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SSRN 2023年

作者： Fuada, Syifaul Duttagupta, Subhasri Adiono, Trio Siregar, Lindawani Program Studi Sistem Telekomunikasi Universitas Pendidikan Indonesia West Java Province Bandung Indonesia Department of Computer Science and Engineering Amrita Viswa Vidyapeetham Keralam Amritapuri-Campus 690525 India Electrical Engineering Department School of Electrical Engineering and Informatics Institut Teknologi Bandung West Java Province Bandung Indonesia Teaching Factory of Manufacturing Electronics Politeknik Negeri Batam Kepulauan Riau Province Batam Indonesia

Street lighting is a public facility that gives highway users’ convenience during their nighttime trips, making it essential to be controlled and monitored using IoT technology. This study aimed to develop a smart lighting system connected through a Mesh network. The system monitors the street light and automatically controls it using the ESP8266 module, a microcontroller with Wi-Fi built-in. The options in selecting the lamp type include Halogen, Fluorescent, LED, or Incandescent. In general, LED is used because it has an advantage in energy-saving over other lamp types. Moreover, the system is equipped with automatic light intensity control through a dimming technique by Pulse Width Modulation (PWM), while BH1750 is used as a light sensor. Furthermore, it monitors the environmental temperature and humidity using DHT22. The communication between the lighting module is based on ESP-Mesh because each node is connected and forwarding data to the destination and the coverage area. ESP-Mesh allows the wireless network to have a scalable architecture in a broader area. It has features to self-organize and self-configure, implying the network could be built and maintained autonomously. Additionally, the system has a web server service with access to control each node and find out the mesh routing table. The results showed that each node could be configured as a mesh network using ESP8266, which sends dimming commands and reads the sensors. The dimming and intensity control was tested using real public street light called NL200D LED module and an off-the-self dimming control driver module, N-Channel MOSFET FQP30N06L. Furthermore, a Mesh connection was tested using five super bright 3mm LEDs representing real street light due to the limitation of the LED module in the laboratory. The street light has three control modes, including on/off-based, intensity control-based or dimming control manually through the website, and intensity control-based automatically using the two se

关键词： Mesh generation

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Brain MR Image Classification for ADHD Diagnosis Using Deep Neural Networks 1

Brain MR Image Classification for ADHD Diagnosis Using Deep ...

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1st International Conference on Machine Vision and Image Processing, MVIP 2020

作者： Abdolmaleki, Sahar Abadeh, Mohammad Saniee Tarbiat Modares University Medical Informatics Department Tehran Iran Tarbiat Modares University Faculty of Electrical and Computer Engineering Tehran Iran

ISBN: (数字)9781728168326

ISBN: (纸本)9781728168326

Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent neurodevelopmental disorder in childhood and adolescence. ADHD diagnosis currently includes psychological tests and depends on ratings of behavioral symptoms, which can be unreliable. Thus, an objective diagnostic tool based on non-invasive imaging can improve the understanding and diagnosis of ADHD. The purpose of this study is classifying brain images by using Artificial Intelligence methods such as clinical decision support system for the diagnosis of ADHD. For this purpose and according to a medical imaging classification system, firstly, image pre-processing is done. Then, a deep multi-modal 3D CNN is trained on GM from structural and fALFF from functional MRI using ADHD-200 training dataset. Finally, with the intention of classifying the extracted features, early and late fusion schemes are employed, and the output scores are classified with the SVM, KNN and LDA algorithms. The evaluation of the proposed approach on the ADHD-200 testing dataset revealed that the presence of personal characteristics alone increased the classification accuracy by 3.79%. In addition, using a combination of early, late fusion and personal characteristics together improved the accuracy of the classification by 5.84%. Among the three classifiers LDA showed better results and achieved a classification accuracy of 74.93%. The comparison of results showed that the combination of early and late fusion as well as considering personal characteristics has a significant effect on enhancing classification accuracy. As a result of this, the reliability of this medical decision support system is increased. © 2020 IEEE.

