Reviewerly/exHarmony huggingface dataset:
MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms.

Missing data is an important problem in machine learning practice. Starting from the premise that imputation methods should preserve the causal structure of the data, we develop a regularization scheme that encourages any baseline imputation method to be causally consistent with the underlying data generating mechanism. Our proposal is a causally-aware imputation algorithm (MIRACLE). MIRACLE iteratively refines the imputation of a baseline by simultaneously modeling the missingness generating mechanism, encouraging imputation to be consistent with the causal structure of the data. We conduct extensive experiments on synthetic and a variety of publicly available datasets to show that MIRACLE is able to consistently improve imputation over a variety of benchmark methods across all three missingness scenarios: at random, completely at random, and not at random.
MEM AS A PHENOMENON IN CREOLIZED INTERNET COMMUNICATION

Existing types and types of communication in the world of virtual reality provide global interpersonal and group communications of modern society. One way to transmit information on the Internet is a phenomenon called «meme». The distribution of memes in actively functioning chat rooms has become a powerful factor shaping public opinion and even modern mentality.
Syntactically Diverse Adversarial Network for Knowledge-Grounded Conversation Generation

Generative conversation systems tend to produce meaningless and generic responses, which significantly reduce the user experience.In order to generate informative and diverse responses, recent studies proposed to fuse knowledge to improve informativeness and adopt latent variables to enhance the diversity.However, utilizing latent variables will lead to the inaccuracy of knowledge in the responses, and the dissemination of wrong knowledge will mislead the communicators.To address this problem, we propose a Syntactically Diverse Adversarial Network (SDAN) for knowledgegrounded conversation model.SDAN contains an adversarial hierarchical semantic network to keep the semantic coherence, a knowledgeaware network to attend more related knowledge for improving the informativeness and a syntactic latent variable network to generate syntactically diverse responses.Additionally, in order to increase the controllability of syntax, we adopt adversarial learning to decouple semantic and syntactic representations.Experimental results show that our model can not only generate syntactically diverse and knowledge-accurate responses but also significantly achieve the balance between improving the syntactic diversity and maintaining the knowledge accuracy.
Quantum Cryptography: The Future of Secure Data Communication

This paper deals with an introduction to quantum cryptography, differences between conventional and quantum cryptography, its applications, drawbacks & its advantages over conventional cryptography. Quantum cryptography is the phenomenon of making cryptographic tasks using the principles of quantum physics. Quantum key distribution (QKD) is one of the leading applications of quantum cryptography which makes the applications, servers, databases, emails & social media handles more securable, i.e., not hackable. Quantum cryptography is a trail to permit two users of a common communication channel to transmit or create secure information between them. This information which is in the form of a random string of bits can be converted to even more strong and unbreakable if used with the help of quantum cryptography. The advantage of quantum cryptography over conventional cryptography is that the information which is being stored or shared is in the quantum state which means it is highly impossible to hack or copy the data. The Heisenberg uncertainty principle and quantum entanglement can be exploited as a system of secure communication in this process. Quantum cryptography takes advantage of quantum’s multiple states, including its “no change theory,” which implies it cannot be unwittingly interrupted. The Uncertainty principle states that it is impossible to simultaneously know the accurate position and momentum of a particle. Quantum cryptography holds both guarantees and threats for our current crypto logical infrastructure. The pretty obvious threat is that quantum computers might decrypt data that has already been encrypted using many of our current systems. However, it conjointly holds the promise of secure communications channels for key distribution. Sooner or later, with correct & clean usage of quantum technology, it may even be possible to build entire encryption systems that are considered unbreakable and unbackable.How to cite this article: Nallana A. Quantum Cryptography: The Future of Secure Data Communication. Int J Hum Comp Inter Data Min 2021; 4(1): 12-16.
Comparative Analysis of Cluster, Utility, Grid and Cloud Computing

Computing is a progressing technique. As part of this progression, computing needs directed by Applications always exceed accessible technology. Computer architects are always looking for quick and more powerful computing systems. With the emergence of Cloud computing, virtualization or automatic computing has progressed. This paper will give an overview of several forms of computing. It will highlight several features of Cluster, Grid, utility, and cloud computing, their working, the differences between them, and the advantages and disadvantages for each.
3D modelling of survey scene from images enhanced with a multi-exposure fusion

