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MIS-AVoiDD: Modality Invariant and Specific Representation for Audio-Visual Deepfake Detection 22

MIS-AVoiDD: Modality Invariant and Specific Representation f...

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22nd IEEE international conference on Machine learning and Applications, ICMLA 2023

作者： Katamneni, Vinaya Sree Rattani, Ajita University of North Texas Dept. of Computer Science and Engineering Denton United States

ISBN: (纸本)9798350345346

Deepfakes are synthetic media generated using deep generative algorithms and have posed a severe societal and political threat. Apart from facial manipulation and synthetic voice, recently, a novel kind of deepfakes has emerged with either audio or visual modalities manipulated. In this regard, a new generation of multimodal audio-visual deepfake detectors is being investigated to collectively focus on audio and visual data for multimodal manipulation detection. Existing multimodal (audio-visual) deepfake detectors are often based on the fusion of the audio and visual streams from the video. Existing studies suggest that these multimodal detectors often obtain equivalent performances with unimodal audio and visual deepfake detectors. We conjecture that the heterogeneous nature of the audio and visual signals creates distributional modality gaps and poses a sig-nificant challenge to effective fusion and efficient performance. In this paper, we tackle the problem at the representation level to aid the fusion of audio and visual streams for multimodal deepfake detection. Specifically, we propose the joint use of modality (audio and visual) invariant and specific representations. This ensures that the common patterns and patterns specific to each modality representing pristine or fake content are preserved and fused for multimodal deepfake manipulation detection. Our experimental results on FakeAVCeleb and KoDF audio-visual deepfake datasets suggest the enhanced accuracy of our proposed method over SOTA unimodal and multimodal audio-visual deepfake detectors by 17.8% and 18.4%, respectively. Thus, obtaining state-of-the-art performance. © 2023 IEEE.

关键词： Audio-visual Deepfake Detection Deepfakes Modality Invariant Modality Specific Features

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Classification of Organic and Recyclable Waste Using a Deep learning Approach 1

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2nd international conference on Advances in data-driven Computing and intelligent Systems, ADCIS 2023

作者： Graceline Jasmine, S. Jagadish, Tarun Shabrez, Md. Febin Daya, J.L. School of Computer Science and Engineering Vellore Institute of Technology—Chennai Campus Chennai India Electric Vehicles Incubation Testing and Research Center Vellore Institute of Technology—Chennai Campus Chennai India

ISBN: (数字)9789819995240

ISBN: (纸本)9789819995233

In this paper, we propose an automated waste classification system that uses the help of deep learning and image processing methods. The main objective of our proposed system is to streamline the classification of waste into organic and recyclable. To achieve this, we used image processing techniques to improve the image quality of recyclable and organic waste from a publicly available dataset and trained our model using convolutional neural networks (CNN). The model achieved a high validation accuracy of 90.65%. We compared this model with four pre-trained models based on their accuracy and loss. Our proposed model for automating waste classification holds the potential to improve waste management practices and can also reduce the amount of waste that ends up in landfills. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolution

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PMSM System Identification by Knowledge-informed Machine learning 22

PMSM System Identification by Knowledge-informed Machine Lea...

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22nd IEEE international conference on Industrial Informatics, IndIN 2024

作者： Yu, Yue Tong, Jiageng Xia, Jinhui Su, Jinya Li, Shihua School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China

ISBN: (纸本)9798331527471

Because of its excellent efficiency, compact dimensions, and accurate control features, Permanent Magnet Synchronous Motor (PMSM) are experiencing widespread applications across various industries. By accurately characterizing the dynamic behavior of PMSM systems through system identification, engineers can ensure that PMSM motors reach their maximum potentials and meet the stringent requirements of modern industrial and technical systems while reducing energy consumption and maintenance costs. The traditional recursive least squares method is sensitive to noises, and unable to accurately identify parameters in complex environments. Pure data-driven models lack interpretability and require complex model architecture and computational costs. To this end, this work draws knowledge-informed neural ordinary differential equations (NODEs) for system identification, which embeds system prior knowledge into the NODEs for more efficient and accurate model learning. Comparative simulations show that this method not only obtains a higher-precision system model, but also significantly reduces the amount of training data and computation costs. © 2024 IEEE.

