Reviewerly/exHarmony huggingface dataset:
Coarse Mask Guided Interactive Object Segmentation

Interactive object segmentation aims to produce object masks with user interactions, such as clicks, bounding boxes, and scribbles. Click point is the most popular interactive cue for its efficiency, and related deep learning methods have attracted lots of interest in recent years. Most works encode click points as gaussian maps and concatenate them with images as the model's input. However, the spatial and semantic information of gaussian maps would be noised through multiple convolution layers and won't be fully exploited by top layers for mask prediction. To pass click information to top layers exactly and efficiently, we propose a coarse mask guided model (CMG) which predicts coarse masks with a coarse module to guide the object mask prediction. Specifically, the coarse module encodes user clicks as query features and enriches their semantic information with backbone features through transformer layers, coarse masks are generated based on the enriched query feature and fed into CMG's decoder. Benefiting from the efficiency of transformer, CMG's coarse module and decoder module are lightweight and computationally efficient, making the interaction process more smooth. Experiments on several segmentation benchmarks demonstrate the effectiveness of our method, and we get new state-of-the-art results compared with previous works.
Efficient Trajectory Planning for Coordinated Arrival of Fixed-Wing UAV Swarm

This paper proposes an efficient centralized trajectory planning algorithm for fixed-wing swarms to address the problem of coordinated arrival. A Dubins-based initial path sampling method is designed in the front end to provide a good initial guess considering kinodynamic feasibility. Then a joint optimization approach is carried out in the back end where constraints are formulated as a penalty in the cost function. Moreover, a well-formed trajectory representation is utilized to realize spatial-temporal optimization with few variables, leading to the high computational efficiency. A replanning strategy is also introduced to handle the situation when the target trajectory changes. Numerous simulation results demonstrate the efficiency and feasibility of the algorithm.
Final Words

In this concluding chapter, we summarize the key payoffs of the causal-model-based approach to causal inference, point to a set of important limitations of the approach, and sketch out what we see as next steps in strengthening model-based inference.
Survey of Structural Analysis in Mathematical Expression Recognition

AbstractAutomated identification of mathematical expressions (MEs) is essential in transforming scientific and engineering documents into electronic form. Even though character and symbol recognizers have achieved commendable performance for digitizing documents, structure analysers still face a challenge in correctly interpreting the maths expressions. This review paper compares the salient aspects of past works dealing with structure analysis of printed and handwritten MEs. To the best of our knowledge, no previous work has done a systematic study of structural analysis methods in mathematical expression recognition. We present distinguishing aspects of different grammars and their production rules for semantic parsing of ME. Our study contributes by providing information on the existing datasets, their desirable properties, different evaluation measures, distinguishing aspects of techniques used and future research directions in structural analysis.KEYWORDS: Deep learningMathematical expression recognitionOfflineOnlineStructural analysisSyntactic parsing Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsRidhi AggarwalRidhi Aggarwal received a BTech in electrical and electronics engineering and an ME in electronics product design and technology. She is currently working towards her PhD degree from IIT Jodhpur.Shilpa PandeyShilpa Pandey (PhD 2019) is an assistant professor in the Department of CSE at Pandit Deendayal Energy University, Gujarat. Email: shilpaiitj@gmail.comAnil Kumar TiwariAnil Kumar Tiwari (member, IEEE) is a professor at Department of EE at IIT Jodhpur. His research interests include development of algorithms for image and video compression, noise minimization, interpolation, healthcare and watermarking. Email: akt@iitj.ac.inGaurav HaritGaurav Harit (PhD 2007) is a professor in the Department of CSE at the IIT Jodhpur since 2010. His areas of interest are document image analysis and video analysis. Email: gharit@iitj.ac.in
Perspectives from Naive Participants and Experienced Social Science Researchers on Addressing Embodiment in a Virtual Cyberball Task

We describe the design of an immersive virtual Cyberball task that included avatar customization, and user feedback on this design. We first created a prototype of an avatar customization template and added it to a Cyberball prototype built in the Unity3D game engine. Then, we conducted in-depth user testing and feedback sessions with 15 Cyberball stakeholders: five naive participants with no prior knowledge of Cyberball and ten experienced researchers with extensive experience using the Cyberball paradigm. We report the divergent perspectives of the two groups on the following design insights; designing for intuitive use, inclusivity, and realistic experiences versus minimalism. Participant responses shed light on how system design problems may contribute to or perpetuate negative experiences when customizing avatars. They also demonstrate the value of considering multiple stakeholders’ feedback in the design process for virtual reality, presenting a more comprehensive view in designing future Cyberball prototypes and interactive systems for social science research.
Anime-like Character Face Generation: A Survey

