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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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ES-Mask: Evolutionary Strip Mask for Explaining Time Series Prediction 37

ES-Mask: Evolutionary Strip Mask for Explaining Time Series ...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Sun, Yifei Song, Cheng Lu, Feng Li, Wei Jin, Hai Zomaya, Albert Y. National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Centre for Distributed and High Performance Computing School of Computer Science University of Sydney Australia

ISBN: (纸本)9781577358800

Machine learning models are increasingly used in time series prediction with promising results. The model explanation of time series prediction falls behind the model development and makes less sense to users in understanding model decisions. This paper proposes ES-Mask, a post-hoc and model-agnostic evolutionary strip mask-based saliency approach for time series applications. ES-Mask designs the mask consisting of strips with the same salient value in consecutive time steps to produce binary and sustained feature importance scores over time for easy understanding and interpretation of time series. ES-Mask uses an evolutionary algorithm to search for the optimal mask by manipulating strips in rounds, thus is agnostic to models by involving no internal model states in the search. The initial experiments on MIMIC-III data set show that ES-Mask outperforms state-of-the-art methods. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Time series

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Integrating Artificial Intelligence for Sustainable and Adaptable Architecture 2

Integrating Artificial Intelligence for Sustainable and Adap...

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Lalitha, Valapula Suneetha, Regidi Basi Reddy, A. Mate, Nilesh R. Mujeebunnisa, Syeeda India Sanketika Vidya Parishad Engineering College Department of Ece Visakhapatnam India Mohan Babu University School of Computing Department of Computer Science and Engineering Tirupati India Centre for Distance and Online Education Pune India Cmr University School of Science and Computer Studies Bengaluru India

ISBN: (纸本)9798350364699

In nature, flexibility is essential, and AI may enhance it in architecture. The article integrates AI to develop self-learning buildings that can adapt to future obstacles. This research aims to create structures that use sensors to gather environmental data and respond. Phases comprise this study's strategy. In Phase 1, AI in architecture is extensively researched. That study in Phase 1 guided Phase 2's building design. The parametric building design in phase 2 uses machine learning and computational design techniques. Grasshopper scripting and Rhino modelling were used to create an algorithm that would create shapes that would both biomimetic the process of coral growth and be tailored to the project's chosen location. The selection procedure for the produced experimental investigations is shown in Phase 3. To determine which type used the least amount of energy, many evaluations were conducted, including measurements of sunshine, radiation, and shadows. Phase 4 involves developing the shape to improve the building's efficiency. Phase 5 involves conducting performance assessments to demonstrate that the final form is highly adaptable and climatic or ecologically sensitive. This building's radiation exposure, according to the research, is between 200 and 300 kWh/m2. According to the shadow study, the building shape casts a shadow for eight hours. According to the analysis, the building's shape makes it more adaptive by reducing its energy use. An AI engine technology is used in the last stage to forecast the building's future growth. By incorporating technology into building design, we may create flexible structures that reduce building energy use and contribute to the creation of a sustainable world. © 2024 IEEE.

关键词： Green buildings

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Predicting Hepatocellular Carcinoma Graft Survival Rate in Post Liver Transplantation Using DeepHit 5

Predicting Hepatocellular Carcinoma Graft Survival Rate in P...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Rajeev, Devi Remya, S. Nayyar, Anand Nair, Krishnanunni Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India School of Computer Science Duy Tan University Da Nang Viet Nam Centre for Organ Transplantation Digestive Diseases Gastrointestinal Surgery AIMS Kochi India

Hepatocellular carcinoma (HCC) is still a severe worldwide health issue, and liver transplantation is frequently the primary curative option for people who meet the criteria for it. It is challenging to predict the long-term graft survival of HCC patients after transplantation. However, it is an essential clinical decision-making task. The DeepHit algorithm is used in this study to predict the long-term survival of grafts using a deep learning-based approach. In this study, we used the dataset collected from the Amrita Institute of Medical science & Research centre, Kochi, with patient-specific information on age, gender, tumor size, AFP levels, cirrhosis status, treatment approach, recurrence, comorbidity, and time to transplant failure. The temporal correlations in patient data are modeled, and censoring is considered using the DeepHit technique. It blends survival analysis with the benefits of deep neural networks. DeepHit predicts long-term graft survival in liver cancer patients after transplantation with 94 % accuracy, outperforming conventional methods and improving prognosis and quality of life. © 2024 The Authors. Published by Elsevier B.V.

