Reviewerly/exHarmony huggingface dataset:
Time-Sensitive and Distance-Tolerant Deep Learning-Based Vehicle Detection Using High-Resolution Radar Bird's-Eye-View Images

Advanceddriver assistance systems (ADASs) and autonomous vehicles rely on differenttypes of sensors, such as cameras, radar, ultrasonic, and LiDAR to sense thesurrounding environment. Compared with the other types of sensors,millimeter-wave automotive radar has advantages in terms of low hardware costand reliable object detection under poor weather conditions, such as snow,rain, or fog, and doesn't suffer from light condition variations, such asdarkness. High-resolution radar bird's-eye-view (BEV) obtained from radarrange-azimuth spectra through a polar-to-Cartesian coordinate transformcontains targets’ geometric information that can be learned by deep neuralnetworks for object detection. Compared to radar point clouds, there is noinformation loss in radar BEV. Unlike RGB images, radar BEVs are single-channelgrayscale images with unique characteristics such as inconsistent resolutionand SNR. Therefore, directly implementing an image-based object detectionnetwork is not an optimal solution for object detection using radar BEV. Wepropose a Temporal-fusion, Distance tolerant single stage object detectionNetwork, termed as, TDRadarNet, to robustly detect vehicles up to 100 metersunder various driving scenarios. DRadarNet leverages historical radar frames toexploit temporal features and separates far and near fields to addressinconsistent resolution in radar frames. With qualitative and quantitativeresults, we show that TDRadarNet achieves 68.9% in precision and 66.8% inrecall, and 67.8% in F1-score, which outperforms the state-of-the-artimage-based object detection networks by 10.6%, 17.1%, and 14.1%.
Persuasive Boundary Point Based Face Detection Using Normalized Edge Detection in Regular Expression Face Morphing

Face recognition, one of the most important study areas, has become very interesting in the last 20 years. The reason comes from automated authentication, the need for a surveillance system, face recognition and man-computer interface design for the face-to-face system of interest, and the like. In the past few years, from a video to face-to-face tracking, commercial enforcement applications have played a huge role in the extreme range. Video, face-to-face surveillance and surveillance from the database, although this work provides a unique technology. In conjunction with the video, we propose a new approach to real-time face detection. Trigger boundary corner detection techniques are a high-speed template matching method using a corner separator. Corner detection technology, designed for front-face detection, is mandatory. Face Capture and then match the data stored on the server in face detection. On the server, from the face dataset, it is given that no data set can execute the appropriate matching task. In the data set that has been trained, the face image is stored in the server, which is detected by adjusting all of the angel’s face corner points. Additional details about the face, in order to identify the face, a specific image will be added. In this, the face is captured by the web camera and tracking the detection. Our simulation results are an advancement in the technique of face detection and high-speed processing that collects information about the face from the server.
Challenges in Teaching English Literature: A Teacher's Perspective

In Indonesia, teaching English through literature (literary text) in primary school has challenges because it does not have a curriculum. Especially English subject is only local content that does not have to include in primary school. A school that never implemented English in their learning must be difficult to provide teaching. So, this study aims to research the challenges in teaching English literature in primary school from a teacher’s perspective and the strategies used to solve the challenges. The method used in this research is qualitative descriptive and the subject is an English teacher of 6th grade in MI Nadlatul Ulama Malang. Data were collected through an unstructured interview via WhatsApp and met in person. The researchers analyzed the data based on the results of the interview. The research found three challenges an English teacher of 6th grade at MI Nadlatul Ulama faced in learning English literature. The three challenges are in the aspect of learning media, students’ English skill gap, and the time of the English lessons. Some strategies are used to make teaching done effectively. To increase students' motivation, the teacher delivered material using PowerPoint not only on the blackboard. The teacher also accompanies when students have difficulty or are confused about accepting the material provided.
On the Feasibility of Homomorphic Encryption for Internet of Things

Homomorphic encryption (HE) facilitates computing over encrypted data without using the secret keys. It is currently inefficient for practical implementation on the Internet of Things (IoT). However, the performance of these HE schemes may increase with optimized libraries and hardware capabilities. Thus, implementing and analyzing HE schemes and protocols on resource-constrained devices is essential to deriving optimized and secure schemes. This paper develops an energy profiling framework for homomorphic encryption on IoT devices. In particular, we analyze energy consumption and performance such as CPU and Memory utilization and execution time of numerous HE schemes using SEAL and HElib libraries on the Raspberry Pi 4 hardware platform and study energy-performance-security trade-offs. Our analysis reveals that HE schemes can incur a maximum of 70.07% in terms of energy consumption among the libraries. Finally, we provide guidelines for optimization of Homomorphic Encryption by leveraging multi-threading and edge computing capabilities for IoT applications. The insights obtained from this study can be used to develop secure and resource-constrained implementation of Homomorphic encryption depending on the needs of IoT applications.
LEARNING MODULE DEVELOPMENT ON COMPILING EXPOSITION AND ARGUMENTATION TEXT USING PROJECT-BASED LEARNING