In current practice, scene survey is carried out by workers using total stations. The method has high accuracy, but it incurs high costs if continuous monitoring is needed. Techniques based on photogrammetry, with the relatively cheaper digital cameras, have gained wide applications in many fields. Besides point measurement, photogrammetry can also create a three-dimensional (3D) model of the scene. Accurate 3D model reconstruction depends on high quality images. Degraded images will result in large errors in the reconstructed 3D model. In this paper, we propose a method that can be used to improve the visibility of the images, and eventually reduce the errors of the 3D scene model. The idea is inspired by image dehazing. Each original image is first transformed into multiple exposure images by means of gamma-correction operations and adaptive histogram equalization. The transformed images are analyzed by the computation of the local binary patterns. The image is then enhanced, with each pixel generated from the set of transformed image pixels weighted by a function of the local pattern feature and image saturation. Performance evaluation has been performed on benchmark image dehazing datasets. Experimentations have been carried out on outdoor and indoor surveys. Our analysis finds that the method works on different types of degradation that exist in both outdoor and indoor images. When fed into the photogrammetry software, the enhanced images can reconstruct 3D scene models with sub-millimeter mean errors.
Native Chinese Reader: A Dataset Towards Native-Level Chinese Machine Reading Comprehension

We present Native Chinese Reader (NCR), a new machine reading comprehension (MRC) dataset with particularly long articles in both modern and classical Chinese. NCR is collected from the exam questions for the Chinese course in China's high schools, which are designed to evaluate the language proficiency of native Chinese youth. Existing Chinese MRC datasets are either domain-specific or focusing on short contexts of a few hundreds of characters in modern Chinese only. By contrast, NCR contains 8390 documents with an average length of 1024 characters covering a wide range of Chinese writing styles, including modern articles, classical literature and classical poetry. A total of 20477 questions on these documents also require strong reasoning abilities and common sense to figure out the correct answers. We implemented multiple baseline models using popular Chinese pre-trained models and additionally launched an online competition using our dataset to examine the limit of current methods. The best model achieves 59% test accuracy while human evaluation shows an average accuracy of 79%, which indicates a significant performance gap between current MRC models and native Chinese speakers. We release the dataset at https://sites.google.com/view/native-chinese-reader/.
Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts

Most existing methods in vision language pre-training rely on object-centric features extracted through object detection and make fine-grained alignments between the extracted features and texts. It is challenging for these methods to learn relations among multiple objects. To this end, we propose a new method called X-VLM to perform `multi-grained vision language pre-training.' The key to learning multi-grained alignments is to locate visual concepts in the image given the associated texts, and in the meantime align the texts with the visual concepts, where the alignments are in multi-granularity. Experimental results show that X-VLM effectively leverages the learned multi-grained alignments to many downstream vision language tasks and consistently outperforms state-of-the-art methods.
Voice Based Employee Management System Using AWS and Alexa

In the developing and creating world, everything is getting advanced, digital, computerized, and automated. With an over-sized number of labor opportunities, the Human workforce has increased. Thus, there is a need for a system that can handle the information of such a large number of employees in a company. This project untangles the task of maintaining records and data of employees, thanks to its user-friendly GUI. The “EMPLOYEE MANAGEMENT SYSTEM using AWS has been created to abrogate the issues with the existing manual framework. This application is designed in such a simplest way that it can eliminate and in some cases reduce the efforts faced by the live system. Moreover, this method is meant for the actual need of the corporate to holdout operations smoothly and effectively. A Voice command feature is additionally added to the system to avoid a waste of time. The application is coded in such a way that, it avoids mistakes while entering the data. It notifies the employee/admin if he entered the incorrect, or invalid data. No formal knowledge or special training is required by the employee/admin to use this application. Thus, it is approved as an easy to understand and person-friendly utility. The Voice command feature is additionally straightforward to use and informative. This project will allow admin to feature new employees after proper authentication (manual). Admin can even edit departments and posts (in a variety of bands). The database should store all personal details of employees like date of birth, full name, people, address, etc. this method enables employees to fill their time-sheets daily or at intervals. The admin can approve or reject the time-sheet filled by a worker together with remarks. Admin may make workdays, Performance charts, Leave details, and will edit employee details. An employee can access its details using Alexa.
Chapter 3. The Toolkit in Action

