Reviewerly/exHarmony huggingface dataset:
Encapsulation, Inheritance, and Polymorphism

This chapter is all about advanced Java OOP topics like encapsulation, inheritance, and polymorphism. Together they constitute three of the four main theoretical principles of object-oriented programming. The fourth principle, abstraction, is beyond the scope of this text, so we won’t be covering it here. Let’s set the stage for the discussion by defining each of these features of the Java, object-oriented, programming language.
Silent Film Branch Detection Using the Barycenter Algorithm - Focusing on [Man with a Movie Camera] with Dziga Vertov

In today's media environment, there is an increasing demand for a method capable of technically analyzing moving images. In the field of film, which contains vast amounts of data, an efficient method and structure for analyzing moving image information is indispensable. This study presents short segmentation and film analysis methods and results, focusing on sequences through Barycenter algorithms and machine learning methodologies, in Dziga Vertov's "Man with a Movie Camera," a director representing the silent film era. The existing proposed machine learning methodology simultaneously utilizes the Barycenter algorithm method in short segmentation due to its limited accuracy, although the time required for short segmentation is short. The Barycenter algorithm is a method of finding the center of gravity in an image by brightness and saturation; it has high short segmentation accuracy compared to the machine learning methodology that relies on center movement. It is proved that the method of using the Barycenter algorithm presented in this study is more accurate than the existing method. This study aims to contribute as a criticism tool based on quantitative indicators of film as well as to improve our understanding and characterizations of films.
Digital Human Workflow Using a Metahuman Creator - Focused on the Digital Human ‘HUE’ in the XR Opera ‘Chat of the Masters’

With digital imaging technologies, network technology, and the increase in online social interactions, the Metaverse is attracting attention. Many elements make up the Metaverse, but among them, a digital human, also called a virtual human, is attracting attention. Virtual humans created by computer graphics technology exist from the first virtual human 'Adam' to the current 'Rosie' and 'Sua' and are active mainly in the Metaverse and on social media. Currently, with the help of many technology advances, it has become possible to produce digital humans that are difficult to distinguish from real people, and the production cost and creation period are also reduced. For example, on September 4 of 2022, the musical ‘Chats of the Masters’ was performed with the support of the Seongnam Foundation for Arts and Culture. This musical, which was held at Seongnam Art Center Ensemble Theater, included a project to appear in Digital Human 'Hue' of "Emotion Playground", an XR production company. Therefore, in this study, the definition of a digital human is examined through a literature review, and the production workflow of the digital human 'HUE' produced by reducing the cost and time compared to the result is studied. Through this, a practical digital human production process is presented. As a result of this study, it is now possible to create a digital human in less time than required in the existing workflow by using the metahuman creator tool, freely creating the desired appearance with a mesh to metahuman technology. In addition, we envisioned a process that can transmit the facial expressions and actions of actors in real time with motion capture technology and Live Link. This study, in the ever-growing digital era, proposes a workflow that significantly reduces the required steps compared to the existing digital human workflow through the aforementioned musical creation drama, thereby increasing the accessibility to digital human production by single-person creators, such as startup companies and YouTube creators. A new and effective digital human production method for everyone is presented.
Research on XR Based Transmedia Content Design

This study is about XR-based transmedia content, focusing on connectivity. This type of connectivity does not mean merely connections between media or content but rather between users and reality with virtuality. This study sees XR-based transmedia as appropriate for realizing connectivity and aims to explore its possibility through an actual project. As a research method, this study includes literature review and a case analysis, and by analyzing XR-based cases, the researcher found a lack of connectivity in the current cases and thus conducted a project. With content from ASEAN CULTURE HOUSE, the researcher proceeded with the project with students and designed the concept of friendly characters with colors and easily readable text and intuitive typography considering a family with children. To construct transmedia experiences, publishing-based media was applied for understanding and delivering information and XR-based media were applied to add new characters and for better storytelling. Then, the publishing-based media were used again to document results and were shared via SNS amongst users. The resulting two prototypes were evaluated by expert heuristic evaluations and interviews. It was found that simple design and consistency in media interfaces were positively evaluated with the related categories, whereas the categories related to preventing errors or providing help were criticized. In the interviews, experts complimented the trial-making experiences of new content when circulating the media experience. This study attempts to connect virtual content, providing new characters and events to actually existing media content and then returning to the sharing of the experiences of users to realize connectivity, i.e., the direction of transmedia, as mentioned earlier. This study has a limitation as the projects were created as prototypes; however, as XR has the potential to be involved in transmedia more, this study aims to contribute to the XR-based transmedia content realm, which is currently in the very early stages.
Comparative Analysis of Ensemble Models for Software Effort Estimation

