Reviewerly/exHarmony huggingface dataset:
Learning Cognitive-Test-Based Interpretable Rules for Prediction and Early Diagnosis of Dementia Using Neural Networks

Background: Accurate, cheap, and easy to promote methods for dementia prediction and early diagnosis are urgently needed in low- and middle-income countries. Integrating various cognitive tests using machine learning provides promising solutions. However, most effective machine learning models are black-box models that are hard to understand for doctors and could hide potential biases and risks. Objective: To apply cognitive-test-based machine learning models in practical dementia prediction and diagnosis by ensuring both interpretability and accuracy. Methods: We design a framework adopting Rule-based Representation Learner (RRL) to build interpretable diagnostic rules based on the cognitive tests selected by doctors. According to the visualization and test results, doctors can easily select the final rules after analysis and trade-off. Our framework is verified on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset (n = 606) and Peking Union Medical College Hospital (PUMCH) dataset (n = 375). Results: The predictive or diagnostic rules learned by RRL offer a better trade-off between accuracy and model interpretability than other representative machine learning models. For mild cognitive impairment (MCI) conversion prediction, the cognitive-test-based rules achieve an average area under the curve (AUC) of 0.904 on ADNI. For dementia diagnosis on subjects with a normal Mini-Mental State Exam (MMSE) score, the learned rules achieve an AUC of 0.863 on PUMCH. The visualization analyses also verify the good interpretability of the learned rules. Conclusion: With the help of doctors and RRL, we can obtain predictive and diagnostic rules for dementia with high accuracy and good interpretability even if only cognitive tests are used.
Real-time 3D scans of cardiac surgery using a single optical-see-through head-mounted display in a mobile setup

Microsoft HoloLens 2 (HL2) is often found in research and products as a cutting-edge device in Mixed Reality medical applications. One application is surgical telementoring, that allows a remote expert to support surgeries in real-time from afar. However, in this field of research two problems are encountered: First, many systems rely on additional sensors to record the surgery in 3D which makes the deployment cumbersome. Second, clinical testing under real-world surgery conditions is only performed in a small number of research works. In this article, we present a standalone system that allows the capturing of 3D recordings of open cardiac surgeries under clinical conditions using only the existing sensors of HL2. We show difficulties that arose during development, especially related to the optical system of the HL2, and present how they can be solved. The system has successfully been used to record surgeries from the surgeons point of view and the obtained material can be used to reconstruct a 3D view for evaluation by an expert. In a preliminary study, we present a recording of a captured surgery under real-world clinical conditions to expert surgeons which estimate the quality of the recordings and their overall applicability for diagnosis and support. The study shows benefits from a 3D reconstruction compared to video-only transmission regarding perceived quality and feeling of immersion.
A novel MCF-Net: Multi-level context fusion network for 2D medical image segmentation

Medical image segmentation is a crucial step in the clinical applications for diagnosis and analysis of some diseases. U-Net-based convolution neural networks have achieved impressive performance in medical image segmentation tasks. However, the multi-level contextual information integration capability and the feature extraction ability are often insufficient. In this paper, we present a novel multi-level context fusion network (MCF-Net) to improve the performance of U-Net on various segmentation tasks by designing three modules, hybrid attention-based residual atrous convolution (HARA) module, multi-scale feature memory (MSFM) module, and multi-receptive field fusion (MRFF) module, to fuse multi-scale contextual information. HARA module was proposed to effectively extract multi-receptive field features by combing atrous spatial pyramid pooling and attention mechanism. We further design the MSFM and MRFF modules to fuse features of different levels and effectively extract contextual information. The proposed MCF-Net was evaluated on the ISIC 2018, DRIVE, BUSI, and Kvasir-SEG datasets, which have challenging images of many sizes and widely varying anatomy. The experimental results show that MCF-Net is very competitive with other U-Net models, and it offers tremendous potential as a general-purpose deep learning model for 2D medical image segmentation.
Photosensitive Accessibility for Interactive Data Visualizations

