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arXiv 2023年

作者： Varley, Thomas F. Havert, Daniel Fosque, Leandro Alipour, Abolfazl Weerawongphrom, Naruepon Naganobori, Hiroki O'Shea, Lily Pope, Maria Beggs, John School of Informatics Computing & Engineering Indiana University BloomingtonIN United States Department of Psychological & Brain Sciences Indiana University BloomingtonIN United States Department of Physics Indiana University BloomingtonIN United States Program in Neuroscience Indiana University BloomingtonIN United States Indiana University BloomingtonIN United States

Most of the recent work in psychedelic neuroscience has been done using non-invasive neuroimaging, with data recorded from the brains of adult volunteers under the influence of a variety of drugs. While this data provides holistic insights into the effects of psychedelics on whole-brain dynamics, the effects of psychedelics on the meso-scale dynamics of cortical circuits remains much less explored. Here, we report the effects of the serotonergic psychedelic N,N-diproptyltryptamine (DPT) on information-processing dynamics in a sample of in vitro organotypic cultures made from rat cortical tissue. Three hours of spontaneous activity were recorded: an hour of pre-drug control, and hour of exposure to 10µM DPT solution, and a final hour of washout, once again under control conditions. We found that DPT reversibly alters information dynamics in multiple ways: first, the DPT condition was associated with higher entropy of spontaneous firing activity and reduced the amount of time information was stored in individual neurons. Second, DPT also reduced the reversibility of neural activity, increasing the entropy produced and suggesting a drive away from equilibrium. Third, DPT altered the structure of neuronal circuits, decreasing the overall information flow coming into each neuron, but increasing the number of weak connections, creating a dynamic that combines elements of integration and disintegration. Finally, DPT decreased the higher-order statistical synergy present in sets of three neurons. Collectively, these results paint a complex picture of how psychedelics regulate information processing in meso-scale cortical tissue. Implications for existing hypotheses of psychedelic action, such as the Entropic Brain Hypothesis, are discussed. © 2023, CC BY-NC-SA.

关键词： Neurons

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Deep Learning-based Fall Detection Algorithm Using Ensemble Model of Coarse-fine CNN and GRU Networks

Deep Learning-based Fall Detection Algorithm Using Ensemble ...

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IEEE International Workshop on Medical Measurement and Applications (MEMEA)

作者： Chien-Pin Liu Ju-Hsuan Li En-Ping Chu Chia-Yeh Hsieh Kai-Chun Liu Chia-Tai Chan Yu Tsao Department of Biomedical Engineering National Yang Ming Chiao Tung University Taipei City Taiwan Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University New Taipei City Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei City Taiwan

Falls are the public health issue for the elderly all over the world since the fall-induced injuries are associated with a large amount of healthcare cost. Falls can cause serious injuries, even leading to death if the elderly suffers a “long-lie.” Hence, a reliable fall detection (FD) system is required to provide an emergency alarm for first aid. Due to the advances in wearable device technology and artificial intelligence, some fall detection systems have been developed using machine learning and deep learning methods to analyze the signal collected from accelerometer and gyroscopes. In order to achieve better fall detection performance, an ensemble model that combines a coarse-fine convolutional neural network and gated recurrent unit is proposed in this study. The parallel structure design used in this model restores the different grains of spatial characteristics and capture temporal dependencies for feature representation. This study applies the FallAllD public dataset to validate the reliability of the proposed model, which achieves a recall, precision, and F-score of 92.54%, 96.13%, and 94.26%, respectively. The results demonstrate the reliability of the proposed ensemble model in discriminating falls from daily living activities and its superior performance compared to the state-of-the-art convolutional neural network long short-term memory (CNN-LSTM) for FD.

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Application of online science practicum by using Microsoft Teams and Learning Management System (LMS) during the Covid 19 pandemic 10

Application of online science practicum by using Microsoft T...

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10th National Physics Seminar, SNF 2021

作者： Pertiwi, W.A. Sanjaya, L.A. Nadiyah Hasirah Haramen, M. Budi, A.S. Zarvianti, E. Puspa, R.W.D. Sianipar, L.K. Qoiriyah, W. Sitompul, H. Islamic Education Management Study Program Faculty Islamic of Education and Teacher Training Muhammad Azim Institute Jambi Indonesia Department of Physics Faculty of Mathematics and Natural Science Universitas Negeri Jakarta Indonesia Sharia Economics Study Program Faculty of Sharia and Law Muhammad Azim Islamic Institute Jambi Indonesia Physics Education Study Program STKIP Muhammadiyah Sungai Penuh Indonesia Kartika VIII-1 Senior High School Jakarta Indonesia Department of Physics Education Universitas Pelita Harapan Tangerang15811 Indonesia Informatics Engineering Study Program Faculty of Engineering Sheikh Yusuf Islamic University Tangerang Indonesia

