Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Electronic Medical Records System with Smart Contract and Consensus Algorithm in Cloud Environment

The blockchain is a peer-to-peer distributed ledger technology that works on the precept of “write-once-read-only.” In a blockchain, pieces of information are arranged in the form of blocks, and these blocks are linked together using the hash value of previous blocks. The blocks in a blockchain mechanism are appended only, which means that once information is stored in a block and it cannot be changed; no one tampers the block’s content. The traditional electronic medical records (EMRs) based system stores the patients’ information in a local database or server, which provides centralization of information, and traditional EMRs are more centric on the health providers. So, security and sharing of patients’ information are difficult tasks in the traditional EMR system. The blockchain mechanism has the potential to resolve these existing problems. Due to the appended-only-ledger principle and decentralization of blocks between the network participants, blockchain technology is suited to the EMR system. In this article, first, we discuss all the existing EMR systems and discuss their drawbacks. Keeping all the drawbacks in our mind, we propose a blockchain-based medical record system that utilizes clouding technology for storage purposes. Furthermore, we have designed a smart contract and consensus algorithm for our proposed EMR. Our system only uses a permissioned blockchain model so that only verified and authenticated users can generate their data and participate in the data-sharing system.
Extrapolating distortion correction with local measurements for space-based multi-module splicing large-format infrared cameras

Conventional distortion correction methods with the classical models, including radial, decentering, and thin prism distortions and with the interpolation template, depend heavily on the evenly distributed measurement data on the entire focal plane. However, owing to the restricted cubage of the vacuum tank and the large size of the assembled camera, there is no more extra space for the amounted large-format camera to adjust with the 2D turntable during laboratory vacuum experiment, which, accordingly, makes the collected measurement points gathered in just one module of the focal plane and eventually results in poor correction accuracy of the mentioned approaches. Here, in terms of the problems above, an extrapolating distortion correction method with local measurements for space-based multi-module splicing large-format infrared cameras was proposed in this paper. Benefiting from the polynomial model not being affected by the distribution of data, a third-order polynomial model adopted for distortion correction is solved by using local measurements and extrapolated reasonably, which guarantees the global camera calibration. Experimental results show that the mean distortion error can be corrected within 0.5 pixels. This method overcoming the deficiency of local test points can effectively improve the correction accuracy of the large-format camera and provide a new idea for global high-precision calibration of on-orbit payloads based on local measurements.
A Lightweight Deep Learning Framework for Galaxy Morphology Classification

Abstract With the construction of large telescopes and the explosive growth of observed galaxy data, we are facing the problem to improve the data processing efficiency while ensuring the accuracy of galaxy morphology classification. Therefore, this work designed a lightweight deep learning framework, EfficientNet-G3, for galaxy morphology classification. The proposed framework is based on EfficientNet which integrates the Efficient Neural Architecture Search algorithm. Its performance is assessed with the data set from the Galaxy Zoo Challenge Project on Kaggle. Compared with several typical neural networks and deep learning frameworks in galaxy morphology classification, the proposed EfficientNet-G3 model improved the classification accuracy from 95.8% to 96.63% with F1-Score values of 97.1%. Typically, this model uses the least number of parameters, which is about one tenth that of DenseNet161 and one fifth that of ResNet-26, but its accuracy is about one percent higher than them. The proposed EfficientNet-G3 can act as an important reference for fast morphological classification for massive galaxy data in terms of efficiency and accuracy.
Development of an Efficient and Secured E-Voting Mobile Application Using Android

Smart technologies, particularly the development of the Internet, are employed to enhance the quality of human existence. Thanks to the Internet’s explosive expansion, more and more tasks can now be completed quickly and easily compared to the earlier times. E-voting is a relatively recent field that has been identified. Voting can be conducted in a variety of methods, including in person at a polling place, online, and via a mobile application. The security of applications cannot be disregarded given the internet’s explosive growth. In order to prevent phishing attacks, we created an Android application and included a 3-step security process before voting. Students can now vote online from any location at any time using a mobile device. Android Studio is used to create and deploy the application. While creating the voting application, this research adheres to the software development life cycle. The result of this research is the creation of a mobile application that is user-friendly for students and serves as a practical tool for letting them vote with three levels of security.
Development and validation of a model for surveillance of postoperative bleeding complications using structured electronic health records data

