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检索条件"任意字段=12th International Symposium on From Data Models and Back, DataMod 2024"

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12th international symposium on from data models and back, datamod 2024

12th International Symposium on From Data Models and Back, D...

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12th international symposium on from data models and back, datamod 2024

ISBN: (纸本)9783031879074

the proceedings contain 9 papers. the special focus in this conference is on from data models and back. the topics include: Modelling COVID-19 with a SIR Variant Using Real-World data: A Case Study in Brazil;verifying Properties of Activities of Daily Living;explaining Pain: On the Impact of Physiological Signals in Pain Prediction;Exploring the Trade-Offs in ECG Biometrics: Impact of Acquisition Time and Temporal Separation;Risk Management and Process Optimization in Industry 4.0: Integrating Sensors with Critical Path and FMEA;Beyond Flattening: Detecting Concurrency Anomalies Using K-NN Graph-Based Modeling in Object-Centric Event Logs;Towards Achieving Energy Efficiency and Service Availability in 6G O-RAN via Formal Verification.

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10th international symposium on from data models and back, datamod 2021, held as a satellite event of the 19th international Conference on Software Engineering and Formal Methods, SEFM 2021

10th International Symposium on From Data Models and Back, D...

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10th international symposium on from data models and back, datamod 2021, held as a satellite event of the 19th international Conference on Software Engineering and Formal Methods, SEFM 2021

ISBN: (纸本)9783031160103

the proceedings contain 12 papers. the special focus in this conference is on from data models and back. the topics include: A Web-Based Tool for Collaborative Modelling and Analysis in Human-Computer Interaction and Cognitive Science;ten Years of datamod: the Synergy of data-Driven and Model-Based Approaches;analysis and Verification of Robustness Properties in Becker-Döring Model;a Secure User-Centred Healthcare System: Design and Verification;model-Based Security Assessment on the Design of a Patient-Centric data Sharing Platform;Towards Model Checking Video Streams Using VoxLogicA on GPUs;privacy Risk and data Utility Assessment on Network data;detecting Anxiety Trends Using Wearable Sensor data in Real-World Situations;combining Quantitative data with Logic-Based Specifications for Parameter Inference;preface.

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Modelling COVID-19 with a SIR Variant Using Real-World data: A Case Study in Brazil 12th

Modelling COVID-19 with a SIR Variant Using Real-World Dat...

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12th international symposium on from data models and back, datamod 2024

作者： Leto, Daniel Wanner, Elizabeth Alamino, Roberto Webber, thais Centro Federal de Educação Tecnológica de Minas Gerais Belo Horizonte Brazil Aston University Birmingham United Kingdom

ISBN: (纸本)9783031879074

this study utilises a SIR model variant to analyse the dynamics of COVID-19 transmission in Brazil. the model incorporates compartments for different population states and optimises infection and loss of immunity rates to achieve a closer fit to real-world data, improving the characterisation of diseases’ behaviours and informing public health strategies. data from the Our World in data portal, including information on vaccines, variants’ dominance, and new developed cases, is used to calibrate the model by minimising the quadratic error between its projections and actual case numbers. Key parameters include infection rates, loss of immunity, vaccination, and the duration of infection and exposure. the study addresses mathematical challenges and discusses non-mathematical variables that influence outcomes. the goal is to explain infection dynamics in Brazil over time by determining infection and loss of immunity rates through simulations, considering various scenarios, including the impact of vaccination. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： COVID-19 Single-objective optimisation SIR Modelling

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Proceedings - 2024 12th international symposium on Computing and Networking, CANDAR 2024

Proceedings - 2024 12th International Symposium on Computing...

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12th international symposium on Computing and Networking, CANDAR 2024

ISBN: (纸本)9798331528362

the proceedings contain 36 papers. the topics discussed include: an analysis of relationship between social bots and echo chambers on X in modern Japan;multithreaded edge-assisted visual SLAM with keyframe backup mechanism;real-time phase retrieval using on-the-fly training of sample-specific surrogate models;FPGA-oriented two-phase bundled-data circular self-timed pipeline;decentralized attribute-based credentials with short attribute proofs from DualRing;CaT: a category-oriented transformer for text and sentiment analysis in Japanese hotel overall ratings prediction;and random number generation based on cryptocurrency prices and linear feedback shift register.

