Reviewerly/exHarmony huggingface dataset:
The Application of Visualized Tools in English Writing Teaching from Perspective of Key Competence: A Survey Study

Writing, as one of the expressive language skills, plays a significant role in learning and teaching of English. In the new round of teaching reform oriented to "key competence", thinking visualization tools have been widely welcomed by teachers and students. What’s more, they are becoming an important tool for teaching transformation. Questionnaire, interview and classroom observation were implemented to analyze the current situation of writing tasks in high school English classrooms, to reveal its actual use in classroom teaching, to analyze teachers' understanding of thinking visualization tools and key competence, as well as to know about different levels of students' perceptions of using thinking visualization tools in writing, based on schema theory, visualization theory and constructivist theory. It is found that teachers were lack of the understanding of thinking visualization tools and key competence. Different thinking visualization tools were implemented in different learning stages. In addition, a part of thinking visualization tools is used by teachers as auxiliary teaching means, such as mind-map and concept-map. This study sheds light on the understanding and application of thinking visualization tools and key competence.
Exploring and Improving the Quality of Master's Degree Students in Local Universities

In recent years, influenced by the surge in the number of master's degree applicants and the new crown epidemic, the scale of graduate student admissions has maintained a relatively stable growth trend. While promoting the development of postgraduate education in China, the expansion of graduate students has also made universities face the challenge of declining quality of student sources. Therefore, this paper takes the total admission data of master's degree students from 2018 to 2022 of university A as the research sample, and analyzes the quality condition of student source of university A by analyzing the application ratio, overall admission, source of candidates and other basic conditions. It is found that: firstly, the admission ratio of university A is higher than the overall national admission ratio and reaches 5.63 in 2022. The proportion of admitted candidates of first-volunteer shows a wavy upward trend, while the proportion of admitted candidates of transfer is generally decreasing and shows a wavy downward trend. Secondly, the proportion of graduated students of double first-class construction universities among admitted candidates is relatively small, and the admitted candidates of A universities are mainly dominated by candidates of the first and second batches of undergraduates. Meanwhile,the percentage of admitted candidates of management and economics-related majors reached 70%. Thirdly, the analysis of the basic situation of admitted candidates found that the proportion of our students in the total admissions varied between 25% and 30%, which was relatively large. The proportion of admitted candidates from previous years is much higher than the proportion of admitted freshmen, with the proportion varying between 50% and 60%. The proportion of admitted candidates in the province is generally decreasing, but still occupies 70% of the total. In this paper, we analyze the situation related to the student source of college A, gain an in-depth understanding of its student source quality situation, and propose a path to improve the student source quality, which has important practical significance for college A to improve the student source quality structure and alleviate the continuous decline of student source quality.
Research on the Attribute Identification and Copyright Protection Mode of Online Game Works

In the context of today's big data, the game industry is developing rapidly, and the frequency of introducing new online games is becoming faster and faster. Online games have complex characteristics, consisting of many elements such as game dynamic screens, computer software, game rules, scene maps, character images, text introductions, and background music. In addition, according to the classification of game modes, online games are also divided into music, games, quizzes, cultivation, and other games. This article has studied the definition and classification of online games, the constitutive requirements of works in copyright law, and the determination of infringement through literature review. It has conducted different determinations of games as a whole and as a split. By analogy with domestic methods of determining audio-visual works and computer software works, it has summarized practical trends and past judicial cases, proposed to establish online games as a separate type of copyright works, and expounded its necessity and feasibility. At the same time, it proposes a protection mode of "integrating integration and separation" to protect the interests of all copyright owners to the greatest extent and more completely. This mode aims to protect the entire online games while also allowing some elements to be removed, which can further promote industry development and economic flow.
Basic Education and Reading Readiness Skills: An Appraisal of Early Childhood Kindergarten in Imo State, South-East of Nigeria

Reading is a basic tool of education and also a crucial aspect in the Nigerian language curriculum. As a receptive skill, it involves the ability to meaningfully interpret or decode written or graphic symbols of a language. It is not merely about how to pronounce words; it is significant that the children understand the meaning of the words they read in order to unfold the hidden treasure of knowledge and also participate in the words’ universal culture and civilization. It becomes imperative to kindle reading culture among the children who are faced with several competing leisure time activities. The early childhood or the kindergarten, being the best time to imbibe reading culture and early literacy skills in children, is also the time to get them reading ready. This research, therefore, was mainly initiated to investigate how reading readiness could enhance children’s early literacy and factors that affect reading readiness among the kindergarten in Imo State. The sample of the study was drawn from the population of nursery/ primary school teachers in the three educational zones using stratified sampling technique. The main objective of the study being to ascertain the essence of reading readiness in building early childhood literacy skill; thus, a qualitative research method was employed. The findings indicated that the majority of the kindergarten teachers are unqualified and inexperienced to impart reading culture into the children. Other factors are overcrowded classrooms and poor environment that prohibit one-to-one teacher/pupil relationship in the reading class. It was suggested that the government should always organize all-inclusive workshops to train the teachers on the steps in teaching reading. Also, parents should always be provided with regular information on the reading progress of their children for them to make their own input in the reading skill of their children. All the above will help to build a solid reading skill which will promote basic functional education in Imo State.
SmartRecorder: An IMU-based Video Tutorial Creation by Demonstration System for Smartphone Interaction Tasks

This work focuses on an active topic in the HCI community, namely tutorial creation by demonstration. We present a novel tool named SmartRecorder that facilitates people, without video editing skills, creating video tutorials for smartphone interaction tasks. As automatic interaction trace extraction is a key component to tutorial generation, we seek to tackle the challenges of automatically extracting user interaction traces on smartphones from screencasts. Uniquely, with respect to prior research in this field, we combine computer vision techniques with IMU-based sensing algorithms, and the technical evaluation results show the importance of smartphone IMU data in improving system performance. With the extracted key information of each step, SmartRecorder generates instructional content initially and provides tutorial creators with a tutorial refinement editor designed based on a high recall (99.38%) of key steps to revise the initial instructional content. Finally, SmartRecorder generates video tutorials based on refined instructional content. The results of the user study demonstrate that SmartRecorder allows non-experts to create smartphone usage video tutorials with less time and higher satisfaction from recipients.
Automatic and high-accuracy matching method for a blade inspection system integrating fringe projection profilometry and conoscopic holography

Abstract The geometric dimensions and tolerances of blades, which are critical parts of turbomachinery with complex features, must be strictly controlled to ensure the efficiency and safety of the engine. Optical-based inspection systems for blades are increasingly receiving attention because of their high efficiency and flexibility. However, as a key issue in blade inspection, the matching of the part coordinate system and machine coordinate system directly determines the measurement accuracy and automation. The blade surface is complex and has no obvious features, and accurate and rapid matching thus remains a challenging problem to solve. To overcome these problems and realize the accurate inspection of blade profiles, an automatic and high-accuracy matching method for a blade measurement system integrating fringe projection profilometry (FPP) and conoscopic holography (CH) is proposed in this paper. First, automatic rough matching is realized making use of the ability of FPP to quickly obtain high-resolution cloud of points and improving the four-point congruent sets algorithm. The path of the CH measurement based on the calibration and rough matching result is then planned, to sample high-precision and uniform cloud-of-points data on the blade surface. Finally, a fine matching optimization algorithm is implemented with the signal-to-noise ratio as the weight. The results of simulation experiments and inspection case studies demonstrate that the proposed matching method is efficient and accurate.
EarPPG: Securing Your Identity with Your Ears

Wearable devices have become indispensable gadgets in people's daily lives nowadays; especially wireless earphones have experienced unprecedented growth in recent years, which lead to increasing interest and explorations of user authentication techniques. Conventional user authentication methods embedded in wireless earphones that use microphones or other modalities are vulnerable to environmental factors, such as loud noises or occlusions. To address this limitation, we introduce EarPPG, a new biometric modality that takes advantage of the unique in-ear photoplethysmography (PPG) signals, altered by a user's unique speaking behaviors. When the user is speaking, muscle movements cause changes in the blood vessel geometry, inducing unique PPG signal variations. As speaking behaviors and PPG signals are unique, the EarPPG combines both biometric traits and presents a secure and obscure authentication solution. The system first detects and segments EarPPG signals and proceeds to extract effective features to construct a user authentication model with the 1D ReGRU network. We conducted comprehensive real-world evaluations with 25 human participants and achieved 94.84% accuracy, 0.95 precision, recall, and f1-score, respectively. Moreover, considering the practical implications, we conducted several extensive in-the-wild experiments, including body motions, occlusions, lighting, and permanence. Overall outcomes of this study possess the potential to be embedded in future smart earable devices.
D-Touch: Recognizing and Predicting Fine-grained Hand-face Touching Activities Using a Neck-mounted Wearable

