Reviewerly/exHarmony huggingface dataset:
Comparing translation accuracy in Belt and Road Malaysia children’s literature: Malay and Chinese native speakers vs ChatGPT

Authors:
raw_author_name: Yoke Lian Lau
raw_affiliation_string: Centre for the Promotion of Knowledge and Language Learning, Universiti Malaysia Sabah
raw_author_name: Zi Xian Yong
raw_affiliation_string: Centre for the Promotion of Knowledge and Language Learning, Universiti Malaysia Sabah
raw_author_name: Zi Hong Yong
raw_affiliation_string: Centre for the Promotion of Knowledge and Language Learning, Universiti Malaysia Sabah
raw_author_name: Anna Lynn Abu Bakar
raw_affiliation_string: Centre for the Promotion of Knowledge and Language Learning, Universiti Malaysia Sabah
raw_author_name: Bavani Arumugam
raw_affiliation_string: Centre for the Promotion of Knowledge and Language Learning, Universiti Malaysia Sabah

Abstract: The study investigates the translation processes of human and artificial intelligence translators in comparison. Human translators consist of a Chinese native speaker and belt and road translators. Different versions of artificial intelligence translators comprise ChatGPT 3.5 and ChatGPT 4.0. The research methodology employed is a keyword detection technique. One human translator and one translator powered by artificial intelligence achieved the highest scores in keyword detection, according to the results. Human translators continue to be indispensable in the field of translation, particularly in the translation of literary works. However, the research group is optimistic that artificial intelligence will soon be able to resolve this issue.
Simulation of a physical reservoir made of a Ag<sub>2</sub>S islands network

Authors:
raw_author_name: Tsuyoshi HASEGAWA
raw_affiliation_string: Department of Pure and Applied Physics, Graduate School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku, Tokyo 169-8555, Japan

Abstract: Abstract Recently, a physical reservoir operation utilizing atomic switch technologies was demonstrated. Atomic switch operates by controlling the formation and annihilation of a metal filament between two electrodes using solid-state electrochemical reactions. In this study, we simulated the operation of an atomic switch-based reservoir by arranging modeled atomic switches in a network. The aim of this study is to confirm that nonlinear transformation and short-term memory in a reservoir operation observed in the experiment can be realized by the integration of atomic switches showing nonvolatile bipolar operation. We incorporated these characteristics by making a simple operating model of a single atomic switch, which successfully reproduced major characteristics of the experimental results of a reservoir operation.
Gesture-Based Interactions: Integrating Accelerometer and Gyroscope Sensors in the Use of Mobile Apps

Authors:
raw_author_name: Sergio Caro-Alvaro
raw_affiliation_string: Universidad de Alcalá, Departamento de Ciencias de la Computación, 28805 Madrid, Spain
raw_author_name: Eva Garcia-Lopez
raw_affiliation_string: Universidad de Alcalá, Departamento de Ciencias de la Computación, 28805 Madrid, Spain
raw_author_name: Antonio Garcia-Cabot
raw_affiliation_string: Universidad de Alcalá, Departamento de Ciencias de la Computación, 28805 Madrid, Spain

Abstract: This study investigates the feasibility and functionality of accelerometer and gyroscope sensors for gesture-based interactions in mobile app user experience. The core of this innovative approach lies in introducing a dynamic and intuitive user interaction model with the device sensors. The Android app developed for this purpose has been created for its use in controlled experiments. Methodologically, it was created as a stand-alone tool to both capture quantitative (time, automatically captured) and qualitative (behavior, collected with post-task questionnaires) variables. The app’s setting features a set of modules with two levels each (randomized presentation applied, minimizing potential learning effects), allowing users to interact with both sensor-based and traditional touch-based scenarios. Preliminary results with 22 participants reveal that tasks involving sensor-based interactions tend to take longer to complete when compared to the traditional ones. Remarkably, many participants rated sensor-based interactions as a better option than touch-based interactions, as seen in the post-task questionnaires. This apparent discrepancy between objective completion times and subjective user perceptions requires a future in-depth exploration of factors influencing user experiences, including potential learning curves, cognitive load, and task complexity. This study contributes to the evolving landscape of mobile app user experience, emphasizing the benefits of considering the integration of device sensors (and gesture-based interactions) in common mobile usage.
Digitalization-growth nexus: some empirical evidence from four regions with a Muslim majority

