Reviewerly/exHarmony huggingface dataset:
Faculty Engagement: A Study on the Higher Education Institutions’ Setting

Faculty who are highly engaged are indeed an asset to any organization, whereas disengaged faculty might end up being a major liability. Faculty are the storehouses of knowledge for nurturing the manpower needs of the nation and, hence, for satisfying the aspirations of the people for a good and humane society. Thus, this study aimed to discuss faculty engagement in the Higher Education Institution setting. The study utilized empirical research through desk review to evaluate the different dimensions of faculty engagement. The findings show that universities across the world portray a different picture of educational values, higher education system and faculty so employed. Faculty who are highly engaged are indeed an asset to any organization, whereas disengaged faculty might end up being a major liability. Nearly all studies conducted on faculty members show a growing tendency for absenteeism, intention to leave the profession, and early retirement of teachers, all of which reflect their disengaged condition. Faculty engagement is a significant predictor of enhanced student learning, which is the goal of all educational reforms. Engaged faculty will be more enthusiastic about investing more time and energy in teaching students. Further studies can be conducted to enhance the understanding of the effects of selected psychographic variables on faculty’s job engagement and organizational engagement in the Philippines' educational context.
PRE-PROCESSING DATA ON MULTICLASS CLASSIFICATION OF ANEMIA AND IRON DEFICIENCY WITH THE XGBOOST METHOD

Anemia and iron deficiency are health problems in Indonesia and globally. In Multiclass Classification, data problems often occur, such as missing data, too many variables, and unbalanced data. Then pre-processing data will be carried out using MissForest imputation, Boruta featuring selection, and SMOTE to help improve the performance of the classification model in predicting a particular class. After the data pre-processing process is carried out, classification modeling will be carried out using the XGBoost algorithm. It was found that when pre-processing the data could improve the performance of the model in predicting multiclass classification for cases of anemia and iron deficiency in women in Indonesia by 0.815 for the accuracy value and 0.9693 for the AUC value
Enabling immersive experiences in challenging network conditions

Immersive experiences, such as remote collaboration and augmented and virtual reality, require delivery of large volumes of data with consistent ultra- low latency across wireless networks in fluctuating network conditions. We describe the high-level design behind a data delivery solution that meets these requirements, provide synthetic simulations of the design, describe and discuss the performance of a software implementation of the design, and provide performance results of the software implementation delivering data with consistent ultra-low latency in network conditions based on real- world measurements that demonstrate the efficacy of the solution.
Manual and non-manual sign language recognition framework using hybrid deep learning techniques

Sign language recognition is a significant cross-modal way to fill the communication gap between deaf and hearing people. Automatic Sign Language Recognition (ASLR) translates sign language gestures into text and spoken words. Several researchers are focusing either on manual gestures or non-manual gestures separately; a rare focus is on concurrent recognition of manual and non-manual gestures. Facial expression and other body movements can improve the accuracy rate, as well as enhance signs’ exact meaning. The current paper proposes a Multimodal –Sign Language Recognition (MM-SLR) framework to recognize non-manual features based on facial expressions along with manual gestures in Spatio temporal domain representing hand movements in ASLR. Our proposed architecture has three modules, first, a modified architecture of YOLOv5 is defined to extract faces and hands from videos as two Regions of Interest. Second, refined C3D architecture is used to extract features from the hand region and the face region, further, feature concatenation of both modalities is applied. Lastly, LSTM network is used to get spatial-temporal descriptors and attention-based sequential modules for gesture classification. To validate the proposed framework we used three publically available datasets RWTH-PHONIX-WEATHER-2014T, SILFA and PkSLMNM. Experimental results show that the above-mentioned MM-SLR framework outperformed on all datasets.
Neuroaesthetics: science, art and digital culture of the XXIst century

The article considers neuroaesthetics as an important direction in modern science, neural networks that are actively included in art and culture. The network as a metaphor for our society is becoming the basis for the humanists’ reflections on the features of modern digital culture, the tools of neuroaesthetics help scientists, physiologists, art critics, and culturologists to better understand the characteristics of a person. A few provisions of torii art have also been proven from the standpoint of the work of our brain in the framework of neuroscience. The possibilities of neuroaesthetics and neuroprogramming have become part of the life of a modern person, manifesting themselves both in the use of graph theory and in the modeling of various objects associated with cinema and art practice. An important point is the criticism of neuroaesthetics for trying to present its conclusions as universal, but as human history shows, it is impossible. A separate issue is the criticism of neural networks by a few creative people, for whom digital projects based on neuroaesthetics and neural networks are identified by competitors. On the other hand, for a few creative people, neural networks have become a valuable tool for creativity, the creation of interdisciplinary projects of humanistic significance. As history shows, modern technologies help us solve principal issues, but cannot completely replace a person. The development of neuroaesthetic science expands our knowledge about the visual perception of works of art and other qualities of a person and the culture he creates. Latest information about sensations will lead us to an understanding of perceptual aesthetic experiences.
The Impact of Industrialisation on Zambia’s Economic Growth

