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Secure and Low-Complexity Medical Image Exchange Based on Compressive Sensing and LSB Audio Steganography

Informatica (Slovenia) 2023年第6期47卷 65-74页

作者： Al-Chaab, Wafaa Abduljabbar, Zaid Ameen Abood, Enas Wahab Nyangaresi, Vincent Omollo Mohammed, Hussein M. Ma, Junchao Department of Mathematics College of Science University of Basrah Basrah61004 Iraq Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah61004 Iraq Technical Computer Engineering Department Al-Kunooze University College Basrah61001 Iraq Huazhong University of Science and Technology Shenzhen Institute Shenzhen430074 China Jaramogi Oginga Odinga University of Science & Technology Bondo40601 Kenya Department of Computer Science Shatt Al-Arab University College Basra61001 Iraq College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China

Patients’ data constitutes the vast majority of information exchanged over the Internet. The magnitude of maintaining its security and privacy directly correlates to the importance and privacy of the patients themselves. The manipulation of this data negatively impacts the patient's life and treatment, thus the researchers were inclined to find solutions for this confidentiality challenge by concealing the data to prevent unauthorized access. The process of data concealment is more complex when dealing with large amounts of data. Often, the proportions of the medical image are sizable, which creates challenges when compressing data while maintaining high accuracy. In this paper, a system for securing and compressing medical images based on the Compressive Sensing (CS) principle is presented. The medical image is divided into 8×4 sub-matrices that are multiplied by a 3×8 sensor matrix consisting of Gaussian random numbers. The proposed solution reduces the image's original size by about 30% and conceals it as a random distribution inside an audio (wav) file for more security, using the LSB technique for low complexity. For reconstruction, the sensed image is multiplied by the pseudo-inverse of Moore-Penrose. The statistical analysis proved the efficacy of the system in compression and recovery with reduced cost and time consumption, combined with reduced distortion of the cover file;it was also judged to be increasingly efficient compared to previous research. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Compressed sensing

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IMF: Interactive Multimodal Fusion Model for Link Prediction 23

IMF: Interactive Multimodal Fusion Model for Link Prediction

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2023 World Wide Web Conference, WWW 2023

作者： Li, Xinhang Zhao, Xiangyu Xu, Jiaxing Zhang, Yong Xing, Chunxiao Department of Computer Science and Technology Tsinghua University Beijing China School of Data Science City University of Hong Kong Hong Kong School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781450394161

Link prediction aims to identify potential missing triples in knowledge graphs. To get better results, some recent studies have introduced multimodal information to link prediction. However, these methods utilize multimodal information separately and neglect the complicated interaction between different modalities. In this paper, we aim at better modeling the inter-modality information and thus introduce a novel Interactive Multimodal Fusion (IMF) model to integrate knowledge from different modalities. To this end, we propose a two-stage multimodal fusion framework to preserve modality-specific knowledge as well as take advantage of the complementarity between different modalities. Instead of directly projecting different modalities into a unified space, our multimodal fusion module limits the representations of different modalities independent while leverages bilinear pooling for fusion and incorporates contrastive learning as additional constraints. Furthermore, the decision fusion module delivers the learned weighted average over the predictions of all modalities to better incorporate the complementarity of different modalities. Our approach has been demonstrated to be effective through empirical evaluations on several real-world datasets. The implementation code is available online at https://***/HestiaSky/IMF-Pytorch. © 2023 Owner/Author.

关键词： Knowledge graph

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A Case Study on Context-Aware Neural Machine Translation with Multi-Task Learning 25

A Case Study on Context-Aware Neural Machine Translation wit...