Project management is an essential step for the success of any software project. One of the most significant tasks in software project management is estimating the cost and effort of software development at the start of the project. The primary purpose of this research is to study the impact of tuning the base learner hyperparameter with different machine learning/ensemble models to improve estimator accuracy. We used random forest, support vector regression, and elastic net as the base learners. In this study, Albrecht, Desharnais, and China datasets were used for experimentation. We also performed feature selection and considered only those features that have strong correlation with target feature, i.e., effort. The mean magnitude relative error (MMRE) and PRED(25) results demonstrate that utilizing elastic net as the base learner for AdaBoost outperforms the other models.
Bounds on Learnability of Neural Networks

Deep learning (DL) got remarkable triumph in wide variety of problems like medical imaging, detection of frauds, e-commerce, health care, etc. It has arguably emerged as a futuristic technique. But designing a neural networks model is still a challenge. Despite the development of many theoretical bounds, there is no clear answer to what and why deep learning models generalize. Many studies have been conducted to develop a better understanding of generalization in deep neural networks. A survey of studies is conducted that aim to better understand the theoretical bounds of neural networks (NN). An empathetic knowledge of the generalization theory will enable researchers/developers to model more cost-effective and powerful networks.
Intelligent System for Detecting Email Spam Messages Using GRU

With the era of digital communication, email has become critical and nearly vital in today's world. Emails and various social networking sites have grown considerably over the past decade, and their use has increased because of this growth. To take advantage of the widespread usage of spam, cybercriminals send it via email and online social networking sites and through a variety of other distribution channels. Phishing and email fraud are two issues that affect most people who use the Internet. Spam has become a big concern in recent years, with email spam being the most well-known. Despite the high level of performance provided by antivirus software, computers can be infected by various sources, including Trojans, bots and, most typically, spam messages. A reliable method of detecting spam communications must be implemented to avoid this problem. This work develops deep learning-based classifiers using Gated Recurrent Unit (GRU) to see ham and spam messages. The proposed GRU with TF-IDF vectorizer has reported the highest accuracy for detecting spam emails.
MPTR: A Metadata-Driven Prospective Tag Recommendation Model Based on Integrative Classification

The existing World Wide Web is moving toward Web 3.0. It is semantic-based Web and is the existing Web’s extension by technologies and standards which helps machines to interpret the information available on Web. Moreover, since Web contains billions of information in form of text, audio, video, images, etc., tags play significant role in recommending the most appropriate data. This paper proposes a framework for image tag recommendation which is semantically inclined and will generate new tags based on existing tags using ontology and semantic strategies. The proposed framework utilizes large volumes of metadata available for the similar images and utilizes this information while generating the new tags by comparing against various semantic similarity measures. This framework incorporates classification using decision trees, RNN and the enriched metadata driven by available datasets in generating new tags for the image and recommends these tags till the user is satisfied with the tag recommended. Furthermore, the comparison and relevance of the recommended tag have been done using various semantic similarity measures such as SemantoSim, KL divergence and Cosine Similarity. This model yields an average accuracy of 95.33% and the lowest FDR of 0.058 which surpasses the existing approaches.
Punjabi Indian Sign Language Dictionary Using Synthetic Animation

Singh, Gurdeep Goyal, Vishal Goyal, LalitObjective: Sign language is the primary form of communication for those who are deaf or hard of hearing. Modern technology necessitates the design of a 3D video dictionary for Indian Languages (ISL). Although there are several human video sign language dictionaries available, we want to construct the Punjabi ISL dictionary utilizing computer-produced animations rather than actual people. Methods/Statistical Analysis: Sign language is different from English, Punjabi, Hindi, etc., in that it cannot be spoken or written. In Punjabi ISL, the most regularly used words are grouped and then translated to the sign language writing notation (HamNoSys—Hamburg Notation System). The HamNoSys notation is changed into Signing Gesture Markup Language (SiGML) so that a synthetic animation of the sign can be made. Findings: In terms of memory usage, uniformity, and scalability, synthetic animation is superior to human film. Synthetic animations may be altered to meet the needs of the audience, but human videos cannot. However, these computer-generated animations may lack the natural and non-manual component of the sign’s expression. Applications/Improvements: It is feasible to utilize this dictionary to construct a system that translates Punjabi phrases into Punjabi ISL.
An Efficient Recommendation Generation Using Extended Jaccard Similarity

