Reviewerly/exHarmony huggingface dataset:
Futures

A future is a thread (or process) that promises to return a value in the future once the associated behaviour has completed. It is thus a future value. It provides a very simple way of firing off behaviour that will either be time consuming to execute or which may be delayed due to expensive operations such as Input/Output and which could slow down the execution of other elements of a program. This chapter discusses futures in Python.
Data Classes

Python 3.7 introduced a special type of class called a data class. Data classes can be used to represent data-oriented concepts. That is, concepts that represent data but tend not to have much related functionality. Such a class might contain several properties but other than member functions for equality or string conversions they do not contain any behaviour. They are often used in larger applications as they are a useful way to group associated attributes together using a named concept and as such have far more semantic meaning than say a simple dictionary. This chapter presents such data classes.
Python Metaclasses and Meta Programming

This chapter looks at metaprogramming and metaclasses in Python. Metaprogramming relates to the idea that a Python program can generate or modify code dynamically at runtime. This is a very powerful (although potentially dangerous) feature in Python which many statically compiled languages such as C++ do not provide. This chapter introduces metaprogramming before discussing three ways in which metaprogramming can be achieved in Python; using decorators, metaclasses and dynamic code execution.
Structural Pattern Matching

Structural pattern matching was introduced into Python in version 3.10. Although the basic form of structural pattern matching looks very much like pattern matching in other languages, there are some specific features that relate to data classes. In this chapter we will look at how data classes can be used with structural pattern matching.
Events in Tkinter User Interfaces

This chapter introduces event handling within a GUI application and Tkinter’s event handling mechanism in particular.
Python Type Hints

Python is a dynamically typed language right—well yes it is however there is a feature known as Type Hints that allows typing information to be provided when functions and methods are defined. These Type Hints are extremely useful and can help a developer understand what types are expected by a function or a method and indeed what types are likely to be returned.
Pandas and Data Analytics

The COVID-19 pandemic has been the defining feature of the first years of the 2020s, and the related data has been the focus of much research. Many organisations, including Google and the UK Government, have made this data publicly available, so that it can be analysed by academics, students and other interested parties. Such analysis can be achieved using a variety of tools from MATLAB, through R to Python and its data analytics stack that includes the Pandas data analytics library and Matplotlib graphing library. In this chapter we will look at two data sets that can be obtained free from public sources and how the Python Pandas library can be used to perform some simple data analytics.
Introduction to Games Programming

Games programming is performed by developers/coders who implement the logic that drives a game. This chapter introduces the reader to the world of games development.
Introduction to Computer Graphics

Computer Graphics are everywhere; they are on your TV, in cinema adverts, the core of many films, on your tablet or mobile phone and certainly on your PC or Mac as well as on the dashboard of your car, on your smart watch and in children's electronic toys.
On Ultra-Sharp Queueing Bounds

Martingale-based techniques render sharp bounds in several queueing scenarios, but mainly in heavy-traffic and subject to the degree of burstiness. We present a related technique to render ultra-sharp bounds across all utilization levels and for various degrees of burstiness.
ViterbiNet Transformed

&lt;p&gt;The article focuses on the problem of detecting transmitted data over an additive Gaussian noise channel. We propose an integration of various deep-learning architectures with the classical Viterbi algorithm for channel estimation and equalization, to improve the estimation performance.&lt;br&gt; The goal is to design communication schemes that are oblivious to the channel coefficients.&lt;/p&gt; &lt;p&gt;The study utilizes various deep-learning architectures to address the challenges of signal detection and data reconstruction over AWGN channels in communication systems, by learning the log-likelihood ratio (LLR) /priors of the channel as a function of the channel memory. We draw inspiration from the ViterbiNetmodel-based architecture and explore the implementation of novel deep neural network (DNN) architectures for channel estimation. These architectures include Convolutional Neural Networks (CNN), Transformer Encoder, Residual Convolutional Networks as well as SIONNA (by Nvidia) architecture.&lt;/p&gt; &lt;p&gt;The performance of these architectures was evaluated by bit-error-rate (BER) across different channel conditions by signal-noise-ratio (SNR). The results show an improvement of up to 1dB in signal-to-noise ratio performance in the moderate and high signal-to-noise ratio regions compared to previously proposed learning-based solutions. The experiments demonstrate the potential of DNN model-based architectures for symbol detection and channel equalization in communication systems.&lt;/p&gt; &lt;p&gt;Our work details the methodology used in the experiments, including the details of the DNN architecture implementation, data-set generation, channel model, training process, evaluation metrics, and semi-supervised online training method. The experiments demonstrate the performance of the proposed architectures and compare them to the classical Viterbi algorithm (with known CSI RS) and online Viterbi-Net pervious deep-learning-based architecture.&lt;/p&gt; &lt;p&gt;In conclusion, the article presents the integration of different model-based deep-learning architectures into the Viterbi algorithm for signal reconstruction of transmitted data over an additive Gaussian noise channel. The proposed architectures show promising results in improving the signal-to-noise ratio performance compared to existing learning-based solutions. These findings contribute to the advancement of communication systems and highlight the potential for further improvements in performance and robustness.&lt;/p&gt;
Collecting metadata for science blog posts

