Reviewerly/exHarmony huggingface dataset:
Future Public Parks: Integrating Facilities for Locative Augmented Reality Games

Contemporary youth culture has caused public parks to be used as locations for them to play Locative Augmented Reality Games (LARG). Nevertheless, the existing public parks were not designed for LARG, which requires wide space for youth to have rigorous physical activities and movement. Therefore, there is a need to integrate facilities for players of LARG in the development of future public parks. This research aims to evaluate the characteristics and components of public parks often chosen by youngsters to play locative augmented reality games and to recommend additional facilities to be integrated into future public parks for all-inclusiveness. This research uses Vlog and On-site observation to evaluate components of public parks. It also employs an open-ended questionnaire survey for collecting primary data on needed additional facilities for future public parks. For reason of Movement Control Order (MCO) during the Covid19 pandemic, the research could evaluate only three (3) public parks that are located in Kuala Lumpur, Malaysia. The finding shows that public parks need design improvement to integrate spaces and facilities for Locative Augmented Reality games players (LARG). The research implies that the function of public parks evolves based on demands from visitors. This research indicates that public parks should be designed to cater for digital recreational activities, triggered by contemporary youth culture and relevant to the digital future.
Exploring the Role of Explainability in AI-Assisted Embryo Selection

In Vitro Fertilization is among the most widespread treatments for infertility. One of its main challenges is the evaluation and selection of embryo for implantation, a process with large inter- and intra-clinician variability. Deep learning based methods are gaining attention, but its opaque nature compromises their acceptance in the clinical context, where transparency in the decision making is key. In this paper we analyze the current work in the explainability of AI-assisted embryo analysis models, identifying the limitations. We also discuss how these models could be integrated in the clinical context as decision support systems, considering the needs of clinicians and patients. Finally, we propose guidelines for the sake of increasing interpretability and trustworthiness, pushing this technology forward towards established clinical practice.
SAT-IT: The Interactive SAT Tracer

In this work we present the Interactive SAT Tracer (SAT-IT), a visual and interactive tool to monitor and illustrate the basic algorithms for solving the Boolean Satisfiability Problem (SAT). We consider three algorithms with progressively increasing sophistication: simple backtracking, its extension with unit-propagation, so called Davis-Putnam-Logemann-Loveland (DPLL), and its further extension with clause-learning and conflict driven, so called Conflict-Driven Clause Learning (CDCL). The motivation of this tool is to provide an environment where the user can see the full trace of a SAT solving process in a compact but detailed way and understand the reasons why each variable assignment, backtrack or back-jump occurs. Moreover, we want the user to be able to control the evolution of the solving process at the desired pace, and let they choose what branchings (or decisions) must be done. Being able to control the solving process and having detailed information of this process, results in many pedagogical and research applications such as understanding algorithms or designing encodings.
Identification Of Online Recruitment Fraud (ORF) Through Predictive Models

Job postings online have become popular these days due to connecting to job seekers around the world. There are also instances where the fraudulent employer posts a job online and expects people to apply to these postings. These fraudulent employers impend job seekers' privacy, spawns fake job offers, and wanes. We perceived that most of the Online Recruitment Fraud (ORF) has matching features. Though the user cannot categorize them, we propose using various predictive models like Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest, Naïve Bayes, or Logistics Regression to detect them effortlessly. Dataset with 17780 job postings was downloaded from Kaggle to identify which proposed model best predicts the fraudulent job posting. The dataset includes 14 features to determine whether online job posting is fraudulent or non-fraudulent. 70% of these job postings train the model, and the remaining 30% test the model's efficiency. The outcomes of each model are predicted using four evaluation metrics – Classification Accuracy (CA), Precision, Recall and F-1 score. The research found its suitability from two sides: the websites can identify fake jobs before being published, and job seekers are sheltered from fraudulent job postings.
Standards of Building e-Learning Systems in Higher Education

At the beginning of the 21st century, virtual education has been positioned as an alternative modality, among other aspects, due to psycho-pedagogical advances and constant technological innovation. There has also been an exponential increase in educational proposals online due to the influence exerted by globalization and the increase in the use of information and communication technologies to provide educational services. Thus, virtual teaching and the development of learning objects represent permanent research phenomena in all disciplinary areas. Digital learning is characterized by the convergence of methods, technologies, applications and services aimed at sustaining learning via the web-portals. Therefore, online training considers as fundamental aspects for the learning development of skills in the management of technological platforms. It is also clear that online education is intended to penetrate the education market at all levels, and this is what also prompts reflection on the potential impact on education in the present century.
The Effects of a Constructively Aligned Virtual Reality Setting on Professionals' Knowledge, Motivation and Perceptions

