Reviewerly/exHarmony huggingface dataset:
Taking Stock and Glimpsing Beyond

This abstract is not so much a summary of the big stones laid thus far, but rather some point-work in the cementing that holds them all together. It is one of the longest of the chapter abstracts, for this, the shortest of the chapters. A summary is given of what the Constructive Logicist has been able to attain by means of core reasoning in accordance with natural-deduction rules of introduction and elimination, especially for carefully de signed abstraction operators. The naturals and the rationals are fully within Kroneckerian purview. But now one must prepare to engage with the reals. The hard deductive work was to obtain access to the naturals, and the Dedekind-Peano truths about them. Once entitled to the naturals, one’s passage to the rationals is somewhat easier, courtesy of apt forms of expression afforded by mereology, and another pair of ‘single-barreled’ in troduction and elimination rules, this time for fractional abstraction. Looking ahead to dealing with the reals, however, one has to be mindful of the complications involved in their inherently infinitary nature. One’s ‘<italic>ontological</italic> entitlement’ to them will notbe as straightforward as it was in the case of the naturals and the rationals. But one now has a <italic>methodological</italic> bridgehead from the earlier treatment of the rationals: a way of pro ducing a numerical representation for a ‘sub-unit part’. This makes one’s grasp of the reals closely cognate with one’s grasp of the rationals (except for the matter of the reals’ uncountability). This is reflected in the similarity of structure between Schema Q and Schema R.
The Translation Analysis of Illocutionary Change in Jojo Moyes’ The Last Letter from Your Lover Novel Translation into Indonesian

Translation is crucial to effective communication between different languages and conveys meaning from one language to another. It can enlarge knowledge about all languages in all parts of the world. Through the techniques and strategies in the translation, there would be no difficulty in understanding a text in a certain language. In the process of translation, a change in structures or elements of a certain language might exist. The structures in every language are unique, and this is often a challenge for a translator to comprehend them. That’s why the researcher is interested in conducting this research. The scope of this research is an Illocutionary change strategy, especially on the application or the changes in the text due to the use of this strategy. This research is conducted to obtain factual data of illocution change in the translation of the sourcebook and analyze the translation results in order to convey the meaning from the source language. The result of this research is crucial to prevent the confusion of understanding the translation result meaning in the target language, especially for the target language readers or learners. Another significance of this research is to add knowledge for those interested in learning the translation. This research uses the qualitative method. The data analysis technique is based on the translation results or the closest equivalence in Indonesian. The results of the analysis show that there is an Illocutionary change strategy that is used by the translator, and certain categories in the translation results are found.
IMPLEMENTASI MODEL ACCELERATED FAILURE TIME (AFT) BERDISTRIBUSI LOG-LOGISTIK PADA PASIEN PENYAKIT JANTUNG BAWAAN

Log-Logistic Accelerated Failure Time (AFT) model is survival analysis that is used when the survival time follows Log-Logistic distribution. Log-Logistic AFT model can be used to estimate survival time, survival function, and hazard function. Log-Logistic AFT model was formed by regressing covariates linierly against the log of survival time. Regression coefficients are estimated using maximum likelihood method. This study uses data from Atrial Septal Defect (ASD) patients, which is a congenital disease with a hole in the wall that separates the top of two chambers of the heart by using sensor type III. Survival time as the response variable, that is the time from patient was diagnosed with ASD until the first relapse and uses age, gender, treatment status (catheterization/surgery), defect size that is the size of the hole in the heart terrace, pulmonary hypertension status, and pain status as predictor variables. The result showed that variable gender, treatment status, defect size, pulmonary hypertension status, and pain status affect the first recurrence of ASD patients, so it is found that category of female, untreated patient, defect size ≥12mm, having pulmonary hypertension, having chest pain tend to have first recurrence sooner than the other category.
Improved MSRN-Based Attention Block for Mask Alignment Mark Detection in Photolithography

Wafer chips are manufactured in the semiconductor industry through various process technologies. Photolithography is one of these processes, aligning the wafer and scanning the circuit pattern on the wafer on which the photoresist film is formed by irradiating light onto the circuit pattern drawn on the mask. As semiconductor technology is highly integrated, alignment is becoming increasingly difficult due to problems such as reduction of alignment margin, transmittance due to level stacking structure, and an increase in wafer diameter in the photolithography process. Various methods and research to reduce the misalignment problem that is directly related to the yield of production are constantly being conducted. In this paper, we use machine vision for exposure equipment to improve the image resolution quality of marks for accurate alignment. To improve image resolution quality, we propose an improved Multi-Scale Residual Network (MSRN) that combines Attention Mechanism using a Multi-Scale Residual Attention Block to improve image resolution quality. Our proposed method can extract enhanced features using two different bypass networks and attention blocks with different scale convolution filters. Experiments were used to verify this method, and the performance was improved compared with previous research.
Dual-MGAN: An Efficient Approach for Semi-supervised Outlier Detection with Few Identified Anomalies

