Reviewerly/exHarmony huggingface dataset:
Preparing Students in the Master’s Programme in Pedagogical Education to Design Educational Routes as a Condition for the Implementation of the Personalised Education Idea

The purpose of the paper is to show the methodological features of teaching students in the Master’s programme in Pedagogical Education to design educational routes in their preparation for the implementation of the personalised education idea. The authors analysed the essence of personalisation of the learning process, considered the methods of developing pupils’ individual educational routes, described the educational task of designing an individual educational route by Pedagogical Education students (using mathematics as an example). Scientific novelty lies in developing a methodology for training future teachers to design pupils’ individual educational routes. As a result of practical evaluation of the proposed methodology, the orienting basis of the teacher’s actions and criteria for assessing the developed routes have been formulated.
FitDepth: Fast and lite 16bits depth image compression algorithm

Abstract This article presents a fast parallel lossless technique and a lossy image compression technique for 16-bit single-channel images. Nowadays, such techniques are “a must” in robotics and other areas where several depth cameras are used. Since many of these algorithms need to be run in lowprofile hardware, the should be very fast and customizable. As depth images represent surfaces, the idea is to split the image into a set of polynomial functions that each describes a part of the surface. The algorithm herein proposed can achieve similar —or better— compression rate and specially high speed rates than existing techniques. It has the potential of being fully parallelizable and running on many cores. This feature makes it specially useful for handling and streaming multiple cameras. The algorithm is assessed in different situations and hardware. Its implementation is rather simple and is carried out with LIDAR captured images. Therefore, this work is accompanied by an open implementation in C++.
PELATIHAN PEMBUATAN MEDIA AJAR DAN SIMULASI UJIAN ANBK BERBASIS DARING

The education sector is one of the most impacted areas of the COVID-19 pandemic. Schools, which generally hold offline meetings, now have to transform into online sessions. With this pandemic, the teaching process must be "forced" to be online. The assignment model is usually given in physical mode (questions on paper, done and collected) is no longer relevant because of the limitations of physical meetings. On the other hand, students need an explanation from the teacher directly because they are familiar with the context of offline learning. Judging from the lack of computer ownership, whether owned by students or their parents, the technological literacy of the student and the teacher is required to carry out this teaching and learning process properly. To answer this problem, the Informatics and Electrical Study Program at Sanata Dharma University held training activities for making teaching media for SD Negeri Sawah teachers, who ultimately played an important role in improving teachers' technological literacy in carrying out online learning. In addition, at the school's request, we also held a phase 2 training targeting fourth-graders in preparation for the ANBK, a computer-based national exam they had to pass in the following year, which successfully increased the student's confidence to sit the test.
A study on the method of measuring the metaverse based on intelligent science

The metaverse has become the latest hot spot of research on virtual space in the age of intelligence. In order to better measure the boundary of the metaverse, this paper proposes a measurement method based on big data and cognitive intelligence. Through the quantitative study of the three main characteristics of the metaverse, a method to measure the metaverse and the real world is given to provide a reference for scholars studying this field.
Artificial Intelligence, the transformation of rheumatology? – Part I

In a two-part text we will discuss the concepts of artificial intelligence and its application in medicine, as well as its incursion into rheumatology and the challenges it poses in the short, medium, and long term.
ONLINE CREATIVE WRITING TRAINING FOR STUDENTS IN KEMLOKO VILLAGE, BLITAR REGENCY

The ability to write as a form of proficiency in a variety of literature is a very important form of writing for students in schools in Kemloko Village. It is assumed that as intellectuals, they must have skills and master the language in a variety of literature. As a form of concern and service to the village community assisted by the Indonesian Language and Literature Study Program, it is necessary to hold these activities to create a young generation who is ready to face the progress of the times in the era of globalization. Thus, the Indonesian Language and Literature Study Program held the creative writing activity as an alternative step to prepare the younger generation who are ready to face the future by proficiently writing and reading. The activity was carried out online as well as an anticipation to break the chain of the spread of Covid-19. Furthermore, to support these activities, good cooperation is needed between the Indonesian Language and Literature Study Program, the Faculty of Cultural Sciences, Airlangga University with the Blitar Regency PORBUDPAR Service, and the MI School Committee and Kemloko Village Village. This strategy can create positive synergies by forming writing groups so that these activities can be applied in a structured and sustainable manner by producing a collection of interesting and quality poems and short stories even though they are online. Furthermore, the collection of students' writings will be made into a collection of books that have an ISBN and can be distributed to various levels of society.
SpiderNet: Hybrid Differentiable-Evolutionary Architecture Search via Train-Free Metrics

