Reviewerly/exHarmony huggingface dataset:
Online English speaking instruction in junior high schools: Readiness and obstacles

In the midst of a pandemic, teaching speaking online becomes one of the challenges. This study investigates whether or not English teachers were prepared to teach speaking virtually. Consequently, the purpose of this study was to determine the English teachers' readiness and obstacles to teaching speaking online during the pandemic Covid-19. The researcher utilized mixed research methods to conduct this study. The information was gathered via questionnaires and interviews. The experts have tested and validated these instruments. The data were then quantitatively and qualitatively analyzed. Quantitative data were analyzed with the aid of SPSS statistical calculations. In contrast, qualitative data were analyzed using multiple steps, including data reduction, data presentation, and conclusion. The results indicated that the majority of English teachers were prepared to implement online speaking instruction during a pandemic, despite the fact that some teachers were not quite prepared due to several obstacles. As for the challenges encountered by English teachers, there were online speaking assessments, poor connections, low interaction, and a lack of learning devices. Shortly, English teachers prepared to teach online speaking to students in order to improve the students' speaking ability, despite the emergence of certain obstacles.
Category-Oriented Localization Distillation for SAR Object Detection and a Unified Benchmark

Despite much research progress in synthetic aperture radar (SAR) object detection, the performance of SAR object detection has encountered a bottleneck limited by the imaging mechanism of SAR. In this work, we investigate how to perform robust SAR object detection by distilling the category knowledge from optical images in the training stage. To this end, we propose a novel knowledge distillation method called Category-oriented Localization Distillation (CoLD), which employs the optical object detection network as the teacher to guide the SAR object detection network. To introduce the category prior knowledge of the teacher network in the localization knowledge transferring, a category-oriented partition module is designed in CoLD to decouple candidate bounding boxes into target and non-target ones according to the category information in optical images. Through box decoupling, the accuracy and efficiency of SAR object detection can be significantly improved. Moreover, an IoU-based weighting module is introduced in CoLD to guide the student network focusing more on high-quality candidate boxes by adaptively changing the weight of each candidate bounding box based on the corresponding IoU score in the teacher network. In addition, a unified benchmark dataset is created for the evaluation of optical information guided SAR object detection, which consists of 14,665 optical and SAR image pairs in the training set and 3,666 SAR images in the testing set. Extensive experiments on the dataset demonstrate the effectiveness of our CoLD against state-of-the-art methods. The dataset is available at: https://github.com/mmic-lcl/Datasets-and-benchmark-code.
Analysis of Translation Techniques and Quality of Charactonyms in The Silmarillion by J. R. R. Tolkien

&lt;p&gt;The object of this study is translation of the charactonyms in the book &lt;em&gt;The Silmarillion&lt;/em&gt; by J. R. R. Tolkien. This objective of this study is to analyze the techniques used by the translator in translating the charactonyms and the impact of the techniques on the quality of the translation in terms of accuracy and acceptability. The data are charactonyms found the book, their translations and the quality assed by researcher and informants. The data are collected using content analysis and focus group discussion. The researcher found 196 charactonyms from the novel &lt;em&gt;The &lt;/em&gt;Silmarillion.The researcher also found that translator uses 27 techniques that use six of Fernandes’ (2006) techniques—rendition, copy, addition, transcription, transposition, and deletion—either as single or combined technique, 3 techniques are single techniques and 24 are combined techniques. The overall score for the accuracy of the translation is 2.98 and for acceptability is 2.45, making the overal score being 2.72.&lt;/p&gt;
A Survey on the Reading Habit of Indonesian Preservice EFL Teachers

Reading is a useful activity to get information which can expand students’ knowledge. This research is aims to investigate pre-service EFL teachers’ reading habit at STKIP PGRI Pasuruan. This survey research collected data from 130 pre-service EFL students who were randomly selected majoring in English and Education as the respondents. Questionnaires was distributed to collect the information regarding their reading habit,and were triangulated through observation. The statistical evidence showed that the student teachers’ interest and frequency of reading as well as their reading habit were poor. The main purpose of reading for the students, the majority of whom download the reading resources from the internet, was to gain knowledge. Most of the students preferred readings from websites and printed books.
Multi-Sensors for Human Activity Recognition

Human activity recognition (HAR) has made significant progress in recent years, with growing applications in various domains, and the emergence of wearable and ambient sensors has provided new opportunities in the field [...]
Protecting classical-quantum signals in free-space optical channels

