Reviewerly/exHarmony huggingface dataset:
DECAF: Deep Extreme Classification with Label Features

Extreme multi-label classification (XML) involves tagging a data point with its most relevant subset of labels from an extremely large label set, with several applications such as product-to-product recommendation with millions of products. Although leading XML algorithms scale to millions of labels, they largely ignore label metadata such as textual descriptions of the labels. On the other hand, classical techniques that can utilize label metadata via representation learning using deep networks struggle in extreme settings. This paper develops the DECAF algorithm that addresses these challenges by learning models enriched by label metadata that jointly learn model parameters and feature representations using deep networks and offer accurate classification at the scale of millions of labels. DECAF makes specific contributions to model architecture design, initialization, and training, enabling it to offer up to 2-6% more accurate prediction than leading extreme classifiers on publicly available benchmark product-to-product recommendation datasets, such as LF-AmazonTitles-1.3M. At the same time, DECAF was found to be up to 22x faster at inference than leading deep extreme classifiers, which makes it suitable for real-time applications that require predictions within a few milliseconds. The code for DECAF is available at the following URL: https://github.com/Extreme-classification/DECAF
Leprosy Screening Based on Artificial Intelligence: Development of a Cross-Platform App

Background According to the World Health Organization, achieving targets for control of leprosy by 2030 will require disease elimination and interruption of transmission at the national or regional level. India and Brazil have reported the highest leprosy burden in the last few decades, revealing the need for strategies and tools to help health professionals correctly manage and control the disease. Objective The main objective of this study was to develop a cross-platform app for leprosy screening based on artificial intelligence (AI) with the goal of increasing accessibility of an accurate method of classifying leprosy treatment for health professionals, especially for communities further away from major diagnostic centers. Toward this end, we analyzed the quality of leprosy data in Brazil on the National Notifiable Diseases Information System (SINAN). Methods Leprosy data were extracted from the SINAN database, carefully cleaned, and used to build AI decision models based on the random forest algorithm to predict operational classification in paucibacillary or multibacillary leprosy. We used Python programming language to extract and clean the data, and R programming language to train and test the AI model via cross-validation. To allow broad access, we deployed the final random forest classification model in a web app via shinyApp using data available from the Brazilian Institute of Geography and Statistics and the Department of Informatics of the Unified Health System. Results We mapped the dispersion of leprosy incidence in Brazil from 2014 to 2018, and found a particularly high number of cases in central Brazil in 2014 that further increased in 2018 in the state of Mato Grosso. For some municipalities, up to 80% of cases showed some data discrepancy. Of a total of 21,047 discrepancies detected, the most common was “operational classification does not match the clinical form.” After data processing, we identified a total of 77,628 cases with missing data. The sensitivity and specificity of the AI model applied for the operational classification of leprosy was 93.97% and 87.09%, respectively. Conclusions The proposed app was able to recognize patterns in leprosy cases registered in the SINAN database and to classify new patients with paucibacillary or multibacillary leprosy, thereby reducing the probability of incorrect assignment by health centers. The collection and notification of data on leprosy in Brazil seem to lack specific validation to increase the quality of the data for implementations via AI. The AI models implemented in this work had satisfactory accuracy across Brazilian states and could be a complementary diagnosis tool, especially in remote areas with few specialist physicians.
PREDICTING SOFTWARE BUGS OF NEWLY AND LARGE DATASETS THROUGH A UNIFIED NEURO-FUZZY APPROACH: RELIABILITY PERSPECTIVE

Reliability of software is an essential concern for users for a long time. Software reliability is mainly obtained through modeling and estimating. There are numerous methods for reducing the failure rate. However, the existing methods are nonlinear. Hence the parameter estimation of these methods is difficult. This paper concerns on estimation and prediction of software reliability through different soft computing methods for improving the reliability of software. For estimation and prediction, the authors of this paper take two soft computing methodologies, including fuzzy logic and neural network. The outcomes seem to give satisfactory results on large datasets. For experiments, this paper is using two different large datasets of Apache server and MyLyn application software for showing the effectiveness of the results. The proposed methods of prediction would be useful for practitioners to simplify the procedures during software development in large datasets for reducing failures of software.
The Technology Education System Staffing: Problems and Solutions