Postoperative bleeding complications surveillance is done primarily through manual chart review. The purpose of this study was to develop and validate a detection model for postoperative bleeding complications using structured electronic health records data.Patients who underwent operations at 1 of 5 hospitals within our local health system between 2013 and 2019 and whose complications were reported by the American College of Surgeons National Surgical Quality Improvement Program were included. Electronic health records data were linked to American College of Surgeons National Surgical Quality Improvement Program data using personal health identifiers. Electronic health records predictors included diagnosis codes mapped to PheCodes, procedure names, and medications within 30 days after surgery. We defined bleeding events as the transfusion of red blood cell components within 30 days after surgery. The knockoff filter and the lasso were used to develop a model in a training set of operations from January 2013 to March 2017. Performance of each model was tested in a held-out data set of patients who underwent operations from March 2017 to October 2019.A total of 30,639 patients were included; 1,112 patients (3.6%) had a bleeding event. Eight predictor variables were selected by the knockoff filter. When applied to the test set, specificity was 94%, sensitivity was 94%, area under the curve was 0.97, and accuracy was 93%. Calibration was consistent in lower predicted risk patients, whereas the model slightly overpredicted risk in high-risk patients.We created a parsimonious, accurate model for identifying patients with bleeding complications. This model can be used to augment manual chart review for surveillance and reporting of perioperative bleeding complications, enabling inclusion of all surgeries in quality improvement efforts.
An Image Retrieval System Using Deep Learning to Extract High-Level Features

The usual procedure used in Content Based Image retrieval (CBIR), is to extract some useful low-level features such as color, texture and shape from the query image and retrieve images that have a similar set of features. However, the problem with using low-level features is the semantic gap between image feature representation and human visual understanding. That is why many researchers are devoted for improving content-based image retrieval methods with a particular focus on reducing the semantic gap between low-level features and human visual perceptions. Those researchers are mainly focused on combining low-level features together to have a better representation of the content of an image, which make it closer to the human visual perception but still not close enough to reduce the semantic gap. In this paper we’ll start by a comprehensive review on the recent researches in the field of Image Retrieval, then we propose a CBIR system based on convolutional neural network and transfer learning to extract high-level features, as an initiative part of a larger project that aims to retrieve and collect images containing the Arabic language for natural language processing tasks.
State-Domain Equations and Their Quantum Computing Solution Based HHL Algorithm

Quantum computing is becoming a major new field in engineering. Adaptation of classical computation concepts to this emerging technological reality is underway. In this study, a sampling-based approach is used to adapt state-domain equations to HHL-based formulation to obtain a homologous quantum algorithm for solving the dynamic equations in the state domain. The results obtained by the quantum computing approach are very close to the real response of the system and to the power-series-based exact solution. This work represents a significant contribution to the advancement of dynamic systems modeling and solving in state-domain adapted for use in quantum computers in the future.
VAL: Volume and Access Pattern Leakage-Abuse Attack with Leaked Documents

Searchable Encryption schemes provide secure search over encrypted databases while allowing admitted information leakages. Generally, the leakages can be categorized into access and volume pattern. In most existing SE schemes, these leakages are caused by practical designs but are considered an acceptable price to achieve high search efficiency. Recent attacks have shown that such leakages could be easily exploited to retrieve the underlying keywords for search queries. Under the umbrella of attacking SE, we design a new Volume and Access Pattern Leakage-Abuse Attack (VAL-Attack) that improves the matching technique of LEAP (CCS '21) and exploits both the access and volume patterns. Our proposed attack only leverages leaked documents and the keywords present in those documents as auxiliary knowledge and can effectively retrieve document and keyword matches from leaked data. Furthermore, the recovery performs without false positives. We further compare VAL-Attack with two recent well-defined attacks on several real-world datasets to highlight the effectiveness of our attack and present the performance under popular countermeasures.
Micro-network Based Convolutional Neural Network with Integration of Multilayer Feature Fusion Strategy for Human Activity Recognition

