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Lightweight Hybrid CNN Model for Face Presentation Attack Detection 2nd

Lightweight Hybrid CNN Model for Face Presentation Attack De...

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2nd International Conference on information Technologies and Their Applications

作者： Turhal, Ugur Yilmaz, Asuman Gunay Nabiyev, Vasif Bayburt Univ Bayburt Turkiye Karadeniz Tech Univ Trabzon Turkiye

ISBN: (纸本)9783031734199;9783031734205

Today, face recognition systems are widely used in many areas that require biometric-based verification, especially because they are contactless and require low user cooperation. Despite their ease of implementation, these systems are vulnerable to attacks. Especially the increasing use of social media, makes it easier to spoof face recognition systems. Therefore, it is very important to develop new methods for Face Presentation Attack (FPA) detection. In this study, FPA detection performances of the lightweight Convolutional Neural Network (CNN) models -MobileNetV3-Small, ShuffleNet, SqueezeNet- and lightweight hybrid CNN models designed with combinations of these networks were investigated. In the experiments, the change in FPA detection performance was examined by giving the cropped face regions and the whole images as input to the networks. Using the whole image instead of the cropped face region significantly improved the FPA detection performance of the CNN models. These results showed that non-facial areas carry important information in FPA detection. Also, the FPA detection performance of the proposed lightweight hybrid CNN models were better than the single use of the networks for both inputs. 0.17% HTER performance was achieved with the hybrid CNN model built from the combination of ShuffleNet + SqueezeNet on the Replay-Attack dataset.

关键词： Face Presentation Attack Detection Face Spoofing Deep Learning Lightweight CNN Model Fake Image Detection

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An Exercise in Uppaal: Modelling the Circadian Clock of a Cyanobacteria 4th

An Exercise in Uppaal: Modelling the Circadian Clock of a Cy...

引用

4th International Conference on Artificial Intelligence Logic and Applications

作者： Iglesias, Antonio Figueiredo, Daniel Madeira, Alexandre Univ Aveiro Res Ctr Math & Applicat CIDMA Aveiro Portugal Assoc Innovat & Biomed Res Light & Image AIBILI Coimbra Portugal

ISBN: (纸本)9789819603534;9789819603541

As in engineering, many phenomena of natural sciences such as biology and chemistry can be modelled using state-transition systems. In this context, the solid tools and theories from computer science represent a valuable resource to analyse, and reason about, such kind of systems. This paper contributes in this direction by using the Uppaal model checker to approach the circadian clock of a cyanobacteria, a challenging case study from biology that was already treated using discrete frameworks.

关键词： Uppaal Circadian rhythm Probabilistic model Checking

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Hybrid AI Techniques for Non-invasive Fault Detection with Experimental Validation 37th

Hybrid AI Techniques for Non-invasive Fault Detection with E...

引用

37th International Conference on computer Applications in Industry and Engineering

作者： Lee, Hoon Cheok, Ka. C. Oakland Univ Elect & Comp Engn Dept Rochester MI 48309 USA

ISBN: (纸本)9783031762727;9783031762734

This paper will provide a non-invasive fault detection solution with Artificial Intelligence (AI) techniques. Non-invasive methods would not require system and machine modification. This solution would use the existing system to collect visual, audio, and vibration data for diagnostics. Because the early signs of fault are numerous, subtle, complex, and difficult to classify and detect, with mathematics and signal processing, the collected diagnostic data will be processed and analyzed for unique patterns and features to be used as input to AI tools. These features will be used to train the AI tools. The subtle characteristics are learned through training;when completed, the trained AI tools can detect them in real-time. Each collected data will be able to detect different faults than others. Some early stages of faults are better detected and diagnosed by visual images than by vibration or audio. Others are better with audio because the cause of the fault is embedded deep inside a machine. For example, an Instrumental Panel (IP) showing engine rpm, and a speedometer complemented by engine sound and wheel vibrations can reveal non-obvious anomalies that might not be shown on the IP. Sound and vibration would be able to provide the early telltale signs of anomalies inside the engine and wheels. These theories are experimented with and validated on an actual vehicle and will show that a non-invasive fault detection solution is a viable solution to early fault detection.

关键词： Non-invasive fault-detection hybrid AI fuzzy inference system

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Smart Tourism Ecosystem: From Connectivity to Value Co-creation

Smart Tourism Ecosystem: From Connectivity to Value Co-creat...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Serra, Pedro Vaz Seabra, Claudia Caldeira, Ana Univ Coimbra Coimbra Portugal CEGOT Geog & Spatial Planning Res Ctr Coimbra Portugal

ISBN: (纸本)9783031834349;9783031834356

Technological changes have always been essential for tourism and the developments that took place in contemporary times, which initiated the concept of smart, are recognised as likely to cause a change in basic assumptions in tourism activity [1, 2]. The concept of a smart tourism ecosystem [3] integrates multiple elements, procedures and activities underlying a location, where technology-based instruments and solutions converge, in a market environment, enhancing differentiating experiences [4]. A conceptual approach is proposed, through a literature review, where the smart tourism ecosystem vision is highlighted, and technological innovation generates and renews the social arrangements that, translated into institutions, are responsible for integrating resources exchanged between different actors. We are facing a virtuous system, where a systematic attitude towards innovation leads to the competitiveness and sustainability of the interested parties, with the unavoidable role of connectivity and the consequent co-creation of value. Relevant implications are envisaged at the levels of management and public policies, considering the obtaining of added value, enhancers of market balances, and the use of technology with a view to long-range outcomes.

