Reviewerly/exHarmony huggingface dataset:
Category-Guided Visual Question Generation (Student Abstract)

Visual question generation aims to generate high-quality questions related to images. Generating questions based only on images can better reduce labor costs and thus be easily applied. However, their methods tend to generate similar general questions that fail to ask questions about the specific content of each image scene. In this paper, we propose a category-guided visual question generation model that can generate questions with multiple categories that focus on different objects in an image. Specifically, our model first selects the appropriate question category based on the objects in the image and the relationships among objects. Then, we generate corresponding questions based on the selected question categories. Experiments conducted on the TDIUC dataset show that our proposed model outperforms existing models in terms of diversity and quality.
SLIQ: Quantum Image Similarity Networks on Noisy Quantum Computers

Exploration into quantum machine learning has grown tremendously in recent years due to the ability of quantum computers to speed up classical programs. However, these ef- forts have yet to solve unsupervised similarity detection tasks due to the challenge of porting them to run on quantum com- puters. To overcome this challenge, we propose SLIQ, the first open-sourced work for resource-efficient quantum sim- ilarity detection networks, built with practical and effective quantum learning and variance-reducing algorithms.
Out-of-Distribution Generalization by Neural-Symbolic Joint Training

This paper develops a novel methodology to simultaneously learn a neural network and extract generalized logic rules. Different from prior neural-symbolic methods that require background knowledge and candidate logical rules to be provided, we aim to induce task semantics with minimal priors. This is achieved by a two-step learning framework that iterates between optimizing neural predictions of task labels and searching for a more accurate representation of the hidden task semantics. Notably, supervision works in both directions: (partially) induced task semantics guide the learning of the neural network and induced neural predictions admit an improved semantic representation. We demonstrate that our proposed framework is capable of achieving superior out-of-distribution generalization performance on two tasks: (i) learning multi-digit addition, where it is trained on short sequences of digits and tested on long sequences of digits; (ii) predicting the optimal action in the Tower of Hanoi, where the model is challenged to discover a policy independent of the number of disks in the puzzle.
Improving End-to-End Speech Translation by Leveraging Auxiliary Speech and Text Data

We present a method for introducing a text encoder into pre-trained end-to-end speech translation systems. It enhances the ability of adapting one modality (i.e., source-language speech) to another (i.e., source-language text). Thus, the speech translation model can learn from both unlabeled and labeled data, especially when the source-language text data is abundant. Beyond this, we present a denoising method to build a robust text encoder that can deal with both normal and noisy text data. Our system sets new state-of-the-arts on the MuST-C En-De, En-Fr, and LibriSpeech En-Fr tasks.
Implementation of an Automated Grading System for Formalized Answer Sheet Using EasyOCR

In this paper, we have developed an automated scoring system for structured formalized answer sheets composed of printed problem numbers and handwritten answers.In the initial stage, we performed gradient normalization to accurately recognize the problem numbers and answers, and subsequently applied the EasyOCR Framework for character recognition.Notably, we introduced a location correction algorithm to accurately detect the positions of numbers, thereby improving the detection rate from the original 70% to 100%.The number recognition model of EasyOCR originally had an accuracy of 89%, but we improved it to 93% through transfer learning.Experimental results showed that the implementation of an automatic scoring system using EasyOCR was feasible, and this system is expected to increase the efficiency of grading tasks in the educational field.
FORMATION OF HEALTH CULTURE AND SELF-PRESERVATION BEHAVIOR OF YOUNG PEOPLE IN THE SYSTEM OF HIGHER PROFESSIONAL EDUCATION

