Reviewerly/exHarmony huggingface dataset:
Pemanfaatan Limbah Styrofoam sebagai Bahan Tambah Paving Block

This research aims to analyze the effect of styrofoam on the compressive strength value of paving blocks and to analyze the effect of styrofoam on the water absorption value of paving blocks. The benefits of research are that it can reduce pollution and provide solutions for recycling styrofoam waste, providing knowledge and innovation in the use of styrofoam waste as a basic material for making paving blocks and analyzed using trial &amp; error methods. This paving block is made from a mixture of sand, styrofoam, and cement which is made in 3 (three) different mixture composition variations. Variation 1 with a composition of 50% sand and 50% styrofoam without added cement, variation 2 with a composition of 50% sand, 30% styrofoam, and 20% cement, and variation 3 with a composition of 50% sand, 15% styrofoam and 35% cement. This paving block is made with a length of 200mm, width of 100mm, and thickness of 80mm. Testing was carried out referring to the SNI 03-0691-1996 standard. From the test results for variation 1, the average compressive strength value was 15.23 MPa, and the average water absorption value was 0.1134% (quality category C). For variation 2, an average compressive strength value was obtained of 28.32 MPa, and an average water absorption value of 0.0821% (quality category B). Variation 3 obtained an average compressive strength value of 36.72 MPa, and an average water absorption value of 0.0635% (quality category B).&#x0D;
DAE: Distribution-Aware Embedding for Numerical Features in Click-Through Rate Prediction

Numerical features are an important type of input for CTR prediction models. Recently, several discretization and numerical transformation methods have been proposed to deal with numerical features. However, existing approaches do not fully consider compatibility with different distributions. Here, we propose a novel numerical feature embedding framework, called Distribution-Aware Embedding (DAE), which is applicable to various numerical feature distributions. First, DAE efficiently approximates the cumulative distribution function by estimating the expectation of the order statistics. Then, the distribution information is applied to the embedding layer by nonlinear interpolation. Finally, to capture both local and global information, we aggregate the embeddings at multiple scales to obtain the final representation. Empirical results validate the effectiveness of DAE compared to the baselines, while demonstrating the adaptability to different CTR models and distributions.
Rosetta Stone Application on Students’ Pronunciation

Expressing an idea or explaining something was needed to pay attention to English pronunciation. There are many digital platforms for learning English, especially for practicing pronunciation. Rosetta Stone is a unique language-learning application that makes it easy for students to learn because it is flexible. The aim of this research is to find out whether there is a difference in the influence of the use of Rosetta Stone Application and PowerPoint media on students' pronunciation. This research was conducted at SMA Negeri 1 Parungkuda. The researcher used a Quasi Experimental design for this research. The researcher involved students of class XI IPA 1 as the experimental class and class XI IPA 2 as the control class. The research instrument was an oral test to measure the influence of students' pronunciation on vowels, especially diphthongs with a total of 20 oral questions were used as research instruments. Data was collected through pre-test and post-test and were analyzed using the t-test with SPSS 22. The results of the independent t-test calculation showed a t-test value of 2,100, while the t-table is 1,9954 at a significance level of 0.05 and the degrees of freedom (df) is 68. The t-count is higher than the t-table (2,100 1,9954). It means that there was a difference in the influence of the use of Rosetta Stone Application and PowerPoint media. The different that distinguishes the experimental and control groups is the result of the final scores of the two classes, in which the experimental group has higher scores than the control group. In conclusion, the results between the experimental group and the control group have a significant difference.
Australian cities are quiet during lockdown. Earthquake scientists are making the most of it

Meghan S. Miller, &lt;em&gt;Australian National University&lt;/em&gt; and Louis Moresi, &lt;em&gt;Australian National University&lt;/em&gt; Our responses to the COVID-19 pandemic have dramatically changed human activity all over the world. People are working from home, schools are closed in many places, travel is restricted, and in some cases only essential shops and businesses are open. Scientists see signs of these changes wherever they look.
Chronological attribution of Sinhalese inscriptions using deep learning approaches