The strategic objective of modern technological education is to prepare children for life in a high-tech competitive world. The implementation of this task encounters the problems of material and technical equipment of educational institutions and their staffing. An integrated approach based on the pooling of all resources and efforts of interested parties is needed to organize a technological education modern system. Training programs for teachers should take into account their interdisciplinary nature, and include the psychological, pedagogical, and natural-science components, so as technical aspects. The important segment of technology education is training of technical specialists without pedagogical background to work at schools and universities, as well as training of school teachers and university lectures based on the modern technological order. In this regard, a new look at the IGIP programs is possible, these program goals and objectives interpretation should be applied to the teacher training within the system of high-school technological education. The article considers the approaches to solving the problems of high-school technological education system staffing, presents the comparative analysis of the current educational programs. The experience in training technical university teachers, masters of industrial instruction, technology teachers should be adjusted in accordance with the global changes in science, technique and technology.
Automatic multilabel detection of ICD10 codes in Dutch cardiology discharge letters using neural networks

Abstract Standard reference terminology of diagnoses and risk factors is crucial for billing, epidemiological studies, and inter/intranational comparisons of diseases. The International Classification of Disease (ICD) is a standardized and widely used method, but the manual classification is an enormously time-consuming endeavor. Natural language processing together with machine learning allows automated structuring of diagnoses using ICD-10 codes, but the limited performance of machine learning models, the necessity of gigantic datasets, and poor reliability of terminal parts of these codes restricted clinical usability. We aimed to create a high performing pipeline for automated classification of reliable ICD-10 codes in the free medical text in cardiology. We focussed on frequently used and well-defined three- and four-digit ICD-10 codes that still have enough granularity to be clinically relevant such as atrial fibrillation (I48), acute myocardial infarction (I21), or dilated cardiomyopathy (I42.0). Our pipeline uses a deep neural network known as a Bidirectional Gated Recurrent Unit Neural Network and was trained and tested with 5548 discharge letters and validated in 5089 discharge and procedural letters. As in clinical practice discharge letters may be labeled with more than one code, we assessed the single- and multilabel performance of main diagnoses and cardiovascular risk factors. We investigated using both the entire body of text and only the summary paragraph, supplemented by age and sex. Given the privacy-sensitive information included in discharge letters, we added a de-identification step. The performance was high, with F1 scores of 0.76–0.99 for three-character and 0.87–0.98 for four-character ICD-10 codes, and was best when using complete discharge letters. Adding variables age/sex did not affect results. For model interpretability, word coefficients were provided and qualitative assessment of classification was manually performed. Because of its high performance, this pipeline can be useful to decrease the administrative burden of classifying discharge diagnoses and may serve as a scaffold for reimbursement and research applications.
Sharing VM Resources With Using Prediction of Future User Requests for an Efficient Load Balancing in Cloud Computing Environment

The problem of balancing user requests in cloud computing is becoming more serious due to the variation of workloads. Load balancing and allocation processes still need more optimizing methodologies and models to improve performance and increase the quality of service. This article describes a solution to balance user workload efficiently by proposing a model that allows each virtual machine (VM) to maximize the serving number of requests based on its capacity. The model measures VMs' capacity as a percentage and maps groups of user requests to appropriate active virtual machines. Finding the expected patterns from a big data repository, such as log data, and using some machine learning techniques can make the prediction more efficiently. The work is implemented and evaluated using some performance metrics, and the results are compared with other research. The evaluation shows the efficiency of the proposed approach in distributing user workload and improving results.
Uncertainty-Aware Variational-Recurrent Imputation Network for Clinical Time Series

Electronic health records (EHR) consist of longitudinal clinical observations portrayed with sparsity, irregularity, and high dimensionality, which become major obstacles in drawing reliable downstream clinical outcomes. Although there exist great numbers of imputation methods to tackle these issues, most of them ignore correlated features, temporal dynamics, and entirely set aside the uncertainty. Since the missing value estimates involve the risk of being inaccurate, it is appropriate for the method to handle the less certain information differently than the reliable data. In that regard, we can use the uncertainties in estimating the missing values as the fidelity score to be further utilized to alleviate the risk of biased missing value estimates. In this work, we propose a novel variational-recurrent imputation network, which unifies an imputation and a prediction network by taking into account the correlated features, temporal dynamics, as well as uncertainty. Specifically, we leverage the deep generative model in the imputation, which is based on the distribution among variables, and a recurrent imputation network to exploit the temporal relations, in conjunction with utilization of the uncertainty. We validated the effectiveness of our proposed model on two publicly available real-world EHR datasets: 1) PhysioNet Challenge 2012 and 2) MIMIC-III, and compared the results with other competing state-of-the-art methods in the literature.
Intelligent detection and recognition system for mask wearing based on improved RetinaFace algorithm