Human health has always been a socially significant element that most acutely determines the peculiarities of modern society state, as evidenced by the results of numerous sociological studies.It is known that health is the most important value both in the life of an individual and of the whole society.In this regard, since ancient times, various aspects of health maintaining and strengthening, formation of the foundations of self-preservation behavior and a culture of health have been studied in medicine, philosophy, psychology, sociology and pedagogy.Today, there is a progressive aggravation in the health of all population groups, but the health of young people is of a particular concern, which is associated with a significant decrease in the number of this population category, deterioration of their somatic, mental and reproductive health.Health in modern society is considered as a global problem, which at the level of society determines the possibilities for survival and at the individual level as the possibilities of an individual for selfpreservation, self-knowledge and self-actualization.First of all, it is about the formation of a complex of qualities and abilities of an individual, which is aimed at self-development and connected not only with the desire to occupy a certain social position, which will allow an individual to realize himself as a social subject, revealing and improving his potential, but also to live a full life and bring a great № 8( 22) 2023 164 benefit to society.Today young generation is left without reliable social reference points.The destruction of traditional forms of socialization increased the personal responsibility of young people for their fate, putting them in front of the necessity of choice, revealed the unwillingness of most of them to join new social relations.Modern social realities require a qualitative understanding of a place and role of health and self-preservation behavior in the system of values, analysis of value orientations and life practices of students as agents of social change.Such a view of the problem will open opportunities for a deeper understanding of the most significant determinants of a healthy lifestyle, differences and similarities in everyday behavior of this social group, formation of dominant self-preservation behaviors that can be formed through education.Possessing the basics of health and self-preservation behavior is the main value of young generation and requires the assimilation of a certain amount of knowledge and skills acquired at the beginning of socialization in an educational establishment.It is what this article is dedicated to.
Retracted: Development and Optimization of Network Music Course Resources Based on Data Mining Technology under the Personalized Online Education Environment

[This retracts the article DOI: 10.1155/2022/2876063.].
Retracted: Construction of AI Environmental Music Education Application Model Based on Deep Learning

[This retracts the article DOI: 10.1155/2022/6440464.].
Evaluation of dynamic thermal comfort of virtual reality headsets in motion

Virtual reality (VR) headset has been increasingly applied in various activities with longer wearing time, such as gaming or training. Users no longer stay in a static state but are more likely to perform a series of body movements. This paper focuses on the dynamic thermal comfort of head mounted displays (HMDs) to explore the VR experience in state of motion. Eight participants wore three types of HMDs to conduct thermal comfort tests while doing light exercises. Thermal comfort was quantified by subjective rating and miniature data logger measurement. The results showed that wearing HMDs in motion caused more subjective thermal discomfort than in rest. Low airtightness of HMDs led to a lower increase in temperature and humidity inside the device in motion, yet it would also reduce the immersiveness of the VR experience due to light leakage. Furthermore, there was no significant difference in the subjective discomfort level of the three types of devices. In order to improve the overall user experience, the development of HMDs should consider design tradeoffs in terms of materials, shape, finishing etc., maximizing user comfort while ensuring optimal audiovisual display effect.
Application of Educational Context Data using Artificial Intelligence Methods

Today the web generates a large amount of data, the same ones that come from social networks, online platforms, communities, cloud computing, etc., but one type of data has not been recognized for its relevance and that is data from Learning Management Systems like Moodle in the educational context. Considering this context, this research will apply some Artificial Intelligence methods and techniques such as the TSA methodology, Text mining, and Sentiment Analysis to assess the data about the opinion of the students, converting them into stable information structures that allow their reflection and analysis. The work carried out focuses on determining the level of user satisfaction, in this case, the students, of the virtual learning platforms. The results obtained show that applying Artificial Intelligence allows obtaining relevant information that helps to undertake improvement actions by authorities and managers in the educational context based on the opinion of the students, detecting important problems in online learning during these times of COVID-19 we are just past.
THE EFFECT OF USING BIG BOOK MEDIA BASED ON PEER TUTORS TO STUDENTS’ COGNITIVE ABILITY OF MADRASAH IBTIDAIYAH

This research describes the effect of using Big Book media based on peer tutors to cognitive abilities in the fifth-grade students at MIN 1 Makassar. This research used quantitative methods with a pre-experimental design one-shot case study. The sample of this study was 57 fifth-grade students. The results of this research have been obtained. First, the use of Big Book media based on peer tutors in the fifth-grade students of MIN 1 Makassar was in the good category with an average value in the interval 113-118. Second, the cognitive abilities were fairly high at intervals of 66-76. Third, the hypothesis test results using one sample t-test analysis indicated the effect of using Big Book media based on peer tutors on the students’ cognitive abilities. Therefore, this study is expected to be a consideration for schools and teachers to use Big Book media to accommodate students' learning needs.&#x0D; Abstrak:&#x0D; Penelitian ini menjelaskan pengaruh penggunaan media Big Book berbasis tutor sebaya terhadap kemampuan kognitif siswa kelas V di MIN 1 Makassar. Penelitian ini menggunakan metode kuantitatif dengan desain pre-experimental one-shot case study. Sampel penelitian ini sevanyak 57 siswa kelas V. Hasil penelitian yang telah diperoleh, pertama, penggunaan media Big Book berdasarkan tutor sebaya pada siswa kelas V MIN 1 Makassar berada pada kategori baik dengan nilai rata-rata pada interval 113-118. Kedua, kemampuan kognitif cukup tinggi dengan interval 66-76. Ketiga, hasil uji hipotesis dengan menggunakan analisis one sample t-test menunjukkan adanya pengaruh penggunaan media Big Book berbasis tutor sebaya terhadap kemampuan kognitif siswa. Oleh karena itu, penelitian ini diharapkan dapat menjadi bahan pertimbangan bagi sekolah dan guru untuk menggunakan media Big Book yang mampu mengakomodasi kebutuhan belajar peserta didik.
BigMac: Performance Overhead of User Plane Integrity Protection in 5G Networks

