Reviewerly/exHarmony huggingface dataset:
Unified multivariate trace estimation and quantum error mitigation

Calculating the trace of the product of $m\phantom{\rule{4pt}{0ex}}n$-qubit density matrices (multivariate trace) is a crucial subroutine in quantum error mitigation and information measure estimation. We propose a unified multivariate trace estimation (UMT) which conceptually unifies the previous qubit-optimal and depth-optimal approaches with tunable quantum circuit depth and number of qubits. The constructed circuits have $\ensuremath{\lceil}(m\ensuremath{-}1)/s\ensuremath{\rceil}$ or $n\ensuremath{\lceil}(m\ensuremath{-}1)/s\ensuremath{\rceil}$ depth corresponding to $(s+m)n$ or $s+mn$ qubits for $s\ensuremath{\in}{1,\ensuremath{\cdots},\ensuremath{\lfloor}m/2\ensuremath{\rfloor}}$, respectively. Such flexible circuit structures enable people to choose suitable circuits for different hardware devices. We apply UMT to virtual distillation to achieve exponential error suppression and design a family of concrete circuits to calculate the trace of the product of eight and nine $n$-qubit density matrices. A numerical example shows that the additional circuits still mitigate the noise expectation value under the global depolarizing channel.
Survival Rate Prediction of Blood Cancer (Leukemia) Patients Using Machine Learning Algorithms

Survival rate prediction for medical diseases is a complex task that requires high precision. With a low survival rate among reported patients, leukemia is a type of cancer of blood which is caused by the abnormal growth of white blood cells. It is critical to numerically evaluate the rate of survivability of patients suffering from leukemia. To this end, this paper employs a comprehensive database, namely Surveillance, Epidemiology, and End Results (SEER) maintained by The National Cancer Institute in MD, USA, to construct a survivability model for leukemia patients. To accurately predict the survival months of the patients, we develop a multi-class classification problem by binning the target variable into four bins. The resulting accuracy is improved by utilizing a multi-tier classification framework. Although, the final numerical results hold significance from biological viewpoint, it is recommended that a clinically relevant model be drawn with caution.
Mathematics intelligent tutoring system for learning multiplication and division of fractions based on diagnostic teaching

Abstract A one-on-one dialogue-based mathematics intelligent tutoring system (ITS) for learning multiplication and division of fractions was developed and evaluated in this study. This system could identify students’ error types and misconceptions in real-time by using a block-based matching method. The adaptive dialogue-based instruction was supported by a response-driven tutoring model, which was constructed based on the diagnostic teaching methodology. Instructional strategies including provoking cognitive conflict, problem simplification and representational teaching were used in the tutoring model of the system. Effectiveness of the math ITS in remedial instruction was evaluated through a quasi-experimental study. The participants of the study were 66 sixth graders chosen from central Taiwan. They were divided into an experimental group of 35 and a control group of 31. One week after the pretest, the experimental group received 2-h one-on-one instruction via the math ITS, while the control group took a 2-h conventional teacher instruction with the same teaching content in the classroom. All participants took a post-test within 2 days after the remedial instruction. The results showed that the experimental group using the math ITS significantly outperformed the control group. Further analysis indicated that the math ITS had a significant effect on the lesser-performing group (the lower 75% in the pretest score). In addition, a usability and user experience survey showed that students were willing and likely to learn mathematics using the dialogue-based math ITS.
Methods for Georeferencing Linear and Non-Linear Media Content

Various methods, for example image recognition, speech-to-text algorithms, and natural language processing, can be used to capture location references in linear and non-linear media content.The methods differ in terms of the technologies, procedures or media, such as the audio track or video images.Our investigation, which is based on the metadata of a video, reveals that georeferencing media content is possible, and that, using examples from the ARD Mediathek, the genre and thus the content of a video can influence the results.
<scp>Breaking Bad</scp>: Quantifying the Addiction of Web Elements to JavaScript

While JavaScript established itself as a cornerstone of the modern web, it also constitutes a major tracking and security vector, thus raising critical privacy and security concerns. In this context, some browser extensions propose to systematically block scripts reported by crowdsourced trackers lists. However, this solution heavily depends on the quality of these built-in lists, which may be deprecated or incomplete, thus exposing the visitor to unknown trackers. In this article, we explore a different strategy by investigating the benefits of disabling JavaScript in the browser. More specifically, by adopting such a strict policy, we aim to quantify the JavaScript addiction of web elements composing a web page through the observation of web breakages. As there is no standard mechanism for detecting such breakages, we introduce a framework to inspect several page features when blocking JavaScript, that we deploy to analyze 6,384 pages, including landing and internal web pages. We discover that 43% of web pages are not strictly dependent on JavaScript and that more than 67% of pages are likely to be usable as long as the visitor only requires the content from the main section of the page, for which the user most likely reached the page, while reducing the number of tracking requests by 85% on average. Finally, we discuss the viability of currently browsing the web without JavaScript and detail multiple incentives for websites to be kept usable without JavaScript.
Application of an artificial neural network model to predict the change of moisture during drying of sturgeon bone marrow

