Reviewerly/exHarmony huggingface dataset:
Pengaruh Desain Media Pembelajaran dengan Program Adobe Flash CS 6 untuk Belajar Berhitung

Media is one of the tools to facilitate the teacher in delivering the material, so that students easily and quickly understand the material presented. For that the author designed the learning media using Adobe Flash CS 6 program to learn to know numbers and counting. Type of research used is experiment. As for the population is suluruh students of Islamic Kindergarten Masythah Bukittinggi, sampling technique is a technique of Purposive Sampling. Technique of data collecting is done by test result of student learning, that is doing normality test and homogeneity test result obtained by t test, based on result of analysis of t test diole = 3,62&gt; from t table 1,66 at the real level = 0, 05 means Ha accepted, means there are differences in student learning outcomes by using instructional media design and by using conventional methods. Data obtained by student learning result by using learning media design better than student learning result with conventional method. The average learning outcomes of the students in the experimental class were 80.73 with the standard deviation of 12.56, while the control class was 69.39 with the standard deviation of 12.74. From the test results of coefficient of determination that there is influence of learning media design based on Macromedia Flash to student learning result is 88,60%.
Using augmented reality for organizational learning: An overview of possible applications and potentials

Organizations and enterprises face a fast-paced and rapidly changing environment today. To be able to keep up with the environment’s changes, organizational learning is important. Augmented reality (AR) might be a technology that can help to improve the efficiency and possibilities of organizational learning. AR offers a technical possibility to extend the real environment with additional information. Teaching is one of the various areas in AR that might make sense for. Here, the added value from AR, such as an increase in learning success and motivation, has been identified. At the same time, collaborative learning and working on virtual models are possible through AR. In this process, people can understand contexts more efficiently through real-time feedback, as well as a different perspective on objects and processes. Despite its potential, the application of AR for organizational learning is a topic that is underexplored. This study aims to shed light on the potential that AR can have for educational purposes. Based on a review of existing literature, we show different applications of AR for learning and outline and summarize the results of prior studies
Scene Classification for Sports Video Using Transfer Learning

Abstract: The quality of scene classification is particularly crucial because it is a key step in both video summarization and video analysis. This article proposes a strategy to accomplish high-quality scene classification and focuses on several practical implementation gaps over the previous methodologies. We identify five scene types, including batting, bowling, boundary, crowd, and close-up, using cricket as a case study. For scene categorization, we use the ResNet152 model which is pre-trained on imagenet. The proposed method employs new, fully connected layers in an encoder fashion. We also prepared a dataset of cricket video and manually labelled them into five classes which was later used to train the model. The model achieved mean accuracy of 99.15 upon training on the dataset.
Entering the era of decentralization: An investigation of new trends in data management

Data build the foundation for many modern technologies like artificial intelligence. Data are of increasing relevance for companies. Nowadays, however, the management of data is no longer only an intraorganizational phenomenon. To get access to more data, companies are increasingly willing to share their own data with other organizations. Furthermore, decentralized technologies like blockchain are supposed to have potentials for the management, storage, or sharing of data. This study aims to investigate three current trends of decentralization within data management: Interorganizational data governance, data ecosystems, and blockchain-based data management. We provide an initial overview of these developments and present avenues for future research
SInGE: Sparsity via Integrated Gradients Estimation of Neuron Relevance

The leap in performance in state-of-the-art computer vision methods is attributed to the development of deep neural networks. However it often comes at a computational price which may hinder their deployment. To alleviate this limitation, structured pruning is a well known technique which consists in removing channels, neurons or filters, and is commonly applied in order to produce more compact models. In most cases, the computations to remove are selected based on a relative importance criterion. At the same time, the need for explainable predictive models has risen tremendously and motivated the development of robust attribution methods that highlight the relative importance of pixels of an input image or feature map. In this work, we discuss the limitations of existing pruning heuristics, among which magnitude and gradient-based methods. We draw inspiration from attribution methods to design a novel integrated gradient pruning criterion, in which the relevance of each neuron is defined as the integral of the gradient variation on a path towards this neuron removal. Furthermore, we propose an entwined DNN pruning and fine-tuning flowchart to better preserve DNN accuracy while removing parameters. We show through extensive validation on several datasets, architectures as well as pruning scenarios that the proposed method, dubbed SInGE, significantly outperforms existing state-of-the-art DNN pruning methods.
The “open” notation as a tool for creating a composer’s text (self‑commentary)

