Reviewerly/exHarmony huggingface dataset:
Verification of the integration process of algorithms and sensor data for mental health applications

Mood disorders are becoming more frequent and, especially after the pandemic years, the importance of analysing and preventing such disorders has become clear. In the worst cases, people can suffer from depression or bipolar disorder leading to hospitalization or sick leave with serious economic and social consequences for the individual and their environment. Thanks to the development of technology, there are increasingly useful portable devices for monitoring individual daily activity. The data collected is very useful for understanding not only the individual's environment but also for characterizing their emotional profile. However, good monitoring requires the use of a diverse set of information sources. Medical consultations are the traditional source of information but in many cases this information is lacking or insufficient. The new sources of information are diverse: smart devices such as smart watches or even the mobile phone itself, portable sensors of various types and even activity records on social networks. All these data can be integrated and processed in such a way that a characterization profile of behaviour related to the emotional state of the individual is determined. However, the manufacturers of these devices apply aggregation algorithms to the monitored data to provide the client with a friendlier and easier to interpret version, but they do not usually provide the raw data collected by the sensors (accelerometer, gyroscope, temperature, etc.). There is still no regulated standardization that obliges the manufacturer to provide the data to the owners of the devices. Both raw data and aggregation algorithms work like a black box in most cases. The information presented by the device applications (generally apps) is very relevant and the interpretation of the users has direct consequences on their behaviour (behaviour modification, medication administration, etc.). For this reason, it is essential to verify the algorithms used in the previous process, guaranteeing that the information integrated (and presented) really corresponds to the information collected by the sensors. In this paper we present a suitable system for the verification of aggregated data from personal activity monitoring sensors. The system includes a parsing algorithm that makes the data structure and relates it to the output. The effectiveness of the algorithm has been tested with real data over a period of two years and for both daytime activity and sleep quality monitoring. The algorithm is perfectly scalable to be used on any device, so the computer system presented can be useful for future computer auditing of this type of process.
Imersive Virtual Reality during work out with movable sports equipment: the effect on oculomotor, disorientation, and nausea before and after training

The combination of immersive virtual reality and sport has garnered significant attention in the academic world and the industry domains. There is a decent amount of literature that shows positive effects but also critical voices when it comes to learning and wellbeing in a virtual world during work out. In our study we postulate the hypothesis that subjects with a higher degree of predisposition in simulation sickness will show higher values in all three subscales of the simulation sickness questionnaire (SSQ) while doing exercise with sports gear with moveable parts in a fully immersive VR application than subjects with lower degrees of predisposition in motion sickness. However, our data did not support the hypothesis. Surprisingly, we even found a disproportionate significant improvement in the oculomotor subscale of the SSQ. These results are being discussed and further studies are being suggested.
Design of an Efficient NTT/INTT Architecture with Low-Complex Memory Mapping Scheme

Number Theoretic Transform (NTT) and Inverse NTT (INTT) are the key algorithms used to speed up polynomial multiplications in post-quantum cryptography (PQC). This brief proposes a cross-bank-write-back (CBWB) memory mapping scheme for memory-based NTT/INTT architecture with conflict-free access patterns. The proposed scheme enables a light-weight memory access patterns for high-radix butterflies. Moreover, it makes the datapath between the memory banks and the butterfly unit straightforward and hardware-friendly. Based on the CBWB scheme, this work also proposes a solution to adopt a single decimation strategy for both NTT and INTT in PQC accelerators to reduce hardware resources. As a case study, an efficient NTT/INTT architecture has been implemented on the FPGA using the parameters of the PQC algorithm Falcon. Experimental results show that our design achieves better Area-Time Product (ATP) compared to the state-of-the-art designs.
Towards the quantum candela: a calibrated flux of five million single photons per second delivered by a single-mode fiber

In this work we demonstrate a record optical power of single-photon flux measured on a SI-traceable trap detector. This flux was measured using a single-mode fiber, allowing for the absolute calibration of a superconducting nanowire detector. Single-photons from an InGaAs quantum dot in a micropillar cavity were determined to have an optical power of 1.06±0.03pW or 4.95±0.14 ×10^6 photons per second as measured on the calibrated trap detector. The single-photon fluorescence at this power exhibited a g(2)(τ=0) of 0.13.
Research Trends in Online Learning in Higher Education: A Systematic Review on Student Emotion (2017 – 2021)

