Reviewerly/exHarmony huggingface dataset:
Applying Text Feature Walks Strategy in Higher Education to Improve Students’ Critical Reading Comprehension

This study was conducted to investigate the efficacy of text feature walks strategy in helping students in higher education to improve their critical reading comprehension through the expository texts. It also explored the feedback from students related to the implementation of Text Feature Walks in the virtual classroom. There were 40 students of English Education Study Program in a private university becoming the sample in this study and they were equally divided into experimental and control groups. To measure their improvement in reading comprehension, all students were given pre- and post reading tests in the form of multiple choice. The data obtained were analyzed by using paired sample and independent sample t-tests. The results of paired sample t-test showed that there was a significant improvement in reading (Md=33.13, p=0.000) in experimental group. In addition, the result of independent sample t-test indicated that there were significant difference in reading (Md=39.70, p=0.000) of both groups.
ABOUT OF FORECASTING POSSIBILITY OF HYDROLOGICAL TIME SERIES

It is offered forecast the natural time series (hydrological time series) by methods the deterministic chaotic dynamic. At analysis of the time series reveal the hidden regularities at raw data’s. Hereinafter revealled regularities use for realization of the forecast of input data. Hydrological time series either as chaotical time series possible forecast only at determined number step onward. For hydrological time series exists the limit of forecasting (forecasting horizon). The identical prediction possible unless come behind of the forecasting horizon. The aim of the work there is design of the procedure the investigations of the natural time series on possibility of the forecasting. As raw data’s were used the mean day data of consuption for the large flat river of the Ukraine (length is 4*365 points). For forecasting of the natural time series it was used designed an autors modification of the method of Local Approximation. The forecasting horizon of time series was defined by means of factor the linear correlation (on how much points onward (maximum) possible forecast the time series for conservation of the factor the correlation within the range of 0.7 … 1.0). The explored dependency of the forecasting horizon from parameter DIM (Dimensionality Reconstrusted the phase space). The results of the research say for essential determinism of time series (hydrological) – the time series is forecasted on 15 … 22 points onward. Software was designed for investigations of the natural time series (hydrological and hydrochemistry) for forecasting (to find forecasting horizon). The called on investigation has shown that the method of Local Approximation more efficient than classical methods the forecast (for classical methods the adequate forecast possible only on 1 … 2 points onward).
Investigating Knowledge Management in Human-Computer Interaction Design

Developing interactive systems is a challenging task. It involves concerns related to human-computer interaction (HCI), such as usability and user experience. Therefore, HCI design must be addressed when developing such systems. HCI design often involves people with different backgrounds, which makes communication and knowledge transfer a challenging issue. In this scenario, knowledge management can support understanding concepts from different knowledge areas and help learn from previous experiences. Aiming at investigating how knowledge management has supported HCI design and contributed to the development of interactive systems, we performed a mapping study in the literature and analyzed 15 publications reporting the use of knowledge management in HCI design. Following that, we conducted a survey with 39 HCI design professionals to find out how knowledge has been managed in their HCI design practice. In this paper, we present the studies and discuss their main findings. In summary, the results indicate that knowledge management has been used in HCI design mainly to improve product quality and reduce the effort and time spent on design activities. However, there is a need for simpler and more practical knowledge-based solutions to support HCI design. Such approaches would be capable of reaching more HCI design practitioners that could benefit from them.
Penggunaan Buku Cerita Bergambar Elektronik Interaktif di Sekolah Dasar (Studi kasus di Kelas 2 SDN Bangka 05 Jakarta Selatan)

Technology is growing very fast, including on medium that used to study in primary school. Reading lesson have many strategic to used, one of them is using an interactive electronic picture book. Present research revealed how interactive electronic picture book used in grade 2 SDN Bangka 05, Jakarta Selatan. Book created by the teacher used in this lesson, combine the image, text, and voice in order to more interactive and gain the student interest. Before the lesson start, teacher diagnosed student reading skill using printed book, and the result show that student read the book with a flat voice, there is no intonation while the reading. Narrative qualitative method used in this research. The result show there is increasing enthusiastic among student compared when using printed book. Beside that there is an increasing on reading comprehensive too and student can show their understanding about story through role playing in the class. Further research needed to conduct in other school with variety resources.
Transformable Dilated Convolution by Distance for LiDAR Semantic Segmentation

