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Validation of cognitive architectures by use cases: Examplified with the psychoanalytically-inspired ARS model implementation

Validation of cognitive architectures by use cases: Examplif...

引用

ieee International symposium on Industrial Electronics (ISIE)

作者： Dietmar Bruckner Friedrich Gelbard Samer Schaat Alexander Wendt Institute of Computer Technology Vienna University of Technology Vienna Austria

Evaluation and validation of software architectures in cognitive science often lack objective evaluation criteria and test mechanisms. Hence, in this article we present an in-house developed use case to evaluate the cognitive architecture ARS, together with its psychoanalytically-inspired functionalities. The use case was developed by a team of psychoanalysts and comprehends human's behavior patterns in a competitive situation. We describe the preconditions, the development, and the setting of the use case, followed by the results and screenshots to document the dataflow within the modules. Different outcomes of the use case depending on the variation of personality parameters of the ARS system play a pivotal role. Furthermore, we provide a general description of the ARS system, of the top-down development process, and of the psychoanalytic theory behind it. We give insight into functionalities of ARS like the decision making process and the association of objects. Finally, we evaluate these functionalities by implementing and simulating the developed use case.

关键词： multi-agent systems computational modeling Data structures Cost accounting Cognitive science Software agents

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KOptim: Kubernetes Optimization Framework

KOptim: Kubernetes Optimization Framework

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ieee International symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Tarek Menouer Christophe Cérin Patrice Darmon CGI France Puteaux France University Sorbonne Paris Nord Villetaneuse France

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

As Kubernetes has become a de-facto standard for a portable, extensible, open-source platform for managing containerized workloads and services, this paper presents a new Kubernetes Optimization Framework called KOptim, which provides a generic schema for scheduling and resource management. Compared to existing Kubernetes frameworks and scheduling procedures, the advantages of KOptim are: (i) it facilitates the configuration of container resources using Service Level Agreement (SLA) classes, and (ii) it optimizes the online scheduling of containers using a multi-criteria decision making (MCDM) algorithm. In short, the main contribution of KOptim is to simplify Kubernetes scheduler optimization, as well as other issues related to container configuration and validation. In KOptim, we propose to configure and manage container resources using a new model with three SLA classes related to CPUs, memory, and storage space. Each SLA class has three Quality of Services (QoS): Premium, Advanced, and Best Effort. Using the KOptim SLA classes, users can select a range of values for a service instead of specifying a fixed amount of resources for each container. This is designed to help non-expert users who do not need to know exactly how many resources must be used to configure their containers easily. In the scheduling step, we suggest using a mechanism based on a multi-criteria decision algorithm that allows users to consider several criteria during the online scheduling decision. In summary, KOptim is a generic framework for Kubernetes distributions even K3s and MicroK8s which are adapted to Fog and Edge computing. Experimental results demonstrate the potential of our framework to improve performance in terms of runtime and energy consumption.

关键词： Energy consumption Processor scheduling computational modeling Quality of service Containers MCDM Resource management

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A Methodology for Developing Deep Reinforcement Learning Trading Strategies: A Case Study in the Futures Market

A Methodology for Developing Deep Reinforcement Learning Tra...

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Proceedings of the ieee/IAFE computational intelligence for Financial Engineering (CIFEr)

作者： Leonardo Conegundes Adriano C. M. Pereira Departamento de Ciencia da Computacao Universidade Federal de Minas Gerais Belo Horizonte Brazil

ISBN: (数字)9798350354836

ISBN: (纸本)9798350354843

Quantitative algorithmic trading and machine learning have transformed financial markets by introducing advanced computational techniques for market analysis, decision-making, risk management, and order execution. This paper explores Deep Reinforcement Learning (DRL) for developing day trading strategies within the Ibovespa futures market, the world's most traded stock index futures contract. We chose the Ibovespa market for its high liquidity and volatility, which present unique challenges and opportunities for trading. We propose a novel methodology for the development, evaluation, and deployment of DRL-powered strategies. This methodology involves model generation, approval, selection, and execution, supported by comprehensive metrics for assessing returns, risk, and stability. We introduce the FUT-DRL trading strategy, a new approach that models the trading problem as a Partially Observable Markov decision Process using DRL agents and the Deep Q-Network algorithm to navigate market complexities. Our empirical study, utilizing a diverse five-year dataset under varied conditions, shows that FUT-DRL outperforms traditional strategies, such as trend-following and mean - reversion, achieving an annualized return over 100 % and a Probabilistic Sharpe Ratio of 0.96. This research advances the integration of machine learning with quantitative trading, offering a robust framework for strategy development, and highlighting DRL's effectiveness in the volatile Ibovespa market, demonstrating the potential of adaptive and intelligent systems to evolve trading strategies in rapidly changing financial markets.

关键词： Measurement Machine learning algorithms Navigation Markov decision processes Stability criteria Deep reinforcement learning Probabilistic logic Risk management Indexes Intelligent systems

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Evaluation of Improved MobileNetV3-YOLO Models for IoT Devices by Fuzzy multi-criteria decision-making

Evaluation of Improved MobileNetV3-YOLO Models for IoT Devic...