Online learning enables learners to learn everywhere and at any time in various forms and various educational activities including full-online, hybrid, blended, synchronous, and asynchronous.During the participation in these educational activities, emotions and personality play a significant role in parts of adaptive online learning systems among students.There is no review examining the broader aspect of research themes in online learning with student emotion in the last recent five years.Therefore, this paper presents a systematic review of university students' emotions in connection with virtual learning based on 50 articles published between 2017 and 2021.Hence, the objective was to analyse the articles for concepts and theoretical background related to virtual learning and emotions and examined for publication trends and patterns, research themes, research methods, and research settings and compared with the research themes from the previous decades.This study is through systematic literature reviews by descriptive research based on the document analysis on previous studies and literature on virtual learning and emotions from online journals, gained from web-based journals and articles indexed by Web of Science (WOS) and Scopus.The general finding of this article shows that 70% of the articles used quantitative methods and mixed methods.Students' emotions were mainly studied through online learning.Furthermore, the findings display that online learning in higher education is a growing field as evidenced by the increasing variety of research topics, methods, and researchers.All of the results were presented in detail with the relevant literature and recommendations for further research.
Research on LG Chem’s Spin-off of LG Energy Solutions and ESG Management

[Purpose]This study analyzes the accounting treatment for the corporate spin-off through a case analysis, and examines the relationship between the corporate spin-off and ESG management. Specifically, we study the accounting treatment for the establishment of LG Energy Solutions by LG Chem in December 2020, and examine the impact of the spin-off decision on the ESG management of LG Chem and LG Energy Solutions.&#x0D; [Methodology]We examine the accounting treatment on the spin-off in accordance with the K-IFRS and analyze the detailed accounting process through the audit report of LG Chem and LG Energy Solutions. In addition, by comparing the ESG ratings of LG Chem and LG Energy Solutions with companies in the same industry, we review the relationship between spin-off and ESG management.&#x0D; [Findings]LG Chem operates the petrochemical business, battery business, advanced materials business, and life science business. Among them, the petrochemical business and battery business account for more than 80% of total revenue and net assets. LG Chem has received relatively lower grades than its competitors in the environment sector of ESG rating. This implies the possibility of suffering losses in the ESG rating due to the petrochemical business, despite the possibility of receiving better rating in the battery business. In order to improve the ESG rating, LG Chem needs to increase the year-on-year improvement rate of each indicator.&#x0D; [Implications]The case analysis of this study is expected to contribute to accounting education by examining the accounting treatment of the corporate spin-off and analyzing the case of LG Chem in detail. In addition, it is meaningful that it has expanded previous research by anlyzing the relation between the corporate spin-off and ESG management which is a non-financial factor.
A study on the fixed-term teachers profession from the view of Myeongli Science

Since the right choice of a job can be an essential task to pursue social and economic stability, interest and demand for career aptitude tests are on the rise, especially for college students and job seekers. I am continuing to study the occupational characteristics of various fields in order to expand the area of use of Myeongli science (命理學), a traditional study in the East, as a realistic alternative to career aptitude tests.&#x0D; In this paper, a sample of 13 teachers currently working as fixed-term teachers at public elementary, middle, and high schools in Korea and 7 teachers who started teaching on a fixed-term basis but passed the teacher appointment test in the middle and are currently working as regular teachers are analyzed. analyzed.&#x0D; As a result of the case analysis, it was confirmed that some of the nine disadvantageous factors, such as vigorous activity(亂立) or biased(偏重), shockinduced movement(衝動), helplessness(無力), or lack(不足), were mixed in Gyeok(格) and Yongshin (用神), Ilgan (日干), Sipseong (十星). Of the total 20 people Saju (四柱), 4 used Gwanseong(官星) as Gyeok(格), and 10 used it as Kyuk-yongsin(格用神) or Chofu-Yongshin(調候用神). As a result of analyzing the trend of luck in the year of passing the teacher appointment test of seven former fixed-term teachers (currently regular teachers), four were helped by Gwanseong-un(官星運), and three were supplemented with JaeSung-un(財星運) and Inseong-un(印星運), which coexist with inertia.&#x0D; Due to the nature of the teacher's job of acquiring and educating knowledge through learning, the interaction power of the sigjaegwan-in(食財官印) is necessary to the extent that it is difficult to determine the importance, so partial bias or lack can weaken the link between win-win. Therefore, it was confirmed that the win-win structure needs to be stably constructed in harmony with balance, and among them, it was confirmed that the difference between fixed-term teachers and regular teachers was significant in the status of education officials, so the weight and role of Gwanseong(官星) were significant.
Certified Logic-Based Explainable AI – The Case of Monotonic Classifiers

