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RT-PPS: Real-time privacy-preserving scheme for cloud-hosted IoT data

Journal of High Speed Networks 2025年第1期31卷 71-89页

作者： Elhoseny, Mohamed Riad, Khaled College of Computing and Informatics University of Sharjah Sharjah27272 United Arab Emirates Faculty of Computers and Information Mansoura University Mansoura35516 Egypt Computer Science Department College of Computer Sciences & Information Technology King Faisal University Al-Ahsa 31982 Saudi Arabia Mathematics Department Faculty of Science Zagazig University Zagazig44519 Egypt

The Internet of Things (IoT) is a rapidly growing network of devices that can communicate with each other and with cloud-based services. These devices generate vast amounts of data that can be used to provide valuable insights into user behavior, environmental conditions, and other important factors. However, as this data is collected and processed by cloud-hosted services, there is a growing concern about privacy and security. Without adequate protection, sensitive information could be exposed to hackers or other malicious actors, putting both individuals and organizations at risk. To address this challenge, real-time privacy-preserving techniques can be used to protect IoT data without compromising its value. This paper introduces an efficient Real-time privacy-preserving scheme (RT-PPS) for cloud-hosted IoT data. RT-PPS employs multi-authority attribute-based encryption on a hybrid cloud environment to keep data secure and private, while still allowing it to be processed and analyzed by cloud-hosted services. RT-PPS has efficient response time and resource consumption, which gives it the ability to handle a huge number of concurrent users at the same time without notable delay. The proposed RT-PPS has been validated through extensive experimental evaluation on a variety of configurations. Moreover, the proposed scheme has been computationally compared with the state-of-the-artwork. RT-PPS has shown excellent performance, effectiveness, and efficiency. The RT-PPS encryption time for a 1 GB dataset while considering 1024 slices is approximately 1000 ms. Also, the RT-PPS decryption time for a 1 GB ciphertext while considering 1024 slices are approximately 235 ms. Finally, RT-PPS is proven secure against any polynomial-time attacks and their variations that have at most a negligible advantage in the introduced security model. Moreover compared to most of the state-of-the-artwork, RT-PPS reduced the ciphertext size and lowered the computations in the encryption, key g

关键词： Ciphertext

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A Novel Divide and Conquer Solution for Long-term Video Salient Object Detection

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Machine Intelligence Research 2024年第4期21卷 684-703页

作者： Yun-Xiao Li Cheng-Li-Zhao Chen Shuai Li Ai-Min Hao Hong Qin State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China College of Computer Science and Technology China University of Petroleum(East China)Qingdao 266580China Department of Computer Science Stony Brook UniversityNew York 11794USA

Recently,a new research trend in our video salient object detection(VSOD)research community has focused on enhancing the detection results via model self-fine-tuning using sparsely mined high-quality keyframes from the given *** such a learning scheme is generally effective,it has a critical limitation,i.e.,the model learned on sparse frames only possesses weak generalization *** situation could become worse on“long”videos since they tend to have intensive scene ***,in such videos,the keyframe information from a longer time span is less relevant to the previous,which could also cause learning conflict and deteriorate the model ***,the learning scheme is usually incapable of handling complex pattern *** solve this problem,we propose a divide-and-conquer framework,which can convert a complex problem domain into multiple simple ***,we devise a novel background consistency analysis(BCA)which effectively divides the mined frames into disjoint *** for each group,we assign an individual deep model on it to capture its key attribute during the fine-tuning *** the testing phase,we design a model-matching strategy,which could dynamically select the best-matched model from those fine-tuned ones to handle the given testing *** experiments show that our method can adapt severe background appearance variation coupling with object movement and obtain robust saliency detection compared with the previous scheme and the state-of-the-art methods.

关键词： Video salient object detection background consistency analysis weakly supervised learning long-term information background shift.

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Hyperparameter optimization: Classics, acceleration, online, multi-objective, and tools

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Mathematical Biosciences and Engineering 2024年第6期21卷 6289-6335页

作者： Tan, Jia Mian Liao, Haoran Liu, Wei Fan, Changjun Huang, Jincai Liu, Zhong Yan, Junchi Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China College of Systems Engineering National University of Defense Technology Changsha China

Hyperparameter optimization (HPO) has been well-developed and evolved into a well-established research topic over the decades. With the success and wide application of deep learning, HPO has garnered increased attention, particularly within the realm of machine learning model training and inference. The primary objective is to mitigate the challenges associated with manual hyperparameter tuning, which can be ad-hoc, reliant on human expertise, and consequently hinders reproducibility while inflating deployment costs. Recognizing the growing significance of HPO, this paper surveyed classical HPO methods, approaches for accelerating the optimization process, HPO in an online setting (dynamic algorithm configuration, DAC), and when there is more than one objective to optimize (multi-objective HPO). Acceleration strategies were categorized into multi-fidelity, bandit-based, and early stopping;DAC algorithms encompassed gradient-based, population-based, and reinforcement learning-based methods;multi-objective HPO can be approached via scalarization, metaheuristics, and model-based algorithms tailored for multi-objective situation. A tabulated overview of popular frameworks and tools for HPO was provided, catering to the interests of practitioners. © 2024 the Author(s), licensee AIMS Press.