The COVID-19 can be transmitted through airborne droplets, aerosols and other carriers.In order to better reduce people's risk of infection, individuals need to wear masks to prevent the spread of the virus when going out to public places, seeking medical treatment, and taking public transportation.This paper is based on the improved RETINAFACE algorithm, which effectively realizes the detection of mask wearing, and on the basis of this algorithm, realizes the function of judging whether the mask is worn correctly.In the face recognition algorithm, this paper designs a face recognition algorithm with higher accuracy.The system combines a face mask wearing detection algorithm, a mask standard wearing detection algorithm and a face recognition algorithm.In addition, this article adds a voice prompt module to better assist the integrity of the system's functions.The test results of the final experiment show that the system can effectively achieve the purpose of face mask detection and recognition.
Unsupervised Fault Detection on Unmanned Aerial Vehicles: Encoding and Thresholding Approach

Unmanned Aerial Vehicles are expected to create enormous benefits to society, but there are safety concerns in recognizing faults at the vehicle’s control component. Prior studies proposed various fault detection approaches leveraging heuristics-based rules and supervised learning-based models, but there were several drawbacks. The rule-based approaches required an engineer to update the rules on every type of fault, and the supervised learning-based approaches necessitated the acquisition of a finely-labeled training dataset. Moreover, both prior approaches commonly include a limit that the detection model can identify the trained type of faults only, but fail to recognize the unseen type of faults. In pursuit of resolving the aforementioned drawbacks, we proposed a fault detection model utilizing a stacked autoencoder that lies under unsupervised learning. The autoencoder was trained with data from safe UAV states, and its reconstruction loss was examined to distinguish the safe states and faulty states. The key contributions of our study are, as follows. First, we presented a series of analyses to extract essential features from raw UAV flight logs. Second, we designed a fault detection model consisting of the stacked autoencoder and the classifier. Lastly, we validated our approach’s fault detection performance with two datasets consisting of different types of UAV faults.
Converting 2D-Medical Image Files “DICOM” into 3D- Models, Based on Image Processing, and Analysing Their Results with Python Programming

This paper presents the possibility of converting (2D) medical image data (Digital Imaging and Communications in Medicine (DICOM) files) to 3D model. Medical data and image processing software’s, namely Seg3D2 and ImageVis3D, were used to analyze images, create 3D models of the liver and export them in OBJ images for performing a range of surgical procedures, and measure the accuracy of the size and weight of the liver, kidneys and arteries with their conformity to DICOM file. It is compared to the image processing before and after the conversion stage of medical image using the Python language program to ensure the integrity of the images after the conversion process is identical to the original pictures of DICOM without causing any distortions or changes to it. We reduce file size while maintaining the model’s highest quality, while employing mixed reality techniques, applied on Liver Surgical Operation [living donor liver Transplantation (LDLT)].
A plasmonically enhanced route to faster and more energy-efficient phase-change integrated photonic memory and computing devices

Over the past 30 years or more, chalcogenide phase-change materials and devices have generated much scientific and industrial interest, particularly as a platform for non-volatile optical and electronic storage devices. More recently, the combination of chalcogenide phase-change materials with photonic integrated circuits has begun to be enthusiastically explored, and among many proposals, the all-photonic phase-change memory brings the memristor-type device concept to the integrated photonic platform, opening up the route to new forms of unconventional (e.g., in-memory and neuromorphic) yet practicable optical computing. For any memory or computing device, fast switching speed and low switching energy are most attractive attributes, and approaches by which speed and energy efficiency can be improved are always desirable. For phase-change material-based devices, speed and energy consumption are both enhanced the smaller the volume of phase-change material that is required to be switched between its amorphous and crystalline phases. However, in conventional integrated photonic systems, the optical readout of nanometric-sized volumes of phase-change material is problematic. Plasmonics offers a way to bypass such limitations: plasmonic resonant structures are inherently capable of harnessing and focussing optical energy on sub-wavelength scales, far beyond the capabilities of conventional optical and photonic elements. In this work, we explore various approaches to combine the three building blocks of Si-photonics, resonant plasmonic structures, and phase-change materials to deliver plasmonically enhanced integrated phase-change photonic memory and computing devices and systems, underlining the inherent technical and theoretical challenges therein.
A multi-objective optimization for resource allocation of emergent demands in cloud computing

