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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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Adversarial-Learning-Based Taguchi Convolutional Fuzzy Neural Classifier for Images of Lung Cancer

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IEEE Access 2024年 12卷 72766-72776页

作者： Lin, Cheng-Jian Lin, Xue-Qian Jhang, Jyun-Yu National Chin-Yi University of Technology Department of Computer Science and Information Engineering Taichung41170 Taiwan National Taichung University of Science and Technology Department of Computer Science and Information Engineering Taichung40401 Taiwan

Deep learning technology has extensive application in the classification and recognition of medical images. However, several challenges persist in such application, such as the need for acquiring large-scale labeled data, configuring network parameters, and handling excessive network parameters. To address these challenges, in this study, we developed an adversarial-learning-based Taguchi convolutional fuzzy neural classifier (AL-TCFNC) for classifying malignant and benign lung tumors displayed in computed tomography images. In the framework of the developed AL-TCFNC, a fuzzy neural classifier replaces a conventional fully connected network, thereby reducing the number of network parameters and the training duration. To reduce experimental cost and training time, the Taguchi method was used. This method helps to identify the optimal combination of model parameters through a small number of experiments. The transfer learning of models across databases often results in subpar performance because of the paucity of labeled samples. To resolve this problem, we used a combination of maximum mean discrepancy and cross-entropy for adversarial learning with the proposed model. Two data sets, namely the SPIE-AAPM Lung CT Challenge data set and LIDC-IDRI Lung Imaging Research data set, were used to validate the AL-TCFNC model. When the AL-TCFNC model was used for transfer learning, it exhibited an accuracy rate of 89.55% and outperformed other deep learning models in terms of classification performance. © 2013 IEEE.

关键词： Fuzzy neural networks

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An enhanced AMBTC for color image compression using color palette

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Multimedia Tools and Applications 2024年第11期83卷 31783-31803页

作者： Xiong, Lizhi Zhang, Mengtao Yang, Ching-Nung Kim, Cheonshik School of Computer Science Nanjing University of Information Science and Technology Nanjing China Department of Computer Science and Information Engineering National Dong Hwa University Hualien Taiwan Department of Computer Engineering Sejong University Seoul Korea Republic of

Digital image has been used in various fields as an essential carrier. Many color images have been constantly produced since their more realistic description, which takes up much storage space and network bandwidth. Thus, color image compression has become an essential key technology. Absolute Moment Block Truncation Coding (AMBTC) has been widely studied as one of the classical image compression methods. However, in the existing methods, the visual quality of the reconstructed images and the compression rate are all relatively low. Therefore, this paper proposes an enhanced AMBTC for color image compression using a color palette. In the proposed method, the K-means clustering algorithm is utilized for training the image's palette pattern. The color palette obtained by K-mean will be more suitable for reconstructing this image than the standard color palette, and the visual quality will be higher. The six clustered central pixels are matched with the palette through a color difference formula, and the obtained index values are used as the quantization levels. Huffman coding is used to build a bitmap to achieve a higher compression rate, that is, a lower bit rate. At last, a block of a color image can be represented by six index values and a bitmap. Experimental results and theoretical analysis demonstrate that the proposed method has better visual quality and bit rate than similar schemes. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Color

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Multi-dimensional information-driven many-objective software remodularization approach

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Frontiers of computer science 2023年第3期17卷 45-62页

作者： Amarjeet PRAJAPATI Anshu PARASHAR Amit RATHEE Department of Computer Science Engineering&Information Technology Jaypee Istitute of Information TechnologyNoida 201307India Department of Computer Science&Engineering Thapar Institute of Engineering&TechnologyPunjab 147004India Department of Computer Science Government College BarotaHaryana 131301India

Most of the search-based software remodularization(SBSR)approaches designed to address the software remodularization problem(SRP)areutilizing only structural information-based coupling and cohesion quality ***,in practice apart from these quality criteria,there require other aspects of coupling and cohesion quality criteria such as lexical and changed-history in designing the modules of the software ***,consideration of limited aspects of software information in the SBSR may generate a sub-optimal modularization ***,such modularization can be good from the quality metrics perspective but may not be acceptable to the *** produce a remodularization solution acceptable from both quality metrics and developers’perspectives,this paper exploited more dimensions of software information to define the quality criteria as modularization ***,these objectives are simultaneously optimized using a tailored manyobjective artificial bee colony(MaABC)to produce a remodularization *** assess the effectiveness of the proposed approach,we applied it over five software *** obtained remodularization solutions are evaluated with the software quality metrics and developers view of *** demonstrate that the proposed software remodularization is an effective approach for generating good quality modularization solutions.