Neural Architecture Search (NAS) algorithms are intended to remove the burden of manual neural network design, and have shown to be capable of designing excellent models for a variety of well-known problems. However, these algorithms require a variety of design parameters in the form of user configuration or hard-coded decisions which limit the variety of networks that can be discovered. This means that NAS algorithms do not eliminate model design tuning, they instead merely shift the burden of where that tuning needs to be applied. In this paper, we present SpiderNet, a hybrid differentiable-evolutionary and hardware-aware algorithm that rapidly and efficiently produces state-of-the-art networks. More importantly, SpiderNet is a proof-of-concept of a minimally-configured NAS algorithm; the majority of design choices seen in other algorithms are incorporated into SpiderNet's dynamically-evolving search space, minimizing the number of user choices to just two: reduction cell count and initial channel count. SpiderNet produces models highly-competitive with the state-of-the-art, and outperforms random search in accuracy, runtime, memory size, and parameter count.
Development of soft competences of future pedagogues in educational activities

The article examines the experience of organising educational activities of students in the field of pedagogic education, we analyse the interpretation of the concept of software skills and determines the significant role of the discipline in the formation of competences significant for the pedagogue. The author gives a description of the technology of discipline implementation, methods and techniques of organising students' interaction in the educational process. The article also presents the results of the introduction of technological methods of work in practical classes and a survey of students based on the results of its development.
HFT-ONLSTM: Hierarchical and Fine-Tuning Multi-label Text Classification

Many important classification problems in the real-world consist of a large number of closely related categories in a hierarchical structure or taxonomy. Hierarchical multi-label text classification (HMTC) with higher accuracy over large sets of closely related categories organized in a hierarchy or taxonomy has become a challenging problem. In this paper, we present a hierarchical and fine-tuning approach based on the Ordered Neural LSTM neural network, abbreviated as HFT-ONLSTM, for more accurate level-by-level HMTC. First, we present a novel approach to learning the joint embeddings based on parent category labels and textual data for accurately capturing the joint features of both category labels and texts. Second, a fine tuning technique is adopted for training parameters such that the text classification results in the upper level should contribute to the classification in the lower one. At last, the comprehensive analysis is made based on extensive experiments in comparison with the state-of-the-art hierarchical and flat multi-label text classification approaches over two benchmark datasets, and the experimental results show that our HFT-ONLSTM approach outperforms these approaches, in particular reducing computational costs while achieving superior performance.
OVERCOMING THE CHALLENGES IN SCIENCE EDUCATION

The impact of science education in the daily activities of man and of course the world can never be over emphasized because science education form the basis for the acquisition of the scientific common senses that are applied in the solution of certain problems. The world is changing every day, therefore, science education which is the vehicle for advancement; innovations and national development have to change too. The paper looks into the concept of science education, goals of science education, emerging challenges in science education and way forward. Suggestions were also made on how best to overcome the challenges in science.
The comprehensive methodology for diagnosing upbringing competences in future pedagogues

The article discusses the problems of developing a methodology for assessing students upbringing competences. The authors present the results of a theoretical analysis of the problem of diagnosing the competences of a future upbringer. The article presents a meaningful and structural model of the competence of a future upbringer, identifies the barriers that arise in the diagnosis of readiness for upbringing activities. The authors proposed criteria for assessing the formation of upbringing competences (cognitive, activity and value-motivational) and developed their own assessment methods for each of these competences. Each of these methods allows one determining the level of formation of upbringing competences according to one of the criteria. The content of the techniques is correlated with the content of the competences themselves. The article presents a description of the achievement test, a motivational questionnaire, a value-orientational game, a set of case tasks, and also identifies ways to validate the proposed comprehensive methodology, which can become the basis for the qualification exam of a future upbringer.
Using Improved BPNN to Predict the Time of Social Poverty Reduction under the Background of Internet