Statement of the problem. Musical notation is an open, symbolic system of composer’s thinking. At the dry stage of its development, it becomes individualized in the creativity of a number of composers, which is thought out the development of new forms of overcoming the material through advanced piano techniques and atypical logic. The article introduces the concept of “open” musical notation as a method of musical composition. On the example of compositional analysis of his “Engravings” for piano (2021), the author sets out its principles. Analysis of recent research and publications. A number of works are devoted to the theme of notation, which can be divided into: 1) on history and typology of traditional notation (Rastall, 1983; Strayer, 2013; Ship, 2016; Kachmar, 2019); 2) non-traditional notation (Stone, 1980; Dubinets, 1999; Muzhchil, 2016). There are researches that reveal the idea of “open” work by U. Eco (1979), which corresponds to the theme of the article, among which – “From a Work to an ‘Open’ Work: Research Experience” (Zavialova &amp; Kalashnyk &amp; Savchenko &amp; Stakhevuch &amp; Smirnova,2020). Main objective(s) of the study is to identify the foundations of the composer’s thinking on the example of the cycle “Engravings” for piano and an attempt to propose a method of “open” notation, as a different way of creation. The scientific novelty is that it offers a method of musical composition, which was obtained by the author through many years of working, namely – the method of “open” musical notation. Methodology focuses on such methods of contemporary cognitivism as: genealogical – identifying the causal links between the creation of the method of “open” notation; contextual and comparative – the study of forms of existence of non-identical types of composer’s thinking; compositional and semantics – the disclosure of the principles of the structure of works in the context of the poetics of creativity. Results and conclusions. The article attempts to theoretically substantiate the method of “open” musical notation, which was used to create the author’s last opuses –“Engravings” for piano (2021) and “The Three Dead Kings” for viola solo (2022). Each type of notation used in the circles is a universe with a unique hierarchical structure, the elements of which are subject to the laws created by the author. Compositional and semantics analysis of “Engravings” reveals the positions of the method. It is manifested in the use of atypical clefs/staffs, the logic and existence of which correspond to the general idea and concepts of each part separately.
Effect of family and peer tutoring programs on reading comprehension skills

This study aims to study the effectiveness of peer tutoring programs through peers and families in improving reading comprehension skills. This study used a quasi-experimental method involving a sample of 450 elementary school students consisting of 250 and 200 students each to take part in a tutoring program with family members and friends. In addition, this study also involved 450 tutors, a combination of tutors from family members and peers. Students' reading comprehension skills were assessed through pretest and posttest. The pretest and posttest were conducted to see a comparison of the effectiveness of the two tutoring programs in improving reading comprehension skills. Family demographic data were generated through questionnaires and interviews. The results showed that peer tutoring programs conducted by peers and family members were equally effective in improving reading comprehension skills. This peer tutoring program is able to control reading skills intensively and evaluate reading comprehension skills compared to self-assessment. So, peer tutoring programs in general can improve students' reading comprehension skills. The implication of this research is that the role of the family is very vital in supporting student literacy activities. Families must actively participate in helping their child's cognitive abilities. Keywords: Peer tutoring, reading comprehension ability, peer tutoring, family tutoring
Be More Active! Understanding the Differences between Mean and Sampled Representations of Variational Autoencoders

The ability of Variational Autoencoders to learn disentangled representations has made them appealing for practical applications. However, their mean representations, which are generally used for downstream tasks, have recently been shown to be more correlated than their sampled counterpart, on which disentanglement is usually measured. In this paper, we refine this observation through the lens of selective posterior collapse, which states that only a subset of the learned representations, the active variables, is encoding useful information while the rest (the passive variables) is discarded. We first extend the existing definition to multiple data examples and show that active variables are equally disentangled in mean and sampled representations. Based on this extension and the pre-trained models from disentanglement lib, we then isolate the passive variables and show that they are responsible for the discrepancies between mean and sampled representations. Specifically, passive variables exhibit high correlation scores with other variables in mean representations while being fully uncorrelated in sampled ones. We thus conclude that despite what their higher correlation might suggest, mean representations are still good candidates for downstream tasks applications. However, it may be beneficial to remove their passive variables, especially when used with models sensitive to correlated features.
Parity Permutation Pattern Matching