关键词： software restructuring remodularization multiobjective optimization software coupling and cohesion

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Vision-based Robotic Arm Control for Screwdriver Bit Placement Tasks

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Sensors and Materials 2024年第3期36卷 1003-1018页

作者： Lin, Cheng-Jian Lin, Pei-Jung Shih, Chi-Huang Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung411 Taiwan Department of Information Engineering and Computer Science Feng Chia University Taichung407 Taiwan

Robotic arms are widely used in the automation industry to package and deliver classified objects. When the products are small objects with very similar shapes, such as screwdriver bits with slightly different threads, pointed tips, and thicknesses, object selection and assembly often lead to misjudgment. We have developed a practical robotic arm control system based on vision detection techniques for screwdriver bits' placement. In addition to effectiveness, easy deployment and high flexibility in the field are also taken into account. The vision-based system consists of four processing stages in the following order: world coordinate conversion from image pixel coordinates, object detection, edge detection, and object orientation. In the first stage, a manual two-point marking method is proposed to easily configure the coordinate conversion for robot operating system (ROS)-based manipulators. For the following stages, we focus on the fine integration of state-of-the-art methods for the technical feasibility of the screwdriver bit placement. Such integration includes the selection between object detection methods and the data flow control among system stages. The experimental results show that (1) in detecting screwdriver bits, You Only Look Once (YOLO) v4 outperforms YOLOv7 and Single Shot MultiBox Detector at an accuracy rate of 99.51%;(2) in the edge detection, the object detection output can better illustrate the object contour than a whole image, achieving a mean absolute error of 0.86% in estimating the object angle;and (3) a successful real-time replacement rate of 96% is achieved for 12 screwdriver bits randomly scattered on a conveyor belt. © 2024 M Y U Scientific Publishing Division. All rights reserved.

关键词： Robotic arms

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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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A Review on Smart Healthcare employing Quantum Internet of Things

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IEEE engineering Management Review 2024年 1-9页

作者： Sutradhar, Kartick Venkatesh, Ranjitha Venkatesh, Priyanka Department of Computer Science and Engineering Indian Institute of Information Technology Sri City India Department of Computer Science and Engineering GITAM School of Technology Bengaluru India Department of Computer Science and Engineering Presidency University Bengaluru India

The Quantum Internet of Things (QIoT) in the healthcare industry holds the promise of transforming patient care, diagnostics, and medical research. Quantum-enhanced sensors, communication, and computation offer unprecedented capabilities that can revolutionize how healthcare services are delivered and experienced. This paper explores the potential of QIoT in the context of smart healthcare, where interconnected quantum-enabled devices and systems create an ecosystem that enhances data security, enables real-time monitoring, and advances medical knowledge. We delve into the applications of quantum sensors in precise health monitoring, the role of quantum communication in secure telemedicine, and the computational power of quantum computing in drug discovery and personalized medicine. We discuss challenges such as technical feasibility, scalability, and regulatory considerations, along with the emerging trends and opportunities in this transformative field. By examining the intersection of quantum technologies and smart healthcare, this paper aims to shed light on the novel approaches and breakthroughs that could redefine the future of healthcare delivery and patient outcomes. IEEE

关键词： Internet of things

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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 article 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: 1) coarse-grained and fine-grained features can be extracted to accurately recognize target activities through densely connected convolutional networks with improvement design;2) crowdsourced monitoring and sensing can be actively triggered for a target as the abnormal activity of the target is detected;and 3) 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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Ensemble Learning Through Evolutionary Multitasking: A Formulation and Case Study

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IEEE Transactions on Emerging Topics in Computational Intelligence 2024年第4期8卷 3081-3094页

作者： Liaw, Rung-Tzuo Wen, Yu-Wei Fu Jen Catholic University Department of Computer Science and Information Engineering New Taipei24205 Taiwan National United University Department of Computer Science and Information Engineering Miaoli36063 Taiwan

Evolutionary machine learning has drawn much attentions on solving data-driven learning problem in the past decades, where classification is a major branch of data-driven learning problem. To improve the quality of obtained classifier, ensemble is a simple yet powerful strategy. However, gathering classifiers for ensemble requires multiple runs of learning process which bring additional cost at evaluation on the data. This study proposes an innovative framework for ensemble learning through evolutionary multitasking, i.e., the evolutionary multitasking for ensemble learning (EMTEL). There are four main features in the EMTEL. First, the EMTEL formulates a classification problem as a dynamic multitask optimization problem. Second, the EMTEL utilizes evolutionary multitasking to resolve the dynamic multitask optimization problem for better convergence through the synergy of common properties hidden in the tasks. Third, the EMTEL incorporates evolutionary instance selection for saving the cost at evaluation. Finally, the EMTEL formulates the ensemble learning problem as a numerical optimization problem and proposes an online ensemble aggregation approach to simultaneously select appropriate ensemble candidates from learning history and optimize ensemble weights for aggregating predictions. A case study is investigated by integrating two state-of-the-art methods for evolutionary multitasking and evolutionary instance selection respectively, i.e., the symbiosis in biocoenosis optimization and cooperative evolutionary learning and instance selection. For online ensemble aggregation, this study adopts the well-known covariance matrix adaptation evolution strategy. Experiments validate the effectiveness of the EMTEL over conventional and advanced evolutionary machine learning algorithms, including genetic programming, self-learning gene expression programming, and multi-dimensional genetic programming. Experimental results show that the proposed framework ameliorates state-o

关键词： Machine learning

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