In the experiment of this article, the artificial neural network (ANN) was used to establish the sturgeon bone marrow drying model. Further, the effects of different temperatures (40, 60, and 80°C), humidities (0, 20, and 40%), and air velocities (8, 16, and 25 m/s) on the drying characteristics of sturgeon bone marrow were stud-ied. The studies had shown that with the increase of drying temperature, the acceleration of air velocity, and the decrease of humidity, the sturgeon bone marrow can be dried in the shortest period of 100 min. This study used ANN to feasibly predict dried sturgeon bone marrow moisture ratio, based on the time, temperature, humidity, and air velocity drying inputs. The results revealed that 11 hidden neurons were selected as the best configuration to predict the moisture ratio. This network was able to predict moisture ratio with R value 0.996. This model correctly predicted the optimal drying conditions and established that temperature is the single most significant factor in determining the drying time of sturgeon bone marrow. It is expected that this system will have broader application in other food drying requirements.
Artificial Intelligence Techniques in Text and Sentiment Analysis

Of late, text and sentiment analysis have become essential parts of modern marketing. These play a vital role in the division of natural language processing (NLP). It mainly focuses on text classification to examine the intention of the processed text; it can be of positive or negative types. Sentiment analysis dealt with the computational treatment of sentiments, opinions, and subjectivity of text. This chapter tackles a comprehensive approach for the past research solutions that includes various algorithms, enhancements, and applications. This chapter primarily focuses on three aspects. Firstly, the authors present a systematic review of recent works done in the area of text and sentiment analysis; second, they emphasize major concepts, components, functionalities, and classification techniques of text and sentiment analysis. Finally, they provide a comparative study of text and sentiment analysis on the basis of trending research approaches. They conclude the chapter with future directions.
Certified

In this new series experts give advice on certification best practice. In this opening feature SGS offers tips on working with North America’s network of safety testing labs.
Structural-interpretative modeling of strategies for achieving the mission of education in an entrepreneurial and community-oriented university

Structural-interpretative modeling of strategies for achieving the mission of education in an entrepreneurial and community-oriented university
Pengembangan Media Pembelajaran QuizWhizzer dan Kinemaster untuk Meningkatkan Motivasi Belajar Siswa di Sekolah Dasar

This study aims to develop digital learning media using the video editor application kinemaster and Quizwhizzer as an evaluation to increase students' learning motivation in learning IPAS. Research The development of this learning media uses qualitative research methods. The research was conducted by observation to determine the success of student learning, researchers conducted pretest and posttest to students. From the observation results, it can be seen that learning using the video editor application kinemaster and quizwhizzer can increase student learning motivation, this can be seen when learning takes place, many students are active.
Developing and validating a highly sensitive platelet clump detection model for the Sysmex haematology analyser

Mainstream haematology analysers (HAs) are reported to have low detection sensitivity for platelet clumps. In this study, a deep learning (DL) algorithm, convolutional neural network (CNN), was implemented to detect platelet clumps.Adenosine diphosphate (ADP) was used to induce platelet aggregation to mimic platelet clumps detected (PCD) samples. Six types of leukocyte scattergrams were collected from the Sysmex XN-10. Then, multiple CNNs were trained and validated by scattergrams in a fivefold cross-validation (CV) method. Finally, the CNN model with the best CV accuracy was tested with practical routine work samples.A total of 386 samples (190 PCD and 196 negative samples) and 4253 samples (150 PCD and 4103 negative samples) were eligible for CNN training and practical test, respectively. The CNN with the highest CV accuracy was trained by using scattergrams of side scatter (SSC) vs. forward scatter (FSC) from the white count and nucleated red blood cells (WNR) channel, whose mean area under the curve (AUC), accuracy, specificity and sensitivity were 0.968, 0.940, 0.937 and 0.942, respectively, in the CV. In the practical test, the AUC, accuracy, specificity and sensitivity of the CNN were 0.916, 0.961, 0.860 and 0.965, respectively. The dispersed spots presenting around the leucocytes in the WNR channel may be a sign of platelet clumping.This study demonstrates that the CNN algorithms can identify platelet clumps based on optical information from dedicated leukocyte channels and has a higher ability to detect platelet clumps than the XN-10 device's internal algorithm under practical circumstances.
Automated Priority Prediction for Bug Reports Using Comment Intensiveness Features and SMOTE Data Balancing

