Reviewerly/exHarmony huggingface dataset:
REVISION AS A KEY STAGE OF THE TRANSLATION PROCESS

Being a part of professional translation, translation revision is considered to be a means of translation quality assessment.A translator should never call the translation finished if it has not been checked.Quality in translation is assessment of its strong and weak points and, therefore, it cannot be taken for granted.Revision and translation have many common features and at the same time these processes are different.In foreign literature translation revision is characterized by lack of consensus regarding its terms and definitions.The majority of definitions may be explained by the fact that translation studies sphere is in a flux, so is the term "revision".In the article revision is defined as operartions used to check and improve the quality of the translated text by eliminating grammatical, lexical ans stylistic mistakes through the comparison of the target text with the source one.The revision types are identified taking into account different criteria.The following types have been analyzed in the article: monolingual (or unilinguala revisor focuses on the translated text itself) and comparative (or bilingual -a revisor constatnly compares the target text with the source one); self-revision (when translators themselves reread and revise their own translations) and other-revision (a translation is read by a revisor); full and partial (the revision of only some parts of the translated text).Their peculiarities have been discussed.The number of steps of the revision process have been examined.Having analyzed the scientific literature on revision parameters those suggested by Mossop (2014) have been reviewed being detailed and systematic, used by revisors themselves and in the process of their training.The process of revision has no clear boundaries and can occur at any stage of translation, it is worth paying as much attention to as the translation of the text itself.
Development of Interactive Learning Media Based on Articulate Storyline 3 Theme 5 My Heroes on Student Learning Interest Grade IV Elementary School

This study aims to develop media based on Articulate Storyline 3 on Theme 5 My Heroes, determine the feasibility and interest of grade IV students at SD Negeri 115486 Ujung Padang. This research is a type of development research (Research and Development) using the ADDIE development model which includes five stages, namely: Analysis, Design, Development, Implementation, and Evaluation. The subjects in this study were media design expert validators, material experts, learning experts (teachers) and grade IV elementary school students. The instrument used to collect data is a questionnaire. The data analysis technique used in this research is descriptive qualitative and descriptive quantitative. Based on data analysis obtained through the validity test of material experts, media design experts, teacher responses, and student responses. The results of validation by material experts are 72% including the valid / good category. The results of validation by media design experts are 66% including the valid / good category. The validation results of the fourth grade teacher's response were 72.5% including a very valid / good category, while for student responses, 85% included a very valid / good category. Based on the percentage results from material experts, media design experts, teacher responses and student responses, then calculated the average results obtained 86.2% including in the category very good / very feasible to use in the learning process. Based on the feasibility of media from the input of material experts, namely lecturers, media experts, namely lecturers and learning experts, namely the response of grade IV teachers, as well as student responses to learning media. From the data from the validation results, the development of Articulate Storyline media material on the Struggle of Heroes developed by researchers is declared very feasible to use as teaching materials in the learning process.
PENGEMBANGAN MEDIA CHATBOT SCAFFOLDING UNTUK MENDUKUNG PEMBELAJARAN PROJECT BASED LEARNING SECARA DARING

This study aims to produce chatbot scaffolding media with a PjBL model in e-lematika to improve student learning outcomes in class VIII Cartesian cooordinat material that is appropriate for use in the learning process. This research is a development research using the ADDIE model which consist of 5 stages, namely analyze, design, development, implementation and evaluation. There are three criteria used to measure the quality of chatbot scaffolding media, namely 10 valid, the validity of the media is obtained from validation tests by material experts and design experts. 2) practical, the practicality of the media is obtained from the teacherâ€™s practicality questionnaire and student practicality questionaire and 3) effective, the effectiveness of the media is obtained from the student response questionnaire and student learning achievement tests. The result of this study indicate that: 1) the result of the percentage validity of material experts are 96.36% (very valid). and design experts are 86.66% (valid); 2) the percentage of practicality by educators is 72.50% (practical) and by students 75.05% (practical); 3) the percentage of the effectiveness seen form the student response questionnaire 75.09% (effective) and the persentageof the completeness of student learning outcomes is 81.25% (very good).
Potential Elements for Development of a Versatile Cognitive Function Screening Tool