The world has been overturned by the power of the Internet, and all walks of life are constantly changing with the trend of the times. Therefore, with the help of the effective technology and means of the Internet, people can start from a more complex perspective. Analyze the social environment and economic situation, and focus on poverty. By actively monitoring the number of poor people in society, we can grasp the actual situation of poverty. Starting from the depths of the problem, aiming at the characteristics of poor groups, this paper excavates the scale data of poor people. Finally, the improved BP neural network model is used to predict the time when the society gets rid of poverty and reduces poverty. The results show the following: (1) After <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>P</mi> </math> value judgment and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>T</mi> </math> value test, the experiment determined 12 indicators and proportion of poverty. The most prominent indicators of contribution rate are T2, T4, and T5. The most significant changes in <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>T</mi> </math> test results were T8 and T12. (2) The reformed BP network has less iteration times, shorter training time, and higher prediction accuracy. The network converges 30 times, and the fitting accuracy reaches the best, and the ideal state is 0.000936578. Training the poverty alleviation data from 2016 to 2020 with the model, the final poverty alleviation work has achieved remarkable results. (3) According to the hidden layer nodes of neural network structure, the final number of hidden layer nodes is determined to be 11. (4) Comparing the absolute error and APE error of the prediction results. The predicted value is very close to the actual value, which can correctly reflect the mapping relationship of poverty reduction and the result is reliable. (5) Compare the predicted values of BPNN model before and after modification; it is found that the new BPNN model is more accurate in prediction. The predicted population is close to the actual population, and the error level curve approaches 0%. After empirical test, the improved BPNN model is accurate and effective. Through the poverty reduction prediction of the model, people can observe the actual situation more intuitively. By evaluating the effectiveness of social poverty alleviation, we can optimize the next poverty alleviation strategy and adjust the strategic policy.
A Contrastive Cross-Channel Data Augmentation Framework for Aspect-based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task, which focuses on detecting the sentiment polarity towards the aspect in a sentence. However, it is always sensitive to the multi-aspect challenge, where features of multiple aspects in a sentence will affect each other. To mitigate this issue, we design a novel training framework, called Contrastive Cross-Channel Data Augmentation (C3 DA), which leverages an in-domain generator to construct more multi-aspect samples and then boosts the robustness of ABSA models via contrastive learning on these generated data. In practice, given a generative pretrained language model and some limited ABSA labeled data, we first employ some parameter-efficient approaches to perform the in-domain fine-tuning. Then, the obtained in-domain generator is used to generate the synthetic sentences from two channels, i.e., Aspect Augmentation Channel and Polarity Augmentation Channel, which generate the sentence condition on a given aspect and polarity respectively. Specifically, our C3 DA performs the sentence generation in a cross-channel manner to obtain more sentences, and proposes an Entropy-Minimization Filter to filter low-quality generated samples. Extensive experiments show that our C3 DA can outperform those baselines without any augmentations by about 1% on accuracy and Macro- F1. Code and data are released in https://github.com/wangbing1416/C3DA.
The Comparison National Institute of Standards and Technology and Digital Forensics Research Workshop Method in Instant Messenger Services

There have been many crimes committed through the internet (cybercrime).Especially for the problem of cybercrime, digital forensics is needed, where the handling is the same as in other forensic fields, only in the handling of digital forensics, the media used to obtain evidence is through mobile devices and other digital devices.This researchaims to compare the results of the tools used, namely MOBILedit Forensic, Magnet AXIOM, and FTK Imager.As well as knowing the differences in digital forensic methods between the methods of the National Institute of Standards and Technology and Digital Forensics Research Workshop.In this research, it is necessary to carry out research stages including case simulation used to obtain data to be investigated next is a comparison of tools used to determine the application that has the best extraction results, the next stage is to analyze NIST method in which there are four stages including Collection, Examination, Analysis and Reporting and DFRWS method that consists of six stages, including Identification, Preservation,Collection,Examination, Analysis, and Presentation, then an analysis of the differences between two methods is carried out.The results showed the use of tool MOBILedit Forensic WhatsApp with the extraction percentage in the form of group chat and picture by 50% and tool Magnet AXIOM by 50%, and tool FTK Imager by 25%, while the use of tool Magnet AXIOM WhatsApp Business, the extraction percentage in the form of group chat and picture 50% and a tool MOBILedit Forensic is 0% and tool FTK Imager 25%.This researchcan determine the strengths and weaknesses of the forensic method between NIST and DFRWS methods in handling cyberbullying and online shop fraud, with NIST method is more inclined to reporting evidence and for handling cases that are not too complex, while DFRWS method is more directed to handling by investigators and for handling more complex cases.
Randomized Maximum Likelihood via High-Dimensional Bayesian Optimization

