Reviewerly/exHarmony huggingface dataset:
TranslateIT: Android-Based Mobile Application for Multilingual Translation

Over the years, the issue of linguistic variation has posed a significant barrier to successful information collaboration. In modern times, learning multiple languages can be both time consuming and a hectic process because of the language difference, and it can also be an expensive expenditure if a tutor is appointed for teaching the same. Language interpreters must be able to communicate in and understand both languages, because the traditional approach to handling the problem of translation is neither productive nor advantageous. TranslateIT is a mobile application that can be used to translate among 11 native languages that includes English, Afrikaans, Arabic, Belarusian, Bulgarian, Bengali, Catalan, Czech, Hindi, Urdu, and Welsh. Therefore, we have developed an android-based mobile application called TranslateIT which is a multi-language translator which has been made using firebase ML in Android Studio using Java. Because of this application, tourists will be able to communicate more effectively with locals and gain access to more relevant information, which will help them learn more about their surroundings.
Identification of Generative Adversarial Network Forms, Open Issues, and Future Study Areas: A Study

Generative Adversarial Network is an emerging technology and research area in machine learning from the time 2014. Generative Adversarial Networks (GANs) is a novel class of deep generative models that has recently gained significant attention. GANs learn complex and high-dimensional distributions implicitly over images, audio, and data. Though carry countless exhilarating prospects, these solicitations likewise increase the mindfulness of the hazard of bogus imageries which may basis enormous destruction. This work highlights some key GAN forms, problems, and research gaps in this study. In addition, we address the benefits that GAN could provide to humans as well as potential solutions. To end with, centred on the perceptions enlarged, we extant encouraging study advices in this hastily emergent arena.
Augmented Reality: Application of ICT Tools for Innovative Pedagogy

With the everyday advancements in information communications technology (ICT), many technological developments have colossally affected education, and one such example is augmented reality (AR). The goal of this research paper is the visualization of the difficult concepts by utilizing the AR technology, which can be a means to innovative learning for the kids seeking elementary education. The paper highlights the importance of utilizing the idea of incorporating learning through AR, instead of letting the kids watch videos on screen. In the current paper, the innovative learning results have been shown picturizing a simple concept of the solar system working. Here, free 3D models of the planets are downloaded from Sketchfab, thereby converted into.glb form, imported, implemented, and developed in PlugXR software. Such applications of AR can envision creative minds to an ever-increasing extent of learning, and unlike the YouTube videos that provide the students with the knowledge, these AR experiences allow for the kids to remain active in their real surroundings.
Synthetically Generated High-Resolution Reflected Ultra Violet Imaging System (RUVIS) Database Using Generative Adversarial Network

In this era of deep learning and artificial intelligence, where a large scale high-quality dataset is a must, we present a high resolution (1024*1024) synthetically generated latent fingerprint RUVIS dataset with pore level details. It will assist the research community to overcome the privacy issue concerns which hinders the large scale manual collection of fingerprint datasets. For this, the training dataset has been collected using RUVIS equipment which works on the principle of reflection of ultraviolet light, whereas synthetic prints are generated using StyleGan2-Ada. Our experimentation suggests that the synthetically generated latent fingerprints are not only diverse and unique but also highlights pore level details. Further, the experimental analysis has been done using structural similarity index (SSIM) and multi-scale structural similarity (MS-SSIM).
Rationale for the Selection of Intelligent Multi-mode Automatic Control Systems by Means of Pairwise Comparisons of Alternatives

The article deals with the problem of multi-criteria selection of intelligent multi-mode automatic control systems (MACS) for aeration regime in biotechnological complexes. The problem of choosing the optimal MACS is connected with the development of methods and algorithms of intellectualization of solving applied problems when building automated control systems (ACS). The presented approach provides reducing the study of the multi-criteria problem of choice to the pairwise comparison of alternatives. The main stages of solving the task are realized: formation of alternative MACSs, selection and assignment of criteria, assessment and analysis of alternatives by the method of pairwise comparison according to the specified criteria. The results of such assessment are presented as matrices of expert evaluations and a vector of evaluations of alternatives at each level of comparison. The received numerical values of the global priorities matrix were used for the substantiating selection of intellectual MACSs for aeration mode as an optimum decision variant on the alternative with the maximum value of global priority. The application of the developed methodology for solving the problem of multi-criteria choice of the optimal alternatives is effective in the composition of the methodological support system for decision-making of the applied automated process control systems (APCS). This will increase the efficiency of decision-making and reduce time costs through the possibility of accounting for the most important criteria, and if necessary, accounting for a greater number of criteria by forming them into limited groups with subsequent multi-stage application of the procedures described in this paper.
Exploration of the Requirements of College English Follow-Up Teaching Courses Through Mobile Web Application