Recently, anime-style modeling has become increasingly popular. Automatic anime character generation can reduce costs and save manpower. There have been numerous related studies that need to be summarized and analyzed. This paper presents an overview of anime character face generation. Firstly, it summarizes the types and methods of 2D face generation for anime characters. Secondly, we introduce methods for anime character face modeling, including deep learning-based approaches and other approaches. Finally, we summarize the challenges of anime character face generation and evaluate their performance.
K-Nearest Neighbors Analysis for Public Sentiment towards Implementation of Booster Vaccines in Indonesia

In order to prevent the spread of COVID-19 in Indonesia, the Government of the Republic of Indonesia has been implementing a booster vaccine program since January 12th, 2022, with priority for the elderly and vulnerable groups as well as those who got the second C-19 vaccine longer than 6 months. The implementation of this program raised many pros and cons among public which were expressed either positively or negatively through social media. Therefore, sentiment analysis is needed to examine these phenomenons. This study aims to determine the positive and negative response from public by employing K-Nearest Neighbor method. A total of 2,000 commentary data were collected to be in turn classified based on positive and negative sentiments. There are 500 comments used as training data and divided equally to positive and negative class, each consists of 250 data. Using the value of K = 9, the results show a positive sentiment of 43% while a negative sentiment of 57%. Based on the validity test using 10-fold cross validation, an accuracy of 82.60% was obtained, a recall value was 82.60% with a precision of 83.89%.
The Question of Studying Information Entropy in Poetic Texts

One of the approaches to quantitative text analysis is to represent a given text in the form of a time series, which can be followed by an information entropy study for different text representations, such as “symbolic entropy”, “phonetic entropy” and “emotional entropy” of various orders. Studying authors’ styles based on such entropic characteristics of their works seems to be a promising area in the field of information analysis. In this work, the calculations of entropy values of the first, second and third order for the corpus of poems by A.S. Pushkin and other poets from the Golden Age of Russian Poetry were carried out. The values of “symbolic entropy”, “phonetic entropy” and “emotional entropy” and their mathematical expectations and variances were calculated for given corpora using the software application that automatically extracts statistical information, which is potentially applicable to tasks that identify features of the author’s style. The statistical data extracted could become the basis of the stylometric classification of authors by entropy characteristics.
A Novel Gender Classification Model based on Convolutional Neural Network through Handwritten Text and Numeral

Human handwriting is used to investigate human characteristics in various applications, including but not limited to biometric authentication, personality profiling, historical document analysis, and forensic investigations. Gender is one of the most distinguishing characteristics of human beings. From this point forth, we propose a novel end-to-end model based on Convolutional Neural Network (CNN) that automatically extracts features from a given handwritten sample, which contains both handwritten text and numerals unlike the related work that uses only handwritten text, and classifies its owner’s gender. In addition to proposing a novel model, we introduce a new dataset that consists of 530 gender-labeled Turkish handwritten samples since, to the best of our knowledge, there does not exist a public gender-labeled Turkish handwriting dataset. Following an exhaustive process of hyperparameter optimization, the proposed CNN featured the most optimal hyperparameters and was both trained and evaluated on this dataset. According to the experimental result, the proposed novel model obtained an accuracy as high as 74.46%, which overperformed the state-of-the-art baselines and is promising on such a task that even humans could not have achieved highly-accurate results for, as of yet.
STUDENT’S DIFFICULTIES ON WRITING DESCRIPTIVE TEXT

This research investigates an overview of students' difficulties in writing descriptive text through linguistic and psychology problems. This research presents a case study as qualitative research of students in eighth grade of SMP Darussalam Medan. The sample comprised 10 students (4 males and 6 females) through purposive sampling. This study used observation, documentation, and interview. In this study, data collection from each instrument was as follows: document data collection through student descriptive writing and direct interview data collection via video call on WhatsApp. The data analysis procedure of this study was followed by Miles and Hubermen in 1984 which covered data reduction, data interpretation, and drawing a conclusion. Based on the observation result supported by the document and interview through the triangulation method, students found difficulties in using punctuation and capitalization in writing descriptive. The students’ felt difficulty in using grammar lack of vocabulary and mechanics. In addition, the practice of writing essays also makes students skilled in writing. With practice, students will know the difficulties they face in writing to know the quality of their writing.&#x0D; &#x0D; Keywords: Difficulties, Writing skill, Descriptive text.
AN ANALYSIS OF STUDENTS' ABILITY IN WRITING AN INVITATION LETTER