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International Conference on computational intelligence and Applications (ICCIA)

作者： Shiyan Du Jiacheng Li Masato Noto Field of Electrical Electronics and Information Engineering Graduate School of Engineering Kanagawa University Yokohama Japan Department of Applied Systems and Mathematics Kanagawa University Yokohama Japan

ISBN: (数字)9798350352214

ISBN: (纸本)9798350352221

Fire detection is crucial in natural disaster prevention, but the limited computational resources of Internet of Things (IoT) devices impose strict requirements for lightweight and efficient models. This study aims to evaluate and compare the performance of various object detection models for fire detection to optimize model selection in resource-constrained environments. We selected the YOLOv8 series, YOLOv10 series, and our improved MobileNetV3-YOLOv8n and MobileNetV3-YOLOv10n models for evaluation. The results demonstrate that the MobileNetV3-YOLOv8n model achieves the best overall performance, followed by the YOLOv8n and MobileNetV3-YOLOv10n models. Sensitivity analysis confirms the stability of these results under minor variations in metric weights, particularly within the top three rankings. On the basis of these findings, we provide specific model selection recommendations for IoT devices with varying computational capabilities.

关键词： Performance evaluation Analytical models Uncertainty Sensitivity analysis computational modeling Object detection Analytic hierarchy process Hardware computational efficiency Internet of Things

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Energy-Saving decision making for Aerial Swarms: PSO-based Navigation in Vector Fields

Energy-Saving Decision Making for Aerial Swarms: PSO-based N...

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ieee symposium Series on computational intelligence

作者： Palina Bartashevich Doreen Koerte Sanaz Mostaghim Faculty of Computer Science University of Magdeburg Germany

This paper presents the Navigation Wind PSO (NW-PSO) as a search mechanism for aerial micro-robots with limited battery capacity acting in environments with unknown external dynamics (such as wind). The proposed method uses the concepts of multi-criteria decision making and let the individuals to decide on their movements almost following the flow with small course deviations in order to save as much energy as possible. One of the goals is to investigate how the arising premature energy-loss on individual level affects the performance of collective search. The experiments show that NW-PSO can save a good amount of energy as well as perform search behavior with good approximation of the global optimal solution almost without awareness regarding disturbance factors and particle loss.

关键词： Robots Batteries decision making Energy consumption Navigation Wind Perturbation methods

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Assessing cloud computing SaaS adoption for enterprise applications using a Petri net MCDM framework

Assessing cloud computing SaaS adoption for enterprise appli...

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ieee symposium on Network Operations and Management

作者： Maristella Ribas Alberto S. Lima Neuman Souza Antão Moura Flávio R. C. Sousa Germano Fenner Techne Engenharia e Sistemas Sao Paulo Brazil Federal University of Ceara Fortaleza Brazil Federal University of Campina Grande (UFCG) Brazil

ISBN: (纸本)9781479909117

Cloud computing has become a very popular technology for IT infrastructure (infrastructure as a service - IaaS), with big players offering attractive pricing schemes. Software vendors are interested in offering products in the cloud as services. However, in order to evaluate software as an offer of service (SaaS) for a given enterprise application, we need to assess some qualitative factors other than just price-security and trust being the most relevant factors. In this work, we propose a Petri net multi-criteria decision making (MCDM) framework to evaluate SaaS as opposed to an on-premises alternative. The proposed framework can help IT managers in deciding between the two options, as well as being able to help software vendors in their efforts to establish SaaS pricing.

关键词： Software as a service Security Licenses Educational institutions Petri nets computational modeling

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Myocarditis Diagnosis: A Method using Mutual Learning-Based ABC and Reinforcement Learning

Myocarditis Diagnosis: A Method using Mutual Learning-Based ...

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International symposium on computational intelligence and Informatics

作者： Saba Danaei Arsam Bostani Seyed Vahid Moravvej Fardin Mohammadi Roohallah Alizadehsani Afshin Shoeibi Hamid Alinejad-Rokny Saeid Nahavandi Adiban Institute of Higher Education Semnan Iran Department of mechanical engineering of biosystems Urmia university Department of exercise physiology & health science University of tehran Internship in UNSW BioMedical Machine Learning Lab Sydney NSW Australia Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Waurn Ponds Victoria Australia UNSW Data Science Hub The University of New South Wales (UNSW Sydney) Sydney New South Wales Australia BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney Sydney NSW Australia

ISBN: (纸本)9798350398830

Myocarditis occurs when the heart muscle becomes inflamed and inflammation occurs when your body’s immune system responds to infections. It can be diagnosed using cardiac magnetic resonance image (MRI), a non-invasive imaging technique with the possibility of operator bias. This paper proposes a hybrid method of deep reinforcement learning-based algorithms and meta-heuristics algorithms. A mutual learning-based artificial bee colony (ML-ABC) is employed for initial weight, which adjusts the candidate food source generated with the higher fitness between two individuals determined by a mutual learning factor. Moreover, a sequential decision-making process investigates the imbalanced classification issue, in which a convolutional neural network (CNN) is used as the foundation for policy architecture. At first, initial weights are produced using the ML-ABC algorithm. After that, the agent receives a sample at each phase and classifies it, obtaining environmental rewards. The minority class receives more rewards than the majority class. Eventually, the agent discovers an ideal strategy with the aid of a specific reward function and a beneficial learning environment. We evaluate our proposed approach on the Z-Alizadeh Sani myocarditis dataset based on standard criteria and demonstrate that the proposed method gives superior myocarditis diagnosis performance.

