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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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An Intelligent Hybrid GenAI Model for Software Testing

An Intelligent Hybrid GenAI Model for Software Testing

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2024 IEEE International Conference on Intelligent Computing and Emerging Communication Technologies, ICEC 2024

作者： Maharana, Tannistha Agrawal, Nitya Sharma, Vandana Alkhayyat, Ahmed Kalinga Institute of Industrial Technology School of Computer Engineering Bhubaneswar India Christ University Computer Science Department Bengaluru India The Islamic University College of Technical Engineering Najaf Iraq

ISBN: (纸本)9798331508432

Software Testing, a well-defined process and a major component of software development. It covers various methods such as execution of a software program, predicting errors, to meet user needs, and ensure that the system is working correctly. Due to the advanced development in technology around the world, new methods are introduced to test the software. This summary talk discusses the current state of the art of AI(Artificial Intelligence) for software testing. The purpose of software testing is validation, verifying and detection of errors. For the study, we explored around 30 articles from different research surveys. 12-15 articles were selected for the final review. Artificial intelligence(AI) which has emerged in a promising approach to software testing in recent years. The main aim of this paper was to review how Artificial Intelligence(AI) works in Software testing. This paper had also proposed many key offerings of AI in the upcoming future for the area of software testing. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhanced Speech Emotion and Gender Recognition Using Hybrid CNN-LSTM Models 4

Enhanced Speech Emotion and Gender Recognition Using Hybrid ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Priyadharshini, S. Sundar, G. Naveen Narmadha, D. Thangavel, Senthil Kumar Ajibesin, Adeyemi Abel Kolandapalayam Shanmugam, Selvanayaki Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Cape Peninsula University of Technology Faculty of Informatics and Design Department of Information Technology South Africa Ashland University Department of Mathematics and Computer Science United States

ISBN: (纸本)9798331523923

The current paper proposes a new approach for peripheral speech emotion analysis and gender estimation incorporating the best machine learning architectures such as CNNs and LSTMs. Its correct depiction of emotions and the variation of the gendered speech to indicate manifold signal-to-noise ratio make the approach accommodate the difficulty inherent in recognizing emotions from the recording of voices as well as predicting the gender of a speaker. It is then able to ally with preprocessing and collection of a vast range of different speech samples, feature extraction of pitch, tone and spectral data. These features serve as input to the CNNs and LSTMs: LSTMs work well at temporal regular features and the CNNs work well in feature extraction by location (spatio feature). The approach is tested on the RAVDESS and IEMOCAP datasets and establishes a near perfect match to prior approaches with reference to recognition accuracy of emotion analysis and gender prediction. Thus, applying the identified dual method approach, which could be described by such characteristics as highly accurate data pre-preprocessing, clear indication of the features of interest, a different CNN-LSTM model joint training, it offers a reasonably good platform to gain certain insights into such characteristics of the speech as our experiment demonstrates. This work offers satisfactory configurations on concerns such as human computer relation, customer relation and mental health surveillance to suggest that other applications could be achieved in future. © 2025 IEEE.

关键词： Convolutional neural networks

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Classification of cervical spine disease using convolutional neural network

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Multimedia Tools and Applications 2024年第41期83卷 88963-88979页

作者： Bezabh, Yohannes Agegnehu Salau, Ayodeji Olalekan Abuhayi, Biniyam Mulugeta Ayalew, Aleka Melese Department of Information Technology University of Gondar Gondar Ethiopia Department of Electrical/Electronics and Computer Engineering Afe Babalola University Ado-Ekiti Nigeria Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

Cervical spondylosis myelopathy (CSM) is a degenerative disorder of the cervical spine’s disks and joints, leading to neurological disability. Typically, this condition causes increasing discomfort and neurologic deterioration. Cervical spine pain disease classification using deep learning can assist medical professionals in making early and more precise medical diagnosis of cervical abnormalities. The main focus of the assessment for doctors is the classification of cervical spine diseases from X-rays images, which is valuable for patients to indicate the various symptoms they are faced with. Identifying cervical spine pain injuries (CPI) is a critical aspect of the diseases segmentation and classification. Manually explaining the high-dimensional feature space makes it challenging to identify the exact category and level of severity. This paper introduces a Convolutional Neural Networks (CNN) concatenation feature extraction method to help in the diagnosis of cervical spine pain disease from X-ray images. An innovative CNN-concatenation model-based fully connected classifier and a four-class system are applied to classify X-ray image data in this research. The CNN approach is employed to automatically classify X-ray images into healthy (normal) and with cervical spine disease (abnormal). CSM is currently diagnosed mostly through clinical evaluation using imaging methods such as X-ray and computed tomography (CT). Due to its inexpensive cost and minimal radiation dose, X-ray is commonly used. The proposed approach achieved a training accuracy of 99.98%, a validation accuracy of 98.29%, and a testing accuracy of 97.82% of classification from experiments. These results indicate that the image preprocessing, data augmentation, and CNN approaches provide an efficient classification method for identifying and accurately classifying cervical spine diseases. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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PROMO: PROactive MObility-support model for task scheduling in fog computing

