Reviewerly/exHarmony huggingface dataset:
Academic Reading: Using SQ3R Method to Improve Students' Reading Skill

Reading is an active process that involves a person focusing on the reading text. It helps students improve their critical thinking skills and reading skills. The ability to read is needed in student learning today. By reading, students get a lot of information. Not only to retrieve information but also to learn grammar, vocabulary, and pronunciation. Therefore, a method is needed to improve students’ reading skills, some of which are often used, such as the SQ3R method and critical reading. This study aims to determine the role of the SQ3R method and critical reading in improving students' reading skills. This study uses a qualitative method in the form of interviews conducted virtually through a communication application (WhatsApp) with students at an Indonesian higher education. The results of this study show that the SQ3R reading method is helpful for students in improving their reading skills and critical thinking.
TRCaptionNet: A novel and accurate deep Turkish image captioning model with vision transformer based image encoders and deep linguistic text decoders

Image captioning is known as a fundamental computer vision task aiming to figure out and describe what is happening in an image or image region. Through an image captioning process, it is ensured to describe and define the actions and the relations of the objects within the images. In this manner, the contents of the images can be understood and interpreted automatically by visual computing systems. In this paper, we proposed the TRCaptionNet a novel deep learning-based Turkish image captioning (TIC) model for the automatic generation of Turkish captions. The model we propose essentially consists of a basic image encoder, a feature projection module based on vision transformers, and a text decoder. In the first stage, the system encodes the input images via the CLIP (contrastive language?image pretraining) image encoder. The CLIP image features are then passed through a vision transformer and the final image features to be linked with the textual features are obtained. In the last stage, a deep text decoder exploiting a BERT (bidirectional encoder representations from transformers) based model is used to generate the image cations. Furthermore, unlike the related works, a natural language-based linguistic model called NLLB (No Language Left Behind) was employed to produce Turkish captions from the original English captions. Extensive performance evaluation studies were carried out and widely known image captioning quantification metrics such as BLEU, METEOR, ROUGE-L, and CIDEr were measured for the proposed model. Within the scope of the experiments, quite successful results were observed on MS COCO and Flickr30K datasets, two known and prominent datasets in this field. As a result of the comparative performance analysis by taking the existing reports in the current literature on TIC into consideration, it was witnessed that the proposed model has superior performance and outperforms the related works on TIC so far. Project details and demo links of TRCaptionNet will also be available on the project?s GitHub page (https://github.com/serdaryildiz/TRCaptionNet).
The Impact of Civilian Investigative Agency Resources on the Timeliness of Police Misconduct Investigations

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Autonomous Navigation of Unmanned Aerial Vehicle: Investigating Architectures and Techniques for a Flexible Platform

Unmanned aerial vehicles (UAVs) have a significant impact on the aircraft industry and are used for both military and civilian purposes. The partial or full automation of UAVs is an active research domain that is attracting interest from both the industrial and academic sectors. This paper presents a comprehensive review of UAVs, their architectures, and techniques used for autonomous navigation. Autonomous navigation requires the ability to recognize and avoid obstacles, which can be achieved using visual, non-visual, or hybrid techniques. These methods use computer vision and machine learning algorithms and are implemented on a computer system, which may or may not be embedded in the vehicle. After investigating the existing architectures and techniques, a flexible platform is designed. This platform combines the advantages of the existing architectures, materials, and methods. It is based on a compact architecture with the possibility of external communication. This platform is mainly composed of a companion computer for complex tasks like obstacle detection and path planning during mission. When implementing this platform, it is crucial to take into account the capabilities of the companion computer processor and appropriately select techniques. Finally, the paper suggests potential areas for future research in this field.
The Smart Kalman Filter: A Deep Learning-Based Approach for Time-Varying Channel Estimation

In digital wireless communications, the received signal can be strongly altered by the environment and may contain Inter-Symbol Interference (ISI). To remove or reduce the ISI i.e. equalize, the impulse response of the propagation channel can be estimated. The Kalman Filter (KF) is an inescapable estimation algorithm in linear systems because of its optimality in terms of Minimum Mean Square Error (MMSE) under certain assumptions. However, in real conditions, implementations of KF are often difficult because of the necessity of hand-tuning parameters. In this paper, we present the Smart Kalman Filter (SKF), a hybrid architecture that combines a KF and the power of neural networks to extract relevant features from data to benefit from an adaptive KF that is automatically well tuned over time. We demonstrate in this paper that the proposed SKF is up to 5dB better at low Signal-to-Noise Ratio (SNR) and 3dB better at high SNR than Least Square (LS) algorithm in a time-varying channel estimation context with abrupt Doppler frequency variations.
The Effects of Using Youtube-Based Videos on English Speaking Performance of Grade 2 Students at Iris Primary School

