Reviewerly/exHarmony huggingface dataset:
Doing Media Linguistic Research

This textbook offers an interdisciplinary, comprehensive and state-of-the-art overview of the media linguistics approaches to explain and understand digital communication and multimodality. Linking the fields of communication studies, applied linguistics and journalism, it grounds communication practices in a deep understanding of the social and societal implications of language use in digital media. The tools to analyse multimodal texts are analysed in light of the advantages and constraints that different communication modes pose, both individually and in combination. Aimed at upper level undergraduates and graduates in applied linguistics, communication and media studies, including journalism and PR, this textbook contains case studies and professional examples highlighting the interplay between language use and digital communication and encouraging the reader to reflect on the themes covered, and put the acquired knowledge into practice. Online resources for students include videos, writing techniques, a guide to multimodal texts analysis, additional case studies and a glossary.
Denoising and enhancement of medical images by statistically modeling wavelet coefficients

At present, medical imaging plays an important role in health monitoring system, mainly in diagnosis and detection of various diseases. Preprocessing of medical image modalities is highly essential for accurate estimation of diseases. Generally ultrasound and optical coherence tomography (OCT) images are affected by speckle noise and MR images are affected by random noise. This chapter presents an image denoising and enhancement algorithm for medical image modalities, such as ultrasound image, magnetic resonance (MR) image and OCT image. First the speckle noise is converted to additive noise by applying homomorphic filter, and then the medical images are wavelet decomposed using Haar wavelet, and the resulted wavelet coefficients are modeled using Gaussian scale mixture (GSM) model to extract the original image variance. Next, a median absolute deviation (MAD) estimator is used to find the Gaussian noise variance of the noisy images. At last the minimum mean square error (MMSE) estimator is used to efficiently reduce the noise by recovering the accurate wavelet coefficients. The experimental results demonstrate that the proposed scheme achieved promising results than the state-of-the-art methods.
Addressing Class Imbalance in Federated Learning via Collaborative GAN-Based Up-Sampling

Federated learning (FL) is an emerging learning framework that enables decentralized devices to collaboratively train a model without leaking their data to each other. One common problem in FL is class imbalance, in which either the distribution or quantity of the training data varies in different devices. In the presence of class imbalance, the performance of the final model can be negatively affected. A straightforward approach to address class imbalance is up-sampling, by which data of minority classes in each device are augmented independently. However, this up-sampling approach does not allow devices to help each other and therefore its effectiveness can be greatly compromised. In this paper, we propose FED-CGU, a collaborative GAN-based up-sampling strategy in FL. In FED-CGU, devices can help each other during up-sampling via collaboratively training a GAN model which augments data for each device. In addition, some advanced designs of FED-CGU are proposed, including dynamically determining the number of augmented data in each device and selecting complementary devices that can better help each other. We test FED-CGU with benchmark datasets including Fashion-MNIST and CIFAR-10. Experimental results demonstrate that FED-CGU outperforms the state-of-the-art algorithms.
Computer-aided Moorish geometric design and patterns

Humans have been striving to decorate their surroundings with various visual elements, such as shapes, textures, and symbols, since ancient times.A lot of times the inspiration for these patterns and textures came from nature, which has always motivated people to create aesthetically pleasing patterns.Geometry assists us to comprehend and reproduce numerous patterns found in nature.Hence, producing geometric patterns has been an integral part of art and ornamental interior design.Exploring a design process for recreating ornamental patterns has led to the development of software for generating Moorish patterns using computer-aided tools.The generated geometric patterns are two-dimensional elements, and could be transformed into three-dimensional objects before they can be used in numerous aspects in interior and architectural design.Thus, combining a two-dimensional pattern with a three-dimensional graphical software, adding extra features and functions to it, provides a powerful tool for artists and designers.The Moorish geometric patterns, renowned for their intricate interlacing and strongly geometric designs, have a long history embedded in multiple cultures.Nevertheless, modern-day computer software has been utilized to incorporate Moorish geometric design into the process of creating interior design-furniture and even in architecture planning and building structures.Computer-aided Moorish geometric design and patterns can influence the manner in which designers and architects create their work.This research traces some of the historical backdrop of Moorish style, the posthumous dynamic of the development of geometric patterns, the advances in PC programming that empower its utilization, and the potential applications of this correlation.The study object was geometric patterns as a form of decoration in architecture and design in the context of geometry and form creating with the purpose of aesthetic search.The study subject was the creation of geometric patterns and the ways possible to make this process faster and more efficient in the work of today's designer and architects.The study purpose was to investigate the source of making geometric design patterns, in terms of employing them in decorating interior spaces and in architecture in general by a brief exploration in historical samples.By inspecting an example of how PC helped Moorish geometric designs can be used in the process of designing interior places or in architecture planning, this exploration will show how innovation can be utilized to produce works of art and design.The last section concludes by summarizing the main aspects of this work and explaining points for further improvements.
Complete, Fully Automatic Detection and Classification of Benign and Malignant Breast Tumors Based on CT Images Using Artificial Intelligent and Image Processing

