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22nd International Symposium INFOTEH-JAHORINA, INFOTEH 2023

作者： Karatkevich, Andrei Grobelna, Iwona Agh University of Science and Technology Dept. of Applied Computer Science Kraków Poland University of Zielona Góra Autom. Control Electronics and Electr. Eng. Zielona Góra Poland

ISBN: (纸本)9781665475464

The optimal deadlock avoiding, deadlock recovery, as well as deadlock detection in Petri nets are the NP-hard problems. For this reason, heuristic algorithms for finding the approximate solutions of such problems are of a great interest. In the paper a heuristic method of deadlock recovery for the S3PR nets, which are often used for modelling of manufacturing systems, is proposed. The method is based on the idea of resetting the process instances in the S3PR nets and a heuristic for breaking the cycles in the wait-for graphs. The method does not require any time consuming operations such as construction of the reachability graph, explicit detecting of all reachable deadlocks or all elementary cycles in a wait-for graph, hence it is quick. The simple recovery transitions are added to the net providing recovery from both global and local deadlocks. © 2023 IEEE.

关键词： Petri nets

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Design of a Gamified Training System for Human-Robot Cooperation

Design of a Gamified Training System for Human-Robot Coopera...

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2022 International Conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2022

作者： Ateş, Gizem Kyrkjebø, Erik Dept. of Computer Science Electrical Eng. and Mathematical Sciences Western Norway University of Applied Sciences Førde Norway

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

Human-Robot Cooperation (HRC) is a field which focuses on employing the best skills of both the robot and the human working together to achieve a common or shared task more efficiently. In most cases, both the human and the robot should know the status of each other and interchange data accordingly. There are several successfully researched HRC systems in the literature proposing solutions to various industrial problems yet few of them are used actively in real-world tasks. One of the major reasons for this gap is that the developed HRC systems do not offer an effective training procedure. The availability of a versatile training setup to get the optimal efficiency is important in addition to evaluating the developed HRC system's usability. Recent studies show that serious games offer effective training outcomes in various sectors such as the military, disaster drills, aviation, health etc. This paper presents an open-source gamified modular training design for HRC applications. It shows how the HRC system can be trained using serious games, what game elements can be utilized and how the learning curve of the user can be measured to evaluate the usability and efficiency. The proposed design is demonstrated through a real-world Cooperative Lifting (co-lift) scenario. The main motivation is to constitute a baseline for effective training of the HRC systems so that the gap between research and successful innovation in the HRC field becomes more narrow. © 2022 IEEE.

关键词： Serious games

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Human Body Action Recognition based on 3D Feature Fusion over RGB Scenes

Human Body Action Recognition based on 3D Feature Fusion ove...

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International Conference on Information Technology (ICIT)

作者： Heba Nsour Zaid Mustafa Maria Al-Sendah Dept. of Computer Science Al-Balqa Applied University Albalqa Jordan Dept. of Computer Information Systems Al-Balqa Applied University Albalqa Jordan

Human body action recognition (HBAR) is an important area of research in machine learning and image processing due to its vast range of applications. Similarly, estimating various components of human anatomy from RGB scenes are essential to human action tracking. The present study involves the implementation of human key body point detection for pose estimation. During abstracted silhouettes and vital human points, the proposed system has extracted two main features, such as 3D features and distance features. Once the relevant attributes have been incorporated, executing precise tasks and strategies is critical to achieving maximum features. Therefore, we used the t- SNE-based data refinement technique for optimal feature selection. Finally, for training, a classification model random forest is utilized. The presented system is verified on a recognized benchmark dataset, i.e., the UCF sports database. The experimental settings have revealed that our proposed system has attained better performance and outperformed other present state-of-the-art methods regarding mean recognition rate.

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Park-Space: A Parking Space Indicator 3

Park-Space: A Parking Space Indicator

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Khanduja, Dhairya Mittal, Janvi Choudhury, Tanupriya Kotecha, Ketan Singh, Manhar Sinha, Suryansh School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun248007 India Cse Dept. Uttarakhand Dehradun248007 India Cse Dept. Maharashtra Pune412115 India Symbiosis Center for Applied Artificial Intelligence Pune412115 India

ISBN: (纸本)9798350343274

The Parking Spot Indicator Application is a software program that aims to simplify the process of finding parking spaces in busy lots for drivers. Utilizing sensors installed in each spot, it monitors their occupancy status in real-time and presents this information through an easy-to-use interface. This application offers drivers, lot operators, and the environment several benefits. Drivers can save time and reduce stress by avoiding the hassle of searching for a spot, thanks to the real-time updates provided by the application. Operators can optimize resources, reduce congestion, and increase revenue by effectively managing their lots. Additionally, this application helps minimize traffic congestion and emissions by reducing circling time spent looking for parking spots. It is designed to be scalable and adaptable to various types of lots, including outdoor areas, multi-level structures, and underground facilities. The user-friendly interface provides a simple experience with a real-time map displaying available spots highlighted. © 2023 IEEE.

