Reviewerly/exHarmony huggingface dataset:
Physical Reservoir-Based Health Monitoring of a Structure with Nonlinear Attachments

The purpose of this work is to discuss the possibility of the concept of physical reservoir computing (PRC) in the field of structural health monitoring (SHM) by regarding the target structure of SHM as the physical reservoir. To this end, the dynamics of the structure, which is assumed extrinsically linear, is tailored to be strongly nonlinear by installing nonlinear attachments. Our purpose is then to detect the change occurred in this augmented physical reservoir. As one possible methodology to achieve this, we propose in this study to train the output layer to learn a specific nonlinear mapping of the input so that the increase of the error may indicate the change of the reservoir. Numerical experiments are presented to examine the validity of the proposed concept.
Compliance with the Code of Conduct and Teaching Performance

The main purpose of this study was to determine the compliance with the code of conduct and teaching performance of 347 public elementary teachers of 6th Congressional District of Negros Occidental using stratified random sampling. The descriptive research design was used. The modified survey questionnaire adapted from the study of Jongue (2010) and approved Individual Performance Commitment and Review Form (IPCRF) for S.Y. 2015-2016 was utilized to get the needed data. The data collected was analyzed using frequency and percentages, mode and Pearson-r correlation coefficient. Moreover, Focus Group Discussion (FGD) with 10 participants was utilized to enrich and supplement the results of the study. Results revealed that in terms of profile, majority of the participants were female, ages 31-40 and 11-20 years in length of service. For the teaching position, majority of the participants were Teacher 1 and Bachelor degree holders. For school location, majority of the participants were in the municipality. Furthermore, findings showed that the extent of the compliance with the code of conduct of the participants was “Always” and their overall teaching performance was very satisfactory. The study found significant differences in the variables and grouped according to the code of conduct. Finally, there were significant relationships found in various components of code of conduct and teaching performance. Recommendations on the findings for the capacity build-up of the participants in complying the code of conduct and teaching performance were given. Lastly, a Teachers Development Program (TDP) was proposed.
LECTURER PROFESSIONAL COMPETENCIES IN IMPROVING STUDENTS' MIND PATTERNS IN THE ERA OF DIGITALIZATION

Lecturers' professional competence in improving students' mindsets, namely evaluating the performance of lecturers who have the qualifications displayed in carrying out their obligations to provide educational services to the community. There are four lecturer competencies, namely (1) having knowledge about learning and human behavior; (2) have knowledge and mastery of the field of study they are studying; (3) have the right attitude about oneself, colleagues and the field of study one is developing; and (4) have technical teaching skills. The research method that the researcher will use is library research with a qualitative method, namely reviewing literature related to the professional competence of lecturers. Qualitative methods prioritize observing phenomena. There are two types of mindset, namely: First, Fixed Mindset. Second, Growth Mindset, namely a mindset (view) that can be developed through practice, training, appropriate ways/methods. The efforts to improve student mindsets, especially in this era of digitalization, are by motivating a mindset that continues to develop, not a mindset that remains in place. for example (1) Study and work, (2) Study will be a lifelong challenge. (3) Learning no longer has to be at school or university. (4) Educational institutions are slow to adapt. (5). Organizational awareness is needed to continue learning.
Elastic Wave Field Neural Networks for Structural Health Monitoring: An Analytical and Numerical Study of Multiple Neurons

The purpose of this study is to develop a novel concept of smart structural systems recognizing their own structural integrity by an embodied high density sensor network. In our concept, a number of sensor nodes are embedded in the host structure, each of which reacts point-wise to the structural vibration with a simple rule. This allows the whole nodes to be mutually coupled through the elastic field, forming a neural network that incorporates the dynamic characteristics of the host structure as the coupling weights. In the previous study, we presented that a single-neuron network as its minimum configuration can exhibit a bifurcation of its dynamics behavior, which can be used to detect the change of the network due to damages. In this study, the formulation of networks with multiple neurons deployed in a structure with single-mode approximation is presented particularly focusing on the bi- furcation analysis to reveal how the behavior of the network is drastically altered depending of the network and structural parameters. Numerical analysis is conducted to examine the validity of the bifurcation analysis.
The Implementation of Augmented Reality Based on Vuforia and Unity for Interactive Learning in Introducing Ragam Randang Objects

