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Semantic-Aware Pseudo-Labeling for Unsupervised Meta-Learning

IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5475-5488页

作者： Ouyang, Tianran Dong, Xingping Ye, Mang Du, Bo Shao, Ling Shen, Jianbing Wuhan University School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan430072 China University of Chinese Academy of Sciences UCAS-Terminus AI Lab Beijing101408 China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science 314100 China

In unsupervised meta-learning, the clustering-based pseudo-labeling approach is an attractive framework, since it is model-agnostic, allowing it to synergize with supervised algorithms to learn from unlabeled data. However, the pseudo-labels suffer from clustering noise and semantic chaos problems, further impacting the effectiveness of meta-learning. In this paper, we analyze and optimize the pseudo-labeling process, including encoding and clustering, aiming to generate semantic-like pseudo-labels to narrow the gap between unsupervised and supervised meta-learning. First, during the encoding, we observe that the embedding space of existing methods lacks clustering-friendly properties, which is the primary reason for clustering noise. To address this issue, we minimize the inter-to-intra-class similarity ratio to generate clustering-friendly embedding features and validate our approach through comprehensive experiments. Then, during the clustering, we find that the semantic quality of pseudo-labels is not adequately controlled, resulting in semantic chaos of pseudo-labels. We propose a semantic-stability index to measure the semantic quality of pseudo-labels quantitatively. Based on this index, we propose the Semantic-aware Pseudo-label Reassignment mechanism to generate semantic-like pseudo-labels for all samples. Our approach is model-agnostic and can easily be integrated into existing supervised methods. To demonstrate its generalization ability, we integrate it into two representative algorithms: MAML and EP. The results on three main few-shot benchmarks clearly show that the proposed method achieves significant improvement compared to state-of-the-art models. Notably, our approach also outperforms the corresponding supervised method in three tasks. © 1979-2012 IEEE.

关键词： Latent semantic analysis

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A Prediction Method of Consumer Buying Behavior Based on Attention Mechanism and CNN-BiLSTM

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第2期7卷 375-385页

作者： Wang, Jian-Nan Cui, Jian-Feng Chen, Chin-Ling College of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China College of Software Engineering Xiamen University of Technology Xiamen361024 China School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China School of Information Engineering Changchun Sci-Tech University Changchun130600 China Department of Computer Science and Information Engineering Chaoyang University of Technology Taichung41349 Taiwan

A prediction method of consumer buying behavior based on attention mechanism and CNN-BiLSTM is proposed, to fully extract the effective features of the behavior and make its prediction more accurate and stable. In this paper, the CNN model is used to automatically extract local features of the consumer buying behavior to select and optimize important information. The BiLSTM model is used to collect sequence patterns of buying behavior to find the correlation among features. The attention mechanism is used to assign different weights to the state values of the hidden layers of each neural network so that the hidden layers can better express important information to obtain more accurate and stable predictions. The experiment results demonstrate that the proposed prediction method has advantages in not only accuracy but stability over other models. © 2022, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Forecasting

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Secure Energy Efficiency for ARIS Networks With Deep Learning: Active Beamforming and Position Optimization

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IEEE Transactions on Wireless Communications 2025年第6期24卷 5282-5296页

