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International Symposium on Applied Machine Intelligence and informatics (SAMI)

作者： Jaroslav Marko Anton Baláź Department of Computer and Informatics Faculty of Electrical Engineering and Informatics Technical University of Košice Košice Slovakia

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

The paper deals with the issue of collecting and displaying geographic information about roads in Slovakia with emphasis on information about obstacles on roads. The main objective is to design a solution for collecting and displaying the roads based on the analysis of existing systems and identifying the requirements for an optimal design. The paper starts with an analysis of available solutions and the development of requirements for the most favourable design, followed by the planning of an optimal solution for collecting and displaying road obstruction data. The focus of the paper is on the development of a method for creating and modifying geographic obstacle information, the design of a textual and graphical display of the information, and the design of a three-level model for user access to the application. The paper concludes with a discussion of the proposed solution, comparing the requirements for the optimal design with the developed design.

关键词： Roads Planning Object recognition informatics Machine intelligence

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Development of a Website-Based Financial Transaction Management System Using Agile with the Scrum Framework 10

Development of a Website-Based Financial Transaction Managem...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Latifah, Ayu Ramdan, Galih Muhammad Sidik, Muhamad Lutfhi Farid Faturrohman, Nadhif Nursyaban, Dzikri Ramelan, Agus Rahman, Fachrur Razy School of Electrical and Informatics Engineering Bandung Institute of Technology Bandung Indonesia Institut Teknologi Garut Department of Computer Science Garut Indonesia Universitas Sebelas Maret Electrical Engineering Departement Solo Indonesia Metal Industry Polytechnic of Morowali Department of Installation and Electrical Engineering Central Sulawesi Indonesia

ISBN: (纸本)9798350339543

The process of managing financial transactions, especially in the education sector, is still mostly carried out conventionally, so it has the potential to cause many risks, such as loss or damage to documents related to difficulties in searching for data as needed, transaction processes that take quite a long time, financial records that are not properly recorded. Well, it is prone to discrepancies between the nominal filing and the nominal disbursed conventional representations of financial statements that make the stakeholder evaluation process uncomfortable, and many other things. Therefore this research was conducted to build a website-based information system that can prevent this from happening in the future. The method used in the development of this information system is the Agile method with the Scrum framework. The Scrum framework consists of five stages, starting from Initiate, Plan, and Estimate, Implement, Review and Retrospect, and Release. This website was built using the Javascript programming language using the Vue JS framework for the Front-end. With this website-based application, it is hoped that transaction activities can be carried out better and correctly without being limited by space and time. © 2023 IEEE.

关键词： Websites

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Predictive World Models from Real-World Partial Observations 1

Predictive World Models from Real-World Partial Observations

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1st IEEE International Conference on Mobility, Operations, Services and Technologies, MOST 2023

作者： Karlsson, Robin Carballo, Alexander Fujii, Keisuke Ohtani, Kento Takeda, Kazuya Nagoya University Graduate School of Informatics Nagoya Japan Gifu University Department of Electrical Electronic and Computer Engineering Gifu Japan Nagoya University Tier Iv Graduate School of Informatics Nagoya Japan

ISBN: (纸本)9798350319958

Cognitive scientists believe adaptable intelligent agents like humans perform reasoning through learned causal mental simulations of agents and environments. The problem of learning such simulations is called predictive world modeling. Recently, reinforcement learning (RL) agents leveraging world models have achieved SOTA performance in game environments. However, understanding how to apply the world modeling approach in complex real-world environments relevant to mobile robots remains an open question. In this paper, we present a framework for learning a probabilistic predictive world model for real-world road environments. We implement the model using a hierarchical VAE (HVAE) capable of predicting a diverse set of fully observed plausible worlds from accumulated sensor observations. While prior HVAE methods require complete states as ground truth for learning, we present a novel sequential training method to allow HVAEs to learn to predict complete states from partially observed states only. We experimentally demonstrate accurate spatial structure prediction of deterministic regions achieving 96.21 IoU, and close the gap to perfect prediction by 62 % for stochastic regions using the best prediction. By extending HVAEs to cases where complete ground truth states do not exist, we facilitate continual learning of spatial prediction as a step towards realizing explainable and comprehensive predictive world models for real-world mobile robotics applications. Code is available at https://***/robin-karlsson0/predictive-world-models. © 2023 IEEE.

