Reviewerly/exHarmony huggingface dataset:
Assessment and Analysis of Fidelity and Diversity for GAN-based Medical Image Generative Model

Recently, various researches on medical image generation have been suggested, and it becomes crucial to accurately evaluate the quality and diversity of the generated medical images.For this purpose, the expert's visual turing test, feature distribution visualization, and quantitative evaluation through IS and FID are evaluated.However, there are few methods for quantitatively evaluating medical images in terms of fidelity and diversity.In this paper, images are generated by learning a chest CT dataset of non-small cell lung cancer patients through DCGAN and PGGAN generative models, and the performance of the two generative models are evaluated in terms of fidelity and diversity.The performance is quantitatively evaluated through IS and FID, which are one-dimensional score-based evaluation methods, and Precision and Recall, Improved Precision and Recall, which are two-dimensional score-based evaluation methods, and the characteristics and limitations of each evaluation method are also analyzed in medical imaging.
Blockchain and the digital transformation of the public sector: The Greek experience

The present study aims to investigate the key organizational factors that impact on the blockchain adoption in the Greek public sector during the current pandemic, which caused many changes in the ways that organizations operate and offer their services. A qualitative method was used through 78 structured interviews with public servants, regarding the digital transformation and the internal factors that hinder the successful integration of blockchain technologies during the current pandemic. The results showed that factors such as employees’ training, organizational culture and leadership, in addition to the lack of equipment, had great impact on the adoption and the successful integration of blockchain in the Greek public administration. Another crucial finding is that public servants consider the current pandemic as an opportunity and not as a threat for radical changes and improvements in the provision of their services, while they also stated that blockchain technologies can improve the ways that they provide public services to citizens and thus they will enhance the sense of public trust to public sector.
EFL University Teachers’ Beliefs About Learner Autonomy and the Effect of Online Learning Experience

Learner autonomy (LA) has always been viewed as an essential component of successful language learning. It is one of the factors that contributes to creating a learning community in which learners are capable of developing their knowledge and skills. Since it plays an essential role in language learning process, it can be reflected in both students&amp;rsquo; and teachers&amp;rsquo; beliefs as well as practices. Despite its importance, EFL teachers&amp;rsquo; perceptions are insufficiently explored in the Saudi context. To address this gap, this study deals with teachers&amp;rsquo; own reflection on how they perceive learner autonomy in Saudi universities and what they do to make their students autonomous learners. It also explores their views about the effect of the online teaching experience during COVID-19 pandemic on LA. To do this, a questionnaire designed by Borg and Al-Busaidi (2012) was used in addition to semi-structured interviews to explore in more detail the teachers&amp;rsquo; perceptions of LA. The analysis of quantitative and qualitative data showed that teachers hold a variety of perceptions of LA and have highly positive attitudes towards enhancing it. They also believe that it is theoretically desirable to promote LA among EFL students; yet, it is partially attainable in the practical ground. Additionally, the experience of online learning during the COVID-19 pandemic and the shift from face-to-face setting have created more opportunities for more practices of LA. Teachers&amp;rsquo; role cannot be disregarded since they create adequate opportunities for LA development. Although a number of obstacles that would hinder LA can be identified, some solutions can be of great appeal in encouraging LA in EFL contexts.
Identifying Language Learning Strategies for Reading among Malaysian TESL Postgraduates in a Public University

One of the most vital skills needed by postgraduate students to excel in their tertiary education is the ability to read effectively.Reading skill is viewed as a necessity for postgraduate students as they are needed to effectively indulge in in-depth academic reading to pursue their research topics.Thus, this study was aimed to identify the language learning strategies employed by TESL postgraduates, especially in curating and enhancing their reading skills.50 postgraduates from a public university in Malaysia were chosen to participate in the study via convenience sampling.This study employed a quantitative approach with a survey research design whereby the data collection was done by employing an online questionnaire which was adapted from the (Young Learner's Language Strategy Use Survey, 2002; Language Strategy Use Survey, 2005).The adapted online questionnaire consisted of 24 items on a 4point scale.The data obtained from the online questionnaire was analysed quantitatively using descriptive analysis focussing on frequency, percentage and mean using the Statistical Package for the Social Sciences (SPSS) software.The findings revealed that Malaysian TESL postgraduates' tend to employ all of the language learning strategies in order to enhance their reading skills.Additionally, the study also found that the most employed language learning strategies were memory strategies with a mean value of 3.64 while the least employed language learning strategies were social strategies with a mean value of 2.85.These findings provide useful information for stakeholders to use as a basis to employ necessary steps to improve the reading skills among postgraduate students in Malaysia.
PENGARUH MEDIA FLASHCARD TERHADAP KEMAMPUAN MENGENAL HURUF ANAK USIA 5-6 TAHUN