Authors:
raw_author_name: Mehmed Ganic

Abstract: Purpose- The purpose of this study is to investigate whether regional variations exist in the development of the digital economy and how they impact growth in the four global regions where Muslims predominate. Methodology- The study employs empirical examination of 48 countries with Muslim majority divided in four regions (Middle East and North Africa region, Europe and Eurasia region, South Asia, East Asia region and the Pacific and Africa region) between 2000 to 2021. The study employs Kao Residual Cointegration Test, and the Long-run Valuation of Fully Modified Ordinary Least Squares (FMOLS/DOLS-Dynamic). A certain number of specific variables in the econometric model will be employed to measure the level of digitization on economic growth, such as: Digital economy infrastructure proxied by Individuals using the internet, Digital economic openness proxied by ICT product exports and Digital technology competitiveness proxied by Research and Development as share of GDP. Findings- The analysis reveals that despite the fact that the digital economy made a positive contribution to economic growth in both Sub-Saharan Africa and Europe and Euroasia, the impact on these regions is less than that on the Middle East and North Africa, South Asia, East Asia, and the Pacific countries due to the underdeveloped infrastructure of the digital economy The least effect of digitalization on growth was found in Sub-Saharan African countries with low incomes.. Conclusion- Based upon the analysis, it may be concluded that digitalization can considerably boost economic growth, but its benefits may differ depending on how developed a nation is. There is a clear geographical imbalance in the development of the digital economy across 48 nations with a majority of Muslims .To increase overall GDP growth, those countries need to look into policies that will help increase ICT and the digital economy use. Keywords: Digital economy, economic growth, ICT, the Muslim Word, panel cointegration model. JEL Codes: O40, O47, E22.
Improve exploration in deep reinforcement learning for UAV path planning using state and action entropy

Authors:
raw_author_name: Hui Lv
raw_affiliation_string: School of Information and Automation, Qilu University of Technology, 3501 University Road, Changqing District, Jinan City, Shandong Province, China, Jinan, 250353, CHINA
raw_author_name: Shibo Li
raw_affiliation_string: School of Information and Automation, Qilu University of Technology, 3501 University Road, Changqing District, Jinan City, Shandong Province, China, Jinan, 250353, CHINA
raw_author_name: Baolong Zhu
raw_affiliation_string: School of Information and Automation, Qilu University of Technology, 3501 University Road, Changqing District, Jinan City, Shandong Province, China, Jinan, 250353, CHINA

Abstract: Abstract Despite being a widely adopted development framework for unmanned aerial vehicle (UAV), deep reinforcement learning is often considered sample inefficient. Particularly, UAV struggles to fully explore the state and action space in environments with sparse rewards. While some exploration algorithms have been proposed to overcome the challenge of sparse rewards, they are not specifically tailored for UAV platform. Consequently, applying those algorithms to UAV path planning may lead to problems such as unstable training processes and neglect of action space comprehension, possibly causing negative impacts on the path planning results. To address the problem of sparse rewards in UAV path planning, we propose an information-theoretic exploration algorithm, Entropy Explorer (EE) , specifically for UAV platform. The proposed EE generates intrinsic rewards based on state entropy and action entropy to compensate for the scarcity of extrinsic rewards. To further improve sampling efficiency, a framework integrating EE and Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithms is proposed. Finally, the TD3-EE algorithm is tested in AirSim and compared against benchmarking algorithms. The simulation outcomes manifest that TD3-EE effectively stimulates the UAV to comprehensively explore both state and action spaces, thereby attaining superior performance compared to the benchmark algorithms in the realm of path planning.
Techniques Toward Quantum Computing System Scaling

