Reviewerly/exHarmony huggingface dataset:
Technocracy as the Ideology

The study examines the position of a man and society in the philosophy of technology and technocracy development that were connected with the technological progress in Europe and North America in the 19th century. The issue of relations within the “man – machine” system has become a great interest among sociologists, historians, psychologists and philosophers. The study is based on methods of analysis and synthesis of scientific writings from Plato to F. Dessauer, who referred to the important role of science and technology in the society development. The technology increasing impact on people’s lives was reflected in the emergence of technocratic concepts. They reflected the vision of technological evolution modern problems and a vision of its future development. The cornerstone of the technocracy concepts was the idea of political power transition to the technocrats’ social layer. However, during the 20th century, technocrats’ categories like scientific and technological intelligentsia, plants and factories heads, scientists and engineers did not constitute a unified political power. They were invited to discuss the economic development problems, as experts, where they were adapted to the existing political regimes rather than trying to modify them. The technocratic strata of population transformation, their political views and preferences, the impact on the socio-political situation in the country had become the main topics in the respective philosophical concepts. Thus, one important issue is man’s status in a technological society, which in turn is a relevant topic for study in the 21 st century, the century of technology. Keywords: philosophy of technology, “man – machine” system, technocracy, scientific and technological progress, political power
Prediction and Performance Evaluation of Coagulant in Water Purification Plant Based on Interval-Based Incremental Granular Model

In this paper, we design an interval-based incremental granular model(IGM) and compare and analyze the prediction performance according to the method of generating information granule in the output space. It is a structure that combines an incremental granular model with a linear regression(LR) model and a granular model(GM). The existing granular model uses the context-based fuzzy C-means(CFCM) clustering method, but in this paper, an interval-based granular model using the interval-based fuzzy C-means(IFCM) clustering method is used. Granular models are designed by information granules generated in the input and output spaces. The information granules generated in the input space are directly induced by the interval generated in the output space. In general, when creating an interval in the output space, a method of dividing the interval evenly and not overlapping is used. In this paper, in addition to the general segmentation method, we compare and analyze prediction performance by adding a method of flexibly and non-overlapping segments based on a probability distribution, and a method of equally dividing segments at regular intervals. Fuzzy-related prediction models are generally evaluated by RMSE and MAPE. However, in the case of an interval-based particle model, the final output is not a numerical value, but a fuzzy number value in the form of an interval, which is not suitable for evaluating the granular model. In order to evaluate the prediction performance of the interval-based incremental granular model, the performance index method using the coverage and specificity of the interval, which is the result of the granular model, is used. In order to confirm the validity of the granular model, an experiment was conducted using predicted data for the injection of a coagulant at a water purification plant.
Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data

Weak supervision has shown promising results in many natural language processing tasks, such as Named Entity Recognition (NER). Existing work mainly focuses on learning deep NER models only with weak supervision, i.e., without any human annotation, and shows that by merely using weakly labeled data, one can achieve good performance, though still underperforms fully supervised NER with manually/strongly labeled data. In this paper, we consider a more practical scenario, where we have both a small amount of strongly labeled data and a large amount of weakly labeled data. Unfortunately, we observe that weakly labeled data does not necessarily improve, or even deteriorate the model performance (due to the extensive noise in the weak labels) when we train deep NER models over a simple or weighted combination of the strongly labeled and weakly labeled data. To address this issue, we propose a new multi-stage computational framework -- NEEDLE with three essential ingredients: (1) weak label completion, (2) noise-aware loss function, and (3) final fine-tuning over the strongly labeled data. Through experiments on E-commerce query NER and Biomedical NER, we demonstrate that NEEDLE can effectively suppress the noise of the weak labels and outperforms existing methods. In particular, we achieve new SOTA F1-scores on 3 Biomedical NER datasets: BC5CDR-chem 93.74, BC5CDR-disease 90.69, NCBI-disease 92.28.
KLASIFIKASI ABSTRAK JURNAL KOMPUTASI MENGGUNAKAN METODE TEXT MINING DAN ALGORITMA SUPPORT VECTOR MACHINE

The University of Lampung especially Computer Science Departement has an online journal that publishes various scientific articles written by researchers both students and lecturers. This scientific article is called the online Computating Journal which is published once every 6 months. But, this online Computating Journal has not been structured and classified into the category of science that more specific. Therefore, in this research the abstract Computating Journal will be classified using text mining techniques to process the abstract become more structured and retrieve information in it. Then, the information in the abstract is extracted as a feature by the TFIDF weighting technique. The proposed classification model uses the support vector machine algorithm that has strong consistency. The model classification will be validated by applying the 10-Fold Cross Validation technique.
Property of long term (1990-2009) seismicity in the region of L&amp;#8217;Aquila Mw6.1 earthquake revealed from machine learning&amp;#160;