Convolutional neural networks (CNN) have shown remarkable performance in enormous computer vision applications over the years, and many works have been done for human activity recognition (HAR) using CNN. However, most of deep learning-based architectures require large training data and plenty of computational resources. Therefore, we proposed a simple and efficient deep learning model for human activity recognition, which works on complex visual data and require less computational resources. In the proposed work, we designed a novel CNN architecture by stacking the repeated components called micro-networks to incorporate multiscale processing in the network layers. We have used a feature fusion strategy to pass the previous layer’s abstract complementary information to the next adjacent layer, believing that each layer encapsulates specific feature maps. Therefore, the hidden complementary information can potentially enhance the feature discrimination capacity of the network and help in learning the network. The proposed architecture is fully trained from scratch with stochastic gradient descent (SGD) optimizer at 0.05 initial learning rate and a softmax classifier is used for activity recognition. The merit of the proposed method over standard deep learning models is its computational efficiency in terms of learnable parameters and computational resources. The proposed model gives good performance on small as well as large size datasets. For authentication of the proposed method several extensive experiments are conducted on publically available datasets, namely UCF-101, HMDB-51, YouTube, and IXMAS datasets. The results have shown the outperformance of the proposed method over the existing state-of-the-art methods.
Efficient Algorithms for Video-Based Engagement Prediction for a MOOC Course

In this paper, we address the engagement prediction task using a two-stage approach. First, two types of features are extracted from each frame on the input video using the OpenFace toolkit and an EfficientNet-based model that was pre-trained for the task of age, gender, and identity prediction. Each set of obtained feature vectors is then separately aggregated into a single video descriptor using statistical functions and a prediction is made using a linear support vector machine. Finally, the scores are fused, and the engagement intensity is predicted. The described ensemble model achieves MSE of 0.0534 on the test set of the EngageWild dataset from the EmotiW 2020 challenge.
Embedded system for mobile interconnection control system of sports training cyclists

Indoor fitness training bicycles are popular among the general public. This paper mainly studies the application of embedded system technology in the mobile interconnected control system of sports training and cycling equipment. This article uses four sub-nodes and sets them on different channels. The dismounting of this bicycle is divided into ten resistance adjustment gears. Since the user is riding, its riding power is affected by two factors, and the three are not simply a simple linear relationship. After each signal acquisition module works normally, connect each module to the wireless body area network terminal node for system networking test. System testing includes signal acquisition test, network communication test and host computer software test. Combine load test and stress test to detect various performance indicators of the system. The results show that the application of embedded system technology can improve the performance of the bicycle.
A Sequence Mining-Based Novel Architecture for Detecting Fraudulent Transactions in Healthcare Systems

With the exponential rise in government and private health-supported schemes, the number of fraudulent billing cases is also increasing. Detection of fraudulent transactions in healthcare systems is an exigent task due to intricate relationships among dynamic elements, including doctors, patients, and services. Hence, to introduce transparency in health support programs, there is a need to develop intelligent fraud detection models for tracing the loopholes in existing procedures, so that the fraudulent medical billing cases can be accurately identified. Moreover, there is also a need to optimize both the cost burden for the service provider and medical benefits for the client. This paper presents a novel process-based fraud detection methodology to detect insurance claim-related frauds in the healthcare system using sequence mining concepts. Recent literature focuses on the amount-based analysis or medication versus disease sequential analysis rather than detecting frauds using sequence generation of services within each specialty. The proposed methodology generates frequent sequences with different pattern lengths. The confidence values and confidence level are computed for each sequence. The sequence rule engine generates frequent sequences along with confidence values for each hospital&#x2019;s specialty and compares them with the actual patient values. This identifies anomalies as both sequences would not be compliant with the rule engine&#x2019;s sequences. The process-based fraud detection methodology is validated using last five years of a local hospital&#x2019;s transactional data that includes many reported cases of fraudulent activities.
LASNet: A Light-Weight Asymmetric Spatial Feature Network for Real-Time Semantic Segmentation

In recent years, deep learning models have achieved great success in the field of semantic segmentation, which achieve satisfactory performance by introducing a large number of parameters. However, this achievement usually leads to high computational complexity, which seriously limits the deployment of semantic segmented applications on mobile devices with limited computing and storage resources. To address this problem, we propose a lightweight asymmetric spatial feature network (LASNet) for real-time semantic segmentation. We consider the network parameters, inference speed, and performance to design the structure of LASNet, which can make the LASNet applied to embedded devices and mobile devices better. In the encoding part of LASNet, we propose the LAS module, which retains and utilize spatial information. This module uses a combination of asymmetric convolution, group convolution, and dual-stream structure to reduce the number of network parameters and maintain strong feature extraction ability. In the decoding part of LASNet, we propose the multivariate concatenate module to reuse the shallow features, which can improve the segmentation accuracy and maintain a high inference speed. Our network attains precise real-time segmentation results in a wide range of experiments. Without additional processing and pre-training, LASNet achieves 70.99% mIoU and 110.93 FPS inference speed in the CityScapes dataset with only 0.8 M model parameters.
Fast and Efficient Image Novelty Detection Based on Mean-Shifts

