Reviewerly/exHarmony huggingface dataset:
韓國早期對中國新文學、台灣文學、自由中國文學的接受及認識 ― 從日據到冷戰初期的翻譯和話語變化為中心

This paper explores the process of Korean recognition on ‘Chinese New Literature’, ‘Taiwanese Literature’ and ‘Free China Literature’ by dividing the period to ‘partial Japanese occupation period’ (1920-1945), ‘the liberation period’ (1945-1950) and the ‘beginning of the Cold War’ (1950-1960). In this paper, the main focus was set to the second and the third periods as the first period can be considered as introductive part. Koreans’ understanding of ‘Chinese new literature’ (mainland-based) and ‘Taiwanese literature’ changed from the influence of the Cold War. Before 1950, Koreans generally recognized the two literatures separately, different from one another. But the imaginary geographies set by the Cold War combined the two. The basic assumption underlying this understanding was that there was a form of literature that should be ostracized from both mainland China and Taiwan. In the perception of the Koreans, Taiwan had become a substitute for ‘Free China’; but the actual literature from mainland China’s and Taiwan had not been included in ‘Free China literature’. The reason for this strange phenomenon is not only because of the variables in the bilateral relations, but also due to the special dialectical perception of Korean of mainland China and Taiwan during the Cold War. Through the exploration of those historical context, this paper finally traced the process of forming the notion of ‘Free China literature’ in Korea during the Cold War. The basic materials of this paper are magazines and newspapers published in Korea until the Cold War, and second-hand materials are related papers and works that have been recently released.
Using Machine Learning Algorithms to Analyze Visualize and Detect Crime Data in Three Dimensional Directions

Crime is a huge problem in our society, and fighting it is crucial. On a daily basis, there are several crimes done on a massive scale. For future reference, a database needs to be kept of all criminal acts and acts of misconduct, as these events are unlikely to be reported later. One of the issues now encountered is keeping up with crime statistics as well as analyzing this data to assist in preventing and solving future crimes. The aim of this project is to gather and analyze datasets containing a multitude of criminal events, and to determine which kinds of criminal activity may occur in the future based on various factors. We will use Machine learning algorithms for crime prediction in Chicago by applying machine learning algorithms to the Chicago criminal data set. Decision Tree, Gaussian Naive Bayes, k-NN, Logistic Regression are all excellent supervised classification tools for this task. With this method, we are attempting to predict crimes by classifying, recognizing patterns, and using effective technologies and tools. We can correlate aspects which can aid our comprehension of future crime patterns by looking at crime data trends that have occurred in the past. Machine learning and visualization approaches are applied to estimating that crimes dispersion over an area in this project. Once the primary records are analyzed and shown based on the need, the procedure shifted to the next stage. A three-dimensional modeling of Cologne's city is constructed using crime statistics. Result visualization in geovirtual environments and real-time situation monitoring in a social protection context result from exploring crimes data analysis in geovirtual environments. Three-dimensional measurements are rapidly becoming the norm in the documenting procedure for crime scenes. By utilizing 3D measuring tools, a richer investigation is provided, revealing the presence of each piece of evidence in relation to the rest of the crime scene.
Static analysis of ReLU neural networks with tropical polyhedra

This paper studies the problem of range analysis for feedforward neural networks, which is a basic primitive for applications such as robustness of neural networks, compliance to specifications and reachability analysis of neural-network feedback systems. Our approach focuses on ReLU (rectified linear unit) feedforward neural nets that present specific difficulties: approaches that exploit derivatives do not apply in general, the number of patterns of neuron activations can be quite large even for small networks, and convex approximations are generally too coarse. In this paper, we employ set-based methods and abstract interpretation that have been very successful in coping with similar difficulties in classical program verification. We present an approach that abstracts ReLU feedforward neural networks using tropical polyhedra. We show that tropical polyhedra can efficiently abstract ReLU activation function, while being able to control the loss of precision due to linear computations. We show how the connection between ReLU networks and tropical rational functions can provide approaches for range analysis of ReLU neural networks.
Metric Learning in Multilingual Sentence Similarity Measurement for Document Alignment

Document alignment techniques based on multilingual sentence representations have recently shown state of the art results. However, these techniques rely on unsupervised distance measurement techniques, which cannot be fined-tuned to the task at hand. In this paper, instead of these unsupervised distance measurement techniques, we employ Metric Learning to derive task-specific distance measurements. These measurements are supervised, meaning that the distance measurement metric is trained using a parallel dataset. Using a dataset belonging to English, Sinhala, and Tamil, which belong to three different language families, we show that these task-specific supervised distance learning metrics outperform their unsupervised counterparts, for document alignment.
Views on Capability-relying Principle and Enlightenment in Primary Foreign Language Teaching