This study uses a quantitative approach with a single experimental research type or single-subject research (SSR) and uses an A-B-A research design. The single-subject design focuses on individual data as the research sample. The subjects in this study were 2 students of group B Tk Al Hidayah Tegalrejo 02 Blitar in the 2021/2022 school year who were identified as not being able to recognize letters well. Data on the ability to recognize letters in the baseline phase and the intervention phase were obtained through observation. Data analysis uses a description of the picture obtained from graphic analysis by taking into account changes in levels or trends. The results of the study show that changes in levels or trends have increased. This shows that the provision of learning using flashcard media affects children's ability to recognize letters.&#x0D; Abstrak&#x0D; Penelitian ini menggunakan pendekatan kuantitatif dengan jenis penelitian eksperimen tunggal atau single subject research (SSR) dan menggunakan desain penelitian A- B- A. Subjek dalam penelitian ini adalah 2 siswa kelompok B Tk Al Hidayah Tegalrejo 02 Blitar pada tahun ajaran 2021/ 2022 yang teridentifikasi belum mampu mengenal huruf dengan baik. Data kemampuan mengenal huruf fase baseline dan fase intervensi diperoleh melalui observasi. Analisa data menggunakan deskripsi gambaran yang diperoleh dari analisa grafik dengan memperhatikan perubahan level atau trend. Hasil penelitian menunjukkan perubahan level atau trend mengalami peningkatan. Hal ini menunjukkan bahwa pemberian pembelajaran menggunakan media flashcard mempengaruhi kemampuan anak dalam mengenal huruf.&#x0D;
RobustLR: Evaluating Robustness to Logical Perturbation in Deductive Reasoning

Transformers have been shown to be able to perform deductive reasoning on a logical rulebase containing rules and statements written in English natural language. While the progress is promising, it is currently unclear if these models indeed perform logical reasoning by understanding the underlying logical semantics in the language. To this end, we propose RobustLR, a suite of evaluation datasets that evaluate the robustness of these models to minimal logical edits in rulebases and some standard logical equivalence conditions. In our experiments with RoBERTa and T5, we find that the models trained in prior works do not perform consistently on the different perturbations in RobustLR, thus showing that the models are not robust to the proposed logical perturbations. Further, we find that the models find it especially hard to learn logical negation and disjunction operators. Overall, using our evaluation sets, we demonstrate some shortcomings of the deductive reasoning-based language models, which can eventually help towards designing better models for logical reasoning over natural language. All the datasets and code base have been made publicly available.
Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts

Question-answering datasets require a broad set of reasoning skills. We show how to use question decompositions to teach language models these broad reasoning skills in a robust fashion. Specifically, we use widely available QDMR representations to programmatically create hard-to-cheat synthetic contexts for real questions in six multi-step reasoning datasets. These contexts are carefully designed to avoid reasoning shortcuts prevalent in real contexts that prevent models from learning the right skills. This results in a pretraining dataset, named TeaBReaC, containing 525K multi-step questions (with associated formal programs) covering about 900 reasoning patterns. We show that pretraining standard language models (LMs) on TeaBReaC before fine-tuning them on target datasets improves their performance by up to 13 F1 points across 4 multi-step QA datasets, with up to 21 point gain on more complex questions. The resulting models also demonstrate higher robustness, with a 5-8 F1 point improvement on two contrast sets. Furthermore, TeaBReaC pretraining substantially improves model performance and robustness even when starting with numerate LMs pretrained using recent methods (e.g., PReasM, POET). Our work thus shows how to effectively use decomposition-guided contexts to robustly teach multi-step reasoning.
The Influence of Economic Literacy and Digital Literacy on Consumptive Behaviour of Students

This study aims to determine the influence of economic literacy and digital literacy on the consumptive behavior of Nusantara PGRI University of Kediri students during the COVID-19 pandemic. While the problems contained in this research are how well the level of student understanding related to economic literacy and digital literacy that has been obtained so far in lectures. The method used in this research was quantitative research methods. The sample used in this study was students of the Economic Education Study Program, Nusantara PGRI University of Kediri, totaling 36 students, and used purposive random sampling. Collecting data by distributing questionnaires via Google form to the respondents. The data analysis technique was multiple regression analysis using SPSS version 23 software. From the results of the multiple linear regression test, it is known that partially the economic literacy variable is 0.028 &lt;0.05, showing a significant influence between economic literacy and student consumptive behavior. Furthermore, the digital literacy variable is 0.599 &gt; 0.05, indicating that there is no significant effect between digital literacy and student consumptive behavior. And from the results of the multiple linear regression test simultaneously of 0.034 &lt;0.05, indicating a significant influence between the variables of economic literacy and digital literacy on the consumptive behavior of students
AUTOMOTIVE RADAR BASED LEAN DETECTION OF VEHICLES

