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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Okada, Masamune Mohd Kamal, Ahmad Akmal Aminuddin Fujisawa, Masaya Tokyo University of Science Department of Information and Computer Technology Tokyo Japan

ISBN: (纸本)9798350355079

Blockchains (BCs) have garnered attention owing to their potential applications in a wide range of fields, including finance and the Internet of Things. Nakajima et al. introduced a secure and decentralized storage system using a permissioned BC and secret sharing. However, this method employs conventional password-based user authentication with a single SDK server. In this study, we introduce a secure and decentralized user authentication system that employs secure computation based on Shamir's (2,2) threshold secret sharing and smart contracts. We demonstrate that employing secure computation using secret sharing facilitates user authentication while maintaining the confidentiality of the original password. Furthermore, we show that even if the information registered on the SDK server is known, an attacker will not be able to recover the original password. © 2024 IEEE.

关键词： Authentication Protocol

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Speech cryptography algorithms: utilizing frequency and time domain techniques merging

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Journal of Ambient Intelligence and Humanized Computing 2024年第10期15卷 3617-3649页

作者： Faragallah, Osama S. Farouk, M. El-sayed, Hala S. El-bendary, Mohsen A. M. Department of Information Technology College of Computers and Information Technology Taif University Al- Hawiya21974 Saudi Arabia Department of Computer Science and Engineering Menoufia University Menouf32952 Egypt Department of Electrical Engineering Menoufia University Shebin El-kom32511 Egypt Department of Electronics Technology Faculty of Technology and Education Helwan University Cairo Egypt

Due to the importance security of sensitive speech and audio signals such as the countries president’s call phone, this research work presents an efficient audio cryptosystem. Audio speech communications play main and essential role in the human daily life, covering many important fields ranging from simple personal private information up to top secret, sensitive and critical information. It is difficult to prevent the eavesdropping by unauthorized person especially 1-D audio scrambling methods, it is built by simple algorithms and has low security level. So, there is a dire need for enhancing audio cryptosystem to be more secure against different types of attacks. In this work, the enhanced audio cryptosystem is proposed, the first approach enhances multiple key audio cryptosystem utilizing the chaotic baker map through enhancing the confusion level for both encryption/decryption algorithms employing time and transform domains. Simulation experiments revealed that the proposed boosted audio cryptosystem on time and transform domains is secure and robust against different types of attacks. The boosted multiple key based audio crypto-system is performed by increasing confusion and enlarging the secret-key space. Results of the simulation experiments revealed that the proposed multiple secret key-enhanced confusion audio cryptosystem is secure and robust with respect to different types of attacks. Moreover, results for transform domains show better results than results for time domain for the three proposed boosted audio cryptosystem. Also, the third proposed audio cryptosystem DWT-based shows the best results among all other proposed audio cryptosystem. Due to the results, robustness and reliability of proposed technique are higher the existing audio cryptographic system. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Cryptography

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A Comprehensive Analysis of Knowledge Graph-based Social Recommendation system 1

A Comprehensive Analysis of Knowledge Graph-based Social Rec...

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1st International Conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Navuluri, Madhavilatha Bhuyan, Hemanta Kumar RGUKT-IIIT Department of Computer Science and Engineering Ongole India Department of Information Technology and Computer Applications Guntur India

ISBN: (纸本)9798331529246

Recommendation systems are crucial due to their high relevance in terms of interpretability and performance. A Social Recommendation system explores how social relations influence user choices and how users select items. In this system, user-embeddings are learned from Knowledge graphs to enhance the recommendations. The social recommendation system can be categorized into three main areas: user recommendation, movie recommendation, and explainable recommendation. User recommendation focusses on providing users with personalized services, movie recommendation enhances the movie-watching experience, and explainable recommendation aims to provide the reasoning behind the suggested items. Data sparsity and the cold start problem are significant challenges faced by RS. The Social RS model addresses these issues by leveraging both user-item interactions and user-user social relationships, thereby improving the overall quality of social network-based recommendations. Methods such as Light Graph Convolutional Network (LGCN), Hyperbolic Recommender (HSR), and Knowledge Enhanced Graph Neural Networks (KEGNN) have been proposed to enhance social recommendation performance. Evaluation metrics such as recall @N, precision @N are commonly used to assess and estimate the effectiveness of recommendation systems. © 2025 IEEE.

