Reviewerly/exHarmony huggingface dataset:
The Effect Of Using Impromptu Speaking Method Towards Students’ Fluency In Speaking At Eleventh Grade Of SMA Negeri 2 Bukik Barisan

This research was due to discover the effect of using impromptu speaking method toward students’ fluency in speaking. This research is quantitative research and the kind of this research is experimental research. The design of this research is Pre experimental research with one group pretest posttest design. The population of the research was the eleventh grade student at SMAN 2 Bukik Barisan, which consisted two classes. In taking the sample, the researcher used purposive sampling technique, in which IPA class became the sample of the research, which consisted of 15 students. The data were gotten from the students’ speaking test that were given before and after treatment was given. The result of the research indicate that the score of the students’ posttest is higher than the score of the students’ pretest. The mean score of pretest is 59.867 where the mean score of students’ posttest is 69.50. It mean that the impromptu speaking speaking method gave contribution toward the students’ fluency in speaking. In addition to test hypothesis, the t-obtained is 14.61 while t-table on α= 0,05 is 1.761. It mean that the t-obtained is higher than t-table. In other words, the Ha was accepted, which stated that there was significaant effect of using Impromptu Speaking method toward students’ fluency in speaking at the Eleventh grade students of SMAN 2 Bukik Barisan. It can be concluded that, using Impromptu speaking method gives significant effect for students in learning speaking especially in speaking fluency. In short, impromptu speaking method can improve students’ fluency in speaking that the teacher gave for them.
Monitoring the Quality of PeduliLindungi Application based on Customer Reviews on Google Play Using Hybrid Naïve Bayes -Laney p' Attribute Control Chart

Indonesia is battling the COVID-19 pandemic. One of the government's strategies to break the virus's transmission chain is to track digital contacts in Indonesia using the PeduliLindungi application. The Google Play comment section is where users can express their opinions about the app. User opinions discovered on Google Play can be used to perform sentiment analysis and quality evaluation. The Naïve Bayes classification can be used to identify how user opinions contain positive, neutral, or negative sentiments in user reviews of the PeduliLindungi app on Google Play. The p and Laney p' charts can be used for quality evaluation. Laney p' control chart is an attribute chart used to monitor the proportion of defects with large and varied sample sizes. The data used in this study is from April 1, 2020, to March 31, 2022. According to the sentiment analysis results of user reviews of the PeduliLindungi app on Google Play, there are more negative reviews than positive classes. The classification accuracy has an Area Under Curve (AUC) value of 89.05%. This result shows that the test data has good classification. The monitoring results using p and Laney p' charts based on ratings and user reviews of the PeduliLindungi app show that the processes are still not statistically controlled. These findings indicate that the app developer still needs to make improvements.
A Study on Computational Thinking for Major of Computer Science

Recent advances in artificial intelligence and Software have resulted in a paradigm shift in education, necessitating greater changes in educational methods and environments. Many countries worldwide have highlighted computational thinking as the core competence related to software education, leading to an increase in the number of studies focusing on computational thinking in Korea as well. Future demands must be analyzed, and curricula must be improved through in-depth education in computational thinking. Therefore, computational thinking curricula currently offered by universities for students majoring and not majoring in related fields must be reformed for precise educational goals. While research on computational thinking for non-major students has been consistently conducted, curricula for major students are inadequate, and there is a lack of learning opportunities because of the expectation that education on computational thinking for computer science major students will be naturally achieved. Thus, for the purpose of improving the computational thinking education for computer science major students, this study conducted a survey consisting of six questions on 'perception of computational thinking' and 11 questions on 'need of computational thinking' among 313 students majoring in computer science at a university in Korea. In the study results, 177 students (56.5%) answered 'I do not know' for the question 'I know what computational thinking is well enough,' indicating that computational thinking education must be expanded and considered not only for non-computer science majors but also for computer science major students.
Research on Strategies for Tripeaks Variant with Various Layouts

A Tripeaks variant game, derived from the classic card game Tripeaks, is gaining popularity among players. In order to evaluate the difficulty of Tripeaks variant games and assist in level design, this paper investigates the playing strategies for Tripeaks variant games. Firstly, three heuristic strategies based on player experience are proposed. Then, reinforcement learning agents are trained and tested on different datasets to evaluate their generalization performance. The experiments demonstrate that Tripeaks variant games have a high degree of randomness and also possess certain strategies. The reinforcement learning agents have some generalization ability, but cannot handle the rich layouts of Tripeaks variant games. Heuristic strategies have stable and efficient performance, and are more suitable for difficulty detection and level design assistance in Tripeaks variant games.
Assessment of the effects of knowledge management on the performance of the Moroccan public and semi-public sectors: An empirical analysis

