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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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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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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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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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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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Smart AI-Based Delivery Robot

Smart AI-Based Delivery Robot

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Vemulapalli, Lavanya Charitha, Kolanti Harshitha, Kuralla Durga Lakshmi Sri, Vallepu Udaya Velagapudi Ramakrishna Siddhartha Engineering College Dept.of Computer Science and Engineering Vijayawada India Velagapudi Ramakrishna Siddhartha Engineering College Dept.of Artificial Intelligence and Data Science Vijayawada India

ISBN: (纸本)9798331512965

Humans are responsible for the majority of delivering the packets over the area of a network. When compared to classic delivery system the Autonomous Delivery Robot(ADR's) is meant to deliver the goods instead of a human involvement. The delivery robots are a contact-less and alternative to human interaction. At their core, we design a budget-friendly prototype for an Autonomous Delivery Robot (ADR) that can deliver packages to their destinations safely. This robot uses a GPS (like a car's navigation system) to find its way around. We use a special kind of computer brain called Artificial Intelligence to understand the best way to go. Once delivery robot know the right path, it follows until it reaches to the final stop. The robot keeps packets secure by putting them in a special container that needs a secret code to open. The secret code to open the container can be sent to the customer mobile when they order something. Once the robot delivers your package, it can find its own way back to where it started. © 2025 IEEE.

关键词： Budget control

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Information theory based clustering of cellular network usage data for the identification of representative urban areas

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Digital Communications and Networks 2024年第6期10卷 1677-1685页

作者： Mihaela I.Chidean Luis Ignacio Jiménez Gil Javier Carmona-Murillo David Cortés-Polo Dept.of Signal Theory and Communication Univ.Rey Juan CarlosFuenlabrada 28942Spain Computer Science University of ValladolidP.de Belén15Valladolid47011Spain Dept.of Computing and Telematics Engineering Univ.de ExtremaduraMérida06800Spain Dept.of Computing and Telematics Engineering Univ.de ExtremaduraCÃceres 10003Spain

The exponential growth of the number of network devices in recent years not only entails the need for automation of management tasks,but also leads to the increase of available network data and metadata.5G and beyond standards already cover those requirements and also include the need to define and use machine learning techniques to take advantage of the data acquired,especially using geolocated Call Detail Record(CDR)data ***,this scenario requires novel cellular network analysis methodologies to exploit all these available data,especially for the network usage pattern in order to ease the management *** this work,a novel method based on information theory metrics like the Kullback-Leibler divergence and data classification algorithms is proposed to identify representative urban areas in terms of the network usage *** validation is performed via computer analysis using the Open Big Data CDR data set in the Milan area for different *** results validate the proposed methodology and also reveal its adaptability in terms of specific scenario *** usage patterns are calculated for each representative area,paving the path to several future research lines in network management,such as network usage prediction based on this methodology and using the comportment time series.

关键词： Clustering 5G Network analysis Multi-feature analysis Kullback-Leibler divergence Cellular networks

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