Reviewerly/exHarmony huggingface dataset:
Statistical Machine Translation

Statistical machine translation (SMT) is a machine translation paradigm that generates translations based on a probabilistic model of translation process, the parameters of which are estimated from parallel text. Modeling, training, and decoding are three fundamental problems in SMT. SMT has evolved from an early word-based approach to recent phrase-based and syntax-based approaches in the past decades. As a data-driven technology, it will continue to develop in the era of big data.
Translation Technology in Japan

In this chapter, a historical overview of research and development of machine translation systems in Japan is first explained. Then, one of the latest government-funded MT projects, research activities related to pre- and post-editing, development of linguistic resources for MT systems, and research on evaluation of MT systems are described.
Development of a Kiswahili Text-to-Speech System based on Tacotron 2 and Wave Net Vocoder

Text-to-Speech (TTS) system converts an input text into a synthetic speech output. The paper provides a detailed description of developing a Kiswahili TTS system using Tacotron 2 architecture and WaveNet vocoder. Kiswahili TTS system will help visually impaired persons to learn, assist persons with communication disorders, and provide a source of input in Voice Alarm and Public Address equipments. Tacotron 2 is a sequence-to-sequence model for building TTS systems. The model consists of three steps: encoder, decoder, and vocoder. The encoder step converts the input text into a sequence of characters. The decoder predicts the sequence of Mel-spectrograms for each generated character sequence. Finally, the vocoder transforms the Mel-spectrograms into speech waveforms. The Kiswahili TTS system developed based on Tacotron 2 obtained a mean opinion score (MOS) of 4.05. The score shows that the speech generated by the system is comparable to human speech. The system implementation is available in the link below https://github.com/Rono8/kiswahili_tts
Segmentation

This chapter provides an introduction to text segmentation, covering all the elements that make up a working algorithm, key considerations in a practical implementation and the impact of design decisions on the performance of a complete machine translation solution. The narrative offers a survey of existing design options and recommendations for advancing the state of the art and managing current limitations.
Alignment

This chapter covers the main algorithms and systems for sentence and word alignment. The focus is on statistical properties of bitexts and the way these properties are exploited for alignment in generative as well as discriminative and heuristic models. In addition, the chapter provides an overview of standard evaluation metrics (for alignment performance) (such as precision, recall, and alignment error rate).
Speech Translation

Speech translation is an advanced computer-based technology that enables speech communication between people who speak different languages. A speech translation system is an integration of speech recognition, machine translation, and speech synthesis. The latest systems are available as smartphone applications. They can perform translation of naturally spoken sentences and support multiple languages.
Translation Technology in the Netherlands and Belgium

In the 1980s, researchers from Belgium and the Netherlands participated in large national and international projects. Disappointing results led to a decade of silence, but in the 2000s, new research projects embraced statistical and example-based machine translation. Dutch, with an estimated 25 million native speakers, is a source or target language in about 15% of current translation technology products.
Conceptual design and preliminary data analysis for classification of plasma disruption event at Aditya-U tokamak

Disruption prediction and its avoidance/mitigation is an essential part of the tokamak operations, particularly for large size tokamak as the disruptions could produce very large heat loads on diverter targets and other Plasma Facing Components (PFC), and large electromagnetic forces on the Vacuum Vessel (VV) can lead to the structural damages. It directs toward disruption research to develop methods for safe and rapid shutdown of high-power tokamak plasmas. To control or avoid the disruption, data-driven methodology using time series of relevant plasma parameters are useful with sufficient anticipation time. The Ohmically heated circular limiter tokamak ADITYA (R0 = 75 cm, a = 25 cm) has been upgraded to a tokamak named the ADITYA Upgrade (ADITYA-U) with an open diverter configuration. The data driven methodology uses supervised learning techniques for classification to develop accurate automatic classifiers from large set of discharges data which will be used for regression to determine key events of time for disruption. Based on extensive literature survey, preliminary data analysis is performed for ~ 8000 previous plasma shots from large dataset of ADITYA/ADITYA-U tokamak archival. Binary classification model is developed to separate two sets of data using Support Vector Machine (SVM) and Neural Network (NN) for ADITYA TOKAMAK. Both models are compared with test data with previous shots and validated with accuracy performance which may lead towards future development of time series data-driven model for accurate prediction of disruption event for ADITYA-U tokamak.
Pengaruh Model Pembelajaran Talking Stick Dipadu dengan Metode Pembelajaran Snowball Drilling terhadap Keterampilan Sosial dan Hasil Belajar Kognitif Siswa

Differences in the characteristics of students cause teachers to find it difficult to choose learning methods so that teachers often use conventional learning methods. This results in a lack of interaction between students, and has an impact on the low social skills and cognitive learning outcomes of students. The purpose of this study was to describe the effect of the talking stick learning model combined with the snowball drilling learning method on the social skills of class X SMA Negeri 1 Kerambitan students and to determine the effect of the talking stick learning model combined with the snowball drilling learning method on the cognitive learning outcomes of class X SMA Negeri 1 students. kinship. This type of research is a quasi-experimental research, the design in this study using a pretest-posttest control group design, with a sampling technique using purposive sampling. The sample of this research is class X A as the experimental class and class X B as the control class. The instruments used in this study were the RPP implementation observation sheet, social skills observation sheet, and cognitive learning outcomes test. The data analysis technique used is descriptive quantitative and t-test. After analyzing the students' social skills data, the average percentage was 16.97 with a good category, while the post-test scores for the control class were 57.1 and the experimental class was 67.4. After testing the hypothesis, the price is obtained (tcount &gt; ttable) or (3.102 &gt; 1.671), so that the hypothesis is accepted, so it can be concluded that there is an effect of the talking stick learning model combined with the snowball drilling learning method on the cognitive learning outcomes of grade X students of SMA Negeri 1 Kerambitan in the academic year 2017/2018, and students' social skills are classified as good.
A Chaos-Based Encryption Algorithm for Database System

This paper introduces a new Database Transposition, Substitution and XORing Algorithm (DTSXA) based on using chaotic maps. It is based primarily on two well-known security properties: confusion and diffusion. A random number generator was depended on to produce the keys for the algorithm of encryption and decryption. The encryption of the Arabic language in addition to the English language was done, besides it can encrypt a table, individual row and individual column. The suggested algorithm was obeyed and analyzed by different tests involving brute force attack analyses, statistical attack analyses (security analysis histogram, correlation coefficient analysis and information entropy analysis), key sensitivity analysis, differential attack analysis, and mean square error analysis. This algorithm passed all the applied analyses well-deservedly, which indicates that the presented encryption algorithm has a high security level due to its large key space and high sensitivity to the change in the cipher keys.
Optimization of Recommender Systems Using Particle Swarms

Background: Recommender systems are one of the most widely used technologies by electronic businesses and internet applications as part of their strategies to improve customer experiences and boost sales. Recommender systems aim to suggest content based on its characteristics and on user preferences. The best recommender systems are able to deliver recommendations in the shortest possible time and with the least possible number of errors, which is challenging when working with large volumes of data. Method: This article presents a novel technique to optimize recommender systems using particle swarm algorithms. The objective of the selected genetic algorithm is to find the best hyperparameters that minimize the difference between the expected values and those obtained by the recommender system. Results: The algorithm demonstrates viability given the results obtained, highlighting its simple implementation and the minimal and easily attainable computational resources necessary for its execution. Conclusions: It was possible to develop an algorithm using the most convenient properties of particle swarms in order to optimize recommender systems, thus achieving the ideal behavior for its implementation in the proposed scenario.
Learning robotic manipulation skills with multiple semantic goals by conservative curiosity-motivated exploration

Reinforcement learning (RL) empowers the agent to learn robotic manipulation skills autonomously. Compared with traditional single-goal RL, semantic-goal-conditioned RL expands the agent capacity to accomplish multiple semantic manipulation instructions. However, due to sparsely distributed semantic goals and sparse-reward agent-environment interactions, the hard exploration problem arises and impedes the agent training process. In traditional RL, curiosity-motivated exploration shows effectiveness in solving the hard exploration problem. However, in semantic-goal-conditioned RL, the performance of previous curiosity-motivated methods deteriorates, which we propose is because of their two defects: uncontrollability and distraction. To solve these defects, we propose a conservative curiosity-motivated method named mutual information motivation with hybrid policy mechanism (MIHM). MIHM mainly contributes two innovations: the decoupled-mutual-information-based intrinsic motivation, which prevents the agent from being motivated to explore dangerous states by uncontrollable curiosity; the precisely trained and automatically switched hybrid policy mechanism, which eliminates the distraction from the curiosity-motivated policy and achieves the optimal utilization of exploration and exploitation. Compared with four state-of-the-art curiosity-motivated methods in the sparse-reward robotic manipulation task with 35 valid semantic goals, including stacks of 2 or 3 objects and pyramids, our MIHM shows the fastest learning speed. Moreover, MIHM achieves the highest 0.9 total success rate, which is up to 0.6 in other methods. Throughout all the baseline methods, our MIHM is the only one that achieves to stack three objects.
Runtime Analysis for Permutation-based Evolutionary Algorithms

While the theoretical analysis of evolutionary algorithms (EAs) has made significant progress for pseudo-Boolean optimization problems in the last 25 years, only sporadic theoretical results exist on how EAs solve permutation-based problems. To overcome the lack of permutation-based benchmark problems, we propose a general way to transfer the classic pseudo-Boolean benchmarks into benchmarks defined on sets of permutations. We then conduct a rigorous runtime analysis of the permutation-based $$(1+1)$$ EA proposed by Scharnow et al. (J Math Model Algorithm 3:349–366, 2004) on the analogues of the LeadingOnes and Jump benchmarks. The latter shows that, different from bit-strings, it is not only the Hamming distance that determines how difficult it is to mutate a permutation $$\sigma $$ into another one $$\tau $$ , but also the precise cycle structure of $$\sigma \tau ^{-1}$$ . For this reason, we also regard the more symmetric scramble mutation operator. We observe that it not only leads to simpler proofs, but also reduces the runtime on jump functions with odd jump size by a factor of $$\Theta (n)$$ . Finally, we show that a heavy-tailed version of the scramble operator, as in the bit-string case, leads to a speed-up of order $$m^{\Theta (m)}$$ on jump functions with jump size m. A short empirical analysis confirms these findings, but also reveals that small implementation details like the rate of void mutations can make an important difference.
A User-Centric Evaluation of Smart Contract Analysis Tools in Decentralised Finance (DeFi)

Blockchain and smart contract technology have led to the creation of an alternative financial system called Decentralised Finance (DeFi) which has grown exponentially in the last year alone to a current value of $76B. Without a central custodian or regulator, non-technical users may find it difficult to assess the security of their favourite projects. In this trustless environment, can the current state-of-the-art smart contract analysis tools be used by non-technical users to protect investors from incurring losses and improving the security in the space? In the paper, we review the literature focusing on well-known vulnerabilities of financial smart contracts and show the scale of successful DeFi attacks. By analysing the root cause of recent exploits of contracts, we assess the feasibility of detecting these vulnerabilities by automatic verification. We investigate 21 analysis tools for detecting vulnerabilities in smart contracts with an in-depth evaluation of six tools: Slither, Mythril, DerScanner, Manticore, Oyente and Securify v2. The tools were evaluated for their efficiency and accuracy against a custom dataset containing 28 vulnerable and 16 healthy smart contracts and are ultimately rated based on how useful they may be from a DeFi user perspective. The results indicate that, while Slither received the highest rating, none of the existing tools can successfully assist DeFi users at present due to lack of reliability or lack of simplicity for the targeted market.
Text sentiment analysis based on two-channel joint model

Convolutional neural networks (CNNs) have a strong feature learning capability. Bi-directional long and short-term memory network (BiLSTM) avoids the problem of gradient disappearance in traditional neural networks and is able to learn contextually relevant features better. The attention mechanism plays the role of feature weight distribution, which can better acquire important features. In this paper, we propose a two-channel joint model for sentiment analysis based on IMDB review texts. And a series of comparison experiments are conducted on the dataset, and the accuracy and RMSE values of the method reach 91.52% and 0.2636, respectively. the results show that the proposed model can effectively improve the accuracy of text classification.
Improving Students’ Reading Literacy During the Covid-19 Pandemic Using the PBL Model and According to the Pisa Context

Several research results show that the reading literacy of high school students is still low, especially during the Covid-19 pandemic.This is also shown by Indonesia's ranking in the Study Program for International Student Assessment (PISA) which is still at level 2 PISA.As an effort to improve it, a class action research has been conducted with the formulation of the problem, "Can the reading literacy of class VIII students be improved by using the 'problem based learning' (PBL) model and texts that are in accordance with the PISA context?"The research was carried out covering the stages of planning, implementation, assessment and observation, as well as reflection.The subjects of this study were students of class VIII.1 SMPN 4 Palembang.The data collection techniques used observations and tests.The analysis was carried out qualitatively and quantitatively.The study was conducted in 3 cycles in one semester with different text contexts according to the context PISA and using the PBL model.The results showed that during the learning process students were very active, responsible, creative, and disciplined.The results of each cycle showed an increase in the average score obtained by students.Initial average score: 56; after cycle 1:73; after cycle 2:80; and after cycle 3 the average score reached: 87.The increase in the average score is not only due to the application of an innovative model but also due to the more contextual context of the text.Practitioners are advised to use texts that are appropriate to the context of PISA in learning and practice, so that students are accustomed to working on PISA type questions.
SVRCI: An Approach for Semantically Driven Video Recommendation Incorporating Collective Intelligence

In the modern era, the recommendation of educational resources, specifically in the form of videos, is a needful task. This paper proposes an SVRCI framework for education video recommendation. It is a query-centric knowledge-driven paradigm where the query terms are enriched by loading knowledge graphs from Google’s knowledge graph (KG) API and subjecting it to structural topic modeling for aggregating relevant topics. Ontology alignment is also achieved to enrich much more relevant auxiliary knowledge. Cosine similarity, Twitter semantic similarity, and concept similarity are the three semantic similarity measures encompassed with differential thresholds for data point selection. The dataset is classified using a solid deep learning GRU classifier and the logistic regression feature control machine learning classifier. The education videos are recommended based on categories or the annotations appended to the videos in the dataset. Overall average precision of 95.66%, accuracy of 96.23%, F-measure of 96.23% and an nDCG of 0.99 has been achieved by this framework.
Method Agnostic Model Class Reliance (MAMCR) Explanation of Multiple Machine Learning Models

Various Explainable Artificial Intelligence (XAI) methods provide insight into the machine learning models by quantitatively analysing the contribution of each variable to the model’s predictions globally or locally. The contribution of variables identified as (un)important by one method’s explanation may not be identified as the same by another method’s explanation for the same machine learning (ML) model. Similarly, the important feature of many well performing ML models that fit equally well on the same data (which are termed as Rashomon set models) may not be the same as each other. While this is the case, providing the explanation based on a single model in the lens of a specific explanation method would be biased over the model/method. Hence, a framework is proposed to describe the consensus variable importance across multiple explanation methods for many almost-equally-accurate models as a method agnostic explanation for the model class reliance. Empirical experiments are carried out on the COMPAS dataset with six XAI (the Sage, Lofo, Shap, Skater, Dalex and iAdditive) methods for verifying whether an inadmissible feature becoming an (un)important feature is consistent across multiple explanation methods and getting the consensus explanation. The results demonstrate the efficiency of the method agnostic model class reliance explanation and its coverage to the model reliance range of all the almost-equally-accurate models of the model class.
Gesture interactive recognition method of moving equipment based on virtual reality technology

The existing interactive recognition methods for gesture recognition of moving equipment have poor recognition effect and low recognition accuracy, which lead to some limitations of gesture interactive recognition. Therefore, a gesture interactive recognition method for moving equipment based on virtual reality technology is proposed. Firstly, we collect and divide the hand gesture features of sports equipment, analyse the common hand gesture categories of sports equipment, and use virtual reality technology to pre-process the hand gesture interactive recognition data of sports equipment. After pre-processing, we complete the hand gesture interactive recognition of sports equipment based on virtual reality technology. The experimental results show that the accuracy of gesture recognition is more than 95%, and that of complex gesture recognition is more than 93%. Compared with the existing gesture recognition methods, this method has higher effectiveness and recognition accuracy, which fully meets the research requirements.
Recognition and Classification of Human Actions Using 2D Pose Estimation and Machine Learning

Recognition and classification of human actions is a fundamental but difficult computer vision task that has been studied by several researchers worldwide in recent years. Pose estimation is a widely used technology for recognizing human actions. It has several applications, especially in the field of computer vision, where it can be used to recognize basic as well as complex human actions. This research provides a novel framework for identifying and classifying human actions which include five categories: standing, walking, waving, punching, and kicking. The dataset used for recognition and classification purposes is generated using the videos that are recorded using a smartphone and a 2D pose estimation technique has been applied to extract the features from the human body. The machine learning (ML) classifiers have been trained on a custom-built dataset. While all algorithms nearly performed well in the classification task, the light gradient-boosting machine (LGBM) outperformed the rest in terms of accuracy (98.80%).
Virginia Cyber Navigator Internship Program (VA-CNIP)

A coalition of Virginia universities, in partnership with the Virginia Department of Elections (ELECT), launched the Virginia Cyber Navigator Internship Program (VA-CNIP) – an innovative educational program to develop future cybersecurity professionals to protect the election infrastructure. The program addresses the need for more skilled cybersecurity professionals, and those who are supporting public services such as elections. This paper provides an overview of the key components of the program: a full semester gateway course covering sociotechnical election topics, a two-day kickoff bootcamp to prepare students for their internship, an internship with an election office, and a one-day debrief and assessment at the end of the internship.
Pengenalan Pola Karakter Tulisan Tangan Aksara Bali Menggunakan Fitur Zoning, Direction, dan Backpropagation

Balinese script has a character with high similarity. Identifying classes between characters requires an optimal pattern recognition model by maximizing the use of feature extraction methods. The use of feature extraction methods in the dataset aims to obtain the characteristic value of each character, in the case of Balinese script data, a method with detailed feature retrieval is used using the zoning method. This study also added the additional factor of the direction feature. The learning method uses a neural network with a backpropagation algorithm. Tests on character data get the highest accuracy in the combination of 16x16 zone ICZ + ZCZ zoning features with the addition of direction features that are 91.18%, ZCZ zone 16x16 zoning and direction with 86.82% accuracy, and ICZ zoning 16x16 and direction zones with 82.43% accuracy. The highest increase in accuracy is found in the ZCZ feature with a difference of addition of 4.36%. The implementation of the model in word testing has an accuracy of 66.2 % and the results of segmentation testing are 97.33 %.