This development research aims to develop and produce a learning module to compile an exposition text. Besides, this research and development also aim to describe the process of developing a material learning module, compiling an exposition text with a Project-Based Learning (PJBL) model for grade VIII SMP students, describing the feasibility of the module based on assessment and validation by media experts, material experts, linguists, and student trials. This research is included in the research category utilizing descriptive quantitative. The resulting quantitative data will be converted into qualitative data to describe the feasibility of the product being developed. The results of the research and development of the module show that the module developed is based on Tessmer's development with two main stages, namely the Preliminary stage which consists of the analysis and design stages, the Formative Evolution stage which consists of Self Evaluation, Prototyping, and Export Review. (Expert Test), One-To-One, Small Group, and Field Test. The validity of the learning module is seen based on the validation test, the results of the validation of media experts reach 75%, material experts 90%, and linguists 75% which can be categorized as valid. Based on product testing at the field test stage, the average score of students was 83.00 which could be categorized as good, while the results of the student response questionnaire obtained a score of 96% which could be categorized as very feasible. Based on the validation test, it can be concluded that the developed learning module can be used both in the student learning process independently and in the Indonesian language learning process, especially in the exposition text material of class VIII students in the class.
ANALYSIS OF TEACHER NEEDS RELATED TO E-LEARNING SCHOOLOGY CONTENT IN CREATIVE WRITING IN MIDDLE SCHOOLS THROUGHOUT PALEMBANG CITY

Learning Indonesian in the text-based 2013 curriculum directs students to be able to produce text-based writings. However, the ability of students to write creatively in middle school found in the field is still in the weak category. Obstacles related to this are increasing considering that Indonesia is currently experiencing the Covid 19 pandemic which requires learning to be carried out from home so that supporting learning media is needed in accordance with existing conditions. Based on the above, a solution is needed to overcome this, one of which is the use of E-Learning Schoology, which is learning that is in accordance with the character of generation Z. This research aimed to determine the needs of teachers regarding E-Learning Schoology content in learning creative writing in secondary schools in Palembang city. This research is a type of research and development. The research object was Indonesian language teachers in SMA in Palembang city. The data collection method used is a survey method with a questionnaire. Data are analyzed quantitatively and qualitatively. The results of the study show that when viewed in terms of necessities, the content of E-Learning Schoology in learning creative writing in secondary schools in Palembang must be adjusted to the principles of learning to write, the principles of electronic / online learning, the Circular of the Minister of Education and Culture No.4 of 2020 concerning Implementation of Education Policies in an Emergency Period of the Spread of Corona Virus Disease (COVID 19) and distance learning guides for teachers during schools closed due to the Covid-19 pandemic. The next finding is that when viewed from the components that are needed, teachers really need E-Learning Schoology in learning creative writing at high schools in Palembang. The last finding is that there are still deficiencies in the implementation of learning creative writing and the media used in secondary schools in Palembang is not adequate yet.
IMPACT OF GEOLOCATION DATA ON AUGMENTED REALITY USABILITY: A COMPARATIVE USER TEST

Abstract. While the use of location-based augmented reality (AR) for education has demonstrated benefits on participants’ motivation, engagement, and on their physical activity, geolocation data inaccuracy causes augmented objects to jitter or drift, which is a factor in downgrading user experience. We developed a free and open source web AR application and conducted a comparative user test (n = 54) in order to assess the impact of geolocation data on usability, exploration, and focus. A control group explored biodiversity in nature using the system in combination with embedded GNSS data, and an experimental group used an external module for RTK data. During the test, eye tracking data, geolocated traces, and in-app user-triggered events were recorded. Participants answered usability questionnaires (SUS, UEQ, HARUS).We found that the geolocation data the RTK group was exposed to was less accurate in average than that of the control group. The RTK group reported lower usability scores on all scales, of which 5 out of 9 were significant, indicating that inaccurate data negatively predicts usability. The GNSS group walked more than the RTK group, indicating a partial effect on exploration. We found no significant effect on interaction time with the screen, indicating no specific relation between data accuracy and focus. While RTK data did not allow us to better the usability of location-based AR interfaces, results allow us to assess our system’s overall usability as excellent, and to define optimal operating conditions for future use with pupils.
THE RECORD OF USING PICTURE SERIES TO DEVELOP STUDENTS’ WRITING SKILLS

