Reviewerly/exHarmony huggingface dataset:
Predicting Length of Stay for Cardiovascular Hospitalizations in the Intensive Care Unit: Machine Learning Approach

Predicting Cardiovascular Length of stay based hospitalization at the time of patients' admitting to the coronary care unit (CCU) or (cardiac intensive care units CICU) is deemed as a challenging task to hospital management systems globally. Recently, few studies examined the length of stay (LOS) predictive analytics for cardiovascular inpatients in ICU. However, there are almost scarcely real attempts utilized machine learning models to predict the likelihood of heart failure patients length of stay in ICU hospitalization. This paper introduces a predictive research architecture to predict Length of Stay (LOS) for heart failure diagnoses from electronic medical records using the state-of-art- machine learning models, in particular, the ensembles regressors and deep learning regression models. Our results showed that the gradient boosting regressor (GBR) outweighed the other proposed models in this study. The GBR reported higher R-squared value followed by the proposed method in this study called Staking Regressor. Additionally, The Random forest Regressor (RFR) was the fastest model to train. Our outcomes suggested that deep learning-based regressor did not achieve better results than the traditional regression model in this study. This work contributes to the field of predictive modelling for electronic medical records for hospital management systems.
Where to relocate?: Object rearrangement inside cluttered and confined environments for robotic manipulation

We present an algorithm determining where to relocate objects inside a cluttered and confined space while rearranging objects to retrieve a target object. Although methods that decide what to remove have been proposed, planning for the placement of removed objects inside a workspace has not received much attention. Rather, removed objects are often placed outside the workspace, which incurs additional laborious work (e.g., motion planning and execution of the manipulator and the mobile base, perception of other areas). Some other methods manipulate objects only inside the workspace but without a principle so the rearrangement becomes inefficient.In this work, we consider both monotone (each object is moved only once) and non-monotone arrangement problems which have shown to be $\mathcal{N}\mathcal{P}$-hard. Once the sequence of objects to be relocated is given by any existing algorithm, our method aims to minimize the number of pick-and-place actions to place the objects until the target becomes accessible. From extensive experiments, we show that our method reduces the number of pick-and-place actions and the total execution time (the reduction is up to 23.1% and 28.1% respectively) compared to baseline methods while achieving higher success rates.
Inclusive GAN: Improving Data and Minority Coverage in Generative Models

Generative Adversarial Networks (GANs) have brought about rapid progress towards generating photorealistic images. Yet the equitable allocation of their modeling capacity among subgroups has received less attention, which could lead to potential biases against underrepresented minorities if left uncontrolled. In this work, we first formalize the problem of minority inclusion as one of data coverage, and then propose to improve data coverage by harmonizing adversarial training with reconstructive generation. The experiments show that our method outperforms the existing state-of-the-art methods in terms of data coverage on both seen and unseen data. We develop an extension that allows explicit control over the minority subgroups that the model should ensure to include, and validate its effectiveness at little compromise from the overall performance on the entire dataset. Code, models, and supplemental videos are available at GitHub .
An IoT-Based Traceable Drug Anti-Counterfeiting Management System

With the rapid development of the social economy, our lives are flooded with all kinds of counterfeit products. The public's attitude of greedy for petty and cheap has encouraged unscrupulous manufacturers to take advantage of the opportunity to provide low-cost counterfeit products, suppress the profits of legitimate manufacturers, and also make the public lose confidence in the quality of the products. At present, the most widely used anti-counterfeiting system based on QR codes on the market. However, existing traceability systems are still mostly built in a centralized manner, and the central agency provides trust guarantees, but the public still has great doubts about the credibility of the central agency. The introduction of blockchain technology can perfectly solve the lack of existing architecture and the environment. In this research, we propose an IoT-based traceable drug anti-counterfeiting management system, a comprehensive plan from drug research and development, certification, production to sales. The framework we propose meets the requirements of information security for data integrity, resistance to replay attacks, irreversible information, and non-repudiation.
Deep Learning-Based Object Detection, Localisation and Tracking for Smart Wheelchair Healthcare Mobility

This paper deals with the development of an Advanced Driver Assistance System (ADAS) for a smart electric wheelchair in order to improve the autonomy of disabled people. Our use case, built from a formal clinical study, is based on the detection, depth estimation, localization and tracking of objects in wheelchair’s indoor environment, namely: door and door handles. The aim of this work is to provide a perception layer to the wheelchair, enabling this way the detection of these keypoints in its immediate surrounding, and constructing of a short lifespan semantic map. Firstly, we present an adaptation of the YOLOv3 object detection algorithm to our use case. Then, we present our depth estimation approach using an Intel RealSense camera. Finally, as a third and last step of our approach, we present our 3D object tracking approach based on the SORT algorithm. In order to validate all the developments, we have carried out different experiments in a controlled indoor environment. Detection, distance estimation and object tracking are experimented using our own dataset, which includes doors and door handles.
Fast demodulation of single-shot interferogram via convolutional neural network