This research study examines the impact of industrialisation on economic growth in Zambia. The study utilised secondary data from Zambian Central Bank and Word-Bank data statistics. The research did specify the model, where GDP was dependent (outcome) variable whereas, FDI, industrial output, interest rate, inflation and currency exchange rate were the independent (predictor) variables. Regression-analysis was used to analyse data using Jamovi-software. The study presents that industrialisation has a positive significant impact on Zambia’s economic growth.
A proposed FAIR approach for disseminating geospatial information system maps

Abstract We present a draft Minimum Information About Geospatial Information System (MIAGIS) standard for facilitating public deposition of geospatial information system (GIS) datasets that follows the FAIR (Findable, Accessible, Interoperable and Reusable) principles. The draft MIAGIS standard includes a deposition directory structure and a minimum javascript object notation (JSON) metadata formatted file that is designed to capture critical metadata describing GIS layers and maps as well as their sources of data and methods of generation. The associated miagis Python package facilitates the creation of this MIAGIS metadata file and directly supports metadata extraction from both Esri JSON and GEOJSON GIS data formats plus options for extraction from user-specified JSON formats. We also demonstrate their use in crafting two example depositions of ArcGIS generated maps. We hope this draft MIAGIS standard along with the supporting miagis Python package will assist in establishing a GIS standards group that will develop the draft into a full standard for the wider GIS community as well as a future public repository for GIS datasets.
Mobile Assisted Language Learning: Advantages and Use among Different Age Groups

Mobile assisted language learning (MALL) facilitates situated, authentic, context aware, contingent and personalized learning. These and more advantages supported by teaching methodology theories are explained and related to suggestions for practical uses of MALL among different age groups (young learners, teenagers and students, and digital immigrants). Criteria for selection of appropriate mobile technologies are also suggested.
STATISTICAL BOUNDED SEQUENCES OF BI-COMPLEX NUMBERS

In this paper, we extend statistical bounded sequences of real or complex numbers to the setting of sequences of bi-complex numbers.We define the statistical bounded sequence space of bicomplex numbers 8 and also define the statistical bounded sequence spaces of ideals I 1 8 and I 2 8 .We prove some inclusion relations and provide examples.We establish that 8 is the direct sum of I 1 8 and I 2 8 .Also, we prove the decomposition theorem for statistical bounded sequences of bi-complex numbers.Finally, summability properties in the light of J.A. Fridy's work are studied.
Tokenized Markets Using Blockchain Technology: Exploring Recent Developments and Opportunities

The popularity of blockchain technology stems largely from its association with cryptocurrencies, but its potential applications extend beyond this. Fungible tokens, which are interchangeable, can facilitate value transactions, while smart contracts using non-fungible tokens enable the exchange of digital assets. Utilizing blockchain technology, tokenized platforms can create virtual markets that operate without the need for a central authority. In principle, blockchain technology provides these markets with a high degree of security, trustworthiness, and dependability. This article surveys recent developments in these areas, including examples of architectures, designs, challenges, and best practices (case studies) for the design and implementation of tokenized platforms for exchanging digital assets.
Grouping of Twitter users according to contents of their tweets

&lt;span&gt;In today’s world most of the people use social networking sites such as Twitter. They share their opinions and their views. through these media. Grouping these users will help us in different ways such as product recommendation, opinion mining, characterization of users based on their way of expressing their feelings. In this work, we present a technique to group the users based on the textual contents of the tweets. This technique is based on an unsupervised approach of machine learning that is clustering. A method is presented for representing the users using vector space model and TF-IDF weight scheme. K-means algorithm is employed for grouping the users using cosine distance as a distance measure. For the evaluation of this method, we construct a Twitter user dataset by using the Twitter application programming interface (API). A new technique is also proposed for characterization of the clusters formed. The experimental results are promising and from the study, it is found that the users in the clusters formed could be well defined by using the proposed cluster characterization technique.&lt;/span&gt;
Chinese Text Sentiment Analysis Based on BERT-BiGRU Fusion Gated Attention

