Reviewerly/exHarmony huggingface dataset:
Data Analysis

This chapter (IV) reports the results of the research. For a better understanding, first, the metadiscourse features model proposed by Hyland was delineated. Then, the metadiscourse features of the Persian monolingual reference corpus (both interactive and interactional) were extracted, analyzed, and reported. Then the metadiscourse features of the English texts (both interactive and interactional) were extracted and analyzed. Once the English texts were analyzed in terms of metadiscourse features, their equivalents in Persian translations (parallel corpus) were analyzed comparatively. Then the metadiscourse features of the English corpus were compared to those of Persian original monolingual (reference) corpus. Moreover, Persian translated corpus (parallel corpus) was compared to that of the Persian original monolingual reference corpus. Finally, the chapter finished with a summary of the results. It is worth mentioning that for the statistical analysis, the SPSS statistical software was employed.
To Infinity and Beyond

In this chapter, whose title is inspired by the motto of Buzz Lightyear, the robot toy appearing in the “Toy Story” movies, we put in evidence the open issues, and the developments that are being enabled both by the advancement of technology and by the results of the many experiences that are being done. We envision how playful robots and not yet implemented robotic games couldDream play be realized in the very next future, and we also present wishful thinking about desirable robotic games that may suggest further developments.
Neural Machine Translation for Malay Text Normalization using Synthetic Dataset

Social media contains valuable information about any individual, political entity, or brand since users posted impromptu and honest opinions which benefit a better data-driven. Social media text is unstructured and contains some issues such as slang, dialect, and short form. Most work addresses the text issue using an approach called text normalization. Text normalization is a preprocessing step to transform noisy words into their standard form and improve the performance of NLP applications, such as Sentiment Analysis, Part-of-speech Tagging, and Name Entity Recognition. However, less work utilizes Neural Machine Translation (NMT) approach to address text problems, specifically for short form and slang. In this work, three different NMT architectures are used to address the text problems. We conducted our experiments by training three different models under the same synthetic dataset. We prepared the dataset by collecting a local Malay news site which contains 46k of sentences and 800k words. The sentences are further processed using rule-based to create a parallel text. We then split the parallel text into training, validation, and test sets. The best model that utilize Transformer architecture achieved 99% accuracy on the validation set which provides an advantage in taking the sentence context during the text normalization process.
Mobile augmented reality: A pedagogical strategy in the university setting

&lt;p&gt;The mobile augmented reality (M-AR) besides being a booming computer technology is an innovative tool that can support the pedagogical process in university classrooms, that is why the present research aims to show a methodological proposal for its implementation, in order to facilitate the learning of spatial reasoning of students, through the visualization and manipulation of three-dimensional virtual objects, promoting the motivation of learning knowledge and topics of the course of industrial design and technical drawing for the career of industrial engineering. A collection of geometric figures has been elaborated with the help of technological tools such as 2D and 3D modeling software, computer-aided design software and augmented reality application software. An updated methodology is proposed, available to any teacher, oriented to the stimulation of mental processes related to spatial reasoning of students, which integrates technological tools in the didactics of the dihedral system and the different graphic projections.&lt;/p&gt;
Gesture Elicitation for Augmented Reality Environments

The study of gesture elicitation in Human-Computer Interaction (HCI) aims to improve the interaction with the computational environment, providing improved usability and a consequent increase in performance in executing their tasks. In environments using augmented reality, gestures are preferable over eye gaze, as, in the former, the user does not take the focus off the field of work. However, unnatural motions are more difficult to memorize, and their use puts any gains from using the technique at risk. Thus, it is necessary to elicit gestures to discover which incorporated movement appears naturally when the user is asked to send a specific command to the interface. This work aims to elicit natural gestures for an augmented reality environment. During the elicitation process, research subjects were presented with stimuli to which they should react by performing gestures that represent commands for the interface in an augmented reality environment, such as, for example, moving forward, backward and changing zoom (scale). As a result, 434 movements were found, with an average of 18 movements per person, where nine actions for the right hand and nine for the left hand were separated. In addition, the movements made by the participants were categorized into bi-manuals, those that were worked with both hands to carry out the interaction, uni-manual movements, which is when the participant used only one hand, and symmetrical bi-manual, when the participant performed a move with one hand that depended on the other hand to complete the interaction. After these analyzes of individual movements, an average of 3 movements considered natural for each interaction were obtained, in which the researchers chose the two most performed by each participant; each movement resulted from an analysis carried out considering the naturalness in which the gesture was performed and also the ease that the participant had to do so. The gesture chosen for each command was the one whose category had the highest number of natural occurrences, the one that had the most performed category by the participants.
Semi-Automated Pre-Processing and Visualization of Fauna Data

