriate entities for their interests. This paper shows how the CESEI Technical Committee tries to solve this difficulty by providing updated information regarding the main publications (journals), events (conferences) and awards to the best academic works. The paper focuses special attention on the activities held during 2020–2021.

关键词： Technological innovation Collaboration Stakeholders Engineering education Research and development

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Author Correction: Spatial communication systems across languages reflect universal action constraints

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Nature human behaviour 2024年第1期8卷 181页

作者： Kenny R Coventry Harmen B Gudde Holger Diessel Jacqueline Collier Pedro Guijarro-Fuentes Mila Vulchanova Valentin Vulchanov Emanuela Todisco Maria Reile Merlijn Breunesse Helen Plado Juergen Bohnemeyer Raed Bsili Michela Caldano Rositsa Dekova Katharine Donelson Diana Forker Yesol Park Lekhnath Sharma Pathak David Peeters Gabriella Pizzuto Baris Serhan Linda Apse Florian Hesse Linh Hoang Phuong Hoang Yoko Igari Keerthana Kapiley Tamar Haupt-Khutsishvili Sara Kolding Katri Priiki Ieva Mačiukaitytė Vaisnavi Mohite Tiina Nahkola Sum Yi Tsoi Stefan Williams Shunei Yasuda Angelo Cangelosi Jon Andoni Duñabeitia Ramesh Kumar Mishra Roberta Rocca Jurģis Šķilters Mikkel Wallentin Eglė Žilinskaitė-Šinkūnienė Ozlem Durmaz Incel School of Psychology University of East Anglia Norwich UK. k.coventry@uea.ac.uk. School of Psychology University of East Anglia Norwich UK. Helmholtz Institute Department of Experimental Psychology Utrecht University Utrecht the Netherlands. Hanse-Wissenschaftskolleg Delmenhorst Germany. Department of English Friedrich-Schiller-Universität Jena Jena Germany. Department of Spanish Modern and Classic Philology University of the Balearic Islands Palma Spain. Language Acquisition and Language Processing Lab Department of Language and Literature Norwegian University of Science and Technology Trondheim Norway. Department of Spanish Language Linguistics and Literature Theory University of Seville Seville Spain. Institute of Estonian and General Linguistics University of Tartu Tartu Estonia. Centre for the Arts in Society Leiden University Leiden the Netherlands. Võro Institute Võru Estonia. Department of Linguistics University at Buffalo Buffalo NY USA. Danieli Telerobot Srl Genoa Italy. Istituto Italiano di Tecnologia (IIT) Genoa Italy. Paisii Hilendarski University of Plovdiv Plovdiv Bulgaria. Department of English University of Nevada Reno NV USA. Department of Slavonic Languages and Caucasus Studies Friedrich-Schiller-Universität Jena Jena Germany. Cognitive Science Department of Humanities Social and Political Sciences ETH Zürich Zürich Switzerland. Cognitive Science and Psycholinguistics Lab Central Department of Linguistics Tribhuvan University Kathmandu Nepal. Centre for Neural and Cognitive Sciences School of Medical Sciences University of Hyderabad Hyderabad India. Department of Communication and Cognition TiCC Tilburg University Tilburg the Netherlands. Max Planck Institute for Psycholinguistics Nijmegen the Netherlands. Department of Chemistry University of Liverpool Liverpool UK. Department of Computer Science University of Manchester Manchester UK. Laboratory for Perceptual and Cognitive Systems Faculty of Computing University o

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IEEE Transactions on Evolutionary Computation: Special Issue on Theoretical Foundations of Evolutionary Computation

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IEEE Transactions on Evolutionary Computation 2018年第4期22卷 632-632页

作者： Oliveto, Pietro S. Auger, Anne Chicano, Francisco Fonseca, Carlos M. Department of Computer Science University of Sheffield United Kingdom INRIA Ecole Polytechnique Paris France Department of Languages and Computing Sciences University of Malaga Spain Department of Informatics Engineering University of Coimbra Portugal

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