Reviewerly/exHarmony huggingface dataset:
Role of matter coherence in entanglement due to gravity

We investigate the quantum nature of gravity in terms of the coherence of quantum objects. As a basic setting, we consider two gravitating objects each in a superposition state of two paths. The evolution of objects is described by the completely positive and trace-preserving (CPTP) map with a population-preserving property. This property reflects that the probability of objects being on each path is preserved. We use the $\ell_1$-norm of coherence to quantify the coherence of objects. In the present paper, the quantum nature of gravity is characterized by an entangling map, which is a CPTP map with the capacity to create entanglement. We introduce the entangling-map witness as an observable to test whether a given map is entangling. We show that, whenever the gravitating objects initially have a finite amount of the $\ell_1$-norm of coherence, the witness tests the entangling map due to gravity. Interestingly, we find that the witness can test such a quantum nature of gravity, even when the objects do not get entangled. This means that the coherence of gravitating objects always becomes the source of the entangling map due to gravity. We further discuss a decoherence effect and an experimental perspective in the present approach.
High resolution data modifies intensive care unit dialysis outcome predictions as compared with low resolution administrative data set

High resolution clinical databases from electronic health records are increasingly being used in the field of health data science. Compared to traditional administrative databases and disease registries, these newer highly granular clinical datasets offer several advantages, including availability of detailed clinical information for machine learning and the ability to adjust for potential confounders in statistical models. The purpose of this study is to compare the analysis of the same clinical research question using an administrative database and an electronic health record database. The Nationwide Inpatient Sample (NIS) was used for the low-resolution model, and the eICU Collaborative Research Database (eICU) was used for the high-resolution model. A parallel cohort of patients admitted to the intensive care unit (ICU) with sepsis and requiring mechanical ventilation was extracted from each database. The primary outcome was mortality and the exposure of interest was the use of dialysis. In the low resolution model, after controlling for the covariates that are available, dialysis use was associated with an increased mortality (eICU: OR 2.07, 95% CI 1.75–2.44, p&lt;0.01; NIS: OR 1.40, 95% CI 1.36–1.45, p&lt;0.01). In the high-resolution model, after the addition of the clinical covariates, the harmful effect of dialysis on mortality was no longer significant (OR 1.04, 95% 0.85–1.28, p = 0.64). The results of this experiment show that the addition of high resolution clinical variables to statistical models significantly improves the ability to control for important confounders that are not available in administrative datasets. This suggests that the results from prior studies using low resolution data may be inaccurate and may need to be repeated using detailed clinical data.
A multi-layer probing approach for video over 5G in vehicular scenarios

Fifth generation (5G) technologies are becoming a reality throughout the world. In parallel, vehicular networks rise their pace in terms of utilization; moreover, multimedia content transmissions are also getting an always increasing demand by their users. Besides the promised performance of 5G networks, several questions still arise among the community: are these networks capable of delivering high quality video streaming services in moving scenarios? What is the relationship between the network conditions and the video quality of experience? To answer to the previous questions, in this paper we propose a multi-layer probing approach able to assess video transmissions over 5G and 4G, combining data from all layers of a communication model, relating events from its origin layers. The probe's potential is thoroughly evaluated in two distinct video streaming use cases, both targeting a vehicular scenario supported by cellular 4G and 5G networks. Regarding the probe's performance, we show that a multitude of performance and quality indicators, from different stack layers, can be obtained. As for the performance of 4G and 5G networks in video streaming scenarios, the results have shown that the 5G links show a better overall performance in terms of video quality-of-experience, granting lower delays and jitter conditions, thus allowing video delay to be diminished and segment buffering to be better performed in comparison to 4G, while still showing adaptability in lightly traffic-saturated vehicular-to-vehicular scenarios.
E-DU: Deep neural network for multimodal medical image segmentation based on semantic gap compensation

U-Net includes encoder, decoder and skip connection structures. It has become the benchmark network in medical image segmentation. However, the direct fusion of low-level and high-level convolution features with semantic gaps by traditional skip connections may lead to problems such as fuzzy generated feature maps and target region segmentation errors. We use spatial enhancement filtering technology to compensate for the semantic gap and propose an enhanced dense U-Net (E-DU), aiming to apply it to multimodal medical image segmentation to improve the segmentation performance and efficiency. Before combining encoder and decoder features, we replace the traditional skip connection with a multiscale denoise enhancement (MDE) module. The encoder features need to be deeply convolved by the spatial enhancement filter and then combined with the decoder features. We propose a simple and efficient deep full convolution network structure E-DU, which can not only fuse semantically various features but also denoise and enhance the feature map. We performed experiments on medical image segmentation datasets with seven image modalities and combined MDE with various baseline networks to perform ablation studies. E-DU achieved the best segmentation results on evaluation indicators such as DSC on the U-Net family, with DSC values of 97.78, 97.64, 95.31, 94.42, 94.93, 98.85, and 98.38 (%), respectively. The addition of the MDE module to the attention mechanism network improves segmentation performance and efficiency, reflecting its generalization performance. In comparison to advanced methods, our method is also competitive. Our proposed MDE module has a good segmentation effect and operating efficiency, and it can be easily extended to multiple modal medical segmentation datasets. Our idea and method can achieve clinical multimodal medical image segmentation and make full use of image information to provide clinical decision support. It has great application value and promotion prospects.
Library on-shelf book segmentation and recognition based on deep visual features

