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Secure 3D Directional Modulation Using Subarrays Based on Planar Frequency Diverse Array with Nonuniform Frequency Offsets

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IEEE Transactions on Communications 2024年第6期73卷 4017-4032页

作者： You, Byungha Lee, In-Ho Jung, Haejoon Duong, Trung Q. Shin, Hyundong Kyung Hee University Department Of Electronics And Information Convergence Engineering Yongin-si17104 Korea Republic of Hankyong National University School Of Electronic And Electrical Engineering Anseong17579 Korea Republic of Memorial University Faculty Of Engineering And Applied Science St. John'sNLA1C 5S7 Canada Queen's University Belfast School Of Electronics Electrical Engineering And Computer Science BelfastBT7 1NN United Kingdom Kyung Hee University Department Of Electronic Engineering Yongin17104 Korea Republic of

Physical-layer security (PLS) is a new paradigm for secure communication without requiring secret key exchange and management. Moreover, PLS with frequency diverse subarray (FDSA) can better control information leakage in the angle-range domain, which mitigates the security weakness of the phased array caused by its lack of range resolution. In this paper, we propose a three-dimensional (3D) directional modulation (DM) using randomized radiation with FDSA for enhanced PLS, employing a planar array. In addition, nonuniform frequency offsets (FOs) are considered as FO configurations (FOCs) for FDSA to concentrate on the mainlobe and suppress the undesired sidelobes in 3D space, where logarithmically increasing FOC (L-FOC), Hamming window-based FOC (H-FOC), and piecewise trigonometric FOC (P-FOC) are introduced. Characterizing the process of selecting the random subsets for randomized radiation, we provide the exact analysis of the secrecy rate of the proposed scheme. Moreover, FOs applied to FDSA and the number of random subsets are optimized with a genetic algorithm (GA)-based optimization strategy. We evaluate the proposed schemes in terms of secrecy rate and vulnerable volume, where the simulation results verify our analysis and show that nonuniform FOCs are a more favorable choice for FDSA compared to uniform FOC (U-FOC). © 1972-2012 IEEE.

关键词： Array signal processing Three-dimensional displays Security Frequency modulation Vectors Frequency diversity Symbols Antenna radiation patterns Wireless communication Phased arrays

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Detection of Toxic Content on Social Networking Platforms Using Fine Tuned ULMFiT Model

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Intelligent Automation & Soft Computing 2023年第1期35卷 15-30页

作者： Hafsa Naveed Abid Sohail Jasni Mohamad Zain Noman Saleem Rao Faizan Ali Shahid Anwar Department of Software Engineering Faculty of ScienceUniversity of LahorePakistan Department of Computer Science COMSATS University IslamabadLahore CampusPakistan Institute for Big Data Analyics and Artifcial Intelligence(IBDAAI) Kompleks Al-KhawarizmiUniversii Tekoogi MARA40450Shah AlamSelangorMalaysia TechnoGenics SMC PVT LTD LahorePakistan Department of Computer and Infomation Science Universiti Teknologi PETRONASBandar Ser IskandarTronohPerakMalaysia Depatment of Information Engineering Technology National Skils University IslamabadSector H-8/1Faiz AhmedFaiz RoadIslamabadPakistan

Question and answer websites such as Quora,Stack Overflow,Yahoo Answers and Answer Bag are used by *** users post questions on these websites to get the answers from domain specific *** websites are multilingual meaning they are available in many different *** problem for these types of websites is to handle meaningless and irrelevant *** this paper we have worked on the Quora insincere questions(questions which are based on false assumptions or questions which are trying to make a statement rather than seeking for helpful answers)dataset in order to identify user insincere questions,so that Quora can eliminate those questions from their platform and ultimately improve the communication among users over the ***,a research was carried out with recurrent neural network and pretrained glove word embeddings,that achieved the F1 score of *** proposed study has used a pre-trained ULMFiT *** model has outperformed the previous model with an F1 score of 0.91,which is much higher than the previous studies.

关键词： Machine learning text mining quora mining artificial intelligence natural language processing

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Cross-lingual ontology matching with CIDER-LM: Results for OAEI 2022 17

Cross-lingual ontology matching with CIDER-LM: Results for O...