In the context of the transformation of the Moroccan public sector, especially in terms of digitalization and human capital development, the objective of this study is to evaluate the effect of knowledge management on knowledge quality and organizational performance. To achieve this, a conceptual model has been adopted that establishes linear relationships between performance, knowledge quality, and knowledge management. The estimation and validation of the model were carried out by administering a questionnaire to a sample of 70 individuals and employing a structural equation model with the PLS approach to analysis the collected data. The study's findings suggest that there is a positive relationship between knowledge quality and performance, as well as a favourable association between knowledge management and knowledge quality. However, regarding the moderating and mediating effects, the study did not find evidence to support the notion that knowledge management plays a mediating or moderating role in the relationship between knowledge quality and performance. Performance is explained by 45.8% through knowledge management and knowledge quality, while knowledge quality is explained by 51% through knowledge management. These results emphasize the importance of public authorities intensifying their efforts to make knowledge management a value-generating factor.
Scale-Adaptive Multi-area Representation for Instance Segmentation

For the instance segmentation task, instance representation directly determines the quality of generated masks, so achieving efficient and accurate instance representation is crucial. Grid-based or box-based instance representation contains redundant information from background or other instances, activation-based instance representation includes a small part of the instance. The instance representation based on current methods is not accurate enough. In order to represent more information of an instance under the condition of excluding irrelevant information, this paper proposes multi-area representation (MAR), which is in the form of a scale-adaptive multi-area activation map generated by a multi-branch structure. MAR can adapt to the structure and pose of an instance, thereby representing the shape and size of the instance. Experiments show that, compared with SparseInst, MARInst can improve the performance of instance segmentation and keep the inference speed and training memory almost unchanged. In particular, MARInst achieved 30.3% AP on the MS COCO 2017 val, and 1.6% AP higher than SparseInst when using the same ResNet-50 backbone, proving the effectiveness of the proposed method.
Ar3dHands: A Dataset and Baseline for Real-Time 3D Hand Pose Estimation from Binocular Distorted Images

Hand pose estimation is an important technology for realtime human-computer interaction. Most existing methods neglect the distorted images captured by wide-angle cameras and tend to have high inference latency particularly without the acceleration of Graphic Process Units (GPUs). In this paper, we propose the first large multi-view distorted hand dataset, Ar3dHands, and develop a simple but effective 3D hand pose estimation algorithm for real-time binocular distorted images which make our method compatible with the wide-angled camera system equipped in miniature visual device like AR/VR glasses. Evaluation shows that our method can achieve state-of-the-art results on several datasets with lower mean 2D end point error and can realize real-time performance on embedded devices without GPUs.
Face Sketch to Image Generation using Generative Adversarial Network

Numerous studies have been conducted in the area of sketch to picture conversion and they got the good outcomes, but sometimes it is not accurate that they observed the blurry boundaries, the mixing of two colors that is the color of hair and face or mixing of both. These results are of the convolution neural networks that are basic of GAN. So to overcome their drawbacks we proposed a novel generative adversarial network using conditional GAN. For that we converted the original image in sketch and both the sketch and original image as reference is applied as input. We got more realistic and sharp colored images as compared to other. We focused on the feature detection, and the results are good. For the experimentation we used the STL-10 dataset. We overcome the problem of mixing of colors and got the different colors for hair, lips, and skin using conditional GAN as compared to CNN modern with increased performance and precision.
Implementasi Cloud Computing Untuk Media Pembelajaran Interaktif Bahasa Inggris Berbasis Android

