Reviewerly/exHarmony huggingface dataset:
Temporal search persistence, certainty, and source preference in dentistry: Results from the National Dental PBRN

The primary goal of this paper was to investigate an old question in a new way: what are the search patterns that professionals demonstrate when faced with a specific knowledge gap?We examine data from a cascading survey question design that captures details about searching for information to answer a self-nominated clinical question from 1027 dental professionals enrolled in the National Dental Practice Based Research Network. Descriptive and conditional logistical regression analysis techniques were used.61% of professionals in our sample choose informal sources of information, with only about 11% looking to formal peer reviewed evidence. The numbers of professionals turning to general internet searches is more than twice as high as any other information source other than professional colleagues. Dentists with advanced training and specialists are significantly more likely to consult peer-reviewed sources, and women in the sample were more likely than men to continue searching past a first source.Speed/availability of information may be just as, or in some cases, more important than credibility for professionals' search behavior. Additionally, our findings suggest that more insights are needed into how various categories of professionals within a profession seek information differently.
Physical education teachers of the basic public education of Minas Gerais in the pandemic of Covid-19: working conditions, health and lifestyle

This study aimed to describe the working and health conditions of physical education teachers in public basic education in the state of Minas Gerais – Brazil during the COVID-19 pandemic. Cross-sectional study, carried out from August to September 2020, via digital form and had the support of the Minas Gerais State Department of Education. The instrument presented variables related to the sociodemographic profile, work and health conditions during the pandemic. The prevalences of the variables were presented. 1,016 physical education teachers participated in the study. Of these, 61.4% were female, 65.8% aged 21 to 40 years and 15.3% worked in the countryside. In relation to working conditions during the pandemic, 99% performed remote activities, 35.7% were dissatisfied with their work, 37% worked much more than they used to and 81.2% presented the interest of students/parents in classes as a difficulty. As for health conditions during the pandemic, 10.3% increased their consumption of alcoholic beverages, 26.1% did not practice physical exercise, 56.5% had increased body weight and 37.8% reported a lot of fear of COVID-19. Physical education teachers of basic education in the state of Minas Gerais were significantly affected by the pandemic, so it is important to implement support strategies.
Simple yet effective 3D ego-pose lift-up based on vector and distance for a mounted omnidirectional camera

Abstract Following the advances in convolutional neural networks and synthetic data generation, 3D egocentric body pose estimations from a mounted fisheye camera have been developed. Previous works estimated 3D joint positions from raw image pixels and intermediate supervision during the process. The mounted fisheye camera captures notably different images that are affected by the optical properties of the lens, angle of views, and setup positions. Therefore, 3D ego-pose estimation from a mounted fisheye camera must be trained for each set of camera optics and setup. We propose a 3D ego-pose estimation from a single mounted omnidirectional camera that captures the entire circumference by back-to-back dual fisheye cameras. The omnidirectional camera can capture the user’s body in the 360 ∘ field of view under a wide variety of motions. We also propose a simple feed-forward network model to estimate 3D joint positions from 2D joint locations. The lift-up model can be used in real time yet obtains accuracy comparable to those of previous works on our new dataset. Moreover, our model is trainable with the ground truth 3D joint positions and the unit vectors toward the 3D joint positions, which are easily generated from existing publicly available 3D mocap datasets. This advantage alleviates the data collection and training burden due to changes in the camera optics and setups, although it is limited to the effect after the 2D joint location estimation.
Association between Clinicopathological Profile and Recurrence of Invasive Breast Carcinoma in Anatomical Pathology FKUI/RSCM

ABSTRACTBackgroundBreast cancer is the most common malignancy and the leading cause of death in women. Invasive breast carcinoma is a breastmalignancy originating from the glandular epithelium of the breast. Approximately 40% of invasive breast carcinomas had recur.There are several factors that influence the occurrence of recurrence, such as age, tumor size, lymphovascular invasion, incisionmargins, hormonal status and therapy. This study aims to determine the clinicopathological profile associated with recurrence 5years after therapy for invasive breast carcinoma.MethodsThis study is a retrospective study with data collection carried out through the archives of the Department of Anatomical Pathology,Faculty of Medicine, University of Indonesia, Dr. Cipto Mangunkusumo General Hospital within a period of 2 years (2014-2015). Theclinicopathological data was searched for up to 5 years of therapy.ResultsFrom the 54 cases of invasive breast carcinoma in this study, all were women with mean age 48.3 years. A total of 18 cases(33.3%) experienced recurrence, especially in patients aged ≤50 years, tumor mass size more than 2 cm and luminal B moleculartype. Tumor margin is difficult to evaluate because many review cases do not submit the tumor margin section. Therapy received bypatients was in accordance with standard procedures’ hospital, both neoadjuvant and adjuvant chemotherapy. From the statisticalanalysis, there was no significant relation between all profiles that studied with recurrence. It is because the comparison of numberof recurrent and non-recurrent cases is not equal.ConclusionOne third of advanced invasive breast carcinomas treated with neoadjuvant and showing a partial response have recurrence,whereas no complete response has recurrence. Tumor size, histologic grade, molecular type and adherence to routine follow-uptreatment influenced the recurrence within 5 years of treatment.
A Framework for Fusing Video and Wearable Sensing Data by Deep Learning

