Reviewerly/exHarmony huggingface dataset:
Pictures as a Learning Media in Teaching Vocabulary

Technological developments in the current era provide many conveniences and benefits for various agencies, including educational institutions. Teachers as one part of educational institutions really need to take advantage of technological developments as a means or media in teaching so that learning does not seem boring. In fact there are many teachers who often focus on books oriented. This causes the students’ learning process to be not optimal, especially in learning foreign languages such as English. Of course, if learning only focuses on books, it will make students feel bored and lazy to learn English. Vocabulary as one of the important aspects in English cannot be taught with books alone, because in learning it requires interesting media to be able to make students more enjoy and interested in learning vocabulary. Based on this, this study aims to determine whether using pictures as learning can improve the vocabulary of students. This study used a pre-experimental design with one group pre-test and post-test. The research subject is class VII with the research instrument is a test. In analyzing the data, in this study the mean score formula was implemented. The results showed that picture as a learning medium greatly affects student learning outcomes in terms of vocabulary. Where, there was a significant increase in students' vocabulary after using pictures as treatment. Picture itself makes students more enthusiastic in learning because the pictures used attract students' attention. Finally, this research also explains the potentials that can be used or developed in the future.
Analisis sitasi pada jurnal berkala arkeologi menggunakan aplikasi “Publish or Perish”

Purpose. This article explains how to determine the quality of a scientific journal by analyzing the level of citation in Berkala Arkeologi journal articles.Methodology. The method used is bibliometric analysis which aims to determine the distribution of citations in an article document in a journal. The data source of this research is Berkala Arkeologi articles for the period of 42 years between 1980 and 2022 which were downloaded using the Publish or Perish 7 application.Results and discussion. The most prolific authors in publishing articles in the journal Berkala Arkeologi are Indah Asikin and T.M Hari Lelono with 24 articles. While the article written by Ph. Subroto has the highest citation among other articles, namely 25 citations. The Journal of Archaeology also publishes articles in a timely manner.Conclusion. Journal Berkala Arkeologi has implemented the periodicity of articles published on time and applies open access. The most prolific authors in publishing articles in the journal Berkala Arkeologi are Indah Asikin and T.M Hari Lelono with 24 articles. While the article written by Ph. Subroto with the title Zonal Patterns of Archaeological Sites published in 1995 turned out to have the highest number of citations among other articles. Thus, the number of articles published by the author is inversely proportional to the number of citations. But this proves that the article still has an influence in the development of archaeology.
TP-FER: An Effective Three-phase Noise-tolerant Recognizer for Facial Expression Recognition

Single-label facial expression recognition (FER), which aims to classify single expression for facial images, usually suffers from the label noisy and incomplete problem, where manual annotations for partial training images exist wrong or incomplete labels, resulting in performance decline. Although prior work has attempted to leverage external sources or manual annotations to handle this problem, it usually requires extra costs. This article explores a simple yet effective three-phase paradigm (“warm-up,” “selection,” and “relabeling”) for FER task. First, the warm-up phase attempts to build an initial recognition network based on noisy samples for discriminative feature extractions and facial expression predictions. Then, the second selection phase defines several rules to choose high confident samples according to prediction scores, and the third relabeling phase assigns two potential labels to those samples for network updating according to a composite two-label loss. Compared with the previous studies, the three-phase learning could effectively correct noisy labels in the ground truth without extra information and automatically assign two potential labels to single-label samples without manual annotations. As a result, the label information is purified and supplemented with few cost, yielding significant performance improvement. Extensive experiments are conducted on three datasets, and the experimental results demonstrate that our approach is robust to noisy training samples and outperforms several state-of-the-art methods.
An Enhanced Northern Goshawk Optimization Algorithm and Its Application in Practical Optimization Problems

