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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Juan Carlos González-Quesada Ignacio Javier Pérez Juan Antonio Morente-Molinera Sergio Alonso Enrique Herrera-Viedma Francisco Javier Cabrerizo Dept. of Computer Science and AI University of Granada Granada Spain Dept. of Software Engineering University of Granada Granada Spain

A fundamental principle of Granular Computing is that of an information granularity distribution and its optimization process. It has been used in system modelling to elevate a numerical model to its granular counterpart, which is more in rapport with reality. For example, in decision-making with fuzzy preference relations, it has been applied to enhance the existing numerical consistency improvement procedures. However, even though different protocols of information granularity distribution have been proposed, only the one based on a uniform and symmetric distribution and the one based on a symmetric but non-uniform distribution have been considered. Given that there exist others, this study aims to analyze how we can take advantage of all of them to improve the consistency of the fuzzy preference relations. Some numerical experiments are also completed to show the performance of these protocols.

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EdgStr: Automating Client-Cloud to Client-Edge-Cloud Transformation

EdgStr: Automating Client-Cloud to Client-Edge-Cloud Transfo...

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International Conference on Distributed Computing systems

作者： Kijin An Eli Tilevich Software Engineering Team Samsung Research Seoul South Korea Dept. of Computer Science Software Innovations Lab Virginia Tech Blacksburg VA United States

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

To harness the potential of edge resources, two-tier client-cloud applications require transformation into three-tier client-edge-cloud applications. Such transformations are hard for programmers to perform correctly by hand. Many cloud services maintain a runtime state that needs to be replicated at the edge. Once replicated, this state must then be synchronized efficiently and correctly. To facilitate the transition to edge computing, we present a framework that automatically transforms client-cloud apps to their client-edge-cloud versions. Our framework, EdgStr, automatically replicates cloud-based services at the edge. EdgStr synchronizes the replicated service state by relying on a third-party Conflict-Free Replicated Data Type (CRDT). It generates code that connects service state changes to CRDT update operations, thus ensuring that the state changes at each replica eventually converge to the same replicated state. As an evaluation, we applied EdgStr to transform representative distributed mobile apps for deployment in dissimilar network and device setups. EdgStr correctly replicates cloud services (targeting the important domain of ***), deploying the resulting replicas on an ad-hoc edge cluster, hosted by Raspberry PI devices. As long as eventual consistency is congruent with the functionality of a cloud service, EdgStr can automatically replicate this service and deploy the replicas at the edge, thus offering the performance benefits of edge-based execution, without the high costs of manual program transformation.

关键词： Performance evaluation Costs Runtime Codes Transforms Manuals Mobile applications

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Speaker's Emotions Recognition Module Based on the GoogleLeNet Neural Network

Speaker's Emotions Recognition Module Based on the GoogleLeN...

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2022 International Conference on Smart Information systems and Technologies, SIST 2022

作者： Tereikovskyi, Ihor Tereikovska, Liudmyla Kryvoruchko, Olena Tereikovskyi, Oleh Tyshchenko, Dmytro Franchuk, Tamara National Technical University Of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Dept. Of System Programming And Specialized Computer Systems Kyiv Ukraine Kyiv National University Of Construction And Architecture Dept. Of Information Technology For Design And Applied Mathematics Kyiv Ukraine Kyiv National University Of Trade And Economics Dept. Of Software Engineering And Cyber Security Kyiv Ukraine National Technical University Of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Dept. Of Department Of Computer Engineering Kyiv Ukraine

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

The article is devoted to the development of means for recognition of the emotions of the speaker, based on the neural network analysis of fixed fragments of the voice signal. The possibility of improving recognition tools through the use of a convolutional neural network of the GoogLeNet type is shown. The expediency of training the network on the examples of the TESS database is determined. A procedure for representing a voice signal in the form of a square grayscale image has been developed. Based on the use of the specified database, the modification of the GoogLeNet network was carried out. The architecture and software implementation of the recognition module have been developed. With the help of computer experiments, it has been established that after 150 epochs of training on a fairly limited training sample, the modified GoogLeNet makes it possible to achieve an accuracy of recognition of the speaker's emotions of approximately 0.97 on test examples. The obtained accuracy is comparable to the accuracy of the best modern systems of this purpose and confirms the promise of using neural network models like GoogLeNet for emotion recognition based on voice signal analysis. The need for further research aimed at reducing the resource intensity of the neural network model is shown. © 2022 IEEE.

关键词： Emotion Recognition

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DORAC: Dynamic-Objects Removal and Clustering for Object Detection

DORAC: Dynamic-Objects Removal and Clustering for Object Det...

