Reviewerly/exHarmony huggingface dataset:
Application of Artificial Intelligence Automatic Diatom Identification System in Practical Cases.

Objective To discuss the application of artificial intelligence automatic diatom identification system in practical cases, to provide reference for quantitative diatom analysis using the system and to validate the deep learning model incorporated into the system. Methods Organs from 10 corpses in water were collected and digested with diatom nitric acid; then the smears were digitally scanned using a digital slide scanner and the diatoms were tested qualitatively and quantitatively by artificial intelligence automatic diatom identification system. Results The area under the curve （AUC） of the receiver operator characteristic （ROC） curve of the deep learning model incorporated into the artificial intelligence automatic diatom identification system, reached 98.22% and the precision of diatom identification reached 92.45%. Conclusion The artificial intelligence automatic diatom identification system is able to automatically identify diatoms, and can be used as an auxiliary tool in diatom testing in practical cases, to provide reference to drowning diagnosis.基于人工智能硅藻自动化识别系统的实际案例应用.目的 探讨人工智能硅藻自动化识别系统在实际案例中的应用，为应用该系统进行硅藻定量分析提供参考，并对该系统所搭载的深度学习模型进行验证。 方法 收集10例水中尸体的器官进行硅藻硝酸消解，利用数字化切片扫描仪将涂片数字化扫描后，使用人工智能硅藻自动化识别系统进行硅藻的定性定量检测。 结果 该人工智能硅藻自动化识别系统所搭载的深度学习模型的受试者操作特征（receiver operator characteristic，ROC）曲线的曲线下面积（area under the curve，AUC）达到98.22%，硅藻识别的查准率达到92.45%。 结论 该人工智能硅藻自动化识别系统实现了硅藻的自动化识别，可用于实际案例中硅藻的辅助检验，并为水中尸体的死因鉴定提供参考依据。.法医病理学；人工智能；硅藻类；溺死.
SOCIAL MEDIA LEARNING PLATFORMS (SMLPs) AS A PERFORMANCE ENHANCER AMONG SENIOR SECONDARY SCHOOL SCIENCE STUDENTS.

The study examined the effects of Social Media Learning Platforms (SMLPs) on secondary school students’ performance in Science in Nigeria. It also investigated the rate of use of social media for the purpose of education or otherwise among science students in Secondary schools in Nigeria. The study was a quasi-experimental of pre-test, post-test research design. The sample for the study was 80 Senior Secondary School two students, selected from two schools in Ondo state, South West, Nigeria. Purposive sampling technique was adopted in the selection of the schools and classes for the study. The choice of the sampling technique used was to ensure that the schools and classes selected have access to cell phones and internet facilities during school period and that the classes are science based. The instruments used for the study were Social Media Students Participation Rating Scale (SMSPRS), Social Media Students’ Performance Test in Science (SMSPTS), Social Media Students’ Questionnaire (SMSQ). Validity and Reliability of the instruments were ensured and determined. Data collated were analyzed using frequency count, mean, standard deviation and t-test. The outcome of the analysis revealed that 98% of the students’ sampled do not use social media for educative learning purposes but for entertainment and socialization. Students exposed to the use of social media platform with conventional teaching-learning method participated better in lessons and performs better in the subject than their counterparts with no use of social media platform
Social assessment of digital reality challenges and modeling the development of cyberphysical systems in the context of the COVID-19 pandemic

A social assessment (technology assessment) of the challenges of digital reality is carried out on the example of the COVID-19 pandemic case in the «smart city»; the analysis of emerging emergent effects in the implementation of digital technologies in the crisis, and the dilemmas inherent in the smart city in this regard is carried out. The methodology of cyberphysical systems crisis modeling and intelligent infrastructures has been developed. Approaches for building a promising system of ethical and axiological regulations and possible compensatory mechanisms for adapting society and people to the challenges of digital reality in conditions of uncertainty and fragility are outlined; cognitive sociotechnical models for managing the processes of digital reality have developed and ways to build roadmaps for their implementation are outlined.
Moral and Ethical Aspect of Teachers’ Work in Inclusive Environment

