Reviewerly/exHarmony huggingface dataset:
It's FLAN time! Summing feature-wise latent representations for interpretability.

Interpretability has become a necessary feature for machine learning models deployed in critical scenarios, e.g. legal systems, healthcare. In these situations, algorithmic decisions may have (potentially negative) long-lasting effects on the end-user affected by the decision. In many cases, the representational power of deep learning models is not needed, therefore simple and interpretable models (e.g. linear models) should be preferred. However, in high-dimensional and/or complex domains (e.g. computer vision), the universal approximation capabilities of neural networks is required. Inspired by linear models and the Kolmogorov-Arnol representation theorem, we propose a novel class of structurally-constrained neural networks, which we call FLANs (Feature-wise Latent Additive Networks). Crucially, FLANs process each input feature separately, computing for each of them a representation in a common latent space. These feature-wise latent representations are then simply summed, and the aggregated representation is used for prediction. These constraints (which are at the core of the interpretability of linear models) allow an user to estimate the effect of each individual feature independently from the others, enhancing interpretability. In a set of experiments across different domains, we show how without compromising excessively the test performance, the structural constraints proposed in FLANs indeed increase the interpretability of deep learning models.
韓中美 스마트 디바이스 기반 어린이용 중국어교육 어플리케이션 현황 분석 연구

With the development of technology, innovations are taking place in the education field, with smart device-based Chinese educational applications drawing attention. This study examines the current status of Chinese educational applications in Korea, China, and the U.S., and explores possible directions to design effective applications for Korean young learners. A total of 179 applications from three countries were investigated and classified based on educational application criteria. Among them, the top five applications were selected from each country using ratings and reviews. Each application was then evaluated and analyzed according to the evaluation criteria. The results demonstrated that applications from the U.S. were the highest scoring overall and highest in terms of content, with applications from China highest in terms of function. The result also presented three common problems, which are limited types of applications, the absence of camera function utilization, and the lack of learning activities to experience Chinese culture. This study recommends further discussions on Chinese educational applications with the broadened scope of application types and target learners.
Shuffling Recurrent Neural Networks

We propose a novel recurrent neural network model, where the hidden state hₜ is obtained by permuting the vector elements of the previous hidden state hₜ₋₁ and adding the output of a learned function β(xₜ) of the input xₜ at time t. In our model, the prediction is given by a second learned function, which is applied to the hidden state s(hₜ). The method is easy to implement, extremely efficient, and does not suffer from vanishing nor exploding gradients. In an extensive set of experiments, the method shows competitive results, in comparison to the leading literature baselines. We share our implementation at https://github.com/rotmanmi/SRNN.
Witness Protection A Fundamental Need in Criminal Justice System

The witness is considered to be one of the most indispensable parts of the criminal justice system. They assist the court by submitting their testimony without any personal gain. While doing so they sustain lots of pain and grief but never stagger from their path. The service rendered by the Witness in the justice administration system cannot be ignored. Whittaker Chambers said,“In search of truth, he plays that sacred role of the sun, which eliminates the darkness of ignorance and illuminates the face of justice, encircled by devils of humanity and compassion.”iTherefore, the witness should be kept in the highest place of honour. However, it has been found that the witness faces lots of troubles during the course of the trial process. The allowances which are paid to the witness are not feasible, the facilities which are offered to the witnesses during court stay are inadequate and apart from these issues they face unjustified adjournments. Often, witnesses are ill-treated by the court officials, harassed and threatened by the accused and sometimes even lose their life during the trial. These all incidences happen due to lack of effective witness protection mechanism in our country. This, in turn, battered the faith of witnesses in the Criminal Justice System and due to which they hesitate to come forward to lend their testimony or often turn hostile. Therefore, witnesses should be provided with adequate protection. Hon‟ble Supreme Court of India and Law commission in various instance highlighted the need for a comprehensive policy on witness protection. In this research paper, the researcher shall underline the trouble faced by the witnesses in the criminal justice system and elaborate on the issue of witness protection in India.
Crowd Manager during Pandemic using Video Analytics

As the COVID-19 pandemic sweeps through the world, we are in need of a system that can manage the flow of people. With the increase of Covid-19 cases, crowds need to be monitored, be it in a public place or in a building. Human monitoring can be quite tiresome and expensive, making way for the upcoming of automated crowd monitoring systems. In this paper, a framework for detection and analysis of crowd flow is proposed. The automated crowd flow analysis and mask detection in buildings eliminates the need for manual monitoring
A Machine Learning Approach for Credit Card Fraud Detection

