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A Novel CAPTCHA Recognition System Based on Refined Visual Attention

computers, Materials & Continua 2025年第4期83卷 115-136页

作者： Zaid Derea Beiji Zou Xiaoyan Kui Monir Abdullah Alaa Thobhani Amr Abdussalam School of Computer Science and Engineering Central South UniversityChangsha410083China College of Computer Science and Information Technology Wasit UniversityWasit52001Iraq Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha67714Saudi Arabia Electronic Engineering and Information Science Department University of Science and Technology of ChinaHefei230026China

Improving website security to prevent malicious online activities is crucial,and CAPTCHA(Completely Automated Public Turing test to tell computers and Humans Apart)has emerged as a key strategy for distinguishing human users from automated ***-based CAPTCHAs,designed to be easily decipherable by humans yet challenging for machines,are a common form of this ***,advancements in deep learning have facilitated the creation of models adept at recognizing these text-based CAPTCHAs with surprising *** our comprehensive investigation into CAPTCHA recognition,we have tailored the renowned UpDown image captioning model specifically for this *** approach innovatively combines an encoder to extract both global and local features,significantly boosting the model’s capability to identify complex details within CAPTCHA *** the decoding phase,we have adopted a refined attention mechanism,integrating enhanced visual attention with dual layers of Long Short-Term Memory(LSTM)networks to elevate CAPTCHA recognition *** rigorous testing across four varied datasets,including those from Weibo,BoC,Gregwar,and Captcha 0.3,demonstrates the versatility and effectiveness of our *** results not only highlight the efficiency of our approach but also offer profound insights into its applicability across different CAPTCHA types,contributing to a deeper understanding of CAPTCHA recognition technology.

关键词： Text-based CAPTCHA recognition refined visual attention web security computer vision

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A Novel Reliable and Trust Objective Function for RPL-Based IoT Routing Protocol

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computers, Materials & Continua 2025年第2期82卷 3467-3497页

作者： Mariam A.Alotaibi Sami S.Alwakeel Aasem N.Alyahya Department of Computer Engineering College of Computer ScienceKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science and Information Technology Shaqra UniversityShaqra11961Saudi Arabia

The Internet of Things (IoT) integrates diverse devices into the Internet infrastructure, including sensors, meters, and wearable devices. Designing efficient IoT networks with these heterogeneous devices requires the selection of appropriate routing protocols, which is crucial for maintaining high Quality of Service (QoS). The Internet engineering Task Force’s Routing Over Low Power and Lossy Networks (IETF ROLL) working group developed the IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) to meet these needs. While the initial RPL standard focused on single-metric route selection, ongoing research explores enhancing RPL by incorporating multiple routing metrics and developing new Objective Functions (OFs). This paper introduces a novel Objective Function (OF), the Reliable and Secure Objective Function (RSOF), designed to enhance the reliability and trustworthiness of parent selection at both the node and link levels within IoT and RPL routing protocols. The RSOF employs an adaptive parent node selection mechanism that incorporates multiple metrics, including Residual Energy (RE), Expected Transmission Count (ETX), Extended RPL Node Trustworthiness (ERNT), and a novel metric that measures node failure rate (NFR). In this mechanism, nodes with a high NFR are excluded from the parent selection process to improve network reliability and stability. The proposed RSOF was evaluated using random and grid topologies in the Cooja Simulator, with tests conducted across small, medium, and large-scale networks to examine the impact of varying node densities. The simulation results indicate a significant improvement in network performance, particularly in terms of average latency, packet acknowledgment ratio (PAR), packet delivery ratio (PDR), and Control Message Overhead (CMO), compared to the standard Minimum Rank with Hysteresis Objective Function (MRHOF).