The purpose is to analyze the implementation status and students' requirements of English follow-up teaching courses in universities and provide a basis for improving the quality of English teaching in universities. Here, a detailed analysis is conducted for college students and their FL (Foreign Language) requirements through basic theories and RA (Requirements Analysis) model. Then, the implementation status of college English follow-up courses is analyzed, including its advantages and disadvantages. A total of 150 English majors and 150 non-English majors (international economy and trade) in Xi'an International Studies University are selected as subjects. Afterward, 300 QSs (Questionnaire Survey) are distributed, and 280 valid QSs are collected. The data are analyzed with STAT (Statistical) SW (Software). The results show that both English majors and international economic and trade majors prefer the original English textbooks over school-teachers-written textbooks, and students love humorous teachers more. Students from both majors like the curriculum arrangement of integration of English skills, intercultural knowledge, and humanistic literacy the best. Meanwhile, the requirement of English majors is stronger for the integrated curriculum. The teaching mode and teaching assessment method of English majors are quite different from those of international economic and trade majors. English majors are more inclined to combine online teaching, classroom teaching, and thematic lectures, while classroom teaching is the last thing they want. At the same time, they assess English follow-up teaching through curriculum papers rather than examinations. It shows that different majors have different requirements for follow-up teaching courses. Universities provide a customized and localized follow-up teaching mechanism for students.
Blending multiple algorithmic granular components: a recipe for clustering

Emerging trends in algorithm design have shown that hybrid algorithms, which combine or merge multiple algorithms, can create synergies to overcome the inherent limitations of the underlying individual algorithms. There are two broad types of hybridization: collaborative—individual algorithms tackle an instance of the problem sequentially or in parallel and exchange information accordingly while solving the problem; integrative—individual algorithms are dedicated to tackling different aspect(s) of the problem-solving process. In this research, we propose a schema for an enhanced form of integrative hybridization that blends granular algorithmic components from multiple algorithms to derive a new singular clustering algorithm. As a case study, we examine the ant clustering algorithm (a swarm intelligence algorithm that is based on the natural phenomenon of brood sorting in some species of ants); highlight the strengths and weaknesses of the algorithm; and present a blend of algorithmic components from Tabu search into the algorithm to improve its exploration strategy and solution quality. Empirical results from applying the blended algorithm to clustering benchmark datasets show improved clustering validation measures for the proposed blended hybrid algorithm compared to other hybridization of the same underlying individual algorithms. Besides, the quality of clusters uncovered by this hybrid algorithm competes favorably with those uncovered using popular clustering algorithms such as DBSCAN and mean shift.
Rancang Bangun Penyusunan Laporan Keuangan Berbasis Excel Pada PT Raksaka Teknologi Indonesia

The object of this research is PT Raksaka Teknologi Indonesia, a company engaged in the IT sector, having its address at Jalan girlfriend No. 2A, Ketabang, Jl. Gayungsari Timur No.16 (MGI-2) Genteeng District, Surabaya. The purpose of this study was to determine the existing transaction recording system at PT Raksaka Teknologi Indonesia and to design the preparation of the financial statements of PT Raksaka Teknologi Indonesia using Microsoft Excel for Accounting. The research design used is a system design method with research data sources using primary data. At PT Raksaka Teknologi Indonesia, it is in recording transactions using Microsoft Excel which only has a general journal input worksheet. So that the design of the preparation of financial statements using Excel for Accounting (EFA) is the right solution in the constraints faced by the company. It is expected that PT Raksaka Teknologi Indonesia can implement accounting applications designed for business activities in businesses. Excel for accounting (EFA) which is designed as simple as possible and can be understood by employees of PT Raksaka Teknologi Indonesia.
Risk-weighted cryptocurrency indices