Outlier detection is an important task in data mining, and many technologies for it have been explored in various applications. However, owing to the default assumption that outliers are not concentrated, unsupervised outlier detection may not correctly identify group anomalies with higher levels of density. Although high detection rates and optimal parameters can usually be achieved by using supervised outlier detection, obtaining a sufficient number of correct labels is a time-consuming task. To solve these problems, we focus on semi-supervised outlier detection with few identified anomalies and a large amount of unlabeled data. The task of semi-supervised outlier detection is first decomposed into the detection of discrete anomalies and that of partially identified group anomalies, and a distribution construction sub-module and a data augmentation sub-module are then proposed to identify them, respectively. In this way, the dual multiple generative adversarial networks (Dual-MGAN) that combine the two sub-modules can identify discrete as well as partially identified group anomalies. In addition, in view of the difficulty of determining the stop node of training, two evaluation indicators are introduced to evaluate the training status of the sub-GANs. Extensive experiments on synthetic and real-world data show that the proposed Dual-MGAN can significantly improve the accuracy of outlier detection, and the proposed evaluation indicators can reflect the training status of the sub-GANs.
SubModRank: Monotone Submodularity for Opinionated Key-phrase Extraction

With the rise of World Wide Web (WWW), a large volume of structured and unstructured data in textual form, has become available. This has led to various data and information processing tasks, one of which is key-phrase extraction. It is concerned with identifying important words and phrases which can comprise a meaningful summary of the input text. Current unsupervised methods for key-phrase extraction do not explicitly account for the opinion expressing side of the text. This is important for certain type of data, for example, product reviews, since they contain user opinion about the product and its aspects. Therefore, the extracted key-phrases should reflect both factual information as well as the opinionated part of the text. In this paper, we present SubModRank, an unsupervised approach for automated key-phrase extraction, based on submodular function optimization, for which a partial enumeration based greedy algorithm has been used. The resultant key-phrases are able to capture both objective as well as subjective side of the text, outperforming state of the art unsupervised key-phrase extraction methods in terms of precision, recall and F1-score.
Hierarchical Soil Classification using Gaussian Processes

&amp;lt;p&amp;gt;The goal of this work is to perform soil classification with uncertainty quantification for a structured treatment of the output classes. Uncertainty can help in this setting to make predictions more informative with regard to class relationships. This is of particular interest due to the often highly related nature of the distinguished soil types. Incorporating knowledge about class structure into the model also provides opportunity for improving the model accuracy. Our main focus, however, is to enable modellers to better understand and work with this structure during analysis.&amp;lt;br&amp;gt;For example, post-hoc aggregation of class labels into supersets facilitates applications such as letting the model choose an ontological level on which it can confidently distinguish the output class. It can likewise be used to determine the combined probability of specified classes that share a property of interest.&amp;lt;br&amp;gt;Technically, this works by learning a latent Gaussian distribution, for example using a Gaussian Process model, and mapping it to a distribution over the class probabilities. We demonstrate this approach, explore possible applications for exploiting uncertainty information, in particular with regard to the class hierarchy, and compare the performance of different model variants in terms of accuracy and calibration.&amp;lt;/p&amp;gt;
Quantum Error Correction

This chapter discusses quantum noise and techniques for quantum error correction, a necessity for quantum computing. It discusses bit-flip errors, phase-flip errors, and their combination. The formalism of quantum operations is introduced, along with the operator-sum representation, and Krauss operators. With this, the chapter discusses the depolarization channel and imprecise gates, as well as (briefly) amplitude and phase damping. For error correction, repetition codes are introduced to motivate Shor’s 9-qubit error correction technique.
Research on Identification Technology for Electric Power Industry based on Negative List

According to the characteristics of power industry data, this paper studies the extraction technology of sensitive words in negative list, identifies the metadata of negative list based on sensitive thesaurus, and discusses the method of automatic identification of negative list by combining metadata with data content, which provides support for accurate control of power data.
THE ANALYSIS OF STUDENTS OPINION ABOUT THE TEACHERS SKILLS USING SUPERVISED LEARNING

