Reviewerly/exHarmony huggingface dataset:
A Study on Conversion of Image Caption to Speech Using Neural Network Models

In this world of artificial intelligence, the technologies are getting advanced in all fields, including captioning of images is also very trending technology it is useful in all fields. Captioning of images will be performed using neural network models based on feature extraction to get appropriate caption for given image. The paper is the study of the image caption generation and the conversion of captions into speech format. The models used for this conversion are CNN, RNN, LSTM, and how captioning can be converted into speech format is explained. The conversion of image caption is most useful for many application and blind people. The blind people does not need to depend on other person to perform their regular activities as guidance device can be used, using this conversion technology.
Artificial Intelligence for Biomedical Informatics

Biomedical Informatics is the application of information technology and computational techniques to analyze and process the biomedical data starting from molecular level to social level, to discover new knowledge and to cater to the information needs of different user groups like biomedical researchers, health workers and patients. Recent advancement in machine learning based artificial intelligence has potential to provide meaningful outcomes when applied to biomedical data. Much of the biomedical knowledge lies buried in biomedical research articles as natural language text. In this chapter we have described three important deep learning based natural language processing techniques - Information Retrieval, Named Entity Recognition and Knowledge Graphs. Information retrieval can help in retrieving relevant information based on user queries. Named entity recognition helps in identifying biomedical entities like disease, symptom, gene, drug etc. Knowledge graphs could help in knowledge discovery by unifying different information into a single data model and provide tools for inferencing.
Media Ownership and Digital Authenticity in Slum TV

This chapter considers alternative media ownership in Kenya through Slum TV, a digital project created in 2008 by artist and photographer, Sam Hopkins. His vision was to authentically film and document the community of Mathare (a collection of slums in Nairobi, Kenya) by training the residents to use mobile cameras and affordable video editing equipment. The purpose of this was to empower locals to represent themselves through a digital identity. Hopkins argues that how most western representations of the slums, through major news channels, printed newspapers and film and literature, usually depict a typical 2D portrayal of the slums as an area of poverty or violence, which is an artificial representation. This chapter considers the ubiquity of the digital image and the power it has in defining societies through its influential omnipresence. Arming the Mathare locals with cameras enables them to take ownership of their identity back. Positive documentaries that feature families, home life and a positive education system are used as a counter-narrative to contest the hegemony of western interpretation. The residents use digital storytelling to positively and authentically portray their lives. This chapter incorporates an original interview I conducted with Hopkins to consider the portrayal of identity through Slum TV, and questions whether digital representation can ever be truly authentic.
Special Issue on The Workshop on MAthematical performance Modeling and Analysis (MAMA 2022)

The complexity of computer systems, networks and applications, as well as the advancements in computer technology, continue to grow at a rapid pace. Mathematical analysis, modeling and optimization have been playing, and continue to play, an important role in research studies to investigate fundamental issues and tradeoffs at the core of performance problems in the design and implementation of complex computer systems, networks and applications.
Mass detection in mammographic images using improved marker-controlled watershed approach

Mass detection in mammogram plays a vital role for early diagnosis of breast cancer. However, screening of masses is a challenging task for radiologist, due to contrast variation, noisy mammographic images and imprecise edges. In this paper, improved marker-controlled watershed approach is presented to segment and detects precise suspicious regions from mammograms. Morphological operations and threshold technique has been used in the proposed algorithm, to suppress artefacts and pectoral region. Magnitude gradient was computed to obtain mass edges. Finally, internal and external markers were determined and watershed transform was applied on modified gradient image, to segregate suspicious region. Proposed approach was applied on 140 mammograms from two datasets, MIAS and DDSM. The performance of proposed approach in terms of true positive fraction yields 93.7% and 94.3% respectively, at the rate of 0.72 and 0.45 average false positive per image. Thus, achieved results depicts that the proposed approach gives better results for mass detection, helping radiologists in diagnosis at an early stage.
A Study on William Benton Scranton’s Life and Medical Work

It is conventionally known among the Korean church that Henry Appenzeller is regarded as the first Methodist missionary in Korea. However, William Scranton was not only appointed the first Methodist missionary in Korea, but also arrived at Seoul, Korea as an ordained minister two months earlier than Appenzeller (while Scranton arrived on May 4th in 1885, Appenzeller arrived in July of 1885). Furthermore, Scranton was a medical missionary who established the first mission hospital (Si-Byoung-Won) and an evangelical missionary who planted several Methodist churches such as A-hyun church, Sangdong church, and Dongdeamoon church. Nevertheless, Scranton has been hidden because of his mysterious resignation from the Methodist mission in 1907. Thus, this paper will highlight Scranton’s life and significant contribution to the Korea mission.
Exploring the User Experience (UX) of University Learning Management System (LMS)

