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8th International computer Science Symposium in Russia, CSR 2013

作者： Spirakis, Paul G. Panagopoulou, Panagiota N. Computer Engineering and Informatics Department University of Patras Greece Computer Technology Institute and Press Diophantus Greece

ISBN: (纸本)9783642385353

We investigate here several categories of strategic games and antagonistic situations that are known to admit potential functions, and are thus guaranteed to either possess pure Nash equilibria or to stabilize in some form of equilibrium in cases of stochastic potentials. Our goal is to indicate the generality of this method and to address its limits. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Stochastic systems

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(POSTER) A Holistic IoT-Enabled Approach for Indoor Air Quality Control 19

(POSTER) A Holistic IoT-Enabled Approach for Indoor Air Qual...

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19th Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023

作者： Kalioras, Fotios Filios, Gabriel Karatzas, Stylianos Nikoletseas, Sotiris University of Patras Department of Computer Engineering and Informatics Greece University of Patras Computer Technology Institute and Press Diophantus Department of Computer Engineering and Informatics Greece University of Patras Department of Civil Engineering Greece

ISBN: (纸本)9798350346497

Indoor air quality (IAQ) is an important yet often overlooked aspect of public health, with poor IAQ contributing to a significant number of diverse health problems worldwide. Existing air quality standards have failed to fully address the problem, however emerging technologies in the field of IoT have demonstrated promise in effectively addressing this issue. This concept paper advocates a hybrid hierarchy system for IAQ monitoring that incorporates both offline and online connectivity. The system utilizes sensing elements that can perform a wide range of functions, including autocalibration, pollution event detection, and more, autonomously from the network. By combining a gateway device, the system's capabilities are enhanced, providing increased data granularity, additional calibration and sensing options as well as connection with Building Management Systems (BMS). Furthermore, by connecting the gateway to the cloud, the system can perform more advanced data analysis and machine learning, providing users with insights into not only air quality but also general environmental quality. Key features of the envisioned system include the integration of subjective perception of pollution via crowdsourcing, a TinyML technology at the edge, digital twins, and an optimised redundancy of sensors that can alleviate poor accuracy and drift as well as capture the spatial dispersion of pollution. © 2023 IEEE.

关键词： Crowdsourcing

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A combined TCP aware scheduling and assembly scheme for OBS networks

A combined TCP aware scheduling and assembly scheme for OBS ...

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2009 6th International Conference on Broadband Communications, Networks and Systems, BROADNETS 2009

作者： Ramantas, Kostas Vlachos, Kyriakos Computer Engineering and Informatics Department Research Academic Computer Technology Institute University of Patras Rio Greece

ISBN: (纸本)9789639799493

The efficient transmission of TCP traffic over OBS networks is a challenging problem, due to the high sensitivity of TCP congestion control mechanism to losses. In this work, we propose solutions to this problem, through the burst assembly and scheduling mechanism of OBS. We investigate and propose algorithms to achieve performance enhancements for TCP traffic by taking into account TCP state information. We prove our claims with references to well-established TCP models, and validate the proposed algorithms through simulations.

关键词： Optical burst switching

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Patrassense: Participatory monitoring of environmental conditions in urban areas using sensor networks and smartphones

Patrassense: Participatory monitoring of environmental condi...

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15th Panhellenic Conference on informatics, PCI 2011

作者： Vikatos, Pantelis Theodoridis, Evangelos Mylonas, Georgios Tsakalidis, Athanasios Department of Computer Engineering and Informatics University of Patras Greece Computer Technology Institute and Press Patras Greece

ISBN: (纸本)9780769543895

This work discusses PatrasSense, a system utilizing a range of technologies for monitoring environmental conditions, based on the concept of participatory monitoring. We discuss here the main issues in this category of applications, and its relation to the Real World Internet vision. We are interested in the use of sensors for collecting data related to the quality of life in urban areas, making it publicly available. We describe a possible architecture for such a system and a plan for deployment in the city of Patras, Greece. We have thus far implemented a set of features and conducted an initial set of small-scale experiments using sensor devices mounted on vehicles. We report on the respective results. © 2011 IEEE.

关键词： Sensor networks

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Profiling TCP traffic in optical burst switching networks

Profiling TCP traffic in optical burst switching networks

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7th International ICST Conference on Broadband Communications, Networks, and Systems, BROADNETS 2010

作者： Ramantas, Kostas Vlachos, Kyriakos Computer Engineering and Informatics Department and Research Academic Computer Technology Institute University of Patras Rio Greece

ISBN: (纸本)9783642303753

The efficient transmission of TCP traffic over OBS networks is a challenging problem, due to the high sensitivity of TCP congestion control mechanism to losses. In this paper, a traffic profiling scheme is proposed for gathering TCP flow statistics, which are used to estimate steady-state performance of TCP traffic over OBS networks. The burst assembly unit, in parallel to the assembly process, can inspect TCP traffic, keeping traffic statistics that can be used for throughput estimations. In this paper, we detail the traffic profiling and estimation mechanism and also provide analytical and simulation results to assess its performance. © 2012 ICST institute for computer Science, Social informatics and Telecommunications engineering.