Abstract. One of the most critical features of autonomous vehicles is the detection of road active objects such as vehicles and pedestrians. The autonomous vehicles’ navigation planning and manoeuvre decision-making are aided by the detection of such active objects, resulting in safe and efficient navigation. Deep Convolutional Neural Networks (CNNs) have recently advanced to become one of the state-of-the-art ways to solving detection challenges, particularly in the autonomous vehicle area. Deep CNNs typically use a large number of processing layers with a high number of kernels per layer to enable detection of the target classes which also demands the use of powerful hardware units. In this research, we present a tailored lean detection strategy for vehicle detection using radar observations. The proposed method employs a compact set of convolutions, as well as pixel classification and a customized selection of kernels and kernel sizes, to provide an efficient technique that greatly decreases detection burden and enables real-time processing on average processing units. A training dataset is used to train the convolution window sizes and the pixel classifiers. Finally, the pixel classified grids are processed to identify the vehicles' bounding boxes. Experimental data sets have been collected using medium-range radar sensors mounted on top of a vehicle to evaluate the suggested approach, the Intersection over Union (IoU) values of the test scenes’ detections range from 0.51 to 0.78.
Penerapan Data Mining Dalam Pemilihan Produk Unggulan dengan Metode Algoritma K-Means Dan K-Medoids

As a business company, PT. XYZ Indonesia is committed to always make improvements to all aspects such as, in terms of determining superior products. To be able to do this, it requires a sufficient source of information to be able to analyze what products are superior or in high demand and what products are less desirable. To find out what products enter the superior product cluster, then researchers do product grouping using the clustering method. In the clustering method there are two types of cluster analysis that have interrelated algorithms, namely k-means and k-medoids. The result of research already conducted that from the value of Davies Bouldin to the k-means algorithm is -0.430 and from the value of Davies Bouldin k-medoids is -1,392 which means that the Davies Bouldin value for the k-medoids method has the smallest Davies Bouldin value so the grouping results using the k-means method are more appropriately used on the issue of superior product selection.
Predictive Factors of Positive E-Learning Experience

During the COVID-19 pandemic, medical schools and residencies have utilized electronic learning (e-learning). Factors such as internet access, age, degree of introversion/extroversion, and propensity to adopt new technologies impact attitudes toward e-learning. This study investigates family medicine educators' satisfaction, effectiveness, and feasibility perceptions of e-learning, characterizes demographic factors impacting attitudes, and identifies which aspects of e-learning are important to educators.In fall 2020, a cross-sectional survey via the 2020 Council of Academic Family Medicine's (CAFM) Educational Research Alliance (CERA) general membership survey was conducted. Members of CAFM-affiliated associations were invited by email to participate.The response rate for the survey was 20.1% (n=862). Of the respondents, 40.4% (n=311) reported satisfaction with e-learning, 47.8% (n=368) found e-learning feasible, and 24.2% (n=186) reported e-learning met their educational goals. No differences were found in satisfaction, feasibility, or effectiveness scores according to generation, introvert/extrovert status, or technology adopter status. Interactive capabilities were the most important factor for e-learning satisfaction (55.9%) and effectiveness (62.0%). Sufficient time was the most frequently selected factor for ease of adoption. Baby Boomer respondents reported platforms not user-friendly, insufficient prior experience as the greatest obstacle more frequently than other generations, and insufficient time less frequently than other generations. Otherwise, rankings of e-learning factors were similar among groups.Satisfaction with and perceived feasibility and effectiveness of e-learning varies among family medicine educators. No differences were found in satisfaction, feasibility, or effectiveness scores according to generation, introvert/extrovert status, or technology adopter status. Respondents consistently ranked interactive capabilities most important for e-learning satisfaction and effectiveness. More research is needed to compare student and learner perspectives regarding e-learning.
Implementation of Short Video Click-Through Rate Estimation Model Based on Cross-Media Collaborative Filtering Neural Network

