Reviewerly/exHarmony huggingface dataset:
EFL Students’ Challenges In Learning Speaking Skills During Online-to-Offline Transition Era

This research aimed to explore the challenges faced by EFL students’ in learning speaking skills during the online-to-offline transition era, then the difference between the experience of learning speaking skills during online and offline, and also explain the potential solutions that EFL students’ do to overcome the challenges. The site of this research was an English study program at one public university in Jambi. The participants in this study were five fourth-semester students consisting of 3 different classes who have completed speaking lessons in 1-3 semesters. This research employed qualitative study. In collecting the data, the researcher used interviews as a primary instrument. The results of this research indicated that there were two themes of challenges faced by EFL students in speaking English, including lack of vocabulary (academic word issues), and nervousness (communication apprehension). This research also revealed that there were four themes of differences in online and offline learning, including learning process (inattentiveness unmotivated, attentiveness and motivated), classroom activities (non-interactive and interactive learning), time management (undisciplined and disciplined learning), and learning outcomes (understanding the material). Last but not least, this research also pointed out that the participants used three strategies or potential solutions to overcome their challenges. Those strategies are metacognitive strategies (preparing vocabulary), cognitive strategies (entertainment platform), and social strategies (asking and practice). Finally, the transition era gives new experiences to students. They do not experience challenges in terms of environment and internet issue as they face during online learning.
On-the-Fly Dual Reduction Method on Transient Fluid Topology Optimization

Abstract Transient topology optimization (TO) requires huge computational resources in terms of both computing time and storage. In this study, we have applied the proper orthogonal decomposition (POD) based on-the-fly dual reduction method on TO problems for time-dependent incompressible fluid channel optimization. The fluid motion was governed by time-dependent Navier-Stokes equation. Total energy dissipation over a given period was considered as the cost function, and total fluid volume was constrained during optimization process. POD based reduced order modeling (ROM) was applied to both the primal equations and the adjoint equations for reduction. We examined the reduction efficacy though examples including U-bend channel and nozzle. Tolerance and snapshot set size were studied as hyper-parameters to improve the reduction performance. The best cases can reach 99% of skip ratios, 50% reduction in computational time, and 96% saving of memory storage.
Design and User Experience Evaluation of 3D Product Information in XR Shopping Application

Abstract Current 3D product information pages are believed to enrich the online shopping experience since it provides a more immersive experience. However, existing solutions still retain the 2D UI elements in the product information design, preventing users from fully immersing themselves in the virtual environment and further degrading the shopping experience. Therefore, how to redesign the presentation of product information in a virtual environment is an essential challenge. In this work, we first construct a design space based on previous design cases of product information design in virtual environments, which contains three dimensions: visual presentation, user interaction and spatial relation. Then nine new 3D information design solutions are produced by combining elements in the design space. After that, we devised a user study to compare these solutions and investigate how different dimensions affect users’ shopping experience and task efficiency of the primary objective in shopping. The results reveal that users’ spatial memory and task efficiency are both impacted by how information is organized in terms of visual presentation dimension, and that users’ hedonic experiences are influenced by the differences in the constituent elements of information. It is found that the different spatial relationship between 3D information and 3D product lead to differences in pragmatic quality.
Augmented Reality for the Positioning of Fasteners and Adhesives in Sheet Metal Joining Processes in the Automotive Industry

Abstract The manual sheet metal joining process in the automotive industry relies on visual guides to locate standardized joint positions. Traditionally, operators place physical templates on the workpiece to mark the exact positions of each joint prior to operating the machinery. In attempts to eliminate this step, applications of augmented reality (AR) that provide positional information through digital means have been introduced. AR offers additional functionalities such as displaying animations and tolerances; however, existing works on the topic often rely on markers in the workspace and face limitations due to ergonomics and workpiece complexity. This paper introduces an AR solution that uses model targets and digital templates to overlay the positional information of joints on the workpiece, enhancing manufacturing visualization. In addition, the positional and diametrical relative accuracies of joints are measured and provided to the user in real-time. Functionalities such as animations, a user interface (UI), and a progress tracking mechanism additionally enhance the process for the user. Unity and Vuforia Engine together with a sample workpiece were used to demonstrate the concept. The resulting solution is free of physical markers in the workspace, resistant to accidental occultation, and suitable for a variety of sheet metal joining processes and workpiece complexities.
Gaze-Based Augmented Reality Interfaces for Micro- and Macro-Scale Tasks

