Reviewerly/exHarmony huggingface dataset:
ADDPC-SMOTE: An Oversampling Algorithm Based on Density Difference Peak Clustering and Spatial Distribution Entropy

Most of the existing oversampling algorithms based on clustering do not consider the spatial distribution of Majority class, and it is easy to overlap classes and ignore important information points when synthesizing new samples. To solve this problem, this paper analyzes the influence of the spatial distribution on the oversampling process, and proposes an oversampling algorithm based on Adaptive Density Difference Peak Clustering and Spatial Distribution Entropy. Firstly, the spatial distribution situation of two classes samples is introduced into the clustering process, and the local density difference is used to cluster of Minority class by the peak value, so as to achieve scientific and reasonable selection of sub-cluster centers and reduce the occurrence of class overlap. At the same time, the method of determining the truncation distance according to the previous experience is change. The spatial distribution situation of two classes samples is characterized by constructing Spatial Distribution Entropy. On this basis, the automatic selection and optimization of truncation distance are realized. Then the boundary points and sparse points are screened according to the absolute value of local density difference, and the sampling probabilities of each minority class sample are determined to focus on these important information points. Finally, Spatial Distribution Entropy is used to evaluate the synthetic samples set to ensure that they can balance the distribution of the two classes samples in the dataset. To test the effectiveness of the algorithm, five oversampling algorithms are used to perform comparative experiments on four classifiers and 16 common datasets. The results show that compared with SMOTE, K-means-SMOTE, BS-SMOTE, ADASYN, DPC-SMOTE, the algorithm has significantly improved in all evaluation indexes.
HSGA: A Hybrid LSTM-CNN Self-Guided Attention to Predict the Future Diagnosis From Discharge Narratives

The prognosis of a patient’s re-admission and the forecast of future diagnoses is a critical task in the process of inferring clinical outcomes. The discharge summaries recorded in the Electronic Health Records (EHR) are stinking rich but they are also heterogeneous, sparse, noisy, and biased, and hinder the learning algorithms that aim to extract actionable insights from them. The existing approaches use the current admission’s International Classification of Diseases (ICD) codes as input but they do not fully describe the progression of the patient. Other systems apply the attention mechanisms directly to these notes without the guidance of a domain knowledge resulting in distorted predictions. In this work, we propose a hybrid LSTM-CNN self-guided attention model that aims to predict the ICD diagnosis that is likely to cause the next readmission within 90 days since the current discharge using the discharge narratives. Because the notes contain unnecessary tokens, the model leverages the recent advances in deep learning to predict the patient’s future diagnosis by reducing the number of tokens from the notes to be considered for prediction. We use a 1D CNN (Convolutional Neural Network) to capture all features from the note and concurrently an LSTM (Long Short-Term Memory) is used to extract the features of clinically meaningful Concept Unique Identifiers (CUI) that are fetched from the note itself to build a knowledge base. The textual knowledge base guides the learning module about which n-grams from the note to focus on for prediction. We consider 3 prediction scenarios; diagnosis category prediction, the probability of the occurrence of one of the top 20 disease conditions and ICD9 codes prediction. For the diagnosis category prediction, the model achieves a Macro-Average ROC of 0.82, an AUROC of 0.87 for most the Top 20 most appearing diseases prediction, and a Micro-RECALL of 0.84 for ICD9 codes prediction. The predictive accuracy of the model is assessed through the prediction of heart failure onset and for all these prediction scenarios the results show that the hybrid approach outperforms the existing baselines.
Differentiable Neural Architecture, Mixed Precision and Accelerator Co-Search

Quantization, effective Neural Network architecture, and efficient accelerator hardware are three important design paradigms to maximize accuracy and efficiency. Mixed Precision Quantization is a process of assigning different precision to different Neural Network layers for optimized inference. Neural Architecture Search (NAS) is a process of automatically designing the neural network for a task and can also be extended to search for the precision of each weight and activation matrix. In this paper, we develop the following three methods: (i) Fast Differentiable Hardware-aware Mixed Precision Quantization Search method to find optimal precision, (ii) Joint Differentiable hardware-aware Architecture and Mixed Precision Quantization Co-search, (iii) Joint Accelerator, Architecture, and Precision triple co-search to find best possibilities in all the three worlds. We demonstrate the effectiveness of our proposed methods targeting Bitfusion accelerator by searching mixed precision models on MobilenetV2. We achieve better accuracy-latency trade-off models than the manually designed and previously proposed search methods.
Future of Communication and Interaction

