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arXiv 2021年

作者： van der Hoeven, Dirk Fusco, Federico Cesa-Bianchi, Nicolò Dept. of Computer Science Università degli Studi di Milano Italy Dept. of Computer Control and Management Engineering Sapienza Università di Roma Italy DSRC Dept. of Computer Science Università degli Studi di Milano Italy

We study the problem of online multiclass classification in a setting where the learner’s feedback is determined by an arbitrary directed graph. While including bandit feedback as a special case, feedback graphs allow a much richer set of applications, including filtering and label efficient classification. We introduce Gappletron, the first online multiclass algorithm that works with arbitrary feedback graphs. For this new algorithm, we prove surrogate regret bounds that hold, both in expectation and with high probability, for a large class of surrogate losses. Our bounds are of order B√ρKT, where B is the diameter of the prediction space, K is the number of classes, T is the time horizon, and ρ is the domination number (a graph-theoretic parameter affecting the amount of exploration). In the full information case, we show that Gappletron achieves a constant surrogate regret of order B2K. We also prove a general lower bound of order max {B2K, √T} showing that our upper bounds are not significantly improvable. Experiments on synthetic data show that for various feedback graphs our algorithm is competitive against known baselines. Copyright © 2021, The Authors. All rights reserved.

关键词： Directed graphs

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Forecasting Breakdowns of Electric Motors of a Sugar Factory Using Machine Learning Methods

Forecasting Breakdowns of Electric Motors of a Sugar Factory...

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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Nataliia Zaiets Nataliia Lutska Lidiia Vlasenko Andrii Zhyltsov dept. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine dept. of Automation and Computer Technologies of Control Systems National University of Food Technologies Kyiv Ukraine dept. of Software Engineering and Cyber Security State University of Trade and Economics Kyiv Ukraine dept. of electromagnetic systems The Institute of Electrodynamics of the National Academy of Sciences of Ukraine Kyiv Ukraine

the paper demonstrates the process of developing mathematical models for identifying breakdowns of electric motors using machine learning methods. The authors have developed three mathematical models for identifying breakdowns of electric drives with a power of 55 kW, 1500 rpm on the example of sugar production: model for quadratic discriminant analysis; binary classification decision tree; feed-forward neural network model. The best structures and parameters of the models were determined by machine learning methods. The performance of the models on the training and test sets was more than 96% by F-score. It was proposed to use all three models independently for diagnosing breakdowns of electric drives, while the decision maker makes the final decision on the replacement or repair of electrical equipment. This approach is justified and effective because of the diversity of models. The developed models, except for assessing the state of the engine, can also be used for simulation modelling, forecasting, and serve as information support for process operators in organizing equipment maintenance. The availability of real-time information about the state of equipment in production will allow timely repair or replacement of equipment, thereby reducing the risks of stopping production and increasing the resource efficiency of technological processes.

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Predictive Analysis for Sales Forecasting Using Machine Learning: Leveraging Data-Driven Insights for Enhanced Business Planning and Decision-Making

Predictive Analysis for Sales Forecasting Using Machine Lear...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Jonardan Koner Shailesh Shivaji Deore S. Roja Kalyani Nilesh Sahastrabuddhe Prashant Kalshetti Anthony Rose NICMAR University Pune Department of Computer Engineering SSVPS Bapusaheb Shivajirao Deore College of Engineering Dhule Maharashtra Department of Computer Science Engineering and Applications Bharathidasan University Trichy-23 Dr. D. Y. Patil Institute of Management and Research Pimpri Pune Dept. of BBA Dr. D Y Patil Vidyapeeth Global Business School and Research Centre Pune Institute of Management and Entrepreneurship Development Pune Maharashtra India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Within the modern trade scene, leveraging prescient investigation for deal estimating has developed as a basic procedure for organizations pointing to optimize their decision-making forms. This consideration centres on the application of the XG Boost calculation, a well-known machine learning method, to tackle data-driven experiences for upgraded deals determining, thus encouraging prevalent commerce arranging. Through an in-dept. investigation of authentic deals information, the inquiry about illustrates the noteworthiness of consolidating machine learning strategies, especially the XG Boost calculation, for exact and solid deal forecasts. By leveraging a comprehensive dataset including different variables affecting deals, such as client socioeconomics, regular patterns, showcasing activities, and financial markets, this exhibits the algorithm's capability to capture complex connections inside the information and produce exact figures. Besides, the investigation emphasizes. interpretability of the XGBoost calculation, empowering partners to comprehend the fundamental components affecting deal expectations. This straightforwardness cultivates a more profound understanding of the driving powers behind deal patterns, encouraging educated decision-making and proactive commerce arranging.

