Textual Descriptions Research Articles

The Effect of Point Illustration Explanation (PIE) Strategy Towards Students' Descriptive Writing at the Tenth Grade Students of SMAN 1 Malingping, Lebak-Banten in Academic Year 2023/2024

This study aims to evaluate the Effect of the Point Illustration Explanation (PIE) Strategy on Students' Writing Skills in Descriptive Texts at the Tenth Grade of SMAN 1 Malingping, Lebak-Banten, in the 2023/2024 academic year. The PIE strategy involves using concrete examples, in-depth explanations, and clear illustrations to help students understand and develop their descriptive writing skills. This quantitative study employs a quasi-experimental method with a sample of 74 tenth-grade students divided into an experimental class (using the PIE strategy) and a control class (using the KWL strategy). Data were collected through pre-tests and post-tests, and analysed using normality tests, homogeneity tests, and paired t-tests with SPSS 22. The results indicate a significant effect of the PIE strategy on students' descriptive writing skills, as evidenced by a t-value of -28.066 with df 74 and a two-tailed significance of 0.000, meaning Ha is accepted and Ho is rejected. In conclusion, the PIE strategy significantly improves the descriptive writing skills of tenth-grade students at SMAN 1 Malingping, enhancing their understanding of descriptive writing structures and techniques, increasing motivation, helping them develop ideas more thoroughly, and improving their ability to present clear and creative descriptions.

Mesh-controllable multi-level-of-detail text-to-3D generation

Text-to-3D generation is a challenging but significant task and has gained widespread attention. Its capability to rapidly generate 3D digital assets holds huge potential application value in fields such as film, video games, and virtual reality. However, current methods often face several drawbacks, including long generation times, difficulties with the multi-face Janus problem, and issues like chaotic topology and redundant structures during mesh extraction. Additionally, the lack of control over the generated results limits their utility in downstream applications. To address these problems, we propose a novel text-to-3D framework capable of generating meshes with high fidelity and controllability. Our approach can efficiently produce meshes and textures that match the text description and the desired level of detail (LOD) by specifying input text and LOD preferences. This framework consists of two stages. In the coarse stage, 3D Gaussians are employed to accelerate generation speed, and weighted positive and negative prompts from various observation perspectives are used to address the multi-face Janus problem in the generated results. In the refinement stage, mesh vertices and faces are iteratively refined to enhance surface quality and output meshes and textures that meet specified LOD requirements. Compared to the state-of-the-art text-to-3D methods, extensive experiments demonstrate that the proposed method performs better in solving the multi-face Janus problem, enabling the rapid generation of 3D meshes with enhanced prompt adherence. Furthermore, the proposed framework can generate meshes with enhanced topology, offering controllable vertices and faces with textures featuring UV adaptation to achieve multi-level-of-detail(LODs) outputs. Specifically, the proposed method can preserve the output’s relevance to input texts during simplification, making it better suited for mesh editing and rendering efficiency. User studies also indicate that our framework receives higher evaluations compared to other methods.

Development of a Remedial Module for Description Text Material Based on Local Wisdom Containing Class AKM for High Class Students

Development of a Remedial Module for Description Text Material Based on Local Wisdom Containing Class AKM for High Class Students

Investigating the Effects of Using Google Classroom on the Student’s Engagement in Writing Instruction During Covid-19 Outbreak

English is a foreign language that has been taught from elementary school to tertiary level. The aim of teaching English in junior high school is to help recognizing word structures well. The present study were focused on 1) examining the student’s engagement in the online classroom during a pandemic situation and more specifically on descriptive text lessons; 2) investigating how teachers use Google Classroom in teaching and how the students learning activity use Google Classroom as an online platform. This study aimed to investigate the effect of using Google Classroom on student engagement in learning during this pandemic. The participant in this study is one English teacher and thirty students in one of junior high schools in Ciamis. This research approach is qualitative with the type of case study. The present study employs observation and in-depth interviews to collect the data. This study used a qualitative descriptive analysis methodology consisting of data reduction, data display, conclusion, and verification. Completed data is obtained by the data collection process then the data must be analysed and interpreted. Based on the research that has been done, the effect of using Google Classroom in this junior high school is not too influential on the learning process. The reason is that many students cannot access the internet because of the network conditions where they live. So, the English teachers use other learning apps as a tool to help the teaching and learning process of their students so that they can continue to follow the lesson.

