Reviewerly/exHarmony huggingface dataset:
Information Control and Trust in the Context of Digital Technologies

Modern information societies are characterized by a virtually unbroken trend to network more and more technical artifacts. Among other factors, this trend is illustrated by the proliferation of the Internet of Things and associated phenomena such as “pervasive computing” or “ubiquitous computing”. However, networking simultaneously creates a range of vulnerabilities: Security gaps, the repression of data protection, breaches of privacy, hacking or cyber-attacks, the surveillance of telecommunications, et cetera. The essential feature of all these phenomena is a loss of information control. Technological methods used to exercise control over the collection, processing and dissemination of personal information largely failed, particularly in the context of digital media have. One consequence of this failure and the subsequent loss of information control is a loss of trust in digital services and platforms. Conversely, this loss of information control tends to be compensated by evermore and increased IT security measures, making the use of digital media more complex and successful cooperation increasingly difficult. This again calls for a new, purely social approach to dealing with the loss of information control described in more detail in the paper.
Grey Numbers for Sentiment Analysis and Natural Language Processing

Various sentiment lexicons have been created over time for helping researchers and other interested parties in better determining the sentiments associated with a message or a text. The sentiment lexicons contain words and sequences of words (called tokens) pre-classified in positive, negative and neutral categories. Some of the lexicons provide even a numerical polarity for each token in order to better convey the perception of the given token. The construction of the lexicons includes a wide range of approaches, each of them providing advantages and disadvantages related to the time needed to build such a lexicon, the number of words and expressions included in the lexicon and the accuracy of the polarity associated to each token. In the present chapter, we aim to combine various-existing lexicons to create a lexicon that is both more accurate and comprehensive. The grey arithmetic on intervals is used with the newly created lexicon for improving the results of the sentiment analysis. A practical application is provided in order to prove the applicability of the proposed approach.
Improving the Efficiency of Urban Wastewater Treatment Plants Based on Information Modeling

The process of forming an information model of an object taking into account the green building standard is considered. The basis of such a model is a system of biological phyto-purification of air, composed of typical photo-bioreactors. The presence of standard modules makes it possible to integrate air purification complexes with sewage treatment plants, agricultural complexes, large closed condominiums, industrial enterprises. Purification and absorption of carbon dioxide occurs due to the growth of unicellular chlorella algae, which are a separate product and can serve as raw materials for the production of medicines and agricultural products. Based on the created model, the issues of efficient use of water, reduction of the carbon footprint, favorable transformation of the landscape due to restoration of the natural environment and purification of reservoirs are solved. The modular structure of biological phyto-purification of air allows you to create three options for using the system—basic, local and hybrid.
Graph Theoretical Model And Analysis Of Carbon Cycle

The importance of gases like carbon dioxide, nitrogen, sulfur, etc and their cycling through ecosystems has been studied a number of times so far. It is now appropriate to recognize the cyclic patterns of these gases to a greater extent to seek out the general patterns that may exist amongst them and subsequently the effects resulting from inadvertent or purposeful human interaction with them. This study derives a mathematical model for the carbon cycle aimed to consider all the possible interactions in a system in order to have a highly balanced ecosystem. Graph theoretical models, variable permanent adjacency matrix models and permanent functions of these systems based on Graph theory-matrix algebrapermanent function methodology were developed. This will help in the assessment of the earth' s ecosystems.
Contribution of the Nonformal Education Labsite in Empowering Digital Technology-Based Communities

The purpose of this research is to reveal the various components of vocational skills in the labsite programme, labsite strategies in improving the vocational skills of technology-based communities.The research method used is using a qualitative approach with a case study research type.The target of this research is the labsite village of the Department of Non Formal Education, namely in Gubugklakah Village, Poncokusumo Sub-district, Benjor Village, Tumpang Subdistrict, and Pait Village, Kasembon Sub-district along with the labsite village social groups.The research results show that the contribution of the Non Formal Education Labsite in digital information technology-based community empowerment is as follows; (1) shaping the mindset of the community to transform and adapt to using digital technology, (2) providing training related to digital literacy, digital finance and digital economy, (3) the village government facilitates facilities and infrastructure for using digital technology such as free internet networks, computers that can be accessed publicly and administrative services that are fast and reduce the use of paper, (4) conducting socialisation related to personal data protection and digital security, and (5) forming a special team for assistance in using digital technology.
Understand Evolution Better Applying the Computer

