Reviewerly/exHarmony huggingface dataset:
Online ELT during the Covid-19 Pandemic. A Case Study on Students’ Perspectives

The Covid-19 pandemic has raised interest within many interdisciplinary fields. Researchers in the field of education were particularly interested in reporting teachers’ and students’ experiences (e.g., Hoti, Dragusha, and Ndou 2022) during the period of Emergency Remote Teaching (ERT) (Hodges et al. 2020) and Sustained Remote Teaching (SRT) (Stewart, Baek, and Lowenthal 2022). This article aims to provide a further contribution to education studies by exploring students’ experiences in an English language course organised by an Italian University Language Centre during the 2020/21 academic year. More specifically, it focuses on how interactions were accomplished in foreign language virtual classrooms and whether students’ perceptions on online teaching have changed with SRT. Results showed that although students still preferred face-to-face lessons, they highly appreciated specific tools of online teaching (e.g., recordings) and wished they could be implemented in traditional lessons. The data also confirmed the importance of interactions in modern foreign language virtual classrooms and found that most students who had an overall negative experience with online teaching were also students who had more difficulties in interacting during classes.
An improved multi-task least squares twin support vector machine

Abstract In recent years, multi-task learning (MTL) has become a popular field in machine learning and has a key role in various domains. Sharing knowledge across tasks in MTL can improve the performance of learning algorithms and enhance their generalization capability. A new approach called the multi-task least squares twin support vector machine (MTLS-TSVM) was recently proposed as a least squares variant of the direct multi-task twin support vector machine (DMTSVM). Unlike DMTSVM, which solves two quadratic programming problems, MTLS-TSVM solves two linear systems of equations, resulting in a reduced computational time. In this paper, we propose an enhanced version of MTLS-TSVM called the improved multi-task least squares twin support vector machine (IMTLS-TSVM). IMTLS-TSVM offers a significant advantage over MTLS-TSVM by operating based on the empirical risk minimization principle, which allows for better generalization performance. The model achieves this by including regularization terms in its objective function, which helps control the model’s complexity and prevent overfitting. We demonstrate the effectiveness of IMTLS-TSVM by comparing it to several single-task and multi-task learning algorithms on various real-world data sets. Our results highlight the superior performance of IMTLS-TSVM in addressing multi-task learning problems.
Shortest Coordinated Motion for Square Robots

We study the problem of determining minimum-length coordinated motions for two axis-aligned square robots translating in an obstacle-free plane: Given feasible start and goal configurations, find a continuous motion for the two squares from start to goal, comprising only robot-robot collision-free configurations, such that the total Euclidean distance traveled by the two squares is minimal among all possible such motions. We present an adaptation of the tools developed for the case of discs by Kirkpatrick and Liu [Characterizing minimum-length coordinated motions for two discs. Proceedings 28th CCCG, 252-259, 2016; CoRR abs/1607.04005, 2016.] to the case of squares. Certain aspects of the case of squares are more complicated, requiring additional and more involved arguments over the case of discs. Our contribution can serve as a basic component in optimizing the coordinated motion of two squares among obstacles, as well as for local planning in sampling-based algorithms, which are often used in practice, in the same setting.
TEALL: Technology-Enhanced Active Language Learning

It is undeniable that technologies have impacted the educational field positively, from the economy to education. Electronic devices such as computers and the Internet have helped educators ease their jobs in educating the learners. ESL learners need to trail the requirement of the global workforce in being proficient in the English language. A few gaps have been identified for other researchers to conduct further study. More empirical evidence is needed to sustain this theory and explore the unknown issues from these research cycles. Hence, this paper aims to discuss the technology used to enhance active learning in the ESL context. This review discusses teaching and learning with technology. Computer-Assisted Language Learning (CALL) and Mobile-Assisted Language Learning (MALL) boost students’ motivation by integrating audio-visual aids and games. The use of technology in language learning contributes positively to a great learning experience in active English language learning. The technology used is adaptable for students to engage in active learning at any English language proficiency level. Teachers and learners should prepare themselves with various technology learning activities for significant benefits.
Dimension Reduction for Photonic Processing Using Tailored Disorder

Integrated optical processing networks enable high computation speeds combined with low energy consumption. We present here a scheme for dimension reduction for optical neural networks, by orders of magnitudes, while still reaching high classification accuracies.
Tunable probabilities from the quantum vacuum

We demonstrate the generation of random bits with tunable probability distribution in an optical parametric oscillator. Bits are encoded into the phase statistics of the signal field, which are tuned by a small bias field.
Technique for Enhancing the Chaotic Characteristics of Chaotic Maps Using Delayed Coupling and Its Application in Image Encryption