The processing priorities for software bug reports are important for software maintenance. Predicting the priorities for bug reports is the subject of many software engineering studies. This study proposes a priority prediction method that uses comment intensiveness features and a Synthetic Minority Over-sampling Technique (SMOTE)-based data balancing scheme. Experiments use datasets for three open-source projects: Eclipse, Mozilla and OpenOffice. The effectiveness of the proposed approach is determined using five classification models: Multinomial Naïve Bayes, Support Vector Machines, Random Forest, Extra Trees and eXtreme Gradient Boosting. The results show that the CIS-SMOTE-based models achieve 0.6078 Precision, 0.4927 Recall, 0.4465 F1-score and 0.7836 Accuracy in priority perdition. The results also show that CIS-SMOTE-RF, CIS-SMOTE-ET and CIS-SMOTE-XGB outperform two advanced priority prediction approaches, eApp and cPur, in terms of all performance measures.
Open Research Data in the Open Science Ecosystem and Business Environment

Today, one can observe shifts in the research landscape, which is formed by digitization and open science principles. The open science movement continues to gain momentum, attention and debate. In parallel with the principle of unity, open science gives rise to a taxonomy of several related ideas, guidelines and concepts, such as open access, open replicable research and open data. Over the past fifteen years, research institutions have focused on open access to publications. However, recently the focus of attention has shifted to research data as a “new currency” in research activities and their distribution in open access, and the guiding principles of data management are becoming crucial for the wide implementation of open science practices and the effective use of data in research, industry, business and other sectors of the economy. In this context, it is relevant to carry out a thorough study of primary scientific works on open science issues and to study the role of the concept of “open research data” in the paradigm of a holistic ecosystem of open science and business ecosystem. In this work, it is proposed to use the methods of quantitative and qualitative bibliometric analysis, which allows to identify the main trends and form the basis for further research. The information base for this work was the international scientometric database Scopus, which enables to analyze bibliographic data using built-in tools and import them for external use in the VOSviewer software. The study revealed an increasing trend in the number of publications on the subject under study, with the highest annual growth rate in 2017 (76%) and 2019 (66%). Qualitative bibliographic analysis made it possible to analyze the most cited and, therefore, trending works on the selected topic. In terms of the number of citations per year, the results show that the studies with such directions in open science as open program code (open source); data/research reproducibility, research data management; open access to publications (open access) are most popular. In addition, a cluster analysis of the co-prevalence of keywords was conducted. It formed clusters dedicated to both institutional and infrastructural problems of the development of open science and research data. Separately, the results of the analysis create a scientific basis for further research into the key determinants of the effectiveness of the implementation of a proper research data management system at the micro, meso, and macro levels. It will improve the effectiveness of the implementation of scientific developments from one field of knowledge to another, while achieving increased interdisciplinary research. In parallel with this, interested persons of the real sector of the economy get the opportunity to analyze scientific results, determining the possibility of their adoption in their own activities.
A multiplexed synthesizer for non-Gaussian photonic quantum state generation

Abstract Disposing of simple and efficient sources for photonic states with non-classical photon statistics is of paramount importance for implementing quantum computation and communication protocols. In this work, we propose an innovative approach that drastically simplifies the preparation of non-Gaussian states as compared to previous proposals, by taking advantage from the multiplexing capabilities offered by modern quantum photonics tools. Our proposal is inspired by iterative protocols, where multiple resources are combined one after the other for obtaining high-amplitude complex output states. Here, conversely, a large part of the protocol is performed in parallel, by using a single projective measurement along a mode which partially overlaps with all the input modes. We show that our protocol can be used to generate high-quality and high-amplitude Schrödinger cat states as well as more complex states such as error-correcting codes. Remarkably, our proposal can be implemented with experimentally available resources, highlighting its straightforward feasibility.
Learning for early childhood using the IcanDO platform: Breakthroughs for golden age education in Arabic learning

Early childhood is an age of children whose learning tendencies rely on games, therefore this study aims to describe the IcanDO platform for early childhood in learning Arabic. The research was conducted with a qualitative approach, the data sources were early childhood teachers. Data collected by observation techniques, interviews and documentation studies. The result of the research is that IcanDO as a learning platform is interesting for early childhood, with this platform they can play to learn Arabic. In accordance with the data, it was found that the IcanDO platform used can stimulate early childhood learning, stronger memory, personalized learning can be implemented, children's thinking skills can be trained and children's multilingual abilities are also growing. Researchers recommend that the use of various platforms that support the implementation of education in difficult conditions continue to be developed. The limitation of this research is that IcanDO and its effectiveness have not been studied comprehensively, therefore the aspects that have not been touched in this research can be the work of future researchers.
Faster DAN: Multi-target Queries with Document Positional Encoding for End-to-End Handwritten Document Recognition