Now a days on line transactions became a critical and essential a part of our lives. As frequency of transactions is growing, style of dishonorable transactions are growing chop-chop. On the way to reduce back dishonorable transactions, device gaining knowledge of algorithms like naive bayes, deliver regression, j48 and adaboost etc. are noted in the course of this paper. An equal set of algorithms are enforced and examined exploitation an internet dataset. Via comparative evaluation it may be terminated that Supply regression and adaboost algorithms carry out higher in fraud detection.The rise in e-commerce commercial enterprise has reason companion degree exponential increase inside the use of credit cards for on line purchases and consequently they has been surge in the fraud related to it .in recent years, for banks has turn out to be terribly troublesome for sleuthing the fraud in grasp card machine. System learning plays a widespread position for sleuthing the grasp card fraud in the transactions. For Predicting those transactions banks build use of assorted gadget gaining knowledge of methodologies, beyond facts has been accrued and new options are been used for reinforcing the prophetical strength. The overall performance of fraud sleuthing in master card transactions is substantially suffering from the sampling method on facts-set, choice of variables and detection techniques used. This paper investigates the performance of deliver regression, call tree and random woodland for grasp card fraud detection. Dataset of master card transactions is accrued from kaggle and it consists of an entire of two, eighty four, 808 grasp card transactions of a European financial institution data set.
PEDAGOGICAL PROMOTION OF THE STUDENT’S ECOTECHNICAL CULTURE

The author sees the problem of research in the search for ways to perfect the process of developing the student’s ecotechnical culture. The article considers the features of the concept of “ecotechnical culture” as content guidelines for pedagogical assistance in the development of the student’s ecotechnical culture. The author of the article, using the methods of comparative analysis of scientific literature, determined socio-historical, practical and theoretical prerequisites that consider the concept of ecotechnical culture as an element of general human culture, the method house of systemic analysis revealed the composition, structure and functions of ecotechnical culture, determined the criteria, indicators and levels of development of ecotechnical culture of the student. The concept of “eco-technical culture of man” was synthesized, its systemic features, level characteristics were identified and described, the organizational and substantive relationship of the identified characteristics of the concept and measures of pedagogic assistance for the development of the student’s ecotechnical culture was substantiated. The results of the systematic analysis of the terminology apparatus made it possible to offer meaningful interdisciplinary means for the development of the student’s ecotechnical culture, as guidelines in strengthening the significant level of pedagogical means. Recommendations in the organization of the educational process and the choice of the content of education aimed at the development of human ecotechnical culture can serve as practical models for teachers-practitioners. Pedagogical assistance to development of integrative property of the student – an ecotechnical culture can be realized through the integrative, cross-disciplinary nature of content of educational process, variety of types of educational and pedagogical interaction: educational, educational, research.
Visual representation of CORDEX climate data by using QGIS

&amp;lt;p&amp;gt;The majority of studies are dedicated to the analysis of climate change and climate models with no regard for data visualization part. Therefore, this research is aimed at highlighting challenges, with an emphasis on spatial referencing that can occur while visualizing CORDEX data. CORDEX data are stored in NetCDF file format, and sometimes georeferencing may be misconceived in QGIS software. For this reason, two techniques of georeferencing data are examined in this work. The first way of data georeferencing is re-projecting coordinates from original projection to an interpolated latitude/longitude grid. The second way is re-encrypting initial data file so that QGIS is able to interpret projection information. Preference of using QGIS explained by two reasons: it is open source GIS application and it has expanded visualization toolkit.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In addition, there are a great deal of climate models based on CORDEX data for some regions whereas there is a lack of climate projections for particular areas. In this regard, carrying out analysis for the region of Kazakhstan is beneficial. Outcomes of this research may stimulate spreading local climate models for Kazakhstan territory. Results are represented in the form of maps of Kazakhstan illustrating temperature change over 21&amp;lt;sup&amp;gt;st&amp;lt;/sup&amp;gt; century time period.&amp;lt;/p&amp;gt;
SU-NLP at TREC NEWS 2020.

