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data-driven Optimization of Complex Systems (DOCS), International Conference on

作者： Xinyu Tian Zesen Zhuang Luyao Zhang Department of Computer Science Duke University Durham United States Pratt School of Engineering Duke University Durham United States Data Science Research Center and Social Science Division Duke Kunshan University Suzhou China

ISBN: (数字)9798350377842

ISBN: (纸本)9798350377859

The Merge changes Ethereum from Proof-of-Work (PoW) to the more secure and less energy-intensive Proof-of-Stake (PoS) mechanism. However, the existence of malicious valida tors still threatens the security of Ethereum, primarily through a discouragement attack. How can we redesign the incentive mech-anism in PoS Ethereum for a more secure blockchain? For this quest, we, for the first time, apply the cutting-edge reinforcement mechanism design method-an interdisciplinary approach at the intersection of reinforcement learning (RL) and mechanism design-to staking mechanism designs. We abstract a generalized staking mechanism as a game environment and implement an RL method for the blockchain as a mechanism designer to explore the optimal incentive design. Our reinforcement mechanism design outperforms the status quo in cultivating honest validators. Furthermore, we identify Advantage Actor-Critic (A2C) as the most efficient RL algorithm among the three alternatives, which intuitively performs better when the initial proportion of honest validator is larger. Our interdisciplinary approach of generalized abstraction could be adapted to analyze the incentive design in any PoS blockchain and beyond.

关键词： Proof of stake Mechanism design Reinforcement learning Games Proof of Work Security Complex systems Optimization

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Handwashing Action Detection System for an Autonomous Social Robot

Handwashing Action Detection System for an Autonomous Social...

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2022 IEEE Region 10 International Conference, TENCON 2022

作者： Sasidharan, Sreejith Prabha, Pranav Pasupuleti, Devasena Das, Anand M Kapoor, Chaitanya Manikutty, Gayathri Pankajakshan, Praveen Rao, Bhavani Amrita Vishwa Vidyapeetham Ammachi Labs Amritapuri India Amrita Vishwa Vidyapeetham Department of Mechanical Engineering Amritapuri India Amrita Vishwa Vidyapeetham Department of Computer Science Amritapuri India VP Data Science and Ai Cropin Ai Labs Bengaluru India

ISBN: (数字)9781665450959

ISBN: (纸本)9781665450959

Young children are at an increased risk of contracting contagious diseases such as COVID-19 due to improper hand hygiene. An autonomous social agent that observes children while handwashing and encourages good hand washing practices could provide an opportunity for handwashing behavior to become a habit. In this article, we present a human action recognition system, which is part of the vision system of a social robot platform, to assist children in developing a correct handwashing technique. A modified convolution neural network (CNN) architecture with Channel Spatial Attention Bilinear Pooling (CSAB) frame, with a VGG-16 architecture as the backbone is trained and validated on an augmented dataset. The modified architecture generalizes well with an accuracy of 90% for the WHO-prescribed handwashing steps even in an unseen environment. Our findings indicate that the approach can recognize even subtle hand movements in the video and can be used for gesture detection and classification in social robotics. © 2022 IEEE.

关键词： Deep learning

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Incorporating Transfer Learning in Engine Health Assessment for Accurate Remaining Useful Life Prediction 3

Incorporating Transfer Learning in Engine Health Assessment ...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Karpagavalli, C. Nelson, A. Raja, R. Saranya, E. Soudagar, Manzoore Elahi M. Ramaian, Christu Paul Ramco Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Rajapalayam626117 India Panimalar Engineering College Department of Mathematics Tamil Nadu Chennai600123 India Pandian Saraswathi Yadav Engineering College Department of Electronics and Communication Engineering Tamil Nadu Sivagangai630561 India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Tamil Nadu Coimbatore641202 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of VLSI Microelectronics Tamil Nadu Chennai602105 India Hindustan Institute of Technology and Science Department of Automobile Engineering Tamil Nadu Chennai603103 India

ISBN: (纸本)9798350396157

Aircraft engines are critical, therefore any move to make them safer, more reliable, and fuel-efficient is highly encouraged. To solve the challenges of flight safety and maintenance expense during aircraft engine operation, a prognostics and health management system is deployed. This system is primarily concerned with flaw detection, health evaluation, and life prediction. Making decisions about aircraft engine operation and maintenance is strongly reliant on estimating the remaining useful life (RUL). This study provides an ensemble transfer learning model for RUL prediction using the C-MAPSS turbofan engine dataset. Pre-processing procedures such as filtering and normalization are used to improve the dataset. The proposed ensemble model for RUL prediction is then trained on the processed data. Some of the well-known transfer learning models that are compared include Xception, ResNet-50, and VGG-16. The ensemble model outperforms the other models, with the highest R2 value of 0.9901. Furthermore, the ensemble model is very effective;it can predict RUL in just 37 seconds, which suggests that it could be useful for keeping an eye on engine health and making decisions about aircraft. © 2024 IEEE.