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17th International Workshop on Ontology Matching, OM 2022

作者： Vela, Javier Gracia, Jorge Department of Computer Science and Systems Engineering University of Zaragoza María de Luna 1 Zaragoza50018 Spain

In this paper, the CIDER-LM cross-lingual matching system is presented, as well as the results it achieved during the OAEI (Ontology Alignment Evaluation Initiative) 2022 campaign. This is the first appearance of CIDER-LM in OAEI where it only participated in MultiFarm, the track for cross-lingual ontology alignment evaluation. The matching system uses a pre-trained multilingual language model based on transformers, fine-tuned using the openly available portion of the MultiFarm dataset. The model calculates the vector embeddings of the labels associated to every ontology entity and its context. The confidence degree between matching entities is computed as the cosine similarity between their associated embeddings. CIDER-LM is novel in the use of multilingual language models for cross-lingual ontology matching. Its initial version obtained promising results in the OAEI'22 MultiFarm track, attaining a modest precision but the best overall performance in recall. © 2022 Copyright for this paper by its authors.

关键词： Embeddings

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A Reconfigurable Actuation Unit for Modular Cable-Driven Robots 16th

A Reconfigurable Actuation Unit for Modular Cable-Driven Rob...

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16th International Federation of Theory of Machines and Mechanisms World Congress, IFToMM WC 2023

作者： Chaparro-Rico, Betsy Dayana Marcela Russo, Matteo Cafolla, Daniele Independent Researcher Swansea United Kingdom Department of Industrial Engineering University of Rome Tor Vergata Via del Politecnico 1 Rome00133 Italy Faculty of Science and Engineering Department of Computer Science Swansea University Swansea United Kingdom

ISBN: (纸本)9783031457692

To address most of the cable-driven robot challenges, this work aims to create a fully modular, portable, customizable, adaptable, collaborative, and reconfigurable cable-driven robot. An actuation unit composed of a reconfigurable SP (Spherical-Parallel) serial architecture that manipulates a cable is the core of the proposed design. The proposed actuation unit can be connected in series with others and can collaborate in parallel with other units to control a load/end-effector position using multiple cables. Actuation units collaborating between them in parallel configuration can be connected to different reference frames such as chairs, tables, stretchers, and other shaped frames. The proposed modular design for cable-driven robots is ideal for performing tasks where scenarios and user requirements change, and the robot-obstacle and the robot-load/end-effector interaction are variable such as rehabilitation applications. The conceptual design of the proposed actuation unit is described in this paper. Simulations showed the feasibility of the proposed design. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cables

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High Efficiency Image Correction for Object Detection Improvement in Low-Power Underwater Drones

High Efficiency Image Correction for Object Detection Improv...

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2023 MTS/IEEE U.S. Gulf Coast, OCEANS 2023

作者： Shindo, Kazuto Katayama, Takafumi Song, Tian Shimamoto, Takashi Tokushima University Department of Science and Engineering Minami-Josanjima 2-1 Tokushima City770-8506 Japan Institute of Technology and Science Graduate School of Engineering Tokushima University Computer Systems Engineering Minami-Josanjima 2-1 Tokushima City770-8506 Japan

ISBN: (纸本)9798218142186

In this paper, we propose a highly efficient underwater image correction method that leverages structural similarity evaluation along with real-time performance from a traditional FUnIEGAN algorithm. The proposed approach is designed for implementation on an embedded GPU module (Jetson Xavier NX), ensuring its practicality and versatility. Furthermore, our method incorporates algorithmic enhancements to significantly enhance color correction performance. To optimize computational resources, we employ automatic mixed precision (AMP), effectively reducing redundancy and enabling rapid processing. As a result, our method demonstrates improved object detection outcomes, achieving an impressive increase in frames per second (FPS) from approximately 55 FPS to about 77 FPS. © 2023 The Marine Technology Society (MTS).

