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Cheap and secure metatransactions on the blockchain using hash-based authorisation and preferred batchers

Blockchain(Research and Applications) 2023年第2期4卷 41-66页

作者： William Hughes Tobias Magnusson Alejandro Russo Gerardo Schneider Dept.of Computer Science and Engineering University of Gothenburg41296GothenburgSweden Dept.of Computer Science and Engineering Chalmers University41296GothenburgSweden

Smart contracts are self-executing programs running in the blockchain allowing for decentralised storage and execution without a ***-chain execution is expensive,with miners charging fees for distributed execution according to a cost model defined in the *** particular,transactions have a high fixed *** present MultiCall,a transaction-batching interpreter for Ethereum that reduces the cost of smart contract executions by gathering multiple users’transactions into a *** current implementation of MultiCall includes the following features:the ability to emulate Ethereum calls and create transactions,both from MultiCall itself and using an identity unique to the user;the ability to cheaply pay Ether to other MultiCall users;and the ability to authorise emulated transactions on behalf of multiple users in a single transaction using hash-based authorisation rather than more expensive *** improves upon a previous version of *** experiments show that MultiCall provides a saving between 57%and 99%of the fixed transaction cost compared with the standard approach of sending Ethereum transactions directly.

关键词： Ethereum Domain-specific language Interpreter Gas optimisation

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A secure double spectrum auction scheme

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Digital Communications and Networks 2024年第5期10卷 1415-1427页

作者： Jiaqi Wang Ning Lu Ziyang Gong Wenbo Shi Chang Choi School of Computer Science and Engineering Northeastern UniversityShenyang110004China School of Computer Science and Technology Xidian UniversityXi©anChina Dept.of Computer Engineering Gachon University1342Seongnam-daeroSujeong-guSeongnam-si13120G School of Computer and Communication Engineering Northeastern UniversityQinhuangdao066004China

With the arrival of the 5G era,wireless communication technologies and services are rapidly exhausting the limited spectrum *** auctions came into being,which can effectively utilize spectrum *** of the complexity of the electronic spectrum auction network environment,the security of spectrum auction can not be *** scholars focus on researching the security of the single-sided auctions,while ignoring the practical scenario of a secure double spectrum auction where participants are composed of multiple sellers and *** begin to design the secure double spectrum auction mechanisms,in which two semi-honest agents are introduced to finish the spectrum auction *** these two agents may collude with each other or be bribed by buyers and sellers,which may create security risks,therefore,a secure double spectrum auction is proposed in this *** traditional secure double spectrum auctions,the spectrum auction server with Software Guard Extensions(SGX)component is used in this paper,which is an Ethereum blockchain platform that performs spectrum auctions.A secure double spectrum protocol is also designed,using SGX technology and cryptographic tools such as Paillier cryptosystem,stealth address technology and one-time ring signatures to well protect the private information of spectrum *** addition,the smart contracts provided by the Ethereum blockchain platform are executed to assist offline verification,and to verify important spectrum auction information to ensure the fairness and impartiality of spectrum ***,security analysis and performance evaluation of our protocol are discussed.

关键词： Secure double spectrum auction SGX technology Privacy information Ethereum platform Verification

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3-Multi ranked encryption with enhanced security in cloud computing

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Digital Communications and Networks 2023年第2期9卷 313-326页

作者： YeEun Kim Junggab Son Reza M.Parizi Gautam Srivastava Heekuck Oh Major in Bio Artificial Intelligence Dept.of Computer Science and EngineeringHanyang UniversityAnsanRepublic of Korea Department of Computer Science Kennesaw State UniversityMariettaUSA Department of Software Engineering Kennesaw State UniversityMariettaUSA Department of Mathematics and Computer Science Brandon UniversityBrandonCanada Research Center for Interneural Computing China Medical UniversityTaichungTaiwanChina Dept.of Computer Science and Engineering Hanyang UniversityAnsanRepublic of Korea

Searchable Encryption(SE)enables data owners to search remotely stored ciphertexts selectively.A practical model that is closest to real life should be able to handle search queries with multiple keywords and multiple data owners/users,and even return the top-k most relevant search results when *** refer to a model that satisfies all of the conditions a 3-multi ranked search ***,SE schemes that have been proposed to date use fully trusted trapdoor generation centers,and several methods assume a secure connection between the data users and a trapdoor generation *** is,they assume the trapdoor generation center is the only entity that can learn the information regarding queried keywords,but it will never attempt to use it in any other manner than that requested,which is impractical in real *** this study,to enhance the security,we propose a new 3-multi ranked SE scheme that satisfies all conditions without these security *** proposed scheme uses randomized keywords to protect the interested keywords of users from both outside adversaries and the honest-but-curious trapdoor generation center,thereby preventing attackers from determining whether two different queries include the same ***,we develop a method for managing multiple encrypted keywords from every data owner,each encrypted with a different *** evaluation demonstrates that,despite the trade-off overhead that results from the weaker security assumption,the proposed scheme achieves reasonable performance compared to extant schemes,which implies that our scheme is practical and closest to real life.