Metadata are an important feature of every scholarly resource. For science blog posts – which are published with far fewer resources than for example journal articles or books – there is an additional requirement: make the metadata collection as painless as possible.
Appearance Enhancement for Camera-captured Document Images in the Wild

Camera-captured document images usually suffer from various appearance degradations, which hamper the clarity of content and preclude subsequent analysis and recognition systems. Most existing methods are tailored for one or relatively few degradations, making them feasible only in limited scenarios. However, in real-world applications, degradations are more diverse, and different degradations may arise simultaneously in a single image. To remedy this limitation, we aimed to achieve appearance enhancement for camera-captured document images in the wild, where degradations exhibit more diversity and may coexist simultaneously within the same image. To realize this, we propose a new end-to-end neural network called GCDRNet, which consists of two cascaded subnets, GC-Net and DR-Net. The GC-Net is used for global context modeling, and the DR- Net is used for detail restoration through a multi-scale and multi-loss training strategy. To train and validate GCDRNet in real-world scenarios, we constructed a new benchmark called RealDAE, which contains 600 real-world degraded document images that are carefully annotated with pixel-wise alignment. To the best of our knowledge, RealDAE is the first dataset that targets multiple degradations in the wild. Extensive experiments validated the superiority and advancement of our GCDRNet and RealDAE compared to existing methods and datasets, respectively. In addition, experiments also demonstrated that image appearance enhancement as a pre-processing procedure can effectively improve the performance of downstream tasks, such as text detection and recognition.
CTIF-Net: A CNN-Transformer Iterative Fusion Network for Salient Object Detection

Capturing sufficient global context and rich spatial structure information is critical for dense prediction tasks. Convolutional Neural Network (CNN) is particularly adept at modeling fine-grained local features, while Transformer excels at modeling global context information. It is evident that CNN and Transformer exhibit complementary characteristics. Exploring the design of a network, that efficiently fuses these two models to leverage their strengths fully and achieve more accurate detection, represents a promising and worthwhile research topic. In this paper, we introduce a novel CNN-Transformer Iterative Fusion Network (CTIF-Net) for salient object detection. It efficiently combines CNN and Transformer to achieve superior performance by using a parallel dual encoder structure and a feature iterative fusion module. Firstly, CTIF-Net extracts features from the image using the CNN and the Transformer, respectively. Secondly, two feature convertors and a feature iterative fusion module are employed to combine and iteratively refine the two sets of features. The experimental results on multiple SOD datasets show that CTIF-Net outperforms 17 state-of-the-art methods, achieving higher performance in various mainstream evaluation metrics such as F-measure, S-measure, and MAE value. The code will be publicly available.
A Machine Learning Model for the Prediction of Heart Attack Risk in High-Risk Patients Utilizing Real-World Data

Heart disease is a significant global public health concern that impacts a vast number of individuals worldwide. The early identification of patients at risk of heart attack can significantly reduce mortality rates. In this research study, we employed machine learning methods to develop a model that predicts the likelihood of a heart attack. To create the model, we collected a real-world dataset of patient features, including demographic information, medical history, and lifestyle factors. We pre-processed the data to eliminate any missing values and standardized the features to ensure uniformity across the dataset. Additionally, we utilized feature engineering techniques to identify the most significant factors that contribute to the development of heart attacks. We evaluated several machine learning algorithms such as logistic regression, decision trees, and random forest to identify the most effective ones based on traditional metrics including accuracy, precision, recall, F1-score, Mathew correlation, ROC, and AUC. Our algorithm produced highly accurate predictions for heart attack risk. Our results demonstrate that machine learning algorithms can effectively predict heart attacks and identify high-risk patients. The model can be integrated into electronic health records to facilitate prompt identification and intervention by healthcare providers. However, our study has limitations that need to be addressed, including the requirement for validation on a larger and more diverse dataset as well as the challenge of interpreting the model. Future research may incorporate additional data sources, advanced machine-learning techniques, and improved model interpretability. Our heart attack prediction model holds significant potential as a valuable tool for healthcare practitioners to identify high-risk patients and decrease heart attack rates.
Teaching Software Performance Evaluation to Undergrads: Lessons Learned and Challenges

