Reviewerly/exHarmony huggingface dataset:
What EFL Students Think about Methods Used to Instruct Their Language

EFL classes are offered at the secondary school level in Indonesia. To ensure the success of the English as a Foreign Language (EFL) classroom, educators in the field must use effective pedagogical practices. Assessing teachers' performance in the classroom can have far-reaching implications on the quality of education in the classroom. A useful strategy is to analyze the pupils' perspectives. Research was therefore done to detail which techniques students found useful and which they found less, as well as the reasons behind these judgments. Twenty-nine college students participated in this study via a systematic random sampling strategy. Focus group discussion (FGD) was used to collect data, which was then analyzed descriptively using a qualitative methodology. First, the analysis revealed that (1) students viewed the Communicative Method, Cooperative Learning, the Competency-Based approach, and Community Language Learning as effective strategies for teaching and learning languages. While the Lecturing Method, the Grammar-Translation Method, the Direct Method, and the Task-Based Method are all considered inefficient. (2) The strategies that students found useful and those that they found ineffective. (3) Three distinct senses will recognize an efficient strategy. These include studying outside the class, reviewing material from the previous session, and repeating it before moving on to the new one.
Learning Knowledge Representation with Entity Concept Information

The goal of Knowledge Representation Learning (KRL) is to learn an accurate knowledge representation that conforms to human understanding. Currently, many works have used entity multi-source information to improve the entity representation semantic precision, such as entity description, attribute, and visual information. However, few methods consider the timeliness in KRL, which significantly affects representation learning performance. In this paper, we attempt to utilize concept information with human understanding to learn an accurate, time-stable knowledge representation. Specifically, we first build a novel Knowledge Graph (KG) - Structure Concept Graph (SCG), which can provide entity structure and concept information jointly. Based on the SCG, we devise a novel KRL model that can embed entity concept information to ensure accuracy and timeliness of improving the KRL’s effect with entity structure information. We evaluate our method on two downstream tasks: the knowledge graph completion task and the zero-shot task. Experimental results on real-world datasets show that our method outperforms other baselines by building effective entities’ representations from their concept information. The source code of this paper can be obtained from https://anonymous.4open.science/r/CKRL-adma2023 .
Attack based on data: a novel perspective to attack sensitive points directly

Abstract Adversarial attack for time-series classification model is widely explored and many attack methods are proposed. But there is not a method of attack based on the data itself. In this paper, we innovatively proposed a black-box sparse attack method based on data location. Our method directly attack the sensitive points in the time-series data according to statistical features extract from the dataset. At first, we have validated the transferability of sensitive points among DNNs with different structures. Secondly, we use the statistical features extract from the dataset and the sensitive rate of each point as the training set to train the predictive model. Then, predicting the sensitive rate of test set by predictive model. Finally, perturbing according to the sensitive rate. The attack is limited by constraining the L0 norm to achieve one-point attack. We conduct experiments on several datasets to validate the effectiveness of this method.
Students Talking Time (STT) Technique to Elevate Speaking Skills in Senior High School Students

This study investigates the efficacy of the Student Talking Time (STT) technique in enhancing the speaking skills of second-year senior high school students. Employing a Classroom Action Research (CAR) approach based on Kurt Lewin's cyclic design, the study comprises four phases: planning, acting, observing, and reflecting. Data collection methods include observations, questionnaires, and tests. The findings indicate successful implementation of the STT technique, meeting predefined criteria for success. In the diagnostic test, 12.5% of students achieved the minimal mastery level criterion of Indonesian school, rising to 43.75% in the first cycle's post-test and a remarkable 75% in the second cycle's post-test. Observations reveal increased student confidence and active participation in class discussions and conversations, reflecting improved speaking skills. Questionnaire results indicate high student interest (69.68%) in using the STT technique for speaking practice, with students acknowledging enhancements in their speaking abilities. In conclusion, this study demonstrates the effectiveness of the STT technique in enhancing the speaking skills of senior high school students, as evidenced by improved test scores, heightened confidence, and strong student interest in this teaching approach.
Effectiveness of Group Work Strategy on the Achievement of EFL Secondary Schools Students in Ma’rib City