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Assessing the Integrity of Mobile Platform-Based Sensor data Pipelines 12

Assessing the Integrity of Mobile Platform-Based Sensor Data...

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12th international symposium on Digital Forensics and Security, ISDFS 2024

作者： Shropshire, Jordan McDonald, Todd School of Computing University of South Alabama Mobile United States

ISBN: (纸本)9798350330366

this paper describes the development and evaluation of a framework for assessing the integrity of sensor data flows within mobile sensor-reliant systems such as UAVs, robots, and autonomous vehicles. these platforms rely on internal pipelines for collecting, processing, and distributing sensor inputs to their command and control components. If the integrity of this sensor data feed is compromised then the platform could pose a safety risk. Hence, the new framework is designed to be integrated into the platform so it can detect compromised sensor data pipelines. It ingests raw data directly from sensor input groups and predicts the corresponding values which should emerge from the pipeline under normal conditions. It uses multi-output prediction models for prediction. Predicted and actual output value are compared to determine if the data pipeline has been compromised. If properly integrated, this framework could increase cyber-physical system resilience. © 2024 IEEE.

关键词： Machine learning

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SOLVING INVERSE PROBLEMS WIth LATENT DIFFUSION models VIA HARD data CONSISTENCY 12

SOLVING INVERSE PROBLEMS WITH LATENT DIFFUSION MODELS VIA HA...

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12th international Conference on Learning Representations, ICLR 2024

作者： Song, Bowen Kwon, Soo Min Zhang, Zecheng Hu, Xinyu Qu, Qing Shen, Liyue University of Michigan United States Kumo.AI Microsoft United States

Latent diffusion models have been demonstrated to generate high-quality images, while offering efficiency in model training compared to diffusion models operating in the pixel space. However, incorporating latent diffusion models to solve inverse problems remains a challenging problem due to the nonlinearity of the encoder and decoder. To address these issues, we propose ReSample, an algorithm that can solve general inverse problems with pre-trained latent diffusion models. Our algorithm incorporates data consistency by solving an optimization problem during the reverse sampling process, a concept that we term as hard data consistency. Upon solving this optimization problem, we propose a novel resampling scheme to map the measurement-consistent sample back onto the noisy data manifold and theoretically demonstrate its benefits. Lastly, we apply our algorithm to solve a wide range of linear and nonlinear inverse problems in both natural and medical images, demonstrating that our approach outperforms existing state-of-the-art approaches, including those based on pixel-space diffusion models. © 2024 12th international Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Inverse problems

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Investigation of Efficiency and Accuracy of Deep Learning models and Features with Electroencephalogram (EEG) data for Binary Classification 12

Investigation of Efficiency and Accuracy of Deep Learning Mo...

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12th international symposium on Digital Forensics and Security, ISDFS 2024

作者： O'Brien, Kelly Brown, Liam Goncalves, Joaquim Technological University of the Shannon: Midwest Limerick Ireland 2Ai - School of Technology Ipca Barcelos Portugal Lasi - Associate Laboratory of Intelligent Systems Guimarães Portugal

ISBN: (纸本)9798350330366

Electroencephalogram (EEG) data is regularly used with Machine Learning to further develop the Medical Technology field. this research investigates the effectiveness of the popular Deep Learning models with a binary classification problem with the subjects' EEG data. the dataset used for this research comprised of EEG data available online that was recorded from human subjects who were labelled as either alcoholic or control. Each subject was presented visual stimuli and brainwave data was recorded through 64 electrodes located on each subject's scalp. three types of features were included in this study: Raw signal data, Discrete Wavelet Transform (DWT) and Continuous Wavelet Transform (CWT). the study investigated how several Neural Network models performed when trained with the different features. the models were Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU) and Temporal Convolutional Network (TCN). the Convolutional Neural Network performed the best with the highest overall accuracy and the best AUC Score when trained with Raw data and Discrete Wavelet Transform. the Temporal Convolutional Network yielded the best AUC Score when trained with Continuous Wavelet Transform. © 2024 IEEE.

关键词： Electroencephalography

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Innovative Methodologies for Leak Detection Using Hydrophone data and Machine Learning Techniques 12

Innovative Methodologies for Leak Detection Using Hydrophone...