In this report, a method is proposed to demodulate a single-shot interferogram based on deep learning using a convolutional neural network (CNN). The time required to demodulate a single interferogram is on the order of milliseconds. The root mean square (RMS) of the residual wavefront between the original wavefront and the estimated wavefront reached the order of 10−2 λ. Moreover, the cost was lower than using commercial dynamic and phase-shifting interferometers. This method yielded superior real-time performance and high precision. The results indicated that this approach based on deep learning is robust and feasible for demodulating single-shot interferograms.
Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective

Neural Architecture Search (NAS) has been explosively studied to automate the discovery of top-performer neural networks. Current works require heavy training of supernet or intensive architecture evaluations, thus suffering from heavy resource consumption and often incurring search bias due to truncated training or approximations. Can we select the best neural architectures without involving any training and eliminate a drastic portion of the search cost? We provide an affirmative answer, by proposing a novel framework called \textit{training-free neural architecture search} (TE-NAS). TE-NAS ranks architectures by analyzing the spectrum of the neural tangent kernel (NTK), and the number of linear regions in the input space. Both are motivated by recent theory advances in deep networks, and can be computed without any training. We show that: (1) these two measurements imply the trainability and expressivity of a neural network; and (2) they strongly correlate with the network's actual test accuracy. Further on, we design a pruning-based NAS mechanism to achieve a more flexible and superior trade-off between the trainability and expressivity during the search. In NAS-Bench-201 and DARTS search spaces, TE-NAS completes high-quality search but only costs 0.5 and 4 GPU hours with one 1080Ti on CIFAR-10 and ImageNet, respectively. We hope our work to inspire more attempts in bridging between the theoretic findings of deep networks and practical impacts in real NAS applications.
Measurement-free preparation of grid states

Abstract Quantum computing potentially offers exponential speed-ups over classical computing for certain tasks. A central, outstanding challenge to making quantum computing practical is to achieve fault tolerance, meaning that computations of any length or size can be realized in the presence of noise. The Gottesman-Kitaev-Preskill code is a promising approach toward fault-tolerant quantum computing, encoding logical qubits into grid states of harmonic oscillators. However, for the code to be fault tolerant, the quality of the grid states has to be extremely high. Approximate grid states have recently been realized experimentally, but their quality is still insufficient for fault tolerance. Current implementable protocols for generating grid states rely on measurements of ancillary qubits combined with either postselection or feed forward. Implementing such measurements take up significant time during which the states decohere, thus limiting their quality. Here, we propose a measurement-free preparation protocol, which deterministically prepares arbitrary logical grid states with a rectangular or hexagonal lattice. The protocol can be readily implemented in trapped-ion or superconducting-circuit platforms to generate high-quality grid states using only a few interactions, even with the noise levels found in current systems.
Manifold Learning for Knowledge Discovery and Intelligent Inverse Design of Photonic Nanostructures: Breaking the Geometric Complexity

Here, we present a new approach based on manifold learning for knowledge discovery and inverse design with minimal complexity in photonic nanostructures. Our approach builds on studying submanifolds of responses of a class of nanostructures with different design complexities in the latent space to obtain valuable insight about the physics of device operation to guide a more intelligent design. In contrast to the current methods for inverse design of photonic nanostructures, which are limited to preselected and usually overcomplex structures, we show that our method allows evolution from an initial design toward the simplest structure while solving the inverse problem.
An Efficient Parallel Secure Machine Learning Framework on GPUs

Machine learning is widely used in our daily lives. Large amounts of data have been continuously produced and transmitted to the cloud for model training and data processing, which raises a problem: how to preserve the security of the data. Recently, a secure machine learning system named SecureML has been proposed to solve this issue using two-party computation. However, due to the excessive computation expenses of two-party computation, the secure machine learning is about 2× slower than the original machine learning methods. Previous work on secure machine learning mostly focused on novel protocols or improving accuracy, while the performance metric has been ignored. In this article, we propose a GPU-based framework ParSecureML to improve the performance of secure machine learning algorithms based on two-party computation. The main challenges of developing ParSecureML lie in the complex computation patterns, frequent intra-node data transmission between CPU and GPU, and complicated inter-node data dependence. To handle these challenges, we propose a series of novel solutions, including profiling-guided adaptive GPU utilization, fine-grained double pipeline for intra-node CPU-GPU cooperation, and compressed transmission for inter-node communication. Moreover, we integrate architecture specific optimizations, such as Tensor Cores, into ParSecureML. As far as we know, this is the first GPU-based secure machine learning framework. Compared to the state-of-the-art framework, ParSecureML achieves an average of 33.8× speedup. ParSecureML can also be applied to inferences, which achieves 31.7× speedup on average.
Can Shallow Neural Networks Beat the Curse of Dimensionality? A Mean Field Training Perspective

