Reviewerly/exHarmony huggingface dataset:
English Translation proofreading System based on Information Technology: Construction of semantic Ontology Translation Model

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Studying the characteristics of SQL-related development tasks: An empirical study

Abstract A key function of a software system is its ability to facilitate the manipulation of data, which is often implemented using a flavour of the Structured Query Language (SQL). To develop the data operations of software (i.e, creating, retrieving, updating, and deleting data), developers are required to excel in writing and combining both SQL and application code. The problem is that writing SQL code in itself is already challenging (e.g., SQL anti-patterns are commonplace) and combining SQL with application code (i.e., for SQL development tasks) is even more demanding. Meanwhile, we have little empirical understanding regarding the characteristics of SQL development tasks. Do SQL development tasks typically need more code changes? Do they typically have a longer time-to-completion? Answers to such questions would prepare the community for the potential challenges associated with such tasks. Our results obtained from 20 Apache projects reveal that SQL development tasks have a significantly longer time-to-completion than SQL-unrelated tasks and require significantly more code changes. Through our qualitative analyses, we observe that SQL development tasks require more spread out changes, effort in reviews and documentation. Our results also corroborate previous research highlighting the prevalence of SQL anti-patterns. The software engineering community should make provision for the peculiarities of SQL coding, in the delivery of safe and secure interactive software.
Analyzing the Emerging Trends of Digital Literacy Among Muslim Students Zahid Hussain

The purpose of this investigation was to gain a comprehensive understanding of current technologies among Islamic university students so that they could make wise and stable use of their literacy online.We think that students' ability with ICT is directly related to how well they use digital technology resources.We decided to base our study on data discoveries that have been published in a number of journals that are accessible through the Google Scholar information web browser and were published between 2010 as well as 2021 in order to make it simple for readers to understand.To find accurate and trustworthy data, we apply analytical research under phenomenological research.For instance, during the data evaluation process, we examine, assess, and analytically analyze the results before coming up with conclusions that address the study's research questions.One conclusion is that students are able to access learning materials using their understanding of online media to comprehend digital literacy.Individuals had also got training and then used internet devices responsibly, resulting in their favourable and smart use of digital technology as an extra reference resource for appropriate and sensible speech, sports, and entertainment components.Future research should be able to benefit from these research results.
TPoet: Topic-Enhanced Chinese Poetry Generation

Chinese poetry generation has been a challenging part of natural language processing due to the unique literariness and aesthetics of poetry. In most cases, the content of poetry is topic related. In other words, specific thoughts or emotions are usually expressed regarding given topics. However, topic information is rarely taken into consideration in current studies about poetry generation models. In this article, we propose a topic-enhanced Chinese poetry generation model called TPoet in which the topic model is integrated into the Transformer-based auto-regressive text generation model. By feeding topic information to the input layer and heterogeneous attention mechanism, TPoet can implicitly learn the latent information of topic distribution. In addition, by setting multiple identifiers such as segment, rhyme, and tone, the model can explicitly learn the constraints of generated poems. Extensive experimental results show that the quality of TPoet-generated poems outperforms the current advanced models or systems, and the topic consistency and diversity in generated poems have been significantly improved as well.
Subject-based knowledge organisation: An OER for supporting (digital) humanities research

Humanities scholars can today engage in research inquiry using data from a range of varied collections which are often characterised by poor subject access, often resulting in systems that underperform and even effectively prevent access to data, information and knowledge. In spite of the availability of professional standards and guidelines to provide quality-controlled subject access through knowledge organisation systems (KOS), subject access in such collections is rarely based on KOS. At the same time, KOS themselves may come with problems such as being slow to update, being rigidly structured and not incorporating end-users’ vocabulary. It may therefore be useful to consider methods for remediating these deficiencies in KOSs, such as collecting user-generated metadata via social tagging or complementing automated indexing techniques with manual ones. To help address the above problems, the paper discusses these challenges and points to possible solutions in different contexts. It does so by reflecting on an open educational resource (OER) devoted to this theme, titled Introduction to Knowledge Organisation Systems for Digital Humanities. It was developed as part of an EU project called DiMPAH (Digital Methods Platform for the Arts and Humanities), 2021–2023, creating seven OERs for inclusion in DARIAH Teach.
Development of Macaroni Fusilli E-Commerce Application Based on Android