Although previous research on Virtual Reality (VR) demonstrated the effects of particular learning environment characteristics on learning, none of these studies constructed their virtual learning environment from a constructive alignment perspective. Therefore, this experimental study aims to investigate the impact of a constructively aligned virtual classroom setting, adopting an Artificial Intelligence (AI) training, on professionals’ knowledge on AI. This experimental condition was compared with a control condition, consisting of a similar constructively aligned AI-training within a traditional face-to-face setting. Learning outcomes were measured using a pre-test post-test validated multiple-choice test. Additionally, motivation and perceptions, which are considered as crucial intermediate variables, were assessed using questionnaires. Results revealed significant improvements in learning from pre-test to post-test with no statistical difference between the conditions. Following the principle of constructive alignment, professionals perceived the VR classroom environment as motivating as the traditional setting. As a result, professionals perceived the VR classroom setting to the same extent as the traditional learning environment. These findings reveal that improvements in learning outcomes of professionals can be realized if environments are designed based on the principle of constructive alignment irrespective of the VR or traditional settings.
Teaching Vocabulary To Young Learners: Combination Of Semantic Mapping And Bingo Game

Students need to have many vocabularies in their mind in order to master four language skills in English namely listening, speaking, reading and writing. In addition, students should acquire number of words and they should know the meaning of the words then use them accurately. The vocabulary mastery would help students in developing their, understanding, and also enhancing the process of transferring information better. Teaching vocabulary is a challenge for teachers especially in teaching young learners. As the characteristics of young learner who love learning through playing, it is a need for the teacher to have an innovative strategy in teaching vocabulary. In addition, the combination of semantic mapping and bingo games will be the solution of the teaching vocabulary to young learners. This combination make the students learn words together with other students based on their background knowledge and review the vocabularies by implementing bingo games.
A New Non-Convex Framework to Improve Asymptotical Knowledge on Generic Stochastic Gradient Descent

Stochastic gradient optimization methods are broadly used to minimize non-convex smooth objective functions, for instance when training deep neural networks. However, theoretical guarantees on the asymptotic behaviour of these methods remain scarce. Especially, ensuring almost-sure convergence of the iterates to a stationary point is quite challenging. In this work, we introduce a new Kurdyka Łojasiewicz theoretical framework to analyze asymptotic behavior of stochastic gradient descent (SGD) schemes when minimizing non-convex smooth objectives. In particular, our framework provides new almost-sure convergence results, on iterates generated by any SGD method satisfying mild conditional descent conditions. We illustrate the proposed framework by means of several toy simulation examples. We illustrate the role of the considered theoretical assumptions, and investigate how SGD iterates are impacted whether these assumptions are either fully or partially satisfied.
Optimalisasi Tracking Rekam Medis Dengan Barcode di Puskesmas Lendah II

In 2014, primary health centres Lendah II was implemented health information system for primary care (Simpus). the use of simpus can help the physician and health practicioners entry data of patient, import dan export data and also improved theirs performance. The implementation of medical records for primary health centres an important role in managing patient data. The use of technology and information systems in the health sector has an impact on optimizing services. One of them is by utilizing a barcode system to support storage at primary health centres Lendah II This study aims to help the storage tracking system in the filing room using barcodes. Data capture with FGD for needs analysis, then application design and trials. This research uses research and develompent. The results of the study by making class diagrams, barcode applications and examples of barcodes applied to poly service names and medical record numbers. Barcodes will be printed and affixed to the patient's medical record to facilitate tracking of the The barcode of the patient's medical record number will also be affixed to the patient's BPJS card, making it easier for officers to check the patient's medical record number on the simpus. There is a scanner reader tool that will make it easier for officers to read barcode codes.
AUTOMATIC CONTROL COMPUTER SYSTEM OF PRESSURE IN THE LIQUID CARBON DIOXIDE TANK

Abstract. The article was reviewed the automatic control system structural diagram of pressure in the liquid carbon dioxide tank for the production of sparkling and champagne wines and was obtained the efficiency by the enterprise when the system is implemented in the work process are considered. Keywords: Computer system, automatic regulation, production.
Fine-Grained Traffic Prediction of Communication-and-Collaboration Apps Via Deep-Learning: A First Look at Explainability

