Reviewerly/exHarmony huggingface dataset:
Identification of Sickle Cells in Erythrocytes Images Using Shape Descriptors

Sickle cell anemia is a blood disorder which is widespread across world with an estimate that about 30% of total population will be affected by the end of 2050. Especially in India, the frequency of sickle cell trait can reach up to 35%, thereby increasing attention over the topic for research. It is necessary to detect the disorder as much as it is for finding a cure to it. Therefore, a system that detect the sickle cells from the blood smear images or erythrocytes images is required. The affected cells change their shape from circular to sickle shape, due to the presence of typical hemoglobin called hemoglobin S. Nowadays, the elongated affected cells are identified using image processing techniques. The shape descriptors which are the vital features used to identify any shapes in an image. The contrast and brightness of blood smear images may not be consistent as the acquisition of these images depends on various factors like luminance and chrominance. To detect the sickle cells properly in the blood smear images, the histogram equalization technique is applied to improve the contrast of the image. The image is converted to binary in order to find the boundary of the cells in the image. In the binary image, each normal cell will have holes, Since the normal red blood cell have the shape of a doughnut that has been pressed in the middle slightly. The boundary of all the cells in the image is traced and the holes inside each cells are filled. Each cell is considered as a connected component for which the eccentricity is calculated. The eccentricity is the shape descriptor which is the ratio of the major axis and minor axis of the connected components. If the eccentricity is greater than threshold, the cells are identified as sickle cells.
Underwater Image Enhancement and Fish Detection

The oceans cover over 70 percent of the surface of the earth. Underwater images are vital to explore these oceans, but they are impacted by quality degradation. The quality degradation occurs mainly due to absorption and scattering of light in water. Degradation in underwater images is caused by problems like low contrast, haziness, colour deviation and blurring. This in turn has made it difficult to explore, study and unravel the mysteries of the oceans. Good quality images are vital to study and research about existing marine ecosystems and also to discover numerous undiscovered species present underwater. There are various dehazing, restoration and enhancement techniques available to improve this scenario. We in this paper look to study and analyse the idea of building a system to combine some of the dehazing, restoration and enhancement algorithms available to obtain quality enhanced images. Further the system would also perform fish detection to obtain better accuracy using the enhanced images. We go about studying and analysing the image outputs obtained from individual algorithms and combination of algorithms and look to open up a possibility of further study of combining restoration and enhancement algorithms to improve the quality of images. We have proposed the idea of adding fish detection to this system as there has always been a difficulty of identifying and differentiating fish from other objects and marine life in underwater images. Doing so would help in studying about and even discovering new species. The fish detection models suffer from the image quality degradation problems and it impacts the accuracy. Hence, we have brought about the idea to enhance images to improve the efficiency and accuracy of fish detection. Therefore, we propose a system which would have various dehazing, restoration and enhancement algorithms working in tandem to produce an enhanced version of the input image. Next, the enhanced image is used to improve the accuracy of fish detection. Our review of combining restoration algorithms with enhancement algorithms and analysing the improvement in accuracy of fish detection by enhancing would provide researchers to further look into the opportunities available for improvement.
Data pseudo-labeling while adapting BERT for multitask approaches

Nowadays, BERT models have found wide use in the NLP field.However, standard BERT architecture training can be stifled by the lack of labels for different tasks while treating multitask settings as a one-task multilabel setting.For every example, we have labels from this example's source task but not from other tasks.This article addressed this issue, exploring eight different data pseudo-labeling approaches in the GLUE 4-task setting.These approaches do not require changes in samples or model architecture.One of the presented techniques excels results on RTE from the original article, by 6.2 %, and falls behind the original article on QQP, MNLI, and SST only by 0.5-1.2%.This way also excels other pseudo-labeling approaches explored in the article by 0.5-2% on average if we consider similar tasks.However, for tasks that are dissimilar to each other, different proposed approach yields the best results.
“Reverse engineering” research portfolio synergies and tradeoffs from domain expertise in minimum data contexts

In research portfolio planning contexts, an estimate of research policy and project synergies/tradeoffs (i.e. covariances) is essential to the optimal leveraging of institution resources. The data by which to make such estimates generally do not exist. Research institutions may often draw on domain expertise to fill this gap, but it is not clear how such ad hoc information can be quantified and fed into an optimal resource allocation workflow. Drawing on principal components analysis, I propose a method for "reverse engineering" synergies/tradeoffs from domain expertise at both the policy and project level. I discuss extensions to other problems and detail how the method can be fed into a research portfolio optimization workflow. I also briefly discuss the relevance of the proposed method in the context of the currently toxic relations between research communities and the donors that fund them.
Problematika Pembelajaran Sejarah Sastra di Kampus Wilayah Perbatasan Negara Kesatuan Republik Indonesia-Republik Demokratik Timor Leste