Recent advances in handwritten text recognition enabled to recognize whole documents in an end-to-end way: the Document Attention Network (DAN) [9] recognizes the characters one after the other through an attention-based prediction process until reaching the end of the document. However, this autoregressive process leads to inference that cannot benefit from any parallelization optimization. In this paper, we propose Faster DAN, a two-step strategy to speed up the recognition process at prediction time: the model predicts the first character of each text line in the document, and then completes all the text lines in parallel through multi-target queries and a specific document positional encoding scheme. Faster DAN reaches competitive results compared to standard DAN, while being at least 4 times faster on whole single-page and double-page images of the RIMES 2009, READ 2016 and MAURDOR datasets. Source code and trained model weights are available at https://github.com/FactoDeepLearning/FasterDAN .
Statistical Shape Modeling of Biventricular Anatomy with Shared Boundaries

Statistical shape modeling (SSM) is a valuable and powerful tool to generate a detailed representation of complex anatomy that enables quantitative analysis and the comparison of shapes and their variations. SSM applies mathematics, statistics, and computing to parse the shape into a quantitative representation (such as correspondence points or landmarks) that will help answer various questions about the anatomical variations across the population. Complex anatomical structures have many diverse parts with varying interactions or intricate architecture. For example, the heart is a four-chambered anatomy with several shared boundaries between chambers. Coordinated and efficient contraction of the chambers of the heart is necessary to adequately perfuse end organs throughout the body. Subtle shape changes within these shared boundaries of the heart can indicate potential pathological changes that lead to uncoordinated contraction and poor end-organ perfusion. Early detection and robust quantification could provide insight into ideal treatment techniques and intervention timing. However, existing SSM approaches fall short of explicitly modeling the statistics of shared boundaries. In this paper, we present a general and flexible data-driven approach for building statistical shape models of multi-organ anatomies with shared boundaries that captures morphological and alignment changes of individual anatomies and their shared boundary surfaces throughout the population. We demonstrate the effectiveness of the proposed methods using a biventricular heart dataset by developing shape models that consistently parameterize the cardiac biventricular structure and the interventricular septum (shared boundary surface) across the population data.
Imaging bridges pathology and radiology

In recent years, medical disciplines have moved closer together and rigid borders have been increasingly dissolved. The synergetic advantage of combining multiple disciplines is particularly important for radiology, nuclear medicine, and pathology to perform integrative diagnostics. In this review, we discuss how medical subdisciplines can be reintegrated in the future using state-of-the-art methods of digitization, data science, and machine learning. Integration of methods is made possible by the digitalization of radiological and nuclear medical images, as well as pathological images. 3D histology can become a valuable tool, not only for integration into radiological images but also for the visualization of cellular interactions, the so-called connectomes. In human pathology, it has recently become possible to image and calculate the movements and contacts of immunostained cells in fresh tissue explants. Recording the movement of a living cell is proving to be informative and makes it possible to study dynamic connectomes in the diagnosis of lymphoid tissue. By applying computational methods including data science and machine learning, new perspectives for analyzing and understanding diseases become possible.
CIFNet: context information fusion network for cloud and cloud shadow detection in optical remote sensing imagery

Accurately and automatically detecting cloud and cloud shadow is one of the key steps in the analysis of optical remote sensing imagery. Currently, most cloud and cloud shadow detection methods are prone to false detection, and some clouds and cloud shadows may be missing from the detection results. Due to the lack of contextual information extraction and fusion capabilities, the accuracy of these cloud detection algorithms cannot be guaranteed. This article proposes a deep learning-based convolutional neural network context information fusion network (named CIFNet) to solve the problem of cloud and cloud shadow detection on Gaofen-1 wide field of view optical remote sensing imageries. First, we preprocess the dataset and make the corresponding labels. Next, we design a Resblock-cloud to capture global and local features and prevent the network from degrading. Then, we utilize a global context fusion module to fuse different levels of global context information by dense skip-connections. Finally, a multiscale context fusion module is designed to extract multiscale contextual relations between cloud and cloud shadow. Experimental results show that the proposed CIFNet method can obtain better cloud and cloud shadow boundaries in complex situations and therefore outperforms the competing methods.
Generative Avatar Non-Fungible Token Collections

Examines all the hype about profile picture (PFP) NFTs