Accessibility guidelines place restrictions on the use of animations and interactivity on webpages to lessen the likelihood of webpages inadvertently producing sequences with flashes, patterns, or color changes that may trigger seizures for individuals with photosensitive epilepsy. Online data visualizations often incorporate elements of animation and interactivity to create a narrative, engage users, or encourage exploration. These design guidelines have been empirically validated by perceptual studies in visualization literature, but the impact of animation and interaction in visualizations on users with photosensitivity, who may experience seizures in response to certain visual stimuli, has not been considered. We systematically gathered and tested 1,132 interactive and animated visualizations for seizure-inducing risk using established methods and found that currently available methods for determining photosensitive risk are not reliable when evaluating interactive visualizations, as risk scores varied significantly based on the individual interacting with the visualization. To address this issue, we introduce a theoretical model defining the degree of control visualization designers have over three determinants of photosensitive risk in potentially seizure-inducing sequences: the size, frequency, and color of flashing content. Using an analysis of 375 visualizations hosted on bl.ocks.org, we created a theoretical model of photosensitive risk in visualizations by arranging the photosensitive risk determinants according to the degree of control visualization authors have over whether content exceeds photosensitive accessibility thresholds. We then use this model to propose a new method of testing for photosensitive risk that focuses on elements of visualizations that are subject to greater authorial control – and are therefore more robust to variations in the individual user – producing more reliable risk assessments than existing methods when applied to interactive visualizations. A full copy of this paper and all study materials are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://osf.io/8kzmg/</uri> .
Predicting Heavy Metal Concentrations in Shallow Aquifer Systems Based on Low-Cost Physiochemical Parameters Using Machine Learning Techniques

Monitoring ex-situ water parameters, namely heavy metals, needs time and laboratory work for water sampling and analytical processes, which can retard the response to ongoing pollution events. Previous studies have successfully applied fast modeling techniques such as artificial intelligence algorithms to predict heavy metals. However, neither low-cost feature predictability nor explainability assessments have been considered in the modeling process. This study proposes a reliable and explainable framework to find an effective model and feature set to predict heavy metals in groundwater. The integrated assessment framework has four steps: model selection uncertainty, feature selection uncertainty, predictive uncertainty, and model interpretability. The results show that Random Forest is the most suitable model, and quick-measure parameters can be used as predictors for arsenic (As), iron (Fe), and manganese (Mn). Although the model performance is auspicious, it likely produces significant uncertainties. The findings also demonstrate that arsenic is related to nutrients and spatial distribution, while Fe and Mn are affected by spatial distribution and salinity. Some limitations and suggestions are also discussed to improve the prediction accuracy and interpretability.
Application of Artificial Intelligence Computer Intelligent Heuristic Search Algorithm

In order to transform three-dimensional space path planning into two-dimensional plane path planning problem and greatly reduce the search time, an intelligent heuristic search algorithm based on artificial intelligence is proposed. The heuristic search algorithm is analyzed and introduced, and A is chosen. A two-dimensional spatial environment model of picking robot path planning is investigated, and a spatial model of picking robot path planning is established by raster method. Then, considering the whole day operation time, the whole day operation time is divided into several periods. With the help of heuristic search algorithm, the most reasonable operation time interval of each period is found, so as to provide reliable reference for the decision-making organization of urban rail transit operation on how to arrange the train rationally. The experimental results show that the improved A <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mo>∗</mo> </math> algorithm can significantly improve the moving path of the picking robot and make the planned path smoother, which confirms the feasibility and superiority of the improved algorithm. The operation decision of urban rail transit is obtained through experiments. After 114 iterations of the heuristic search algorithm, the optimal value is 6.83353635 <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>e</mi> </math> -001, while the average optimal value is 6.83551939 <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>e</mi> </math> -001. After 231 iterations of particle swarm optimization algorithm, the optimal value is 6.83650785 <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi>e</mi> </math> -001. The average optimal value is 6.83745935 <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <mi>e</mi> </math> -001. After 789 iterations, the genetic algorithm obtains the optimal value of 6.83921100 <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"> <mi>e</mi> </math> -001, and the average optimal value is 6.84410765 <math xmlns="http://www.w3.org/1998/Math/MathML" id="M7"> <mi>e</mi> </math> -001. Through the comparative analysis, it can be seen that compared with the other two optimization algorithms, the heuristic search algorithm is significantly better than the other two optimization algorithms, both in terms of the optimal value and the number of optimization iterations. The results show that the heuristic search algorithm is a fast, accurate, and reliable optimization method to solve the problem of accurate scheduling of urban rail transit departure interval. It is proved that the intelligent heuristic search algorithm of artificial intelligence computer can realize the path planning effectively.
Sustainable Development Research of Green Smart Park in High-End Manufacturing Based on Internet of Things