The evolution of populations creates new species; the individual living being or protein is, after all, determined within narrow limits by the specific genomeGenomes. New populations with ever new typical characteristics (through mutationMutations and, in the case of sexual reproduction, through recombinationRecombination) are always created, which allow an almost optimal adaptation to the prevailing environment, less environmentally related characteristics are less often passed on in the population (selectionSelection). However, many variants are also neutral or new structures only appear abruptly when enough mutationsMutations are present (neutral pathwaysPathways in RNA structures; “punctuated equilibrium”Punctuated equilibrium according to Gould). PhylogenyPhylogeny helps to infer the evolution of different species on the basis of shared or non-shared characteristics via calculated predecessors. There are faster (neighbor joining) and more accurate methods (parsimonyParsimony, most accurate maximum likelihoodMaximum-likelihood). Accompanying sequence and secondary structureSecondary structure analyses reveal conserved and variable regions as well as the evolution of functional domainsDomains. Most accurate phylogenetic trees require much practice and systematic comparison of all available information (e.g. alternative phylogenetic trees; marker proteins).
Directed Fuzzy Incidence Graphs

This chapter presents directed fuzzy incidence graphs, a novel mathematical model for analyzing a wide range of stochastic networks.
The Bad and the Ugly: Reflections on the Use of Invigilator App by First-Year Students in English Proficiency for University Studies at an Odel University in South Africa

This study explores the first-year students in English Proficiency for University Studies' experiences of e-proctoring challenges when they used the Invigilator app during online exams at a mega open and distance e-learning (ODeL) university in South Africa.Current research indicates that the few proctoring technologies that are available on the market remain largely untried and untested, thus the amount of research available on the students' experiences with the Invigilator app is limited.The study seeks to fill that gap by exploring the students' experiences with this app when they used it for the first time.A mixed methods approach was used involving ten (n = 10) students who completed questionnaires, twenty (n = 20) who took part in online interviews, and five (n = 5) email queries sent by the students during the exam session.The principal findings from the questionnaires, interviews and email queries indicate that while students had experienced challenges with the Invigilator app as they often felt inadequately prepared to use it, they still supported e-proctoring compared to physical invigilation.Future research should focus on how to adequately prepare the students to use the Invigilator app with confidence.
Analysis of the Needs of Elementary School Learning Environment Model Based on Green School

A healthy and green learning environment in schools will greatly support the achievement of a smart, healthy, and quality generation.Therefore, it is necessary to make an effort to create a school that has a good learning environment.It's just that a healthy and green learning environment is not only physical but comprehensive in every system in the school.So it is necessary to develop a comprehensive green school-based learning environment model to support the achievement of a quality generation.This study aims to analyze the need for a green school-based learning environment model.Needs analysis includes policies, curriculum, activities, and infrastructure.According to the results of the needs analysis, the authors recommend the development of an integrated green school-based learning environment model in integrated thematic learning.
Managing Changes in a DevOps Project

They say that change is the only constant. They also say that anything that does not grow withers away. This is true in the software industry. No matter how old the software or the service is, changes happen all the time. No matter how legacy the application is, it still needs to be maintained and updated with the organizational needs.
The Impact of Leadership on Employee Motivation in the Jordanian Telecommunication Sector

The main objective of the current study is to examine the relationship between leadership style and employee motivations from the point of view of motivation in the Jordanian telecommunication sector the population is 70 the appropriate sample size will be 59, (Telecommunication Regulatory Commission 2023), therefore the sample of this study is 59 directors, a total of 59 questionnaires were distributed to the respondent’s development model by (Bass et al. 2003) employee motivation Scale, results showed it appears that transformational leadership and transactional leadership was correlated with employee motivation, but laissez-faire leadership has no significant relationship with employee motivation.
Reducing Deep Learning Complexity Toward a Fast and Efficient Classification of Traffic Signs

In our digital society, where the technology is changing at an accelerated rate, many of human activities have become easier due to the exponential growth of many intelligent devices, especially mobiles ones. These technologies have changed many aspects of our daily life, including among others the way we use and control our cars. Improving the driving experience, and enhancing the environmental perception to support safety, especially in VANETs, represents in effect one of the most recent application fields of these technologies. This safety improvement includes among others traffic signs recognition. Although the high performances of 2D CNNs in ensuring this recognition, their computational complexity limits however their use in this type of devices characterized by their low resources. While we find recently that the 1D networks gains more attention and popularity in many areas of research, and that due to their low-cost implementation and computational complexity compared to the traditional 2D CNNs. Therefore, to benefit from the advantages of these two types of networks, we have applied instead of the full connections between the nodes, a final 1D module based essentially on one-dimensional separable convolutions to the final features’ vector, and that using different sets of kernels. The training, the validation and the testing results show that, the proposed approach is fast and efficient in terms of inference time and classification accuracy, and that using low resources environments and well-known public datasets (Belgium and CURE-TSR datasets).
Awareness of Arabic Teachers in Academic Integrity Towards Students Completing Tasks in Abu Dhabi: Data Analysis Approach

