Reviewerly/exHarmony huggingface dataset:
Accountant competency needs in the industrial revolution 4.0 Era: Focus on soft skills

The industrial revolution 4.0 can be an opportunity, challenge or threat for various professions including the Accountant. One of the challenges that must be faced is technological disruption. If the Accountants can’t adapt to changes, there is no guarantee they will survive in competing with others. Accounting students who will become the next Accountants must prepare themselves to become competitive professional Accountants. This research has a purpose to answer the problem formulation and analyze the skills of accounting students in preparing for Accountants competency in the fourth industrial revolution era. The research method used is a case study with a descriptive analysis approach. Data collection techniques applied are observation, in-depth interviews, and documentation. The results of this study indicate that accounting students have soft skills including Intellectual skills, Technical and Functional skills, Personal skills, Interpersonal and communication skills, and Organizational and Business Management skills. Soft skills of accounting students have an effect on Accountant competency. Hopefully, those skills can become innovation and reference for the development of skills of accounting students in preparing for Accountant competency in the industrial revolution 4.0 era.
Refurbished Computer Giveaway

Metropolitan State University of Denver serves a diverse student population, with significant percentages of enrolled students experiencing housing insecurity and food insecurity during the academic year. A percentage of students also lack reliable access to technology resources, preventing them from completing coursework at home and causing them to fall behind. The Information Technology Services department strives to provide our students with the technology they need to become successful during their time at MSU Denver. With this is mind and in hopes of providing the best educational experience for our students, the ITS department identified an opportunity to refurbish and distribute out of service laptops and desktops to the student population rather than disposition these devices. As such, ITS designed and hosted their first lottery-based refurbished computer giveaway in April 2019 for students, issuing 160 devices to students selected from over 2,600 initial reservations. This poster session will provide an overview of the program developed, challenges faced and the results of the first year of the initiative, as well as goals for future years of the service offering.
Haematological Patterns of Anaemia in Geriatric Patients and Its Etiology: A Study of 500 Cases

Introduction: Anaemia in the elderly is a global health problem, since it is associated with significant morbidity and mortality. The World Health Organization’s criteria consider anaemia when level of haemoglobin is less than 13g/dl in male and 12g/dl in female. Anaemia is easier to miss among the elderly because symptoms such as fatigue, generalized weakness, or mental confusion can be linked to the ageing process. However, anaemia should never be considered as a natural part of growing older.&#x0D; Aim: To investigate the haematological pattern of anaemia and its underlying etiology in elderly Indian patients.&#x0D; Materials and Methods: A prospective study undertaken over a span of 2 years in a tertiary care hospital in Western Maharashtra. The study included 500 geriatric (&gt;65years) anaemic patients who underwent a complete haemogram and the results were correlated with peripheral blood smear findings. Other diagnostic tests were done as indicated.&#x0D; Results: The spectrum encompassed all patterns of anaemia with the most common being normocytic normochromic pattern (57.26%). Overall, there was slight male preponderance (53%) with 65-74 years (69%) being the most common age group afflicted. Maximum number of elderly people had moderate anaemia(56%) in this study; and chronic disease (27.2%) was the most common etiological cause of anaemia followed by iron deficiency (23.4%). Upper gastrointestinal lesions (59%) were the most common causes of iron deficiency anaemia in the present study followed by nutritional deficiency (31.6%).&#x0D; Conclusion: Correlation of haemogram and peripheral blood smears is a crucial first step to identify the haematological pattern of anaemia and advise subsequent investigations for assessing and diagnosing it’s etiology. Thus, it plays a role in deciding the management protocol and contributes towards a better quality of life for geriatric anemic patients.
Deconstructing the Iron Boomerang—Quantitative Predictions of Hematite, Ochreous, and Vitreous Goethite Mixtures

The so-called iron boomerang is constructed by using a combination of spectral metrics derived from the 900 nm 6A1→4T1 Fe3+ crystal field absorption. Previous work showed the iron boomerang provides qualitative information about the mineralogical type of spectral datasets of Western Australian iron ore deposits. That work is expanded to demonstrate how the shape of the iron boomerang is driven by a linear mixing regime between hematite and ochreous and vitreous goethite. The iron boomerang enveloping shape is defined by mixing pathways between 3 different 2-endmember systems of hematite–ochreous goethite, hematite–vitreous goethite and ochreous–vitreous goethite and a 3-endmember mixing regime in the interior of the boomerang. This provides a novel methodology of quantifying the relative hematite, ochreous, and vitreous goethite content from spectrally derived data. A combination of 4 spectral metrics as input features to a random forest regression model results in modelling root mean squared errors (RMSE) of approximately 4% when all endmembers are known and in a system where the endmembers are not known the RMSE is approximately 10%. Additionally, a means of identifying potential hematite endmembers from a spectral dataset is presented. Lastly, the regression models are applied to 3 iron ore diamond drillcore from the Hamersley Province in Western Australia and demonstrates that the proportions of hematite, ochreous, and vitreous goethite can be estimated downhole which, in turn, provides ore zone delineation and hardcap and hydrated zone identification.
Enhancement of the Healthcare Sector and the Medical Cyber-Physical System with the Help of Blockchain Technology

