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arXiv 2021年

作者： Ranno, Nathan Si, Dong Division of Computing and Software Systems University of Washington Bothell BothellWA98011 United States

Advances in imagery at atomic and near-atomic resolution, such as cryogenic electron microscopy (cryo-EM), have led to an influx of high resolution images of proteins and other macromolecular structures to data banks worldwide. Producing a protein structure from the discrete voxel grid data of cryo-EM maps involves interpolation into the continuous spatial domain. We present a novel data format called the neural cryo-EM map, which is formed from a set of neural networks that accurately parameterize cryo-EM maps and provide native, spatially continuous data for density and gradient. As a case study of this data format, we create graph-based interpretations of high resolution experimental cryo-EM maps. Normalized cryo-EM map values interpolated using the non-linear neural cryo-EM format are more accurate, consistently scoring less than 0.01 mean absolute error, than a conventional tri-linear interpolation, which scores up to 0.12 mean absolute error. Our graph-based interpretations of 115 experimental cryo-EM maps from 1.15 to 4.0 Å resolution provide high coverage of the underlying amino acid residue locations, while accuracy of nodes is correlated with resolution. The nodes of graphs created from atomic resolution maps (higher than 1.6 Å) provide greater than 99% residue coverage as well as 85% full atomic coverage with a mean of than 0.19 Å root mean squared deviation (RMSD). Other graphs have a mean 84% residue coverage with less specificity of the nodes due to experimental noise and differences of density context at lower resolutions. The fully continuous and differentiable nature of the neural cryo-EM map enables the adaptation of the voxel data to alternative data formats, such as a graph that characterizes the atomic locations of the underlying protein or macromolecular structure. Graphs created from atomic resolution maps are superior in finding atom locations and may serve as input to predictive residue classification and structure segmentation methods. This

关键词： Graphic methods

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Using convolutional neural networks to detect learner's personality based on the Five Factor Model

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Computers and Education: Artificial Intelligence 2023年 5卷

作者： El Bahri, N. Itahriouan, Z. Abtoy, A. Brahim Belhaouari, S. Information System and Software Engineering Laboratory Abdelmalek Essadi University Tetouan Morocco Systems and Sustainable Environment Laboratory Private University of Fez Fez Morocco Division of Information and Computing Technology Hamad Bin Khalifa University Doha Qatar

Aiming at the detection of learners' personalities which can help us to enhance the educational learning process, we trained three Convolutional Neural Networks (CNNs) architectures (ResNet50, VGG16, AlexNet) with different datasets for predicting the Five Factor Model (FFM) of personality (Neuroticism, Openness to experience, Extraversion, Conscientiousness and Agreeableness) from the analysis of facial features using Facial Action Coding System (FACS). As well, we compared the three CNNs model results by using multiple evaluation metrics: accuracy, loss, confusion matrix and Reciever Operator Charactetristic- Area under the ROC Curve (ROC-AUC), to decide the most useful model for our use case. Our proposed methodology is based on three steps: The base step where the face’ characteristics are detected and cropped from each video frame using a facial landmark detection algorithm. The second step aims to detect in real time face Action Units (AUs) traits which figured in each frame and compute the highest AU probability appeared on frames sets. The third step is used to decide based on detected AUs combinations personalities according to FFM by using a pre-trained decision tree algorithm. Detected personalities are designed to be stored in a database as a dataset to be exploited and studied in multiple contexts particularly in the analysis of student personality in learning platforms. © 2023 The Authors

关键词： CNNs FACS FFM Learner's personality MBTI

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Psychosis iREACH: Reach for Psychosis Treatment using Artificial Intelligence

Psychosis iREACH: Reach for Psychosis Treatment using Artifi...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jonathan Lee Sarah Kopelovich Sunny Chieh Cheng Dong Si Computing & Software Systems Division University of Washington - Bothell Bothell WA Department of Psychiatry and Behavioral Sciences University of Washington - Seattle Seattle WA School of Nursing & Healthcare Leadership University of Washington - Tacoma Tacoma WA

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Psychosis iREACH aims to optimize the delivery of evidence-based cognitive behavioral therapy to family caregivers who have a loved one with psychosis. It is an accessible digital platform that can utilize the user’s intent and entities to determine the appropriate response. The platform is implemented based on an artificial intelligence and natural language understanding (NLU) framework, RASA. We developed the web application of the platform, and the chatbot has been integrated into the platform to collect data and evaluate the performance. The results showed that the NLU model’s accuracy, precision, recall, and F1- score of the intent prediction are 88.31%, 89.80%, 88.22%, and 88.65% respectively. The link to the website is https://***/.