Abstract Augmented Reality (AR) and gaze-based user interfaces (UI) can reduce a user’s cognitive load in both robotics and manufacturing applications. This paper describes the development and evaluation of a gaze-based AR system to assist users with an assembly task for future use in human-robot interaction (HRI) that is both scalable and benchmarked to determine its efficacy in different scales of environments and with different densities of AR-inclusive items. Testing includes analysis of the effects of a dense environment on the system. The system worked for all initial participants with qualitative positive feedback on the system. Constructive criticism focused on tailoring the dwell time to the individual. The results provide future developers with guidance to determine the practical density of an AR-inclusive environment.
An Efficient Surrogate Modeling Method for Reliability-Based Global Path Planning of Off-Road Autonomous Ground Vehicles

Abstract The off-road autonomous ground vehicle operation is challenging because of the complex and highly uncertain working environment they operate in. This brings risks to their mission success. Physics-based global path planning combining physics-informed surrogate modeling with path planning is recently employed to address this issue by ensuring a reliability to complete the mission. However, the current two-stage methods require a relatively high computational cost to obtain the reliability map undermining the practicability for real-life applications. To decrease the computational cost, this study proposes an efficient surrogate modeling framework for physics-based path planning under uncertainty. The proposed method couples adaptive surrogate modeling with path planning sequentially through several iterations of path planning and path verification. Instead of obtaining an accurate reliability map for the whole target area in the two-stage approach, the proposed method only refines the map in the vicinity of the path identified, thereby reducing the computational cost. The final path is determined if the path verification is successful; i.e. the specified mission reliability for the path is met. The presented case study demonstrates that the proposed method can significantly reduce the number of surrogate refinement iterations and reduce the computational cost for physics-based path planning.
Evaluating and Developing Human-Centered Design Workshops for Engagement of Co-Designers Living and Working in Sub-Saharan Africa

Abstract Co-design is an equity-centered method of human-centered design that offers the opportunity for diverse voices (including designers and those for whom a solution is being created) to design together. As experts in their own lived experiences, co-designers provide valuable insight to help contextualize the problem being solved, leading to more feasible, viable, and desirable solutions. In particular, co-design has the potential to aid design for global health equity as health issues have many complex social influences. However, methods for practicing co-design and for evaluating that practice, especially in the context of global health, are largely understudied. This study aims to fill this gap by conducting qualitative coding and thematic analysis of survey responses from co-designers engaged in human-centered design workshops in Kenya and Uganda. From this analysis, we have gained insights on the knowledge gained by co-designers, what they enjoyed about the co-design process, what was challenging for them, and what suggestions they had for improving the workshops themselves. The findings of this study reveal that while co-designers gained knowledge and had an enjoyable experience, barriers to successful co-design continue to persist, including time constraints and language barriers. This study highlights the need for further research to overcome these barriers, by refining co-design curriculum and evaluation tools, in order to achieve more successful and equitable design.
An Enhanced Timed Elastic Band Method for Autonomous Navigation and its Collision Avoidance Reliability Analysis

Abstract The timed elastic band (TEB) method is an optimization-based navigation algorithm which uses the idea of an elastic band to connect an agent’s current pose to its goal position. The method is composed of three main levels: i) a feasible global path, ii) deforming collision-free path in real time, and iii) a control method to move the robot along the path. Static and moving obstacles deform the elastic band but the agent will always be pulled toward the goal position while avoiding obstacles. The key issue is the required computation efficiency to calculate the collision-free path in real time under dynamically changing environment. In literature, various case studies have been implemented to demonstrate the performance considering various constraints. However, its collision avoidance reliability under safety critical navigation scenarios has not yet been studied. The major contributions of this paper are summarized as follows. Firstly, an enhanced version of the TEB (i.e., eTEB) is proposed to improve the computational efficiency by explicitly considering non-holonomic constraints of the TEB method. Secondly, collision avoidance reliability analysis of the eTEB method is systematically conducted considering non-reactive moving obstacles. Results indicate that the eTEB method is much more reliable than the DWA for head-on collision avoidance with a single non-reactive moving obstacle at a time.
Measuring the Health of Makerspaces During Large Disruptions Such As the COVID-19 Pandemic