5G introduces a series of new security features that overcome known issues of the previous mobile generations. One of these features is integrity protection for user plane data. While this addition protects against manipulations like DNS spoofing, it also introduces extra overhead to user plane traffic. As it is optional to enable, this additional overhead can be the decision point for network operators to avoid the additional security feature. In this work, we investigate the overhead induced by different integrity protection algorithms and test the burden they add to the workload of a device. Our results indicate how visible performance differences would be on the end-devices of users, and how the performance of the algorithms differs in isolation. With these results we aim to initiate a discussion regarding the benefits of enabling user plane integrity protection and to overcome misconceptions regarding the performance impairments for end users.
Exploring the World of Game Development: A Python-based Space Shooter Game

Abstract: This research paper discusses the development of a Space Shooter game using Python programming language. The objective of the game is to shoot and destroy alien spaceships while avoiding their attacks. The game is designed using Pygame,a Python module specifically created for game development. The paper begins with an introduction to the project, followed by a literature review of related work in game development using Python. The details of the project, including the game’s features, design, and code implementation, are then presented. The future scope of the project is discussed, highlighting potential improvements and enhancements. Finally, the paper concludes bysummarizing the project’s key findings and contributions. The development of a Space Shooter game using Python provides an exciting opportunity for game enthusiasts to explore the world of game development using a popular and versatile programming language. The result of coding a space shooter game is a fully functional and interactive game where players control a spaceship to combat enemy ships in a dynamic space environment. Players can move theirspaceship horizontally, shoot projectiles, and avoidobstacles. The game features multiple levels, increasing difficulty, and keeps track of scores and lives. The graphics and animations are rendered, creating an immersive space-themed experience. Collision detection accurately detects when projectiles hit enemies or obstacles, providing a satisfying gameplay experience. The game includes game over conditions and allows players to restart or quit, providing a complete and enjoyable gaming experience
Ideas on Digitally Supported Individualization of Teaching and Learning for Evolving Competency Requirements

The world is changing rapidly, mainly due to the digitalization of all areas of living. A huge amount of information is accessible via the Internet, and since it is no longer possible for individual humans to keep track of it, artificial intelligence (AI) is analyzing this data. In this rapidly changing world, students have to be educated for a successful career during their whole working life. These boundary conditions lead to completely new challenges for the education of students that are unprecedented in this form. Digitalization in education can help to cope with these challenges but can only be a means, not a goal. Personal interaction with students remains the most important task in education to address individual weaknesses and further develop strengths and talents. With the increasing amount of openly available information and the consequently increasing diversity of experiences within the group of students, differentiation is advancing to become the key to successful education. Digitization can help with this challenging task and support communication between students and their experienced instructors. But computers cannot replace human interaction and attempts to improve teaching efficiency by replacing this communication with electronic means endangers the learning success for complex concepts. This article analyzes education demands and possibilities for digitally supported teaching and learning.
Using SMS to improve TB treatment adherence and cure rate

From the findings of this study, it is evident that technological applications like SMS have improved healthcare services in many settings and it still offers great potential to achieve more use in TB programs. Considering this information and working with various health sector stakeholders,I proposed implementing this project in the Sidama region, Ethiopia. It is common knowledge that the health system operates in a challenging, ever-evolving environment. One of the key causes of this transition is technological advancement. There is an opportunity to employ technology to support healthcare services. This project hopes to change the culture and experience of using technology and its application to TB care and healthcare services in general. This project proposal adheres to the agile project management approach's guiding principles since it provides an opportunity to revise decisions throughout the lifecycleof the project.
Needs-analysis to the Sasaknese Locality-Embedded English Curriculum Development for Tourim Vocational High Schools