Breast cancer is the most common type of cancer in women, and early detection is important to significantly reduce its mortality rate. This study introduces a detection and diagnosis system that automatically detects and classifies breast tumors in CT scan images. First, the contours of the chest wall are extracted from computed chest tomography images, and two-dimensional image characteristics and three-dimensional image features, together with the application of active contours without edge and geodesic active contours methods, are used to detect, locate, and circle the tumor. Then, the computer-assisted diagnostic system extracts features, quantifying and classifying benign and malignant breast tumors using a greedy algorithm and a support vector machine. The study used 174 breast tumors for experiment and training and performed cross-validation 10 times (k-fold cross-validation) to evaluate performance of the system. The accuracy, sensitivity, specificity, and positive and negative predictive values of the system were 99.43%, 98.82%, 100%, 100%, and 98.89% respectively. This system supports the rapid extraction and classification of breast tumors as either benign or malignant, helping physicians to improve clinical diagnosis.
Mediating Effect of Institutional Image on the Relationship between Student Satisfaction and Student Loyalty in Higher Learning Institutions Using the HEdPERF Model

Higher learning institutions in Malaysia have proliferated to fulfil the demands of students to further their studies at the higher level of education.Some higher learning institutions keep promoting their educational services and programmes to capture the interest of these potential students to further their studies in their institutions.Therefore, this study aims to obtain feedback on student satisfaction with service quality provided by the higher learning institutions.Institutional image has played an important role as a mediating variable in this study.The population for this study is 756 students and the sample size required for the study is 90 students based on G-Power.The questionnaires has been distributed to the students online by using the simple random sampling technique.However, only 370 have completed and returned the questionnaires, which have then been gathered and analysed by using PLS 3.3.There have been nine hypotheses constructed for the study.The three dimensions of the HEdPERF model, which are academic aspect, non-academic aspect, and programmes, have supported the hypotheses.However, the remaining two dimensions, which are access and reputation, have rejected the hypotheses.Thus, institutional image and student satisfaction are identified to have supported the hypotheses.Institutional image is a vital mediator between student satisfaction and student loyalty.This study has been conducted to determine whether or not the students are satisfied with services provided by the institution.The institutional image portrayed by the institution could be a pivotal factor in student satisfaction, which leads to student loyalty towards the institution.The results of this study will help to improve its current services to the students and its institutional image too.It can be further explored for future research by adding new variables in HEdPERF.In addition, conducting the same survey on other local public and private universities in Terengganu is also recommended before applying it to the whole country.
Numeracy Literacy Guidance Using Jarimatika Method on Students of Elementary School 1 Lamokato, Kolaka Regency, Southeast Sulawesi Province, Indonesia

This guidance intends to provide guidance to class students of elementary school 1 Lamokato in using the Jarimatika method. This guidance is carried out because it is motivated by the low numeracy literacy of students at elementary school 1 Lamokato. This has an impact on the low ability of students to count. Therefore, guidance is needed for students using a different method from the method used by teachers at elementary school 1 Lamokato. This training was carried out from June to August 2022 at elementary school 1 Lamokato. The methods used in this training are demonstration methods, question and answer, and practice using the Jarimatika method. The students were very enthusiastic during the training. Students are interested in using the Jarimatika method. Students actively ask and answer about the Jarimatika method. Each student practices the method of Jarimatika in front of the class. The result of this training activity is that there is an increase in students' numeracy literacy in this case the ability to count.
MULTIMODAL SPEECH RECOGNITION BASED ON AUDIO AND TEXT DATA