As a kind of effective tool in solving complex optimization problems, intelligent optimization algorithms are paid more attention to their advantages of being easy to implement and their wide applicability. This paper proposes an enhanced northern goshawk optimization algorithm to further improve the ability to solve challenging tasks. Firstly, by applying the polynomial interpolation strategy to the whole population, the quality of the solutions can be enhanced to keep a fast convergence to the better individual. Then, to avoid falling into lots of local optimums, especially late in the whole search, different kinds of opposite learning methods are used to help the algorithm to search the space more fully, including opposite learning, quasi-opposite learning, and quasi-reflected learning, to keep the diversity of the population, which is noted as a multi-strategy opposite learning method in this paper. Following the construction of the enhanced algorithm, its performance is analyzed by solving the CEC2017 test suite, and five practical optimization problems. Results show that the enhanced algorithm ranks first on 23 test functions, accounting for 79.31% among 29 functions, and keeps a faster convergence speed and a better stability on most functions, compared with the original northern goshawk optimization algorithm and other popular algorithms. For practical problems, the enhanced algorithm is still effective. When the complexity of the TSP is increased, the performance of the improved algorithm is much better than others on all measure indexes. Thus, the enhanced algorithm can keep the balance between exploitation and exploration and obtain better solutions with a faster speed for problems of high complexity.
Neural Dynamics in Speech Processing: Properties, Specificity and Open Questions

Human speech is a particularly relevant acoustic stimulus for our species, due to its role of information transmission during communication. Speech is inherently a dynamic signal, and a recent line of research focused on neural activity following the temporal structure of speech. We review findings that characterise neural dynamics in speech processing and compare them with temporal aspects in human speech. We highlight properties and constraints that both neural and speech dynamics have, suggesting that auditory neural systems are optimised to process human speech. We then discuss the speech-specificity of neural dynamics and their potential mechanistic origins, and summarise open questions in the field.
Improved Belgian AI Algorithm for Dynamic Management in Action Role-Playing Games

Artificial intelligence in games is one of the most challenging tasks in academia and industry. In action role-playing games, how to manage combat effectively is a key issue related to game development and the player’s experience. The Belgian artificial intelligence (BAI) algorithm is a classic but limited method that is widely used for combat management between the player and enemies. To address the poor adaptability of BAI, this paper proposes an improved Belgian artificial intelligence (IBAI) algorithm with dynamic difficulty adjustment (DDA) and implements two systems separately based on BAI and IBAI in Unreal Engine 4. Advantages on 12 parameters—10.086 mean total score greater, and 0.079 standard deviation smaller—demonstrate that the system based on IBAI has higher adaptability and a better player experience by comparing the two systems in different situations and inviting players to participate in gameplay experiences and questionnaires. The robust dynamic management mechanism of IBAI can help game designers and developers achieve the combat system of action role-playing games more efficiently, thus, shortening the development cycle and improving the player retention rate.
An Optimization-Based Motion Planner for Car-like Logistics Robots on Narrow Roads

Thanks to their strong maneuverability and high load capacity, car-like robots with non-holonomic constraints are often used in logistics to improve efficiency. However, it is difficult to plan a safe and smooth optimal path in real time on the restricted narrow roads of the logistics park. To solve this problem, an optimization-based motion planning method inspired by the Timed-Elastic-Band algorithm is proposed, called Narrow-Roads-Timed-Elastic-Band (NRTEB). Three optimization modules are added to the inner and outer workflow of the Timed-Elastic-Band framework. The simulation results show that the proposed method achieves safe reversing planning on narrow roads while the jerk of the trajectory is reduced by 72.11% compared to the original method. Real-world experiments reveal that the proposed method safely and smoothly avoids dynamic obstacles in real time when navigating forward and backward. The motion planner provides a safer and smoother trajectory for car-like robots on narrow roads in real time, which greatly enhances the safety, robustness and reliability of the Timed-Elastic-Band planner in logistics parks.
How Things Take Up Space: A Grounded Theory of Presence and Lived Space

In cognitive science, it is unclear what precisely presence (both in the sense of objecthood and immersion) refers to in lived experience. The present study addresses the research question of what the relationship between presence is and lived space. A hundred and seventeen phenomenological interviews were conducted with 14 participants. We sampled their experience in a transdiagnostic manner. That is, we observed how the experience of presence changes both in circumstances appraised as positive (e.g., sexual intimacy) and negative (e.g., psychopathology). Our grounded theory suggests that presence is a phenomenon that is comprised of all available sensory knowledge, however, it itself is not present in any one sensory modality. Presence takes the form of a disembodied sense of solidity. Our findings can be related to the notion of transmodality in contemporary qualitative phenomenology (i.e., the idea that there are some aspects of experience that can be readily translated from one sensory modality to another. Further, how presence (in its capacity as immersion) is related to lived space can shed further light on the formation of delusions, suggesting that it is based on sensory alterations rather than changes in belief. Finally, the observation that presence as it appears in lived space need not perfectly correspond to the objective situation, can elucidate extant discussion on whether presence is an amodal aspect of consciousness.
Using Artificial Intelligence to improve Writing Fluency for The Preparatory Stage Students in Distinguished Governmental Language Schools."

