Reviewerly/exHarmony huggingface dataset:
“I Have to Do Something About It” - An Exploration of How Dashboards Invoke Self-Reflections in Chronic Obstructive Pulmonary Disease Patients

Chronic Obstructive Pulmonary Disease (COPD) patients need to track their symptoms for health professionals to adapt treatments in a timely manner in case of health deterioration. Clinicians typically analyzed the tracked data and recommended actions to patients who acted as mere data collectors. Consequently, patients have little agency and motivation to self-track. Two studies investigated how digital dashboards influenced patients’ motivation, agency, and reflections. Study 1 (one week) focused on how five patients used a paper diary to self-track and reflect on their symptoms. Additionally, the patients evaluated a tablet-based digital dashboard using four data visualisations. Study 2 looked at how five patients tracked and reflected on their data using a tablet-based dashboard for two weeks. By using reflective questions to prompt patients to compare and reflect on time series charts with data annotations, patients gained new knowledge about what factors might influence their symptoms and identified actions to improve their health (e.g. increase oxygen supplements). This strengthened their sense of agency and motivated them to participate more in the management of their condition.
Understanding Barriers of Missing Data in Personal Informatics Systems

Advanced personal informatics (PI) tools enable users to collect and reflect on a wide range of personal data. Researchers consider missing data, or discontinuous (sparse) data caused by device malfunctions or human errors, an important barrier for adopting PI tools in their daily routines. While a lot is known about why missing data occurs, less is known about its impact on user reflection or how tools can be designed to mediate/reduce its negative effects in PI systems. In this work, we focused on exploring the importance and impact missing data has on user reflection and extracting insights to improve the design of PI reflection tools. We present a semi-structured interview to investigate the impact of missing data on users’ daily usage on two user groups, trainees and maintainers. We then provide design implications for incorporating visualization of estimated data (synthetic data) in the reflection stage, as a potential solution to the missing data problem. In this work, we provided data-driven implications for the design of future PI tools to help users reflect upon and mitigate missing data in their tracking activities.
PROBLEMS AND COUNTERMEASURES FOR ONLINE CHINESE CHARACTER TEACHING AT THE NATIONAL UNIVERSITY OF HUMANITIES IN UKRAINE

This paper analyses the teaching of Online Chinese characters to Ukrainian students. The author singles out two problems of Online Chinese characters teaching/learning at the National University of Humanities. According to the problems, four countermeasures of online Chinese characters teaching have been put forward: to strengthen the analysis of homophones and shapes, to improve the sound-speaking use, to maintain a tolerance to the Chinese characters, to strengthen the Chinese character consciousness, to attach importance to Pinyin teaching, and to use the real teaching corpus to improve online Chinese characters writing.
SISTEM INFORMASI KEPERAWATAN PADA RUMAH SAKIT DR AK GANI DENGAN METODE RAPID APPLICATION DEVELOPMENT (RAD)

Abstract : The information age is a period that involves a lot of information in decision making, both by individuals, companies and government agencies. The data is processed using a well-integrated system to produce an accurate and useful information system. To manage nurses, as well as perform duty shift attendance, and also make reports already using computerization, the head nurse only monitors the nurse's activities, the system development method uses RAD, the programming language PHP and MySQL as the database, the results that can be achieved are between nursing and the head nurse can well integrated.
Reading Instruction and Interventions in Multigrade Schools in Quezon

This study aimed to undermine the realities of reading instruction in three multigrade schools in Quezon province during the COVID-19 pandemic. They analyzed data obtained from Phil-IRI in the past three years and focused on how teachers implemented reading instruction in multigrade settings. The researchers investigated the interventions they employed. Results revealed that parental involvement played a critical role in reading instruction. Teachers espoused varied reading strategies and focused on material selection, integration, and contextualization. Formative strategies, home visitations, and interventions that involved parents were implemented. Keywords: multigrade, reading, reading instruction, strategies, reading skills, last mile school
A Study on College English Education after COVID-19: Comparison of Learners' Perceptions of Cooperative Learning in Level-differentiated Face-to-face and Non-face to face Classes.