关键词： Smart tourism ecosystem Value co-creation Tourism experience Stakeholders Connectivity

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One-Class Convolutional Neural Network for Arabic Mispronunciation Detection 4th

One-Class Convolutional Neural Network for Arabic Mispronunc...

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4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Lounis, Meriem Dendani, Bilal Bahi, Halima Univ Badji Mokhtar Dept Comp Sci Annaba Algeria Univ Badji Mokhtar LISCO Lab Annaba Algeria

ISBN: (纸本)9783031821554;9783031821561

In today's interconnected world, humans live in a closely connected community, where it is essential to acquire proficiency in multiple languages to interact with others effectively. Therefore, research in computer-assisted language learning (CALL) is a dynamic study area, focusing on pronunciation mastery as the most challenging aspect. Pronunciation assessment is a keystone in computer-assisted pronunciation teaching (CAPT) systems. It aims to detect mispronunciations and provide informative feedback to learners. This task has been approached as a classification problem. Herein, the amount of available data is of great importance during the training stage. However, datasets are more likely to be imbalanced. This paper tackles the issue of imbalanced datasets by suggesting a semi-supervised approach using the one-class classification (OCC) method. We trained a convolutional neural network (CNN) to detect mispronounced words, whilst the CNN is exclusively trained based on well-pronounced ones. The experiments were conducted on the freely accessible Arabic speech mispronunciation detection dataset (ASMDD). The obtained results show an accuracy of about 84% on unseen pronunciations.

关键词： computer-assisted pronunciation teaching (CAPT) mispronunciation detection one-class classification (OCC) convolutional neural network (CNN)

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ViTSigat: Early Black Sigatoka Detection in Banana Plants Using Vision Transformer 12th

ViTSigat: Early Black Sigatoka Detection in Banana Plants Us...

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12th Ecuadorian Conference on information and Communication Technologies

作者： Charco, Jorge L. Yanza-Montalvan, Angela Zumba-Gamboa, Johanna Alonso-Anguizaca, Jose Basurto-Cruz, Edgar Univ Guayaquil Delta & Kennedy Ave Guayaquil Ecuador

ISBN: (纸本)9783031754302;9783031754319

This paper presents a model based on Vision Transformer for black Sigatoka detection in early stages, which is called ViTSigat. A comparative of the performance with other proposed model based on Convolutional Neural Network is performed. As first step, a preprocessing is applied to a dataset of 98 videos, which were recorded from a banana plantation using a mobile phone with high resolution camera. The obtained images from each video generated a total of 1500 images, which were normalize and divided in four stages/categories. Different setting parameters were used to train ViTSigat and CNN models. Likewise, both models used the same optimizer, loss function and learning rate. The obtained results were evaluated considering the accuracy, precision, recall and f1-score metrics, including the confusion matrix and ROC curves. A saliency map is used to show the relevant areas where the leaves could be affected due to the infection by black Sigatoka. The experimental results of the proposed models show that the ViTSigat model based on transformer encoder obtains better performance since its attention modules focus on important features of input data instead of take attention to non-useful information.

关键词： Vision Transformer Convolutional Neural Network Black Sigatoka Attention modules

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Deep Reinforcement Learning-Driven Path Optimization in Multi-UAV Edge Computing Systems 4th

Deep Reinforcement Learning-Driven Path Optimization in Mult...

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4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Adhikari, Pronab Kumar Tomar, Abhinav Netaji Subhas Univ Technol Dept Comp Sci & Engn New Delhi India

ISBN: (纸本)9783031837821;9783031837838

Aerial drones, in general known as Unmanned Aerial Vehicles (UAVs), holds a longstanding history of being utilized within mobile networks as network processors;however, a shift has been observed where they are now being utilized as mobile servers within the framework of Mobile Edge Computing (MEC). This evolution is primarily attributed to their inherent flexibility, portability, robust line-of-sight communication capabilities, and cost-effectiveness, which allows for adaptable usage scenarios, thereby contributing to an increase in their utilization within both research and commercial settings. The essential characteristics of aerial drones have made them increasingly popular across a wide spectrum of civilian services, such as transportation, industrial monitoring, agriculture, forest fire management, and wireless services. Within the scope of this project, the focus lies on exploring MEC networks utilizing Unmanned Aerial Vehicles, where these UAVs undertake computational tasks provided by mobile terminal users (TUs). In order to guarantee the Quality-of-Service (QoS) for each TU, the UAV makes real-time modifications to its flight path by taking into consideration the positions of the mobile TUs, with the ultimate goal of improving the overall performance of the network and enhancing the user experience.