Augmented Reality (AR) is a technology that combines objects from the real world and virtual or virtual objects with real-time or direct conditions. The merging of real and virtual objects occurs because of the right technological support while the interactions that are carried out can occur using certain devices. AR is a variation of Virtual Environments (VE), better known as Virtual Reality (VR). This VR technology allows users to fully immerse themselves in a virtual environment. While Augmented Reality technology is developing very fast, many applications in Indonesia use AR technology. This study uses the waterfall system development method, where the research flow will follow the phases of the waterfall. This application is built using Unity and Vuforia. The purpose of this research is an interactive application to make it easier for students and museum visitors to understand augmented reality through mobile devices. So that museum visitors and students can see objects containing information interactively
Topic Modeling for Text Structure Assessment: The case of Russian Academic Texts

Background: Automatic assessment of text complexity levels is viewed as an important task, primarily in education. The existing methods of computing text complexity employ simple surface text properties neglecting complexity of text content and structure. The current paradigm of complexity studies can no longer keep up with the challenges of automatic evaluation of text structure.&#x0D; Purpose: The aim of the paper is twofold: (1) it introduces a new notion, i.e. complexity of a text topical structure which we define as a quantifiable measure and combination of four parameters, i.e. number of topics, topic coherence, topic distribution, and topic weight. We hypothesize that these parameters are dependent variables of text complexity and aligned with the grade level; (2) the paper is also aimed at justifying applicability of the recently developed methods of topic modeling to measuring complexity of a text topical structure.&#x0D; Method: To test this hypothesis, we use Russian Academic Corpus comprising school textbooks, texts of Russian as a foreign language and fiction texts recommended for reading in different grades, and employ it in three versions: (i) Full Texts Corpus, (ii) Corpus of Segments, (iii) Corpus of Paragraphs. The software tools we implement include LDA (Latent Dirichlet Allocation), OnlineLDA and Additive Regularization Of Topic Models with Word2vec-based metric and Normalized Pairwise Mutual Information.&#x0D; Results: Our findings include the following: the optimal number of topics in educational texts varies around 20; topic coherence and topic distribution are identified to be functions of grade level complexity; text complexity is suggested to be estimated with structural organization parameters and viewed as a new algorithm complementing the classical approach of text complexity assessment based on linguistic features.&#x0D; Conclusion: The results reported and discussed in the article strongly suggest that the theoretical framework and the analytic algorithms used in the study might be fruitfully applied in education and provide a basis for assessing complexity of academic texts.
The Effect Of Reinforcement On Writing Skill At Eleventh Grade Students In SMK Negeri 1 Pematang Siantar

The problem in this study is the lack of reinforcement, the lack of student writing skills. This study aims to determine The Effect Of Reinforcement On Writing Skill At Eleventh Grade Students In Smk Negeri 1 Pematang Siantar. The population in this study were eleventh grade OTKP students and the samples in this study were eleventh grade OTKP-2 students totaling 31 students. This type of research is non-experimental quantitative research, data collection techniques using a questionnaire expressed in the form of a Likert Scale and documentation. The test results show that (1) there is a significant influence between reinforcement on writing ability in eleventh grade students of OTKP SMK Negeri 1 Pematang Siantar obtained tcount&gt;ttable of (3,082&gt;2,045) with significance &lt;0.05 (0.000&lt;0.05), then in the determination test results obtained R Square of 0,24, this means that reinforcement (X1) is distributed by 24%, while the other 76% is explained by other variable not discussed in this study.
Utilizing M-Reader for Extensive Reading: Implications for Seminarian Identity at Mataloko Seminary

Introduced in 2021, M-Reader is a digital application designed to promote Extensive Reading. Mataloko Seminary, recognizing its potential, has employed M-Reader to enhance both student literacy and identity formation. With this context in mind, this research sought to answer two pivotal questions: (1) How has Mataloko Seminary integrated Extensive Reading into its curriculum? and (2) To what extent does Extensive Reading via M-Reader influence the identity of seminarians on their journey to priesthood? Employing a narrative inquiry methodology, the study meticulously examined the application of M-Reader in extensive reading practices and its consequent impact on seminarian identity. Data was meticulously collated through semi-structured interviews with three students from grades 10 and 12, all hailing from Mataloko. This data, systematically categorized and analyzed using Microsoft Excel, revealed that M-Reader-supported extensive reading at Mataloko Seminary adheres to ten established principles of extensive reading. Furthermore, the program emerged as a pivotal tool in molding the seminarians' identity across four critical dimensions: human, intellectual, pastoral, and spiritual. These findings underscore the potential benefits for other educational institutions targeting comprehensive identity development: the incorporation of digital extensive reading tools like M-Reader can be a game-changer.
How Far Can I Go? Video Project-Based Learning as a Meaning-Making Process to Promote Students’ Speaking Proficiency