关键词： Searchable encryption Multi-keyword search Multiple data owners/users Ranked search Enhanced security

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Predictive Precision: A Comparative Study of ML in Alzheimer's Diagnosis 1

Predictive Precision: A Comparative Study of ML in Alzheimer...

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1st International Conference on Trends in engineering Systems and Technologies, ICTEST 2024

作者： Sujatha, E. Subiksha, M. Saveetha Engineering College Dept.of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350307795

Alzheimer's disease has become a significant contemporary health concern, impacting approximately 45 million individuals. This degenerative brain condition, prevalent among the elderly, lacks a well-defined etiology and pathogenesis. Dementia serves as the primary instigator, progressively inflicting damage upon brain cells and leading to a decline in cognitive functions such as thinking and reading. Those afflicted typically experience an average of 8 years of lifespan after the discernible onset of symptoms. However, this duration may vary, spanning from four to 20 years, contingent upon factors such as age and overall health. Addressing this critical issue, the integration of ML systems gives a great solution for disease prediction. The primary objective is to identify Dementia in diverse patient populations. This study encapsulates the outcomes and analyses derived from the detection of Dementia from many ML algorithms. The dataset collected from the Open Access Series of Imaging Studies (OASIS) serves as the foundational resource for system development. Comprising MRI data from 150 patients from the age gap of 60 to 96, this also undergoes comprehensive scrutiny and application across multiple machine learning *** ensuing comparative analysis seeks to ascertain the efficacy of different models in delivering optimal results. Through this investigation, the aim is to delineate the model that exhibits superior performance for the detection of Alzheimer's disease within the context of the OASIS dataset. © 2024 IEEE.

关键词： Diagnosis

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Anomaly Detection Control Through One-class Learning in Defect Inspection Systems

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Journal of Institute of Control, Robotics and Systems 2025年第4期31卷 328-333页

作者： Jeon, Janggun Kim, Namgi Dept of AI Computer Engineering and Dept of computer science Kyonggi University Korea Republic of

This paper presents a novel image-level anomaly detection control method for defect inspection, which comprises featureextraction from high-dimensional data and feature-learning to analyze the observed patterns. we focus on the feature-learning step, especially in Deep SVDD. To determine anomalies from images of object suspected to be defective, Deep SVDD learns the decision boundaries for the normal feature distribution in the latent space. When a new image is received by the defect inspection system, Deep SVDD maps its features to the latent space and detects anomalies based on whether the observed features fall within the decision boundaries for normal features. The decision boundary of Deep SVDD is determined based on the center-point, which is the center position of the decision boundary. The center-point is randomly fixed at the beginning of training of Deep SVDD. However, this limits the possibility of finding the optimal decision boundary. Therefore, we propose a center-point-moving method to optimize the center-point location and improve the performance of Deep SVDD. To confirm the suitability of the proposed method, we conducted experiments on MVTec-AD, a defect inspection dataset for several products in the manufacturing industry. Based on the results, the proposed method improved the performance of Deep SVDD in 11 of the 15 class domains in the MVTec-AD dataset. © ICROS 2025.

关键词： Inspection equipment

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Unforgettable Password Generation Using LoRA Fine-Tuned Large Language Model

Unforgettable Password Generation Using LoRA Fine-Tuned Larg...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Sameer, Mohammed Sudharsan, K. Benazir Begum, A. Hindustan Institute Of Technology And Science Dept.of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350364828

In response to the challenges posed by complex and hard-to-remember passwords in traditional authentication systems, this research introduces an innovative approach leveraging Low Rank Adaption (LoRA) fine-tuned Large Language Models (LLMs) to generate natural language-like strong passwords. Focused on striking a balance between usability and security, proposed system methodology employs smaller-sized pre-trained LLMs, adapted using LoRA fine-tuning, to produce passwords that are both strong and memorable. By addressing the limitations of current password generation practices, proposed system framework aims to encourage user adoption of secure practices, contributing to a positive societal impact through improved cybersecurity hygiene. The proposed solution involves deploying a fine-tuned LLM and serving them through an frontend web UI which emphasizes resource efficiency scalability of deployment of such model, and ease of using the generated passwords in real life authentication purposes fostering good cybersecurity practices among non-technical users. © 2024 IEEE.

