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Preventing 51% Attack by Using Consecutive Block Limits in Bitcoin

IEEE Access 2024年 12卷 77852-77869页

作者： Mahmood Babur, Sohail Ur Rehman Khan, Shafiq Yang, Jing Chen, Yen-Lin Soon Ku, Chin Yee Por, Lip Government Murray College Department of Information Technology Sialkot51010 Pakistan Capital University of Science and Technology Department of Computer Science Islamabad44730 Pakistan Universiti Malaya Department of Computer System and Technology Kuala Lumpur50603 Malaysia National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia

In permissionless blockchain systems, Proof of Work (PoW) is utilized to address the issues of double-spending and transaction starvation. When an attacker acquires more than 50% of the hash power of the entire network, they gain the ability to engage in double-spending activities, posing a significant threat to the PoW consensus algorithm. This research focuses on the consensus algorithm employed in the Bitcoin system, explaining how it operates and the security challenges it faces. The proposed modification to the PoW algorithm imposes a restriction on miners: they are not allowed to accept consecutive blocks from the same miner into the final local blockchain to prevent the 51% attack problem. This modification supports transactions that require six confirmations. In the event an attacker attempts a 51% attack with a private chain that consists of fewer than 6 blocks, it becomes easier to detect a double-spending attack before accepting the attacker's private chain. The modified algorithm introduces a 'Safe Mode Detection Algorithm' that scrutinizes incoming blocks for adjustments at the top of the local blockchain. If inconsistencies are identified, the consensus algorithm proceeds cautiously by comparing the UTXO dictionaries from the attacker's chain with those from the miner's own blockchain. This meticulous comparison aims to detect instances of double-spending. If such instances are detected, the miner rejects the attacker's chain, establishing a double-spend-free environment and thwarting 51% attacks. © 2013 IEEE.

关键词： Blockchain

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FUGAN: A GAN Based Facial Reconstructor for Accurate Unveiling of Hidden Faces 27th

FUGAN: A GAN Based Facial Reconstructor for Accurate Unveili...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Sadhukhan, Mrinmoy Bhattacharya, Indrajit Dutta, Paramartha Department of Computer and System Sciences Siksha Bhavana Visva Bharati Bolpur India Department of Computer Applications Kalyani Government Engineering College Kalyani India

ISBN: (纸本)9783031781711

Image-to-image translation has become a prominent trend in the field of computer vision. This innovative technique is widely employed for generating concealed facial features from given noise. It proves particularly useful in reconstructing obscured facial regions covered by masks. The underlying technology at the core of this method is the Generative Adversarial Network (GAN). The primary mechanism of GAN-based neural networks relies on an encoder-decoder structure combined with a discriminator network. The discriminator plays a crucial role in training the network to distinguish between authentic and generated images. FUGAN, a novel approach in this domain, incorporates a self-attention block and has demonstrated superior performance to other models. With an impressive SSIM and BRISQUE score of 0.928 and 27.990, respectively, FUGAN is at the forefront of achieving state-of-the-art results. The architecture of the FUGAN neural network is built upon the UNET framework, with modifications to enhance its effectiveness. These modifications involve increasing the network depth and incorporating batch instance normalization as a regularizer to achieve the desired outcomes. The resulting model attains a smaller size and exhibits high accuracy in image regeneration. This streamlined and efficient FUGAN model is well-suited for deploying low-resource Internet of Things (IoT) devices. Its capability to regenerate facial features from masked regions makes it instrumental in surveillance systems, detecting individuals wearing masks and reconstructing their facial details. Github link (FUGAN: https://***/mrinmoy-sadhukhan/FUGAN-model) of our work is provided. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Gallium nitride

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An Improvised Genetic Algorithm Technique on Task Scheduling in Edge and Cloud Computing systems 15

An Improvised Genetic Algorithm Technique on Task Scheduling...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Pitty, Nagarjuna Hemalatha, N. Venkata Ramana, K. Sajitha, L.P. Ramesh Babu, P. Babu, M.Dinesh Indian Institute of Science Karnataka Bengaluru India Tamilnadu Chennai India QIS College of Engineering and Technology Department of CSE Andrapradesh Ongole India R.M.K Engineering College Department of Computer Science and Business System Tamilnadu Kavaraipettai India Wollega University College of Engineering and Technology Department of Computer Science Oromia Region Nekemte Ethiopia Rajalakshmi Institute of Technology Department of Mechanical Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350370249

In current years, part cloud computing systems have received huge adoption because of their capability to provide low latency and high bandwidth offerings to users. however, one of the primary challenges in these structures is green undertaking scheduling in the presence of dynamic and heterogeneous sources. To deal with this trouble, genetic set of rules (GA) optimization strategies were proposed as a potential solution. This approach utilizes genetic operators, which include mutation and crossover, to conform a populace of potential solutions and choose the best one. by way of considering various constraints and objectives, consisting of strength consumption and make span, the GA-primarily based project scheduling technique can efficaciously optimize useful resource allocation in area cloud computing structures, at the end improving their performance and resource utilization. © 2024 IEEE.