关键词： Machine learning algorithms Decision making Machine learning Organizations Solids Prediction algorithms Planning Predictive analytics Forecasting Socioeconomics

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Constrained Reinforcement Learning via Dissipative Saddle Flow Dynamics

arXiv

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arXiv 2022年

作者： Zheng, Tianqi You, Pengcheng Mallada, Enrique The Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States The Dept. of Industrial Engineering and Management Peking University Beijing China

In constrained reinforcement learning (C-RL), an agent seeks to learn from the environment a policy that maximizes the expected cumulative reward while satisfying minimum requirements in secondary cumulative reward constraints. Several algorithms rooted in sampled-based primal-dual methods have been recently proposed to solve this problem in policy space. However, such methods are based on stochastic gradient descent-ascent algorithms whose trajectories are connected to the optimal policy only after a mixing output stage that depends on the algorithm’s history. As a result, there is a mismatch between the behavioral policy and the optimal one. In this work, we propose a novel algorithm for constrained RL that does not suffer from these limitations. Leveraging recent results on regularized saddle-flow dynamics, we develop a novel stochastic gradient descent-ascent algorithm whose trajectories converge to the optimal policy almost surely. © 2022, CC BY.

关键词： Reinforcement learning

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Question Answering Over Knowledge Graphs Using Bert Based Relation Mapping

SSRN

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SSRN 2022年

作者： Suneera, C.M. Prakash, Jay Kumar Singh, Pramod Dept. of Computer Science and Engineering National Institute of Technology Calicut India ABV - Indian Institute of Information Technology and Management Gwalior India

A Knowledge Graph (KG) is a structured form of knowledge describing real-worldentities, properties, and relationships as a graph. Question Answeringover Knowledge Graphs (KGQA) allows people to ask questions in natural languageand extract answers from KG accurately and more quickly. The maintask of a KGQA is to convert a natural language query to the correspondingstructured query form like SPARQL. However, generating the precise SPARQLquery from a question is a challenging task and is highly error-prone. Here wepropose a question answering framework that uses Knowledge Graph to answersimple questions without using SPARQL. Question classification, dependencyparsing, entity linking, BERT-based relation finding, and answer extraction arethe main modules of the approach. We used the DBpedia as the knowledgegraph and tested the end-to-end system with a subset of QALD-4, LC-QuAD,and SimpleQuestions datasets. © 2022, The Authors. All rights reserved.

关键词： Knowledge graph

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Exploring the Mental State Intersection by Brain-computer Interfaces, Cellular Automata and Biofeedback

Exploring the Mental State Intersection by Brain-Computer In...

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Eurocon

作者： Tommaso Colafiglio Domenico Lofù Paolo Sorino Fabrizio Festa Tommaso Di Noia Eugenio Di Sciascio Dept. of Electrical and Information Engineering (DEI) Politecnico di Bari Bari (Italy) Bari Italy Dept. of Computer Automatic and Management Engineering (DIAG) Sapienza Università di Roma Roma (Italy) Roma Italy Conservatorio di Musica E.R. Duni - Matera Matera (Italy) Matera Italy

Brain-computer Interfaces (BCI) allows systems to be controlled by signals derived from Electroencephalogram (EEG) analysis. Several low-cost electroencephalographs are available on the market that provides high-quality EEG signals. A very interesting approach in this domain is to represent a user's mental state by using an EEG signal. In this paper, we propose a method to represent and describe the user's mental state by exploiting Cellular Automata (CA). Using BCI, CA, and Autoencoder Neural Network we provide a graphical description and audible representation of the current mental state of the user wearing the device. We demonstrated experimentally the capability of generating a representation of the mental states, both CA and sound texture, while at the same time can be exploited as a biofeedback approach.

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Expression of Concern for: Taxi Origin-Destination Demand Prediction using DNN Algorithm with NYU Database

Expression of Concern for: Taxi Origin-Destination Demand Pr...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Kumar A. Shukla Apoorva Reddy Radhey Shyam Meena Shubh Almal School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu India Dept. of Industrial and Management Engineering Indian Institute of Technology Kanpur India

ISBN: (数字)9781665437899

ISBN: (纸本)9781665437905

Because of its colossal possible application in huge scope keen transportation frameworks, taxi request expectation has as of late drawn in expanded research interest. Most past methodologies, then again, just considered taxi request forecast in beginning regions, neglecting the demonstrating of the interesting circumstance of objective travellers. We accept that assigning taxicabs to every area ahead of time founded exclusively on taxi beginning interest is wasteful. In this paper, we present a significant and fascinating errand called taxi beginning objective interest expectation, which means to estimate taxi interest between all-area matches from now on. Its key hardships come from delineating how to proficiently gather a wide scope of relevant information to learn request designs. In this paper, a clever Deep Neural Network (DNN) with Deep learning-based models outflanked conventional AI techniques in an assortment of characterization undertakings, including beginning and objective perspectives. Broad tests and examinations on an expansive dataset plainly show that our DNN beats other looked at techniques for foreseeing taxi beginning objective interest.