The lifestyle change originated from the COVID-19 pandemic has caused a measurable impact on Internet traffic in terms of volume and application mix, with a sudden increase in usage of communication-and-collaboration apps. In this work, we focus on four of these apps (Skype, Teams, Webex, and Zoom), whose traffic we collect, reliably label at fine (i.e. per-activity) granularity, and analyze from the viewpoint of traffic prediction. The outcome of this analysis is informative for a number of network management tasks, including monitoring, planning, resource provisioning, and (security) policy enforcement. To this aim, we employ state-of-the-art multitask deep learning approaches to assess to which degree the traffic generated by these apps and their different use cases (i.e. activities: audio-call, video-call, and chat) can be forecast at packet level. The experimental analysis investigates the performance of the considered deep learning architectures, in terms of both traffic-prediction accuracy and complexity, and the related trade-off. Equally important, our work is a first attempt at interpreting the results obtained by these predictors via eXplainable Artificial Intelligence (XAI).
Code Switching Used by Certified and Non-Certified English Teachers

This research conducted to analyzed the code switching used by certified and non-certified teachers. The researcher applied the qualitative method. The subject of this research was certified and non-certified English teachers at Senior High School 1, 7, and 13 Bone consisted in 2 teachers that were certified and 2 teachers that were non-certified. The data were collected through audio recording, observation checklist, and interviews to find out type of code switching used by teachers, factor of code switching used by teachers, and certified and non-certified English teacher perception of code switching used. The result of this research that there were five types of code switching used by certified teacher in the classroom. They were inter-sentential, intra-sentential, metaphorical, emblematic and establishing continuity switching. Non-certified teachers also used also five types of code switching, they are inter-sentential, intra-sentential, metaphorical, tag and establishing continuity switching. The used of code switching of the teachers dominated by intra-sentential and inter-sentential switching both for certified and non-certified teachers where the usage frequency was over in certified teachers. The occurrence of code switching appears more in certified teachers. The factors of code switching used were intention of clarifying the speech content for interlocutor, repetition used for clarification, translate the unknown vocabulary and talking about particular topic. Certified English teachers perceive that the used of code switching is not directly affected by certification. However, the teacher consciously admits that it affects the teacher's performance when managing the class and in their professional appearance at school. On the other hand non-certified teacher used code switching influenced by their experiences to provide an even understanding for students and main the class situation.
Influence of Augmanted reality text learning media description of the learning independence of class students VII SMP in central Lampung

This research aims to develop Augmented Reality learning media descriptive text material for class VII SMP and testing the effect of using Augmented Reality learning media for descriptive text on the learning independence of class VII SMP students in Central Lampung. The method used to develop Augmented Reality learning media is method development of Borg and Gall. The population in this study were class VII students of SMP Negeri 3 Terbanggi Besar for the 2023/2024 academic year with a sample size of 75 students. This research uses four data collection techniques, namely interviews, observation, questionnaires and tests. The results of development research show two things. First, the Augmented Reality learning media developed with the Borg and Gall concept was declared feasible by material expert validators, learning media experts and practitioners with percentage scores of 83.33%, 85% and 78%. Second, based on the feasibility test of the developed Augmented Reality learning media, an average score of 90.8 was obtained which is included in the very feasible category; The effectiveness test using N-Gain obtained a value of 0.74 which is included in the high category. This means that Augmented Reality learning media is suitable for use in learning descriptive text material and has been proven to be effective in improving students' reading comprehension abilities of descriptive text.
Standard Units in Miniatures of the Manuscript no. 20 in Russian State Library, Fund 173.I