Given two permutations, a pattern $$\sigma $$ and a text $$\pi $$ , Parity Permutation Pattern Matching asks whether there exists a parity and order preserving embedding of $$\sigma $$ into $$\pi $$ . While it is known that Permutation Pattern Matching is in $$\textsf{FPT}$$ , we show that adding the parity constraint to the problem makes it $$\textsf{W}[1]$$ -hard, even for alternating permutations or for 4321-avoiding patterns. However, it remains in $$\textsf{FPT}$$ if the text avoids a fixed permutation, thanks to a recent meta-theorem on twin-width. On the other hand, as for the classical version, Parity Permutation Pattern Matching remains polynomial-time solvable when both permutations are separable, or if both are 321-avoiding, but $$\textsf{NP}$$ -hard if the pattern is 321-avoiding and the text is 4321-avoiding.
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 Publishing. • Type of peer review: Double Anonymous • Conference submission management system: Morressier • Number of submissions received: 67 • Number of submissions sent for review: 67 • Number of submissions accepted: 24 • Acceptance Rate (Submissions Accepted / Submissions Received × 100): 35.8 • Average number of reviews per paper: 2 • Total number of reviewers involved: 10 • Contact person for queries: Name: Robbi Rahim Email: robbirahim@ieee.org Affiliation: Sekolah Tinggi Ilmu Manajemen Sukma
Modelling of risk factors that influence malaria infection using Binary Logistic Regression

Abstract Malaria is a parasitic disease that is currently endemic in many countries, including in Indonesia. Batubara District in North Sumatera is one of the malaria-endemic areas. This study was conducted to make a mathematical model based on risk factors that influence malaria infection. The data used is secondary data from other researchers. The method used to model risk factors that influence malaria infection is the binary logistic regression with a categorical response variable, namely diagnosis by microscopic examination. There are 10 variable predictors; mosquito nets ( X 1 ), quality use of mosquito nets ( X 2 ), condition of the livingenvironment ( X 3 ), knowledge ( X 4 ), attitudes ( X 5 ), actions ( X 6 ), quality of health workers ( X 7 ), use of mosquito coils (X8), use of topical insect repellent ( X 9 ), and access of health workers ( X 10 ). Maximum likelihood estimation is used to findout the parameter estimates. From the 10 predictor variables studied, it was found that only 8 variables had a significant effect on the model. Based on the odds ratio, the most significant variable is the quality of health workers with 16,5. The ability of predictor variables in explaining the response variable in the model is 87, 8%. The classification function in this study is 96, 3%.
Cloud Computing and Big Data: An Overview

Cloud computing is one of the bleeding edge advances used by far most of the people. Cloud clients are mentioned to present the individual private data to the cloud by the internet. Big data or we can say huge information is an information examination system that contributes to the fast development of different applications utilizing in everyday life like interpersonal organization investigation, semantic web examination and bio-informatics arrange investigation. The model of cloud computing is spreading wherever so as to convey huge information administrations. In this paper give an overview and brief details about the recent innovation used technologies big data and cloud computing.
Blockchain Technology for Advancement of Smart Cities

Today we undoubtedly live in one of the fastest-changing times humanity has ever seen. Blockchain is revolutionizing the whole world by bringing a new geometry to security, efficiency, and stability of systems and data. Blockchain Technology is more than just Bitcoin but it is a foundational technology that can disrupt every industry. The architecture of blockchain technology enables everybody on the system to trust the system. The incarnation of new technologies for the development of smart cities gives new ways of rethinking different services. Given the continuous development of urban areas, a new approach to urban government innovation is required for smart cities and smart technologies are essential for it. The objective of smart cities mission is to provide smart solutions to drive economic growth, increase the quality of life, development of urban ecosystems, etc. Blockchain enables our cities to exchange data with a high degree of reliability and transparency without the need for a centralized administrator. As a result, several venture capital firms and large enterprises are investing in blockchain technology research and trials to re-imagine traditional practices and business models. In this paper, we specifically address the question of how blockchain technology may benefit the development of urban areas.
Split Ways: Privacy-Preserving Training of Encrypted Data Using Split Learning

