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2024 IEEE International Conference on Big Data, BigData 2024

作者： Vidal, Nicolas Gong, Qian Reshniak, Viktor Archibald, Rick Klasky, Scott Oak Ridge National Laboratory Computer Science and Math. Division Oak RidgeTN United States

ISBN: (纸本)9798350362480

In the compression of scientific data, error-controlled compressors enable to considerably decrease the size of the dataset while maintaining adequate levels of accuracy. In this paper, we note that multi-level refactoring scheme such as MGARD i) rely on an approximation of the data based on the interpolation of coefficients, ii) estimate the resulting error with global metrics on the dataset. To improve on these two aspects, we propose a method that aims to divide the original dataset into blocks based on their smoothness and refactors each block separately with the most relevant interpolation order. We show the relevance of such a method on tailored datasets and the benefits and challenges when applying it to large scientific data. © 2024 IEEE.

关键词： Interpolation

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A Framework for Compressing Unstructured Scientific Data via Serialization

A Framework for Compressing Unstructured Scientific Data via...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Reshniak, Viktor Gong, Qian Archibald, Rick Klasky, Scott Podhorszki, Norbert Oak Ridge National Laboratory Computer Science and Math. Division Oak RidgeTN United States

ISBN: (纸本)9798350362480

We present a general framework for compressing unstructured scientific data with known local connectivity. A common application is simulation data defined on arbitrary finite element meshes. The framework employs a greedy topology preserving reordering of original nodes which allows for seamless integration into existing data processing pipelines. This reordering process depends solely on mesh connectivity and can be performed offline for optimal efficiency. However, the algorithm's greedy nature also supports on-the-fly implementation. The proposed method is compatible with any compression algorithm that leverages spatial correlations within the data. The effectiveness of this approach is demonstrated on a large-scale real dataset using several compression methods, including MGARD, SZ, and ZFP. © 2024 IEEE.

关键词： Mesh generation

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ECDSA-based tamper detection in medical data using a watermarking technique

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International Journal of Cognitive Computing in Engineering 2024年第1期5卷 78-87页

作者： Ch, Rupa Vivek K, Naga Srivastava, Gautam Reddy Gadekallu Department CSE VR Siddhartha Engineering College Andhra Pradesh Vijayawada520 007 India Dept of Math and Computer Science Brandon University MB Canada Dept. of Computer Science and Math Lebanese American University Beirut1102 Lebanon Centre of Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara Punjab Rajpura140401 India Lebanese American University Department of Electrical and Computer Engineering Byblos Lebanon Lovely Professional University Division of Research and development Phagwara India Jiaxing University College of Information Science and Engineering Jiaxing China

Telemedicine is a form of healthcare delivery that employs communication technology to provide medical care to patients remotely. The use of telemedicine has seen a significant increase in recent years, presenting challenges such as patient privacy, data security, the need for reliable communication technology, and the potential for misdiagnosis without a physical examination. Digital Watermarking can assist in addressing such issues by incorporating a unique identifier into an image that can be used to authenticate its validity. To tackle these issues, this study proposes a robust digital watermarking approach tailored to brain medical images, combining hashing, the Elliptic Curve Digital Signature Algorithm (ECDSA), and the Integer Wavelet Transform-Discrete Cosine Transform (IWT-DCT). This method utilizes the Secure Hash Algorithm (SHA-256) to first segment the brain's Region of Interest (RoI). Subsequently, the hashed RoI, along with an ECDSA signature, is embedded into the high-frequency sub-bands of the medical image using IWT-DCT. The embedding process strategically alters the coefficients of the high-frequency sub-bands to accommodate the signature while minimizing perceptual distortion. The technique leverages the robustness of transformed-domain image watermarking techniques against various attacks and combines it with SHA-256 for integrity and ECDSA for authentication purposes. The results demonstrate that the suggested approach is robust to a variety of image processing techniques, including noise addition, filtering, and compression while maintaining high levels of imperceptibility. Key metrics such as the Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and Structural Similarity Index (SSIM) were used to evaluate performance. The suggested strategy exhibited a substantial improvement over existing methods. The PSNR increased to 68.67, indicating higher image quality, while the MSE reduced to 0.96, demonstrating closer pixel values to the or

关键词： Mean square error

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Collaborative Intrusion Detection System for Intermittent 10 Vs Using Federated Learning and Deep Swarm Particle Optimization 11

Collaborative Intrusion Detection System for Intermittent 10...