This paper aims to investigate the level of Arabic language teachers’ awareness of the values of academic integrity and its relationship to the motivation to complete tasks among students of the third cycle in the Emirate of Abu Dhabi. The study sample consisted of (50) male and female teachers, and their students; (150) male and female students. The study followed the descriptive analytical approach and adopted two tools: the questionnaire to measure the degree of awareness of Arabic language teachers about the values of academic integrity, and the task completion questionnaire for the twelfttwelfth-gradents. The results of the study showed that the level of awareness of Arabic language teachers about the values of academic integrity was high. It was also noted that there were statistically significant differences in the level of awareness of Arabic language teachers for the twelfth grade due to the gender variable and such differences were in favor of males. It was also noted that there were no statistically significant differences due to the educational qualification variable, and there were differences due to the educational qualification (bachelor’s degree) variable. The results of the questionnaire’s analysis of the motivation to complete the tasks by the twelfth-grade students showed that it was statistically significant. Based on the results, the study came out with several recommendations.
Personalised recommendation method of college English online teaching resources based on hidden Markov model

Aiming at the problem of the high comprehensive evaluation index of recall and precision in the traditional personalised recommendation methods of college English online teaching resources, a personalised recommendation method of college English online teaching resources based on hidden Markov model is proposed. First, extract the label features, student learning behaviour features, and time weight features. Then, pre-process the extracted college English online teaching resource data, build a recommendation model based on the pre-processing results, and use a hidden Markov model to process the hidden data to obtain the maximum likelihood estimates the parameter. Finally, input the parameter into the recommendation model to obtain the optimal parameter, that is, the optimal recommendation result. The simulation results show that the comprehensive evaluation index values of precision and recall of the recommended results of the proposed method are within 0.9 and 0.5, which has a good recommendation effect and meets the needs of the development of network teaching.
Lightweight Detection Algorithm of Substation Instrument Based on Improved YOLOv4 Algorithm

Under the requirements of new power system, the intelligent process of substation is being promoted rapidly, among which the instrument inspection plays an important role in this process. The target detection algorithms based on computer vision and deep learning technology have been applied to the substation inspection image in many studies to replace the traditional manual inspection . While the requirements for storage and computing power in deep neural network are too high to meet for patrol robots, resulting the great challenges to deploying the algorithm to the mobile terminal. There are three improvements based on the target detection algorithm YOLOv4 to realize the easier deployment by using the lightweight network MobileNetv3 to replace the backbone feature extraction network in the original network, using the depth wise separable convolution to replace the traditional convolution and introducing pre-fused of features. The improved strategy is trained by using the instrument images collected in the real scene and is compared with the original algorithm with the same test sets, showing that their recognition and positioning accuracy are basically close while the inference speed significantly reducing, which can make it more convenient to deploy on the terminal.
Predicted Phase Using Deep Neural Networks to Enhance Esophageal Speech

It is well known that the intelligibility and comprehension of pathological voices are notoriously poor, and since esophageal speech is one of those voices, the main idea behind our approach is to use Deep Neural Networks to enhance this speech. For that reason, the source phase and the target phase was aligned using the Fast Dynamic Time Warping method (FastDTW), in order to train the neural network. To retain the source speaker identity, the predicted phase along with source cepstral coefficients were used to reconstruct the output signal. The results obtained by this approach gave good performances.
A Deep Neural Network Architecture for Extracting Contextual Information

The exponential growth of textual data makes it challenging to retrieve pertinent information. Numerous methods for automating keyphrase extraction have emerged from earlier studies. Keyphrases have been used extensively to analyze, organize, and retrieve text content across various domains. Previous works have yielded numerous viable strategies for automated keyphrase extraction. They rely on domain-specific knowledge and features and select and rank the most relevant keyphrases. In this paper, we propose a deep neural network architecture based on word embedding and a Bidirectional Long Short-Term Memory Recurrent Neural Network “Bi-LTSM”. This architecture can capture the hidden context and the main topics of the document. Experimental analysis of benchmark datasets reveals that our proposed model achieves noteworthy performance compared to baselines and previous approaches for keyphrase extraction.
The Reality of Artificial Intelligence Skills Among Eighth-Grade Students in Public Schools

