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16th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discover, CyberC 2024

作者： Deng, Qinghua Chen, Lanxiang Zhu, Yizhao Mu, Yi City University of Macau Faculty of Data Science 999078 China Hanshan Normal University School of Computer and Information Engineering Chaozhou521041 China Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fuzhou China

ISBN: (纸本)9798331506896

Symmetric searchable encryption (SSE) is crucial for promoting cloud storage. It enables users to encrypt private data and outsource it to untrusted servers while retaining query capabilities. To prevent the cloud from obtaining users' private data, researchers have proposed numerous schemes to protect users' privacy. However, the issue of volume hiding has received less attention. Recent studies have demonstrated that volume leakage can be exploited to infer query *** mitigate the aforementioned leakage problem, we introduce an efficient volume-hiding SSE scheme supporting conjunctive queries, denoted as VHXT. The primary strategy for implementing volume hiding comprises two aspects: first, employing a bitmap-based inverted index to obscure the query response length of the least frequent keyword, and second, introducing laconic PSI for efficient membership testing. The laconic PSI enables our scheme to achieve approximately optimal overhead. Moreover, security analysis and performance evaluation demonstrate that VHXT effectively prevents volume leakage with minimal overhead. © 2024 IEEE.

关键词： information leakage

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All-fiber orbital-angular-momentum mode encoding system based on helical fiber gratings

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Photonics Research 2025年第4期13卷 960-967页

作者： YU CHEN MING GAO YUTAO WANG XINYUE HUANG HUI HAO XIAOQI NI HUALI LU HUA ZHAO PENG WANG XIN WANG HONGPU LI School of Computer and Electronic Information Nanjing Normal University Intelligent Optoelectronic Sensing Technology Innovation Center Nanjing Normal University College of Electronic Engineering Nanjing Xiaozhuang University Key Laboratory of Optoelectronic Materials and Devices Chinese Academy of Sciences Faculty of the Engineering Shizuoka University

In this study, we propose and demonstrate an all-fiber orbital-angular-momentum (OAM) mode encoding system, where through helical fiber gratings (HFGs), binary symbols are encoded to or decoded from two OAM modes with topological charges (TCs) of ?1 and +1, respectively. We experimentally validate that the OAM mode generated by a clockwise-helix HFG (cHFG) can be converted back into fundamental mode by using an HFG with a helix orientation opposite to that of the c HFG, i.e., ccHFG.

关键词： Fiber optic communications Mode conversion Optical imaging Photonic crystal fibers Spatial light modulators Spiral phase

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Secure beamforming designs for maximizing secrecy sum rate in MISO-NOMA networks

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Digital Communications and Networks 2025年第1期11卷 83-91页

作者： Yanbo Zhang Zheng Yang Jingjing Cui Xianfu Lei Yi Wu Jun Zhang Chao Fang Zhiguo Ding College of Photonic and Electronic Engineering Fujian Normal UniversityFuzhou 350007China School of Electronics and Computer Science University of SouthamptonSouthampton SO171BJUK Institute of Mobile Communications Southwest Jiaotong UniversityChengdu 610031China Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and TelecommunicationsNanjing 210003China Faculty of Information Technology Beijing University of TechnologyBeijingChina School of Electrical and Electronics Engineering The University of ManchesterManchester M139PLUK

In this paper,the application of Non-Orthogonal Multiple Access(NOMA)is investigated in a multiple-input single-output network consisting of multiple legitimate users and a potential *** support secure transmissions from legitimate users,two NOMA Secrecy Sum Rate Transmit Beam Forming(NOMA-SSR-TBF)schemes are proposed to maximise the SSR of a Base Station(BS)with sufficient and insufficient transmit *** BS with sufficient transmit power,an artificial jamming beamforming design scheme is proposed to disrupt the potential eavesdropping without impacting the legitimate *** addition,for BS with insufficient transmit power,a modified successive interference cancellation decoding sequence is used to reduce the impact of artificial jamming on legitimate *** specifically,iterative algorithm for the successive convex approximation are provided to jointly optimise the vectors of transmit beamforming and artificial *** results demonstrate that the proposed NOMA-SSR-TBF schemes outperforms the existing works,such as the maximized artificial jamming power scheme,the maximized artificial jamming power scheme with artificial jamming beamforming design and maximized secrecy sum rate scheme without artificial jamming beamforming design.