This study aimed to investigate the effect of using some proposed AI activities on enhancing EFL writing fluency for the preparatory stage students in Distinguished Governmental Language Schools.Participants of the study were 33students in preparatory three from Hassan Abu Bakr governmental language school in the academic year 2021-2022.The researcher`s instruments included writing fluency components checklist, a rating scoring rubric and a pre/post writing fluency test.Participants were taught through using some relevant AI applications designed by the researcher that included Minecraft game with grammarly mood, Semantris vocabulary AI game and virtual reality in AI mood that enabled the students to practise using English in vivid situations.Participants' scores on the pre and posttest were statistically analyzed using T-test and effect size.The researcher also used a qualitative analysis of the students' writing fluency level.Results of the study revealed the positive effect of using AI applications on enhancing third preparatory stage students` writing fluency.
Transforming the generative pretrained transformer into augmented business text writer

Abstract This study uses transformers architecture of Artificial neural networks to generate artificial business text for a given topic or theme. The implication of the study is to augment the business report writing, and general business writings process with help of generative pretrained transformers (generative pretrained transformer (GPT)) networks. Main focus of study is to provide practical use case for GPTs models with help of big data. Our study model has 355 million model parameters and trained for three months on GPU enable devices using 2.3 billion text tokens(is available as open-source data now). Text tokens are collected with help of rigorous preprocessing, which includes; shortlisting of Subreddits of Fortune 500 companies and industries, listed on US-based social news aggregation online portal called “Reddit”. After shortlisting, millions of submission of users during the five years, are parsed to collect the URLs out of it. 1.8 million working URLs are scrutinized. Business text is parsed, cleaned, and converted into word embeddings out of uniform resoruce locator (URLs). The result shows that both models; conditional interactive and random sampling, generate text paragraphs that are grammatically accurate and stick to the given topic.
An interpretable approach for automatic aesthetic assessment of remote sensing images

The increase of remote sensing images in recent decades has resulted in their use in non-scientific fields such as environmental protection, education, and art. In this situation, we need to focus on the aesthetic assessment of remote sensing, which has received little attention in research. While according to studies on human brain's attention mechanism, certain areas of an image can trigger visual stimuli during aesthetic evaluation. Inspired by this, we used convolutional neural network (CNN), a deep learning model resembling the human neural system, for image aesthetic assessment. So we propose an interpretable approach for automatic aesthetic assessment of remote sensing images. Firstly, we created the Remote Sensing Aesthetics Dataset (RSAD). We collected remote sensing images from Google Earth, designed the four evaluation criteria of remote sensing image aesthetic quality-color harmony, light and shadow, prominent theme, and visual balance-and then labeled the samples based on expert photographers' judgment on the four evaluation criteria. Secondly, we feed RSAD into the ResNet-18 architecture for training. Experimental results show that the proposed method can accurately identify visually pleasing remote sensing images. Finally, we provided a visual explanation of aesthetic assessment by adopting Gradient-weighted Class Activation Mapping (Grad-CAM) to highlight the important image area that influenced model's decision. Overall, this paper is the first to propose and realize automatic aesthetic assessment of remote sensing images, contributing to the non-scientific applications of remote sensing and demonstrating the interpretability of deep-learning based image aesthetic evaluation.
A Study on the Design Procedure of Re-Configurable Convolutional Neural Network Engine for FPGA-Based Applications

Convolutional neural networks (CNNs) have become a primary approach in the field of artificial intelligence (AI), with wide range of applications. The two computational phases for every neural network are; the training phase and the testing phase. Usually, testing is performed on high-processing hardware engines, however, the training part is still a challenge for low-power devices. There are several neural accelerators; such as graphics processing units and field-programmable-gate-arrays (FPGAs). From the design perspective, an efficient hardware engine at the register-transfer level and efficient CNN modeling at the TensorFlow level are mandatory for any type of application. Hence, we propose a comprehensive, and step-by-step design procedure for a re-configurable CNN engine. We used TensorFlow and Keras libraries for modeling in Python, whereas the register-transfer-level part was performed using Verilog. The proposed idea was synthesized, placed, and routed for 180 nm complementary metal-oxide semiconductor technology using synopsis design compiler tools. The proposed design layout occupies an area of 3.16 × 3.16 mm2. A competitive accuracy of approximately 96% was achieved for the Modified National Institute of Standards and Technology (MNIST) and Canadian Institute for Advanced Research (CIFAR-10) datasets.
Fish Migration Optimization with Dynamic Grouping Strategy for Solving Job-Shop Scheduling Problem

