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Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Rodrigo Silva José De la cruz Lenis Wong Information Systems Engineering Program Universidad Peruana de Ciencias Aplicadas Lima Perú

During emergencies like COVID-19, maintaining educational quality involves integrating non-specialized online. Therefore, a framework is proposed to improve the authenticity and traceability in the assignation of grades in online classrooms of educational institutions in Perú. The proposed framework will impact students and teachers. The former, will be able to have more confidence in their exam grades, which will avoid request to the professors to review them. The latter will trace their students learning in an organized way, which will allow them to assess them continuously. The framework consists of three phases: (1) design of the blockchain architecture, (2) design of the web application architecture and (3) implementation of the web application. It was validated with the application of two experiments made with the support of twenty Peruvian students and a survey conducted with three experts. The validation results show that the proposed framework reduces the time required for the request of study certificates by 99.96% and the survey results by the experts showed that “Traceability”,”Authenticity”, “Performance”, and “Usability” obtained an average rating of 4.83 (very high), 4.7 (very high), 4 (high), and 4 (high) respectively.

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The Practical Model DAM for Analyzing Data Lake Architectures in a Financial Institution in Peru

The Practical Model DAM for Analyzing Data Lake Architecture...

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Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Cristhian Zaña Ramirez Sergio Benancio Mauricio Lenis Wong Information Systems Engineering Program Universidad Peruana de Ciencias Aplicadas Lima Perú

As the volume of data generated in the financial sector continues to grow organizations are challenged to effectively manage this vast amount of information. However, the implementation of a technological solution, especially in highly regulated financial environments with stringent security and privacy requirements, does not achieve the expected result. This study proposes a novel model, called DAM (Data Lake Analysis Model), to analyze the implemented Data Lake architecture of a Peruvian financial institution in order to identify opportunities for improvement and close the gaps that may arise in the management of large volumes of data. This methodology consists of 3 levels: (1) data and internal process analysis, (2) architecture and tools analysis, and (3) data governance analysis. A case study was conducted in a financial institution, and interviews and questionnaires were conducted with analysts and data scientists. The results at the three levels of analysis were very positive, with scores of 4.33, 4.06, and 4.11, respectively. In addition, the results of the questionnaire indicate that the experts rated the analysis as “good”, suggesting that the DAM model is effective in addressing the challenges of financial data management in the Peruvian context.

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TOOL For Predicting Plans and Improving Solid Waste Management by Applying Time Series and AWS

TOOL For Predicting Plans and Improving Solid Waste Manageme...

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Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Limber Jaimes Yudit Garriazo Jymmy Dextre Information Systems Engineering Program Universidad Peruana de Ciencias Aplicadas Lima Perú

Currently, the constant population growth in Peru leads to the generation of large amounts of solid waste, most of these originate from domestic sources and are collected by municipalities. The inefficiency in solid waste collection plans generates rework and therefore operational overhead. Likewise, it pollutes the environment due to waste accumulated in the streets and dissatisfaction from the neighbors. Therefore, an IoT tool is proposed, which will have a solar panel, sensor, chip inside the containers that served to train the algorithm and predict solid waste collection plans for decision-making of a municipality using the time series model, arima and Random Forest. This tool has 3 stages: analysis, design, and validation. Stage 1 evaluates machine learning models and IoT architectures. Stage 2 describes the design and software architecture of the tool. And, in stage 3, the deployment of IoT devices in a municipality of Lima was simulated and its operation was validated with the solid waste collection area. The result of the implementation of the tool in a sector of Villa El Salvador managed to increase the efficiency of solid waste collection plans from 81 % to 96 % effectiveness in said sector. In addition, this reduced operational costs by approximately 8 % and increased the satisfaction of neighbors by not having solid waste accumulated in the streets.

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Approach for Personalized Recommendations to Enhance Customer Service Process in Peruvian Restaurants using OpenAI Contextual Chatbot

Approach for Personalized Recommendations to Enhance Custome...

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IEEE International Congress on Electronics, Electrical engineering and Computing (INTERCON)

作者： Fabiola Cieza Romero Sebastian Pajes León Lenis Wong Information Systems Engineering Program Universidad Peruana de Ciencias Aplicadas Lima Peru

In the current digital era, efficiently accessing relevant information is crucial for various applications such as restaurant recommendation systems, website searches, book recommendations, among others. This study presents an approach for personalized recommendations in improving the customer service process in restaurants using OpenAI's Contextual Chatbot. The approach consists of six phases: (1) Data preprocessing, (2) Embedding and storage, (3) Scheduled updating, (4) Document retrieval, (5) Context adaptation and request creation, and (6) Response generation. OpenAI's text embeddings are used to convert application data into vectors and store them in a vector database. These vectors are used to retrieve similar records and generate contextualized responses using the GPT-3.5 model. The chatbot's performance is evaluated in terms of accuracy and user satisfaction. Two scenarios were used in the experimentation: (a) with the proposed solution and (b) without the solution. The results demonstrated an operational efficiency of 86.67% with the proposed solution and the versatility of the proposed methodology, showcasing its potential for application in a wide range of domains, including websites, books, PDFs, and other forms of documentation.

