Reviewerly/exHarmony huggingface dataset:
Research on teacher development strategy of Guangdong independent college after transformation based on swot-ahp

After transformation, Guangdong independent colleges are urgent to build a completely independent and stable teaching staff in a short time. How to examine the difficulties, clear strategies and explore the optimal development path, is substantial significance for promoting teacher development and school development. The research establishes the SWOT-AHP model of teacher development strategies, the weight degree of the strengths, weaknesses, opportunities and threats factors are analyzed. The optimal strategies for teacher development is WT, it means eliminating weaknesses and avoiding threats. Finally, suggestions on teacher development strategies are put forward.
INTEGRATING MULTIMEDIA AND DIGITAL TECHNOLOGY IN SECONDARY SCHOOL ENGLISH LANGUAGE TEACHING (ELT) PROGRAM

Computer technology has been used in teaching foreign languages since the 1980s, which led to the emergence of a special direction in the methodology of teaching foreign languages, which examines the theoretical and practical aspects of the use of these technologies in the course of teaching a foreign language. In Kazakhstan, this direction is called "computer linguodidactics", the most common term used abroad is CALL (Computer-assisted language learning). With the technological progress, more and more advanced technical devices appeared with significant linguodidactic potential. Today, in the practice of teaching foreign languages, a wide range of technical devices are used, both stationary (for example, a computer, interactive whiteboard) and mobile (tablet computers, smartphones), which are combined under the concept of "digital technology". The appearance of these technical tools also influenced the ways of presenting information, which today is most often given in a multimedia format, that is, in several forms simultaneously: text, graphic, video, audio, interactive. Key words: digital technologies, self-development, active learning, generations Z and α, ICT.
An Improved YOLOv5 Real-time Traffic Sign Detection Algorithm

Focusing on the problems of complex detection network structure, multiple network parameters, and low detection accuracy in the process of traffic sign detection, a lightweight traffic sign detection algorithm MC-YOLOv5 is proposed. Firstly, the backbone feature extraction structure CSPDarkNet-53 in YOLOv5 is replaced by Mobilenetv2, and the depth separable convolution is introduced to reduce the network parameters and computation; Then, the K-means++ clustering algorithm is used to generate a priori anchor frame automatically; Finally, CBAM attention mechanism is introduced into the network to strengthen the representation ability of essential features, reduce feature loss, and improve the network feature extraction ability. The average accuracy of the MC-YOLOv5 algorithm is verified by experiments on TT100K traffic sign dataset [email protected] it reaches 90.0%, the Recall rate reaches 98%, the detection accuracy of MC-YOLOv5 model is greatly improved compared with the original YOLOv5 model, and the model volume is reduced by 67%. The experimental results show that the algorithm can better meet the requirements of accuracy and real-time traffic sign detection, and realize the model's lightweight.
The Interrelation of Psychological Security of University Educational Environment and Psychological Well-Being of Its Subjects

The article presents the results of the correlation study of psychological security of university educational environment and psychological well-being of its subjects. The study has revealed the correlation between the concept of university educational environment and that of psychological wellbeing, as well as interrelation between them. The study has found that the higher psychological well-being of its subjects is the more favorable psychological climate is in their view and vice versa (p ≤ 0,01). Moreover, there is a correlation between satisfaction with educational environment and all components of psychological well-being of its subjects. It has been proved that the more secure a subject of educational environment feels in it the more psychologically unscathed he or she is, and conversely, the higher psychological well-being of a subject is the more secure he or she feels (p ≤ 0,01).
Creating Your Own Private Ethereum Test Network

Now that you have a solid understanding of how a blockchain works, you are ready to see how to interact with one. In this chapter, you will learn how to make use of an Ethereum client to create a private Ethereum blockchain network within your local network. Doing this provides you with the chance to learn how to interact with an Ethereum blockchain without needing to worry about synchronizing with the entire mainnet (which may take up more than one terabyte of disk space on your computer).
Understanding the Science Behind Blockchain: Cryptography

The reason you are reading this book is because you want to understand what a blockchain is, how it works, and how you can write smart contracts on it to do cool things. And while I perfectly understand that you are excited to get started in this first chapter, we need to take a step back and look at one fundamental technology that makes blockchain possible: cryptography.
Storing Proofs Using Smart Contracts

In the previous chapter, you saw what a smart contract is, how it works, and how to create one. In this chapter, you will learn one good use case of a smart contract and at the same time learn some advanced techniques in Solidity.
Strengthening Research of SYSTRAN in the Field of Artificial Intelligence Automatic Translation

