Reviewerly/exHarmony huggingface dataset:
Research on Multi-interactive Teaching of College English under the Environment of Information Technology

Multi-interactive teaching model is an important way to improve the quality of college English teaching, and it is also the key foundation to improve students' understanding of college English teaching content. In the increasingly mature environment of information technology application, students' learning enthusiasm will be fully stimulated, multi-interactive communication will be deepened, and students' English core literacy will be effectively enhanced by giving full play to the advantages of information technology, to upgrade the intelligence of college English teaching system. Based on the analysis of the multi-interactive teaching mode of college English under the information technology environment, this paper puts forward some practical strategies for innovating the multi-interactive teaching of college English by using information technology.
The research on data mining technology in the analysis of students scores

On the basis of being familiar with the traditional decision tree algorithm, some improvements are made to the C4.5 algorithm to reduce the mining time. After preliminary processing of students’ scores, the improved algorithm is used to mine the management association rules among students’ scores, and the results of association rules are analyzed and interpreted.
DIGITAL MODERNITY AND ITS CONSTRUCTIVE IMPACT ON THE PERFORMANCE OF WORKERS IN TOURISM ORGANIZATIONS: AN ANALYTICAL STUDY IN THE PREMIUM CLASS HOTELS OF THE CITY OF BAGHDAD

The current research tests the effect of digital modernity and its components (hardware, software, communication networks, individuals) in improving the performance of workers. The research problem was represented in several questions that were answered to know the extent of the hotel management’s interest in supporting and applying digital modernity, and then determining the effect between its variables. (48) of (managers, heads of departments, heads of divisions) in the premium hotels of the city of Baghdad, and data and information were collected using a questionnaire, and the data was analyzed using the statistical program (SPSS). The research reached several results, including: that there is a statistically significant effect between the research variables (digital modernity, improving the performance of workers), and these results are identical to the research hypotheses. The research presented a number of recommendations, perhaps the most prominent of them: the most important of which was the necessity of applying digital modernity by the surveyed hotels because of its importance in solving many problems, including the negatives of paper handling.
Media Intelligent Content Production in the AI Horizon

With the advent of the era of artificial intelligence (AI), all walks of life are facing the subversion and reconstruction from characterization to the core, and the media industry that is highly cohesive with the society is naturally no exception.In the past 10 years, the media has emphasized the intelligent intelligence system of artificial intelligence—improved the communication effectiveness of content by increasing immersion and “presence” experience, reformed the content production system, and designed a new mechanism that meets the new media content production standards. [Objective] This paper studies the two aspects of content production mechanism and mode of production, fmethod] through literature analysis, deep observation of specific practice cases, and experimental analysis, [conclusion] proposes an effective content production mechanism, and proves that with the latest VR/AR (Visual Reality/Augmented Reality) information presentation technology, media can produce content products with immersive and better experience.This article conducted in-depth research on the two professional media, participated in the content production process of VR and AR according to the research requirements, and successfully presented the content works.
Research on a New Mode of Oral English Teaching Based on Computer Network Assistance

Oral English teaching is an important form of cultivating students' English accomplishment and improving their English learning effect. In the context of intelligent education, by giving full play to the advantages of computer network and actively reconstructing the new mode of Oral English teaching, we will, on the basis of optimizing the form of oral English teaching and by integrating high-quality oral English teaching resources, to promote students to participate in oral English learning and speaking practice in a conscious and spontaneous way. Based on the analysis of the existing problems in spoken English teaching, this paper puts forward the construction and implementation of a new spoken English teaching model based on computer network.
Phish-Defence: Phishing Detection Using Deep Recurrent Neural Networks

In the growing world of the internet, the number of ways to obtain crucial data such as passwords and login credentials, as well as sensitive personal information has expanded. Page impersonation, often known as phishing, is one method of obtaining such valuable information. Phishing is one of the most straightforward forms of cyberattack for hackers and one of the simplest for victims to fall for. It can also provide hackers with everything they need to get access to their target's personal and corporate accounts. Such websites do not offer a service, but instead, gather personal information from users. In this paper, we achieved state-of-the-art accuracy in detecting malicious URLs using recurrent neural networks. Unlike previous studies, which looked at online content, URLs, and traffic numbers, we merely look at the text in the URL, which makes it quicker and catches zero-day assaults. The network has been optimised to be utilised on tiny devices like Mobiles, and Raspberry Pi without sacrificing the inference time.
Multi-modal Universal Embedding Representations for Language Understanding