The aim of this study was to determine the effectiveness of group work strategy on the achievement of EFL secondary school students in Ma’rib city. To achieve the objectives of the study, a quasi-experimental design was organized with experimental and control groups. The experimental group (n=30) and the control group (n=30) were purposefully chosen to participate in the experiment among the 10th-grade students in Al-Methaq secondary school in Ma’rib city during the first semester of the study year (2021-2021). The study instrument was an achievement test administered before and after conducting the experiment. The validity and reliability of the instrument were ensured. The collected data were analyzed using the Statistical Package for Social Sciences (SPSS). The obtained data were calculated and tabulated for analysis using descriptive statistics (means, standard deviation, and rank mean). The results of the study showed a positive effect of using group work strategy on students’ achievement in English. In addition, there were statistically significant differences in the mean scores between the two groups in English achievement. This result was attributed to the use of group work strategy. In conclusion, the study recommends implementing active learning strategies in teaching and learning English to ensure better outcomes for students.
Assessing the Academic Performances in Online and Modular Learning among the Junior High School STE Students of Tanauan City Integrated High School

Education is the foundation of society, like technology, which drives globalization and industrialization. Computers are replacing books in education. One click can Google anything. These changes are relevant to COVID 19, the current global situation. Amid the pandemic, government agencies propose online learning for 2020-2021. This study assessed high school students' online and modular learning performance at Tanauan City Integrated High School. The researcher used descriptive quantitative method to determine the online and modular student performance. Pearson r was used to determine the relationship between respondent demographics and student performance. The results show that students' online and modular learning performances vary based on their personal benefits, such as being satisfied with their work and motivated to pass. Online learning received a higher positive (excellent) response for academic performance, class standing, and attendance than modular learning.
Pengaruh Penggunaan Media Boneka Tangan (Hand Puppet) terhadap Peningkatan Bahasa Reseptif Anak Usia 5-6 Tahun di PAUD SOS HKBP Sitabotabo

The purpose of this study was to determine the effect of using Hand Puppet media on the improvement of receptive language for children aged 5-6 years at PAUD SOS HKBP Sitabotabo. The method used in this research is quantitative inferential statistics. The population is all children aged 5-6 years in PAUD SOS HKBP Sitabotabo, totaling 30 people. Data were collected using a positive closed questionnaire of 30 items. The results of data analysis showed that there was an effect of using Hand Puppet Media on the Improvement of Receptive Language for Children aged 5-6 Years in PAUD SOS HKBP Sitabotabo.: 1) Test requirements analysis: a) positive relationship test obtained values rxy = rtabel(a=0,05,n=30) = 0,361 Thus, it is known that there is a positive relationship between the X variable and the Y variable. b) The test of a significant relationship is obtained by the value of thitung= 3,980 ttabel(a=0,05,dk=n-2=28 )= 2,048 thus there is a significant relationship between variable X and variable Y. 2) Test the effect: a) Test the regression equation, the regression equation is obtained b) Test the coefficient of regression determination (r2) = 36%. 3) Test the hypothesis by using the F test obtained Fhitung ≥ Ftabel that is 15,84 1,62 Thus Ha is accepted and H0 is rejected.
RANCANG BANGUN MEDIA PEMBELAJARAN INTERAKTIF ARTICULATE STORYLINE PADA MATA PELAJARAN IPS JENIS-JENIS PEKERJAAN DI SEKOLAH DASAR

This research is motivated by optimizing the concept of types of work including jobs based on geographic location in social studies learning for grade IV in elementary schools, so that interactive learning media articulated storylines for types of work were developed. This study aims to produce products from the design of interactive learning media articulate storyline, describe the feasibility of the media from the validation results of experts including material experts, linguists and media experts, as well as user responses, namely teachers and students, to the use of interactive learning media. By using articulate storyline interactive learning media, it is hoped that the media can help students at different levels of abstraction because of the images in the multimedia. This study uses the Design and Development method using the ADDIE model development (Analyze, Design, Development, Implementation, and Evaluation). Participants in this study involved material experts, linguists and media experts to provide an assessment of this interactive learning media. Data collection techniques carried out on this media used unstructured questionnaires and interviews. The results of this study indicate that the articulate storyline interactive learning media gets a very feasible category as a medium to be used in learning, especially social studies. Based on the results of limited product trials, this interactive learning media obtained a very feasible category from material experts with aspects of material, display, program, learning and language. The responses from users, namely teachers and fourth grade students at SDN Bedahan 02 gave an assessment in the "very feasible" category with aspects of material, appearance, language, and use. So from these results, it shows that interactive learning media articulate storyline for material types of decent work is used in social studies learning for grade IV elementary schools.
Analysis Covid-19 Vaccine Effectiveness Using SIR Model

