Reviewerly/exHarmony huggingface dataset:
A Proposed Solution to Problems in Learning the Knowledge Needed by Self-Driving Vehicles

Three problems in learning knowledge for self-driving vehicles are: how a finite sample of information about driving, N, can yield an ability to deal with the infinity of possible driving situations; the problem of generalising from N without over- or under-generalisation; and how to weed out errors in N. A theory developed with computer models to explain a child’s learning of his or her first language, now incorporated in the SP System, suggests: compress N as much as possible by a process that creates a grammar, G, and an encoding of N in terms of G called E. Then discard E which contains all or most of the errors in N, and retain G which solves the first two problems.
Ensemble Classifiers Employed for Spam Review Detection

The advancement of technology and the use of internet have changed many aspects of human culture over the years.Today, consumers take confidence in e-commerce platforms like amazon and eBay for comprehensive understanding of products and services when making a purchase decision.Here the web or user-generated content from consumers of such products and services, known as reviews, are exploited by spam reviewers to falsely promote or downgrade some targeted products.Despite potential solutions, Identifying and preventing review spam are still one of the top challenges faced by web search engines today.Therefore, in the quest to provide a more improved and efficient classification of review spam, this research probed different techniques in order to find most effective solution to spam detection.The research employed three base classifiers, Naïve Bayes, Support Vector Machines and Logistic Regression to form ensemble classifiers complimented with Arching classifier.The Arching classifier performs the weighted voting that produces the final class label with performance and accuracy higher than either of the individual base classifiers.Cross-validation is used as evaluation metrics to measure the performance or effectiveness of the ensemble classifiers while the experimental results shows that the ensemble classifiers achieve the best results compared to the single based classifier in terms of Precision, Recall, F1-measure and Accuracy.
An In-Depth Study of Retail Sales Trend and Pattern based on Exploratory Data Analysis

A world driven by consumer’s means that we are mostly directed by the demands of individuals, which we might call customers. As the world changes, we must plan and restructure our processes for assessing trends and staying on top of the charts with our sales figures. To analyses the shopping or order putting behavior of clients, we have developed an EDA method. As a result of using exploratory data analysis, we were able to determine the monthly patterns in sales. Based on categorical variables, we employed a thorough procedure for sales analysis. We also looked at the top 10 selling goods in terms of profit and quantity. To guarantee that clients have a positive purchasing experience, the shipping method is crucial. As a result, the customer's favorite delivery method is also researched from the analyzed data. Further, we looked at the most lucrative product categories and concentrated on the category that generated profits, in order to understand not only what consumers buy, but also how and why they do so. What product categories customers are most interested in, which product categories have the greatest conversion rates, which of your items are popular both qualitatively and quantitatively, were all revealed through this research. Improved sales & order pipelines may be boosted with better sales & order data analysis. If the firm analyses order data trends, it will be able to keep up with product and pricing changes, utilize optimal distribution channels and continue to improve earnings.
The impact of the pandemic on education

About one and a half years after the pandemic began, in every corner of the world, we have a clearer picture of the effects of the pandemic on education, and the conclusions are worrying, which means that the problem is deeply felt in education. The rapid need for digitization, the challenges of a new curriculum adapted to teaching online, the negative psycho-social impact of pupils resulting from isolation, anxiety, insecurity, and in particular the increase in school drop-out rates due to lack of access to online learning, There are some of the realities facing the global education system due to Covid-19. Strengthening educational institutions alongside teachers can be a key condition for the recovery of education in the post-pandemic period, undeniable, with the support of central and local public authorities.
Neural networks with trainable matrix activation functions

The training process of neural networks usually optimize weights and bias parameters of linear transformations, while nonlinear activation functions are pre-specified and fixed. This work develops a systematic approach to constructing matrix activation functions whose entries are generalized from ReLU. The activation is based on matrix-vector multiplications using only scalar multiplications and comparisons. The proposed activation functions depend on parameters that are trained along with the weights and bias vectors. Neural networks based on this approach are simple and efficient and are shown to be robust in numerical experiments.
PEMANFAATAN INTERNET BERBASIS SWADAYA MASYARAKAT UNTUK AKTIVITAS PEMBELAJARAN DARING SISWA SEKOLAH

