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27th International Conference on System Theory, control and Computing, ICSTCC 2023

作者： Alexandrescu, Adrian Mironeanu, Cătălin Faculty of Automatic Control and Computer Engineering Gheorghe Asachi Technical University of Iaşi Iaşi Romania

ISBN: (纸本)9798350337983

There is an increasing need for computational and storage capabilities for complex distributed applications. Existing solutions need to be deployed in an environment that allows for an increase in performance, scalability, and availability. This paper takes looks at the state-of-the-art regarding methods that take existing applications and make them more efficient by using Cloud services. The novelty of the paper consists of a proposed framework for deploying applications on three major Cloud providers (i.e., Amazon's AWS, Google Cloud and Microsoft Azure) and on the OpenStack open-source Cloud. After the main services from the four Cloud providers are identified, different deployment methods are described depending on the Cloud services and on the requirements of the application. Also, some examples of migrations are discussed with reference to specific Cloud provider services. The proposed solution for Anything-as-a-Service (YaaS) is a straightforward framework for taking different types of applications and migrating them to the Cloud. Therefore, the deployed applications benefit from Cloud features such as resource pooling, availability or scalability, while also being wary of the incurring costs. © 2023 IEEE.

关键词： Web services

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UPB at IberLEF-2023 AuTexTification: Detection of Machine-Generated Text using Transformer Ensembles

UPB at IberLEF-2023 AuTexTification: Detection of Machine-Ge...

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2023 Iberian Languages Evaluation Forum, IberLEF 2023

作者： Preda, Andrei-Alexandru Cercel, Dumitru-Clementin Rebedea, Traian Chiru, Costin-Gabriel Computer Science and Engineering Department Faculty of Automatic Control and Computers University Politehnica of Bucharest Bucharest060042 Romania

This paper describes the solutions submitted by the UPB team to the AuTexTification shared task, featured as part of IberLEF-2023. Our team participated in the first subtask, identifying text documents produced by large language models instead of humans. The organizers provided a bilingual dataset for this subtask, comprising English and Spanish texts covering multiple domains, such as legal texts, social media posts, and how-to articles. We experimented mostly with deep learning models based on Transformers, as well as training techniques such as multi-task learning and virtual adversarial training to obtain better results. We submitted three runs, two of which consisted of ensemble models. Our best-performing model achieved macro F1-scores of 66.63% on the English dataset and 67.10% on the Spanish dataset. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Deep learning

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Learning the Dynamic Environment of an Original Game Using Hierarchical Reinforcement Learning Methods 27

Learning the Dynamic Environment of an Original Game Using H...

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27th International Conference on System Theory, control and Computing, ICSTCC 2023

作者： Batalan, Vlad Leon, Florin Faculty of Automatic Control and Computer Engineering "Gheorghe Asachi" Technical University of Iași Iași Romania

ISBN: (纸本)9798350337983

This paper compares the performance of two reinforcement learning algorithms, Q-Learning and MAXQ-0, in learning to play an original game. An extension of MAXQ-0 algorithm, MAXQ-P is introduced, which enhances the variety of the tree nodes with simple, ordered and repetitive nodes. The hierarchical approach provided by MAXQ-P finds the optimal solution faster than the flat Q-Learning approach but converges more slowly. Furthermore, the performance of the MAXQ-P algorithm decreases after a certain number of episodes due to representation error in the weights of the model. To address this issue, the model is periodically tested with an exploration value of 0, and if the model successfully finds the solution, it is stored for future use. This study provides insights into the benefits and drawbacks of using hierarchical reinforcement learning algorithms for complex tasks and highlights the importance of carefully designing and training such algorithms for optimal performance. © 2023 IEEE.

关键词： Reinforcement learning

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Understanding Deep RL agent decisions: a novel interpretable approach with trainable prototypes 4

Understanding Deep RL agent decisions: a novel interpretable...