关键词： Aircraft engines

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WOBTree:a write-optimized B+-tree for non-volatile memory

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Frontiers of computer science 2021年第5期15卷 37-51页

作者： Haitao WANG Zhanhuai LI Xiao ZHANG Xiaonan ZHAO Song JIANG School of Computer Science and Engineering Northwestern Polytechnical UniversityXi'An 710072China Key Laboratory of Big Data Storage and Management Northwestern Polytechnical UniversityMinistry of Industry and Information TechnologyXi'An 710072China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’An 710072China Department of Computer Science and Engineering University of Texas at ArlingtonArlingtonTX 76010USA

The emergence of non-volatile memory(NVM)has introduced new opportunities for performance optimizations in existing storage *** better utilize its byte-addressability and near-DRAM performance,NVM can be attached on the memory bus and accessed via load/store memory instructions rather than the conventional block *** this scenario,a cache line(usually 64 bytes)becomes the data transfer unit between volatile and non-volatile ***,the failure-atomicity of write on NVM is the memory bit width(usually 8 bytes).This mismatch between the data transfer unit and the atomicity unit may introduce write amplification and compromise data consistency of node-based data structures such as B+-*** this paper,we propose WOBTree,a Write-Optimized B+-Tree for NVM to address the mismatch problem without expensive *** minimizes the update granularity from a tree node to a much smaller subnode and carefully arranges the write operations in it to ensure crash consistency and reduce write *** results show that compared with previous persistent B+-tree solutions,WOBTree reduces the write amplification by up to 86× and improves write performance by up to 61× while maintaining similar search performance.

关键词： non-volatile memory B+-tree atomic granularity mismatch write amplification performance optimization

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Prediction of Liver Disease using Machine Learning Models with PCA

Prediction of Liver Disease using Machine Learning Models wi...

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2021 International Conference on data Analytics for Business and Industry, ICDABI 2021

作者： Al Telaq, Burair Hassan Hewahi, Nabil University of Bahrain Big Data Science and Analytics College of Science Sakir Bahrain University of Bahrain College of Information Technology Department of Computer Science Sakir Bahrain

ISBN: (纸本)9781665416566

The liver is one of the most vital organs of the human body. There are various disorders of liver that need early treatment by doctors. Early diagnosis and treating the patients are significant to reduce the risk. Healthcare system can benefit from various Machine Learning (ML) models to predict diseases in early stage. The aim of this study is to predict liver disease using different ML models applied on Indian Liver Patient dataset (ILPD). The models used on this work are Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), Artificial Neural Network (ANN) and various versions of Ensemble Learning (EL). The results show that ensemble learning of KNN, RF, and SVM achieved the highest accuracy of 88%. Moreover, ensemble learning of KNN with RF obtained the highest True Positive Rate (TPR) of 99% on negative cases. In addition, this paper found that the Principal Component Analysis (PCA) technique has good impact to increase the accuracy. © 2021 IEEE.

关键词： Forecasting

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A Multi-Language Toolkit for the Semi-Automated Checking of Research Outputs

IEEE Transactions on Privacy

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IEEE Transactions on Privacy 2025年第1期2卷 55-66页

作者： Preen, Richard J. Albashir, Maha Davy, Simon Smith, Jim University of the West of England Department of Computer Science and Creative Technologies BristolBS16 1QY United Kingdom University of Oxford Bennett Institute for Applied Data Science OxfordOX1 2JD United Kingdom

This article presents a free and open source toolkit that supports the semi-automated checking of research outputs (SACRO) for privacy disclosure within secure data environments. SACRO is a framework that applies best-practice principles-based statistical disclosure control (SDC) techniques on-the-fly as researchers conduct their analyses. SACRO is designed to assist human checkers rather than seeking to replace them as with current automated rules-based approaches. The toolkit is composed of a lightweight Python package that sits over well-known analysis tools that produce outputs such as tables, plots, and statistical models. This package adds functionality to (i) automatically identify potentially disclosive outputs against a range of commonly used disclosure tests;(ii) apply optional disclosure mitigation strategies as requested;(iii) report reasons for applying SDC;and (iv) produce simple summary documents trusted research environment staff can use to streamline their workflow and maintain auditable records. This creates an explicit change in the dynamics so that SDC is something done with researchers rather than to them, and enables more efficient communication with checkers. A graphical user interface supports human checkers by displaying the requested output and results of the checks in an immediately accessible format, highlighting identified issues, potential mitigation options, and tracking decisions made. The major analytical programming languages used by researchers (Python, R, and Stata) are supported by providing front-end packages that interface with the core Python back-end. © 2024 IEEE.