The continued advances in artificial intelligence (AI), including those in machine learning (ML), raise concerns regarding their deployment in high-risk and safety-critical domains. Motivated by these concerns, there have been calls for the verification of systems of AI, including their explanation. Nevertheless, tools for the verification of systems of AI are complex, and so error-prone. This paper describes one initial effort towards the certification of logic-based explainability algorithms, focusing on monotonic classifiers. Concretely, the paper starts by using the proof assistant Coq to prove the correctness of recently proposed algorithms for explaining monotonic classifiers. Then, the paper proves that the algorithms devised for monotonic classifiers can be applied to the larger family of stable classifiers. Finally, confidence code, extracted from the proofs of correctness, is used for computing explanations that are guaranteed to be correct. The experimental results included in the paper show the scalability of the proposed approach for certifying explanations.
Factors Predicting Teachers’ Job Satisfaction in Korean Secondary Schools

T is research aims to reveal the combinations of conditions leading to Korean teacher job satisfaction.Te data of this study are derived from the 2018 Teaching and Learning International Survey (TALIS).Teacher job satisfaction was approached as a composite scale with two dimensions: job satisfaction with profession and job satisfaction with work environment.Te explanatory variables were preparation for pedagogical content knowledge, teacher self-efficacy in general, teacher self-efficacy in multicultural classrooms, teachers' innovative climate, teacher-student relations, social perceptions of teacher value, and professional development barriers.Te findings using fsQCA are as follows; 1) the presence of social perceptions of teacher value is a necessary condition for a high level of job satisfaction with the profession.2) Four distinct paths were extracted to explain the high level of job satisfaction with the profession.3) Four distinct paths were shown clearly to explain the low level of job satisfaction with the profession.4) None of the conditions is a necessary condition for job satisfaction with the work environment.5) Five distinct paths were singled out to explain the high level of job satisfaction with the work environment.6) Tree distinct paths were identified to explain the low level of job satisfaction with the work environment.Te findings revealed Korean teacher job satisfaction in multicultural secondary schools by outlining several combinations of factors that lead to paths explaining job satisfaction with profession and job satisfaction with work environment.Some implications and a few recommendations to improve teacher job satisfaction are suggested.
Physical education graduate students' learning about innovation skill development

The cultivation of the innovation ability of students is of great significance for the development of a country and the growth of individuals.However, the current cultivation of the innovation ability of postgraduates majoring in physical education has some shortcomings temporarily, such as the lack of the cultivation of innovation ability in the curriculum system, and the shortage of an atmosphere to stimulate innovation ability in the learning environment and the monotony of teaching method.To this end, our study blends motivation theory and practical experience into the cultivation system of postgraduates majoring in physical education, puts forward corresponding construction idea and methods based on inquiry-based teaching method, conducts a deep analysis on the characteristics of the cultivation of postgraduates majoring in physical education and constructs a cultivation path for the innovation ability of postgraduates majoring in physical education based on motivation theory, with an aim to accelerate the construction of the specialty of physical education, quickly improve the teaching level of physical education in colleges and universities and foster excellent senior talents majoring in physical education for sports and educational undertakings.
Vision-based Real-Time Disaster Recognition Monitoring System using Raspberry Pi and Deep Learning Model

Natural disasters are destructive forces that greatly affect all people around the world. In the Philippines, earthquakes, tropical cyclones, and floods are some of the most frequent disasters that struck the country in recent years. Several studies utilizing technology have been conducted with varying degrees of success to reduce the impact of these uncontrollable events. Different from others, this paper investigates the use of a Deep Learning model deployed in a Raspberry Pi 3b for on-the-ground, real-time, automated disaster recognition and monitoring. It aims to empower emergency responders and people in the community to easily detect disasters as they happen in real-time, reducing the loss of life and damage to property. To this end, a novel low-cost monitoring system is proposed. Experiments and a survey made to emergency responders were conducted to validate the system’s feasibility and acceptability. Results revealed that the proposed system detects disasters with a high degree of performance. Also, it utilizes a low CPU and memory footprint while achieving seven frames per second processing rate during disaster recognition. In addition, the respondents find the system clear, helpful, innovative, and easy to use. Hence, the system is capable of recognizing disasters in real-time, proving acceptable and beneficial to people in the community.
Aspect-Oriented Lexicon-Based Sentiment Analysis of Students’ Feedback