Posterior sampling for high-dimensional Bayesian inverse problems is a common challenge in real-world applications. Randomized Maximum Likelihood (RML) is an optimization based methodology that gives samples from an approximation to the posterior distribution. We develop a high-dimensional Bayesian Optimization (BO) approach based on Gaussian Process (GP) surrogate models to solve the RML problem. We demonstrate the benefits of our approach in comparison to alternative optimization methods on a variety of synthetic and real-world Bayesian inverse problems, including medical and magnetohydrodynamics applications.
ANALISIS DAN DESAIN SISTEM INFORMASI PENGARSIPAN SURAT MASUK DAN SURAT KELUAR PADA DINAS LINGKUNGAN HIDUP PROVINSI SUMATERA BARAT

Abstract: The Environmental Service of West Sumatra Province is a government agency that provides services and organizes benefits in the environmental field. The activities of the Department of the Environment include: formulation of policies; policy implementation; implementation of evaluation and reporting; administrative implementation; fostering the UPT and the Environmental Service Functional Group. In a government agency, information exchange activities cannot be separated from correspondence activities. Letters are used by the Environmental Service as a means of communication in collaboration with other individuals or agencies. The letter filing system at the Environmental Service still uses paper media as the main archive so that it often experiences difficulties in finding data, errors in data writing, and data loss. The purpose of this study was to produce an information system for archiving incoming and outgoing letters at the Environmental Service of West Sumatra Province. This study uses the Waterfall method, where user needs are the main point in making the system. The results of this study are an information system that can facilitate the filing system of incoming and outgoing mail that is currently running at the Environmental Service of West Sumatra Province and minimizes the risks that will occur.Keywords:Analysis, Archiving, Design, Information Systems, Waterfall
An Extendable, Efficient and Effective Transformer-based Object Detector

Transformers have been widely used in numerous vision problems especially for visual recognition and detection. Detection transformers are the first fully end-to-end learning systems for object detection, while vision transformers are the first fully transformer-based architecture for image classification. In this paper, we integrate Vision and Detection Transformers (ViDT) to construct an effective and efficient object detector. ViDT introduces a reconfigured attention module to extend the recent Swin Transformer to be a standalone object detector, followed by a computationally efficient transformer decoder that exploits multi-scale features and auxiliary techniques essential to boost the detection performance without much increase in computational load. In addition, we extend it to ViDT+ to support joint-task learning for object detection and instance segmentation. Specifically, we attach an efficient multi-scale feature fusion layer and utilize two more auxiliary training losses, IoU-aware loss and token labeling loss. Extensive evaluation results on the Microsoft COCO benchmark dataset demonstrate that ViDT obtains the best AP and latency trade-off among existing fully transformer-based object detectors, and its extended ViDT+ achieves 53.2AP owing to its high scalability for large models. The source code and trained models are available at https://github.com/naver-ai/vidt.
LaMemo: Language Modeling with Look-Ahead Memory

Although Transformers with fully connected self-attentions are powerful to model long-term dependencies, they are struggling to scale to long texts with thousands of words in language modeling.One of the solutions is to equip the model with a recurrence memory.However, existing approaches directly reuse hidden states from the previous segment that encodes contexts in a uni-directional way.As a result, this prohibits the memory to dynamically interact with the current context that provides up-todate information for token prediction.To remedy this issue, we propose Look-Ahead Memory (LaMemo) 1 that enhances the recurrence memory by incrementally attending to the right-side tokens, and interpolating with the old memory states to maintain long-term information in the history.LaMemo embraces bi-directional attention and segment recurrence with an additional computation overhead only linearly proportional to the memory length.Experiments on widely used language modeling benchmarks demonstrate its superiority over the baselines equipped with different types of memory.
Detecting Text Formality: A Study of Text Classification Approaches

Formality is one of the important characteristics of text documents. The automatic detection of the formality level of a text is potentially beneficial for various natural language processing tasks. Before, two large-scale datasets were introduced for multiple languages featuring formality annotation -- GYAFC and X-FORMAL. However, they were primarily used for the training of style transfer models. At the same time, the detection of text formality on its own may also be a useful application. This work proposes the first to our knowledge systematic study of formality detection methods based on statistical, neural-based, and Transformer-based machine learning methods and delivers the best-performing models for public usage. We conducted three types of experiments -- monolingual, multilingual, and cross-lingual. The study shows the overcome of Char BiLSTM model over Transformer-based ones for the monolingual and multilingual formality classification task, while Transformer-based classifiers are more stable to cross-lingual knowledge transfer.