关键词： Cost reduction

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Big data Between Quality and Security 22nd

Big Data Between Quality and Security

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22nd international conference on Hybrid intelligent Systems, HIS 2022 and the 18th international conference on Information Assurance and Security, IAS 2022

作者： El Balbali, Hiba Abou El Kalam, Anas Talha, Mohamed Cadi Ayyad University National School of Applied Sciences Marrakech Morocco

ISBN: (纸本)9783031274084

data is one of the most precious assets an organization can have;it may have a huge impact on its longterm performance, or even, existence. Hence, data quality and data security remain a real challenge. Ensuring the quality and security of data should never be considered as an expense, but as a wise investment. On the one hand, data should be protected to prevent attacks or violations of their confidentiality, integrity, and availability. On the other hand, it must be of high quality to ensure the efficiency of the decision-making process. Unfortunately, the majority of existing works deal with quality and security separately whereas these two areas are closely related and may be jointly addressed;this can be handled in three ways: quality and security could be mutually blocked, security can be used to improve quality, and inversely, quality can serve to enhance security. In this paper, we present a study on big data quality and security, the conflict between them as well as our proposed approach that uses Artificial Intelligence to enforce quality in the service of security by improving the quality of log data before applying Machine learning algorithms to detect threats. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Big data

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An intelligent Healthcare System for automated Diabetes Diagnosis and Prediction using Machine learning 2

An Intelligent Healthcare System for Automated Diabetes Diag...

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2nd international conference on Machine learning and data engineering, ICMLDE 2023

作者： Nissar, Iqra Mir, Waseem Ahmad Shaikh, Tawseef Ayoub Areen, Tuba Kashif, Mohammad Khiani, Simran Hussain, Asif Jamia Millia Islamia New Delhi110025 India GH Raisoni College of Engineering and Management Pune412203 India National Institute of Technology Srinagar190006 India Maulana Mukhtar Ahmad Nadvi Technical Campus Malegoan423203 India Sri Sai Group of Institutions Punjab 145001 India

The progression of biotechnological and health science fields has instigated a substantial proliferation of data, encompassing high-throughput genetic information and extensive clinical data derived from expansive electronic health record repositories. In the domain of biosciences, the indispensability of machine learning and data mining methodologies has surged, attaining heightened significance for the purpose of converting extant information into usable knowledge. Diabetes mellitus is a metabolic condition that affects human health. Extensive research on diabetes (diagnosis, therapy, etc.) has generated vast volumes of data. Manual diagnosis of diabetes has always been a time-consuming task. Therefore, automatic detection and diagnosis of diabetes using artificial intelligence and machine learning is gaining prominence. In our work, we have devised a novel architecture using machine learning for the automatic diagnosis of diabetes. We implemented our model using many algorithms and found that random forest is the most optimized and accurate one for classification purposes. It produced the highest accuracy of 98.07%, precision, recall, F1-score of 98%, and logarithmic loss of 0.03 using the mRMR feature selection method and 0.2 test split. It produced a recall of 97%, the precision of 97%, an F1-score of 97%, an accuracy of 96.79%, and a log loss of 0.11 on 0.3 test split with mRMR feature selection. Also, the proposed model has performed better than most state-of-the-art models. © 2024 Elsevier B.V.. All rights reserved.

关键词： data mining

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Hierarchical learning of Generative Automaton Models from Sequential data 22nd

Hierarchical Learning of Generative Automaton Models from ...