关键词： Object detection

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The effect of resampling techniques on the performances of machine learning clinical risk prediction models in the setting of severe class imbalance: development and internal validation in a retrospective cohort

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Discover Artificial Intelligence 2024年第1期4卷 1-17页

作者： Ke, Janny Xue Chen DhakshinaMurthy, Arunachalam George, Ronald B. Branco, Paula Department of Anesthesia St. Paul’s Hospital Providence Health Care 1081 Burrard Street VancouverBCV6Z1Y6 Canada Department of Anesthesiology Pharmacology and Therapeutics University of British Columbia VancouverBC Canada Department of Anesthesia Pain Management & ampPerioperative Medicine Dalhousie University HalifaxNS Canada School of Computer Science Carleton University OttawaON Canada Mount Sinai Hospital University of Toronto TorontoON Canada School of Electrical Engineering and Computer Science University of Ottawa OttawaON Canada

Purpose: The availability of population datasets and machine learning techniques heralded a new era of sophisticated prediction models involving a large number of routinely collected variables. However, severe class imbalance in clinical datasets is a major challenge. The aim of this study is to investigate the impact of commonly-used resampling techniques in combination with commonly-used machine learning algorithms in a clinical dataset, to determine whether combination(s) of these approaches improve upon the original multivariable logistic regression with no resampling. Methods: We previously developed and internally validated a multivariable logistic regression 30-day mortality prediction model in 30,619 patients using preoperative and intraoperative features. Using the same dataset, we systematically evaluated and compared model performances after application of resampling techniques [random under-sampling, near miss under-sampling, random oversampling, and synthetic minority oversampling (SMOTE)] in combination with machine learning algorithms (logistic regression, elastic net, decision trees, random forest, and extreme gradient boosting). Results: We found that in the setting of severe class imbalance, the impact of resampling techniques on model performance varied by the machine learning algorithm and the evaluation metric. Existing resampling techniques did not meaningfully improve area under receiving operating curve (AUROC). The area under the precision recall curve (AUPRC) was only increased by random under-sampling and SMOTE for decision trees, and oversampling and SMOTE for extreme gradient boosting. Importantly, some combinations of algorithm and resampling technique decreased AUROC and AUPRC compared to no resampling. Conclusion: Existing resampling techniques had a variable impact on models, depending on the algorithms and the evaluation metrics. Future research is needed to improve predictive performances in the setting of severe class imbalance. ©

关键词： Adversarial machine learning

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Empowering Real-Time Insights Through LLM, LangChain, and SAP HANA Integration 6th

Empowering Real-Time Insights Through LLM, LangChain, and SA...

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6th International Conference on Recent Innovations in Computing, ICRIC 2023

作者： Easin Arafat, Md. Asuah, Georgina Saha, Sourav Orosz, Tamas Data Science and Engineering Department Faculty of Informatics Eötvös Loránd University Pázmány Péter Str. 1/A Budapest1117 Hungary Department of Computer Science and Engineering United International University United City Madani Avenue Dhaka1212 Bangladesh

ISBN: (纸本)9789819734412

Real-time integration within the SAP HANA (High-Performance Analytics Appliance) environment through open-source components signifies a substantial financial investment in information technology (IT) projects. The prevalence of budget overruns, delays in implementation, and imprecise forecasts remain significant factors contributing to project failures. This has led many companies to adopt the usage of real-time insights in planning and decision-making facilities. By keeping the fundamental components of SAP, businesses can upgrade their information systems without having to completely reconfigure them, which proves to be cost-effective. This study initiates an exploration of the intricate integration of large language model (LLM), LangChain, and SAP HANA, underlining the intrinsic value of real-time insights and their multi-faceted applications. The study's main objective is to provide individuals with a road map for utilizing these essential components, customized to their enterprise's specific phase of development. This study highlights how important this integration is and provides a strong push in the direction of operational effectiveness and long-term expansion. The study equally outlines the challenges and considerations of such components in operational effectiveness and long-term expansion. The integrated framework offers the following key benefits, linguistic precision, seamless language-technology integration, and robust analytics infrastructure. As the integrated framework develops, it becomes a revolutionary instrument that changes how real-time insights and decision-making are conducted across a variety of industries. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Budget control

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Self-Learning Based Dependable Offloading Optimization in Semi-Trusted Vehicular Edge Computing and Networks

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8141-8157页

作者： Li, Xuehan Jing, Tao Li, Ruinian Wang, Xiaoxuan Yan, Yu Fan, Xin Huo, Yan Yu, Fei Richard Beijing Jiaotong University School of Electronic and Information Engineering National Engineering Research Center of Advanced Network Technologies Beijing100044 China Bowling Green State University Department of CS Bowling GreenOH43403 United States Beijing Forestry University School of Information Science and Technology Beijing100083 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