Systems of machine translation of texts from one language to another simulate the work of a human translator. Their performance depends on the ability to understand the grammar rules of the language. In translation, the basic units are not individual words, but word combinations or phraseological units that express different concepts. Only by using them, more complex ideas can be expressed through the translated text. The main feature of machine translation is different length for input and output. The ability to work with different lengths of input and output provides us with the approach of recurrent neural networks. A recurrent neural network (RNN) is a class of artificial neural network that has connections between nodes. In this case, a connection refers to a connection from a more distant node to a less distant node. The presence of connections allows the RNN to remember and reproduce the entire sequence of reactions to one stimulus. From the point of view of programming, such networks are analogous to cyclic execution, and from the point of view of the system, such networks are equivalent to a state machine. RNNs are commonly used to process word sequences in natural language processing. Usually, a hidden Markov model (HMM) and an N-program language model are used to process a sequence of words. Deep learning has completely changed the approach to machine translation. Researchers in the deep learning field has created simple solutions based on machine learning that outperform the best expert systems. In this paper was reviewed the main features of machine translation based on recurrent neural networks. The advantages of systems based on RNN using the sequence-to-sequence model against statistical translation systems are also highlighted in the article. Two machine translation systems based on the sequence-to-sequence model were constructed using Keras and PyTorch machine learning libraries. Based on the obtained results, libraries analysis was done, and their performance comparison.
Online Library Management App Madrasah Aliyah Negeri Binjai

Technological changes to date are growing increasingly rapidly in various fields of life both in the field of health, the field of government, especially in the field of education. The world of education can also undeniably have a rapid impact on the development of technology, one of which is the means of school learning, namely the library. The library provides a platform for every student and student to develop their knowledge insight with various types of books. The use of technology to make library applications is needed in schools. This library application is made web-based and online, in this application will also be made sms reminder so that students are not late in returning books. This will greatly facilitate and speed up the performance of library officers who previously still use ledger records. So with the system that researchers designed at the Madrasah Aliyah State Binjai library can make library officers work more effectively and efficiently
The Moderating Role of Gender Difference in The Behaviors of Omantel Frontline Service Employees Connecting The Relationship Between Service Quality and Customer Satisfaction

Omantel is a leading telecom operator in Oman, where more recently there have been calls from its customers focusing on service quality to increase customer satisfaction, and organizational profitability.Parasuraman, Zeithaml, and Berry (1988) SERVQUAL Model did not break down the gender construct into male and female ignoring the gender difference in the behaviors of Frontline Service Employees (FSEs).Therefore, the current study adds this breakdown to their theory.This study is an attempt to investigate whether the gender difference (male and female gender) in the behaviors of FSEs at Omantel outlets in Muscat moderates the relationship between service quality dimensions namely: Tangibles, Responsiveness, Assurance, Empathy, Reliability, Job Requirements, Rapport, and Job Satisfaction and Omantel Customer Satisfaction.The target population for this study consists of 384 subscribers who are used to visit or contact Omantel outlets in Muscat (the capital of Oman), but 328 Omantel subscribers responded to the online questionnaire adapted from SERVQUAL scale and used to reach out the respondents who have been selected using nonprobability convenience sampling.Structured questionnaire data was analyzed by SPSS SEM and AMOS.This quantitative research found that the behaviors of both male and female FSEs moderated the relationships between all service quality dimensions where female FSEs moderated these relationships stronger than their counterparts.
A Study of Zillennials in Uttarakhand Using RStudio

The purpose of this study is to understand the behaviour of Zillennials.The study has used constructs like personal innovativeness, technologicalinnovativeness, gadget lovers, and technological opinion leadership for gain-ing insight into the overall behaviour of Zillennials. The study involvequantitative research where descriptive research design is used. This studymakes use of both primary and secondary data. Survey research method hasbeen used in this study where primary data is collected through a question-naire. The questionnaire consists of 16 items where each of these items isevaluated on a five-point Likert scale ranging from 1 (Strongly Disagree)to 5 (Strongly Agree). The data is collected from a sample of 300 studentsfrom various colleges of Pithoragarh belonging to the category of Zillennials.Purposive sampling technique is used to collect the data. The study usesdescriptive measures of central tendency, cronbach alpha, and confirmatoryfactor analysis technique. The data is analysed in RStudio with the helpof readxl, psych, lavaan, and summarytools packages. The results of thisstudy reveal that Zillennials in Uttarakhand have personal innovativeness andtechnological opinion leadership.
Building up of Students’ Vocabulary Mastery through Knowing by Heart Strategy

