Reviewerly/exHarmony huggingface dataset:
Optimization Study of Renal CT Image Classification Using Convolution Neural Networks

Computer tomography (CT), also known as computed tomography, is a medical imaging method that generates detailed and precise horizontal or axial images of targeted regions of the body for diagnostic purposes. In this particular study, the focus was on performing image classification of CT scan images of the Kidney. This organ plays an essential role in detoxification, fluid balance, and maintaining electrolyte levels in the human body. The dataset used for the study contained 12,446 images into four labeled classes - Cyst, Tumor, Stone, and Normal. Convolution Neural Network (CNN) model is built for binary (Normal and Tumor) and multi-image Classification to classify the Renal CT images into four classes. CNN uses ReLU as an activation function for classifying these images. The Model is evaluated and compared based on Accuracy and Loss performance metrics by compiling the CNN model with different deep learning TensorFlow Keras optimizers for Version 2.11.0 for Binary Image Classification at 10,20, and 50 epochs. The CNN model is assembled with Adam Optimizer and Follows the Regularized Leader (FTRL) Optimizer. The Testing and training Accuracy and Losses achieved are evaluated with a learning rate of 0.001 and 0.0001. Adam Optimizer outperforms to be the best Optimizer for Binary and Multi-Image Classification.
YFDM: YOLO for detecting Morse code

Abstract With the increasing complexity of the shortwave communication environment, the efficiency and accuracy of the manual detection of Morse code no longer meet actual needs. Therefore, this paper proposes a Morse code detection algorithm called YFDM. For the time–frequency image of the received signal, a combination module of deformable convolution and C3 is used to enhance the backbone network’s attention to the abstract semantics and location information of Morse code. GSConv and VOV-GSCSP modules are used to build a lightweight neck network. Finally, the confidence propagation cluster (CP-Cluster) algorithm is used to filter the detection frame. In an ablation experiment, the parameters and giga floating-point operations per second (GFLOPs) of YFDM were 5.961 M and 9.74 G, respectively, 15.11% and 38.9% less than those of YOLOv5. Moreover, when WIoUv1 was used as the loss function of the bounding box, the AP0.5:0.95 and frames per second (FPS) values of the algorithm reached the highest values, 0.68 and 72.4. The experimental results indicate that the algorithm can effectively reduce the weight of the model while ensuring the detection accuracy and inference speed.
A Structured Narrative Prompt for Prompting Narratives from Large Language Models: Sentiment Assessment of ChatGPT-Generated Narratives and Real Tweets

Large language models (LLMs) excel in providing natural language responses that sound authoritative, reflect knowledge of the context area, and can present from a range of varied perspectives. Agent-based models and simulations consist of simulated agents that interact within a simulated environment to explore societal, social, and ethical, among other, problems. Simulated agents generate large volumes of data and discerning useful and relevant content is an onerous task. LLMs can help in communicating agents’ perspectives on key life events by providing natural language narratives. However, these narratives should be factual, transparent, and reproducible. Therefore, we present a structured narrative prompt for sending queries to LLMs, we experiment with the narrative generation process using OpenAI’s ChatGPT, and we assess statistically significant differences across 11 Positive and Negative Affect Schedule (PANAS) sentiment levels between the generated narratives and real tweets using chi-squared tests and Fisher’s exact tests. The narrative prompt structure effectively yields narratives with the desired components from ChatGPT. In four out of forty-four categories, ChatGPT generated narratives which have sentiment scores that were not discernibly different, in terms of statistical significance (alpha level α=0.05), from the sentiment expressed in real tweets. Three outcomes are provided: (1) a list of benefits and challenges for LLMs in narrative generation; (2) a structured prompt for requesting narratives of an LLM chatbot based on simulated agents’ information; (3) an assessment of statistical significance in the sentiment prevalence of the generated narratives compared to real tweets. This indicates significant promise in the utilization of LLMs for helping to connect a simulated agent’s experiences with real people.
Disaster Mitigation and First Aid Counseling: Building Kupuk Village Preparedness

