Reviewerly/exHarmony huggingface dataset:
PENGARUH EKONOMI KELUARGA, LINGKUNGAN TEMAN SEBAYA, DAN KESADARAN MASYARAKAT DALAM PENGAMBILAN KEPUTUSAN UNTUK MELANJUTKAN PENDIDIKAN KE PERGURUAN TINGGI (Studi pada SMK Swasta di Kecamatan Banjarnegara)

ABSTRACT               This research was conducted at some private Vocational Schools in Banjarnegara district. The purpose of the study was to examine the influence of family economic income, peer environment, and public awareness in decision making to continue their higher education at collage. At some reasons, students in private Vacational Schools in Banjarnegara basicly need to work for economic reasons, because some parts of region are still classified as families with low economic income.  On the other reason students in the region should be more focused and eager to work to help lift their family’s economy, but in this case students also still make the decision to continue their education to collage.               The data used in this research were primary data which was obtained through questionnaires. The sampling technique was the probability sampling technique. Data analysis in this research used multiple regression analysis with SPSS 24. In this research indicated that the family economic income affected decision making, peer environment did not affect the decision making, public awareness affects the decision making. Family economic income variables, peer environment, and community awareness simultaneously affected decision making as much as 52.4%, and the remaining 47.6% was influenced by other variables excluded in this study.
A Real-Time Deep Transfer Learning-Based Classification and Social Distance Alert Framework Based on Covid-19

Introduction: Covid-19 is a novel virus that has exponentially increased the number of infected persons and the death of human beings into millions within a few months.This virus spreads when a person comes into contact with another person, coughing, sneezing and droplets.Objective: To avoid loss of lives, human direct assistance and early precaution, automated systems required for reducing the number of cases.Deep Learning can facilitate human life much better way by automating human visual intelligence into machine intelligence. Methods:In this novel research work, we are implementing transfer learning methodology to improve the learning of a related objective task on top of base deep learning model in developing a mask/non-mask detection model along with changing the hyperparameters and data augmentation technique by using less input dataset for smart healthcare, smart home in reducing and detecting corona cases. Results:We used object detection model Single Shot Multi-box Detector and classification model mobile net, which achieved significant accuracy and much faster for both training and inference with prediction accuracy of 87% with IOU=.75 on our own created trained dataset comparable with other real-time object detection model such as Faster Regional Convolutional Neural Network by tuning the hyperparameters. Conclusion:The automated system not only reduces the false alarm but also enhanced the performance accuracy by detecting the mask and non-mask due to which the number of covid-19 cases can be reduced at an early stage.
Mode of Combination of Production, Teaching and Research of College English Based on Online Education Platform

This paper introduces an English learning system based on Web technology. In this paper, the design scheme and key technical problems of the system are described in detail. Firstly, the algorithm and strategy of intelligent test paper generation in the system are analyzed, and a mathematical model of intelligent test paper generation is established based on the requirements of practicability and operability of the algorithm. This paper designs an optimization method based on the improved genetic algorithm to solve the model, which can generate the combination mode of industry, University and research randomly according to the combination of production, teaching and research indicators, so as to meet the personalized needs of different users. Secondly, through the integration of Ajax technology, the time process control of the combination of English production, teaching and research in the system online assessment is realized. This solves the "bottleneck" problem caused by B / S structure and slows down the data flow.
Minimal parsimonious chunking of written language: investigating the storage-computation trade-off as a driving principle in chunk formation

Visual word identification is the process that allows the brain to recognize a familiar and meaningful word from an ordered collection of letters. Chunking seems to play an important role in this process: rather than jumping from single letters to words, the brain seems to group letters in smaller units. Some recent studies suggest to oust morphemes, the smallest meaning-bearing units in language, from their role as building blocks in chunking: they should instead be replaced by letter chunks which do not necessarily have an explicit connection with semantics, but which could be explained by statistical regularities in letter co-occurence. However, the exact principles according to which these chunks emerge in skilled readers are still unclear. The algorithm developed in the thesis tries to answer this question, looking for the set of chunks that optimizes the trade-off between the storage of many different units and the computational effort needed to process completely new words. This optimization problem is formally translated in the minimization of a one-parameter function featuring two competing terms, the number of stored chunks and the average number of chunks per word. The parameter in the objective function allows us to adjust the relative weight of the two players in this competition, and potentially mirrors psychologically meaningful phenomena, such as the progressive mastering of literacy. Since we used a massive database to learn the chunks, many computational tricks are introduced to accelerate the algorithm, which otherwise would not be able to compute a solution in a finite time. A natural improvement of the algorithm is then to assign different weights to the chunks. We considered here the concept of “chunk productivity”, which we defined as the number of times that a chunk is used to identify a word – although this concept turned out to be fairly elusive. Finally, in order to evaluate the algorithm’s performance against real psychological data, we tested its ability to account for priming, the time saving in the identification of a target word (e.g., deal) that is brought about by a related one (the prime, e.g., dealer). The core idea is that priming is larger when the prime is chunked onto its target (dealer=deal+er). This naive reader, whose only goal is to find the best compromise between storage and computation without any semantic or morphological information, surprisingly proves able to select many interesting affixes and chunks. Nevertheless, it only partially accounts for human performance. This can be considered a further hint supporting the hypothesis that chunks could partially emerge in a language-independent mechanism, which could take into account, among other factors, the computation and storage trade-off.
Artificial Neural Network Design Approaches to Multi-Channel Information Analysis