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11th IEEE International Conference on Data science and Advanced Analytics, DSAA 2024

作者： Ullah, Farhan Srivastava, Gautam Mostarda, Leonardo Cacciagrano, Diletta University of Camerino Division of Computer Science Camerino Italy Brandon University Dept. of Math and Computer Science BrandonMBR7A 6A9 Canada University of Perugia Department of Mathematics and Computer Science Perugia Italy School of Software Northwestern Polytechnical University Xian China

ISBN: (纸本)9798350364941

Intelligent vehicles have significantly influenced the advancement of Intelligent Transportation Systems (ITS). Smart city consumers increasingly depend on vehicular cloud services, highlighting the need for a stronger Internet of Vehicles (IoV s) architecture. Moreover, smart cities deliver high-performance cloud services using multiple technologies, increasing concerns about communication security across entities exchanging indi-vidual requester data. An intelligent privacy-preserving Intrusion Detection System (IDS) is needed to secure IoV data. This work presents a Federated Learning (FL) approach for intermittent IoVs that uses Deep Swarm Particle Optimisation (DSPO) to choose features optimally while protecting user privacy. This approach enables remote IoVs to access shared data securely, ensuring operational confidentiality and privacy. By integrating DPSO with FL, it enhances data analysis and model training for IoV s, optimizing deep learning models for efficient feature selection in secured distributed environments. This cooperative technique not only protects data privacy but also fosters collaboration among IoV devices. We evaluate the proposed method using two standard datasets, namely CICloV2024 and CICEVSE2024. Despite the intermittent nature of IoVs and imbalanced datasets, our approach gives the highest performance. © 2024 IEEE.

关键词： Federated learning

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The Meaning of Independence in Local Institutions from Neutrosophic Perspective

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Neutrosophic Sets and Systems 2025年 77卷 463-478页

作者： Mohammed, Alaa A. Abdulrahman, Sabah S. Khalid, Huda E. Smarandache, Florentin Salama, A.A. University of Telafer Legal Affairs Department Telafer University Iraq University of Telafer Telafer University Telafer Iraq Mathematics Physics and Natural Science Division University of New Mexico 705 Gurley Ave GallupNM87301 United States Dept. of Math and Computer Sci. Faculty of Science Port Said University Egypt

It is well known that the term vagueness is spread in all aspects of our lives, this manuscript will clarify the meaning of independence (strict meaning of independence, the illusory meaning of independence, Oscillating between the true meaning of independence, and the fallacy of claiming independence and not applying it), this triplet actually refers to the three components that the Neutrosophic theory standing on which is (truthiness, falsity, indeterminacy), the strategy of applying the notion of independency in the local institutions, enlighten on the allegation of applying the independency notion in some governments’ institutions without a real impact of this term, and studying the meaning of independence from the perspective of a country legal system, all these issues and some comparisons in applying the term (independency) between some countries such as Iraq, France, Algeria, USA, etc. have been enclosed in this article. © 2025, University of New Mexico. All rights reserved.

关键词： Laws and legislation

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Optimizing Semantic Enrichment of Biomedical Content through Knowledge Sharing 4

Optimizing Semantic Enrichment of Biomedical Content through...

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4th Knowledge-Aware and Conversational Recommender Systems Workshop, KaRS 2022

作者： Abbas, Asim Mbouadeu, Steve Bisram, Avinash Iqbal, Nadeem Keshtkar, Fazel Bukhari, Syed Ahmad Chan Division Of Computer Science Math & Science Collins College of Professional Studies St. John’s University NYC Queens United States

Each day a vast amount of unstructured content is generated in the biomedical domain from various sources such as clinical notes, research articles and medical reports. Such content contain a sufficient amount of efficient and meaningful information that needs to be converted into actionable knowledge for secondary use. However, accessing precise biomedical content is quite challenging because of content heterogeneity, missing and imprecise metadata and unavailability of associated semantic tags required for search engine optimization. We have introduced a socio-technical semantic annotation optimization approach that enhance the semantic search of biomedical contents. The proposed approach consist of layered architecture. At First layer (Preliminary Semantic Enrichment), it annotates the biomedical contents with the ontological concepts from NCBO BioPortal. With the growing biomedical information, the suggested semantic annotations from NCBO Bioportal are not always correct. Therefore, in the second layer (Optimizing the Enriched Semantic Information), we introduce a knowledge sharing scheme through which authors/users could request for recommendations from other users to optimize the semantic enrichment process. To guage the credibility of the the human recommended, our systems records the recommender confidence score, collects community voting against previous recommendations, stores percentage of correctly suggested annotation and translates that into an index to later connect right users to get suggestions to optimize the semantic enrichment of biomedical contents. At the preliminary layer of annotation from NCBO, we analyzed the n-gram strategy for biomedical word boundary identification. We have found that NCBO recognizes biomedical terms for n-gram-1 more than for n-gram-2 to n-gram-5. Similarly, a statistical measure conducted on significant features using the Wilson score and data normalization. In contrast, the proposed methodology achieves an suitable ac