This study aims to compare learners' perceptions of online non-face-to-face versus face-to-face classes and examine how learners perceive cooperative learning used to improve communication skills. To this end, a survey was conducted on students who had taken level-differentiated college English classes in the first-semester non-face-to-face and the second-semester face-to-face in 2022. We found the following results. First, learners expressed a slightly lower level of satisfaction with face-to-face classes than with non-face-to-face classes. Second, high-level students in both non-face-to-face and face-to-face classes reported significantly higher satisfaction levels with the professors’ teaching ability and communication and interaction with students. Finally, in both non-face-to-face and face-to-face classes, it was found that students recognized cooperative learning through communication with the professors and interaction between learners as meaningful. In the post-COVID-19 era, a blended teaching method that combines the advantages of face-to-face and non-face-to-face education with cooperative learning will be a good way to improve learners’ communication skills and motivate them to learn. (Catholic Kwandong University)
Human Behavior Recognition via Hierarchical Patches Descriptor and Approximate Locality-Constrained Linear Coding

Human behavior recognition technology is widely adopted in intelligent surveillance, human-machine interaction, video retrieval, and ambient intelligence applications. To achieve efficient and accurate human behavior recognition, a unique approach based on the hierarchical patches descriptor (HPD) and approximate locality-constrained linear coding (ALLC) algorithm is proposed. The HPD is a detailed local feature description, and ALLC is a fast coding method, which makes it more computationally efficient than some competitive feature-coding methods. Firstly, energy image species were calculated to describe human behavior in a global manner. Secondly, an HPD was constructed to describe human behaviors in detail through the spatial pyramid matching method. Finally, ALLC was employed to encode the patches of each level, and a feature coding with good structural characteristics and local sparsity smoothness was obtained for recognition. The recognition experimental results on both Weizmann and DHA datasets demonstrated that the accuracy of five energy image species combined with HPD and ALLC was relatively high, scoring 100% in motion history image (MHI), 98.77% in motion energy image (MEI), 93.28% in average motion energy image (AMEI), 94.68% in enhanced motion energy image (EMEI), and 95.62% in motion entropy image (MEnI).
Resilience of Healthy Cities in the Post-pandemic Era: Findings Based on Internet of Things Data and Artificial Intelligence Algorithms

This study aims to improve urban resilience in the face of public health emergencies. Therefore, based on the urban resilience development theory, a resilience evaluation index system for healthy cities is constructed. In addition, the Back Propagation Neural Network (BPNN) algorithm and entropy method are combined to empirically evaluate 7 representative cities in the Yangtze River Delta urban agglomeration. Linear regression analysis is carried out on the index weight of the evaluation system, relative proximity degree, and score of urban resilience. The results reveal that social environment and infrastructure are two important factors in the resilience evaluation of healthy cities. Nanjing and Shanghai have relatively good resilience, while Ma'anshan has the worst resilience. It is also found that economic status is the most critical factor affecting urban resilience. Thus, this study enriches the theories related to urban resilience, expands the evaluation system of urban resilience, and opens up a new approach to the quantitative evaluation of urban resilience.
Reward shaping with hierarchical graph topology

Reward shaping using GCNs is a popular research area in reinforcement learning. However, it is difficult to shape potential functions for complicated tasks. In this paper, we develop Reward Shaping with Hierarchical Graph Topology (HGT). HGT propagates information about the rewards through the message passing mechanism, which can be used as potential functions for reward shaping. We describe reinforcement learning by a probability graph model. Then we generate a underlying graph with each state is a node and edges represent transition probabilities between states. In order to prominently shape potential functions for complex environments, HGT divides the underlying graph constructed from states into multiple subgraphs. Since these subgraphs provide a representation of multiple logical relationships between states in the Markov decision process, the aggregation process rich correlation information between nodes, which makes the propagated messages more powerful. When compared to cutting-edge RL techniques, HGT achieves faster learning rates in experiments on Atari and Mujoco tasks.
IMPLICATIONS OF THE INTRODUCTION OF RETAIL DIGITAL CURRENCIES BY CENTRAL BANKS FOR INTERNATIONAL ECONOMIC RELATIONS

The article systematizes the consequences of the introduction into circulation of the so-called third form of money – retail digital currencies of central banks. Forms of money are evolving under the influence of various factors, and the current period is characterized by the spread of both private digital assets and digital currencies of central banks. Empirical experience has not yet been accumulated, and the article reviews the results of current theoretical research on the possible consequences of the possibility of issuing digital currencies of central banks, presents possible characteristics of this form of money. The further spread of the studied digital currencies depends on their configuration and on the settings of the so-called currency design, the variants of which are presented in the article. The authors analyze the consequences of the introduction of retail digital currencies from the perspective of their possible impact on the transmission mechanism, on the structure of monetary circulation, as well as on the use of the national currency as a global means of payment. It is concluded that the formation of new models of the functioning of monetary systems, taking into account the spread of digital currencies of central banks, will change the existing economic, financial and trade relations both within individual states and in the global space.
Conversation-Based Assessments in Education: Design, Implementation, and Cognitive Walkthroughs for Usability Testing