In this paper, we introduced a web-based dashboard using multidimensional techniques to visualize the distribution and characteristics of the fauna. 41 types of fauna data were collected from Belum Temenggor. Among them are birds, mammals, primates, bats, fish, and amphibians. The data are pre-processed and visualized to show the trends and patterns of the fauna. A survey is conducted to evaluate the dashboard. Most of the respondents agreed that the dashboard helped them to extract and understand the information easily, as well as visualize the trends and patterns of the fauna data interactively.
Multimodal Semantic Mismatch Detection in Social Media Posts

Short videos have become the most popular form of social media in recent years. In this work, we focus on the threat scenario where video, audio, and their text description are semantically mismatched to mislead the audience. We develop self-supervised methods to detect semantic mismatch across multiple modalities, namely video, audio and text. We use state-of-the-art language, video and audio models to extract dense features from each modality, and explore transformer architecture together with contrastive learning methods on a dataset of one million Twitter posts from 2021 to 2022. Our best-performing method benefits from the robustness of Noise-Contrastive loss and the context provided by fusing modalities together using a cross-transformer. It outperforms state-of-the-art by over 9% in accuracy. We further characterize the performance of our system on topic-specific datasets containing COVID-19 and Russia-Ukraine related tweets, and shows that it outperforms state-of-the-art by over 17% in accuracy.
Sentiment Analysis and Recommendation of Book Reviews

Reviews are an indispensable part of any unit. Be it, business, non-profits, institutions or any type of service which deals with the masses, are in critical need of constructive reviews and opinions for their betterment and growth. In business and trade especially, customer satisfaction is what pays. A product, which was manufactured with extreme efforts, brainstorming and the demanding work of multiple people would never matter if the receiving end were enjoying it. The product's worth is decided by whether it works towards fulfilling your customer's needs, otherwise the whole purpose of the products existence would be defeated. When it comes to trade, not only the product but the service and delivery the company has to offer too matters. Whether the product is their own product or somebody else's idea, e-commerce websites take up the responsibility to get it through to the buyer. From the information and details of the product showing on their website/app to delivery confirmations, delivery duration, the customer receiving undamaged goods and the quality of the product delivered all contribute to the stars and written opinions rendered by the buyers. Hence reviews involve an amount of emotional conduct in them. Online book reviews are regarded as one of the most important sources of customer feedback. In the current environment, people can learn about books and make decisions based on online review sites.
MRIGAT: Many-to-many Recommendation using Interaction based Knowledge Graph Attention

Recommendation systems combining Graph Neural Networks and Knowledge Graphs have been successfully applied in various domains. However, most existing approaches only consider one-to-one or one-to-many user-item interactions and cannot cater to many-to-many recommendation scenarios, such as providing prescriptions based on clinical diagnosis and medical history. Providing such a recommendation requires considering the implicit interaction within input user features as well as output candidates. In this paper, we propose a two-stage knowledge graph attention aggregation mechanism that helps recommend drug combinations for a patient based on his conditions. First, a disease-drug interaction graph is constructed and integrated with the medical domain knowledge, forming a collaborative knowledge graph. Secondly, an intra-feature attention aggregation is performed to obtain the representations of diseases and drugs based on drug-drug and disease-disease interactions, respectively. Thirdly, an inter-feature attention aggregation is performed using the disease-drug interaction to better represent a user's condition. Finally, the user's condition representation is concatenated with other user features to generate the final user representation for the prescription recommendation. Experiments with realistic datasets show that our approach can outperform existing recommendation systems by 4.3% and 6.1% in precision and recall, respectively.
Data-Driven Ordinary Differential Equations Model for Predicting Missing Data and Forecasting Crude Oil Prices

Predicting missing dataMissing data and forecastingForecasting are essential in many research and analysis. Solving missing dataMissing data problems is very crucial before executing data analysis since without solving it accurately, data analysis will produce biased results. This will impact the forecastingForecasting of future data. This chapter presents the application of two data-drivenData-driven ordinary differential equationsOrdinary differential equation (ODE) fitted models with Nelder-MeadNelder-Mead and least-square algorithms. Runge-Kutta-Fehlberg 4-5th orderRunge-Kutta-Fehlberg 4-5th order utilized for predicting missing dataMissing data and forecastingForecasting future data. The predicted and forecasted data were compared with quadraticQuadratic and cubicCubic polynomialPolynomial least-squareLeast-square fitted model results. The experiment utilized Crude oilCrude oil prices data for performance analysis. Findings show that predicting missing dataMissing data and forecastingForecasting future data applying the data-drivenData-driven ordinary differential model using Runge-Kutta-Fehlberg produce good missing dataMissing data predictionPrediction and the best future data forecastingForecasting among compared methods.
Communicative guiding system for physically challenged person