A study of this caliber can be identified as a profound source for a wealth of knowledge as the aim of this study is to present chronological attribution of Sinhalese inscriptions based on deep learning approaches. Inscriptions shed light on a multitude of information such as chronicled civilizational thought, economic status, language evolution, cultural boundaries, details of royal officers, local rules, ethnic groups, land tenure, religious activities, beliefs, and trade and industries. Inscriptions are major assets to showcase inclusive of listed above, multitude information; hence, the benefits served by a study of high caliber, especially to the historical heritage research and to the heritage tourism. Several computer-aided solutions have been proposed to resolve the recognition of inscriptions in the Sri Lankan context. But this paper proposes an optimized classification. A dataset of five hundred images of original Sinhalese inscriptions dating from the 3rd century BC to the present was used to train and test the models. This study adopts four deep learning models to classify Sinhalese inscriptions: a newly proposed convolutional neural network model, and the pre-trained models Inception-v3, VGG-19, and ResNet-50. Palaeographical and morphological rules were adopted in the manual classification of Sinhalese inscriptions into a number of eras, namely, the Early Brahmi (3rd century BC to 1st century AD), Late Brahmi (2nd century AD to 4th century AD), Transitional Brahmi (5th century AD to 7th century AD), Medieval Sinhala (8th century AD to 14th century AD), and Modern Sinhala (15th century AD to the present). The results of the study indicate promising outcomes with accuracies of 70.66%, 85.94%, 57.44%, and 58.77% respectively for used four models. Further, the study revealed that the Inception-v3 model outperformed in classifying the Sinhalese inscriptions in respective eras.
Unsupervised Multi-Modal Representation Learning for High Quality Retrieval of Similar Products at E-commerce Scale

Identifying similar products in e-commerce is useful in discovering relationships between products, making recommendations, and increasing diversity in search results. Product representation learning is the first step to define a generalized product similarity metric for search. The second step is to extend similarity search to a large scale (e.g., e-commerce catalog scale) without sacrificing quality. In this work, we present a solution that interweaves both steps, i.e., learn representations suited to high quality retrieval using contrastive learning (CL) and retrieve similar items from a large search space using approximate nearest neighbor search (ANNS) to trade-off quality for speed. We propose a CL training strategy for learning uni-modal encoders suited to multi-modal similarity search for e-commerce. We study ANNS retrieval by generating Pareto Frontiers (PFs) without requiring labels. Our CL training strategy doubles retrieval@1 metric across categories (e.g., from 36% to 88% in category C). We also demonstrate that ANNS engine optimization using PFs help select configurations appropriately (e.g., we achieve 6.8× search speed with just 2% drop from the maximum retrieval accuracy in medium size datasets).
Exploring Data Mining Applications and Techniques: A Comprehensive Research Survey

Every second, huge amount of data is generated and accumulated. This data could possibly be used in forecasting the future. Data mining uses this data and generates valuable information which can be transformed into relevant knowledge. Data mining is a technique of identifying outliers, behaviours, trends of patterns and relationship among huge datasets. It is hugely associated with the skill of decision making. The knowledge on a relevant subject will help in understanding future trends. This survey paper supplies the overview of data mining, the processes involved, the scope it can offer, its different techniques and multiple applications. Data mining is a great model of using data efficiently.
Penerapan English Song dalam Mengenalkan Bahasa Inggris di Taman Kanak-kanak Yari Internasional School

Songs are an excellent way to teach English to young children so that they can actually support their capacity to recite and recall basic English vocabulary and expressions, which is the basis of this post. This study also attempts to determine the viability and usefulness of using the English song to teach English to kindergarten students at Yari International School. The ability of children to understand concepts of speaking, reading, and writing is one of the English recognition skills that will be tested. This study employs a descriptive qualitative methodology. Additionally, this study employs methods for gathering data such as observation, interviews, and documentation. The author's method of data analysis includes data reduction, data presentation, and conclusion
On the Thresholding Strategy for Infrequent Labels in Multi-label Classification

In multi-label classification, the imbalance between labels is often a concern. For a label that seldom occurs, the default threshold used to generate binarized predictions of that label is usually sub-optimal. However, directly tuning the threshold to optimize F-measure has been observed to overfit easily. In this work, we explain why this overfitting occurs. Then, we analyze the FBR heuristic, a previous technique proposed to address the overfitting issue. We explain its success but also point out some problems unobserved before. Then, we first propose a variant of the FBR heuristic that not only fixes the problems but is also more justifiable. Second, we propose a new technique based on smoothing the F-measure when tuning the threshold. We theoretically prove that, with proper parameters, smoothing results in desirable properties of the tuned threshold. Based on the idea of smoothing, we then propose jointly optimizing micro-F and macro-F as a lightweight alternative free from extra hyperparameters. Our methods are empirically evaluated on text and node classification datasets. The results show that our methods consistently outperform the FBR heuristic.
Komposit Zeolit-Polivinil Alkohol sebagai Adsorben untuk Menurunkan Kesadahan Air Tanah