During the Covid19 pandemic, universities around the world had to transformed from the classroom learning into an online learning environment.Varieties of learning Management System (LMS) were developed by respective universities and software companies to assist the learning process.In Universiti Teknologi MARA (UiTM), UFuture platform is developed to assist in the teaching and learning process.However, there is little information on the user experience (UX) among the students interacting with UFuture.This paper attempt to address the user experience of UFuture usage.The data were gathered from a focus group study consists of 50 postgraduate students that has experiences using UFuture platform for learning.The study used a standard User Experiences Questionnaire (UEQ) comprised of 26 questions in measuring six (6) scales which are Attractiveness, Perspicuity, Efficiency, Dependability, Stimulation, and Novelty.The descriptive analysis result indicates neutral evaluation, where the mean values of each scale within -0.8 to 0.8.In addition, the benchmarking between UiTM UFuture against UEQ dataset shows that the learning platform scored "below average".For future work it is suggested, element of attractiveness, efficiency, stimulation and dependability of UFuture platform should be enhanced to heighten the overall student's user experience (UX).
The changes of early childhood teachers' perception and practice in play using teacher community that supports children's play based on revised Nuri curriculum

Objectives The purpose of this study is to examine the change process of teachers' perception and practice according to the formation and operation of a teacher community based on the revised Nuri curriculum with cooperation with the research team. Methods Two teacher communities with 7 early childhood teachers was operated in cooperation with researchers for two months, conducting action research on changes in teachers’ understanding of play, recognition of roles, communication process, and utilization of collective intelligence. Results Changes through the teacher community have been classified into three categories. The first was the concerns about the play style and the teacher's role, the second was the change in teachers' perception and practice, and the third was the dynamic change in the play of children according to the teacher's support. Conclusions First, through the teacher community, teachers shared their concerns and sought advice from their peers to get ideas on how to operate and support play, and to reflect on their own teaching methods. Second, the teacher community facilitated that teachers supported young children to play a leading role in using play spaces and media, leading to immersion in play.&#x0D;
Development of Online Learning Support Group Counseling Program for Academic Probation College Students: Focused on Strengths and Mental Coaching Techniques

Objectives The purpose of this study was to develop an online learning support group counseling program for academic probation college students, focusing on strength coaching and mental coaching techniques, and to explore their satisfaction on the program and the effects of the program on improving their academic achievement. Methods To achieve the goals of this study, first of all the program was developed based on the group counseling program development model suggested by Chang-Dai Kim(2011) and video counseling operation manual suggested by Soo-Youn Soh(2020), which is composed of 5 sessions, and each session took 50 minutes. Next, a questionnaire was administered to 86 academic probation college students who participated in the program to measure their program satisfaction. And their grade point averages were analyzed with two dependent-sample t-tests to see if they improved with 60 students enrolled in classes of the semester right after participating in the program. Results The results of this study are as follows. First, academic probation college students showed high satisfaction(4.2) with the program. Second, it was showed that they received help in 5 main areas of college life adjustment(self management, interpersonal relationship, career, academic, understanding of major and academic information). Third, 80% of the students enrolled in classes of the semester right after participating in the program improved in grade point average of the semester and did not receive academic probation. Conclusions It was found that the program developed in this study could contribute to college life adjustment and academic achievement of academic probation college students. Also, the discussion and implications based on the main results of this study were provided for the operation of online learning support group counseling programs using strength coaching and mental coaching techniques.&#x0D;
Is There a Place for Music in Foreign Language Teaching?