In recent years, machine learning has made good progress in Computer Vision (CV), Natural Language Processing (NLP), and Vision + Language (V + L). However, most existing pre-training models just focus on single-modal (i.e., using only linguistic or visual features for training) or multi-modal (i.e., using both linguistic and visual features for training) scenarios, and can only use single-modal data or limited multi-modal data. This means that models for different scenarios need to be pre-trained separately, which requires a lot of computing resources and time. In this paper, we propose a universal method to train a general pre-training model to solve the problems in different scenarios and modalities. Moreover, we find that the model pre-trained with multi-modal data performs better in the single-modal downstream tasks. We use the General Language Understanding Evaluation (GLUE) benchmark for single-modal tasks to evaluate our model, which outperforms Bidirectional Encoder Representations from Transformers (BERT) in four tasks. For Vision + Language (V + L) tasks, we test our model on downstream tasks such as Visual Question Answering (VQA) and achieve similar performance to the current top-level model.
The Legal Nature of Quantum Communications

The article discusses legal issues related to quantum communications. Special attention is paid to the principles on which the work of quantum communications is based. The necessity of observing the uniformity of the conceptual apparatus in the system of Russian law is pointed out. The review and comparison of the main strategic planning acts of the European Union, the Russian Federation and other countries in the field of quantum technologies and quantum communications is given. The main foreign and international standards are analyzed. Considerable attention is paid to the attempt to give the concept of quantum communications as an information technology, which can be fixed in the adopted acts. The main legal problems associated with the introduction of quantum communications are identified. The conclusion is made about the negative consequences of excessive regulation of this area.
Library and Information Education in the Digital Age: Students’ Expectations

In the context of an evolving digital environment of library education, it is important to study the needs and expectations of students as participants of the educational process and the direct consumers of educational services. This paper presents the results of a questionnaire survey of students studying on the specialty “Library and Information Activities” at the Belarusian State University of Culture and Arts. The objective of the survey was to determine the students’ vision of new trends in educational practice and the prospects for the development of library education associated with digitalization. The study was conducted by the working group as a part of the contract research “Explore trends and ways of development of library and information education in the digital age”. The survey touched upon such research areas as a promising vector for the development of library education, students’ readiness for library activities in a digital environment, and the use of information and communication technologies in the learning process. It was found that the students consider the information and technological courses as a promising vector for the development of library education (information courses as the basis for training). In the courses taught at the Faculty of Information and Document Communications, students would like to focus on remote user service, programming, data analytics, digital technologies, neural networks, nanotechnologies, digital marketing, web design basics, the creation and design of electronic resources, the basics of working with graphic editors and other. Assessing the importance of various types of library work in the digital environment, students marked “Working with electronic catalogs and databases” as the most important one. Least of all, students consider themselves ready for the “Work to create electronic information resources” and “Work to provide access to electronic information resources”. In the process of distance learning during the epidemic COVID-19, the greatest difficulty was the increase of the workload, the complication of the learning process. Of less significance were the technical communication problems. А set of measures from the viewpoint of students to improve the quality and competitive advantages of library and information education in the digital age is defined.
User-­Oriented Approach to Content Improvement in Official Library Websites

The creation of official websites of modern libraries has opened up for them fundamentally new opportunities to present a wide range of information resources and services for remote users. The purpose of the article is to reveal the problem of determining the rational composition and placement of headings and subheadings in the content structure of library sites and to present a typical content model as one of the possible means to ensure the orientation of users on the sites of the same type of libraries from the standpoint of a user-oriented approach. A typical model of the content of the official website of the central libraries of the subjects of the Russian Federation is proposed. It can be used in any of the libraries of the types under consideration (republican, regional, autonomous districts and regions, as well as central libraries of cities of federal subordination). The proposed typical model is aimed at overcoming the unreasonable diversity of structural elements by reducing the existing list of headings and subheadings and bringing them to uniformity and to provide mutual understanding between library site developers and remote users. The unification of the content structure of official library websites can become a condition not only for improving the quality of the content of individual sites, but also for ensuring their compatibility to form uniform digital library and information space in Russia.
Design of a source data management system for data middle platform

As a mature mechanism for enterprise management to use and process data in recent years, data middle platform has been widely used and developed by enterprises with its unique data screening and management organization mode.Because the data middle platform can establish a proprietary business model and system structure according to different enterprises, it can also have strong applicability.This paper makes a structural analysis on the current popular data middle platform, and makes an innovative management design from the source data management level to optimize the traditional data middle platform, so as to realize a more efficient and centralized management system.
A Comparative Study on Mapping Experience of Typical Battery Electric Vehicles Based on Big Data Text Mining Technology