关键词： Training Privacy Costs Databases Pipelines Buildings Predictive models

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Identification of Essential Genes with NemoProfile and Various Machine Learning Models 16th

Identification of Essential Genes with NemoProfile and Vario...

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16th International Symposium on Bioinformatics Research and Applications, ISBRA 2020

作者： Wang, Yangxiao Kim, Wooyoung Division of Computing and Software Systems School of Science Technology Engineering and Mathematics University of Washington Bothell 18115 Campus Way NE BothellWA98011-8246 United States

ISBN: (纸本)9783030578206

Genes are sequences of nucleotide in DNA that encode proteins. Essential genes are a type of genes that are critical and indispensable for an organism’s survival. Many network-based algorithms have been developed to identify essential genes. We introduce a novel approach to predict essential genes that are based on network motif profiles (NemoProfile) and various machine learning models. Experimental results show that NemoProfile is an effective data feature generated from biological networks, and balanced data is a critical factor to improve the overall performance. © 2020, Springer Nature Switzerland AG.

关键词： Genes

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Beyond current boundaries: Integrating deep learning and AlphaFold for enhanced protein structure prediction from low-resolution cryo-EM maps

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Computational biology and chemistry 2025年 119卷 108494页

作者： Xin Ma Dong Si Division of Computing and Software Systems University of Washington Bothell 18115 Campus Way NE Bothell 98011 WA USA. Electronic address: chloexma@uw.edu. Division of Computing and Software Systems University of Washington Bothell 18115 Campus Way NE Bothell 98011 WA USA. Electronic address: dongsi@uw.edu.

Constructing atomic models from cryo-electron microscopy (cryo-EM) maps is a crucial yet intricate task in structural biology. While advancements in deep learning, such as convolutional neural networks (CNNs) and graph neural networks (GNNs), have spurred the development of sophisticated map-to-model tools like DeepTracer and ModelAngelo, their efficacy notably diminishes with low-resolution maps beyond 4 Å. To address this critical gap, this study introduces DeepTracer-LowResEnhance, an innovative computational framework that uniquely integrates structural predictions from AlphaFold with a deep-learning-based map refinement strategy specifically tailored to enhance low-resolution maps. Unlike existing techniques, our approach leverages the strengths of AlphaFold's sequence-based predictions combined with advanced neural network-driven refinement processes to significantly improve map interpretability and modeling accuracy. DeepTracer-LowResEnhance demonstrates substantial and consistent improvements on an extensive dataset comprising 37 diverse protein cryo-EM maps, covering resolutions from 2.5 to 8.4 Å and including 22 challenging cases below 4 Å resolution. DeepTracer-LowResEnhance achieves an average TM-score improvement of 3.53x compared to baseline DeepTracer predictions. Notably, our enhanced methodology showed performance gains across 95.5% of the tested low-resolution datasets. A comparative analysis alongside traditional sharpening methods such as Phenix's auto-sharpening illustrates DeepTracer-LowResEnhance's superior capability in rendering more detailed and precise atomic models, thereby pushing the boundaries of current computational structural biology methodologies.

关键词： AlphaFold Computational structural biology Cryo-EM Deep learning

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NemoLib: Network Motif Libraries for Network Motif Detection and Analysis 16th

NemoLib: Network Motif Libraries for Network Motif Detection...

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16th International Symposium on Bioinformatics Research and Applications, ISBRA 2020

作者： Kim, Wooyoung Brader, Zachary Arthur Division of Computing and Software Systems School of Science Technology Engineering and Mathematics University of Washington Bothell 18115 Campus Way NE BothellWA98011-8246 United States

ISBN: (纸本)9783030578206

Network motifs are frequent and unique subgraph patterns located inside networks, and have been applied to solve various biological problems. Due to the high computational costs of performing network motif analysis, various tools have been created to make the process more efficient. However, existing tools lack extensible functionality and provide limited output formats. This restricts the ability to use network motif analysis for extensive and exhaustive experiments in real problems. We provide NemoLib (Network Motif Libraries) as a general purpose tool for detection and analysis of network motifs. It is an easily adoptable and highly accessible tool with a focus on efficiency and extensibility. © 2020, Springer Nature Switzerland AG.

