Reviewerly/exHarmony huggingface dataset:
Using machine learning to explore the characteristics of eye movement patterns and relationship with cognition ability of Chinese children aged 1–6 years

Researchers have begun to investigate the relationship between eye movement characteristics of gaze patterns and cognitive abilities, and have attempted to use eye-tracking technology as a new method to evaluate cognitive abilities. Traditional eye movement analysis methods typically separate spatial and temporal information of eye movements, mostly analyze averaged data, and consider individual differences as noise. In addition, current eye movement studies on gaze patterns mostly involve adults, while research on infants and toddlers is limited with small sample sizes and narrow age ranges. It is still unknown whether the conclusions drawn from adult-based research can be applied to children. Consequently, eye movement research on gaze patterns in children is necessary. To address the concerns stated above, this study used the Hidden Markov machine learning method to model gaze patterns of 330 children aged 1–6 years while observing faces freely, and analyzed characteristics of eye movement gaze patterns. Additionally, we analyzed the correlation between gaze patterns of 31 toddlers aged 1–3 years and 37 preschoolers aged 4–6 years, and the different dimensions of cognitive abilities. The findings indicated that children exhibited holistic and analytic gaze patterns while observing different faces freely. More children adopted a holistic gaze pattern, and there were age-specific gaze pattern characteristics and regularities. Gaze patterns of toddlers may be correlated with their adaptive abilities and gaze patterns of preschoolers may be correlated with their visual space abilities. Specifically, toddlers aged 1–3 years showed a moderate negative correlation between the H-A scale and the adaptive dimension, while preschoolers aged 4–6 years showed a low negative correlation between the H-A scale and the visual space dimension. This study may provide new insights into the characteristics of children’s eye-movement gaze patterns during face observation, and potentially offer objective evidence for future research aimed at promoting the use of eye-tracking technology in the assessment of toddlers’ adaptive abilities and preschoolers’ visual space abilities in the field of face perception.
A Complete V-Equational System for Graded lambda-Calculus

Modern programming frequently requires generalised notions of program equivalence based on a metric or a similar structure. Previous work addressed this challenge by introducing the notion of a V-equation, i.e. an equation labelled by an element of a quantale V, which covers inter alia (ultra-)metric, classical, and fuzzy (in)equations. It also introduced a V-equational system for the linear variant of lambda-calculus where any given resource must be used exactly once. In this paper we drop the (often too strict) linearity constraint by adding graded modal types which allow multiple uses of a resource in a controlled manner. We show that such a control, whilst providing more expressivity to the programmer, also interacts more richly with V-equations than the linear or Cartesian cases. Our main result is the introduction of a sound and complete V-equational system for a lambda-calculus with graded modal types interpreted by what we call a Lipschitz exponential comonad. We also show how to build such comonads canonically via a universal construction, and use our results to derive graded metric equational systems (and corresponding models) for programs with timed and probabilistic behaviour.
Peer Review Statement

All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. Type of peer review: Single Anonymous Conference submission management system: Morressier Number of submissions received: 58 Number of submissions sent for review: 35 Number of submissions accepted: 35 Acceptance Rate (Submissions Accepted / Submissions Received × 100): 60.35 Average number of reviews per paper: 3.00 Total number of reviewers involved: 55 Number of final published papers:25 Contact person for queries: Name: Gow-Yi Tzou Email: gtzou@uow.edu.au Affiliation: University of Wollongong, Australia Chung Chou University of Science and Technology, Taiwan
Application of onboarding in product IT project team management

The article examines the peculiarities of IT project management in product companies. Usually, the team of such a project will be updated for a long time. The intensity of such an update will depend on the number of the company's products, the growth of their functionality and the number of customers. Team management is considered in the sense of adapting new team members to the features of the product that is developed and administered by the IT company. The peculiarity of this process is that it should be connected with the features of the product. The training processes should take place in the form of mentoring, in the role of leading specialists of the company. The article reviews literature sources on approaches to training and adoption of new employees. The role of intellectual capital in the created values for the company and the product is highlighted. Onboarding technology is analyzed, its advantages and disadvantages are determined. Based on the results of the analysis, an approach was developed for the adoption of new members of the product project team in two directions: 1) courses to improve technical competences; 2) activities to acquaint newcomers with business processes and the company's corporate culture. The measures that were developed for specific projects in the above-mentioned directions are presented.
An Automatic Vehicle Speed Controlling based on Traffic Signs Recognition using Convolutional Neural Network