The research objective of this is to see how to apply science practicum online using Microsoft Teams and Learning Management System (LMS). The form of this research is classroom action research which aims to apply the online science practicum during the Covid 19 pandemic. Therefore, it is carried out using two cycles in the application of online science practicum. The subjects of this study were students of the seventh semester in the Natural Science practicum course. Through observation and evaluation, it is shown that in the cycle I the percentage of practicum implementation which includes aspects of preparation, process and data and practicum results is 87.5%, in cycle II it has increased to 92.5%. In the assessment aspect of the practicum report, the average practicum assessment obtained in the first cycle was 3.09 from the highest score of 4 and in the second cycle was 3.7, while the aspect of student knowledge about the use of the Microsoft team which was taken using google form was 93%. In the aspect of student knowledge about the use of Learning Management System (LMS) by 75% and aspects of student understanding in the implementation of online science practicum using Microsoft Teams by 75%. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Students

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Hand-Woven Fabric Motif Recognition Methods: A Systematic Literature Review

Hand-Woven Fabric Motif Recognition Methods: A Systematic Li...

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International Conference on informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Handrie Noprisson Ermatita Ermatita Abdiansah Abdiansah Vina Ayumi Mariana Purba Marissa Utami Doctoral Program in Engineering Universitas Sriwijaya Palembang Indonesia Faculty of Computer Science Universitas Sriwijaya Palembang Indonesia Program of Informatics Management Politeknik Anika Palembang Indonesia Faculty of Engineering Muhammadiyah University of Bengkulu Bengkulu Indonesia

ISBN: (纸本)9781665427340

Knowledge of hand-woven motif recognition is only owned by the older generation, which has not been passed down to the younger generation. Moreover, computer technology can be used to support the recognition of traditional woven fabric motifs. The aim of this research is to conduct a literature review on hand-woven fabric motif recognition, with an emphasis on performance accuracy, techniques, and datasets utilized. This research included three distinct stages of systematic literature review (SLR): planning, execution, and reporting of findings. We found several datasets between 924,845 data and the number of classes between 3–25 class. Based on research result, we obtained several methods for hand-woven fabric motif recognition focused on performance examination. We recommended image pre-processing method, including Adaptive Filtering Denoising, Adaptive Wiener Filtering, Histogram Equalization, Gradient Pyramid (GP) Decomposition. Moreover, we suggested five feature extraction methods, including Radon Transform, Wavelet Transform, Locally Rotation Invariance Measure (LBP-ROR), Transform Invariant Low-Rank Textures (TILT) and Histogram of Oriented Gradients (HOG). For learning method, we recommend Fuzzy C-Mean (FCM), Convolutional neural network (CNN), Deep Neural Network (DNN), MobileNets, Inception-v3 and ResNet-50.

关键词： Wavelet transforms Histograms Wiener filters Systematics Bibliographies Neural networks Fabrics

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An Analysis Method for Metric-Level Switching in Beat Tracking

arXiv

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arXiv 2022年

作者： Chiu, Ching-Yu Müller, Meinard Davies, Matthew E.P. Su, Alvin Wen-Yu Yang, Yi-Hsuan Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University Academia Sinica Taiwan International Audio Laboratories Erlangen Germany Department of Informatics Engineering Centre for Informatics and Systems The University of Coimbra University of Coimbra Portugal Department of CSIE National Cheng Kung University Taiwan Taiwan AI Labs Taiwan

For expressive music, the tempo may change over time, posing challenges to tracking the beats by an automatic model. The model may first tap to the correct tempo, but then may fail to adapt to a tempo change, or switch between several incorrect but perceptually plausible ones (e.g., half- or double-tempo). Existing evaluation metrics for beat tracking do not reflect such behaviors, as they typically assume a fixed relationship between the reference beats and estimated beats. In this paper, we propose a new performance analysis method, called annotation coverage ratio (ACR), that accounts for a variety of possible metric-level switching behaviors of beat trackers. The idea is to derive sequences of modified reference beats of all metrical levels for every two consecutive reference beats, and compare every sequence of modified reference beats to the subsequences of estimated beats. We show via experiments on three datasets of different genres the usefulness of ACR when being utilized alongside existing metrics, and discuss the new insights that can be gained. © 2022, CC BY.

