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Security in Social Networks and Media

Advances in Electronics, Communication, Computing and Intelligent Information Systems (ICAECIS), International Conference on

作者： Jyothi A P Vinayak N Badiger Vinyas Shivakumar Dept. of Computer Science Faculty of Engineering and Technology Ramaiah University Of Applied Sciences Bengaluru Karnataka India Dept. of Electronics and Communication Faculty of Engineering and Technology Ramaiah University Of Applied Sciences Bengaluru Karnataka India

In today’s data-driven world, data privacy and security is of utmost importance. Social media platforms have become the main source of data breaches due to the sheer number of users who are exposed to these risks. It is essential to understand the viable risks associated with such breaches in order to ensure that personal data remains secure. In this paper, we have discussed and reviewed the different forms of digital privacy breaches, their risks, possible preventive strategies and responsibilities of Institutions and Governments to handle digital security.

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Efficient Convolutional Forward Modeling and Sparse Coding in Multichannel Imaging

arXiv

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arXiv 2024年

作者： Wang, Han Kvich, Yhonatan Pérez, Eduardo Römer, Florian Eldar, Yonina C. Applied AI Signal Processing and Data Analysis Fraunhofer Institute for Nondestructive Testing Saarbrücken Germany Faculty of Math and Computer Science Weizmann Institute of Science Rehovot Israel Dept. Electronic Measurements and Signal Processing Technische Universität Ilmenau Ilmenau Germany

This study considers the Block-Toeplitz structural properties inherent in traditional multichannel forward model matrices, using Full Matrix Capture (FMC) in ultrasonic testing as a case study. We propose an analytical convolutional forward model that transforms reflectivity maps into FMC data. Our findings demonstrate that the convolutional model excels over its matrix-based counterpart in terms of computational efficiency and storage requirements. This accelerated forward modeling approach holds significant potential for various inverse problems, notably enhancing Sparse Signal Recovery (SSR) within the context LASSO regression, which facilitates efficient Convolutional Sparse Coding (CSC) algorithms. Additionally, we explore the integration of Convolutional Neural Networks (CNNs) for the forward model, employing deep unfolding to implement the Learned Block Convolutional ISTA (BC-LISTA). Copyright © 2024, The Authors. All rights reserved.

关键词： Ultrasonic testing

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Efficient Convolutional Forward Modeling and Sparse Coding in Multichannel Imaging

Efficient Convolutional Forward Modeling and Sparse Coding i...

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European Signal Processing Conference (EUSIPCO)

作者： Han Wang Yhonatan Kvich Eduardo Pérez Florian Römer Yonina C. Eldar Applied AI Signal Processing and Data Analysis Fraunhofer Institute for Nondestructive Testing Saarbrücken Germany Dept. Electronic Measurements and Signal Processing Technische Universität Ilmenau Ilmenau Germany Faculty of Math and Computer Science Weizmann Institute of Science Rehovot Israel

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

关键词： Convolutional codes Adaptation models Convolution Computational modeling Signal processing algorithms Imaging Transforms Encoding Computational efficiency Sparse matrices

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Optimizing Airline Networks: A Comparative Analysis of Graph-Based Techniques

Optimizing Airline Networks: A Comparative Analysis of Graph...

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2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Iftikhar, Mahnoor Ali, Raja Hashim Saleem, Memoona Shahzadi, Nazia Arshad, Usama Khan, Talha Ali Zeeshan Ijaz, Ali Ali, Nisar Imad, Muhammad Gik Institute of Engg. Sciences & Tech Faculty of Computer Science & Engg Khyber Pakhtunkhwa Pakistan Ai Research Group Gik Institute of Engg. Sciences & Tech Faculty of Computer Science & Engg Khyber Pakhtunkhwa Pakistan Univ. of Europe of Applied Sciences Dept. of Tech & Software Engg Berlin Germany University of Regina Faculty of Electronic Systems Engg Regina Canada

ISBN: (纸本)9798350315974

In the vast expanse of global travel, the intricate web of interconnected airline networks serves as the lifeline of modern aviation. Amidst the bustling realm of global aviation, where time and resources are of the essence, the optimization of airline networks emerges as a paramount pursuit, unleashing the power to streamline operations, enhance connectivity, and elevate the travel experience for passengers worldwide. In this paper, four prominent graph-based algorithms, namely A*, Bellman-Ford, bidirectional search, and Dijkstra's algorithm, were used and compared for optimization of airline networks. These algorithms leverage the inherent structure of the airways network, represented as a graph, to determine the most efficient routes. By evaluating the execution times, path lengths, and complexities of these algorithms, this research equips the airline industry with valuable insights for optimizing airways path-finding. With optimized path-finding, the skies become more navigable, fostering a seamless travel experience for passengers and bolstering the economic growth driven by the aviation sector. © 2023 IEEE.