We prove that the gradient descent training of a two-layer neural network on empirical or population risk may not decrease population risk at an order faster than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$t^{-4/(d-2)}$</tex-math></inline-formula> under mean field scaling. The loss functional is mean squared error with a Lipschitz-continuous target function and data distributed uniformly on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$d$</tex-math></inline-formula> -dimensional unit cube. Thus gradient descent training for fitting reasonably smooth, but truly high-dimensional data may be subject to the curse of dimensionality. We present numerical evidence that gradient descent training with general Lipschitz target functions becomes slower and slower as the dimension increases, but converges at approximately the same rate in all dimensions when the target function lies in the natural function space for two-layer ReLU networks. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>Impact Statement</i>–Artificial neural networks perform well in many real life applications, but may suffer from the curse of dimensionality on certain problems. We provide theoretical and numerical evidence that this may be related to whether a target function lies in the hypothesis class described by infinitely wide networks. The training dynamics are considered in the fully non-linear regime and not reduced to neural tangent kernels. We believe that it will be essential to study these hypothesis classes in detail to choose an appropriate machine learning models for a given problem. The goal of the article is to illustrate this in a mathematically sound and numerically convincing fashion.
Privacy-Preserving IoT Data Aggregation Based on Blockchain and Homomorphic Encryption

Data analytics based on the produced data from the Internet of Things (IoT) devices is expected to improve the individuals’ quality of life. However, ensuring security and privacy in the IoT data aggregation process is a non-trivial task. Generally, the IoT data aggregation process is based on centralized servers. Yet, in the case of distributed approaches, it is difficult to coordinate several untrustworthy parties. Fortunately, the blockchain may provide decentralization while overcoming the trust problem. Consequently, blockchain-based IoT data aggregation may become a reasonable choice for the design of a privacy-preserving system. To this end, we propose PrivDA, a Privacy-preserving IoT Data Aggregation scheme based on the blockchain and homomorphic encryption technologies. In the proposed system, each data consumer can create a smart contract and publish both terms of service and requested IoT data. Thus, the smart contract puts together into one group potential data producers that can answer the consumer’s request and chooses one aggregator, the role of which is to compute the group requested result using homomorphic computations. Therefore, group-level aggregation obfuscates IoT data, which complicates sensitive information inference from a single IoT device. Finally, we deploy the proposal on a private Ethereum blockchain and give the performance evaluation.
Shape and Texture Aware Facial Expression Recognition Using Spatial Pyramid Zernike Moments and Law’s Textures Feature Set

Facial expression recognition (FER) requires better descriptors to represent the face patterns as the facial region changes due to the movement of the face muscles during an expression. In this paper, a method of concatenating spatial pyramid Zernike moments based shape features and Law’s texture features is proposed to uniquely capture the macro and micro details of each facial expression. The proposed method employs multilayer perceptron and radial basis function feed forward artificial neural networks for recognizing the facial expressions. The suitability of the features in recognizing the expressions is explored across the datasets independent of the subjects or persons. The experiments conducted on JAFFE and KDEF datasets demonstrate that the concatenated feature vectors are capable of representing the facial expressions with better accuracy and least errors. The radial basis function based classifier delivers a performance with an average recognition accuracy of 95.86% and 88.87% on the JAFFE and KDEF datasets respectively for subject dependent FER.
International collaboration framework for the calculation of performance loss rates: Data quality, benchmarks, and trends (towards a uniform methodology)