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International Journal of computers and Applications 2022年第11期44卷 1092-1101页

作者： Kaur, Navjeet Kumar, Ashok Kumar, Rajesh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Application Lovely Professional University Phagwara India Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India

In fog computing, closer proximity of fog devices to the end-user provides several benefits, such as reduced End-to-End (E2E) delay and better Quality of Service (QoS). Although, fog computing gives promising benefits of uninterrupted services to mobile users. The critical challenge is to decide when and where to re-schedule tasks according to the user's mobility. This paper targets this issue and proposes a Proactive Mobility-support (PROMO) model for re-scheduling tasks to mitigate the issues that influence QoS in mobility. The PROMO model is implemented and validated on MobFogSim, which significantly improves delay with 37% and around 17% downtime during task re-scheduling. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Fog computing

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Partial Discharge Classification With 1D Convolutional Neural Network 14

Partial Discharge Classification With 1D Convolutional Neura...

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14th International Conference on the Properties and Applications of Dielectric Materials, ICPADM 2024

作者： Cheypoca, Thepjit Promphanich, Wiboon Thway, Aung Ye Hankae, Angelina Phimpissada Jeenmuang, Siwakorn Pattanadech, Norasage School of Engineering King Mongkut's Institute of Technology Ladkrabang Instrumentation and Control Engineering Dept. Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Computer Engineering Dept. Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Electrical Engineering Dept. Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Mechatronics Engineering Dept. Bangkok Thailand

ISBN: (纸本)9798350374605

This paper introduces a novel approach for classifying with the 1D Convolutional Neural Network model for partial discharge patterns, that consists of corona discharge, surface discharge and internal discharge. The PD measuring circuit suggested in IEC 60270:2000 is used to record Partial discharge signals. Independent parameters such as phase and charge of PD patterns were recorded. The Artificial Neural Network for the classification model was constructed. Moreover, 2×1D CNN feature extraction was utilized to reduce the curse of dimensionality in the dense layer of the proposed PD classification model. 80% of the recorded data will be used as a training data and 20% recorded data was used for testing of the classification models. Impacts of neuron numbers and network architecture on the PD classification performance will be observed. © 2024 IEEE.

关键词： Convolutional neural networks

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BASIL:Binary Anchor-Based Smart Indoor Localization

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Tsinghua Science and technology 2025年第1期30卷 1-17页

作者： Zhe Yang Yanjun Li Yufan Zhang Yun Pan Chung Shue Chen Yi-hua Zhu School of Computer Science and Technology Zhejiang University of TechnologyHangzhou 310023China College of Information Science&Electronic Engineering Zhejiang UniversityHangzhou 310027China Nokia Bell Labs Paris-Saclay CenterMassy 91300France

Indoor localization has been challenging research due to the invalidity of the global navigation satellite system in indoor *** advances in ambient assistive living have shown great power in detecting and locating persons living in their homes,especially using the ON/OFF binary *** this paper,we exploit the Bluetooth low-energy beacons as device-based binary anchors under the lowest transmission power to turn any indoor activity and facility interaction into a binary location *** binary anchors are fused with an extended Kalman filter based pedestrian dead-reckoning using a factor graph optimization,with extra constraints including the normalized magnetic loop closure which is optimized using an attenuation factor,and a rapidly-exploring random tree-based map collision *** proposed system provides a cost-effective,scalable,and robust localization for common indoor *** experimental results show an effective sub-meter precision for the long-term trajectories,and a small amount of anchors is enough for significant calibration in large scenarios.