Cognitive function screening tools are so useful in detection of cognitive impairment resulted from both neurodegenerative, non-neurodegenerative, neurodevelopmental and non-neurodevelopmental disorders. Moreover, it is also used as cognitive aptitude tests or general cognitive ability tests in non-clinical settings such as in various recruitment processes. However, most of the cognitive function screening tools (CFST) available now is highly subjective and don’t encompass a wide range of cognitive biomarkers and also don’t apply a wide range of cognitive reagents. Whether cognitive function screening tools are pen-paper basedor digital, these essential elements have to be taken into consideration to ensure versatility, efficacy and accuracy of cognitive function screening tools. The same principle can be applied in development of cognitive function training tools (CFTT) that can potentially provide effective cognitive rehabilitation. A dual-functional model has been proposed and demonstrated that can potentially be used in pre-clinical and non-clinical settings.
Robust JPEG Steganography Based on the Robustness Classifier

Abstract The application of image steganography in popular image-sharing social networks requires robustness because the JPEG recompression in social networks changes the DCT coefficients of uploaded images. Currently, most robust steganography algorithms rely on the resistance of embedding to the general JPEG recompression process, while operations in a specific compression channel are usually ignored, which reduces the robustness performance. Besides, the state-of-the-art robust steganography needs to upload the cover image to social networks several times to acquire the robust cover image, which may be insecure in terms of behavior security. In this paper, a robust steganography method based on the softmax outputs of a trained classifier and protocol message embedding is proposed. In the proposed method, a deep learning-based robustness classifier is trained to model the specific process of the JPEG recompression channel. The prediction result of the classifier is used to select the robust DCT blocks to form the embedding domain. The selection information is embedded as the protocol messages into the middle-frequency coefficients of DCT blocks. To further improve the recovery possibility of the protocol message, a robustness enhancement method that decreases the predicted non-robust possibility of the robustness classifier by modifying low-frequency coefficients of DCT blocks is proposed. The experimental results show that the proposed method is universal and can be implemented in different JPEG compression channels after finetuning the classifier, and it has better robustness performance compared with state-of-the-art robust steganography and does not have the disadvantage in terms of behavior security, and it has better security performance compared with the state-of-the-art method when embedding large-sized secret messages.
Suspended sediment distribution pattern using remote sensing techniques in the Sambas River of Sambas Regency

The Sambas River is part of the watershed of Sambas River which covers 71.03% of the total area of Sambas Regency. This river exhibits a mixed tidal regime with a tendency towards a double daily tide, which can cause sedimentation. Monitoring of this sedimentation can be conducted using remote sensing methods. The aim of this research is to analyze the concentration of suspended sediments based on direct measurements and appropriate algorithm processing. The data processing results from satellite images in 2022 using the Budhiman, Nurandani, and Lestari algorithms have a similar range of values to the direct measurements, ranging from 72 mg/L to 84 mg/L, although there are some data points that fall outside this range. However, the Budhiman algorithm demonstrates the best correlation with the direct measurements. The processed satellite data shows fluctuating values, indicating fluctuations in suspended sediment concentrations. Based on the conducted research, it is evident that the distribution pattern of suspended sediments in a river system can be analyzed using remote sensing techniques.Keywords: Remote Sensing; Suspended Sediment; Watershed
Verification Based Algorithm Selection

Algorithm selection is a general purpose optimization based on meta-learning. It uses meta-features to select algorithms on a case by case basis for the most accurate result. In real world problems such as image understanding or image segmentation the meta-features are difficult to obtain due to high diversity and lack of structure in the input data. Therefore algorithm selection in real world problems has been traditionally using image features that are also used for the task processing. Such features however might are not well suited for algorithm selection due to their task specific purpose. In order to solve this problem we propose an improved verification based approach to algorithm selection. First, we build a verification table indicating each algorithms ability to generate content that would satisfy verification for each verification attribute. Using these statistics as basis, we create a multi-algorithm verification map (MAVM). This map is built as a statistical co-occurrences of individual algorithm's ability to satisfy relational verification process of different attributes. The MAVM is reformatted so that for each relational attribute we obtain a co-occurrence of success of each algorithm. The resulting table then used for selection based on the verification result given an algorithm. The advantages of the proposed method are lesser computational cost, possibility to determine ideal combination of algorithms on a case by case basis and provides a basis for algorithm design.
Improved-GCN-based collaborative filtering recommendation model