关键词： Deep neural networks

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SCAPE: Searching Conceptual Architecture Prompts using Evolution 13

SCAPE: Searching Conceptual Architecture Prompts using Evolu...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Lim, Soo Ling Bentley, Peter J. Ishikawa, Fuyuki University College London Department of Computer Science London United Kingdom University College London Autodesk Research Department of Computer Science London United Kingdom National Institute of Informatics Tokyo Japan

ISBN: (纸本)9798350308365

Conceptual architecture involves a highly creative exploration of novel ideas, often taken from other disciplines as architects consider radical new forms, materials, textures and colors for buildings. While today's generative AI systems can produce remarkable results, they lack the creativity demonstrated for decades by evolutionary algorithms. SCAPE, our proposed tool, combines evolutionary search with generative AI, enabling users to explore creative and good quality designs inspired by their initial input through a simple point and click interface. SCAPE injects randomness into generative AI, and enables memory, making use of the built-in language skills of GPT -4 to vary prompts via text-based mutation and crossover. We demonstrate that compared to DALL. E 3, SCAPE enables a 67% improvement in image novelty, plus improvements in quality and effectiveness of use;we show that in just three iterations SCAPE has a 24% image novelty increase enabling effective exploration, plus optimization of images by users. We use more than 20 independent architects to assess SCAPE, who provide markedly positive feedback. © 2024 IEEE.

关键词： Generative adversarial networks

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Optimizing Deepfake Detection with Transfer Learning Methods 1

Optimizing Deepfake Detection with Transfer Learning Methods

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1st International Conference on Innovations in Communications, Electrical and computer Engineering, ICICEC 2024

作者： Kingsy Grace, R. Ragavan, M. Karthikeyan, N. Prakalathan, M. Saveetha, V. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India KGiSL Institute of Technology Department of Computer Science & Business Systems Coimbatore India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376517

The goal of the proposed system is to identify whether a video has endured software manipulation or not. This system specially deals with identifying deepfake videos from real ones. As new techniques emerged to make deepfake, a handful of photos and fake videos are spreading at an alarmingly increasing rate, which is indistinguishable from the real ones to the naked eye. The proposed system uses CNN with transfer learning to predict the degree of manipulation involved. The user uploads the video that they want to check;the video is saved on the server, and then they experience the process of deepfake detection. Then the user is presented with a plot showing the degree of the fakeness of the video and the accuracy of the prediction. The user can then assume based on the plot, as it shows the frames where manipulation might be involved. © 2024 IEEE.

关键词： Convolutional neural networks

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Machine Learning-Based Big Data Analytics in Smart Cities: A Survey of Current Trends and Future Research Directions 11

Machine Learning-Based Big Data Analytics in Smart Cities: A...

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11th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2024

作者： Althabahi, Mariam Hassan Ahmad Jan, Mian Brik, Bouziane Foufou, Sebti University of Sharjah College of Computing and Informatics Department of Computer Science Sharjah27272 United Arab Emirates

ISBN: (纸本)9798350367300

Efficient utilization of Big data in smart cities is crucial for smooth operation of urban environments. Machine learning-enabled big data analytics is essential for optimizing city operations, improving resource management, and enhancing the quality of urban life. By leveraging machine learning (ML) algorithms to process and analyze the vast amounts of data generated in smart cities, authorities can gain insights and make real-time data-driven decisions. This article provides a comprehensive survey of how ML techniques are applied to analyze the large volumes of data generated by smart cities to improve urban living. Various ML algorithms, such as supervised, unsupervised, and reinforcement learning (RL) are discussed by highlighting their roles in numerous applications. Moreover, their distinguishing features are examined, enabling the selection of the most suitable algorithms for various applications in smart cities. Finally, the survey explores various challenges and suggests numerous research directions. © 2024 IEEE.

关键词： Adversarial machine learning

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SwinVid:Enhancing Video Object Detection Using Swin Transformer

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computer systems science & Engineering 2024年第2期48卷 305-320页

作者： Abdelrahman Maharek Amr Abozeid Rasha Orban Kamal ElDahshan Computer Science Department Faculty of Artificial Intelligence and InformaticsBenhaEgypt Mathematics Department Faculty of SciencesAl-Azhar UniversityCairoEgypt Department of Computer Science College of Science and Arts in QurayyatJouf UniversitySakakaSaudi Arabia

