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Authorization of Aadhar data using Diffie Helman key with enhanced security concerns

Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 8639-8658页

作者： Karthika, K. Rangasamy, Devi Priya Anna University Chennai India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India

In today’s digital era, the security of sensitive data such as Aadhaar data is of utmost importance. To ensure the privacy and integrity of this data, a conceptual framework is proposed that employs the Diffie-Hellman key exchange protocol and Hash-based Message Authentication Code (HMAC) to enhance the security. The proposed system begins with the preprocessing phase, which includes removing noise, standardizing formats and validating the integrity of the data. Next, the data is segmented into appropriate sections to enable efficient storage and retrieval in the cloud. Each segment is further processed to extract meaningful features, ensuring that the relevant information is preserved while reducing the risk of unauthorized access. For safeguarding the stored Aadhaar data, the system employs the Diffie-Hellman key exchange protocol which allows the data owner and the cloud service provider to establish a shared secret key without exposing it to potential attackers. Additionally, HMAC is implemented to verify the identity of users during the login process. HMAC enhances security by leveraging cryptographic hash functions and a shared secret key to produce a distinct code for each login attempt. This mechanism effectively protects the confidentiality and integrity of stored data. The combination of Diffie-Hellman key exchange and HMAC authentication provides a robust security framework for Aadhaar data. It ensures that the data remains encrypted and inaccessible without the secret key, while also verifying the identity of users during the login process. This comprehensive approach helps preventing unauthorized access thereby protecting against potential attacks, instilling trust and confidence in the security of Aadhaar data stored in the cloud. Results of the article depict that the proposed scheme achieve 0.19 s of encryption time and 0.05 s of decryption time. © 2024 – IOS Press. All rights reserved.

关键词： Sensitive data

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Unleashing the potential of ChatGPT in education: Revolutionizing learning and collaboration

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IEEE Potentials 2024年第6期43卷 13-22页

作者： Chandrasekaran, Balaji Nair, Smrithy Girijakumari Sreekantan College of Engineering Guindy Anna University Department of Computer Science and Engineering Chennai600025 India Vellore Institute of Technology School of Computer Science and Engineering Chennai600127 India

ChatGPT, an advanced language model powered by artificial intelligence, has emerged as a transformative tool in the field of education. This article explores the potential of ChatGPT in revolutionizing learning and collaboration within educational settings. By leveraging natural language processing and machine learning, ChatGPT offers an interactive and personalized learning experience, enabling students to engage in dynamic conversations and receive instant feedback. Moreover, ChatGPT may facilitate collaborative learning by promoting effective communication and knowledge sharing among students and educators. This article examines the various applications of ChatGPT in education, including virtual tutoring, adaptive learning, and intelligent chatbots. It also discusses the benefits and considerations associated with implementing ChatGPT, such as ethical considerations, data privacy, and potential biases. While acknowledging the limitations and challenges, this article emphasizes the promising potential of ChatGPT in transforming education by enhancing student engagement, fostering critical thinking skills, and expanding access to quality education. © 1988-2012 IEEE.

关键词： Adversarial machine learning

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Boosting Transferability and Discriminability for Time Series Domain Adaptation 38

Boosting Transferability and Discriminability for Time Serie...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Liu, Mingyang Chen, Xinyang Shu, Yang Li, Xiucheng Guan, Weili Nie, Liqiang School of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Data Science and Engineering East China Normal University China School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen China

Unsupervised domain adaptation excels in transferring knowledge from a labeled source domain to an unlabeled target domain, playing a critical role in time series applications. Existing time series domain adaptation methods either ignore frequency features or treat temporal and frequency features equally, which makes it challenging to fully exploit the advantages of both types of features. In this paper, we delve into transferability and discriminability, two crucial properties in transferable representation learning. It's insightful to note that frequency features are more discriminative within a specific domain, while temporal features show better transferability across domains. Based on the findings, we propose Adversarial CO-learning Networks (ACON), to enhance transferable representation learning through a collaborative learning manner in three aspects: (1) Considering the multi-periodicity in time series, multi-period frequency feature learning is proposed to enhance the discriminability of frequency features;(2) Temporal-frequency domain mutual learning is proposed to enhance the discriminability of temporal features in the source domain and improve the transferability of frequency features in the target domain;(3) Domain adversarial learning is conducted in the correlation subspaces of temporal-frequency features instead of original feature spaces to further enhance the transferability of both features. Extensive experiments conducted on a wide range of time series datasets and five common applications demonstrate the state-of-the-art performance of ACON. Code is available at https://***/mingyangliu1024/ACON. © 2024 Neural information processing systems foundation. All rights reserved.