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22nd international conference on Software engineering and Formal Methods, SEFM 2024

作者： von Berg, Benjamin Aichernig, Bernhard K. Rindler, Maximilian Štern, Darko Tappler, Martin Institute of Software Technology Graz University of Technology Graz Austria TU Wien Vienna Austria AVL List GmbH Graz Austria

ISBN: (纸本)9783031773815

Passive automata learning is a method for inferring automaton models from a given collection of observations of system behavior (traces). It has been applied to reactive systems with probabilistic behavior. In particular, IOAlergia is a well known algorithm for inferring models in the form of deterministically labeled Markov decision processes from system traces. The quality of the resulting model depends heavily on the provided data set and suffers if data is scarce. However, in many cases additional knowledge about the system is available. This work aims to incorporate knowledge about system modes into the learning process in order to improve model quality for low-data scenarios. This is done by splitting the traces according to system modes, learning individual models for each mode and combining those models into one model with sub-regions corresponding to individual system modes. In our evaluation on artificial models, our method outperforms the baseline in at least 90 % of cases for all considered metrics. This method was developed to learn generative models of human driving behavior. data from recorded test drives on highways was used to learn a hierarchical stochastic model of typical acceleration behavior of human drivers. In the automotive industry, such models make the simulations of driving emissions more realistic. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Markov processes

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MBSE Net: Addressing data Scarcity and Model Interpretability in Predicting UHPC Performance for Bridge Infrastructure 22

MBSE Net: Addressing Data Scarcity and Model Interpretabilit...

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22nd IEEE international conference on Smart City, SmartCity 2024

作者： Zou, Zefeng Jiang, Nan Yu, Zesheng Li, Ziyi Zhang, Jing Wang, Yufei School of Information and Software Engineering East China Jiaotong University Nanchang China

ISBN: (纸本)9798331540487

Bridges serve as critical infrastructure, enabling the movement of people and goods across regions, thus underscoring the importance of their durability and reliability. Different key components of bridges demand varied material performance characteristics. Traditional materials like standard concrete often exhibit deficiencies in strength, crack resistance, and durability, leading to cracks and damage in essential bridge parts. Ultra-High Performance Concrete (UHPC) addresses these shortcomings, but its complex component interactions pose new challenges for predicting mechanical properties. Machine learning models designed for this purpose often lack generalization capability, while deep learning models face issues related to data scarcity and model interpretability. To tackle the challenge of data scarcity, we introduced a self-supervised pre-training approach that divides the prediction task into two stages: feature learning followed by prediction. Designing an encoder capable of extracting multi-scale effective feature representations from limited data is particularly challenging. After extensive tuning, we developed the Multi-Branch Self-Encoding Network (MBSE Net). This network achieved R2 values exceeding 0.99 in predicting both compressive strength (CS) and flexural strength (FS), significantly outperforming other methods. To enhance model interpretability, we implemented two visualization techniques to improve transparency and credibility from different perspectives. These techniques elucidate the key factors influencing UHPC performance and provide insights into the optimal material proportion design to meet the performance requirements of critical bridge components. © 2024 IEEE.

关键词： Self-supervised learning

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Impact of Stress on Sleep Levels: A Comparative Machine learning Study Based on Wearable data 22

Impact of Stress on Sleep Levels: A Comparative Machine Lear...

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22nd IEEE international conference on Machine learning and Applications, ICMLA 2023

作者： Barati, Ronak Duffett-Leger, Linda Moshirpour, Mahtab Moshirpour, Mohammad University of Calgary Department of Electrical and Software Engineering CalgaryAB Canada University of Calgary Department of Nursing CalgaryAB Canada University of Calgary Department of Psychology CalgaryAB Canada University of California Irvine Department of Informatics IrvineCA United States