LiDAR semantic segmentation is essential in autonomous vehicle safety. A rotating 3D LiDAR projects more laser points onto nearby objects and fewer points onto farther objects. Therefore, when projecting points onto a 2D image, such as spherical coordinates, nearer objects appear larger than more distant objects. Recognizing a closer object requires a larger receptive field, whereas recognizing a nearer object requires a smaller receptive field. However, existing CNNs have always used the same receptive field, making it difficult to express objects of various sizes in a single-sized receptive field, restricting their performance in terms of the recognition of larger (or nearer) objects that require a larger receptive field. In response to these limitations, we propose a transformable dilated convolution (TD Conv) to adjust the convolution filter’s size according to the input distance. Leveraging the distance information of LiDAR and dilated convolution, a large convolution was applied to nearby objects, and a small convolution was applied to farther objects. The proposed method yielded good performance when recognizing nearer objects or larger objects such as roads and buildings and showed similar performance to the conventional method for farther or smaller objects. To test the proposed method, we used the SemanticKITTI dataset.
INTEGRATION OF FRAMEWORK ISO 27001 AND COBIT 2019 IN SMART TOURISM INFORMATION SECURITY PT. YoY INTERNATIONAL MANAGEMENT

Information technology that is growing rapidly becomes a very high threat to information systems. PT. YoY Management Internasional which will manage the location-based smart tourism application so that it is necessary to protect company information, in order to avoid interference and threats that can harm the company. In this study, an analysis of information technology (IT) governance was carried out using the COBIT 2019 framework, by aligning the company's strategies and goals into existing processes in COBIT 2019 which were then mapped into ISO 27001 for information security management. The purpose of this research is to manage information security using the COBIT 2019 framework and the ISO 27001:2013 standard. The research method used in this research is descriptive qualitative. The results obtained in this study are in the form of several recommendations for policies in managing information security in smart tourism applications in accordance with the COBIT 2019 and ISO 27001:2013 standards.
Analytical Framework for Facial Expression on Game Experience Test

Game experience testing, an essential process for developing and servicing high-quality games, aims to evaluate and improve the play experience from the user perspective.Previous game experience tests are usually held offline with recruited test participants.
Kontribusi Teknologi Informasi dan Komunikasi terhadap Rata-Rata Lama Sekolah di Pulau Jawa

Education is one of the capital in building human resources. Through education, people can add knowledge and skills that will be used in the future or the present. The number of facilities offered in Java Island, many young people who prefer to study in the region compared to the outer region of Java Island. The use of Technology, Information, and Communication (ICT) in the field of education has a large role in the learning process. The purpose of this study is to describe the Mean Years of Schooling and ICT and analyze the influence of ICT on the increase in average length of school in Java Island. The data used is sourced from data from the Statistics Indonesia-BPS in the period 2017 until 2020. The results showed the Mean Years of Schooling, internet users, and computer users tended to increase in Java. The variables of internet users, computer users, and capital expenditures have a positive and significant influence on the Mean Years of Schooling in Java while the percentage of poverty does not have a significant influence. Therefore, collaboration between various parties is needed in improving ICT support infrastructure. The government should be able to allocate funds for education appropriately. In addition, the school should register students who have difficulty in accessing the internet.
3D-MTR: 3D Reconstruction Algorithms for Deep Understanding of Images

Three-dimensional data can improve the performance of computer vision downstream tasks. In this paper, computer vision is introduced from the two-dimensional world to the three-dimensional world through the three-dimensional reconstruction algorithm. A 3D reconstruction algorithm, 3D Masked autoencoder Transformer Reconstruction (3D-MTR), is proposed for deep understanding of series view to restore 3D model. 3D-MTR can reconstruct 3D models with high-level semantic information in the series of views. Among them, in order to accurately extract and fuse the feature vectors with spatial information in the series of views, the MAE self-supervised learning pre-training is performed on the two-dimensional encoder; in order to make the reconstructed model more realistic, the Transformer-based two-dimensional encoder is compared with adversarial training. Combined, the model is updated through advanced semantic information; in order to make up for the uncontrollable defect of adversarial training, a repair network is added at the end of the model. In order to verify the validity of the model, a comparison experiment was conducted with three classic 3D reconstruction models on the ShapeNet dataset, using the Chamfer distance and the intersection-over-union ratio as the model evaluation indicators, and the experiments were conducted. Perform qualitative and quantitative analysis.
Evaluating Variational Quantum Circuit Designs for Knowledge Graph Completion