The system for biannual teachers’ evaluation made by students, acquires a large amount of information that becomes difficult to be analyzed by a manager. It is true that delivers a final score, but it is calculated based only on the answers to the questions with several pre-defined answer options. In their questionnaire, there are also open questions in which students can express themselves freely. These answers contain valuable information, but an educational manager needs a long time to read them all individually. In this paper is proposed an automatic module for interpreting the answers. The module will report to the educational manager only the answers that need to be analyzed. The module uses natural language processing techniques, and supervised machine learning for selecting the answers to be analyzed in a certain context.
Association Between Cytological and Histological Grading of Breast Cancer and its Usefulness in Patient Treatment

Introduction: Breast cancer is the most common cancer in women throughout the globe, and the modern therapeutic available options have been widely researched currently. For a developing nation with limited resources, such as Pakistan, a quick, affordable, and least invasive technique like FNAC may give useful information. Aim: The aim of this study was; 1 Perform and evaluate cytomorphological classification of all breast cancer patients using the Robinson's three-tier grading system. 2 Histopathological evaluation and classification of all samples of MRM by using a modified BR classification system. 3 Correlation of histo-morphological and cytomorphological classification systems. Place and Duration: In the Pathology department of Khyber Teaching Hospital Peshawar for six months duration from February 2018 to July 2018. Material and methods: A prospective study was conducted in which two independent observers classified all breast cancer patients on the basis of cytology and histology of the tissue and cyto-histological correlation. Results: This study provided good individual correlation and inter-observer agreement in the cytopathological classification of breast cancer aspirates. Conclusion: Cytopathological classification may be useful for these patients and should therefore be included in routine reports. Keywords: Breast carcinoma; Cytopathology; Robinson classification system
Chaotic mixing in laminar flows through rocks

&amp;lt;p&amp;gt;Recent studies have shown that chaotic advection is spontaneously produced by laminar flows through granular media such as bead packs, strongly impacting solute mixing rates. This has strong implications for many reactive and biological processes in the subsurface. Chaotic dynamics could also be key in a wide range of environmental and industrial applications driven by mixing. Beside granular media, there is still no evidence that chaos broadly arises in the large variety of porous architectures that exist. In particular, it is unknown how the pore structure and topology can control chaotic dynamics.&amp;lt;br&amp;gt;In this study, we numerically investigate the mixing behavior of solute for a wide range of natural and engineered porous material that goes from carbonates and sandstones to beadpacks. We quantify chaotic advection by measuring Lagrangian stretching statistics (Lyapunov exponent) and its impact on mixing by estimating the decay of solute concentration variance. We find that stretching and mixing rates vary significantly between the different classes of porous architectures. A simple topological model is proposed to explain this behavior.&amp;lt;/p&amp;gt;
A perfect play in 4×12 board of Othello

In 2015, Hiroki Takeshita started to analyze a reduced-form Othello board with a 4x6 board.Later, he analyzed the 4×8 and 4×10 boards and discovered perfect play.The analysis of 4×12 board Othello was sub-problemed by Takeshita and further sub-problemed by Toya Shotaro in 2019.In this paper, we present the solution to the remaining subproblem of 4 × 12 board Othello and the discovery of perfect play.
A Study on the Development of 21st Century Life Ballet Education Program: Focused on the analysis of life ballet education

This thesis proposes the need for an educational program that the life ballet education aim for as a community dance activity for social community and present the purpose of the development of educational programs. In addition, it is based on the fact that the subjects participating in the community dance are diverse and the institutions that perform life ballet are diverse. The final purpose of this thesis is to classify educational programs by subject and institution of life ballet that anyone can participate in, and to present information related to the establishment of differentiated strategies for each educational program. As described above, the future direction of the life ballet education program is presented as a community dance activity. It can design and apply a new direction in the actual field according to the life ballet education environment. A Life ballet as a community dance is an area that can be activated through the establishment of new strategy that can expand the participation of members of the social community, and it will lead to more participation.
Online signature verification using deep learning based aggregated convolutional feature representation

Handwritten signature for the identification and authentication of an individual has been widely used in the biometric systems. Due to the intra-class and inter-class variabilities, signature verification has become one of the most challenging problem in the biometric technology. Furthermore, the offline handwritten signature can be forged by the skilled persons due to its static nature. Therefore, in this paper a deep learning-based method using convolutional neural network (CNN) for online signature verification has been developed. Different values of the convolutional kernels such as 1×1, 3×3 and 5×5 are used to extract the discriminative features at multi-scales. The features of the initial and middle layers of the CNN are combined to create more powerful features. An up-sampling method with bilinear interpolation has been used to add the features of convolutional layers with different spatial dimensions. Both the addition and concatenation methods have been used to aggregate the convolutional features. A convolutional transpose method is applied to increase the depth of the convolutional layers while performing an addition operation on the layers with different depths. Finally, the concatenated features are passed to the fully connected layers for high-level feature extraction and classification. To evaluate the performance of the proposed method, an android application was developed where; a custom database of 985 online signatures collected from 197 users has been created. The problem of inadequate training data for online signature verification has been addressed through the data augmentation method. The experimental results show that the deep aggregated convolutional feature representation method achieves an accuracy of 99.32% on the custom developed online signature database.
Handwriting to Text Conversion for English Language Using Deep Learning