The article is devoted to the research of the problem of pedagogical ethics, specifically the norms of communication and interaction between teachers and students with special educational needs. One of the most important requirements to pedagogic profession is clear social and professional position of its representatives. The profession encourages the teachers to expresses themselves as the subjects of pedagogical activity. The position of the teacher is a system of those intellectual, volitional and emotional-evaluative attitudes to the world, pedagogical reality and pedagogical activity in particular, which are the source of teachers’ activity. It is determined, on the one hand, by the demands, expectations and opportunities provided by society. On the other hand, there are internal, personal sources of activity such as aspiration, experience, incentives and goals of the teachers, their values, outlook, and ideals. The purpose of this article is to clarify moral and ethical principles of co-operation of teachers and students with special educational needs and to put forward a number of practical tips on how to communicate with them. In the context of the given article, such key concepts as “inclusive education”, “children with special educational needs (SEN)”, “morality”, “pedagogical tact” and others have been analyzed; such aspects of pedagogical morality of teachers as ethical principles of cooperation “teacher - student with SEN”, rules of conduct and norms of communication with students with SEN have also been formulated. In our opinion, the main ethical principles of cooperation in an inclusive environment involve the principle of humanism, the principle of nature, the principle of combining respect for the personality of the pupil with sensible discipline, the principle of democratization, the principle of positive background, the principle of the subject - subjective interaction, the principle of individual approach to students, and acmeological principle. In the table we have given examples of speech clichés which we recommend to use, or, on the contrary, to avoid in speaking and writing communication with students in an inclusive environment. We have also offered a set of practical recommendations as to communication between teachers and students with special educational needs based on moral standards and pedagogical ethics.
Fast intra-mode decision algorithm for virtual reality 360 degree video based on decision tree and texture direction

Virtual reality 360-degree video is now widely used in various fields. However, the huge amount of data caused by its high resolution increases the coding complexity. While the existing video coding framework, such as HEVC, is not optimized according to the characteristics of virtual reality video, in order to accelerate the speed of intra-mode decision process in video coding, a new algorithm is proposed in this paper. Each virtual reality 360-degree video frame is divided into two regions, namely the pole region and the equatorial region. The number of candidate modes in the pole region is reduced in advance according to the statistical distribution pattern of the modes, and intra-mode in the equatorial region is determined by the cart decision tree using the texture contrast of the coding unit. The experimental results show that compared with the original HM16.20, the proposed algorithm can save the coding time by 28.3%, while only increasing the bd-rate by 0.612%.
Deep Semantic Compliance Advisor for Unstructured Document Compliance Checking

Unstructured document compliance checking is always a big challenge for banks since huge amounts of contracts and regulations written in natural language require professionals' interpretation and judgment. Traditional rule-based or keyword-based methods cannot precisely characterize the deep semantic distribution in the unstructured document semantic compliance checking due to the semantic complexity of contracts and regulations. Deep Semantic Compliance Advisor (DSCA) is an unstructured document compliance checking platform which provides multi-level semantic comparison by deep learning algorithms. In the statement-level semantic comparison, a Graph Neural Network (GNN) based syntactic sentence encoder is proposed to capture the complicate syntactic and semantic clues of the statement sentences. This GNN-based encoder outperforms existing syntactic sentence encoders in deep semantic comparison and is more beneficial for long sentences. In the clause-level semantic comparison, an attention-based semantic relatedness detection model is applied to find the most relevant legal clauses. DSCA significantly enhances the productivity of legal professionals in the unstructured document compliance checking for banks.
Multivariate Probability Calibration with Isotonic Bernstein Polynomials

Multivariate probability calibration is the problem of predicting class membership probabilities from classification scores of multiple classifiers. To achieve better performance, the calibrating function is often required to be coordinate-wise non-decreasing; that is, for every classifier, the higher the score, the higher the probability of the class labeling being positive. To this end, we propose a multivariate regression method based on shape-restricted Bernstein polynomials. This method is universally flexible: it can approximate any continuous calibrating function with any specified error, as the polynomial degree increases to infinite. Moreover, it is universally consistent: the estimated calibrating function converges to any continuous calibrating function, as the training size increases to infinity. Our empirical study shows that the proposed method achieves better calibrating performance than benchmark methods.
THE QUEST FOR HOLY GRAILS IN LONG-TERM ECOLOGICAL RESEARCH PROGRAMS: POTENTIAL DANGERS AND SOLUTIONS

Long-term Ecological Research programs, LTERs, are necessary to understand processes that occur in time-scales longer than the period of theses, dissertations, and grants from most funding agencies. A basic result of a long-term study is the production long-term time series, but the establishment of general patterns and processes require data integration between many long-term studies and networks. These two main targets became like Holy Grails in LTERs, objectives on their own, many times dissociated from the questions they should provide answers. I discuss the advantages and potential pitfalls of these targets becoming Holy Grails in LTER, and Long-Term Socio-Ecological Research as a potential new Holy Grail.
SISTEM PAKAR UNTUK DIAGNOSA PENYAKIT KEJIWAAN DENGAN MENGGUNAKAN METODE TEOREMA BAYES

