Reviewerly/exHarmony huggingface dataset:
Enabling children’s genuine participation in digital design and fabrication: instructors’ perspective

Abstract Digital fabrication laboratories (Fab Labs) are accessible to the public, including children and families. However, a variety of technical skills, tools, and expertise are needed in these spaces, and Fab Lab staff—instructors working there—are in a key role in helping Fab Lab users. There is, however, a lack of research on how these instructors can inspire children's genuine participation in Fab Labs. We analyze the challenges faced by four instructors during two months of Fab Lab workshops with 7–12-year-old children. The children worked in child-only or child-parent groups on informal digital design and fabrication activities. Based on genuine participation principles derived from the literature, we designed our workshops, which included the creation of instructor guideline. At the end, we developed a post-workshop framework in which the instructors provided feedback about their performance and experiences. We provide recommendations to help instructors support children's genuine participation in digital design and fabrication.
Quality of Experience of Immersive Media – New Challenges

In the past decade, significant progress in imaging and computing technologies has unleashed the creation of more accurate representations of the physical world, enabling immersive and interactive experiences that more closely resemble reality [1]. As a consequence, immersive applications have attracted a lot of interdisciplinary attention, with the development of multimodal human-computer interactions that allow users to fully immerse themselves within realistic or virtual environments or to interact with real/virtual elements seamlessly blended with the physical world [6]. Immersive experiences can be found at different points along the “virtuality continuum,” ranging from photorealistic settings, through mixed reality approaches, to completely virtual environments.
Bytes and Rhythms – Delving into the Tech-Driven Evolution of Food and Music

In the rapidly advancing domain of design and technology, the transition from a predominantly human-centred approach to a more encompassing more-than-human perspective is gaining traction. This paper investigates the implications of such a shift, focusing on the domains of music creation and culinary experiences—two intrinsic human activities deeply embedded within larger ecosystems. Utilising immersive Virtual Reality (VR) methodologies, we examine the intricate relationship musicians have with their sonic environments and the profound connections diners establish with the food life-cycle. Our research highlights the constraints of an approach that prioritises only human experiences, unveiling potential avenues for more holistic, interconnected engagements. By incorporating more-than-human elements, from the ambient subtleties influencing musical performances to the comprehensive journey of food from origin to consumption, we advocate for a design perspective that is both holistic and integrative. This perspective not only enriches human experiences but also promotes a balanced coexistence with the entities that influence our daily interactions.
WSI2ML – An Open-Source Whole Slide Image Annotation Software for Machine Learning Applications

Machine learning (ML) has emerged as a powerful tool for improving the clinical pathology routine; however, developing novel ML research requires a complex multidisciplinary team effort. Therefore, a software for tackling the challenges of effectively annotating, building, and validating ML models is desirable. In this work we present WSI2ML, a web-based platform that provides a friendly interface suited for ML computational pathology research to be easily performed. Compared to similar tools currently available, the proposed software provides a complete toolset for each stage of ML workflow. We demonstrate the usefulness and functionality of WSI2ML by analyzing the performance results obtained in a tissue recognition task using a novel gastric cancer research dataset that is currently being developed with the tool. The software, documentation, installation instructions and related annotation handling library is freely available at https://luanvcmartins.github.io/WSI2ML/.
Instruction Tuning Text-to-SQL with Large Language Models in the Power Grid Domain

This paper explores the large language models to address the Text-to-SQL task in real-world scenarios in the electricity domain. To tackle the lack of training data and corresponding databases for vertical domain real-world scenarios, the paper devised specific prompts to leverage ChatGPT for data generation, achieving significant improvements in annotation efficiency through automated data generation. Furthermore, to apply the powerful semantic parsing and generation capabilities of large language models to Text-to-SQL, the paper utilized a large language model for instruction tuning for SQL generation. This model has undergone secondary pre-training with electrical knowledge, tailoring it to the specific SQL generation task. On the power grid test set, the paper’s matching accuracy reached 65.7%, and the execution accuracy reached 80.9%. Additionally, the paper conducted further tests on various general large language models for zero-shot learning and single-sample prompt-based Text-to-SQL. The results indicate that while simple single-table queries can be achieved, meeting the requirements for complex queries remains challenging.
Metaverse Libraries