In recent years, a large amount of multi-channel data has been collected due to advances in technology such as with computers and the Internet. However, obtaining and labelling data are still laborious and time-consuming. Yet another issue that adds to the difficulty is finding important channels and features from multi-channel data since having enough channels alone does not guarantee designing efficient algorithms due to scalability problems. In this thesis, a generative model and hierarchical learning models are introduced to deal with the aforementioned issues. First, the learning process of Variational Autoencoders is analysed. Taking into account the role of the mean and the standard deviation, which are used in the reparameterization trick, we propose a new generative model. The proposed model is modified from the original Autoencoder architecture which is used for dimensionality reduction. The model preserves the architecture of the Autoencoder by removing the reparameterization trick and becomes a generative model by extension of the mapping of the decoder from a discrete latent space to a continuous latent space. The model is compared with VAE and MMD on three benchmark datasets: MNIST, Fashion-MNIST and SVHN datasets. The experimental results show that the difference of the accuracy of the test set when training ANNs using synthetic data generated by the proposed model is less than 10% when training it using the original training set in MNIST and Fashion-MINST datasets. In addition, further experiments are carried out to investigate the impact of the number of the training set when training generative models. The results show that the accuracy of the test set decreases less than 10% when the number of the training set decreases in the NNIST and the Fashion-MNIST dataset. Second, two types of hierarchical learning models are proposed. Designing these models began with the idea of utilizing an innate hierarchy of targets. The first type of model, HAL, is proposed when targets are discrete. This model involves inserting the auxiliary block to output the auxiliary scores from the coarse classes. These scores are distributed based on the corresponding coarse classes. Although the model improves the accuracy of a test set, it has the disadvantage of requiring the coarse classes at the test phase. The second type of models are proposed when targets are continuous. C-FNNs and HADNNs are proposed to perform the regression task by utilizing the coarse classes. C-FNNs and HADNNs are evaluated on three benchmark indoor localization datasets, examples of multi-channel data. Results show that C-FNNs increase the floor accuracy by 30% at least and 60% at most in the three datasets. However, C-FNNs require more than three times the parameters than the baseline. HADNNs achieve better accuracy than C-FNNs and require 1.2 times the parameters than the baseline at most. Third, human motion data is analysed in order to show the importance of the relationship between sensor locations and motion types when identifying motion…
THE DEVELOPMENT OF COGNITIVE INDEPENDENCE IN CHILDREN OF PRESCHOOL AGE THROUGH CASE TECHNOLOGIES

The article reveals modern approaches to the concept of «cognitive independence» and defines the main components of the formation of cognitive independence in older preschool children.Based on the analysis of scientific research, the definition of «case technologies» as applied in a kindergarten is given. A model for the development of cognitive independence in older preschool children has been developed using case technologies. It defines the main goal and the underlying objectives arising from the goal. The content of the model presents the main typesof cases: photo cases, case illustrations, role design. The authors substantiated scientific approaches, principles, requirements for the use of case technologies in preschool education. Recommendations are given on the use of various types of case technologies in the classroom on fiction, the formation of elementary mathematical concepts and familiarization with the surrounding world. Shown are diagnostic techniques aimed at determining the level of cognitive independence in older preschool children.
Modeling and Forecasting GDP per Capita in Rwanda

Using annual time series data on GDP per capita for Rwanda from 1960 to 2017, the study analyzes GDP per capita using the Box – Jenkins ARIMA technique. The ADF tests showed that Rwandan GDP per capita data is I (1). Based on the AIC and the Theil’s U, the study presents the ARIMA (3, 1, 1) model. The diagnostic tests further showed that the presented parsimonious model is quite stable and acceptable. The results of the study indicate that living standards in Rwanda will continue to slowly improve over the next decade. Vision 2020’s “middle income status” goal is likely not to be achieved, although this does not obliterate Rwanda’s success story of significantly reducing extreme poverty. Indeed, Rwanda’s economy is growing and other African countries must learn from Rwanda’s experiences. The study offers policy prescriptions in an effort to help policy makers for Rwanda on how to promote and maintain the much needed sustainable growth in Rwanda.
Development of Teaching Materials for Writing Biographical Texts Based on Typonymy of Street Names in Gowa Regency