Authors:
raw_author_name: Shuai-Peng Wang
raw_affiliation_string: Beijing Computational Science Research Center, Beijing, China
raw_author_name: Yulong Liu
raw_affiliation_string: Beijing Academy of Quantum Information Sciences, Beijing, China
raw_author_name: Hanjun Jiang
raw_affiliation_string: School of Integrated Circuits, Tsinghua University, Beijing, China
raw_author_name: Tiefu Li
raw_affiliation_string: School of Integrated Circuits, Tsinghua University, Beijing, China

Abstract: It is expected that a useful quantum computing (QC) system would require millions of qubits within one system. The state-of-the-arts QC systems contain hundreds of qubits, which is still several orders of magnitude away from the ultimate goal. To scale the QC system, namely, to increase the number of qubits and QC circuit layers, and also to improve the performance such as fidelity, robustness, etc., we need to develop scalable techniques including high-density qubit devices, ultra-low power qubit interfacing circuits, 3D interconnection and packaging etc. This article will review the recent reported device, circuit and system-level techniques that contribute to the QC system scaling and may ultimately lead to a fully functional quantum computer.
How to Support ML End-User Programmers through a Conversational Agent

Authors:
raw_author_name: João Felipe Nicolaci Pimentel
raw_affiliation_string: Northern Arizona University, Flagstaff, Arizona, USA
raw_author_name: Marco Gerosa
raw_affiliation_string: Northern Arizona University, Flagstaff, Arizona, United States of America
raw_author_name: Igor Steinmacher
raw_affiliation_string: Northern Arizona University, Flagstaff, Arizona, United States of America
raw_author_name: Anita Sarma
raw_affiliation_string: Oregon State University, Corvallis, Oregon, United States of America

Abstract: Machine Learning (ML) is increasingly gaining significance for enduser programmer (EUP) applications. However, machine learning end-user programmers (ML-EUPs) without the right background face a daunting learning curve and a heightened risk of mistakes and flaws in their models. In this work, we designed a conversational agent named "Newton" as an expert to support ML-EUPs. Newton's design was shaped by a comprehensive review of existing literature, from which we identified six primary challenges faced by ML-EUPs and five strategies to assist them. To evaluate the efficacy of Newton's design, we conducted a Wizard of Oz within-subjects study with 12 ML-EUPs. Our findings indicate that Newton effectively assisted ML-EUPs, addressing the challenges highlighted in the literature. We also proposed six design guidelines for future conversational agents, which can help other EUP applications and software engineering activities.
AIGC-Assisted Instructional Design for the Self-Regulated Learning Course on "Implementing 2D Animation for the Ziyun Temple Stories"

Authors:
raw_author_name: Li, Kun Chung

Abstract: The instructional design effectively utilizes generative AI to assist in the self-regulated learning course "2D Animation Implementation of Chiayi Temple Stories". It adopts 2D animation and the PPVIO teaching model, leveraging generative AI to create 2D animation scripts and 2D animations. This enhances and sharpens students' storytelling, 2D animation production, and the ability to use generative AI. It also promotes social care, communication, collaboration, and practical application skills. Within the domain of animation graphics, tools like Midjourney and other image-generating AIs are used, with animations completed using the Cartoon Animator 5 software. In this teaching design, teachers play a guiding and mentoring role, leading students to ask more precise prompts to make good use of ChatGPT, and guiding students to overcome the setbacks of self-regulated learning. With the assistance of AI generation tools, students can not only achieve cross-disciplinary learning objectives but, more importantly, reduce the time needed to learn drawing tools. This allows students, even those not specializing in IT, to enhance their digital capabilities in a shorter period. Two suggestions are put forth. Firstly, in the creative section, although the use of AI generation tools is convenient, once technical barriers are removed, the educational content should elevate the level of students' professional creation, such as emphasizing artistic styles. Secondly, regarding self-regulated learning, self-regulated learning should not be pursued for its own sake, nor should learning be for the mere act of learning. It's crucial to stress self-monitoring, self-regulation, and self-discipline to truly grasp the essence of self-regulated learning.
A Comparative Analysis of Feature Selection Algorithms in Cross Domain Sentiment Classification