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4th Italian Workshop on Explainable Artificial Intelligence, XAI.it 2023

作者： Borzillo, Caterina Ragno, Alessio Capobianco, Roberto Department of Computer Control and Management Engineering Sapienza University of Rome RM Rome00185 Italy Sony AI

Deep reinforcement learning (DRL) models have shown great promise in various applications, but their practical adoption in critical domains is limited due to their opaque decision-making processes. To address this challenge, explainable AI (XAI) techniques aim to enhance transparency and interpretability of black-box models. However, most current interpretable systems focus on supervised learning problems, leaving reinforcement learning relatively unexplored. This paper extends the work of PW-Net, an interpretable wrapper model for DRL agents inspired by image classification methodologies. We introduce Shared-PW-Net, an interpretable deep learning model that features a fully trainable prototype layer. Unlike PW-Net, Shared-PW-Net does not rely on pre-existing prototypes. Instead, it leverages the concept of ProtoPool to automatically learn general prototypes assigned to actions during training. Additionally, we propose a novel prototype initialization method that significantly improves the model's performance. Through extensive experimentation, we demonstrate that our Shared-PW-Net achieves the same reward performance as existing methods without requiring human intervention. Our model's fully trainable prototype layer, coupled with the innovative prototype initialization approach, contributes to a clearer and more interpretable decision-making process. The code for this work is publicly available for further exploration and applications. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Reinforcement learning

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A Moreau Envelope Approach for LQR Meta-Policy Estimation 63

A Moreau Envelope Approach for LQR Meta-Policy Estimation

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Aravind, Ashwin Toghani, Mohammad Taha Uribe, Cesar A. Iit Bombay Centre for Systems and Control MH Mumbai India Rice University Department of Electrical and Computer Engineering HoustonTX United States

ISBN: (纸本)9798350316339

We study the problem of policy estimation for the Linear Quadratic Regulator (LQR) in discrete-time linear timeinvariant uncertain dynamical systems. We propose a Moreau Envelope-based surrogate LQR cost, built from a finite set of realizations of the uncertain system, to define a meta-policy efficiently adjustable to new realizations. Moreover, we design an algorithm to find an approximate first-order stationary point of the meta-LQR cost function. Numerical results show that the proposed approach outperforms naive averaging of controllers on new realizations of the linear system. We also provide empirical evidence that our method has better sample complexity than Model-Agnostic Meta-Learning (MAML) approaches. © 2024 IEEE.

关键词： Cost functions

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A Decentralized Paper Dissemination System Employing Blockchain Technology, Peer Review and Expert Badges 27

A Decentralized Paper Dissemination System Employing Blockch...

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27th International Conference on System Theory, control and Computing, ICSTCC 2023

作者： Bărbuţă, Delia-Elena Alexandrescu, Adrian Faculty of Automatic Control and Computer Engineering Gheorghe Asachi Technical University of Iaşi Iaşi Romania

ISBN: (纸本)9798350337983

Peer review represents the status-quo when it comes to evaluating research articles that are submitted to conferences and journals. The significance of a computer science article is given by the prestige of the publication and is correlated with the inclusion in the ISI Web of Science index. This paper discusses the issues of the current paper publication paradigm and proposes a decentralized approach to the paper dissemination and the peer review processes. On the one hand, decentralization and transparency are obtained by employing smart contracts, through blockchain technology. On the other hand, an optimization of the paper rating system is obtained by employing a system of expert badges, based on NFTs, which ensure that the peer review process is just and that only specialists in the fields associated to the contributed paper offer proficient feedback. Other proposed facets include the remuneration of reviewers, a method of allowing the proposed system to expand based on the community's input, and a solution for allowing the organization of conferences. © 2023 IEEE.

关键词： Smart contract

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Cloud-based Architecture for Deploying a Distributed Ambient Assisted Living Environment 27

Cloud-based Architecture for Deploying a Distributed Ambient...

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27th International Conference on System Theory, control and Computing, ICSTCC 2023

作者： Botezatu, Nicolae Alexandrescu, Adrian Faculty of Automatic Control and Computer Engineering Gheorghe Asachi Technical University of Iaşi Iaşi Romania

ISBN: (纸本)9798350337983

Ambient Assisted Living (AAL) is starting to become the norm as more and more smart devices and sensors are installed in people's homes. This is an important aspect in improving the quality of life, especially for the elderly and for people with disabilities. The solution presented in this paper approaches this paradigm in a large scale deployment context, as it proposes an AAL architecture on three layers (Edge, Dew, and Cloud), which is capable of handling high amount of sensor data in near real-time. Using a distributed system of rules engines, the system is able to take actions based on the acquired data. An important novel concept is the"new home deployment", which facilitates the integration of the devices from a new installation site - the software setup of the new edge infrastructure is done by using a dedicated Cloud service. In order to showcase the practicality and efficiency of the architecture, this paper presents the intricacies of the employed services and service-interaction when deploying the solution on the OpenStack Cloud platform. © 2023 IEEE.