The proposed article is addressed to the phenomenon of typical graphic elements used in Russian late medieval visual art; the study is based on the material of miniatures of the wellknown illuminated manuscript of the Commentated Prophet Book from the collection of the Moscow Theological Academy. Previously, a detailed comparison of the drawing of paper icon samples, and later of the icons themselves of the 15th–16th centuries as well, led to the conclusion that icon painters widely use a set of the same systematically repeating graphic elements. They can always be seen in almost any icon, in different numbers and different combinations, in the most dissimilar images and regardless of who or what is depicted on the icon: the Savior, the Virgin or a prophet; regardless of whether the image in front of us is a half-length image, shoulder-length or full-length figure; regardless of the pose, gestures and type of clothing. Those elements are used constantly, although their number and set may vary significantly from case to case. In addition, their graphics vary quite freely: the proportions within the block and its dimensions change relative to other parts of the image, straight lines may be more or less curved, or part of the block may be hidden by another detail or, on the contrary, shown two or more times. Sometimes some part of the block is inverted, shifted or reduced, but the structure of the block remains the same. This observation makes it possible to significantly clarify the ideas about the principles of icon-painter work in the 15th–17th centuries: the drawing, for example, of a human figure and their robes was not copied literally from icon to icon, neither was invented completely anew each time; the image was more or less “constructed” from already existing ready-made blocks, either in the literal sense, or they filled in a ready-made general compositional scheme or a part of it, which itself can be considered as a standard block of a higher order. The miniatures of the manuscript under study serve as a clear example of the use of standard blocks in the Moscow illuminated book of the 15th century. The article for the first time compares a large number of deesis and prophetic images.
Average Estimates in Line Graphs Are Biased Toward Areas of Higher Variability

We investigate variability overweighting, a previously undocumented bias in line graphs, where estimates of average value are biased toward areas of higher variability in that line. We found this effect across two preregistered experiments with 140 and 420 participants. These experiments also show that the bias is reduced when using a dot encoding of the same series. We can model the bias with the average of the data series and the average of the points drawn along the line. This bias might arise because higher variability leads to stronger weighting in the average calculation, either due to the longer line segments (even though those segments contain the same number of data values) or line segments with higher variability being otherwise more visually salient. Understanding and predicting this bias is important for visualization design guidelines, recommendation systems, and tool builders, as the bias can adversely affect estimates of averages and trends.
Interpretable Inference and Classification of Tissue Types in Histological Colorectal Cancer Slides Based on Ensembles Adaptive Boosting Prototype Tree

Digital pathology images are treated as the “gold standard” for the diagnosis of colorectal lesions, especially colon cancer. Real-time, objective and accurate inspection results will assist clinicians to choose symptomatic treatment in a timely manner, which is of great significance in clinical medicine. However, Manual methods suffers from long inspection cycle and serious reliance on subjective interpretation. It is also a challenging task for existing computer-aided diagnosis methods to obtain models that are both accurate and interpretable. Models that exhibit high accuracy are always more complex and opaque, while interpretable models may lack the necessary accuracy. Therefore, the framework of ensemble adaptive boosting prototype tree is proposed to predict the colorectal pathology images and provide interpretable inference by visualizing the decision-making process in each base learner. The results showed that the proposed method could effectively address the “accuracy-interpretability trade-off” issue by ensemble of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> adaptive boosting neural prototype trees. The superior performance of the framework provides a novel paradigm for interpretable inference and high-precision prediction of pathology image patches in computational pathology <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">.</i>
SCcheck: A Novel Graph-driven and Attention-enabled Smart Contract Vulnerability Detection Framework for Web 3.0 Ecosystem

With the rapid progress of technology, Web 3.0 has emerged as a transformative force in the digital realm. It is characterized by decentralization, user-centric data ownership, and the implementation of cryptographic techniques. Smart contracts, as a core component of Web 3.0, play a pivotal role in driving its evolution by enabling novel functionalities and various application. However, given the substantial financial significance of smart contracts and their inherent transparency, the accessibility of their source code to all opens potential avenues for attackers to identify and exploit vulnerabilities. Therefore, the detection of security vulnerabilities in smart contracts has become significantly important. Existing smart contract vulnerability detection tools mostly rely on expert-defined rules, leading to high false positive rates. To address this problem, this paper proposes an efficient and automated framework that combines Graph and Attention for detecting smart contract vulnerabilities. This framework takes into account the code structure of smart contracts, extracts nodes, and constructs a contract graph, utilizing dataflow to represent the different semantics of variable nodes at different locations. Additionally, a bidirectional multilayer Transformer framework is constructed and trained with our dataset, utilizing the information from the nodes. The framework achieves state-of-the-art levels of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Accuracy$</tex-math></inline-formula> 92.72%, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Recall$</tex-math></inline-formula> 82.81%, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$F1_{score}$</tex-math></inline-formula> 87.54%, respectively. These results show that our framework can effectively detect security vulnerabilities in smart contracts and has the potential to improve their security.