Abstract: This research is a type of research and development that aims to produce teaching materials for writing biographical texts using various creative techniques that are valid for learning to write biographical texts. Based on the development activities and the results of the validation of teaching materials, it was concluded that writing poetry teaching materials using various creative techniques was declared valid and suitable for use in learning to write poetry in class X SMA. Overall, the requirements for developing teaching materials have been met in the development activities carried out. Based on the validator's assessment of the feasibility of content, language, presentation, and graphics. Teaching materials as a whole are suitable for use in an effort to improve students' skills in writing biographical texts. Keywords : teaching materials, biographical texts, typonymy of street names
Statistical models for point-counting data

Abstract Point-counting data are a mainstay of petrography, micropalaeontology and palynology. Conventional statistical analysis of such data is fraught with problems. Commonly used statistics such as the arithmetic mean and standard deviation may produce nonsensical results when applied to point-counting data. This paper makes the case that point-counts represent a distinct class of data that requires different treatment. Point-counts are affected by a combination of (1) true compositional variability and (2) multinomial counting uncertainties. The relative magnitude of these two sources of dispersion can be assessed by a chi-square statistic and test. For datasets that pass the chi-square test for homogeneity, the ‘pooled’ composition is shown to represent the optimal estimate for the underlying population. It is obtained by simply adding together the counts of all samples and normalising the resulting values to unity. However, more often than not, point-counting datasets fail the chi-square test. The overdispersion of such datasets can be captured by a random effects model that combines a logistic normal population with the usual multinomial counting uncertainties. This gives rise to the concept of a ‘central’ composition as a more appropriate way to average overdispersed data. Two- or three-component datasets can be displayed on radial plots and ternary diagrams, respectively. Higher dimensional datasets may be visualised and interpreted by Correspondence Analysis (CA). This is a multivariate ordination technique that is similar in purpose to Principal Component Analysis (PCA). CA and PCA are both shown to be special cases of Multidimensional Scaling (MDS). Generalising this insight to multiple datasets allows point-counting data to be combined with other data types such as chemical compositions by means of 3-way MDS. All the techniques introduced in this paper have been implemented in the provenance R -package, which is available from http://provenance.london-geochron.com .
Research and Design of Sensor Data Management System Based on Distributed Storage

Sensor data is one of the basic source of big data in the Internet of Things(IoT). It is the data that is sensed, measured, and transmitted by sensors with real-time and dynamic characteristics. With the development of the IoT and big data technologies, new industries such as smart transportation, smart cities, and smart agriculture have gradually formed, resulting in an increasing demand for large-scale data analysis and calculations. However, the storage architecture based on relational databases has been unable to meet the current needs in terms of data storage and data read and write speed. Therefore, this paper mainly studies the sensor data management system based on distributed storage, builds a Hadoop cluster, proposes a storage structure that combines the relational database MySQL with the distributed database HBase, designs a data migration module to satisfy real-time sensor data monitoring and massive data storage requirements. In order to meet the needs of enterprises in actual production, SSM framework technology is used to build a sensor data management platform, covering the functions of system management, sensor data management, data exception management, sensor data real-time monitoring, sensor node status monitoring, data statistical analysis, etc. The platform has good security and reliability, and can effectively improve the management efficiency of the enterprise. Finally, the experiment provides that the system has obvious advantages in data upload and query ability compared with the traditional storage architecture. It provides a good data environment for data analysis and data mining in the future.
제 2 언어 처리: 피상적 구조 가설 재고

This paper aims to consider the shallow structure hypothesis through empirical findings. Based on their work on some syntactic domains(syntactic ambiguity and syntactic dependency), Clahsen and Felser propose the shallow structure hypothesis that the real-time sentence processing of non-native learners is qualitatively different from that of native speakers. It means that non-native learners compute shallower and less detailed syntactic representations than native speakers do during sentence comprehension. To investigate the validity of hypothesis the results of their long distance wh-dependency study, which is most closely associated with the hypothesis, are discussed in the paper. Even though the hypothesis has much effect on the field of second language acquisition, there exists a disparity between the hypothesis and empirical evidence.
Library Guides: Summer 2021 Changes in the Library: Home

