Reviewerly/exHarmony huggingface dataset:
Application of Artificial Intelligence in Forensic Audit

Artificial Intelligence (AI) is nowadays an emerging technology growing rapidly. Since forensic audit and accounting is itself a new concept in India so the use of Artificial intelligence in forensic audit can be proved as a game-changer. This article discusses about the introduction of Artificial intelligence &amp; forensic audit and, the need of AI in forensic audit and the tools of AI used in the forensic audit. At last, we can clearly say that artificial intelligence has a very important role in detecting fraud and preventing fraud.
E-Summary Teaching Materials with Hannafin &amp; Peck Models for Training Participants in the Human Resources Development Agency

This study aims to describe the design of electronic teaching materials packaged in e-summary form, and to determine the validity of e-summary teaching materials according to experts and product trials. This type of research is development research using the Hannafin &amp; Peck model. The types of data in this study are quantitative and qualitative data. The research data collection used the document recording method and the questionnaire method. The data analysis technique used is descriptive quantitative analysis technique, and descriptive qualitative. The results of the study (1) The design of this e-summary teaching material consists of three stages, namely the needs analysis stage, the design stage, the development and implementation stage. And at each stage it passes through the evaluation stage. (2) The validity of the development of e-summary teaching materials is measured through (a) the results of the content/content expert review by obtaining a percentage of 93.3% being in very good qualification, (b) the results of the e-summary linguist review being 87.5% being on good qualifications (c) the results of the review of individual trials show that the percentage of the overall subject is 97.1% with very good qualifications and (d) the results of the small group trial review show that the percentage of the overall subject is 96.3% with very good qualifications. . In general, the research results show that e-summary is valid with very good quality and is suitable for use in the learning process in creative thinking training. Suggestions that can be given from the results of this study are that this e-summary teaching material can be used optimally in the learning process so that it can increase the training participants' self-study motivation and make it easier to understand the training material.
Internet of Things (IoT) Based Patient Fall Prediction and Monitoring System

A large portion of death from an external cause in a hospital setting or care facility is caused by patient getting injured or hurt by falling down due to either disability or medication or by accident. A fall could lead to sustaining severe injuries and fatalities in some rare cases. It is highly unfortunate to get a call from the hospital stating that their family member has been injured by falling down. Elderly people are prone to fall more when compared to others and are also more likely to be fatal. In case of a fall, first aid should be provided immediately so that the fall does not cause fatal consequences. There are very few existing hospitals that can treat elderly people with intensive care and be proactive in tending to the patients. There are also limited technology that can help with detecting the fall. In most cases they turn out to be tardy as there would already have been consequences from the fall. In order to overcome this scenario, we may need special devices and gadgets. Instead of fall detection, determining the fall before it occurs and preventing it, will reduce the cost of treatment, the repercussions involved from the fall and also not make the fall lethal. This paper suggests the techniques of fall prevention that can abate the fall significantly in advance.
Serology Suggests Adequate Safety Measures to Protect Healthcare Workers From COVID-19 in Shiga Prefecture, Japan

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An experimental study on a conversational agent in software testing lessons

Nowadays, few professionals understand the techniques and testing criteria to systematize the software testing activity in the software industry. Towards shedding some light on such problems and promoting software testing, professors in the area have established Massive Open Online Courses as educational initiatives. However, the main limitation is the professor’s lack of supervision of students. A conversation agent called TOB-STT has been defined in trying to avoid the problem. A previous study introduced TOB-STT; however, it did not analyze its efficacy. This article addresses a controlled experiment that analyzed its efficacy and revealed it was not expressive in its current version. Therefore, we conducted an in-depth analysis to find what caused this result and provided a detailed discussion. The findings contribute to the TOB-STT since the experimental results show that improvements need to be made in the conversational agent before we use it in Massive Open Online Courses.
Stitching technique based on SURF for Hyperspectral Pushbroom Linescan Cameras