关键词： Mobile robots

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Optimizing Magnetic Induction Sensors for Non-Obtrusive Vital Signs Monitoring: Impact of Current Control on Operational Quality 46

Optimizing Magnetic Induction Sensors for Non-Obtrusive Vita...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Momeni, Mahdi Radomski, Adrian Guler, Ulkuhan Teichmann, Daniel University of Southern Denmark Sdu Health Informatics and Technology The Maersk Mc-Kinney Moller Institute Denmark Worcester Polytechnic Institute Department of Electrical and Computer Engineering WorcesterMA United States

ISBN: (纸本)9798350371499

This paper investigates the advancement of magnetic induction-based heart and respiration rate sensing by actively controlling the coil current. This is realized through the implementation of a current-starved inverter mechanism. Experiments show a notable level of accuracy of the proposed circuit in measuring heart and respiration activity when compared to a reference sensor. The direct manipulation of current levels was found to have a direct impact on the signal strength. Incrementing the overall current within the proposed circuit from 60 mA to 100 mA resulted in an augmentation of the output amplitude of the heart rate signal from 8.5 mV to 27 mV, accompanied by a marginal enhancement in beat-to-beat interval accuracy. Moreover, the proposed sensor demonstrates noteworthy precision in monitoring the respiratory rate when compared with the reference sensor under different current values, exhibiting the same trend in signal strength. This finding offers valuable insight for the development of future power-optimized magnetic induction sensors with enhanced robustness. © 2024 IEEE.

关键词： Contact sensors

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University department Recommendations Using Subject-Score-Based Skyline Queries

University Department Recommendations Using Subject-Score-Ba...

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2024 International Conference on Information Technology and Computing, ICITCOM 2024

作者： Damarjati, Cahya Wicaksana, Galang Suripto, Slamet Wijayanto, Heri Setyawan, Haris Chen, Hsing-Chung Universitas Muhammadiyah Yogyakarta Department of Information Technology Yogyakarta Indonesia Universitas Muhammadiyah Yogyakarta Department of Electrical Engineering Yogyakarta Indonesia Universitas Mataram Department of Informatics Engineering Mataram Indonesia Asia University China Medical University Department of Computer Science and Information Engineering Department of Medical Research Taichung Taiwan

ISBN: (纸本)9798350379839

Selecting the right university department is a critical decision-making problem for high school graduates, significantly influenced by limited guidance and personal preferences. Traditional counseling methods often do not fully utilize available academic data, resulting in less effective recommendations. This study proposed a Subject-Score-Based Skyline Queries Algorithm to enhance decision-making by recommending university departments based on candidates' high school subject scores. It used the score differences and minimum score rule in addition to the native Skyline Queries. The Algorithm identifies departments where a candidate's grades meet or exceed minimum requirements across multiple academic subjects, thereby suggesting optimal matches. This approach aims to mitigate uncertainty and enhance decision confidence among prospective students, potentially reducing dropout rates and improving academic outcomes. Experimental results demonstrate the method's effectiveness in providing personalized recommendations, underscoring its applicability beyond university admissions to various decision-making contexts. Future research could extend this approach by incorporating non-academic factors for more comprehensive guidance. © 2024 IEEE.

关键词： Students

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MRI Joint Super-Resolution and Denoising based on Conditional Stochastic Normalizing Flow

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期6卷 1472-1487页

作者： Liu, Zhenhong Wang, Xingce Wu, Zhongke Zhu, Yi-Cheng Frangi, Alejandro F. Beijing Normal University School of Artificial Intelligence Beijing China Peking Union Medical College Hospital Department of Neurology Beijing China The University of Manchester Christabel Pankhurst Institute Division of Informatics Imaging and Data Sciences School of Health Sciences Department of Computer Science School of Engineering United Kingdom Department of Cardiovascular Sciences Department of Electrical Engineering Leuven Belgium Alan Turing Institute London United Kingdom