关键词： Mechatronics computational modeling Metaheuristics Reinforcement learning Myocardium Markov processes Classification algorithms

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Distributed Mobile Cloud Computing: A multi-user Clustering Solution

Distributed Mobile Cloud Computing: A Multi-user Clustering ...

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Ad-Hoc and Sensor Networks symposium

作者： Jessica Oueis Emilio Calvanese Strinati Sergio Barbarossa CEA LETI Sapienza University of Rome

ISBN: (纸本)9781479966653

Edge computing through local mobile cloud computing platforms is a key enabler for coping with the ever increasing data traffic requirements. A key enabler for this technology is the awaited ultra-dense deployment of radio access points for future 5G networks. Local cloud platforms allow maintaining a scalable network design by jointly managing local radio and computational resources. The Fog, a platform with rich services, introduces distributed intelligence at the edge of the network where entities such as radio access points form a local computing resources pool. In this paper, we address the problem of radio access points clustering for fog computing applications. We focus on the multi-user case where the local cloud resources are to be shared by several devices. We propose a novel clustering algorithm in which management functionalities are split into two layers: centralized and decentralized. The proposed strategy compromises centralized optimality with decentralized distribution intelligence for faster and less complex decision making. We compare, through simulations, the performance of the proposed algorithm to centralized and decentralized strategies, and show how it can achieve good quality of experience.

关键词： access points Clustering algorithms Community radio distributed intelligence Cloud Computing Decentralized Solution Solutions Radio Network design Platform TWO-LAYER Mobile

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Tolerance-Based Group Scoring Scheme Promotes Cooperation in Public Goods Game

Tolerance-Based Group Scoring Scheme Promotes Cooperation in...

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ieee International symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Haiping Zhang Gang Wang Hongjun Qiu Weitong Hu Lanping Yu Hangzhou Dianzi University Information Engineering School Hangzhou China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China

The evolution of cooperation in social dilemmas has always been a subject of considerable concern in various fields, such as economics, sociobiology and social science. Previous studies have shown that reputation can sustain cooperation in multi-player public goods games through indirect reciprocity. Unfortunately, most studies only consider the impact of personal performance, and while the effect of group performance is still unclear. Here, we propose a reputation mechanism that combines the groups' performance and players' performance. Simulations show that the situation only consider group scoring can not sustain cooperation, but when the players' difference is considered, the population will achieve a high level of cooperation.

关键词： Games Sociology Statistics decision making Mathematical model Probability computational modeling

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An AHP/TOPSIS-Based Approach for an Optimal Site Selection of a Commercial Opening Utilizing GeoSpatial Data

An AHP/TOPSIS-Based Approach for an Optimal Site Selection o...

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International Conference on Big Data and Smart Computing (BIGCOMP)

作者： Salman Ahmed Shaikh Mohsin Ali Memon Milos Prokop Kyoung-sook Kim Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology Tokyo Japan Department of Software Engineering Mehran Univeristy of Engineering and Technology Pakistan University of Edinburgh Edinburgh UK

ISBN: (数字)9781728160344

ISBN: (纸本)9781728160351

One of the most important factors in the success of a business is its location, therefore the problem of identifying an optimal site for a new commercial opening has remained the focus of the research community. The ideal business site depends on many criteria including competitors, ease of access, traffic condition, etc. Precisely, optimal business site selection is a multi criteria decision making (MCDM) problem, which deals with the selection of the best alternative from several potential candidates based on several criteria. In this work we present an AHP/TOPSIS hybrid approach to identify an optimal site for a commercial opening, given a set a candidate sites. Both the AHP and TOPSIS are MCDM methods, however the hybrid approach is chosen as it results in the reduction of the computational complexity, and the reduction in the manual effort required in the construction of the AHP pairwise comparison matrices. The proposed approach ranks all the candidate sites based on their performance scores and the candidate with the highest score is identified as the optimal site. A nexus of complex factors such as competition in the area, traffic conditions, and area popularity are considered for ranking the candidates, where the factor values are computed with the assistance of real GeoSpatial data. The proposed approach is flexible and can effectively incorporate any number of factors to rank the candidates. The effectiveness of the proposed approach is proved with the help of a case study to identify an optimal location for a new gas station in the New York City.

关键词： Analytic hierarchy process Geospatial analysis TOPSIS decision making

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