The evaluation of feedback collected from students at the end of the year is very essential for every educational institution. It is important to improve the teaching–learning process and the annual appraisal process. The existing approach utilizes a Likert scale questionnaire, which allows students to express their level of agreement or disagreement with given statements or provide a neutral response. Additionally, the feedback form includes open-ended questions where students can provide textual feedback. This study introduces a Lexicon-based approach to automatically analyze the textual feedback concerning different aspects of teaching. Aspect-based Sentiment Analysis (ABSA) of student feedback aims to identify sentiments expressed toward various aspects of teachers, such as their ability to address student doubts and their overall knowledge. This study explores linguistic characteristics found in sentences, including negation, modifiers and contact shifters. To assess the sentiment of a sentence, the SentiWordNet lexicon is utilized to assign scores to individual words. Based on these scores, the sentence is categorized as either positive, negative or neutral. According to the experimental findings, the Aspect-Oriented Lexicon-Based (AOLB) approach demonstrates superior performance compared to other baseline methods when it comes to accurately scoring sentiment. The approach achieved a high accuracy rate of 94% for the student feedback dataset-I, 74% for the student feedback dataset-II, 55% for laptop reviews and 59% for restaurant reviews in the SemEval 2014 dataset-III.
Vocabee: Empowering Language Learning Through Descriptive Text - A Novel Approach to Vocabulary Instruction

This research was conducted to design Vocabee as a teaching and learning media to teach Vocabulary to the eighth-grade students of SMP Negeri 5 Hulu Gurung. The form of this research was Design and Development. This design dan development research was conducted to produce innovative teaching media. The method of this research was adapted from Branch (2009), that consists of three phases namely analyzing, designing, and developing. In the Analyze phase found that the actual conditions of students and the teacher. Design phase was done to prepare the appropriate material to create the application. Develop phase was done to create an essential part of the Vocabee using software called Articulate Storyline 3. The designed product was completed after have an expert validation regarding to the needs and already valid to be use by expert validator. As the result, Vocabee is useable to support the process of teaching and learning vocabulary to the eighth-grade students of SMP Negeri 5 Hulu Gurung.
Unleashing the Power of Clustering: Enhancing Descriptive Text Writing through Innovative Teaching Techniques

Students have to learn the two parts language skill, they are: receptive language skills and productive language skills. Receptive language skills include listening and reading. While, productive language skills include speaking and writing. One of the productive language skills discussed is writing. Productive language skill means language has output as language learning’s outcome. Because of that, when students are writing, students need to express their thoughts to create a written deliverable. The objectives of the research are: 1) to find out the ability in writing descriptive text of the students taught without using clustering technique, 2) to find out the ability in writing descriptive text of the students taught using clustering technique, 3) to find out whether or not there is a significant difference of the writing ability in descriptive text between the students taught using clustering technique and those taught without using clustering technique. This research used quasi-experimental quantitative with pre- and post- tests. In quasi-experimental quantitative research, there are two groups of participants one group receives a study treatment while the other does not. In this study, the researcher used clustering as a teaching approach, specifically for creating descriptive language. This study was carried out at SMP N 10 Semarang, which has a population of class VIII students. This study used a quasi-experimental approach with pre- and post-tests. The students were given a pre-test, a treatment, and a post-test. It took the writer eight meetings to collect data. In this study, the researcher employed VIII C as the experimental class of 31 students and VIII D as the cont trol class of 32 students. A test was conducted to determine the extent to which students’ writing skill increased without being taught by clustering technique, in other words, the students received the traditional treatment from an initial average value of 62.34 to a final average value of 60.59, the findings that were displayed from the descriptive test indicated that the average value of the class that was not provided treatment decreased by 1,75 points. Keywords: Writing, Clustering Technique, Descriptive Text
Alternating (In)Dependence-Friendly Logic

Hintikka and Sandu originally proposed Independence Friendly Logic () as a first-order logic of imperfect information to describe game-theoretic phenomena underlying the semantics of natural language. The logic allows for expressing independence constraints among quantified variables, in a similar vein to Henkin quantifiers, and has a nice game-theoretic semantics in terms of imperfect information games. However, the semantics exhibits some limitations, at least from a purely logical perspective. It treats the players asymmetrically, considering only one of the two players as having imperfect information when evaluating truth, resp., falsity, of a sentence. In addition, truth and falsity of sentences coincide with the existence of a uniform winning strategy for one of the two players in the semantic imperfect information game. As a consequence, does admit undetermined sentences, which are neither true nor false, thus failing the law of excluded middle. These idiosyncrasies limit its expressive power to the existential fragment of Second Order Logic (). In this paper, we investigate an extension of , called Alternating Dependence/Independence Friendly Logic (), tailored to overcome these limitations. To this end, we introduce a novel compositional semantics, generalising the one based on trumps proposed by Hodges for . The new semantics (i) allows for meaningfully restricting both players at the same time, (ii) enjoys the property of game-theoretic determinacy, (iii) item recovers the law of excluded middle for sentences, and (iv) grants the full descriptive power of . We also provide an equivalent Herbrand-Skolem semantics and a game-theoretic semantics for the prenex fragment of , the latter being defined in terms of a determined infinite-duration game that precisely captures the other two semantics on finite structures.