Covid-19 is a new coronavirus disease that was labelled a pandemic by the World Health Organization (WHO) in March 2020. SIR Model is a versatile compartmental mathematical tool that may be used to simulate any pandemic dynamic, including the current Covid-19 out- break. In the conventional SIR model, the total population (N) is divided into three categories: susceptible(S), infected(I), and recovered(R). So, this research focused on finding the basic re- productive number, Ro of Covid-19 by using the Next Generation Matrix. Ro is greater than 1 means viruses begin to spread the population and Ro less than 1 means disease is about to vanish from the population. It is also analyze and compare the transmission of Covid-19 with and without vaccination. To apply this, the data from government websites is used to find the total number of cases and recover. With the help of mathematical software such as Maple to find the result of the graph. From the result produce from Maple, it can be observed that the slope of with vaccination is bigger than the slope of without vaccination. It clearly shows the comparison between them. The findings improved by having vaccination and then transmission rate low is good to decrease virus Covid-19 from infection.
Speech Recognition and Sending Alert Message If Abusive Words are Detected

Abstract: This paper introduces an innovative approach to bolstering the field of speech recognition. Speech recognition, a pivotal process in facilitating computer systems' understanding and response to natural language commands, has garnered considerable importance in today's digital landscape. Our novel method aims to enable users to issue voice commands with enhanced accuracy and efficiency. This approach encompasses several critical steps: the intake of a single audio input, effective noise reduction, and the utilization of pattern matching for audio recognition. To fortify the system's robustness, we've integrated an error tolerance mechanism capable of accommodating human errors and mitigating environmental noise.What sets our method apart is its ability to circumvent the need for storing extensive audio data for matching, leading to a substantial reduction in both space and time complexity when compared to conventional systems. In our experiments, we have exclusively employed a database containing an array of predefined voice patterns, effectively eliminating the necessity for end-user training.
Exploring the Possibilities of Continuous Quality Improvement as a Capacity Building Tool to Teacher Training Institution: Centering on A Universities

The purpose of this study is to provide practical help for the improvement of the quality of teachers' education in preparation for the competency diagnosis of teacher education institutions and to support the voluntary development of teachers' competencies at A University. The subjects of the study were 304 CQI of curricula(2022-1) and the research questions were the core contents of lecture evaluations of students, satisfaction and dissatisfaction factors of teacher education curricula perceived by teachers. As a result of the analysis, it was confirmed that the quality of classes is improving based on key words, it was confirmed that students' demands for various teaching methods are increasing; third, the teacher satisfaction factors were psychological factors and the dissatisfaction factors were still poor educational environments. Therefore, it is necessary to develop a CQI questionnaire that focuses on strengthening the capacity of teacher training and to collaborate with and support relevant departments to improve the quality of teaching.
Pembuatan Augmented Reality Menggunakan Metode Marker Based Tracking Sebagai Media Edukasi Budidaya Ikan Lele Di Bomo Krajan Banyuwangi

The lack of information is one of the challenges faced by people who want to start catfish farming in Banyuwangi, due to a lack of knowledge about catfish farming methods. This is what motivates the author to provide information recommendations using Augmented Reality-based technology as an educational medium. The implementation aims to reach people who want to learn how to farm catfish, from selecting fingerlings to the harvesting process. The method used in creating this learning media is the application of AR (Augmented Reality) technology. Augmented Reality can provide easily understandable and absorbable supporting information. This technique also offers an attractive interface by combining various multimedia aspects, including visuals, audio, and text simultaneously. Augmented reality-based video is applied through brochures. Testing on different smartphones resulted in effective marker recognition in lighting conditions of 10 Lux and above, at distances below 20cm. Marker recognition becomes challenging when markers are torn, wet, scribbled, or damaged.
The Effect of the Level of Digital Information Use among Older Adults Living Alone on Their Quality of Life and the Moderating Effect of Sense of Coherence

This study was to examine the effect of the level of digital information utilization of seniors living alone on their quality of life, and to verify the moderating effect of sense of coherence. For this purpose, secondary data analysis from a nationwide survey of elderly people living alone was used. Also, a hierarchical regression analysis was conducted with 477 people. The results showed that the level of digital information use and quality of life of seniors living alone were related, and that sense of coherence was also related to quality of life. However, moderating effect of the level of digital information use and sense of coherence did not appear in their impact on quality of life. This study, which confirmed the impact of the level of digital information use and sense of coherence on the quality of life for seniors living alone, is expected to serve as basic data for establishing digital support policies for seniors living alone in the future.
IMPLEMENTASI ALGORITMA APRIORI UNTUK MENENTUKAN PRODUK TERLARIS PADA TOKO I_DOCRAFT