关键词： Air transportation

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QDarts: A Quantum Dot Array Transition Simulator for finding charge transitions in the presence of finite tunnel couplings, non-constant charging energies and sensor dots

arXiv

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arXiv 2024年

作者： Krzywda, Jan A. Liu, Weikun van Nieuwenburg, Evert Krause, Oswin 〈aQaL〉 Applied Quantum Algorithms Universiteit Leiden Netherlands Dept. of Computer Science University of Copenhagen Copenhagen Denmark Instituut-Lorentz and LIACS Universiteit Leiden Netherlands

We present QDarts, an efficient simulator for realistic charge stability diagrams of quantum dot array (QDA) devices in equilibrium states. It allows for pinpointing the location of concrete charge states and their transitions in a high-dimensional voltage space (via arbitrary two-dimensional cuts through it), and includes effects of finite tunnel coupling, non-constant charging energy and a simulation of noisy sensor dots. These features enable close matching of various experimental results in the literature, and the package hence provides a flexible tool for testing QDA experiments, as well as opening the avenue for developing new methods of device tuning. © 2024, CC BY.

关键词： Nanocrystals

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A study on transformer-based Object Detection

A study on transformer-based Object Detection

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2021 International Conference on Intelligent Technologies, CONIT 2021

作者： Vaidwan, Hritik Seth, Nikhil Parihar, Anil Singh Singh, Kavinder Delhi Technological University Dept. of Applied Physics New Delhi India Delhi Technological University Dept. of Computer Science and Engineering New Delhi India

ISBN: (纸本)9781728185835

This paper focuses on transformers based end-to-end object detection methods. End to end object detection is a new paradigm that has got attention in recent times. It does not require complex hand-engineered components such as non-max suppression to detect objects inside an image. Various methods are proposed to date to enhance fully end-to-end object detectors, most of them are based on the attention mechanism. In this work, we analyze some algorithms which involve transformers for the purpose of object detection. We discuss end-to-end models in which we have focused on Adaptive clustering-based transformers which solve attention encoder redundancy, Deformable Detection Transformers in which the attention module attends a limited collection of key sampling points, Unsupervisedly pre-trained Detection Transformers which are pre-trained on random query patches from the given image to improve accuracy and finally the Transformer-based Set Prediction using FCOS. These enhanced models not only improve the mean average precision of the model but also improves the total convergence time. © 2021 IEEE.

关键词： Object detection

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Object-aware Multi-criteria Decision-Making Approach using the Heuristic data-driven Theory for Intelligent Transportation Systems

Object-aware Multi-criteria Decision-Making Approach using t...

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International Conference on Data science and Advanced Analytics (DSAA)

作者： M S Mekala Elyan Eyad Gautam Srivastava School of Computing Robert Gordon University Aberdeen UK Dept. of Math and Computer Science Brandon University Brandon Canada

Sharing up-to-date information about the surrounding measured by On-Board Units (OBUs) and Roadside Units (RSUs) is crucial in accomplishing traffic efficiency and pedestrians safety towards Intelligent Transportation Systems (ITS). Transferring measured data demands $\geq$10Gbit/s transfer rate and $\geq$1GHz bandwidth though the data is lost due to unusual data transfer size and impaired line of sight (LOS) propagation. Most existing models concentrated on resource optimization instead of measured data optimization. Subsequently, RSU-LiDARs have become increasingly popular in addressing object detection, mapping and resource optimization issues of Edge-based Software-Defined Vehicular Orchestration (ESDVO). In this regard, we design a two-step data-driven optimization approach called Object-aware Multi-criteria Decision-Making (OMDM) approach. First, the surroundings-measured data by RSUs and OBUs is processed by cropping object-enabled frames using YoLo and FRCNN at RSU. The cropped data likely share over the environment based on the RSU Computation-Communication method. Second, selecting the potential vehicle/device is treated as an NP-hard problem that shares information over the network for effective path trajectory and stores the cosine data at the fog server for end-user accessibility. In addition, we use a nonlinear programming multi-tenancy heuristic method to improve resource utilization rates based on device preference predictions (Like detection accuracy and bounding box tracking) which elaborately concentrate in future work. The simulation results agree with the targeted effectiveness of our approach, i.e., mAP($\geq$71%) with processing delay ($\leq3.5\times 10^{6}$bits/slot), and transfer delay ($\leq$3Sms). Our simulation results indicate that our approach is highly effective.

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Using Authenticated Encryption for Securing Controller Area Networks in Autonomous Mobile Platforms

Using Authenticated Encryption for Securing Controller Area ...