关键词： UAV Mobile Edge Computing Quality-of-service

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Design and Deployment of an IoT System for Hydrometeorological Monitoring in an Andean Catchment 4th

Design and Deployment of an IoT System for Hydrometeorologic...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Duque, Felipe Salinas, Edison Torres, Renato Merino, Pabel Samaniego, Natalia Univ Nacl Loja Carrera Ingn Ambiental Ave Pio Jaramillo Alvarado Loja 1101608 Ecuador Univ Nacl Loja Carrera Ingn Elect & Telecomunicac Ave Pio Jaramillo Alvarado Loja 1101608 Ecuador Univ Nacl Loja Ctr Invest Trop Ambiente & Biodivers CITIAB Carrera Ingn Forestal Ave Pio Jaramillo Alvarado Loja 1101608 Ecuador

ISBN: (纸本)9783031840777;9783031840784

The Andean catchments, crucial for their biodiversity and water resource supply in the region, are under increasing pressure due to threats such as deforestation, soil degradation, and climate change. These threats not only alter rainfall and runoff patterns but also compromise the water supply and other vital ecosystem services. Consequently, hydrometeorological monitoring of these areas becomes essential to assess and measure these impacts effectively. This study focuses on the design and implementation of an advanced system for collecting, transmitting, storing, and real-time visualization of hydrometeorological data from an Andean catchment using Internet of Things (IoT) technology. The IoT system was installed in the Los Nogales research catchment (NOREC), located in the city and province of Loja, Ecuador. It is designed to overcome the economic and logistical obstacles that are inherent in traditional methods of environmental data collection and transmission. Both simulation and field tests validated the system's ability to reliably collect, transmit, store, and visualize data. Furthermore, the system was designed to use the well-known licence-free ISM bands, which reduces operating costs. The installed IoT system will facilitate research aimed at better understanding the impacts of threats, such as climate change, to which NOREC and other Andean catchments are exposed. The results of this study not only demonstrate the viability of using IoT technology in hydrometeorological monitoring but also pave the way for its application in other regions with similar characteristics.

关键词： IoT Hydrometeorological Monitoring Andean catchments NOREC

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Comparison of Machine Learning Based Anomaly Detection Methods for ADS-B System 2nd

Comparison of Machine Learning Based Anomaly Detection Metho...

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2nd International Conference on information Technologies and Their Applications

作者： Cevik, Nursah Akleylek, Sedat HAVELSAN Ankara Turkiye Ondokuz Mayis Univ Computat Sci Samsun Turkiye Univ Tartu Inst Comp Sci Tartu Estonia Istinye Univ Dept Comp Engn Istanbul Turkiye

ISBN: (纸本)9783031734199;9783031734205

This paper introduces an anomaly/intrusion detection system utilizing machine learning techniques for detecting attacks in the Automatic Detection System-Broadcast (ADS-B). Real ADS-B messages between Turkiye's coordinates are collected to train and test machine learning models. After data collection and pre-processing steps, the authors generate the attack datasets by using real ADS-B data to simulate two attack scenarios, which are constant velocity increase/decrease and gradually velocity increase or decrease attacks. The efficacy of fivemachine learning algorithms, including decision trees, extra trees, gaussian naive bayes, k-nearest neighbors, and logistic regression, is evaluated across different attack types. This paper demonstrates that tree-based algorithms consistently exhibit superior performance across a spectrum of attack scenarios. Moreover, the research underscores the significance of anomaly or intrusion detection mechanisms for ADS-B systems, highlights the practical viability of employing tree-based algorithms in air traffic management, and suggests avenues for enhancing safety protocols and mitigating potential risks in the airspace domain.

关键词： ADS-B Anomaly Detection System Intrusion Detection System IDS Machine Learning Avionics Security Cyber Security

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Automated Legality Detection on Privacy Policy Based on Deep Learning 4th

Automated Legality Detection on Privacy Policy Based on Deep...

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4th International Conference on Artificial Intelligence Logic and Applications

作者： Li, Juan Zhao, Chenggong Sang, Jinze Gao, Yan Cent South Univ Sch Law Changsha 410083 Peoples R China Cent South Univ Sch Automat Changsha 410083 Peoples R China

ISBN: (纸本)9789819603534;9789819603541

The Personal information Protection Law (PIPL) have strengthened the protection of personal information in China, and regulators have accordingly strengthened their supervision over the collection and use of personal information. Privacy policies play a pivotal role in safeguarding users' privacy rights, promoting self-governance among developers, and rectifying the power imbalance between operators and users. It is essential to evaluate the legality of the privacy policies established by operators. Despite their importance, current privacy policy text is often quite lengthy and obscure, hindering user comprehension and under-mining their efficacy in safeguarding users' informational rights. We construct the CCPP-181 dataset and develop a method for the automatic detection of privacy policy compliance.

关键词： Privacy policy Text classification Deep learning Personal information protection

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