English as an international language play a more important role in many parts of our life nowadays. Master English, especially speaking skill, is importantly need for the students to allow them to communicate with other people global. This study aimed to investigate the efficacy of utilizing YouTube videos to improve students' speaking performance using YouTube-based videos in language classroom. The data were taken using an instrument in the form of speaking tests given at the beginning and at the end of the study and an observation on students’ speaking performance in the classroom during the treatment of using YouTube-based videos. The results showed that there were significant differences between students’ pretest and posttest scores. In particular, before employing YouTube-based videos in the classroom, students’ speaking skills were not really good enough; however after the course students performed considerably better on the post-tests than pretest. Therefore, it is claimed that the YouTube-based videos approach is truly beneficial and effective for improving students’ speaking performance.
Network of International Cooperative Learning Educators and Enthusiasts (NICLEE): Cultivating Global Connections

Desmond Tutu speaks to the fundamental truth that the human species relies on cooperation for its survival. While we may see ourselves as individuals, the reality is that we are all interconnected and our fates are intertwined. This is especially true when it comes to learning and education. We cannot learn in isolation; we need teachers, mentors, peers, and a community that supports us. The act of learning itself is a cooperative process, where we share knowledge and ideas with others to construct deeper understanding. In short, the quote reminds us that we are not alone in this world, and that our success and happiness are tied to the success and happiness of others. It is only through cooperation and collaboration that we can achieve great things and create a better future for all.
Creation of Educational Innovations through Cloud-based Constructivism and Connectivism Learning for Undergraduates

This research examined the process of creating educational innovations through cloud-based constructivism and connectivism learning for undergraduates. The objectives were as follows: (1) To examine the cloud-based constructivism and connectivism learning model&amp;rsquo;s roles in helping undergraduates create educational innovations; and (2) To evaluate the learning and innovation skills of students participating in cloud-based constructivism. The population in the study consisted of 60 undergraduates of Phetchaburi Rajabhat University, acquired using simple random sampling. The undergraduates were enrolled in a course for designing and developing games for education and computer-assisted mathematics instruction. The independent variable was the format of the constructivism and connectivism learning model for undergraduates involving cloud technology to promote innovative education. The dependent variable was the result of innovative education. The research findings were as follows: (1) Participants&amp;rsquo; overall satisfaction with the model was at the highest level (x &amp;nbsp;= 4.53, S.D. = 0.60). (2) Out of the 60 students, 21 created innovative educational workpieces. Four workpieces were accepted for academic publication, showing that this model allowed learners to analyze media, promote innovative education, and apply educational innovations.
Study of Students in Reading Interest Through Humor Story at SMP Negeri 18 Baubau

The problem statement based on the background of the research, the research of problems statement formulated, is there any significant difference Study of Students in Reading Interest through Humor Story at SMP Negeri 18 Baubau. This research used a quantitative approach with applying Descriptive methods. Population on this research was the eighth grade students of SMP Negeri 18 Baubau consisted of 256 students. The sample was taken by using a purposive sampling technique consisted of 30 students. The results of this research showed that the based on T table the value was 1,991 and T count was 62,129. It can conclude that t count was 62,129 &gt; 1,991 of T table, so that Ho was rejected and H1 was accepted. It means that there was not significant difference Study of students’ interest in reading through humor story.
ASSESSING THE RECOVERY LEVELS OF DRUG ADDICTS IN REHABILITATION CENTERS USING DRUG ADDICTION RECOVERY INSTRUMENTS

Abstract: Drug addiction recovery is a fundamental factor in helping drug users restart normal life and get back into society. It is also a vital stage of drug treatment, as it can be an indication or evidence of whether the procedures used during treatment are suitable for helping addicts with drug problems. This study focuses on identifying the level of recovery of drug addicts undergoing rehabilitation treatment at CCRC Besut quantitatively based on the built DART instrument. The Drug Addiction Recovery Test (DART) consists of four main components: drug dependency, relapse, drug resiliency, and mental strength. Every component has 20 items, so the total number of items in DART is 80. A total of 123 treated addicts in the Cure and Care Rehabilitation Centre (CCRC) in Besut were chosen as survey respondents. Study data was analysed using descriptive analysis for each component. As for the level of recovery, the study showed that 52% of the respondents were fully discharged from the CCRC, 37% could be released as outpatients, 9% of respondents could not be released and needed to be monitored, and 2% could not be released and needed to be given intensive treatment. The study proves that the instrument can be used to measure the recovery level of addicts treated at the CCRC. Additionally, this instrument would be able to help government institutions, especially the CCRC, treat the drug addiction crisis effectively in the future. &#x0D; Keywords: Addiction Recovery; Drug Dependency; Drug Possible Relapse; Drug Resiliency; Client Mental Strength
Investigating aspects of Korean contemporary Poetry in Korean language education textbooks: Focusing on cultural elements