On-shelf book segmentation and recognition are crucial steps in library inventory management and daily operation. In this paper, a detailed investigation of related work is conducted. RFID and barcode-based solutions suffer from expensive hardware facilities and long-term maintenance. Digital Image processing and OCR techniques are flawed due to a lack of accuracy and robustness. On this basis, we propose a visual and non-character system utilizing deep learning methods to accomplish on-shelf book segmentation and recognition tasks. Firstly, book spine masks are extracted from the image of on-shelf books by instance segmentation model, followed by affine transformation to rectangle images. Secondly, a spine feature encoder is trained to learn the deep visual features of spine images. Finally, the book inventory search space is constructed and the similarity metric between spine visual representations is calculated to recognize the target book identity. To train the models we collect high-resolution datasets of 10k-level and develop a data annotation software accordingly. For validation, we design simulated scenarios of recognizing 3.6k IDs from 5.6k book spines and achieve a best top1 accuracy of 99.18% and top5 accuracy of 99.91%. Furthermore, we develop a prototype of a mobile library management robot with embedded edge intelligence. It can automatically perform on-shelf book image capturing, spine segmentation and recognition, and target book grasping workflow. • Deep learning paradigm of on-shelf books segmentation and recognition. • Deep visual features of the whole spine are extracted to recognize a book. • 10k-level datasets of spine instance segmentation and recognition are collected. • A spine identity annotation software is developed for dataset construction. • A prototype of automatic library management robot is developed for smart library.
An Intelligent Based Symmetrical Classification of Online Shop Selling Counterfeit Products

In recent years, the social network has become popular and people have started trading transactions on the Internet. Many counterfeit websites have begun to appear which create websites with counterfeit products or use the digital advertiser’s services to promote their websites on social media. Malicious sellers disguise high-quality products to attract consumers since buyers cannot receive transparent information. If there is asymmetry information, a secondary market will be formed. To solve the above problems, this research explored the machine-learning-based method to classify counterfeit and legitimate websites with symmetry information. The data set is 1612 websites used in this paper and a total of 15 feature values and takes 804 counterfeit websites and 808 legitimate websites. The Random Forest and Deep Neural Network algorithms were used to classify fake websites. This study also used statistical tests, such as Chi-square and ANOVA detection, to compare the importance of features in feature selection. The experiment results show that the RF accuracy is 99.2% and the DNN accuracy is 93.2%. The RF Precision and Recall are 100% and 98.5%, respectively. The DNN Precision and Recall are less than RF. Then, the RF F1-score is 99.2% which is higher than DNN.
Emerging learning technologies for future of work and education in engineering

Wall thickness is an important parameter used in the characterization of the surface quality of chevron corrugated plates. However, current measurement techniques such as contact mechanical or destructive modes are timing-consuming and are limited by the complex morphology of the measured object. In this study, an optical measurement system is established for experimental evaluation of the wall thickness. Its optical design and working characteristic are described based on the triangulation principle. By performing normal vector estimation, and combining with the search results about the best possible reconstructed mesh surface, the wall thickness at each measurement point can be evaluated. A series of comparison experiments with the maximum deviation 0.043 mm show that our proposed method can provide effective and automatic technical support for dimensional detection of complex components.
Deep Presentation Bias Integrated Framework for CTR Prediction