What causes object detection in video to be less accurate than it is in still images?Because some video frames have degraded in appearance from fast movement,out-of-focus camera shots,and changes in *** reasons have made video object detection(VID)a growing area of research in recent *** object detection can be used for various healthcare applications,such as detecting and tracking tumors in medical imaging,monitoring the movement of patients in hospitals and long-term care facilities,and analyzing videos of surgeries to improve technique and ***,it can be used in telemedicine to help diagnose and monitor patients *** VID techniques are based on recurrent neural networks or optical flow for feature aggregation to produce reliable features which can be used for *** of those methods aggregate features on the full-sequence level or from nearby *** create feature maps,existing VID techniques frequently use Convolutional Neural Networks(CNNs)as the backbone *** the other hand,Vision Transformers have outperformed CNNs in various vision tasks,including object detection in still images and image *** propose in this research to use Swin-Transformer,a state-of-the-art Vision Transformer,as an alternative to CNN-based backbone networks for object detection in *** proposed architecture enhances the accuracy of existing VID *** ImageNet VID and EPIC KITCHENS datasets are used to evaluate the suggested *** have demonstrated that our proposed method is efficient by achieving 84.3%mean average precision(mAP)on ImageNet VID using less memory in comparison to other leading VID *** source code is available on the website https://***/amaharek/SwinVid.

关键词： Video object detection vision transformers convolutional neural networks deep learning

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Query Optimization Framework for Graph Database in Cloud Dew Environment

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computers, Materials & Continua 2023年第1期74卷 2317-2330页

作者： Tahir Alyas Ali Alzahrani Yazed Alsaawy Khalid Alissa Qaiser Abbas Nadia Tabassum Department of Computer Science Lahore Garrison UniversityLahorePakistan Faculty of Computer Science and Information Systems Islamic University Madinah Madinah42351Saudi Arabia Networks and Communications Department College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Science Virtual University of PakistanLahorePakistan

The query optimizer uses cost-based optimization to create an execution plan with the least cost,which also consumes the least amount of *** challenge of query optimization for relational database systems is a combinatorial optimization problem,which renders exhaustive search impossible as query sizes *** in CPU performance have surpassed main memory,and disk access speeds in recent decades,allowing data compression to be used—strategies for improving database performance *** performance enhancement,compression and query optimization are the two most *** reduces the volume of data,whereas query optimization minimizes execution *** the database reduces memory requirement,data takes less time to load into memory,fewer buffer missing occur,and the size of intermediate results is more *** paper performed query optimization on the graph database in a cloud dew environment by considering,which requires less time to execute a *** factors compression and query optimization improve the performance of the *** research compares the performance of MySQL and Neo4j databases in terms of memory usage and execution time running on cloud dew servers.

关键词： Query optimization compression cloud dew decompression graph database

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An intrusion detection system using ranked feature bagging

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International Journal of Information Technology (Singapore) 2024年第2期16卷 1213-1219页

作者： Azhagiri, M. Rajesh, A. Karthik, S. Raja, K. Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer Science and Engineering Jain University Bangalore India Department of IT College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India

An Intrusion Detection System monitors the network for any malicious attacks. It is an ideal tool for protecting extensive business networks from any kind of attack. In this paper, an Intrusion Detection System using Bagging and ranked features has been proposed. The sample datasets for training the ensemble of classifiers in the bagging approach are obtained by generating subsets of the features ranked according to their importance in the classification process. OneR algorithm has been used to rank the features. The decision tree has been used as the base classifier in the bagging approach. The proposed system was able to predict attacks with an accuracy of 99.71% with a minimal number of features and outperformed other similar approaches discussed in the literature. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Bagging CIC-IDS 2017 dataset Decision tree Ensemble Feature selection Intrusion detection system OneR feature selection

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A Novel Framework for Gauging Information Extracted from Smartphones Using Neutrosophic Logic

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Neutrosophic Sets and systems 2025年 76卷 154-171页

作者： Abou Alzahab, R.M. Ismail, Amr Abd Elkhalik, S.H. Shams, M.Y. El-Bakry, H.M. Salama, A.A. Department of Mathematics and Computer Science Faculty of Science Port Said University Egypt Department of Cybersecurity College of Engineering and Information Technology Buraydah Private Colleges Buraydah51418 Saudi Arabia Department. of Communication and Computer Engineering Air Defense University Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Department of Information Systems. Faculty of Computer and Information Science Mansoura University Egypt

Smartphones contain a vast amount of information about their users, which can be used as evidence in criminal cases. However, the sheer volume of data can make it challenging for forensic investigators to identify and use the most relevant information. Neutrosophic logic is a generalization of fuzzy logic that allows for uncertainty and vagueness, making it a more potent tool for dealing with the ambiguity of real-world data. The proposed framework aims to identify and utilize the most relevant information for forensic investigators, making it easier to solve criminal cases using Neutrosophic logic. This novel approach provides a more powerful tool for dealing with the ambiguity of smartphone data, ultimately improving the accuracy and effectiveness of forensic investigations. Our research has utilized Neutrosophic logic to evaluate the degree of truth, falsity, or Indeterminacy of this information. Additionally, this study analyzes conversations between individuals using Excel’s fuzzy lookup add-in to determine the percentage of truth and false in each conversation. The results were compiled into a dataset and utilized a Neutrosophic Python code to evaluate the information. The results indicate the percentage of truth, false, and Indeterminacy in each conversation and which can be used to determine its admissibility as evidence and which not. © 2025, University of New Mexico. All rights reserved.

关键词： Smartphones

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