Both cameras and IoT devices have their particular capabilities in tracking human behaviors and statuses. Their correlations are, however, unclear. In this work, we propose a framework for integrating video and wearable sensing data for smart surveillance, such as person identification and tracking. Using biometric features such as fingerprint, iris, gait, and face may lead to good recognition results. However, these approaches all have their limitations in distance and privacy concerns. In this work, we present a data fusion framework based on deep learning for fusing the aforementioned data. Here, using deep learning is to help adaptively learn the hidden bindings of these data. We demonstrate how to retrieve data of interest from IoT devices, which are attached on human objects, and correctly tag them on the human objects captured by a camera, thus correlating video and IoT data. Potential applications of this framework include smart surveillance and friendly visualization. We then show a case study, including integrating video data with body movement and physiological data.
Major Security Issue That Facing Social Networks with Its Main Defense Strategies

The Social Network Service "SNS" has enabled significant advancements in a wide variety of scientific fields, and as a result, it has become an extremely popular subject in both academia and business. SNSs can be extremely beneficial to users because they eliminate economic and geographical barriers and can be used for job searching, entertainment and education. Regardless of the economic and social benefits, protecting businesses and users' security and privacy remains a critical issue that must be addressed. It is critical to address and evaluate social network service challenges, as they vary according to the variety of SNS sites. Thus, by discussing SNS challenges alongside available and potential solutions, users, developers, and businesses can identify relevant and timely responses to specific threats, resulting in the best SNS-based services possible. The objective of this article is to discuss the inherent challenges of social networking sites and some critical solutions for resolving them. We extracted and analyzed seminal papers to add to the corpus of literature by focusing on several critical challenges in the social network service domain and shedding light on how these challenges affect a variety of domains, including users, sites, and business. The most frequently mentioned difficulties concerned privacy risks, anonymity risks, malware, spam, identity theft, phishing, business data, social content, technical issues, and psychological difficulties. By incorporating previously discovered solutions, this paper addressed these issues. The implications for both researchers and practitioners have been discussed.
Performance Comparison between Pytorch and Mindspore

Deep learning has been well used in many fields. However, there is a large amount of data when training neural networks, which makes many deep learning frameworks appear to serve deep learning practitioners, providing services that are more convenient to use and perform better. MindSpore and PyTorch are both deep learning frameworks. MindSpore is owned by HUAWEI, while PyTorch is owned by Facebook. Some people think that HUAWEI's MindSpore has better performance than FaceBook's PyTorch, which makes deep learning practitioners confused about the choice between the two. In this paper, we perform analytical and experimental analysis to reveal the comparison of training speed of MIndSpore and PyTorch on a single GPU. To ensure that our survey is as comprehensive as possible, we carefully selected neural networks in 2 main domains, which cover computer vision and natural language processing (NLP). The contribution of this work is twofold. First, we conduct detailed benchmarking experiments on MindSpore and PyTorch to analyze the reasons for their performance differences. This work provides guidance for end users to choose between these two frameworks.
Spline-based specimen shape optimization for robust material model calibration

Abstract Identification from field measurements allows several parameters to be identified from a single test, provided that the measurements are sensitive enough to the parameters to be identified. To do this, authors use empirically defined geometries (with holes, notches...). The first attempts to optimize the specimen to maximize the sensitivity of the measurement are linked to a design space that is either very small (parametric optimization), which does not allow the exploration of very different designs, or, conversely, very large (topology optimization), which sometimes leads to designs that are not regular and cannot be manufactured. In this paper, an intermediate approach based on a non-invasive CAD-inspired optimization strategy is proposed. It relies on the definition of univariate spline Free-Form Deformation boxes to reduce the design space and thus regularize the problem. Then, from the modeling point of view, a new objective function is proposed that takes into account the experimental setup and constraint functions are added to ensure that the gain is real and the shape physically sound. Several examples show that with this method and at low cost, one can significantly improve the identification of constitutive parameters without changing the experimental setup.
SVM and Naïve Bayes Algorithm Comparison for User Sentiment Analysis on Twitter