This research is motivated by the occurrence of the COVID-19 pandemic so that the government has implemented large-scale social distancing, one of the areas affected by this application in the field of education. so that learning that was previously carried out in the classroom has now become online learning so that new problems arise in the field. This study aims to determine the use of self-help-based internet on the effectiveness of online learning for school students. The theoretical basis in this research 1. Theory of Online Learning 2. Theory of Self-help 3. Theory of Community Education. The data collection technique used a qualitative approach with the type of case study research and tend of descriptive. The instruments of this research are interviews, observations, and literature studies. This research was conducted at RT 02 RW 08 Sukatani Village, Ngamprah District, West Bandung Regency. Samples were taken of 5 people, namely 1 manager, 2 parents, and 2 students The results showed that the existence of non-government-based internet can increase the efficiency and effectiveness of students in online learning as well as save costs.
D2C-SR: A Divergence to Convergence Approach for Real-World Image Super-Resolution

In this paper, we present D2C-SR, a novel framework for the task of real-world image super-resolution. As an ill-posed problem, the key challenge in super-resolution related tasks is there can be multiple predictions for a given low-resolution input. Most classical deep learning based approaches ignored the fundamental fact and lack explicit modeling of the underlying high-frequency distribution which leads to blurred results. Recently, some methods of GAN-based or learning super-resolution space can generate simulated textures but do not promise the accuracy of the textures which have low quantitative performance. Rethinking both, we learn the distribution of underlying high-frequency details in a discrete form and propose a two-stage pipeline: divergence stage to convergence stage. At divergence stage, we propose a tree-based structure deep network as our divergence backbone. Divergence loss is proposed to encourage the generated results from the tree-based network to diverge into possible high-frequency representations, which is our way of discretely modeling the underlying high-frequency distribution. At convergence stage, we assign spatial weights to fuse these divergent predictions to obtain the final output with more accurate details. Our approach provides a convenient end-to-end manner to inference. We conduct evaluations on several real-world benchmarks, including a new proposed D2CRealSR dataset with x8 scaling factor. Our experiments demonstrate that D2C-SR achieves better accuracy and visual improvements against state-of-the-art methods, with a significantly less parameters number and our D2C structure can also be applied as a generalized structure to some other methods to obtain improvement. Our codes and dataset are available at https://github.com/megvii-research/D2C-SR
The Highs and Lows of Simple Lexical Domain Adaptation Approaches for Neural Machine Translation

Machine translation systems are vulnerable to domain mismatch, especially in a lowresource scenario.Out-of-domain translations are often of poor quality and prone to hallucinations, due to exposure bias and the decoder acting as a language model.We adopt two approaches to alleviate this problem: lexical shortlisting restricted by IBM statistical alignments, and hypothesis re-ranking based on similarity.The methods are computationally cheap, widely known, but not extensively experimented on domain adaptation.We demonstrate success on low-resource out-of-domain test sets, however, the methods are ineffective when there is sufficient data or too great domain mismatch.This is due to both the IBM model losing its advantage over the implicitly learned neural alignment, and issues with subword segmentation of out-of-domain words.
Evaluating the multilingual capabilities of the OCCAM workflow : a case of digitised historical newspapers

Increased digitization of historical newspapers by cultural heritage institutions1 has allowed humanities scholars to expand their corpora in terms of volume and diversity. This has accompanied an increase in the use of computational tools2 . Simultaneously this has also brought to rise a new set of questions around the accuracy and value of this data and related studies. In this presentation we will focus on the multilingual challenge of studying historical newspapers from the lens of Belgium: questioning how a humanities researcher with an understanding of one language Dutch or French can accurately implement a study of a national press without the knowledge of the other most populous language. This question is core to the workflows being developed in the OCCAM (OCR, ClassificAtion & Machine Translation) project’s digital humanities case. OCCAM implements a workflow for the integration of image classification, Translation Memories (TMs), Optical Character Recognition (OCR), and Machine Translation (MT) to support the automated translation of scanned documents.We explain this through the case of the Belgian press using a set of multilingual historical newspapers from KBR- the Royal Library of Belgium’s historical newspaper collections: BelgicaPress. Through examples from a set of Dutch and French language newspapers from the early 1900s we explain how images of textual sources in multiple languages can efficiently be OCRed using the machine learning based model of PERO. In a subsequent workflow, the results of the OCR are then fed through a machine translation module. Originally developed for the machine translation of contemporary documents, we will report on the results using our digitized historical newspapers test case, to afford research of these historical documents.
Homography augumented momentum constrastive learning for SAR image retrieval.