Chaotic systems are widely used in many scientific fields for their dynamic characteristics. This study proposes a new delayed coupling method, which not only disturbs the control coefficient in chaotic maps but also affects their function structure, such that using this improved method will produce chaotic maps with better effect. The numerical simulation results prove that the delayed coupling method can greatly improve the chaotic characteristics of chaotic maps. Furthermore, an image encryption algorithm based on the delayed coupling Logistic map is proposed. Several numerical simulations indicate that the image encryption algorithm has a high level of security, and can compete with other encryption algorithms.
Digital Marketing Application to Increases Livestock Sales in Sacrificial Cages in Ganggang Panjang Sidoarjo Village Based on Android

The need for qurban animals both for the needs of the Eid al-Adha holiday and needs other than Eid al- Adha such as aqiqah, or other important events are very much needed where in the selection of sacrificial animals such as goats or cows are both very important. types of sacrificial goats and cows, the condition of the sacrificial animals so that the results given are very good. In the process of selecting, purchasing, distributing sacrificial animals, they still use the manual method, which is less efficient, so there is a need for an application system that can assist in selecting, purchasing, and distributing sacrificial animals. In running the business, most of the service processes that are applied are still manual by checking the condition of the animal by coming to the place so that it is difficult for the customer to come twice or contact by phone. Then, in managing transaction data and issuing notes, it is also still manual. "DIGITAL MARKETING APPLICATION TO INCREASING LIVESTOCK SALES IN QURBAN STAGES IN GANGGANG PANJANG VILLAGE, SIDOARJO BASED ON ANDROID" was created to simplify the process of buying and selling livestock services so as to facilitate work and improve service in these small and medium enterprises. From the results of this study it can be concluded that this system can increase effectiveness because it is not done manually, the features in the information system are easy to use and the transaction data management runs well.
USE OF NEURAL NETWORKS FOR PREDICTION OF COMPLICATIONS OF THE GESTATION PERIOD

The article discusses in detail the development, use and use of neural networks for practical healthcare, especially in predicting the development of complications and preventing the development of complications. This is especially relevant now given the expected risk of adverse outcomes during pregnancy. Neural networks are obtained by themselves, simple elements are built among themselves - formal neurons. Most of the work on neuroinformatics is devoted to the transfer of various algorithms for solving problems in such networks. The core of the ideas used is the idea that neurons can be modeled by fairly simple automata, and the complexity of the brain, the flexibility of its functioning, and other important qualities are determined by the connections between neurons. The purpose of this study: was the development of neural network expert programs as the most important method for researching medical data. Our study was the result of testing the possibility of using neural networks in obstetrics and gynecology to predict gestational complications and the development of placental insufficiency. The definition of the risk group was carried out on the basis of the model developed by us. A neural network for prediction was developed, the possibility of using this model in obstetrics and gynecology was tested to predict gestational complications in 1024 women with a high risk of developing this pathology. Confirming the effectiveness of the proposed model, based on the data obtained from a prospective study, we calculated the indicators of its sensitivity, specificity, positive and negative predictive value. The result of the study confirms that the trained neural network makes a reliable prediction of the development of gestational complications and the development of insufficiency of the placental complex. As a result of the research, an important conclusion can be drawn that neural networks are the most important method for studying medical data, universal and highly effective, focused on active use in practical medicine.
Object embedding using an information geometrical perspective

Abstract Acquiring vector representations of objects is essential for applying machine learning, statistical inference, and visualization. Although various vector acquisition methods have been proposed considering the relationship between objects in target data, most of them are supposed to use only a specific relevance level. In real-world data, however, there are cases where multiple relationships are contained between objects, such as time-varying similarity in time-series data or various weighted edges on graph-structured data. In this paper, a vector acquisition method which assigns vectors in a single coordinate system to objects preserving the information given by multiple relations between objects is proposed. In the proposed method, a logarithmic bilinear model parameterized by representation vectors is utilized for approximating relations between objects based on a stochastic embedding idea. The inference algorithm proposed in this study is interpreted in terms of information geometry: the m -projection from the probability distribution constructed from observed relations on the model manifold and the e -mixture in the model manifold are alternately repeated to estimate the parameters. Finally, the performance of the proposed method is evaluated using artificial data, and a case study is conducted using real data.
Comparing Measured Agile Software Development Metrics Using an Agile Model-Based Software Engineering Approach versus Scrum Only