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Cyber Science and Technology Congress (CyberSciTech), IEEE

作者： Kwanho Kiml Dokyung Kim Kanghyun Jo Dept. of Electrical Electronic and Computer Engineering (University of Ulsan) Intelligent Systems Laboratory Ulsan Republic of Korea

ISBN: (数字)9798331532093

ISBN: (纸本)9798331532109

Object detection using deep learning approach is being utilized for many fields such as autonomous driving, traffic analysis, etc. However, deep learning approach shows some fundamental limitations related to collecting datasets or explainabilty caused by its unclearness of the process for reaching a specific conclusion. This paper proposes DORAC which refers to Dynamic-Objects Removal And Clustering for object detection and does not use any deep neural networks. DORAC can be operated at videos captured by fixed camera. Frames of a video are averaged for removing dynamic objects. Then, the confidence map obtained from subtraction between video frame and averaged image is processed by thresholding and clustering. Experiments show that proposed method outperforms the state-of-the-art deep learning models trained with remarkable dataset by over 5% for our industry videos. In addition to metrics related to accuracy, frame rate of the method proposed in this paper is also 0.5 frames faster than YOLOv9-tiny model in CPU, averagely.

关键词： Deep learning Measurement Industries Accuracy Object detection Artificial neural networks Cameras Autonomous vehicles Videos

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A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning

arXiv

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arXiv 2025年

作者： Rozada, Sergio Paternain, Santiago Bazerque, Juan Andrés Marques, Antonio G. Dept. of Signal Theory and Communications King Juan Carlos University Madrid Spain Dept. of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States Dept. of Electrical and Computer Engineering University of Pittsburgh PittsburghPA United States

In pursuit of reinforcement learning systems that could train in physical environments, we investigate multi-task approaches as a means to alleviate the need for massive data acquisition. In a tabular scenario where the Q-functions are collected across tasks, we model our learning problem as optimizing a higher order tensor structure. Recognizing that close-related tasks may require similar actions, our proposed method imposes a low-rank condition on this aggregated Q-tensor. The rationale behind this approach to multi-task learning is that the low-rank structure enforces the notion of similarity, without the need to explicitly prescribe which tasks are similar, but inferring this information from a reduced amount of data simultaneously with the stochastic optimization of the Q-tensor. The efficiency of our low-rank tensor approach to multi-task learning is demonstrated in two numerical experiments, first in a benchmark environment formed by a collection of inverted pendulums, and then into a practical scenario involving multiple wireless communication devices. Copyright © 2025, The Authors. All rights reserved.

关键词： Multi-task learning

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A 0.77-pJ/bit 40-Gb/s/pin Single-Ended Hybrid DAC-Based Transmitter for Memory Interfaces

A 0.77-pJ/bit 40-Gb/s/pin Single-Ended Hybrid DAC-Based Tran...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Sanghyuk Seo Yong-un Jeong Jeakwang Yun Jaewook Kim Suhwan Kim Dept. of Electrical and Computer Engineering Seoul National University Seoul Korea Dept. of Semiconductor Systems Engineering Sejong University Seoul Korea Dept. of DRAM Design SK Hynix Icheon Korea

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

This paper presents a single-ended hybrid digital-to-analog converter (DAC)-based transmitter (TX) using four-level pulse-amplitude modulation (PAM-4) for memory interfaces. The proposed hybrid DAC composed of voltage mode (VM) driver unit segments and current mode (CM) driver unit segments reduces the total number of unit segments without increasing complexity in impedance matching. Therefore, the hybrid DAC significantly reduces area and the associated power consumption. Digital signal processing (DSP)-based feed-forward equalization (FFE) provides precise and flexible equalization. The proposed linearity enhancement technique compensates for additional non-linearity caused by short-length devices in the CM driver segments. The proposed TX operating at 40Gb/s/pin consumes 30.9mW with 1.0/0.9/0.15V supply voltages, achieving 0.77pJ/bit energy efficiency and a level separation mismatch ratio (RLM) of 0.989. The TX occupies an active area of 0.023mm 2 .

关键词： Power demand Transmitters Digital-analog conversion Impedance matching Memory management Linearity Voltage

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SIMHEURISTIC AND LEARNHEURISTIC FOR SOLVING STOCHASTIC AND/OR DYNAMIC PORTFOLIO OPTIMIZATION PROBLEMS 11

SIMHEURISTIC AND LEARNHEURISTIC FOR SOLVING STOCHASTIC AND/O...

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11th Operational Research Society Simulation Workshop, SW 2023

作者： Li, Yuda Polat, Onur Juan, Angel A. Calvet, Laura Kizys, Renatas Dept. of Computer Science Universitat Oberta de Catalunya Barcelona Spain Dept. of Applied Statistics and Operations Research Universitat Politcnica de Valncia Alcoy Spain Dept. of Public Finance Bilecik Şeyh Edebali University Bilecik Turkey Dept. of Telecomm. and Systems Engineering Autonomous University of Barcelona Sabadell Spain Dept. of Banking and Finance Southampton Business School University of Southampton Southampton United Kingdom

ISBN: (纸本)9781713870951

Constructing portfolio by proper asset selection to maximize return and minimize risk has been considered an essential task for investment activities. Rich portfolio optimizations with realistic constraints are NP-hard problems and are commonly solved using metaheuristics. However, financial markets are characterized by their high volatility and uncertainty, and metaheuristics do not fully account for these random and/or dynamic components, which renders them unrealistic in the presence of heightened uncertainty and dynamism in financial markets. Therefore, this paper proposes a simulationoptimization approach specifically, a simheuristic algorithm to deal with the stochastic version of the problem and a learnheuristic algorithm for solving the dynamic version of the problem. Computational experiments are performed on a benchmark instance to illustrate the advantages of the proposed methodologies and analyze how the solutions change in response to a different degree of stochasticity, dynamism, and minimum required return. © SW *** rights reserved

关键词： Financial markets

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An Image Analysis-Based Automated Method using Deep Learning for Grain Counting

An Image Analysis-Based Automated Method using Deep Learning...