作者： Wang, Dawei Wang, Zijun Zhao, Hongbo Zhou, Fuhui Alfarraj, Osama Yang, Weichao Mumtaz, Shahid Leung, Victor C. M. Northwestern Polytechnical University School of Electronics and Information Shaanxi Xi’an710072 China Beihang University School of Electronic and Information Engineering Beijing100191 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing210000 China King Saud University Community College Computer Science Department Riyadh11437 Saudi Arabia Nottingham Trent University Department of Computer Science NottinghamNG1 4FQ United Kingdom Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen528060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Incorporating an active reconfigurable intelligent surface on an autonomous aerial vehicles (AAVs), denoted as an aerial reconfigurable intelligent surface (ARIS), introduces a novel dimension for secure transmissions. Given the constraint of limited battery capacity in AAVs, energy management emerges as a key challenge within AAV networks. In response, we propose a secure energy efficiency (SEE) transmission scheme for ARIS networks, where active ARIS is strategically deployed to enhance information security. In addition, a SEE optimal problem is formulated by considering the imperfect wiretap channel state information to optimize the active beamforming vector and the ARIS position. For this non-convex problem, we first reformulate the fractional SEE objective into an equivalent form and subsequently decompose it into two distinct subproblems: optimizing the AAV’s position and designing the active beamforming. For the AAV’s position optimization, we propose a sophisticated deep deterministic policy gradient algorithm that enables the AAV to autonomously determine the optimal ARIS position through a self-learning strategy. Regarding beamforming design, we transform this aspect into a quadratic constrained quadratic programming problem and design an alternating direction multiplier method to optimize the reflection coefficient. Subsequently, an alternating optimization algorithm is proposed to synergistically solve these subproblems. Empirical simulations validate our proposed scheme, indicating an improvement in SEE of up to 47.2%. This significant improvement underscores the efficacy of the proposed ARIS-assisted secure transmission scheme in enhancing both security and energy efficiency in AAV networks. © 2002-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Human Gait Recognition Algorithm Based on MobileNetV1 with Attention Mechanism 3

Human Gait Recognition Algorithm Based on MobileNetV1 with A...

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3rd International Conference on Artificial Intelligence and computer engineering, ICAICE 2022

作者： Zhang, Jinsha Zhang, Xuedong School of Computer and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China

ISBN: (纸本)9781510663473

For embedded modern equipment, the current gait recognition algorithm model is difficult to deploy on it due to a large amount of gait frame image data, slow network processing speed, complex structure and low computational efficiency. In this paper, a lightweight convolutional network model integrating the attention mechanism is proposed. The algorithm first performs morphological processing on the image, extracts the gait contour image, and calculates the gait energy image;Integrates the attention mechanism with MobileNetV1;The feature information of the image is effectively extracted, and the parameters of the network are reduced. A number of body method validation experiments are conducted in the CAISIAB gait database of the Chinese Academy of sciences, and the experimental results are significantly improved with other deep learning models. © 2023 SPIE.

关键词： Computational efficiency

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Can end-user feedback in social media be trusted for software evolution: Exploring and analyzing fake reviews

Can end-user feedback in social media be trusted for softwar...

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作者： Khan, Javed Ali Ullah, Tahir Khan, Arif Ali Yasin, Affan Akbar, Muhammad Azeem Aurangzeb, Khursheed Department of Computer Science Faculty of Physics Engineering and Computer Science University of Hertfordshire Hatfield United Kingdom School of Computer & Technology Beijing Institute of Technology Beijing China M3S Empirical Software Engineering Research Unit University of Oulu Oulu Finland School of Software Northwestern Polytechnical University Shaanxi Xian China Software Engineering Department Lappeenranta-Lahti University of Technology Lappeenranta Finland Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

End-user feedback in social media platforms, particularly in the app stores, is increasing exponentially with each passing day. software researchers and vendors started to mine end-user feedback by proposing text analytics methods and tools to extract useful information for software evolution and maintenance. In addition, research shows that positive feedback and high-star app ratings attract more users and increase downloads. However, it emerged in the fake review market, where software vendors started incorporating fake reviews against their corresponding applications to improve overall software ratings. For this purpose, we conducted an exploratory study to understand how end-users register and write fake reviews in the Google Play Store. We curated a research data set containing 68,000 end-user comments from the Google Play Store and a fake review generator, that is, the Testimonial generator (TG). Its purpose is to understand fake reviews on these platforms and identify the common patterns potential end-users and professionals use to report fake reviews by critically analyzing the end-user feedback. We conducted a detailed survey at the University of science and Technology Bannu, Pakistan, to identify the intelligence and accuracy of crowd-users in manually identifying fake reviews. In addition, we developed a ground truth to be compared with the results obtained from the automated machine and deep learning (M&DL) classifier experiment. In the survey, 512 end-users participated and recorded their responses in identifying fake reviews. Finally, various M&DL classifiers are employed to classify and identify end-user reviews into real and fake to automate the process. Unlike humans, the M&DL classifiers performed well in automatically classifying reviews into real and fake by obtaining much higher accuracy, precision, recall, and f-measures. The accuracy of manually identifying fake reviews by the crowd-users is 44.4%. In contrast, the M&DL classifiers obtained an