This study compares the reliability of estimation, productivity, and defect rate metrics for sprints driven by a specific instance of the agile approach (i.e., scrum) and an agile model-Bbased software engineering (MBSE) approach called the integrated Scrum Model-Based System Architecture Process (sMBSAP) when developing a software system. The quasi-experimental study conducted ten sprints using each approach. The approaches were then evaluated based on their effectiveness in helping the product development team estimate the backlog items that they could build during a time-boxed sprint and deliver more product backlog items (PBI) with fewer defects. The commitment reliability (CR) was calculated to compare the reliability of estimation with a measured average scrum-driven value of 0.81 versus a statistically different average sMBSAP-driven value of 0.94. Similarly, the average sprint velocity (SV) for the scrum-driven sprints was 26.8 versus 31.8 for the MBSAP-driven sprints. The average defect density (DD) for the scrum-driven sprints was 0.91, while that of the sMBSAP-driven sprints was 0.63. The average defect leakage (DL) for the scrum-driven sprints was 0.20, while that of the sMBSAP-driven sprints was 0.15. The t-test analysis concluded that the sMBSAP-driven sprints were associated with a statistically significant larger mean CR, SV, DD, and DL than that of the scrum-driven sprints. The overall results demonstrate formal quantitative benefits of an agile MBSE approach compared to an agile alone, thereby strengthening the case for considering agile MBSE methods within the software development community. Future work might include comparing agile and agile MBSE methods using alternative research designs and further software development objectives, techniques, and metrics.
PEMBUATAN MATERNITY JACKET SEDERHANA SEBAGAI ALAT PERAGA PRAKTIKUM PEMERIKSAAN TINGGI FUNDUS UTERI DENGAN METODE LEOPOLD

Uterine fundal height measurement using Leopold method is the one of the learning outcome in antenatal care course that must be achieved by midwifery students. For practical teaching in laboratory, the lecturer and the students usually use a phantom with synthetic rubber material. As it made of synthetic rubber, this phantom become stiff and lack of real tactile sensation to measure fundal height. This research aimed to make a simple maternity jacket as practical teaching aid which fabricable and affordable so the students can determine the fundal height boundary. This study used research and development method. The number of respondents was 32 with purposive sampling and 2 experts. This research taken place in Midwifery Laboratory of Poltekkes Kemenkes sorong from April to November 2021. The research instrument use questionnaire. The data was analyzed by converting the qualitative form to quantitative using Likert scale, and the mean score result was converted back to qualitative form. Feasibility analysis by calculating the score of the respondents’ assessment results compared to the ideal score. The results showed that the respondents strongly agreed that this product is feasible with feasibility percentage of 96.5%. Based on the results of the assessment, it is expected that this product can be used as a practical teaching aid and can improve students' skills in fundal height measurement. Further research is needed for the development of this practical teaching aids so that it can be used for a complete Leopold examination.
A neurocomputational variable on welfare tradeoffs explains the function and form of cyberaggression

OPINION article Front. Behav. Neurosci., 27 July 2023Sec. Individual and Social Behaviors Volume 17 - 2023 | https://doi.org/10.3389/fnbeh.2023.1034564
Multiple Instance Learning with Trainable Soft Decision Tree Ensembles

A new random forest-based model for solving the Multiple Instance Learning problem under small tabular data, called the Soft Tree Ensemble Multiple Instance Learning, is proposed. A new type of soft decision trees is considered, which is similar to the well-known soft oblique trees, but with a smaller number of trainable parameters. In order to train the trees, it is proposed to convert them into neural networks of a specific form, which approximate the tree functions. It is also proposed to aggregate the instance and bag embeddings (output vectors) by using the attention mechanism. The whole Soft Tree Ensemble Multiple Instance Learning model, including soft decision trees, neural networks, the attention mechanism and a classifier, is trained in an end-to-end manner. Numerical experiments with well-known real tabular datasets show that the proposed model can outperform many existing multiple instance learning models. A code implementing the model is publicly available.
Digital Book of Indonesian Rare Flora Using Augmented Reality

The uniformity of the flora is still not widely known. Therefore, natural knowledge has an important role in education considering the limitations of print media as a learning medium. Innovation by utilizing Augmented Realityp technology is very helpful as a learning medium that can support the learning process more effectively and efficiently, on the other hand Augmented Reality that displays 3D models of objects in real time can also increase interest in learning in knowing Indonesia's rare flora attractively and interactively.So, researchers designed and developed the application "Digital Flora Langka Indonesia Using Augmented Reality" with SDLC (Software Development Life Cycle) methods which are expected to help as an interesting learning medium.
240 Gbps Quantum Random Number Generator with Photonic Integrated Chip