Balinese Women and Gender Equality in Tourism Industry

Purpose: This research aims to raise awareness among industry players and the wider community, especially those working in the tourism industry, regarding gender equality in the Bali tourism industry. Research methods: The research methodology employed is a literature review. Analysis of textual descriptions was carried out using the Gender Equality Continuum Assessment concept. Results and discussion: The findings reveal injustices faced by Balinese women working in the tourism sector, including the perpetuation of stereotypes implying limitations in managing the tourism sector and inadequate legal protections. These findings underscore the necessity of raising public awareness about gender inequality and enforcing legal protections for women in the tourism sector. Implications: By implementing gender equality policies and raising awareness of gender roles, the tourism industry is expected to create a more inclusive and fair environment thereby promoting social justice and fostering economic development.

IQAGPT: computed tomography image quality assessment with vision-language and ChatGPT models

Large language models (LLMs), such as ChatGPT, have demonstrated impressive capabilities in various tasks and attracted increasing interest as a natural language interface across many domains. Recently, large vision-language models (VLMs) that learn rich vision–language correlation from image–text pairs, like BLIP-2 and GPT-4, have been intensively investigated. However, despite these developments, the application of LLMs and VLMs in image quality assessment (IQA), particularly in medical imaging, remains unexplored. This is valuable for objective performance evaluation and potential supplement or even replacement of radiologists’ opinions. To this end, this study introduces IQAGPT, an innovative computed tomography (CT) IQA system that integrates image-quality captioning VLM with ChatGPT to generate quality scores and textual reports. First, a CT-IQA dataset comprising 1,000 CT slices with diverse quality levels is professionally annotated and compiled for training and evaluation. To better leverage the capabilities of LLMs, the annotated quality scores are converted into semantically rich text descriptions using a prompt template. Second, the image-quality captioning VLM is fine-tuned on the CT-IQA dataset to generate quality descriptions. The captioning model fuses image and text features through cross-modal attention. Third, based on the quality descriptions, users verbally request ChatGPT to rate image-quality scores or produce radiological quality reports. Results demonstrate the feasibility of assessing image quality using LLMs. The proposed IQAGPT outperformed GPT-4 and CLIP-IQA, as well as multitask classification and regression models that solely rely on images.

An open chest X-ray dataset with benchmarks for automatic radiology report generation in French

Medical report generation (MRG), which aims to automatically generate a textual description of a specific medical image (e.g., a chest X-ray), has recently received increasing research interest. Building on the success of image captioning, MRG has become achievable. However, generating language-specific radiology reports poses a challenge for data-driven models due to their reliance on paired image-report chest X-ray datasets, which are labor-intensive, time-consuming, and costly. In this paper, we introduce a chest X-ray benchmark dataset, namely CASIA-CXR, consisting of high-resolution chest radiographs accompanied by narrative reports originally written in French. To the best of our knowledge, this is the first public chest radiograph dataset with medical reports in this particular language. Importantly, we propose a simple yet effective multimodal encoder–decoder contextually-guided framework for medical report generation in French. We validated our framework through intra-language and cross-language contextual analysis, supplemented by expert evaluation performed by radiologists. The dataset is freely available at: https://www.casia-cxr.net/.

Descriptive Text Writing in the Perspective of Surface Strategy Taxonomy

The purpose of the study was to figure out the types of errors in the perspective of surface strategy taxonomy found in students’ descriptive writing. This study employed qualitative research. The population of the study was the tenth graders of SMA Islam Sultan Agung 1 Semarang. The sample of the study was 38 students of the tenth graders of X-IPS 1. They were given a descriptive text writing test. The result of the test were analyzed by using Surface Strategy Taxonomy. It classified the errors into four categories: omission, addition, misinformation, and misordering. The result of the study showed that the total errors were 91 errors. The errors consisted of omission with 38 errors (41.76%), misinformation with 33 errors (36.27%), addition with 14 errors (15.38%), and misordering with 6 errors (6.59%). From the data above, it can be concluded that omission was the most common error made by the students.

PathCLIP: Detection of Genes and Gene Relations From Biological Pathway Figures Through Image-Text Contrastive Learning.