Granular zeolite-polyvinyl alcohol (PVA) composites without glutralaldehyde and zeolite-PVA composites with the addition of glutaraldehyde were carried out in this study and used as adsorbents. This study aims to reduce groundwater hardness, determine the optimum zeolite size in composite mixtures, and determine the characterization of functional groups in composites. The stages of the research included: preparation and activation of zeolite with variations in zeolite sizes of 100, 60 and 45 mesh and variations in composition of zeolite-polyvinyl alcohol 1:1; 1:2; 2:1; 2:3 and 3:2. Furthermore, the preparation of PVA solution concentration of 9% and the formation of the composite. The results showed that the optimum size of the zeolite on the composite without glutaraldehyde was 60 mesh with a decrease in hardness of 68% while the ratio of the optimum zeolite-PVA composition was at a ratio of 3:2 with a decrease in hardness of 34 mg/L. The results of Langmuir and Freundlich isotherm analysis showed the presence of chemical bonds because the determination value (R2) of the adsorption isotherm was &gt; 0.9. The results of functional group characterization showed OH- in the zeolite-PVA composite without glutaraldehyde at a wavelength of 3363.01 cm-1 while in the composite with glutaraldehyde at a wavelength of 3345.89 cm-1. The C=C functional group appeared at a wavelength of 1628.73 cm-1 for the zeolite-PVA composite without glutaraldehyde and 1635.86 cm-1 for the composite with glutaraldehyde. At a wavelength of 788.70 cm-1 and 794.40 cm-1, the Si-O symmetric stretching vibrations show in the zeolite structure.
NAVIGATING EMOTIONS ACROSS BORDERS: DEEP LEARNING-DRIVEN LOCATION-INFORMED SENTIMENT ANALYSIS OF TWITTER

Emotion assessment, a pivotal domain in the realm of natural language processing, holds significant importance in comprehending the feelings expressed through textual content. It has progressed to include the intricate interplay of emotions found in textual information. This paper introduces a pioneering approach to sentiment analysis by amalgamating deep learning techniques and geographic context within the realm of Twitter data. Leveraging an expanded sentiment class set that includes positive, negative, neutral, mixed, ambiguous, happy, sad, angry, fearful, and surprised. Our framework aptly captures diverse emotional expressions. Incorporating location-based sentiment analysis unveils cross-border sentiment dynamics, enriching our understanding of how emotions resonate within various geographical regions.&#x0D; We present a meticulously designed deep learning model that seamlessly integrates textual content and location information. Through the utilization of text vectorization, embedding layers, and advanced classification techniques, our model achieves exceptional accuracy, F1-score, precision, and recall values. The temporal analysis of tweet timestamps uncovers temporal engagement trends, while the examination of tweet lengths underscores the dynamic range of expression within the Twitter character limitation.&#x0D; Furthermore, our investigation into the locations reveals Twitter's global presence, with the United States, United Kingdom, and Ukraine emerging as key hubs of activity. This geographical insight augments our comprehension of the platform's diverse user interactions.&#x0D; This paper not only offers insights into sentiment analysis but also paves the way for future research exploring sentiment dynamics, language variations, and real-time interactions within the Twitter landscape.
A Comparative Study of Parallel and Distributed Big Data programming models: Methodologies, Challenges and Future Directions

According to a survey conducted in 2021, users share about 4 petabytes of data on Facebook daily. The exponential increase in data (called big data) plays a vital role in machine learning, internet of things (IoT), and business intelligence applications. Due to the rapid increase in big data, research in big data programming models gained much interest in the past decade. Today, many programming paradigms exist to handle big data, and selecting an appropriate model for a project is critical for its success. This study provides an in-depth analysis of big data programming models such as MapReduce, Directed Acyclic Graph (DAG), Bulk Synchronous Parallel (BSP), and SQL. We conduct a comparative study of distributed and parallel big data programming models and categorize these models into three classes: traditional data processing, graph-based processing, and query-based processing models. Furthermore, we evaluate these programming models based on different parameters like performance, data processing, storage, fault-tolerant, suitable language, and machine learning support. Finally, we highlight the benchmark datasets used for big data programming models and discuss the challenges of models along with future directions for the research community.
Clustering of information and analytical support for scientific research on designing digital educational

information and analytical support are substantiated, which makes it possible to understand more deeply and in detail the results of scientific research obtained by researchers, to determine the value of their scientific output, and to determine
Meningkatkan Kemampuan Menulis Pengalaman Pribadi Melalui Metode Pemodelan Bentuk Teks Kelas VIII di Madrsah Tsanawiyah Negeri 8 Kampar

In this research, it is hoped that the teacher will be able to play his role as a learning innovator. Increasing the teacher's teaching creativity is absolutely developed. This study uses a classroom action research approach to obtain data and analysis through reflective, participatory, and collaborative studies. Program development is based on data and information from students, teachers, and the social setting of the class naturally through two stages of the class action research cycle. To improve students' personal experience writing skills, classroom action research was carried out using the text form modeling method. This research was conducted at MTsN 8 Kampar with two cycles. In the first cycle there were some who had not been able to do the task properly because the teacher had not given clear instructions. In the second cycle, students and teachers have been able to create a conducive learning situation so that student learning activities have increased. Likewise the product results in the form of personal experience writing also experienced an average increase from 75 to 83. In the implementation of cycle one and cycle two it can be concluded that learning with the text form modeling method can increase activity and product results of personal experience writing in class VIII students at MTsN 8 Kampar
Penerapan Metode Belajar Kooperatif Tipe Think Pair Share (TPS) untuk Meningkatkan Motivasi Belajar Peserta Didik