Capability-relying principle is defined as a principle that teaching has to keep pace with children's developmental level. In other words, foreign language teaching in primary school must adapt to students’ age, psychological characteristics and their acceptance capacity, which requires foreign language teachers, in language teaching activity, regard their teaching objects as children and according to students’ needs and acceptance capacity, they plan the teaching schedule, allocate the teaching time and choose the teaching methods reasonably and scientifically on the basis of understanding correctly and applying to the scientific principle. Hence, capacity-relying principle has an indelible and profound guiding significance in today's foreign language teaching.
An Integrated Approach To Information Technology Training In Non-Specialised Education

Modern Information Technology Is A Technology That Based On New Approaches To Youth Studying In Educational Institutions, Can Organize A Learning Process Related To The Knowledge, Qualifications And Skills Formation, Which Will Bring Education To A New Quality Level. Information And Communication Technologies - Technologies That Perform The Functions Of Routing Switching (Characterization) Of Communication Between Network Computers For Information Transfer.
人类文字的单一起源与演变脉络图谱 (The Single Origin and Knowledge Map of Human Writing Characters)

Chinese abstract: 摘要：人类各民族的文字看起来各不相同，语言发音更是千差万别。但实际上它们之间有盘根错节的关系。以前的研究对字母文字之间的传承关系已基本达成共识，建立起了字母文字单一起源理论，并提出了字母与古埃及圣书体象形符号存在演变关系的假设。但是对象形文字系统，特别是古埃及文字与古汉语之间的关系仍研究非常少。本文对当代文字比较研究成果进行了系统的综述，结合《发现夏朝》、《文明起源》两书的原创性研究成果，绘制了一副人类文字传播与影响的脉络图谱，完成东西方语言文字的中西合璧与总体拼图。English abstract: The writing characters of different nationalities seem to be different, and the pronunciations of languages are also different. But in fact, they are intertwined. Previous studies have basically reached a consensus on the evolutionary relationship between alphabetical languages, established the theory of single origin of alphabets, and put forward the hypothesis that there is an evolutionary relationship between the original alphabets and the ancient Egyptian hieroglyphs. However, there are few studies on the relationships between hieroglyphic writing systems, especially between ancient Egyptian writing and ancient Chinese characters. Combining the original research results of two books: Discovery of Xia Dynasty and The Origin of Civilization, this article makes a systematic review of the achievements in the comparative study of contemporary human writing systems, seeks to establish the Egyptian origin of all well-researched Hieroglyphs and all alphabetical systems evolved, achieves the combination of Chinese and Western languages and the overall puzzle of human writing systems.
Improvisation in Pop-Rock Music

Improvisation is a fundamental and unavoidable aspect of the human experience in general, a specific trait of human practices, often linked to expertise or competence, that displays itself at various levels in all dimensions of life. Unless the authors believe in the totally predetermined character of all events, every action seems to imply, to a greater or a lesser degree, contingency and the possibility of facing the surprising, the unknown, the unforeseen and the unexpected. However, jazz is only one among the many different musical genres in which improvisation plays a significant role. Although jazz, along with “radical,” “free” or “non-idiomatic” forms of improvised music, is the kind of music most people tend to associate with improvisation, this must not eclipse the fact that improvisation has played and continues to plays an equally relevant role in other musical genres.
LibGuides: Human Library at UCA: 2019 Human Library Catalog

The University of Central Arkansas will host a Human Library event on April 9, 2019 in Torreyson Library
Convergence Analysis of Particle Swarm Optimization via Illustration Styles

Particle swarm optimization (PSO) is the most important and popular algorithm to solving the engineering optimization problem due to its simple updating formulas and excellent searching capacity. This algorithm is one of evolutionary computations and is also a population-based algorithm. Traditionally, to demonstrate the convergence analysis of the PSO algorithm or its related variations, simulation results in a numerical presentation are often given. This way may be unclear or unsuitable for some particular cases. Hence, this paper will adopt the illustration styles instead of numeric simulation results to more clearly clarify the convergence behavior of the algorithm. In addition, it is well known that three parameters used in the algorithm, i.e., the inertia weight w, position constants c1 and c2, sufficiently dominate the whole searching performance. The influence of these parameter settings on the algorithm convergence will be considered and examined via a simple two-dimensional function optimization problem. All simulation results are displayed using a series of illustrations with respect to various iteration numbers. Finally, some simple rules on how to suitably assign these parameters are also suggested
Image Steganography Using Pixel Value Differencing (PVD) Technique Based on Firefly Algorithm

