Reviewerly/exHarmony huggingface dataset:
peRISCVcope: A Tiny Teaching-Oriented RISC-V Interpreter

The fast advances of computer systems translate into a growing demand of methodologies and tools to introduce those novelties into classes. Among the plethora of those advances, virtualization has become an essential technology in almost every relevant system stack, from connected cars to hyperscaled cloud servers. However, introducing those technologies into the classroom remains a challenging task because of the huge complexity of their software components that may hinder the learning process of students. peRISCVcope aims to help in this area by proposing a tiny yet powerful interpreter to dig into virtualization technologies, such as the implementation of trap&emulate hypervisors. With less than 2,000 lines of code, and thanks to the conciseness of the RV32I base instruction set of RISC-V, peRISCVcope enables students to make virtualization knowledge their own. This paper presents our experiences developing and testing a virtualization laboratory where students implement parts of an interpreter. After the practical experience, peRISCVcope has been proved as a useful pedagogical tool, and, most importantly, students have positively rated the experience.
Application of multi-objective comprehensive optimization model of soil and water conservation based on genetic algorithm

Soil and water conservation as the focus of the development of urban construction, in the development of modern economic and technological innovation, international scholars applying artificial intelligence to construct the integrated optimization model of soil and water conservation, in order to provide effective reference for solving the problem of soil erosion, avoid the destruction of their homes by human beings rely on, really realize the goal of sustainable development. As a basic part of artificial intelligence research, genetic algorithm plays an active role in solving IP problems in combinatorial optimization. Therefore, on the basis of understanding the genetic algorithm and the research status of soil and water conservation, this paper mainly discusses the application effect of the multi-objective comprehensive optimization model of soil and water conservation with the genetic algorithm as the core. The final experimental results prove that the comprehensive optimization model constructed by RAGA accelerated genetic algorithm proposed in this paper can comprehensively improve the multi-parameter optimization ability, and has reliability and applicability characteristics in practice, which provides an effective basis for the soil and water conservation work of urban construction and development in the new era.
A Critical Review on Steps Applied to Conduct Activities in Reading Classes to Improve Students’ Vocabulary Development and Expand Vocabulary Use

A majority of people consider intense reading as a great strategy to improve competence in vocabulary and language skills. However, educators believe that the time commitment required to use the intensive reading technique is a major barrier to its adoption. In order to teach students about short stories, novels, poems, and other types of creative writing, literature professors frequently use the intense reading method. The value of the intensive reading technique is a topic of much investigation. This study compared reading to intensive reading and considered how the two could be integrated. The primary objective of this study is to determine whether the Intensive Reading Method works well for teaching new vocabulary words utilizing its suggested procedures. In order to explore the intensive reading approach to improve language proficiency in writing, reading comprehension, and vocabulary, a systematic literature review was used. The results showed that the procedures taken in 70% of the studies were acknowledged as essential for carrying out the intensive reading approach. The Intensive Reading Method was employed in 14 out of 20 research studies, and it was proven to be successful in assisting learners in acquiring new vocabulary. Keywords: Vocabulary learning; Vocabulary Development; Intensive Reading Method
Self-Supervised Push-Frame Super-Resolution With Detail-Preserving Control And Outlier Detection

Self-supervised training enables the application of deep-learning based methods for multi-image super-resolution of satellite imagery. In this work we propose two improvements on the self-supervised Deep-Shift-and-Add (DSA) method introduced by Nguyen et al. First, we demonstrate how the self-supervised loss of DSA can be extended to provide the image interpreter with a spatially varying parameter to control the trade-off between detail preservation and noise removal at test time. Second, we endow the DSA architecture with a mechanism that enables the network to be robust to outliers produced for example by dead pixels, reflections or registration errors.
Approaches to Defining Priors