Machine learning (ML) graph convolution network (GCN) has been applied in various fields, including recommendation systems, and has achieved great success. The core goal of the recommendation system is to infer user preferences so as to actively provide users with valuable and targeted information. Collaborative Filtering (CF) is a classic and widely used recommendation method. However, GCN-based recommendation methods still have many problems. For example, GCN recursively merges messages from different order neighborhoods, it is difficult to distinguish between different node messages, which leads to training difficulties, and problems such as over-smoothing have great constraints on the recommendation model. Different from the current GCN-based method, in view of the above problems, it is proposed to use a simple GCN model to separately aggregate neighbor messages in different orders for collaborative filtering, and then aggregate them in a hierarchical manner without introducing other model parameters. After that, the dropout idea was transferred to the model, by generating multiple views of the same node from the three dimensions of nodes, edges and graphs to reduce the effect of over-smoothing, which prevented over-fitting and improved the performance of the model. The experimental results on three datasets demonstrate the effectiveness of the proposed model.
Game Development Treasure Based Learning Discovery in Early Children in TK Az-Zikri Perbaungan District

This study aims to develop a treasure trove game based on discover learning to determine its feasibility and is ready to be used as a learning medium for early childhood at AZ ZIKRI Kindergarten, Perbaungan sub-district. This research is a type of research and development or research and development (R&amp;D). This study uses the ADDIE model which focuses on developing discover learning-based treasure games which are equipped with a media use guidebook to make it easier for teachers to make and use discover learning-based treasure games. The instrument used to collect data is using a questionnaire on the validator based on the validation results of material experts getting a score of 90% and media experts of 83%. And interview instruments given by the teacher before and after getting a score of 93%. So it can be said that the development of a treasure game based on discovery learning in early childhood is very appropriate to be used as a learning medium in the Perbaungan sub-district.
Development of Digital Learning Media Helped Capcut on Theme Care Against Living Things in Class IV SD

This research is to find out the Development of CapCut-Assisted Digital Learning Media on the Theme of Caring for Living Creatures. The research design that I used in this study was Development or Research and Development (R&amp;D). The model used in this development is the ADDIE model. The research subjects of the development of CapCut-assisted digital learning media are media design experts, material experts. The object in this study is CapCut-assisted digital learning media on the theme of caring for living things which was created to assist teachers in thematic learning of class IV SD. Research on product development in the form of digital learning media assisted by CapCut on the theme of caring for living things for class IV SD NEGERI 0209 BAHAL BATU, which will be carried out in June 2023. To analyze the data collected from the questionnaire it can be grouped into 2 types of data, namely descriptive qualitative and quantitative descriptive. Qualitative data were obtained from a questionnaire containing criticism and suggestions from media and material validator experts. The data obtained as a reference for revising the product being developed. The data obtained as a guideline for revising the resulting product. Based on the results of the calculations above, the overall assessment results from material experts reach 70%. If the percentage obtained reaches 61% to 80% then the CapCut-assisted digital learning video is categorized as feasible. From the calculation above, it is obtained that the percentage is 70%, so the CapCut video is categorized as feasible. Based on the results of material expert validation, learning material on the theme of caring for living things through CapCut interactive video is feasible to be developed in science learning with a percentage of 80%, this means that learning media is included in the "decent" category.
Learning a fair and privacy-preserving graph neural network from private and limited sensitive attributes

&lt;p&gt;Learning a fair and privacy-preserving graph neural network from private and limited sensitive attributes&lt;/p&gt;
Abnormal Behavior Detection in Online Exams Using Deep Learning and Data Augmentation Techniques

Massive open online courses (MOOCs) and other forms of distance learning have gained popularity in recent years. The success of remote online exam proctoring determines the integrity of the exam. Deep-learning-powered proctoring services have also grown in popularity. A large number of samples are needed for deep-learning training. The network’s generalization ability is poor due to insufficient training data or an uneven lack of variation. This study illustrates how to analyze students’ anomalous behavior by utilizing a YOLOv5 deep model trained using newly produced dataset. To overcome insufficient training data for deep-learning-related issues, this paper proposes a data-augmentation method based on semantic segmentation. The MobileNetV3 model was used to get an image semantic segmentation mask, which was used to get a binary mask, which in turn was used to replace the image background by using conditional subtraction with randomly selected background images. Finally, randomly pixel-based color augmentation was added to the resulting image. The behavioral detection model used in this study achieved 0.98 mean average precision (mAP) on the produced dataset, showing acceptable detection precision. The experimental findings indicate that the suggested augmentation method improves behavioral detection precision by more than 0.3%.
Transforming Healthcare: Leveraging Vision-Based Neural Networks for Smart Home Patient Monitoring