Abstract: Because of the rising use of digital technology in all businesses and in all day-to-day activities to store and communicate information, recognition systems in writing have become a prominent study topic and development. Humans still require handwriting copies to be converted into digital copies that can be shared and preserved electronically. Handwriting recognition is one of the most active study areas, and deep neural networks are being used in it. Humans find it simple to recognise handwriting, but computers find it tough. Nowadays, technologies that detect handwriting letters, characters, and figures assist people in doing more sophisticated activities that would otherwise take a long time and be costly. The purpose of this project is to turn handwritten notes into typed documents. We aim to transform handwritten English characters into a computer-readable format using a paragraph as an input, process the paragraph with cursive writing and symbols support, and then train a neural network algorithm to recognize and display the text. CNN is the neural network model that we used. The image can be uploaded by the user. To eliminate background noise, the system pre-processes the input. The machine then looks for text sections in the image. The system then displays the text that is contained in the image to the user. To conduct horizontalvertical segmentation, we used OpenCV. Keywords: Handwriting, Bi-LSTM, Convolutional Neural Network, Text Conversion, Deep Learning, OpenCV
RANCANG BANGUN SISTEM INFORMASI PERPUSTAKAAN BERBASIS WEB UNTUK EFEKTIVITAS LAYANAN PEMINJAMAN DAN PENGEMBALIAN BUKU

The library is one of the facilities that support the learning activities of students or students to get references or sources of knowledge and information. With the development of technology, people think to be able to work more effectively and efficiently. One of them is to make the conventional system into a computerized system. By utilizing the facilities of a website that is connected to the internet, the library can be more and more efficient. The purpose of this study is to design a web-based library information system to help librarians solve problems in school libraries. In this study, a web-based information system was designed using the PHP MySQL programming language. With this system, it is hoped that it will be able to overcome various needs to find books and facilitate school administration in borrowing books and making reports.
Long-depth-volume 3D measurements using multithread monocular fringe projection profilometry

Stereo-assisted fringe projection profilometry (FPP) has been broadly applied to 3-D measurements because of its accuracy and stability advantages. However, absolute phase unwrapping requires an ever-increasing number of cameras to continuously enlarge the measurement depth volume without spatial uncertainty, which inevitably reduces calibration reliability and system flexibility. This paper proposes a multithread monocular FPP to implement long-depth-volume 3-D measurements. For achieving the multithread FPP technique, a galvanomirrorbased camera-projector system with variable extrinsic parameters is developed. The typical combination of a camera and a projector is virtually converted into multiple monocular active vision systems through fast viewpoint switching. Based on the spatio-temporal correlation of multiple viewpoints, each wrapped phase can obtain a possible fringe order map. Subsequently, with the geometric constraints of the system, the wrapped phase maps extracted from image sequences are converted into relative phase distributions. Combining the possible fringe orders and the relative phase map, the absolute phase distribution is determined by choosing the correct phase map from all potential candidates. Experimental results demonstrate that the proposed method could accomplish long-depth-volume 3-D measurements with a measuring accuracy of 0.02 mm.
Canopy�K-means Combined Collaborative Filtering Using RMSE-minimization

Collaborative filtering is one of the most conventional algorithm in recommendation system. However, CF suffers from data sparsity and scalability issues. Thus, we propose Canopy–K-means Combined Collaborative Filtering (CK-ComCF) to solve the challenge of data sparsity and scalability. In particular, the prediction outcomes of user-based CF and item-based CF are integrated using a weighting approach, which is based on the root-mean-square error minimization. Experiment results based on two real-life datasets of MovieLens and Net-flix Prize demonstrate that the proposed RMSE-minimization method outperforms the traditional CF methods, improving the accuracy by 64.24% (UbCF with MovieLens) and 13.72% (IbCF with Netflix Prize). The proposed CKComCF model outperforms the existing improved CF method, reducing the calculation time by 41.84% (MovieLens) and 64.77% (Netflix Prize).