关键词： Scheduling algorithms

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Need for Artificial Neural Networks in today’s world 4th

Need for Artificial Neural Networks in today’s world

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4th Annual International Conference on Data science, Machine Learning and Blockchain Technology, AICDMB 2023

作者： Siddesh Kumar, N.M. Promod Kumar, B.M. Haloor, Bheem Kumar Haran, Pranadhi Manoj Gowda, K.S. Hemashree, S.S. Department of Mechanical Engineering PES College of Engineering Mandya India Department of Computer Science and Engineering PES College of Engineering Mandya India Department of Electrical and Engineering PES College of Engineering Mandya India Department of Information Science and Engineering PES PES College of Engineering Mandya India

ISBN: (纸本)9781032426853

Artificial Neural Networks (ANNs) are widely used in various fields due to their versatile applications. They are capable of handling complex functions and can adapt to new conditions. Their applications range from data interpretation, drug design, pattern recognition, text translation, credit card fraud detection, medical diagnosis and more. ANNs have become an essential tool in today’s world due to their ability to interpret large amounts of data, making them crucial in fields such as medical diagnosis and credit card fraud detection. The versatility of ANNs makes them applicable in many different industries and continues to contribute to their growing popularity. The current work focuses on exploring the wide range of applications of ANNs and their significance in our world today. © 2024 The Author(s).

关键词： Diagnosis

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Electroencephalography Machine Learning Features and Methods for Early Diagnosis and Classification of Parkinson’s Disease (2013–2023): A Review

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IEEE Sensors Journal 2025年第12期25卷 21017-21032页

作者： Emad-Ud-Din, Muhammad Almuteb, Ibrahim Wang, Ya Hubbard, James E. Texas A&M University Department of Computer Science College StationTX77843 United States Texas A&M University Department of Biomedical Engineering College StationTX77843 United States Department of Electrical and Computer Engineering College Station77843 United States Texas A&M University Department of Mechanical Engineering College StationTX77843 United States

Exploring the Machine Learning (ML) features and methods based on electroencephalography (EEG) and quantitative electroencephalographic (qEEG) holds the potential to early diagnosis and classification of Parkinson’s disease (PD) and can bring convenience to the decision-making process for healthcare professionals. PD is clinically heterogeneous, with high variability between patients in terms of motor and non-motor symptoms. This nature makes it extremely challenging for early diagnosis, and severity classification using traditionally questionnaire-based standards. EEG and qEEG provide objective signals with rich features that can potentially provide a translational alternative. In the past decade, a host of ML-based features and algorithms have been developed to detect and classify PD in patients using a range of EEG and qEEG datasets. Various methodologies, including graph-CNNs, deep RNNs, and hybrid models, are discussed within the context of early diagnosis and classification, each offering unique insights and performance levels. This review highlights the multifaceted nature of PD diagnosis and classification research. It addresses the challenges and limitations encountered in existing techniques, emphasizing the need for further improvements in accuracy, sensitivity, and specificity. This review provides a standardized insight into the evolving landscape of PD diagnosis and classification. It showcases the remarkable progress that has been made in utilizing EEG and qEEG data while emphasizing the ongoing quest for effective and convenient ML based methods for early diagnosis and classification. © 2001-2012 IEEE.

关键词： Electroencephalography

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Improving insulation performance with woven flax/PP hybrid composite reinforced by synthetic alumina and aluminum trihydrate

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Interactions 2024年第1期245卷 1-16页

作者： Selvam, Prabu Saravanan, P. Palanisamy, Satishkumar Umate, Laxmikant Saminathan, Rajasekaran School of Computer Science and Engineering VIT University Tamilnadu Chennai India School of Computing SRM Institute of Science and Technology Tiruchirappalli Campus Tiruchirappalli India Department of Mechanical Engineering Rathinam Technical Campus Tamilnadu Coimbatore India Directorate of Research Jawaharlal Nehru Medical College Datta Meghe Institute of Medical Sciences Maharashtra Wardha India Department of Mechanical Engineering College of Engineering and Computer Science Jazan University Jazan Saudi Arabia