This research aims to explore and describe some of the problems experienced in the learning History of Literature courses in Indonesian Language and Literature Education courses on the campus of the border region of the Unitary State of the Republic of Indonesia-Democratic Republic of Timor Leste (NKRI-RDTL). One of the campuses located in the border area is the University of Timor (Unimor). The problems explored will be balanced with several solutions and recommendations, to reduce some of the problems represented. In this case, the method used is a qualitative descriptive method using an e-ethical paradigm. This paradigm was used by Clifford Geertz in The Religion of Java which was presented to Abangan Santri dan Priyayi dalam Masyarakat Jawa (1981) by Aswab Mahasin. About Timorese culture, Gregor Neonbasu applied this paradigm in his monograph entitled Citra Manusia Berbudaya: Sebuah Monografi tentang Timor dalam Perspektif Melanesia (2017). The subject of this research is the study activity learning History of Literature inactive students of the first semester (odd) study program of Indonesian Language and Literature Education Faculty of Educational Sciences, University of Timor academic year 2019—2020. Based on the results of the study concluded that the problem faced in the learning History of Literature courses on the campus of the border area of NKRI-RDTL is due to five aspects, namely material aspects or teaching materials, methods, media, students, and literacy.
Research on the Design of Mass Recommendation System Based on Lambda Architecture

In the era of big data, in order to increasing the information data for conforms to the personalized needs of content, research scholars put forward based on the Lambda mass recommendation system architecture design, it can not only to the recessive and dominant behavior of users of the system data collection storage and research analysis, can also be based on the analysis of cascading hybrid algorithm to explore how to carry out real-time recommendation. Therefore, on the basis of understanding the research and development achievements of recommender systems at home and abroad in recent years, and based on the understanding and analysis of Lambda architecture and cascading hybrid algorithm, this paper aims at how to design a massive recommender system in line with users’ behavior, and makes clear the recommendation effect by combining with system testing.
PENERAPAN MODEL PROBLEM BASED LEARNING UNTUK MENINGKATKAN KEMAMPUAN BERPIKIR ILMIAH DAN PRESTASI BELAJAR SISWA (Studi pada Mata Pelajaran Matematika Siswa Kelas 5 SD Negeri 11 Merapi Barat Kecamatan Merapi Barat)

Thisstudy aimstodescribetheapplicationofproblembasedlearningmodel to improvescientificthinking andstudents’achievement.Thisresearch usesclassroom actionresearchandquasi-experimentalmethods.Theresearchsubjectsare30students ofthefifthgradeofsemestertwoofthe2019/2020academicyearatSDNegeri11MerapiBarat.Forthequasi-experimentalsamplesare24studentsinclassV/Band24 studentsinclassV/CinMerapi Baratas thesecond group.Datacollectionmethodsin thisstudyusedobservation sheets ofstudents'scientificthinkingandtestinstrumentsto measurestudents’achievement.Analysisofresearchdatausingdescriptivestatistics thatisaverage(mean).Analysisof theresultsofstudentlearningresearchbeforeand aftertheactionisanalyzedusingqualitatifandquantitatifdata.From theresultsofthe study,itshowsthattheapplicationof theproblembasedlearningmodelcanimprove scientificthinkingandstudents’achievementinmathematicssubjectofthefifthclass SDNegeri11Merapi Barat
Get Rich Syndrome: Examining the Fight against Cybercrime in Enugu State, Nigeria

Despite the large scale provisions within the Nigerian legal framework that address the issue of cyber frauds, there is an alarming increase in cyber-offences in Nigeria. This necessitated the present study that employed semi-structured interviews to draw data from civil servants from grade level twelve and above and business owners aged 40 years and over [N = 34]. The study participants were recruited through a purposive sampling method and data were analyzed thematically. Results show that individuals and different organizations are often hit through direct hacking, malware planting, and many other more sophisticated means by cyber-criminals. The study calls for the Nigerian leaders to reach a consensus on the meaning of cyber fraud, the effects and roles each community must play to reach an agreed goal. Parents need to also balance euphoria in their children with training and preparations for the harsh environments in the real world.
Cluster analysis of variability of key parameters of the economic environment in the framework of diffusion of blockchain technologies