关键词： Taps

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Dynamic Deployment and Control of an NDN Network for Military Multi-UAVs Based Surveillance Applications

Dynamic Deployment and Control of an NDN Network for Militar...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Otávio A. Rocha da Cruz Carlos Eduardo Pereira Antonio Santos da Silva João Paulo J. da Costa Paulo Mendes Edison Pignaton de Freitas Graduate Program in Electrical Engineering Federal University of Rio Grande do Sul Rio Grande do Sul Brazil Institute of Informatics Federal University of Rio Grande do Sul Rio Grande do Sul Brazil Hamm-Lippstadt University of Applied Sciences (HSHL) Hamm Germany Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Airbus Taufkirchen Germany

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

The military usage of Unmanned Aerial Vehicles (UAVs) has garnered attention, especially after their employment in the Ukrainian war. Despite the most commented lethal usage, they have many other applications from which surveillance for imagery acquisition is one of primal importance. Using a standalone UAV for this purpose is well-known, but to cope with the scale of battlefield operations, using multi- UAV systems is an asset of great value. However, these systems rely on ad hoc networks that require solutions beyond conventional ones based on the Internet Protocol (IP). This paper addresses this concern by proposing a communication support mechanism for multi-UAV military surveillance systems based on the Information-Centric Networks (ICN) paradigm. The proposed approach consists of the dynamic deployment of an ICN network based on microservices architecture, where the communication services of each UAV are deployed according to their resources. The solution is validated in a simulated battlefield scenario where a surveillance UAV provides data demanded by other nodes. The results demonstrate that the proposed solution minimizes the data delivery delays by successfully deploying the customized set of microservices to support the transmission, even when a UAV with a low battery level is replaced at runtime.

关键词： Military communication Runtime Protocols Surveillance Information-centric networking Microservice architectures Autonomous aerial vehicles

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A deep learning approach to Lung Nodule Growth Prediction using CT image combined with Demographic and image features 23

A deep learning approach to Lung Nodule Growth Prediction us...

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7th International Conference on Medical and Health informatics, ICMHI 2023

作者： Li, Ai-Hsien Adams Nguyen, Duc-Khanh Lai, Yen-Jun Chien, Ting-Ying Chiu, Yen-Ling Chan, Chien-Lung Yang, Pan-Chyr Division of Cardiology Far Eastern Memorial Hospital Taipei220 Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Department of Information Management Yuan Ze University Taoyuan320 Taiwan Department of Radiology Far Eastern Memorial Hospital Taipei220 Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital Taipei Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan320 Taiwan Institute of Biomedical Sciences Academia Sinica Taipei106 Taiwan Department of Internal Medicine College of Medicine National Taiwan University Taipei100 Taiwan Center of Genomic Medicine National Taiwan University Taipei100 Taiwan

ISBN: (纸本)9798400700712

Lung cancer remains a significant global public health concern, being the leading cause of cancer-related deaths worldwide. Despite recent medical advancements, the disease still has a high mortality rate, making early detection and treatment critical for improving patient outcomes. Accurate prediction of nodule growth is key to early lung cancer treatment, but current assessments offer unclear indications of future growth, leading physicians to recommend costly and anxiety-provoking follow-up appointments every 3 to 12 months. To address this need, this study develops an ensemble deep learning approach that combines demographic data with existing CT images to predict whether lung nodules will grow, potentially reducing unnecessary examinations and easing the burden on patients. Our study, conducted on 862 patients with 1004 nodules, produced promising preliminary results with Accuracy, Sensitivity, Precision, F1 Score, and AUC of 0.66, 0.66, 0.67, 0.66, and 0.71, respectively. The proposed method provides a promising support system to empower patients to make informed decisions about seeking medical attention and helps physicians facilitate early treatment regimens, leading to improved patient outcomes and potentially saving lives. © 2023 ACM.

关键词： Forecasting

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Employing texture features of chest x-ray images and machine learning in covid-19 detection and classification

Mendel

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Mendel 2021年第1期27卷 9-17页

作者： Alquran, Hiam Alsleti, Mohammad Alsharif, Roaa Qasmieh, Isam Abu Alqudah, Ali Mohammad Harun, Nor Hazlyna Binti Department of Biomedical Systems and Informatics Engineering Yarmouk University Irbid21163 Jordan The Institute of Biomedical Technology King Hussein Medical Center Royal Jordanian Medical Service Amman11855 Jordan College of applied medical Sciences Radiological Science Program King Saud University Jeddah21435 Saudi Arabia Data Science Research Lab School of Computing Universiti Utara Malaysia Sintok Kedah06010 Malaysia