In online advertising, click-through rate (CTR) prediction typically utilizes click data to train models for estimating the probability of a user clicking on an item. However, the different presentations of an item, including its position and contextual items, etc., will affect the user's attention and lead to different click propensities, thus the presentation bias arises. Most previous works generally consider position bias and pay less attention to overall presentation bias including context. Simultaneously, since the final presentation list is unreachable during online inference, the bias independence assumption is adopted so that the debiased relevance can be directly used for ranking. But this assumption is difficult to hold because the click propensity to the item presentation varies with user intent. Therefore, predicted CTR with personalized click propensity rather than debiased relevance should be closer to real CTR. In this work, we propose a Deep Presentation Bias Integrated Framework (DPBIF). With DPBIF, the presentation block containing item and contextual items on the same screen is introduced into user behavior sequence and predicted target item for personalizing the integration of presentation bias caused by different click propensities into CTR prediction network. While avoiding modeling with the independence assumption, the network is capable of estimating multiple integrated CTRs under different presentations for each item. The multiple CTRs are used to transform the ranking problem into an item-to-position assignment problem so that the Kuhn-Munkres (KM) algorithm is employed to optimize the global benefit of the presentation list. Extensive offline experiments and online A/B tests are performed in a real-world system to demonstrate the effectiveness of the proposed framework.
DIRECT COSTS CORRESPONDING TO VASCULOGENIC ULCER DRESSINGS PERFORMED IN A COMPREHENSIVE WOUND CARE UNIT

Objective: to assess the mean direct costs corresponding to vasculogenic ulcer dressings performed by Nursing professionals on patients treated at a Comprehensive Wound Care Unit. Method: A quantitative and exploratory-descriptive research study, conducted in March, August and September 2020 at a Comprehensive Wound Care Unit located in Vitória da Conquista, Bahia, Brazil. The costs were calculated multiplying the time spent (timed) by the professionals by the cost of direct labor, adding the costs of the inputs. Data analysis was performed by means of descriptive statistics. Results: The total mean direct costs were as follows: US$ 11.90 (SD±10.79) for outpatient dressings (n=42), US$ 7.22 (SD±8.69) for home dressings (n=22) and US$ 3.10 (SD±2.57) for hospital dressings (n=11), with a significant contribution from the costs of topical therapies: US$ 9.82 (SD±10.55), US$ 5.60 (SD±8.43) and US$ 1.30 (SD±0.22), respectively. Conclusion: the results may support a review of the inputs required for vasculogenic ulcer dressings, notably, regarding topical therapies and materials/solutions.
Development of an Analog Gauge Reading Solution Based on Computer Vision and Deep Learning for an IoT Application

In many industries, analog gauges are monitored manually, thus posing problems, especially in large facilities where gauges are often placed in hard-to-access or dangerous locations. This work proposes a solution based on a microcontroller (ESP32-CAM) and a camera (OV2640 with a 65° FOV lent) to capture a gauge image and send it to a local computer where it is processed, and the results are presented in a dashboard accessible through the web. This was achieved by first applying a Convolutional Neural Network (CNN) to detect the gauge with the CenterNet HourGlass104 model. After locating the dial, it is segmented using the circle Hough transform, followed by a polar transformation to determine the pointer angle using the pixel projection. In the end, the indicating value is determined using the angle method. The dataset used was composed of 204 gauge images split into train and test sets using a 70:30 ratio. Due to the small size of the dataset, a diverse set of data augmentations were applied to obtain high accuracy and a well-generalized gauge detection model. Additionally, the experimental results demonstrated adequate robustness and accuracy for industrial environments achieving an average relative error of 0.95%.
Clinical and temporal characterization of COVID-19 subgroups using patient vector embeddings of electronic health records

Abstract Objective To identify and characterize clinical subgroups of hospitalized Coronavirus Disease 2019 (COVID-19) patients. Materials and Methods Electronic health records of hospitalized COVID-19 patients at NewYork-Presbyterian/Columbia University Irving Medical Center were temporally sequenced and transformed into patient vector representations using Paragraph Vector models. K-means clustering was performed to identify subgroups. Results A diverse cohort of 11 313 patients with COVID-19 and hospitalizations between March 2, 2020 and December 1, 2021 were identified; median [IQR] age: 61.2 [40.3–74.3]; 51.5% female. Twenty subgroups of hospitalized COVID-19 patients, labeled by increasing severity, were characterized by their demographics, conditions, outcomes, and severity (mild-moderate/severe/critical). Subgroup temporal patterns were characterized by the durations in each subgroup, transitions between subgroups, and the complete paths throughout the course of hospitalization. Discussion Several subgroups had mild-moderate severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections but were hospitalized for underlying conditions (pregnancy, cardiovascular disease [CVD], etc.). Subgroup 7 included solid organ transplant recipients who mostly developed mild-moderate or severe disease. Subgroup 9 had a history of type-2 diabetes, kidney and CVD, and suffered the highest rates of heart failure (45.2%) and end-stage renal disease (80.6%). Subgroup 13 was the oldest (median: 82.7 years) and had mixed severity but high mortality (33.3%). Subgroup 17 had critical disease and the highest mortality (64.6%), with age (median: 68.1 years) being the only notable risk factor. Subgroups 18–20 had critical disease with high complication rates and long hospitalizations (median: 40+ days). All subgroups are detailed in the full text. A chord diagram depicts the most common transitions, and paths with the highest prevalence, longest hospitalizations, lowest and highest mortalities are presented. Understanding these subgroups and their pathways may aid clinicians in their decisions for better management and earlier intervention for patients.
Mobile Forensic Signal Instant Messenger Services in Case of Web Phishing using National Institute of Standards and Technology Method