关键词： Semantics

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The Semantics and Collocations Relation in Food Reviews 34th

The Semantics and Collocations Relation in Food Reviews

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34th International Florida Artificial Intelligence Research Society Conference, FLAIRS-34 2021

作者： Shi, Ledong Bukhari, Syed Ahmad Chan Keshtkar, Fazel Division of Computer Science Math & Science St. John’s University

Finding our favorite dishes have became a hard task since restaurants are providing more choices and varieties. On the other hand, comments and reviews of restaurants are a good place to look for the answer. The purpose of this study is to use computational linguistics and natural language processing to categorise and find semantic relation in various dishes based on reviewers’ comments and menus description. Our goal is to implement a state-of-the-art computational linguistics methods such as, word embedding model, word2vec, topic modeling, PCA, classification algorithm. For visualizations, t-Distributed Stochastic Neighbor Embedding (tSNE) was used to explore the relation within dishes and their reviews. We also aim to extract the common patterns between different dishes among restaurants and reviews comment, and in reverse, explore the dishes with a semantics relations. A dataset of articles related to restaurant and located dishes within articles used to find comment patterns. Then we applied t-SNE visualizations to identify the root of each feature of the dishes. As a result, to find a dish our model is able to assist users by several words of description and their interest. Our dataset contains 1,000 articles from food reviews agency on a variety of dishes from different cultures: American, i.e. ’steak’, hamburger;Chinese, i.e. ’stir fry’, ’dumplings’;Japanese, i.e., ’sushi’. © 2021by the authors. All rights reserved.

关键词： Computational linguistics

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Tuning the Interpolation Basis in a Multigrid Decomposition for Local Error Control

Tuning the Interpolation Basis in a Multigrid Decomposition ...

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IEEE International Conference on Big Data

作者： Nicolas Vidal Qian Gong Viktor Reshniak Rick Archibald Scott Klasky Computer Science and Math. Division Oak Ridge National Laboratory Oak Ridge TN USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

关键词： Measurement Interpolation Codes Production Big Data Benchmark testing Compressors Entropy Error correction Tuning

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A Framework for Compressing Unstructured Scientific Data via Serialization

A Framework for Compressing Unstructured Scientific Data via...

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IEEE International Conference on Big Data

作者： Viktor Reshniak Qian Gong Rick Archibald Scott Klasky Norbert Podhorszki Computer Science and Math. Division Oak Ridge National Laboratory Oak Ridge TN USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

We present a general framework for compressing unstructured scientific data with known local connectivity. A common application is simulation data defined on arbitrary finite element meshes. The framework employs a greedy topology preserving reordering of original nodes which allows for seamless integration into existing data processing pipelines. This reordering process depends solely on mesh connectivity and can be performed offline for optimal efficiency. However, the algorithm’s greedy nature also supports on-the-fly implementation. The proposed method is compatible with any compression algorithm that leverages spatial correlations within the data. The effectiveness of this approach is demonstrated on a large-scale real dataset using several compression methods, including MGARD, SZ, and ZFP.

关键词： Correlation Codes Pipelines Big Data Data processing Throughput Compressors Topology Finite element analysis Compression algorithms

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A framework for compressing unstructured scientific data via serialization

arXiv

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arXiv 2024年

作者： Reshniak, Viktor Gong, Qian Archibald, Rick Klasky, Scott Podhorszki, Norbert Computer Science and Math. Division Oak Ridge National Laboratory Oak RidgeTN United States

We present a general framework for compressing unstructured scientific data with known local connectivity. A common application is simulation data defined on arbitrary finite element meshes. The framework employs a greedy topology preserving reordering of original nodes which allows for seamless integration into existing data processing pipelines. This reordering process depends solely on mesh connectivity and can be performed offline for optimal efficiency. However, the algorithm’s greedy nature also supports on-the-fly implementation. The proposed method is compatible with any compression algorithm that leverages spatial correlations within the data. The effectiveness of this approach is demonstrated on a large-scale real dataset using several compression methods, including MGARD, SZ, and ZFP. Copyright © 2024, The Authors. All rights reserved.

关键词： Mesh generation

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