Hearing loss affects around 360 million people worldwide. According to the WHO, there are 328 million people and 32 million children who are deaf. Deaf and dumb individuals account for over 9.1 billion persons on the planet. Only 700 schools in India teach sign language, and many people are unaware that sign language exists. Despite the fact that technology has made human existence easier, there are still those people who are finding it difficult. We offer a new electronic device in this idea that will make communication easier for them. The wearable electronic device aids persons with speech, hearing, and visual impairments. This wearable electronic aid will be used by people with the aforementioned difficulties who do not know sign language to communicate with others.
Modularisation

In early programming languages, programs were monolithic: A program consisted of one file or one deck of punched cards. Reuse of code was done by copying parts of a program into another program. While this can work reasonably well for small programs, the link between the original and the copy is lost, so errors fixed in one of these are not also fixed in the other.
Applying Neutrosophic Iadov Technique for assessing an MDD-based approach to support software design

Performing a correct architectural design is essential to satisfy the quality requirements of a software. In this phase, the high-level components that will compose the system, as well as their relationships, are defined. Since the architects must struggle with complex and challenging tasks in this phase, providing them with advanced and helpful tools and methods is suitable. For example, MDD-based approaches are a valuable means to deal with the complexity during the software development process, particularly during the architectural design stage. However, despite the notable benefits of this type of approaches, the architects are often sceptical about adopting new technologies. Hence, before formally adopting new methods or tools, it is suitable to consider the opinion of those using them. In that sense, this paper aims to describe the results of an assessment of an MDA-based approach to support the architectural design. This assessment was carried out applying the Iadov neutrosophic technique. This technique has been extensively applied in a wide variety of domains to analyze the satisfaction level of potential users of different proposals. The results indicate a high satisfaction level of potential users of the assessed approach.
Application Research of Edge Computing in Condition Monitoring of Construction Equipment

The development of construction intelligence has resulted in a normalization of the condition monitoring of construction equipment. The efficiency and convenience of information systems provide construction equipment monitoring with basic data and platform guarantees. However, this field still faces challenges, such as high manual intervention, questionable data reliability, late data delivery, and high demand for cloud computing resources. This paper proposes a monitoring system framework for construction equipment conditions based on edge computing to overcome the above problems. This project aims to design a basic protocol suitable for edge computing, build a security and collaborative scheduling scheme, relieve the pressure on the central server in monitoring equipment conditions, and at the same time improve the real-time security and reliability of monitoring activities.
How did mathematics teacher in state vocational high school 1 Bulakamba, Brebes manage online learning?

The learning activities during the COVID-19 pandemic required an extra effort from teachers to conduct online learning for the students as they could not attend school as usual. This study aimed to describe how the Mathematics teacher in State Vocational High School 1 Bulakamba, Brebes had conducted online learning during the COVID-19 pandemic. The data were collected through online interviews and by examining research documents and other related resources to describe the school's online learning process during the COVID-19 pandemic. The results showed that Mathematics online learning in the school was conducted using YouTube and Google Form. The teacher developed learning materials on YouTube and shared it with the students. A social media platform was also used; thus, the students were able to discuss and ask any questions. The teacher used Google Form to record the student's attendance and evaluate learning. Based on the interview, the teacher admitted that online learning activities during the COVID-19 pandemic using YouTube and Google Form were satisfactory. The student's performance from the exercise and exam results showed that most of them understood learning materials well.
Mathematics learning based on augmented reality: A relevant mathematics teaching content and enhanced student abilities

During the COVID-19 pandemic, the material delivering on learning should be more innovative. Now, the application of technology in learning process can be a solution, such as Augmented Reality (AR). AR technology was implemented in various way, one of which in education. This study aims to explore the integration of AR in mathematics learning and see the potential of the technology AR used, in terms of what abilities can be improved. The method used in this research is literature review. Data obtained from scientific journals, conference proceedings, and other related sources. The results showed that the mathematical materials used in the use of AR technology are geometry and integer operations. Then there are seven abilities identified to increase in mathematics learning based on augmented reality. So AR can be an alternative learning medium that can be used during this pandemic.
A Solution of Stream Computing Processing Real-time Service Based on flume