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12th IEEE international symposium on Signal, Image, Video, and Communications, ISIVC 2024

作者： Mohammed, Essouabni Jamal, El Mhamdi Abdelilah, Jilbab Mohammed V University in Rabat Research Laboratory E2SN Ensam Morocco

ISBN: (纸本)9798350385267

the aim of this research is to develop innovative methodologies for leak detection using hydrophone data and machine learning techniques. the database used has been shared publicly and contains several recordings from various sensors, including hydrophones. the data was collected in a laboratory environment simulating real potable water distribution network topologies. the study focused on recordings without water demand, as they reflect the ideal conditions for leak detection. An outlier analysis revealed that their number was significantly higher in pipes without leaks compared to those with leaks. Two models, Random Forest and SVM, were employed to classify the data based on a threshold defined from this analysis. Using the number of outliers as a discriminant feature, thanks to the acoustic pressure measured by hydrophones, allowed for accurate leak detection. the models showed promising performance, with an average accuracy of 0.95 for the Random Forest model and 0.9 for the SVM model. the methodologies presented in this study proved effective for leak detection using hydrophone data. the combination of outlier analysis and machine learning techniques offers considerable potential to improve leak detection in water distribution networks. © 2024 IEEE.

关键词： Leak detection

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Handling Imbalanced data for Detecting Scams in Ethereum Transactions Using Sampling Techniques 12

Handling Imbalanced Data for Detecting Scams in Ethereum Tra...

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12th international symposium on Digital Forensics and Security, ISDFS 2024

作者： Krishnan, Lakshmi P. Vakilinia, Iman Reddivari, Sandeep Ahuja, Sanjay School of Computing University of North Florida JacksonvilleFL United States

ISBN: (纸本)9798350330366

Blockchain technology and cryptocurrencies have captured global attention due to their numerous and versatile features, resulting in several industries and services adopting cryptocurrencies as payment methods. the advantages including user anonymity, open source, and tamper-proof transactions, have contributed to its popularity. However, these advantages have also attracted scammers who exploit the technology's features to engage in fraudulent activities, leading to a growth in crypto-frauds. To prevent and identify these frauds, various detection and prevention methodologies have been proposed, mainly using machine learning algorithms to identify scams as anomalies or outliers. the performance of such models depends on the datasets used and the features engineered. Often, these models face the challenge of having limited amounts of data that are scam-labeled. Under such circumstances, the model performs poorly due to an imbalanced dataset. Similarly, the features engineered and selected to train the model are also very important in detecting scams. With the help of sampling techniques, we propose to create a dataset that is researchready and addresses the data imbalance problem. Additionally, we list the resources that can be used to collect labeled data. Furthermore, we discuss the practical significance of features and various feature engineering strategies in detecting scams from transactions in Ethereum. © 2024 IEEE.

关键词： Blockchain

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PROTEIN DISCOVERY WIth DISCRETE WALK-JUMP SAMPLING 12

PROTEIN DISCOVERY WITH DISCRETE WALK-JUMP SAMPLING

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12th international Conference on Learning Representations, ICLR 2024

作者： Frey, Nathan C. Berenberg, Daniel Zadorozhny, Karina Kleinhenz, Joseph Lafrance-Vanasse, Julien Hötzel, Isidro Wu, Yan Ra, Stephen Bonneau, Richard Cho, Kyunghyun Loukas, Andreas Gligorijević, Vladimir Saremi, Saeed Genentech United States Antibody Engineering Genentech United States Department of Computer Science New York University United States Center for Data Science New York University United States

We resolve difficulties in training and sampling from a discrete generative model by learning a smoothed energy function, sampling from the smoothed data manifold with Langevin Markov chain Monte Carlo (MCMC), and projecting back to the true data manifold with one-step denoising. Our Discrete Walk-Jump Sampling formalism combines the contrastive divergence training of an energy-based model and improved sample quality of a score-based model, while simplifying training and sampling by requiring only a single noise level. We evaluate the robustness of our approach on generative modeling of antibody proteins and introduce the distributional conformity score to benchmark protein generative models. By optimizing and sampling from our models for the proposed distributional conformity score, 97-100% of generated samples are successfully expressed and purified and 70% of functional designs show equal or improved binding affinity compared to known functional antibodies on the first attempt in a single round of laboratory experiments. We also report the first demonstration of long-run fast-mixing MCMC chains where diverse antibody protein classes are visited in a single MCMC chain. © 2024 12th international Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Antibodies

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