Reviewerly/exHarmony huggingface dataset:
중국 대학의 한국어 교과과정 재검토 및 개선 방향 탐색

According to the foreign language talent training objective raised by the Ministry of Education of China, it is necessary to review and revise the Korean language curriculum of universities in China. In particular, the recent foreign language talent training objective raised the demand for knowledge of humanities and social sciences, so universities in China should review and improve the Korean language curriculum. Therefore, in this paper, as a part of Korean language curriculum improve process of Guangdong University of Foreign Studies, we looked at the current status of Korean language curriculum at 31 universities in China, in order to find the problems and find the directions for improvement.
Research and Strategies on the Prevention of Speculative Teaching Behaviors in the New National Teachers’ Qualification Examination

There are two sectionsâ€”written examination and interviewâ€”in the new National Teachersâ€™ Qualification Examination. However, the speculative education teaching behavior, a general term of utilitarian education and teaching behavior with the fundamental purpose of successfully passing the interview section and finally obtaining the National Teachersâ€™ Qualification Certificate, lies in the interview section, and it is a false presentation of education and teaching ability. This paper lists some typical speculative teaching behaviors in interview section, analyzes the main reasons lying in the time and space limitation, imperfect evaluation mechanism, harmful guidance of training institutions and competition between non-normal students, and gives some preventive strategies including strengthening the cultivation of practical ability of pre-service teachers; setting corresponding thresholds for non-normal candidates; adding assessment and evaluation of examineesâ€™ growth records; guiding normatively training behavior of training institutions; increasing vocational guidance and psychological guidance.
Unravel the Black Box between Fear and Security Coping Behavior: The Role of Learning from Failure

Leadership and organizational learning theorists have argued that psychological safety is associated with learning from failures at all three levels - individual, group, and organization. Meanwhile, behavioral information systems (IS) security researchers have produced various frameworks to explain individuals' psychological and behavioral responses to security threats and risks. Their findings, however, do not explain how learning from failure - a critical factor for improvements at multiple levels - unfolds in the IS security paradigm where fear plays a key role in exerting impacts on individuals' emotional state and subsequent behavioral manifestations. We propose that a cross-paradigmatic examination of the theoretical underpinnings of psychological safety, fear, leadership roles, and threat-response appraisal may benefit our academic society, who continuously seeks innovative ways to elucidate befuddling, sometimes conflicting, security-based behaviors. We theorize how the presence of moderate fear stimuli, in conjunction with psychological safety, may augment learning performance and thus positively affect the security coping intention and behavior of information workers. We also initiated a discussion of moving beyond the dichotomy of what we learn better from - success vs. failure, by suggesting that an organic permutation of learning from success and failure experiences, if carefully assessed and calibrated, appears to serve our best interests.
Trio for violin, violoncello and harp

Author(s): Ma, Lingbo | Abstract: This music score was submitted for the Kaleidoscope 2020 Call for Scores, an open access collaboration with the UCLA Music Library.
LibGuides: Board Games: How to Add Games to the Library

Welcome to the William James College Board Game Library! This is a guide for work-study students who are adding games to the library.
Embedding Oriented Adaptable Semantic Annotation Framework for Amharic Web Documents

The Web has become a source of information, where information is provided by humans for humans and its growth has increased necessity to get solutions that intelligently extract valuable knowledge from existing and newly added web documents with no (minimal) supervisions. However, due to the unstructured nature of existing data on the Web, effective extraction of this knowledge is limited for both human beings and software agents. Thus, this research work designed generic and embedding oriented framework that automatically annotates semantically Amharic web documents using ontology. This framework significantly reduces manual annotation and learning cost used for semantic annotation of Amharic web documents with its nature of adaptability with minimal modification. The results have also implied that neural network techniques are promising for semantic annotation, especially for less resourced languages like Amharic in comparison to language dependent techniques that have cost of speed and challenge of adaptation into new domains and languages. We experiment the feasibility of the proposed approach using Amharic news collected from WALTA news agency and Amharic Wikipedia. Our results show that the proposed solution exhibits 70.68% of precision, 66.89% of recall and 68.53% of f-measure in semantic annotation for a morphologically complex Amharic language with limited size dataset.
Management Information System Development Concept School Database