关键词： indoor localization sensor fusion graph optimization binary anchor loop closure

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Evaluation of Different Network Coding Algorithm Strategies for Wireless Systems 5th

Evaluation of Different Network Coding Algorithm Strategies ...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Parameswaran, Murali Savita Kannagi, A. Panwar, Rahul School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Electrical Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India

ISBN: (纸本)9789819780426

These research objectives are to evaluate and compare distinct community coding algorithms that can be applied in wireless networks to maximize network efficiency, throughput, and facts switch velocity. Special procedures are taken, such as coding in any respect of nodes within the community or sure nodes, which includes area nodes or routers. One-of-a-kind coding algorithm techniques might be tested in this study, including threshold coding and coded pooling. The chosen method will depend on the type of community, the scale, the traffic charge, and the favored performance goals, which include latency or bandwidth. Through leveraging network coding, packets can be transmitted and retransmitted without re-encoding. Studies have been carried out to evaluate the overall performance of diverse network coding algorithms for wireless networks inclusive of flooding, random linear network coding (RLNC), most distance separable codes (MDSC), directional network coding (DANC) and layered community coding (LNC). At the same time, MDSC and directional network coding have been proven more potent in factor-to-factor conversation eventualities. LNC is the most efficient average in many instances because of its ability to modify in real-time to changing link conditions. All of those algorithms provide a couple of advantages for wireless networks. This abstract affords an overview of the unique algorithm techniques, evaluates their effectiveness in numerous scenarios, and highlights their blessings for wireless networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network coding

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Enhancing the Resolution of ChestX-ray Images with SRGAN and Sub-Pixel CNN 7

Enhancing the Resolution of ChestX-ray Images with SRGAN and...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Reddy, K. Lokeshwar Sudharson, S. Natarajan, B. Theetchenya, S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science And Engineering Chennai India Computer Science And Engineering Vellore Institute of Technology Chennai India Computer Science And Engineering Sona College of Technology Salem India

ISBN: (纸本)9798350371406

A crucial diagnostic tool for respiratory and heart disorders is chest X-ray imaging. However, the limited resolution of acquired images can hinder accurate interpretation and subsequent treatment decisions. To address this challenge, we employ deep learning-based super-resolution techniques, specifically SRGAN and ESPCN, to enhance the chest X-ray images. In this study, we proposed a modified ESPCN and present our findings on the application of SRGAN and ESPCN for enhancing chest Xray images. The enhanced image resolution enables radiologists to visualize fine details and subtle abnormalities that were previously difficult to discern. This enhancement significantly improves image interpretation and diagnostic accuracy. Moreover, our findings demonstrate the potential of super-resolution techniques for medical education and training. Through extensive experimentation, we saw significant visual improvements, as evidenced by PSNR(peak signal-to-noise ratio) of SRGAN and proposed ESPCN are 40.46 and 40.17, and achieved SSIM values 0.84 and 0.71 respectively. In conclusion, our study showcases the efficacy of SRGAN and ESPCN in enhancing chest X-ray imaging. By improving image quality and diagnostic accuracy, these techniques have the potential to revolutionize medical diagnostics, leading to better patient outcomes and a more effective healthcare system. © 2023 IEEE.

关键词： Image resolution

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Towards optimized tensor code generation for deep learning on sunway many-core processor

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Frontiers of computer Science 2024年第2期18卷 1-15页

作者： Mingzhen LI Changxi LIU Jianjin LIAO Xuegui ZHENG Hailong YANG Rujun SUN Jun XU Lin GAN Guangwen YANG Zhongzhi LUAN Depei QIAN State Key Laboratory of Software Development Environment Beijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China National University of Singapore Singapore 119077Singapore State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214000China Science and Technology on Special System Simulation Laboratory Beijing Simulation Center Beijing 100854China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China

The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application *** the existing deep learning compilers,TVM is well known for its efficiency in code generation and optimization across diverse hardware *** the meanwhile,the Sunway many-core processor renders itself as a competitive candidate for its attractive computational power in both scientific computing and deep learning *** paper combines the trends in these two ***,we propose swTVM that extends the original TVM to support ahead-of-time compilation for architecture requiring cross-compilation such as *** addition,we leverage the architecture features during the compilation such as core group for massive parallelism,DMA for high bandwidth memory transfer and local device memory for data locality,in order to generate efficient codes for deep learning workloads on *** experiment results show that the codes generated by swTVM achieve 1.79x improvement of inference latency on average compared to the state-of-the-art deep learning framework on Sunway,across eight representative *** work is the first attempt from the compiler perspective to bridge the gap of deep learning and Sunway processor particularly with productivity and efficiency in *** believe this work will encourage more people to embrace the power of deep learning and Sunwaymany-coreprocessor.

关键词： sunway processor deep learning compiler code generation performance optimization

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