关键词： Libraries

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Chinese Landmark Recognition

Chinese Landmark Recognition

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International Conference on computing, Networking and Communications (ICNC)

作者： Hao Wu Min Chen Division of Computing and Software Systems School of STEM University of Washington Bothell

ISBN: (数字)9781728149059

ISBN: (纸本)9781728149066

Landmark recognition is a useful yet challenging task due to the lack of large annotated landmark datasets. Previous related work often neglects non-English speaking areas, such as Chinese landmarks. Tests using commercial landmark recognition APIs also indicate the incompleteness of such landmark data. Our project releases Chinese landmark and attraction dataset for recognition of human-made and natural landmarks in China. The dataset contains 42,548 images depicting 987 unique landmarks. Moreover, we build a landmark recognition model based on Convolution Neural Network and transfer learning techniques. Lastly, an iOS application is built based on our landmark recognition model. It allows user to do landmark prediction on images taken by camera and images from device gallery.

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Deep Vision in Analysis and Recognition of Radar Data: Achievements, Advancements and Challenges

arXiv

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arXiv 2023年

作者： Liu, Qi Yang, Zhiyun Ji, Ru Zhang, Yonghong Bilal, Muhammad Liu, Xiaodong Vimal, S. Xu, Xiaolong The Wuxi University 333 Xishan Avenue Wuxi214105 China The School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China The Division of Computer and Electronic Systems Engineering Hankuk University of Foreign Studies Korea Republic of The School of Computing Edinburgh Napier University EdinburghEH10 5DT United Kingdom The Department of Artificial Intelligence & DS Ramco Institute of Technology Tamilnadu India

Radars are widely used to obtain echo information for effective prediction, such as precipitation nowcasting. In this paper, recent relevant scientific investigation and practical efforts using Deep Learning (DL) models for weather radar data analysis and pattern recognition have been reviewed;particularly, in the fields of beam blockage correction, radar echo extrapolation, and precipitation nowcast. Compared to traditional approaches, present DL methods depict better performance and convenience but suffer from stability and generalization. In addition to recent achievements, the latest advancements and existing challenges are also presented and discussed in this paper, trying to lead to reasonable potentials and trends in this highly-concerned *** Codes I.2, I.4, I.5 © 2023, CC BY-NC-SA.

关键词： Extrapolation

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A Survey on Explainable Artificial Intelligence for Cybersecurity

arXiv

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arXiv 2023年

作者： Rjoub, Gaith Bentahar, Jamal Wahab, Omar Abdel Mizouni, Rabeb Song, Alyssa Cohen, Robin Otrok, Hadi Mourad, Azzam King Hussein School of Computing Sciences Princess Sumaya University for Technology Jordan Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Computer and Software Engineering Polytechnique Montréal Montréal Canada Electrical Engineering and Computer Science Department Khalifa University Abu Dhabi United Arab Emirates Department of EECS Khalifa University Abu Dhabi United Arab Emirates David R. Cheriton School of Computer Science University of Waterloo Canada Cyber Security Systems and Applied AI Research Center Dept. of CSM Lebanese American University Lebanon Division of Science New York University Abu Dhabi United Arab Emirates

The "black-box" nature of artificial intelligence (AI) models has been the source of many concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a rapidly growing research field that aims to create machine learning models that can provide clear and interpretable explanations for their decisions and actions. In the field of cybersecurity, XAI has the potential to revolutionize the way we approach network and system security by enabling us to better understand the behavior of cyber threats and to design more effective defenses. In this survey, we review the state of the art in XAI for cybersecurity and explore the various approaches that have been proposed to address this important problem. The review follows a systematic classification of cybersecurity threats and issues in networks and digital systems. We discuss the challenges and limitations of current XAI methods in the context of cybersecurity and outline promising directions for future research. © 2023, CC BY.

关键词： Cybersecurity

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ViDupe-Duplicate Video Detection as a Service in Cloud

ViDupe-Duplicate Video Detection as a Service in Cloud

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IFIP/IEEE International Symposium on Integrated Network Management

作者： Gousiya Farheen Shaik Min Chen Division of Computing and Software Systems School of STEM University of Washington Bothell Bothell USA

ISBN: (纸本)9781728106182;9783903176157

Detecting near-duplicate videos in the cloud is computationally intensive due to sheer number of videos and their large file sizes. To address this challenge, we propose to improve algorithm efficiency by enhancing and revising existing metadata and content comparison methods, and to enable public access by developing a web service. As a proof of concept, a duplicate video detection system called ViDupe is developed that can quickly scans user's Google drive for duplicates.

关键词： Google Streaming media Fingerprint recognition Cloud computing Feature extraction Discrete cosine transforms Conferences

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