Abstract The effects of digitization can now be seen in almost all life areas—from avatars to mixed reality. Communication will continue to change in the future. Telephones, smartphones, and the Internet are changing human interaction. With chatbots and artificial intelligence, modern ways of establishing contact are possible. One thing is for sure, 20 years from now, using a cell phone to chat with friends will be as outdated as using a landline phone today. By then, mixed reality technology will allow people to see virtual copies of their friends in the real world and see the world through friends’ eyes and help them accomplish real-world tasks. Today’s VR and AR systems show only a fraction of what is possible with mixed reality. MR systems can share many communication signals and allow people to communicate in ways that have never been possible.
Uncertainties in geochemistry

Certainly, all geochemical measurements have uncertainties. Here I present an overview of random, systematic, and gross errors, and treatment of random errors using statistical method. Standard deviation is a simple but the most important concept in error analysis. I review six probability distributions related to geochemistry, including normal/Gaussian distribution, log-normal distribution, gamma distribution, uniform distribution, triangular distribution, and Poisson distribution and their respective standard deviations. I highlight the use of standard deviations to determine the limit of detection and limit of quantification during instrument analysis. I provide an overview of propagation of random errors and systematic errors. In the end, I review reduced Chi Squared and its application for testing probability distributions. Examples to estimate uncertainty propagation are provided.
Asynchronous Narrative Audio-Messages: An Internet-Based Qualitative Method for Data Collection in International Research

Abstract The postdigital era is witness to significant changes in (digital) communication. The opportunities this brings for research, however, have not yet been extensively explored. This chapter elaborates on current international developments around digitalization and examines the need for corresponding research methods in the social sciences. Following an overview of internet-based data collection methods, Kleinlein introduces the new method of Asynchronous Narrative Audio-Messages (ANAs), which facilitates comprehensive qualitative research in the postdigital era. A critical reflection on the facilitating and constraining effects of the method illustrates its potential areas of use. Finally, the practical implementation of the method is illustrated by an ongoing international research project that focuses on inclusive schooling practices of teachers from a transcultural perspective.
Postdigital Participation in Education: An Introduction

Abstract In their introduction, the editors explore the complexities and contexts around the ambiguous term “postdigital,” particularly the “messy” entanglements of the digital and analog, material and symbolic, technology and sociality that it encompasses. The editors also introduce the concept of media constellations as the interwoven relationalities at the heart of a postdigital society whose “post” does not mean “overcome” but rather that digitality has become part of complex, everyday interrelationships and correlations. With its particular interest in participation, the book’s introduction lays out how participating in the “postdigital condition” is tightly knit with power relations and political economies. This performative aspect in an educational context means that contemporary media constellations shape participation just as the practices, actors, and technologies of participation shape these very constellations.
Postdigital Participation in Education: A Postscript

Abstract This postscript takes the form of a conversation between the book’s editors, who pull together the discursive strands explored in the book’s chapters in response to three guiding questions: How is the postdigital deployed in the chapters of this book? How do contemporary media constellations shape participation? And who participates how in these contemporary media constellations? Addressing these questions together, as necessitated by the intricate entanglements of the postdigital condition, the editors discuss the complexities around the term “postdigital” and the practices and implications for power relations that both influence and derive from the use of technology in education, in society, in culture, and in politics.
Sensor Capsules with Augmented Reality Integration

Medical imaging often lacks real-time, detailed views of internal organs, impeding diagnoses. This study introduces an innovative solution: ingestible sensor capsules combined with live augmented reality (AR). This approach aims to facilitate high-resolution, real-time examination of the body. The system consists of biocompatible capsules housing miniature cameras, pH sensors, and wireless transmitters. Ingested like pills, these capsules travel through the body, transmitting data. Healthcare providers use AR headsets to receive this data, displaying a 3D virtual model of the examined organ, enhanced with sensor data and a live endoscopic view.Despite technical challenges like sensor miniaturization and wireless bandwidth, the potential benefits are immense. Successful implementation could transform non-invasive examinations of internal cavities, providing valuable insights into conditions like inflammation and cancer.With further advancements in key enabling technologies, AR-assisted swallowable sensor capsules have the potential to complement traditional medical imaging methods. They offer healthcare professionals information-rich, dynamic 3D visualizations of the human body from within, contributing to earlier and more accurate diagnoses.
InstantILL Recognized at Premier ILL Conference

While building InstantILL, alongside our partners at IUPUI University Library, we documented our engineering work and unusual partnership… While building InstantILL, alongside our partners at IUPUI University Library, we documented our engineering work and unusual partnership in a paper that you ca
Mostly Behavioristic Theories