关键词： IoT LLNs RPL objective function OF MRHOF OF0 routing metrics reliability trustworthiness

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Combinatorial Multi-Armed Bandits with Fairness Constraints: An Online Convex Optimization Perspective

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Journal of Artificial Intelligence Research 2025年 82卷 1909-1942页

作者： Chen, Xiaosong Zhuang, Hanqin Liu, Yang Xu, Huanle Lau, Wing Cheong Department of Computer and Information Science University of Macau Macau China Department of Computer Science Shanghai University Shanghai China Department of Information Engineering The Chinese University of Hong Kong Hong Kong

The problem of multi-armed bandit (MAB) with fairness constraints has emerged as an important research topic recently. For such problems, one common objective is to maximize the total rewards within a fixed number of pull rounds, while satisfying the fairness requirement of a minimum selection fraction for each individual arm in the long run. Previous works have made substantial advancements in designing various online selection solutions for MAB, however, when incorporating such fairness constraints, they fail to achieve a sublinear regret bound. In this paper, we study a combinatorial MAB problem with concave objective and fairness constraints. In particular, we design a new selection algorithm that solves MAB problems from an online convex optimization perspective. Our algorithm is computationally efficient, and more importantly, manages to achieve a sublinear regret bound of O(√T ln T) with high probability guarantees in T selection rounds. We also extend our framework to include more general knapsack constraints. Finally, we assess the performance of our algorithm through extensive simulations and real dataset applications, demonstrating its significant advantages over baseline schemes. ©2025 The Authors.

关键词： Convex optimization

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Privacy-preserving explainable AI: a survey

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science China(information sciences) 2025年第1期68卷 23-56页

作者： Thanh Tam NGUYEN Thanh Trung HUYNH Zhao REN Thanh Toan NGUYEN Phi Le NGUYEN Hongzhi YIN Quoc Viet Hung NGUYEN School of Information and Communication Technology Griffith University School of Computer and Communication Sciences Ecole Polytechnique Federale de Lausanne Faculty of Mathematics and Computer Science University of Bremen Faculty of Information Technology HUTECH University Department of Computer Science Hanoi University of Science and Technology School of Electrical Engineering and Computer Science The University of Queensland

As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures. Our contribution to this field comprises a thorough analysis of research papers with a connected taxonomy that facilitates the categorization of privacy attacks and countermeasures based on the targeted explanations. This work also includes an initial investigation into the causes of privacy leaks. Finally, we discuss unresolved issues and prospective research directions uncovered in our analysis. This survey aims to be a valuable resource for the research community and offers clear insights for those new to this domain. To support ongoing research, we have established an online resource repository, which will be continuously updated with new and relevant findings.

关键词： privacy-preserving explainable AI privacy attacks privacy defences PrivEx PPXAI

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Proactive Crowdsourced Monitoring and Sensing With Expansible Activity Recognition Based on Internet of Things Localization

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IEEE Internet of Things Journal 2025年第11期12卷 17674-17686页

作者： Chen, Lien-Wu Liao, Chun-Wei Liu, Jun-Xian Feng Chia University Department of Information Engineering and Computer Science Taichung407 Taiwan

This paper proposes a Proactive Crowdsourced Monitoring and Sensing (PCMS) framework with the designed Smart iBeacon device to accurately recognize the activities of an equipped target, exclusively customize the recognition model of a specific target, and actively trigger cooperative tracking of nearby smartphones for an abnormal target based on Internet of Things (IoT) localization. According to our review of relevant research, PCMS is the first framework that provides the following features: i) coarse-grained and fine-grained features can be extracted to accurately recognize target activities through densely connected convolutional networks with improvement design, ii) crowdsourced monitoring and sensing can be actively triggered for a target as the abnormal activity of the target is detected, and iii) deep learning model of target activity recognition can be exclusively customized for a specific target to improve the recognition accuracy based on the dedicated activity data of the target. An Android-based prototype with stationary iBeacon nodes and the Smart iBeacon is implemented to verify the feasibility and superiority of our PCMS framework. Experimental results show that our framework outperforms existing methods and can accurately recognize target activities for abnormal event detection and proactive crowdsourced tracking in a real-time manner. © 2014 IEEE.