Current cryptocurrency indices are generally capitalization- or volume-weighted, assigning heavy weights to leading coins like Bitcoin. Such indices offer limited risk diversification and limited exposure to the success of altcoins. We propose the Smart Beta Indices of Cryptocurrencies (SBICs), which weight the components based on their dynamic risks and dependency structure. The SBICs provide better risk diversification, adding 0.13–0.31 Sharpe ratios and 24.42%–31.61% annualized returns over the cap-weighted index. The SBICs produce significant alphas using a cryptocurrency five-factor model that controls for exposures to the market, size, value, reversal, and betting-against-cryptocurrency-beta risks. Our results from the cryptocurrency market challenge a traditional opinion that smart beta outperformance generally results from the greater assumption of factor risks.
Analysing cross-cutting competencies learning in an online entrepreneurship context

Research in teaching innovation encourages leveraging the evolution of digital technologies from using the device to learning with the device, which means a change from using information and communicational technologies to learning and knowledge technologies. Nevertheless, although the feasibility of implementing active and interactive methodologies to improve education is widely recognised, more research is needed to obtain evidence on the subjects and contents with the most significant potential for success. In addition, the most recent literature claims greater attention to the improvement of transversal skills, as they are critical in the student's professional future. Thus, the main objective of this study is to contribute to the development of immersive learning aimed at improving the cross-cutting skills of university students. Developing a teaching activity where the student acts as an entrepreneur in electronic distribution channels faces the research question. The student-company-university collaboration is the basis for enhancing the transversal skills of the Degree in Marketing and Market Research at the University of Zaragoza (Spain). The student participates in each step of the program as an active agent. The teacher tutors the work teams in each process step, and the Palbin Company provides the necessary technical support. The students give the information to analyse the success of the experience through two surveys -pre and post-workshop, following the methodology used in previous literature. While the activity planning is complex, the students show an excellent mood during the experience. The comparison between expectations and performance offers significant success from the student's point of view. As a result, the student improves cross-cutting competencies and gains confidence, satisfaction with their learning at university, and professional experience.
Controller for Unmanned Aerial Vehicle Using Parallel Mechanism

It is difficult for a human operator to find roll, pitch, yaw (RPY) that indicates the desired direction of unmanned aerial vehicle (UAV) in a three-dimensional space. Herein, a controller for UAV was developed allowing the human operator controlling the direction of UAV without finding RPY information. The algorithm implemented in the controller automatically calculated RPY information of UAV from the normal vector of the end effector. The developed controller was designed using a parallel mechanism. The joint angles of the controller were measured using potentiometers to estimate the normal vector of the end effector. Five subjects participated in an experiment to control a vector in three-dimensional space to follow a randomly generated target vector using the developed controller and the thumb sticks. The performance of the two controllers was evaluated by two methods: measuring the required time to reduce the error between the controlled vector and the target vector to be less than 0.1 cm and calculating a normalized error between the controlled vector and the target vector after manipulating the controlled vector for 10 seconds. When using the developed controller, the difference in control ability between subjects was reduced, and both required time and normalized error were generally reduced.
New corporate players and educational policy: How might the Australian competition and consumer commission help us to understand AI’s associations with educational policy?

In the last decade education has experienced a shift from privatization to commercialization. This paper argues that the commercialization of education has evolved more recently as a result of artificially intelligent corporate players, enabling forms of insights sales called ‘Dark Advertising’. It unpacks how Dark Advertising are profiting from data-driven predictions that reveal where demand is emerging, rather than responding to perceived problems by examining reports by the Australian Competition and Consumer Commission (ACCC). Able to produce techno-solutions ‘just in time’ through Dark Advertising, Dark Advertising are considered to be enabling new forms of governance and influencing educational policy. Findings of the examination reveal associations in terms of teachers’ privacy, ability to provide consent, and agency. Arguably, circumnavigating Codes of Conduct and Privacy legislation, the author calls for greater scrutiny into various information asymmetries associated with Insight Sales strategies that predict, nudge and experiment with teachers’ behavior for profit.
DCE-IVI: Density-based clustering ensemble by selecting internal validity index

As each clustering algorithm cannot efficiently partition datasets with arbitrary shapes, the thought of clustering ensemble is proposed to consistently integrate clustering results to obtain better division. Most of ensemble research employs a single algorithm with different parameters to clustering. And this can be easily integrated, however it is hardly to divide complex datasets. Other available methods integrate different algorithms, it can divide datasets from different aspects, but fail to take outliers into account, which produces negative effects on the partition results. In order to solve these problems, we clustering datasets with three different density-based algorithms. The innovation of this paper is described as: (1) by setting dynamic thresholds, lower frequency evidence in the co-association matrix is gradually deleted to obtain multiple reconstructed matrices; (2) these reconstructed matrices are analyzed by hierarchical clustering to obtain basic clustering results; (3) an internal validity index is designed by the compactness within clusters and the correlation between clusters, which is used to select the final clustering result. By this innovation, the clustering effect is significantly improved. Finally, a series of experiments are designed, and the results verify the improvement and effectiveness of the proposed technique (DCE-IVI).
Keyphrase extraction using deep and wide learning features

