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Towards Personalized Fitness with Multi-modal Wearable Heart Rate Monitors: A Proposal 6

Towards Personalized Fitness with Multi-modal Wearable Heart...

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6th International Conference on Vocational Education and Electrical engineering, ICVEE 2023

作者： Nugraha, Alfian Dwi Puspitaningayu, Pradini Hidayatullah, Rahmat Pramukantoro, Eko Sakti Widayaka, Parama Diptya Rochmawati, Naim State University of Surabaya Dept. of Informatics Engineering Surabaya Indonesia State University of Surabaya Dept. of Electrical Engineering Surabaya Indonesia Brawijaya University Faculty of Computer Science Malang Indonesia

ISBN: (纸本)9798350326642

Nowadays, the trend for home workouts is getting more and more popular. The growth of wearable fitness trackers from basic accelerometers, pedometers, pulse detectors, and ECGs also supports the advancement of personal fitness monitoring. Everyone has a distinct level of fitness, in theory. The heart rate, which is correlated with physical activity, such as sports, demonstrates how this organ functions. As the muscles work harder at a high intensity, the heart rate will also rise. This paper presents the design and implementation of a multimodal wearable heart rate monitoring system. The software is Android-based and is designed to be able to receive heart rate data sent by two different wearable heart rate monitors simultaneously using Bluetooth low-energy protocol. One uses an optical heart monitor and the other uses an electrocardiogram or ECG. The implementation also includes IoT for real-Time data acquisition and a web-based application for wider accessibility. The system is evaluated by collecting packets from two devices for 60 minutes. The results underscore the feasibility of the system in collecting users' heart rate data either using different devices. Furthermore, it serves as a foundation for future studies aiming to expand the personalization capabilities of wearable devices in healthcare and fitness. © 2023 IEEE.

关键词： Electrocardiograms

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Interactive Educational Game Development Using Modified Single Stage Detector 28

Interactive Educational Game Development Using Modified Sing...

引用

28th International computer Science and engineering Conference, ICSEC 2024

作者： Usmanto, Kristiawan Dwi Budiyanta, Nova Eka Yuniarno, Eko Mulyanto Adi, Cyprianus Kuntoro Universitas Sanata Dharma Dept. of Informatics Yogyakarta Indonesia Institut Teknologi Sepuluh Nopember Dept. of Electrical Engineering Surabaya Indonesia Universitas Katolik Indonesia Atma Jaya Dept. of Electrical Engineering Jakarta Indonesia Institut Teknologi Sepuluh Nopember Dept. of Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350366860

This research develops a real-Time hand gesture detection system using the You Only Look Once (YOLO) algorithm for simple math learning applications aimed at children with special needs. This application is a simple drag and drop game using hand gesture indicators. The system utilizes computer vision and deep learning technology to recognize hand gestures in videos which are then converted into interactive inputs in learning media. System testing involves hyperparameter tuning methods, including learning rate, image size, and momentum adjustments. The test results show that the YOLO algorithm with hyperparameter tuning method with hyperparameter values of learning rate 0.0001, image size 320, and momentum 0.937 is effective in detecting hand gestures with an mAP@0.5: 0. 95 score of 93.27%. The test results provide the potential for using a real-Time hand gesture detection system as an interesting and interactive learning method for children with disabilities, especially for those who have limitations in communication. © 2024 IEEE.

关键词： Gesture recognition

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On Utilizing Unused Slots In Topology-Transparent TDMA MAC Policies for Ad Hoc Networks

On Utilizing Unused Slots In Topology-Transparent TDMA MAC P...

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2022 Global Information Infrastructure and Networking Symposium, GIIS 2022

作者： Dragonas, Vasileios Kallergis, Dimitrios Tsoumanis, Georgios Oikonomou, Konstantinos Ionian University Department of Informatics Corfu Greece University of West Attica Dept. of Informatics & Computer Engineering Aegaleo Greece University of Ioannina Dept. of Informatics & Telecommunications Arta Greece