This paper presents our work and submissions for the TREC 2020 News Track: background linking and wikification tasks. For the background linking task, we investigated utilization of transformer-based architectures as either to map news articles to sentence embeddings, or to extract summaries of queried news articles or to re-rank the candidate documents. Even though none of these approaches outperformed our regular full-text baseline search approach, they provided some insights and future research directions. In the wikification task, after extracting and linking the entities to their corresponding entities, we analyzed two ranking approaches; one depends on entities positions within the text and the other depends on vector similarities between the news article and entity’s linked page.
Cloud based Scalable Object Recognition from Video Streams using Orientation Fusion and Convolutional Neural Networks

Object recognition from live video streams comes with numerous challenges such as the variation in illumination conditions and poses. Convolutional neural networks (CNNs) have been widely used to perform intelligent visual object recognition. Yet, CNNs still suffer from severe accuracy degradation, particularly on illumination-variant datasets. To address this problem, we propose a new CNN method based on orientation fusion for visual object recognition. The proposed cloud-based video analytics system pioneers the use of bi-dimensional empirical mode decomposition to split a video frame into intrinsic mode functions (IMFs). We further propose these IMFs to endure Reisz transform to produce monogenic object components, which are in turn used for the training of CNNs. Past works have demonstrated how the object orientation component may be used to pursue accuracy levels as high as 93\%. Herein we demonstrate how a feature-fusion strategy of the orientation components leads to further improving visual recognition accuracy to 97\%. We also assess the scalability of our method, looking at both the number and the size of the video streams under scrutiny. We carry out extensive experimentation on the publicly available Yale dataset, including also a self generated video datasets, finding significant improvements (both in accuracy and scale), in comparison to AlexNet, LeNet and SE-ResNeXt, which are the three most commonly used deep learning models for visual object recognition and classification.
A Context Aware Approach for Generating Natural Language Attacks.

We study an important task of attacking natural language processing models in a black box setting. We propose an attack strategy that crafts semantically similar adversarial examples on text classification and entailment tasks. Our proposed attack finds candidate words by considering the information of both the original word and its surrounding context. It jointly leverages masked language modelling and next sentence prediction for context understanding. In comparison to attacks proposed in prior literature, we are able to generate high quality adversarial examples that do significantly better both in terms of success rate and word perturbation percentage.
SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption

Self-supervised contrastive representation learning has proved incredibly successful in the vision and natural language domains, enabling state-of-the-art performance with orders of magnitude less labeled data. However, such methods are domain-specific and little has been done to leverage this technique on real-world tabular datasets. We propose SCARF, a simple, widely-applicable technique for contrastive learning, where views are formed by corrupting a random subset of features. When applied to pre-train deep neural networks on the 69 real-world, tabular classification datasets from the OpenML-CC18 benchmark, SCARF not only improves classification accuracy in the fully-supervised setting but does so also in the presence of label noise and in the semi-supervised setting where only a fraction of the available training data is labeled. We show that SCARF complements existing strategies and outperforms alternatives like autoencoders. We conduct comprehensive ablations, detailing the importance of a range of factors.
Integrated language skills materials and varied activities to improve students speaking skills

This study aims to improve students speaking skills through the implementation of integrated language skills materials and varied activities at the English Literature Study Program of Pamulang University. Based on the initial data collected, it was found that the students did not master the use of grammar, vocabulary and pronunciation to comprehend and to speak fluently. The students became unconfident to speak. This concern led to a study design of action research conducted in a one-semester course. To maximize the results of the study, a task-based learning model was adapted. The participants were 15 students in their third semester. The data are taken from the oral tests and observation. The findings reveal that the students speaking skills significantly improve. The average Speaking scores are based on the aspects assessed: grammar, vocabulary, pronunciation, comprehension, and fluency. In cycle 1, students who achieved average score ≥ 70 was only 13.33%, and in cycle 2 was 40%. Within those two cycles, less than 70 % of the students average scores were below 70, which means they couldnt meet the criterion of learning completeness. However, in cycle 3 more than 75% of the students achieved their speaking score ≥ 70 which suits the learning completeness criterion. To see the overall progress throughout semester pre-test and post-test were compared and discussed further in this paper.
Bernstein polynomial multiwavelets direct method for certain physical variational problems

In this article, variational problems are solved using a direct method derived from Bernstein polynomials multiwavelets (BPMWs). Based on the BPMWs, the operational matrix of integration is provided, which converts the variational problem into an algebraic equation system. Some numerical examples are given, and their findings are compared with the exact solution and some existing methods to demonstrate the efficiency and accuracy.
Efektivitas Akun Instagram @bukittinggiku Terhadap Pemenuhan Kebutuhan Informasi