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Audit model using controls from ISO/IEC 27002 for the information security of electronic voting based on IoT and Blockchain

Audit model using controls from ISO/IEC 27002 for the inform...

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Advanced Learning Technologies on Education & Research (ICALTER), IEEE International Conference on

作者： Edson Paredes Marco Mendez Jymmy Dextre Information Systems Engineering Program Universidad Peruana de Ciencias Aplicadas Lima Perú

In recent years, significant progress has been made in the integration of technologies in voting systems with the aim of improving the transparency of the process. This study proposes an audit model based on the ISO/IEC 27002 standard to evaluate electronic voting systems that make use of Blockchain technology and the Internet of Things (IoT), with the aim of improving security in voting processes. To validate this model, an evaluation of the security control was carried out by applying this international standard to a voting system that integrates Blockchain and IoT. In our analysis of 6 security controls applied to the reality of electronic voting, the system passed all established tests; Therefore, it can be concluded that the risk of compromising system information has been significantly mitigated. This research contributes to the advancement of the security of electronic voting systems and provides a solid foundation for future developments in this field. The combination of emerging technologies and international standards promotes trust, security and therefore mitigates any risk of compromising an electronic voting system by following the best practices of the international standard ISO/IEC 27002.

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Proposing a Low-Rank Approximation Method with Mathematical Guarantees for High-Dimensional Tensor Data

Proposing a Low-Rank Approximation Method with Mathematical ...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Hasegawa, Hiroki Yata, Kazuyoshi Okada, Yukihiko Kunimatsu, Jun University of Tsukuba Program in Service Engineering Tsukuba Japan Sciences University of Tsukuba Institute of Pure and Applied Tsukuba Japan University of Tsukuba Institute of Systems and Information Engineering Tsukuba Institute for Advanced Research Center for Artificial Intelligence Research Tsukuba Japan University of Tsukuba Institute of Medicine Tsukuba Japan

ISBN: (纸本)9798350362480

The analysis of high-dimensional tensor data presents significant challenges for traditional methods such as CANDECOMP/PARAFAC (CP) decomposition and Higher-Order Singular Value Decomposition (HOSVD) due to the size, complexity, and noise inherent in the data. Recent studies have emphasized the critical need for noise-robust approaches capable of reducing dimensionality while preserving data integrity, particularly for matrix-form data, underscoring the necessity of theoretical advancements in noise reduction. In this study, we propose a novel method, Noise-Reduced Higher-Order Singular Value Decomposition (NR-HOSVD), which integrates noise reduction techniques into the HOSVD framework to enhance dimensionality reduction for matrix data. In comparative simulations with CP decomposition and HOSVD, NR-HOSVD consistently demonstrated lower relative errors as the rank increased, particularly for high-dimensional tensors, surpassing the performance of conventional methods beyond a certain rank threshold. Furthermore, NR-HOSVD exhibited superior stability and faster convergence on large-scale datasets compared to existing approaches. These findings suggest that NR-HOSVD provides a more accurate and efficient approach to high-dimensional tensor data analysis, making it particularly well-suited for applications in big data and machine learning. In the future, NR-HOSVD is expected to serve as a pivotal method for enhancing the accuracy and efficiency of high-dimensional tensor data analysis, with broad applicability anticipated in fields such as large-scale data processing and precision medicine. © 2024 IEEE.

关键词： Data integrity

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Implementing NIST CSF and CIS CSC in Gas Industry: A Model's Effectiveness and Acceptance Analysis

Implementing NIST CSF and CIS CSC in Gas Industry: A Model's...

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Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Roberto Villarreal Flavio Alarcón Luis Torrejón Information Systems Engineering Program Universidad Peruana de Ciencias Aplicadas Lima Perú

During 2020, the pandemic intensified global cyberattacks, increasing by 50% in 2021. Since 2022, the war in Ukraine has exacerbated this situation, particularly in the oil and gas industry. This sector, considered critical infrastructure, faces cybersecurity challenges that demand a proactive approach combining technological and behavioral controls. Therefore, a cybersecurity model based on NIST CSF (National Institute of Standards and Technology Cybersecurity Framework) and CIS CSC (Center for Internet Security Critical Security Controls) is proposed to protect the technological platform of a natural gas company. This model consists of four stages: environment analysis, scope and risk appetite, control design, and finally, its development. This model was validated through the evaluation of the implemented controls and a survey of the staff, obtaining a 92.69% effectiveness and an 81.55% acceptance, thus achieving level 2 of NIST CSF, meeting the established objective.