关键词： Optical burst switching

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Flip Learning: Weakly Supervised Erase to Segment Nodules in Breast Ultrasound

arXiv

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arXiv 2025年

作者： Huang, Yuhao Chang, Ao Dou, Haoran Tao, Xing Zhou, Xinrui Cao, Yan Huang, Ruobing Frangi, Alejandro F. Bao, Lingyun Yang, Xin Ni, Dong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Lab Shenzhen University Shenzhen China Marshall Laboratory of Biomedical Engineering Shenzhen University Shenzhen China School of Computing University of Leeds Leeds United Kingdom Shenzhen RayShape Medical Technology Co. Ltd Shenzhen China Division of Informatics Imaging and Data Science School of Health Sciences University of Manchester Manchester United Kingdom Department of Computer Science School of Engineering University of Manchester Manchester United Kingdom Department of Electrical Engineering Department of Cardiovascular Sciences KU Leuven Belgium Alan Turing Institute London United Kingdom NIHR Manchester Biomedical Research Centre Manchester Academic Health Science Centre Manchester United Kingdom Department of Ultrasound Affiliated Hangzhou First People’s Hospital School of Medicine Westlake University China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China

Accurate segmentation of nodules in both 2D breast ultrasound (BUS) and 3D automated breast ultrasound (ABUS) is crucial for clinical diagnosis and treatment planning. Therefore, developing an automated system for nodule segmentation can enhance user independence and expedite clinical analysis. Unlike fully-supervised learning, weakly-supervised segmentation (WSS) can streamline the laborious and intricate annotation process. However, current WSS methods face challenges in achieving precise nodule segmentation, as many of them depend on inaccurate activation maps or inefficient pseudo-mask generation algorithms. In this study, we introduce a novel multi-agent reinforcement learning-based WSS framework called Flip Learning, which relies solely on 2D/3D boxes for accurate segmentation. Specifically, multiple agents are employed to erase the target from the box to facilitate classification tag flipping, with the erased region serving as the predicted segmentation mask. The key contributions of this research are as follows: 1) Adoption of a superpixel/supervoxel-based approach to encode the standardized environment, capturing boundary priors and expediting the learning process. 2) Introduction of three meticulously designed rewards, comprising a classification score reward and two intensity distribution rewards, to steer the agents’ erasing process precisely, thereby avoiding both under- and over-segmentation. 3) Implementation of a progressive curriculum learning strategy to enable agents to interact with the environment in a progressively challenging manner, thereby enhancing learning efficiency. Extensively validated on the large in-house BUS and ABUS datasets, our Flip Learning method outperforms state-of-the-art WSS methods and foundation models, and achieves comparable performance as fully-supervised learning algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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USEFUSE: Uniform stride for enhanced performance in fused layer architecture of deep neural networks

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Journal of Systems Architecture 2025年 166卷

作者： Muhammad Sohail Ibrahim Muhammad Usman Jeong-A Lee Department of Mechanical Systems Engineering Kumoh National Institute of Technology Gumi-si Republic of Korea Faculty of Informatics and Data Science University of Regensburg Regensburg 93053 Germany Department of Computer Engineering Chosun University Gwangju Republic of Korea

Convolutional Neural Networks (CNNs) are crucial in various applications, but deploying them on resource-constrained edge devices poses challenges. This study presents the Sum-of-Products (SOP) units for convolution, which utilize low-latency left-to-right bit-serial arithmetic to minimize response time and enhance overall performance. The study proposes a methodology for fusing multiple convolution layers to reduce off-chip memory communication and increase the overall performance. An effective mechanism detects and skips inefficient convolutions after ReLU layers, minimizing power consumption without compromising accuracy. Additionally, efficient tile movement guarantees uniform access to the fusion pyramid. An analysis demonstrates the uniform stride strategy improves operational intensity. Two designs cater to varied demands: one focuses on minimal response time for mission-critical applications, and another focuses on resource-constrained devices with comparable latency. This approach notably reduced redundant computations, improving the efficiency of CNN deployment on edge devices.

