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检索条件"任意字段=19th International Conference on Algorithmic Learning Theory"

共 547 条记录，以下是11-20 订阅

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2024 IEEE 19th international conference on the Perspective Technologies and Methods in MEMS Design, MEMSTECH 2024 - Proceedings

2024 IEEE 19th International Conference on the Perspective T...

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19th IEEE international conference on the Perspective Technologies and Methods in MEMS Design, MEMSTECH 2024

ISBN: (纸本)9798350378627

the proceedings contain 31 papers. the topics discussed include: Bilow – knee-prothesis study;electrodynamic modeling the motion devices of MEMS;application of bayes theory model to determine the optimal variant of technological process for production of MEMS components;the monitoring of automated platforms for testing Internet of things devices;simulation of parameters and modes of wave propagation in electromechanical systems using methods of inverse problems theory;advanced computer-aided optimization of quartz resonator-based sensors;improvement of the control system of the robotic mobile platform;segmentation of medical images using deep learning and texture enhancement based on fractional derivative operators;modelling of the dynamic process in the microbeam of the MEMS resonators;exploring the potential of cellulose acetate membranes for precise gas permeation in microfluidic devices;and investigation of magnetic properties of cryogenically cooled electrolyte pure iron powder.

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ARES 2024 - 19th international conference on Availability, Reliability and Security, Proceedings

ARES 2024 - 19th International Conference on Availability, R...

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19th international conference on Availability, Reliability and Security, ARES 2024

ISBN: (纸本)9798400717185

the proceedings contain 202 papers. the topics discussed include: accuracy evaluation of SBOM tools for web applications and system-level software;enhancing secure deployment with ansible: a focus on least privilege and automation for Linux;behavioral modelling for sustainability in smart homes;a web browser plugin for users' security awareness;analysis of the windows control flow guard;if it looks like a rootkit and deceives like a rootkit: a critical examination of kernel-level anti-cheat systems;systematic analysis of label-flipping attacks against federated learning in collaborative intrusion detection systems;operation assessment in cyberspace: understanding the effects of cyber deception;exploring the influence of the choice of prior of the variational auto-encoder on cybersecurity anomaly detection;and evaluation of cyber situation awareness - theory, techniques and applications.

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algorithmic Forex Trading Using Q-learning 19th

Algorithmic Forex Trading Using Q-learning

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19th international conference on Artificial Intelligence Applications and Innovations (AIAI)

作者： Zahrah, Hasna Haifa Tirtawangsa, Jimmy Telkom Univ Bandung Indonesia

ISBN: (纸本)9783031341137;9783031341113;9783031341106

the forex market is a difficult market for traders to succeed. the high noise and volatility of the forex market make the traders very hard to open and close position accurately. Many approaches have been proposed to overcome these difficulties, including algorithmic trading. this research proposed a framework for algorithmic trading using Q-learning with the help of LSTM. the proposed framework uses a finite state space in reinforcement learning to use holding time and higher timeframe market data. the state space is designed so that the agent can open and close positions flexibly, without being restricted by a fixed time window. this allows the agent to take profits and avoid losses. the proposed framework was trained and tested using 15 years' worth of historical data of the EUR/USD currency pair in 5-min timeframe data. the system was evaluated based on various metrics such as profit, drawdown, Sharpe ratio, holding time, and delta time. the results show that with its designed finite state space and flexible time window, the proposed framework achieved consistent profits, reduced losses, and increased overall profits. this suggests that the proposed framework may be a suitable solution for forex market trading.

关键词： Q-learning LSTM Forex

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A Straggler-resilient Federated learning Framework for Non-IID Data Based on Harmonic Coding 19

A Straggler-resilient Federated Learning Framework for Non-I...

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19th international conference on Mobility, Sensing and Networking (MSN)

作者： Tang, Weiheng Chen, Lin Chen, Xu Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China