Both a serious academic text and an intriguing story, this seventh edition reflects a significant update in research, theory, and applications in all areas. It presents a comprehensive view of the historical development of learning theories from behaviorist through to cognitive models. The chapters also cover memory, motivation, social learning, machine learning, and artificial intelligence. The author's highly entertaining style clarifies concepts, emphasizes practical applications, and presents a thought-provoking, narrator-based commentary. The stage is given to Mrs Gribbin and her swashbuckling cat, who both lighten things up and supply much-needed detail. These two help to explore the importance of technology for simulating human cognitive processes and engage with current models of memory. They investigate developments in, and applications of, brain-based research and plunge into models in motivation theory, to name but a few of the adventures they embark upon in this textbook.
Summary, Synthesis, and Integration

Both a serious academic text and an intriguing story, this seventh edition reflects a significant update in research, theory, and applications in all areas. It presents a comprehensive view of the historical development of learning theories from behaviorist through to cognitive models. The chapters also cover memory, motivation, social learning, machine learning, and artificial intelligence. The author's highly entertaining style clarifies concepts, emphasizes practical applications, and presents a thought-provoking, narrator-based commentary. The stage is given to Mrs Gribbin and her swashbuckling cat, who both lighten things up and supply much-needed detail. These two help to explore the importance of technology for simulating human cognitive processes and engage with current models of memory. They investigate developments in, and applications of, brain-based research and plunge into models in motivation theory, to name but a few of the adventures they embark upon in this textbook.
Partnering with Montana State to make self-archiving simple with Shareyourpaper.org.

“We’re excited to finally build everything we’ve learned from mediated deposit into a simple tool, rather than a dozen workarounds, this…
A Case of Computer Image Processing Depth Learning Algorithm Based on YOLOv4 Improved Algorithm

Computer image recognition algorithm is the key to unsafe behavior recognition, and it is also the content that must be mastered to learn computer image processing. In order to solve the problem of detecting unsafe behaviors in high voltage areas and intrusion of people in hazardous areas, a detection method of unsafe behaviors in high voltage areas based on the double-layer YOLOv4 algorithm is proposed. The results demonstrate that the average accuracy (mAP) of this model can reach 85.73% in the task of unsafe behavior detection in high-voltage area, and the speed can reach 94ms/piece. It can be seen that this method can accurately detect intrusion into dangerous areas and unsafe behaviors of operators in real time, and can guarantee the safety of power grids and personnel.
Restoration of Damaged Mural Images Based on Recurrent Feature Inference and Global-Local Attention

In the process of repairing damaged mural images, it is often overlooked that these images have unique painting techniques. To address this issue, we propose a method based on recurrent feature inference and global-local attention for restoring damaged mural images. In order to enhance the ability of the network model to capture the structural and textural features specific to mural images, we designed a coherent semantic attention inference module at the feature inference stage of the network model. Additionally, we replaced the original attention module with a global-local attention module in the feature inference stage to strengthen the connection between missing and background regions and generate high-quality feature maps based on the morphological characteristics of the mural. Experimental results show that our method can significantly improve the semantic disorder and pseudo-shadow phenomenon in mural images repaired by other algorithms, and generate more detailed information for damaged mural images, thus making the results of mural image restoration more natural and accurate.
Research on Intelligent Recommendation Algorithm Based on Data Mining in Credit Bank

This article investigates the intelligent recommendation problem in credit banks and designs experiments to validate three algorithms based on association rules, clustering, and classification. In terms of data preprocessing, methods such as data cleaning and feature selection are used. In terms of algorithm implementation, the principles, implementation processes, and pseudocode of the association rule algorithm, clustering algorithm, and classification algorithm are introduced respectively. The results show that all three algorithms can effectively recommend courses suitable for users' learning needs. The research in this article provides new ideas and methods for intelligent recommendation problems in credit banks, and provides some reference for subsequent research.
Integrated multi-branch neural network dehazing algorithm

Recently, the field of image dehazing has achieved rapid development. End-to-end deep learning algorithms achieve good performance. However, we observe that many algorithms perform poorly on the task of image dehazing in high-resolution complex environments. In this paper, aiming at the problems of detail loss, edge blurring and regional haze legacy in the process of dehazing non-uniform haze images with high resolution by existing algorithms, an end-to-end dehazing model integrating multiple neural networks is proposed. To be specific, we introduce a multiple neural network to deal with the aforementioned problems. Our proposed structure consists of three branches: the encoder-decoder network is constructed by transfer learning to reconstruct haze-free features; Full-resolution residual extraction network helps to avoid loss of details in downsampling layers; Swin-transformer scale extraction network makes the network receptive field richer. Then, their different features are then mapped through a learnable fusion tail. Extensive experimental results on real datasets show that our proposed ensemble multi-branch dehazing model has better recovery ability and good generalization ability on multiple datasets.
Research on Flight Control System of Computer Vision Folding Common Axis Reverse Propeller UAV