Sidoarjo is a district located in East Java Province.Various kinds of contemporary snacks, one of which is macaroni, are growing rapidly in this area.However, there are obstacles in access to marketing, namely the difficulty of implementing a marketing system to compete in the digital era.E-commerce is a form of marketing using information technology and is a suitable medium for buying and selling online.The objectives of this study are (1) Developing an android-based e-commerce application for fusilli macaroni.(2) Make it easy to order fusilli macaroni.(3) Attract consumers to buy with creative designs.The method to be used is the ADDIE method, by analyzing, designing, developing, implementing, and evaluating.This research resulted in (1) the BukaOlshop application which is used for the sale of fusilli macaroni.(2) A feature that makes ordering via cell phone easier without having to come to the shop.(3) Attractive and creative design.With the development of increasingly sophisticated technology, it can be used as an online business opportunity because it will make business activities more effective and efficient.
The impact of organizational empowerment practices and learning organization on firm performance

Organizations adapt and implement different strategies and practices in order to improve and enhance its performance and competitiveness in the marketplace. This study investigates the impact of organizational empowerment practices and learning organization on organizational performance of Abu Dhabi National Oil Company (ADNOC) in the UAE. The study contributes to the knowledge by investigating empirical data about organizational empowerment practices, learning organization, and organizational performance and elaborating it in the context of the oil industry in the UAE. The study adopted the quantitative approach and explored participants from different managerial levels. A total number of 212 valid questionnaires were used for analysis. Further, appropriate statistical tests were used to examine the model validation and hypotheses verification. The literature findings suggest that organization performance and organizational commitment increase as the organization invests in training its employees. The study concluded that an increase in communication, rewards, stimulus culture, and sharing of vision among managers, supervisors, or mid-managers could positively impact the performance of ADNOC.
Examining the Structure of QR Codes to Improve Encryption and Decryption

Black and white barcode have become utilised in the past decade to encode more data per square unit. The barcode is a series of bars and spaces organised in a certain pattern. It exemplifies the appropriate approach to provide knowledge. Data is retained gradually in 2D matrices. Such codes are known as two-dimensional shapes codes. Many applications are briefly explored, as well as the structure, symbology, and features of barcodes. Throughout 2007 and 2010, this study paper discussed ways to provide QR codes using word processing software. The major goal of the study is to design and grasp QR code technology in the context of today's society. Component, formatting, style, styling, insert are all terms.
Research progress of computer vision tasks based on deep learning and SAE network

Abstract In recent years, artificial intelligence has gradually become the core driving force of a new round of scientific and technological revolution and industrial transformation, and is exerting a profound impact on all aspects of human life. With the rapid development of Internet big data and high-performance parallel computing, relevant research in computer vision has made significant progress in the past few years, becoming one of the important application branches in the field of artificial intelligence. The exercise of image classification forming part of computer vision tasks involves a large amount of computation, and training based on traditional deep learning (DL) classification models typically involves slow training and low accuracy in many parameters. Thus, in order to solve these problems, an image classification model based on DL and SAE network was proposed. Firstly, the main research of computer vision task-image classification is introduced in detail. Then, the combination framework of deep neural network and SAE network is built. At the same time, the deep neural network was used to carry out convolution operation of the parameters learned by SAE and extract each feature of the image with neurons, so as to improve the training accuracy of the deep neural network. Finally, the traditional deep neural network and SAE network were used for comparative experiment and analysis. Experimental results show that the proposed method has a certain degree of improvement in image classification accuracy compared with traditional deep neural network and SAE network, and the accuracy reaches 97.13%.
Implementation of Newsly Aggregator using Django Framework

The newsly web is a platform where we are filtering, aggregating news and providing the information or news for user friendly experience at one place. Newsly aggregator is a very simple and user-friendly application. In a news aggregator, user can select the websites user want to follow. Visiting many separate websites frequently to find out if the content on the site has been updated can take a long time. Newsly Aggregation technology helps to consolidate many websites into one page that can show only the new or updated information from many sites. Newsly Aggregators reduce the time and effort. News and providing the content for user friendly experience at one place In Newsly we have used HTML, CSS, JavaScript and bootstrap for the front End. We use crispy forms python, Django for the back-end support. This paper will explore the advancements in AI and NLP that are expected to shape the future, as well as the potential implications of human interaction
TN-GAN-Based Pet Behavior Prediction through Multiple-Dimension Time-Series Augmentation