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Involution symmetry quantification using recurrences

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Physical Review E 2024年第2期110卷 024203-024203页

作者： Gabriel Marghoti Thiago de Lima Prado Sergio Roberto Lopes Yoshito Hirata Physics Department Degree Program in Computer Science Interdisciplinary Center for Science Technology and Innovation CICTI Departament of Physics - Telegraphenberg 14473 Potsdam Germany Institute of Systems and Information Engineering

Symmetries are ubiquitous in science, aiding theoretical comprehension by discerning patterns in mathematical models and natural phenomena. This work introduces a method for assessing the extent of symmetry within a time series. We explore both microscopic and macroscopic features extracted from a recurrence plot. By analyzing the statistics of small recurrence matrices, our approach delves into microscale dynamics, facilitating the identification of symmetric time series segments through diagonal macroscale structures on a recurrence plot. We validate our approach by successfully quantifying involution symmetries for three-dimensional dynamical models, specifically, order-2 rotational symmetry in the Lorenz '63 model, and inversion symmetry in the Chua circuit. Our quantifier also detects symmetry breaking in the modified Lorenz model for El Niño phenomenon. The method can be applied in a versatile manner, not only to three-dimensional trajectories but also to univariate time series. Symmetry quantification in time series is promising for enhancing dynamical system modeling and profiling.

关键词： Chaos Dynamical systems Time series analysis

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Action Robust Reinforcement Learning with Highly Expressive Policy

Action Robust Reinforcement Learning with Highly Expressive ...

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IEEE International Conference on systems, Man and Cybernetics

作者： SeongIn Kim Takeshi Shibuya Doctor's Program in Intelligent and Mechanical Interaction Systems University of Tsukuba Ibaraki Japan Faculty of Engineering Information and Systems University of Tsukuba Ibaraki Japan

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In traditional reinforcement learning, there can be a degradation in the control performance of the policy when the environmental parameters differ between the training and application phase. The policy that minimizes this degradation is referred to as a robust policy. A framework called Noisy action Robust Markov Decision Process (NR-MDP) was proposed for training robust policies, and the Action Robust Deep Determin-istic Policy Gradient (AR-DDPG) algorithm was introduced as a method for solving NR-MDP. The optimal policy in NR-MDP includes policies following various probability distributions, whereas AR-DDPG is restricted to deterministic policies. We propose a new robust reinforcement learning method called Action Robust Q-Learning (AR-QL) that enables the training of optimal policies in NR-MDP by leveraging various sampling techniques to extend the representational capacity of policies, targeting an improvement in policy robustness. To validate this, we confirmed that AR-QL can acquire the optimal policy for a simple NR-MDP problem, for which AR-DDPG fails to obtain the optimal policy. Furthermore, we confirmed that the robust performance of policy trained by AR-QL in the OpenAI's InvertedPendulum environment surpasses that of policy trained by AR-DDPG.

关键词： Training Degradation Q-learning Markov decision processes Toy manufacturing industry Probability distribution Robustness Computational efficiency Noise measurement Cybernetics

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Explainable Reinforcement Learning via Causal Model Considering Agent's Intention

Explainable Reinforcement Learning via Causal Model Consider...

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IEEE International Conference on systems, Man and Cybernetics

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Explaining agent's decision can offer valuable insights for designers and end-users. One proposed method for explaining an agent's decision-making involves representing a causal relation between state components and action as a causal model and providing explanations for the decisions made using causal model. However, traditional causal model often faces structural limitation, restricting the range of representable control problems. Additionally, providing accurate explanation becomes challenging in control problems with various types of rewards because agent's intention of an action is unknown. In this study, we introduce a causal model capable of representing a broader range of control problems and a method to provide accurate explanations in control problems with various types of reward structures. Through redefining the relationships between nodes in the causal model, we have enabled a broader representation of control problems. Also, by incorporating agent's intention into the explanation, we have achieved to provide a more precise explanation. To validate the effectiveness of our proposed method, we conducted experiments using OpenAI's LunarLander environment. Using a proposed causal model, we defined the causal model of LunarLander, which could not be represented by conventional causal models. Furthermore, by incorporating the intentions of an agent into the explanation, novel interpretations previously inaccessible have become feasible.

关键词： Accuracy Decision making Reinforcement learning Faces Cybernetics

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