关键词： Magnetic resonance imaging

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LE3D: A Lightweight Ensemble Framework of Data Drift Detectors for Resource-Constrained Devices

arXiv

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arXiv 2022年

作者： Mavromatis, Ioannis Sanchez-Mompo, Adrian Raimondo, Francesco Pope, James Bullo, Marcello Weeks, Ingram Kumar, Vijay Carnelli, Pietro Oikonomou, George Spyridopoulos, Theodoros Khan, Aftab Toshiba Europe Ltd. Bristol United Kingdom Department of Electrical and Electronic Engineering University of Bristol Bristol United Kingdom Department of Engineering Mathematics University of Bristol Bristol United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom

Data integrity becomes paramount as the number of Internet of Things (IoT) sensor deployments increases. Sensor data can be altered by benign causes or malicious actions. Mechanisms that detect drifts and irregularities can prevent disruptions and data bias in the state of an IoT application. This paper presents LE3D, an ensemble framework of data drift estimators capable of detecting abnormal sensor behaviours. Working collaboratively with surrounding IoT devices, the type of drift (natural/abnormal) can also be identified and reported to the end-user. The proposed framework is a lightweight and unsupervised implementation able to run on resource-constrained IoT devices. Our framework is also generalisable, adapting to new sensor streams and environments with minimal online reconfiguration. We compare our method against state-of-the-art ensemble data drift detection frameworks, evaluating both the real-world detection accuracy as well as the resource utilisation of the implementation. Experimenting with real-world data and emulated drifts, we show the effectiveness of our method, which achieves up to 97% of detection accuracy while requiring minimal resources to run. © 2022, CC BY.

关键词： Internet of things

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Reinforcement Learning in Traffic Prediction of Core Optical Networks using Learning Automata

Reinforcement Learning in Traffic Prediction of Core Optical...

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2020 IEEE International Conference on Communications, Computing, Cybersecurity, and informatics, CCCI 2020

作者： Valkanis, Anastasios Beletsioti, Georgia A. Nicopolitidis, Petros Papadimitriou, Georgios Varvarigos, Emmanouel Aristotle University of Thessaloniki Department of Informatics Thessaloniki Greece School of Electrical and Computer Engineering National Technical University of Athens Athens Greece

ISBN: (纸本)9781728120355

The development of technology related to optical core networks provides the appropriate infrastructure for the reliable flow of information, that is constantly increasing with the increase of interconnected users and devices and the evolution of the applications used. Network engineering following traffic prediction is one of the most important factors determining the effectiveness of core networks. Because of its importance, the research community has dealt extensively with traffic forecasting by proposing prediction mechanisms that are mainly based on time series and machine learning models. The objective of traffic forecasting mechanisms is to achieve the maximum possible accuracy while maintaining a low complexity in their operation. In this paper, we propose a novel traffic prediction mechanism that is based on reinforcement learning using Learning Automata. The evaluation of the proposed mechanism is carried out using traffic matrices derived from real measurements in a core network. Our performance simulation results show the effectiveness of the proposed mechanism, which achieves satisfactory forecast accuracy with low operational complexity. © 2020 IEEE.

关键词： Reinforcement learning

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Value-creating Roles Played by the Actors in Open Government Data: A Systematic Literature Review Completed research paper 31

Value-creating Roles Played by the Actors in Open Government...

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31st Australasian Conference on Information Systems, ACIS 2019

作者： Mokobombang, Novy N.R.A. Gutierrez, Jairo Petrova, Krassie School of Engineering Computer & Mathematical Sciences Auckland University of Technology Auckland New Zealand Informatics Department Universitas Hasanuddin Makassar Indonesia

Government-held data are immensely valuable;as well as providing the information required to carry out tasks related to internal governance procedures, additional value may be generated by publishing and making accessible Open Government Data (OGD). The paper aims to identify the underlying factors that influence or are influenced by value creation, from the perspective of the OGD actors engaged in value-creating roles. A systematic literature review was conducted in order to study OGD value creation from the perspective of OGD actors engaged in value-creating roles. The findings of the metasynthesis approach indicate that the value-creating roles of the actors in the OGD ecosystem initiate the value co-creation necessary to turning data value into social and economic benefits for stakeholders, as the outcome of all actors’ efforts and government commitment to OGD initiatives. © 2020 Mokobombang, Gutierrez, Petrova.

关键词： Open Data

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Influence of electric field orientation on the effect of ocular current stimulation using full field electroretinogram

Influence of electric field orientation on the effect of ocu...