Abstract Cloud resource demands, especially some unclear and emergent resource demands, are growing rapidly with the development of cloud computing, big data and artificial intelligence. The traditional cloud resource allocation methods do not support the emergent mode in guaranteeing the timeliness and optimization of resource allocation. This paper proposes a resource allocation algorithm for emergent demands in cloud computing. After building the priority of resource allocation and the matching distances of resource performance and resource proportion to respond to emergent resource demands, a multi-objective optimization model of cloud resource allocation is established based on the minimum number of the physical servers used and the minimum matching distances of resource performance and resource proportion. Then, an improved evolutionary algorithm, RAA-PI-NSGAII, is presented to solve the multi-objective optimization model, which not only improves the quality and distribution uniformity of the solution set but also accelerates the solving speed. The experimental results show that our algorithm can not only allocate resources quickly and optimally for emergent demands but also balance the utilization of all kinds of resources.
Shape-Constrained Symbolic Regression—Improving Extrapolation with Prior Knowledge

We investigate the addition of constraints on the function image and its derivatives for the incorporation of prior knowledge in symbolic regression. The approach is called shape-constrained symbolic regression and allows us to enforce, for example, monotonicity of the function over selected inputs. The aim is to find models which conform to expected behavior and which have improved extrapolation capabilities. We demonstrate the feasibility of the idea and propose and compare two evolutionary algorithms for shape-constrained symbolic regression: (i) an extension of tree-based genetic programming which discards infeasible solutions in the selection step, and (ii) a two-population evolutionary algorithm that separates the feasible from the infeasible solutions. In both algorithms we use interval arithmetic to approximate bounds for models and their partial derivatives. The algorithms are tested on a set of 19 synthetic and four real-world regression problems. Both algorithms are able to identify models which conform to shape constraints which is not the case for the unmodified symbolic regression algorithms. However, the predictive accuracy of models with constraints is worse on the training set and the test set. Shape-constrained polynomial regression produces the best results for the test set but also significantly larger models.
Motion control of an articulated mobile manipulator in 3D using the Lyapunov-based control scheme

Finding feasible solutions to motion planning and control problem of robotic systems in different environments with various applications is an active area of research.This article presents a new solution to the motion planning and control problem of a three-dimensional articulated mobile manipulator comprising a car-like mobile platform and a three-dimensional n-link articulated arm using the Lyapunov-based control scheme.The motion of the system is described as twofold: first, the car-like mobile platform moves from an initial position to its pseudo-target, and second, when the mobile platform is within some predefined distance from the pseudo-target, the end-effector of the robot arm is attracted to its designated target.Therefore, presenting a new 2-Step Algorithm in this paper for dual movement of the articulated mobile manipulator in 3D.In addition, a workspace cluttered with fixed spherical and rod obstacles of random sizes and positions is considered in this research.For the mobile manipulator to avoid an obstacle, the Minimum Distance Technique is adapted where a point on the robot that is closest to an obstacle will avoid the obstacle.The convergence of the two bodies and the stability of the mechanical system are guaranteed by the Lyapunov's direct method.The continuous nonlinear control laws proposed from the control scheme also take into account all mechanical singularities and velocity limitations associated with the system.Theoretical proofs and computer simulations validate the new continuous, acceleration-based, nonlinear, time-invariant control laws.
Sentiment analysis in Bengali via transfer learning using multi-lingual BERT

Sentiment analysis (SA) in Bengali is challenging due to this Indo-Aryan language's highly inflected properties with more than 160 different inflected forms for verbs and 36 different forms for noun and 24 different forms for pronouns. The lack of standard labeled datasets in the Bengali domain makes the task of SA even harder. In this paper, we present manually tagged 2-class and 3-class SA datasets in Bengali. We also demonstrate that the multi-lingual BERT model with relevant extensions can be trained via the approach of transfer learning over those novel datasets to improve the state-of-the-art performance in sentiment classification tasks. This deep learning model achieves an accuracy of 71% for 2-class sentiment classification compared to the current state-of-the-art accuracy of 68%. We also present the very first Bengali SA classifier for the 3-class manually tagged dataset, and our proposed model achieves an accuracy of 60%. We further use this model to analyze the sentiment of public comments in the online daily newspaper. Our analysis shows that people post negative comments for political or sports news more often, while the religious article comments represent positive sentiment. The dataset and code is publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .