Reviewerly/exHarmony huggingface dataset:
Formalizing BPE Tokenization

In this paper, we formalize practical byte pair encoding tokenization as it is used in large language models and other NLP systems, in particular we formally define and investigate the semantics of the SentencePiece and HuggingFace tokenizers, in particular how they relate to each other, depending on how the tokenization rules are constructed.Beyond this we consider how tokenization can be performed in an incremental fashion, as well as doing it left-to-right using an amount of memory constant in the length of the string, enabling e.g. using a finite state string-to-string transducer.
Ordered Context-Free Grammars Revisited

We continue our study of ordered context-free grammars, a grammar formalism that places an order on the parse trees produced by the corresponding context-free grammar.In particular, we simplify our previous definition of a derivation of a string for a given ordered context-free grammar, and present a parsing algorithm, using shared packed parse forests, with time complexity O(n 4 ), where n is the length of the input string being parsed.
[Source Analysis and Risk Assessment of Heavy Metals in Soil of County Scale Based on PMF Model].

This study explores the pollution characteristics, risks, and sources of heavy metals in small-scale areas. Rongcheng District, Jieyang City, Guangdong Province was considered as the study area and enrichment factor (EF), pollution load index (PLI), potential ecological risk index (RI), and US EPA health risk assessment model were used to evaluate its environmental risk. Moreover, the source apportionment of heavy metals was analyzed through correlation analysis, the characteristic of spatial distribution, and a PMF model. The results showed that the mean concentrations of ω(Cr), ω(Hg), ω(As), ω(Pb), ω(Ni), ω(Cd), ω(Cu), and ω(Zn) were 54.87, 0.25, 8.35, 56.00, 15.38, 0.35, 30.56, and 124.23 mg·kg-1, respectively. The mean concentrations of all elements exceeded the local soil background value. In terms of EF level, Cr, As, Pb, and Ni showed negligible accumulation; Zn and Cu showed minor accumulation; and Hg and Cd showed moderate accumulation. The mean value of the pollution load index was 2.37, with a severe pollution level, and the eight elements were in different pollution levels. In total, the study region suffered severe ecological risk, Hg and Cd presented strong ecological risk, and other elements presented slight ecological risk. The non-carcinogenic risks under the three exposure paths were within the acceptable level. The carcinogenic risks (CR) of adults and children were 9.81E-05 and 5.59E-04, respectively, and Cr and As were the main contributors of CR. The results showed that the four sources of heavy metals were transportation sources (37.02%), parent material sources (18.53%), atmospheric deposition sources (26.49%), and industrial sources (17.96%).
Neuro Symbolic Applications

Although these days neural networks and deep learning get equated with AI, there are many systems that combine neural reasoning and learning with symbolic reasoning and learning modules. In the previous chapters, we discussed some of these approaches with more focus on formalisms. In this chapter we point to some of the notable applications, elaborate on some of the applications in visual question answering, and natural language processing domains, and touch upon neurosymbolic reinforcement learning.
Building Readiness of Future Natural Science Teachers for Professional Activities Using Stem Tools

Aim. The aim of the study is to determine the level of readiness of future science teachers to use the STEM approach in the educational process and to develop recommendations for the integration of relevant STEM tools into the educational process of Ukraine. Methods. The study involved the following methods: the focus group method, the two-phase survey, the accompanying survey, the variable impact method, and the statistical analysis. Results. Students majoring in natural sciences are poorly prepared for the integration of STEM tools. Students of the experimental subgroup became more ready to use STEM tools and developed a more structured vision of systemic issues. Comprehensive educational courses are an effective means of preparing future teachers for the integration of STEM tools in their future activities. Conclusions. STEM approach can be effectively integrated into teaching natural sciences through comprehensive educational courses and practical activities that develop the future teachers’ skills. Virtual laboratory technologies demonstrated the greatest effectiveness. Prospects. Prospects for further research focus on the need to verify the obtained results for a wider sample of future teachers of natural sciences and other majors of higher education institutions (HEIs).
INFOGRAPHICS AS A METHOD OF VISUALIZING DIGITAL EDUCATIONAL CONTENT

Purpose. The article is devoted to the topic of effective use of visualization of digital educational content in the educational process, relevant in the conditions of the modern digital educational environment. The subject of the analysis is infographics as a method of visualizing digital educational content. The author aims to reveal the potential of using infographics in the educational process, working with digital content, as a way of accessible explanation of complex, voluminous information.&#x0D; Methodology. The basis of the research is formed by the methods of studying and analyzing publications, which consider aspects of this problem, summarizing their own experience in using infographics in the educational process, as well as systematizing the studied information.&#x0D; Results. The advantages of digital educational content visualization are described and the criteria for evaluating educational infographics are highlighted.&#x0D; Practical implications. The results obtained can be applied in higher and secondary school didactics.
Do Developers Present Proficient Code Snippets in Their README Files? An Analysis of PyPI Libraries in GitHub

A README file plays an essential role as the face of a software project and the initial point of contact for developers in Open Source Software (OSS) projects. The code snippet ranks among the most important content in the README file for demonstrating the usage of software and APIs. While easy to comprehend, code snippets are preferred by clients in order to quickly understand software usage and features. However, proficient code snippets are sometimes found in README files. In this paper, we first investigate the prevalence of each competency level of Python code snippets in the README files. Then, we analyze the relationships between the usage of proficient code snippets and topics of libraries. From our empirical study on 1, 620 README files of PyPI libraries, we find that developers mainly present 92% of basic elements in code snippets. However, developers are likely to present proficient elements in code snippets from topics about Application Framework, Quality Assurance, and User Interface. We therefore (i) encourage developers to mainly present basic code snippets in their README files to attract more newcomers, and (ii) suggest that clients try to understand proficient code snippets if they are adopting libraries from previously mentioned topics.
Testing different function fitting methods for mobile eye-tracker calibration

During calibration, an eye-tracker fits a mapping function from features to a target gaze point. While there is research on which mapping function to use, little is known about how to best estimate the function's parameters. We investigate how different fitting methods impact accuracy under different noise factors, such as mobile eye-tracker imprecision or detection errors in feature extraction during calibration. For this purpose, a simulation of binocular gaze was developed for a) different calibration patterns and b) different noise characteristics. We found the commonly used polynomial regression via least-squares-error fit often lacks to find good mapping functions when compared to ridge regression. Especially as data becomes noisier, outlier-tolerant fitting methods are of importance. We demonstrate a reduction in mean MSE of 20% by simply using ridge over polynomial fit in a mobile eye-tracking experiment.
Strategi Penerapan Ice Breaking dalam Mengatasi Kebosanan Siswa pada Pembelajaran IPS di SMP N 26 Kabupaten Tebo

This research is motivated by the problem of student boredom in the learning process at school. The learning that is carried out is still dominated by listening to the teacher's explanation in class, taking notes or summarizing the material. Learning that only uses books will of course make students bored and not interested in following the ongoing lessons. This study aims to find out whether the application of ice breaking can overcome student boredom in the learning process at SMPN 26 Tebo District. The method used in this study is the experimental quantitative method. Sampling was done by random sampling technique. The population in this study was class VII with a sample of 50 people divided into 25 students in the control class and 25 students in the experimental class. The instruments used were tests and questionnaires. Data collection techniques carried out were tests, questionnaires, documentation. The data analysis technique used in this study is the Normality Test, Homogeneity Test, then proceed with hypothesis testing using the t-test. Based on the analysis of normality test data for all pretest and posttest experimental and control group data, it shows that the significant value of Kolmogorov-Smirnov and Shapiro-Wilk is &gt; 0.05, which means that the data is normally distributed. .05 so that it can be concluded that the data is homogeneous. Then the results of data analysis hypothesis testing using the t-test with a significant value of 0.001. So it can be concluded that the application of ice breaking can overcome student boredom in social studies learning at SMPN 26 Tebo Regency with a significance value of 0.001 &lt;0.05. Thus Ha is accepted.
High-speed information reconciliation with syndrome-based early termination for continuous-variable quantum key distribution

Information reconciliation (IR) is an indispensable component in the post-processing stage of continuous-variable quantum key distribution (CV-QKD), which adopts error-correcting codes to address the asymmetry of secret keys. Currently, low-density parity-check (LDPC) decoding in IR is a post-processing bottleneck in high-speed CV-QKD systems since the upper bound on secret key rate is higher than the information throughput delivered by decoder. In this paper, we study the relationship between the syndrome variation pattern (SVP) in iterative decoding and reconciliation frame error rate. An early termination scheme based on SVP is proposed and applied to multidimensional reconciliation, which can increase information throughput by adaptively adjusting the iteration number of iterative decoding to real-time decoding status. Furthermore, we show that only the resulting syndrome of the highest-rate code part in Raptor-like LDPC codes needs to be calculated to verify whether the reconciliation is successful by studying the convergency of resulting syndrome, which can save a large fraction of computational resources for syndrome calculation. Simulation results show that information throughput of the proposed scheme can be improved by 617.1% compared to the existing scheme when the IR efficiency reaches 97.09%. The proposed scheme points out a new direction for breaking the post-processing bottleneck in high-speed CV-QKD systems.
Implementasi Metode Mesin Rekomendasi User Based Filtering pada Sistem Penyewaan Alat Pertambangan

The problem with most mining companies is a lack of information on the availability of ready-to-use (purchase or rent) heavy equipment and information on providers of heavy equipment spare parts, heavy equipment owners who have difficulty finding a market to sell or rent their equipment. Transaction processes that have been taking place so far have used telephone lines, chats and person-to-person emails where this is felt to be less effective and efficient. This research methodology contains stages regarding the process and procedure of data collection, method analysis procedures, system development procedures as well as the time and place of research. The purpose of this study is to implement the user based filtering recommendation machine method on mining equipment rental systems. The results of the study show that the recommendation system that has been built has succeeded in providing appropriate recommendations to customers who have made mining equipment rental transactions based on the parameters of the results of the recommendation accuracy test. The algorithm of the user based filtering recommendation engine method can be applied to the mining tool recommendation system that has been built. Based on testing the accuracy of the recommendation values, each value obtained in the calculation of the Mean Absolute Error (MAE) is 0.00936 so that the resulting recommendation accuracy is 99.99%. Then for the value of the Mean Absolute Percentage Error (MAPE) obtained a value of 27.84%, and for the value of the Mean Squared Error (MSE) obtained was 0.01702.
A Lightweight Island Model for the Genetic Algorithm over GPGU

This paper presents a parallel approach of the genetic algorithm (GA) over the Graphical Processing Unit (GPU) to solve the Traveling Salesman Problem (TSP). Since the earlier studies did not focus on implementing the island model in a persistent way, this paper introduces an approach, named Lightweight Island Model (LIM), that aims to implement the concept of persistent threads in the island model of the genetic algorithm. For that, we present the implementation details to convert the traditional island model, which is separated into multiple kernels, into a computing paradigm based on a persistent kernel. Many synchronization techniques, including cooperative groups and implicit synchronization, are discussed to reduce the CPU-GPU interaction that existed in the traditional island model. A new parallelization strategy is presented for distributing the work among live threads during the selection and crossover steps. The GPU configurations that lead to the best possible performance are also determined. The introduced approach will be compared, in terms of speedup and solution quality, with the traditional island model (TIM) as well as with related works that concentrated on suggesting a lighter version of the master-slave model, including switching among kernels (SAK) and scheduled light kernel (SLK) approaches. The results show that the new approach can increase the speed-up to 27x over serial CPU, 4.5x over the traditional island model, and up to 1.5–2x over SAK and SLK approaches.
Signal Restoration with Fractional Transform Sparse Representation and Autoencoder

Signa1 denoising is a crucial task in image processing, audio analysis, and communications. Traditional methods often struggle to strike a balance between reducing noise and preserving important signal details. Existing methods exploit the potential of fractional transforms combined with sparse representation techniques. However, these approaches face challenges in achieving the desired balance due to limited sensitivity to signal variations, high computational complexity, restricted adaptability to diverse noise types, and the need for careful selection of transform parameters. To overcome these limitations, we propose a novel method called Fractional Transform Sparse Representation Denoising (FTSRD), which integrates fractional wavelet transform (FWT), fractional DCT (FDCT), sparse representation, and Deep Denoising Autoencoder (DDAE) techniques. The integration of DDAE in FTSRD provides several advantages. DDAE, being a deep learning-based model, has the capability to learn complex noise patterns and extract meaningful signal features. Experimental evaluations were conducted on synthetic signals and ECG signals. The results demonstrate the superior performance of FTSRD in reducing noise while preserving essential structural details. It also surpasses traditional methods in subjective and objective quality measures. In conclusion, FTSRD addresses the challenges associated with noise reduction and signal detail preservation, making it a valuable tool in various denoising applications.
Evolution of Computational Ontologies: Assessing Development Processes Using Metrics

Ontologies serve as a bridge between humans and computers. They encode domain knowledge to connect federated data sources or infer tacit knowledge. While the technology used in ontologies is widely adopted and can be considered mature, much has still to be learned about their evolution and development, as understanding ontology evolution could help ensure better quality control and provide knowledge engineers with helpful modeling and selection guidelines. This paper examines the evolution of computational ontologies using ontology metrics. The authors hypothesize that ontologies follow a similar development pattern. This assumption is broken down into five hypotheses and tested against historical metric data from 69 dormant ontologies and 7,053 versions. The research proved that the development processes of ontologies are highly diverse. While a slight majority of ontologies confirm some of the hypotheses, accepting them as a general rule is not possible. Further, the data revealed that most ontologies have disruptive change events for most measured attributes. These disruptive events are examined regarding their occurrences, combinations, and sizes.
Research on the Assessment Approaches to Pronunciation Training for Pre-Service EFL Teachers

For EFL teacher education, it’s important to polish pre-service teachers’ pronunciation and their pronunciation teaching skills. Currently, the pronunciation training for pre-service EFL teachers in China only focuses on pronunciation improvement and lacks the element of teacher education so that its efficacy still needs improvement. This paper points out the rationale of applying the Learning-Oriented Assessment (LOA) to the pronunciation training for pre-service EFL teachers. Then the author discusses the evaluation approaches integrating assessment of learning, assessment as learning and assessment for learning after reformulating the goals of the pronunciation training for pre-service EFL teachers. Finally, the author analyzes the effectiveness of the evaluation approaches for improving pre-service English teachers’ pronunciation as well as their pronunciation teaching skills.
Pengaruh Pembelajaran Literasi Digital terhadap Minat Belajar Siswa di Tingkat Sekolah Dasar

Digital literacy learning is important in today's digital era, especially at the elementary school level. Digital literacy involves using skills in using digital technology and managing information effectively. In a short time, digital technology has changed the way we learn and work. Therefore, it is important to understand the effect of digital literacy learning on students' interest in learning at the elementary school level. The purpose of this study was to evaluate the effect of digital literacy learning on students' interest in learning at the elementary school level. The research method used in this study is a qualitative research method with a literature review design. Data analysis was carried out by identifying the main findings from previous studies.
Enabling Space-Aware Service Discovery Model in Home Networks through a Compatible Extension to mDNS/DNS-SD

The advent of the smart home, fueled by the rapid emergence of tiny embedded IoT devices and sensors, brings the consideration of space and location information to the forefront of service discovery. In a smart home environment, spaces often have composite capabilities and complex structures. Since residents may have varying preferences based on their location within different spaces, traditional service discovery results may be suboptimal without considering the spatial context. This paper introduces a seamless approach to integrate space and location information with mDNS/DNS-SD, a widely utilized service discovery protocol in home networks. We provide a formal specification for this approach, develop a prototype, and perform a series of experiments to evaluate the efficacy and potential of the proposed method.
Virtual Sensor Data Imputation Using Generative Adversarial Imputation Nets and Pearson Correlation

The term Internet of things (IoT) was introduced into every areas of life; more and more devices are connecting and operating that rely on information collected by IoT sensors. As a matter of fact, sensor failures may either kill the operation of a system or make it malfunctioning because of the interrupted flow of data. Consequently, missing data is a pervasive problem with real-world datasets. Besides that, machine learning methods have recently been applied in several applications and achieved state-of-the art results. Therefore, this work leverages machine learning methods to resolve the missing-data problem. This paper proposes a novel method named PGAIN Virtual Sensor (PGAIN-VS) based on virtual sensors to impute missing data. Remarkably, PGAIN-VS uses the Pearson metric to evaluate the correlations of data among data-collected devices to remove noise and bias potentially causing inaccurate data imputation. PGAIN-VS can produce good estimates for the missing values, so it is absolutely capable of replacing physical sensors during failure time. We evaluated our method on several datasets and compared it to other recent works. Furthermore, the experiments showed that our approach achieved better performance up to 20% in the considered datasets with different metrics.
An Ensemble and Deep Neural Network Based Approaches for Automated Sentiment Analysis

The rise of technology in social media has resulted in an enormous quantity of textual ambiguity. Sentiment analysis provides a crux of subjective opinions stored in a large amount of text such that the data gets segregated into positive and negative. In this research two datasets are used i.e., Amazon Reviews and IMDb wherein, first, implemented machine learning models such as Naive Bayes, XGBoost, etc. for the sentiment analysis out of which Linear SVC performed the best for the IMDb dataset and Amazon Reviews dataset. Furthermore, implemented a deep learning model i.e., Bi-LSTM that outperformed machine learning models for both datasets. Next, implemented BERT, a pre-trained language model, showed better results than Bi-LSTM for both datasets. Lastly, proposed a hybrid CNN-LSTM model, wherein, for feature extraction CNN is used, while LSTM is used for classification. The proposed hybrid model has given the best ROC score for both datasets among all the models used in this paper.
Investigating Students’ Use of the Moodle Platform: A Pilot Study Testing

The use of the Moodle platform during the pandemic has dramatically increased, especially in education and learning. There have been numerous studies on user acceptance of learning technology. However, until now, there has been no evaluation research on user satisfaction by transformational leadership as a mediator. This study evaluated user satisfaction, namely students, based on the UTAUT and Delone McLean models. The method used is random sampling as a pilot study of 2 classes at a private Islamic university in Jakarta, Indonesia. In this study, we used ten variables, namely; Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), System Quality (SQ), Information Quality (IQ), Service Quality (SeQ), Transformational Leadership (TL), User Satisfaction (US). The results showed that eight hypotheses were accepted out of 6, so 75% of this pilot study was successful. We concluded that how students act significantly affects how interested they are in using the Moodle platform.