Writing is one of the most important skills in the English language that students must master at school, while the eleventh grade students of Madrasah Aliyah Al Amiriyyah still had problems in writing, especially in writing invitation letters, most of them were not understand well about part of invitation letter and to design their sentences. Besides they were also less vocabulary in writing invitation letter. So the aim of research is to describe student's ability in writing an invitation letter at the eleventh grade students of Madrasah Aliyah Al Amiriyyah Blokagung Banyuwangi. The method in this research is descriptive qualitative. The data was taken from the results of the researcher's observations on the learning carried out in class, especially on the invitation letter material. The results of the analysis of students' writing skills on an invitation letter are that three students are included in the sufficient category with scores obtained 64, 66, 62 and one student is included in the good category with a score of 75. This indicates that the writing ability of the eleventh grade students of MA Al Amiriyyah on an invitation letter is still low and there is a need for improvement in better writing learning methods.
HyperHuman: Hyper-Realistic Human Generation with Latent Structural Diffusion

Despite significant advances in large-scale text-to-image models, achieving hyper-realistic human image generation remains a desirable yet unsolved task. Existing models like Stable Diffusion and DALL-E 2 tend to generate human images with incoherent parts or unnatural poses. To tackle these challenges, our key insight is that human image is inherently structural over multiple granularities, from the coarse-level body skeleton to fine-grained spatial geometry. Therefore, capturing such correlations between the explicit appearance and latent structure in one model is essential to generate coherent and natural human images. To this end, we propose a unified framework, HyperHuman, that generates in-the-wild human images of high realism and diverse layouts. Specifically, 1) we first build a large-scale human-centric dataset, named HumanVerse, which consists of 340M images with comprehensive annotations like human pose, depth, and surface normal. 2) Next, we propose a Latent Structural Diffusion Model that simultaneously denoises the depth and surface normal along with the synthesized RGB image. Our model enforces the joint learning of image appearance, spatial relationship, and geometry in a unified network, where each branch in the model complements to each other with both structural awareness and textural richness. 3) Finally, to further boost the visual quality, we propose a Structure-Guided Refiner to compose the predicted conditions for more detailed generation of higher resolution. Extensive experiments demonstrate that our framework yields the state-of-the-art performance, generating hyper-realistic human images under diverse scenarios. Project Page: https://snap-research.github.io/HyperHuman/
Database Search Form HTML and JavaScript

This dataset includes HTML and JavaScript templates for the creation of database search forms. These templates provide a simple, consistent codebase for the creation of forms where the database supports requests via the base URL and query string.
Design and experimental validation of a new wheelchair seat stabilization system

Seat tilting wheelchair features can increase the comfort and safety of the user. Although many power wheelchairs have tilting mechanisms, they are often designed with a specific wheelchair model in mind. In this study, a design process for seat tilting mechanisms that can be applied to most rear-wheel drive wheelchair models is developed.Equations were developed to describe the geometrical and load constraints that were used to size the electric actuator that powers the system and define its position. Finally, the equations were used to create the seat tilting mechanism of a prototype wheelchair, which was then tested.The equations yielded coherent results which showed that advantageous actuator positions from a load minimization perspective usually require dimensions that cannot be found in commercial actuators. Also, there are positions in which the load increases exponentially, which should be avoided. The tests showed that the system was able to function properly on the prototype wheelchair and that the actuator position affected the time taken for the actuator to execute different parts of the tilting movement.The design process presented here was successful and modelled by general equations that can be applied to most front-wheel drive wheelchairs. It presents a low-cost option for the design of seat tilting systems, which can increase their accessibility.Developing new systems to provide improvements in assistive technologies is fundamental for social reintegration and quality of life improvement.Wheelchairs with a seat stabilization system for moving through inclined terrain can provide greater comfort and safety to the user.Adding low-cost functionalities to wheelchairs is essential to make them more accessible to people, therefore, this paper provides a design method of a new seat stabilization system applied to low-cost wheelchairs.
World Experience with Augmented Reality Technology in the Field of Cultural Heritage

This article reviews a number of projects of virtual reconstructions of historical objects, using augmented reality technology across the world. We have considered basic examples of projects aimed at the public presentation objects of cultural heritage, as this area is the most common. We have analyzed the applications and developments published in various databases, as well as described the developments from the personal practice of this article's authors. Based on the analysis of the examples given we describe the characteristics, potential opportunities, trends, as well as the limitations and advantages of using this augmented reality technology in the application in the field of preservation of cultural heritage. Our research confirms that augmented reality is a very useful tool in the field of displaying cultural heritage objects, and it greatly helps this field to successfully compete for public attention in the context of total digitalization. We can also say that the technology is gradually moving into the category of a basic tool in the field of public presentation of objects of cultural heritage and may become a tool even in the construction part. This knowledge contributes to a better understanding of the current situation in the world and will enable developers, businesses, and researchers to better navigate the field.
The Development of Global Education in the Korean Social Studies Curriculum