The results of a survey conducted in 2022 with teachers and foreign language university lecturers are presented and analyzed in the article. The opinion of the respondents regarding the use of musical activities in foreign language learning is taken, and their expertise in the field is studied. Conclusions that would be useful for methodology experts are derived. The survey is a part from a more comprehensive research on the relationship between music and language development, good practices for applying music activities in early foreign language teaching and learning and unused opportunities for including music in the teaching process and language learning.
Entropy for item inclination in sub-community based recommender system

Machine learning (ML) entails self-learning via data usage and experience [1].It requires no human intervention to uncover patterns in data [2].Recent work in this area includes classification of liver tumours [3]; extraction of clinical attributes from the breast cancer dataset [4]; prediction of student performance [5]; accurate recognition of complex physical human activity acquired using body-worn sensors [6].Recommender system (RS) is also an exciting application of ML for suggesting relevant items to a user [7].ML driven recommendation engines [8] have become ubiquitous in the last few decades.Intelligent web engines have crept in everywhere, recommending everything from movies, songs, food, social media posts to anything conceivable.Unconsciously, everybody is following these recommendations.The apparent reasons are convenience and satisfaction; else, dealing with a profusion of information on the web is quite cumbersome.
Special issue on Theoretical Topics in Graph Transformation

Due to remarkable development of advanced data, it has gotten drawn-out to recover applicable query items from the web crawler. The customary web search tool furnishes pertinent site pages however with the blend of unessential pages with less coverage rate and high slithering time. To overcome the issues, this paper proposes an Intelligent Multi-Agent design that utilizes Differential Evolution Algorithm (DEA) to extricate more pertinent site pages with high coverage rate and less slithering time. Three intelligent agents, specifically, Trajectory pattern mining agent, Query clustering agent and Web page classification agent are employed to track the relevancy of the pages, hidden web links and classify the searchable and non-searchable forms respectively. The extrication of relevant pages is optimized by the DEA algorithm. DEA calculation like selection, crossover and mutation are utilized for optimizing the extraction process. In any case, in Genetic Algorithm (GA) determination of chromosome depends on the wellness esteem, where the low quality chromosomes don't get by in the future. The Differential Evolutionary activity help the helpless chromosome to get by in the opposition and it will create the upgraded results. The learnt by experience of the intelligent agents help to work on the presentation of the framework. The exploration result shows that the proposed architecture gives the high precision and recall ratesover the current web crawlers. The creeping time for recovering the website pages is likewise diminished utilizing the proposed DEAIMA architecture.
An Analysis on Identifying Real Names of Users from Usernames

Username is one of the most basic parts of both offline and online applications in today&#x2019;s digital world and is used for many purposes such as logging, authentication, and account creation. It is an established practice to obligatorily take a username when creating an account in Online Social Network (OSN) applications as well. However, a username can include various information about its user even though it is often restricted to be a short string. In this study, an analysis is conducted on usernames of Turkish users of Facebook which is a popular OSN application. The results obtained by using a lexicon-based approach showed that it is possible to correctly detect the first names from usernames with a percent of up to 69.23. On the other hand, the success of gender identification based on detected first names has also reached up to a percent of 86.20.
An Analysis on Disentanglement in Machine Learning

Learnt representations by Deep autoencoders is not capable of decomposing the complex information into simple notion. In other words, attributes of samples are entangled in the basis vectors spanning the learned space. This leads to significant errors in deep learning algorithms. In order to avoid these errors, it is necessary to separate the feature space according to the common features shared between classes and to define a simple subspace for each feature. This approach has led to the birth of a new paradigm in Machine Learning, called disentanglement.Roughly, disentangled models can be defined as models that can independently learn the different components of the probability density function that produces the dataset in the feature space. Unfortunately, it is not always possible to learn these models. For this reason, there is still no easily applicable mathematical definition of disentanglement in the literature. In this study, a mathematical definition of the concept of disentanglement will be made and methods and metrics related to this approach will be discussed.
Style-driven Handwritten Character Generation

Handwritten character generation is a popular topic with a variety of applications. Many methods of character generation have been proposed in the literature, but few of these methods focus on preserving the writer&#x2019;s style while producing handwritten characters. Representing handwriting styles involves the challenge of representing both the style of each character and the overall style of the writer. In this study, unlike the studies in the literature, it is tried to produce the characters of the person that we have not seen yet by extracting the handwriting style of the person with limited data. While producing handwritten characters, spline curves of the characters were used as input, as well as handwritten images. In the developed method, a multitasking learning network is proposed by using the Conditional Variable Autoencoder (CVAE) model, which is one of the deep learning methods. In the experiments carried out, three different models were compared and the performance of the Conditionally Variable Autoencoder (CVAE) network trained with spline curves gave better results compared to other models.
Developing Session-based Personalized Accommodation Recommender System by Using LSTM