Keyphrases are important phrases that represent the theme of a document. With the help of keyphrases people can quickly find useful information from massive data. Traditional statistic-based methods for keyphrase extraction only make use of the statistical features of the words and ignore the semantic relationship between words. Recently, the emerging methods based on deep neural network extract keyphrases by capturing the semantic contextual information without considering the statistical features. In this paper, we propose a new keyphrase extraction method based on the neural network architecture composing of deep and wide learning parts. In the deep learning part, BERT (Bidirectional Encoder Representation from Transformers) and Bi-LSTM (Bidirectional Long Short-Term Memory) models are used to capture the contextual semantic information from the word sequence while in the wide learning part several important statistical features are considered to jointly train the keyphrase extraction model. The experimental results on two public datasets show that the performance of our proposed model is better than eight commonly baseline keyphrase extraction methods.
User Identity Linkage across Social Networks with the Enhancement of Knowledge Graph and Time Decay Function

Users participate in multiple social networks for different services. User identity linkage aims to predict whether users across different social networks refer to the same person, and it has received significant attention for downstream tasks such as recommendation and user profiling. Recently, researchers proposed measuring the relevance of user-generated content to predict identity linkages of users. However, there are two challenging problems with existing content-based methods: first, barely considering the word similarities of texts is insufficient where the semantical correlations of named entities in the texts are ignored; second, most methods use time discretization technology, where the texts are divided into different time slices, resulting in failure of relevance modeling. To address these issues, we propose a user identity linkage model with the enhancement of a knowledge graph and continuous time decay functions that are designed for mitigating the influence of time discretization. Apart from modeling the correlations of the words, we extract the named entities in the texts and link them into the knowledge graph to capture the correlations of named entities. The semantics of texts are enhanced through the external knowledge of the named entities in the knowledge graph, and the similarity discrimination of the texts is also improved. Furthermore, we propose continuous time decay functions to capture the closeness of the posting time of texts instead of time discretization to avoid the matching error of texts. We conduct experiments on two real public datasets, and the experimental results show that the proposed method outperforms state-of-the-art methods.
Label correlation for partial label learning

Partial label learning aims to learn a multi-class classifier, where each training example corresponds to a set of candidate labels among which only one is correct. Most studies in the label space have only focused on the difference between candidate labels and non-candidate labels. So far, however, there has been little discussion about the label correlation in the partial label learning. This paper begins with a research on the label correlation, followed by the establishment of a unified framework that integrates the label correlation, the adaptive graph, and the semantic difference maximization criterion. This work generates fresh insight into the acquisition of the learning information from the label space. Specifically, the label correlation is calculated from the candidate label set and is utilized to obtain the similarity of each pair of instances in the label space. After that, the labeling confidence for each instance is updated by the smoothness assumption that two instances should be similar outputs in the label space if they are close in the feature space. At last, an effective optimization program is utilized to solve the unified framework. Extensive experiments on artificial and real-world data sets indicate the superiority of our proposed method to state-of-art partial label learning methods.
Open-Source Game Engine &amp; Framework for 2D Game Development

Currently, the use of game engines and frameworks for the development of video games has become very popular, given they provide many features that allow to reduce the effort and time when building video games. Also, they allow developers to focus on building their idea and not on how to implement each of the technical functionalities of the game. However, these tools are very generic, given they do not have functionalities focused on specific styles or genres such as visual novels, RPG, 2D only, 2.5D only, or 3D only. Therefore, in this research we have proposed a game engine application that uses its own framework to make 2D video games in a simpler and more efficient way. In addition, Entity Component System (ECS), Middleware and Scene Management architectural patterns were used in the process of building the game engine and framework to ensure high performance and scalability of future features. Likewise, our project has a 2D graphical user interface (GUI) for ease of use. Finally, the results of the experiment show that 75% of game developers perceived a reduction in the time and effort required to develop a 2D video game, compared to existing game engines on the market.