Chapter 3 of Library Technology Reports (vol. 57, no. 8), “Using the Toward Gigabit Libraries Toolkit,” covers a few different approaches for how to use the toolkit (as a whole or in pieces). It also uses anecdotes from the pilot process to illustrate how the toolkit can be utilized (e.g., to discuss connection speed problems with an internet service provider beyond describing the connection as “slow”).
Exploring Methods for Generating Feedback Comments for Writing Learning

The task of generating explanatory notes for language learners is known as feedback comment generation. Although various generation techniques are available, little is known about which methods are appropriate for this task. Nagata (2019) demonstrates the effectiveness of neural-retrieval-based methods in generating feedback comments for preposition use. Retrieval-based methods have limitations in that they can only output feedback comments existing in a given training data. Furthermore, feedback comments can be made on other grammatical and writing items than preposition use, which is still unaddressed. To shed light on these points, we investigate a wider range of methods for generating many feedback comments in this study. Our close analysis of the type of task leads us to investigate three different architectures for comment generation: (i) a neural-retrieval-based method as a baseline, (ii) a pointer-generator-based generation method as a neural seq2seq method, (iii) a retrieve-and-edit method, a hybrid of (i) and (ii). Intuitively, the pointer-generator should outperform neural-retrieval, and retrieve-and-edit should perform best. However, in our experiments, this expectation is completely overturned. We closely analyze the results to reveal the major causes of these counter-intuitive results and report on our findings from the experiments.
THE EFFECT OF USING JOURNAL ON THE STUDENTS’ WRITING ABILITY IN RECOUNT TEXT AT GRADE EIGHT STUDENTS’OF SMP NEGERI 2 BALIGE IN ACADEMIC YEAR 2018/2019

This study presents a research  on The Effect of Using Journalon Students’  Writing Ability in Recount Text. The objective of the study was to find out whether there is a significant effect of using journal on the students’ writing ability in recount text. It was conducted by using experimental research design. The population was  grade eight students of SMP Negeri 2 Balige. Forty six students from 220 students distributed in eight classes were taken as the sample by using random sampling technique. The sample divided into two groups, experimental and control groups and each group consists of 23 students. The experimental group was taught by applying using journal. The data were obtained by asking the students to write a recount text. In analyzing the data, the reliability of the test was high, i.e 0,99. The result of the t-test calculation shows that t-observed was 6,88 , while t-table was 3.52 at the level of significance 0,05 with the degree of freedom is 44. It means that the value of t-observed was higher than the value of t-table. Therefore, the hypothesis alternative (Ha) was accepted. The conclusion is there is a significant effect on applying using journal on students’  writing ability in  recount text. It is suggested that teachers should apply using journal as one of the strategy in teaching writing especially in writing recount text.
BENEFITS OF C ++ AS A FIRST LANGUAGE FOR TEACHING PROGRAMMING

This article is about why it is still worth choosing C ++ as the first programming language for teaching students, and a little about the problems of teaching in universities. Some of the judgment regarding the learning process is based on personal teaching experience as well as communication with teachers and students.
Low Resource Quadratic Forms for Knowledge Graph Embeddings

We address the problem of link prediction between entities and relations of knowledge graphs.State of the art techniques that address this problem, while increasingly accurate, are computationally intensive.In this paper we cast link prediction as a sparse convex program whose solution defines a quadratic form that is used as a ranking function.The structure of our convex program is such that standard support vector machine software packages, which are numerically robust and efficient, can solve it.We show that on benchmark data sets, our model's performance is competitive with state of the art models, but training times can be reduced by a factor of 40 using only CPUbased (and not GPU-accelerated) computing resources.This approach may be suitable for applications where balancing the demands of graph completion performance against computational efficiency is a desirable trade-off.
Studying the relationship between depression, academic stress, and female college students’ premenstrual syndrome