In the field of artificial intelligence, automatic translation is an important technology, which can improve the processing and understanding of information by intelligent machines. This paper studies the application of machine translation and strengthens its application in artificial intelligence automatic translation. Firstly, this paper expounds the principle, function and characteristics of artificial intelligence automatic translation, then introduces the principle of machine translation, then studies the application process of machine translation system, and tests the translation performance of machine translation, so as to improve the ability of intelligent machine to process and understand information to a certain extent. Finally, the test results show that machine translation performs well in artificial intelligence automatic translation, and achieves more than 80% results in word translation accuracy, fluency and accuracy.
Creating Non-Fungible Tokens Using ERC-721

Non-fungible tokens (NFTs) seem to be the latest craze in the world of blockchain lately. Chances are you have heard from friends or the media about some guys getting really rich by selling selfies as NFTs. So, what exactly is an NFT and what do you as a developer need to know about NFTs? This chapter explains what an NFT is and how to mint your own NFTs.
Semantic Similarity based Automated Answer Script Evaluation System using Machine Learning Pipeline and Natural Language Processing

Natural language processing is a subfield of computer science, in which the computers try to interrupt human language. Answer script evaluation is a tedious task for universities. So, the system developed to make the computers understand both the answer key and the answer scripts written by students which are later compared answer score between 0 and 1 is given which is known semantic similarity score. The proposed system uses several models like WordNet, latent semantic analysis, word to vector, universal sentence encoder, convolution neural network, and Siamese network to derive the result. Few models among those produce an output which includes several distance scores and similarity scores, while others predict whether these sentences are similar or not. The final similarity score determines how much the answers are semantically equal to the ones given in the answer key. Finally, the marks are awarded based on their equality. The average time required for training is 92 min, and the accuracy of the proposed system is 76.2%. The efficiency of the proposed system is improved due to the invocation of different models to identify the similarity score.
Getting Started

Originally developed in the early 1990s by Tim Berners-Lee, HTML stands for HyperText Markup Language and is a language used to lay out and format documents for the World Wide Web that are designed to be displayed in a web browser. In other words, the HTML code describes the structure of a web page. HTML can be used with other technologies such as Cascading Style Sheets (CSS) to style and format the document and scripting languages such as JavaScript to provide functionality and interactive elements.
Plates for Vanderhooft et al.’s “<i>Yehud</i> Stamp Impression”

Previous articleNext article No AccessPlates for Vanderhooft et al.’s “Yehud Stamp Impression”PDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by Volume 26, Number 1-22022 A journal of ASOR Article DOIhttps://doi.org/10.1086/MAR202226118 © 2022 American Society of Overseas Research. All rights reserved.PDF download Crossref reports no articles citing this article.
The Role of Artificial Intelligence Technology on English Language Learning: A Literature Review

The word (AI) stands for artificial intelligence, a computer-based simulation of human intelligence meant to act like humans. AI is one of the driving forces behind the 4.0 industrial revolution, making teaching and learning more accessible in schools. This study aims to understand the function of AI in ELT and examine AI technologies in ELT. This is a library research project. The findings indicate that AI provides a positive learning environment for learning English. Depending on the learner's current level of English, career needs, or hobbies, it has much potential to create a customized environment where students can simultaneously use their senses to learn English. AI boosts practical abilities like writing and offers a trustworthy simulation dialogue platform like spoken English. It maximizes the teaching impact of English in ELT while increasing students' practice ability. With the advancement of technology and platforms, learning English has gotten simpler. Artificial intelligence technology provides the chance to enhance English linguistic competence. Students may comprehend English more quickly because many different learning technologies are available. Students get access to a wide variety of ELT apps that are built on AI technology. These technologies include Google Translate, Text to Speech (TTS), EnglishAble, Orai, Elsa, Chatbot, Duolingo, Neo platforms, and many others. Using a method that computers and mobile devices can use, these intelligent machines can mimic intelligence and make decisions as people do.
Strategy for Services Anticipation at the Rehabilitation Center of BNN Based on Moving Average and Exponential Smoothing Forecasting Method