Recent high-profile performance-related outages and problems in industry clearly establish the importance of imparting performance evaluation skills to students at the undergrad level.
Secure Edge Server Placement with Non-Cooperative Game for Internet of Vehicles in Web 3.0

Based on cloud-edge computing, the Internet of Vehicles (IoV) is rapidly developing as a key part of greatly facilitating our lives in the era of Web 3.0. Plenty of delay-sensitive tasks are executed in adjacent edge servers (ESs) in IoV, incurring the increasing significance of the distribution of ESs. As a decentralized network, Web 3.0 allows for data and applications to be distributed across multiple nodes, making it more transparent and user-controlled and placing higher demands on security. Specifically, some attackers outside IoV transmit insecure tasks, which may damage the ESs or waste computing resources after being executed in ESs. To achieve the security of Web 3.0, zero-trust security, an enhanced security model, is used, which assumes that all the service requests are not trusted to access the system networks by default unless passing the anomaly detection. Therefore, in this paper, a secure ESP method with the non-cooperative game for IoV in cloud-edge computing, named ESPS, is proposed. By incorporating an anomaly detection mechanism of tasks, ESPS enhances the security of ESP. Eventually, the experiments on the distribution of Telecom base stations in Shanghai, China show the effectiveness of ESPS.
MACHINE LEARNING MODELS TO PREDICT ASTHMA EXACERBATIONS

SESSION TITLE: Asthma Treatment and Longitudinal Studies SESSION TYPE: Rapid Fire Original Inv PRESENTED ON: 10/09/2023 12:00 pm - 12:45 pm PURPOSE: Although clinical, functional and biomarker data can help predict asthma exacerbations, many events remain difficult to avert, and approaches for personalized medicine are lacking. Machine learning uses mathematical and statistical methods to detect patterns across large datasets from electronic health records (EHR) to predict future disease events and provide real-world evidence. The purpose of this study is to train and tune machine learning algorithms for prediction of severe asthma exacerbations in patients with diagnosed asthma and to identify clinical features associated with exacerbations that may be predictive. METHODS: Data from patients with asthma was extracted from the Optum Panther EHR database from 2016–2022. Adults with ≥2 ICD9/10 asthma codes within 1 year but at least 30 days apart were identified. Severe exacerbations were defined as hospitalization for asthma with administration of systemic corticosteroids. Clinical information included demographics, lab results, diagnoses, medications, immunizations, and allergies. Three models were built using XGBoost, Long Short-Term Memory (LSTM) and Transformers algorithms, which were trained, fine-tuned and tested on an independent dataset. RESULTS: Of ~1.1M asthma patients, ~16K experienced ≥1 severe exacerbation. XGBoost was the best predictive algorithm among the three methods tested. Factors associated with exacerbation included history of exacerbation and use of prednisone. Reduced probability of exacerbation was associated with having received albuterol, vitamins, aspirin, cyclobenzaprine and influenza vaccination. CONCLUSIONS: Machine learning confirmed previously known factors associated with increased exacerbation risk and uncovered associations of less understood factors with reduced exacerbation risk in the real world. CLINICAL IMPLICATIONS: Machine learning can leverage high dimensional structural data to help patient risk stratification and personalised care. DISCLOSURES: Employee relationship with Amgen Inc. Please note: June 2021 - present Added 03/15/2023 by Scott Caveney, source=Web Response, value=Salary Employee relationship with Amgen Inc. Please note: June 2021 - present Added 03/15/2023 by Scott Caveney, source=Web Response, value=Stock No relevant relationships by Gilles Gnacadja No relevant relationships by Julie Kim No relevant relationships by Nestor Molfino Employee relationship with Amgen Please note: 6-14-21 to date Added 04/07/2023 by Gianluca Turcatel, source=Web Response, value=Salary No relevant relationships by YI XIAO
Neural Projection Mapping Using Reflectance Fields

We introduce a high resolution spatially adaptive light source, or a projector, into a neural reflectance field that allows to both calibrate the projector and photo realistic light editing. The projected texture is fully differentiable with respect to all scene parameters, and can be optimized to yield a desired appearance suitable for applications in augmented reality and projection mapping. Our neural field consists of three neural networks, estimating geometry, material, and transmittance. Using an analytical BRDF model and carefully selected projection patterns, our acquisition process is simple and intuitive, featuring a fixed uncalibrated projected and a handheld camera with a co-located light source. As we demonstrate, the virtual projector incorporated into the pipeline improves scene understanding and enables various projection mapping applications, alleviating the need for time consuming calibration steps performed in a traditional setting per view or projector location. In addition to enabling novel viewpoint synthesis, we demonstrate state-of-the-art performance projector compensation for novel viewpoints, improvement over the baselines in material and scene reconstruction, and three simply implemented scenarios where projection image optimization is performed, including the use of a 2D generative model to consistently dictate scene appearance from multiple viewpoints. We believe that neural projection mapping opens up the door to novel and exciting downstream tasks, through the joint optimization of the scene and projection images.
Unstuck in Metaverse: Persuasive User Navigation using Automated Avatars