Abstract As the popularity of makerspaces and maker culture has skyrocketed over the past two decades, numerous studies have been conducted to investigate the benefits of makerspaces for university students and how to best establish an inclusive, welcoming environment in these spaces on college campuses. However, unprecedented disruptions, such as the COVID-19 pandemic, have the potential to greatly affect the way that students interact with makerspaces and the benefits that result. In this study, a survey asking about prior makerspace involvement, tool usage, and student demographics was administered to students who use academic makerspaces at two large public universities. Survey data was collected for three semesters (Fall 2020, Spring 2021, and Spring 2022) and spanned both during and after the height of the COVID-19 pandemic. To quantify the differences between the semesters, nestedness and connectance metrics inspired by ecological plant-pollinator networks were utilized. These ecological metrics allow for the structure of the interactions of a network to be measured, with nestedness highlighting how students interact with tools and connectance with the quantity of student-to-tool interaction. The network analysis was used to better gauge the health of the makerspace and the type and frequency of interactions between tools. The raw survey data combined with the ecological metrics provided unique insight into the struggles the makerspaces encountered throughout the pandemic. It was found that nestedness, a measure of system stability, decreases with a decrease in tool usage. Additionally, the higher the connectance the more students interacted with the space. Utilizing metrics such as these and better understanding student tool interactions can aid makerspaces in monitoring their success and maintaining a healthy and welcoming space, as well as tracking the current health of the space. In combination with the survey results, a deep understanding of what challenges the space is facing can be captured.
Hand Assemble Action Recognition Using Spatial Temporal Graph Convolutional Network

Abstract In the manufacturing site, more and more research works have been conducted to apply Human action recognition (HAR) to detect or recognize the human worker’s actions/motions. This research aims to develop a hand gesture recognition framework particularly in a hand assembly production site where the accuracy of human hand motion is crucial for assembly. This research investigated the number of joints that might affect the prediction accuracy of HAR models when using the same image data. In this study, the skeleton information extracted from OpenPose was used to estimate the human action of the assembly operations. The extracted skeleton was then used as the input of Spatial-Temporal Graph Convolutional Networks (ST-GCN) for action recognition. The prediction accuracy of the human action recognition model under different labels and the number of joints were analyzed. The videos collected from the real-world motherboard assembly were used to evaluate the feasibility of the proposed framework. The experiments showed that the action labeling with Right-handedness-head (26 joints) resulted in the best accuracy of the recognition model. This result also implies the skeleton information of handedness might be enough for hand assembly work when the non-handedness hand was only assigned for similar action such as fixing the components.
VariableTypography: Artificial Intelligence Augmented Reading Experience

Abstract Nowadays, digital information has overloaded and reading time has been fragmented due to the significant development of digital devices, such as phones, computers, etc. Optimizing the reading experience has become a challenge. Prior studies on Chinese digital reading have primarily focused on uniform and consistent typography settings. Nevertheless, this standard typography is not applicable to real-world scenarios. Usually, people leverage different typographical variables to emphasize part of a sentence to highlight key information. Inspired by this, we propose a novel screen reading method, dubbed VariableTypography (VT), that highlights key information in various typographic ways by utilizes artificial intelligence tool ChatGPT to identify key information in the text, aiming to enhance the reading experience. In the field of artificial intelligence, significant progress has been made in natural language processing, resulting in innovative tools like ChatGPT. With its low operating threshold and the ability to use natural language for prompt engineering, ChatGPT has attracted widespread attention. In this study, we incorporated ChatGPT into a real-world reading scenario and integrated it with VT to examine its potential for assisted reading. To evaluate the effectiveness of VT, objective and subjective experiments were conducted in naturalistic remote settings with 70 participants using their smartphones in daily Interlude Reading scenarios. The results show that our proposed VT effectively improved readers’ subjective information acquisition while significantly reducing cognitive load during reading, without affecting the original reading efficiency. Additionally, VT is evidenced with good subjective acceptance. We hope that our findings will shed light on the field of digital Chinese readability.
Let’s Chat If You Are Unhappy – The Effect of Emotions on Interaction Experience and Trust Toward Empathetic Chatbots

Abstract Chatbots are now prevalent in obtaining information and executing tasks on behalf of human users. While the design community has paid more attention to streamlining user-chatbot interactions, the existing literature has not thoroughly examined social elements such as emotions and understood how emotions influence user interaction and trust in chatbots. This study proposes to test how participants perceive an empathetic chatbot versus a non-empathetic one under various emotional states (i.e., positive, neutral, negative) when the chatbot facilitates conversations for student advising via an online platform. The study shows the importance of presenting empathetic cues in the design of chatbots or other intelligent agents. The empathetic behavior of the chatbot improves participants’ trust and perception of the chatbot’s performance. The improvement is more salient to people with negative emotions than people who feel neutral or positive. This interaction effect is explained by people’s perceived effort of the chatbot during the interaction with people, even though the chatbot is a technological agent that acts based on algorithms. The results suggest that people attribute human qualities to chatbots in social interaction and highlight the emotional needs of people who experience negative emotions. Therefore, design efforts need to be designated according to people’s dynamic emotional states.
Topology Optimization With Quantum Approximate Bayesian Optimization Algorithm