Abstrak Akun @bukittinggiku adalah media sosial Instagram dalam menyampaikan informasi yang digunakan sebagai pemenuhan kebutuhan informasi followers. Teori yang digunakan dalam penelitian menggunakan teori efektivitas dan teori kebutuhan informasi. Dalam penelitian ini peneliti menggunakan metode penelitian kuantitatif deskriptif. Populasi pada penelitian ini adalah followers akun Instagram @bukittinggiku dengan jumlah followers 201.000. peneliti menggunakan teknik non-probabilty sampling dan mendapatkan jumlah sampel sebesar 100 orang. Hasil penelitian menunjukan adanya pengeruh signifikan antara efektivitas akun Instagram @bukittinggiku terhadap pemenuhan kebutuhan informasi followers. Berdasarkan hasil analisis koefisien determinasi diperoleh R squere sebesar 0,741. Hal ini menjelaskan bahwa besarnya pengaruh variabel efektivitas akun Instagram (X) terhadap Kebutuhan informasi (Y) adalah sebesar 54,90%. Sedangkan sisanya 45,10% dipengaruhi oleh faktor – faktor lain yang tidak dilakukan dalam penelitian ini. Kata Kunci: Efektivitas, Instagram, Kebutuhan, Informasi Abstract The @bukittinggiku account is Instagram's social media in conveying information that is used to fulfill followers' information needs. The theory used in research that uses strong theory and information needs theory. In this study, researchers used descriptive quantitative research methods. The population in this study were followers of the @bukittinggiku Instagram account with 201,000 followers. Researchers use non-probabilty sampling technique and get a sample size of 100 people. The results showed that there was a significant influence between the @bukittinggiku Instagram account on fulfilling followers' information needs. Based on the results of the analysis of the coefficient of determination, the R squere is 0.741. This explains that it is very influential on variables that affect Instagram accounts (X) Information needs (Y) are 54,90%. While the remaining 45,10% is believed by other factors that are not carried out by researchers in this study. Keywords: Effectiveness, Instagram, Needs, Information
FRACTAL APPROACH TO THE USE OF EDUCATIONAL TECHNOLOGIES IN MATH LESSONS (ON THE EXAMPLE OF TEACHING STOCHASTICS)

The development of the information society has entailed the development and implementation into practice of educational institutions, in particular schools, numerous educational technologies based on information and communication technologies. Various methodologies and approaches to the study of modern educational technologies make it possible to determine how effective their application is in the learning process. We tried to reflect the potential of modern educational technologies in math lessons from the position of a still little-studied fractal approach to teaching. The application of a fractal approach in teaching is a relevant study of the present. Many scientists are engaged in the formulation and resolution of certain issues of this problematics. The educational process of general education institutions — schools, which should be built on each of the lessons in the context of a fractal approach, does not go unnoticed. Math lessons are no exception to this, so in the article we tried to indicate possible ways to implement a fractal approach to teaching schoolchildren mathematics. Namely, we presented a list of technologies that can be used in the course of educational activities. In addition, we described the methodological problems of a fractal approach to the use of educational technologies in the learning process. In the article, the methodological problems of the fractal approach to the use of educational technologies in the learning process are considered on the example of one of the lines of the school course in mathematics — probability-statistical, which has become the basis for the development of the entire complex of socio-economic sciences, as well as physics, biology, linguistics, chemistry, astronomy and many other areas. The article contains the author's definition of the concept of «fractal approach in teaching mathematics».
Argument Detection and Rebuttal in Dialog 1

A method is proposed for argumentation on the basis of information that characterizes the structure of arguments. The proposed method can be used both to detect arguments and to generate candidate arguments for rebuttal. No assumption of a priori knowledge about attack and support relations between propositions, advanced by the agents participating in a dialog, is made. More importantly, by using the method, the relations are dynamically established while the dialog is taking place. This allows incremental processing since the agent need only consider the current utterance advanced by the dialog participant, not necessarily the entire argument, to be able to continue processing.
Role of angular in web development

Angular is that the most popular framework for building reactive single-page web applications and artistic elements of the website. because it is that the most used Framework of JavaScript, it absolutely was designed as a full-featured framework to reinforce simplicity and potency. The importance of Angular ahead finish Web Development is given below Developers realize Angular terribly effective in terms of making dynamic, single page application, and supporting MVC (Model-View-Controller) programming structure. At a gift span of your time, within the competitive business surroundings, Angular has emerged because of the superhero because of its intuitiveness and wealthy options. Angular provides some superb capabilities out of the box and may convert your non-trivial jQuery primarily based apps into one thing a lot of powerful and nevertheless a lot easy to keep up with. Some frameworks merely bundle along with the present tools that build app development terribly tough. However, Angular was fastidiously designed to make sure that each tool works optimally and delivers a nice result.