In biomedical literature, biological pathways are commonly described through a combination of images and text. These pathways contain valuable information, including genes and their relationships, which provide insight into biological mechanisms and precision medicine. Curating pathway information across the literature enables the integration of this information to build a comprehensive knowledge base. While some studies have extracted pathway information from images and text independently, they often overlook the correspondence between the two modalities. In this paper, we present a pathway figure curation system named pathCLIP for identifying genes and gene relations from pathway figures. Our key innovation is the use of an image-text contrastive learning model to learn coordinated embeddings of image snippets and text descriptions of genes and gene relations, thereby improving curation. Our validation results, using pathway figures from PubMed, showed that our multimodal model outperforms models using only a single modality. Additionally, our system effectively curates genes and gene relations from multiple literature sources. Two case studies on extracting pathway information from literature of non-small cell lung cancer and Alzheimer's disease further demonstrate the usefulness of our curated pathway information in enhancing related pathways in the KEGG database.

Evaluasi Program Literasi Membaca Sekolah Dasar di SD Negeri 1 Kedungjati Menggunakan Model Goal Free Evaluation

Purpose – Based on a survey conducted by the Program for International Student Assessment (PISA) released in 2019, Indonesia is ranked 62nd out of 70 countries, placing it among the bottom 10 countries with low literacy levels. In response, the Indonesian government promoted the School Literacy Movement (GLS), a comprehensive initiative aimed at transforming schools into learning organizations where all members are literate for life, with the involvement of the community. The purpose of this study was to evaluate the reading literacy movement program at SD Negeri 1 Kedungjat.Method – This research employs a descriptive qualitative design. The study employs the Goal-Free Evaluation model, which enables researchers to evaluate the program without concentrating on its specific goals. Data collection techniques include document studies, observations, and interviews. Data analysis techniques used in this research follow the Miles and Huberman approach, which involves data collection, data reduction, data presentation, and conclusion drawing, with the data presented in descriptive text form.Findings – According to the study findings, the reading literacy movement involves instilling a 15-minute reading habit before commencing lessons. Supporting factors within this movement include a diverse range of reading materials, a conducive environment, and well-equipped libraries. The research indicates that the implementation of the reading literacy movement has been successful and has yielded positive outcomes, despite not being uniformly distributed across all classes. Research Implications – This study outlines the beneficial effects of the school literacy movement on students' reading interest, leading to an increased enthusiasm for reading. Furthermore, it demonstrates that the literacy movement enhances students' comprehension levels.

Attribute binding enhancement improvement for diffusion model based zero-shot image segmentation

As diffusion model's potential ability to accomplish perception tasks being discovered, many researchers have tried to apply diffusion model in segmentation tasks and achieved good results. However, there are not many methods to optimize the diffusion model for segmentation tasks by improving the text side. Even if some research have pointed out that diffusion model sometimes 'misunderstand' prompt and bind attributes to wrong objects. With the development of artificial intelligence and the gradual entry of large models into people's daily lives, in addition to the performance of the model, the model's interactivity and ability to understand natural language are also more important. Based on existing zero-shot diffusion model based segmentation method, this work introduces a new method to enhance attribute binding in the embedding of prompt improve the performance of the model. Through this method, more descriptive text will get better segmentation results, which to some extent, improves the segmentation performance of the model for inputs that are more in line with natural language description habits.

The Effect Using Animated Video as Teaching Media

Teaching writing is not easy. Teacher needs to think the appropriate and effective medium that will be able to attract the students’ interest. Due to this case, an animated video was chosen as a medium to facilitate students' learning process to be more interactive and attract students' interest for composing, particularly to write Descriptive Texts. The research explored the use animated videos to teach descriptive text in grade seventh academic year 2023/2024 at MTsN 6 Tulungagung. The type of experiment utilized is a Quantitative with Pre-Experiment Designs. The sample consisted of 31 students as control group. There are three steps conducting this research: pre-test, treatment, and post-test. The expert formed the validation of the questions. Based of the data analysis results, the pre-experimental class’s mean scores 76.35 and 86.32 in the post-test. Researchers found that the score of Sig. (2-Tailed) is 0.001 less than 0.05, therefore H0 is denied by acceptance of Ha. So in this case it was concluded that the differences in the analysis of the mean between the results of the pre and post-test meant that there were effects of using the animated video as the medium of learning in writing Descriptive Text in class VII in the 2023/2024 school year at MTsN 6 Tulungagung.