关键词： Application programs

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Design of Provably Secure Authentication Protocol for Edge-Centric Maritime Transportation System

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IEEE Transactions on Intelligent Transportation Systems 2023年第12期24卷 14536-14545页

作者： Mahmood, Khalid Shamshad, Salman Ayub, Muhammad Faizan Ghaffar, Zahid Khan, Muhammad Khurram Das, Ashok Kumar National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu610054 China National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan King Saud University Center of Excellence in Information Assurance Riyadh11653 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

The epidemic growth of the Internet of Things (IoT) objects have revolutionized Maritime Transportation Systems (MTS). Though, it becomes challenging for the centralized cloud-centric framework to fulfil the application requirements such as low latency and power utilization. The introduction of the distributed edge-centric framework has recently helped the IoT-enabled MTS to meet these requirements by manipulating the tasks at the edge of the networks. Despite the fact that MTS leverages mobile subscribers by overcoming inherent cloud computing limitations, data security and user privacy requirements in establishing the MTS setup are still non-trivial challenges. In this article, we develop a key agreement solution for mobile users to realize mutual authentication in a single round. Our protocol offers user anonymity to maintain user privacy, and it can prevent physical attacks by physically unclonable functions. Initially, the security analysis is conferred to substantiate our protocol's security persistence or strength. Later, its performance correlation is observed under the assumption of diverse metrics in a predefined empirical setup. The meticulous performance correlation endorses the precedence of our protocol over specified related protocols. © 2000-2011 IEEE.

关键词： Hash functions

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Region-adaptive Concept Aggregation for Few-shot Visual Recognition

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Machine Intelligence Research 2023年第4期20卷 554-568页

作者： Mengya Han Yibing Zhan Baosheng Yu Yong Luo Han Hu Bo Du Yonggang Wen Dacheng Tao School of Computer Science National Engineering Research Center for Multimedia SoftwareWuhan UniversityWuhan430072China Hubei Luojia Laboratory Wuhan430072China School of Computer Science The University of SydneySydney2006Australia JD Explore Academy Beijing101116China School of Information and Electronics Beijing Institute of TechnologyBeijing100081China School of Computer Science and Engineering Nanyang Technological UniversitySingapore639798Singapore

Few-shot learning (FSL) aims to learn novel concepts from very limited examples. However, most FSL methods suffer from the issue of lacking robustness in concept learning. Specifically, existing FSL methods usually ignore the diversity of region contents that may contain concept-irrelevant information such as the background, which would introduce bias/noise and degrade the performance of conceptual representation learning. To address the above-mentioned issue, we propose a novel metric-based FSL method termed region-adaptive concept aggregation network or RCA-Net. Specifically, we devise a region-adaptive concept aggregator (RCA) to model the relationships of different regions and capture the conceptual information in different regions, which are then integrated in a weighted average manner to obtain the conceptual representation. Consequently, robust concept learning can be achieved by focusing more on the concept-relevant information and less on the conceptual-irrelevant information. We perform extensive experiments on three popular visual recognition benchmarks to demonstrate the superiority of RCA-Net for robust few-shot learning. In particular, on the Caltech-UCSD Birds-200-2011 (CUB200) dataset, the proposed RCA-Net significantly improves 1-shot accuracy from 74.76% to 78.03% and 5-shot accuracy from 86.84% to 89.83% compared with the most competitive counterpart.

关键词： Few-shot learning metric-based meta learning concept learning region-adaptive concept-aggregation

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Proposed intelligence systems based on digital Forensics: Review paper

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Materials Today: Proceedings 2023年 80卷 2647-2651页

作者： Ismael Sahib, Hussein Qahatan AlSudani, Mustafa Hasan Ali, Mohammed Qassim Abbas, Haydar Moorthy, Kohbalan Mundher Adnan, Myasar Faculty of Computer Science and Information Technology UniversitiTun Hussein Onn Malaysia Computer Techniques Engineering Department Faculty of Information Technology Imam Ja'afar Al-sadiq University Najaf Iraq Faculty of Computer Systems & Software Engineering Universiti Malaysia Pahang Kuantan Malaysia Islamic University Najaf Iraq Faculty of Engineering School of Computing Universiti Teknologi Malaysia 81310 Johor Bahru Malaysia