ISBN: (数字)9781665490955

ISBN: (纸本)9781665490955

In the age of the Internet of Things, numerous devices of diverse characteristics are communicating with each other. The underlying networks supporting those communications can either use some sort of infrastructure or implement an ad hoc network. For the latter type of networks and depending on the application requirements, Topology-Transparent TDMA MAC policies can be considered as they are inherently suited for dynamic and unstructured topologies. However, such TDMA-based schemes may suffer from low throughput performance, especially in sparse networks, where a significant portion of available time slots remains unused. To address this challenge, a Topology-Transparent TDMA MAC policy is revisited in this paper, which utilizes unused slots by allowing nodes to transmit in them under a common and fixed probability p. Additionally, an improvement is proposed, where every node can calculate its own individual access probability based on information gathered from its local neighborhood. Simulation results demonstrate that the use of the individual access probability leads to improved performance in terms of system throughput. Moreover, the requirement for assigning a value to probability p during the network initialization is relaxed, as this value is now calculated dynamically and distributively during the network operation. © 2022 IEEE.

关键词： Ad hoc networks

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Federated Digital Twin 27

Federated Digital Twin

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27th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2023

作者： Vergara, Christian Bahsoon, Rami Theodoropoulos, Georgios Yanez, Wendy Tziritas, Nikos School of Computer Science University of Birmingham Birmingham United Kingdom Southern University of Science and Technology Dept. of Computer Science and Engineering Shenzhen China University of Thessaly Dept. of Informatics and Telecommunications Lamia Greece

ISBN: (纸本)9798350337846

Digital Twin (DT) is a virtual replica of a physical system that is constantly receiving information from different data sources, enhancing its operations and processes through data analytics, predictions and simulations. The development of DTs relies on advancements in cutting-edge technologies namely IoT, Big data, Cloud computing and Artificial Intelligence;and although it was initially conceived in manufacturing, it is currently contributing to the digital transformation of several fields including aeronautics, healthcare, urban planning and agriculture. The existing body of research suggests that it will be expanded in the next few years with the implementation of sophisticated applications, therefore different proposals to achieve collective work between DTs have been investigated. Nevertheless, much research is needed to develop and validate appropriate mechanisms to ensure its successful deployment in complex real-world cases that require collaboration among individual systems. A Federated Digital Twin (FDT) has been identified as a promising solution for this approach, since it allows the interconnection among autonomous DTs in the virtual space, leveraging their advantages and enabling interaction, collaboration and shared learning. Additionally, since a FDT is envisaged as a network of cooperative DTs, cognitive principles can be applied to assist the overall operations through knowledge acquisition and reasoning, leading to an informed and intelligent decision making. This study aims to expand the FDT concept, develop mechanisms for coordination and synchronization based on well-defined FDT goals and connectionism theory. Furthermore, four architectural styles are provided to enable the integration of collaborative DTs within a federated environment, aiming to improve the operations in complex real-world systems. © 2023 IEEE.

关键词： Metadata

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Dynamic Blockchain Reconfiguration: Balancing the Trilemma Trade-off Using Digital Twins 27

Dynamic Blockchain Reconfiguration: Balancing the Trilemma T...

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27th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2023

作者： Diamantopoulos, Georgios Tziritas, Nikos Bahsoon, Rami Theodoropoulos, Georgios School of Computer Science University of Birmingham Birmingham United Kingdom University of Thessaly Dept. of Informatics and Telecommunications Lamia Greece Southern University of Science and Technology Dept. of Computer Science and Engineering Shenzhen China

ISBN: (纸本)9798350337846

The trilemma trade-off problem between decentralisation, scalability, and security states that in blockchain systems the above properties are negatively correlated. Infrastructure, node configuration, choice of Consensus Protocol, and complexity of the underlying application are cited among the factors that affect the balance of the trade-off. Given that Blockchains are complex, dynamic systems, a dynamic approach to their management and reconfiguration at runtime is deemed necessary to reflect the changes in the state of the infrastructure and application. This work proposes the use of Digital Twins as the means of optimising the trilemma trade-off of blockchain i.e., re-configuring system parameters such as to maximise scalability, decentralisation and security. Specifically, through a bi-directional feedback loop between the system and the digital twin, simulation, what-if analysis and machine learning techniques will be employed for the computation of an optimal configuration given the current system state. Furthermore, a dynamic update mechanism is proposed to allow for blockchain reconfiguration without violating the decentralisation of the system. © 2023 IEEE.