Due to turbulence and tracking errors, free-space optical channels involving mobile transceivers are characterized by a signal's partial loss or complete erasure. This work presents an error-correction protocol capable of protecting a signal passing through such channels by encoding it with an ancillary entangled bipartite state. Beyond its ability to offer protection under realistic channel conditions, our protocol has the ability to transmit both a quantum state and a classical symbol using the same encoded state. We show how the protocol can improve the fidelity of transmitted coherent states over a wide range of losses and erasure probabilities relative to nonencoded direct transmission. In addition, the use of ancillary non-Gaussian entangled bipartite states in the signal encoding is considered, showing how this can increase performance. Finally, we briefly discuss the application of our protocol to the transmission of more complex input states, such as multimode entangled states.
Performance evaluation of a simple feed-forward deep neural network model applied to annual rainfall anomaly index (RAI) over Indramayu, Indonesia

Indramayu is a district in West Java that is known for being the leading producer of rice and brackish salt. The production of these two commodities is strongly influenced by hydroclimatological conditions, making accurate and reliable long-term estimates crucial. In this study, we evaluated a simple feed-forward deep neural network (DNN) model that could potentially be used as a candidate for statistical guidance to improve the accuracy of numerical climate models.We used the spatial average of the accumulated annual rainfall of the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data as an input time series with a time range from 1981 to 2022. This data was then processed into annual rainfall anomaly index (RAI) data. The Annual RAI was divided into training and test sets, and the feed-forward DNN model was fitted to the annual RAI in the training set. The accuracy of the model was then tested in the test set using the root-mean-square error (RMSE) metric.Our study shows that the feed-forward DNN model is not suitable for estimating the annual RAI over Indramayu. This is because the RMSE values are significantly high in both the training and test sets.
Vocabulary Learning Strategies of Graduate Paramedical Students: An Analysis Based on Cognitive Linguistics

Words play an important part in utilising a language efficiently.An advanced level of learning the English language for a professional purpose involves the need to understand and remember words that are extremely important to perform the task correctly and efficiently.The paramedical students need the perfect register and the passive language for their specialization and using the correct word at the appropriate situation is a question of life and death.The task of learning vocabulary for occupational or specific purpose needs specific strategies.The assessment has been carried out using a Schmitt's tool VLSQ (Vocabulary Learning Strategy Questionnaire) to analyse the various ways in which the paramedical students learn and develop their vocabulary.This research is based on theoretical approach of Cognitive linguistics that studies language as it relates to cognition that takes the psychological and anthropological dimensions into significant consideration.The results show that only rote memory method is highly used by them and that the cognitive and metacognitive modes of vocabulary learning strategies to be known to them and that the teachers handling the paramedical students should focus on sharpening their cognitive and metacognitive domains in recording the registers.
Complex structure and efficient characterization of multiphoton split states in integrated circuits

Multiphoton split states, where each photon is in a different spatial mode, represent an essential resource for various quantum applications, yet their efficient characterization remains an open problem. Here, we formulate the general structure of their reduced spatial density matrices and identify the number of real and complex-valued independent coefficients, which in particular completely determine the distinguishability of all photons. Then, we show that this density matrix can be fully characterized by measuring correlations after photon interference in a static integrated circuit, where the required number of outputs scales subquadratically versus the number of photons. We present optimized circuit designs composed of segmented coupled waveguides, representing a linear optical neural network, which minimize the reconstruction error and facilitate robustness to fabrication deviations.
Educator's Strategy in Stimulating Speech Delay for Children with Mild Mental Retardation

When communicating with the surrounding environment, speech delay or speech disorder generally occurs in normal children or children with special needs with language development disorders. Children with special needs with mental retardation tend to have various disorders, including speech delay, which requires treatment as soon as possible with the right strategy and stimulation. This study aims to describe educator strategies in stimulating mild retarded disabled children who experience speech delay. This study uses a descriptive approach through case studies of a child with mild mental retardation category having speech delay disorder. Data collection techniques used observation and interviews conducted directly at SLB-C Dharma Rena Ring Putra I Yogyakarta. At the same time, the data analysis technique uses the Miles and Huberman models with the data analysis stages, namely data reduction, data presentation, concluding, and verification by testing data wettability using triangulation techniques. The study results show that the language development of children with speech delay disorders in children with mild mental retardation can develop optimally through strategies that educators provide with various stimulations. The findings in this study are aspects that significantly influence dealing with speech delay disorder, namely parenting parents, speech therapy activities, educators understanding children's language in communicating, and exciting learning media through videos or pictures.
Improving Storytelling Ability Using Implications of The Read-Aloud Method in Early Childhood