The village of Kupuk, situated in the Bungkal District of the Ponorogo Regency in East Java, is extremely vulnerable to natural disasters such as forest fires and tornadoes. The level of public awareness and readiness for this disaster must still be enhanced. This area also has natural springs, often the location of drowning accidents. To address this issue, students from Muhammadiyah University of Ponorogo participating in a community service program have created an educational video on disaster preparedness and first aid. Creating this video requires identifying disaster risks, collaborating with experts and relevant authorities, collecting in-depth information, conducting first aid simulations, professional video production, expert content validation, and distribution through social media and local community networks. This initiative's results indicate that disaster mitigation and community education play a crucial role in safeguarding communities from the threat of natural disasters. Collaboration between stakeholders and video technology has increased the community's awareness and preparedness for emergency situations. This video has the potential to serve as an illustrative model for other regions confronting comparable threats.
Sentiment Analysis of Political Party Twitter Data using Ensemble Learning Classifier

Sentiment analysis is a field that has gained a lot of popularity in the last decade. We can identify the sentiment of a user in various fields like ‘Movie Reviews’, ‘Hotel reviews’, ‘Online Product reviews’ etc. Analysis of sentiment helps the other user in decision-making. Nowadays, sentiment analysis is also being used in the political scenario to select appropriate candidates/parties. Seeing this fact, we have performed a sentiment analysis on the political Twitter dataset to find out which Indian political party is getting positive/negative reviews. Generally, researchers use Textblob which is a lexicon-based sentiment analyzer to predict sentiment. The lexicon-based technique does not provide good accuracy to predict sentiment therefore in this paper we have applied ensemble learning using boosting techniques along with basic machine learning and deep learning techniques like SVM and LSTM. The result in terms of accuracy and ROC-AUC shows that ensemble learning i.e.LSTM with SVM outperform as compared to other ML technique. The accuracy is 87% and the ROC-AUC score is 93%.
ViStruct: Visual Structural Knowledge Extraction via Curriculum Guided Code-Vision Representation

State-of-the-art vision-language models (VLMs) still have limited performance in structural knowledge extraction, such as relations between objects. In this work, we present ViStruct, a training framework to learn VLMs for effective visual structural knowledge extraction. Two novel designs are incorporated. First, we propose to leverage the inherent structure of programming language to depict visual structural information. This approach enables explicit and consistent representation of visual structural information of multiple granularities, such as concepts, relations, and events, in a well-organized structured format. Second, we introduce curriculum-based learning for VLMs to progressively comprehend visual structures, from fundamental visual concepts to intricate event structures. Our intuition is that lower-level knowledge may contribute to complex visual structure understanding. Furthermore, we compile and release a collection of datasets tailored for visual structural knowledge extraction. We adopt a weakly-supervised approach to directly generate visual event structures from captions for ViStruct training, capitalizing on abundant image-caption pairs from the web. In experiments, we evaluate ViStruct on visual structure prediction tasks, demonstrating its effectiveness in improving the understanding of visual structures. The code is public at \url{https://github.com/Yangyi-Chen/vi-struct}.
Automatic Title Generation with Attention-Based LSTM

The objective of this article is to develop a generalised architecture that can generate headings for any type of document, including articles, book paragraphs, newspaper articles, and legal documents. The title of a document provides a concise overview of its content, allowing readers to quickly understand the main idea without focusing on the specifics. The more attractive the title is, the more the reader is bound to read it. Title generation will be helpful to the editors of newspapers or technical writers to easily find the central theme of the whole article. The Sequence-to-Sequence (Seq-to-Seq) model is implemented in this proposed work for generating the title for the project, article, and research articles. The Seq-to-Seq model utilizes an encoder-decoder recurrent neural network that has an attention layer and Long Short-Term Memory (LSTM). The main strength of the developed framework is the adoption of an attention layers to the encoder-decoder architecture, which assigns more weightage to background words. This proposed model achieves 90% prediction accuracy by including an attention layer into the LSTM.
Text to Data

This chapter covers text-to-data translation. It begins by providing a historical perspective, briefly covering early relevant work on semantic parsing and meaning representation before moving to newer approaches that utilize word embedding, semantic compositionality, knowledge graph, and large language models. Three key topics are covered in depth: (1) converting sentences to structured form, (2) neural semantic parsing, and (3) recent text-to-SQL models, systems, and benchmarks.
Exploring the Theory, Benefits, and Challenges of Mobile Web-Based Learning (MWBL) for English as a Second Language (ESL) Learners

The philosophy, benefits, and challenges of mobile web-based learning (MWBL) for English language learners are examined in this paper. MWBL offers dynamic and participatory exercises that are in line with constructivist and behaviorist theories, combining the benefits of mobile learning and web-based learning. Flexibility, personalized learning experiences, more engagement and motivation, and the encouragement of self-directed learning are all advantages of MWBL. Nevertheless, obstacles to MWBL implementation include teachers' openness to new technology, technological problems with privacy and security, and the demand for user-friendly interfaces. Stakeholders, including the Ministry of Education, state education departments, administrators, and teachers, must work together to provide the infrastructure, support, training, and resources necessary to enable successful MWBL implementation. MWBL can improve by resolving these issues and maximizing the advantages.
AR Patterns: Event-Driven Design Patterns in Creating Augmented Reality Experiences

