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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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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 multi-stage review framework for AI-driven predictive maintenance and fault diagnosis in photovoltaic systems

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Applied Energy 2025年 393卷

作者： Hamza, Ali Ali, Zunaib Dudley, Sandra Saleem, Komal Uneeb, Muhammad Christofides, Nicholas London United Kingdom London United Kingdom Islamabad Pakistan Department of Electrical Engineering Computer Engineering and Informatics Frederick University Nicosia Cyprus

The photovoltaic (PV) sector encounters challenges such as high initial costs, reliance on weather, susceptibility to faults, irregularities in the grid, and degradation of components. Predictive maintenance (PdM) aims to proactively identify issues, thereby enhancing reliability and efficiency but may lack specific fault details without additional diagnostic efforts. This research presents an advanced PdM and fault diagnosis framework that integrates fault pattern analysis, severity assessments, and critical fault predictions. It aims to improve the functionality of PV systems, minimize downtime, and enhance reliability by identifying and analyzing specific fault patterns. Consequently, our article provides a critical review of current Artificial Intelligence (AI) methodologies for PdM and fault diagnosis in PV systems. Moreover, this study highlights the significance of data standardization and offers recommendations on how PdM, when combined with fault diagnosis, can utilize various data sources to anticipate faults in advance, assess their severity, and optimize system performance and maintenance activities. To the best of the authors’ knowledge, no such review study exists. © 2025

关键词： Predictive maintenance

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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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Mask Wearing Classification using CNN 7

Mask Wearing Classification using CNN

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7th International Conference on Advanced informatics: Concepts, Theory and Applications, ICAICTA 2020

作者： Pramita, Maharani Devira Kurniawan, Budi Utama, Nugraha Priya School of Electrical and Informatics Engineering Bandung Institute of Technology Bandung Indonesia Prosa.ai Computer Vision Department Bandung Indonesia

ISBN: (纸本)9781728180380

Indonesian official team for handling COVID-19 stated that from total 358,659 tested people, 42,762 of them are confirmed to be positive COVID-19, approximately 11.7% from total tested people and continues to increase every day. Therefore, wearing a mask becomes a new habit to adapt new normal life, in order to prevent COVID-19 virus spread in Indonesia. It is important for everyone to wear a mask especially in public places with high intense of human interaction. Public area managers need to monitor and ensure everyone is wearing mask properly. To do a better public places monitoring, this work proposes a deep-learning based image classification model to detect whether a person is wearing a mask or not wearing a mask in real-time condition. Proposed architecture is using Convolutional Neural Network (CNN) to learn from annotated private dataset to match the Indonesian characteristics. Result gives 97% accuracy for classifying face. © 2020 IEEE.

关键词： Convolutional neural networks

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Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments 6

Deep Reinforcement Learning-based Obstacle Avoidance for Rob...

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6th IEEE International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2024

作者： Li, Keqin Chen, Jiajing Yu, Dezhi Dajun, Tao Qiu, Xinyu Lian, Jieting Ji, Ryan Zhang, Shengyuan Wan, Zhenyu Sun, Baiwei Hong, Bo Ni, Fanghao Han, Jiaqi Ama University Department of Computer Science Quezon Philippines Courant Institute New York University 251 Mercer St New YorkNY10012 United States University of California Berkeley University Avenue and Oxford St School of Information BerkeleyCA94720 United States Carnegie Mellon University Master of Science in Mechanical Engineering School of Engineering 5000 Forbes Avenue PittsburghPA United States Northeastern University College of Engineering Seattle United States School of Professional Studies New York University Master of Science in Database Technology 50 West 4th Street New YorkNY United States Cornell University College of Engineering 616 Thurston Ave IthacaNY14853 United States Cornell University Master of Professional Science in Applied Statistics-Data Science College of Computing and Information Science 616 Thurston Ave IthacaNY United States Independent Researcher Beijing China University of California Irvine Master of Computer Science Donald Bren School of Information and Computer Sciences 510 Aldrich Hall IrvineCA United States Northern Arizona University School of Informatics Computing and Cyber Systems FlagstaffAZ United States Shanghai Institute of Technology Shanghai China

ISBN: (纸本)9798350389418

This paper proposes a deep reinforcement learning based on the warehouse environment, the mobile robot obstacle avoidance Algorithm. Firstly, the value function network is improved based on the pedestrian interaction. We can learn to obtain the relative importance of the current state and the historical trajectory state as well as the joint impact on the robot's obstacle avoidance strategy. Secondly, the reward function of reinforcement learning is designed based on the spatial behaviour of pedestrians, and the robot is punished for the state where the angle changes too much, so as to achieve the requirement of comfortable obstacle avoidance. Finally, the feasibility and effectiveness of the deep reinforcement learning-based mobile robot obstacle avoidance algorithm in the warehouse environment in the complex environment of the warehouse are verified through simulation experiments. © 2024 IEEE.

关键词： Deep reinforcement learning

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