Deep learning-based image retrieval has been emphasized in computer vision. Representation embedding extracted by deep neural networks (DNNs) not only aims at containing semantic information of the image, but also can manage large-scale image retrieval tasks. In this work, we propose a deep learning-based image retrieval approach using homography transformation augmented contrastive learning to perform large-scale synthetic aperture radar (SAR) image search tasks. Moreover, we propose a training method for the DNNs induced by contrastive learning that does not require any labeling procedure. This may enable tractability of large-scale datasets with relative ease. Finally, we verify the performance of the proposed method by conducting experiments on the polarimetric SAR image datasets.
뉴 미디어 을 활용한 한국 언어문화 교육의 병행적 학습 모형 연구 - 가치 문화를 중심으로-

This study was intended to examine the educational utility and utilization of new media in the digital environment, paying attention to the popular culture production and communication methods of .According to this goal, linguistic informatics as a quantitative study and text interpretative method as a qualitative study were applied to verify the educational utility of as a media text and to present a specific classroom teaching model based on this data.The Korean value culture education plan considering the learner characteristics of Koreans, foreigners, and overseas Koreans was constructed as a class model and data. This can be seen that there is a continuous need for research on ways to present interpretation and critique of media content in the field of teaching and learning and to use it educationally. (Yonsei University)
Evaluating Multiway Multilingual NMT in the Turkic Languages

Despite the increasing number of large and comprehensive machine translation (MT) systems, evaluation of these methods in various languages has been restrained by the lack of high-quality parallel corpora as well as engagement with the people that speak these languages. In this study, we present an evaluation of state-of-the-art approaches to training and evaluating MT systems in 22 languages from the Turkic language family, most of which being extremely under-explored. First, we adopt the TIL Corpus with a few key improvements to the training and the evaluation sets. Then, we train 26 bilingual baselines as well as a multi-way neural MT (MNMT) model using the corpus and perform an extensive analysis using automatic metrics as well as human evaluations. We find that the MNMT model outperforms almost all bilingual baselines in the out-of-domain test sets and finetuning the model on a downstream task of a single pair also results in a huge performance boost in both low- and high-resource scenarios. Our attentive analysis of evaluation criteria for MT models in Turkic languages also points to the necessity for further research in this direction. We release the corpus splits, test sets as well as models to the public.
Healthcare and Cyber Physical Systems

In several facets of our daily lives, including how people access and receive healthcare services, the revolutionary trend of Industry 4.0 has been introduced. The fundamental innovative structures of smart healthcare galaxies are mounting in dimensions as well as density when we change in the direction of Healthcare Industry 4.0. Aimed at creation Healthcare Industry 4.0 significant enormous, composed information are accurately treated. As per requirements in place system offer helpful knowledge in addition recommendation. Nowadays we are going into the period of imaginative specialized arrangements. These arrangements assume a significant job in the development of the country like Cyber Physical System (CPS). Cyber Physical System is using in healthcare. Today's world-wide major issues of healthcare is coronavirus disease covid-19. This study emphases on the forthcoming growth commands beneath the Industries 4.0 and highpoint precise the growth strategy of healthcare. Moreover, the security and privacy of Cyber Physical System are discussed for secure CPS System.
김희상의 「초등국어어전」에 대하여

This article describes the contents of 「Cho-deung-gug-eo-eo-jeon」, the first approved grammar textbook written by Hee-Sang Kim, who had been educated in Chinese and English for a long time, with the stimulation of English grammar. The first edition was published in 1909, and a revised edition was published in 1910. This article compares and reviews editions, focusing on 「(Revised) Cho-deung-gug-eo-eo-jeon (Vol. 1, Vol. 2, Vol. 3)」, which was published by Yuil Seogwan on February 8, 1910, stored in Sejong University Academic Information Service.It was confirmed that there is no change in the contents of the first edition and the revised version of Vol. 2, which is currently comparable, but only changes in spelling and examples. Three versions of Vol. 1 is compared and reviewed, and confirmed the possibility that there could be more than two edition. In Vol. 3, the contents of which were first introduced through this article, it is reviewed that most of the contents in 「Joseon Eojeon」 and 「Uligultul」 were presented at the elementary education level, and the content on writing is also presented for elementary education.
Semantic-guided Automatic Natural Image Matting with Trimap Generation Network and Light-weight Non-local Attention