Revisiting 3D visual grounding with Context-aware Feature Aggregation

3D visual grounding is the task of accurately locating objects in a three-dimensional scene based on textual descriptions. Current approaches mainly depend on downsampling and extracting the point features for fusion with text features. However, the main challenges of these methods are the poor point feature resolution and limited local context during multi-modal fusion, causing visual-linguistic misalignment, particularly for small objects described in the text. The intuitive solution is to get additional object-related point features, gathering more contextual information to augment the representation capability of multimodal features, thereby promoting the representation capabilities of multimodal features. Based on this, we introduce a novel 3D visual grounding framework named Context-aware Feature Aggregation (CFA). The CFA framework includes two key modules: (1) Point Augmented Aggregation Module (PAM), designed to compensate for downsampling-induced information loss by augmenting sampled points with neighboring context for more discriminative features; and (2) Dual Contextual Grouping Attention Module (DCGAM), which iteratively refines features and geometry coordinates from PAM, capturing more global context. We assess the performance of our CFA framework on two point-based datasets: ScanRefer and Nr3D/Sr3D. The CFA framework exhibits efficiency in 3D visual grounding, surpassing the performance of previous methods by experimental results.

The Implementation of PORPE Strategy for the Teaching of Descriptive Reading Text

Reading is Reading comprehension ability is very important to improve learning achievement, because it increases vocabulary, fluency and language proficiency in various subjects, thereby influencing all material and teaching in each subject. This research investigates the application of the PORPE strategy in comprehending descriptive reading texts and its impact on student comprehension using a mixed methods research approach. The data was collected using observation, pre-test and post-test. It can be seen in this research, that the PORPE strategy, which consists of the stages of predicting, organizing, training, practicing and evaluating, can significantly improve students' understanding and analysis of descriptive texts, develop critical thinking and reading skills, and encourage active participation. In this research found, that there was a significant increase in students' average scores after implementing the PORPE strategy, from 57.37 to 80.37, which showed a statistically significant difference in scores before and after treatment. The PORPE strategy improved students' reading comprehension and critical thinking skills by facilitating interactive learning, increasing understanding of descriptive texts and fostering students' positive contributions.

E-Book Text Description Assisted by Artificial Intelligence (AI) Contains Pancasila Student Profile Dimensions of Global Diversity

E-Book Text Description Assisted by Artificial Intelligence (AI) Contains Pancasila Student Profile Dimensions of Global Diversity

Leveraging Conditional Generative Adversarial Networks (cGANs) for enhanced artistic creation: Exploring quality improvement and content control through conditional inputs

Conditional Generative Adversarial Networks (cGANs) have revolutionized digital art by enabling the creation of high-quality, artistically coherent images guided by conditional inputs. This paper examines key factors influencing the performance of cGANs, including the quality of training data, network architecture improvements, and loss function optimization. We introduce a mathematical model to quantify training data quality, emphasizing dataset diversity, data augmentation, and cleaning. Network architectural enhancements such as residual connections, attention mechanisms, and progressive growing are explored for their impact on image quality. Additionally, we discuss the integration of conditional inputs, such as labels and textual descriptions, for precise content control. Challenges in balancing realism with artistic expression, managing mode collapse, and interpreting conditional inputs are also addressed. This study provides a comprehensive framework for enhancing cGAN-generated artworks, offering insights into applications in personalized art generation, art restoration, and collaborative art projects.

Parent eHealth Preferences: Perceived Credibility and Personal Reactions to AbilitiCBT, BEAM, and Triple P Online

Parental eHealth needs and preferences are unknown. Evidence from in-person programs shows that programs that prioritize parent preferences have higher enrollment and adherence. Better knowledge of parental impressions and preferences based on current eHealth programs could help identify programs that are most in line with parental values, goals, and needs. Accordingly, the present study aimed to compare parental perceptions and preferences based on textual descriptions of three eHealth programs that have been prescribed to parents: AbilitiCBT (mental health-focused), BEAM (mental health and parenting-focused), and Triple P Online (parenting-focused). 177 parents of 0-5-year-old children in the United States were recruited through MTurk. Mental health symptoms in this sample were high (70.1% clinically concerning depression and/or anxiety symptoms and 74.6% clinically concerning parenting stress symptoms). Results showed that Triple P was less likely to be chosen than AbilitiCBT or BEAM; AbilitiCBT seemed more helpful to participants. There was considerable variability, and all programs were preferred by at least 17% of parents. Overall, the present study suggests that parents experiencing high psychological distress are less likely to choose to participate in a parenting program without mental health support and that it is important to offer diverse psychosocial service options to meet the needs of more parents. Further research is needed to identify specific program characteristics that parents prefer as well as parents’ rationale for their choices, which would help better tailor interventions to their preferences.