关键词： NOMA Secure transmission Artificial jamming Beamforming design

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Fuzzy Hybrid Coyote Optimization Algorithm for Image Thresholding

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computers, Materials & Continua 2022年第8期72卷 3073-3090页

作者： Linguo Li Xuwen Huang Shunqiang Qian Zhangfei Li Shujing Li Romany F.Mansour School of Computer Nanjing University of Posts and TelecommunicationsNanjing210003China School of Computer and Information Engineering Fuyang Normal UniversityFuyang236041China Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

In order to address the problems of Coyote Optimization Algorithm in image thresholding,such as easily falling into local optimum,and slow convergence speed,a Fuzzy Hybrid Coyote Optimization Algorithm(here-inafter referred to as FHCOA)based on chaotic initialization and reverse learning strategy is proposed,and its effect on image thresholding is *** chaotic initialization,the random number initialization mode in the standard coyote optimization algorithm(COA)is replaced by chaotic *** sequence is nonlinear and long-term unpredictable,these characteristics can effectively improve the diversity of the population in the optimization ***,in this paper we first perform chaotic initialization,using chaotic sequence to replace random number initialization in standard *** combining the lens imaging reverse learning strategy and the optimal worst reverse learning strategy,a hybrid reverse learning strategy is then *** the process of algorithm traversal,the best coyote and the worst coyote in the pack are selected for reverse learning operation respectively,which prevents the algorithm falling into local optimum to a certain extent and also solves the problem of premature *** on the above improvements,the coyote optimization algorithm has better global convergence and computational *** simulation results show that the algorithmhas better thresholding effect than the five commonly used optimization algorithms in image thresholding when multiple images are selected and different threshold numbers are set.

关键词： Coyote optimization algorithm image segmentation multilevel thresholding logistic chaotic map hybrid inverse learning strategy

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A Distributed Feature Selection Approach Over Hadoop for Accurate Lung Cancer Classification Based on Grasshopper Algorithm and Rough Sets

SSRN

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SSRN 2023年

作者： Song, Xu Zhou, Hongyu Teo, Brian Sheng Xian Abo-Alnadr, Ayman Hamed, Ahmed School of Computer and Information Engineering Anyang Normal University Anyang455000 China School of Software Engineering Anyang Normal University Anyang455000 China School of Graduate Studies Shah Alam40100 Malaysia Kafrelsheikh University Egypt Department of Computer Science Faculty of Computers and Information Damanhour University Damanhour22511 Egypt

In the specialized field of lung cancer research, precise feature selection has become paramount, especially given the extensive and intricate datasets available. Many of these datasets house a plethora of features, of which a substantial number may be redundant, leading to potential inaccuracies and increased computational demands. Although the Rough Set (RS) and Multigranular Rough Set (MGRS) models have demonstrated efficacy in feature selection, their computational complexities can be limiting. To address this, we introduce an innovative solution, integrating the MGRS with the Grasshopper Optimization Algorithm (GOA)—a metaheuristic technique derived from grasshopper foraging behaviors. To manage the expansive lung cancer data, we employ the Hadoop framework for streamlined distributed processing. By distributing the enhanced GOA tasks within Hadoop, we aspire to efficiently process large-scale datasets. The proposed algorithm's efficacy is assessed using dedicated lung cancer datasets, benchmarked via classifiers such as Random Forest and K-Nearest Neighbor. Preliminary results highlight the superior performance of our approach compared to prevalent metaheuristic strategies, with the MGRS model enhancing performance notably when employed as an objective function. © 2023, The Authors. All rights reserved.

关键词： Feature Selection

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Point-by-Point Semantic Segmentation of 3D Indoor Scenes with Few-Shot Based on ADMPTI

Point-by-Point Semantic Segmentation of 3D Indoor Scenes wit...