Image novelty detection is a repeating task in computer vision and describes the detection of anomalous images based on a training dataset consisting solely of normal reference data. It has been found that, in particular, neural networks are well-suited for the task. Our approach first transforms the training and test images into ensembles of patches, which enables the assessment of mean-shifts between normal data and outliers. As mean-shifts are only detectable when the outlier ensemble and inlier distribution are spatially separate from each other, a rich feature space, such as a pre-trained neural network, needs to be chosen to represent the extracted patches. For mean-shift estimation, the Hotelling T2 test is used. The size of the patches turned out to be a crucial hyperparameter that needs additional domain knowledge about the spatial size of the expected anomalies (local vs. global). This also affects model selection and the chosen feature space, as commonly used Convolutional Neural Networks or Vision Image Transformers have very different receptive field sizes. To showcase the state-of-the-art capabilities of our approach, we compare results with classical and deep learning methods on the popular dataset CIFAR-10, and demonstrate its real-world applicability in a large-scale industrial inspection scenario using the MVTec dataset. Because of the inexpensive design, our method can be implemented by a single additional 2D-convolution and pooling layer and allows particularly fast prediction times while being very data-efficient.
ISSA: Generic Pipeline, Knowledge Model and Visualization Tools to Help Scientists Search and Make Sense of a Scientific Archive

Abstract Faced with the ever-increasing number of scientific publications, researchers struggle to keep up, find and make sense of articles relevant to their own research. Scientific open archives play a central role in helping deal with this deluge, yet keyword-based search services often fail to grasp the richness of the semantic associations between articles. In this paper, we present the methods, tools and services implemented in the ISSA project to tackle these issues. The project aims to (1) provide a generic, reusable and extensible pipeline for the analysis and processing of articles of an open scientific archive, (2) translate the result into a semantic index stored and represented as an RDF knowledge graph; (3) develop innovative search and visualization services that leverage this index to allow researchers, decision makers or scientific information professionals to explore thematic association rules, networks of co-publications, articles with co-occurring topics, etc. To demonstrate the effectiveness of the solution, we also report on its deployment and user-driven customization for the needs of an institutional open archive of 110,000+ resources. Fully in line with the open science and FAIR dynamics, the presented work is available under an open license with all the accompanying documents necessary to facilitate its reuse. The knowledge graph produced on our use-case is compliant with common linked open data best practices.
Construction of high-dimensional cyclic symmetric chaotic map with one-dimensional chaotic map and its security application

Classical one-dimensional chaotic map has many ideal characteristics which is quite suitable for many different kinds of scientific fields, especially cryptography. In this paper, we propose an idea of constructing high-dimensional (HD) cyclic symmetric chaotic maps by using one-dimensional (1D) chaotic map. Two constructed 3D cyclic symmetric chaotic maps are taken as the examples, named three-dimensional cyclic symmetric logistic map (3D-CSLM) and three-dimensional cyclic symmetric Chebyshev map (3D-CSCM), respectively. Numerical experiments show that the new maps possesses better dynamical performances, and their parameters have a wider range, compared with the original map. Furthermore, to verify its effect in image encryption, a novel image encryption algorithm based on 3D-CSLM and DNA coding is proposed. DNA method for image encryption can improve the efficiency of permutation and diffusion. Firstly, the algorithm uses 3D-CSLM to generate chaotic sequences for DNA operation rule selection and pixel permutation. Then through the DNA XOR operation to achieve diffusion, and finally form an encrypted image. Several simulation tests results indicate that the proposal has a promising security performance and strong anti-attack ability.
Classifying encyclopedia articles: Comparing machine and deep learning methods and exploring their predictions

This article presents a comparative study of supervised classification approaches applied to the automatic classification of encyclopedia articles written in French. Our dataset includes all 70k text articles from Diderot and d’Alembert’s Encyclopédie (1751-72). In a two-task experiment we test combinations of (1) text vectorization methods (bags-of-words and word embeddings) and (2) traditional Machine Learning and newer Deep Learning classification methods (including transformer architectures). In addition to evaluating each approach, we review the results quantitatively and qualitatively. The best model obtains an average F-score of 86% for 38 classes. Using network analysis, we highlight the difficulty of labeling semantically close classes. We also discuss misclassifications in order to understand the relationship between content and different ways of ordering knowledge. We openly release all code and results, and data is available on request.1