Conversational agents have been widely used in education to support student learning. There have been recent attempts to design and use conversational agents to conduct assessments (i.e., conversation-based assessments: CBA). In this study, we developed CBA with constructed and selected-response tests using Rasa—an artificial intelligence-based tool. CBA was deployed via Google Chat to support formative assessment. We evaluated (1) its performance in answering students’ responses and (2) its usability with cognitive walkthroughs conducted by external evaluators. CBA with constructed-response tests consistently matched student responses to the appropriate conversation paths in most cases. In comparison, CBA with selected-response tests demonstrated perfect accuracy between system design and implementation. A cognitive walkthrough of CBA showed its usability as well as several potential issues that could be improved. Participating students did not experience these issues, however, we reported them to help researchers, designers, and practitioners improve the assessment experience for students using CBA.
Exploring Fragmented Data: Environments, People and the Senses in Virtual Reality

Abstract Taking into consideration that archaeologists and historians are today more frequently encouraged to think in terms of digital transduction of historical materials, this chapter focuses on the potentials and pitfalls of ‘visualizing’ ‘recreating’ and ‘re-enacting/experiencing the senses’ in Virtual Reality (thereon VR) environments. More precisely, we focus on the very idea of sensory immersion for archaeological enquiry, research, study and dissemination. This chapter draws upon four VR projects at Humlab, Umeå University. The first is an example of using archaeological data for supporting the interpretation process in a Mesolithic site, environment from GIS to an immersive platform. The second is a result of collaborative work with the project ancient dance modern dancers (Slaney et al. 2018) in capturing the intangible art of Roman Pantomime in the theatre of Pompeii on Virtual Reality. The third is the implementation of interactive tools for an immersive study of photogrammetric models of medieval rock-cut settlements while the fourth is an assessment of the implementation of VR Google Earth in teaching ancient topography for undergraduate archaeology students. We show how important and interesting research is made in the process of tool experimentation and tool development.
Dynamic Programming on a Quantum Annealer: Solving the RBC Model

We introduce a novel approach to solving dynamic programming problems, such as those in many economic models, on a quantum annealer, a specialized device that performs combinatorial optimization. Quantum annealers attempt to solve an NP-hard problem by starting in a quantum superposition of all states and generating candidate global solutions in milliseconds, irrespective of problem size. Using existing quantum hardware, we achieve an order-of-magnitude speed-up in solving the real business cycle model over benchmarks in the literature. We also provide a detailed introduction to quantum annealing and discuss its potential use for more challenging economic problems.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
ACADEMIC MOBILITY: EUROPEAN AND ORIENTAL VECTORS OF COOPERATION

The article deals with the issues related to the international academic mobility. Some key notions within the academic sphere have been specified (academic mobility, academic integration, academic normative documents). The author outlines the European and Oriental vectors of academic cooperation (involvement of the academic staff and students into the programmes of international advanced training and / or internship; joint participation in scientific grant programmes; international co-supervision of Bachelor, Master, PhD research).
A Novel OPT-GBoost Approach for Predicting Direct Normal Irradiance

An accurate estimation of a location’s solar potential is critical for solar system evaluation. Solar radiation variation and the absence of measuring stations at each site necessitate the development of credible prediction models. For accurate projections of solar plant production, a high forecast accuracy of direct normal irradiance is necessary, which has increased solar plant profitability. The OPT-GBoost model proposed in this paper uses an enhanced XGBoost classifier to estimate direct normal irradiance. We require extensive hyper-parameter tuning to design a more effective system that employs a classifier. As a result, we updated XGBoost’s hyper-values and trained the model with the new parameters obtained from the hyperparameter adjustment system OPTUNA. The validation of the model was conducted using the National Solar Radiation Database (NRSDB). We obtained superior results with our approach compared to other systems proposed in recent years by other authors. For example, using the coefficient of determination R2, we reached 99.96% in the NRSDB database.
MULTICULTURAL COMPETENCE AS A COMPONENT OF THE PROFESSIONAL COMPETENCE OF PRE-SERVICE PRE-SCHOOL EDUCATORS

The article presents a theoretical analysis of the problem related to the development of pre-service pre-school educators’ multicultural competence; the approaches of scientists to the definition of the phenomenon "multicultural competence" have been summarized; the essence of multicultural competence as a theoretical and practical readiness to carry out of pre-service educators’ professional activities under conditions of a multicultural society has been revealed; a survey of applicants for the specialty 012 Pre-school education was conducted to determine the state of multicultural competence formation; some ways of further scientific research in the context of multicultural education have been defined.
ECO-TRANSLATOLOGY ENGLISH-CHINESE TRANSLATION TECHNIQUES FOR EST

