Reviewerly/exHarmony huggingface dataset:
Hardware descriptions code completion based on a pre-training model

The code completion function, as a coder-friendly tool, appears in almost all IDEs. With the development of artificial intelligence technology in recent years, intelligent code completion techniques have gradually demonstrated better results than traditional code completion. Most code completion tools focus on imperative programming languages, while the development of intelligent tools in hardware description languages is far behind. In this paper, we propose a model that can be directly applied to code completion on hardware descriptions based on the GPT-2 model. The method can achieve the same level of accuracy without designing complex neural network structures. In addition, the method also has fast training speed, strong generalization ability, and the ability to capture the localness of the code.
PSVD: Post-Training Compression of LSTM-Based RNN-T Models

RNN-T has received a lot of attention recently since it achieves state-of-art WER in automatic speech recognition. To run the RNN-T model in real-time on resource-limited edge devices, model compression is often required. However, typical compression methods are still challenging to apply to RNN-T. First, it takes a lot of fine-tuning time and computing resources (e.g., up to several weeks even with multiple GPUs) due to the large size of training speech dataset and slow training speed of LSTMs. Second, the ASR training pipeline and datasets are often proprietary and not publicly available. Thus it is hard to fine-tune the pre-trained model after compression. In this paper, we propose PSVD (Post-training SVD) which could effectively improve the WER of a SVD com-pressed RNN-T model without requiring the large training dataset and costly back-propagation. Based on a small amount of test data, PSVD could quickly post-train a SVD compressed RNN-T model by leveraging alight-weight linear regression. In particular, we observed that multiple iterations of layer-sequential linear regression is effective in optimizing the compressed model. Compared to SVD compression with-out fine-tuning, PSVD could improve WER by up to 8.36% at a fraction of the compute time. To the best of our knowledge, our work is the first to leverage post-training compression for LSTM and RNN-T.
The peculiarities of international business communication on the Internet

This article examines the problem of business communication on the Internet on the example of the Russian and Chinese languages. The relevance of this research is substantiated by the progressive international relations, and lies in studying the peculiarities of the culture of international business communication on the Internet, namely between China and Russia. Although the economic cooperation between the two countries are rapidly developing, the problem of mutual intelligibility remains acute. In the globalization era, the Internet is crucial factor that effects the specifics of the culture of business communication. The article aims to reveal the peculiarities of international business communication on the Internet, taking into account the specifics of the parties to communication on the example of China and Russia). The subject of this research is the specifics of international business communication on the Internet between China and Russia. The object is the China-Russia business communication via the Internet. The scientific novelty lies in comprehensive analysis of the forms and specifics of international business communication on the Internet between China and Russia. The main conclusions are as follows: business communication on the Internet is affected by such factors as mentality, national traditions, established etiquette norms and rules; the Russian and Chinese business communication on the Internet is characterized by standardization, use of speech patterns, clich&amp;eacute;; Russian and Chinese business representatives must have command of both languages alongside broad knowledge on culture and traditions of the partner country, show flexibility manifested in the use of modern technologies of conducting business, namely the Internet and its capabilities.
Penggunaan Sumber Belajar Berbasis Information Technologi dalam Meningkatkan Hasil Belajar Siswa Pelajaran IPS di Kelas VIII MTs Nuris Silo

The use of learning resources in the teaching and learning process is very necessary for both teachers and students. Learning resources are all kinds of sources that exist outside of students whose existence facilitates the learning process. Information technology is defined as science in the field of computer-based information and its development is very rapid. In the world of education, the existence of information technology as a medium is very necessary. Teachers can use learning resources obtained from the internet in the form of ebooks, journals and others so that students' insights can be broadened. MTs Nuris Silo in the learning process, the teacher uses Information Technology-based learning resources in Social Studies Subjects for class VIII, namely the teacher uses student books in the form of ebooks, uses videos, articles from the internet, and pictures. The results of the use of technology-based learning resources can facilitate the learning process. students understand what is explained by the teacher from those who do not know to know, from those who do not understand to understand. Learning outcomes are an important aspect for educators, because by assessing student learning outcomes, educators obtain information about student learning progress. This has been proven by the results of the assignments given by the teacher.
Practical Survey on MapReduce Subgraph Enumeration Algorithms