This study as a part of the process of English curriculum development aimed to examine the existing problems of the current curriculum implementation and the target needs and learning needs (local wisdom insertion). It drew on focus group discussion, interview, and content analysis. The results of the target need analysis on local knowledge and language competence for developing skills and abilities, needs-based, and contextual learning showed that a lack of the gaps as the fundamental problems elicited from the current curriculum. It was also found that in terms of Wants, the students preferred to knowledge and vocational background-based curriculum contents. English learning design was associated with tourism competencies embedded with local wisdom. The local wisdom can be developed into the English curriculum; values of knowledge, social norms, and cultural acculturations, the marriage systems, patterns of family, and the other social-cultural activities, and the elements of aesthetics. The study encapsulates that English teachers need to initiate developing a local wealth-based English curriculum in favor of orienting the students to develop local knowledge.
Secure QR-Code Generation in Healthcare

QR codes have become ubiquitous across several industries, including e-commerce, education, and healthcare.In the healthcare sector, QR codes are increasingly used to relay essential information regarding medical products, patient history, and healthcare education.In addition, QR codes have proven to help secure and preserve patient records during transmissions.This paper aims to develop and analyze the implementation of QR code technology for healthcare appli-cations.The proposed approach involves generating a unique QR code for each patient's information, facilitating data transmission between nodes such as hospitals.With its small size, the QR code provides a simple solution to transfer patient records, reducing internet capacity requirements and minimizing latency.The proposed system utilizes advanced techniques such as Diffie Hellman and logistic map for key generation and distribution, hash algorithm and Lorenz equation for key mask generation, the salting algorithm for data integrity, DNA encoding, and Huffman Algorithm for data coding.The proposed system's usability was tested with 366 patient records, and the results indicate that 99.76% of data was saved with a compression ratio of 416.8 for each patient record.Moreover, the comparative analysis demon-strated that the proposed strategy outperforms other data capacity, security, and integrity methods.Overall, the proposed system can revolutionize data transmission and security in the healthcare sector, ensuring the safety and confidentiality of patient records.
Accelerating the Training of Video Super-resolution Models

Despite that convolution neural networks (CNN) have recently demonstrated high-quality reconstruction for video super-resolution (VSR), efficiently training competitive VSR models remains a challenging problem. It usually takes an order of magnitude more time than training their counterpart image models, leading to long research cycles. Existing VSR methods typically train models with fixed spatial and temporal sizes from beginning to end. The fixed sizes are usually set to large values for good performance, resulting to slow training. However, is such a rigid training strategy necessary for VSR? In this work, we show that it is possible to gradually train video models from small to large spatial/temporal sizes, \ie, in an easy-to-hard manner. In particular, the whole training is divided into several stages and the earlier stage has smaller training spatial shape. Inside each stage, the temporal size also varies from short to long while the spatial size remains unchanged. Training is accelerated by such a multigrid training strategy, as most of computation is performed on smaller spatial and shorter temporal shapes. For further acceleration with GPU parallelization, we also investigate the large minibatch training without the loss in accuracy. Extensive experiments demonstrate that our method is capable of largely speeding up training (up to $6.2\times$ speedup in wall-clock training time) without performance drop for various VSR models.
Multi-View Domain Adaptive Object Detection on Camera Networks

In this paper, we study a new domain adaptation setting on camera networks, namely Multi-View Domain Adaptive Object Detection (MVDA-OD), in which labeled source data is unavailable in the target adaptation process and target data is captured from multiple overlapping cameras. In such a challenging context, existing methods including adversarial training and self-training fall short due to multi-domain data shift and the lack of source data. To tackle this problem, we propose a novel training framework consisting of two stages. First, we pre-train the backbone using self-supervised learning, in which a multi-view association is developed to construct an effective pretext task. Second, we fine-tune the detection head using robust self-training, where a tracking-based single-view augmentation is introduced to achieve weak-hard consistency learning. By doing so, an object detection model can take advantage of informative samples generated by multi-view association and single-view augmentation to learn discriminative backbones as well as robust detection classifiers. Experiments on two real-world multi-camera datasets demonstrate significant advantages of our approach over the state-of-the-art domain adaptive object detection methods.