To address the problem that Word2vec static encoding cannot give accurate word vectors about contextual semantics and cannot solve the problem of multiple meanings of words, we propose to use the BERT pre-training model as a word embedding layer to obtain word vectors dynamically; we introduce the gating idea to improve on the traditional attention mechanism and propose BERT-BiGRU-GANet model.The model firstly uses the BERT pre-training model as the word vector layer to vectorize the input text by dynamic encoding; secondly, uses the bi-directional gated recursive unit model (BiGRU) to capture the dependencies between long discourse and further analyze the contextual semantics; finally, before output classification, adds the attention mechanism of fusion gating to ignore the features with little relevance and highlight the key features with weight ratio features.We conducted several comparison experiments on the Jingdong public product review dataset, and the model achieved an F1 value of 93.06%, which is 3.41%, 2.55%, and 1.12% more accurate than the BiLSTM, BiLSTM-Att, and BERT-BiGRU models, respectively.It indicates that the use of the BERT-BiGRU-GANet model has some improvement on Chinese text sentiment analysis, which is helpful in the analysis of goods and service reviews, for consumers to select goods, and for merchants to improve their goods or service reviews.
Towards a new healthy food decision-making system

Latterly, food recommendation systems have received high attention due to their importance to healthy living. On the recommendation domain, most studies focus on recommendations that suggest healthy products for each user based on their profiles. These types of recommender systems offer additional functionality to persuade users to change their buying behavior profitably. However, these systems must highlight the health preferences of the users and their health problems must be adequately taken into account. In this work, healthy food products recommender systems (RS) are our interest study and more specifically using content-based filtering. We represented this content by the food product composition. Our goal was to provide a healthy recommendation to consumers or citizens around the world, especially at this time when disease abounds. Thus, we developed our new healthy recommendation system (HRS). In this paper, we present a new recommendation process for individuals in the area of healthy eating. Furthermore, we analyze the existing state of the art in recommender system techniques and implement an algorithm that responds to this new process with very satisfactory results from the beginning, to conclude we discuss the research challenges related to the development of this kind of HRS.
Effectiveness of E-learning Mode for Teaching English Language at Dong Nai Technology University

The goal of using e-learning platforms in teaching English at many Vietnamese colleges and universities is to increase students' competency in the language. At Dong Nai Technology University, e-learning systems, including DNTU-LMS and Canvas, have recently been introduced. They have been employed as essential instruments for teaching and learning at several university faculties, notably during the Covid-19 epidemic, because of their flexibility and efficiency. The purpose of the current research was to ascertain the opinions of EFL students from the Faculty of Foreign Languages on the use of an e-learning platform. To take part in this research, 100 EFL students were randomly selected. A questionnaire was used to obtain the data. The findings showed that the implementation of e-learning was successful after assessing student reactions. Students are comforted and happy by e-learning, which also encourages them to study English.
Teachers' Views on the Application of Educational Technologies in the Classroom: A Case of Selected Tshwane West Secondary Schools in Gauteng

The teaching profession is moving toward the use of educational technologies (ET) in the form of tablets, laptops, and reliable internet in the classroom to improve the quality of teaching and learning in schools. The use of these tools in the Fourth Industrial Revolution (4IR) is believed to positively impact how teachers deliver the curriculum. Teachers’ views on the application of educational technologies in the classroom are examined in this paper. The opinions of the teachers are crucial since they could influence whether or not the technologies are accepted. This study used a qualitative approach through semi-structured interviews with 12 teachers from one district in Gauteng province. A purposive sampling method was used to select these teachers on the basis that Gauteng is the leading province in ICT integration in schools. Data were analysed thematically from teachers’ responses. The Activity Theory framework was used to underpin the study. According to the findings, teachers voiced their dissatisfaction with the use of these technologies and other issues they face, such as poor technical support, a lack of equipment for maintaining educational technologies, inferior educational technology infrastructure, and unreliable Internet connectivity. Additionally, teachers called on the Department of Basic Education to provide professional development in technology integration and to equip schools with infrastructure as well as Wi-Fi.
Decision Tree Learning Based Feature Selection and Evaluation for Image Classification