Subgraph enumeration is a basic task in many graph analyses. Therefore, it is necessary to get this task done within a reasonable amount of time. However, this objective is challenging when the input graph is very large, with millions of nodes and edges. Known solutions are limited in terms of scalability. Distributed computing is often proposed as a solution to improve scalability. However, it has to be done carefully to reduce the overhead cost and to really benefit from the distributed solution. In this work we provide a comprehensive overview of several Map-Reduce subgraph enumeration algorithms which currently represent the state of the art. We identify and describe the main conceptual approaches, giving insight on their advantages and limitations, and provide a summary of their similarities and differences.
Geostatistical Study of Landfill Contaminated Topsoil and Impacts of Urban Waste on Its Immediate Environment (Gafsa, South-West Tunisia)

Inadequate waste management coupled with increasing urban expansion, industrial developments produces significant alterations in the physical environment and increases trace metallic element concentration in soil. In this study, spatial concentrations of Chromium (Cr), Copper (Cu), Nickel (Ni), Zinc (Zn) and Cadmium (Cd) in 16 sampling sites from the Gafsa landfill were investigated using a Kriging interpolation and multivariate statistical analysis. Their respective concentrations are 3.96, 0.42, 63.80, 79.75 and 6.01 mg kg−1. The spatial distribution of trace element factor coordinates shows higher values in the eastern part of the study area, with important loadings on Zn (−0.881) and Cu (−0.922). However, the areas of higher Ni (0.753) and Cr (0.786) contents were mainly distributed in the south and the southwest of landfill. They were indicating that both intrinsic and extrinsic factors controlled these trace elements. These results can be considered as baseline information for other soil quality monitoring studies in Gafsa city.
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The study aimed to determine the teaching competencies of vocational education teachers in basic schools affiliated to the Directorate of Education in Ajloun Governorate in the Hashemite Kingdom of Jordan in competencies (implementation, and teaching methods), where the researcher used the descriptive approach in addition to using the questionnaire, which consisted of (26) items as a study tool. The study population consisted of all teachers of vocational education in the schools of Ajloun Governorate, who numbered (115) teachers, and the sample of the study consisted of (50) teachers. This is ahead of the adequacy of implementation, which was at a rate of (71%), and to a large degree for each of them.
Vehicle Tracking with Crop-based Detection

End-to-end production of vehicle tracking data from video in real-time and with high accuracy remains a challenging problem due to the computational cost of object detection on each frame. In this work we present Tracking with Crop-based Detection, a method for speeding object tracking in constrained contexts (with stable cameras and relatively-predictable object motion) such as vehicle traffic monitoring. We leverage this context to provide a strong prior for object locations, which we use to 1.) boost detection speed by detecting objects only in regions corresponding to object priors on most frames and 2.) inform the selection of the detector output for each object. We evaluate Crop-based Detection as an extension to the KIOU object tracker (Crop-KIOU) on the UA-DETRAC dataset. The proposed tracker outperforms all other reported algorithms in terms of PR-MOTA, PR-MOTP, and mostly tracked objects on the UA-DETRAC benchmark, establishing a new state-of-the-art. Relative to tracking by detection with KIOU, Crop-KIOU achieves a 26% higher frame-rate and increases accuracy. Furthermore, Tracking with Crop-based Detection can be combined with frame skipping; we show a 149% increase in framerate relative to KIOU with no decrease in accuracy using this combination of methods.
Improving the Robustness of the Bug Triage Model through Adversarial Training

Recently, research on automated bug triage using deep neural networks is being actively conducted. Unfortunately, there have been few studies on improving the robustness of the bug triage models through adversarial training. In this paper, we present an approach to evaluate and improve the robustness of the bug triage model. We present a new method for generating adversarial bug reports. We exploit the test coverage to compare the robustness of various models for bug triage. The experimental results suggest that our model has better robustness. In addition, the proposed technique for adversarial data generation is superior compared to the existing techniques in three aspects: adversarial data generation time, document similarity, and word change rate.
Information for Rural Development in Africa

Information is one of the major contributors to sustainable development in Africa. Access to relevant information would offset the emerging challenges in market supply chains, climate change and shrinking natural resources. At the same time, information can help to reduce poverty and creating economic opportunity for the majority of the rural populace in Africa. Further growth of ICT can enhance lifelong learning and impart skills and knowledge for individual and societal growth. This growth in skills and knowledge would also impact access and use of information in agriculture, health, transportation and economy. Information is very important in business by presenting data in a way that can be interpreted by management and support growth in the industry. Relevant information can support teaching and learning and improve quality sustainable education development in Africa. The chapter proposes that access and use of information in Africa can be enhanced by designing and improving the existing information infrastructures such as the internet and information centres. Legislations such as Access to Information and others would compel African governments and institutions to reconsider the role of information for sustainable development and consequently use it for decision making and competitive advantage.
Is the Energy-Hungry Bitcoin Beneficial for Portfolio Risk Reduction?