A pose feedback control method based on reverse sliding mode was designed. The dynamic modeling is carried out. A method of position, attitude and positioning control for foldable coaxial twin-rotor unmanned aerial vehicle based on reverse sliding mode is proposed. The dynamic modeling and aerodynamic modeling of cylindrical coaxial unmanned helicopter are carried out. A method to solve the nonlinear control problem of the system is proposed by using the sliding mode theory. MATLAB was used to simulate it. Experimental results show that this method can improve the stability of aircraft.
Security Challenges of Digital Transformation in Smart Cities: Case of Banking Sector

Banking traces back to 2000 BC in Assyria, India and Sumeria. Merchants used to give grain loans to farmers and traders to carry goods between cities. In ancient Greece and Roman Empire, lenders in temples, provided loans, and accepted deposits while performed change of money. The archaeological evidence uncovered in India and China corroborates this. The major development in banking came predominantly in the mediaeval, Renaissance Italy, with the major cities Florence, Venice and Genoa being the financial centres. Technology has become an inherent and integral part of our lives. We are generating a huge amount of data in transfer, storage and usage, with greater demands of ubiquitous accessibility, inducing an enormous impact on industry and society. With the emergence of smarter cities and societies, the security challenges pertinent to data become greater, impending impact on the consumer protection and security. The aim of this chapter is to highlight if SSI and passwordless authentication using FIDO-2 protocol assuage security concerns such as authentication and authorisation while preserving the individual's privacy.
Investigating the Influence of Blockchain in Building Trust Network – Smart Transport Networks in a Smart City

This study aims to identify blockchain-related innovation trends that can improve trust networks in a smart city's transport and supply chain networks. Trust networks are crucial in building and maintaining the trust of citizens in smart cities. By promoting transparency and accountability, facilitating collaboration and innovation, enhancing citizen participation and protecting privacy and security, trust networks can help to ensure that smart cities are developed and implemented in a responsible and sustainable way. A systematic literature review identifies 60 conceptual and empirical studies. This research focuses on the current problems and developing procurement and supply chain strategy and the potential benefits of using blockchain in these areas. It suggests ways for the smart city's transport and supply chain networks to utilise blockchain to improve operations and supply chain strategy and identifies innovation trends related to blockchain. The study also includes a systematic literature review and Blockchain Transformation and Influence model as the basis to enhance trust networks in the supply chain.
Animals in the Wild: Using Crowdsourcing to Enhance the Labelling of Camera Trap Images

Artificial intelligence and its subarea of computer vision became one of the top research areas in the past decade. Gathering information from all kinds of images is beneficial for various applications. In most of those applications, the images are either collected in a controlled (i.e. with known light conditions) environment or the object of interest is the dominant part of the image. Detecting small and only partly visible objects in the background within varying lighting and environmental conditions requires a well-labelled training and validation dataset. This work combines automatic labelling with a web app for label confirmation. It offloads the tiring manual labelling and checking process using a simple webpage and crowdsourcing to validate the image labels. Depending on the domain, even an inexperienced user can quickly check several hundreds of images within a couple of minutes. In this work, we present and evaluate the concept and our toolchain for this assisted labelling approach.
Improved Path Planning Algorithms for Non-Holonomic Autonomous Vehicles in Industrial Environments with Narrow Corridors: Roadmap Hybrid A* and Waypoints Hybrid A*

This paper proposes two novel path planning algorithms, Roadmap Hybrid A* and Waypoints Hybrid A*, for car-like autonomous vehicles in logistics and industrial contexts with obstacles (e.g., pallets or containers) and narrow corridors. Roadmap Hybrid A* combines Hybrid A* with a graph search algorithm applied to a static roadmap. The former enables obstacle avoidance and flexibility, whereas the latter provides greater robustness, repeatability, and computational speed. Waypoint Hybrid A*, on the other hand, generates waypoints using a topological map of the environment to guide Hybrid A* to the target pose, reducing complexity and search time. Both algorithms enable predetermined control over the shape of desired parts of the path, for example, to obtain precise docking maneuvers to service machines and to eliminate unnecessary steering changes produced by Hybrid A* in corridors, thanks to the roadmap and/or the waypoints. To evaluate the performance of these algorithms, we conducted a simulation study in an industrial plant where a robot must navigate narrow corridors to serve machines in different areas. In terms of computational time, total length, reverse length path, and other metrics, both algorithms outperformed the standard Hybrid A*.