Analyses of Johannes Kepler’s Sunspot Drawings in 1607: A Revised Scenario for the Solar Cycles in the Early 17th Century

Telescopic sunspot observations began in 1610 and captured subsequent solar cycles. In combination with proxy reconstructions on an annual scale, these data sets indicate a gradual transition between regular solar cycles and the Maunder Minimum. The telescopic sunspot observations missed the beginning of the first telescopic solar cycle (Solar Cycle −13), leaving room for considerable uncertainty as to its temporal evolution. Before these early telescopic observations, however, Kepler made solar observations using camerae obscurae and recorded a sunspot group in three solar drawings in 1607. Here, we make use of Kepler’s sunspot drawings and descriptive texts to identify his observational sites and time stamps. We have deprojected his sunspot drawings and compared the reported positions with our calculations of the inclination of the solar equator as seen from these sites at that time. These results locate the reported sunspot group near the solar equator eastward from the central meridian. This contrasts with telescopic sunspot drawings from the 1610s that show sunspot groups in the higher heliographic latitudes. Therefore, what Kepler saw was probably a sunspot group from Solar Cycle −14, rather than one from Solar Cycle −13. These records allow us to place the beginning of Solar Cycle −13 between 1607 and 1610. In comparison with the 14C-based solar-cycle reconstructions, our result supports regular solar-cycle durations around the 1610s, rather than any suggested extreme extensions of the solar-cycle duration(s) around the 1610s.

AI models collapse when trained on recursively generated data

Stable diffusion revolutionized image creation from descriptive text. GPT-2 (ref. 1), GPT-3(.5) (ref. 2) and GPT-4 (ref. 3) demonstrated high performance across a variety of language tasks. ChatGPT introduced such language models to the public. It is now clear that generative artificial intelligence (AI) such as large language models (LLMs) is here to stay and will substantially change the ecosystem of online text and images. Here we consider what may happen to GPT-{n} once LLMs contribute much of the text found online. We find that indiscriminate use of model-generated content in training causes irreversible defects in the resulting models, in which tails of the original content distribution disappear. We refer to this effect as ‘model collapse’ and show that it can occur in LLMs as well as in variational autoencoders (VAEs) and Gaussian mixture models (GMMs). We build theoretical intuition behind the phenomenon and portray its ubiquity among all learned generative models. We demonstrate that it must be taken seriously if we are to sustain the benefits of training from large-scale data scraped from the web. Indeed, the value of data collected about genuine human interactions with systems will be increasingly valuable in the presence of LLM-generated content in data crawled from the Internet.

Enhancing text-audio generation by music classification and Retrieval-Augmented Generation

Recent advancements in deep learning have propelled the development of AI systems capable of generating music that resonates with human emotions and preferences. However, current music generation models still struggle to align generated music with detailed textual descriptions and maintain consistency, especially for longer compositions. This paper presents an innovative approach to address these challenges by integrating genre classification and retrieval-augmented generation (RAG) into the music generation pipeline. We train advanced CNN architectures, including ResNet-50, GoogleNet, and VGG16, for accurate genre classification. The classifier is then incorporated into a RAG framework, where the most relevant pre-classified music piece is retrieved based on the input text query. The retrieved audio and the text description are then fed into the MUSICGEN model to generate a new music piece that inherits attributes from both inputs. We evaluate our system through a double-blind human study, comparing the outputs of the original MUSICGEN model with our RAG-enhanced model. The results demonstrate a significant improvement in the ability of the RAG-enhanced model to generate music embodying specific stylistic elements, as evidenced by higher average confidence scores from participants. Our work represents a significant step towards more personalized and context-aware AI-generated musical experiences, laying the foundation for future advancements in this exciting field.