Abstract The IEA PVPS Task 13 group, experts who focus on photovoltaic performance, operation, and reliability from several leading R&amp;D centers, universities, and industrial companies, is developing a framework for the calculation of performance loss rates of a large number of commercial and research photovoltaic (PV) power plants and their related weather data coming across various climatic zones. The general steps to calculate the performance loss rate are (i) input data cleaning and grading; (ii) data filtering; (iii) performance metric selection, corrections, and aggregation; and finally, (iv) application of a statistical modeling method to determine the performance loss rate value. In this study, several high‐quality power and irradiance datasets have been shared, and the participants of the study were asked to calculate the performance loss rate of each individual system using their preferred methodologies. The data are used for benchmarking activities and to define capabilities and uncertainties of all the various methods. The combination of data filtering, metrics (performance ratio or power based), and statistical modeling methods are benchmarked in terms of (i) their deviation from the average value and (ii) their uncertainty, standard error, and confidence intervals. It was observed that careful data filtering is an essential foundation for reliable performance loss rate calculations. Furthermore, the selection of the calculation steps filter/metric/statistical method is highly dependent on one another, and the steps should not be assessed individually.
Investigating the Effectiveness of Virtual Reality for Culture Learning

People who are to live, study and work abroad will face more challenges in the new cultural environment and suffer more acculturative stress. Virtual Reality (VR), by which an immersive learning environment can be built, may help them adapt to a foreign culture at a lower cost of time and money. In order to work out a design method for culture learning in VR, we have designed a VR application so that learners can experience and learn the typical western festival culture – Christmas culture – in an immersive environment. To evaluate the effectiveness of the VR method, 50 EFL Chinese university students were enrolled in our experiments and randomly assigned to the VR group and the non-VR group, the data was drawn from cultural knowledge questionnaire, behavior test and Intercultural Sensitivity Scale (ISS). The ANCOVA revealed no major effect for group factor on knowledge learning. Similarly, the Mixed ANOVA identified no major effect for group factor on behavior learning and attitude learning. There was no interaction effect between time and group in all experiments. Our results show that the VR method is preferred by most of the participants, but it shows no remarkable advantage over the non-VR method. Moreover, regression analysis between the culture learning and the sense of presence in VR shows that presence has the potential to improve the performance of intercultural interaction engagement. Our findings are of practical value for culture learning in VR.
Deep Learning and Transfer Learning for Automatic Cell Counting in Microscope Images of Human Cancer Cell Lines

In biology and medicine, cell counting is one of the most important elements of cytometry, with applications to research and clinical practice. For instance, the complete cell count could help to determine conditions for which cancer cells could grow or not. However, cell counting is a laborious and time-consuming process, and its automatization is highly demanded. Here, we propose use of a Convolutional Neural Network-based regressor, a regression model trained end-to-end, to provide the cell count. First, unlike most of the related work, we formulate the problem of cell counting as the regression task rather than the classification task. This allows not only to reduce the required annotation information (i.e., the number of cells instead of pixel-level annotations) but also to reduce the burden of segmenting potential cells and then classifying them. Second, we propose use of xResNet, a successful convolutional architecture with residual connection, together with transfer learning (using a pretrained model) to achieve human-level performance. We demonstrate the performance of our approach to real-life data of two cell lines, human osteosarcoma and human leukemia, collected at the University of Amsterdam (133 training images, and 32 test images). We show that the proposed method (deep learning and transfer learning) outperforms currently used machine learning methods. It achieves the test mean absolute error equal 12 (±15) against 32 (±33) obtained by the deep learning without transfer learning, and 41 (±37) of the best-performing machine learning pipeline (Random Forest Regression with the Histogram of Gradients features).
A multiple combined method for rebalancing medical data with class imbalances

Most classification algorithms assume that classes are in a balanced state. However, datasets with class imbalances are everywhere. The classes of actual medical datasets are imbalanced, severely impacting identification models and even sacrificing the classification accuracy of the minority class, even though it is the most influential and representative. The medical field has irreversible characteristics. Its tolerance rate for misjudgment is relatively low, and errors may cause irreparable harm to patients. Therefore, this study proposes a multiple combined method to rebalance medical data featuring class imbalances. The combined methods include (1) resampling methods (synthetic minority oversampling technique [SMOTE] and undersampling [US]), (2) particle swarm optimization (PSO), and (3) MetaCost. This study conducted two experiments with nine medical datasets to verify and compare the proposed method with the listing methods. A decision tree is used to generate decision rules for easy understanding of the research results. The results show that (1) the proposed method with ensemble learning can improve the area under a receiver operating characteristic curve (AUC), recall, precision, and F1 metrics; (2) MetaCost can increase sensitivity; (3) SMOTE can effectively enhance AUC; (4) US can improve sensitivity, F1, and misclassification costs in data with a high-class imbalance ratio; and (5) PSO-based attribute selection can increase sensitivity and reduce data dimension. Finally, we suggest that the dataset with an imbalanced ratio >9 must use the US results to make the decision. As the imbalanced ratio is < 9, the decision-maker can simultaneously consider the results of SMOTE and US to identify the best decision.