Battery electric vehicles (BEV) are the core innovation of low-carbon travel transformation. However, there are still few evaluation studies on the user experience of its users. This paper is based on the text mining of big data natural language processing. Taking the user experience reviews of typical Battery electric vehicles as the research object, by comparing the user experience reviews of users of two typical electric vehicles that are more popular in China on Quora with Chinese characteristics, the specific research models are Tesla Model 3 which is representative of international brands and BYD Han EV which represents a domestic brand. After data collection, filtering, extraction, analysis, and mapping. The specific findings are as follows: Firstly, In terms of the vehicle hardware itself, the user experience of both focuses on the “battery”, Model3 Focusing on “brake”, the user experience of Han EV pays more attention to “appearance” and “rear space”; Secondly, in terms of vehicle software configuration, both pay attention to “system”, and Han EV users focus more on the features that are not obvious, Model 3 its user experience focuses on “charging”, “marketing” and “performance”; Thirdly, in terms of subjective feeling, both focus on “driving” and “experience”, and Model 3 focuses more on “owner” and “price”. Overall, we have the conditions to conclude that in the context of the experience economy, the application of artificial intelligence technology uses big data to actively “restore” user experience in real scenarios, and optimize the user experience of traditional subjective questionnaires based on small data. Ground on the research of group characteristics, explore the possibility of individual user experience characteristics that can highly fit the characteristics of user groups in the future.
“StudySandboxx: A Tool for Scraping, Sandboxing, Preserving, and Preparing Interactive Web Sites for Use in Human-computer Interaction and Behavioral Studies”

Human-computer interaction and computer-mediated behavioral psychology research studies often rely on capturing user interaction data to characterize online behaviors. IRB considerations, site policies, and/or security and privacy concerns may force researchers to use screenshots or offline copies of pages of interest, instead of live websites, in their study designs. These interaction modalities reduce the fidelity and contextual realism of web content and often affect interface aesthetic quality – due to broken links, missing images, and/or malfunctioning scripts. StudySandboxx is a tool that allows websites to be saved exactly as they appear online. The tool sandboxes websites in a way that removes dangerous scripts that threaten privacy and security. Saved websites are encapsulated into a single portable file that contains all related website resources. Finally, the tool also supports certain types of permutations commonly used in research – such as changing links in a page. The project is housed within a GitHub repository at <a href="https://github.com/gewethor/study-sandbox" target="_blank">https://github.com/gewethor/study-sandbox</a>.
HotLT: LT Code-Based Secure and Reliable Consortium Blockchain Storage Systems

The consortium blockchain is deployed in many key industries because decentralization is irreversible and traceable. However, the storage system of the alliance chain usually adopts a full copy method, which leads to an explosive increase in storage overhead over time. What is worse, this will become a hidden threshold for companies to join the alliance to a greater extent and then evolve into an industry-centric model again. In this paper, we propose a novel storage solution named HotLT, which adopts a distributed storage system coding scheme to reduce the storage overhead of the alliance chain and enhance scalability. Secondly, to solve the bottleneck of concurrent access to encoded data and improve the average decoding speed, HotLT dynamically adjusts the generating matrix during the encoding process to adopt different encoding strategies for data blocks of different hot. Finally, the data access frequency is further divided, low-complexity coding is performed on data with high access frequency, and high-complexity coding is performed on cold data. Compared with the encoding time of traditional LT encoding, the experimental results show that the encoding time of HotLT is almost the same, and its average access speed gradually approaches the speed of direct reading as the proportion of access hot data increases.
Feature Subset Selection Techniques with Machine Learning

Scientists and analysts of machine learning and data mining have a problem when it comes to high-dimensional data processing. Variable selection is an excellent method to address this issue. It removes unnecessary and repetitive data, reduces computation time, improves learning accuracy, and makes the learning strategy or data easier to comprehend. This chapter describes various commonly used variable selection evaluation metrics before surveying supervised, unsupervised and semi-supervised variable selection techniques that tend to be often employed in machine learning tasks including classification and clustering. Finally, ensuing variable selection difficulties are addressed. Variant selection is an essential topic in machine learning and pattern recognition, and numerous methods have been suggested. This chapter scrutinizes the performance of various variable selection techniques utilizing public domain datasets. We assessed the quantity of decreased variants and the increase in learning assessment with the selected variable selection techniques and then evaluated and compared each approach based on these measures. The evaluation criteria for the filter model are critical. Meanwhile, the embedded model selects variations during the learning model's training process, and the variable selection result is automatically outputted when the training process is concluded. While the sum of squares of residuals in regression coefficients is less than a constant, Lasso minimizes the sum of squares of residuals, resulting in rigorous regression coefficients. The variables are then trimmed using the AIC and BIC criteria, resulting in a dimension reduction. Lasso-dependent variable selection strategies, such as the Lasso in the regression model and others, provide a high level of stability. Lasso techniques are prone to high computing costs or overfitting difficulties when dealing with high-dimensional data.
Neural Machine Translation with Synchronous Latent Phrase Structure