The addition of microparticles of aluminum trihydrate (ATH) and alumina (AL) to flax-based polypropylene (PP) composites enhances their mechanical strength and fire resistance. The composite materials were produced via the vacuum bagging technique. Various weight ratios of fillers (ATH & AL) to polypropylene were obtained through the process of ultrasonication (3, 6, 9, and 12 wt%). The mechanical properties of the hybrid composites were thoroughly evaluated, including their thermal conductivity, flammability, flexural and tensile strengths, and moisture absorption. Based on XRD analysis, it has been determined that the flax composite contained AL and ATH microparticles. The research indicates that the use of additional synthetic fillers in the flax-based composite reduces both the burning and moisture absorption rates, in comparison to typical flax/PP composites. The incorporation of a hybrid composite consisting of 9 wt % AL and 3 wt % ATH results in a 10.36% enhancement in tensile strength and a 3.21% improvement in flexural strength. The improved interfacial bonding strength between the fibers and resin can be attributed to the uniform dissemination of ATH/AL particles. The stress transfer rate will be enhanced as a result of the efficient cross-linking it produces. This was verified by the utilization of scanning electron microscopy (SEM). The AL/ATH hybrid composites possess a thermal conductivity of 0.0596 W/mk as an additional characteristic. The thermal insulation property of the composites remains unaffected by the presence of flame retardant AL and ATH particles, as their thermal conductivity is below 0.065 W/mk. These composites are suitable for applications in the automotive, aerospace, and construction sectors, where lightweight, durable, and fire-resistant materials are needed for interior panels, insulation materials, and structural components. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Tensile strength

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A systematic literature review of visual feature learning: deep learning techniques, applications, challenges and future directions

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Multimedia Tools and Applications 2024年 1-58页

作者： Abdullahi, Mohammed Oyelade, Olaide Nathaniel Kana, Armand Florentin Donfack Bagiwa, Mustapha Aminu Abdullahi, Fatimah Binta Junaidu, Sahalu Balarabe Iliyasu, Ibrahim Ore-ofe, Ajayi Chiroma, Haruna Department of Computer Science Ahmadu Bello University Zaria Zaria Nigeria Department of Mechanical Engineering Ahmadu Bello University Zaria Nigeria Department of Computer Engineering Ahmadu Bello University Zaria Nigeria College of Computer Science and Engineering University of Hafr Al Batin Hafar Al-Batin31991 Saudi Arabia School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast Belfast United Kingdom

Visual Feature Learning (VFL) is a critical area of research in computer vision that involves the automatic extraction of features and patterns from images and videos. The applications of VFL are vast, including object detection and recognition, facial recognition, scene understanding, medical image analysis, and autonomous vehicles. In this paper, we propose to conduct extensive systematic literature review (SLR) on VFL based on deep learning algorithms. The paper conducted an SLR covering deep learning algorithms such as Convolutional Neural Networks (CNNs), Autoencoders, and Generative Adversarial Networks (GANs) including their variants. The review highlights the importance of VFL in computer vision and the limitations of traditional feature extraction techniques. Furthermore, it provides an in-depth analysis of the strengths and weaknesses of various deep learning algorithms for solving problems in VFL. The discussion of the applications of VFL provides an insight into the impact of VFL on various industries and domains. The review also analyzed the challenges faced by VFL, such as data scarcity and quality, overfitting, generalization, interpretability, and explainability. The discussion of future directions for VFL includes hybrid techniques, unsupervised feature learning, continual learning, attention-based models, and explainable AI. These techniques aim to address the challenges faced by VFL and improve the performance of the models. The systematic literature review concludes that VFL is a rapidly evolving field with the potential to transform many industries and domains. The review highlights the need for further research in VFL and emphasizes the importance of responsible use of VFL models in various applications. The review provides valuable insights for researchers and practitioners in the field of computer vision, who can use these insights to enhance their work and ensure the responsible use of VFL models. © The Author(s), under exclusive licence to

关键词： computer vision

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Machine learning classifiers with pre-processing techniques for rumour detection on social media: an empirical study

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International Journal of Cloud Computing 2022年第4期11卷 330-344页

作者： Al-Sarem, Mohammed Al-Harby, Muna Saeed, Faisal Hezzam, Essa Abdullah College of Computer Science and Engineering Taibah University Medina Saudi Arabia Saba'a Region University Mareb Yemen Information System Department College of Computer Science and Engineering Taibah University Medina Saudi Arabia

