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GPNST: An Improved Graphical Password Authentication Scheme Leveraging Neural Style Transfer Technique on Smartphones

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International Journal of Information Security 2025年第3期24卷 1-34页

作者： Ray, Palash Giri, Debasis Obaidat, Mohammad S. Jana, Manasi Sasmal, Mahuya Alenazi, Mohammed J. F. Haldia Institute of Technology West Bengal Haldia India Maulana Abul Kalam Azad University of Technology West Bengal Nadia India King Abdullah II School of Information Technology The University of Jordan Amman Jordan School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Department of Computational Intelligence School of Computing SRM University SRM Nagar TN Kattankulathur603203 India School of Engineering The Amity University UP Noida201301 India King Saud University Riyadh Saudi Arabia

The growing prevalence of smartphones and the increasing dependence on digital services have made it crucial to have authentication techniques that are both secure and user-friendly. This study examines the effectiveness and usability of graphical passwords named GPNST as an alternative authentication method on mobile devices. Graphical passwords use users’ capacity to remember visuals or patterns, offering a possible equilibrium between security and user pleasure. GPNST is a two-factor authentication technique that requires users to remember content and style images, or stylized images, as portfolio images. Additionally, a unique key token is assigned to each user device and securely sent to the server using the reversible data hiding approach, therefore mitigating several potential attacks. The research uses a combination of quantitative analysis to evaluate the strength of passwords and qualitative evaluations to measure user satisfaction and memorability. We investigate the influence of several design elements in graphical passwords, such as the intricacy of images and the layout of grids, on both the level of security and ease of use. In addition, the study evaluates the robustness of graphical passwords against popular forms of attacks, such as shoulder surfing and brute-force efforts. The findings provide valuable insights regarding the practicality of incorporating graphical passwords into smartphone authentication systems. This has implications for designers, developers, and users who are looking for secure and user-friendly solutions in the constantly changing field of mobile technology. The GPNST scheme demonstrates outstanding performance in our assessment test, with an accuracy rate of up to 94.80% and a login time of 9.61 seconds. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Static random access storage

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Instance Segmentation of Ice-Covered Transmission Line in Unmanned Aerial Vehicle Images 5

Instance Segmentation of Ice-Covered Transmission Line in Un...

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5th International Seminar on Artificial Intelligence, Networking and Information technology, AINIT 2024

作者： Pan, Nianyi Sun, Jiacheng Zhou, Xinghong Li, Xiaomao School of Computer Engineering and Science Shanghai University Shanghai China School of Future Technology Shanghai University Shanghai China Research Institute of Usv Engineering School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (纸本)9798350385557

Unmanned aerial vehicle (UAV) detection of ice-covered transmission lines (ITL) is a crucial task for winter power grid maintenance. Instance segmentation offers robustness in handling complex scene variations in UAV images while providing instance-level information. However, the quality of instance segmentation heavily relies on precise pixel-level annotations, while predictions from low-cost annotations tend to be coarse. Therefore, this paper proposes a soft label mask optimization (SLMO) method to achieve high-quality segmentation results of ITL with only coarse annotations. Furthermore, to alleviate loss of feature details during multi-scale feature fusion, a pixel shuffle module (PSM) is introduced to replace the conventional interpolation sampling method. Combining these methods, this paper proposes a SLMO Network (SLMONet) based on the efficient SparseInst framework. Extensive experiments on our ITL datasets validate the effectiveness of SLMONet in segmenting ITL. © 2024 IEEE.

关键词： Electric lines

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Connecting Cities: IoST Innovation for Smarter Cities

Connecting Cities: IoST Innovation for Smarter Cities

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2024 International Conference on Recent Innovation in Smart and Sustainable technology, ICRISST 2024

作者： Kaur, Mandeep Aron, Rajni Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India SkillTech University Ugdx School of Technology Atlas Maharashtra Mumbai400070 India

ISBN: (纸本)9798350309140

The Internet of Things (IoT) is becoming the next big thing in the history of the Internet. It is important to figure out what areas IoT can be used and what study problems these areas offer. IoT will be important in every part of human lives, from smart towns and healthcare to smart farming, smart living, smart shopping, and intelligent environments. Even though IoT-enabling technologies have come a long way in the past few years, many problems still need to be fixed. Turning a city into a "smart city"is very difficult, but it can be done with the help of technologies that enable the Internet of Smart Things (IoST). The Internet of Things (IoT) idea grew out of different types of end systems, so there will be many study problems to solve. This paper talks about how the growth of smart cities will be achieved by improving general infrastructure with the help of IoT and what problems this might cause. © 2024 IEEE.