Split Learning (SL) is a new collaborative learning technique that allows participants, e.g. a client and a server, to train machine learning models without the client sharing raw data. In this setting, the client initially applies its part of the machine learning model on the raw data to generate activation maps and then sends them to the server to continue the training process. Previous works in the field demonstrated that reconstructing activation maps could result in privacy leakage of client data. In addition to that, existing mitigation techniques that overcome the privacy leakage of SL prove to be significantly worse in terms of accuracy. In this paper, we improve upon previous works by constructing a protocol based on U-shaped SL that can operate on homomorphically encrypted data. More precisely, in our approach, the client applies Homomorphic Encryption (HE) on the activation maps before sending them to the server, thus protecting user privacy. This is an important improvement that reduces privacy leakage in comparison to other SL-based works. Finally, our results show that, with the optimum set of parameters, training with HE data in the U-shaped SL setting only reduces accuracy by 2.65% compared to training on plaintext. In addition, raw training data privacy is preserved.
Information Retrieval: Recent Advances and Beyond

In this paper, we provide a detailed overview of the models used for information retrieval in the first and second stages of the typical processing chain. We discuss the current state-of-the-art models, including methods based on terms, semantic retrieval, and neural. Additionally, we delve into the key topics related to the learning process of these models. This way, this survey offers a comprehensive understanding of the field and is of interest for for researchers and practitioners entering/working in the information retrieval domain.
Functional-analytical reconstruction of high contrast inhomogeneities

In practice of acoustic tomography, for example, in medical applications and ocean tomography, the relative deviation of sound speed from its background value usually does not exceed 10-30%. At the same time, in electromagnetic applications, the equivalent contrasts can be noticeably higher than 60%. Since the inverse electromagnetic problem can be reduced in some approximation to Helmholtz equation, a formal comparison of reconstruction results obtained for different "acoustic" contrast and corresponding "dielectric" contrast is possible. In this work examples of such reconstructions are presented, which were obtained by using the functional-analytical algorithm described in works of R.G. Novikov. Previously, the advantages of this algorithm for solving practical problems of acoustic tomography were demonstrated. Results obtained in the present work show that functional-analytical algorithm can also be applied to reconstructing inhomogeneities with high "dielectric" contrast. Moreover, the functional algorithm also perfectly reconstructs very small "dielectric" contrast, recovering of which can be difficult for other approaches due to weak backscattering.
Document Summarization with Text Segmentation

In this paper, we exploit the innate document segment structure for improving the extractive summarization task. We build two text segmentation models and find the most optimal strategy to introduce their output predictions in an extractive summarization model. Experimental results on a corpus of scientific articles show that extractive summarization benefits from using a highly accurate segmentation method. In particular, most of the improvement is in documents where the most relevant information is not at the beginning thus, we conclude that segmentation helps in reducing the lead bias problem.
Phoneme-Level BERT for Enhanced Prosody of Text-to-Speech with Grapheme Predictions

Large-scale pre-trained language models have been shown to be helpful in improving the naturalness of text-to-speech (TTS) models by enabling them to produce more naturalistic prosodic patterns. However, these models are usually word-level or sup-phoneme-level and jointly trained with phonemes, making them inefficient for the downstream TTS task where only phonemes are needed. In this work, we propose a phoneme-level BERT (PL-BERT) with a pretext task of predicting the corresponding graphemes along with the regular masked phoneme predictions. Subjective evaluations show that our phoneme-level BERT encoder has significantly improved the mean opinion scores (MOS) of rated naturalness of synthesized speech compared with the state-of-the-art (SOTA) StyleTTS baseline on out-of-distribution (OOD) texts.
Statistical Theory of Differentially Private Marginal-based Data Synthesis Algorithms

Marginal-based methods achieve promising performance in the synthetic data competition hosted by the National Institute of Standards and Technology (NIST). To deal with high-dimensional data, the distribution of synthetic data is represented by a probabilistic graphical model (e.g., a Bayesian network), while the raw data distribution is approximated by a collection of low-dimensional marginals. Differential privacy (DP) is guaranteed by introducing random noise to each low-dimensional marginal distribution. Despite its promising performance in practice, the statistical properties of marginal-based methods are rarely studied in the literature. In this paper, we study DP data synthesis algorithms based on Bayesian networks (BN) from a statistical perspective. We establish a rigorous accuracy guarantee for BN-based algorithms, where the errors are measured by the total variation (TV) distance or the $L^2$ distance. Related to downstream machine learning tasks, an upper bound for the utility error of the DP synthetic data is also derived. To complete the picture, we establish a lower bound for TV accuracy that holds for every $\epsilon$-DP synthetic data generator.