As the field of intelligent libraries continues to evolve, Metaverse technology presents a promising opportunity for driving innovation and improving library services. This chapter delves into the potential of Metaverse to transform libraries into fully-intelligent spaces. The authors will explore the underlying technologies and theoretical foundations of Metaverse, and examine how it can be applied to enhance library collection management, reading spaces, cultural promotion, reading experiences, and user services. By harnessing the power of Metaverse, intelligent libraries have the opportunity to create immersive experiences and achieve significant progress in their digital transformations.
Using Everyday Objects as Props for Virtual Objects in First Person Augmented Reality Games: An Elicitation Study

In this paper, we present an elicitation study which explores how people use common household objects as props to control virtual objects in augmented reality first-person perspective games. 24 participants were asked to select items from a range of common household objects to use as controllers for three different types of virtual object: a sword, shield, and crossbow. Participants completed short gameplay tasks using their selected items and rated the AR experience using the Augmented Reality Immersion (ARI) questionnaire. Results found no strong consensus linking any specific household object to any virtual object across our test group and, in addition, those who chose the most commonly selected object for each task did not have significantly higher scores on the ARI questionnaire compared to those who did not. A short post-experiment interview indicated a few key factors that were important to participants when selecting their household object, such as shape, size, grip feel and weight distribution. Based on our findings we recommend that developers provide the ability for users to choose which household objects to use as props based on the user's own preferences, and that they design intuitive ways for users to interact with virtual objects.
Application of the Think-Pair-Share Type Cooperative Model on Poetry Appreciation Learning

Learning poetry appreciation is essentially learning to explore the values contained in the poem. However, the facts show that until now, poetry is still considered as one of the most difficult and boring subjects. Learning with the Think-Pair-Share Cooperative Model in Poetry Appreciation Learning for class X MA Hidayatul Ummah Pringgoboyo for the 2021/2022 academic year can help students improve their learning achievement. The purpose of this study was to determine the effectiveness of Think-Pair-Share Cooperative learning on Poetry Appreciation material in terms of: (1) teacher activity, (2) student activity, (3) student learning completeness, and (4) student response in class X MA. Hidayatul Ummah Pringgoboyo. The source of data in this study were students of cass X MA Hidayatul Ummah Pringgoboyo. Tis research used descriptive qalitative methods. Data collection techniques used are observation methods, tests, and questionnaires.Obtained from student complete learning the teacher positive value is 93,75&amp; and the student’s positive activity is 95.16%, and the result of the stdent’s positive response are 78%.Poetry in clas X MA Hidayah Ummah Pringgoboyo Acad Observation methods are used to determine the activities of teachers and in the 2021/2022 academic year.
CLHHN: Category-aware Lossless Heterogeneous Hypergraph Neural Network for Session-based Recommendation

In recent years, session-based recommendation (SBR), which seeks to predict the target user’s next click based on anonymous interaction sequences, has drawn increasing interest for its practicality. The key to completing the SBR task is modeling user intent accurately. Due to the popularity of graph neural networks (GNNs), most state-of-the-art (SOTA) SBR approaches attempt to model user intent from the transitions among items in a session with GNNs. Despite their accomplishments, there are still two limitations. First, most existing SBR approaches utilize limited information from short user–item interaction sequences and suffer from the data sparsity problem of session data. Second, most GNN-based SBR approaches describe pairwise relations between items while neglecting complex and high-order data relations. Although some recent studies based on hypergraph neural networks have been proposed to model complex and high-order relations, they usually output unsatisfactory results due to insufficient relation modeling and information loss. To this end, we propose a category-aware lossless heterogeneous hypergraph neural network (CLHHN) in this article to recommend possible items to the target users by leveraging the category of items. More specifically, we convert each category-aware session sequence with repeated user clicks into a lossless heterogeneous hypergraph consisting of item and category nodes as well as three types of hyperedges, each of which can capture specific relations to reflect various user intents. Then, we design an attention-based lossless hypergraph convolutional network to generate sessionwise and multi-granularity intent-aware item representations. Experiments on three real-world datasets indicate that CLHHN can outperform the SOTA models in making a better tradeoff between prediction performance and training efficiency. An ablation study also demonstrates the necessity of CLHHN’s key components.
The formation of «digital philosophy» as a new direction of philosophy