Steganography deals with the ways of hiding communicated data in such a way that it remains confidential. Finding best position inside cover image to embed text message, maintaining a reasonable trade-off between security, robustness, higher bit embedding rate and imperceptibility are some of the challenges of steganography system. Hence, this paper presents firefly algorithm for finding best positions inside cover image in order to embed text message into cover image using Pixel Value Differencing (PVD) technique. Four different cover image was used. Experimental result showed the cover image with selected location using firefly algorithm as well as the stego image using PVD technique. The stego image was evaluated using Peak Signal to Noise Ratio (PSNR) and Mean square Error (MSE). Firefly Algorithm with PVD technique produced a promising result for image steganography.
Classics at the Hamburg Open Online University

Justine Diemke, Nadine Leisner and Alexandra Trachsel present three examples of e-Learning from the Hamburg Open Online University (HOOU). Each Classics department has developed multimedia applications and playful learning scenarios like quizzes: the eManual of Ancient History, the online presence Ariadne which provides basic archaeological information about the ancient Mediterranean, and Antike Heute in Hamburg, an online quiz focusing on Greco-Roman mythology.
Bitcoin hits $11K: A timeline of cryptocurrency's rise, fall and rebound

Bitcoin’s price has climbed steadily in recent months as Facebook prepared to launch its digital currency, Libra.
Camera Based Smart Surveillance System

This paper is the survey of Smart camera based surveillance monitoring system using Raspberry pi. Camera based surveillance is important in all sectors, they can be colleges and hospitals, shopping malls and other challenging indoor and outdoor environments require high end cameras. This paper focus on low-cost project on single board computer Raspberry Pi. This is new technology and far less expensive and, it is being used as a main platform for video detection and acquisition. It can be used with involvement of mobile network (internet) to provide essential security and surveillance to our properties and for other control applications. The security system records information and transmits it via network to a Smart Phone using web application Raspberry pi.
Spatial Transformation Based 2D Image Segmentation for Single Human Pose Estimation with Improved Efficiency

Human pose evaluation on multi-person is challenging than human pose evaluation on single-person . Even when the human detectors have given their best results, small errors are available in localization part and recognition. These errors in detection lead to failures in human pose estimation.  So, we propose an architecture for human pose evaluation although when the errors are present in  the human detection. The model consists of two main steps: first step  is to detect the human and next  step is to evaluate the human pose for the detected human. The components used primarily for human pose estimation is the STN(Spatial Transformer Network), SPPE(Single Person Pose Estimator) and Pose-NMS(Non-Maximum Suppression). This method is tested on the MPII(multi-person) dataset.
Research on acquisition on authors sources of Sci-Tech journals based on AARRR Model

The term "Growth hacker" originated from Silicon Valley. It refers to people who use data-driven marketing, market-oriented products, and technical means to achieve growth goals. According to Gartner's survey, 70% of high-tech companies have set up jobs to Growth hackers. Hotmail, Google, Facebook, twitter, Xiaomi, Zhihu, airbnb and other enterprises have used AARRR model to improve their business. In order to explore the measures to expand contributions and improve the width and depth of contributions for sci-tech journals, this paper adopts the growth model of AARRR (a growth hacker model), and adopts the comparative analogy method to analyze the links affecting contributions. The author will make management suggestions for (1) acquiring authors, (2) induced registration, (3) achievement on publication, (4) improving impact, and (5) attracting new authors. Its management Suggestions include: (1) the authors obtaining, (2) uses of the crawler software acquisition author information, (3) uses contributions solicitation and filling way to attract register, (4) reminds and encourage the author submit papers, (5) simplifies steps (6) optimizes the keywords (7) pushes published papers (8) uses existing author connections to attract new authors.
BloomNet: A Robust Transformer based model for Bloom's Learning Outcome Classification.

Bloom taxonomy is a common paradigm for categorizing educational learning objectives into three learning levels: cognitive, affective, and psychomotor. For the optimization of educational programs, it is crucial to design course learning outcomes (CLOs) according to the different cognitive levels of Bloom Taxonomy. Usually, administrators of the institutions manually complete the tedious work of mapping CLOs and examination questions to Bloom taxonomy levels. To address this issue, we propose a transformer-based model named BloomNet that captures linguistic as well semantic information to classify the course learning outcomes (CLOs). We compare BloomNet with a diverse set of basic as well as strong baselines and we observe that our model performs better than all the experimented baselines. Further, we also test the generalization capability of BloomNet by evaluating it on different distributions which our model does not encounter during training and we observe that our model is less susceptible to distribution shift compared to the other considered models. We support our findings by performing extensive result analysis. In ablation study we observe that on explicitly encapsulating the linguistic information along with semantic information improves the model on IID (independent and identically distributed) performance as well as OOD (out-of-distribution) generalization capability.
A combined local and global structure module for human pose estimation