Beginning from the onset of the Covid-19 pandemic, crypto assets have intensely been in the spotlight and have attracted significant investor attention. By being the first blockchain product, Bitcoin is the first crypto asset and still dominates the entire crypto market capitalization. In this study, we shed light on whether this energy-hungry crypto asset is an effective tool for portfolio volatility reduction from the perspective of the Modern Portfolio Theory. Based on a two-year period from April 2019 to April 2021, which includes the extreme impacts (crash and rally) of the pandemic on markets, we conclude that Bitcoin is not a beneficial instrument for volatility reduction if short-selling is not allowed. After removing this restriction, Bitcoin has very small negative investment weights in minimum variance portfolios. In other words, short-selling Bitcoin slightly reduces portfolio volatility.
Abstract A Study on the 'Dailiness of Erudition'

The Covid-19 pandemic has fundamentally changed the traditional rules in the global realm of the political, economic, social, cultural sides. Especially, in the educational dimension, the Covid-19 pandemic has caused some changes in terms of teaching & learning methods and on-line instructing system. By doing so, there were some questions what is the essentials of education. In the era of the fourth industrial revolution, human beings should practice lifelong erudition. A future society could be a society that lives together like the community as conviviality. This study examined the changes in daily life under the pandemic circumstance, and reconceptualized the reformatting of daily life, habitus of erudition. And, it discussed the effects of changes in daily life and recommended the potentials of the Science of Erudition for daily life.
Indian Languages Requirements for String Search/comparison on Web

The document formats and protocols that based on character data is mainly prepared for the web. These protocols and formats can be access as resources that contain the various text files that cover syntactic content and natural language content in some structural markup language. In order to process these types of data, it requires various string based operations such as searching, indexing, sorting, regular expressions etc. These documents inspect the text variations of different types and preferences of the user for string processing on the web. For this purpose, W3C has developed two documents Character Model: String Matching and searching that act as building blocks related two these problems on the web and defining rules for string manipulation i.e. string matching and searching on the web. These documents also focus on the different types of text variations in which same orthographic text uses different character sequences and encodings. The rules defined in these documents act as a reference for the authors, developers etc. for consistent string manipulation on the web. The paper covers different types of text variations seen in Indian languages by taking Hindi as initial language and it is important that these types of variations should reflect in these documents for proper and consistent Indian languages string manipulations on the web.
Blockchain-Based Sea Waybill Trust Mechanism

In traditional maritime trade, Bill of lading (B/L) transaction mode has many risks, such as poor timeliness and complicated procedures. Sea waybill (SWB) can effectively remedy the defects of bill of lading, but the crisis of trust is always the core problem that hinders the further development of sea waybill. In recent years, with the influence of shipping digitalization and the improvement of trust in the overall market, especially the emerging digital technology led by blockchain has brought a feasible solution to the problem of sea waybill trust. This article first briefly introduces the technology and theory of blockchain, and then according to its technical characteristics build system architecture to meet the requirements of shipping business blockchain and integrating it into the sea waybill trade, thus forming a complete sea waybill trust mechanism based on the technology of blockchain theory model, provides a train of thought for scholars after the study.
ON THE CONTROLLABILITY OF LINEAR EVOLUTIONARY FUNCTIONAL-DIFFERENTIAL SYSTEMS

New theorem on the controllability of linear functional-differential system of evolutionary type and algorithm for practical verification of controllability are obtained, which can be applied even in the case when the coefficients of system are not continuously differentiable over the time interval under consideration. Special cases of this system are nonstationary systems with distributed and concentrated delay, integro-differential systems with a Voltaire integral and ordinary differential systems. The main results obtained are formulated in the form of 12 theorems and 3 corollaries. On their basis an algorithm for practical verification of the controllability of the system under consideration using a computer is built. Examples illustrating the operability of the obtained theorems and the algorithm are given. The algorithm is implemented in the Maple 17 package for examples of second-order differential systems with delay.
EveryAction acquires event management platform Mobilize