Recommendation system is considered to be the best approaches for providing personalized services for the customers which is available in the recent times. Through the available interest of the customers, it will find out the relevant information about the customer. A similar measure is utilized to accomplish the similarity between two clients or things through registered data. Therefore, in this process, efficient similarity simulations were performed by considering all user rating vectors to classify related EM algorithms as an extreme probability estimation technique in the presence of latent variables. In addition, Movie Lens, a well-known data sets used in the recommender systems area, can be used to evaluate the performance of the selected framework. The suggested Extended Jaccard similarity (EJS) appears to be more accurate and efficient in generating.
Effective Security Algorithms for Big Data Clouds

Big data cloud provides high capacity and added imaginative types of informatics to enhance discovery, decision-making, and process improvement. A cloud computing system uses encryption and decryption from a data bank to access and store data. As part of this system, authentication is done using the consumer name and verification code, authorization is provided by the cloud security service provider, the data is encrypted when uploaded, and OTP is used to decrypt the data when accessed. Different cryptographic schemes used for encryption are hybridized ABE and AES as Scheme 1 and the proposed novel algorithm named secure dynamic bit standard (SDBS) as Scheme 2 which offers high security for the data stored by the end user.
BiCropRec: A Bi-Classifier Approach for Crop Recommendation Based on Inclusion of Semantic Intelligence and Topic Modelling

Better crop yield is the sole aim for the working farmers all over the world. Finding new and effective technologies can help all the farmers yield good quality crops with better profits across all categories of crops. In this paper, a BiCropRec framework for crop recommendation has been proposed which integrates latent semantic indexing for topic modelling. The BiCropRec model is knowledge centric and uses two distinct classifiers in order to increase the heterogeneity of classification. The model integrates upper domain ontologies and a knowledge graph and uses several semantic similarity computation schemes to yield best in class recommendation maintaining a high diversity index. Experimentations have indicated that the proposed BiCropRec framework has yielded much higher performance measures when compared to the baseline models. BiCropRec has furnished an overall F-measure of 97.66% with a very low false discovery rate of 0.03.
Oversampled Deep Fully Connected Neural Network Towards Improving Classifier Performance for Fraud Detection

Fraud-related losses and damages are plaguing the financial services industry and other industries that deal with money. In recent years, the machine learning approach to fraud prevention and detection has received a great deal of press, and this has shifted industry attention away from rule-based detection mechanisms further toward ML-based solutions. However, there are subtle and hidden events in user behavior that may go unobserved but still indicate possible fraud. As the digital transactions are improving in all the sectors, real-time fraud detection with credit card transaction remains open-challenging issue for the researchers to work. With this view, this paper focus on designing a new fully connected neural network integrated with oversampling the classes for detecting the fraud classes. With respect to above scenario, this paper uses the credit card transaction dataset from KAGGLE with the dataset size of 284,807 with 31 features having 284,315 valid transactions and 492 fraud transactions. The data are preprocessed and normalized for splitting the training and testing dataset. The deep fully connected neural network is designed, and the original dataset is fitted to extract the fraud classes. The class distribution is analyzed and found to have imbalanced target data distribution, and it is applied to oversample the target classes. The oversampled dataset size is increased to result in 569,614 number of rows. The oversampled dataset is fitted to deep fully connected neural network to analyze the performance. Then, the dataset target classes are fitted with weight for class 0 as 0.50, and class 1 as 289.44, and then applied to deep fully connected neural network to examine performance. The above three models are compared with accuracy, recall, precision, and AUC and ROC curve. The paper is implemented with Python under NVidia Tesla V100 GPU server, and the trained model is implemented using TensorFlow. Experimental results show that the proposed oversampled deep fully connected neural network exhibits the accuracy with 99%, precision with 82%, and recall with 79% when compared to traditional and weighted fully connected neural network model.
Using Nursing Notes to Predict Length of Stay in ICU for Critically Ill Patients