Background: Many women suffer from acute or severe premenstrual syndrome (PMS). Studies show that PMS is often affected by the levels of stress women experience in day-to-day life. This research examined the relationship between depression, academic stress, and the severity of PMS in college students.Methods: This was a cross-sectional descriptive study. Its participants included 300 female college students from three faculties within a South Korea university. Participants completed questionnaires, and descriptive statistics, t-test, ANOVA, Spearman’s correlation, and multivariate analyses were performed on questionnaire data using SPSS 23.0 software.  Findings: PMS was found to be significantly negatively correlated with physical activity and positively correlated with perceived health status, eating salty foods, depression, and academic stress. The factors associated with PMS among college students were physical activity (β=-.12, p=.001), academic stress (β=.59, p<.001), and depression (β=.03, p<.001). These variables explained 70% of the variance in PMS, and academic stress was the most influential factor.Applications: This study’s findings imply that universities should develop specific programs to help students cope with academic stress, engage in physical activity, and combat depression in order to help female college students experience less severe PMS.
Managing Social Distancing using Smart Detector

This paper presents a methodology for social distance detection using deep learning models and algorithms such as YOLO and CNN. Deep learning is one of those technologies which have greatly enhanced the overall experience of the technology that humans use. Deep learning has brought a lot of changes from self-driven cars made by Tesla to the smallest object detection model. Deep learning, artificial intelligence and machine learning provide a way to be able to put things to use. The purpose of this paper is to be able to implement real-time object detection to detect social distancing.
Types for Tables: A Language Design Benchmark

Context: Tables are ubiquitous formats for data. Therefore, techniques for writing correct programs over tables, and debugging incorrect ones, are vital. Our specific focus in this paper is on rich types that articulate the properties of tabular operations. We wish to study both their expressive power and _diagnostic quality_. Inquiry: There is no "standard library" of table operations. As a result, every paper (and project) is free to use its own (sub)set of operations. This makes artifacts very difficult to compare, and it can be hard to tell whether omitted operations were left out by oversight or because they cannot actually be expressed. Furthermore, virtually no papers discuss the quality of type error feedback. Approach: We combed through several existing languages and libraries to create a "standard library" of table operations. Each entry is accompanied by a detailed specification of its "type," expressed independent of (and hence not constrained by) any type language. We also studied and categorized a corpus of (student) program edits that resulted in table-related errors. We used this to generate a suite of erroneous programs. Finally, we adapted the concept of a datasheet to facilitate comparisons of different implementations. Knowledge: Our benchmark creates a common ground to frame work in this area. Language designers who claim to support typed programming over tables have a clear suite against which to demonstrate their system's expressive power. Our family of errors also gives them a chance to demonstrate the quality of feedback. Researchers who improve one aspect -- especially error reporting -- without changing the other can demonstrate their improvement, as can those who engage in trade-offs between the two. The net result should be much better science in both expressiveness and diagnostics. We also introduce a datasheet format for presenting this knowledge in a methodical way. Grounding: We have generated our benchmark from real languages, libraries, and programs, as well as personal experience conducting and teaching data science. We have drawn on experience in engineering and, more recently, in data science to generate the datasheet. Importance: Claims about type support for tabular programming are hard to evaluate. However, tabular programming is ubiquitous, and the expressive power of type systems keeps growing. Our benchmark and datasheet can help lead to more orderly science. It also benefits programmers trying to choose a language.
Semantic Resizing of Charts Through Generalization: A Case Study with Line Charts

Inspired by cartographic generalization principles, we present a generalization technique for rendering line charts at different sizes, preserving the important semantics of the data at that display size. The algorithm automatically determines the generalization operators to be applied at that size based on spatial density, distance, and the semantic importance of the various visualization elements in the line chart. A qualitative evaluation of the prototype that implemented the algorithm indicates that the generalized line charts preserved the general data shape, while minimizing visual clutter. We identify future opportunities where generalization can be extended and applied to other chart types and visual analysis authoring tools.
An Empirical Study on Android Phone as a Good Companion during Pandemic

The use of smartphone is gradually becoming a compelling learning tool used to enhance teaching and learning in modern pandemic education system. Its usage ensures flexible course delivery, makes it possible for learners to access online learning platforms, access course resources and interact digitally. The purpose of this study was to examine the use and effects of the Android phone as a learning tool in online education among Post graduate students Hyderabad. The survey research design and questionnaires were employed for the study. The major objectives of the study were: to find out the students’ perceived ease of use of a Android phone in learning activities, to determine the perceived usefulness of Android phone in students’ academic achievement, to investigate the effect of the use of the Android phone in students’ learning activities, and to investigate the factors that inhibit the use of a Smartphone as a learning tool. The findings revealed that the use of Android phone performed remarkable roles among the distance learning students of Hyderabad in their academic activities.