Hyperspectral imaging (HSI) has a crucial role in material classification tasks. The primary type of sensors in this field are linescan sensors, offering a high spatial and spectral resolution and improving the quality of the captured hypercubes. However, these cameras have inherent problems such as the need of dedicated hardware to move the camera over the scene and the necessity of pre-processing the raw captures in order to obtain the actual hypercube. This work makes an analysis and an adaptation of stitching techniques to apply them to the medical area. Also, it includes different methods to post-process the hypercubes obtained and it analyzes the SURF algorithm to improve processing times. The results obtained show that a stitching technique based on SURF is suitable for the medical area.
Contamination of Sudanese Banknotes with Acinetobacter radioresistens

a peer-reviewed, open access online international journal which publishes original research papers. The journal welcomes submission from scholars and experts for possible publication from all over the world. The scope of the journal includes: Pharmaceutical research, chemistry and biochemistry of naturally occurring compounds, biological evaluation of crude extracts, ethnomedicine, traditional and complementary medicine, ethnopharmacology, biomedical research, Biotechnology, Evaluation of natural substances of land and sea and of plants, microbes and animals, pharmacognosy, bioavailability, clinical, pharmacological, toxicological studies and pharmacokinetics of phytochemicals, Isolation and characterization of compounds, structure elucidation, synthesis and experimental biosynthesis of natural Product as well as developments of methods in these areas are welcomed in the journal.
Factors of Pricing in Russian Public Higher Education Institutions

The paper analyzes the factors affecting the tuition fee in Russian public higher education institutions. The purpose of the study is to identify pricing factors and measure their influence on tuition fees at bachelor and specialist educational programs. The relevance of the research is characterized by the need for effective pricing policy tool for public universities with consideration of their peculiarities and external factors. Empirical basis of this research is formed by Monitoring of enrollment quality at Russian universities - 2018 and Monitoring of performance of Russian higher education institutions - 2018. More than five thousand observations in the context of educational programs have been analyzed via multiple log-linear regression. The key limitations of the study are database specification, that excludes the information on private universities and their branches, and a wide qualitative indicators dispersion within enlarged groups caused by the heterogeneity of Russian educational space. Regression analysis results shows a significant influence of external factors on the tuition fees in Russian public universities with the absence of ability to affect them. Particularly, social and economic indicators of the region, competition level in the local markets, as well as the volume of state funding create a significant influence on tuition fees. The influence is supported by two basic external factors: low solvent demand for tuition-based educational services from population and the presence of two gravity centers among the heterogeneity of the Russian educational space - Moscow and Saint Petersburg.
Bayesian Augmentation of Deep Learning to Improve Video Classification

Traditional automated video classification methods lack measures of uncertainty, meaning the network is unable to identify those cases in which its predictions are made with significant uncertainty.This leads to misclassification, as the traditional network classifies each observation with same amount of certainty, no matter what the observation is.Bayesian neural networks are a remedy to this issue by leveraging Bayesian inference to construct uncertainty measures for each prediction.Because exact Bayesian inference is typically intractable due to the large number of parameters in a neural network, Bayesian inference is approximated by utilizing dropout in a convolutional neural network.This research compared a traditional video classification neural network to its Bayesian equivalent based on performance and capabilities.The Bayesian network achieves higher accuracy than a comparable non-Bayesian video network and it further provides uncertainty measures for each classification.
Mineralogical and Geochemical Characteristics of Carbonate Rocks for Lime Industry in Ban Pong, Chiang Mai Province, Northern Thailand

One of the oldest lime manufactures in Chiang Mai Province is located in a hillside village, Ban Pong, Hang Dong District, the western part of Chiang Mai City. Villagers still have conserved traditional production methods using wood-fired kilns and the primary material selection, an Ordovician marble. There are 2 massive amounts of carbonate rocks distributed in Chiang Mai Province; Permian limestone and Ordovician marble. However, the Ordovician carbonate rocks in Ban Pong were selected to produce high-quality lime by their primitive method (man-made) for a long time. Petrographical studies suggest that the Ordovician rock samples show a granoblastic texture with a slightly foliation represents by mica flakes. They are made up mainly of calcite, with very small amounts of quartz, muscovite, talc, and opaque minerals that can be identified as marble. The mineral compositions are according to the value of CaO, SiO2, Al2O3, K2O, and Fe2O3 in whole-rock analysis by XRF. The geochemical data suggest that marble in the Ban Pong area has a high CaO ratio and contains small amounts of muscovite, quartz, talc, and opaque minerals that may reduce the decomposition temperature during the calcination process. The Ordovician marble in Ban Pong is an appropriate raw material for traditional lime manufacture to saving energy in the production system.&#x0D; HIGHLIGHTS&#x0D; &#x0D; Limestone has been a geologic material in the lime industry from the past to the presence of human society&#x0D; Villagers in Ban Pong (Chiang Mai Province, Thailand) selected the western Ordovician limestone mountains to produce lime by a primitive process instead of the eastern Permian limestone mountains&#x0D; The mineral- and chemical compositions of the Ordovician limestones were intensively analyzed to characterize the suitable raw materials for a primitive lime kiln&#x0D; The mineral composition indicated that the Ordovician limestone in this area is metamorphosed into an impure fine-grained marble&#x0D; &#x0D; GRAPHICAL ABSTRACT
Online Discrepancy with Recourse for Vectors and Graphs