One of the Indonesian government’s responses to the COVID-19 pandemic is making policies related to restrictions on public services which affects the organizational resilience of the Rehabilitation Center of the National Narcotics Board (BNN). This research aimed to determine the historical pattern of the influence of public service policies during the COVID-19 pandemic on the client population, to forecast the client population for 3 (three) months ahead, and to analyze strategies for anticipating rehabilitation services at the Rehabilitation Center of BNN. This research method is quantitative by using a moving average (MA) and exponential smoothing forecasting model. Based on the validity test, MA is the best forecasting model, which indicates a possibility of a spike in male clients with the same amount in the pre-pandemic period, as many as 310 people, and the average female client is 7 people. Meanwhile, adolescent clients show inaccurate prediction results with MAPE 149.825. Strategies that can be implemented to anticipate a spike in the number of clients if it reaches the highest forecasting point are: increasing the budget, modifying the rehabilitation program for female and adolescent clients, a balanced staff composition, and the availability of facilities and infrastructure.
Enhancing Online Search Security Through Bayesian Trust Network Implementation

The article focuses on the development of an information search and protection system based on a Bayesian trust network as a measure of document relevance to the user’s query. The result is the development of search system structures and algorithms with relevance evaluation when searching the Internet, the implementation of data transmission with an adaptive database for storing decisions. If the need arises, when the goal set before the user cannot be achieved without involving additional information, the adaptive database sends a request to the search system, which in turn collects the necessary information. Mathematical formalization of the definition of relevant decisions was carried out. A graph was modelled, which was built based on Bayesian Trust Networks (BTN) in the GeNIe application package.
The formation of digital competence by means of information and communication technologies among students of higher education

The increasing digitalization in all spheres of human activity demands the improvement of digital competence in the educational field. This article examines how information and communication technologies (ICT) can help form the digital competence of higher education students, enhancing their skills in everyday, professional, and educational activities. The study focuses on the development of digital competence in university students through the use of ICT. The objective is to create an effective policy for the development of students' digital competence through the use of information technologies applicable in the educational process. The study's methodology includes outlining the key principles of digital competence, the current state of digital skills development, and the existing means for their development, as well as analyzing the organization of the educational process with the support of ICT. The findings of the study can be used to develop internal policies in universities, improve the skills of teaching staff, and allocate additional time to disciplines requiring attention to the study of digital tools applicable in practice.
Optimization Metrics for the Evaluation of Batch Schedulers in HPC

Machine Learning techniques are taking a prominent position in the design of system softwares. In HPC, many work are proposing to use such techniques (specifically Reinforcement Learning) to improve the performance of batch schedulers. Their main limitation is the lack of transparency of their decision. This underlines the importance of choosing correctly the optimization criteria when evaluating these solutions. In this work, we discuss bias and limitations of the most frequent optimization metrics in the literature. We provide elements on how to evaluate performance when studying HPC batch scheduling. We also propose a new metric: the standard deviation of the utilization, which we believe can be used when the utilization reaches its limits. We then experimentally evaluate these limitations by focusing on the use-case of runtime estimates. One of the information that HPC batch schedulers use to schedule jobs on the available resources is user runtime estimates: an estimation provided by the user of how long their job will run on the machine. These estimates are known to be inaccurate, hence many work have focused on improving runtime prediction.
Extraction of Radiological Characteristics From Free-Text Imaging Reports Using Natural Language Processing Among Patients With Ischemic and Hemorrhagic Stroke: Algorithm Development and Validation

Background Neuroimaging is the gold-standard diagnostic modality for all patients suspected of stroke. However, the unstructured nature of imaging reports remains a major challenge to extracting useful information from electronic health records systems. Despite the increasing adoption of natural language processing (NLP) for radiology reports, information extraction for many stroke imaging features has not been systematically evaluated. Objective In this study, we propose an NLP pipeline, which adopts the state-of-the-art ClinicalBERT model with domain-specific pretraining and task-oriented fine-tuning to extract 13 stroke features from head computed tomography imaging notes. Methods We used the model to generate structured data sets with information on the presence or absence of common stroke features for 24,924 patients with strokes. We compared the survival characteristics of patients with and without features of severe stroke (eg, midline shift, perihematomal edema, or mass effect) using the Kaplan-Meier curve and log-rank tests. Results Pretrained on 82,073 head computed tomography notes with 13.7 million words and fine-tuned on 200 annotated notes, our HeadCT_BERT model achieved an average area under receiver operating characteristic curve of 0.9831, F1-score of 0.8683, and accuracy of 97%. Among patients with acute ischemic stroke, admissions with any severe stroke feature in initial imaging notes were associated with a lower probability of survival (P&lt;.001). Conclusions Our proposed NLP pipeline achieved high performance and has the potential to improve medical research and patient safety.
Security Policy Learning