Effective learning activities in the classroom that motivate students toward language proficiency should be thoughtfully implemented. One notable method is task-technology fit (TTF), which combines task-based language learning (TBLL) and project-based learning (PjBL). This study explores this intersection through video project-based learning (VPBL). It seeks to understand how VPBL interventions influence students' speaking proficiency, especially in areas of complexity, accuracy, and fluency, while also examining the role of students' self-efficacy and self-reflection. Utilizing a pre-experimental design without a control group, this research was conducted among second-year students of the English Education Program at Universitas PGRI Kanjuruhan Malang in their Speaking course. 37 students participated in a speaking test prompt and answered self-reflection and self-efficacy questionnaires, while 8 students underwent a more detailed interview regarding their VPBL experience. Results showed a general enhancement in speaking proficiency, primarily due to VPBL's unique structure and the student's capacity for self-reflection. Although improvements in fluency and accuracy were noted, there was not significant progress in complexity, indicating that while students utilized VPBL for refining their speaking skills, further efforts are required to advance their linguistic intricacy.
Evaluation of Large Language Models Using an Indian Language LGBTI+ Lexicon

Large language models (LLMs) are typically evaluated on the basis of task-based benchmarks such as MMLU. Such benchmarks do not examine the behaviour of LLMs in specific contexts. This is particularly true in the LGBTI+ context where social stereotypes may result in variation in LGBTI+ terminology. Therefore, domain-specific lexicons or dictionaries may be useful as a representative list of words against which the LLM’s behaviour needs to be evaluated. This paper presents a methodology for evaluation of LLMs using an LGBTI+ lexicon in Indian languages. The methodology consists of four steps: formulating NLP tasks relevant to the expected behaviour, creating prompts that test LLMs, using the LLMs to obtain the output and, finally, manually evaluating the results. Our qualitative analysis shows that the three LLMs we experiment on are unable to detect underlying hateful content. Similarly, we observe limitations in using machine translation as means to evaluate natural language understanding in languages other than English. The methodology presented in this paper can be useful for LGBTI+ lexicons in other languages as well as other domain-specific lexicons. The work done in this paper opens avenues for responsible behaviour of LLMs in the Indian context, especially with prevalent social perception of the LGBTI+ community.
SQL Injection Detection in Cloud Computing with Machine Learning Algorithms

Due to the coronavirus crisis, a lot of companies all over the world started a fast digitalization of their business and became more comfortable with the digital world. In this way, a lot of people in the digital domain, including net developers and system engineers, are exposed to different types of risks that they may not completely know or understand. SQL injection is one such vulnerability that can be exploited by attackers to inject malicious data into a database via an application programming interface (API) call request that uses SQL syntax. Cloud computing (IaaS) and network as a service (Naas) provide companies with a wide range of advantages. However, the same advantages can also pose certain vulnerabilities to the organization. In this paper we will focus on SQL injection attacks and make a comparison between different Machine learning algorithms that can be used to detect SQL injection attacks, such as "Naive Bayes" and "K-NN", SVN, and decision tree, we will also use a deep learning algorithm CNN, this comparison will show the potential that machine learning algorithms can offer in terms of cloud security.
Evaluation of Large Tweet Dataset for Emotion Detection Model: A Comparative Study between Various ML and Transformer

Specific emotion detection in written human language is a challenging problem in various research fields, including psychology, neuroscience, and computer science. Twitter is a suitable source for collecting large emotion datasets, as users have provided tweets with emotion hashtags (e.g., #fear, #anger, #sadness, #joy, #surprise, and #disgust) expressing their emotions. However, the criteria for data collection, i.e., the position of representative or synonymous emotion hashtags, remains unclear. Next to this unclarity, we assess the suitability of various machine learning (ML) algorithms for this purpose. In this study, we collected over five million tweets (n=5,645,139) with 24 emotion hashtags and investigated the efficacy of different criteria for collecting tweets. Contrary to previous research, we found that applying any position of representative emotion hashtags can achieve strong performance, rather than applying the last position of synonymous emotion hashtags. Our study shows that the RoBERTa-large transformer model outperforms deep learning algorithms and traditional ML algorithms in terms of specific emotion detection in tweets, especially when trained on a dataset with a balance between size and quality. We also found that larger datasets are more efficient for RoBERTa model training than smaller datasets. Along with these empirical contributions, we share the collected emotion dataset.
Several Families of Self-Orthogonal Codes and Their Applications in Optimal Quantum Codes and LCD Codes