The rapid increase in popularity of social media helped the users to easily post and share information with others. However, due to uncontrolled nature of social media platforms, such as Twitter and Facebook, it becomes easy to post fake news and misleading information. The task of detecting such problem is known as rumour detection. This task requires data analytics tools due to the massive amount of shared content and the rapid speed at which it is generated. In this work, the authors aimed to study the impact of different text pre-processing techniques on the performance of classifiers when performing rumour detection. The experiments were performed on a dataset of tweets on emerging breaking news stories which cover several events of Saudi political context (EBNS-SPC). The results have shown that pre-processing techniques have a significant impact on increasing the performance of machine learning methods such as support vector machine (SVM), multinomial naïve Bayes (MNB), and K-nearest neighbour (KNN) classifiers. However, the classifiers react differently when different combinations of pre-processing techniques were used. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Support vector machines

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An artificial systems,computational experiments and parallel execution-based surface electromyogram-driven anti-disturbance zeroing neurodynamic strategy for upper limb human-robot interaction control

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CAAI Transactions on Intelligence Technology 2024年第2期9卷 511-525页

作者： Yongbai Liu Keping Liu Gang Wang Jiliang Zhang Yao Chou Zhongbo Sun Department of Mechanical Engineering Changchun University of TechnologyChangchunChina Department of Control Engineering Changchun University of TechnologyChangchunChina School of Electrical and Information Engineering Jilin Engineering Normal UniversityChangchunChina College of Information Science and Engineering Northeastern UniversityShenyangChina Department of Electrical and Computer Engineering Brigham Young UniversityProvoUtahUSA

In recent years,intelligent robots are extensively applied in the field of the industry and intelligent rehabilitation,wherein the human-robot interaction(HRI)control strategy is a momentous part that needs to be ***,the efficacy and robustness of the HRI control algorithm in the presence of unknown external disturbances deserve to be *** deal with these urgent issues,in this study,artificial systems,computational experiments and a parallel execution intelligent control framework are constructed for the HRI *** upper limb-robotic exoskeleton system is re-modelled as an artificial *** on surface electromyogram-based subject's active motion intention in the practical system,a non-convex function activated anti-disturbance zeroing neurodynamic(NC-ADZND)controller is devised in the artificial system for parallel interaction and HRI control with the practical ***,the linear activation function-based zeroing neurodynamic(LAF-ZND)controller and proportionalderivative(posterior deltoid(PD))controller are presented and *** results substantiate the global convergence and robustness of the proposed controller in the presence of different external *** addition,the simulation results verify that the NC-ADZND controller is better than the LAF-ZND and the PD controllers in respect of convergence order and anti-disturbance characteristics.

关键词： neural network pattern recognition robotics signal processing

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Indentation behavior of a semi-infinite piezoelectric semiconductor under a rigid flat-ended cylindrical indenter

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Applied Mathematics and Mechanics(English Edition) 2024年第4期45卷 649-662页

作者： Shijing GAO Lele ZHANG Jinxi LIU Guoquan NIE Weiqiu CHEN State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures Shijiazhuang Tiedao UniversityShijiazhuang 050043China School of Mechanical Engineering Shijiazhuang Tiedao UniversityShijiazhuang 050043China Hebei Key Laboratory for Mechanics of Intelligent Materials and Structures Department of Engineering MechanicsShijiazhuang Tiedao UniversityShijiazhuang 050043China State Key Laboratory of Computer Aided Design and Computer Graphics Key Laboratory of Soft Machines and Smart Devices of Zhejiang ProvinceDepartment of Engineering MechanicsZhejiang UniversityHangzhou 310027China

This paper theoretically studies the axisymmetric frictionless indentation of a transversely isotropic piezoelectric semiconductor(PSC)half-space subject to a rigid flatended cylindrical *** contact area and other surface of the PSC half-space are assumed to be electrically *** the Hankel integral transformation,the problem is reduced to the Fredholm integral equation of the second *** equation is solved numerically to obtain the indentation behaviors of the PSC half-space,mainly including the indentation force-depth relation and the electric potential-depth *** results show that the effect of the semiconductor property on the indentation responses is limited within a certain range of variation of the steady carrier *** dependence of indentation behavior on material properties is also analyzed by two different kinds of *** element simulations are conducted to verify the results calculated by the integral equation technique,and good agreement is demonstrated.

关键词： piezoelectric semiconductor(PSC) insulating indenter electromechanical response singular integral equation finite element simulation

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