Extract Defining priors remains an issue of perennial debate in the Bayesian framework. There is no principled way of defining priors that has gained universal or even widespread acceptance. While this might well be an elusive goal, one would hope that, at least for some large class of commonly used models (e.g., generalized linear mixed models), there ought to be at least a conventional approach to setting priors to guide the data analysts. Those working within the frequentist framework do not have such problems, as we operate in a state of almost complete amnesia as if the world starts again with every data analysis. We may criticize Bayesian statistics all we want, but it is the explicit specification of the assumed priors what allows us to debate their influence on the results. We have pointed out that priors tend to have a larger influence on the results of data analyses when we are trying to detect smaller magnitudes of effects of the explanatory variables with data sets of modest size. Unfortunately, this is the most common situation with experimental and survey data in the life sciences. Effect sizes of large magnitude are likely to have been detected in previous studies. The amount of information contained in large (meaning many hundreds of thousands of data points) data sets is likely to overwhelm most reasonable priors. Priors can also be used to regularize results by reducing the chances of obtaining unrealistically large effect sizes due to chance, as may occur with data sets of modest size.
EE136 Cost of Illness in Patient With Synovial Sarcoma in México (2010-2021)

The sinovial sarcoma is a malignant mesenchyma. Although sarcomas are a rare disease, they account for approximately 1% of the total tumors, and are responsible for 2% of cancer mortality. This represents between 5 and 10% of all soft tissue sarcomas, and is the fourth frequency, after malignant fibrous histiocytoma, liposarcoma and rhabdomyosarcoma. The objective of this study is to estimate the total medical cost of illness of Synovial Sarcoma at the National Institute of Cancerology (Mexico City), in the last 10 years (2010-2021).
RANCANG BANGUN SISTEM PENGGAJIAN DENGAN METODE WATERFALL PADA PT.LANGGENG ENGINEERING TEKNIK INDONESIA

The payroll system is a continuous process related to employee management. Processing employee data apart from calculating the amount of salary to be received and employee income tax, the payroll system is also used to allocate labor costs to products. Payroll data management requires an information system that can manage payroll data accurately and on time. PT Langgeng Engineering Teknik Indonesia is a company engaged in general trading, supplying industrial goods, infrastructure, fabrication, mechanics, electrical and maintenance services and pinting really needs a computerized payroll system by managing employee attendance data and working hours. The payroll system using the Waterfall method makes it easier for companies to manage employee salary data so that they can obtain precise and accurate salary calculations
Learning about the force of gravity: findings from a survey research

Abstract In recent years of social distance due to the pandemic, augmented reality (AR) with its ability to overlay real phenomena with digital content has become even more attractive for making physics experiments accessible to every student. Therefore, we have recently developed an AR application for Android mobile devices that allows users to study how the gravitational force between two spherical bodies depends on their masses and their mutual distance. The purpose of this study is to investigate students' experience of using this application. To that end, 85 undergraduate students from the University of Zagreb were asked to use our AR application for completing a task related to the concept of gravity. Thereafter, they were administered five open-ended and 30 close-ended questions from three different questionnaires: System Usability Scale (SUS), User Experience (UX), and Handheld Augmented Reality Usability Scale (HARUS). Analysis of students’ responses revealed that students perceived the application as easy to use, consistent, self-explanatory, and enjoyable. Valuable feedback was provided on possible improvements to the application.
Separated syntax and semantics modeling for cross-domain aspect-level sentiment classification

神经网络在方面级情感分类任务上已经取得了良好的性能. 然而, 由于复杂且耗时的数据标注流程, 方面级情感分类在很多领域上是低资源甚至是零资源的, 这限制了该任务在实际场景中的应用. 为了解决这个挑战性的问题, 本文关注跨领域的方面级情感分类, 并提出一种基于语法和语义分割的跨领域方面情感分类方法. 具体而言, 针对不同领域用词差异造成的领域漂移和注意力泛化问题, 本文首次提出利用单纯的语法信息来获取可在领域之间迁移的语法注意力, 并引入与目标领域相近的文档情感分类任务来增强神经网络模型对目标领域的情感识别能力, 最终从语法和语义两个层面分别提升模型的注意力机制和文本上下文表示. 实验在6个跨领域方面级情感分类任务上进行, 结果表明, 本文的方法在6个任务上都取得了最先进的性能, 在平均准确率和平均macro F1-score两个指标上比之前最好的模型DIFD分别提升7.14%和7.6%. 此外, 即使以大规模预训练模型BERT、BERT-ADA、RoBERTa等作为骨干网络, 本文的方法仍能实现3.5%以上的平均准确率提升和平均macro F1-score提升.
Evaluating morphosemantic demotivation through experimental and distributional methods