The purpose of this study is to know the concept of developing a school database management information system. The model that will be used in this study is the spiral model. Each cycle includes 1) communication with users, 2) planning, 3) risk analysis, 4) prototype or engineering, 5) construction, and 6) system evaluation. The results of the study, the SIM database model of the school that was developed, still has shortcomings that must be corrected. Deficiencies at the concept development stage will be a reference for developers to improve. Suggestions from the validator were corrected and subsequently as a basis for designing the School Database SIM model at the system development stage. Conclusions, the concept of developing a school database system in the Department of Education and Culture of Musi Rawas Regency includes; a) developing a school database system for the Education Office admin (home menu, profile menu and submenus, report menu and submenu, admin menu and submenu, and logout menu), b) developing a school database system for school admin (home menu, menu and submenu profiles, menus and student submenus, educators and educational staff menus and submenus, facilities and infrastructure menus and submenus, curriculum menus and submenus, public relations menus and submenus, financial menus and submenus, report menus and submenus, and log out menus).
Sistem Informasi Penjualan Kosmetik

Web-based Cosmetic Sales Information System is an information system that provides or sells cosmetic products.However, many websites only sell cosmetics but do not meet the needs of consumers.Even though there is a cosmetics sales information system, consumers continue to attend seminars or classes held offline by the organizer to increase knowledge about cosmetics.This study aims to create a Sales Information System that can solve the constraints experienced by consumers.These system needs include consumers being able to take part in online seminars, being able to interact with fellow consumers through cyberspace and consumers being able to take beauty classes online.The results of this study are Cosmetic Sales Information Systems which include sales catalogs, online seminar features, online beauty class features, online discussion forum features, and payments.
A Delicate Union of Batching and Parallelization Models in Distributed Computing and Communication

The Fork-Join (FJ) model has been extensively studied in the past due to its natural ability to capture computation and communication systems that employ split and merge techniques, i.e., boosting performance through splitting and parallelizing the input and finally merging the results. This model finds applications ranging from multipath communications to distributed databases.In this work, we explore the effect of batching within FJ systems, i.e., when servers collect the input to benefit from a so-called speedup, observed as a service time reduction due to batching. We numerically compare the performance when the speedup assumes different analytical forms. Our numerical evaluation shows that the steady-state waiting time of such an FJ system is heavily dependent upon the form of the speedup achieved through batching while the optimization thereof is non-trivial.
Is Starting Unregistered Beneficial for Firms? The Moderating Role of Institutional Context

Building on the resource-based view and social network theory, we examine the relationship between informality and firm performance. Based on an empirical investigation covering 121 countries over the 2006-2018 period we show that firms that started unregistered perform better than firms that started registered. We also show that the relationship between time spent unregistered and firm performance is curvilinear, taking and inverted-U shape, and that firms operating in countries marked by poorly functioning formal institutions benefit longer from staying unregistered. Our findings have important implications both for managers and for policy makers.
Do Small Businesses Pay More? Firm Size, Informal Payments, and Mitigating Strategies in Africa

Bribery and corruption remain as one of the most binding constraints that businesses in Africa have to deal with. In this paper, we sort to understand the effect of firm size on demand for and payment of bribes by African firms and the strategies needed to overcome them. We developed and tested a contingent framework. We argued that size represents a selectionist pressure on firms to cede to corrupt demands while strategies (formalization, relational intensity and foreign participation) from the adaptive perspective of the organization are tools to deal with these pressures. We test our framework using data from the World Bank Enterprise Surveys in Africa. We find that the liability of smallness effect exists as the larger a firm gets the fewer bribes in percentage terms it pays out to corrupt public officials. We find also that formalization, relational intensity and foreign participation are significant moderators of this effect. The study makes a contribution by testing a contingent framework that integrates the selectionist and adaptive views of organizing.
Long-Tail Predictions with Continuous-Output Language Models

Neural language models typically employ a categorical approach to prediction and training, leading to well-known computational and numerical limitations. An under-explored alternative approach is to perform prediction directly against a continuous word embedding space, which according to recent research is more akin to how lexemes are represented in the brain. Choosing this method opens the door for for large-vocabulary, language models and enables substantially smaller and simpler computational complexities. In this research we explore a different important trait - the continuous output prediction models reach low-frequency vocabulary words which we show are often ignored by the categorical model. Such words are essential, as they can contribute to personalization and user vocabulary adaptation. In this work, we explore continuous-space language modeling in the context of a word prediction task over two different textual domains (newswire text and biomedical journal articles). We investigate both traditional and adversarial training approaches, and report results using several different embedding spaces and decoding mechanisms. We find that our continuous-prediction approach outperforms the standard categorical approach in terms of term diversity, in particular with rare words.
LibGuides: Graphic Novels: Apps/Interactive