关键词： Convolution neural network Online arithmetic Most-significant-digit-first arithmetic CNN acceleration Layer fusion

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Hidden Markov Models for Sentiment Analysis in Social Media 4

Hidden Markov Models for Sentiment Analysis in Social Media

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4th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2019

作者： Perikos, Isidoros Kardakis, Spyridon Paraskevas, Michael Hatzilygeroudis, Ioannis Computer Engineering and Informatics Department University of Patras Computer Technology Institute and Press diophantus Patras Greece Department of Electrical and Computer Engineering University of Peloponnese and Computer Technology Institute and Press diophantus Patras Greece

ISBN: (纸本)9781728108865

User-generated content in social media contain very meaningful information about public sentiment and stance and require automated methods to analyze them extract knowledge from them. The recognition of sentiments, emotions and attitudes in textual data is necessary for understanding public stance and is highly desired for various services and procedures. In this work, we examine the performance of Hidden Markov Models (HMM) in the recognition of sentiments and opinions in text. Hidden Markov Models constitute a quite suitable approach given the special characteristics of the textual data. In particular, they can utilize the sequential nature of textual data, a piece of information that traditional machine learning approaches fail to fully take into account. We performed several experiments in order to assess the performance of different HMM-based methods under various training parameters and architectures. The evaluation results indicate that Hidden Markov Models achieve superior performance compared to traditional machine learning algorithms and highlight that they are scalable and accurate in analyzing user generated content and in specifying opinions and attitudes. © 2019 IEEE.

关键词： Hidden Markov models

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Evaluation of teleteaching services over ATM and IP networks

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Telematics and informatics 2003年第1期20卷 1-18页

作者： Bouras, Ch. Gkamas, A. Kapoulas, V. Tsiatsos, Th. Computer Technology Institute Riga Feraiou 61 GR-26221 Patras Greece Computer Engineering and Informatics Department University of Patras Riga Feraiou 61 GR-26221 Patras Greece

This paper presents the components of a synchronous teleteaching application, their underlying technology and their interaction with network services. In addition, it presents the architecture and design of a synchronous teleteaching application used over the public ATM network in Greece. This paper presents various teleteaching scenarios over an ATM network infrastructure with the use of native ATM and IP over ATM. In each scenario we present the standards used, the logical components, the resource demands (network and equipment) and its special characteristics. For each scenario we give detailed information about the transmission and frame rates and the video delay. The main result of our experiments is that broadband networks offer many capabilities for high quality teleteaching, and generally speaking, high quality telematic services. © 2002 Elsevier Science Ltd. All rights reserved.

关键词： computer aided instruction

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Fair and explainable Myocardial Infarction (MI) prediction: Novel strategies for feature selection and class imbalance correction

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computers in Biology and Medicine 2025年 184卷 109413-109413页

作者： Akter, Simon Bin Akter, Sumya Tuli, Moon Das Eisenberg, David Lotvola, Aaron Islam, Humayera Fernandez, Jorge Fresneda Hüttemann, Maik Pias, Tanmoy Sarkar Martin Tuchman School of Management New Jersey Institute of Technology Newark NJ07102 United States Greenlife Medical College & Hospital Dhaka Bangladesh Department of Computer Science Virginia Tech College of Engineering BlacksburgVA United States Institute for Data Science and Informatics University of Missouri Columbia United States Department of Biochemistry Microbiology and Immunology Wayne State University School of Medicine DetroitMI United States Department of Information Management and Business Analytics Montclair State University Feliciano School of Business NJ United States Department of Oncology Wayne State University School of Medicine DetroitMI United States Center for Molecular Medicine and Genetics Wayne State University School of Medicine DetroitMI United States

The rising incidences of myocardial infarction (MI), often affecting individuals without traditional risk factors, highlight the urgent need for improved early detection using personal health data. However, health surveys and electronic health records (EHRs) frequently suffer from class imbalances, leading to prediction biases and differences between specificity and sensitivity, which hinder reliable model development despite the valuable insights contained in these datasets. To address this, we have introduced a novel approach to enhance MI risk prediction using self-reported attributes from the Behavioral Risk Factor Surveillance System (BRFSS) and the National Health Interview Survey (NHIS) dataset. Our approach incorporates three innovative techniques: the Dual-Path Artificial Neural Network (DP-ANN) to mitigate biased decision making across imbalanced datasets, the Triple Criteria Selection (TCS) for unbiased feature selection, and Minority Weighted Sampling (MWS) to tackle challenges of uncontrolled minority class sampling. These methods collectively enhance MI prediction and feature relevance. The DP-ANN model has achieved balanced performance, with an average specificity of 80%, sensitivity of 82%, and AUC–ROC of 89.5%, improving imbalance variance by approximately 14.96% compared to prior studies. By outperforming other models across four heavily imbalanced datasets, our approach demonstrates robustness and generalizability. Additionally, SHapley Additive exPlanations (SHAP) analysis has revealed key predictors and risk factors for MI, such as coronary heart disease and bronchitis in females, and stroke among individuals aged 35–54. In conclusion, our study provides a robust model for healthcare professionals to assess MI risk through targeted factors, promoting early detection and potentially improving patient outcomes. © 2024

关键词： Electronic health record

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