The success of the metaverse depends on technological advancements as well as human-centered designs that care for human users in fully immersive virtual environments. Without substantial spatial knowledge of the virtual environment, users can feel lost or disoriented in the metaverse. This can cause distress and poor overall user experience. This article introduces an unstuck feature that aims at guiding users out of irreconcilable situations. The feature delivers persuasive user navigation using automated avatars which were driven by machine learning models. A user experiment with mobility and eye-gaze tracking has been conducted to evaluate the effectiveness of the unstuck feature.
Self-Supervised Multi-Source Heterogeneous Data Fusion Using Encode and Decode Attention for Intelligent Medical Device Communication Analysis

With the development of artificial intelligence, the application of intelligent medical devices is gradually becoming popular. Intelligent medical devices can access many types of medical data through sensors and analyze these data to monitor, diagnose and treat patients. Intelligent medical device communication is an important research area that is of great significance to the development of the medical field. Intelligent medical device communication technology allows medical devices to communicate and share information with each other, thereby improving the efficiency and accuracy of medical devices. However, due to the heterogeneous nature of medical data and the diversity of data sources, it has become a challenge to integrate and analyze these data effectively. In order to achieve data interaction and sharing between different devices, it is necessary to fuse heterogeneous data from multiple sources to improve data integrity and usability. In this paper, we propose a self-supervised multi-source heterogeneous data fusion method using encode and decode attention for intelligent medical device communication analysis. The method improves data fusion by automatically learning the correlation between data through self-supervised learning and using encoding and decoding attention to strengthen the correlation between data. The experimental results show that the proposed method can effectively improve the accuracy and efficiency of medical data fusion and provide a strong support for communication between intelligent medical devices. This research integrates different types of medical information to provide better decision support for physicians and to promote medical technology advancement and health management.
Live traffic analysis on S1-MME interface using LSTM autoencoder

The spreading of mobile communication networks resulted in a vast amount of signaling traffic on the control plane. It is quite challenging to analyze the extensive number of message sequences, recognize patterns and identify anomalies manually. However, cutting-edge machine learning methods offer excellent tools to explore unusual events in signaling traffic to provide countermeasures in the mobile network. The paper proposes a data exploration method with an LSTM-Autoencoder based latent space model to visualize manifolds controlling the generation of control plane traffic. The model has been validated on a live LTE signaling traffic captured on the S1-MME interface.
Multi-Channel Spatio-Temporal GCN for Human Pose Forecasting

Human pose forecasting predicts future human postures based on a given sequence of human postures. The fusion of spatio-temporal joint dependencies has a significant impact on model output, but research in this area is relatively scarce. This paper introduces a spatiotemporal graph convolutional neural network that effectively fuses multi-channel information with learnable multi-channel topology. We have designed a multi-branch module to capture temporal features from diverse spatial domains. Moreover, we initiatively adopted DST to optimize long-term forecasting. Our network has been evaluated on 3DPW and AMASS datasets commonly used in motion prediction. The results of the experiments showed that our methods were up to the current state of the art.
Shuffled Frog-leaping Algorithm Based on Heap and Fractional Order

The traditional shuffled frog-leaping algorithm uses sorting algorithm and exhaustion method during population partitioning and local evolution, respectively, which leads to inefficient operation of the algorithm. In addition, it does not make full use of the useful information contained in the updated individuals during the subpopulation update process. Therefore, a new shuffled leapfrog algorithm based on heap and fractional order is proposed. Firstly, a heap storage structure is used to maintain the population of frogs to optimize the efficiency of the algorithm. At the same time, makes the population in a chaotic state at initialization increasing the global disturbance to improve the overall optimization performance. Secondly, the fractional order operator is introduced in the local update strategy of the algorithm, and the Euclidean distance method is used to select the most recently updated individuals containing useful information to construct a new fractional order local update strategy, which makes the algorithm increase the local disturbance and memory in the subpopulation loop iteration, so as to improve the population diversity. Finally, the algorithm is verified to have stronger merit-seeking ability through simulation experiments.