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)Online Discrepancy with Recourse for Vectors and GraphsAnupam Gupta, Vijaykrishna Gurunathan, Ravishankar Krishnaswamy, Amit Kumar, and Sahil SinglaAnupam Gupta, Vijaykrishna Gurunathan, Ravishankar Krishnaswamy, Amit Kumar, and Sahil Singlapp.1356 - 1383Chapter DOI:https://doi.org/10.1137/1.9781611977073.57PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract The vector-balancing problem is a fundamental problem in discrepancy theory: given T vectors in [–1, 1]n, find a signing σ(a) ∊ {±1} of each vector a to minimize the discrepancy ‖ Σa σ(a) · a‖∞. This problem has been extensively studied in the static/offline setting. In this paper we initiate its study in the fully-dynamic setting with recourse: the algorithm sees a stream of T insertions and deletions of vectors, and at each time must maintain a low-discrepancy signing, while also minimizing the amortized recourse (the number of times any vector changes its sign) per update. For general vectors, we show algorithms which almost match Spencer's offline discrepancy bound, with O(n polylog T) amortized recourse per update. The crucial idea behind our algorithm is to compute a basic feasible solution to the linear relaxation in a distributed and recursive manner, which helps find a low-discrepancy signing. We bound the recourse using the distributed computation of the basic solution, and argue that only a small part of the instance needs to be re-computed at each update. Since vector balancing has also been greatly studied for sparse vectors, we then give algorithms for low-discrepancy edge orientation, where we dynamically maintain signings for 2-sparse vectors in an n-dimensional space. Alternatively, this can be seen as orienting a dynamic set of edges of an n-vertex graph to minimize the discrepancy, i.e., the absolute difference between in- and out-degrees at any vertex. We present a deterministic algorithm with O(polylog n) discrepancy and O(polylog n) amortized recourse. The core ideas are to dynamically maintain an expander-decomposition with low recourse (using a very simple approach), and then to show that, as the expanders change over time, a natural local-search algorithm converges quickly (i.e., with low recourse) to a low-discrepancy solution. We also give strong lower bounds (with some matching upper bounds) for local-search discrepancy minimization algorithms for vector balancing and edge orientation. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-707-3 https://doi.org/10.1137/1.9781611977073Book Series Name:ProceedingsBook Code:PRDA22Book Pages:xvii + 3771
On the Neural Network Solution of One-Dimensional Wave Problem

Artificial neural network has become an emerging popular method to handle various problems, especially in case where it has deep multiple neural layers. In this study, we use a deep artificial neural network model to solve one-dimensional wave equation, without any external datasets. Different type of boundary conditions, i.e., Dirichlet, Neumann, and Robin, are used. We analyze the model learning capabilities in a set of settings, such as data setup and the model width and depth. We also present some discussions of advantages and disadvantages of the model in comparison with other matured existing techniques to solve wave equation.
The Phenomenon of Complexity and Quantum Semiotics

The article is devoted to the discussion of some characteristic features of the “complexity paradigm” (Moraine). It is emphasized that thinking in complexity needs an adequate conceptual optics, methodological support, which can be rep­resented by a conceptual assemblage – a heterogeneous perceptual and com­municative network of concepts of quantum mechanics and Peirce semiotics, considered ontogenetically in terms of processality, temporality, becoming. The situation with thinking in complexity is such that this optics should arise as a certain set of cognitive operations as a certain topologically connected set of semiotically meaningful operations in the very process of realizing a cognitive situation as becoming specifically complex. And this awareness is a key prereq­uisite for the emergence of that epistemological situation, which we call “meet­ing with complexity”. Or, more precisely, thinking together with complexity. It is emphasized that the encounter with complexity itself implies the presence of a kind of sensitivity, an intuitive predisposition to its conscious recognition as such. We need such a conceptual character as the “observer of complexity”, un­derstood in the unity of the multiplicity of cybersemiotic, synergetic and quan­tum mechanical perspectives. A character who traces his ancestry from the quan­tum-relativistic physics of the twentieth century and the procedural philosophy of Pierce, Bergson and Whitehead.It is emphasized that in Peirce's scheme there is a specific, context-dependent relationship between signs and objects with which they are connected through the third element, an interpreter that plays the role of a connecting link between them" And this semiotic interpreter of Peirce is, in fact, a semiotic observer-a participant in the becoming turbulent flow of temporal complexity, synergistically included in the self-organizing quantum Universe.
Early Identification of Maternal Cardiovascular Risk Through Sourcing and Preparing Electronic Health Record Data: Machine Learning Study