Basically, a school for early childhood takes work. Many factors influence this, including the shy factor that makes you afraid to speak or tell stories. If the problem is not resolved immediately, it will be difficult for the child to express his feelings. The Read-Aloud method trains children to be able to retell the contents of fairy tales and stories to increase children's vocabulary. Packaging the Read Aloud method uses the media of illustrated children's story books and fairy tale books that can make young children interested in following them. In this study, the authors used a qualitative descriptive method. Data collection techniques used are observation, interviews, and documentation. The results of this study indicate that the implications of the Read Aloud method can stimulate early childhood development by increasing vocabulary so children's language skills can develop properly. Obstacles in this study were limited by changes in the behavior of research subjects which made researchers increase research time.
Rancang Bangun Purwarupa Mobil Bergerak Menggunakan Pengendali Suara Berbasis Mikrokontroler Atmega328

One type of prototype car with interesting special abilities to develop is a voice-controlled prototype car based on the ATmega328 microcontroller module EasyVR, Motor Driver as the stepper motor controller, and using wheels with a stepper motor as the driver. EasyVR Commander serves as the voice recorder. In the control system, several voice commands are used. The first command is "ROBOT", waiting for the LED to light up, and then the second command is "FORWARD", which makes the prototype car move forward. The first command is "ROBOT", waiting for the LED to light up, and then the second command is "REVERSE", which makes the prototype car move backward. The first command is "ROBOT", waiting for the LED to light up, and then the second command is "RIGHT", which makes the prototype car move to the right. The first command is "ROBOT", waiting for the LED to light up, and then the second command is "LEFT", which makes the prototype car move to the left. The first command is "ROBOT", waiting for the LED to light up, and then the second command is "ACTION", which triggers a reaction from the prototype car. The prototype car will move according to voice commands and pressure through sound waves.
RTBF as an Effort to Establish Legal Protection for Victims of Deepfake Pornography in Indonesia

RTBF (Right to be Forgotten) is a legal concept that allows individuals to request a removal or deletion of electronic documents or information about themselves that are inaccurate or no longer relevant. RTBF can be used as a legal basis for addressing cases of Deepfake Pornography, which involves the creation of manipulated pornographic content using Deepfake technology, where someone's face is inserted into another person's body in various media formats, such as images or videos. The current regulation of RTBF in Article 26 (3) of UU ITE is considered to have legal vague, resulting in the failure to achieve legal protection for victims of Deepfake Pornography in Indonesia. This raises the issue of the scope and limitations of the use of the Right to be Forgotten (RTBF), as well as the implementation of an adoption model for RTBF in cases of Deepfake Pornography in Indonesia. Research findings indicate that legal vague regarding the implementation of RTBF in Indonesia can be addressed by conducting a comparative study with other countries that also apply RTBF to cybercrime, including Deepfake Pornography. Indonesia is still lagging behind in detailed and clear regulations regarding the implementation of RTBF in the country, as well as in addressing Deepfake Pornography. Also, there is currently a lack of clear procedures in the UU ITE regarding the use of RTBF, and the ineffectiveness of the authorities handling Deepfake Pornography cases.
Recommending Target Actions Outside Sessions in the Data-poor Insurance Domain

Providing personalized recommendations for insurance products is particularly challenging due to the intrinsic and distinctive features of the insurance domain. First, unlike more traditional domains like retail, movie etc., a large amount of user feedback is not available and the item catalog is smaller. Second, due to the higher complexity of products, the majority of users still prefer to complete their purchases over the phone instead of online. We present different recommender models to address such data scarcity in the insurance domain. We use recurrent neural networks with 3 different types of loss functions and architectures (cross-entropy, censored Weibull, attention). Our models cope with data scarcity by learning from multiple sessions and different types of user actions. Moreover, differently from previous session-based models, our models learn to predict a target action that does not happen within the session. Our models outperform state-of-the-art baselines on a real-world insurance dataset, with ca. 44K users, 16 items, 54K purchases and 117K sessions. Moreover, combining our models with demographic data boosts the performance. Analysis shows that considering multiple sessions and several types of actions are both beneficial for the models, and that our models are not unfair with respect to age, gender and income.
Future Warfare and Responsibility Management in the AI-based Military Decision-making Process