ISBN: (纸本)9798350358261;9798350358278

Federated learning (FL) has recently emerged as a promising learning paradigm, that enables local gradient update and global model aggregation and synchronization. With the advantages of data privacy protection and communication overhead reduction, FL has been widely adopted in a multitude of edge intelligence and IoT applications. However, except the benefits, FL also suffers from stragglers effect and non-IID data, leading to unpredictable training delay and unstable convergence. Meanwhile, coding theory-based approaches have been proposed for stragglers mitigation in distributed computing, i.e., coded computing. Motivated by coded computing, there are several works introduce coding techniques into federated learning. In this paper, to further exploit the potential of coded federated learning, we propose HarFL, a stragglers resilient federated learning framework for non-IID data based on harmonic coding, which is suitable for general machine learning model with multivariate polynomial gradients and achieves a better stragglers mitigation than former coded federated learning schemes with the same coded computation redundancy. We first describe the basic harmonic coded federated learning framework with two phases: encoded data sharing and gradient results decoding. Moreover, we formulate an optimization problem aiming to maximize the successful probability of decoding, which is proved to be NP-hard. An efficient approximate algorithm with theoretical performance guarantee is also developed to mathematically solve the formulated problem. Finally, simulation results demonstrate the superiority of HarFL over several compared schemes.

关键词： Federated learning Coded computing Stragglers effect Non-IID data

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Preparing a Dataset of Ransomware BTC Addresses for Machine learning Purpose 19th

Preparing a Dataset of Ransomware BTC Addresses for Machine ...

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19th international conference on Dependability of Computer Systems (DepCoS-RELCOMEX)

作者： Rodwald, Przemyslaw Polish Naval Acad Dept Comp Sci Smidowicza 69 PL-81127 Gdynia Poland

ISBN: (纸本)9783031618567;9783031618574

Ransomware is a class of malicious software that encrypts the files of an infected machine and demands payments, mainly in cryptocurrencies such as Bitcoin. One of the challenges faced by law enforcement is identifying suspected Bitcoin addresses. the aim of this article is to create a dataset of historical ransom addresses as training data for future research. this dataset consists of two elements: the first is a compilation of suspicious addresses from various data sources, and the second comprises custom-calculated parameters for these addresses. this dataset serves as a starting point for future research on the automatic identification of ransom addresses.

关键词： bitcoin address ransomware dataset

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Mediated Effect of Product Quality on Product Familiarity, Low-Price, and Social Interactions Affecting Urge to Buy Impulsively in Live-Streaming Shopping 19

Mediated Effect of Product Quality on Product Familiarity, L...

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19th international conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Ko, Hsiu-Chia Department of Information Management Chaoyang University of Technology Taichung Taiwan

ISBN: (纸本)9798331507817

Live-streaming shopping (LSS) quickly monetizes traffic by triggering the urge to buy impulsively (UBI). For experienced LSS consumers, past viewing memories and marketing stimuli may influence their UBI. Grounded in the stimuli-organism-response theory and social interaction theory, this study aims to explore how product familiarity, monetary savings, social interactions, and product quality influence UBI. the mediating role of product quality between these stimuli and UBI is also examined. Data were collected from an online questionnaire of 233 experienced LSS participants. the results reveal that product familiarity, perceived monetary savings, and word of mouth enhance perceived product quality, which in turn positively affects UBI. While observing others’ purchases directly drives UBI, text-based positive word of mouth influences UBI through perceived product quality. this study shows that for experienced LSS consumers, UBI can be driven by cognitive factors, with perceived product quality as a crucial mediator. the findings provide valuable insights for streamers to enhance consumer engagement and drive sales through effective marketing stimuli and strong social interactions. © 2025 IEEE.

关键词： live-streaming shopping monetary savings observational learning product familiarity product quality urge to buy impulsively word of mouth

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Workload Characterization in HPC Environment for Auto-scaling of Resources - Preliminary Study 19

Workload Characterization in HPC Environment for Auto-scalin...

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19th IEEE-India-Council international conference (INDICON)

作者： Barve, Mahesh Sinha, Sharad Hardikar, Rahul Padmakar Gunturu, Ashok Mallik, Writtam Indian Inst Technol Tata Consultancy Serv SMCS HPC Farmagudi Goa India Indian Inst Technol Sch Math & Comp Sci SMCS Farmagudi Goa India Tata Consultancy Serv HPC Enterprise Growth Grp Pune Maharashtra India

ISBN: (纸本)9781665473507

Machine learning and deep learning techniques have been shaping many sectors, one of them is High-Performance Computing. As predicting workloads assists decision-making and resource management, many statistical and machine learning techniques have been employed to predict workloads. this paper presents several machine learning and ensemble learning techniques along with a neural network approach to predict the run-time of a scientific application CP2K. It is a molecular dynamics simulation software. Its run-time was characterized under various resource allocation and algorithmic parameters. the data thus generated were used to train Random Forest, SVM, XGBoost, CatBoost, GradientBoost, and Neural Network models. the trained models help predict its run time under different resource allocation and algorithmic parameters thus enabling better resource allocation request by HPC users. though all the models have shown promising results during both training and testing phases, the Random Forest has beaten all the models in terms of Mean Squared Error (MSE).