关键词： Knowledge graph

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The role of explainability and transparency in fostering trust in AI healthcare systems: a systematic literature review, open issues and potential solutions

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Neural Computing and Applications 2024年第4期37卷 1999-2034页

作者： Eke, Christopher Ifeanyi Shuib, Liyana Department of Information System FCSIT University of Malaya Kuala Lumpur50603 Malaysia Department of Computer Science Faculty of Computing Federal University of Lafia Nasarawa State Lafia Nigeria

The healthcare sector has advanced significantly as a result of the ability of artificial intelligence (AI) to solve cognitive problems that once required human intelligence. As artificial intelligence finds more applications in healthcare, trustworthiness must be guaranteed. Even while AI has the potential to improve healthcare, there are still challenging issues because it is yet to be widely adopted, especially when it comes to transparency. Concerns about comprehending the internal workings of AI models, possible biases, model robustness, and generalizability are raised by their opacity which makes them function like black boxes. A solution for worries over the transparency of AI algorithms is explainable AI. Explainable AI seeks to enhance AI explainability and analytical capabilities, particularly in vital industries like healthcare. Even though earlier research has examined several explainable AI-related topics, such as a lexicon, industry-specific overviews, and applications in the healthcare industry, a thorough analysis concentrating on the function of explainable AI in building trust in AI healthcare systems is required. In an effort to close this gap, a systematic literature review that adheres to PRISMA principles that analyze relevant papers that were published between 2015 and 2023 was done in this paper. To determine the critical role that explainable AI plays in fostering trust, this study examines widely utilized methodologies, machine learning and deep learning techniques, datasets, performance measures and validation procedures used in AI healthcare research. In addition, research issues and potential research directions are also discussed in this research. Thus, this systematic review provides a thorough summary of the present status of research on explainability and transparency in AI healthcare systems, thus illuminating crucial factors that affect user trust. The results are intended to assist researchers, policymakers and healthcare professi

关键词： Adversarial machine learning

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A Coarse to Fine Detection Method for Prohibited Object in X-ray Images Based on Progressive Transformer Decoder 24

A Coarse to Fine Detection Method for Prohibited Object in X...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Ma, Chunjie Du, Lina Gao, Zan Zhuo, Li Wang, Meng Shandong Jinan China School of Computer Science and Technology Shandong Jianzhu University Shandong Jinan China Faculty of Information Technology Beijing University Of Technology Beijing China School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China

ISBN: (纸本)9798400706868

Currently, Transformer-based prohibited object detection methods in X-ray images appear constantly, but there are still some shortcomings such as poor performance and high computational complexity for prohibited object detection with heavily occlusion. Therefore, a coarse to fine detection method for prohibited object in X-ray images based on progressive Transformer decoder is proposed in this paper. Firstly, a coarse to fine framework is proposed, which includes two stages: coarse detection and fine detection. Through adaptive inference in stages, the computational efficiency of the model is effectively improved. Then, a position and class object queries method is proposed, which improves the convergence speed and detection accuracy of the model by fusing the position and class information of prohibited object with object queries. Finally, a progressive Transformer decoder is proposed, which distinguishes high and low score queries by decreasing confidence thresholds, so that high-score queries are not affected by low-score queries in the decoding stage, and the model can focus more on decoding low-score queries, which usually correspond to prohibited object with severe occlusion. The experimental results on three public benchmark datasets (SIXray, OPIXray, HiXray) demonstrate that compared with the baseline DETR, the proposed method achieves the state-of-the-art detection accuracy with a 21.6% reduction in model computational complexity. Especially for prohibited objects with heavily occlusion, accurate detection can be carried out. © 2024 ACM.

关键词： Decoding

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Speaker diarization based on X vector extracted from time-delay neural networks (TDNN) using agglomerative hierarchical clustering in noisy environment

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International Journal of Speech technology 2025年第1期28卷 13-26页

作者： Khadar, K. V. Aljinu Sunil Kumar, R.K. Sameer, V.V. Department of Computer Science University of Calicut Tenhippalam Kerala Malappuram673635 India Department of Information Technology Kannur University Mangattuparamba Kerala Kannur670567 India Department of Computer Science Farook College Kerala Kozhikode673 632 India

This paper introduces a speaker diarization system using speaker embedding parameters, specifically the x-vector. By incorporating auto-correlated MFCC features for x-vector extraction using a pre-trained time delay neural network, the system exhibits enhanced adaptability to noise variations. Speaker clustering is accomplished through agglomerative clustering with PLDA scoring as the distance metric, making the system particularly valuable for potential speaker identification, especially in forensic applications. The system’s noise adaptability is thoroughly evaluated by integrating various types of noise such as Red, Pink, and white noise across a wide range of signal-to-noise ratios (20 dB to − 20 dB). Additionally, the system’s performance is comprehensively assessed by varying speech duration and adjusting the number of speakers, highlighting its robustness and effectiveness in real-world scenarios. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Vectors

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Robust Human Interaction Recognition Using Extended Kalman Filter

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computers, Materials & Continua 2024年第11期81卷 2987-3002页

作者： Tanvir Fatima Naik Bukht Abdulwahab Alazeb Naif Al Mudawi Bayan Alabdullah Khaled Alnowaiser Ahmad Jalal Hui Liu Faculty of Computing&Artificial Intelligence Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Engineering College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Cognitive Systems Lab University of BremenBremen28359Germany

In the field of computer vision and pattern recognition,knowledge based on images of human activity has gained popularity as a research *** recognition is the process of determining human behavior based on an *** implemented an Extended Kalman filter to create an activity recognition system *** proposed method applies an HSI color transformation in its initial stages to improve the clarity of the frame of the *** minimize noise,we use Gaussian *** of silhouette using the statistical *** use Binary Robust Invariant Scalable Keypoints(BRISK)and SIFT for feature *** next step is to perform feature discrimination using Gray *** that,the features are input into the Extended Kalman filter and classified into relevant human activities according to their definitive *** experimental procedure uses the SUB-Interaction and HMDB51 datasets to a 0.88%and 0.86%recognition rate.