Blockchain is a new technology that has changed the world scenario. The popularity gain of blockchain is so high that big companies and banks have started adopting this technology. The integrity, transparency, user-friendliness, peer-to-peer connection, and safety against hackers make blockchain an ideal model to use in the future. The most famous use of blockchain is in cryptocurrency; whenever someone says blockchain, Bitcoin strikes our mind. The reason is the amount of work or research done on Bitcoin and ethereum. The central pillar of blockchain is a smart contract. Authors in this chapter discuss the same vision of a smart contract for its deployment in hospital management systems. The authors aim to achieve the results based on smart contracts in blockchain without the involvement of any third party. Further, the authors also give a glimpse of a fantastic future to improve corruption in the healthcare system.
Korean economic growth factor analysis with total factor productivity

Korea has recorded negative real GDP growth only three times in the last 60 years: the oil crisis in 1980, the financial crisis in 1998, and the COVID-19 crisis in 2020. While the economic recession for the first time in 22 years may be attributed to COVID-19, it is more noteworthy is that the growth rate of the Korean economy has been continuously declining. To find counter measures for the decline in the economic growth rate, it is necessary to analyze the cause of the decline in the growth rate. The factors of economic growth can generally be divided into changes in input factors such as labor and capital and productivity. We analyzed the relative contribution of each input factor from 1982 to 2020. Our result suggests that the contributions of capital and labor to economic growth are decreasing over time, and the contribution of TFP is gradually increasing. This study is employing annual time series data to provide up-to-date estimates of TFP and exploring the determinants of TFP to help detect future growth engines for the long-run sustainable development in Korea.
Association of Celiac Disease and Inflammatory Bowel Disease: A Systematic Review and Metaanalysist

Introduction: Immune-mediated diseases such as celiac disease and inflammatory bowel disease (IBD) are still unable to be fully recognized, although the etiologic factors are multifactorial. Material and Method: This meta-analysis taken literature based on full-text English journals published in the last 20-years ago (range 2002-2022). This article aims to discuss one-way or two-way connection between IBD inflammatory bowel disease and celiac disease CeD. The databases that we use in writing this article are Sage Pub, Pubmed and Google Schoolar. Result: Several researches have presented that with IBD in patients, CeD is more common than the general population, although these results are inconsistent. Conclusion: One of the risk factors associated with the association between IBD and CeD is genetic susceptibility, which can be clearly identified from a previous family history of the disease. Keyword: Celiac Disease; Crohn’s Disease; IBD (Inflammatory Bowel Disease); Ulcerative Colitis
SYSTEMS FOR INVARIANT TARGET RECOGNITION BASEDON CENTRAL IMAGE CHORD TRANSFORMATION

Target recognition (TR) is widely used in different military and civil applications and permits enhanced intelligence and autonomously operating platforms design. The article describes existing systems for TR such as deep learning aided computer vision; target tracking architecture, based on the tracking-by-detection paradigm; a target detection dataset; deep neural networks; a system for the management of a plurality of sensors; a target recognition architecture, adaptive to operational conditions and a target detection system, based on the theory of multi-temporal recognition. Unfortunately, the existing systems do not orient for real-time processing or can be applied for synthetic aperture radar images only, or used for image processing of soft targets, etc. This article presents the data regarding proposed new systems for targets recognition and determination of their parameters, based on central image chord transformation. The systems’ main processing units are described. The structures of the elaborated systems and the principles of their functioning are presented. The models of data processing flow in the systems are described. The determination of the processing time of the operations, realized in the systems was made and the estimation of the throughput of the systems was done. The optimization of the elaborated systems was made. The results regarding systems’ characteristics are presented.
Preface – Special Issue on selected and extended papers from FACS 2019