As vehicular edge computing and networks (VECONs) emerge, Internet of vehicles (IoVs) is gaining significant advantages. To provide users with dependable services, VECONs need to offer efficient and secure task offloading services. Unfortunately, little effort has been put into addressing security issues such as the risk of privacy exposure when offloading data. Modeling and optimizing offloading dependability are affected by these issues. To address these concerns, a dependable three-layer offloading framework involving blockchain technology is presented in this paper, with fully homomorphic encryption (FHE) algorithm incorporated in order to mitigate privacy exposure risks. The framework divides tasks reasonably and introduces a novel measure of offloading dependability called cost from energy consumption, delay, and privacy exposure risk (cEDP) for constructing dependable offloading optimization. Additionally, a self-learning based dependable privacy preservation offloading (SDPPO) algorithm is presented that is capable of solving the optimal offloading problem in a decentralized manner. Simulation results show that the SDPPO algorithm outperforms its counterparts in terms of faster convergence speed, better cEDP convergence results, and improved scalability and tunability characteristics. © 1967-2012 IEEE.

关键词： Differential privacy

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From Missing Pieces to Masterpieces: Image Completion With Context-Adaptive Diffusion

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 6073-6087页

作者： Shamsolmoali, Pourya Zareapoor, Masoumeh Zhou, Huiyu Felsberg, Michael Tao, Dacheng Li, Xuelong University of York Department of Computer Science YorkYO10 5DD United Kingdom East China Normal University SCEE Shanghai200050 China Shanghai Jiao Tong University Department of Electrical Engineering Shanghai200240 China University of Leicester School of Computing and Mathematical Sciences LeicesterLE1 7RH United Kingdom Linköping University Computer Vision Laboratory Linkoping581 83 Sweden Nanyang Technological University College of Computing and Data Science 639798 Singapore of China Telecom Beijing100033 China

Image completion is a challenging task, particularly when ensuring that generated content seamlessly integrates with existing parts of an image. While recent diffusion models have shown promise, they often struggle with maintaining coherence between known and unknown (missing) regions. This issue arises from the lack of explicit spatial and semantic alignment during the diffusion process, resulting in content that does not smoothly integrate with the original image. Additionally, diffusion models typically rely on global learned distributions rather than localized features, leading to inconsistencies between the generated and existing image parts. In this work, we propose ConFill, a novel framework that introduces a Context-Adaptive Discrepancy (CAD) model to ensure that intermediate distributions of known and unknown regions are closely aligned throughout the diffusion process. By incorporating CAD, our model progressively reduces discrepancies between generated and original images at each diffusion step, leading to contextually aligned completion. Moreover, ConFill uses a new Dynamic Sampling mechanism that adaptively increases the sampling rate in regions with high reconstruction complexity. This approach enables precise adjustments, enhancing detail and integration in restored areas. Extensive experiments demonstrate that ConFill outperforms current methods, setting a new benchmark in image completion. © 1979-2012 IEEE.

关键词： computer aided design

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Expedited Block Transmission in Blockchain Network by using Clusters

Expedited Block Transmission in Blockchain Network by using ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Hua, Xiaoqi Chen, Jian Zhang, Peiyun Fu, Zhangjie Zhu, Haibin Lu, Kezhong Ren, Jigang School of Software Nanjing University of Information Science and Technology Nanjing210044 China Engineering Research Center of Digital Forensics of Ministry of Education The School of Computer Science Nanjing University of Information Science and Technology Nanjing210044 China School of Computer Science Nanjing University of Information Science and Technology Nanjing210044 China Nipissing University Department of Computer Science and Mathematics North BayONP1B 8L7 Canada School of Big Data and Artificial Intelligence Chizhou University Chizhou247000 China

ISBN: (纸本)9781665410205

Blockchain technology has garnered increasing attention from researchers. Because blockchain systems may contain malicious or spatially limited nodes that may delay block verification and reduce block transmission rate, this work proposes a block transmission model by designing and using special clusters. This work proposes the cluster formation and selection mechanisms. Nodes are grouped into clusters in a blockchain, and clusters with high fitness values are chosen to transmit blocks by calculating their trust values and block transmission rates. The proposed method is compared with the peers: Layer-Chain, BlockP2P-EP and RNS. According to experimental findings, the proposed method is superior to its peers regarding the time needed for block synchronization and transmission, block occupation storage ratio, transaction throughput, and block transmission success ratio. © 2024 IEEE.

关键词： Blockchain

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