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IICPilot: An Intelligent Integrated Circuit Backend Design Framework Using Open EDA

arXiv

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arXiv 2024年

作者： Jiang, Zesong Zhang, Qing Liu, Cheng Cheng, Long Li, Huawei Li, Xiaowei SKLP Institute of Computing Technology Chinese Academy of Sciences Beijing China Institute of Advanced Technology University of Science and Technology of China Hefei China Dept. of Computer Science University of Chinese Academy of Sciences Beijing China School of Control and Computer Engineering North China Electric Power University Beijing China

Open-source EDA tools are rapidly advancing, fostering collaboration, innovation, and knowledge sharing within the EDA community. However, the growing complexity of these tools, characterized by numerous design parameters and heuristics, poses a significant barrier to their widespread adoption. This complexity is particularly pronounced in integrated circuit (IC) backend designs, which place substantial demands on engineers’ expertise in EDA tools. To tackle this challenge, we introduce IICPilot, an intelligent IC backend design system based on LLM technology. IICPilot automates various backend design procedures, including script generation, EDA tool invocation, design space exploration of EDA parameters, container-based computing resource allocation, and exception management. By automating these tasks, IICPilot significantly lowers the barrier to entry for open-source EDA tools. Specifically, IICPilot utilizes LangChain’s multi-agent framework to efficiently handle distinct design tasks, enabling flexible enhancements independently. Moreover, IICPilot separates the backend design workflow from specific open-source EDA tools through a unified EDA calling interface. This approach allows seamless integration with different open-source EDA tools like OpenROAD and iEDA, streamlining the backend design and optimization across the EDA tools. © 2024, CC BY-NC-SA.

关键词： Space research

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Analysis and Detection of Melanoma through Collective Intelligence with AI

Analysis and Detection of Melanoma through Collective Intell...

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Annual IEEE Symposium on computer-Based Medical Systems

作者： Enrique Fernández-Morales Carlos L. Sanchez-Bocanegra Rafael Pastor-Vargas José-Juan Pereyra-Rodriguez Juan M. Haut José Alberto Benítez-Andrades Communications and Control Systems Dpt. Computer Science Faculty UNED Madrid Spain Communications and Control Systems Dpt. Computer Science Faculty UNED Malaga Spain Virgen Del Rocio Hospital Universitario Seville Spain School of Tecnology University of Extremadura Cáceres Spain Dept. of Electric Systems and Automatics Engineering Universidad de León León Spain

ISBN: (数字)9798350384727

ISBN: (纸本)9798350384734

Cancer is a widespread global health problem, claiming millions of lives each year, and skin cancer represents a significant threat as it is one of the most common types. Early tumor detection via medical imaging is critical for effective treatment. Leveraging artificial intelligence, particularly novel models like Transformers, presents promising avenues for improved diagnosis. This paper explores the efficacy of a Collective Intelligence approach using AI in classifying cancerous and non-cancerous tumors, aiming to reduce classification errors and support clinical decision-making. We created five different configurations using various datasets to compare the results. The results show solid performance for the CI in the evaluated tasks, reaching up to 75.89% accuracy. The lack of images in certain classes significantly contributes to overfitting. It is suggested to explore data expansion strategies and improve consistency in image capture for future work.

关键词： Accuracy Melanoma Collective intelligence Image capture Transformers Solids Artificial intelligence

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DROPO: Sim-to-Real Transfer with Offline Domain Randomization

arXiv

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arXiv 2022年

作者： Tiboni, Gabriele Arndt, Karol Kyrki, Ville Dept. of Control and Computer Engineering Politecnico di Torino Francesco Ferrucci Street 112 Torino10141 Italy Intelligent Robotics Group Dept. of Electrical Engineering and Automation Aalto University Maarintie 8 Espoo02150 Finland

In recent years, domain randomization over dynamics parameters has gained a lot of traction as a method for sim-to-real transfer of reinforcement learning policies in robotic manipulation;however, finding optimal randomization distributions can be difficult. In this paper, we introduce DROPO, a novel method for estimating domain randomization distributions for safe sim-to-real transfer. Unlike prior work, DROPO only requires a limited, precollected offline dataset of trajectories, and explicitly models parameter uncertainty to match real data using a likelihood-based approach. We demonstrate that DROPO is capable of recovering dynamic parameter distributions in simulation and finding a distribution capable of compensating for an unmodeled phenomenon. We also evaluate the method in two zero-shot sim-to-real transfer scenarios, showing successful domain transfer and improved performance over prior methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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