Health care data are fragmenting as patients seek care from diverse sources. Consequently, patient care is negatively impacted by disparate health records. Machine learning (ML) offers a disruptive force in its ability to inform and improve patient care and outcomes. However, the differences that exist in each individual's health records, combined with the lack of health data standards, in addition to systemic issues that render the data unreliable and that fail to create a single view of each patient, create challenges for ML. Although these problems exist throughout health care, they are especially prevalent within maternal health and exacerbate the maternal morbidity and mortality crisis in the United States.This study aims to demonstrate that patient records extracted from the electronic health records (EHRs) of a large tertiary health care system can be made actionable for the goal of effectively using ML to identify maternal cardiovascular risk before evidence of diagnosis or intervention within the patient's record. Maternal patient records were extracted from the EHRs of a large tertiary health care system and made into patient-specific, complete data sets through a systematic method.We outline the effort that was required to define the specifications of the computational systems, the data set, and access to relevant systems, while ensuring that data security, privacy laws, and policies were met. Data acquisition included the concatenation, anonymization, and normalization of health data across multiple EHRs in preparation for their use by a proprietary risk stratification algorithm designed to establish patient-specific baselines to identify and establish cardiovascular risk based on deviations from the patient's baselines to inform early interventions.Patient records can be made actionable for the goal of effectively using ML, specifically to identify cardiovascular risk in pregnant patients.Upon acquiring data, including their concatenation, anonymization, and normalization across multiple EHRs, the use of an ML-based tool can provide early identification of cardiovascular risk in pregnant patients.
Sound i historisk perspektiv: oppdagelse, naturalisering, kanonisering

This chapter addresses technological development as a driving force of musical development during the history of recorded music. The study is organized around three moments, which in various ways have contributed to forming new ways of producing music, and thereby also have left their audible marks on the sound of the music. The first example demonstrates how the development of the electric microphone contributed to new vocal expressions already in the 1930s. The second example takes up how magnetic tape technology has affected the status of recording, the possibility of multitrack recording and for experimenting with the sound of new, virtual spaces in recordings. The third example is the gated reverb on drums, which left a definitive mark on the sound of the 1980s. The overall aim of this chapter, then, is to provide an inroad to understanding the concept of sound in a historic perspective, through processes of discovery, naturalisation and canonisation.
Building Mathematical Concepts Through Traditional Games to Develop Counting Skills for Early Childhood

In recent years, fun learning methods have almost disappeared from school classrooms. In addition, there is a reduction in active and creative playtime so that students feel bored. This study aims to build (C5) simple mathematical concepts with the traditional game Dakon and Snakes and Ladders in developing numeracy skills in children with high and low order thinking abilities. The type of research used is quasi-experimental research (quasi-experimental) with a 2x2 factorial design. The subjects in this study were 60 children in group B. The data collection techniques used in this study were tests and observations. The instrument used is a test question, and the observation sheet uses five indicators to measure the ability to count ata analysis using Two Way ANOVA test. This research aims to build a simple mathematical concept with traditional dakon games to develop numeracy skills in children with higher-order thinking skills, which gives better results than the traditional snake and ladder game with a Mean Difference value of 1.533 and a Significance value of 0.0001. Building simple mathematical concepts with traditional dakon games to develop numeracy skills in children with low-level thinking abilities gave better results than the traditional snake and ladder game with a Mean Difference value of 2.867 and a Significance value of 0.0001. Thus, it can be concluded that the traditional dakon game is very well used to build simple mathematical concepts, especially numeracy skills.
Fast Pupil center localization system based on SSD Cascade gradient

In order to solve the problem of low recognition and high misrecognition of traditional eye tracking system, which uses cascade classifier to obtain face image. This paper proposes a model of SSD(Single Shot MultiBox Detector) combined gradient algorithm. The method, firstly, put the SSD in depth study of facial model to replace the cascade classifier, with a face image segmentation the eye part of the detected image. Secondly using the improved gradient localization algorithm to locate the pupil center position, and then through the proposed simple judgment mechanism on the rationality of the pupil center again decrease the misrecognition, finally get the pupil center. Experimental results show that the proposed algorithm can achieve a detection rate of 5.42 frames per second and improve the detection accuracy by 6%.
Chromatic Fracture Cubes

In this note, we construct a higher-dimensional version of the chromatic fracture square. We then categorify the resulting chromatic fracture cubes obtaining a decomposition of the category of E(n)-local spectra into monochromatic pieces.