关键词： Cloud computing architecture

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ScDCMAC:adaptive feedback clustering of scRNA-seq data with deep gene correlation mining

ScDCMAC:adaptive feedback clustering of scRNA-seq data with ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Pang, Shanchen Wang, Ruiqian Wu, Wenhao Li, Hengxiao Wu, Jiarui Zhang, Yuanyuan Wang, Shudong College of Computer Science and Technology Qingdao Institute of Software Qingdao China Qingdao University of Technology School of Information and Control Engineering Qingdao China

ISBN: (纸本)9798350386226

Cluster assignment is crucial for analyzing single-cell RNA sequencing (scRNA-seq) data, essential for studying cell diversity and biological functions at the single-cell level. Deep learning-based clustering methods have been extensively applied in scRNA-seq data analysis. However, current deep models frequently overlook the deep exploration of potential gene correlation, resulting in networks lacking guidance from gene correlation information when learning hidden layer features. This paper proposes an adaptive feedback fuzzy clustering method based on deep gene correlation mining(scDCMAC). The deep gene correlation mining network employs contrast learning to compute the contrast loss on two cell similarity matrices, constructed from denoised autoencoder hidden layer features and original input features, to uncover potential gene correlation. The adaptive feedback clustering network further denoises the hidden layer features using a self-attention mechanism, computes the affiliation matrix, and supervises the denoising autoencoder's dimensionality reduction using the affiliation matrix. Experiments on 13 real scRNA-seq datasets demonstrate that scDCMAC excels in clustering accuracy and stability, showing great potential in scRNA-seq sample analysis. © 2024 IEEE.

关键词： Dimensionality reduction

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An Improved YOLOv5s-Based Smoke Detection System for Outdoor Parking Lots

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computers, Materials & Continua 2024年第8期80卷 3333-3349页

作者： Ruobing Zuo Xiaohan Huang Xuguo Jiao Zhenyong Zhang State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China School of Information and Control Engineering Qingdao University of TechnologyQingdao266520China State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou310027China Key Laboratory of Computing Power Network and Information Security Ministry of EducationQilu University of Technology(Shandong Academy of Sciences)Jinan250353China Text Computing&Cognitive Intelligence Engineering Research Center of National Education Ministry Guizhou UniversityGuiyang550025China

In the rapidly evolving urban landscape,outdoor parking lots have become an indispensable part of the city’s transportation *** growth of parking lots has raised the likelihood of spontaneous vehicle combus-tion,a significant safety hazard,making smoke detection an essential preventative ***,the complex environment of outdoor parking lots presents additional challenges for smoke detection,which necessitates the development of more advanced and reliable smoke detection *** paper addresses this concern and presents a novel smoke detection technique designed for the demanding environment of outdoor parking ***,we develop a novel dataset to fill the gap,as there is a lack of publicly available *** dataset encompasses a wide range of smoke and fire scenarios,enhanced with data augmentation to ensure robustness against diverse outdoor ***,we utilize an optimized YOLOv5s model,integrated with the Squeeze-and-Excitation Network(SENet)attention mechanism,to significantly improve detection accuracy while maintaining real-time processing ***,this paper implements an outdoor smoke detection system that is capable of accurately localizing and alerting in real time,enhancing the effectiveness and reliability of emergency *** show that the system has a high accuracy in terms of detecting smoke incidents in outdoor scenarios.

关键词： YOLOv5s smoke detection deep learning SENet

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Consensus Tracking of Multi-Agent Systems in The Presence of Byzantine Agents Using Model Predictive control 8

Consensus Tracking of Multi-Agent Systems in The Presence of...

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8th International Conference on control, Instrumentation and Automation, ICCIA 2022

作者： Rezaei, Mohammad Amin Bagheri, Peyman Hashemzadeh, Farzad University of Tabriz Faculty of Electrical and Computer Engineering Department of Control Engineering Tabriz Iran

ISBN: (纸本)9781665495691

This paper deals with the consensus tracking of multi-agent systems in the presence of some Byzantine agents. Steering the agents toward a predefined reference via model predictive control and, besides, encountering the misbehaving agents, are the main aims of the paper. Using graph properties alongside the Weighted-Mean-Subsequence-Reduced (W-MSR) algorithm led to a control method for special kinds of multi-agent systems. Combining model predictive control with W-MSR results in a new algorithm called Reference-Based-W-MSR (RBW-MSR), which has made the consensus point fixed according to a predetermined reference. The effectiveness of the proposed algorithm would be illustrated via simulation results in which different scenarios have been examined to exhibit the success of the method. © 2022 IEEE.

关键词： Multi agent systems

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