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International Symposium on Wireless Personal Multimedia Communications (WPMC)

作者： Amar Rasheed Mohamed Baza Mohammad Khan Narashimha Karpoor Cihan Varol Gautam Srivastava Dept. of Computer Science Sam Houston State University Huntsville TX USA Dept. of Computer Science College of Charleston Charleston SC USA Department of Computing East Tennessee State University Johnson City TN USA Department of Math & Computer Science Brandon University Canada

Controller Area Networks (CAN) has become the dominant data communication and networking system used in today's smart vehicles, autonomous mobile platforms, and industrial control systems. Although CAN bus, a vehicle bus standard designed to allow microcontrollers and devices to communicate with each other's applications provides robust and reliable internode data communication, existing CAN technology lacks support for secure data communication and data authentication. This paper explores the utilization of a lightweight and power-efficient authenticated data encryption engine based on TinyJAMBU-128. The proposed system is suitable for autonomous mobile platforms with CAN bus capability. In our proposed testbed, CAN data frames transmitted over the bus will be encrypted and authenticated with minimal power consumption using TinyJAMBU-128. We have analyzed the performance of TinyJAMBU-128 against three parameterized data encryption modules: AES-128/192/256, CAMELLIA-128/192/256, and ARIA-128/192/256. Based on our simulation data, CAN frames encrypted and authenticated via TinyJAMBU-128 required 22%, 17%, and 15% less average energy consumption compared to AES, CAMELLIA, and ARIA respectively.

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A Lightweight Encryption Method for Preserving E-Healthcare Data Privacy Using Dual Signature on Twisted Edwards Curves 1

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5th International Conference on computer and Communication Technologies, IC3T 2023

作者： Farhat, Suryya Kumar, Manoj Vaish, Arti Dewangan, Bhupesh Kumar Choudhury, Tanupriya Kotecha, Ketan Department of Applied Science and Humanities ADGITM Delhi110053 India Department of Mathematics and Statistics Gurukula Kangri Vishwavidyalaya Haridwar249404 India Department of Computer Science and Engineering OP Jindal University Raigarh496109 India Lavale Campus Maharashtra Pune412115 India Pune412115 India Research Professor CSE Dept. Graphic Era Deemed to be University Uttarakhand Dehradun248002 India

ISBN: (数字)9789819997077

ISBN: (纸本)9789819997060

Due to covid-19 pandemic, research in e-healthcare system is gaining popularity because in most of the cases e-healthcare system does not require to present patient physically at doctor’s door. The reason behind this is that e-healthcare system eliminates the necessity for patient to travel at healthcare centres for doctor’s prescription or to collect their diseases diagnostic reports. In the present paper, we have proposed an algorithm, which may be capable to provide integrity for e-healthcare data and preserve its privacy also. The proposed scheme combines dual signature with EdDSA. Dual signature looks similar to double signature but it is quite different;it is a technique of coupling to different values of varied natures preserving them anonymous to varied objects securely. From recent research, it has been proved that elliptic curve cryptography is more suitable for resource constraint (lightweight) device when compared with RSA. Being an improvement of ECDSA, EdDSA signature generation is egregious and affordable. Moreover, EdDSA signature verification, being computationally very hard task, cannot be carried out by e-healthcare devices. Although edge computing devices having no operational constraints can easily perform this task. These characteristics make our proposal more elegant, perky, and efficient strategy for e-healthcare system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Diagnosis

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An Effective Prediction of Rainfall Using Machine Learning Technique

An Effective Prediction of Rainfall Using Machine Learning T...

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International Conference on Electronics, Communication and Computing Technologies (ICECCT)

作者： Nisha Rana Guru Prasad M S Ashutosh Sharma Pratap M S M Anand Kumar Aditya Pai H Dept. of Computer Science and Engineering Doon Institute of Engineering And Technology (DIET) Rishikesh India Dept. Computer Science and Engineering Graphic Era Deemed University Dehradun India Dept. of Applied Science (Chemistry) Doon Institute of Engineering And Technology (DIET) Rishikesh India School of Computing and Information Technology Reva University Bengaluru India Dept. of Computer Application Graphic Era (Deemed to be University) Dehradun India Dept. of Computer Science and Engg. Graphic Era (Deemed to be University) Dehradun India

Climatic information such as temperature, humidity, and precipitation are useful for agriculturists, businesses, researchers, and the government. Estimating precipitation is a major factor that affects the environment and is one of the most important aspects of meteorological science. In this study, factual approaches and machine learning methods are used to estimate and forecast meteorological parameters for the government. Estimating precipitation is a major factor that affects the environment and is one of the most important aspects of meteorological science. Approaches are used to forecast and estimate precipitation. Daily observations were used as part of the experiment. Validation of results using real-world data is used to check the accuracy of forecasting model experimentation. The experimental results show that distribution, correlation, line-plots, and neural networks perform well for forecasting meteorological characteristics and have the best classification accuracy when compared to other types of machine learning methods for precipitation forecasting.

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