Bahasa Inggris merupakan bahasa internasional yang memiliki peran penting dalam menghubungkan berbagai masyarakat di seluruh dunia. Data dari survei EF EPI (English Proficiency Index) menunjukkan bahwa Indonesia memiliki skor kemampuan bahasa Inggris sebesar 52.12. Sebagai respons terhadap hal ini, sebuah platform media pembelajaran berbasis Android telah dikembangkan. Platform ini berkomunikasi dengan server cloud yang diatur melalui teknologi Docker. Aplikasi ini menjalani pengujian sistem menggunakan metode black box testing. Hasil dari pengujian ini menunjukkan bahwa aplikasi ini memiliki tingkat kelayakan sebesar 94% dari 50 pengguna yang berpartisipasi dalam pengujian. Selain itu, pengujian kepuasan aplikasi dengan melibatkan 50 responden menunjukkan bahwa 85% dari mereka merasa puas dengan aplikasi pembelajaran ini, sementara 15% menyatakan ketidakpuasan. Hasil dari implementasi performa cloud computing mendapatkan nilai rata-rata 8,38 permintaan per detik pada waktu respon 1 detik dengan 1/1000 permintaan. Ketika pengguna meningkat 10/1000 pengguna mendapatkan nilai rata-rata 34,19 permintaan per detik dengan waktu respon 1 detik. Cloud computing pada aplikasi berfungsi dengan baik dan aplikasi dapat diakses sampai 1000 pengguna.Kata kunci : Android, Media Pembelajaran, Cloud Computing, Blackbox Testing AbstractEnglish is an international language that plays a crucial role in connecting various societies around the world. Data from the EF EPI (English Proficiency Index) survey indicates that Indonesia has an English language proficiency score of 52.12. In response to this, an Android-based media learning platform has been developed. This platform communicates with a cloud server managed through Docker technology. The application undergoes system testing using the black box testing method. The results of this testing show that the application has a feasibility rate of 94% among the 50 users who participated in the testing. Furthermore, a satisfaction test involving 50 respondents revealed that 85% of them are satisfied with this learning application, while 15% expressed dissatisfaction. The performance implementation of cloud computing yielded an average of 8.38 requests per second with a 1-second response time at 1/1000 requests. When the user load increases to 10/1000 users, the average rate beccame 34.19 requests per second with a 1-second response time. Cloud computing in the application functions effectively, and the application can be accessed by up to 1000 users.Keywords : Android, Media Pembelajaran, Cloud Computing, Blackbox Testing
Pemodelan Klasifikasi Lama Waktu Pencapaian Jabatan Fungsional Lektor Kepala menggunakan Optimizer Parameter Support Vector Machine

ABSTRAKPemenuhan dosen dengan jabatan fungsional lektor kepala dan guru besar menjadi sangat penting dalam memperoleh akreditasi unggul bagi perguruan tinggi. Salah satu upaya pemenuhan dengan melakukan klasifikasi dosen dari sisi lama waktu pencapaian jabatan fungsional lektor kepala dari lektor pada Universitas Sebelas Maret dibagi menjadi tiga, yaitu cepat, sedang, dan lambat. Variabel yang digunakan dalam klasifikasi antara lain usia, tempat studi, lama studi, international research, sertifikasi dosen, jabatan structural dan bidang ilmu dari staf pengajar. Penelitian ini melakukan klasifikasi menggunakan algoritma Support Vector Machine dengan dataset sejumlah 520 data. K-fold Cross Validation digunakan untuk membagi dataset menjadi data latih dan data uji, dengan k=5. Hasil pengujian model diperoleh rata-rata akurasi terbaik menggunakan Support Vector Machine sebesar 86.39% dengan Optimizer Parameter sedangkan rata-rata akurasi Support Vector Machine tanpa parameter sebesar 80.92%.Kata kunci: klasifikasi, jabatan fungsional, Support Vector Machine, K-fold Cross Validation ABSTRACTThe fulfillment of lectures on achieving associate professor and professor position holds tremendous significance for gaining excellent institution Accreditation Predicate. Sebelas Maret Univesity took measures to achieve this objective by carrying out thorough the classification on the length of achieving associate professor from instructor position and split into 3 grades namely fast, medium and slow. The features used for conducting the classification are age, place of study, the length of the study, the amount of international publication, lecturer certification, lecturer’s structural position and field of study. In this study, the Support Vector Machine algorithm was utilized to classify a dataset consisting of 520 data. To ensure reliable results, K-fold Cross Validation was applied to divide the dataset into training and test data, with k=5. The evaluation of the model's performance revealed that the Support Vector Machine achieved an impressive average accuracy of 86.39%. In contrast, the average accuracy of the Support Vector Machine to 80.92% without parameters.Keywords: classification, associate lectures position, support vector machine, K-fold Cross Validation
TDM copyright for AI in Europe: a view from Portugal

Abstract The development of artificial intelligence (AI) justified the introduction at the level of the European Union (EU) of a new copyright exception regarding text and data mining (TDM) for purposes of scientific research conducted by research organizations and entities responsible for cultural heritage. This exception is binding and mandatory, as Member States have to provide it in their domestic laws and it cannot be excluded by contract nor by technical measures. In addition to this exception for scientific purposes, a general TDM exception is provided, which is also binding but only applies by default, meaning that it can be excluded by contract and/or by technical measures. The default TDM exception also applies to computer programs, raising the question whether acts allowed under the Software Directive can now be excluded by contract and/or technical measures. It is far from clear whether the new regime extends or, on the contrary, restricts freedom of innovation in AI and whether and how the TDM copyright will contribute to the development of AI and encourage ‘machine learning’ in Europe. The Portuguese implementation of the new ‘TDM copyright’ has been faithful to EU law, and therefore, AI development in Portugal will be carried out under the copyright terms of the Digital Single Market Directive, which seeks a fair balance between the principle of high-level protection of rightholders, the rights and freedoms of users and the good functioning of the digital market in the EU.
Data Modifications in Blockchain Architecture for Big-Data Processing