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25th Annual Conference of the European Association for Machine Translation, EAMT 2024

作者： Appicharla, Ramakrishna Gain, Baban Pal, Santanu Ekbal, Asif Bhattacharyya, Pushpak Department of Computer Science and Engineering Indian Institute of Technology Patna India Wipro AI Lab45 London United Kingdom School of AI and Data Science Indian Institute of Technology Jodhpur India Department of Computer Science and Engineering Indian Institute of Technology Bombay India

ISBN: (纸本)9781068690709

In document-level neural machine translation (DocNMT), multi-encoder approaches are common in encoding context and source sentences. Recent studies (Li et al., 2020) have shown that the context encoder generates noise and makes the model robust to the choice of context. This paper further investigates this observation by explicitly modelling context encoding through multi-task learning (MTL) to make the model sensitive to the choice of context. We conduct experiments on cascade MTL architecture, which consists of one encoder and two decoders. Generation of the source from the context is considered an auxiliary task, and generation of the target from the source is the main task. We experimented with German-English language pairs on News, TED, and Europarl corpora. Evaluation results show that the proposed MTL approach performs better than concatenation-based and multi-encoder DocNMT models in low-resource settings and is sensitive to the choice of context. However, we observe that the MTL models are failing to generate the source from the context. These observations align with the previous studies, and this might suggest that the available document-level parallel corpora are not context-aware, and a robust sentence-level model can outperform the context-aware models. © 2024 The authors, © 2024 European Association for Machine Translation.

关键词： Neural machine translation

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Robust EEG-based Emotion Recognition Using an Inception and Two-sided Perturbation Model 46

Robust EEG-based Emotion Recognition Using an Inception and ...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Sartipi, Shadi Cetin, Mujdat University of Rochester Department of Electrical and Computer Engineering Rochester United States University of Rochester Goergen Institute for Data Science Department of Electrical and Computer Engineering Rochester United States

ISBN: (纸本)9798350371499

Automated emotion recognition using electroencephalogram (EEG) signals has gained substantial attention. Although deep learning approaches exhibit strong performance, they often suffer from vulnerabilities to various perturbations, like environmental noise and adversarial attacks. In this paper, we propose an Inception feature generator and two-sided perturbation (INC-TSP) approach to enhance emotion recognition in brain-computer interfaces. INC-TSP integrates the Inception module for EEG data analysis and employs two-sided perturbation (TSP) as a defensive mechanism against input perturbations. TSP introduces worst-case perturbations to the model's weights and inputs, reinforcing the model's elasticity against adversarial attacks. The proposed approach addresses the challenge of maintaining accurate emotion recognition in the presence of input uncertainties. We validate INC-TSP in a subject-independent three-class emotion recognition scenario, demonstrating robust performance. © 2024 IEEE.

关键词： Emotion Recognition

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A Bayesian-based approach for extracting the pion charge radius from electron-electron scattering data

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Chinese Physics C 2021年第8期45卷 12-27页

作者： Alam A Hidayat Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara UniversityJakarta 11480Indonesia Computer Science Department BINUS Graduate Program-Master of Computer ScienceBina Nusantara UniversityJakarta 11480Indonesia

In this study,we utilize a potentially versatile Bayesian parameter approach to compute the value of the pion charge radius and quantify its uncertainty from several experimental e^(+)e^(-) datasets for the pion vector form *** employ dispersion relations to model the pion vector form factor to extract the *** model selection is used to determine the order of polynomial appearing in the form factor formulation that can be supported by the data,adapting the computation of Bayes evidence and Bayesian effective complexity based on Occam's *** findings indicate that five out of six used datasets favor the nine-parameter model for radius extraction,and accordingly,we average the radii from the *** some inconsistencies with the most updated radius values,our approach may serve as a more intuitive method of addressing parameter estimations in dispersion theory.

关键词： Bayesian complexity dispersion hadron pion

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Ensuring Success in Quick Commerce by Evaluating Its Application Development Capability Maturity Using COBIT 2019 8

Ensuring Success in Quick Commerce by Evaluating Its Applica...