Based on the observations results, the learners had difficulty in exploring their ideas. They took more time and could only write some sentences. Another barrying that students faced was that they could not write well-organized paragraphs. The students also often had difficulty in learning grammar, especially English tenses. Lack of vocabulary mastery was also the cause of writing difficulties. They stated that writing in English was difficult and complicated. That’s why the researchers used classroom action research to apply picture serias as media. It was conducted by two cycles.The subjects were seventeen students of tenth grade science student at Banyuwangi. To get the data, the researchers used observation, interview, and test. The research result could be stated that using the picture series improved the learners’ writing narrative text competence. They could improve their writing gradually from the first cycle to the second one. The picture series that implemented significantly improved their skill in English writing.
THE EFFECT OF PROBLEM-BASED LEARNING MODEL TOWARDS STUDENTS’ COMPREHENSION OF THE ENGLISH READING TEXT

This research entitles The Effect of Problem-Based Learning Model (PBL) on the Students’ comprehension of the English Reading Text. It is aimed at finding out the differences in students’ comprehension of English reading text using the PBL and the TBL models. The methodology used was an experimental method in which there were two groups available. The first group was an experimental group that was treated using the PBL model and the second group was a control group that was treated using the TBL model. The learning models were applied to find out the one which had the greatest effect on the students’ achievement in English reading comprehension. Since the subject delivered was Reading IV, the sampling technique used was purposive sampling, the sample chosen was students of semester IV, English Department of State Polytechnics of Sriwijaya. Based on the result of two-way analysis of variance or ANOVA AB on the level of significance of α = 0,05, it is found that Fhitung = 4,555 dan Ftabel (0,05;1:40) = 4,08. Based on the value of Significance on the table of Tests of Between-Subjects Effects on line A that if it is less than 0.05, the result of the test is significant. In the table of 4.12, the Sig value for line A was 0.039, less than 0.05. Then it is concluded that students treated using the PBL model were far higher in English reading text comprehension compared to those who were treated using the TBL model.
AUGMENTING EFL STUDENTS’ SPEAKING SKILLS THROUGH THE MYSTERY BOX GAME

This study examined how employing the Mystery Box game affected tenth-grade students' speaking skills at a senior high school in Lhokseumawe, Aceh, Indonesia. It employed pre-experimental quantitative research. A number of 25 students from class X-3 were purposively chosen as the sample of this study. In collecting the data, the researchers used tests (pre-test and post-test) as the instrument, and the results were analyzed by using statistics. The results showed that the mean scores of the pre-test and post-test were 37.2 and 73.2, respectively. The mean score of the post-test was higher than the mean score of the pre-test, with the T-test value of 4,556, and the critical value in the t-table (0.05) of 1.711. This implies that the t-score is higher than the t-table (t- score = 4.534 t-table = 1.711), and thus the alternative hypothesis (Ha) of this research was accepted and the null hypothesis (Ho) was rejected. In conclusion, teaching English by using the Mystery Box game to improve these EFL students’ speaking skills succeeded. Consequently, the use of this game is among the alternative media to be used by English teachers in augmenting their students’ skills in speaking English. When students achieve the accuracy and mastery of speaking skills, at a later stage, it can help them move to the improvement of other language skills
LEARNERS’ DIGITAL LITERACY IN THE ONLINE LEARNING DURING COVID-19

This research investigates the understanding of digital literacy in the online learning process during covid-19 for students in the Higher Education students in Indonesia. The objectives of this study are to describe the aspects of digital literacy in the concepts of Bawden, they are basic skill of digital literacy, attitudes and perspectives, background knowledge of the information, technology and iinformation skills, and attitudes and perspectives. This study employ qualitative with the subjects are 117 students; these students were determined using purposive sampling technique with the criteria who were joining the learning classroom actively. The collecting data technique was using 20 questions in the questionnaire and it was analyzed using descriptive quantitative techniques. The results in this study reveal that all of the respondents indicate have good criteria in digital literacy. They have good characteristics in basic skill of digital literacy; have good criteria in background knowledge of the information; have good skill in technology and information; as well as good attitude and perspective of digital literacy. The conclusion is the higher education students have a good digital literacy in the aspect of basic skill of digital literacy, they have a good digital literacy in the aspect of background knowledge of information, and they have a good digital literacy in the term of technology and information skills as well as strong attitude and perspective on digital information.
A BIBLIOMETRIC ANALYSIS OF EFL READING FLUENCY FROM 2016 TO 2021