This study examined the development of global education in South Korean social studies curriculum. It analyzed the curriculum changes from the post-liberation era to 2022, focusing on each period to understand the transformations in global education reflected in the national curriculum. The research investigated the stages of introducing global education for international understanding, its establishment for nationalistic globalization, the leap towards global citizenship education, and the inclusion of global education in the latest curriculum reflecting the concept of global competence. Through this analysis, I aimed to review how the perspective of global education has been reflected, maintained, transformed, and developed in South Korean social studies curriculum. Based on the findings, I suggested the direction of global education in schools in the future.
A systematic review of digital innovations in technology-enhanced learning designs in higher education

In the years prior to the COVID-19 pandemic, there was considerable innovation in designing and implementing teaching and learning with technology in fully online, face-to-face and blended modes. To provide an overview of technology-enhanced learning in higher education, we conducted a systematic literature review following PRISMA guidelines of digital innovations in learning designs between 2014 and 2019, prior to emergency remote teaching responses to the COVID-19 pandemic. From 130 publications, we identified eight overlapping categories of digital technologies being deployed across higher education fields: simulation and augmented or virtual reality; Web 2.0; learning management systems; mobile learning; gamification and serious games; various technologies in classrooms; massive open online courses; and other software, websites, applications and cloud computing. We use these publications, supplemented with findings from selected meta-analyses and systematic reviews of specific technologies, as examples to guide educators designing technology-enhanced learning activities in changing circumstances that may require blended or fully online delivery. As the 130 publications had mixed perceived quality, levels of evidence and details of learning designs and evaluation presented, we suggest educators share their innovations following reporting guidelines relevant to their research methodologies, enabling others to consider transferability to other contexts and to build on their work. Implications for practice or policy: Leaders and administrators should support staff development of technological pedagogical content knowledge and teaching as design for student learning. Educators and instructional designers, in designing learning experiences, should consider adult learning theories, inclusive practices and digital equity and leverage multiple technologies to facilitate students learning their curricula. In educational research or scholarship of teaching and learning, researchers should provide sufficient detail to enable readers to assess transferability to their own contexts.
Application and Exploration of Digital Storytelling through Metaverse Avatar Customization in University General Education Courses: The Impact on Digital Efficacy and Learner Experience and Perception

The purpose of this study is to explore whether a digital storytelling course utilizing metaverse avatar customization influences learners' digital self-efficacy and how learners perceive this experience. The study was conducted with 135 learners who took a general education course in the Seoul metropolitan area during the first semester of 2023. Digital self-efficacy was analyzed using a pre- post test and paired-sample t-tests for its sub-dimensions (digital self-confidence, digital preference, digital utility) all showed significantly higher results in the post-test compared to the pre-test, indicating an impact on learners' digital self-efficacy. Additionally, learner perceptions of experiencing the metaverse revealed that character creation through the metaverse platform allowed learners to express characters creatively, fostered active team communication, unleashed team ideas to the fullest, facilitated the expansion of new experiences and knowledge, increased confidence in using metaverse, and improved expression skills. This study is significant in its focus on the educational application effects of a digital storytelling course utilizing metaverse avatar customization and the learner experiences in the avatar customization process.
Personalized Breast Cancer Screening

The summary is as follows: Novel machine learning model reduces breast cancer screening delays by 2-3 months via personalization.
ANALISIS UNTUK MEMPREDIKSI STOK ASET DI PT. XYZ MENGGUNAKAN ALGORITMA APRIORI

There was an error in the staff's recording of goods and calculations, making it difficult to locate the necessary data. A collection of data that has been recorded and calculated ultimately renders the data useless. The purpose of the study was to determine whether the RapidMiner application's use of data mining can aid in stock prediction and data analysis. For stock predictions, the researcher employs the a priori algorithm approach in this instance. The findings indicate that employees will request the most support in 2022—a printer—if they request a laptop by as much as 7.9%. The laptop category will dominate in 2021, and the PC category will dominate in 2022. Therefore, the highest level of confidence for 2022 is that employees will request a printer if they request a PC and an external hard disk of 100%. The conclusion states that employees can use the RapidMiner application to perform data mining by applying the a priori algorithm, which aids in data analysis and predicts employee supply requirements.