ISBN: (纸本)9798350345346

Inadequate sleep is a significant factor in chronic conditions such as cardiovascular disease, diabetes, and mental health disorders, and includes not just sleep duration but also the quality of sleep. Although addressing sleep disorders is complex, mitigating their physical and psychological consequences through effective monitoring and control measures is achievable using wearable devices. While there have been a variety of successful works using wearable devices to classify sleep levels, this study investigates the feasibility of machine learning (ML) approaches based on wearable data to predict different levels of sleep by considering physiological parameters including stress. Stress has long been established as one of the most crucial physiological parameters influencing sleep levels. In this paper, we leveraged data collected from wrist-worn devices to classify the levels of sleep using two different datasets: one considering the level of stress and the other excluding it. The objective is to achieve optimal classification performance by employing a diverse set of seven ML-based classifiers, including Support Vector Machines (SVM), Logistic Regression, K-Nearest Neighbors (KNN), Decision Trees, Random Forests, Naive Bayes, and XGBoost. By comparing the performances of the models on these two datasets, we observed that the model incorporating stress inputs exhibited superior accuracy in predicting sleep, highlighting the significant role of stress in sleep levels. Moreover, the findings from the models and their comparison indicate that the Random Forest and XGBoost algorithms outperform other ML methods, exhibiting an impressive accuracy of 95 % in accurately predicting different sleep levels. © 2023 IEEE.

关键词： De-cision Trees K-Nearest Neighbors (KNN) Logistic Regression Naive Bayes Random Forests Sleep levels measurement Smartwatches Stress Support Vector Machines (SVM) Wrist-worn wearable devices XGBoost

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Deep Recurrent Q Networks for Urban Traffic Signal Control 22

Deep Recurrent Q Networks for Urban Traffic Signal Control

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22nd COTA international conference of Transportation Professionals (CICTP) - Intelligence, Connectivity, and Mobility

作者： Zhang, Xiongfei Mo, Huijuan Ma, Hongzhuang Luo, Qin Shenzhen Technol Univ Guangdong Rail Transit Intelligent Operat & Maint Shenzhen Peoples R China Shenzhen Univ Mechatron & Control Engn Coll Shenzhen Peoples R China

ISBN: (数字)9780784484265

ISBN: (纸本)9780784484265

Traditional traffic signal control methods have not played an effective role in alleviating traffic congestion. In order to make better signal control decisions, this paper combines deep recurrent Q network (DRQN) to carry out in-depth research on the optimization of signal controls. The core idea of this approach is to set up a deep neural network to learn the Q-function of reinforcement learning from state inputs and performance output. Specific state, action and reward functions were defined in reinforcement learning, a long short-term memory (LSTM) network was used to fit the state, then the appropriate signal control strategy was proposed. SUMO software was used for simulation experiments to create a single intersection traffic signal control.

关键词： Reinforcement learning

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Improving Information Freshness in Edge-Assisted Smart Grids: An AoI-Aware Routing Strategy for Neighborhood Area Networks 22

Improving Information Freshness in Edge-Assisted Smart Grids...

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22nd IEEE international conference on Industrial Informatics, IndIN 2024

作者： Farag, Hossam Kotb, Mostafa Stefanović, Čedomir Gidlund, Mikael Department of Electronic Systems Aalborg University Denmark Department of Informatics Universität Hamburg Germany Department of Electrical Engineering Aswan University Egypt Mathematics Department Aswan University Egypt Department of Computer and Electrical Engineering Mid Sweden University Sweden

ISBN: (纸本)9798331527471

The integration of edge controllers into smart grid infrastructures facilitates advanced functionalities and high responsiveness, thereby bolstering the overall efficiency of the energy grid. The freshness of the sensing information received at the edge controller, captured by the Age of Information (AoI) metric, is vital to maintain system stability, where outdated information may lead to incorrect responses to grid conditions, potentially causing inefficiencies or system disruptions. However, the timeliness of the transmitted updates is mainly compromised by the delay and congestion within the routing links in the Neighborhood Area Network (NAN). In this work, we develop an intelligent routing strategy to improve the AoI of the Routing Protocol for Low-power and lossy networks (RPL), which is the common routing protocol for smart grids. Our proposed method is based on an AoI-aware parent selection mechanism, by which a node becomes attached to the parent with the highest probability of delivering a packet within a predefined AoI threshold. The prediction is made based on a supervised machine learning model, trained using the collection of heterogeneous routing metrics. The performance of the proposed method is evaluated via extensive discrete-event simulations and the results show its potency to improve the peak AoI and the AoI violation probability compared to the standard RPL. © 2024 IEEE.

关键词： Edge computing

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