This study aims to describe the planning, implementation and evaluation of increasing the learning motivation of students in class X IPS 3 MAN 1 Pekanbaru for the 2022/2023 academic year using the TPS (Think Pair Share) method. This research is a classroom action research (CAR) and the data is presented through qualitative and quantitative descriptive data. The learning motivation of class X IPS 3 students is still low, researchers are trying to increase it by applying the TPS type cooperative method. This research was conducted in two cycles. Each cycle consists of 4 stages, namely: planning, implementation, action, observation and reflection. The subjects of this study were 34 class X IPS 3 students for the 2022/2023 academic year. The results showed that learning motivation was 71% in cycle 1, 80% in cycle 2. There was an increase of 9% of students who were motivated, became active, both physically, mentally and socially in the learning process
Nilai-Nilai Pendidikan Karakter Dalam Kesenian Kompang

Kompang art is a traditional art that still exists today, and is still widely used in formal events such as plain flour pats. Kompang is an art that already has a lot of customs in it which shows identity as a Malay nation that upholds art with a touch of Islamic law. In this paper the author is interested in discussing the character values contained in the Kompang art in Pasiran Village. Using a qualitative descriptive method, this study conducted in-depth interviews with one of the informants, namely Mr. Kasim who lives in Pasiran Village who is the owner of the Kompang Pasiran art studio. Then do an analysis that this Kompang art has existed for a long time, and has moral and aesthetic values in it. Where Hindu teachings are still felt so strong in the use of the tools used in every plain flour patting event, but the Islamic teachings that have been assimilated into it bring prayers and sholawat in the book of al Barzanji to make the connection with Islamic law so felt. With the existence of Islamic teachings, this will make the character and personality of the local community better in attitude, behavior, in accordance with Islamic teachings contained in the Kompang art
Choosing Your Friends: Shaping Ethical Use of Anonymity Networks (Transcript of Discussion)

Good morning everyone! I’m Daniel and this is joint work together with Ceren and Alastair. We’ve been thinking about something maybe a bit less technical: how one could shape the use of anonymity networks. We are all working on them, and sometimes we feel we don’t talk sufficiently often about this aspect.
Efficient Graph Embedding Method for Link Prediction via Incorporating Graph Structure and Node Attributes

Link prediction is a crucial task in graph analysis that aims to predict the existence of missing links in a graph. Graph embedding methods have gained popularity for link prediction by learning low-dimensional vector representations for nodes. However, most existing methods have not effectively utilized the graph structure and node attributes in attributed graphs. Additionally, higher-order neighborhood nodes that have high correlation with the source node are often disregarded. To address these issues, we propose an efficient graph embedding method called GSNA ( $$\textbf{G}$$ raph $$\textbf{S}$$ tructure and $$\textbf{N}$$ ode $$\textbf{A}$$ ttributes), which effectively incorporates graph structure and node attributes to learn nodes representation for link prediction. Specifically, node sequences are generated using random walk, and then the nodes with high frequency are screened out. Then, the Top-N list of high-order neighborhood nodes with high correlation is obtained by calculating attribute similarities between nodes. Afterwards, a new graph is generated and passed through a multi-head attention mechanism to further learn the representations for link prediction tasks. Experiments conducted on several benchmark datasets demonstrate the effectiveness and efficiency of our proposed method for link prediction.
ECOSYSTEM OF SCHOOL EDUCATION QUALITY: FORMATION AND DEVELOPMENT

The article analyzes the concepts of &amp;quot;ecosystem&amp;quot; and &amp;quot;ecosystem approach&amp;quot;, their evolution in the field of business, economics, the possibility of using the potential of the ecosystem approach in relation to solving management issues and assessing the quality of education at school. Attention is focused on the presence of analogies, common trends and approaches in the business environment and the field of general education. The signs and manifestations of the ecosystem approach in relation to the ecosystem of the quality of school education are considered (cooperation and competition; formation of new values of activity and usefulness of the school; the focus on integrated solutions; the effect of saving on diversity). A special place in the article is given to the interpretation of the quality of school education as a multidimensional and interdisciplinary phenomenon. The characteristic of the subjects of activity that ensure the quality of education within the school ecosystem and the value creation process is given. The article also presents and discusses the results of a survey of teaching staff of secondary schools in St. Petersburg and the Leningrad region on the formation and development of the ecosystem of the quality of school education. On the basis of theoretical and experimental studies generalizing conclusions are formulated, prospects for further study of the phenomenon of &amp;quot;ecosystem of school education quality&amp;quot; are determined.