Augmented Reality (AR) and Mixed Reality (MR) enable superimposing digital content onto the real world. These technologies have now matured to a point where low-code/no-code editors for AR development have emerged. However, existing collections of design principles for AR often fall short, either being too generic or overly focused on low-level details. This makes it challenging to identify the essential patterns necessary for creating captivating AR experiences. This paper addresses this issue by introducing high-level AR design patterns encompassing fundamental concepts for crafting immersive AR experiences. Event-Condition-Action rules are leveraged as a generic abstraction from the reactive behavior of AR software systems to establish a unified framework. AR-specific behavioral patterns and augmentation patterns are presented in detail. Additionally, a uniform pattern diagram schema is proposed that ensures consistent presentation and technology-agnostic documentation of AR design patterns, facilitating their effective use in design and creation of AR applications.
CHARACTERISTICS OF EMOTIONAL INTELLIGENCE OF STUDENTS WITH DIFFERENT LEVELS OF DEVELOPMENT OF COMMUNICATIVE ABILITIES

The article presents the results of theoretical and empirical research of psychological peculiarities of emotional intelligence of students with different levels of development of communicative abilities. Theoretical analysis revealed that emotional intelligence is mostly defined as a person's ability to realize, accept and manage emotional states and feelings, both their own and other people's, which is formed during a person's life in communication and professional activities. Thus, emotional intelligence is, on the one hand, the ability to understand, analyze and control one’s own feelings and emotions, and on the other it is the ability to feel and understand the mood of the environment. &#x0D; At the same time, the need for a more detailed practical study of the problem was confirmed, as the issue of the specifics of the emotional intelligence of the personality depending on the level of development of its communicative area remains insufficiently resolved. &#x0D; The data of the empirical study of the peculiarities of emotional intelligence of students with different levels of development of communicative abilities showed that the least students have the lowest level of general emotional intelligence, and the most number of students has the low level. The higher the level of development of emotional intelligence the subjects have, the more moderate they are in interaction with society, more sociable and have a positive emotional background, not susceptible to negativity. A high level of development of communicative and organizational abilities in the studied sample was also revealed. &#x0D; It was proved that emotional intelligence has a direct relationship with the communicative abilities of the personality in the student age, namely, the higher the level of emotional intelligence of students is, the higher is the level of development of their communicative abilities. In practice, this is manifested in the fact that the better the researched students are oriented in emotions, the more accurately they are able to recognize certain emotions of other people, and the more able they are to understand and control their feelings, the more they are able to communication with others and to active communicative activities.
The Role of App Update and User Feedback

Developing a mobile app that users will love can be a challenging task even with a great idea and a talented development team. It’s impossible to anticipate every user’s need or preference. That’s where user feedback comes in collecting feedback from app users is essential for improving app’s functionality, user experience, and overall success. It provides us with valuable insights into what users like, what they don’t like, and what they want to see improved. In turn, this information allows us to make data-driven decisions about our app’s development. Our work explores the rule of user reviews in app updates based on release notes. For this purpose, we collected user reviews and released notes for Spotify, the number one app in the music category in apple app store, as the research data. Then, we manually removed non-informative parts of each release notes and manually determined the relevance of the app reviews with respect to the release notes. Our empirical results show that more than 60% of the matched reviews are actually irrelevant to the corresponding release notes. When zooming in at these relevant user reviews, we found that around half of them were posted before the new release and referred to requests, suggestions, and complaints. Whereas the other half of the relevant user reviews were posted after updating the apps and concentrated more on bug reports and praise.
Forgery Detection on Handwritten Signatures using Convolutional Neural Networks

Abstract: Handwritten signatures play a significant part in numerous parts of our day-to-day events and aid in authenticating our personal information all over the world from banks to many government and private institutions. On the other hand, the usage of these handwritten signatures is accompanied by the problems of signature replication, counterfeit signatures and identity theft by both professional and amateur people alike. And so, there is a need for a system to help in differentiating and isolating the real signatures from their copied lookalikes but this task turns out to be really challenging. In recent years, there have been lots of advances in the field of Deep Learning, which is increasingly becoming a part of daily lives. One such deep learning technique called Convolutional Neural Networks, is an interesting tool for analysing pictures and other types of data. This method could be applied to detect and analyse handwritten signatures taken as inputs in order to classify if it is original or forged.
The Verbal and Visual Signs of Memes Found on Pinterest