关键词： Application programs

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Secure Risk Management Approach for Enhancing the Performance of Cloud-based Cooperative Spectrum Sensing CRNs

Secure Risk Management Approach for Enhancing the Performanc...

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IEEE International Symposium on Network Computing and Applications

作者： Victor Balogun Xavier Paquette Oluwafemi A. Sarumi Dept. of Applied Computer Science University of Winnipeg Winnipeg MB Canada Institute of Computer Science Heinrich-Heine-University Duesseldorf Duesseldorf Germany

ISBN: (数字)9798331510183

ISBN: (纸本)9798331510190

The increasing demand for radio spectrum, spurred by an upsurge in interconnected devices, necessitates innovative management solutions. Cognitive Radio Networks (CRN) offer a promising approach to dynamically allocate spectrum resources. This study examines the Cooperative Spectrum Sensing (CCSS) architecture, leveraging cloud computing and big data analytics, to enhance CRN efficacy. We present a risk analysis identifying key vulnerabilities, explore regulatory considerations within the Canadian spectrum landscape, and propose an augmented architecture incorporating preventative and defensive strategies. We also discuss challenges encountered in the implementation phase and suggest future research trajectories for cloud-assisted CCSS systems.

关键词： Cloud computing Cooperative communication computer architecture Big Data Trajectory Sensors Risk management Security Cognitive radio Radio spectrum management

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An IoHT System Utilizing Smart Contracts for Machine Learning -Based Authentication

An IoHT System Utilizing Smart Contracts for Machine Learnin...

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2023 International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2023

作者： Upreti, Kamal Vats, Prashant Borkhade, Gauri Raut, Ranjana Dinkar Hundekari, Sheela Parashar, Jyoti Dept. of Computer Science Delhi NCR Ghaziabad India Scse Manipal University Jaipur Dept. of Cse Rajasthan Jaipur India Bharati Vidyapeeth College of Engineering Dept. of Electronics & Telecommunication Engg Maharashtra Navi Mumbai India Sant Gadge Baba Amravati University Pgtd Applied Electronics Maharashtra Amravati India Mit Adt University Loni Kalbhor Pune Pune India Adgitm Dept. of Computer Science & Engg. New Delhi India

ISBN: (纸本)9798350314878

The Internet of Healthcare Things (IoHT) and blockchain technologies have made it feasible to share data in a secure and effective manner, but it is still challenging to ensure the data's veracity and privacy. This paper presents a blockchain authentication method that utilizes Machine Learning (ML) techniques that use smart contracts to ensure the security and privacy of IoHT data. The process utilizes smart contracts to manage access control and ensure data integrity, and deep learning algorithms to identify and validate the accuracy of user data. Furthermore, the approach improves the resilience and dependability of the authentication process and permits secure data ex-change between multiple IoHT systems. The proposed approach provides a potentially revolutionary solution to enhance the safety and confidentiality of IoHT data. It has the potential to fundamentally change how healthcare is provided in the future. © 2023 IEEE.

关键词： Blockchain

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Evaluating the Benefits of Asymmetry Features for Emotion Recognition

Evaluating the Benefits of Asymmetry Features for Emotion Re...

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Canadian Conference on Electrical and computer Engineering (CCECE)

作者： Camilo E. Valderrama Fatima Islam Mouri Sergio G. Camorlinga Dept. of Applied Computer Science University of Winnipeg Winnipeg MB Canada

Emotion recognition is a topic of interest in Affective Computing (AC). While deep learning architectures have gained popularity for classification tasks, their reliance on large datasets limits their applicability when data availability is scarce. An alternative approach is feature engineering, which involves extracting relevant features to train supervised machine learning models. Neuroscientific theories on emotion processing, such as the lateralization theory, have motivated the introduction of asymmetry features for emotion prediction. However, none of these studies have statistically evaluated whether including asymmetrical features could reduce classification error or computational time. To address that direction, the current work compared two approaches for emotion recognition. The first approach used features extracted from individual EEG channels, while the second used asymmetry features calculated by matching pairs of EEG nodes. The two approaches were compared in terms of performance and fitted computational time. The comparison indicated that the performances of both approaches were not statistically significant. Notably, the asymmetry approach required less computational time for the training stage. This finding implies that incorporating asymmetry features in emotion recognition models is viable when computational resources are limited, without significantly compromising performance.