The sales of pajama products on i_docraft have not yet leveraged data mining algorithms to analyze transactional data for optimizing sales. To avoid underperforming pajama models and determine which pajama models sell well, the utilization of the Apriori algorithm is necessary. The Apriori algorithm can discern these patterns based on transactional data. This study conducts a transactional data analysis using data mining with the Apriori algorithm. By employing this algorithm, the most frequently sold pajama products can be identified, allowing for prioritization of these models and the development of marketing strategies for other types of pajamas based on a comparison of their strengths and commonly high sales figures. The processed data yields associations rules for concurrently sold pajama items. Based on the results of the final association rules meeting both predetermined minimum support and confidence criteria, for instance, if a product with item code 7 (Cherrypie Nightdress) is purchased, then a product with item code 17 (3 in 1 Lotso Set) will likely be bought with a support value of 22.58% and a confidence value of 100%.
A deep learning aided differential distinguisher improvement framework with more lightweight and universality

Abstract In CRYPTO 2019, Gohr opens up a new direction for cryptanalysis. He successfully applied deep learning to differential cryptanalysis against the NSA block cipher SPECK32/64, achieving higher accuracy than traditional differential distinguishers. Until now, one of the mainstream research directions is increasing the training sample size and utilizing different neural networks to improve the accuracy of neural distinguishers. This conversion mindset may lead to a huge number of parameters, heavy computing load, and a large number of memory in the distinguishers training process. However, in the practical application of cryptanalysis, the applicability of the attacks method in a resource-constrained environment is very important. Therefore, we focus on the cost optimization and aim to reduce network parameters for differential neural cryptanalysis.In this paper, we propose two cost-optimized neural distinguisher improvement methods from the aspect of data format and network structure, respectively. Firstly, we obtain a partial output difference neural distinguisher using only 4-bits training data format which is constructed with a new advantage bits search algorithm based on two key improvement conditions. In addition, we perform an interpretability analysis of the new neural distinguishers whose results are mainly reflected in the relationship between the neural distinguishers, truncated differential, and advantage bits. Secondly, we replace the traditional convolution with the depthwise separable convolution to reduce the training cost without affecting the accuracy as much as possible. Overall, the number of training parameters can be reduced by less than 50% by using our new network structure for training neural distinguishers. Finally, we apply the network structure to the partial output difference neural distinguishers. The combinatorial approach have led to a further reduction in the number of parameters (approximately 30% of Gohr’s distinguishers for SPECK).
CRAG: A Guideline to Perform a Cybersecurity Risk Audits

The cybersecurity risk audit is a relatively new field. The objective of cybersecurity risk audits is to identify deficiencies or deviations in cybersecurity countermeasures. Currently, cybersecurity risk audit guidelines do not include an internal control approach that aligns with cybersecurity standards such as ISO 27001 or the NIST CSF. Internal control is essential for addressing cybersecurity risk audits. This article proposes a cybersecurity risk audit guideline called CRAG (Cybersecurity Risk Audit Guideline), created using the SADT (Structured Analysis and Design Technique) model. CRAG aims to be comprehensive in the various applications that a cybersecurity risk audit guideline can have. The CRAG guideline consists of seven steps and 28 activities, as well as the content that the resulting audit report should include. Additionally, this article provides guidelines for its proper implementation, as well as examples of its potential applications.
A belief rule-based fault diagnosis method for Multi-UAVs under missing attribute data

To reasonably improve the missing attribute data and effectively integrate sample data and uncertain expert knowledge, this paper proposes a new fault diagnosis method based on a belief rule base (BRB). In the case of missing attribute data, the maximum likelihood estimation (MLE) method is used to complete the missing attribute data, and the fault diagnosis method of the multi-UAVs is constructed by using other complete attribute data as the input information of BRB. Finally, the effectiveness of the proposed method is verified by experimental simulation.
Target Combat Intention Recognition Based on Improved Fisher Information Matrix

Recognizing the opponent’s intention is an important part in situational assessment and make the operation decision. Based on the pattern recognition technology of artificial intelligence, a combat intention recognition algorithm according to the value of Fisher information matrix is proposed. The target’s combat intention knowledge rule base is constructed and the relative Fisher information matrix is designed to evaluate the target’s current state information. The target combat intention recognition problem is transformed into an optimization problem according to the Fisher information matrix. The contribution index is defined to eliminate redundant information during the discrimination process, which improves the accuracy and efficiency of the target combat intention recognition. The proposed algorithm is used on multi-target combat intention recognition and the simulation results verified that the proposed algorithm is effective.