Today, the problem of digitization attracts special attention of science and society. Because its role in the development of social institutions, in the organization of everyday life and in the socialization of an individuals is increasingly evident. Philosophy is also contributing to the vision of digitization, creating a conceptual framework for understanding it, and assessing its social and humanitarian consequences. Due to digitization, social development and changes in the technological field have caused the emergence of a huge flow of information. Modern social philosophy today is experiencing a digital turning point, which is characterized not only by changes in the means of knowledge, but also by the transition of society to a new, digital stage in its development. Understanding the new model of the digital society requires looking at the history of its formation. Even simple use of digital technologies in humanitarian sciences, including philosophy, has not only resulted in methodological and methodical difficulties, but also determining the field of its application has become a serious problem. In the middle of the 20th century, due to the development of computer technology and the emergence of the digital revolution, «digital philosophy» began to form as a new direction of philosophy. The article discusses philosophy and humanities, their place and role in society and culture in the age of digitization. Also, the main definitions of the concept of «digital philosophy» are considered, and the features and methodologies of the research subject are defined.
Hybrid Model for Robotic Surveillance using Advance Computing Techniques with IoT

The primary purpose of this research is to design and construct a surveillance robot with the potential to reduce the number of people killed in combat. The Internet of Things (IoT) technology is utilised to tackle the issues that arise from directing the movement of the robot through short-range communication. The movement of the robot may be controlled from almost any location in the globe using an Android smartphone. This research work is divided into five different phases: manually running the robot via the use of IoT technology; automatically controlling the robot through the use of an Android device or a personal computer; Wireless Streaming live video captured by a night vision camera equipped with audio, advanced computational capabilities, and metal detecting sensors. The execution will begin as soon as the metal detector and the advanced computer sensors identify the robot and cause it to stop moving. These days, the remote monitoring and surveillance of the homes is more important than ever before. Additionally, the camera is equipped with a face recognition technology that enables it to identify the person whose motion was detected and prompted the camera's activation. The in-vehicle voice assistant will start a conversation with the caller if they are approved to use the system. It is only when there is an unauthorised person present that a notification will be delivered. Along with the notice, a live webcam broadcast will begin, and it will also contain photographs of the intruder. The participants in this research are being monitored using Internet of Things (IoT)-based robots that make use of an Arduino UNO microcontroller. These robots may be controlled by a computer or a smartphone. The objective is to design a remote-controlled spy car that is capable of carrying out continuous surveillance in hostile conditions. The robot is equipped with a wireless camera, which enables it to collect real-time streaming throughout the day as well as during the night. The user will physically control these robot movements in order to operate them. Because of this robot, the amount of human contact that occurs in a hazardous location that requires constant monitoring and safety measures is instantly decreased. An advanced computer sensor, an ultrasonic sensor, and a gas sensor, each of which is linked to an Arduino board, make up the whole of the system. The board also serves as the connection point for the other sensors. An Android application may control the navigation of the robot from a distant location by using WiFi connection. In the event that a person gets buried under a structure as a result of a natural disaster like as an earthquake, the sophisticated computer sensor that the robot has may be used to locate them.
Efikasi Diri Siswa EFL Indonesia Terhadap Prestasi Belajar (Studi Kasus Pada Tahun Penerapan PBI 2017 1.2)

This study aims to find out how self-efficacy impacts Indonesian EFL learners.learning achievement. This research was conducted at UIN Alauddin Makassar. The research subjects were six students Department of English Education class of 2017 PBI 1.2. The method used in this research is qualitative research using a case study design. Instruments used to collect the data is an interview guide to explore sources of self-efficacy. Interview data were analyzed based on data analysis procedures consisting of compaction data, data presentation, and drawing conclusions/ verification data. The results of the study show that students who have a high grade point average have efficacy high self-esteem, seen from his confidence in answering questions and seen from the source of self-efficacy students who have a high grade point average have high self-efficacy in experience and mastery physiological state. Students who have a moderate grade point average have high self-efficacy because they have High confidence in answering questions. and seen from the source of student self-efficacy having a moderate achievement index has high self-efficacy in representative experiences and circumstances physique. Students who have a low grade point average have low self-efficacy because they have no sense High confidence in their ability to answer questions. So, students who have low achievement index has low self-efficacy in mastery of experience and physical condition.Based on these findings, the researcher recommends that it is important for students to identify their self-efficacy because self-efficacy is very important for students to deal with problems in academics College. In addition, students with high academic self-efficacy tend to persist in challenging academic assignments and strive to achieve success in academic tertiary institutions. while students with low self-efficacy tend to give up easily and lack the confidence to succeed come on stage. given assignment
Use Of Interactive Learning Media To Improve Language Capabilities Early Children

