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Demystifying Ethereum account diversity:observations,models and analysis

Frontiers of computer Science 2022年第4期16卷 119-130页

作者： Chaofan Wang Xiaohai Dai Jiang Xiao Chenchen Li Ming Wen Bingbing Zhou Hai Jin Clusters and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabWuhan430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan430074China School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan430074China Centre for Distributed and High Performance Computing School of Information TechnologiesThe University of SydneyCamperdownNew South Wales2006Australia

Blockchain platform Ethereum has involved millions of accounts due to its strong potential for providing numerous services based on smart *** massive accounts can be divided into diverse categories,such as miners,tokens,and exchanges,which is termed as account diversity in this *** benefit of investigating diversity are multi-fold,including understanding the Ethereum ecosystem deeper and opening the possibility of tracking certain abnormal ***,the exploration of blockchain account diversity remains *** the most relevant studies,which focus on the deanonymization of the accounts on Bitcoin,can hardly be applied on Ethereum since their underlying protocols and user idioms are *** this end,we present the first attempt to demystify the account diversity on *** key observation is that different accounts exhibit diverse behavior patterns,leading us to propose the heuristics for classification as the *** then raise the coverage rate of classification by the statistical learning model Maximum Likelihood Estimation(MLE).We collect real-world data through extensive efforts to evaluate our proposed method and show its ***,we make an in-depth analysis of the dynamic evolution of the Ethereum ecosystem and uncover the abnormal arbitrage *** for the former,we validate two sweeping statements reliably:(1)standalone miners are gradually replaced by the mining pools and cooperative miners;(2)transactions related to the mining pool and exchanges take up a large share of the total *** latter analysis shows that there are a large number of arbitrage transactions transferring the coins from one exchange to another to make a price difference.

关键词： blockchain Ethereum classification account diversity network analysis

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Utilizing Artificial Neural Networks and Multivariate Patient Data for Anemia Detection using WEKA based Approach for Diagnosis 2

Utilizing Artificial Neural Networks and Multivariate Patien...

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2nd International Conference on Automation, Computing and Renewable systems, ICACRS 2023

作者： Bhargava, Neeraj Rathore, Pramod Singh Vaishnav, Parul Rai, Manish School of Engineering & System Sciences MDS University Ajmer India Department of Computer and Communication Engineering Manipal University Jaipur India MDS University Ajmer India Manipal University Jaipur India

ISBN: (纸本)9798350340235

This research study describes a method that uses neural networks to figure out if a woman has anemia. An artificial neural network is a type of computer network that is based on the real neural networks that make up the structure of the human brain. In this paper, the WEKA tool is used for the classification process carried out using an ANN structure, which is referred to as one of the data mining approaches and is based on multiple perceptions. The network has seven inputs (RBC, HCT, MCV, MCH, MCHC, RDW, and WBC) and an output (decision). Simulations on 567 patients demonstrate that the Artificial Neural Network (ANN) accurately diagnose the disease. This study investigates the utilization of Artificial Neural Network (ANN)-supervised machine learning techniques for predicting anemia by using the data from Kaggle. © 2023 IEEE.

关键词： Face mask detection face recognition mask standard wearing detection Single Shot Multibox Detector algorithm

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An RFID-Based Battery-Less Vibration Monitoring system for Electrical Appliances

An RFID-Based Battery-Less Vibration Monitoring System for E...

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2022 International Symposium on Information Technology and Digital Innovation, ISITDI 2022

作者： Song, Zequn Sun, Ran Rahmadya, Budi Takeda, Shigeki Engineering Graduate School Of Science And Engineering Ibaraki University Department Of Electrical And Electronic System Hitachi Japan Engineering Ibaraki University Department Of Electrical And Electronic System Hitachi Japan Andalas University Computer System Department Padang Indonesia

ISBN: (数字)9781665461160

ISBN: (纸本)9781665461160

This paper presents a vibration monitoring system for electrical appliances. This system is based on RFID sensors and edge processing technologies. For long-term monitoring, two different operation modes referred to as standby and active modes are introduced. The difference between the two modes is radio wave radiation times. The standby mode is useful to reduce energy consumption and temperature increase of an RFID reader, and amount of data uploaded to a network. This mode also detects a beginning of a vibration event caused by the motor of an electrical appliance. The standby mode subsequently triggers the active mode. The active mode accurately monitors the vibration event and keeps the measured data only for the active mode. Experiments for monitoring a refrigerator demonstrate that the proposed modes enable efficient vibration detections. This system can prevent unintended COVID-19 vaccine disposals caused by the problematic operation and management of refrigerators. © 2022 IEEE.