关键词： and Networks IoT Services and Management Smart Cities

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Playing Cards Classification and Detection Using Sequential CNN Model Through Machine Learning Techniques Using Artificial Intelligence

Playing Cards Classification and Detection Using Sequential ...

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2024 International Conference on E-Mobility, Power Control and Smart Systems, ICEMPS 2024

作者： Mittal, Khushi Gill, Kanwarpartap Singh Chauhan, Rahul Sharma, Manish Sunil, G. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed To Be University Computer Science & Engineering Uttarakhand Dehradun248002 India School of Computer Science & Artificial Intelligence SR University Warangal India

ISBN: (纸本)9798350394399

The task of identifying and classifying playing cards within images or video streams in computer vision involves employing a Sequential Convolutional Neural Network (CNN) model. This process combines image categorization and object recognition, presenting a sophisticated and engaging implementation of machine learning techniques. The significance of classifying and detecting playing cards extends to artificial intelligence and computer vision, with numerous practical applications. Systems for classifying and detecting playing cards utilizing artificial intelligence (AI) rely on computer vision methods and deep learning algorithms to achieve precise identification and categorization. These technological solutions not only enhance automation and efficiency across various tasks but also open up new possibilities for interaction and enjoyment in the domain of playing card games, making them accessible and engaging in diverse industries and user experiences. The primary objective of this research is to categorize playing cards and predict their expected accuracy, targeting a result of 75 percent. The high level of accuracy demonstrated in this study holds potential benefits for future researchers exploring this technology and subject matter. © 2024 IEEE.

关键词： computer vision

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Optimized FOREX Rate Prediction Using Hybrid Machine Learning Algorithm 1

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2nd International Conference on Intelligent Computing Systems and Applications, ICICSA 2023

作者： Latha, Challa Madhavi Bhuvaneswari, S. Soujanya, K.L.S. Department of Computer Science School of Engineering and Technology Pondicherry University Pondicherry Karaikal India Department of Information Technology CMR College of Engineering and Technology Medchal Hyderabad Kandlakoya India

ISBN: (数字)9789819754120

ISBN: (纸本)9789819754113

This research examines the application of fuzzy time series (FTS) models and different machine learning techniques to anticipate changes in foreign exchange (FOREX) data. The statistics used in this investigation include the actual FOREX prices and the high and low values of four different currencies. These statistics begin from January 2012 to November 2022;the data is available on the Reserve Bank of India website. This article investigates the significant flaws present in the literature information on FTS. This article identifies fundamental flaws in all FTS-based algorithms presently available in the literature and shows how to fix them. The study then describes the unique structure of improvised FTS. The Hybrid Machine Learning Algorithm is the name given to this structure. The purpose is to solve the identified issues present in FTS-based models previously published in the literature. The result was compared with the before and after optimization processes. After implementing the proposed hybrid machine learning algorithm, the result shows the better accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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S-MGHSTN: Towards An Effective Streaming Traffic Accident Risk Prediction Framework

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IEEE Transactions on Knowledge and Data engineering 2025年第7期37卷 4285-4298页

作者： Chen, Minxiao Yuan, Haitao Jiang, Nan Zheng, Zhihan Bao, Zhifeng Zhou, Ao Jiang, Jiaxin Wang, Shangguang Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore RMIT University School of Computing Technologies MelbourneVIC3000 Australia National University of Singapore School of Computing 117417 Singapore