Summary The lexicalization of morphologically complex words, i.e. their inclusion in the lexicon, can involve a loss of semantic compositionality. Such a phenomenon, called demotivation, has been overlooked in both morphological and lexical studies, notably regarding its gradual nature. This paper compares two measures of demotivation based on experimental and distributional semantics approaches. It builds on the evaluation of 78 pairs of French verbs and derived nouns selected to represent three levels of demotivation. The comparison of the two approaches using speakers’ judgements and word vector similarity indicates convergence on the identification of demotivation degrees within a continuum, while also highlighting specific aspects of each method. The study provides direction to further research on morphosemantic demotivation, bridging together semantic, morphological and methodological considerations.
Understanding the Poetry as a Complex Analytical and Synthetic Process of the Cognitive Sphere of the Personality

the purpose of our research was to conduct an empirical study of the psy-chological characteristics of future philologists of contemporary poetry and to assess the development of personal qualities of students who are the part of the structure of reading competence and, in our opinion, will facilitate students’ un-derstanding of poetry to develop their ability to reflect their mental states, goals and speech-thinking competence, creative imagination, richness of associations, awareness of one’s own experiences, the ability to in-depth analysis and com-menting on poetry.methods of the research. The following theoretical methods of the research were used to solve the tasks formulated in the article: the categorical method, structural and functional methods, the methods of the analysis, systematization, modeling, generalization. The experimental method was the method of organi-zing empirical research.the results of the research. It was proved thatthe most common reasons for the difficulty of understanding poetry had been: surreal poetics; postmodern poetics; original and unusual worldview; deliberate outrage; intellectual begin-ning; high symbolism of images; high subjectivity; complex philosophical load; accumulation of worlds; mental unusualness; intertextuality; experimentalism; complex, metaphoric imagery; associative multifacetedness of semantic series; complex subtexts; guidelines for the destruction of poetic form and providing ex-periments with it; presence of reminiscences; complex allusions; intuitive grasp of Ukrainian archaisms; speech experiments; outdated and complex vocabulary; unusually large proportion of obscene vocabulary; a large number of author’s innovations in poetry.conclusions. We interpret the understanding of the text as a complex ana-lytical-synthetic process, which ends with penetration into the deep semantics of some message. It was shown that understanding had taken a place on two levels – on informational-cognitive one and on the semantic level. We offer the criteria for understanding the content of the message, which are related to the processes of extracting and generating the meaning of the text. We proved, that the main criterion of understanding the poetry is the restoration of the meaning of the original message, the construction of its internal semantic structure, which is characterized by the criteria of depth and completeness of understanding.
Neapstrādāta mašīntulkojuma izmantošana

The article „Use of Raw Machine Translation” addresses several examples of raw MT on sites available in Latvian to every internet user. The aim of the article is to examine the objectives of using the raw MT and to find out, by using a comparative analysis of examples, how precisely the raw MT transfers the source text’s message into the target text in Latvian. Along with the use of the content, objectives and ethical aspects of providing and using the raw MT are studied. Also, an insight into neural MT has been given since it has facilitated the use of MT. The article emphasises the multifaceted essence of MT and identifies several issues. Conclusions state that the raw MT is usable; however, there must be a clear use objective, and a user must always be informed that the content has been translated automatically and can contain errors. The article also concludes that not always raw MT has been used reasonably, i.e., after consideration of key principles for using thereof. Such principles, in turn, are related to ethical aspects that, regarding artificial intelligence, are of special significance.
Rekurence kā teksta saistījuma līdzeklis latviešu zinātniskajos rakstos