It has been reported that grammatical information is useful for machine translation (MT) tasks. However, the annotation of grammatical information incurs significant human costs. Furthermore, it is not trivial to adapt grammatical information to MT because grammatical annotation usually employs tokenization standards that might not capture the relation between two languages and consequently, subword tokenization such as byte-pair-encoding is used to alleviate out-of-vocabulary problems; however, this might not be compatible with those annotations. In this work, we introduce two methods to incorporate grammatical information without supervising annotation explicitly: first, the latent phrase structure is induced in an unsupervised fashion from an attention mechanism; and second, the induced latent phrase structures in the encoder and decoder are synchronized so that they are compatible with each other using constraints during training. We demonstrate that our approach performs better in two tasks: translation and word alignment, without extra resources. We found that the induced phrase structures enhance the precision of alignments through the synchronization constraint after exact phrase and alignment structure analysis.
Impact of Blockchain on Academic Publishing

Blockchain technology creates a distributed environment where the transactions/records are stored securely and immutably. This would be quite useful in the field of publications, especially in open access academic publications. Academic data is huge and dynamic, a contribution of many authors worldwide, which needs to be updated upon publishing any novel research work. So, maintaining its integrity is a bit tedious with the centralized system architecture. Blockchain manages the aforementioned open access data publication issue using digital rights management, consensus mechanism, and smart contract. This chapter discusses the potential of blockchain technology in the academic publishing sector.
Preliminaries

Figure 2.1 presents the overall procedure of crowdsourced testing. The project manager provides a test task for crowdsourced testing, including the software under test and test requirements. The crowdsourced testing task is usually in the format of open call, so a large number of crowd workers can sign in to perform the task based on its test requirements, and are required to submit crowdsourced test reports. The project manager then inspects these submitted test reports, confirm whether it is a bug, debug and fix it. Note that not every test report involves a bug, and different reports might describe the same bug (i.e., duplicate reports).
Analysis of the role of charts in disambiguating sentences in technical manuals

企業等におけるマニュアルや手順書は, 団塊世代の大量退職に伴う労働力の減少や企業内の技術・ノウハウの継承の断絶が危惧された2007，2012年問題において注目され, 多くの企業がマニュアル化を進めてきた. 着目した鉄道車両用のマニュアルには多くの図表が使用されている. 図表の活用は読み手に対して, 動機付けや理解の促進に繋がることが明らかにされており, 暗黙知を有効に継承する役目を持つと考えられる. 本研究では, マニュアル内の暗黙知抽出の手がかりとするため，技術者が文のみを読み曖昧であると判断した文を技術継承の鍵となる暗黙知を含む文として，それを補完する図との関係を明らかにすることを目的とする. 分析の結果，技術者が曖昧であると判断した文の根拠となる語が図表によって補完され, その曖昧さが解消されていることがわかった. また, 図表によって補完された曖昧な文と補完されなかった曖昧な文を比較することでその特徴を明らかにした.
Current trends in the pedagogical staff professional development in the multicultural space of the USA

The article deals with some aspects of teacher and research-pedagogical staff professional development in the USA.The reforms in the system of education in Ukraine over the recent years, which presuppose finding ways of improving
A Novel Method of Deepfake Detection

Deep-Fake is a novel artificial media technology that uses the likeness of someone else to replace people in existing photographs and films. Deep Learning, as the name implies, is a type of Artificial Intelligence that is used to create it. It is critical to develop counter attacking approaches for detecting fraudulent data. This research examines the Deep-Fake technology in depth. The Deep-Fake Detection discussed here is based on current datasets, such as the Deep-Fake Detection Challenge (DFDC) and Google’s Deep-Fake Detection dataset (DFD). The creation of a bespoke dataset from high-quality Deep-Fakes was utilised to test models. The results of both with and without Transfer Learning were analysed. Finally, the trained models were used to spot well-known deep-fakes of former US President Barack Obama and well-known actor Tom Cruise. A comparison study was performed on all three models. The findings show that the detection are generally domain-specific tasks, however that using Transfer Learning considerably improves the model performance parameters, whereas convolutional RNN gives sequence detection advantage.