Traffic accidents pose a significant risk to human health and property safety. To address this issue, predicting their risks has garnered growing interest. We argue that a desired prediction solution should demonstrate resilience to the complexity of traffic accidents. In particular, it should adequately consider the streaming nature of data and key related aspects, such as regional background, accurately capture both proximity and similarity while bridging the disparities, and effectively address the sparsity. However, these factors are often overlooked or difficult to incorporate. In this paper, we propose a novel streaming multi-granularity hierarchical spatio-temporal network. Initially, we innovate by incorporating remote sensing data, facilitating the creation of hierarchical multi-granularity structure and the comprehension of regional background. We construct multiple high-level risk prediction tasks to enhance model's ability to cope with sparsity. Subsequently, to capture and bridge spatial proximity and semantic similarity, region features and multi-view graph undergo encoding processes to distill effective representations, followed by a graph-enhanced representation alignment module that reconciles their disparities. At last, an alternating experience replay with a dual-memory buffer is employed to accommodate streaming data scenarios. Extensive experiments on two real datasets verify the superiority of our model against the state-of-the-art methods. © 1989-2012 IEEE.

关键词： Accidents Semantics Remote Sensing Correlation Urban Areas Streaming Media Roads Predictive Models Encoding Data Models Traffic Accident Risk Prediction Streaming Data Spatio Temporal Data Remote Sensing Car Accident Risk Of Traffic Accidents Hierarchical Structure Sparsity Regional Characteristics Remote Sensing Data Streams Semantic Similarity Spatial Proximity Background Regions Similarity Graph Experience Replay Short Term Memory Point Of Interest Semantic Features Incremental Learning Spatiotemporal Characteristics Weight Training Node Features Self Supervised Learning Traffic Risk Traffic Prediction Remote Sensing Images Point Of Interest Data Semantic Graph Feature Encoder Geographical Correlation Graph Features Temporal Modulation Concept Drift

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Idecomp: imbalance-aware decomposition for class-decomposed classification using conditional GANs

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Discover Artificial Intelligence 2023年第1期3卷 31页

作者： Buczek, Patryk Zidan, Usama Gaber, Mohamed Medhat Abdelsamea, Mohammed M. School of Computing and Digital Technology Birmingham City University Birmingham100190 United Kingdom Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Department of Computer Science Faculty of Computers and Information Assiut University Assiut71515 Egypt

Medical image classification tasks frequently encounter challenges associated with class imbalance, resulting in biased model training and suboptimal classification performance. To address this issue, the combination of class decomposition and transfer learning has proven to be effective in classifying imbalanced medical imaging datasets. Nevertheless, in order to further augment the performance gains achieved through the utilisation of class decomposition within deep learning frameworks, we propose a novel model coined imbalance-Aware Decomposition for Class-Decomposed Classification (iDeComp) model. By incorporating a conditional Generative Adversarial Network (GAN) model, iDeComp is capable of generating additional samples specifically tailored to underrepresented decomposed subclasses. This paper investigates the application of iDeComp using two different medical imaging datasets. iDeComp selects underrepresented samples from the training set of the sublevel classes within each dataset, which are then employed to train separate conditional Deep Convolutional GAN (DCGAN) models and verification models. The conditional DCGAN model is responsible for generating additional samples, while the verification model critically evaluates the appropriateness of the synthesised images. Subsequently, the resulting augmented samples are utilized to train the classification model. To assess the effectiveness of iDeComp, we employ various evaluation metrics including accuracy, precision, recall, and F1 score. The results obtained from our experiments clearly indicate that iDeComp outperforms existing approaches in terms of classifying both imbalanced datasets. © The Author(s) 2023.

关键词： Generative adversarial networks

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Evolution-guided Bayesian optimization for constrained multi-objective optimization in self-driving labs

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npj Computational Materials 2024年第1期10卷 2171-2181页

作者： Andre K.Y.Low Flore Mekki-Berrada Abhishek Gupta Aleksandr Ostudin Jiaxun Xie Eleonore Vissol-Gaudin Yee-Fun Lim Qianxiao Li Yew Soon Ong Saif A.Khan Kedar Hippalgaonkar School of Materials Science and Engineering Nanyang Technological UniversitySingapore 639798Singapore Institute of Materials Research and Engineering(IMRE) Agency for ScienceTechnology and Research(A*STAR)2 Fusionopolis WayInnovis#08-03Singapore 138634Republic of Singapore Department of Chemical and Biomolecular Engineering National University of SingaporeSingapore 117585Singapore School of Mechanical Sciences Indian Institute of Technology GoaGoa 403401India Institute of Sustainability for Chemicals Energy and Environment(ISCE2)Agency for ScienceTechnology and Research(A*STAR)1 Pesek RoadSingapore 627833Republic of Singapore Department of Mathematics National University of SingaporeSingapore 119077Singapore Institute for Functional Intelligent Materials National University of SingaporeSingapore 117544Singapore School of Computer Science and Engineering Nanyang Technological UniversitySingapore 639798Singapore