Prospects for the development of the national economy in the context of integration of blockchain technologies into the system of economic processes indicate the need to develop methods and algorithms for a formalized assessment of their impact on the key parameters of socio-economic dynamics. Meanwhile, if in the scientific and expert space, although it is extremely rare, but still one can find works devoted to certain aspects of this scientific and methodological problem, then, unfortunately, it should be stated that the study of the impact of the technologies under study on certain sectors of the economy does not occur in information-analytical and scientific spaces at all. Unfortunately, the methods of empirical study of the influence of blockchain technologies on the parameters of economic development at the current time not only have not found their consolidated solution, but are completely characterized by fragmentation and patchiness. This study is an attempt to strengthen the positions of formalized approaches to the study of the scientific and practical problem. The paper proposes an algorithm for studying the dynamics of the gross value added of the sectors of the Russian economy through the prism of a possible transformation of the key parameters of the functioning of the financial and real sectors of the economy as a result of the diffusion of blockchain technologies. Relying on the hypotheses put forward, cointegration models were built for the studied types of economic activity, which make it possible to identify the contribution of exogenous factors, corrected under the pressure of penetration of distributed data storage technologies into the economic environment, on the degree of added value growth. This, in turn, made it possible to carry out a cluster analysis of the studied sectors of the economy in terms of their level of sensitivity to institutional transformations caused by the integration of blockchain technologies into the economic environment.
Sistem Informasi Kalkulasi Bahan Baku Berbasis Mobile pada PT. Sari Burger Indonesia

PT. Sari Burger Indonesia, with one of its branches, is located at Jl. Teuku Umar, Level 21 Shopping Mall, Denpasar Bali. In realizing the company's goals by simplifying the process of calculating raw materials to be purchased by the company, there is a manager or, more precisely, an assistant manager in charge of calculating the raw materials to be purchased. This system is made using Data Flow Diagrams (DFD) design; this system is made based on Android, using android studio software, and with the help of Firebase, which has many readily available features, besides that this system uses the SMA (Single Moving Average) method. , this method is used during the forecasting calculation process. With this system, the goal of calculating the number of raw materials to be purchased becomes easier. The calculation process that was previously manual can now be easier to use a computerized system based on Android because the data inputted will be stored in the system in performing calculations. In addition, calculations that have been saved can also be downloaded using a file with a pdf extension. The system can also forecast the remaining stock, which will be used in estimating the amount of stock remaining next month.
Automated CATS system for distance learning

This paper discusses a new automated training system called CATS. The proposed system covers all the main components of the educational process, including the simple and convenient formation of educational material, tasks for laboratory works, tests to check knowledge, allows you to monitor the progress of students, the process of studying educational content, check completed work for plagiarism, send incorrectly completed tasks to correction, keep an electronic journal and much more. The automated CATS system has been introduced into the educational process at the Belarusian National Technical University and is actively used, especially in the context of the COVID-19 pandemic. In the spring of 2020 alone, more than a thousand users have registered in the CATS system. The intellectual component in the CATS system allows you to implement a unique training program, which is based on the existing knowledge and the level of perception of the educational material by the students. As mathematical methods, it is proposed to use the analysis of expert systems, as well as artificial neural networks. These mathematical methods made it possible to develop adaptability algorithms, their software implementation and testing in the educational process. Users are provided with a web application and its mobile clients for iOS and Android operating systems. Mobile applications are localized in Russian, Belarusian, English and German. By formalizing the intellectual processes that are carried out by both the teacher and the student, it is possible to automate a certain part of their functions, reduce the cost of manual labor, which will make it easier to control the educational process, and make the training itself more effective.
A Multi-ELM Model for Incomplete Data

This paper presents a novel model of Extreme Learning Machines (ELMs) for incomplete data.ELMs are fast accurate randomized neural networks.Nevertheless ELM can only be applied on the complete dataset.Therefore, a novel Multi-ELM Model for incomplete data is proposed, consisting of multiple secondary ELMs and one primary ELM.The secondary ELMs are approximating the primary ELM's hidden neurons' outputs for the data with missing values.As summarized in the experimental Section, this model can be applied on data with any missing patterns, without using imputations and can outperform the traditional imputation methods within a reasonable fraction of missing values, as it avoids the noises introduced by imputations.
Attendance Management Based on MVC and Laravel in the Municipal Government of Pekanbaru