This research is based on the changing "Mood" of AUD children, so this is inline with an interactive learning model, in encouraging children's curiosity for knowledge, it is important to know the teacher's strategy in using interactive learning media to improve early childhood language skills, The intended ability includes the ability that children use to communicate, both in the form of writing, speech, body code and sign language. In an effort to reveal the facts from the findings when this research was conducted, the method used in this study was a quantitative method. In its implementation, the sample from this study was tested on a limited scale and also tested on a wide scale. For the limited-scale trial consisting of 2 teachers and 1 class of children in group B1 totaling 15 people, while for the wide-scale test consisting of 2 teachers with 2 classes of children in groups B1 and B2 totaling 30 children. To support the iteration of the required data, this research in the implementation of collecting data uses direct observation and written test. The data analysis technique applied was using the average score, percentage and t-test. It is concluded that the development of interactive multimedia-based teaching materials is feasible to use to improve language skills based on limited and broad tests. And the development model is effective in improving children's language skills
Generating Control Command for an Autonomous Vehicle Based on Environmental Information

This paper presents a novel CNN architecture using an end-to-end learning technique to predict the steering angle for self-driving cars. The front camera is the only sensor used to generate this control command. This network was trained and tested on Sully Chen Public Dataset, which contains image frames and steering angle data for each image. The test outcomes demonstrated that this model could generate a reasonably accurate steering angle for autonomous vehicles and perform about 60% better than the networks designed from 2017 to 2021. In addition, the problem of overfitting in previous networks has mainly been addressed with the help of the new network architecture and different data preprocessing.
THE BIDIRECTIONAL RELATIONSHIP BETWEEN HIGHER EDUCATION AND INNOVATION: EMPIRICAL EVIDENCE FROM MENA REGION

The purpose of thispaper is to examine the bidirectional relationship between two sets of variables, higher education and training (eight indicators) and innovation (seven indicators), using the canonical correlation analysis (CCA). The study utilisesdata published by the World Economic Forum Reports for seven years (2012-2018) for a sample of 12 countries in the MENA area.CCA is used to analyse the relationship between the two sets of variables. It helps in the evaluation of the interchangeable relationships between the two sets of multiple variates. More in-depth analysis of the nature of such a relationship between the two sets of variables is provided through redundancy analysis to identify the percentage of the variance in each set that is interpreted by the other set, andthe commonality analysis to determine the variance of canonical function that is due to unique or standard variables. Canonicalanalysis shows the causality between the two endogenous sets of variables. Also, the findings suggest that technology alone is not an antidote, while other factors might have a significant impact on innovation. Commonality analysis shows that the role of quality of management school in explaining the variation in canonical function of innovation in common with “quality of education system”, “quality of math and science”, “internet access”, “training availability and “staff training produce”.
MIPE: Towards Cleaning the Datasets of Autonomous Driving Systems using Multi-modal Information Processing and Confident Learning

Numerous autonomous driving systems employ deep learning-based image object detection schemes for their navigation. For developing reliable autonomous driving systems, the training process of the deep image object detectors must be performed in a precise manner. Existence of samples with erroneous labels, e.g., erroneous bounding boxes, in the training datasets of autonomous driving systems leads to a reduction in their performance and a decrease in their reliability in real-life situations. Given these explanations, in this paper, we propose a novel erroneous bounding box detection scheme for identifying the bounding boxes in the training datasets that are annotated wrongly, and avoid their use in the training process of autonomous driving systems. Specifically, we employ two efficient techniques, namely, multi-modal information processing and confident learning, in the development of the proposed scheme. In the multi-modal information processing, we first obtain the instance segmentation maps of the images using deep image instance segmentation networks, and then, utilize their interactions with the spatial coordinates of the bounding boxes, along with the spatial coordinates themselves, to generate discriminative sets of features for the task of erroneous bounding box detection. Further, by using the confident learning technique, we leverage the statistical information of the estimated erroneous statuses of the bounding boxes, to further enhance the performance of the task of erroneous bounding box detection. The results of extensive experimentations demonstrate the effectiveness of the proposed erroneous bounding box detection scheme in cleaning the datasets of autonomous driving systems, compared to the state-of-the-art data selection schemes.