Magnetic resonance imaging (MRI) is often limited by noise and low-resolution (LR), which can impact the precision of the diagnosis and treatment of patients. LR images and mixed noise (e.g., Gaussian noise, Rician noise, and Impulse noise) are inherent in MR images, and current approaches typically address image super-resolution (SR) reconstruction and denoising separately, resulting in a discrepancy between the actual MRI data distribution and the reconstructed images. This research introduces a new algorithm SRDSNF, the stochastic normalizing flow-based MR image SR and denoising model, which tackles SR and denoising simultaneously through a stochastic normalizing flow. Our method integrates the encoded information of the input image as a conditional variable in each reverse step of the stochastic normalizing flow, ensuring a consistent representation of the spatial distribution between the reconstructed image and the original data. Additionally, we incorporate range-null space decomposition and subsequence sampling techniques to increase the consistency between the original and constructed data and accelerate model generation. To assess the efficacy of our approach, we conducted experiments on the BrainWeb and NFBS datasets, which include simultaneous SR and denoising, standalone denoising, and standalone SR tasks. The results of the experiments illustrate that our method achieves superior SR and denoising performance with fewer sampling steps, closely approximating the ground truths. Furthermore, our results surpass those of existing methods in various tasks, showing improvement of up to 4.64 dB in PSNR and 13.8% in SSIM achieved by our SRDSNF model was contextualized against state-of-the-art approaches such as AMIR, SwinIR, and InstructIR. These methods typically report PSNR improvements ranging from 0.5 to 2 dB and SSIM increases of 3-6%, underscoring the potential clinical value of our methodology. © 2020 IEEE.

关键词： Stochastic systems

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Collaborative learning-based inter-dependent task dispatching and co-location in an integrated edge computing system

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Digital Communications and Networks 2024年第6期10卷 1837-1850页

作者： Uchechukwu Awada Jiankang Zhang Sheng Chen Shuangzhi Li Shouyi Yang School of Software Henan Institute of Science and TechnologyXinxiang 453003China School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China Department of Computing and Informatics Bournemouth UniversityPoole BH125BBUK School of Electronics and Computer Science University of SouthamptonSouthampton SO171BJUK Faculty of Information Science and Engineering Ocean University of ChinaQingdao 266100China

Recently,several edge deployment types,such as on-premise edge clusters,Unmanned Aerial Vehicles(UAV)-attached edge devices,telecommunication base stations installed with edge clusters,etc.,are being deployed to enable faster response time for latency-sensitive *** fundamental problem is where and how to offload and schedule multi-dependent tasks so as to minimize their collective execution time and to achieve high resource *** approaches randomly dispatch tasks naively to available edge nodes without considering the resource demands of tasks,inter-dependencies of tasks and edge resource *** approaches can result in the longer waiting time for tasks due to insufficient resource availability or dependency support,as well as provider ***,we present Edge Colla,which is based on the integration of edge resources running across multi-edge *** Colla leverages learning techniques to intelligently dispatch multidependent tasks,and a variant bin-packing optimization method to co-locate these tasks firmly on available nodes to optimally utilize *** experiments on real-world datasets from Alibaba on task dependencies show that our approach can achieve optimal performance than the baseline schemes.

关键词： Edge computing Collaborative learning Resource utilization Execution time Edge federation Gang scheduling

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Comparative Analysis of Movie Recommendation Systems Using Filtering Techniques on IMDB and Rotten Tomatoes 15

Comparative Analysis of Movie Recommendation Systems Using F...

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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Majhi, Santosh Kumar Sahani, Preetipadma Pradhan, Rosy Acharya, Biswaranjan Grivokostopoulou, Foteini Kanavos, Andreas Gerogiannis, Vassilis C. Department of Computer Science and Information Technology Bilaspur India Veer Surendra Sai University of Technology Department of Computer Science and Engineering Odisha Burla India Veer Surendra Sai University of Technology Department of Electrical Engineering Odisha Burla India Marwadi University Department of Computer Engineering Ai Rajkot India Computer Technology Institute and Press 'Diophantus' Patras Greece Ionian University Department of Informatics Corfu Greece University of Thessaly Department of Digital Systems Larissa Greece