This study tends to search the reality of artificial intelligence skills among eighth-grade students in public schools in the city of Al-salt – Jordan, and the descriptive analytical approach was used, where the study tool was developed to measure the skills of artificial intelligence by the researchers, The original community of the study consisted of all students (male and female) in public schools in the city of Al-salt for the first semester of the academic year 2022–2023, and their number is (788) male and female students distributed over (64) schools by using the simple random method. (349) male and female sample students were used. The results indicated that the level of artificial intelligence skills was a moderate degree, Where the dimension of basic skills came in the first place and then advanced skills, and later the dimension of intermediate skills and finally the special skills. Based on the results found, the study recommended that the results of this study should be used to improve students’ digital learning for students in the various curricula.
Leveraging Blockchain and Machine Learning to Improve IoT Security for Smart Cities

Recently, Internet of Things (IoT) infrastructures are developing various applications in sustainable smart cities and societies. However, there are numerous challenges in smart cities, such as security, privacy, trust, verifiability, communication latency, scalability, and centralization preventing faster adaptations of IoT. Machine Learning (ML) is an important analytic tool that provides a scalable and accurate analysis of data in real time. However, there are several obstacles to designing and developing a usable large data analysis tool utilizing ML, such as centralized architecture, security, and privacy, resource limits, and a lack of sufficient training data. Blockchain, as opposed to that, promotes a decentralized architecture as new technology. It encourages the secure sharing of data, and resources among the various nodes of the IoT network, removing centralized control and overcoming ML’s current difficulties. As a result, this study provides a smart city intrusion detection system. This system consists of three modules: a trust module based on designing an address-based blockchain reputation system, a two-level privacy module based on blockchain-based enhanced Proof of Work technique, and an intrusion detection module. We provide a blockchain-IPFS integrated Edge-Fog-Cloud infrastructure, named Cloud-Block, Fog-Block, and Edge-Block, to utilize the system proposed for smart cities, related to the inherited strengths and shortcomings of Edge-Fog-Cloud architecture. The sBoT-IoT and TON-IoT datasets are utilized to evaluate the system. Finally, a comparison of our implementation results shows that our system outperforms other state-of-the-art systems.
Comparing International Phonetic Alphabet Method and Natural Spelling Method in English Teaching of China

English education has a long history and a great influence in China.However, there are still many shortcomings and limitations in English education in China, a large part of which is due to the two existing mainstream English teaching methods in China.The Main findings of this paper were: 1) introduce the development history of English Teaching in China; 2) extend the description of the introduction and application of the two current English teaching methods: the International Phonetic Alphabet method and the Natural Spelling method; 3) give some suggestions and improvement methods based on a comparison and analyze the existing teaching methods in detail.This paper will inspire future researchers to develop better teaching programs for Chinese English learners with mainstream English teaching methods.
Research on the Evolution of Mobile Game UIs

Thanks to the increase of mobile terminal devices, mobile games have gradually become an important part of human life.People use games to please themselves or kill time.The user interface of excellent games can attract users or make it easier for users to immerse in them.By studying the changes in the game user interface from its birth to the present, this paper discusses the internal reasons for its changes and the future development direction.This paper studies the game user interface in the era of the Saipan system and the era of smart machines through qualitative methods and discusses the current mainstream game user interface types.Through research, it is found that the mobile phone hardware has a serious impact on the game user interface, and as the mobile phone screen gradually becomes larger, the game user interface can present more content, and the interaction between the user and the game system also presents a variety of modes.
A Study of Teaching Decisions of Yangqin Teachers Based on Educational Requirements

The Yangqin was introduced to China in the late Ming Dynasty, initially popular in the coastal area of Guangdong and then gradually spread to the mainland.Through the continuous improvement of successive generations of artists, it gradually evolved into a characteristic national musical instrument in the practice of traditional folk arts.The "embodied" experience of the yangqin in vocal performance, both technical and performance, is a way to gain a deeper understanding of vocal singing and "embodied" theory through my own embodied experience, and to improve my vocal singing direction by combining my own performance and learning theory.This study focuses on the teaching of the yangqin and on teachers' aspects of core literacy in yangqin teaching.Both teachers' empirical views of curriculum decisions profoundly influence the effectiveness of teachers' curriculum decisions in the context of core literacy.