Lifted (family-based) static analysis based on abstract interpretation is capable of analyzing all variants of a program family (or any other configurable software system), simultaneously, in a single run without generating any of the variants explicitly. The elements of the underlying lifted domain are tuples, which maintain one property per system variant. Still, explicit property enumeration in tuples, one by one for all variants, immediately yields combinatorial explosion. This is particularly apparent in the case of program families that, apart from Boolean features, contain also numerical features with large domains, thus giving rise to astronomical configuration spaces.The key for an efficient lifted analysis is a proper handling of variability-specific constructs of the language (e.g., feature-based runtime tests and #if directives). In this work, we introduce new symbolic representations of the lifted domain that can efficiently analyze program families with numerical features. This makes sharing between property elements corresponding to different variants explicitly possible. In the first approach, elements of the new lifted domain are decision trees, in which decision nodes are labeled with linear constraints defined over numerical features and the leaf nodes belong to an existing single-program analysis domain. The lifted domain is parametric in the choice of the domains for representing linear constraints and leaf nodes. Furthermore, we propose another alternative approach for efficient lifted analysis. We encode a program family with numerical features as a family with only Boolean features, and then use a BDD lifted domain to analyze the resulting program family.To illustrate the potential of our representations, we have implemented an experimental lifted static analyzer, called SPLNum2Analyzer, for inferring invariants of #if-annotated C programs. The tool implements all three approaches for lifted analysis based on abstract interpretation: tuple-based, decision tree-based, and BDD-based. It uses existing numerical abstract domains (e.g., intervals, octagons, polyhedra) from the APRON library as parameters. An empirical evaluation on benchmarks from SV-COMP and BusyBox yields promising results indicating that our tool can be successfully used for analyzing program families with very large configuration spaces.
Generating Courses in an Intelligent Tutoring System

In this paper, we present a knowledge-based system intended to support automatic course generation for a particular target group. This generation is realized by a reasoning process on the curriculum and is guided by the set of knowledge which should be acquired by the learner. The idea is to produce a modifiable course graph that would allow to control the evolution of a learner’s cognitive structure. The course produced is directly exploitable by an intelligent tutoring system (ITS) or by a human professor.
Plant Fault Diagnosis by Integrating Fault Cases and Rules

This paper proposes a method of combining diagnosis rules and fault cases to support computer-aided fault diagnosis of plants. This method helps users to maintain and use the rules and the cases by representing them in a unified format. The rules and the cases are used by analogical retrieval. The rules and the cases are represented in two modes: the natural language/index mode. The latter is made from the former by abstraction. The natural language mode is used for the detailed explanation, while the index mode for easier understanding of the contents. The effectiveness of this method is demonstrated through the prototype system for the control and maintenance computer of the water treatment plant. The customer support engineers, accepting the claims from the plant operators, abstract the keywords and input them into the system. The system diagnoses by searching for the relevant rules and cases.
Implementation of the Balanced Scorecard as a Strategy in Improving the Quality of Education

The basic need of someone who is classified as important is education. People can develop his intelligence, self-potential, skills and shape personality with an education. Through education, a person can also obtain information and knowledge to train and develop his character. One of the places where people can get education is school. Schools as educational institutions have the duty and responsibility to produce students with character, broad insight, acceptable to society, especially consumers or users and able to compete at a global level. To form quality students, of course, the educational institution itself must have qualified and qualified quality. To determine the performance of educational institutions, a measuring instrument is needed that can assess each process of the running of educational institutions. The Balanced Scorecard (BSC) is a measuring tool which is also one of the management systems used to measure the efficiency of the performance of the institution. In the application or implementation of BSC into educational institutions, it is necessary to pay attention to the elements and factors that are important so that the implementation is successful and able to improve the quality of educational institutions.
Raman identification of epidote-group minerals in turbiditic sediments from the Bengal Fan (IODP Exp. 354): a complementary tool to better constrain metamorphic grade of source rocks.

&amp;lt;p&amp;gt;Together with amphibole and garnet, epidote-group minerals are one of the three most important heavy minerals found in orogenic sediments (Garzanti and And&amp;amp;#242;, 2007). Their chemical composition and optical properties vary markedly with temperature and pressure conditions, and thus provide useful information in provenance analysis on the metamorphic grade of source rocks.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The aim of this study is to devise an efficient and quick method, with micrometric resolution to distinguish among the different species of the epidote group during routine point-counting of heavy-mineral slides, which can be applied on a vast ranges of grain-sizes from fine silt to medium sand.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The geochemical variability of epidote-supergroup minerals from different source rock collected in different sectors of the Alpine orogenic belt was first investigated by coupling Raman Spectroscopy, Scanning Electron Microscopy, and Energy-dispersed X-ray Spectroscopy (SEM-EDS). The geochemical composition, optical properties, and Raman fingerprints of these standard epidote grains were described and in-house database of Raman spectra was created, combining geochemical data and Raman response in the low wavenumbers region and OH stretching bands. A program, written in Matlab&amp;amp;#174; language, has been established which allows to obtain a quick estimate of the amount of iron from the Raman spectra in the clinozoisite-epidote series.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Raman spectra of detrital epidotes contained in turbiditic sediments of the Bengal Fan (IODP Expedition 354) were next compared with Raman spectra of epidote-group standards to determine their composition. The identification and relative amount of detrital epidote, clinozoisite and zoisite in silt- and sand-sized deep-sea sediments contribute to constrain the metamorphic grade of Himalayan source rocks, reconstruct the erosional evolution of the Himalayan orogen, and provide information on climate change and strengthening of the Indian Ocean monsoon throughout the Neogene and Quaternary.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;Key words:&amp;lt;/strong&amp;gt; epidote, provenance, Himalaya, Raman spectroscopy, Microprobe analyses, optical microscope.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Garzanti, E., And&amp;amp;#242; S., 2007. Plate tectonics and heavy-mineral suites of modern sands. In: Mange, M.A., Wright, D.T. (Eds.), Heavy Minerals in Use, Developments in Sedimentology Series, 58. Elsevier, Amsterdam, pp. 741-763.&amp;lt;/p&amp;gt;
FreezEye Tracker: novel fast and precise multitasking platform for tracking eye movements