关键词： Blockchain

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Context-aware Container Orchestration in Serverless Edge Computing

Context-aware Container Orchestration in Serverless Edge Com...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Guan, Peiyuan Chen, Chen Chen, Ziru Cai, Lin X. Hao, Xing Taherkordi, Amir University of Oslo Dept. of Informatics Oslo Norway University of Cambridge Dept. of Computer Science and Technology Cambridge United Kingdom Illinois Institute of Technology Dept. of Electrical and Computer Engineering Chicago United States Northwest Univesity School of Information Science and Technology Xi'an China

ISBN: (纸本)9798350351255

Adopting serverless computing to edge networks benefits end-users from the pay-as-you-use billing model and flexible scaling of applications. This paradigm extends the boundaries of edge computing and remarkably improves the quality of services. However, due to the heterogeneous nature of computing and bandwidth resources in edge networks, it is challenging to dynamically allocate different resources while adapting to the burstiness and high concurrency in serverless workloads. This article focuses on serverless function provisioning in edge networks to optimize end-to-end latency, where the challenge lies in jointly allocating wireless bandwidth and computing resources among heterogeneous computing nodes. To address this challenge, We devised a context-aware learning framework that adaptively orchestrates a wide spectrum of resources and jointly considers them to avoid resource fragmentation. Extensive simulation results justified that the proposed algorithm reduces over 95% of converge time while the end-to-end delay is comparable to the state of the art. © 2024 IEEE.

关键词： Quality of service

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On Min-Max Graph Balancing with Strict Negative Correlation Constraints 34

On Min-Max Graph Balancing with Strict Negative Correlation ...

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34th International Symposium on Algorithms and Computation, ISAAC 2023

作者： Kuo, Ting-Yu Chen, Yu-Han Frosini, Andrea Hsieh, Sun-Yuan Tsai, Shi-Chun Kao, Mong-Jen Dept. of Computer Science National Yang-Ming Chiao-Tung University Hsinchu Taiwan Dept. of Computer Science and Information Engineering National Cheng-Kung University Tainan Taiwan Dept. of Mathematics and Informatics University of Florence Italy Dept. of Computer Science and Information Engineering National Chi-Nan University Puli Taiwan

ISBN: (纸本)9783959772891

We consider the min-max graph balancing problem with strict negative correlation (SNC) constraints. The graph balancing problem arises as an equivalent formulation of the classic unrelated machine scheduling problem, where we are given a hypergraph G = (V, E) with vertex-dependent edge weight function p: E × V 7→ Z≥0 that represents the processing time of the edges (jobs). The SNC constraints, which are given as edge subsets C1, C2, . . ., Ck, require that the edges in the same subset cannot be assigned to the same vertex at the same time. Under these constraints, the goal is to compute an edge orientation (assignment) that minimizes the maximum workload of the vertices. In this paper, we conduct a general study on the approximability of this problem. First, we show that, in the presence of SNC constraints, the case with maxe∈E |e| = maxi |Ci| = 2 is the only case for which approximation solutions can be obtained. Further generalization on either direction, e.g., maxe∈E |e| or maxi |Ci|, will directly make computing a feasible solution an NP-complete problem to solve. Then, we present a 2-approximation algorithm for the case with maxe∈E |e| = maxi |Ci| = 2, based on a set of structural simplifications and a tailored assignment LP for this problem. We note that our approach is general and can be applied to similar settings, e.g., scheduling with SNC constraints to minimize the weighted completion time, to obtain similar approximation guarantees. Further cases are discussed to describe the landscape of the approximability of this prbolem. For the case with |V | ≤ 2, which is already known to be NP-hard, we present a fully-polynomial time approximation scheme (FPTAS). On the other hand, we show that the problem is at least as hard as vertex cover to approximate when |V | ≥ 3. © Ting-Yu Kuo, Yu-Han Chen, Andrea Frosini, Sun-Yuan Hsieh, Shi-Chun Tsai, and Mong-Jen Kao;licensed under Creative Commons License CC-BY 4.0.

关键词： Computational complexity

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Predicting Fish-Mortality: Artificial Neural Networks vs Symbolic Regression 14

Predicting Fish-Mortality: Artificial Neural Networks vs Sym...