关键词： Sensitive data

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Control, Confidentiality, and the Right to be Forgotten

arXiv

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arXiv 2022年

作者： Cohen, Aloni Smith, Adam Swanberg, Marika Vasudevan, Prashant Nalini Department of Computer and Data Science University of Chicago United States Department of Computer Science Boston University United States Department of Computer Science National University of Singapore Singapore

Recent digital rights frameworks give users the right to delete their data from systems that store and process their personal information (e.g., the "right to be forgotten" in the GDPR). How should deletion be formalized in complex systems that interact with many users and store derivative information? We argue that prior approaches fall short. Definitions of machine unlearning Cao and Yang [2015] are too narrowly scoped and do not apply to general interactive settings. The natural approach of deletion-as-confidentiality Garg et al. [2020] is too restrictive: by requiring secrecy of deleted data, it rules out social functionalities. We propose a new formalism: deletion-as-control. It allows users’ data to be freely used before deletion, while also imposing a meaningful requirement after deletion—thereby giving users more control. Deletion-as-control provides new ways of achieving deletion in diverse settings. We apply it to social functionalities, and give a new unified view of various machine unlearning definitions from the literature. This is done by way of a new adaptive generalization of history independence. Deletion-as-control also provides a new approach to the goal of machine unlearning, that is, to maintaining a model while honoring users’ deletion requests. We show that publishing a sequence of updated models that are differentially private under continual release satisfies deletion-as-control. The accuracy of such an algorithm does not depend on the number of deleted points, in contrast to the machine unlearning literature. © 2022, CC0.

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Conformalized adaptive forecasting of heterogeneous trajectories 24

Conformalized adaptive forecasting of heterogeneous trajecto...

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Proceedings of the 41st International Conference on Machine Learning

作者： Yanfei Zhou Lars Lindemann Matteo Sesia Department of Data Sciences and Operations University of Southern California Los Angeles CA Department of Computer Science University of Southern California Los Angeles CA

This paper presents a new conformal method for generating simultaneous forecasting bands guaranteed to cover the entire path of a new random trajectory with sufficiently high probability. Prompted by the need for dependable uncertainty estimates in motion planning applications where the behavior of diverse objects may be more or less unpredictable, we blend different techniques from online conformal prediction of single and multiple time series, as well as ideas for addressing heteroscedasticity in regression. This solution is both principled, providing precise finite-sample guarantees, and effective, often leading to more informative predictions than prior methods.

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Using Video Games as a Tool in Math Education

Using Video Games as a Tool in Math Education

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Smart Information Systems and Technologies (SIST), IEEE International Conference on

作者： Ruslan Omirgaliyev Diyar Mukhidenov Ayazhan Sydyk Department of Computer Engineering Astana IT University Astana Kazakhstan Department of Computational and Data Science Astana IT University Astana Kazakhstan

Today, you can see how children are increasingly spending their free time playing desktop or mobile video games. The question then becomes whether a simple hobby can be turned into a rewarding learning pastime? In the introduction of the article, the current situation and the problem that video games can create and the direction in which it can be solved using video games will be considered. The literature review considers some interesting aspects and conclusions of earlier similar studies. The methodology will present plans for quantitative and qualitative surveys of the population and a proposal for the development of a gaming product for educational purposes. The results will show what the situation is and what people's opinion about the use of video games in education. All the data obtained will help in the development of an educational project. Summing up, we will talk about the meaning and significance of the project and similar ideas in the modern world.

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Local Borsuk-Ulam, Stability, and Replicability

arXiv

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arXiv 2023年

作者： Chase, Zachary Chornomaz, Bogdan Moran, Shay Yehudayoff, Amir Department of Mathematics Technion Israel Department of Mathematics Computer Science and Data and Decision Sciences Technion Google Research Israel

We use and adapt the Borsuk-Ulam Theorem from topology to derive limitations on list-replicable and globally stable learning algorithms. We further demonstrate the applicability of our methods in combinatorics and topology. We show that, besides trivial cases, both list-replicable and globally stable learning are impossible in the agnostic PAC setting. This is in contrast with the realizable case where it is known that any class with a finite Littlestone dimension can be learned by such algorithms. In the realizable PAC setting, we sharpen previous impossibility results and broaden their scope. Specifically, we establish optimal bounds for list replicability and global stability numbers in finite classes. This provides an exponential improvement over previous works and implies an exponential separation from the Littlestone dimension. We further introduce lower bounds for weak learners, i.e., learners that are only marginally better than random guessing. Lower bounds from previous works apply only to stronger learners. To offer a broader and more comprehensive view of our topological approach, we prove a local variant of the Borsuk-Ulam theorem in topology and a result in combinatorics concerning Kneser colorings. In combinatorics, we prove that if c is a coloring of all non-empty subsets of [n] such that disjoint sets have different colors, then there is a chain of subsets that receives at least 1 + ⌊n/2⌋ colors (this bound is sharp). In topology, we prove e.g. that for any open antipodal-free cover of the d-dimensional sphere, there is a point x that belongs to at least (Equation presented) sets. © 2023, CC BY.

关键词： Topology

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