During the transition from industrial production to intelligent, there are still a large number of manufacturing industries in the traditional production mode. The problems of slow information transmission, difficult integrated production management, and complicated process parameter control have led to disconnect decision-making from actual production. In addition, the current manufacturing industry is more of a traditional management method, and the upgrade and iteration speed are slow, and it is not combined with the technology of the Internet of Things. Therefore, the purpose of this article is to use the Internet of Things monitoring technology to explore the improvement of high-end manufacturing and analyze the problems and solutions in the process of consistency of ecological environment and economic effects. This article will use the research methods of specific problems and specific analysis to compare the data and draw conclusions. This article will discuss the technical mechanism of the Internet of Things monitoring and introduce the collection and data of production status data of each station equipment in the production workshop during the production process: improving the current production model, learning foreign experience at the same time, quickly carrying out load curve and data center data accumulation on the service capabilities of the park, verifying multiple operating models, and simultaneously exploring new management models. Based on comparative advantage analysis, the study draws similarities and differences in the development of high-end manufacturing industries at home and abroad, learns advanced experience, proposes improved methods and paths, combines new environmental protection paths, and finally proposes energy operation in the park. Build a green and intelligent park to provide a theoretical reference for sustainable development. The research results show that computer monitoring technology and methods have effectively improved the production environment of high-end manufacturing, combined with the development status of the existing ecological and intelligent composite production system; the infrastructure construction of industrial parks in the new era, IT support, services, manpower resources, and industries has developed rapidly.
Curvilinear Structure Tracking Based on Dynamic Curvature-penalized Geodesics

Geodesic models are considered as a fundamental and powerful tool in the applications of curvilinear structure extraction, where the target structures are usually modeled as geodesic paths connecting prescribed points. Despite great advances in geodesic models, it still remains an unsolved problem of detecting weak curvilinear structures from complicated scenarios. In this paper, a dynamic high-order geodesic model for curvilinear structure extraction is introduced to alleviate the shortcuts or short branches combination problems suffered in the classical geodesic approaches. For that purpose, we take into account the nonlocal pattern of curvilinear structures and the local curvature of geodesic paths for the construction of geodesic metrics. Accordingly, the proposed model is able to blend the benefits from the on-the-fly nonlocal smoothness property, curvature regularization and appearance coherence penalization. The nonlocal smoothness property carried out via a local bending operator is constructed to provide a quantitative measure of geodesic advancing directions, meanwhile the coherence penalization is established to guarantee the consistency of the local appearance features extracted via a vessel detector. The experiment results on synthetic and real images illustrate that the proposed method obtains outperformance when compared to the classical geodesic-based tracing algorithms.
Artificial Bee Colony Based Gabor Parameters Optimizer (ABC-GPO) for Modulation Classification

Modulation classification is one of the essential requirements in the various cognitive radio applications where prior information about the incoming signal is unknown. The modulation classification using a pattern recognition approach can be achieved in 2 modules: first, parameters are extracted from the noisy signal, and then feature selection is carried out using a Gabor filter network (GFN). In the second module, features are exploited for classification purposes. The modulation formats considered for the purpose of classification are BPSK, QPSK, 8PSK, 16PSK, 64PSK, 4FSK, 8FSK, 16FSK, QAM, 8QAM, 16QAM, 32QAM, and 64QAM. The Gabor filter parameters and weights of the adaptive filter are attuned using the Delta rule and recursive least square (RLS) algorithm until the cost function is minimized. In the end, the artificial bee colony (ABC) algorithm is used to optimize the Gabor parameters as well as the classifier’s performance. The simulation results show the supremacy of the proposed classifier structure.
The Influence of Product Quality and Purchase Decisions on Helmet LTD's Brand Image

This excogitate administers to consequence how the aftermath of aftermath transcendence and bad mark carved digital audiotape on invest in encampments for Helmets Ltd (Case excogitate of Stie Bina Karya Tebing Tinggi students).The resident therein excogitate were undergraduates of Stie Bina Karya Tinggi Cliffs, semester VI attitudes b, c and d.The demonstration was condemned as severals as 74 respondents with the apportionment course of action victimisationing the census, namely the across-theboard resident was sampled on account of the constitution was hardly any than 100.The transformation psychoanalysis inclination inoffensive pre-owned was conglomeration unidimensional transformation and to valuation the possibleness inoffensive pre-owned t valuation and f valuation at considerable coextensive or alpha = 5 %.The valuation solicitations were analyzed or clarified victimisationing the SPSS narrative 17 program.backed on the valuation borderline determination it was 0. 237 or 23.This make evident that if the aftermath transcendence iridescent (X1), and bad mark carved digital audiotape iridescent (X2) channel mark explanation for the venture in steadfastness iridescent (Y) by 23. 7%, the distinguished 76. 3% (100% -23.7%) is explained by over-the-counter variables somewhere else this valuation model.In the conglomeration unidimensional transformation valuation consequences obtained the succeeding transformation equation: Y = 3. 688 + 0. 485X1 -0.The consequences of the possibleness valuation native land that: H1 is accepted, it channel mark be seen from that the depletion of tcount 4. 634 > ttable 1. 9934, in this procedure moreover with a significance depletion of 0. 000 <0. 05 so the transcendence of the aftermath has an concatenation response on invest in decisions.H2 is accepted, the depletion of tcount -2.693 < t tabularize 1. 9934, as well as the significance depletion of 0. 009 < 0. 05 so bad mark carved digital audiotape has an concatenation response on invest in decisions.H3 is accepted, it channel mark be seen from that the depletion of Fcount 12. 318 > Ftable 2.
CloudFL: A Zero-Touch Federated Learning Framework for Privacy-aware Sensor Cloud