关键词： Authentication

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BlogGen- A Blog Generation Application Using Llama-2

BlogGen- A Blog Generation Application Using Llama-2

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Verma, Amitasha Anand Kurupudi, Deshik Sathyalakshmi, S. Hindustan Institute Of Technology And Science Dept.of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

In recent years, Large Language Models (LLMs) have emerged as transformative tools in natural language processing (NLP) and artificial intelligence (AI) *** excel in generating coherent and contextually relevant text across a wide range of applications one such segment focusing on content generation for websites, blogs and creative writing platforms. In such cases,with an open-source LLM like Llama-2, any person or business can use it for their means without having to pay licensing fees. This includes deploying the LLM to their own infrastructure and fine-tuning it to fit their own needs eliminating the exposure of data to third party interfaces, also making it task specific. This paper introduces a unique model to generate customised blogs by fine-tuning Llama-2 model and perform a comparison with GPT-3.5 and BERT. © 2024 IEEE.

关键词： Blogs

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Ocularis Enigma - Redefining Ocular Disease Detection using Ensemble CNN Innovations 15

Ocularis Enigma - Redefining Ocular Disease Detection using ...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Rekha, Baanu S. Shanmugam, Pavadharni Poorni, R. Dept.of Computer Science and Engineering Sri Venkateswara College of Engineering Kanchipuram India

ISBN: (纸本)9798331300579

The Ocular diseases are progressive and patients are unaware they have a disease it is better to recognize it and cure it before it is too *** earlier proposed model aims to use a single CNN architecture such as DenseNet121, VGG19, EfficientNet etc… which achieved less *** proposed model aims to develop an automated system based on deep learning that could aid in earlyocular disease *** proposed model can detect Glaucoma, Cataracts and Diabetic Retinopathy along with the normal eye *** utilizes contrast-limited adaptive histogram equalization (CLAHE) for pre-processing the fundus image, while a CNN classifier is used for the diagnosis of the *** enables the usage of multi-label classes to analyze coloured fundus *** of various CNN models is applied to find the model with the highest accuracy. © Grenze Scientific Society, 2024.

关键词： Deep learning

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Preprocessing, Feature Extraction, and Classification Methodologies on Diabetic Retinopathy Using Fundus Images

Preprocessing, Feature Extraction, and Classification Method...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Basreddy, Veerendra Veerashetty, Sachinkumar Dept.of Computer Science and Engineering Kalaburagi India

ISBN: (纸本)9798350315455

Significant progress has been made throughout the detection of Diabetic Retinopathy (DR) over the past few decades because of the utilization of Deep-Learning (DL) techniques. Accurate and economical identification of DR using Fundus Images (FI) is made possible by combining state-of-the-art methods for image-processing, feature-extraction, and classification algorithms. This survey aims to explore and analyze the latest developments in preprocessing, feature extraction, and classification approaches, highlighting their key ideas and contributions. Novel approaches to feature extraction, involving traditional handcrafted features and DL-based methods, are investigated to capture relevant characteristics of DR lesions effectively. Furthermore, the survey explores diverse classification algorithms, including Support-Vector-Machines (SVM), Convolutional-Neural-Networks (CNNs), and ensemble methods, which enable accurate categorization of DR severity levels. This survey provides valuable insights into the state-of-the-art methodologies for DR detection, facilitating a comprehensive understanding of the key approaches and innovations in preprocessing, feature extraction, and classification techniques. © 2023 IEEE.

关键词： Classification Diabetic Retinopathy Feature Extraction Fundus Images Preprocessing

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RFID-based 3D human pose tracking: A subject generalization approach

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Digital Communications and Networks 2022年第3期8卷 278-288页

作者： Chao Yang Xuyu Wang Shiwen Mao Dept.of Electrical and Computer Engineering Auburn UniversityAuburnAL36849-5201USA Dept.of Computer Science California State UniversitySacramentoCA95819-6021USA

Three-dimensional (3D) human pose tracking has recently attracted more and more attention in the computer vision field. Real-time pose tracking is highly useful in various domains such as video surveillance, somatosensory games, and human-computer interaction. However, vision-based pose tracking techniques usually raise privacy concerns, making human pose tracking without vision data usage an important problem. Thus, we propose using Radio Frequency Identification (RFID) as a pose tracking technique via a low-cost wearable sensing device. Although our prior work illustrated how deep learning could transfer RFID data into real-time human poses, generalization for different subjects remains challenging. This paper proposes a subject-adaptive technique to address this generalization problem. In the proposed system, termed Cycle-Pose, we leverage a cross-skeleton learning structure to improve the adaptability of the deep learning model to different human skeletons. Moreover, our novel cycle kinematic network is proposed for unpaired RFID and labeled pose data from different subjects. The Cycle-Pose system is implemented and evaluated by comparing its prototype with a traditional RFID pose tracking system. The experimental results demonstrate that Cycle-Pose can achieve lower estimation error and better subject generalization than the traditional system.

关键词： Radio-frequency identification(RFID) Three-dimensional(3D)human pose tracking Cycle-consistent adversarial network Generalization

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