Knowledge graphs (KGs) are informative knowledge bases for various applications such as semantic web search or question answering. Since KGs often have missing facts mainly due to large construction costs, KG completion is a fundamental problem. Nowadays, as KGs have become large-scale, KG completion has become gradually intractable for classical computers. One promising direction is to utilize quantum circuits to capture complex structural information that entities and relations in KGs have. By encoding entities into quantum states and representing relations between entities as quantum circuits running on the quantum states, it becomes able to score and predict plausible missing entities or relations. However, it is still unclear how to design the quantum circuits to enhance KG completion accuracy. In this paper, we evaluate variations of quantum circuits in terms of KG completion accuracy, and investigate the relationship between the accuracy and the quality of the circuits (e.g., expressibility and entangling capacity). The experiments using a real KG show some correlation between the completion accuracy and the goodness of the circuits.
Robotic education in Russia and abroad

The article analyzes the level of robotization and the state of robotic education in different countries, their importance at the present stage of development of industry, services and other sectors of the economy. In a technological society in which robotics is actively introduced into everyday life, education should be aimed at developing the skills necessary to work with robots and intelligent automation systems. Under the sanctions imposed against Russia, the development of domestic industrial and service robots is especially important, and this requires qualified specialists and technicians.
Endomorphisms of Lifted Planning Problems

Classical planning tasks are usually modelled in the PDDL which is a schematic language based on first-order logic. Nevertheless, most of the current planners turn this first-order representation into a propositional one via the grounding process. It is well known that the grounding process may cause an exponential blowup. Therefore it is important to detect which grounded atoms are redundant in a sense that they are not necessary for finding a plan and therefore the grounding process does not need to generate them. This is usually done by a relaxed reachability analysis, which can be improved by employing structural symmetries. Symmetries are bijective self-maps preserving the structure of the PDDL task. In this paper, we introduce a new method which is based on self-maps preserving the structure but which need not be bijective. We call these maps PDDL endomorphisms and we show that they can be used for pruning of redundant objects even if they appear in a reachable atom. We formulate the computation of endomorphisms as a constraint satisfaction problem (CSP) that can be solved by an off-the-shelf CSP solver.
Construction of two-dimensional multiparameter mathematical models of the problem of the theory of nonlinear filtration of fluids

The article discusses the construction and development of algorithms for solving two-dimensional generalized mathematical models of the filtration process of Newtonian and non-Newtonian fluids. The multiparameter generalized model includes 13 known and previously unknown models. The classification of the models is carried out in accordance with the laws of filtration and the algorithms for the solution are grouped. Partial numerical solutions of individual problems are presented.
Iterative Robust Ellipsoid Fitting Based on M-Estimator With Geometry Radius Constraint

The ellipsoid fitting technique has been popularly used in many application scenarios, especially in triaxial sensor calibration in recent years. The conventional method to achieve ellipsoid fitting is the pseudo-inverse technique with algebraic constraints to obtain proper fitting results. This technique ensures the least mean square (LMS) error relative to all measured data and prevents trivial solutions. However, this approach lacks robustness against outliers resulting in nonideal fitting performance in real calibration situations. The M-estimator technique with several robust cost functions is adopted and applied in this research to attenuate the outlier’s effect to offer a more accurate fitting performance. In contrast, the constraint with geometric meaning is also applied and examined to prevent trivial solutions. These two methods are achieved by using the iterative constrained gradient descent (G.D.) method to approach the proper ellipsoid fitting. The performances are validated through synthesis and real measured data. Through calibrated experiments, the proposed methods are found to be able to increase robustness against outliers and prevent parameters of ellipsoid degeneration toward trivial solutions.
Conflict-Based Search for Optimal Multi-Agent Path Finding

We present a new two-level search algorithm for optimal multi-agent path finding called Conflict Based Search (CBS). At the high level, a search is performed on a tree based on conflicts between agents. At the low level, a search is performed only for a single agent at a time. Experimental results on various problems shows a speedup of up to a full order of magnitude over previous approaches.
PENERAPAN MODEL PEMBELAJARAN KOOPERATIF TIPE TALKING STICK UNTUK MENINGKATKAN KETERAMPILAN BERBICARA DAN PRESTASI BELAJAR SISWA