Intelligent sensing solutions bridge the gap between the physical world and the cyber-physical systems by digitizing the sensor data collected from sensor devices. Sensor cloud networks provide physical and virtual sensing device resources and enable uninterrupted intelligent solutions to end-users. Thanks to advancements in machine learning algorithms and big data, the automation of mundane tasks with artificial intelligence is becoming a reliable smart option. However, existing approaches based on centralized Machine Learning (ML) on sensor cloud networks fail to ensure data privacy. Moreover, centralized ML works with the pre-requisite to transfer the entire training dataset from end devices to a central server. To address this, we propose a Quantized Federated Learning (FL) based approach, called CloudFL, to ensure data privacy on end devices in a sensor cloud network. Our framework enables a personalized version of FL implementation and enhances privacy and security with cryptosystem tools to obfuscate the information of the FL process from unauthorized access. Furthermore, microservices of our approach provide software as a service implementation of FL with instances of cloud servers that require zero-touch on local data for training.
Quantum-Enabled Distributed Unit Commitment

The penetration of distributed energy resources (DERs) within the microgrids (MGs) increases the complexity of Unit Commitment (UC) problem. In this paper, the idea of quantum computing (QC) is introduced in power system distributed optimization to solve the combinatorially complex UC problem. To take advantage of QC, the quantum-amenable model of UC problem is innovatively developed. Then, by using the hybrid nature of quantum-inspired alternate direction method of multipliers (ADMM) and by exploiting the superposition and entanglement of quantum bits (qubits), the classical ADMM coordinates the subproblems to obtain feasible solutions. Additionally, this paper uses a decomposition and coordination-supported framework for quantum optimization, which allows to solve problems larger that currently available quantum computers are capable of solving. The quantum distributed UC results are compared with those from its classical counterpart, which validate the efficacy of quantum computing,
Quantum communication using a quantum switch of quantum switches

The quantum switch describes a quantum operation in which two or more quantum channels act on a quantum system with the order of application determined by the state of an order quantum system. And by suitably choosing the state of the order system, one can create a quantum superposition of the different orders of application, which can perform communication tasks impossible within the framework of the standard quantum Shannon theory. In this paper, we consider the scenario of one-shot heralded qubit communication and ask whether there exist protocols using a given quantum switch or switches that could outperform the given ones. We answer this question in the affirmative. We define a higher-order quantum switch composed of two quantum switches, with their order of application controlled by another order quantum system. We then show that the quantum switches placed in a quantum superposition of their alternative orders can transmit a qubit, without any error, with a probability higher than that achievable with the quantum switches individually. We demonstrate this communication advantage over quantum switches useful as a resource and those that are useless. We also show that there are situations where there is no communication advantage over the individual quantum switches.
Investigating the Role and Interplay of Narrations and Animations in Data Videos

Abstract Combining data visualizations, animations, and audio narrations, data videos can increase viewer engagement and effectively communicate data stories. Due to their increasing popularity, data videos have gained growing attention from the visualization research community. However, recent research on data videos has focused on animations, lacking an understanding of narrations. In this work, we study how data videos use narrations and animations to convey information effectively. We conduct a qualitative analysis on 426 clips with visualizations extracted from 60 data videos collected from a variety of media outlets, covering a diverse array of topics. We manually label 816 sentences with 1226 semantic labels and record the composition of 2553 animations through an open coding process. We also analyze how narrations and animations coordinate with each other by assigning links between semantic labels and animations. With 937 (76.4%) semantic labels and 2503 (98.0%) animations linked, we identify four types of narration‐animation relationships in the collected clips. Drawing from the findings, we discuss study implications and future research opportunities of data videos.