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14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023

作者： Aravanis, Theofanis Ilias, Aristeidis Hatzilygeroudis, Ioannis Spiliopoulos, Georgios University of the Peloponnese Dept of Mechanical Engineering Patras Greece University of Patras Dept of Computer Engin. and Informatics Patras Greece Kefalonia Fisheries S.A. Kefalonia Greece

ISBN: (纸本)9798350318067

In this paper, we present a comparison between a collection of Artificial Neural Networks (ANNs) - specifically, Multilayer Perceptrons - and a collection of Symbolic Regression (SR) models, all developed for predicting fish-deaths due to infectious diseases in aquaculture. The implemented Machine-Learning models are able to identify patterns that indicate an increased risk of fish-deaths, and provide early warning-alerts to fish-farmers and other stakeholders, enabling them to take necessary measures to prevent or minimize losses of fishes. The models were trained using real-world data acquired from a large Greek fish-farming unit, and evaluated in a validation dataset (distinct from the training dataset) based on their Mean Absolute Error (MAE) performance. The study found that, for each disease considered, the corresponding ANN model outperformed the respective SR model in terms of MAE. However, the ANNs sacrificed interpretability for higher performance, while the developed SR models, despite lower performance, managed to produce transparent and understandable mathematical expressions for estimating fish-deaths. Overall, this study not only provides a valuable method for generating early warning-alerts of pathogenetic circumstances to the interested stakeholders, improving the efficiency and sustainability of fish-farming operations, but it also sheds light on the strengths and limitations of the developed ANN and SR models. © 2023 IEEE.

关键词： Forecasting

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Dual-Branch Neural Networks for Predicting Shared Bikes 17

Dual-Branch Neural Networks for Predicting Shared Bikes

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17th International Conference on Ubiquitous Information Management and Communication, IMCOM 2023

作者： Yang, Huigyu Raza, Syed M. Le, Duc-Tai Kim, Dongsoo S. Choo, Hyunseung Sungkyunkwan University Dept. of Superintelligence Engineering Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Indiana Uiversity-Purdue University Dept. of Electrical and Computer Engineering Indianapolis United States Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9781665453486

Bike sharing is efficient urban mobility method as a user determines the rental period upon his/her demand. A dock-based sharing system designates places (i.e., bike docks or stations) where a user rents or returns a bike. Due to regionally and temporally erratic nature of urban mobility demand, bike imbalance among stations inevitably occurs and a service operator rebalances bikes to prevent service impairment. The precise prediction of imbalance for each station is essential to optimize the rebalancing schedule. This paper presents Dual-Branch Neural Networks (DBNNs) employing Long Short-Term Memory (LSTM) to classify the imbalance level of each station along with the prediction of bike counts. To address the dual objectives, the model trains by backpropagating both classification and prediction losses to the dual output branches individually, and jointly updates the trainable parameters of LSTM layers. The results showcase that the proposed model predicts bike counts within 8% average error for next 24 hours, and achieves 94% accuracy of classification for the same time period. © 2023 IEEE.

关键词： Forecasting

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Income Forecasting for Multi-Category Product using ARIMA Approach

Income Forecasting for Multi-Category Product using ARIMA Ap...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Rizki Elisa Nalawati Asep Taufik Muharram Bambang Warsuta Ayres Pradiptyas Gresia Andayani Mauldy Laya Dept. of Informatics and Computer Engineering Politeknik Negeri Jakarta Depok Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

The rapid development of e-commerce platforms has brought significant changes in consumer behavior and business marketing strategies. In facing increasingly tight competition, some online stores need to predict revenue for the products they have. However, most online stores have more than one category, so it is necessary to evaluate predictions for each category. This problem can be handled by implementing Autoregressive Integrated Moving Average (ARIMA). This approach can be used to handle non-stationarity by having "integrated" components become stationary through differencing. The average MAPE value of all categories of cosmetic products is 12.144%. The development of this prediction system is in accordance with the stages in CRISP-DM. This approach provides a structured framework for carrying out data mining projects with six clear phases.

关键词： Seminars Consumer behavior Electronic commerce Data mining Intelligent systems Information technology Forecasting Business

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