We present a novel platform for eye tracking showing high speed and accuracy in a wide range of realizable visual tasks. The optical setup consists of the scanning laser ophthalmoscope, the actual tracker (the FreezEye Tracker), and a visual projector for task presentation. The MEMS-based tracker scans the retina with a framerate of 1.24 kHz, providing high angular and temporal resolution. Advanced algorithms allow for precise reconstruction of the eye trajectory covering the range of movements from small microsaccades to high amplitude saccades. The high quality of the generated data provides an abundance of data potentially useful for diagnostic purposes.
4 Outlier-robust Kalman filtering framework based on statistical similarity measure

This chapter introduces the statistical similarity measure to quantify the similarity between two random vectors, which is then employed to develop an outlierrobust Kalman filtering framework. The approximation errors and the stability of the filter are analyzed and discussed. To implement the filter, a fixed-point iterative algorithm and a separate iterative algorithm are given, their local convergent conditions are also provided, and comparisons are made. In addition, selection of the similarity function is considered, and four exemplary similarity functions are established. Then the relationships between the filter and other existing outlier-robust Kalman filters are revealed. Simulation examples are used to illustrate the effectiveness and potential of the filtering scheme.
White Blood Cell Classification Using Conventional and Deep Learning Techniques

White blood cells (WBCs) play an essential part in examining the health status of an individual. The decision about blood disease diagnosis requires recognition, detection, and characteristics of a blood sample under test. Manual assessment methods-based blood cell classification leads to intra- and inter-level changes in the results acquired by haematologists. As a result, the automatic classification of WBCs is of great importance in the area of medical image analysis. Currently, automatic classification of blood cells is done through blood smear images. The conventional WBC classification system follows a group of tasks to be performed sequentially. This motivated us to adopt deep learning to classify blood cells with raw images and enhance prediction accuracy. The blood smear gives vital information on disease when it is visualized under a microscope. This analysis is instrumental in diagnosing many diseases, such as malaria, leukaemia, anaemia, etc. The WBC count decides the disorders, and structural differences like size, colour, and shape variations aid in disease diagnosis. Assessment of WBCs through manual means provides data such as morphological analysis and total and differential WBC count. The disadvantages of this manual evaluation have led to observational errors and laborious tasks. Computer-based WBC evaluation automatically reduces haematologists’ workload. Blood cell classification through automatic methods provides fast results and also handles massive data effectively. Automated classification of WBCs can be done in two ways, namely conventional and deep learning methods. In this chapter, a review of the classification of blood cells using traditional and deep learning methods is discussed.
Generative Adversarial Modeling of Tropical Precipitation and the Intertropical Convergence Zone

&amp;lt;p&amp;gt;In this study we evaluate the use of generative adversarial networks (GANs) to model satellite-based estimates of precipitation conditioned on reanalysis temperature, humidity, wind, and surface latent heat flux.&amp;amp;#160; We are interested in the climatology of precipitation and modeling it in terms of atmospheric state variables, in contrast to a weather forecast or precipitation nowcast perspective. &amp;amp;#160;We consider a hierarchy of models in terms of complexity, including simple baselines, generalized linear models, gradient boosted decision trees, pointwise GANs and deep convolutional GANs. To gain further insight into the models we apply methods for analyzing machine learning models, including model explainability, ablation studies, and a diverse set of metrics for pointwise and distributional differences, including information theory based metrics.&amp;amp;#160; We find that generative models significantly outperform baseline models on metrics based on the distribution of predictions, particularly in capturing the extremes of the distributions.&amp;amp;#160; Overall, a deep convolutional model achieves the highest accuracy.&amp;amp;#160; We also find that the relative importance of atmospheric variables and of their interactions vary considerably among the different models considered.&amp;amp;#160;&amp;lt;/p&amp;gt;