Dates and frequently asked questions about UC Library Search launch and building re-opening
Balancing public-private partnerships in a digital age: CBDCs, central banks and technology firms

What roles exist for public and private partnerships within the context of central bank digital currencies (CBDCs), in an increasingly digitalized global system? Do central bank digital currencies (CBDCs) serve as public goods rather than tools which should primarily remain within the realm and governance of private sector firms? What challenges or risks are presented through the use of CBDCs and how can such risks be mitigated through current existing structures - as well as models which have been propounded in relation to public – private partnerships? This paper aims to contribute to the literature on the topic through a consideration of several variants and models of CBDCs under which the public private partnership would function, namely the synthetic CBDC (sCBDC) and the two-tiered CBDC. Further, two other types of CBDCs, namely the wholesale CBDC and the retail CBDC will be distinguished - as well as the account based CBDC, which is contrasted to CBDCs based on digital tokens. Whilst concerns for privacy and security remain paramount and cannot be undermined, particularly from the perspectives of distributed ledger technologies (and blockchains – through which such platforms operate), such concerns need to be weighed against the need for identification since regulators will be better supported in their goals in enforcing the law, as well as identifying fraudulent operations, where sufficient identification procedures have been put in place
FORMATION OF PROFESSIONAL SELF-ASSESSMENT OF A FUTURE SPECIALIST IN THE PROCESS OF TEACHING PRACTICE

The article deals with psychological and pedagogical aspects of the formation of professional self-esteem of future teachers. The value of the reflexive level in professional activity is analyzed. Psychological and pedagogical aspects and main approaches to the formation of professional self-assessment of future specialists are defined. The main stages of formation of reflection and professional assessment in the course of pedagogical practice are considered. An alternative system for organizing teacher training based on the reflexive-humanistic model of teacher education is proposed. The purposeful formation of the reflexive component of students 'practical activity allowed us to significantly adjust the professional and value orientations and the" I-concept " of students
Correlation of Robinson’s Cytological Grading of Breast Carcinoma with Bloom Richardson’s Histological Grading – A Teaching Institutional Experience in Mandya, Karnataka

BACKGROUND Breast cancer is increasing in developing countries and the management options are wide; therefore, providing the surgeon with accurate prognostic information on which mode of therapy will be chosen becomes important. Fine needle aspiration cytology (FNAC) is a routinely used initial investigation of choice for rapid diagnosis of breast cancer. Apart from diagnosis of cancer, it also has the ability to predict the grade on smears which will add its diagnostic value without any additional morbidity or expense for the patients. Among various cytological grading systems, Robinson grading is most commonly used for breast carcinoma in fine needle aspirates. The purpose of this study is to evaluate the correlation between Robinson’s cytological grading and Bloom Richardson’s histological grading. METHODS This is a 3 - year retrospective analytical study. 40 cases of infiltrating duct carcinoma (IDC) of breast diagnosed on cytology were included in the study. Cytological grading was done using Robinson’s grading and corresponding histopathology slides were taken, and histological grading using Bloom Richardson’s system was done. Finally, correlation between cytological and histological grading was done using statistical package for social sciences (SPSS) software. RESULTS Age of the patients varied between 32 and 70 yrs. Cytologically, 32.5 % cases were grade I, 40 % were grade II and 27.5 % cases were grade III respectively. Histologically 22.5 %, 47.5 % and 30 % cases were grade I, grade II, and grade III, respectively. Concordance rate between grade I tumours in cytology and histology was 53.84 %, for grade II tumours it was 75 %, and for grade III tumours it was 63.63 %. The absolute concordance rate was 65 %. CONCLUSIONS Robinson’s cytological grading (RCG) of breast carcinoma correlates well with Bloom - Richardson’s histological grading system and could be a helpful parameter in selecting a neoadjuvant treatment for the breast cancer patients on fine needle aspiration cytology alone. KEYWORDS Breast Carcinoma, Robinson’s Cytological Grading, Bloom Richardson’s Histological Grading
Survey and Comparison of String Matching Algorithms

There are many applications which makes use of pattern matching algorithm. Most of current websites implements pattern matching algorithm in order to display the results faster. There exist different kind of data such as image, text, video, audio. In order to deal with such kind of data different pattern matching algorithms are used. One algorithm performs well in particular type of data, while it degrades in other kind of data. Our aim is to find best pattern matching algorithm.One of the key aspects of any string-matching algorithm is how fast the string matching is done along with the degree of search performance. This paper offers a survey on various String-matching algorithms along with the comparative analysis to provide a brief idea regarding the better algorithm for improving the search performance..