关键词： Pattern recognition geometric features activity recognition full-body texture

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Human Activity Recognition Using AutoML Approach 4

Human Activity Recognition Using AutoML Approach

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4th IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2024

作者： Alboueishi, Nadeen H. Nagem, Tarek A. M. Abdelnabi, Esra A. University of Benghazi Faculty of Information Technology Department of Artificial Intelligence and Computer Systems Benghazi Libya University of Benghazi Faculty of Education Department of Computer Science Benghazi Libya

ISBN: (纸本)9798350372632

This paper focuses on improving human activity recognition using smartphones by training the model using H2O AutoML on a dataset called WISDM. This dataset was collected from smartphone users performing various human activities such as walking, jogging, standing, sitting, upstairs, and downstairs, and was used to train the model. The model's performance was improved using H2O AutoML and evaluated using various performance metrics such as classification accuracy and F1 score. The results showed significant improvement in the model's performance in identifying various human activities, indicating the effectiveness of using H2O AutoML to improve machine learning-based models for human activity recognition using smartphones. This model can be used in various applications such as fitness, healthcare, and smart application control. It is expected to improve users' quality of life and performance in various fields. © 2024 IEEE.

关键词： Smartphones

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Multidimensional image morphing-fast image-based rendering of open 3D and VR environments

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虚拟现实与智能硬件(中英文) 2025年第2期7卷 155-172页

作者： Simon SEIBT Bastian KUTH Bartosz von Rymon LIPINSKI Thomas CHANG Marc Erich LATOSCHIK Game Tech Laboratory Faculty of Computer ScienceNuremberg Institute of TechnologyNuremberg 90489Germany Department of Electrical Engineering and Computer Science Coburg University of Applied Sciences and ArtsCoburg 96450Germany Human-Computer Interaction Group Institute of Computer ScienceUniversity of WuerzburgWuerzburg 97074Germany

Background In recent years,the demand for interactive photorealistic three-dimensional(3D)environments has increased in various fields,including architecture,engineering,and ***,achieving a balance between the quality and efficiency of high-performance 3D applications and virtual reality(VR)remains *** This study addresses this issue by revisiting and extending view interpolation for image-based rendering(IBR),which enables the exploration of spacious open environments in 3D and ***,we introduce multimorphing,a novel rendering method based on the spatial data structure of 2D image patches,called the image *** this approach,novel views can be rendered with up to six degrees of freedom using only a sparse set of *** rendering process does not require 3D reconstruction of the geometry or per-pixel depth information,and all relevant data for the output are extracted from the local morphing cells of the image *** detection of parallax image regions during preprocessing reduces rendering artifacts by extrapolating image patches from adjacent cells in *** addition,a GPU-based solution was presented to resolve exposure inconsistencies within a dataset,enabling seamless transitions of brightness when moving between areas with varying light *** Experiments on multiple real-world and synthetic scenes demonstrate that the presented method achieves high"VR-compatible"frame rates,even on mid-range and legacy hardware,*** achieving adequate visual quality even for sparse datasets,it outperforms other IBR and current neural rendering *** Using the correspondence-based decomposition of input images into morphing cells of 2D image patches,multidimensional image morphing provides high-performance novel view generation,supporting open 3D and VR ***,the handling of morphing artifacts in the parallax image regions remains a topic for future resea

关键词： computer graphics 3D real-time rendering computer vision Image morphing Virtual reality

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An Implementation of Brain-computer Interface in Research Activities 15

An Implementation of Brain-Computer Interface in Research Ac...

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15th International Scientific and Practical Conference on Environment. technology. Resources, ETR 2024

作者： Mamchur, Dmytro Peksa, Janis Department of Information Technology Turiba University Riga Latvia Computer Engineering and Electronics Department Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Ukraine Institute of Information Technology Faculty of Computer Science and Information Technology Riga Technical University Riga Latvia

ISBN: (纸本)9781713899570

The paper provides brief overview of Brain-computer Interface (BCI), highlighting BCI-based assisting technologies for disabled persons in healthcare applications as one of the key priorities. A proposed solution for implementation OpenBCI-based hardware and software in research on computer typing assistance system described, including general view of experimental test bench and software approach description. An experimental verification of a possible computer typing assistance system is presented, as well as basic test results obtained along with their interpretation and discussion. © 2024 Dmytro Mamchur, Janis Peksa.

关键词： Brain computer interface

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