This paper presents D-Touch, a neck-mounted wearable sensing system that can recognize and predict how a hand touches the face. It uses a neck-mounted infrared camera (IR), which takes pictures of the head from the neck. These IR camera images are processed and used to train a deep-learning model to recognize and predict touch time and positions. The study showed D-Touch distinguished 17 Facial related Activity (FrA), including 11 face touch positions and 6 other activities, with over 92.1% accuracy and predict the hand-touching T-zone from other FrA activities with an accuracy of 82.12% within 150 ms after the hand appeared in the camera. A study with 10 participants conducted in their homes without any constraints on participants showed that D-Touch can predict the hand-touching T-zone from other FrA activities with an accuracy of 72.3% within 150 ms after the camera saw the hand. Based on the study results, we further discuss the opportunities and challenges of deploying D-Touch in real-world scenarios.
FlexType: Flexible Text Input with a Small Set of Input Gestures

In many situations, it may be impractical or impossible to enter text by selecting precise locations on a physical or touchscreen keyboard. We present an ambiguous keyboard with four character groups that has potential applications for eyes-free text entry, as well as text entry using a single switch or a brain-computer interface. We develop a procedure for optimizing these character groupings based on a disambiguation algorithm that leverages a long-span language model. We produce both alphabetically-constrained and unconstrained character groups in an offline optimization experiment and compare them in a longitudinal user study. Our results did not show a significant difference between the constrained and unconstrained character groups after four hours of practice. As expected, participants had significantly more errors with the unconstrained groups in the first session, suggesting a higher barrier to learning the technique. We therefore recommend the alphabetically-constrained character groups, where participants were able to achieve an average entry rate of 12.0 words per minute with a 2.03% character error rate using a single hand and with no visual feedback.
VR-LENS: Super Learning-based Cybersickness Detection and Explainable AI-Guided Deployment in Virtual Reality

Virtual reality (VR) systems are known for their susceptibility to cybersickness, which can seriously hinder users' experience. Therefore, a plethora of recent research has proposed several automated methods based on machine learning (ML) and deep learning (DL) to detect cybersickness. However, these detection methods are perceived as computationally intensive and black-box methods. Thus, those techniques are neither trustworthy nor practical for deploying on standalone VR head-mounted displays (HMDs). This work presents an explainable artificial intelligence (XAI)-based framework VR-LENS for developing cybersickness detection ML models, explaining them, reducing their size, and deploying them in a Qualcomm Snapdragon 750G processor-based Samsung A52 device. Specifically, we first develop a novel super learning-based ensemble ML model for cybersickness detection. Next, we employ a post-hoc explanation method, such as SHapley Additive exPlanations (SHAP), Morris Sensitivity Analysis (MSA), Local Interpretable Model-Agnostic Explanations (LIME), and Partial Dependence Plot (PDP) to explain the expected results and identify the most dominant features. The super learner cybersickness model is then retrained using the identified dominant features. Our proposed method identified eye tracking, player position, and galvanic skin/heart rate response as the most dominant features for the integrated sensor, gameplay, and bio-physiological datasets. We also show that the proposed XAI-guided feature reduction significantly reduces the model training and inference time by 1.91X and 2.15X while maintaining baseline accuracy. For instance, using the integrated sensor dataset, our reduced super learner model outperforms the state-of-the-art works by classifying cybersickness into 4 classes (none, low, medium, and high) with an accuracy of and regressing (FMS 1–10) with a Root Mean Square Error (RMSE) of 0.03. Our proposed method can help researchers analyze, detect, and mitigate cybersickness in real time and deploy the super learner-based cybersickness detection model in standalone VR headsets.
Interviewing the Interviewer: AI-generated Insights to Help Conduct Candidate-centric Interviews

The most popular way to assess talent around the world is through interviews. Interviewers contribute substantially to candidate experience in many organizations' hiring strategies. There is a lack of comprehensive understanding of what makes for a good interview experience and how interviewers can conduct candidate-centric interviews. An exploratory study with 123 candidates revealed critical metrics about interviewer behavior that affects candidate experience. These metrics informed the design of our AI-driven SmartView system that provides automated post-interview feedback to Interviewers. Real-world deployment of the system was conducted for three weeks with 35 interviewers. According to our study, most interviewers found that SmartView insights helped identify areas for improvement and could assist them in improving their interviewing skills.
Masktrap: Designing and Identifying Gestures to Transform Mask Strap into an Input Interface

Embedding technology into day-to-day wearables and creating smart devices such as smartwatches and smart-glasses has been a growing area of interest. In this paper, we explore the interaction around face masks, a common accessory worn by many to prevent the spread of infectious diseases. Particularly, we propose a method of using the straps of a face mask as an input medium. We identified a set of plausible gestures on mask straps through an elicitation study (N = 20), in which the participants proposed different gestures for a given referent. We then developed a prototype to identify the gestures performed on the mask straps and present the recognition accuracy from a user study with eight participants. Our results show the system achieves 93.07% classification accuracy for 12 gestures.
Pearl: A Technology Probe for Machine-Assisted Reflection on Personal Data