One way to detect psychiatric illnesses is to utilize advanced and modern technology, namely the field of study of Artificial Intelligence (AI) that is able to mimic human intelligence. One part of artificial intelligence is an expert system (expert system). In general the expert system (expert system) is one of the fields of computer science that utilizes computers so that they can behave intelligently like humans. This system seeks to adopt human knowledge into computers, so that computers can solve problems as is usually done by experts. The expert system will provide a list of symptoms until they can identify the object based on the answers received. This system is intended for early diagnosis of 4 types of mental illness, namely: Schizophrenia, Psychopaths, Obsessive and Depression. The processed data is obtained from psychiatrists. and the expected outcome of this system is to be able to detect psychiatric diseases early with the help of a system in which knowledge is taken from the knowledge of a psychiatrist.
Support System for Determination of Low-Income Students Scholarship (BSM) with Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)

Muhammadiyah 3 Middle School in Purwokerto is the school that organizes the Low-Income Students Scholarship (BSM) program every first semester held in each new school year. During this time, processing student data and other equipment have been processed with manual calculations, as well as data storage using only Microsoft Excel. In selecting ranking, it still uses paper. The paper calculation on the selection of BSM recipients in the previous year is often lost and hard to find already needed, also there is no particular system for processing the data so that the subjective method is still needed by relying trusts on personal. The purpose of this study is the creation of a Decision Support System (DSS) application for Determining Low-Income Students Scholarship (BSM) using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method at Muhammadiyah 3 Middle School in Purwokerto so that the selection process of Low-Income Students Scholarship (BSM) can be used. So that it is right on target to students who are entitled to BSM and can store data safely. The system development method used is a waterfall.
Multilevel Cross-Aware RGBD Semantic Segmentation of Indoor Environments

Semantic segmentation is the main step towards scene understanding which is one of the most important tasks of computer vision. As the depth and color information are independent, the combination of depth and RGB images can improve the quality of semantic labeling. In this paper, we proposed a multilevel cross-aware network (MCA-Net) for RGBD semantic segmentation to jointly reason about 2D appearance and depth geometric information. Our MCA-Net utilizes basic residual structure to encode texture information and depth geometric information respectively. Multilevel cross-aware fusion modules are designed to fuse multi-scale complementary features extracted from RGB and depth images. The proposed network produces high quality segmentation results of RGBD images particularly in indoor environments. The experiments conducted on Scannet dataset demonstrate the effectiveness of apperceiving and fusing multilevel features and that proposed MCA-Net outperforms state-of-the-art methods.
Analysis on the Teaching Reform of Linear Algebra in Applied Universities

Based on the requirements of application-oriented talent training and the characteristics of linear algebra, this paper analyzes the current situation of linear algebra teaching, and puts forward that the teaching of linear algebra should emphasize the application, pay attention to the application of knowledge, and drive the teaching of linear algebra with practical problems. Pay attention to the new technology means, using the network teaching to improve the teaching effect; Combined with the study of the professional, increase the application of case teaching, not only to improve the teaching effect of linear algebra, but also conducive to the cultivation of students' ability.
Scaling Up AND/OR Abstraction Sampling

Abstraction Sampling (AS) is a recently introduced enhancement of Importance Sampling that exploits stratification by using a notion of abstractions: groupings of similar nodes into abstract states. It was previously shown that AS performs particularly well when sampling over an AND/OR search space; however, existing schemes were limited to ``proper'' abstractions in order to ensure unbiasedness, severely hindering scalability. In this paper, we introduce AOAS, a new Abstraction Sampling scheme on AND/OR search spaces that allow more flexible use of abstractions by circumventing the properness requirement. We analyze the properties of this new algorithm and, in an extensive empirical evaluation on five benchmarks, over 480 problems, and comparing against other state of the art algorithms, illustrate AOAS's properties and show that it provides a far more powerful and competitive Abstraction Sampling framework.
Enhancing Text Classification via Discovering Additional Semantic Clues from Logograms

Text classification in low-resource languages (eg Thai) is of great practical value for some information retrieval applications (eg sentiment-analysis-based restaurant recommendation). Due to lacking large-scale corpus for learning comprehensive text representation, bilingual text classification which borrows the linguistics knowledge from a rich-resource language becomes a promising solution. Despite the success of bilingual methods, they largely ignore another source of semantic information---the writing system. Noting that most low-resource languages are phonographic languages, we argue that a logographic language (eg Chinese) can provide helpful information for improving some phonographic languages' text classification, since a logographic character (ie logogram) could represent a sememe or a whole concept, not only a phoneme or a sound. In this paper, by using a phonographic labeled corpus and its machine-translated logographic corpus both, we devise a framework to explore the central theme of utilizing logograms as a "semantic detection assistant''. Specifically, from a logographic labeled corpus, we first devise a statistical-significance-based module to pick out informative text pieces. To represent them and further reduce the effects of translation errors, our approach is equipped with Gaussian embedding whose covariances serve as reliable signals of translation errors. For a test document, all seeds' Gaussian representations are used to convolute the document and produce a logographic embedding, before being fused with its phonographic embedding for final prediction. Extensive experiments validate the effectiveness of our approach and further investigations show its generalizability and robustness.
Theories of Probability, Information and Graphs in Applied Geophysics