Nonprofit Business AdvisorVolume 2021, Issue 377 p. 12-12 News EveryAction acquires event management platform Mobilize First published: 15 January 2021 https://doi.org/10.1002/nba.30943Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume2021, Issue377February 2021Pages 12-12 RelatedInformation
Errors, Good Programming Practices, and Debugging

Regardless of how proficient, diligent, and careful a programmer you are, writing code with errors is unavoidable; this can be one of the most frustrating parts of programming. As such, dealing with errors preemptively, mentally, and emotionally is a critical part of becoming a proficient programmer. This chapter will provide a formal definition of errors, provide good programming practices that will help you avoid making errors, and show you some Python tools to help you find errors when you make them.
Models for finding quality of affirmation and points of intervention in an academic discussion forum

In academic discussion forums, people exchange thoughts, discuss, argue, and learn from each other. However, sometimes discussions can be inconclusive, messy, and vague. In order to make academic discussions meaningful, relevant interventions may need to be made whenever discussions lose clarity. Generally, academic discussion forum data is in the form of a sequence of questions, answers, and comments on the answers. All this data relating to a question (i.e. question, answers, and comments), considered together, is called an affirmation. If we can assess the clarity of an affirmation, it will be possible to find points where we can intervene in an academic discussion in order to steer it towards agreement or conclusion. We have trained and tested a model for assessing the clarity of an affirmation which is considered to be based on scores of agreements, disagreements, and partial answers. We have also trained a model for finding points where we can intervene whenever a discussion is leading to disagreement or inconclusiveness. We extend our previous work on assessing the quality of a discussion thread using a Random Forest classifier, by proposing an integrated affirmation assessment system here with a new sentence-level model. The sentence-level model gave us points where we could intervene in order to steer the discussion towards agreement or conclusion. The F1 score for the sentence-level model is 66.6%, and for the combined model it is 67.11%. In the paper, we have explained the models with experimentation and discussed the example by running the simulation of the model to show how the intervention works.
The Need of a Developmental Perspective on Juvenile Justice Policy

While the purpose of juvenile justice is to help juveniles develop in a healthy way, the Korean juvenile justice system has been pushed to implement a tougher policy on juvenile issues. In particular, it has been suggested to lower the minimum age of criminal responsibility so that young juveniles can be sent to criminal court. With this in mind, the current study reviewed how the juvenile issues were viewed and approached in the Korean juvenile justice system. Although juvenile offenders are still growing, it seems that they are viewed as a miniature of adult criminals. In addition, it might not be recognized that those juveniles’ development is in progress, even while they serve disposition. Furthermore, it appears that there is a lack of developmental perspective on juvenile issues, resulting in limitation of addressing juvenile delinquency. Based on the review, the need of developmental perspective among juvenile professionals and researchers were proposed.
Breast Cancer Prediction using Supervised Machine Learning Techniques

Breast cancer is a form of tumor that develops in the breast tissues. It is the most frequent cancer in women worldwide, and it is one of the leading causes of mortality in women. Therefore, early detection and diagnosis will benefit patients who have breast cancer. This paper has compared nine supervised machine learning techniques named Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest Classifier (RFC), Decision Tree Classifier (DTC), Logistic Regression (LR), Gaussian Naive Bayes (GNB), Artificial Neural Network (ANN), XGBoost Classifier (XGB) and AdaBoost Classifier (ADB) in order to classify the tumors into benign and malignant. The data set is from the UCI Machine Learning repository, which is used for predicting breast cancer. The performance of models is measured with respect to the accuracy, precision, recall, and F1 score. The results reveal that the ANN obtained the highest accuracy, precision, and F1 score of 0.993, 0.998, and 0.991, respectively.
Inductive inference with incompleteness

We present an axiomatic model of a process wherein likelihoods of eventualities are compared based on data. One eventuality is perceived as more likely than another whenever the data corroborates this conclusion. However, the correct relevance of records to the eventualities under consideration may be impossible to ascertain with any degree of surety due to multiple interpretations of the data, formalized by allowing the evaluator to entertain multiple weighting functions. The evaluator ranks one eventuality as more likely than another whenever its total weight over the entire database is higher, according to all relevance-weighting functions. Otherwise, the comparison is indecisive.