&amp;lt;p&amp;gt;On April 6th 2009 (01:32 UTC) strong earthquake of magnitude M&amp;lt;sub&amp;gt;W&amp;lt;/sub&amp;gt;6.1 occurred near the city of L&amp;amp;#8217;Aquila in the Abruzzo region in the Central Apennines of Italy. Due to the extensional processes the Abruzzo region is characterized by prominent historical seismicity. However, before the 2009 event the background seismic activity is characterised as sparse and mostly clustered in space and time. The general lack of events, especially small magnitude events before the 2009 event motivated us to study the long-term near-fault seismicity before the large earthquake occurrence. To achieve this we first have to extend the existing catalog. We take into consideration the data from the AQU (42.354, 13.405) station that has been recorded in the city of L&amp;amp;#8217;Aquila, near Paganica fault responsible for the 2009 event, during an extensive period of 29-years, 19 years before the event itself. The catalog extension is performed by applying the two-stage convolutional neural network pipeline for earthquake detection and characterisation (epicentral distance and magnitude) using three component signal station waveforms. The algorithm allows us to successfully detect ~800 local events (less than 10 km from the AQU station) in the period 1990-2009. We here present a detailed analysis of this catalog including waveforms characterization to derive new insights about the long term preparation processes(es) occuring before the 2009 M&amp;lt;sub&amp;gt;w&amp;lt;/sub&amp;gt;6.1 earthquake.&amp;lt;/p&amp;gt;
Predefined Project Scope Changes and its Causes for Project Success

In project management, the project scope is the base of significant project planning processes such as estimating the cost, schedule and building work breakdown structure. Poor project scope definition directly affects project cost and schedule. Accordingly, dealing with unrealistic scope definition of cost and schedule may lead to failing a project. Besides, changes in project scope have a negative and positive impact on project success. This paper aims to predefine the potential scope changes to keep the project scope on track and identify any weakness in scope definition at the early stages of a project. Enhancing project scope quality has a massive impact on the success of a project and it adds more control over project scope. A method is proposed to improve the quality of project scope and increase the efficiency of controlling scope changes. The proposed method will help to avoid scope creeping by defining a clear statement of work, increase the learning opportunities of the development team to optimize its processes, reducing the communication gap between the clients and development team and screen adjustments of new tasks. The proposed method is validated using a survey and the results are found encouraging.
SAR-U-Net: squeeze-and-excitation block and atrous spatial pyramid pooling based residual U-Net for automatic liver segmentation in Computed Tomography

Background and objective: In this paper, a modified U-Net based framework is presented, which leverages techniques from Squeeze-and-Excitation (SE) block, Atrous Spatial Pyramid Pooling (ASPP) and residual learning for accurate and robust liver CT segmentation, and the effectiveness of the proposed method was tested on two public datasets LiTS17 and SLiver07. Methods: A new network architecture called SAR-U-Net was designed. Firstly, the SE block is introduced to adaptively extract image features after each convolution in the U-Net encoder, while suppressing irrelevant regions, and highlighting features of specific segmentation task; Secondly, ASPP was employed to replace the transition layer and the output layer, and acquire multi-scale image information via different receptive fields. Thirdly, to alleviate the degradation problem, the traditional convolution block was replaced with the residual block and thus prompt the network to gain accuracy from considerably increased depth. Results: In the LiTS17 experiment, the mean values of Dice, VOE, RVD, ASD and MSD were 95.71, 9.52, -0.84, 1.54 and 29.14, respectively. Compared with other closely related 2D-based models, the proposed method achieved the highest accuracy. In the experiment of the SLiver07, the mean values of Dice, VOE, RVD, ASD and MSD were 97.31, 5.37, -1.08, 1.85 and 27.45, respectively. Compared with other closely related models, the proposed method achieved the highest segmentation accuracy except for the RVD. Conclusion: The proposed model enables a great improvement on the accuracy compared to 2D-based models, and its robustness in circumvent challenging problems, such as small liver regions, discontinuous liver regions, and fuzzy liver boundaries, is also well demonstrated and validated.
Pedagogical Conditions for the Stage Image Formation of the Vocal Teacher in the Process of Professional Training