This study concerns with the term of semiotics that is aimed at describing the verbal and visual signs as well as analyzing the functions and meaning of signs. The data of this study were taken from popular memes in 2020 on Pinterest or www.pinterest.com. The data were collected by observation method. The collected data were analyzed by descriptive qualitative method. Furthermore, the data were presented by formal and informal methods. There were three theories applied. The theory of semiotic was proposed by Saussure (1983), the theory of meaning was proposed by Barthes (1997). In addition, the theory of color was driven by Wierzbicka (1996). The result of this study showed that there were 6 verbal signs and 8 visual signs based on 3 data used in total. The visual signs are the most sign used on each meme. The purpose is to attract people on the internet when communicating with friends and shared for humorous purpose because it also conveys someone’s idea.
PERANCANGAN SISTEM ABSENSI KEHADIRAN KARYAWAN MENGGUNAKAN BARCODE BERBASIS CLIENT SERVER

The attendance system aims to make it easier in terms of employee attendance at the office and this system uses visual basic software. The problem why you have to use this system is because the employee attendance method still uses the manual method so that it cannot be effective and efficient at work. The advantage that can be obtained by using this system is that it can make it easier for employees in terms of attendance at the office by using computer facilities using barcodes. The benefits provided to the company are to simplify and speed up the work of employees so that they can improve employee discipline at the company PT. Priorit
Morphological and Syntactical Analysis on Advertising Billboards Used by Hotels in Banjarmasin

It is important to have knowledge of the language to make no mistakes occur in the language. Although previous studies have discussed linguistics and advertising, it is still very rare, and almost very few have discussed the morphology and syntax of hotel billboards in detail. Therefore, to fill this void, this study aimed to examine the morphological and syntactical analysis of advertising billboards used by hotels in Banjar masin. This study used descriptive qualitative research as its methodology. The billboards from various hotels in Banjarmasin served as the study's objects and location. The researcher specifically selected the images from the billboard advertisements that were captured. Then, the data were gathered through making observations and documentation. The findings discovered in the morphology section that numerous hotel advertisements had issues adding suffixes, which were lack of suffix-ly, suffix-s/es for verbs, and suffix-s/es for nouns. Furthermore, the phrase or sentence from the existing hotel billboard has a structural flaw in the syntactic part which is difficult to distinguish with morphology elements. . This study is intended to assist lecturers in the teaching-learning process in terms of morphology and syntax. It is expected that this study would be useful to future researchers conducting pertinent literature.
Two-Stage Graph Convolutional Networks for Relation Extraction

The purpose of relation extraction is to extract semantic relationships between entities in sentences, which can be seen as a classification task. In recent years, the use of graph neural networks to handle relation extraction tasks has become increasingly popular. However, most existing graph-based methods have the following problems: 1) they cannot fully utilize dependency relation information; 2) there is no consistent criterion for pruning dependency trees. To address these issues, we propose a two-stage graph convolutional networks for relation extraction. In the first stage of the model, the node representation, dependency relation type representation and dependency type weight jointly generate new node representations, fully utilizing the dependency relation information. In the second stage, with the help of the adjacency matrix derived from the dependency tree, the graph convolution operation is performed. In this way, the model can automatically complete the pruning operation. We evaluated our proposed method on two public datasets, and the results show that our model outperforms previous studies in terms of F1 score and achieves the best performance. Further ablation experiments also confirm the effectiveness of each component in our proposed model.
Integrating Iot and Blockchain Technology for Securing Sensors Communication

Blockchain offers new approaches to manage and store data. The technical trajectory of blockchain makes it beneficial in various industries because of its benefits for tamperproof and immutable data security. The increasing use of IoT devices will surely result in a smarter world. A growing number of applications, including environment monitoring, building automation, smart metering, surveillance, and asset tracking, are turning to networks of wireless sensors, each of which is capable of a combination of processing, communication, and sensing. The Internet of Things (IoT) has facilitated the transition to a connected and intelligent digital environment, allowing smart manufacturing, improved decision-making and data analytics. In this study, we review the various IoT and Blockchain frameworks and discuss how integration of IoT and Blockchain resolves existing gaps.