The possible ways for formalization of geophysical-geological investigations are outlined.It is shown that all the available geophysical-geological information can be represented by the classic three-level model.The main aim of the paper is a problem of determination of set of means composing the notion www.videleaf.com"geophysical-geological prospecting" (relative to some fixed feature) by assumed reliabilities of the means.The reliability of geological prospecting means is considered at the level of local determination.Reliabilities of information obtaining by separate mean and set of means are analyzed in detail.Suggested procedure of determining reliability for means and sets of means relative to feature are based on improved methodology of conditional probability utilization.The ways providing the increment of reliability of geological means are proposed.The applicability of proposed methods is shown on simplified examples.Estimating the efficiency of individual geophysical methods and their combination is analyzed.Practical employment of probabilities of type I and type II errors are shown.A selection of number of geophysical methods to solving different problems usually has no theoretical substantiation.The solution to this "four color" mathematical problem enables to assume that two independent geophysical methods are sufficient theoretically to characterize the geological-geophysical peculiarities of the area under study.
A Scene Text Detector for Text with Arbitrary Shapes

The performance of text detection is crucial for the subsequent recognition task. Currently, the accuracy of the text detector still needs further improvement, particularly those with irregular shapes in a complex environment. We propose a pixel-wise method based on instance segmentation for scene text detection. Specifically, a text instance is split into five components: a Text Skeleton and four Directional Pixel Regions, then restoring itself based on these elements and receiving supplementary information from other areas when one fails. Besides, a Confidence Scoring Mechanism is designed to filter characters similar to text instances. Experiments on several challenging benchmarks demonstrate that our method achieves state-of-the-art results in scene text detection with an F-measure of 84.6% on Total-Text and 86.3% on CTW1500.
Parsing into Variable-in-situ Logico-Semantic Graphs

We propose variable-in-situ logico-semantic graphs to bridge the gap between semantic graph and logical form parsing.The new type of graph-based meaning representation allows us to include analysis for scope-related phenomena, such as quantification, negation and modality, in a way that is consistent with the state-of-the-art underspecification approach.Moreover, the well-formedness of such a graph is clear, since model-theoretic interpretation is available.We demonstrate the effectiveness of this new perspective by developing a new state-of-the-art semantic parser for Minimal Recursion Semantics.At the core of this parser is a novel neural graph rewriting system which combines the strengths of Hyperedge Replacement Grammar, a knowledgeintensive model, and Graph Neural Networks, a data-intensive model.Our parser achieves an accuracy of 92.39% in terms of ELEMENTARY DEPENDENCY MATCH, which is a 2.88 point improvement over the best data-driven model in the literature.The output of our parser is highly coherent: at least 91% graphs are valid, in that they allow at least one sound scoperesolved logical form.
Brazilian Cardiology Interventional Registry during COVID-19 pandemic (RBCI-COVID19)

Background The outbreak of coronavirus disease 2019 (COVID-19), a disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was characterized as a pandemic by the World Health Organization, with high fatality rate. The number of registered cases has skyrocketed, as well as the number of deaths. Half of the patients with COVID-19 have chronic conditions, especially cardiovascular and cerebrovascular diseases, followed by diabetes mellitus, increasing their morbidity and mortality. Several studies have demonstrated this correlation, however a [...]
A conversation with Tempestt Neal

Ubiquity's senior editor Dr. Bushra Anjum chats with Dr. Tempestt Neal to discuss her work to improve access control on mobile devices, specifically developing continuous authentication methods.
Diagnosa Kesulitan Belajar Matematika Siswa Kelas V MI NW Pancor Kopong NTB

This study wants to reveal the factors of low student achievement, especially in mathematics. The factors of learning difficulties experienced by students of class V MI NW Pancor Kopong. Learning difficulties experienced by students result in underachievement in these subjects. The study was conducted to determine the factors of learning difficulties experienced by fifth grade students of MI NW Pancor Kopong. This research is a qualitative descriptive study. The subject of the research was the fifth grade students who had difficulty learning, namely students who had KKM scores that did not meet the UAS. The data collected in the form of documentation data, and interviews. Data collection was carried out using questionnaires, documentation, and interviews. Data analysis to illustrate psychological factors of learning difficulties by calculating the percentage of questionnaire results in fifth grade students of MI NW Pancor Kopong. and Data analysis to determine the psychological factors of learning difficulties by recording the results of the interview.