Objectives This study aims to analyze modern poetry from a cultural perspective rather than a literary aspect in Korean language education, and to present a way to use modern poetry more effectively in Korean language edu-cation by analyzing cultural elements.&#x0D; Methods For this study, 55 modern poems, excluding overlapping psalms, were analyzed from 75 contemporary poems contained in 141 Korean textbooks at 12 universities in Seoul, which have the highest distribution of aca-demic learners. In order to analyze modern poetry contained in Korean textbooks, poetry was classified as a cul-tural element using the cultural category of the third stage of the “Standard Curriculum” prepared at the national level.&#x0D; Results Using the cultural category of the “Checking and Complementing the Use of the International Standard Korean Curriculum,” 55 modern poems were classified into cultural elements such as “Living Culture,” “Language Culture,” “History Culture,” “Art Culture,” “Traditional Culture,” “Instrumental Culture,” and “Information Culture.” The frequency of cultural items classified as cultural elements was 55 times for “language culture,” 37 times for “geographic culture,” 36 times for “spiritual culture,” 18 times for “living culture,” and 17 times for “receiving culture.”&#x0D; Conclusions As a result of analyzing 55 modern poems contained in 141 Korean textbooks as cultural elements of the “standard curriculum,” they were concentrated on “language culture,” followed by “geographical culture” and “mental culture.” Psalms that can be classified into various cultural elements including low frequency ‘institutional culture’, ‘traditional culture’, ‘historical culture’, and ‘art culture’ should be added.
PRI Modulation Classification in EW Systems Using Deep Learning

The analysis of radar signals is a critical task in Electronic Warfare (EW) environments and decides the nature of counter employments. For an Electronic Support (ES) system, the challenge is to efficiently recognize the source of the threat radiation. In dense EW situations, detection of Pulse Repetition Interval (PRI) modulation modes of radar signal significantly aids the manifestation of emitter in the process of radar emitter recognition. Developments in Artificial Intelligence (AI) methods suggest that this emerging technology can be very effective for such purposes. In this direction, an automatic approach for recognizing eight kinds of complex PRI modulation types based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN) is proposed. The CWT is used to decompose the PRI modulation sequence to obtain different time–frequency components, and the CNN is used to extract features from the 2D scalogram composed of temporal and spectral elements for deriving appropriate class decisions. The simulation result shows that the proposed method not only enhances the performance but is also robust in an environment with noisy content. The recognition accuracy is 98.2% with 30% spurious pulses in the simulation environment.
Poimanje deteta, detinjstva, razvoja i učenja iz postmoderne perspektive

The paper presents postmodern perspectives on child and childhood, development and learning, which rely on mutually intertwined starting points of poststructuralism, sociocultural theory, sociology of childhood and relational pedagogy. In order to advocate for childrenʼs rights and to support their well-being, it is very important to understand how specific socio-cultural-political context shapes childrenʼs lives, identities, development and learning. Аdditionаlly, the paper indicates the significance of continuous critical reexamination of dominant discourses about child and childhood, child’s nature and children’s needs, what children are, can be and should be, what is natural, normal and necessary in development, as well as how children learn, how they should learn and what is important for learning. Since the educational practice depends on existing beliefs and constructions, emphasis is placed on problematizing, deconstructing and reconstructing discourses that are taken “for granted”.
A Study on the English Writing Class and Learners’ Opinions

Objectives The purpose of this study is to suggest not only recognition of Englisn writign for learners but also val-uable solution for liberal arts English writing classes. For this end, this research implemented English writing classes with three different activities during one semester and analyzed the result of employed questionnaire after classes.&#x0D; Methods This research was based on the responsest of a total of 99 college freshmen with different majors who volutarily answered the questionnaire. All respondents from different majors took the same formatted writing class which consisted of basic writing lessons and three different writing activities such as individual writing, peer feedack, and collaborative writing. After completing the course, they responded the degree of satisfaction with the English writing classes and the writing activities, and their opinions about them.&#x0D; Results According to the results of the questionnaire, the satisfaction of the English writing class was 4.28 out of 5, that of writing activities was 4.02 out of 5. While the most useful activity for male learners was collaborative writing, that for female learners was peer feedback. According the major, the pattern was various, but collabo-rative writing was responded as the most useful one by overall opinions. The results implied that peer feedback or collaborative writing was better than individual writing because learners could review their writing objectively by other’s opinion on their writing and it was very helpful.&#x0D; Conclusions Although the need of English writing was paced to the rapid globalization era, the research of writing class was not enough due to the difficulties from both learners and teachers. Learners, however, showed that how they were satisfied with the English writing class and its activities. In addition, they responded that the English writing class fostered their English language abilities. The English writing class, therefore, should provide more helpful one through attempt of various and flexible writing activities.
A framework for covariate-specific ROC curve estimation, with application to biometric recognition