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DDRM-PR: Fourier Phase Retrieval using Denoising Diffusion Restoration Models

arXiv

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arXiv 2025年

作者： Kaya, Mehmet Onurcan Oktem, Figen S. Dept. of Applied Mathematics and Computer Science DTU Lyngby2800 Denmark Dept. of Electrical and Electronics Eng. METU Ankara06800 Turkey

Diffusion models have demonstrated their utility as learned priors for solving various inverse problems. However, most existing approaches are limited to linear inverse problems. This paper exploits the efficient and unsupervised posterior sampling framework of Denoising Diffusion Restoration Models (DDRM) for the solution of nonlinear phase retrieval problem, which requires reconstructing an image from its noisy intensity-only measurements such as Fourier intensity. The approach combines the model-based alternating-projection methods with the DDRM to utilize pretrained unconditional diffusion priors for phase retrieval. The performance is demonstrated through both simulations and experimental data. Results demonstrate the potential of this approach for improving the alternating-projection methods as well as its limitations. Copyright © 2025, The Authors. All rights reserved.

关键词： Inverse problems

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Predictive Insights Beyond Boundaries: Integrating Clinical Data for Hepatitis C Forecasting Using Machine Learning and Deep Learning

Predictive Insights Beyond Boundaries: Integrating Clinical ...

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International Conference on Electrical, computer and Communication Engineering (ECCE)

作者： Fatema Tuj Jenin Al-Amain Khandaker Mohammad Mohi Uddin Faizul Ahmed Robin Md. Ferdous Bin Hafiz Dept. of Computer Science and Engineering Southeast University Dhaka Bangladesh Dept. of Information and Electrical Engineering Hamburg University of Applied Sciences Hamburg Germany

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

A person's well-being and wellness are seriously hampered by the chronic diseases' lasting implications and recurrent signs. A startling 58 million people worldwide suffer from an incurable chronic form of hepatitis C, disease is brought about by a virus called hepatitis C (HCV). Additionally, it results in 290,000 deaths every year from cirrhosis and liver cancer, both of which linked to HCV. The goal of this venture is to reliably identify and anticipate hepatitis C utilizing Machine Learning (ML) and deep Learning (DL) techniques. It does this by utilizing ML and DL algorithms' capacity to scan big datasets and spot patterns and associations that help with diagnosis. Predictive models were created utilizing blood values from 615 patients, including both hepatitis patients and healthy blood donors, using a variety of approaches comprising SVM, Logistic regression (LR), KNN, LSTM and others. The dataset underwent extensive preprocessing, which included managing missing values, scaling, and diminished dimensionality using Principal Component Analysis (PCA). After a variety of ML along with the SVM classifier showed the best result, giving an accuracy concerning 98.33%, recall concerning 94.17%, precision concerning 93.99%, and F1-score concerning 94.16%.

关键词： Support vector machines Deep learning Accuracy Liver diseases Predictive models Reliability Long short term memory Viruses (medical) Medical diagnostic imaging Principal component analysis

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Device Distribution Scheme of Random Access in Space-Air-Ground Integrated Network for Massive IoT 13

Device Distribution Scheme of Random Access in Space-Air-Gro...

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13th International Conference on Information and Communication Technology Convergence, ICTC 2022

作者： Oh, Joohyun Park, Joohan Youn, Jiseung Im, Jeong-Ju Cho, Sunghyun Major in Bio-Artificial Intelligence Hanyang University Dept. of Applied Artificial Intelligence Ansan Korea Republic of Hanyang University Dept. of Computer Science & Engineering Ansan Korea Republic of Major in Bio-Artificial Intelligence Hanyang University Dept. of Computer Science & Engineering Ansan Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

To support massive random access trials of devices with diverse QoS is a major challenge for massive machine-type communications. Space-air-ground integrated network(SAGIN) can be a promising solution for the congestion issues because the SAGIN can extend preamble pool. In this paper, we propose a device distribution scheme to maximize random access success rate while conserving delay of the packet in SAGIN. The proposed scheme consists of two phases. One is an algorithm in which the device selects the appropriate SAGIN infrastructure to maximize the random access success rate. The other is an algorithm in which devices that have failed previous random access select an infrastructure different from the previously selected infrastructure to resolve congestion. We compare the performance of the proposed algorithm with two benchmark algorithms in terms of random access success rate and delay. © 2022 IEEE.

关键词： Benchmarking

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