The purpose of this research was positively to find out whether the knowing by heart strategy is applicable/ effective or not to build up the student’s vocabulary mastery of SMPN 2 Patampanua especially for the second year. In sum, this research was adopted by experimental research design. The population of this research was the second year students of SMP Negeri 2 Patampanua Pinrang in 2012-2013 academic years, which are spread out in 246 students. Then, the sample was taken from VIII.6 with amount 40 students and VIII.4 on amount 40 students. The sample selected by using cluster random sampling technique toward total sample 80 students. The data was obtained by distributing instrument as multiple choice tests and filling the blank test. The overall result of data analysis showed that the mean score on post-test of experiment class (73,06) was higher than the post-test of control class (66,69), and the t-test value (3,593) was greater than t-table value in the level (2,021). However, alternative hypothesis failed to be rejected. It could be concluded that the application of the knowing by heart strategy as the first applied strategy was able to build up the student’s vocabulary mastery in learning English at SMPN 2 Patampanua for the second year.
PCROD: Context Aware Role based Offensive Detection using NLP/ DL Approaches

With the increased use of social media many people misuse online platforms by uploading offensive content and sharing the same with vast audience. Here comes controlling of such offensive contents. In this work we concentrate on the issue of finding offensive text in social media. Existing offensive text detection systems treat weak pejoratives like ‘idiot‘ and extremely indecent pejoratives like ‘f***‘ as same as offensive irrespective of formal and informal contexts . In fact the weakly pejoratives in informal discussions among friends are casual and common which are not offensive but the same can be offensive when expressed in formal discussions. Crucial challenges to accomplish the task of role based offensive detection in text are i) considering the roles while classifying the text as offensive or not i) creating a contextual datasets including both formal and informal roles. To tackle the above mentioned challenges we develop deep neural network based model known as context aware role based offensive detection(CROD). We examine CROD on the manually created dataset that is collected from social networking sites. Results show that CROD gives better performance with RoBERTa with an accuracy of 94% while considering the context and role in data specifics.
Semi-automatic Summarization of Spoken Discourse for Recording Ideas using GPT-3

Although sticky notes are generally used to record and structure ideas stated in a face-to-face workshop, participants sometimes forget to write down their stated ideas due to the inconvenience. This study aims to apply speech recognition to record the ideas stated in a face-to-face workshop using a model created by fine-tuning the GPT-3 to choose the utterances to be recorded and then paraphrase spoken utterances. In the evaluation experiment, we compared the effects of including preceding and following utterances in addition to the summarizing and paraphrasing target utterance, and demonstrated that including only preceding utterances in addition to the summarizing and paraphrasing target utterance in the model input resulted in an F1 value over 0.8 for the selection of utterances to be recorded and ROUGE-1 up to 0.48 for paraphrasing utterance content.
Amortizing Division and Exponentiation

Multiparty computation (MPC) has developed rapidly in the past three decades. The research on general MPC only considers addition and multiplication in a circuit because they are Turing-complete. However, when we consider other arithmetic operations, such as division and exponentiation, the customed protocols are often more efficient. In this work, we optimize the overhead of computing division and exponentiation. Our main idea is to use vector oblivious linear-function evaluation (VOLE) to generate correlation multiplication triples and use these triples to compute correlation multiplication in division and exponentiation protocols. Our method can reduce the cost of a single division to strictly no more than 2 multiplications. In the batch setting, we reduce the cost of n correlation division to almost the same as that of one division. In addition, we use the same method to reduce the cost of n correlation private exponentiation by about $$33\%$$ .
Data Privacy using Multi-Keyword Search and Coordinate Matching over Encrypted Documents

Abstract: With cloud computing, businesses have access to notjust data storage but also to networks and computers. Most cloud service providers operate on a pay-as-you-go basis, which means that their customers no longer have to invest much in infrastructure to take use of their services. As these services are not hosted on the company’s local network, they are far simpler to administer and implement than more conventional infrastructure options. Cloud computing’s appeal stems from these aspects, explaining its ever- increasing popularity. The benefits of storing information and programs on the cloud outweigh the disadvantages. As such, it raises issues that must be addressed head-on to guarantee a safe cloud computing setting. With more and more sensitive information being stored on the cloud at present, there are growing privacy and security issues about this information. The data is encrypted before being saved in the cloud to avoid this problem. An effective search methodis also required since the quantity of data typically kept is quite large. The two most important components of cloud computing that we cover here are encryption and searching. We propose a multi-keyword search method to search through encrypted clouddata in addition to a safe and efficient encryption technique for the data and queries stored in the cloud.