Human pose estimate can be used in action recognition, video surveillance and other fields, which has received a lot of attentions. Since the flexibility of human joints and environmental factors greatly influence pose estimation accuracy, related research is confronted with many challenges. In this paper, we incorporate the pyramid convolution and attention mechanism into the residual block, and introduce a hybrid structure model which synthetically applies the local and global information of the image for the analysis of keypoints detection. In addition, our improved structure model adopts grouped convolution, and the attention module used is lightweight, which will reduce the computational cost of the network. Simulation experiments based on the MS COCO human body keypoints detection data set show that, compared with the Simple Baseline model, our model is similar in parameters and GFLOPs (giga floating-point operations per second), but the performance is better on the detection of accuracy under the multi-person scenes.
Impact of Integrated Personnel Payroll Information System on Employees Satisfaction in Kogi State

Performance of firms either public or private sector largely depends on employees’ satisfaction hence their satisfaction need to be taken with utmost seriousness if firms’ immediate and strategic objectives must be attained. This study titled integrated personnel payroll and information system and employees’ satisfaction is carried out to examine the impact of integrated personnel payroll and information system on employees’ satisfaction. The research adopts research survey design and respondents were reached using a structured questionnaire. The population of the study is 1100 who are employees of the Federal Polytechnic Idah, Kogi state. The study adopts Godden sample size statistical formula which generated a sample size of 285. However, out of the total of 285 questionnaires distributed only 242 were duly completed and returned giving a retrieval rate of 85%. The data were analyzed using a five point’s likert scale and the analytical tool is linear regression analysis. The finding revealed that adoption of integrated personnel payroll and information system has serve as a veritable tool in enshrining accountability but has threatened employees’ satisfaction owing to its non-domestication to carter for the peculiarity of the Polytechnic sector. Thus, the study recommends that adoption of the payroll system be reviewed and all critical stakeholders be consulted so as to enhanced and sustained employees satisfaction.
Unpaired Depth Super-Resolution in the Wild.

Depth maps captured with commodity sensors are often of low quality and resolution; these maps need to be enhanced to be used in many applications. State-of-the-art data-driven methods of depth map super-resolution rely on registered pairs of low- and high-resolution depth maps of the same scenes. Acquisition of real-world paired data requires specialized setups. Another alternative, generating low-resolution maps from high-resolution maps by subsampling, adding noise and other artificial degradation methods, does not fully capture the characteristics of real-world low-resolution images. As a consequence, supervised learning methods trained on such artificial paired data may not perform well on real-world low-resolution inputs. We consider an approach to depth super-resolution based on learning from unpaired data. While many techniques for unpaired image-to-image translation have been proposed, most fail to deliver effective hole-filling or reconstruct accurate surfaces using depth maps. We propose an unpaired learning method for depth super-resolution, which is based on a learnable degradation model, enhancement component and surface normal estimates as features to produce more accurate depth maps. We propose a benchmark for unpaired depth SR and demonstrate that our method outperforms existing unpaired methods and performs on par with paired.
COVID-19 and the Future of Content Moderation

The coronavirus crisis is disrupting the functioning of content moderation, causing the loss of thousands of jobs in an overlooked profession.
Bitcoin Mining Council faces critics

Some Twitter users called the council an attack on Bitcoin while others claimed the group would drive the currency's mining and transaction costs higher, amount to a bigger waste of energy and make it less viable for payments
Libraries and linked data

With Linked Data, the rich source of libraries’ metadata can be moved into the semantic web environment where its discovery will be increased. In order to achieve this, the library world needs to find ways to replace the MARC standards, used for coding and exchanging bibliographic information. The Library of Congress took the lead when they launched the Bibframe initiative, then OCLC followed with Schema.org.  In tandem, they are breaking down MARC fields and mapping them to Bibframe entities. Most of the Bibframe database is not yet ready for public use, although at least one vendor, Ex Libris Alma, has begun to incorporate Bibframe into their LMS. As a library community, we will have to work together to embrace these new challenges.
The Role of Teachers in Increasing Reading Interest of Primary Schools Through Literation in The Pandemic Time Covid-19

The implementation of education in Indonesia during the COVID-19 pandemic has experienced several visible changes. The health crisis caused by the COVID-19 outbreak has spearheaded online learning concurrently. In this case, the role of the teacher is to guide and to help students in solving the problems which they face regarding their knowledge. Reading is a language skill that is very important in our life. Reading has an important role in giving birth to an intelligent, creative, and critical next generations. By reading, someone can get knowledge and information from all over the world. Reading is a must for individuals who instill themselves as an intellectual. The purpose of this study is to determine the role of teachers in creating literacy in Indonesian subjects. This study uses interview techniques and based on the results of the research that has been done, an effort to build reading literacy for children is through the role of the teacher. The role of the teacher to improve reading literacy is by giving rules, giving orders, giving rewards, facilitating, modeling, guiding, motivating, giving affection, and let the students free to choose their reading sources and materials.