This guide shows the value of Graphic Novels not only as a source of entertainment for kids, but as a tool for increasing learning skills.
Understanding the Impact of Experiment Design for Evaluating Dialogue System Output

Evaluation of output from natural language generation (NLG) systems is typically conducted via crowdsourced human judgments. To understand the impact of how experiment design might affect the quality and consistency of such human judgments, we designed a between-subjects study with four experimental conditions. Through our systematic study with 40 crowdsourced workers in each task, we find that using continuous scales achieves more consistent ratings than Likert scale or ranking-based experiment design. Additionally, we find that factors such as no prior experience of participating in similar studies of rating dialogue system output
Rethinking Platform Strategies in the Age of Distributed Trust

The current overwhelming focus on proprietary platform ecosystems unnecessarily limits current platform literature. The introduction of distributed trust technologies such as blockchain enables new business models and strategic possibilities. Adopting a platform lens, we reveal how distributed trust technologies can replace proprietary platform sponsors at the center of platform-based markets, and afford new roles and opportunities for third-party complementors. In this paper we review the key assumptions of the platform literature which are now being violated. We then update the theory for this new reality, in light of recent technological advances. To elucidate the new theory we present the illustrative case of Cryptokitties, the first successful distributed application (“dApp”) built atop a permissionless blockchain platform.
The Perception of Computer Engineering Department Students at Politeknik Negeri Jember Towards the Use of Duolingo in Learning English

Duolingo application is an online free language educational application. It become more suitable, innovative and applicable way to learn english in EFL classroom. The aim of this study is to find out the students perception towards the use of duolingo application in learning English. The method used in this study is a descrptive research. Then, subject of the study are students at computer engineering department, Politeknik Negeri Jember. The result of this study revealed that computer engineering Department students at Politeknik Negeri Jember come to realize duolingo as a software that is easy to use, interesting and enjoyable for learning English. In addition, duolingo bring a new concept (gamification) in learning English. Thus, it makes them very exciting in learning English.
Unsupervised Continuous Object Representation Networks for Novel View Synthesis

Novel View Synthesis (NVS) is concerned with the generation of novel views of a scene from one or more source images. NVS requires explicit reasoning about 3D object structure and unseen parts of the scene. As a result, current approaches rely on supervised training with either 3D models or multiple target images. We present Unsupervised Continuous Object Representation Networks (UniCORN), which encode the geometry and appearance of a 3D scene using a neural 3D representation. Our model is trained with only two source images per object, requiring no ground truth 3D models or target view supervision. Despite being unsupervised, UniCORN achieves comparable results to the state-of-the-art on challenging tasks, including novel view synthesis and single-view 3D reconstruction.
Similarity and Farness Based Bidirectional Neural Co-Attention for Amharic Natural Language Inference

In natural language one idea can be conveyed using different sentences; higher Natural Language Processing applications get difficulties in capturing meaning of ideas stated in different expressions. To solve this difficulty, different scholars have conducted Natural Language Inference (NLI) researches using methods from traditional discrete models with hard logic to an end-to-end neural network for different languages. In context of Amharic language, even though there are number of research efforts in higher NLP applications, still they have limitation on understanding idea expressed in different ways due to an absence of NLI in Amharic language. Accordingly, we proposed deep learning based Natural Language Inference using similarity and farness aware bidirectional attentive matching for Amharic texts. The experiment on limited Amharic NLI dataset prepared also shows promising result that can be used as baseline for subsequent works.
Human Factors and the use of Simple Language

Communication is one of the human factors. Even two individuals use the same language, either English or their mother tongue, it still has problems. The problems often occur due to the mismatch between expectations and actual communications in accomplishing a task or activity. The information should be communicated to the right individual at the right time using the correct codes, context, terminology, and words. The individual, who received the information, should attend to the sources of information and acknowledge it.
Corporate Data Governance: Are Data Subjects Investors?