Image captioning is a promising technique for remote monitoring of patient behavior, enabling healthcare providers to identify changes in patient routines and conditions. In this study, we explore the use of transformer neural networks for image caption generation from surveillance camera footage, captured at regular intervals of one minute. Our goal is to develop and evaluate a transformer neural network model, trained and tested on the COCO (common objects in context) dataset, for generating captions that describe patient behavior. Furthermore, we will compare our proposed approach with a traditional convolutional neural network (CNN) method to highlight the prominence of our proposed approach. Our findings demonstrate the potential of transformer neural networks in generating natural language descriptions of patient behavior, which can provide valuable insights for healthcare providers. The use of such technology can allow for more efficient monitoring of patients, enabling timely interventions when necessary. Moreover, our study highlights the potential of transformer neural networks in identifying patterns and trends in patient behavior over time, which can aid in developing personalized healthcare plans.
A Systematic Literature Analysis on the Fake Identification

Abstract- Rapid progress in AI, machine learning, and deep learning over the last few decades has resulted in new methodologies and tools for altering multimedia. Though the technology has generally been utilized for respectable causes such as entertainment and education, rogue people have also used it for illegal or evil purposes. High-quality and realistic fake movies, photos, or audios, for example, have been generated to disseminate disinformation and propaganda, incite political division and hatred, or even harass and blackmail people. Deepfake is a term that refers to modified, high-quality, realistic videos. To address the issues posed by Deepfake, many ways have been discussed in the literature. In this study, we undertake a systematic literature review (SLR) to offer an updated overview of the research efforts in Deepfake detection, summarizing 112 relevant papers from 2018 to 2020 that provided a range of techniques. We classify them into four categories: deep learning-based approaches, traditional machine learning-based methods, statistical techniques, and blockchain-based techniques. We also compare the detection capabilities of the various algorithms across different datasets and find that deep learning-based methods outperform other methods in Deepfake detection. Keyword: Deepfake detection, video or image manipulation, digital media forensics, systematic literature review.
Securing Big Data in the Cloud: Privacy, Confidentiality, and Access Controls

Securing Big Data in the Cloud: Privacy, Confidentiality, andAccess Controls
Keeping track of reality: Embedding visual memory in natural behavior

Since immersive virtual reality (IVR) emerged as a research method in the 1980s, the focus has been on the similarities between IVR and actual reality. In this vein, it has been suggested that IVR methodology might fill the gap between laboratory studies and real life. IVR allows for high internal validity (i.e., a high degree of experimental control and experimental replicability), as well as high external validity by letting participants engage with the environment in an almost natural manner. Despite internal validity being crucial to experimental designs, external validity also matters in terms of the generalizability of results. In this paper, we first highlight and summarize the similarities and differences between IVR, desktop situations (both non-immersive VR and computer experiments), and reality. In the second step, we propose that IVR is a promising tool for visual memory research in terms of investigating the representation of visual information embedded in natural behavior. We encourage researchers to carry out experiments on both two-dimensional computer screens and in immersive virtual environments to investigate visual memory and validate and replicate the findings. IVR is valuable because of its potential to improve theoretical understanding and increase the psychological relevance of the findings.
تأثير وسيلة بطاقة الصورة في تعليم المفردات في مدرسة انسان جنديكيا الثانوية باياكمبوه

There is a problem in teaching vocabulary, namely many students do not master vocabulary well, so many people misunderstand. Errors include writing Arabic letters and correct vocabulary. This problem is caused by some teachers who do not apply teaching steps in accordance with existing theory. Meanwhile, it poses a problem. The purpose of this study was to determine the effect of the picture card method in understanding vocabulary, as well as in its translation at Islamic School Insan Cendekia Payakumbuh. In this study, researchers used quantitative methods, namely experimental research which was experimental research in schools with a quasy semi experimental design research with the pretest-posttest one group design type.Based on the results of the research as follows: First, the value group for the students' ability to know the understanding of vocabulary from the pre-test in the entire experimental semester is 1709 (∑X). The researcher found the largest value to be 75 and the smallest to be 38, with a mean of 58.93. Also, the researcher found the results of the students from the post-examination in the seventh semester. It is depicted that the whole number is 29 students (N)), and the result from the group value of the ability of students to know the understanding of vocabulary from the pre-test in the entire experimental class is 2172 (∑X). The researcher found the largest value 86 and the smallest value 56 with a mean of 74.89. Secondly, the researcher also found the students' results from the translation of vocabulary in pre-examination in the control class at Islamic School Insan Cendekia Payakumbuh. It is depicted that the whole number is 29 students (N), and the result of the group value of the ability of students to know the translation of vocabulary from the pre-exam in the control class as a whole is 1608 (∑X). The researcher found the largest value 73 and the smallest value 30 with a mean of 55.44.