关键词： Smartphones

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Hybrid Deep Learning Approach for Automating App Review Classification:Advancing Usability Metrics Classification with an Aspect-Based Sentiment Analysis Framework

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computers, Materials & Continua 2025年第1期82卷 949-976页

作者： Nahed Alsaleh Reem Alnanih Nahed Alowidi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computer Science and EngineeringUniversity of HailHail81451Saudi Arabia

App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their *** the analysis of these reviews is vital for efficient review *** traditional machine learning(ML)models rely on basic word-based feature extraction,deep learning(DL)methods,enhanced with advanced word embeddings,have shown superior *** research introduces a novel aspectbased sentiment analysis(ABSA)framework to classify app reviews based on key non-functional requirements,focusing on usability factors:effectiveness,efficiency,and *** propose a hybrid DL model,combining BERT(Bidirectional Encoder Representations from Transformers)with BiLSTM(Bidirectional Long Short-Term Memory)and CNN(Convolutional Neural Networks)layers,to enhance classification *** analysis against state-of-the-art models demonstrates that our BERT-BiLSTM-CNN model achieves exceptional performance,with precision,recall,F1-score,and accuracy of 96%,87%,91%,and 94%,*** contributions of this work include a refined ABSA-based relabeling framework,the development of a highperformance classifier,and the comprehensive relabeling of the Instagram App Reviews *** advancements provide valuable insights for software developers to enhance usability and drive user-centric application development.

关键词： Requirements engineering(RE) app review analysis usabilitymetrics hybrid deep learning BERT-BiLSTM-CNN

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TRE-DSP: A traceable and revocable CP-ABE based data sharing scheme for IoV with partially hidden policy

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Digital Communications and Networks 2025年第2期11卷 455-464页

作者： Yousheng Zhou Rundong Peng Yuanni Liu Pandi Vijayakumar Brij Gupta College of Computer Science and Technology Chongqing University of Posts and Telecommunications College of Cyber Security and Information Law Chongqing University of Posts and Telecommunications Department of Computer Science and Engineering University College of Engineering Department of Computer Engineering National Institute of Technology Kurukshetra

With the popularity of the Internet of Vehicles(IoV), a large amount of data is being generated every day. How to securely share data between the IoV operator and various value-added service providers becomes one of the critical issues. Due to its flexible and efficient fine-grained access control feature, Ciphertext-Policy Attribute-Based Encryption(CP-ABE) is suitable for data sharing in IoV. However, there are many flaws in most existing CP-ABE schemes, such as attribute privacy leakage and key misuse. This paper proposes a Traceable and Revocable CP-ABE-based Data Sharing with Partially hidden policy for IoV(TRE-DSP). A partially hidden access structure is adopted to hide sensitive user attribute values, and attribute categories are sent along with the ciphertext to effectively avoid privacy exposure. In addition, key tracking and malicious user revocation are introduced with broadcast encryption to prevent key misuse. Since the main computation task is outsourced to the cloud, the burden of the user side is relatively low. Analysis of security and performance demonstrates that TRE-DSP is more secure and practical for data sharing in IoV.

关键词： Privacy-preserving Attribute-based encryption Partially hidden policy Traceability User revocation Internet of vehicles

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Decomposition-based hybrid methods employing statistical, machine learning, and deep learning models for crude oil price forecasting

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Neural Computing and Applications 2025年 1-46页

作者： Purohit, Sourav Kumar Panigrahi, Sibarama Department of Computer Science and Engineering Sambalpur University Institute of Information Technology Burla India Department of Computer Science and Engineering National Institute of Technology Rourkela Odisha Rourkela769008 India

Crude oil prices (COP) profoundly influence global economic stability, with fluctuations reverberating across various sectors. Accurate forecasting of COP is indispensable for governments, policymakers, and stakeholders to make well-informed decisions and effectively mitigate risks. The decomposition-based hybrid models have been showing promising COP forecasting accuracy than other time series forecasting methods. Despite this fact, no systematic study has been conducted to evaluate the true potential of different decomposition-based hybrid methods employing different forecasting models to forecast the COP. Therefore, a hybrid modeling framework is developed by combining efficient decomposition techniques, namely empirical mode decomposition (EMD), ensemble EMD (EEMD), complete EEMD with adaptive noise (CEEMDAN), and variational mode decomposition (VMD) with seven statistical models, fourteen machine learning (ML) models, and six deep learning (DL) models. Further, a systematic study is conducted on the resulting decomposition-based hybrid models to find the best hybrid model for COP forecasting. Three distinct train-test data splits are employed to ensure a reliable evaluation of the models using four performance metrics. Extensive statistical analysis is conducted to identify the optimal combination of the decomposition technique and forecasting model for precise COP prediction. The results demonstrate that the proposed decomposition-based hybrid model employing VMD and Huber Regression is statistically the best method among all alternatives to forecast monthly COP. The proposed hybrid method VMD-Huber Regression improves the root mean square error (RMSE) by 21% than CEEMDAN-ARIMA, 58.31% than EEMD-Theta, 13.18% than EMD-Random Walk, and 49.44% than VMD-TBATS hybrid methods in 60–40 Train-Test split ratio. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Mean square error