Autonomous driving systems and applications in the real time are the trending field these days. To carry out all the necessary operations in accordance with the requirements, these systems do require sophisticated algorithms. One of the most challenging constraints in performing actions by autonomous driving systems is controlling road speed. The novel method proposed for motor controlling speed in accordance with speed stipulations uses a Convolutional Neural Network (CNN) algorithm to scan speed limit sign boards. The effectiveness of our system is contingent on CNNs' ability to properly recognize and classify objects in images, which are then used to extract speed limit information from sign boards. The pace of the robot chassis is regulated by an Arduino microcontroller using the CNN’s output. Through simulations and tests on a real testbed, we show how effective our method is and show that it can maintain the desired speed with a tolerable degree of error.
Revolutionizing Heritage Site Information Retrieval: A Deep Learning Approach Utilizing CNN and SVM for Effective Classification of Cultural Heritage Sites

Categorizing cultural heritage photographs into many categories, such as historical relevance, architectural features, and preservation status, is necessary for retrieving information from heritage sites. This research investigates categorising photographs from historical sites using deep learning methods like Convolutional Neural Networks (CNN) and Support Vector Machines (SVM). For classification, we created a system that used a CNN model with three convolutional layers, four max-pooling layers, a fully connected layer, and an SVM model. Our model classified the 8645 photos in the dataset utilized in this research into six separate categories, with an overall accuracy of 0. 89091. To create a successful historic site information retrieval system, comprehensive data collection and processing, feature extraction, algorithm creation, and assessment metrics are essential. We review the methods and strategies used in each process and the difficulties and constraints faced during the system's development and testing. To obtain high classification accuracy, thorough data processing and model optimization is crucial. Our results show the potential of deep learning techniques for historic site information retrieval. Future research might increase the dataset's size further to verify the efficacy of the CNN and SVM models to include additional categories and pictures. It may also be investigated to optimize the CNN and SVM models further to increase classification accuracy. Overall, our work offers insights into the significance of meticulous data processing and model optimization in creating an efficient system and adds to developing a heritage site information retrieval system utilizing CNN and SVM algorithms.
Penerapan Algoritma Rivest-Shamir-Adleman (RSA) pada Enkripsi Uniform Resource Locator (URL) Website untuk Keamanan Data

PT. Rezeki Surya Gasindo is a company that uses websites to store important company data, one of which is customer personal data. PT. Rezeki Surya Gasindo’s website is protected by a login system. However, a login system alone is not enough to protect the data stored on the website from cases of data theft by third parties. One solution to this problem is to encrypt the website's Uniform Resource Locator (URL) to increase the security level of data stored on the website. In this research, the Rivest-Shamir-Adleman (RSA) algorithm is used for the encryption process. The aim of this research is to determine the process of applying the Rivest-Shamir-Adleman (RSA) algorithm to the Uniform Resource Locator (URL) encryption of the PT. Surya Gasindo's website. The success of applying encryption with the RSA algorithm is observed from changes in the get parameter value that appears in the URL bar. The encrypted message is the customer, the get parameter in the Customer menu, which contains the consumer's personal data. By choosing two large prime numbers, namely 151 and 173, and taking one of the public keys/encryption keys, namely 16379, the result is that the get parameter in the URL bar has changed to the code string 5a9cb05811aa6e4c. The RSA algorithm has been successfully applied to the website URL.
Use of Enhanced Greedy Algorithm for Load Balancing in Cloud Computing

Because of the Internet’s phenomenal growth in recent years, computing resources are now more widely available. It led to the development of a new computing concept known as Cloud Computing, allowing users to share resources such as networks, servers, storage, applications, services, software, and data across multiple devices on demand for economical and fast. Load balancing is an important branch of cloud computing as it optimizes machine utilization by distributing tasks equally over resources. It occurs among physical hosts or Virtual Machines in a cloud environment. Round robin is a commonly used algorithm in load balancing. RR gives a time quantum for each task and is in circular order. It is noted that it suffers from many problems, such as the waste of time and the high cost. In the present study, the greedy algorithm was enhanced and implemented to allocate and schedule tasks that come to the cloud on Virtual Machines in balance. The task with the longest execution time is given to the virtual machine with the least load using an improved greedy algorithm. The outcomes demonstrate that the suggested algorithm outperformed round robin in makespan. Also, all Virtual Machines in the proposed algorithm finish their work simultaneously, whereas round robin is unbalanced.
Analysis and Improvement of Grammar Questions in the Korean Language Teacher Appointment Examination