Text cohesion can be achieved through grammatical and lexico-semantic means. Lexico-semantic means used in text cohesion can be defined as recurrence, as in Latin, the word recurrere means returning to a previous utterance or repeating it. Repetition of lexico-semantic means in the text can be named using a foreign word in the Latvian language, rekursija (recursion in English). In Latvian, the concept rekursija is related to phonology, defining it as ‘the final part of pronouncing a sound, in which speech organs deviate from the previously assumed state characteristic of a particular sound’ (translated from Latvian of the entry in Tēzaurs 2022). Recursion in English linguistics is often understood as the sequential use of grammatical structures, while in German text linguistics, the concept Rekurrenz is defined as the repetition of similar language means, elements, and forms in the text. The research goal is to identify types of cohesive devices used in scientific articles in the Latvian language. The research focuses on full or partial repetition of such cohesive devices as words and word collocations, substitution, etc. in the scientific texts in Latvian that have been included in the Corpus of Latvian Scientific Articles. The Corpus comprises 1401 texts published between 2008 and 2018 covering six scientific branch groups. A qualitative research method, content analysis, has been used. It can be concluded that scientific texts in Latvian contain exact repetitions, partial recurrence, ellipsis, and substitution as lexico-semantic cohesive devices. Research findings show the different density of recurrence means used in scientific articles in different scientific branches and how relatable the used recurrence means are to the article’s title and the indicated key words.
Getting Started with Ubuntu

One of the best things about Ubuntu is that it’s ready for use as soon as it’s installed. As soon as you reboot, you can get almost anything done immediately, and new applications are just a couple of clicks away. Some of the power of Ubuntu is in its differences from other operating systems.
Research on Deep Compression Method of Expressway Video Based on Content Value

Aiming at the problem that the storage space and network bandwidth of expressway surveillance video are occupied largely due to data redundancy and sparse information, this paper proposes a deep compression method of expressway video depth based on content value. Firstly, the YOLOv4 algorithm is used to analyze the content value of the original video, extract video frames with vehicle information, and eliminate unintentional frames. An improved CNN is then designed by adding Feature Pyramids and the Inception module to accelerate the extraction and fusion of features at all levels and improve the performance of image classification and prediction. Finally, the whole model is integrated into HEVC encoder for compressing the preprocessed video. The experimental results show that at the expense of only a 5.96% increase of BD-BR, and only a 0.19 dB loss of BD-PSNR, the proposed method achieves a 64% compression ratio and can save 62.82% coding time compared with other classic data compression methods based on deep learning.
Intent Classification and Slot Filling Model for In-Vehicle Services in Korean

Since understanding a user’s request has become a critical task for the artificial intelligence speakers, capturing intents and finding correct slots along with corresponding slot value is significant. Despite various studies concentrating on a real-life situation, dialogue system that is adaptive to in-vehicle services are limited. Moreover, the Korean dialogue system specialized in an vehicle domain rarely exists. We propose a dialogue system that captures proper intent and activated slots for Korean in-vehicle services in a multi-tasking manner. We implement our model with a pre-trained language model, and it includes an intent classifier, slot classifier, slot value predictor, and value-refiner. We conduct the experiments on the Korean in-vehicle services dataset and show 90.74% of joint goal accuracy. Also, we analyze the efficacy of each component of our model and inspect the prediction results with qualitative analysis.
Self-admitted technical debt classification using natural language processing word embeddings