Reflection on one's personal data can be an effective tool for supporting wellbeing. However, current wellbeing reflection support tools tend to offer a one-size-fits-all approach, ignoring the diversity of people's wellbeing goals and their agency in the self-reflection process. In this work, we identify an opportunity to help people work toward their wellbeing goals by empowering them to reflect on their data on their own terms. Through a formative study, we inform the design and implementation of Pearl, a workplace wellbeing reflection support tool that allows users to explore their personal data in relation to their wellbeing goal. Pearl is a calendar-based interactive machine teaching system that allows users to visualize data sources and tag regions of interest on their calendar. In return, the system provides insights about these tags that can be saved to a reflection journal. We used Pearl as a technology probe with 12 participants without data science expertise and found that all participants successfully gained insights into their workplace wellbeing. In our analysis, we discuss how Pearl's capabilities facilitate insights, the role of machine assistance in the self-reflection process, and the data sources that participants found most insightful. We conclude with design dimensions for intelligent reflection support systems as inspiration for future work.
R-Calculus for Simplified Propositional Logics

Assume that the logical language of propositional logic contains three logical symbols: $$\lnot , \wedge , \vee .$$ There is a classical Gentzen deduction system $$\textbf{G}$$ for propositional logic which is sound and complete with respect to classical semantics (Li 2010; Mendelson 1964; Takeuti 1987).
R-Calculi $$\textbf{R}^{Q_1iQ_2j}/\textbf{R}_{Q_1iQ_2j}$$

We consider sequents of form $$\textbf{G}^{Q_1iQ_2j}$$ and co-sequents of form $$\textbf{G}_{Q_1iQ_2j}$$ (Li 2010; Takeuti and Barwise 1987) and corresponding R-calculi $$\textbf{R}^{Q_1iQ_2j}$$ and $$\textbf{R}_{Q_1iQ_2j}$$ (Li (2007)), where $$Q_1, Q_2\in \mathbf{A ,\textbf{E} }$$ and $$i,j\in {0,1}.$$
From Data Processing to Blockchain Networking: A Recount and Projection of Information Systems Research

In the past seventy years since the sale of the first mainframe computer by IBM in 1953, the world has become an information society that relies on networked computing systems in all aspects of work and life. Along with the evolution of computing, Information Systems has become an indispensable discipline in business and management that serves the needs of other managerial disciplines and performs its own business functions. Further information technology (IT) has become a sine qua non for the survival of a business and a source of much competitive advantage. Without advanced IT systems, an organization cannot survive for long today. In this paper, we recount and evaluate the evolution of Information Systems research since its infancy in the 1980s by empirically analyzing the research that has been published in leading academic journals. We found that research in Information Systems has evolved through several stages. During this evolution the basic disciplines from which researchers have drawn have also shifted considerably. Over this period the underlying technology support for the research has also developed enormously from simple data processing to systems development, client-server networks, electronic commerce, several generations of the Web (1.0, 2.0 and 3.0) and blockchain. Each new technology generation has spawned its own unique set of research issues and in turn influenced the research agenda of the scholars in the IS field in terms of research topics, methods, and theories. History offers a mirror to the past and a lesson for the present, and thus we believe that the results of this study should provide instructional value for new researchers such as doctoral students in Information Systems and other related disciplines.
Perception of the use of Blockchain and its impact on transparency in public institutions

The arrival of Blockchain provides solutions to institutions in different sectors such as finance, also in other areas, such as accounting, audits, legal aspects, smart contracts, the supply chain and the transparency of institutions, which contributes in better management. Hence, this work was focused on investigating 2 objectives, the perception of Blockchain and its impact on the transparency of information in institutions, as well as the existing correlation of both variables. In this work, a descriptive and correlational analytical methodology was used, the sample was made up of 33 university professors and 142 students who have entered the labor sector through their internships and internship processes or are already working and are recent entrants. . The information was collected through a structured questionnaire, with alternatives of multiple answers. The findings showed regarding one of the objectives that the perception of Blockchain is acceptable, understanding its potential that it offers. Regarding the second objective, a correlation of 0.771 was evidenced at a significance level of 0.029, indicating that there is a high and statistically significant relationship between the perception variables on Blockchain and its impact on the transparency of information in institutions.