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Artificial Intelligence Technology (ACAIT), Asian Conference on

作者： Wenjing Gao Zhenxiong Xu Ya Wang Faculty of Computer and Information Engineering Fuyang Normal University Fuyang China

ISBN: (数字)9798331517090

ISBN: (纸本)9798331517106

This paper addresses the challenging task of semantic segmentation of 3D scenes, particularly in indoor environments. Many existing 3D point cloud segmentation methods face significant issues, including a reliance on large datasets, weak generalization ability, and insufficient learning of discriminative features when performing point-by-point semantic segmentation of 3D point clouds. To address these challenges, we propose ADMPTI, a novel approach that employs a few-shot learning method. Additionally, to further enhance the model's generalization capability, we introduce the DenseBaseLearner. This component enables the model to more accurately measure the similarity between samples, thereby better guiding the segmentation process. Furthermore, to more effectively capture the internal relationships within feature categories, our model incorporates various attention mechanisms. By integrating these attention mechanisms, the model can focus on critical information, thereby improving its ability to extract discriminative features. The effectiveness of our proposed method is demonstrated through significant improvements on the benchmark dataset S3DIS.

关键词： Point cloud compression Solid modeling Three-dimensional displays Attention mechanisms Semantic segmentation Feature extraction Indoor environment Data mining Few shot learning Faces

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Relating university students’ online self-regulated English learning to motivational beliefs: a structural equation modelling analysis

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Behaviour and information Technology 2023年第7期42卷 888-903页

作者： Yossatorn, Yossiri Binali, Theerapong Weng, Cathy Awuor, Nicholas O. Department of General Education Faculty of Sciences and Health Navamindradhiraj University Bangkok Thailand Department of Computer Science and Information Engineering National Taiwan Normal University Taipei Taiwan Graduate Institute of Digital Learning and Education National Taiwan University of Science and Technology Taipei Taiwan

This study investigated university students’ online self-regulated English learning (OSEL) and related it to their motivational beliefs in order to probe important psychological factors that may predict how students use self-regulated strategies to facilitate their online English learning. The data was gathered from 337 Thai university nursing students. The instruments used in this study were validated to measure motivational beliefs and OSEL in the context of Thai university students. All motivational belief factors were significantly associated with OSEL. The results of confirmatory factor analysis (CFA) indicated that the construct validity of the questionnaire items was acceptable. Furthermore, the structural modelling analysis (SEM) showed that intrinsic value and cost were significant predictors of all OSEL strategies, namely, behavioural strategies, goal setting, and environment structuring. Meanwhile, academic self-efficacy and attainment value were positively related to certain OSEL factors. Implications for enhancing students’ motivation and self-regulation in online language learning as well as directions for further investigations are discussed. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Students

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Deep Semantic-consistent Penalizing Hashing for Cross-modal Retrieval

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IEEE Transactions on Multimedia 2025年

作者： Qin, Qibing Wu, Lei Zhang, Wenfeng Huang, Lei Nie, Jie Weifang University School of Computer Engineering Weifang China Qufu Normal University School of Computer Science Rizhao China Chongqing Normal University College of Computer and Information Science Chongqing China Ocean University of China Faculty of Information Science and Engineering Qingdao China