ISBN: (纸本)9798350368833

Recommendation systems are pivotal in industries like entertainment, where competitiveness is intense. This study enhances movie recommendations by utilizing rich datasets from IMDB and Rotten Tomatoes, incorporating movie ratings, user profiles, audience scores, and critics' reviews. These elements were subjected to sentiment analysis to refine the data quality. We explored three filtering techniques: content-based, which analyzes movie features;collaborative, which processes user behavior patterns;and hybrid, combining both to leverage their strengths. The effectiveness of these methods was assessed using Mean Average Precision at k (MAP-k) and Root Mean Square Error (RMSE), with our results showing notable improvements in recommendation accuracy. For instance, the hybrid method achieved a MAP ©k of 0.271 and an RMSE of 0.634, indicating enhanced precision and reliability of the recommendations. These findings suggest significant potential for improving user satisfaction and retention in digital platforms, demonstrating the benefit of integrating diverse data sources and analytical techniques in recommendation systems.

关键词： Collaborative filtering

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9th International Conference on Remote Sensing and Geoinformation of the Environment, RSCy 2023

作者： Zampas, Ioannis Moses, Mefe Haralambous, Haris Department of Electrical Engineering Computer Engineering and Informatics School of Engineering Zaria810107 Nigeria Department of Geomatics Ahmadu Bello University Zaria810107 Nigeria Frederick University Frederick Research Center Nicosia1036 Cyprus

ISBN: (纸本)9781510668225

The purpose of the present study is to investigate co-seismic earthquake ionospheric signatures as detected by remote sensing instrumentation situated in Cyprus by exploiting Total Electron Content measurements from calibrated 1 sec RINEX files from the Cyprus Positioning System (CYPOS) GNSS stations and ionograms from the Cyprus Digisonde that is monitoring the electron density profile over Cyprus at 5 min resolution. Through these observations, we established atmospheric wave activity emanating from the earthquake epicenter that took place in Turkey on the 6 February 2023. The major earthquake shock of 7.8 (Mw) struck southern and central Turkey, as well as northern and western Syria km west-northwest of Gaziantep at 01:17 UTC. Subsequently a second 7.6 (Mw) earthquake occurred at 10:24 UTC to the north-northeast in Kahramanmaras Province. Following the first earthquake no ionospheric signatures were detected but the second earthquake around local noon caused notable acoustic wave activity on the ionosphere. © 2023 SPIE.

关键词： Earthquakes

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Retain and Adapt: Online Sequential EEG Classification With Subject Shift

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4479-4492页

作者： Duan, Tiehang Wang, Zhenyi Shen, Li Doretto, Gianfranco Adjeroh, Donald A. Li, Fang Tao, Cui Mayo Clinic Department of Artificial Intelligence and Informatics JacksonvilleFL32216 United States University of Maryland College ParkMD20740 United States Jd Explore Academy Beijing100101 China West Virginia University Lane Department of Computer Science and Electrical Engineering MorgantownWV26506 United States

Large variance exists in Electroencephalogram (EEG) signals with its pattern differing significantly across subjects. It is a challenging problem to perform online sequential decoding of EEG signals across different subjects, where a sequence of subjects arrive in temporal order and no signal data is jointly available beforehand. The challenges include the following two aspects: 1) the knowledge learned from previous subjects does not readily fit to future subjects, and fast adaptation is needed in the process;and 2) the EEG classifier could drastically erase information of learnt subjects as learning progresses, namely catastrophic forgetting. Most existing EEG decoding explorations use sizable data for pretraining purposes, and to the best of our knowledge we are the first to tackle this challenging online sequential decoding setting. In this work, we propose a unified bi-level meta-learning framework that enables the EEG decoder to simultaneously perform fast adaptation on future subjects and retain knowledge of previous subjects. In addition, we extend to the more general subject-agnostic scenario and propose a subject shift detection algorithm for situations that subject identity and the occurrence of subject shifts are unknown. We conducted experiments on three public EEG datasets for both subject-aware and subject-agnostic scenarios. The proposed method demonstrates its effectiveness in most of the ablation settings, e.g. an improvement of 5.73% for forgetting mitigation and 3.50% for forward adaptation on SEED dataset for subject agnostic scenarios. © 2020 IEEE.

关键词： Brain computer interface

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