The development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision *** reach target properties efficiently,these platforms are increasingly paired with intelligent experimental ***,current optimizers show limitations in maintaining sufficient exploration/exploitation balance for problems dealing with multiple conflicting objectives and complex ***,we devise an Evolution-Guided Bayesian Optimization(EGBO)algorithm that integrates selection pressure in parallel with a q-Noisy Expected Hypervolume Improvement(qNEHVI)optimizer;this not only solves for the Pareto Front(PF)efficiently but also achieves better coverage of the PF while limiting sampling in the infeasible space.

关键词： optimization driving constrained

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Remote Sensing Image Segmentation of Wetlands in Macau Based on Machine Learning

Remote Sensing Image Segmentation of Wetlands in Macau Based...

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2023 International Conference on Big Data, Information and Intelligent engineering, BDIIE 2023

作者： Li, Cheng Cui, Hanwen Tian, Xiaolin School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology 999078 China School of Computer Science Zhuhai College of Science and Technology Zhuhai519041 China State Key Laboratory of Lunar and Planetary Sciences Macau University of Science and Technology 999078 China

Wetlands are essential components of our natural ecosystems, performing critical functions that contribute to biodiversity conservation, water purification, climate regulation, and shoreline protection. Remote sensing classification of wetlands, wetland resource surveys and other wetland research is booming, for the development of environmental protection policies continue to provide reliable data. Macau is a coastal city with rich wetland resources, but there is no relevant research on wetlands in Macau. In this paper, we use the high spatial resolution remote sensing data from Gaofen-2 satellite to extract the wetlands of Macau SAR to fill the gap of related research. This paper used two distinct machine learning classifiers, including Random Forest (RF) and Support Vector Machine (SVM), to analyse the high spatial resolution remote sensing data of GF-2 satellite in order to extract wetlands in Macau Special Region Area. In this research, the accuracy of RF model is much higher than that of SVM model, comparing the indexes of overall accuracy (OA) and Kappa coefficient. After analysing the resultant images generated after segmentation, the image segmentation result of RF model is better than that of SVM model. As for the RF model, the overall accuracy (OA) is 0.9941 and Kappa coefficient is 0.9899. © Published under licence by IOP Publishing Ltd.

关键词： Wetlands

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HyperSteiner: Computing Heuristic Hyperbolic Steiner Minimal Trees

HyperSteiner: Computing Heuristic Hyperbolic Steiner Minimal...

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2025 SIAM Symposium on Algorithm engineering and Experiments, ALENEX 2025

作者： García-Castellanos, Alejandro Medbouhi, Aniss Aiman Marchetti, Giovanni Luca Bekkers, Erik J. Kragic, Danica Amsterdam Machine Learning Lab University of Amsterdam Netherlands School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden School of Engineering Sciences KTH Royal Institute of Technology Sweden

ISBN: (纸本)9798331311995

We propose HyperSteiner – an efficient heuristic algorithm for computing Steiner minimal trees in the hyperbolic space. HyperSteiner extends the Euclidean Smith-Lee-Liebman algorithm, which is grounded in a divide-and-conquer approach involving the Delaunay triangulation. The central idea is rephrasing Steiner tree problems with three terminals as a system of equations in the Klein-Beltrami model. Motivated by the fact that hyperbolic geometry is well-suited for representing hierarchies, we explore applications to hierarchy discovery in data. Results show that HyperSteiner infers more realistic hierarchies than the Minimum Spanning Tree and is more scalable to large datasets than Neighbor Joining. Copyright © 2025 by SIAM.

关键词： Trees (mathematics)

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