Abstract Despite its wide scope of applications, topology optimization faces a major challenge, which is its high computational expenses caused by the large number of design variables and the underlying physics. Because the number of possible material layouts increases exponentially, the search for the optimum becomes computationally intractable. Recently, quantum computing emerged as an alternative paradigm for solving optimization problems with potentially much higher efficiency than classical computing. The quantum superposition phenomenon allows quantum computer to perform a parallel search of many possible solutions. In this work, a two-mixer quantum approximate Bayesian optimization algorithm (TM-QABOA) is developed as a hybrid quantum-classical optimization algorithm, where Pauli-X mixers and generalized Grover mixers are applied in an alternating fashion. TM-QABOA enables a dynamic exploration-exploitation balance to improve searching efficiency. The exploration is done with the Pauli-X mixers, whereas the exploitation is enhanced by the generalized Grover mixers through amplitude amplification. Surrogate-based Bayesian optimization is used to optimize the hyperparameters of the quantum circuit, i.e., rotation angles. The algorithm can also be used to solve mixed-integer optimization problems. The feasibility of TM-QABOA to solve optimization problems is demonstrated with two simple examples. One is two-dimensional truss design, and the other is metamaterials design.
Analysis of the Topsis in the Recommendation System of PPA Scholarship Recipients at Universitas Islam Kebangsaan Indonesia

&lt;p&gt;This research implements the TOPSIS method on a recommendation system for Peningkatan Prestasi Akademik (PPA) scholarship recipients. The research data was obtained from the computer and multimedia faculty, UNIKI. The results showed that the TOPSIS method can provide the best alternative based on the highest rank. In this research, the highest rank was obtained from the results for predetermined criteria, namely GPA, achievements, parental dependents and parental income. The highest value obtained is 0.7489. The system built based on a website with the PHP programming language.&lt;/p&gt;
Efficient Mobile Robot Navigation with D* Lite and Bellman Ford Hybrid Algorithm

Robotics is a challenging area which is highly employed in various fields, including industry, logistics, healthcare, and transportation. They need effective and reliable path planning algorithms to navigate in complicated and dynamic situations. Due to its effectiveness and optimality, the hybridized algorithm has recently become a well-liked approach for resolving path planning issues in robotics. In this article, the D* Lite algorithm's implementation was done using Bellman ford Shortest Path Algorithm to achieve improved path quality and their performance assessment was done using different 2D maps. The theoretical foundation of the algorithm, its simulation implementation, and practical findings proving its efficiency regarding computing time, path distance and various other metrics are all presented in this article. It has been verified through both theoretical and empirical results that the novel hybrid technique can enhance the efficiency and effectiveness of the D*Lite. Simulation results are very much effective as D* lite implementation includes bellman ford algorithm for achieving nearly 25% reduced computation time as well as 36% improved optimal path length.
FACTORS AFFECTING STUDENTS’ LOW MOTIVATION IN LEARNING ENGLISH AT SMK MAMBAUL ULUM JAMBI

This study is purposed to find out factor of affecting students’ low motivation in learning English at high school. This study used qualitative case study as the design. To collect the data, the researcher conducted interviews and observations with respondents to find out the problems students’ low motivation in learning English..The interview consisted of 12 questions arranged according to the indicator by Gardner and Dornyei. Researchers used data analysis to identify students’ low motivation in learning English. The result of this study is the students eleventh grade of SMK Mambaul Ulum Jambi are low motivation in learning English. Then, the factors that affected are: internal factors (students self/individual, students’ interest, curiosity) and external factors (family factors, teacher factors, and environment factors). Suggestions for future researchers, it is hoped that this study will serve as a reference for researchers who low the motivation to investigate the same topic and that subsequent researchers will be able to learn more about it.
EFL Teacher Identity Development during Teacher Professional Education Programme

This study aims to identify the development of teacher participants of PPG. The development of the identity is considered urgent to explore especially because PPG is conducted fully online facilitated by an LMS. Instructors and participants are required to conference online and do assignments online as well as teaching practice. The current stud is conducted based on content analysis method involving six respondents. Data was interpreted from recordings during online discussion session.The research findings indicate that the new skills acquired during PPG fall within the area of use of technology, pedagogy, and collaboration.