Authors:
raw_author_name: Lipika Goel
raw_affiliation_string: Gokaraju Rangaraju Institute of Engineering &amp; Technology
raw_author_name: Sonam Gupta
raw_affiliation_string: Ajay Kumar Garg Engineering College, Ghaziabad
raw_author_name: Avdhesh Gupta
raw_affiliation_string: Ajay Kumar Garg Engineering College, Ghaziabad
raw_author_name: Neha Nandal
raw_affiliation_string: Geethanjali College of Engineering and Technology, Cheeryal, Medchal, Hyderabad
raw_author_name: Pradeep Gupta
raw_affiliation_string: Geethanjali College of Engineering and Technology, Cheeryal, Medchal, Hyderabad

Abstract: Background: Cross-domain Sentiment Classification is a well-researched field in sentiment analysis. The biggest challenge in CDSC arises from the differences in domains and features, which cause a decrease in model performance when applying source domain features to predict sentiment in the target domain. To address this challenge, several feature selection methods can be employed to identify the most relevant features for training and testing in CDSC. Method: The primary objective of this study is to perform a comparative analysis of different feature selection methods on the various CDSC tasks. In this study, statistical test-based feature selection methods using 18 classifiers for the CDSC task has been implemented. The impact of these feature selection methods on Amazon product reviews, specifically those in the DVD, Electronics, Kitchen, and TV domains, has been compared. Total 12x18 experiments were conducted for each feature selection method by varying source and target domain pairs from the Amazon product reviews dataset and by using 18 classifiers. Performance evaluation measures are accuracy and f-score. Results: From the experiments, it has been inferred that the CSDC task depends on various factors for a good performance, from the right domain selection to the right feature selection method. We have concluded that the best training dataset is Electronics as it gives more precise results while testing in either domain selected for our study. Conclusion: Cross-domain sentiment analysis is a dynamic and interdisciplinary field that offers valuable insights for understanding how sentiment varies across different domains.
Knowledge Graph Embedding via Triplet Component Interactions

Authors:
raw_author_name: Tao Wang
raw_affiliation_string: School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China
raw_author_name: Bo Shen
raw_affiliation_string: School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China
raw_author_name: Jinglin Zhang
raw_affiliation_string: School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China
raw_author_name: Yu Zhong
raw_affiliation_string: School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China

Abstract: Abstract In knowledge graph embedding, multidimensional representations of entities and relations are learned in vector space. Although distance-based graph embedding methods have shown promise in link prediction, they neglect context information among the triplet components, i.e., the head_entity, relation, and tail_entity, limiting their ability to describe multivariate relation patterns and mapping properties. Such context information denotes the entity structural association inside the same triplet and implies the correlation between entities that are not directly connected. In this work, we propose a novel knowledge graph embedding model that explicitly considers context information in graph e mbedding via t riplet c omponent i nteractions (TCIE). To build connections between components and incorporate contextual information, entities and relations are represented as vectors comprised of two specialized parts, enabling comprehensive interaction. By simultaneously interacting with one-hop related head and tail entities, TCIE strengthens the connections between distant entities and enables contextual information to be transmitted across the knowledge graph. Mathematical proofs and experiments are performed to analyse the modelling ability of TCIE in knowledge graph embedding. TCIE shows a strong capacity for modelling four relation patterns (i.e., symmetry, antisymmetry, inverse, and composition) and four mapping properties (i.e., one-to-one, one-to-many, many-to-one, and many-to-many). The experimental evaluation of ogbl-wikikg2, ogbl-biokg, FB15k, and FB15k-237 shows that TCIE achieves state-of-the-art results in link prediction.
Crossing the digital frontier: are older adults ready for virtual reality workouts?