Currently, the management of employee attendance in the City Government is still done manually, where the leadership does not have any media that can be used to monitor attendance, causing problems including recapitulation and slow employee attendance reporting. The calculation of the percentage (%) of the employee performance allowance (Tukin) is not carried out proportionally according to the attendance discipline. The impact of the current system is undisciplined employee attendance. Attendance management system which aims to manage employee attendance data. Attendance data obtained every morning and evening is stored on a server at the Pekanbaru City Information and Communication Office. This data is then processed to determine the whereabouts of an employee and become a reference for determining the percentage (%) of the performance allowance that will be received. In addition, this attendance management can be used by Regional Leaders and Heads of Services / Institutions to monitor employee attendance quickly and quickly. The research method used for data collection is to use interviews and literature. While the method in software development uses the method of The Rapid Application Development (RAD). Attendance Management is built on the basis of the Laravel framework using the MVC (Model View Controller) method. The MVC programming pattern that separates logic and views makes program code cleaner, easier to trace when troubleshooting or debugging. The Blade feature of laravel makes using html / php templates simple. This application provides online information about employee attendance. Regional Leaders, Heads of Services / Institutions can monitor the condition of employee absenteeism in Pekanbaru City Government online every day. Employee attendance reports for each Department / OPD are generated automatically. The Personnel and Human Resources Development Agency can use it as a monthly attendance summary report.
Formation of a Programming Languages Stack and a methodology of teaching to students specialized in Computer Science at Technical Universities in the context of interdisciplinarity

Rapid development of the computing hardware and software components has created many new technological directions for the use of computer tools. Thus, information, digital and end-to-end technologies for the processing of a variety of data exist and are developing, as well as many different types of platforms for their support, including digital platforms. At the same time, the active development of computing and its sub-disciplines directly impacts on the programming languages used in this area. This article attempts to assess the range of the most important and frequently used programming languages for the formation of a stack thereof and the methodology of their teaching in the context of the interdisciplinarity of modern computer specialties at technical Universities.
Semisupervised Association Learning Based on Partial Differential Equations for Sparse Representation of Image Class Attributes

Semisupervised learning is an idea that addresses how to use a large number of unlabeled samples and a limited number of labeled samples to learn decision knowledge together. In this paper, we propose a multitask multiview semisupervised learning model based on partial differential equation random field and Hilbert independent standard probability image genus attribute model, i.e., shared semantics. In the framework of the image-like genus attribute model, data from different data sources are generated by their shared hidden space representation. Different from the traditional model, this paper uses the Hilbert independence criterion to inscribe the shared relationship of hidden expressions. Meanwhile, to exploit the correlations between labels in the label space as well, this paper uses the partial differential equation random field to inscribe the correlations between different kinds of labels in the label space and the correlations between hidden features and labels. Using the variational expectation-maximization algorithm, the whole generative process model can be inferred. To verify the effectiveness of the model, two artificial datasets and three real datasets are tested in this paper, and the experimental results verify the effectiveness of the algorithm in the paper. On the one hand, it not only improves the classification accuracy of the multiclassification problem and the multilabel problem; it also outputs the association structure between different kinds of labels and between hidden features and labels.
Subset based error recovery

We propose a data denoising method using Extreme Learning Machine (ELM) structure which allows us to use Johnson-Lindenstrauß Lemma (JL) for preserving Restricted Isometry Property (RIP) in order to give theoretical guarantees for recovery. Furthermore, we show that the method is equivalent to a robust two-layer ELM that implicitly benefits from the proposed denoising algorithm. Current robust ELM methods in the literature involve well-studied L1, L2 regularization techniques as well as the usage of the robust loss functions such as Huber Loss. We extend the recent analysis on the Robust Regression literature to be effectively used in more general, non-linear settings and to be compatible with any ML algorithm such as Neural Networks (NN). These methods are useful under the scenario where the observations suffer from the effect of heavy noise. We extend the usage of ELM as a general data denoising method independent of the ML algorithm. Tests for denoising and regularized ELM methods are conducted on both synthetic and real data. Our method performs better than its competitors for most of the scenarios, and successfully eliminates most of the noise.
Pedagogical diagnostics of the levels of formation of professional competence of future managers according to metacognitive skills

The main purpose of the study is to conduct pedagogical diagnosis of the levels of professional competence of future managers according to metacognitive skills. The process of forming a specialist of a qualitatively new level is significantly constrained by the existing gap between the achievements of science and education, therefore, the coefficient of useful use of scientific discoveries and new technologies is sharply reduced, basic knowledge is ousted from the content of educational processes by applied knowledge. The present sets before the society the tasks of training physical culture teachers who mastered not only a high level of professional training. As a result, a pedagogical diagnosis of the levels of professional competence of future managers according to metacognitive skills was conducted.