Behavioral prediction modeling applies statistical techniques for classifying, recognizing, and predicting behavior using various data. However, performance deterioration and data bias problems occur in behavioral prediction. This study proposed that researchers conduct behavioral prediction using text-to-numeric generative adversarial network (TN-GAN)-based multidimensional time-series augmentation to minimize the data bias problem. The prediction model dataset in this study used nine-axis sensor data (accelerometer, gyroscope, and geomagnetic sensors). The ODROID N2+, a wearable pet device, collected and stored data on a web server. The interquartile range removed outliers, and data processing constructed a sequence as an input value for the predictive model. After using the z-score as a normalization method for sensor values, cubic spline interpolation was performed to identify the missing values. The experimental group assessed 10 dogs to identify nine behaviors. The behavioral prediction model used a hybrid convolutional neural network model to extract features and applied long short-term memory techniques to reflect time-series features. The actual and predicted values were evaluated using the performance evaluation index. The results of this study can assist in recognizing and predicting behavior and detecting abnormal behavior, capacities which can be applied to various pet monitoring systems.
A novel multi-level image segmentation algorithm via random opposition learning-based Aquila optimizer

Aquila optimizer (AO) is an efficient meta-heuristic optimization method, which mimics the hunting style of Aquila in nature. However, the AO algorithm may suffer from immature convergence during the exploitation stage. In this paper, two strategies are elegantly employed into conventional AO, such as random opposition-based learning and nonlinear flexible jumping factor, which can efficiently enhance the performance of conventional AO. Experiments on [Formula: see text] benchmark functions and image segmentation demonstrate the effectiveness of the proposed algorithm. Comparison with several state-of-the-art meta-heuristic optimization techniques indicates the efficacy of the developed method.
On-chain global maintenance services: technical, legal and managerial implications

Facility management deals with all activities that are not core business for a company and are consequently outsourced to specialized companies. Maintenance is a fundamental activity in facility management and it is often handled by Global Maintenance Services (GMS) where some maintenance activities are delegated by the company to service providers and are remunerated according to measurable results expressed as Key Performance Indicators. In this context, it would be desirable to have information systems trustable by all involved actors. In this paper, we discuss the design of a blockchain solution capable to support a GMS on-chain. We first introduce the GMS concept and how it is related to the Principal-Agent relationship, and then we show a reference architecture to implement GMS on-chain. We discuss a use case and a proof-of-concept of on-chain GMS in a hospital showing how smart contracts and oracles can be used in this context. We finally discuss the main legal and managerial implications of the proposed approach.
Toru Takemitsu, <i>Between Tides</i>: al di là del suono e del silenzio Analisi generale e riflessioni personali sull’opera

This article is born from my personal reflections while studying Between Tides , therefore contributing to the already existing literature of Toru Takemitsu. A general analysis outlines the structural motives as Sea Theme, played with E Flat, E and A, constitutes a matrix of other thematic cells. The piano writing chosen aim to disassociate timbres underlining the colors of every harmony, while the strings produce a sonorous effect with techniques and positions that go back to the avante-garde of the 1900s. In Between Tides we hear an assiduous research of the sound and beyond it. Playing this work creates a unique performing experience where gesture and sound are inextricably bound together in an intimate relationship, where the sensitivity of Toru Takemitsu was able to seize.
Interpretation of a musical work as a significant component of the professional activity of an accompanist

The subject of the study is the conditions for the development of professional skills of the concertmaster in the music interpretation. The article deals with the problem of interpreting a piece of music as a basic quality of a musician's personality in general, and in particular, in the structure of the concertmaster's professional activity. This article is devoted to the description of the process of working on a musical work of a musician in general, and, in particular, an accompanist. The author considers the pedagogical conditions for the formation of skills and abilities of interpretation of musical works as an important component in the training of a musician-performer, concertmaster; the importance of the concertmaster's communicative skills as one of the factors in the work on the interpretation of a musical work is determined. The role of psychological and professional qualities of a musician in the artistic embodiment of interpretation is revealed, which depend on his musical and auditory representations, intelligence, temperament, emotional sensitivity, musical experience, performing endurance, concentration of attention, ability to control his playing. The novelty of the research lies in the identification of an algorithm of work aimed at creating an interpretation of a musical work in the joint performance of a soloist – concertmaster. The main conclusion of this work is that the use of artistic and pedagogical interpretation in the work of an accompanist on a piece of music can contribute to the development of professionally significant qualities of an accompanist.
Repetitive Path Planning with Experience-Based Bidirectional RRT