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作者： Blum, Maren-Christina Leydolph, Lilly Klee, Sascha Institute of Biomedical Engineering and Informatics Faculty of Computer Sciences and Automation Technische Universität Ilmenau Ilmenau Germany Department of General Health Studies Division Biostatistics and Data Science Karl Landsteiner University of Health Sciences Krems an der Donau Austria

Ocular current stimulation (CS) exhibits potential for the treatment of neurodegenerative ocular diseases. For a full field electroretinogram (ffERG) we found no CS effect on the characteristic waves (a-wave, b'-wave, and b-wave). To investigate whether the orientation of the generated electric field has an influence on the CS effect, this study repeated the previous ffERG study with changing one CS electrode position and compared the results of both studies. In the first study 15 (8 m, 27.5 ± 4.5 years) and in the second study 17 (7 m, 22.0 ± 1.9 years) healthy volunteers were stimulated with an anodal, cathodal, and sham direct CS of 800 μA for 5 min in three sessions (randomized, different days). For both studies, a cut-sized ring rubber electrode was placed around the eye. A square rubber electrode was placed for the first study at the ipsilateral temple and for the second study at the visual cortex. Before (ERG 1) and during (ERG 2) the CS, the ffERG was measured. For both studies, the difference between the ERG 1 and ERG 2 measurement (CS effect) for the three characteristic waves was analyzed and compared between the studies. For statistical analysis, the Mann-Whitney U test with Bonferroni correction was applied (α = 0.05). The comparison of the data distribution showed only slight differences between the studies. The Mann-Whitney U test found no significant difference of the CS effect between the studies for all amplitudes and CS groups (p ≥ 0.0055). In the mean, the latency differences were smaller than the time resolution, therefore no statements for latency effects were possible. It can be concluded that the retinal cells generating the ffERG are not affected by ocular CS in either electrode montages tested. © 2021 by Walter de Gruyter Berlin/Boston.

关键词： Rubber

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Efficient Computing of Disaster-Disjoint Paths: Greedy and Beyond

Efficient Computing of Disaster-Disjoint Paths: Greedy and B...

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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Balázs Vass Erika Bérczi-Kovács Péter Gyimesi János Tapolcai Department of Telecommunication and Media Informatics Faculty of Electrical Engineering and Informatics (VIK) Budapest University of Technology and Economics (BME) and HUN-REN-BME Information Systems Research Group and MTA-BME Future Internet Research Group Faculty of Mathematics and Computer Science Babeş-Bolyai University Cluj Napoca Romania ELTE Eötvös Loránd University Budapest Hungary HUN-REN-ELTE Egerváry Research Group on Combinatorial Optimization

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

In a network topology $G$ , we say a set of st-paths are disaster-disjoint if no disaster strikes more than one path. In this poster, we explore the basic capabilities and limitations of greedy and more advanced algorithms for computing maximal collections of such paths in planar networks. An algorithm is greedy if it generates consecutive paths $P_{1}, P_{2},\ldots$ according to a simple rule. In the simplest setting, the only rule is that $P_{i+1}$ is the closest clockwise disaster-disjoint from $P_{i}$ . We find that the simplest greedy may fail even when 1) $G$ is planar, 2) each disaster region is connected, and 3) each node failure (apart from s and $t$ ) is considered possible. Adding a simple rule explained in [1] yields a correct polynomial-time algorithm for the above problem. Finally, we digest a recent related near-linear runtime algorithm of [2] solving a more general problem and discuss the underlying relations among the foundations of these algorithms.

关键词： Greedy algorithms Visualization Runtime Network topology Disasters Conferences

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Full-field electroretinography examinations of the human eye with the eye diagnostic device PEP-2000-First results

Full-field electroretinography examinations of the human eye...