&lt;p&gt;Aiming at the job-shop scheduling problem (JSP), a dynamic grouping fish migration optimization (DFMO) is proposed to solve it. The DFMO algorithm adopts a multi-group structure to improve the convergence ability of the algorithm. And the opposition-based learning (OBL) strategy is applied to the group with poor overall fitness value to improve its solving environment. This paper proposes three different communication strategies to exchange information between different groups. In order to better determine the communication time between groups, a dynamic detection method based on population diversity is proposed. Compared with the static method of determining the communication time between groups, the proposed method can make the group more fully explore the current area and more hopefully find the optimal solution. The experiment in this paper is divided into two parts, one part is the numerical experiment test, the other part is the JSP problem standard library test. From the experimental results, the DFMO algorithm can obtain good results in both parts of the experiment, and has a good problem optimization ability.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Application of Virtual Reality in Learning the Concepts of Special Relativity and Mass–Energy Equivalence

Einstein described the relationship between mass and energy using the theory of special relativity by a simple equation, E=mc2. Mass–energy equivalence implies that mass can be converted into energy and vice versa. In this study, a virtual reality (VR) system was developed for learners to take a spacecraft travelling at nearly the speed of light to conduct space exploration and understand the concepts of special relativity and mass–energy equivalence. A teaching experiment was conducted to investigate the learning effectiveness and cognitive load of learners by recruiting 60 students as research samples. The experimental group (30 students) used the VR system and the control group (30 students) used physics textbooks for learning special relativity and mass–energy equivalence. The experimental results reveal that the learning effectiveness of the experimental group is higher than that of the control group while the cognitive load of the former is lower than that of the latter. The questionnaire results show that students of the control group had responded positively to learning content, cognitive usefulness, cognitive ease of use, and user satisfaction, indicating that they were satisfied with the learning experience of the VR system.
INFLUENCE OF THE PANDEMIC ON UNIVERSITY ACCESS FOR MEN AND WOMEN: THE CASE OF SANTA CRUZ DE TENERIFE (SPAIN)

The purpose of this article is to analyse the influence of the pandemic on the grades obtained in different subjects that comprise the University entrance exam. With this purpose, we analysed a significant database containing grades related to four consecutive academic annual editions of the entrance exam to the University of La Laguna (Canary Islands, Spain) divided into two periods, pre-pandemic (academic years 2017-2018 and 2018-2019) and the post-pandemic (2019-2020 and 2020-2021). Bivariate data analysis along with multiple linear regression was performed on the data. According to the results, it is confirmed that in the post-pandemic period some grades have suffered a significant boost, and it is also possible to detect the social impact of that increase in social variables, i.e. gender significant differences.
Research on the Influence of Virtual Reality on the Learning Effect of Technical Skills of Science and Engineering College Students: Meta-Analysis Based on 32 Empirical Studies

Virtual reality technology (VR) has been widely used in education and teaching, but the results of a large number of experimental studies on the impact of VR on students’ technical skill learning are not consistent. In this study, a meta-analysis method was used to conduct in-depth quantitative analysis of 32 experimental or quasi-experimental research literatures based on virtual reality in the past 20 years. The results showed that: (1) virtual reality technology has a moderately positive effect on the learning of technical skills of science and engineering college students ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mtext>SMD</mtext> <mo>=</mo> <mn>0.363</mn> </math> ); (2) the effect of VR on cognitive strategies ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mtext>SMD</mtext> <mo>=</mo> <mn>0.487</mn> </math> ) was significantly greater than that of mental skills ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mtext>SMD</mtext> <mo>=</mo> <mn>0.275</mn> </math> ) for technical skills under different measurement dimensions. (3) Different test periods, previous experience levels, and auxiliary methods have different degrees of positive influence on improving students’ technical skills, among which the 40-60 minutes experiment period ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mtext>SMD</mtext> <mo>=</mo> <mn>0.484</mn> </math> ) has the greatest influence on the improvement of technical skills. According to the research conclusions, we suggest to make an overall planning for the construction of VR enabling teaching application scenarios and create a human-machine collaborative interactive teaching model of “theory+VR simulation+feedback” based on flow theory.