Abstract: The problem statement focuses on feature evaluation and selection for image classification using decision tree learning. The objective is to identify the most significant features in an image dataset and train a decision tree classifier using these selected features. The accuracy of an image classifier heavily relies on the quality and relevance of the features used to represent the images. Hence, it is crucial to identify the most important features and eliminate the irrelevant ones to enhance the classifier's accuracy. To implement this approach, we can utilize scikit-learn, a popular machine learning library in Python. The solution must involve training a decision tree classifier on the dataset and extracting feature importances, selecting the top features using modules from sklearn like “SelectFromModel”, and also performing hyperparameter tuning using “GridSearchCV” and training a new decision tree classifier on the selected features with the best hyperparameters. Decision trees are a popular machine learning algorithm that uses a tree-like model of decisions and their possible consequences. By training a decision tree classifier on an image dataset and extracting feature importances, it is possible to identify the most important features and select them for use in a new decision tree classifier that can improve classification accuracy. It is important to note that decision tree learning is a versatile machine learning algorithm that can handle both binary and multiclass classification problems. Additionally, it is advantageous for feature evaluation and selection in image classification tasks. By identifying the most relevant features, this approach can enhance the accuracy of the classifier and reduce computational complexity, making it suitable for large datasets. By following this outlined approach, you can create a project that addresses feature evaluation, selection, and classification accuracy improvement using decision tree learning in the context of image classification
Measuring the Importance of Forensic Accounting and The Necessity of Integrating It into Higher Education Curricula

In the specialist field of accounting known as "forensic accounting," financial knowledge is used to look into and settle legal disputes and financial crimes. The need for forensic accounting knowledge has increased in recent years as businesses and organizations are under more pressure to adhere to complicated standards and stop financial wrongdoing. The trend has been affected by a number of factors, including the growth of international trade, the rapid improvement of technology, as well as the threats posed by ransomware. As a result, many businesses now understand the value of having forensic accountants on staff or retainers to aid in the detection and avoidance of financial crime and investigate and resolve legal issues.The significance of forensic accounting is examined in this essay as the requirement that it be included in curricula for higher education. A brief history of forensic accounting is given at the outset, and then a review of the literature on the subject's significance and efficacy in identifying and preventing fraud follows. To do this, research will be done on the economic effects of fraud, the efficiency of forensic accounting in identifying and preventing fraud, and the state of forensic accounting education right now. Then, a survey of accounting professionals is undertaken to acquire information regarding the perceived significance of forensic accounting and the requirement of integrating it into higher education courses. The survey results are analyzed to ascertain the perceived significance of forensic accounting and the necessity for ongoing study in this field. This article will thoroughly evaluate the significance of forensic accounting, the necessity of incorporating it into higher education courses, and the need for ongoing forensic accounting research using the analyzed survey results.
TOWARD A FRAMEWORK FOR SUSTAINABLE EXPERIENCE DESIGN EDUCATION AND AN ILLUSTRATIVE CASE OF A SERVICE DESIGN THINKING COURSE

Abstract In sustainable experience design education, experience activities of consumers and users, design activities of student designers, and education activities of instructors should be addressed. It would be meaningful to compare various sustainable experience design education efforts considering regional and cultural context. This paper describes a framework where experience, design and education activities are systematically represented with diverse contexts also represented in a structured manner. Student design projects in a service design thinking course are illustrated where sustainability is gently introduced.
Personalized recommendation with hybrid feedback by refining implicit data

In personalized recommender systems, the collaborative filtering (CF) recommendation approaches have been widely used to predict the preferences of users in real-world applications. Among them, Bayesian personalized ranking (BPR) attracts much attention as it can easily explore the binary form of implicit feedback. However, it still suffers from the absence problem of negative feedback. To address this issue, this paper proposes a hybrid-feedback collaborative filtering model by jointly exploiting the explicit and implicit feedback. Based on the assumption that users prefer items with high ratings, this work firstly introduces the definition of explicit rating data to the BPR model and further proposes an improved Bayesian personalized ranking (IBPR) model to jointly extract the implicit feedback features of users and items. The IBPR model alleviates the problem of lack of negative feedback and promotes the anti-noise performance of the recommender system. Then the IBPR and BiasSVD (Biased Singular Value Decomposition) models are combined to further extract explicit latent features of users as well as items and construct the hybrid-feedback CF model. In this model, the user–item ranking matrix is reconstructed based on the extracted implicit feedback features, and the rating matrix is constructed based on the extracted explicit feedback features. Our proposed method is evaluated on five public datasets and achieves the competitive performance.