The field of information security, in general, has seen shifts a traditional approach to an intelligence system. Moreover, an increasing of researchers to focus on propose intelligence systems and framework based on the forensic case studies because of the limitations of traditional methods such as analysis intensive data manually, intelligence visualization to make the evidence more understandable and intelligence system for store data. However, most of these intelligence systems are still facing different limitations. Furthermore, the primary goal of this work analysis popular intelligence system that was used based on forensic. Moreover, propose new algorithms and hybrid model which it's achieved good results in dif-ferent other fields to develop the forensic systems in the future. © 2021

关键词： Digital forensics

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Blockchain Assisted Intrusion Detection System Using Differential Flower Pollination Model

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computers, Materials & Continua 2022年第12期73卷 4695-4711页

作者： Mohammed Altaf Ahmed Sara A Althubiti Dronamraju Nageswara Rao ELaxmi Lydia Woong Cho Gyanendra Prasad Joshi Sung Won Kim Department of Computer Engineering College of Computer Engineering&SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Science and Engineering Chitkara University Institute of Engineering and TechnologyChitkara UniversityPunjab140401India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

Cyberattacks are developing gradually sophisticated,requiring effective intrusion detection systems(IDSs)for monitoring computer resources and creating reports on anomalous or suspicious *** the popularity of Internet of Things(IoT)technology,the security of IoT networks is developing a vital *** of the huge number and varied kinds of IoT devices,it can be challenging task for protecting the IoT framework utilizing a typical *** typical IDSs have their restrictions once executed to IoT networks because of resource constraints and ***,this paper presents a new Blockchain Assisted Intrusion Detection System using Differential Flower Pollination with Deep Learning(BAIDS-DFPDL)model in IoT *** presented BAIDS-DFPDLmodelmainly focuses on the identification and classification of intrusions in the IoT *** accomplish this,the presented BAIDS-DFPDL model follows blockchain(BC)technology for effective and secure data transmission among the ***,the presented BAIDSDFPDLmodel designs Differential Flower Pollination based feature selection(DFPFS)technique to elect ***,sailfish optimization(SFO)with Restricted Boltzmann Machine(RBM)model is applied for effectual recognition of *** simulation results on benchmark dataset exhibit the enhanced performance of the BAIDS-DFPDL model over other models on the recognition of intrusions.

关键词： Internet of things feature selection intrusion detection blockchain security deep learning

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Remote Sensing Based Advance Image Captioning Improved Feature Attention 7

Remote Sensing Based Advance Image Captioning Improved Featu...

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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Sattar, Ammara Assam, Muhammad Alahmadi, Tahani Jaser Bhatti, Uzair Aslam Tang, Hao Aamir, Muhammad Harbin Engineering University Harbin China University of Science and Technology Department of Software Engineering KPK Bannu Pakistan College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia School of Information and Communication Engineering Hainan University Haikou China College of Computer Science Huanggang Normal University Huanggang China

ISBN: (纸本)9798350350890

In the domain of image description generation, the focal point mechanism is crucial for highlighting the most relevant features within an image, thereby influencing the quality of generated descriptions. To address this challenge, we introduce an innovative optimization module called the Enhanced Focal (EF) mechanism, which builds upon the traditional multi-head focal point mechanism. The EF module is designed to assess the correlation levels between objects within an image, thereby guiding the process of subtitle generation more effectively. The EF mechanism integrates the output of the multi-head focal point mechanism with the current context (i.e., the query) to generate two components: an 'information vector' and an 'attention gate.' The 'attention gate' is then applied to the 'information vector' to create a refined focal point mechanism. This refined mechanism undergoes element-wise multiplication with the 'information vector,' producing attention information that reveals a strong correlation between the target and the background in the image. This process leverages the Long Short-Term Memory (LSTM) mechanism to enhance the model's capability to capture intricate relationships. Upon training and evaluating our enhanced model on the MS COCO dataset, we observed significant improvements in key evaluation metrics. The BLEU-1 and METEOR scores reached 73.01% and 28.81%, respectively, showcasing the effectiveness of the proposed EF mechanism in improving image description generation. © 2024 IEEE.

关键词： Data description

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