Repetitive motion planning in semi-structured environments is involved in many robotics applications, such as manufacturing and in-orbit service of spatial robotic arms. In these scenarios, the environment is mainly static, but it may contain some movable obstacles that the robot needs to avoid, and the planning problems that the robot will encounter are very similar. However, most current sampling-based motion planning methods ignore the correlation among tasks, and instead plan each task instance from scratch, or assume that the environment is highly structured. In order to overcome this problem, this paper proposes an experience-based bidirectional rapidly-exploring random trees* (EBRRT*) for repetitive path planning in semi-structured environments. First, an adaptive sampler learned from the demonstration paths by a Gaussian mixture model (GMM) is constructed, which can guide the planner to generate samples in task-related regions. Second, an experience graph is proposed to capture the exploration of configuration space by previous similar tasks. Subsequent tasks can retrieve locally valuable information from the graph to avoid exploring the configuration space repeatedly. In addition, the validity of edges contained in the experience graph is lazily verified during the planning process so that the experience graph can adapt to small environmental changes. Finally, the proposed method is verified in a 2D simulation environment and a 7-DOF manipulator scene. Experimental results show that the proposed method can return a high-quality path in less time than other state-of-the-art sample-based motion planning for repetitive path planning in semi-structured environments.
Scandalous Dystopias: Hyping The Last of Us Part II and Cyberpunk 2077 During the Pandemic

This chapter explores how the COVID-19 pandemic scandalized two of 2020’s most anticipated video games: The Last of Us Part II—a sequel to an older game that adapts zombie media like 28 Days Later and The Walking Dead—and Cyberpunk 2077, based on the cult tabletop roleplaying game Cyberpunk 2020, which adapted Dungeons & Dragons and William Gibson’s novel Neuromancer. The pandemic exposed the risks of building hype by framing these games as adapting other media whose most appealing features they incorporated. Hyping them through their associations with prestigious earlier media increased their visibility but created unmanageable expectations. These discrepancies became increasingly apparent as the pandemic disrupted the games’ carefully planned releases, giving birth in both cases to disastrous scandals.
A Brief Analysis of Chinese Bamboo Flute Ensemble Art

Bamboo flute is a traditional Chinese instrument. In the long history of China, the performance of bamboo flute has been used to express and transmit emotions, enriching the spiritual world of the Chinese people. Nowadays, with the continuous progress of spiritual civilization construction and the unprecedented enhancement of Chinese national cultural confidence, the development trend of the bamboo flute art has been more clear. This paper makes an in-depth analysis of the development history and characteristics of bamboo flute ensemble and the significance of its practice, and shares the author’s opinion on the practice of bamboo flute ensemble, so as to enrich the research theory of Chinese bamboo flute ensemble art and make contributions to its development.
Application of Artificial Intelligence in Ambient Assisted Living to Support Elderly People in Smart Homes

This chapter begins with a grounding in the principles underlying the Ambient Intelligence (AmI) paradigms providing healthcare services to older residents who are living in their homes or in supported housing. This study will explore the complexity of diagnosis and prognosis in healthcare where multiple vital sign parameters are obtained from the patients using smart sensors and Internet of Things (IoT) technologies. Moreover, Ubiquitous/Pervasive computing, wearable technologies, context-awareness and Artificial Intelligence (AI) are investigated to highlight the importance and contribution of these paradigms and disciplines to the intelligence, ubiquitous and adaptivity of the Ambient Assisted Living (AAL) environment. Later in this chapter, the current application of AAL health monitoring will be discussed followed by a discussion on research gaps.
A Condition Evaluation Method of Power Transformer Based on Text Mining Technology

Text analysis of power transformer defect record is a challenging task because of the large number of professional words and the scarcity of labelled text data. These problems make generic models less effective in the power transformer domain. Pre-trained models can help solve these problems as they can be pre-trained on large-scale cross-domain unlabeled data, and can solve tasks in specific domains by continue training on the corpus of specific domains. We introduce the “pre-training, continue-training, fine-tuning” transfer learning approach to the field of power transformer text mining, and propose a language model based on Bidirectional Encoder Representations from Transformers (BERT) to solve the power transformer defect text classification problem. The model is first trained on the Chinese Wikipedia corpus to learn the language representation. We then continue to train the model on the power transformer corpus to learn the expertise. On this basis, this paper compares the accuracy rate, confusion matrix, differential mean $$F_{1}$$ ( $$F_{1micro}$$ ) and other factors with traditional machine learning, deep learning and other methods. The case study demonstrates that our model can automatically and accurately classify the text of power transformer defect record with better classification results.