With the development of computer network, the demand of real-time data processing is growing rapidly, and various processing schemes are blossoming.The problem that the demand of various industries, enterprises and institutions for real-time data processing, and its current project development methods and supporting tools are not compatible is gradually highlighted. Therefore, through flume technology, with the use of Ali cloud products,this paper achieve a real-time business processing scheme.from the direction of data stream - data source, data collection, cache queue, real-time processing calculation and data display four directions.
Research on Optimization of Fireworks Algorithm Based on Gravity Search Operator

In the proposed fireworks algorithm with gravitational search operator (FAGSO), the effect of exchanging the current dimensional position information can be achieved through the mutual gravitational force between particles on the basis of FA; therefore, the improved fireworks algorithm combines the advantages of both, using FA in the early stage, which can find some The improved fireworks algorithm combines the advantages of both, using FA in the first stage, can find some optimal solutions or its domain, and using FAGSO in the later stage, can jump out of the local optimum to some extent, and then better find the global optimum. According to the simulation representations of 68 test functions in the Hedar test set, the improved fireworks algorithm has better performance in terms of finding speed and solution accuracy compared with FA, FAGSO, SPSO, and GA intelligent optimization algorithms.
Soft Sensor for Anaerobic Digestion of Kitchen Waste with Semi-Supervised Extreme Learning Machine-based Graph Convolutional Network

Anaerobic digestion (AD) has become the mainstream technology of kitchen waste. Volatile fatty acid (VFA) is an important parameter for monitoring AD, which currently cannot be monitored in real time and can only be obtained by sampling assays, so soft sensor techniques were introduced to estimate the VFA concentration. In this paper, for the problems that soft sensors exist without considering the spatial relationship between variables, we propose a graph convolutional network (GCN) to construct the graph structure of the spatial relationship between different variables, which extracts spatial feature information of the samples data. In order to prevent the lack of training data from over-fitting problem, we introduce an approach based on extreme learning machine auto-encoder (ELM-AE) to train GCN for feature extraction. Additionally, the idea of manifold regularization is introduced into the model to mine the data of unlabeled samples. Then, the manifold regularization extreme learning machine (MRELM) algorithm is used for the regression prediction. Finally, a graph convolutional network soft sensor based on semi-supervised extreme learning machine (S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -EGCN) is built to predict the concentration of VFA in real time. The simulation on the industrial data of a kitchen waste digestion plant shows that our model achieves good prediction results compared to existing methods.
Il sonetto 26 di Iacopo Sannazaro: «Dolce, amaro, pietoso, irato sdegno»

This article analyses the metre, syntax, argumentative structure and imagery of Sonnet 26 in Iacopo Sannazaro’s Sonetti et canzoni, in relation to the model of Petrarch’s Fragmenta. Particular attention is dedicated to the textual variants present in the manuscript versions with respect to the first edition, exploring how the corrections redesigned the stylistic features of the poem and introduced a bolder depictive effect, which together reshaped the meaning evoked by the work.
Improved Secure Provable Data Possession Scheme for Cloud Storage

With the rapid development of cloud storage technology, effectively verifying the cloud data’s integrity becomes a major issue in cloud storage. Recently, Nayak and Tripathy proposed their cloud auditing protocol. Although the protocol is efficient, the protocol is not secure as Yu et al’s attack shows. Even if the server does not store the users’ data, it is possible to forge the provable data possession proof and pass the integrity audit. We point out the issue in their article and describe in detail the process by which the cloud server forging the proof. We finally give an improved and more secure scheme and analysis its security also.
PENGEMBANGAN MEDIA PEMBELAJARAN VIDEO TUTORIAL MINUMAN MOCKTAIL PADA MATA KULIAH TATA HIDANG

This study aims to..develop a video..tutorial..that.can be,used as a.learning medium for non-alcoholic mocktail material in F&amp;B Services subject, as well as to analyze the-feasibility level of the media that was developed. This research is using Research.and.Development (R&amp;D) method and adapting ADDIE development model. In the evaluation stage, media product trials were carried out to media experts, material experts, linguists, then individual tests were carried out to 3 people, small group tests were 5 people and large group tests were 20 people. The value obtained from media experts is 87.1%, the value obtained from material experts is 80%, the value obtained from linguists is 82.8%, which the value given from all experts to this media falls into the good category. The presentation of the value obtained in the individual test is 96.4% which means that this video tutorial learning media is in very,good category, the small group test results get a value of 91.7% which is included,in the very good category, and the percentage of large group test results also get a value of 91.9% which is also included in the very good category. In conclusion, based on the value given by the experts and students showed that the mocktail drink video tutorial learning media was appropriate to be used as a learning medium in the F&amp;B Services course.