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8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Heruatmadja, Chandra Hermawan Gaol, Ford Lumban Supangkat, Suhono Harso Ranti, Benny Bina Nusantara University Binus Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Institute of Technology Bandung Faculty of Digital Communication and Hotel & Tourism Bandung Indonesia Universitas Indonesia Faculty of Computer Science Computer Science Department Depok Indonesia

ISBN: (纸本)9798350326093

In the digital era, the demand for quick access to goods (q-Commerce) has driven retail companies to develop online shopping applications, aiming to attract more consumers. To cater to the demands of q-Commerce, specialized mobile applications that prioritize fast delivery and user-friendly experiences are crucial. However, developing and maintaining these applications present challenges in terms of speed of development and security. This research focuses on a retail company, PT X, with a q-Commerce application serving 3.5 million active users monthly. The research aims to assess PT X's capability maturity level in application development governance using the COBIT 2019 Capability Maturity Model. Additionally, the study investigates the differences in assessment methods between COBIT 2019 and previous versions. The findings contribute to understanding in which level of capability maturity that companies can achieve in developing quick commerce applications with a large user base. Findings of this research provides an overview for PT X to initiate the necessary initial steps to comply with the good application development according to COBIT 2019 standard. This research also highlights to future researchers that COBIT 2019 is primarily a framework for enterprise-level assessment and need to be adjusted to be suitable for measurements below the enterprise level. © 2023 IEEE.

关键词： Mobile commerce

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Opticurve: an optimized informer-curvelet framework for enhanced hyperspectral image segmentation and classification

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International Journal of Information Technology (Singapore) 2025年 1-16页

作者： Shaw, Kailash Wou Onn, Choo Li, Baihua Department of Computer Science and Engineering (AIML) Vishwakarma Institute of Technology Maharashtra Pune 411037 India Faculty of Data Science and Information Technology INTI International University Persiaran Perdana BBN Putra Nilai Negeri Sembilan Nilai 71800 Malaysia Department of Computer Science Loughborough University Loughborough LE11 3TU United Kingdom

Hyperspectral image (HSI) classification is crucial for applications in climate action, land use analysis, disaster risk reduction, and informed decision-making, given the complex spatial and spectral variations inherent in HSI data. Traditional methods struggle with accurately capturing these variations, necessitating more advanced techniques. This work introduces an Optimized Linformer-Curvelet (OptiCurve) Framework that integrates CNN-based feature extraction, Curvelet Transform for spatial detail capture, and Linformer for efficient feature representation. By combining these techniques, the model enhances HSI segmentation and classification, supporting improved outcomes in critical areas like environmental monitoring and disaster response. The framework is validated on four standard HSI datasets-Indian Pines, Pavia University, Kennedy Space Center, and Houston University-showing significant performance improvements over existing methods. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Climate action Disaster risk reduction Feature extraction Hyperspectral image classification Land use Linformer-Curvelet transform

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TezNet: An Efficient Convolutional Neural Network for Optical Coherence Tomography Classification 6

TezNet: An Efficient Convolutional Neural Network for Optica...

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6th International Conference on Robotics and Automation in Industry, ICRAI 2024

作者： Qaiser, Muhid Abbas, Muhammad Sohail Muhammad, Asif Haider, Khadija Khan, Haroon National University of Computer and Emerging Sciences Department of Artificial Intelligence and Data Science Islamabad Pakistan National University of Computer and Emerging Sciences Department of Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350355703