Tourism sector has been transformed by the advances in the Internet technology. Users can search for information and can select their destination from various alternatives by themselves, which brings the need for personal recommender methods. Personalized recommender system development is a complex topic. Demographic information, series of user clicks, and interactions and hotel features are examined to offer the appropriate set of hotels. Since the user interactions, clicks and hotel history is a time series data, Long Short-Term Memory models is a perfect fit to recommend a set of hotels from this data. In this study, we proposed a session-based accommodation recommender system that uses LSTM and achieved promising results.
A Real-time Queue Tracking Method for Waiting Time Estimation

In daily life, people spend a significant part of their time waiting in queues at numerous places such as banks, airports, cafeterias and market cash registers. Various queue analysis and management tools have been developed to reduce this wasted time. One of these tools is the systems that analyze the queues and calculate the average waiting time. In this study, a computer vision method that calculates the average waiting time by detecting and tracking the people waiting in the queue is proposed. Thanks to this method, it is aimed that people can see how long they will wait before queuing. In the developed method, people waiting in a direction were analyzed by using object detection methods, and the times of joining and leaving the queue were tried to be determined. Due to the high processing load of the object detection algorithms, object tracking algorithms are used so that the method can work in real-time. The method developed according to the experimental studies can process the 640&#x00D7;480 resolution video on a mid-level GPU with %88.46 accuracy and speeds up to 95.51 fps.
Anomaly Detection with Deep Compact Hypersphere

The classifiers that return compact acceptance regions are crucial for the success in anomaly detection. This paper introduces a novel method that approximates the class acceptance regions with compact hypersphere models for anomaly detection. As opposed to the other deep hypersphere classifiers, we treat the hypersphere centers as learnable parameters and update them based on the changing deep feature representations. In addition, we propose a novel loss term that is more robust to label noises within outlier exposure dataset. The experimental results show that the proposed method achieves the state-of-the-art accuracies on the majority of the tested datasets in the context of anomaly detection.
Estimation of Time-Varying Graph Signals by Learning Graph Dictionaries

We study the problem of estimating time-varying graph signals from missing observations. We propose a method based on learning graph dictionaries specified by a set of time-vertex kernels in the joint spectral domain. The parameters of the time-vertex kernels are optimized jointly with the sparse representation coefficients of the signals, so that the learnt representation fits well to the available observations of the time-vertex signals at hand. The missing observations of the signals are then estimated based on their reconstruction with the learnt model. Experimental results on real graph signal data sets show that the proposed method outperforms classical graph-based regression approaches.
Weakly Supervised Blur Assesment and Localization

As smart devices become more common, visual media have also become common. Bur is a defect in videos and photographs taken with non-professional setups. In this study, we work on measuring and assessing the intensity of blur images have.
Investigation of Appropriate Classification Method for EOG Based Human Computer Interface

The reason why real feelings and mood changes can be seen through our eyes is that the eyes provide the most revealing and accurate information of all human communication signs. It is possible to control a human-computer interface by voluntarily moving the eyes, which have an important place in communication. In this study, the appropriate feature and classification methods were investigated to use the Electooculography signs obtained from seven different voluntary eye movements in the human-computer interface. The success of the system is increased by determining the combination that gives the best result from many features by using the sequential forward feature selection method. The developed method reached 93.9% success in the seven-class dataset. The results show that human-computer interface control can be done with high accuracy with voluntary eye movements. Also, the development of a real-time working model is inspiring for work.
Anomaly Detection in Surveillance Videos Using Regression With Recurrent Neural Networks

Security cameras are widely used to detect and prevent crimes, but the number of surveillance videos has increased due to this prevalence. By processing these videos with the help of a suitable machine learning algorithm, unfavorable events can be brought to the attention of expert to manually monitor. Since these unfavorable events are of various types and few in number, this problem can be addressed in the anomaly detection structure. In this study, an anomaly detection algorithm has been developed using the UCF-Crime dataset consisting of 1900 surveillance videos of various lengths. First of all, features were extracted from these videos with the help of a pre-trained artificial neural network (ANN), the size of these features was reduced with another ANN, and the anomaly detection was performed using two different recurrent neural networks, one based on classification and the other based on future feature estimation by regression. Area under receiver operating characteristic (ROC) curve (AUC) was used as the evaluation criterion. At video level, regression method gives a better performance with 88.71% AUC than the classification method which only gives 85.82% AUC, while at video frame level, both methods perform similarly with 73.64% and 73.71%, but there are true positive rate ranges where they perform better than each other.