关键词： high performance computing machine learning CP2K

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Architectural Design Decisions for Machine learning Deployment 19

Architectural Design Decisions for Machine Learning Deployme...

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19th IEEE international conference on Software Architecture (ICSA)

作者： Warnett, Stephen John Zdun, Uwe Univ Vienna Res Grp Software Architecture Vienna Austria

ISBN: (纸本)9781665417280

Deploying machine learning models to production is challenging, partially due to the misalignment between software engineering and machine learning disciplines but also due to potential practitioner knowledge gaps. To reduce this gap and guide decision-making, we conducted a qualitative investigation into the technical challenges faced by practitioners based on studying the grey literature and applying the Straussian Grounded theory research method. We modelled current practices in machine learning, resulting in a UML-based architectural design decision model based on current practitioner understanding of the domain and a subset of the decision space and identified seven architectural design decisions, various relations between them, twenty-six decision options and forty-four decision drivers in thirty-five sources. Our results intend to help bridge the gap between science and practice, increase understanding of how practitioners approach deployment of their solutions, and support practitioners in their decision-making.

关键词： Architectural Design Decisions Machine learning Software Architecture Grey Literature Grounded theory

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Visual Variation: A Versatile Knowledge Visualization Method based on Variation theory 27

Visual Variation: A Versatile Knowledge Visualization Method...

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27th international conference on Information Visualisation (IV) / 19th international conference Computer Graphics, Imaging and Visualization (CGiV)

作者： Eppler, Martin J. Univ St Gallen Mcm Inst St Gallen Switzerland

ISBN: (纸本)9798350341614

the aim of this paper is to introduce a set of five versatile graphic representation formats called visual variations that allow anyone to represent qualitative insights systematically, concisely and in an expansive way. this "bar chart for knowledge" is based on Marton's seminal variation theory to improve learning and comprehension and makes its mechanisms actionable within the realm of a simple graphic notation consisting of five visual patterns: 1. the 'Approaches' pattern to visualize different fitting and problematic solutions to a problem. 2. the concise and expansive 'Scenarios' pattern to visualize possible futures. 3. the chronological 'Segments' pattern to visualize processes and events. 4. the powerful 'Configurations' pattern to depict solution architectures. 5. the memorable "Mutations" pattern based on visual metaphors to represent classifications. these patterns follow the principle that you only understand something if you understand it in more than one way. the paper describes the method's key idea, background, application areas, and evaluation results, as well as numerous examples from the realm of technology, business, and psychology. It concludes by pointing out future development directions for the visual variation methodology.

关键词： notation knowledge representation visual variation learning patterns

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Unveiling the Dynamics of Human Decision-Making: From Strategies to False Beliefs in Collaborative Human-Robot Co-learning Tasks 24

Unveiling the Dynamics of Human Decision-Making: From Strate...

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19th Annual ACM/IEEE international conference on Human-Robot Interaction (HRI)

作者： Kumar, Rajul Huang, Yidi Yao, Ningshi George Mason Univ Elect & Comp Engn Fairfax VA 22030 USA

ISBN: (纸本)9798400703232

As robots become more integrated into humans' daily activities, it is essential to understand how human decision varies during co-learning with robots in real-world scenarios. Despite great advances in developing humanoid robots, which aims to foster a seamless collaborative world where humans and robots coexist, a gap remains in the social bond between humans and robots, particularly in tasks demanding optimal teamwork. In alignment with current pioneering efforts in the human-robot collaboration field, this paper presents an experimental study leading to a rationale analysis and classification of human behavioral dynamics during a joint collaborative pick-and-place task with a robotic arm. Our post-experimental analysis categorized human behavioral dynamics into three distinct broad categories, which are "strategic explorers and decoders", "reactive navigators and dynamic responders", and "score maximizers and ideal collaborators". We provide in-depth analysis for each group, exploring potential reasons for their observed behavioral patterns and irrational decisions substantiated by intuitions from psychological and behavioral game theory, including concepts of false belief and strategy development.

关键词： Human Decision Making Co-learning Joint Tasks Human Robot collaboration False belief

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