The study of pedagogical conditions for the formation of the stage image of vocal teachers is a topical issue in the context of improving the quality of professional music education in Ukraine, as well as in the world. The purpose of the article is to study the pedagogical conditions that contribute to the effective formation of the stage image of the future vocal teacher in the process of his professional education. The methodological basis of the study is the theoretical methods of scientific knowledge (analysis and synthesis, classification method, comparative method). Also, in the context of this study, a theoretical analysis of scientific sources was conducted. The article highlights and substantiates the pedagogical conditions for the formation of the stage image of a vocal teacher in the process of professional education: the creation of models of their own future based on the activation of “I-real” and “I-ideal”; providing motivation for performing and creative stage selfrealization of future specialists; application of personality-oriented models and learning technologies aimed at developing the student's individuality and providing conditions for the development of their abilities and creative self-realization in professional activities; diversification of practical forms of organization of the educational process It is emphasized that the basis of the stage image is a combination of personal and professional characteristics and psychological archetypes, which are the basis of image formation, which should correspond to the character of the vocalist the best human qualities and powerful informational-creative potential of the person. It is emphasized that according to the personality-oriented concept of education, priority is given to the development of personality, rather than the learning process, which is an important aspect in the process of forming the stage image of personality. The practical value of the work is that the results of the study can be used to increase the efficiency of the educational process, as well as to improve the professional qualities of vocal teachers
Website for online Stock Market Trading tips analysis

The website aims for analyzing the accuracy of tips given by the Financial Advisor to the Investors investing in the shares of the Stock Market. The stock market plays a crucial role in the financial aspects of the nation's growth, but Stock Market is highly Complex and risky. On every day's business, millions of traders invest in Stock Market. Most of these Investors either gain or lose money. With the ease of availability of top-notch gadgets, superior technology, and the high-speed internet, youngsters today are adapting the quick and efficient option of Stock Market Trading as an additional source of income.With this Online Trading Platform created by using the Agile Model of SDLC, this website provides tips to the new Investors to invest in the right shares based on the detailed analyzed tips given by the Financial Advisors.
Techniques for Lexical Semantics in Hindi Language

A word having multiple senses in a text introduces the lexical semantic task to find out which particular sense is appropriate for the given context. One such task is word sense disambiguation which refers to the identification of the most appropriate meaning of the polysemous word in a given context using computational algorithms. The language processing research in Hindi, the official language of India, and other Indian languages is constrained by non-availability of the standard corpora. For Hindi word sense disambiguation also, the large corpus is not available. In this work, we prepared the text containing new senses of certain words leading to the enrichment of the available sense-tagged Hindi corpus of sixty polysemous words. Furthermore, we analyzed two novel lexical associations for Hindi word sense disambiguation based on the contextual features of the polysemous word. The evaluation of these methods is carried out over learning algorithms and favourable results are achieved.
Credit card fraud detection

In today's world, due to the rapid increase of technology many countries in the world encouraging cashless transactions. Credit cards are the easiest and fastest mode of payment both in offline as well as online transactions. So, credit cards are becoming a more popular mode of transaction in modern-day life. This also increases fraud cases in credit card transactions. Peoples and companies face a huge amount of loss of money due to these increasing cases of fraud transactions. This also affects the liability of the bank systems and service providers. This is why it becomes very important to detect fraud transactions and avoid financial loss. The good thing is fraud tends to occur in patterns so machine learning algorithms can help us to predict them. Our objective is to reduce the tedious work of the fraud detection process with the help of machine learning and data science.
A THOROUGH STUDY ON PRODUCT RECOMMENDATION

The improvements in the internet infrastructure and the increased affordability has led to an increase in the number of users on this platform. This has put a large impact on the services being offered on this platform especially on the e-commerce websites. These websites cater to the individual and the increased number of users has led to an increase in customer data. This data is highly valuable as it can allow for the effective prediction of customer behavior. Therefore, these predictions can allow for effective and accurate product recommendations based on the interests and the behavior of the customer. To achieve this approach, this research article analyzes a collection of related works based on the paradigm of product recommendation. After a thorough analysis, an improved product recommendation system is devised through the effective implementation of Natural Language Processing and machine learning algorithms. The proposed methodology performs preprocessing, Bag of Words, and TF-IDF along with Fuzzy Artificial Neural Networks and Collaborative Filtering to achieve an effective Product Recommendation system. This approach will be expanded further in the upcoming researches.
2nd Place Solution for Waymo Open Dataset Challenge -- Real-time 2D Object Detection