The aim of the research is to provide an extensive literature review on reading fluency. Articles were located through publish or perish software and the Google scholar database. Scanning by scimagojr.com (Q1, Q2, Q3, and Q4). We analyzed 60 articles from 2016 to 2021. Mendeley software was used to manage and resume the references. We have reviewed this database and attempted to classify articles using VOSviewer software. Four clusters are identified. Cluster 1 consisted of ten items (disability, elementary students, fluency intervention, impact, intervention, reader, reading fluency, reaped reading, student, and teacher), Cluster 2 consisted of seven items word (child, effect, fluency, language, present study, reading, and word), cluster 3 consisted of five items word (comprehension, oral reading fluency, relationship, research, and vocabulary), and cluster 4 consisted of 4 items word (accuracy, development, relation, and study).
Sistem Pakar Untuk Mendeteksi Minat Dan Bakat : Sebuah Systematic Literature Review

We present a systematic literature review of an expert system to detect children's interests and talents. The purpose of this review is to plot all aspects of knowledge in the field of education, especially those using the expert system method, what children do in learning and how expert system methods can help in determining children's interests and talents. We conducted a systematic literature review of published studies that focused on the use of expert systems methods. The systematic review in this study is used to analyze how expert system methods can contribute massively to developments in the field of education. This research can also be used as a guideline for the most widely used expert system method to determine children's interests and talents. Research on determining children's talents and interests from 20 papers in a systematic literature review provides a broad understanding of the research conducted on what makes the system able to determine the best talents and interests for children through data obtained from experts
Response to the UK White Paper: A pro-innovation approach to AI regulation

We propose a risk-based regulatory system for AI based on requiring those who develop and operate AI systems to explain and justify the steps they have taken to mitigate specified risks, and to operate their systems in accordance with those explanations.Liability to compensate for losses caused by the AI has three bases:* Failure to operate in accordance with the risk mitigation explanation* Failure to perform to the same standard as a human counterpart* Failure to take reasonable care in the development and operation of the AI, with a reversed burden of proof.The regulatory scheme also provides for the possibility of regulatory sanctions in appropriate, high risk, cases.
A stacking ensemble classifier-based machine learning model for classifying pollution sources on photovoltaic panels

Solar energy is a very efficient alternative for generating clean electric energy. However, pollution on the surface of solar panels reduces solar radiation, increases surface transmittance, and raises the surface temperature. All these factors cause photovoltaic (PV) panels to be less efficient. To address this problem, a stacking ensemble classifier-based machine learning model is proposed. In this study, different sources of pollution on each solar panel are used, and their power generation is recorded. The proposed model includes gradient boost, extra tree, and random forest classifiers, with the extra tree classifier serving as a meta-learner. The model takes into account various weather features during the training process, including irradiance and temperature, aiming to increase its accuracy and robustness in identifying pollution sources on the PV panel. Moreover, the proposed model is evaluated using various methods in order to examine performance metrics such as accuracy, F1 score, and precision. Results show that the model can achieve an accuracy score of 97.37%. The model's performance is also compared to state-of-the-art machine learning models, demonstrating its superiority in accurately classifying pollution sources on PV panels. By utilizing different sources of pollution and weather features during training, the model can accurately classify different pollution sources, resulting in increased power generation efficiency and the longevity of PV panels. The main results of this study can be used to manage and maintain PV panels since the model can identify PV modules that need to be cleaned to keep producing the most power. Furthermore, the efficiency, reliability, and sustainability of PV panels can be further enhanced by the proposed model.
Text-Controlled GAN Inversion for Image Editing

Abstract This paper presents an optimization-based approach that allows for text-based image editing using Generative Adversarial Networks (GANs) with preserving specific details of the image. While recent Diffusion Models can generate images using text by training with text and image pairs, GANs are trained only with images, making it challenging to control the latent space according to text prompts. Therefore, this paper introduces two-step pivotal mechanisms that enable text instructions for StyleGAN in the latent space while maintaining the original image information during editing. This approach simplifies the editing process and eliminates the need for separate training to get the particular direction in the latent space. The paper demonstrates the effectiveness of this method with diverse input images and written text prompts, showing compelling editing results.
Cross-cutting interaction, inter-party hostility, and partisan identity: Analysis of offensive speech in social media

This study utilizes social media data and deep-learning-based text classification methods to investigate cross-cutting interactions on social media. Our findings reveal that people are more likely to use offensive speech in response to content published by opposing partisans. Furthermore, we demonstrate how inter-party hostility is associated with the partisan identity of both the message sender and the target in the interaction. On one hand, the findings indicate that strong partisans and people who publicly assert their partisan identities tend to attack opposing partisans, suggesting a relationship between the salience of partisan identity and value defense mechanisms. On the other hand, strong partisans, especially politicians, are more likely to be the target of offensive speech from opposing partisans. The disparity in the extent of received offensive speech is argued to result from individuals’ tendency to maintain their partisan identification by expressing hostility toward representative individuals of opposing partisans.