关键词： Forecasting

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Fortified Cuckoo Search Algorithm on training multi-layer perceptron for solving classification problems

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Personal and Ubiquitous computing 2023年第3期27卷 1039-1049页

作者： Thirugnanasambandam, Kalaipriyan Prabu, U. Saravanan, D. Anguraj, Dinesh Kumar Raghav, R.S. Centre for Smart Grid Technologies School of Computer Science and Engineering Vellore Institute of Technology Tami Nadu Chennai Campus Chennai600127 India Department of Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh Vijayawada520007 India Department of Computer Science Engineering VIT Bhopal University Bhopal-Indore Highway Madhya Pradesh Bhopal466114 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur522302 India School of Computing SASTRA Deemed University Tamil Nadu Thanjavur613007 India

Multi-layer perceptron (MLP) in artificial neural networks (ANN) is one among the trained neural models which can hold several layers as a hidden layer for intensive training to obtain optimal results. On the other hand, the classification problem has a high level of attraction towards researchers to increase the accuracy in classification. In ANN, feedforward neural network (FNN) is one model that possesses the art of solving classification and regression problems. When input data is given to FNN, it will apply the sum of product rule and the activation function to map the input with its appropriate output. In the sum of product rule, a term called weights is to be chosen appropriately to map between the input and output. In standard FNN, the weights are chosen in a random way which may lead to slower convergence towards the optimal choice of weight values. In this paper, an effective optimization model is proposed to optimize the weights of MLP of FNN for effective classification problems. Four different datasets were chosen, and the results are interpreted with statistical performance measures. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

关键词： Machine learning

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Enhanced Ship Classification in SAR Imagery using Fine-tuned DenseNet and Self-attention 15

Enhanced Ship Classification in SAR Imagery using Fine-tuned...

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

作者： Chethan, Kosaraju Sai Lokesh, Vadlamudi Reddy, Kunta Vignaj Singh, Rimjhim Padam Srivastava, Smita Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Defense Research & Development Organization Centre for Artificial Intelligence & Robotics Bengaluru India

ISBN: (纸本)9798350370249

The categorization of ships in synthetic aperture radar (SAR) imagery is vital for civilian and military applications, including maritime management and reconnaissance. Deep learning techniques, such as Inception, DenseNet, Fusion Transformers, and EfficientNet, are used to extract multiscale information from SAR images, improving classification capabilities. Advanced solutions are necessary to address challenges in SAR imagery, such as speckle noise, varying illumination conditions, and complex sea clutter. The FUSAR-Ship dataset, which utilizes GF-3 SAR technology, offers a comprehensive collection of diverse scenarios including sea, land, coast, river, and islands. With over 5000 ship chips containing AIS messages and examples of various clutter types, FUSAR-Ship is a benchmark dataset for ship classification tasks. Our research involves thorough testing of advanced classification models using the FUSAR dataset. Through meticulous evaluation of a wide range of ship instances, scales, and maritime scenes, we aim to demonstrate the robustness and adaptability of these models. By refining their architectures and optimizing their parameters, we strive to enhance the capabilities of classification models and advance the performance of ship classification in SAR imagery. Our efforts contribute to enhancing maritime surveillance, management, and reconnaissance in both civilian and military domains. © 2024 IEEE.

关键词： DenseNet201 EfficientNetB1 InceptionV3 MobilenetV3Small RestNet50 Self Attention Ship small object detection Xception

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A Hybrid UNet and Vision Transformer Architecture with Multi-scale Fusion for Brain Tumor Segmentation 5th

A Hybrid UNet and Vision Transformer Architecture with Multi...

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5th International Conference on Medical Imaging and computer Aided Diagnosis, MICAD 2024

作者： Ranjbarzadeh, Ramin Anari, Shokofeh Crane, Martin Bendechache, Malika School of Computing Faculty of Engineering and Computing Dublin City University Dublin Ireland Department of Accounting Economic and Financial Sciences Islamic Azad University South Tehran Branch Tehran Iran ADAPT Research Centre School of Computer Science University of Galway Galway Ireland

ISBN: (纸本)9789819638628

This research presents an advanced neural network architecture for the segmentation of brain tumors. Our suggested model for brain tumor segmentation incorporates the Vision Transformer (ViT) as a 3D encoder alongside a modified UNet architecture to effectively capture both global and local information in MRI data. The model contains a dual-encoder architecture: a 3D ViT encoder captures global contextual information throughout the MRI volume, while a concurrent convolutional encoder emphasizes localized features. The encoder routes are enhanced by skip connections, facilitating efficient feature transmission between the ViT and UNet layers. In the bottleneck, features from both encoders are integrated via a multi-scale fusion block, augmenting the model's capacity to discern intricate spatial hierarchies and tumor changes. The decoder pathway employs a hierarchical upsampling and multi-scale fusion framework, meticulously rebuilding the final segmentation map with a focus on spatial accuracy. This hybrid model integrates the ViT's capacity to capture long-range relationships with the UNet's proficiency in fine-grained spatial localization, yielding a strong segmentation output on the BraTS 2020 dataset that is adept at addressing the complex and diverse architecture of brain tumors in MRI. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Signal encoding