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作者： Schieding, Nikola Reuter, Thomas Grundmann, Andreas Walther, Sebastian Klee, Sascha ICM-Institut Chemnitzer Maschinen-und Anlagenbau E.V. Otto-Schmerbach-Str. 19 Chemnitz09117 Germany Department of General Health Studies Division Biostatistics and Data Science Karl Landsteiner University of Health Sciences Krems an der Donau Austria Institute of Biomedical Engineering and Informatics Faculty of Computer Sciences and Automation TU Ilmenau Ilmenau Germany

Full-field electroretinography (full-field ERG) forms the diagnostic basis for numerous pathologies of the eye. For this reason, fast and accurate diagnostics in the field of ophthalmology are essential. Two examination techniques, full-field ERG and pupillometry were combined in a diagnostic device developed by ICM e.V. to reduce the examination process for both examiners and patients. In this paper, the device is examined for the quality of the full-field ERG measurements. A feasibility study with 12 healthy subjects (3 f, 9 m, 36.33 ± 11.94 years) was conducted to evaluate the device. The results showed that the peak times for both light-and dark-adapted measurements were within the range of the researched literature values. However, the amplitudes were markedly lower in both measurements compared to the averaged literature values (dark-adapted about 8.5-fold and light-adapted about 5.5-fold) and are clearly outside the range of values researched. The main reason for this is the use of cup electrodes, which were placed on the skin of the lower eyelid. Nevertheless, plausible and comparable analysis values could be obtained with the eye diagnostic device PEP-2000. Further studies with wire electrodes will be performed. © 2022 by Walter de Gruyter Berlin/Boston.

关键词： Ophthalmology

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Audio-Driven Talking Face Video Generation with Dynamic Convolution Kernels

arXiv

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arXiv 2022年

作者： Ye, Zipeng Xia, Mengfei Yi, Ran Zhang, Juyong Lai, Yu-Kun Huang, Xuwei Zhang, Guoxin Liu, Yong-Jin MOE-Key Laboratory of Pervasive Computing The Department of Computer Science and Technology Tsinghua University Beijing China The Department of Computer Science and Engineering Shanghai Jiao Tong University China The School of Mathematical Sciences University of Science and Technology China School of Computer Science and Informatics Cardiff University United Kingdom Kwai Inc.

In this paper, we present a dynamic convolution kernel (DCK) strategy for convolutional neural networks. Using a fully convolutional network with the proposed DCKs, high-quality talking-face video can be generated from multi-modal sources (i.e., unmatched audio and video) in real time, and our trained model is robust to different identities, head postures, and input audios. Our proposed DCKs are specially designed for audio-driven talking face video generation, leading to a simple yet effective end-to-end system. We also provide a theoretical analysis to interpret why DCKs work. Experimental results show that our method can generate high-quality talking-face video with background at 60 fps. Comparison and evaluation between our method and the state-of-the-art methods demonstrate the superiority of our method. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolutional neural networks

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A review of deep learning models to detect malware in Android applications

Cyber Security and Applications

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Cyber Security and Applications 2023年 1卷

作者： Mbunge, Elliot Muchemwa, Benhildah Batani, John Mbuyisa, Nobuhle Department of Computer Science Faculty of Science and Engineering University of Eswatini Private Bag 4 Kwaluseni Swaziland Department of Information Technology Faculty of Accounting and Informatics Durban University of Technology P O Box 1334 Durban 4000 South Africa Faculty of Engineering and Technology Botho University Maseru 100 Lesotho

Android applications are indispensable resources that facilitate communication, health monitoring, planning, data sharing and synchronization, social interaction, business and financial transactions. However, the rapid increase in the smartphone penetration rate has consequently led to an increase in cyberattacks. Smartphone applications use permissions to allow users to utilize different functionalities, making them susceptible to malicious software (malware). Despite the rise in Android applications’ usage and cyberattacks, the use of deep learning (DL) models to detect emerging malware in Android applications is still nascent. Therefore, this review sought to explain DL models that are applied to detect malware in Android applications, explore their performance as well as identify emerging research gaps and present recommendations for future work. This study adopted the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines to guide the review. The study revealed that convolutional neural networks, gated recurrent neural networks, deep neural networks, bidirectional long short-term memory, long short-term memory (LSTM) and cubic-LSTM are the most prominent deep learning-based malicious software detection models in Android applications. The findings show that deep learning models are increasingly becoming an effective technique for malicious software detection in Android applications in real-time. However, monitoring and tracking information flow and malware behavior is a daunting task because of the evolving nature of malware and human behavior. Therefore, training mobile application users and sharing updated malware datasets is paramount in developing detection models. There is also a need to detect malicious software before downloading mobile applications to improve the security of Android smartphones. © 2023

关键词： Android applications Deep learning Detection Malicious software Smartphones

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