The novel coronavirus (nCoV-19) was first detected in December 2019. It had spread worldwide and was declared coronavirus disease (COVID-19) pandemic by March 2020. Patients presented with a wide range of symptoms affecting multiple organ systems predominantly the lungs. Severe cases required intensive care unit (ICU) admissions while there were asymptomatic cases as well. Although early detection of the COVID-19 virus by Real-time reverse transcription-polymerase chain reaction (RT-PCR) is effective, it is not efficient;as there can be false negatives, it is time consuming and expensive. To increase the accuracy of in-vivo detection, radiological image-based methods like a simple chest X-ray (CXR) can be utilized. This reduces the false negatives as compared to solely using the RT-PCR technique. This paper employs various image processing techniques besides extracted texture features from the radiological images and feeds them to different artificial intelligence (AI) scenarios to distinguish between normal, pneumonia, and COVID-19 cases. The best scenario is then adopted to build an automated system that can segment the chest region from the acquired image, enhance the segmented region then extract the texture features, and finally, classify it into one of the three classes. The best overall accuracy achieved is 93.1% by exploiting Ensemble classifier. Utilizing radiological data to conform to a machine learning format reduces the detection time and increase the chances of survival. © 2021, Brno University of Technology. All rights reserved.

关键词： COVID-19

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Atmospheric dynamics and early warning system low level windshear for airport runway hazard mitigations 10

Atmospheric dynamics and early warning system low level wind...

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10th International Seminar on New Paradigm and Innovation of Natural Sciences and itsApplication, ISNPINSA 2020

作者： Gernowo, R. Subagio, A. Adi, K. Widodo, A.P. Widodo, C.E. Putranto, A.B. Department of Physics Faculty of Science and Mathematics Diponegoro University Indonesia Department of Informatics Faculty of Science and Mathematics Diponegoro University Indonesia Study Program of Automation Engineering Technology Vocational School Diponegoro University Jl. Prof. Soedarto SH. Tembalang Semarang50275 Indonesia

Natural disaster nowadays often occur in Indonesia due to climate change, especially those related to atmospheric dynamics anomalies. The statutory event of atmospheric turbulence statistically is quite significant in its improvement. This is an important issue to conduct research both in the analysis of atmospheric dynamics and early detection solutions for these events. The Windshear phenomenon is an atmospheric turbulence disaster that occurs below an altitude of 3000 feet, often known as low level windshear (LLWS), which is very dangerous for planes taking take-off or landing. Its effect on the aircraft will result in increased airflow on the wings, so this increased airflow will result in a sudden increase in aircraft speed. If the pilot is not aware of the indication of the windshear, he will instinctively throttle back to reduce/compensate for the aircraft's speed. However, once the plane passes through the wind shear zone, the wind suddenly turns into a downdraft. The design of windshear early detection devices have been developed to complement research on the dynamics of the atmosphere. The results obtained are anomalous changes in temperature and atmospheric pressure at this time, and the windshear sensor can detect it well. © Published under licence by IOP Publishing Ltd.

关键词： Aircraft

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Utilization of Linguistic Data for Learner Assessment on e-Learning: Instrument and Processing

Utilization of Linguistic Data for Learner Assessment on e-L...

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International Conference on informatics and Computing (ICIC)

作者： Wenty Dwi Yuniarti Sri Hartati Sigit Priyanta Herman Dwi Surjono Doctoral Program of Computer Science Universitas Gadjah Mada Yogyakarta Indonesia Universitas Islam Negeri Walisongo Semarang Indonesia Department of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta Indonesia Department of Informatics Engineering Education Faculty of Engineering Universitas Negeri Yogyakarta Yogyakarta Indonesia

ISBN: (纸本)9798350345728

The increasingly massive use of e-Learning illustrates the speed and need for innovation in learning. According to the National Higher Education Standards (SN-Dikti), constructive alignment is required between learning outcomes, processes, and assessments to properly implement learning in e-Learning. Assessment is an essential component of e-Learning. Unfortunately, the problem of assessment in e-Learning is still found. One of them is the limitations of e-Learning in accommodating and processing various assessment data, primarily linguistic. Even though the variety of assessment data, both numerical, linguistic, and a combination of the two, supports a comprehensive assessment. On the other hand, the accommodation of linguistic data raises problems regarding how to process of unifying linguistic data is carried out. Research related to linguistic data using computing with words has been carried out, but it still needs more precise results from the unification of the linguistic data. Therefore, this study proposes providing an assessment instrument to accommodate linguistic data in e-Learning, as well as showing how to process of unifying linguistic data is carried out using 2-Tuple Fuzzy Linguistic. This approach can avoid the loss of assessment information by presenting more informative and precise results in a 2-tuple (s, α) where $s$ indicates the ability level, and α shows a comparison of abilities with other learners and the potential of the learner to achieve higher abilities. This proposal has the potential to be applied in a learner assessment system for higher education e-Learning.

关键词： Technological innovation Electronic learning Instruments Education Linguistics Proposals informatics

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