关键词： COVID-19

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Recommendation systems in Education : A Bibliometric Analysis 9

Recommendation Systems in Education : A Bibliometric Analysi...

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9th International Conference on Business and Industrial Research, ICBIR 2024

作者： Mayatopani, Hendra Murad, Dina Fitria Yahaya, Nor Adnan School of Computing University Malaysia of Computer Science and Engineering Selangor Malaysia Pradita University Information System Tangerang Indonesia Binus Online Bina Nusantara University Information Systems Department Jakarta Indonesia

ISBN: (纸本)9798350383010

In today's digital age, recommendation systems have become an important tool in many sectors, including education. This study presents a comprehensive bibliometric analysis of the literature related to recommendation systems in an educational context. Using major scientific databases and bibliometric analysis tools, we evaluate publications from the last 5 years to identify research trends, collaboration patterns, and methodological developments. Our results show a significant increase in the number of publications and interest in this field, with a wide variety of research themes ranging from personalized learning to optimization of educational resources. The most dominant analytics techniques include collaborative filtering and content-based filtering, while hybrid and deep learning approaches show growing potential. In addition, we identified geographic and institutional concentrations of research, as well as a set of challenges such as data privacy and the need for adaptation to learner dynamics. This research underscores the importance of recommendation systems in improving tailored educational experiences and supporting data-driven academic decisions. We also offer insights for future research directions, including the need for interdisciplinary research and closer international collaboration. © 2024 IEEE.

关键词： Collaborative filtering

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Tailoring Highly Confined Electric-field Based Optical Skyrmions

Tailoring Highly Confined Electric-field Based Optical Skyrm...

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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Chen, J. Shen, X. Zeng, Y. Zhan, Q. University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China University of Shanghai for Science and Technology Shanghai Key Laboratory of Modern Optical System Shanghai200093 China

ISBN: (纸本)9798350375909

Optical skyrmions are quasi-particles with nontrivial topological textures, which hold potential applications in optical information processing, transmission, and storage. There are various three-dimensional physical vectors to construct the optical skyrmions, such as spin skyrmions, Stokes skyrmions, Poynting vector skyrmions, and so on. Here, we present a method to construct tightly focused electric-field skyrmions based on the array antenna radiation theory. A multiple concentric-ring array of dipoles (MCAD) is placed at the focal region of the high numerical aperture (NA) lens to mimic the topological structure of the desired electric field skyrmion, and the radiation pattern of the MCAD is derived to form the incident fields at the pupil planes of the high NA lenses in a 4π focusing setup. The characteristics of the generated focal field are quantitatively evaluated by using the Debye diffraction integral theory. The results demonstrate that two types of electric field vector topologies, i.e., skyrmion and meron, can be created with adjustable polarity, vorticity, and helicity. Additionally, the normal direction of the plane containing the skyrmion or meron can also be customized, increasing the degrees of freedom in tailoring optical skyrmions. © 2024 IEEE.

关键词： Topology

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Method for Evaluating the Performance of Aircraft Collaborative Decision-Making Based on Dynamic Bayesian Network

Method for Evaluating the Performance of Aircraft Collaborat...

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2024 International Conference on Electronics and Devices, Computational Science, ICEDCS 2024

作者： Zhang, Wenbo Yu, Boxiao Tong, Jiahui Li, Jing Beijing Electro-Mechanical Engineering Institute National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing China Beijing Institute of Technology School of Computer Science and Technology Beijing China

ISBN: (纸本)9798331527624

This research addresses the challenge of assessing equipment decision-making effectiveness in environments characterized by uncertainty and multiple influencing factors. We propose a novel approach involving the creation and optimization of a dynamic Bayesian network-based model tailored for equipment decision-making processes. The model facilitates a systematic evaluation of decision-making effectiveness by incorporating various factors, including equipment performance, environmental conditions, and enemy defensive capabilities. A comprehensive assessment system has been developed to simulate the penetration capabilities of different equipment through enemy defenses. Moreover, this study identifies potential errors in the effectiveness evaluation model by analyzing its performance in typical scenarios, pinpointing areas for enhancement. © 2024 IEEE.

关键词： Decision-making Dynamic Bayesian networks Equipment Simulation Uncertainty Management

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Verifiable Secure Aggregation Protocol Under Federated Learning 1st

Verifiable Secure Aggregation Protocol Under Federated Learn...