Retinal diseases such as Choroidal Neovascularization (CNV), Drusen, and Diabetic Macular Edema (DME) can severely impair vision and are challenging to reverse if detected late. Optical Coherence Tomography (OCT) serves as a non-invasive imaging modality to diagnose these conditions through cross-sectional retinal images. Current advancements in retinal disease detection predominantly focus on fine-tuning larger models to achieve high accuracy, often at the expense of substantial training time and data requirements. We introduce an innovative Convolutional Neural Network (CNN) named TezNet that is deeper and wider than existing custom CNNs for the dataset used in this research yet maintains a lightweight architecture with only 3.6 million parameters. This high efficiency is accomplished by leveraging depthwise and spatial separable convolutional techniques without employing preprocessing techniques or noise reduction filters. The data augmentation techniques generate diverse image qualities, ensuring robust predictions across various scenarios. By training exclusively on a single dataset, our model achieves rapid training times and optimal resource utilization, minimizing the risk of overfitting. This blend of advanced parameter optimization, minimal preprocessing, and strategic data augmentation underscores the potential of our CNN model for swift, precise, and resource-efficient retinal disease detection. TezNet model achieves a remarkable 96% accuracy, competing closely with state-of-the-art networks like ResNet50 (over 23M parameters), InceptionV3 (over 25M parameters), and ResNeXt (89M parameters). © 2024 IEEE.

关键词： Optical coherence tomography

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Decentralized brokered enabled ecosystem for data marketplace in smart cities towards a data sharing economy

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Environment Systems and Decisions 2023年第3期43卷 453-471页

作者： Anthony, Bokolo Department of Applied Data Science Institute for Energy Technology Halden 1777 Norway Department of Computer Science and Communication Østfold University College BRA Veien 4 Halden 1757 Norway

Presently data are indispensably important as cities consider data as a commodity which can be traded to earn revenues. In urban environment, data generated from internet of things devices, smart meters, smart sensors, etc. can provide a new source of income for citizens and enterprises who are data owners. These data can be traded as digital assets. To support such trading digital data marketplaces have emerged. data marketplaces promote a data sharing economy which is crucial for provision of available data useful for cities which aims to develop data driven services. But currently existing data marketplaces are mostly inadequate due to several issues such as security, efficiency, and adherence to privacy regulations. Likewise, there is no consolidated understanding of how to achieve trust and fairness among data owners and data sellers when trading data. Therefore, this study presents the design of an ecosystem which comprises of a distributed ledger technology data marketplace enabled by message queueing telemetry transport (MQTT) to facilitate trust and fairness among data owners and data sellers. The designed ecosystem for data marketplaces is powered by IOTA technology and MQTT broker to support the trading of sdata sources by automating trade agreements, negotiations and payment settlement between data producers/sellers and data consumers/buyers. Overall, findings from this article discuss the issues associated in developing a decentralized data marketplace for smart cities suggesting recommendations to enhance the deployment of decentralized and distributed data marketplaces. © 2023, The Author(s).

关键词： data ecosystem data sharing economy Decentralized data marketplace Emerging technologies IOTA technology MQTT broker Smart cities Sustainable society

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Vision transformer for detecting traffic congestion using image mapping

Advances in Transportation Studies

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Advances in Transportation Studies 2025年 66卷 19-38页

作者： Khalfi, A. Guerroumi, M. Haid, T. Laradji, N. Faculty of Computer Science Artificial Intelligence and Data Science Department University of Science and Technology Houari Boumediene Algiers Algeria Matter Sciences and Computer Science Faculty Department of Computer Science University of Djilali Bounaama Khemis Miliana Algeria

Leveraging advanced computer vision techniques, we present a novel method for analyzing and categorizing traffic videos by utilizing an image mapping technique combined with a UniVit (Unit Vision Transformer) model. First, video data is transformed into an image dataset, where each frame undergoes a mapping process based on vehicle detection using the YOLOv8s algorithm. These mapped images are then input into a vision transformer, which utilizes attention mechanisms to identify and analyze complex patterns across frames. The approach is evaluated on the UCSD (University of California San Diego) dataset, which features diverse weather conditions such as clear, overcast, and rainy scenarios, enabling a rigorous assessment under varied environments. Our method achieves a notable accuracy score of 99.89%, surpassing previous approaches on the UCSD benchmark. This improvement underscores the effectiveness of combining image mapping with advanced vision transformers, suggesting promising applications for intelligent traffic management systems aimed at mitigating traffic issues. © 2025, Aracne Editrice. All rights reserved.

关键词： Photomapping

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