In this paper, we analyze the construction of cross-media collaborative filtering neural network model to design an in-depth model for fast video click-through rate projection based on cross-media collaborative filtering neural network. In this paper, by directly extracting the image features, behavioral features, and audio features of short videos as video feature representation, more video information is considered than other models. The experimental results show that the model incorporating multimodal elements improves AUC performance metrics compared to those without multimodal features. In this paper, we take advantage of recurrent neural networks in processing sequence information and incorporate them into the deep-width model to make up for the lack of capability of the original deep-width model in learning the dependencies between user sequence data and propose a deep-width model based on attention mechanism to model users' historical behaviors and explore the influence of different historical behaviors of users on current behaviors using the attention mechanism. Data augmentation techniques are used to deal with cases where the length of user behavior sequences is too short. This paper uses the input layer and top connection when introducing historical behavior sequences. The models commonly used in recent years are selected for comparison, and the experimental results show that the proposed model improves in AUC, accuracy, and log loss metrics.
MODERN DIMENSION OF TASKS OF THE SCIENTIFIC ORGANIZATION OF THE EDUCATIONAL PROCESS OF TRAINING OF TEACHERS OF FINE ARTS IN THE SYSTEM OF ARTISTIC AND ARTISTIC

The urgent problems of scientific organization of educational process of préparation of the teachers of fine arts in system of art-pedagogical éducation on Ukraine are considered and the experience of job in this direction of art graphic faculty о/ the South-Ukrainian pedagogical university.
On Parking Functions and The Tower of Hanoi

The displacement of a parking function measures the total difference between where cars want to park and where they ultimately park. In this article, we prove that the set of parking functions of length $n$ with displacement one is in bijection with the set of ideal states in the famous Tower of Hanoi game with $n+1$ disks and $n+1$ pegs, both sets being enumerated by the Lah numbers.
The Body Scaling Effect and Its Impact on Physics Plausibility

In this study we investigated the effect of body ownership illusion-based body scaling on physics plausibility in Virtual Reality (VR). Our interest was in examining whether body ownership illusion-based body scaling could affect the plausibility of rigid body dynamics similarly to altering VR users' scale by manipulating their virtual interpupillary distance and viewpoint height. The procedure involved the conceptual replication of two previous studies. We investigated physics plausibility with 40 participants under two conditions. In our synchronous condition, we used visuo-tactile stimuli to elicit a body ownership illusion of inhabiting an invisible doll-sized body on participants reclining on an exam table. Our asynchronous condition was otherwise similar, but the visuo-tactile stimuli were provided asynchronously to prevent the onset of the body ownership illusion. We were interested in whether the correct approximation of physics ( true physics ) or physics that are incorrect and appearing as if the environment is five times larger instead ( movie physics ) appear more realistic to participants as a function of body scale. We found that movie physics did appear more realistic to participants under the body ownership illusion condition. However, our hypothesis that true physics would appear more realistic in the asynchronous condition was unsupported. Our exploratory analyses revealed that movie physics were perceived as plausible under both conditions. Moreover, we were not able to replicate previous findings from literature concerning object size estimations while inhabiting a small invisible body. However, we found a significant opposite effect regarding size estimations; the object sizes were on average underestimated during the synchronous visuo-tactile condition when compared to the asynchronous condition. We discuss these unexpected findings and the potential reasons for the results, and suggest avenues for future research.
GSR: A Generalized Symbolic Regression Approach

Identifying the mathematical relationships that best describe a dataset remains a very challenging problem in machine learning, and is known as Symbolic Regression (SR). In contrast to neural networks which are often treated as black boxes, SR attempts to gain insight into the underlying relationships between the independent variables and the target variable of a given dataset by assembling analytical functions. In this paper, we present GSR, a Generalized Symbolic Regression approach, by modifying the conventional SR optimization problem formulation, while keeping the main SR objective intact. In GSR, we infer mathematical relationships between the independent variables and some transformation of the target variable. We constrain our search space to a weighted sum of basis functions, and propose a genetic programming approach with a matrix-based encoding scheme. We show that our GSR method is competitive with strong SR benchmark methods, achieving promising experimental performance on the well-known SR benchmark problem sets. Finally, we highlight the strengths of GSR by introducing SymSet, a new SR benchmark set which is more challenging relative to the existing benchmarks.
VisionARi: An online community-based augmented reality platform for immersive learning

This paper outlines the development of VisionARi, an online community-based augmented reality (AR) platform for immersive learning. At the core of the platform is a public digital library where creators can upload educational AR content, which is then viewable by users on a dedicated mobile application. VisionARi enables educators with little to no experience in immersive technology to begin creating educational AR content in a matter of minutes through short video tutorials and template files, achieved by leveraging popular free-to-use or open-source software such as Unity and Blender. The ubiquity of smartphones enables VisionARi to overcome barriers and issues related to technical competencies and hardware accessibility currently hampering immersive learning, while the community-based ecosystem facilitates online collaboration across multiple disciplines, potentially leading to improved student learning outcomes.