In an autonomous driving system, it is essential to recognize vehicles, pedestrians and cyclists from images. Besides the high accuracy of the prediction, the requirement of real-time running brings new challenges for convolutional network models. In this report, we introduce a real-time method to detect the 2D objects from images. We aggregate several popular one-stage object detectors and train the models of variety input strategies independently, to yield better performance for accurate multi-scale detection of each category, especially for small objects. For model acceleration, we leverage TensorRT to optimize the inference time of our detection pipeline. As shown in the leaderboard, our proposed detection framework ranks the 2nd place with 75.00% L1 mAP and 69.72% L2 mAP in the real-time 2D detection track of the Waymo Open Dataset Challenges, while our framework achieves the latency of 45.8ms/frame on an Nvidia Tesla V100 GPU.
Peranan Orang Tua Dalam Pengembangan Literasi Dini Selama Covid-19 Pada Anak Usia 5-6 Tahun

This study aims to reveal the role of parents in developing children's early literacy during the Covid-19 period, by using a survey method with 26 respondents, namely parents of 13 boys and 13 girls. The analysis was carried out using the percent index formula and SPSS 16 software. The instrument was validatedwith expert judgment techniques and the reliable test showed the number 0.912&gt; 0.60, which means that the questionnaire was reliable. The results of the data analysis showed that the parents in the TK Pembangunan Laboratorium UNPwere in the category of less role, with the average percentage of all skills being 62.09%. Researchers have revealed the role of parents in developing children's early literacy with details of the ability to print motivation skills of 53.34% which means less, print motivation skills of 67.04% which means sufficient, phonological awareness skills of 70.68% which means sufficient, vocabulary skills of 67.21% which means enough, narrative skill is 60.74% which means less, and letter knowledge is 53.55% which means less
File Conversion Application using Kivy and Tesseract – OCR

In the fastest developing world education is the most important factor in all over the country.Indian Rural teachers have a great responsibility on their shoulders to educate the students without getting technological support.They face various challenges like network problems, lack of technical skills, the burden of multitasking.In this research, paper researchers try to make an application for Indian rural teachers to fight technical challenges and overcome the burden on them.
Employing an Adjusted Stability Measure for Multi-criteria Model Fitting on Data Sets with Similar Features

Fitting models with high predictive accuracy that include all relevant but no irrelevant or redundant features is a challenging task on data sets with similar (e.g. highly correlated) features. We propose the approach of tuning the hyperparameters of a predictive model in a multi-criteria fashion with respect to predictive accuracy and feature selection stability. We evaluate this approach based on both simulated and real data sets and we compare it to the standard approach of single-criteria tuning of the hyperparameters as well as to the state-of-the-art technique "stability selection". We conclude that our approach achieves the same or better predictive performance compared to the two established approaches. Considering the stability during tuning does not decrease the predictive accuracy of the resulting models. Our approach succeeds at selecting the relevant features while avoiding irrelevant or redundant features. The single-criteria approach fails at avoiding irrelevant or redundant features and the stability selection approach fails at selecting enough relevant features for achieving acceptable predictive accuracy. For our approach, for data sets with many similar features, the feature selection stability must be evaluated with an adjusted stability measure, that is, a measure that considers similarities between features. For data sets with only few similar features, an unadjusted stability measure suffices and is faster to compute.
Elementos del modelo educativo de la Universidad Tecnológica Fidel Velázquez utilizados en el proceso de enseñanza aprendizaje

The objective of this work is to present the research that was carried out with the students of the Higher University Technician (TSU) careers in Digital Network Infrastructure and Multiplatform Software Development, where the elements of the educational model of the Fidel Velázquez Technological University were identified (UTFV) that are being used in the virtual learning teaching process as a learning strategy in the COVID-19 pandemic. The research that was applied was quantitative, a questionnaire on Google with closed-ended questions was used as an information gathering instrument, and the sample that was taken was on students enrolled from first to fourth semester. Where it was identified that 100% of the students consider that the virtual learning strategy in UTFV is adequate and allows the feedback sessions to be carried out by meet, in addition to the fact that the teacher uses electronic presentations and vodcast as a teaching method in the classroom platform and classes are carried out in 2 synchronous and asynchronous modes. The result obtained is to propose the generation of a platform where Vodcasts are uploaded as a didactic resource that strengthens the learning and competencies of the TSU.