This is a paper that explores the status of data subjects in the era of data capitalism. A data subject is a person whose personal data is being collected, held, or processed by data collectors or processors (e.g., Amazon, Google, or Facebook). Data-driven companies rely on data subjects offering personal information for training algorithms for services. In this article, we maintain that it is time to seriously investigate whether data subjects can be considered as investors. In what follows, first, we preview our thesis, followed by a functional definition of an investor. Then, we develop our argument that data subjects are better understood as investors rather than consumers or labor providers by examining the balance sheet impact of a data contribution to the data firm and the existing legal regime requiring data subjects to retain an ownership interest in their data even after it has been transferred to the data firm.
Specialize, Standardize, Scale, and Save - Digital Innovation in Law Firms

Digitalization currently transforms industries and societies across the globe, bringing firms new opportunities to create and capture value. This paper explores the impact of digitalization on knowledge intensive firms, with a particular focus on the rising opportunities in the legal industry. Digital technologies have enabled new practices among law firms and motivated firms to innovate in regard to their business model. Through a multiple case study of 12 law firms, this paper explores business model innovation in law firms and depicts how digitalization has enabled value to be created and captured in new ways. Based on empirical data, on inventive ways to use digital tools and technology to create and capture value, this paper has identified four digital strategies; Specialize, Standardize, Scale and Save. These can be combined in a model describing four new competitive positions that innovative law firms occupy in the digitalized context. This paper contributes with a new understanding of digital innovation in a traditional field and provides practitioners with valuable advice as how to create and capture value from the rising digital opportunities.
AI Sensing for Robotics using Deep Learning based Visual and Language Modeling

An artificial intelligence(AI) system should be capable of processing the sensory inputs to extract both task-specific and general information about its environment.However, most of the existing algorithms extract only task specific information.In this work, an innovative approach to address the problem of processing visual sensory data is presented by utilizing convolutional neural network (CNN).It recognizes and represents the physical and semantic nature of the surrounding in both human readable and machine processable format.This work utilizes the image captioning model to capture the semantics of the input image and a modular design to generate a probability distribution for semantic topics.It gives any autonomous system the ability to process visual information in a human-like way and generates more insights which are hardly possible with a conventional algorithm.Here a model and data collection method are proposed.
DESIGN HIGH SPEED AND AREA EFFICIENT ARCHITECTURE OF AUTHENTICATED ENCRYPTION & DECRYPTION ALGORITHM

ABSTRACT: This paper presents a performance comparison of new authenticated encryption and decryption(AED) algorithm which are aimed at providing better security and resource efficiency compared to existingstandards. Authenticated encryption and decryption technique guarantee both privacy and integrity. One of themost popular authenticated encryption and decryption algorithm is to improve the speed. In existed systemencryption process is performed (i.e) plain text is converted into cipher text. Because of this there will be noproper security. Hence authenticated encryption and decryption is implemented (i.e) plain text is converted intocipher text & cipher text is converted into plain text. Hence this project gives better security compared to existone.
Modeling information flow through deep convolutional neural networks

In this work we investigate methods for optimizing deep convolutional neural networks (CNN) by 1) reducing the computational complexity and 2) improving classification performance for the task of transfer learning. Based on the work of Chaddad et al. (2019, 2017), the CNN is modeled as a Markov chain, where the filter output at a layer is conditionally independent of the rest of the network, given a set of previous layers. Filter banks at each layer are compressed using principal component analysis (PCA), where a reduced set of orthogonal basis filters are used to reduce the number of convolutions required while preserving classification accuracy. Information theory is then used to quantify the flow of image information through the network. Filter responses with low conditional entropy (CENT) are shown to be highly effective in image classification, and can be used as generic features for effective, noise resistant transfer learning. CENT feature analysis is demonstrated in various contexts including computer-assisted diagnosis of Alzheimer’s disease (AD) from 3D magnetic resonance images (MRI) of the human brain, and object classification in 2D photographs.
LWT-CLAHE Based Color Image Enhancement Technique: An Improved Design

Color image enhancement is one of important process and actually a vital precursory stage to other stages in the field of digital image processing. This is due to the fact that the effectiveness of processes in this stage on the output determines the success of other stages for a quality overall performance. This paper presents a color image enhancement technique using lifting wavelet transform (LWT) and contrast limited adaptive histogram equalization (CLAHE) to overcome the issue of noise amplification, over and under-enhancement in exiting enhancement techniques. Test images from Computer Vision Database were used for the proposed technique and the performance was evaluated using PSNR and SSIM. Result obtained shows an average improvement of 56.4% and 20.98% in terms of PSNR and SSIM respectively.