Biometric traits, such as fingerprints, facial images, and teeth impressions, are often used in forensic analysis to identify crime suspects. Matching such biometric traits is not perfect, and recent reports have indicated the need for quantifiable measures of error rates for (these) possible matches. Often, comparisons between two sets of a trait are scored with a higher score indicating a higher likelihood that the sets are a match. Adjustment of the cutoff for which a match is declared yields a trade-off between false positive and false negative decisions that can be represented by an ROC curve. In this paper we study modeling of such ROC curves conditional on covariates, for example, demographic information about source subjects, quality properties of the underlying biometric measurements, or characteristics of forensic examiners; quantifying how error rates vary in dependence of such covariates is often considerably more meaningful in biometrics and forensics than the “raw” error rates based on the pooled data. We herein develop a framework for estimating covariate-specific ROC curves that integrates robustness, heteroscedasticity, and stochastic ordering. The latter is of specific relevance in the given application since biometric recognition systems are typically calibrated to assign higher scores to matching pairs than to nonmatching pairs. The proposed methodology is demonstrated on accuracy of face recognition and fingerprint matching and also has potential in other domains of application like medical diagnostics.
Critical infrastructure defense: perspectives from the EU and USA cyber experts

Purpose. To examine the components of cyber autonomy according to the insights of seasoned professionals from the European Union (EU) and the United States of America (USA). The value of each element will be calculated by obtaining data from structured in-depth interviews. Methodology. Through an investigation of different aspects of the research, we used the Delphi technique and research interviews include the option of the Interviewee Transcript Review (ITR). The Delphi method is processed in several rounds, usually three, with two rounds being considered as a minimum and in that respect the Delphi method helps our study explore, predict and identify the nature and fundamental elements of Cyber Autonomy. Findings. The study findings demonstrate that elements such as “Policies”, “Reputation management”, and “Infrastructure and Architecture” hold substantial importance within Cyber Autonomy. These elements are considered critical for future perspectives. The research highlights the role of Cyber Autonomy in streamlining cybersecurity approaches, mitigating the impact of cyber-attacks, and safeguarding possible reputation damage. The study also highlights the importance of well-defined implementation methods and the organizational structure in successfully deploying Cyber Autonomy. Originality. The research demonstrates the interdisciplinary nature of cybersecurity and applies a comprehensive approach covering information security, information security policy, technical and economic aspects, noting the important role of governance in the company share value recovery process. Cyber Autonomy could offer a concept of defense reputation that helps to identify potential cyber threats that are further intensified in connection with the development of various platforms for remote control of artificial intelligence, distance learning, and opportunities for autonomous operation of enterprise systems, the influence of multinational companies on financial markets, and automated decision-making systems. Practical value. Experts’ insights are analyzed that can help to provide practical solutions for the Cyber Autonomy and risk management methods for implementing cyber resilience strategy for critical infrastructure. The research provides adjustments to existing cybersecurity frameworks and directives which consider new cyber elements of information security realities. The current study can be used as a guide to confidence-building measures for possible reputation and financial loss, reinforces protection actions against disinformation or negative cyber impact.
Machine Learning-Based Enthalpy Prediction Using Geochemical Monitoring Data in Indonesia Geothermal Fields

Summary The monitoring of geothermal fields involves the measurement of steam fraction and enthalpy, which are used to calculate the chemical content of the reservoir. However, flow rate measurement is often problematic, particularly in the absence of buffer wells, making enthalpy determination difficult. To address this challenge, the authors developed data models to predict enthalpy using machine learning based on fluid chemistry data obtained from each well that is periodically sampled. They used more than 600 data with 36 parameters and applied algorithms such as Gradient Boosting Regressor, Random Forest Regressor, Ada Boost, and Deep Neural Network. The evaluation of the machine learning model outcomes showed that Gradient Boosting Regressor produced the most accurate results.