Based on the analysis of translation examples, this paper studies the application of translation selection adaptation theory in English-Chinese translation of scientific and technological texts from the microscopic point of view, and puts forward three English-Chinese translation techniques of EST from linguistic, cultural and communicative dimensions: appropriately reducing prepositions and conjunctions, researching culture loaded expressions and appropriately adjusting sentence group logic chain.
A progressive segmentation with weight contrast label enhancement for weakly supervised video salient object detection

Abstract Scribble labels have gained increasing attention in the field of weakly supervised video salient object detection (VSOD). Based on scribble labels, latest methods can spread labeled pixels to unlabeled regions using local coherence loss, but predicted objects often lose detail and boundary information. In this work, a novel method based on back‐foreground weight contrast is proposed that adds label enhancement points to facilitate the model to learn the edge, detail and location of salient object. Additionally, a new VSOD framework based on global structural localization is introduced. Enhanced scribble labels are used to assist the model for global localization, and then the located regions are finely segmented by the trained model. Extensive experiments demonstrate that the method achieves the state‐of‐the‐art performance on common VSOD datasets, with an improvement of 3.75%, 4.68%, and 0.88% in S‐measure, F‐measure, and MAE, respectively.
Two-stage sparse multi-kernel optimization classifier method for more accurate and explainable prediction

Since many classifier methods cannot identify and remove redundant observations and unrelated attributes from data, they usually give more inconsistent classification between actual and predicted outputs. Introducing single- or multi-kernel functions to classifier models helps to solve non-linearly separable problems, but it reduces the predictive interpretability. In this paper, we put forward a novel two-stage sparse multi-kernel optimization classifier (TSMOC) method under the framework of combining support vector classifier (SVC) and multiple kernel learning (MKL), aiming to solve the above issues. With our defined row and column multi-kernel matrices, the proposed method employs iterative updates to compute the ℓ0- norm approximations of coefficients and weights, which extract important observations and attributes besides prediction. Based on the experimental results on thirteen real-world datasets, TSMOC generally outperforms the other seven classifiers of SVC, ℓ1- norm SVC, least-squares SVC, LASSO classifier, SimpleMKL, EasyMKL, and DeepMKL. Besides obtaining the best classification accuracy, TSMOC extracts the smallest number of observations and attributes important to prediction and it can provide explainable prediction with their contribution percentages.
Robot trajectory planning for autonomous 3D reconstruction of cockpit in aircraft final assembly testing

The trend towards automation and intelligence in aircraft final assembly testing has led to a new demand for autonomous perception of unknown cockpit operation scenes in robotic collaborative airborne system testing. To address this demand, a robotic automated 3D reconstruction cell which enables to autonomously plan the robot end-camera's trajectory is developed for image acquisition and 3D modeling of the cockpit operation scene. A continuous viewpoint path planning algorithm is proposed that incorporates both 3D reconstruction quality and robot path quality into optimization process. Smoothness metrics for viewpoint position paths and orientation paths are introduced together for the first time in 3D reconstruction. To ensure safe and effective movement, two spatial constraints, Domain of View Admissible Position (DVAP) and Domain of View Admissible Orientation (DVAO), are implemented to account for robot reachability and collision avoidance. By using diffeomorphism mapping, the orientation path is transformed into 3D, consistent with the position path. Both orientation and position paths can be optimized in a unified framework to maximize the gain of reconstruction quality and path smoothness within DVAP and DVAO. The reconstruction cell is capable of automatic data acquisition and fine scene modeling, using the generated robot C-space trajectory. Simulation and physical scene experiments have confirmed the effectiveness of the proposed method to achieve high-precision 3D reconstruction while optimizing robot motion quality.
Background Replacement in Video Conferencing

Article Background Replacement in Video Conferencing Kiran Shahi *, and Yongmin Li 1 Department of Computer Science, Brunel University London, Uxbridge, UB8 3PH, United Kingdom * Correspondence: 2144420@alumni.brunel.ac.uk Received: 17 March 2023 Accepted: 24 April 2023 Published: 23 June 2023 Abstract: Background replacement is one of the most used features in video conferencing applications by many people, perhaps mainly for privacy protection, but also for other purposes such as branding, marketing and promoting professionalism. However, the existing applications in video conference tools have serious limitations. Most applications tend to generate strong artefacts (while there is a slight change in the perspective of the background), or require green screens to avoid such artefacts, which results in an unnatural background or even exposes the original background to other users in the video conference. In this work, we aim to study the relationship between the foreground and background in real-time videos. Three different methods are presented and evaluated, including the baseline U-Net, the lightweight U-Net MobileNet, and the U-Net MobileNet&amp;ConvLSTM models. The above models are trained on public datasets for image segmentation. Experimental results show that both the lightweight U-Net MobileNet and the U-Net MobileNet&amp; ConvLSTM models achieve superior performance as compared to the baseline U-Net model.