The application of artificial intelligence (AI) technology for military use is growing fast. As a result, autonomous weapon systems have been able to erode humans’ decision-making power. Once such weapons have been deployed, humans will not be able to change or abort their targets. Although autonomous weapons have a significant decision-making power, currently they are not able to make ethical choices. This article focuses on the ethical implications of AI integration in the military decision-making process and how the characteristics of AI systems with machine learning (ML) capabilities might interact with human decision-making protocols. The authors suggest that in the future, such machines might be able to make ethical decisions that resemble those made by humans. A detailed and precise classification of AI systems, based on strict technical, ethical, and cultural parameters would be critical to identify which weapon is suitable and the most ethical for a given mission.
Multi-proxy feature learning for robust fine-grained visual recognition

Visual representation for fine-grained visual recognition can be learned by mandatorily enforcing all samples of the same category into a uniform representation. This strict training objective performs well under closed-set setting but is not applicable to data in the wild containing noisy annotations and long-tailed distributions, e.g., it may lead to a feature space biased to head categories. This paper tackles this challenge by pursuing a more balanced and discriminative feature space by first retaining intra-class variances to isolate noises, then eliminating intra-class variances to improve the visual recognition performance. We propose the Compact Memory Updater to maintain a memory bank, which memorizes proxy features to represent multiple typical appearances of each category in the training set. The Proxy-based Feature Enhancement hence leverages proxy features to ensure samples of the same category have similar features. Iteratively running those two modules boosts the robustness and discriminative power of the learnt representation, hence facilitates various fine-grained visual recognition tasks including person re-identification (re-id), image classification and retrieval. Extensive experiments on noisy and long-tailed training sets show this Multi-Proxy Feature Learning (MPFL) framework achieves promising performance. For instance on a training set with 90% one-shot categories, MPFL outperforms the recent long-tailed person re-id method LEAP-AF by 16.9% in rank-1 accuracy.
A Comprehensive Survey on Blockchain Technology Applications in Different Sectors of Pakistan to Develop Smart Cities

A smart city is an amalgamation of various task-oriented systems and technologies that strive to evolve and adapt according to the needs of the city and its infrastructure. These systems aim to provide citizens with efficient services, tackle urban challenges through data-driven analytical means, and minimize the need for human intervention. One technology that can assist in achieving these goals is Blockchain. Blockchain's decentralization of assets can simplify costly and time-consuming processes, minimize expenses, and enhance transparency, security, and data integration. However, being a relatively new technology, Blockchain faces regulatory hurdles which can be attributed to incomplete knowledge of infrastructure by governing organizations. This lack of understanding often leads to confusion while comprehending different infrastructure elements, hindering problem-solving efforts. In this paper, we analyze different sectors of smart cities and their challenges in Pakistan. We also examine the existing parameters to establish the smart city and their way of upgrading infrastructure technology.
E-learning management process and its effects on the community

This research work raises the needs of virtual communities, from the mastery of technologies for the development of student competencies, to meet the commitment of quality standards in education, to generate high school graduates with performance skills so that they can face a working world that requires the application of computer tools. The descriptive statistics showed that the majority of students regularly connect to virtual classes as pedagogical support and reinforcement to comply with the activities of the educational cards of the Let's Learn Together at Home Plan. Students who cannot connect due to lack of internet access, receive the cards in person to comply with the Ministry of Education's requirements
Analisis Perubahan Desain Karakter dalam GIM SERI DREADOUT Pendekatan Manga Matrix

Game is an interactive media that can create a space of imagination. DreadOut is a digitale game that adapts Indonesian culture as an entertainment aspect in the game, By bringing Indonesian ghosts. In its development, visual appearance is important in building an interesting game, one of which is the characters. There are differences in kuntilanak and tuyul characters in Dreaout 1 and Dreadout 2. This research dissects the differences between these characters using a manga matrix approach. It is hoped that this research will be a reference as a development of visual creation of characters in the game, in order to develop interesting characters according to the intended target market.
Digital Access among Economics Students: In Terms of Material Access

The internet and other digital technologies, which provide students access to a prosperity of knowledge and allow them to explore their interests and learn at their own pace, will enable them to do both. The teaching and learning process has grown more effective and meaningful due to ICT's ability to support self-paced learning through various instruments, including assignments, computers, and other devices. Examining the digital divide between economics class 12 students in public and private schools based on access is the main objective of this study. This study used a cross-sectional quantitative survey as its sole data collection method. The researcher used a simple random selection procedure to select 125 respondents from the population as a sample. This study found that private school students have better access than community schools in terms of material access to ICT in school. By giving ICT access in the classroom and persuading parents that it is a fundamental necessity, schools (public and private) can develop highly ICT smartness students in Nepalese.