Security Policies define rules aiming to protect the infrastructure from insider and outsider threats. A key component to achieving this goal is access control policies, where security officers specify how subjects may access objects and under which conditions. However, developing detailed access control policies is a time-consuming and highly error-prone task that may have catastrophic effects if done incorrectly. Therefore, researchers have investigated techniques to automatically learn the security policies. In this chapter, we discuss ML techniques for automatic security policy learning. We organize those techniques in three parts: the first part refers to learning high-level access control policies, such as ABAC policy learning, whereas the second part discusses network security policy learning. While both parts pertain to access control policy learning, the former considers attributes of the subjects, objects, and operations, whereas the latter considers the idiosyncrasies of network environments, such as communication protocols and network addressing. The final part discusses privacy policy learning.
Dynamic Management of Distributed Machine Learning Projects

Given the new requirements of Machine Learning problems in the last years, especially in what concerns the volume, diversity and speed of data, new approaches are needed to deal with the associated challenges. In this paper we describe CEDEs - a distributed learning system that runs on top of an Hadoop cluster and takes advantage of blocks, replication and balancing. CEDEs trains models in a distributed manner following the principle of data locality, and is able to change parts of the model through an optimization module, thus allowing a model to evolve over time as the data changes. This paper describes its generic architecture, details the implementation of the first modules, and provides a first validation.
Letter Archivement Information System in Department Earth Engineering Faculty of Engineering Universitas Syiah Kuala Based on Paperless Office

Incoming and outgoing mail information systems have an important role as a source of information and documentation media. As a source of information, archives are material/data for making the right decisions, so that archives can be said as a system that is interrelated with each other in a unified bond, because archives can support a program of organizational activities, both in terms of planning, implementation and controlling the tasks of the organization concerned, from leading activities to decision-making activities. Archives as documentation media, archives are official evidence that can be accounted for for government activities, therefore it is an obligation for every employee who works in the archives unit in this organization to carry out their duties properly, so that correspondence services can be more effective. and efficient, while the purpose of this study is to analyze the Archives Management System at the Department of Earth Sciences, Faculty of Engineering, Syiah Kuala University. In this study, researchers used descriptive research methods with a qualitative approach. This study aims to develop concepts and facts in depth to answer how the Archives Management System in the Department of Earth Sciences, Faculty of Engineering, Syiah Kuala University. The archive management system at the Department of Earth Sciences, Faculty of Engineering, Syiah Kuala University uses a number system and a dating system that is still less effective but has been running well, because it uses a combined procedure of centralization and decentralization. The Prototyping method is used as a software development model and evaluation activities are also carried out as an assessment of the results of the prototype built. Based on the evaluation of the appearance of the current desktop application. With the prototype design, it is known that each category has increased usability values. Content, Organization and Readability previously had a value of 0.64 and increased to 0.72. The Navigation category was previously 0.69 to 0.71. The previous User Interface Design category was 0.58 to 0.74 and the previous Performance and Effectiveness category was 0.62 to 0.72.
In Celebration of the 30th Meeting of the Conference on Comparative Cognition

This year marks the 30th meeting of the
Watermarking In Medical Images

Abstract—The Internet of Medical Things, smart healthcare systems are becoming ubiquitous in our daily lives. Patients, doctors, and other medical personnel rely on the safe and efficient storage, transmission, and analysis of electronic health records , medical images such as scanning reports X-rays for successful treatment and management of different ailments. A Multi watermarking method is proposed for medical images based on quantum random walk and optimization algorithm. A logo image is used to verifying medical image integrity is embedding in region of interest and text data are embedded in the region of non interest to conceal private hospital and patient informations. This method improves the authenticity and integrity of medical images. A number of experiments are conducted to validate the security ,robustness and capacity of the proposed multi watermarking scheme. Keywords—medical image, quantum random walk, optimization algorithm, Internet of Medical Things
239. Improving the Sensitivity Between a Smartphone Measure of Model-Based Planning and Compulsivity Through Data-Driven Task-Optimizations

Deficits in model-based planning have consistently been linked to compulsivity but several studies have shown these estimates are sensitive to modifications to the task, including transition probabilities, reward rates, instruction and incentives. What remains untested is whether these task modifications affect external validity of model-based planning estimates i.e., its association with compulsivity.