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International Conference on Industrial and Information systems, ICIIS

作者： Ramesh Ajikaran Ashen Iranga Hewarathna Vigneshwaran Palanisamy Charles Joseph Selvarajah Thuseethan Dept. of Computing and Infromation Systems Sabaragamuwa University of Sri Lanka Dept. of Software Engineering Sabaragamuwa University of Sri Lanka Dept. of Computer Science FAU Germany School of Information Technology Deakin University Australia

In the process of quality analysis of grains, number counting is one of the key steps for agricultural production. Traditional manual grain counting methods are time-consuming and subject to human error, and automated methods have the potential to improve accuracy and save time. In this study, we aimed to develop an image analysis-based method to automatically quantify the number of grains in a quicker manner. The 576 grain images were collected manually, and labelImg tagging tool used to annotate to generate a text file with their respective positions by drawing bounding boxes manually. The datasets consist were separated into three groups: training, validation, and test. For object detection, the YOLOv5, YOLOv4, and YOLOv3 algorithms represent cutting-edge deep learning frameworks. They replace the tedious and error-prone manual counting process by precisely identifying and counting grains in images obtained from agricultural fields. This technique helps to increase grain counting's precision and effectiveness. We believe this method will be extremely beneficial in guiding the development of high throughput systems for counting the number of grains in other crops as it performs well with a wide range of backgrounds, picture sizes, grain sizes, as well as various quantities of grain crowding. When compared to the other two approaches, YOLOv4 performed well in terms of accuracy, speed, and robustness (97.65%), demonstrating that the suggested strategy is competitive with other cutting-edge deep networkst.

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Attack Tree Refinements Analysis and Verification by applying Coloured Petri Nets 48

Attack Tree Refinements Analysis and Verification by applyin...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Pasandideh, Shabnam Pereira, Pedro Gomes, Luis Nova School of Science and Technology-Nova University Lisbon Center of Technology and Systems-UNINOVA Dept. Electrical and Computer Engineering Caparica Portugal

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Recently, Graphical Security Models (GrSMs) became widely used for security analysis. The basic formalism called Attack Tree (AT) has been augmented with new attributes covering defence, response, and countermeasure aspects to support security modelling and analysis in vulnerable systems. Although the models have strength in visualising and analysing small attack-defence scenarios, these suffer from lack of scalability when increasing nodes and adaptability with other refinement models to show the dynamic nature and state of systems in interest. In this work, Coloured Petri net (CPN) is used to fulfil the mentioned shortcomings in GrSMs (specifically Tree-based models). It is applied for evaluating each component's interactions, the impact of threats as well as defence systems to mitigate those threats. For that end and pointing out the CPN adaptability with GrSMs, a set of mapping rules are proposed allowing translation of ATs extension into CPN and their analysis. The quantitative analysis aspect is addressed in this work by introducing computing transition. We validate our proposed approach by applying it in an example of SCADA systems cyber-security analysis. © 2022 IEEE.

关键词： Cybersecurity

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Decentralized Drug Transaction Tracking: A Novel Hybrid Blockchain-IPFS Platform

Decentralized Drug Transaction Tracking: A Novel Hybrid Bloc...

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Public Safety Technology (WFPST), IEEE World Forum on

作者： Mouhamadou Mouctar Gueye Wael Jaafar Fatoumata Baldé Dept. Computer Science Université Alioune Diop Bambey Sénégal Dept. Software and IT Engineering École de Technologie Superieure (ÉTS) Montreal Canada

ISBN: (数字)9798350329148

ISBN: (纸本)9798350329155

Counterfeit drugs are a serious global problem that poses threats to public health. It occurs when falsified or counterfeit drugs are intentionally manufactured and introduced into the pharmaceutical supply chain, masquerading as genuine products. This is due to the complexity of the supply chain that involves independent entities. As a result, a drug transits through several stages before reaching the patient. Current drug management systems are centralized, thus creating data transparency and authenticity risks within the supply chain. To bypass this issue, we propose in this paper a novel hybrid and distributed drug transaction tracking platform that leverages a combination of the Interplanetary File System (IPFS) protocol and Ethereum blockchain. Relying on IPFS guarantees the integrity of drug traceability data, while the blockchain ensures the authenticity of this data. We developed our platform and, through experiments, we prove that our method is more efficient, in terms of process costs, than the conventional blockchain-based drug tracking system.

关键词： Drugs Costs Supply chains Smart contracts Public security Blockchains InterPlanetary File System

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