&lt;p&gt;Recent studies show that it is possible to detect technical dept automatically from source code comments intentionally created by developers, a phenomenon known as self-admitted technical debt. This study proposes a system by which a comment or commit is classified as one of five dept types, namely, requirement, design, defect, test, and documentation. In addition to the traditional term frequency-inverse document frequency (TF-IDF), several word embeddings methods produced by different pre-trained language models were used for feature extraction, such as Word2Vec, GolVe, bidirectional encoder representations from transformers (BERT), and FastText. The generated features were used to train a set of classifiers including naive Bayes (NB), random forest (RF), support vector machines (SVM), and two configurations of convolutional neural network (CNN). Two datasets were used to train and test the proposed systems. Our collected dataset (A-dataset) includes a total of 1,513 comments and commits manually labeled. Additionally, a dataset, consisting of 4,071 labeled comments, used in previous studies (M-dataset) was also used in this study. The RF classifier achieved an accuracy of 0.822 with A-dataset and 0.820 with the M-dataset. CNN with A-dataset achieved an accuracy of 0.838 using BERT features. With M-dataset, the CNN achieves an accuracy of 0.809 and 0.812 with BERT and Word2Vec, respectively.&lt;/p&gt;
Penerapan Model Numbered Head Together Untuk Meningkatkan Motivasi Dan Hasil Belajar Siswa

The teacher is very influential in the teaching and learning process, the success achieved by students is inseparable from the role of the teacher. Therefore, the purpose of this study is to describe how the application of the numbered head together (NHT) model can improve student motivation and learning outcomes. The research method used is classroom action research. This research was conducted by means of pre-cycle, first cycle and second cycle. The results showed that in the first cycle there was an increase where 14 students got scores above the average or according to the KKM target but 16 students still got scores below the KKM, which is 75 established national completeness. The average value in this cycle is 68 with the highest value of 80 and the lowest value of 35. However, at this stage it has increased even though it is not significant. the second cycle experienced an increase that was more than in the first cycle, to listen/pay attention to information there were 27%, students were very serious about listening to the teacher's explanations, students/groups were active in asking questions 90%. And the average value of students also increased as high as 85% so that many students reached the KKM value that had been set, and students were very enthusiastic during the learning process
3DSG: A 3D LiDAR-Based Object Detection Method for Autonomous Mining Trucks Fusing Semantic and Geometric Features

Object detection is an essential task for autonomous vehicles to ensure safety. Due to the complexity of mining environments, it is difficult to detect objects accurately and robustly. To address these issues, this paper proposes a novel 3D LiDAR-based object detection method fusing semantic and geometric features for autonomous trucks in mining environments (3DSG). A road region extraction method is presented by establishing a semantic segmentation network with a region searching strategy to eliminate off-road point clouds. To deal with the complexity of unstructured road ground point-cloud segmentation, we propose a cascaded ground detection algorithm by performing semantic segmentation filtering and rectangular grid map filtering. A clustering method is proposed fusing adaptive distance thresholds of Euclidean clusters with semantic segmentation categories to solve the problem of the over- and undersegmentation of objects caused by the sparsity of point clouds. The performance of the proposed method is examined utilizing a real mining dataset named TG-Mine-3D. Compared with state-of-the-art methods, our method achieved higher precision of 66.39%. Moreover, for the truck and pedestrian categories, the performance of our method was significantly improved by 2.66% and 5.80%, respectively. The proposed method running at 51.35 ms achieved real-time performance.
Development of Mathematics E-Comic to Facilitate the Mathematical Communication Ability

This study aims to develop a valid mathematics e-comic learning media for class VIII students on statistical material. The design of the e-comic mathematics learning media development using the PPE stage includes planning (planning), production (production), and evaluation (evaluation). The criteria for achieving quality are carried out by testing so that they are declared valid by experts. The results showed that the e-comic learning media for class VIII students on statistics material for class VIII students on statistics material met the valid criteria. Product validity was obtained based on the assessment of experts which showed that the quality of the learning media was included in the very good category with an average score of 3.65 by the material expert validator, and was included in the very good category with an average score of 3.75 by the media expert validator. Based on the results of the assessment, it can be concluded that the e-comic learning media for class VIII students on statistical material is valid.