Objectives This study critically analyzes questions in the grammar area of the appointment examination from 2014 to 2023 and proposes directions for improvement.&#x0D; Methods Marzano (2007)'s new taxonomy of educational objectives was used to establish a framework for what characteristics grammar questions should have. And using it, grammar questions were analyzed according to the level of thinking and knowledge type.&#x0D; Results The questions corresponding to the subject education were just questions about the content of the sub-ject, most of the information knowledge were about understanding and analysis, and no procedural knowledge was asked.&#x0D; Conclusions The system of subject content and subject education in grammar questions should be restructured, and grammar questions should include questions that evaluate procedural knowledge as well as information knowledge on understanding, analysis, utilization, and metacognition.
Middle school students using artificial intelligence (ChatGPT) the actual writing education

Objectives In this study, middle school students try to write poetry using artificial intelligence (ChatGPT) and ex-plore the possibility of artificial intelligence as a writing tool. Critical issues such as conceptual problems of crea-tivity, plagiarism, ethical problems, and human-specific thinking problems are being discussed from the per-spective that artificial intelligence is capable of creative writing, which is considered a unique area of humans. In an era when the concept of writing is changing, education also needs a new approach and method. Therefore, we would like to actively respond to the era of artificial intelligence and present a new direction for poetry writing education.&#x0D; Methods 148 middle school students (67 female students and 81 male students) in Gyeonggi-do were subjected to poetry writing using artificial intelligence (ChatGPT). First, enter the text introducing yourself at the prompt and ask artificial intelligence to define it in a way that says, “I am~.” After that, a poem was written using the calculated definition, and the final result was submitted after ‘correcting’ it using artificial intelligence again. The results of the survey were analyzed at what stage artificial intelligence was helpful.&#x0D; Results Students were interested in writing poems using artificial intelligence (ChatGPT), and after a series of questions, they derived a metaphor of ‘I am~’. However, I had a lot of trouble generating questions about how to enter the desired result. Later, in the survey, students with confidence in writing showed a positive response to the use of artificial intelligence in the future. After writing poems using artificial intelligence, the more useful the artificial intelligence was, the higher the future use plan was. In addition, artificial intelligence showed an opinion that it is helpful for writing poetry in the ‘expression, rewrite’ stage of writing.&#x0D; Conclusions Artificial intelligence (ChatGPT) is possible to use as one tool that can help you help to help writing. The ability to create questions, objectives, purpose, and genre of genre and genre should be previously developed. The ability to ask questions are soon connected to problems.In addition, education is also necessary to judge the properties of the door to determine the appropriateness and artificial intelligence should be conducted.
Gradient-Semantic Compensation for Incremental Semantic Segmentation

Incremental semantic segmentation focuses on continually learning the segmentation of new coming classes without obtaining the training data from previously seen classes. However, most current methods fail to tackle catastrophic forgetting and background shift since they 1) treat all previous classes equally without considering different forgetting paces caused by imbalanced gradient back-propagation; 2) lack strong semantic guidance between classes. In this paper, to solve the aforementioned challenges, we propose a Gradient-Semantic Compensation (GSC) model, which surmounts incremental semantic segmentation from both gradient and semantic perspectives. Specifically, to handle catastrophic forgetting from the gradient aspect, we develop a step-aware gradient compensation that can balance forgetting paces of previously seen classes by reweighting gradient back-propagation. Meanwhile, we propose a soft-sharp semantic relation distillation to distill consistent interclass semantic relations via soft labels for alleviating catastrophic forgetting from the semantic aspect. In addition, we design a prototypical pseudo re-labeling which provides strong semantic guidance to mitigate background shift. It produces high-quality pseudo labels for background pixels belonging to previous classes by assessing distances of pixels relative to class-wise prototypes. Experiments on three public segmentation datasets provide strong evidence for the effectiveness of our proposed GSC model.
A Secure Architectural Model using Blockchain and Estimated Trust Mechanism in Electronic Consumers

Consumer electronics devices, such as refrigerators, washing machines, TVs, smartphones, and household appliances, have become integral to human activities. However, these devices are vulnerable to security breaches and cyber-criminal threats, which can result in the theft and misuse of sensitive information. Existing security surveys and proposed schemes have encountered limitations in terms of redundancy and effectiveness. In this paper, we present a novel approach that ensures secure and transparent communication in consumer electronics. We introduce a multi-criterion decision-making model called TOPSIS, along with a weighted product model, to enhance the security and accuracy of the system. Furthermore, our proposed scheme employs a blockchain system for continuous tracking and monitoring of devices, ensuring accountability and surveillance of their past communications. Through comprehensive validation and verification against existing approaches using various security metrics, our proposed scheme demonstrates superior performance and effectiveness.
Visibility Estimation Based on Weakly Supervised Learning under Discrete Label Distribution