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Improving 5G Positioning Through Signal-to-Noise Ratio Recognition Training

Improving 5G Positioning Through Signal-to-Noise Ratio Recog...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zheng, Wenqi Chen, Jianing Yang, Junze Chen, Chuhao Li, Wei Yadav, Rahul Meng, Xiangxu College of Computer Science and Technology Harbin Engineering University China

ISBN: (纸本)9798350368741

Fifth-generation communication technology enables advanced indoor positioning with its high bandwidth and frequency capabilities. However, indoor environment variability causes signal propagation fluctuations, making existing models inadequate for accurate location estimation. In this paper, we demonstrate that integrating SNR recognition training with the positioning network can enhance localization performance. Specifically, by leveraging a general neural network to extract common features from 5G Channel State Information (CSI), we employ two carefully designed downstream networks for signal-to-noise ratio (SNR) estimation and location prediction. In order to improve localization performance, we utilize mutual information-based metric to direct each network head towards its specific task, thereby refining and enhancing the features pertinent to each task. Moreover, for enhanced adaptive positioning accuracy, the coefficients linked to the features used in SNR and position estimation are determined based on the hidden features of the corresponding identification head. These coefficients are then utilized to appropriately weigh the hidden features of the location regression module. Experimental results using datasets from real-world scenarios demonstrate that our proposed method significantly improves positioning accuracy over state-of-the-art (SOTA) methods. Code can be available at: https://***/mxx123321/indoor_snr. © 2025 IEEE.

关键词： 5G Channel State Information (CSI) Deep Learning (DL) Fingerprint Indoor positioning

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ZVEFusion: Zero-Shot Visual Enhancement Fusion for Infrared and Visible Images in Low Light

ZVEFusion: Zero-Shot Visual Enhancement Fusion for Infrared ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Duo Shi, Yiqi Zhang, Guoyin Li, Sizhao Zhang, Liguo School of Computer Science and Technology Harbin Engineering University China

ISBN: (纸本)9798350368741

Infrared and visible image fusion (IVIF) aims to generate fused images with prominent targets and rich scene information. However, in low-light conditions, visible images lose accurate texture and color, reducing their ability to provide detailed scene information for fusion. Existing IVIF methods often overlook illumination degradation and cause color distortion when incorporating infrared information. To address these problems, we propose a novel visually enhanced IVIF method tailored for low-light environments. Our method combines low-light image enhancement (LLIE) and IVIF into a single module. First, we adaptively enhance the low-light visible image, ensuring rich texture and color for fusion. Additionally, we introduce a three-channel fusion coefficient map to transform infrared information into visible image, preventing color distortion and highlighting key targets while maintaining details of the fused image. Since infrared and visible images are from different modalities, we map them into the same high-dimensional feature space. We then propose the feature difference to integrate complementary information, producing a fused image with complete content and no redundancy. Notably, our method is zero-shot, requiring only a pair of test infrared and visible images for training. This better meets the complexity of IVIF in various low-light scenes. Extensive experiments show that in low-light conditions, our method surpasses other state-of-the-art (SOTA) methods by providing more natural colors, richer textures, and better alignment with human visual perception. © 2025 IEEE.

关键词： Image Fusion Low Light Visually Enhanced Zero-Shot

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GEMD-UNet: Graph Structure Enhanced Multi-dimensional Learning Unet for Cloud Detection

GEMD-UNet: Graph Structure Enhanced Multi-dimensional Learni...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Chen, Jianing Chen, Chuhao Yang, Junze Li, Wei Yadav, Rahul Zheng, Wenqi College of Computer Science and Technology Harbin Engineering University China

ISBN: (纸本)9798350368741

Cloud detection (CD) in remote sensing images is commonly used in satellite imaging and laser communication. UNet-based methods with multi-level feature caching and interaction learning, are popular for superior CD performance. However, most current CD methods focus on spatial feature enhancement through global feature learning, which often overlooks the detailed perception of color, shape, and boundaries due to insufficient channel interactions. Additionally, these methods tend to increase computational requirements. To ensure flexible feature attention, enhancing both structural and detailed understanding, while maintaining low computational complexity. In this paper, we propose Graph Structure Knowledge Enhanced Multi-Dimensional Learning U-shaped Network (GEMD-UNet) for CD. Specifically, we utilize tensor dimension transformations to cleverly employ two fundamental learnable parameters alternately for spatial/channel relationship learning. Furthermore, to enhance feature knowledge capacity and focus more on local detail, we employ local window partitioning to divide hidden features into multiple independent learning groups and inject two different physically meaningful graph structures knowledge in a non-Euclidean space into features. Experimental results demonstrate that GEMD-UNet outperforms previous SOTA methods on both HRC-WHU and L8SPARCS datasets. Code can be available at: https://***/mxx123321/cloud_model. © 2025 IEEE.