Natural image matting aims to precisely separate foreground objects from background using alpha matte. Fully automatic natural image matting without external annotation is challenging. Well-performed matting methods usually require accurate labor-intensive handcrafted trimap as extra input, while the performance of automatic trimap generation method of dilating foreground segmentation fluctuates with segmentation quality. Therefore, we argue that how to handle trade-off of additional information input is a major issue in automatic matting. This paper presents a semantic-guided automatic natural image matting pipeline with Trimap Generation Network and light-weight non-local attention, which does not need trimap and background as input. Specifically, guided by foreground segmentation, Trimap Generation Network estimates accurate trimap. Then, with estimated trimap as guidance, our light-weight Non-local Matting Network with Refinement produces final alpha matte, whose trimap-guided global aggregation attention block is equipped with stride downsampling convolution, reducing computation complexity and promoting performance. Experimental results show that our matting algorithm has competitive performance with state-of-the-art methods in both trimap-free and trimap-needed aspects.
Extending Representability on the Set of Intervals Endowed with Admissible Orders for the Construction of Interval-valued Fuzzy Operators

In this work, we provide a study on the representability of interval-valued fuzzy connectives considering partial and admissible orders.Our approach considers those orders based on injective aggregation functions which allowed the construction of intervalvalued operators.An immediate result is the construction of the implication used in the Quantum Logic, known as QL-implication, that in our proposal is generalised for intervals, given by means of interval-valued t-(co)norms and interval-valued fuzzy negations.
Student’s self-development and self-realization in the process of professional training

The article reveals the essence of the concepts of self-development and self-realization of a student. The problem of personal and professional development in a higher educational institution is considered. The significance of the development of creativity as a resource in the self-organization of an individual in professional activity is determined.
How to use Fibonacci numbers to teach recursive programming

There are too many examples and programming guides (which, e.g., an internet search for "recursive procedure Fibonacci" will turn up) to count that use Fibonacci numbers as an example to illustrate recursive programming. The motivation for this article is to show why the naive way of doing this is a bad idea, as it is horrendously inefficient. We will exhibit much more efficient ways of computing Fibonacci numbers, both iterative and recursive, and analyze and compare worst case running times and memory usages. Using some mathematical properties of Fibonacci numbers leads to the most efficient method for their computation. For illustration and benchmarking, we will use Maple and its programming language, however, similar behaviour can be demonstrated in almost any other programming language. This exposition combines and explores the mathematical properties of Fibonacci numbers, notions of algorithmic complexity, and efficient Maple programming and profiling techniques, and may be used as an introduction to any of these three subjects. The techniques described can be readily generalized to more general types of linear recurrences with constant coefficients.
Challenges and Solutions in DeepFakes

Deep learning has been successfully appertained to solve various complex problems in the area of big data analytics to computer vision. A deep learning-powered application recently emerged is Deep Fake. It helps to create fake images and videos that human cannot distinguish them from the real ones and are recent off-shelf manipulation technique that allows swapping two identities in a single video. Technology is a controversial technology with many wide-reaching issues impacting society. So, to counter this emerging problem, we introduce a dataset of 140k real and fake faces which contain 70k real faces from the Flickr dataset collected by Nvidia, as well as 70k fake faces sampled from 1 million fake faces generated by style GAN. We will train our model in the dataset so that our model can identify real or fake faces.
3D LEBP and 3D SPIHT based B Mode Echocardiogram Compression using variant transform

Currently, the echocardiograms are adopted widely for attaining accurate diagnosis of the cardiovascular disease. Using echocardiogram as an imaging modality has been developed in a substantial manner over the past decade. Several research works has been executed to fill the gap existing in medical treatment and echocardiogram retain major part in it. Effective means of storage is mandatory for high quality echocardiogram results for diagnosis of heart diseases. Compression techniques are highly suggested for this concern and by efficient compression means, the data loss and bandwidth reduction can be measured. In this current research work, have proposed three-dimensional listless embedded block partitioning (3D LEBP) encoding followed by 3D set partitioning in hierarchical trees (3D SPIHT) for compression in recent research work and achieved better compression ratio with best results. Different types of transfer functions such as Bi-orthogonal Transform, Daubechies Transform, Haar Transform and Mexican Transform are applied in the proposed technique. The Performance evaluation metrics are carried out by considering the factors such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), SSIM (Structure SIMilarity Index) and EPI (Edge Preservation Index). By comparing the outcomes attained and evaluation metrics, the enhanced video compression technique could be suggested.