Self-orthogonal codes have nice applications in many areas including quantum codes, lattices and LCD codes. For a prime power <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</i> , it is in general difficult to construct <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</i> -ary self-orthogonal codes. In the literature, there exists no simple method to judge whether a general <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</i> -ary linear code is self-orthogonal or not. In this paper, we mainly present several families of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</i> -ary self-orthogonal codes and study their applications in quantum codes and LCD codes. Firstly, several families of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</i> -ary linear codes are constructed by some special defining sets. These codes are proved to be self-orthogonal. To this end, we determine the numbers of solutions of some systems of equations over finite fields. Secondly, three families of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</i> -ary quantum codes with unbounded length and minimum distance three are constructed from the self-orthogonal codes. These quantum codes are optimal according to the quantum Hamming bound. In particular, some of them have better parameters than known ones. Thirdly, several families of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</i> -ary LCD codes are constructed from the self-orthogonal codes. Many optimal or almost optimal binary and ternary LCD codes are produced by our constructions. Some binary and ternary LCD codes have better parameters than known ones.
Postdigital Freirean Ecopedagogies

Ecopedagogy has various models for teaching students to critically understand the oft-hidden politics of why unsustainable environmentally violent actions occur. Paulo Friere’s scholarship is foundational to ecopedagogical work, embracing his argument for needing unceasing reinvention to meaningfully contextualize education. This entry discusses postdigital ecopedagogical work to (re)construct education for praxis towards globally all-inclusive socio-environmental justice and planetary sustainability, including and beyond the anthroposphere.
Ensemble adversarial training based defense against adversarial attacks for machine learning-based intrusion detection system

In this paper, a defence mechanism is proposed against adversarial attacks. The defence is based on an ensemble classifier that is adversarially trained. This is accomplished by generating adversarial attacks from four different attack methods, i.e., Jacobian-based saliency map attack (JSMA), projected gradient descent (PGD), momentum iterative method (MIM), and fast gradient signed method (FGSM). The adversarial examples are used to identify the robust machine-learning algorithms which eventually participate in the ensemble. The adversarial attacks are divided into seen and unseen attacks. To validate our work, the experiments are conducted using NSLKDD, UNSW-NB15 and CICIDS17 datasets. Grid search for the ensemble is used to optimise results. The parameter used for performance evaluations is accuracy, F1 score and AUC score. It is shown that an adversarially trained ensemble classifier produces better results.
The Effect Of Pre-Questioning Teaching Technique To The Reading Comprehension Of Grade Ten Students Of Smk Swasta GKPI 1 Pematang Siantar On A Narrative Text

This research aimed to find out the significant effect of the pre-questioning teaching technique on the reading comprehension of Grade Ten students of Smk Swasta Gkpi 1 Pematang Siantar on a narrative text. This research was carried out at SMK Swasta GKPI 1 Pematang Siantar, namely in classes X TKJ A and X TKJ B. The problem statement of this research was the effect of the pre-questioning teaching technique on the reading comprehension of Grade Ten students of SMK Swasta GKPI 1 Pematang Siantar on a narrative text. The researcher used a quantitative method with a quasi-experimental design and used two classes: the experimental class and the control class. The data was collected using a multiple-choice test. The instrument used to collect data was taken from the results of students' pre-test and post-test scores, where the data was calculated manually using MS Excel. The result of this research showed that the average value of the experimental class was 40 in the pre-test and 80,16 score in the post-test. While the average value of the control class was 39,1 score in the pre-test and 65,5 score in the post-test, The results of the students scores on the post-test showed that the use of the pre-questioning teaching technique was very effective in reading narrative text for grade ten students. The t-test calculation showed that the t-test value was higher than the t-table value (3,32&gt;1.670). The null hypothesis (Ho) was rejected. So, it could be concluded that the pre-questioning teaching technique was very significant in developing students reading comprehension, especially in narrative text.
Bio-dome in the classes of the school subject “The world around us” when studying the topic “The water cycle in nature”

The topic "The Water Cycle", which is part of the discipline "The World Around Us", requires visual material for better understanding by younger students. The justification for this statement lies in the predominance of the visual-imaginative type of thinking over the verbal-logical type of thinking in younger students. In this regard, the abstract natural science material in the educational process of primary school, as a rule, is studied through experiments and experiments. We offer for consideration a visual material, which until recently had almost no scientific coverage: a children's scientific and entertaining set "Bio-dome", simulating the biosphere of the planet Earth and assuming interactive participation of students, including work in groups. The aim of this article is to describe the effectiveness of using the Bio-dome in laboratory experiments with elementary school students in studying the topic "The Water Cycle in Nature" based on the results of an empirical study. During the practical research the following methods were used: testing of elementary schoolchildren, laboratory experiment with a bio-dome and a pedagogical experiment. The study was conducted on the basis of the state budgetary educational institution of the city of Moscow "School N 1561". The results showed the effectiveness and prospects of using the bio-dome in the lessons of the discipline "The World Around Us" in the elementary school.