Due to the immutability of blockchain, the integration with big-data systems creates limitations on redundancy, scalability, cost, and latency. Additionally, large amounts of invaluable data result in the waste of energy and storage resources. As a result, the demand for data deletion possibilities in blockchain has risen over the last decade. Although several prior studies have introduced methods to address data modification features in blockchain, most of the proposed systems need shorter deletion delays and security requirements. This study proposes a novel blockchain architecture called Unlichain that provides data-modification features within public blockchain architecture. To achieve this goal, Unlichain employed a new indexing technique that defines the deletion time for predefined lifetime data. The indexing technique also enables the deletion possibility for unknown lifetime data. Unlichain employs a new metadata verification consensus among full and meta nodes to avoid delays and extra storage usage. Moreover, Unlichain motivates network nodes to include more transactions in a new block, which motivates nodes to scan for expired data during block mining. The evaluations proved that Unlichain architecture successfully enables instant data deletion while the existing solutions suffer from block dependency issues. Additionally, storage usage is reduced by up to 10%.
The intervention effect of marketing interactive teaching model on student depression

Background With the increase of academic pressure, depression has gradually become one of the most important diseases in students, affecting students’ learning, life and physical health. As an innovative educational method, interactive teaching model of marketing has the potential to stimulate students’ interest and enhance their enthusiasm for learning, and can become an auxiliary means to relieve students’ depressive symptoms. Subjects and Methods 300 college students were selected as the research subjects, and were divided into the control group and the experimental group according to 150 people in each group. The control group was treated with traditional drug therapy, and the experimental group was treated with marketing interactive teaching model, emphasizing students’ interaction and practice. Hamilton Depression Scale (HAMD) and self-rating Depression Scale (SDS) were used to evaluate the depression status of students. Results The results showed that the Hamilton Depression Scale score of the experimental group was significantly lower than that of the control group ( P &lt; 0.05), and the difference was statistically significant. The self-rating depression scale score of the experimental group decreased by 9.1 points on average, and that of the control group decreased by 4.6 points. Statistical analysis showed that the improvement of depressive symptoms in the experimental group was significantly higher than that in the control group ( P &lt; 0.001). Conclusions The results showed that the interactive teaching model of marketing had a positive effect on the treatment of depression, and could significantly reduce the symptoms of depression and anxiety of students. Acknowledgement The research of higher vocational education teaching reform in Jiangsu City Vocational College is based on the research of the teaching mode of “learning, doing, thinking, innovation” which integrates science and practice—Building a new Teaching Model of Marketing (11-YB-02).
The Implementation of Hypnoteaching Method in Improving Students’ Reading Comprehension

The objective of this research is to find out the implementation of Hypnoteaching method in improving students reading comprehension. The researcher applied quasi-experimental design. The population of this research is the second semester students of Informatics Study Program at Cokroaminoto Palopo in 2022/2023 academic year. The researcher used cluster random sampling technique, where two classes were taken randomly as sample. The sample consists of 27 students of experimental class and 27 students of control class. There were two variables in this research, namely the implementation of Hypnoteaching method as independent variable and the students reading comprehension as dependent variable. The data was collected through reading test administered in pretest and posttest of each class. The result of data analysis shows that t-test value is greater than t-table value (5.34 &gt; 2.056) at level of significance (p) = 0.05 with degree of freedom (df) = 26. It means that there is a significant difference of reading comprehension between two classes and it indicates that null hypothesis (H0) is rejected and alternative hypothesis (H1) is accepted. Therefore, based on the findings and discussion of this research, it can be concluded that the implementation of Hypnoteaching method improves students reading comprehension
Analysis of Korea and Global Monopoly Research Trends Using Topic Modeling and Time-Series Analysis

Many countries have made efforts to prevent the concentration of a few major corporations and monopolistic practices by developing and improving fair trade laws and antitrust regulations. However, the emergence of new industries and changes in trading patterns due to increased online transactions amid the COVID-19 pandemic have created gaps in existing laws. We aim to predict trends in monopolistic industries to proactively prevent such problems by establishing relevant laws or regulations. In order to examine the research trends on domestic and international monopolies, we collected abstracts of papers related to monopoly regulation from the RISS database for domestic papers and the Web of Science for international papers from 1995 to 2021. After preprocessing the data, LDA topic modeling was conducted to extract the top 15 significant topics. Moreover, we proposed a method for calculating the topic's importance to measure how actively research has progressed. We employed the linear regression model to examine the trends in research importance for each topic, and the ARIMA model was applied to predict the future trends in research importance for the next three years. While no significant trends were observed in the international data, the findings from the domestic data indicated a significant increasing volume of research on “online transactions” in the future.