关键词： Cloud detection (CD) Deep Learning Graph Neural Network UNet

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Aggregated Relative Similarity (ARS): a novel similarity measure for improved personalised learning recommendation using hybrid filtering approach

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Multimedia Tools and Applications 2025年第18期84卷 18733-18780页

作者： Pal, Saurabh Dutta Pramanik, Pijush Kanti Choudhury, Prasenjit Department of Computer Science and Engineering National Institute of Technology Durgapur West Bengal Durgapur India School of Computer Applications and Technology Galgotias University Uttar Pradesh Greater Noida India Department of Computer Science and Engineering School of Engineering Brainware University Kolkata India

To improve the effectiveness of online learning, the learning materials recommendation is required to be personalised to the learner material recommendations must be personalized to learners. The existing approaches are ineffective in recommending learning materials according to the learner’s learning situation, the learner’s internal learning characteristics, and fitting to the learner’s preferences and learning suitability. In this work, we propose a hybrid recommendation approach cthat combines the advantages of knowledge-based and collaborative filtering approaches. The knowledge-based filtering approach enables us to find the most suitable learning materials that fit the learner’s situation and learning state by appropriately mapping the learners’ contexts with the metadata of the learning materials. We use collaborative filtering to refine learning material selection by considering learner-learner similarity. We propose a novel similarity measure, aggregated relative similarity (ARS), to determine similar learners based on implicit learner characteristics such as education, knowledge, cognitive ability, and learning style. The experimental results show that the proposed ARS algorithm outperforms popular similarity measures such as Jaccard similarity, Sorensen-Dice coefficient, and overlap coefficient. The overall recommendation performance of the proposed hybrid approach attained mean absolute error (MAE) and root mean square error (RMSE) of 0.681 and 0.9198, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Collaborative filtering

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WSO-KELM: War Strategy Optimization-Based Kernel Extreme Learning Machine for Automatic Software Fault Prediction Model

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Journal of The Institution of Engineers (India): Series B 2025年第1期106卷 145-163页

作者： Elci, J. Brundha Nandagopalan, S. Department of Computer Science and Engineering Vemana Institute of Technology Visvesvaraya Technological University Karnataka Belagavi India Department of Information Science and Engineering Vemana Institute of Technology Visvesvaraya Technological University Karnataka Belagavi India

The software development projects’ testing part is usually expensive and complex, but it is essential to gauge the effectiveness of the developed software. Software Fault Prediction (SFP) primarily serves to detect faults within software components. The scale of software projects is getting larger, making it impossible and time-consuming to analyze the flaws manually. Artificial Intelligence (AI) techniques are mainly incorporated to solve this problem. The massive data retrieved from the software repositories helps in identifying the features that are highly correlated with fault prediction and the features that are not. This paper introduces a Kernel Extreme Learning Machine (KELM) for SFP. To optimize the hyperparameters of the classifier and also enhance the classification accuracy, the War Strategy Optimization (WSO) algorithm is employed here. The experimental evaluations are conducted using different baseline actual-world datasets of software programs such as Java-coded open-source and PROMISE datasets. The experimental outcomes are conducted using different performance metrics that include sensitivity, Accuracy, and F1-score with values of 0.96%, 0.97%, and 0.96%, respectively. The results show that KELM is beneficial, and the optimization provided by the WSO algorithm also helps us obtain higher classification accuracy for the SFP problem and improve the fault detection process. © The Institution of Engineers (India) 2024.

关键词： Software reliability

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Improvement of Formation,Microstructure and Mechanical Properties of TIG Welded TC4 Titanium Alloy by Ultrasonic Coaxial Radiation

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Acta Metallurgica Sinica(English Letters) 2024年第12期37卷 2045-2056页

作者： Yihao Gao Boqiao Ren Yunhao Chen Xinchen Sui Xiaohui Zhao Chenglei Fan Chao Chen Key Laboratory of Automobile Materials School of Materials Science and EngineeringJilin UniversityChangchun130025China State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyHarbin150001China

In this study,the effects of ultrasonic coaxial assisted TIG welding(U-TIG)on the formation,microstructure,and mechanical properties of TC4 titanium alloy welded joints were *** results indicated that at optimal ultrasonic power and welding current settings,remarkable improvements of welded joint were ***,the weld depth-to-width ratio increased by 267%compared to conventional TIG welding,reaching ***,the microhardness of the welded seam increased by 53%,reaching 550.1 HV,while the tensile strength of the welded joint rose to 1197.7 MPa,representing a 12.2%*** compressing effect of ultrasound concentrates the energy of the arc,thereby improving the penetration depth of the welded *** addition,ultrasonic cavitation plays a crucial role in refining the grain structure of the weld seam,consequently enhancing its strength.

关键词： TC4 alloy Ultrasonic coaxial assisted TIG welding(U-TIG) Arc shape Microstructure Mechanical properties

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On the Stability of Random Access with Congestion Control 14th

On the Stability of Random Access with Congestion Control

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14th EAI International Conference on Wireless and Satellite Systems, WiSATS 2024

作者： Xu, Yu Cui, Chen Wang, Zhenyong Guo, Qing School of Electronics and Information Engineering Harbin Institute of Technology Harbin150001 China National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing100871 China Songjiang Laboratory Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9783031861956

The random access protocol is a critical technique widely used in various wireless networks. A significant challenge is analyzing the stability of the random access system when a large number of users transmit packets independently. Although the congestion control policy can enhance the stability of the system, there is currently a lack of quantitative analysis on the stability of random access with congestion control. In this paper, we analyze the random access system with congestion control and quantify its stability. Specifically, we consider a congestion control scheme to enhance the stability of the random access system and present the graphical analytical model. Based on this graphical model, we qualitatively discuss the behavioral variations of the system as it works and quantify its stability. © ICST institute for computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Congestion control (communication)

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