This research is conducted to describe the application of talking stick type of cooperative learning model to increase the speaking skill and the students’ achievement at English Lesson of the tenthgrade at SMA Negeri 1 Kikim Timur. The research methods that used were Classroom Action Research and Quasi Experiment. The subjects of this research were students of the tenth grade SMA Negeri 1 Kikim Timur at the second semester of the academic year 2020/2021,for CAR class was X IPS 1,Experiment class X IPS 3,and Control Class X IPS 2. Each class totaling 27 students. The Research instruments used observation sheet and test. The data analysis used the average score analysis and test consisting of inter-cycle different test and unrelated two sample different test. The result showed that the application of cooperative type of learning model talking stick can increase the speaking skill and the students’ achievement of the tenthgradestudents of English lesson at SMA Negeri 1 Kikim Timur
Impact of the COVID 19 pandemic on the student’s academic performance. Case at the School of Engineering - Universidad Nacional de Colombia Bogotá Campus

This paper analyzes the impact of the COVID-19 pandemic on the student’s academic performance in the School of Engineering at Universidad Nacional de Colombia – Bogota Campus. The impact is assessed from a quantitative approach based on (i) student’s grades, (ii) student’s progress in their curriculum and (iii) dropped courses. In addition, results from a faculty survey (qualitative approach) are presented to expand some explanatory perspectives on the main academic changes during the pandemic. Results show a significant increase in the average numerical grade as well as in the probability of a course being dropped during the pandemic conditions. Furthermore, the student’s average curriculum progress per semester grew approximately 18%. A differentiated academic impact, depending on sex and family income was observed which may be included in future post-pandemic programs. The survey reflects a new faculty’s perspective on evaluation tools and methodologies.
The unsolvable problem or the unheard answer?

Talks at practitioner-focused open-source software conferences are a valuable source of information for software engineering researchers. They provide a pulse of the community and are valuable source material for grey literature analysis. We curated a dataset of 24,669 talks from 87 open-source conferences between 2010 and 2021. We stored all relevant metadata from these conferences and provide scripts to collect the transcripts. We believe this data is useful for answering many kinds of questions, such as: What are the important/highly discussed topics within practitioner communities? How do practitioners interact? And how do they present themselves to the public? We demonstrate the usefulness of this data by reporting our findings from two small studies: a topic model analysis providing an overview of open-source community dynamics since 2011 and a qualitative analysis of a smaller community-oriented sample within our dataset to gain a better understanding of why contributors leave open-source software.
An Explainable AI Model for Hate Speech Detection on Indonesian Twitter

To avoid citizen disputes, hate speech on social media, such as Twitter, must be automatically detected. The current research in Indonesian Twitter focuses on developing better hate speech detection models. However, there is limited study on the explainability aspects of hate speech detection. The research aims to explain issues that previous researchers have not detailed and attempt to answer the shortcomings of previous researchers. There are 13,169 tweets in the dataset with labels like “hate speech” and “abusive language”. The dataset also provides binary labels on whether hate speech is directed to individual, group, religion, race, physical disability, and gender. In the research, classification is performed by using traditional machine learning models, and the predictions are evaluated using an Explainable AI model, such as Local Interpretable Model-Agnostic Explanations (LIME), to allow users to comprehend why a tweet is regarded as a hateful message. Moreover, models that perform well in classification perceive incorrect words as contributing to hate speech. As a result, such models are unsuitable for deployment in the real world. In the investigation, the combination of XGBoost and logical LIME explanations produces the most logical results. The use of the Explainable AI model highlights the importance of choosing the ideal model while maintaining users’ trust in the deployed model.
Eksplorasi Pemberian Kartu Masalah untuk Meningkatkan Kemampuan Numerasi Siswa Kelas VII SMP Negeri 1 Pakis

The results of the 2018 PISA test show that Indonesian students' math ability scores are ranked 7th from the bottom. This shows that the numeracy skills of Indonesian students are low. Based on an interview with one of the teachers at SMP Negeri 1 Pakis, information was obtained that the numeracy skills of students at SMP Negeri 1 Pakis were low. This is due to students' lack of numeracy questions, and teachers often give routine questions requiring procedural skills. This is a classroom action research that explores the teacher's actions to improve the numeracy skills of seventh-grade students of SMP Negeri 1 Pakis Malang by giving problem cards. The research subjects were 32 Class VII D students of SMP Negeri 1 Pakis Malang in the academic year 2021/2022. The research instruments include lesson plans, problem cards, test questions, observation sheets, interview guidelines, and field notes. Data analysis techniques include data reduction, presenting data, and drawing conclusions and verification. The results showed an increase in students with a KKM score of 30.77% and Classical Learning Completeness of 26.26%. The study shows that giving problem cards can improve students' numeracy skills. Through the Problem Card, students are allowed to write down the information asked in their language and are given guidance/instructions to solve the given problem.