With the emergence of the Peduli Protect application, which is used by the government to monitor the spread of Covid-19 in Indonesia, it turns out to be reaping the pros and cons of public opinion on Twitter. From this phenomenon, a research was conducted by mapping the sentiment analysis of twitter users towards the Peduli Protect application. This study aims to compare two classification algorithms that are included in the supervised learning category. The two algorithms are Support Vector Machine (SVM) and Naïve Bayes. The two algorithms are implemented in analyzing the sentiment analysis of twitter user reviews on the Peduli Protect application. The dataset used in this research is tweets of twitter users with a total of 4,782 tweets. Then, compared to how much accuracy and processing time required of the two algorithms. The stages of the method in this research are: collecting data from user tweets with a crawling technique, preprocessing text, weighting words using the TF-IDF method, classification using the SVM and Naïve Bayes algorithm, k-fols cross validation test, and drawing conclusions. The results showed that the accuracy of the SMV algorithm with the k-fold test method was 86% and the split 8020 technique resulted in an accuracy of 79%. Meanwhile, the Naïve Bayes algorithm produces an accuracy of 85% with k-fold, and an accuracy of 80% with a split 8020. From these results it can be concluded that both algorithms have the same level of accuracy, only different in processing time, where Naïve Bayes algorithm is faster with time required 0.0094 seconds.
“Snapshot” of the State of Software Reuse in Colombia

Due to competitive markets, the software business wants faster, better, and cheaper solutions in a short amount of time. Software reuse emerges as a viable solution to these demands since it offers significant benefits, such as increased quality and efficiency and lower development costs and effort, as well as shorter commercialization times. This research aims to study and understand the state of the practice of software reuse in Colombia, to make comparisons with related works, and to offer an instrument for decision-making in companies that adopt these practices. To reach these objectives, three stages were proposed. In the first stage, the research questions were defined. In the second stage, a survey was developed, evaluated, and carried out to validate successful practices and adoption barriers in the context of the Colombian software industry. Finally, the results were analyzed and reported. This paper showed and evidenced the expectations, adoption barriers, and factors influencing the success of software reuse in Colombian industrial environments. In the same way, the experience from the development of this work serves as a roadmap for other regions that want to analyze the current state of reuse. Nevertheless, each organization needs to determine its capabilities and find the appropriate factors to be adopted to its context.
Embedded AI system for interactive vision screen based on human action recognition

In recent years, vision screening has emerged globally for employment (on a yearly basis) within primary and high schools since myopia heavily affects school-aged children. However, this is a laborious and time-consuming task. This article proposes an intelligent system for "self-service" vision screening. Individuals can accomplish this task independently-without any assistance by technical staff. The technical solution involved within this platform is human action recognition realized by pose estimation (real-time human joint localization in images, including detection, association, and tracking). The developed system is based on a compact and embedded artificial intelligence platform, aided by a red-green-blue-D sensor for ranging and pose extraction. A set of intuitive upper-limb actions was designed for unambiguous recognition and interaction. The deployment of this intelligent system brings great convenience for large-scale and rapid vision screening. Implementation details were extensively described, and the experimental results demonstrated efficiency for the proposed technique.
Green Path Project Develops Using Image Processing

Abstract: The Green Path project develops Image Processing which includes changing the nature of an image in the order to improve its pictorial information for human interpretation, for autonomous machine perception. In digital image processing, image is converted to an array of small integers, called pixels, representing a physical quantity such as scene radiance, stored in a digital memory, and processed by computer. Interest in digital image processing methods is in two principal applications areas: first one is improvement of pictorial information for human interpretation; and second one is processing of image data for storage, transmission, and representation for image or machine. Edge detection is one of the most difficult tasks in image processing hence it is a problem of fundamental importance in image processing method. In this paper we find out different steps of digital image processing. Like, a high-speed filter implementation is presented. Then Filter solves the purpose of removing the impulse noise from the image and reducing distortion in the image. The Image Processing is very important in the project. Then indicate a specific spot required for pointing to show the main spot for empty places. So, we required to divide the image pixelization points and coloring into different parts. And last one spotted the spot which is very important for project. Image processing is a method to perform operations on an image to extract information from it or enhance it. Digital image processing has a broad range of applications such as image restoration, medical imaging, remote sensing, image segmentation, etc. Every process requires a different technique. Keywords: Image Processing, scene radiance
Information Hiding Based on Statistical Features of Self-Organizing Patterns