Benefiting from the advantages of low storage cost and high retrieval efficiency, hash learning could significantly speed up large-scale cross-modal retrieval. Based on the prior annotations, most of the available cross-modal hashing usually introduces the margin-based constraint to generate different boundaries for each class in the inference phase, optimizing the model. However, these obtained label-guided penalty boundaries may differ from the primitive semantic relationships between heterogeneous modalities, impairing retrieval performance. Besides, the margin-based constraint is too weak to penalize the classes with low intra-class variances or inter-class correlations, which struggle to learn high-quality embeddings. In this paper, we propose a novel Deep Semantic-consistent Penalizing Hashing framework (DScPH) to learn the consistent penalizing fields for all classes, achieving accurate and efficient cross-modal retrieval. Specifically, by exploring unbalanced intra-class and inter-class correlations, the consistent penalizing loss is introduced into cross-modal retrieval to learn the consistency decision boundaries across classes. During training, the dice-like optimization strategy is developed to balance the pulling penalizing elements and pushing penalizing elements, facilitating the model convergence. Besides, based on the invariance of similarity measures under orthogonal transformations, the alternative quantization is proposed to minimize the errors between the learned continuous embeddings and binary discretization, maintaining the consistency of semantic relationships after performing binary projection. Extensive experiments are conducted on three benchmark datasets, and the comprehensive results validate the efficacy of our proposed DScPH framework, which outperforms the current mainstream deep cross-modal hashing algorithms. The source code of our proposed DScPH framework has been released at https://***/QinLab-WFU/DScPH. © 1999-2012 IEEE.

关键词： Embeddings

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Privacy Protection Authentication Protocol for Consumer Internet of Things in Horizontal Federated Learning Environment

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IEEE Transactions on Consumer Electronics 2025年

作者： Sun, Zhanfang Xu, Jingxiu Li, Jing Zhang, Tingliang School of Electronic Information Engineering Henan Institute of Technology Henan Xinxiang453000 China College of Computer Science and Technology Huanggang Normal University Huanggang438000 China School of Electronic Information and Electrical Engineering Anyang Institute of Technology Henan Anyang455000 China

—With the widespread application of Consumer Internet of Things (CIoT) devices, especially in the fields of smart wearable and health devices, the generation and transmission for a large amount of personal health data has raised concerns about user privacy and data security. In this context, federated learning technology provides a new approach to privacy protection by enabling model training without directly accessing raw data. However, the semi-honest CIoT devices participating in federated learning may lead to the risk of indirect privacy leakage. Therefore, this paper proposes a privacy protection authentication protocol for CIoT in horizontal federated learning environments. This protocol adopts elliptic curve cryptosystem to implement mutual authentication and data transmission between a set of CIoT devices and server, avoiding signaling overload. Then AVISPA and informal security analysis prove that the protocol is secure and can resist known attacks. The experimental results show that the protocol has increased signal overhead and computational overhead by at least 33% and 50%. And the protocol exhibits a variety of security attributes and achieves a balance of security and overhead in performance, which is suitable for horizontal federated learning training environment. © 2025 IEEE.

关键词： Authentication Protocol

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Study of Feature Fusion Models for Human Activity Recognition on Point Clouds from mmWave Radar 9

Study of Feature Fusion Models for Human Activity Recognitio...

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9th IEEE Smart World Congress, SWC 2023

作者： Kong, Lin Zhou, Houpu Zhang, Guochen Huang, Runhe Ma, Yanbo Luo, Yunlong Su, Yun Pontosense Inc. Canada Southwest Jiaotong University School of Information Science and Technology Chengdu China Northwest Normal University College of Computer Science and Engineering Gansu Lanzhou China Hosei University Faculty of Computer and Information Sciences Tokyo Japan

ISBN: (纸本)9798350319804

Human Activity Recognition (HAR) has gained significant attention as a technological response to the ever-increasing global need to monitor the health of elderly populations. This research focused on millimeter-wave (mmWave) radar technology for HAR because of its comparative advantages over competing technologies in privacy protection, superior penetration, and lack of a requirement for ambient light. This research proposes a feature fusion model for detecting activities that incorporates both voxel and Doppler information embedded in pixels. The proposed model achieves high accuracy of 95.70% for recognizing human activities, which is a significant improvement in performance over the performance using a single model, particularly in distinguishing between challenging activity categories. This paper provides an overview of the HAR system based on mmWave radar, including the design of two submodels and the composition of the feature fusion model, as well as an accuracy analysis of the proposed model through experimentation. The study also identifies several challenges that need to be addressed in future research, such as the improvement of dataset categories, the pre-processing of point cloud data, and the development of models with greater accuracy in recognizing similar activity categories. © 2023 IEEE.

关键词： 2D-CNN 3D-CNN Feature Fusion LSTM Point Cloud Voxel

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