This paper proposes an end-to-end neural network model that fully utilizes the characteristic of uneven fog distribution to estimate visibility in fog images. Firstly, we transform the original single labels into discrete label distributions and introduce discrete label distribution learning on top of the existing classification networks to learn the difference in visibility information among different regions of an image. Then, we employ the bilinear attention pooling module to find the farthest visible region of fog in the image, which is incorporated into an attention-based branch. Finally, we conduct a cascaded fusion of the features extracted from the attention-based branch and the base branch. Extensive experimental results on a real highway dataset and a publicly available synthetic road dataset confirm the effectiveness of the proposed method, which has low annotation requirements, good robustness, and broad application space.
The Correlation between Parenting Style, Parent’s Involvement, and Emergent Literacy through Language Ability

This study aims to describe and analyze parenting style, parents' involvement, and emergent literacy through language ability. A quantitative approach with a correlational technique was used to obtain data from a sample of 167 people. The data collection was carried out using instruments consisting of parenting style (20 items), parent’s involvement (20 items), language ability (18 items), and emergent literacy (20 items). Then, the data from this study was analyzed using descriptive analysis, the classical assumption test, multiple linear regression analysis, and path analysis. The results of the study show that there is a direct correlation between parenting style with language ability, parenting style with emergent literacy, parent’s involvement with language ability, parent’s involvement with emergent literacy, and language ability with emergent literacy, and an indirect correlation between parenting style with emergent literacy through language ability and parent’s involvement with emergent literacy through language ability.
Clinical application of convolutional neural network for mass analysis on mammograms

The detection of masses on mammogram represents one of the earliest signs of a malignant breast cancer. However, masses may be hard to detect due to dense breast tissue, leading to false negative results. In this study, we aimed to explore the clinical application of the convolutional neural network (CNN)-based deep learning (DL) system constructed in our previous work as an objective and accurate tool for breast cancer screening and diagnosis in Asian women.This retrospective analysis included 324 patients with masses detected on mammograms at Shenzhen People's Hospital between April and December 2019. (I) Detection: images were independently analyzed by two junior radiologists who were blinded to relative results. Then, a senior radiologist analyzed the images after reviewing all the relevant information as the reference. (II) Classification: masses were classified by the same two junior radiologists and in consensus by two other seniors. Images were also input into the DL system. The sensitivity of detection by junior radiologists and the DL system, effects of different factors [breast density; patient age; morphology, margin, size, breast imaging reporting and data system (BI-RADS) category of the mass] on detection, the accuracy, sensitivity, and specificity of classification, and the area under the receiver operating characteristic (ROC) curve (AUC), were evaluated.A total of 618 masses were detected. The detection sensitivity of the two junior radiologists [78.0% (482/618) and 84.0% (519/618), respectively] was lower than that of the DL system [86.2% (533/618)]. Breast density significantly affected the detection by two junior radiologists (both P=0.030), but not by the DL system (P=0.385). The AUC for classifying masses as negative (BI-RADS 1, 2, 3) or positive (BI-RADS 4A, 4B, 4C, 5) for the DL system was significantly higher compared to those of the two junior radiologists, but not significantly different compared to seniors [DL system, 0.697; junior, 0.612 and 0.620 (P=0.021, 0.019); senior in consensus, 0.748 (P=0.071)].The CNN-based DL system could assist junior radiologists in improving mass detection and is not affected by breast density. This DL system may have clinical utility in women with dense breasts, including reducing the impact caused by inexperienced radiologists and the potential for missed diagnoses.
Illuminating the augmented reality stage: Assessment of lighting conditions for space exploration

Augmented reality (AR) technology faces a prevailing challenge when dealing with harsh lighting environments, which are common in space. The study aims to evaluate the lighting compatibility for successful hand gestures using HoloLens 2, a device that utilizes AR technology for heads-up displays (HUDs), as a potential way to provide critical information to astronauts during extravehicular activities (EVAs). The study modulated luminance (dim, neutral, bright) and hand coverings (bare hand, white glove, black glove) to determine the HoloLens functionality. The results will inform design changes to support the astronaut experience by enhancing hand gesture recognition and reducing time wasted re-gesturing. By understanding the lighting requirements for HoloLens, we can improve the AR experience for future space missions. This study highlights the importance of considering lighting conditions in the development of AR technology, especially in challenging environments like space.