A computational technique for the determination of optimal hiding conditions of a digital image in a self-organizing pattern is presented in this paper. Three statistical features of the developing pattern (the Wada index based on the weighted and truncated Shannon entropy, the mean of the brightness of the pattern, and the p-value of the Kolmogorov-Smirnov criterion for the normality testing of the distribution function) are used for that purpose. The transition from the small-scale chaos of the initial conditions to the large-scale chaos of the developed pattern is observed during the evolution of the self-organizing system. Computational experiments are performed with the stripe-type patterns, spot-type patterns, and unstable patterns. It appears that optimal image hiding conditions are secured when the Wada index stabilizes after the initial decline, the mean of the brightness of the pattern remains stable before dropping down significantly below the average, and the p-value indicates that the distribution becomes Gaussian.
On the Internet of Things, Blockchain Technology for Supply Chain Management (IoT)

Patient’s medical records are now accessible from anywhere and at any time, thanks to the Internet and the changes it has wrought in the healthcare industry. Electronic health records were dogged by a lack of standards, but that was not the only issue. Decentralized online ledgers were already being proposed and used to solve interoperability and privacy issues when blockchain-based systems were first built. As far as technical issues go, scaling, usability, and accessibility stand out. On the one hand, it is difficult to keep secure access control measures on-chain while simultaneously keeping a wide range of medical data off-chain. Finding out who owns what and spreading access control of data is the second challenge in medical settings. Using temporal blockchain, the Secured Healthcare System (SHS) aims to address these problems (TB). As an SHS fundamental building piece, the context-based Merkle tree emphasizes privacy, enhanced integrity management, and access control methods (CBMT). For interoperability and scale control, the framework uses temporal features, HL7 criteria, and IPFS data management (IPFS). Personalized micro booklet (PML) security was found to be affected by the SHS framework, namely, on the time-based shadow notions and the contextual components of the PML (PML). Taking advantage of the architecture’s enormous potential to solve the challenges of siloed data and enabling tamper-proof, secure healthcare transaction has been sought.
A Cluster Analysis of the Acceptance of a Contact Tracing App—The Identification of Profiles for the Italian Immuni Contact Tracing App

Digital contact tracing apps have been introduced by governments as a strategy to limit the spread of the COVID-19 pandemic. Digital contact tracking is an alternative to traditional contact tracing performed by human tracers who have to reconstruct each contact an infected person had in the recent past by means of interviews. The Italian government proposed the Immuni digital contact tracking app as a solution. Immuni uses Bluetooth technology to anonymously register all close contacts a person had: if she tests positive for COVID-19 then all registered contacts are notified. The main aim of the paper is to propose a cluster analysis of some factors concerning the possible acceptance of the Immuni app to build behaviour profiles that explain and predict the possible behaviours of the respondents. The factors considered referred to three different pillars: the technological pillar, investigated by considering factors from the technology acceptance models family; the health pillar, where variables of the health belief model were used; and the sociopolitical pillar, where some values of the respondents were considered as possible barriers to or facilitators of the acceptance of this technology. As a result of the cluster analysis, three behavioural profiles were built: the ProApp profile, the Hesitant profile, and the AntiApp profile. The first is the profile grouping the respondents who intend to use the contact tracing app; the second is more about people who are favourable of the use of the app, but some issues such as privacy reduce the strength of their intention; the last profile is about people who are less favourable to use the app. We are confident that the behaviour profiles found would be useful to build more tailored communication campaigns to help promote the use of the app by managing factors that could either be facilitators or barriers.
A Novel Deep Learning Technique for Higher Education System

This study aims to enhance the interest of university classrooms and the learning efficiency of students. From deep learning, gesture recognition algorithms (GRA) and long short-term memory (LSTM) are adopted to build prediction modules. After the system process is completed, a brand-new human-computer interaction (HCI) system is established. The deep learning GRA augments the operation of the recognition algorithm of the pattern matching method created on the previous image segmentation. Its enticements on two merits of gesture estimation are as follows: rapid detection of gesture joint features and convolutional neural network (CNN) image classification, thereby improving GRA. The recognition rates of “take”, “pinch”, and “point” are 98.6%, 99.5%, and 99.4%, respectively. When the data volume of the LSTM network model is less than 10, the prediction accuracy can reach 70%, and the performance is relatively stable. The designed HCI system can better recognize the teacher’s gesture intent in teaching, execute gesture commands, and optimize the interactive teaching method between the teacher and the computer. This system is a derivative form of gamification teaching. While it improves the interest in teaching, it is also of great significance for improving the teaching efficiency of general higher education.