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Deep Learning-Based Image Watermarking Using Catalan Transform and Non-Negative Matrix Factorization

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IEEE Access 2025年 13卷 68995-69020页

作者： Fahim Hossain Saikat, Md. Provath, Md. Al-Mamun Deb, Kaushik Dhar, Pranab Kumar Shimamura, Tetsuya Department of Computer Science and Engineering Chittagong4349 Bangladesh Saitama University Department of Information and Computer Sciences Saitama338-8570 Japan

In the digital age, embedding imperceptible data into images for authenticating ownership and content integrity through watermarking has gained immense importance. Existing watermarking methods struggle to balance imperceptibility, robustness, and security, resulting in suboptimal peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), normalized correlation (NC) values, along with higher time complexity. To overcome these constraints, this study proposes a new method for image watermarking that uses deep learning and operates within the YIQ color space. The main contribution of the proposed method is its new combination of the Catalan transform (CT) and non-negative matrix factorization (NMF) with an artificial neural network (ANN) for enhanced watermark embedding and extraction. CT enhances pixel intensity while NMF extracts the most significant features from each (m x m) block (with m= 2 in simulations), improving the method's robustness and imperceptibility. Before embedding, the watermark undergoes a new scrambling technique combining adaptive diffusion and non-linear transform, significantly enhancing security. A new embedding equation integrates the watermark into the non-negative elements of the host image blocks. Statistical features from watermarked blocks are used to create a dataset for extraction, and an ANN model trained on this dataset enables accurate watermark recovery. The proposed method achieves a PSNR of 66.43 to 77.02 dB (31.91% improvement), SSIM of 0.99 to 1.00 (1.02% improvement), and NC of 0.99 to 1.00 (1.88% improvement). Its computational complexity is O(n2), a 33.33% reduction compared to traditional O(n3) methods. Security metrics include RE = 0.3094, UACI = 34.21%, and NPCR = 99.15%, demonstrating superior robustness and efficiency over existing approaches. © 2013 IEEE.

关键词： Non-negative matrix factorization

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HCBiL-DMN: an effective food infestation detection from stored food grains using deep learning model

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Multimedia Tools and Applications 2025年第14期84卷 14047-14070页

作者： Vedavathi, N. Suhas Bharadwaj, R. Department of Computer Science and Engineering Vidyavardhaka College of Engineering Karnataka Mysuru570002 India Department of Information Science and Engineering The National Institute of Engineering Karnataka Mysuru570008 India

Food Infestation Detection is more important for food safety and health concerns. It is a challenging task to separate the grains into infested or non-infested. It is found that in the existing system, there is no effective approach for the early detection of pest populations and for classifying crops as infested or non-infested. Therefore, the identification and categorization of early-stage grain infestations is in high demand. To address the above problem, this research presents a better technique for detecting and classifying infested and non-infested grains. Initially, the maize, wheat and olive image datasets are collected, which are then pre-processed using data normalization and adaptive wiener filtering (AdwF) approach to improve the excellence of the input images. Image quality helps in achieving better performance and also helps in analyzing the quality of food grains effectively. Feature extraction is done using the Dense Residual Pyramidal Dilated Convolution Network (DR-PDCNet) technique, and feature selection is done using the Linear Discriminant Coati Optimization (LD-CoA) algorithm. The Hybrid Convolutional Bi-LSTM based Deep Maxout Network (HCBiL-DMN) model is used to detect the infected food grains in the preserved food grains with high accuracy. This research explores the potential and future possibilities for using technology to ensure the quality, assessment, and monitoring of nutritional grains during storage. The performance results are achieved with 98.3% accuracy, 99.12% recall, 98.65% precision, 98.02% MCC, and 99.32% F1 score. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolution

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