A pure resistor is used as a transimpedance device in this work to maximize the operating bandwidth of an on-chip integrated homodyne setup, resulting in a 240 GBits/s quantum random number generator.
Gradient-based Quantum Neural Network Using Quantum Computing Chips

An analytic gradient algorithm for training neural networks in an 8-mode quantum photonic chip is proposed. The algorithm shows 4x faster-training speed and 3% accuracy rate enhancement than existing approaches in solving two classification tasks.
Highly non-degenerate, heralded single photon source on a PPKTP-PIC integrated system

We present a novel, highly non-degenerate, heralded visible-telecom photon-pair source using an integrated system of nonlinear waveguides and photonic circuits. Thermo-optic modulation of the telecom single-photon-state is demonstrated for deployable quantum communication/networking systems.
Momentum exchange in quantum two-electron photon interactions

We show that multiple electrons become correlated with each other through their interaction with light, even in the absence of Coulombic interactions, providing a way to realize largenumber Fock states on reasonable time scales.
Microcomb-driven photonic convolutional engine

We demonstrate a microcomb-driven chip-based photonic convolutional engine utilizing time-wavelength plane stretching approach. A preeminent photonic-core compute density of 1.04 TOPS/mm 2 and a record high weight control precision of 9 bits are achieved.
A Prior Knowledge-Guided Graph Convolutional Neural Network for Human Action Recognition in Solar Panel Installation Process

Human action recognition algorithms have garnered significant research interest due to their vast potential for applications. Existing human behavior recognition algorithms primarily focus on recognizing general behaviors using a large number of datasets. However, in industrial applications, there are typically constraints such as limited sample sizes and high accuracy requirements, necessitating algorithmic improvements. This article proposes a graph convolution neural network model that combines prior knowledge supervision and attention mechanisms, designed to fulfill the specific action recognition requirements for workers installing solar panels. The model extracts prior knowledge from training data, improving the training effectiveness of action recognition models and enhancing the recognition reliability of special actions. The experimental results demonstrate that the method proposed in this paper surpasses traditional models in terms of recognizing solar panel installation actions accurately. The proposed method satisfies the need for highly accurate recognition of designated person behavior in industrial applications, showing promising application prospects.
A Toxic Style Transfer Method Based on the Delete–Retrieve–Generate Framework Exploiting Toxic Lexicon Semantic Similarity

Whether consciously or inadvertently, our messages can include toxic language which contributes to the polarization of social networks. Intelligent techniques can help us detect these expressions and even change them into kinder expressions by applying style transfer techniques. This work aims to advance detoxification style transfer techniques using deep learning and semantic similarity technologies. The article explores the advantages of a toxicity-deletion method that uses linguistic resources in a detoxification system. For this purpose, we propose a method that removes toxic words from the source sentence using a similarity function with a toxic vocabulary. We present two models that leverage it, namely, LexiconGST and MultiLexiconGST, which are based on the Delete –Retrieve–Generate framework. Experimental results show that our models perform well in the detoxification task compared to other state-of-the-art methods. Finally, this research confirms that linguistic resources can guide deep learning techniques and improve their performance.
An Automated English Essay Scoring Engine Based on Neutrosophic Ontology for Electronic Education Systems

Most educators agree that essays are the best way to evaluate students’ understanding, guide their studies, and track their growth as learners. Manually grading student essays is a tedious but necessary part of the learning process. Automated Essay Scoring (AES) provides a feasible approach to completing this process. Interest in this area of study has exploded in recent years owing to the difficulty of simultaneously improving the syntactic and semantic scores of an article. Ontology enables us to consider the semantic constraints of the actual world. However, there are several uncertainties and ambiguities that cannot be accounted for by standard ontologies. Numerous AES strategies based on fuzzy ontologies have been proposed in recent years to reduce the possibility of imprecise knowledge presentation. However, no known efforts have been made to utilize ontologies with a higher level of fuzzification in order to enhance the effectiveness of identifying semantic mistakes. This paper presents the first attempt to address this problem by developing a model for efficient grading of English essays using latent semantic analysis (LSA) and neutrosophic ontology. In this regard, the presented work integrates commonly used syntactic and semantic features to score the essay. The integration methodology is implemented through feature-level fusion. This integrated vector is used to check the coherence and cohesion of the essay. Furthermore, the role of neutrosophic ontology is investigated by adding neutrosophic membership functions to the crisp ontology to detect semantic errors and give feedback. Neutrosophic logic allows the explicit inclusion of degrees of truthfulness, falsity, and indeterminacy. According to the comparison with state-of-the-art AES methods, the results show that the proposed model significantly improves the accuracy of scoring the essay semantically and syntactically and is able to provide feedback.