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1st International Conference on Artificial Intelligence Security and Privacy, AIS and P 2023

作者： Xu, Peiming Zheng, Meiling Xu, Lingling Guangdong Provincial Key Laboratory of Power System Network Security Electric Power Research Institute CSG Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9789819997848

Federated learning is a new machine learning paradigm used for collaborative training models among multiple devices. In federated learning, multiple clients participate in model training locally and use decentralized learning methods to ensure the privacy of client data. However, although federated learning protects the privacy of client data, the update gradients uploaded by clients may still contain sensitive information. To solve this problem, this paper proposes a secure aggregation protocol which can verify the aggregation results under federated learning and protect gradient privacy. The core idea of this aggregation protocol is to use encryption technology to achieve secure computation between clients, ensuring the privacy of gradients during the aggregation process. At the same time, bilinear pairing technology is used to achieve the verifiability of aggregation results, ensuring the correctness and usability of the model after aggregation. In order to evaluate the security of the protocol, this paper conducts a detailed security analysis. The results show that this protocol has higher security properties compared to the existing related protocols. In addition, the computation and communication costs of the protocol are analyzed, which show that the protocol has good credibility and applicability in practical federated learning scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Sensitive data

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Mixed Hierarchical Networks for Deep Entity Matching

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Journal of computer Science & Technology 2021年第4期36卷 822-838页

作者： Chen-Chen Sun De-Rong Shen Engineering Research Center of Learning-Based Intelligent System(Ministry of Education)Tianjin University of Technology Tianjin 300384China School of Computer Science and Engineering Tianjin University of TechnologyTianjin 300384China School of Computer Science and Engineering Northeastern UniversityShenyang 110819China

Entity matching is a fundamental problem of data *** groups records according to underlying real-world *** is a growing trend of entity matching via deep learning *** design mixed hierarchical deep neural networks(MHN)for entity matching,exploiting semantics from different abstract levels in the record internal hierarchy.A family of attention mechanisms is utilized in different periods of entity ***-attention focuses on internal dependency,inter-attention targets at alignments,and multi-perspective weight attention is devoted to importance ***,hybrid soft token alignment is proposed to address corrupted *** order is for the first time considered in deep entity ***,to reduce utilization of labeled training data,we propose an adversarial domain adaption approach(DA-MHN)to transfer matching knowledge between different entity matching tasks by maximizing classifier ***,we conduct comprehensive experimental evaluations on 10 datasets(seven for MHN and three for DA-MHN),which illustrate our two proposed approaches1 *** apparently outperforms previous studies in accuracy,and also each component of MHN is ***-MHN greatly surpasses existing studies in transferability.

关键词： entity matching attention mechanism mixed hierarchical neural network(MHN) domain adaption data integration

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HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion 38

HairDiffusion: Vivid Multi-Colored Hair Editing via Latent D...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Zeng, Yu Zhang, Yang Liu, Jiachen Shen, Linlin Deng, Kaijun He, Weizhao Wang, Jinbao Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images. © 2024 Neural information processing systems foundation. All rights reserved.

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λGrapher: A Resource-Efficient Serverless system for GNN Serving through Graph Sharing 24

λGrapher: A Resource-Efficient Serverless System for GNN Se...

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33rd ACM Web Conference, WWW 2024

作者： Hu, Haichuan Liu, Fangming Pei, Qiangyu Yuan, Yongjie Xu, Zichen Wang, Lin National Engineering Research Center for Big Data Technology and System The Services Computing Technology and System Lab Cluster and Grid Computing Lab in the School of Computer Science and Technology Huazhong University of Science and Technology 1037 Luoyu Road Wuhan China Peng Cheng Laboratory Huazhong University of Science and Technology China School of Mathematics and Computer Science Nanchang University China Paderborn University Paderborn Germany

ISBN: (纸本)9798400701719

Graph Neural Networks (GNNs) have been increasingly adopted for graph analysis in web applications such as social networks. Yet, efficient GNN serving remains a critical challenge due to high workload fluctuations and intricate GNN operations. Serverless computing, thanks to its flexibility and agility, offers on-demand serving of GNN inference requests. Alas, the request-centric serverless model is still too coarse-grained to avoid resource waste. Observing the significant data locality in computation graphs of requests, we propose λGrapher, a serverless system for GNN serving that achieves resource efficiency through graph sharing and fine-grained resource allocation. "Grapher features the following designs: (1) adaptive timeout for request buffering to balance resource efficiency and inference latency, (2) graph-centric scheduling to minimize computation and memory redundancy, and (3) resource-centric function management with fine-grained resource allocation catered to the resource sensitivities of GNN operations and function orchestration optimized to hide communication latency. We implement a prototype of λGrapher based on the representative open-source serverless platform Knative and evaluate it with real-world traces from various web applications. Our results show that λGrapher can achieve an average savings of 61.5% in memory resource and 47.2% in computing resource compared with the state of the arts while ensuring GNN inference latency. © 2024 ACM.

关键词： Efficiency

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