The increasing online dependence of the entire world creates opportunities for cybercrime to occur.One of the problems with instant messaging applications that trigger phishing cybercrimes is the Signal application.Phishing is a criminal activity that uses social engineering techniques.The purpose of this study is to carry out a forensic process on an android smartphone in the form of a web phishing case using the National Institute of Standards and Technology (NIST) method to obtain digital evidence on the Signal Instant Messenger application.This study uses the NIST method with four forensic stages, namely collection, examination, analysis, and reporting.Forensic tools used to collect digital evidence are MOBILedit Forensic Express Pro and Belkasoft Evidence Center.The process in obtaining evidence is by making acquisitions of two smartphones.The acquisition results are used as digital evidence to prove the occurrence of crimes that lead to web phishing cases.The results of this study show that the MOBILedit Forensic Express Pro and Belkasoft Evidence Center tools obtained evidence from the first smartphone in the form of contacts, account phone numbers, text messages, and picture messages.Meanwhile, the second smartphone did not get the expected evidence, either using the MOBILedit Forensic Express Pro or Belkasoft Evidence Center tools, because the smartphone was not rooted and the security of the encrypted storage device system failed at the acquisition stage for digital evidence retrieval.It is hoped that further research will be able to acquire on smartphones the state of the data intentionally or accidentally deleted by the perpetrators and be able to acquire the latest android version.
MRBSChain a novel scalable medical records binance smart chain framework enabling a paradigm shift in medical records management

Medical records management had always been a challenging in healthcare sector. Traditionally, medical records are handled either manually or electronically that are under the stewardship of hospitals/healthcare institutions. A patient centric approach is the new paradigm where patient is an inherent part of the healthcare ecosystem controlling the access and sharing of his/her personal medical care information. Medical care information requires robust security and privacy. Also there are other issues like confidentiality, interoperability, scalability, cost efficiency and timeliness that need to be addressed. To achieve these objectives, this paper proposes a novel-scalable patient centric yet privacy preserving framework for efficient and secure electronic medical records management. In addition, proposed system generates a unified trusted record and authentication role mapping for enforcing secure access control for medical records using complex encryption algorithms. This paper identifies 13 key performance factors for performance comparison of proposed framework with traditional models. Ethereum and Binance Smart Chain acted as a benchmark platform for performance evaluation of MRBSChain on the basis of three metrics (transaction cost, average block time and deployment cost).At last, a comparative analysis of MRBSChain with other state of art blockchain systems on the basis of execution time is presented in the paper.
Full Convexity for Polyhedral Models in Digital Spaces

In a recent work, full convexity has been proposed as an alternative definition of digital convexity. It solves many problems related to its usual definitions, for instance: fully convex sets are digitally convex in the usual sense, but are also connected and simply connected. However, full convexity is not a monotone property hence intersections of fully convex sets may be neither fully convex nor connected. This defect might forbid digital polyhedral models with fully convex faces and edges. This can be detrimental since classical standard and naive planes are fully convex. We propose in this paper an envelope operator which solves in arbitrary dimension the problem of extending a digital set into a fully convex set. This extension naturally leads to digital polyhedra whose cells are fully convex. We present first a generic envelope operator which add points in required directions in parallel and prove that it builds a fully convex set. Then a relative envelope operator is proposed, which can be used to force digital planarity of fully convex sets. We provide experiments showing that our method produces coherent polyhedral models for any polyhedron in arbitrary dimension.
MDA: An Intelligent Medical Data Augmentation Scheme Based on Medical Knowledge Graph for Chinese Medical Tasks

Text data augmentation is essential in the field of medicine for the tasks of natural language processing (NLP). However, most of the traditional text data augmentation focuses on the English datasets, and there is little research on the Chinese datasets to augment Chinese sentences. Nevertheless, the traditional text data augmentation ignores the semantics between words in sentences, besides, it has limitations in alleviating the problem of the diversity of augmented sentences. In this paper, a novel medical data augmentation (MDA) is proposed for NLP tasks, which combines the medical knowledge graph with text data augmentation to generate augmented data. Experiments on the named entity recognition task and relational classification task demonstrate that the MDA can significantly enhance the efficiency of the deep learning models compared to cases without augmentation.