Managing resource critical facilities like Intensive Care Units (ICUs) is an important task for hospital management officials. Predicting how long a patient is going to stay in ICU is considered an important problem for managers. Several attempts have been made to solve this problem using different types of clinical data that are available from the past. While a number of studies have deployed classification models that use structured clinical variables, recent advances in Natural Language Processing models have opened up the possibilities of using unstructured text data like nursing notes, discharge summaries, etc. for prediction. In this work, we have proposed the use of CNN and LSTM based prediction networks along with transformer-based language models for representing the notes data. The proposed model can predict with a much higher accuracy rate than any other existing model. The dataset used for the experiment is MIMIC, which is an anonymized dataset that contains detailed records of around 40,000 patients most of whom were critically ill. We use the first day’s nursing notes for prediction since that can provide most relevant and valuable input to planning.
Prisoners’ Information Dissemination by National Central Library in Tanzania: Challenges and the Way Forward

This study discussed challenges facing the Tanzania National Central Library (TNCL) in providing information services to prisoners. Data were collected using a structured interview and standardized questionnaire that were administered to 41 librarians. Face to Face interview was conducted with the Library Director and two Heads of Departments who were purposefully selected. Each interview lasted between 30 and 40 minutes. All 41 TNCL employees were included in the sample of the study. The quantitative data outputs were presented in the form of descriptive statistics, mainly in tabular form while qualitative data, which were subjected to content analysis were presented mainly in a narrative form. The findings indicated that TNCL uses various sources to disseminate information to prisoners. Moreover, they also indicated that TNCL faces several challenges including inadequate funds, poor infrasture systems, restrictive prison policies and lack of library buildings which is deterring provision of information services to prisoners. The study recommended that all of the raised challenges should be solved to facilitate information provision services to prisoners.
Developing Contemporary Rhythm Skills Through Contemporary R&amp;B

This chapter offers some ways to help students understand the organization of rhythm and meter in contemporary popular music, with a focus on those skills that may be underdeveloped in a traditional music curriculum. In particular, I discuss a number of classroom activities and assignments related to developing rhythm skills within the context of contemporary R&B, using songs from artists such as Mariah Carey, Bobby Brown, Mary J. Blige, and Ne-Yo. The chapter is organized into three main parts. The first describes how drummer’s charts can help students conceptualize and internalize patterns of form, hypermeter, and phrase organization in this style. In the second part, I discuss how a comprehensive description of meter in contemporary R&B requires going beyond traditional meter identification categories, due to how the drumbeat interacts with the time signature and swing rhythms on different metric levels. Finally, Part 3 gives a few examples of rhythm transcription assignments, with a particular focus on the extensive syncopation found in R&B melodies. Overall, the attention to rhythm and meter is intended not only to build fluency with this particular style but also to more broadly develop critical skills for the twenty-first-century musician.
Efficient Steganographic Method Based on Modulo Cube

In this paper, we propose a steganographic method with high capacity based on modulo operation. The secret message is mapped to a cube, the coordinates of the position will be calculated by modulo and then embedded in the cover image. The experimental results show that our proposed method can have a higher capacity with imperceptibility. The modulo operation can make the proposed method have lower computational overhead.
Combining a Bi-LSTM-Based Siamese Network with Word2Vec Algorithm for Classifying High-Dimensional Dataset

The Siamese variant of the bidirectional long short-term memory classification model, called SVBi-LSTM, provides a good solution to overcome this misclassification problem. However, the model limits the data input to the two datasets. If this model is applied to higher-dimensional data for classification, the input dataset should be partitioned vertically into two sets, and the dimensionality of the input data must be reduced. In this paper, we propose a dimensionality reduction method for high-dimensional datasets to fit in SVBi-LSTM. The proposed method combines the attributes with a higher correlation into one set to reduce dimensionality. We compared the correlation coefficients between each pair of attributes in the input dataset. Thereafter, we used the Word2Vec algorithm to convert all the text in the dataset into word vectors and calculate the arithmetic mean of word vectors for each attribute, and we then calculated Pearson’s correlation coefficient for every pair of attributes to obtain a correlation coefficient matrix. We finally separated the higher correlated attributes from the others and partitioned them into two groups to reduce dimensionality. Two datasets were used to verify the feasibility of our method: the News Category Dataset and the IMDb Movies Extensive Dataset. The classification precision of the SVBi-LSTM model in Experiments 1 and 2 are approximately 4% and 5% higher, respectively.