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SWPFOPLD: A Profiling and Optimizing Loader for SW26010Pro Processors 8

SWPFOPLD: A Profiling and Optimizing Loader for SW26010Pro P...

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8th International Conference on computer and Communication Systems, ICCCS 2023

作者： Qian, Hong Wu, Wei Zhu, Qi Wang, Fei Zhao, Jinwei Zheng, Yan School of Computer Science and Technology University of Science and Technology of China Hefei China Jiangnan Institute of Computing Technology System Software Research Department Wuxi China Tsinghua University Department of Computer Science and Technology Beijing China National Research Center of Parallel Computer Engineering and Technology Beijing China

ISBN: (纸本)9781665456128

The Sunway family supercomputers have achieved a series of remarkable achievements. However, the toolchains provided by them are not perfect, which has brought great challenges to the development of high-performance application software. In this paper, a profiling and optimizing tool is proposed to assist people to analyze and optimize the performance of their programs. SWPFOPLD is independent of the application program and gathers the runtime performance data through the PMUs, an automatically hot functions rearrange optimization based on the performance data is furtherly accomplished. The evaluation shows that SWPFOPLD can be easily and effectively used to analyze and optimize the performance of the application programs. © 2023 IEEE.

关键词： Supercomputers

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Take Help from Elder Brother: Old to Modern English NMT with Phrase Pair Feedback 1

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20th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2019

作者： Sen, Sukanta Hasanuzzaman, Mohammed Ekbal, Asif Bhattacharyya, Pushpak Way, Andy Department of Computer Science and Engineering Indian Institute of Technology Patna Bihta India ADAPT Centre School of Computing Dublin City University Dublin Ireland

ISBN: (数字)9783031243370

ISBN: (纸本)9783031243363

Due to the ever-changing nature of the human language and the variations in writing style, age-old texts in one language may be incomprehensible to a modern reader. In order to make these texts familiar to the modern reader, we need to rewrite them manually. But this is not always feasible if the volume of texts is very large. In this paper, we present this rewriting task as a neural machine translation (NMT) problem. We propose an effective approach for training NMT system using a tiny parallel corpus comprising of only 2.7 k parallel sentences. We inject parallel phrase pairs extracted using Statistical Machine Translation (SMT) as additional training examples to NMT. We choose publicly available old-modern English parallel texts for our experiments. Evaluation results show that our proposed approach outperforms the baseline NMT system by more than 18 BLEU points without using any additional training data. © 2023, Springer Nature Switzerland AG.

关键词： Neural machine translation

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An Authentication-Based Image Scrambling Algorithm Using Hybrid Chaotic Maps and SHA

An Authentication-Based Image Scrambling Algorithm Using Hyb...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Ramneshkar, B. Krishnan, R. Bala Kumar, N.Rajesh Karthikeyan, B. Manikandan, G. School of Computing Sastra Deemed University Thanjavur India Srinivasa Ramanujan Centre Sastra Deemed University Department of Computer Science and Engineering Kumbakonam India

ISBN: (纸本)9798350313987

Digital mode of communication is widely used for exchanging various kinds of data through internet. The data that is being transmitted may be used in sensitive applications. Thus, those data have to be secured from various kinds of attacks and must ensure basic security services as well. One such kind of data is Image data which is used in medical imaging and some other sensitive fields as well. There are many algorithms to protect and secure the images during transmission but the algorithm and their security has to be modified constantly to cope up with the technological advancements. So, in this enhanced work, an authentication-based image scrambling algorithm using hybrid chaotic maps and SHA is proposed. RSA provides the initial-values for the chaotic-map. Improved LS will be used to generate random values for XOR operation. SBOX will be generated by using both Improved LS and Henon map. Authentication message will be embedded into the image and it will be XORed and scrambled thrice (SBOX, Arnold and Sub-Image based). Receiver will decrypt and verify the message. This algorithm is expected to be faster, provide good security and resist several attacks. © 2023 IEEE.

关键词： Arnold Transform Authentication Henan Map Image Encryption RSA SHA

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