Authors:
raw_author_name: André Ramalho
raw_affiliation_string: Sport Physical Activity and Health Research & INnovation CenTer, SPRINT, Santarém, Portugal; Sport, Health & Exercise Research Unit (SHERU), Polytechnic Institute of Castelo Branco, Castelo Branco, Portugal
raw_author_name: João Serrano
raw_affiliation_string: Sport Physical Activity and Health Research & INnovation CenTer, SPRINT, Santarém, Portugal; Sport, Health & Exercise Research Unit (SHERU), Polytechnic Institute of Castelo Branco, Castelo Branco, Portugal

Abstract: In the dynamic web of our digital age, virtual reality (VR) is crystallizing as a promising medium for promoting physical activity in older adults, overcoming age-old barriers. This perspective article explores the intricate synergy between VR and physical activity in older adults and highlights the many challenges of aging within a virtual environment. While VR heralds the potential for multisensory interaction and cognitive enhancement, a thorough assessment of its impact is paramount. The reach of VR goes beyond immediate applications and permeates the emotional and psychological realms of the human journey. Such an exploration requires a comprehensive perspective that goes beyond a purely technical assessment. The aim of this article is not to present clear-cut solutions, but to encourage reflection on the diverse impact of VR on our aging population. We argue for a future in which technology complements, rather than eclipses, the depth of human experience.
Digital practices at school, safety and digital citizenship

Authors:
raw_author_name: Jorge Sales E Silva Neto

Abstract: This study aims to address the massification of the internet in today's world, how much people depend on digital technologies today for almost everything in their lives, as well as the digital practices that exist today in schools, as well as security issues. online or digital and digital citizenship. Which, by the way, are topics little discussed in schools or with timid reflections still in the school environment. Because it is a bibliographical research, important contributions from authors who dominate on such subjects and address digital security issues as a whole and that the school universe is still unaware of many issues were described. Research by Cristina Paludo Santos was also described in her article Education, Digital Practices and New Network Risks regarding a project with high school students where the fragility of knowledge regarding digital security was detected, where part of it is shared in this bibliographical research. In view of the above, it is concluded in this work that digital practices exist in schools, however, the risks also and about digital security is a topic that is still little known by the school community and society in general. The risks are inevitable, but it is possible that society can be better informed about them.
Pointer Meter Reading Recognition by Joint Detection and Segmentation

Authors:
raw_author_name: Ying Li
raw_affiliation_string: School of Software, Shandong University, Jinan 250100, China
raw_author_name: Xuemei Li
raw_affiliation_string: School of Software, Shandong University, Jinan 250100, China
raw_author_name: Caiming Zhang
raw_affiliation_string: School of Software, Shandong University, Jinan 250100, China; Shandong Provincial Laboratory of Digital Media Technology, Jinan 250014, China; Shandong Provincial Laboratory of Future Intelligence and Financial Engineering, Yantai 264005, China

Abstract: To handle the task of pointer meter reading recognition, in this paper, we propose a deep network model that can accurately detect the pointer meter dial and segment the pointer as well as the reference points from the located meter dial. Specifically, our proposed model is composed of three stages: meter dial location, reference point segmentation, and dial number reading recognition. In the first stage, we translate the task of meter dial location into a regression task, which aims to separate bounding boxes by an object detection network. This results in the accurate and fast detection of meter dials. In the second stage, the dial region image determined by the bounding box is further processed by using a deep semantic segmentation network. After that, the segmented output is used to calculate the relative position between the pointer and reference points in the third stage, which results in the final output of reading recognition. Some experiments were conducted on our collected dataset, and the experimental results show the effectiveness of our method, with a lower computational burden compared to some existing works.