Reviewerly/exHarmony huggingface dataset:
A Critical Discourse Study of the Construction of Alibaba’s Corporate Image in Chinese and American Newspapers

With the development of China’s digital economy, the corporate image of Chinese Internet enterprise has also become a focus of academic attention. In order to study the corporate image of Chinese Internet enterprises in the digital economy period, this paper takes Alibaba, a leading Chinese Internet enterprise, as the research object. This paper establishes Chinese News Corpus and English News Corpus and analyze characteristics of lexical collocation, concordance line and semantic prosody. It is found that by categorizing Alibaba’s strong collocations, the images of Alibaba’s enterprises that Chinese and American newspapers focus on include the image of the enterprise’s workers, products, services. It is concluded that the image of Alibaba’s employees has a positive influence in Chinese newspapers, while the image in American newspapers is more negative, including sexism. The image of Taobao, Alibaba’s main product, is a platform that contributes to raises the income of the rural population in the Chinese newspaper, while it is a platform for fake products in the American newspaper. Alibaba’s services are portrayed in the Chinese newspaper as diverse services platform and a provider of digital solutions for China’s smart cities, but in the U.S. press as a monopolistic platform subject to the Chinese government.
Development of a Generalized Model for the Protection of a Critical Infrastructure Object from the Destructive Impact of Air Attack Means

Purpose: development of a generalized model for the protection of critical infrastructure objects from the destructive action of an air attack. Theoretical framework: based on the analysis of the use of cruise missiles with radar correlation-extreme algorithms to damage critical infrastructure objects in the conditions of the Russian-Ukrainian war, a generalized model of the protection of critical infrastructure objects from the destructive action of an air attack has been developed. Methods: determined by a set of solved scientific and research tasks and carried out using: methods of system analysis - during studies of the distribution function of the electronic subsystem; numerical modeling methods - when studying the main electrophysical properties of critical infrastructure objects. Results and conclusions: a generalized model of the protection of a critical infrastructure facility against the destructive action of air attack tools has been developed, which allows for the assessment of risks and their management. The developed model can quantify uncertainties, simulate potential scenarios and assess the impact of various factors on the level of risk. This allows decision makers to make informed choices and develop strategies to mitigate risks. Finding implication: the research carried out in the article makes it possible to develop a mathematical model for ensuring the protection of critical infrastructure objects from the destructive impact of various types of air attack means. Uniqueness /value: a mathematical model of the protection of a critical infrastructure object from the destructive effects of air attack provides a powerful basis for understanding, predicting and optimizing air defense, contributing to progress in various fields and enabling evidence-based decision-making.
Exploring the Experiences of School Information Coordinators (SIC) in the New Normal Education

The School Information Coordinators (SIC) ensure efficient communication and information are coordinated and strengthened in their respective schools, especially in the new normal.Despite its successful designation, challenges were experienced in performing their roles, which hampers their effectiveness and functions.This qualitative case study explored the experiences of 12 SIC by interviewing them about their performance of their duties in the new normal education.The School Information Coordinators' duties and responsibilities in the new normal involve verifying information, facilitating the conduct of information, and helping to address the schools' issues and concerns.Furthermore, strategies to disseminate information include initiating collaborative efforts, strengthening relationships, being knowledgeable on tasks, and doing technologically inclined practices.Their challenges include poor internet connection and overloading of functions and responsibilities.However, in dealing with these challenges, School Information Coordinators applied resourceful in problem-solving, increasing work efforts, and seeking help and support.School Information Coordinators encountered different challenges, but efforts to overcome challenges were made possible by applying creativity and flexibility to their assigned designation.
The Integration of Technological Devices in Mathematics Education: A Literature Review

The emergence into the new era requires teachers to employ technological devices to meet the requirements of their students in mathematics education.The incorporation of technology into mathematics learning has the potential to promote teaching and learning of mathematics because these devices can increase effectiveness by means of their features.The use of technology in mathematics education can foster thinking skills, achievement, motivation, and self-efficacy of learners.Mathematics learning requires logical reasoning and problem-solving abilities.Through using textbooks, it is not easy to develop these skills of students; however, technological tools can create an authentic learning environment for students in which they experience learning and promote deeper understanding of mathematical concepts.This article explores the benefits of employing technology in mathematics education.
Two-level QR code scheme based on region matrix image secret sharing algorithm

Quick response (QR) codes have become increasingly popular as a medium for quickly and easily accessing information through mobile devices. However, the open-source nature of QR code encoding poses a risk of information leakage and potential attacks, especially with the growing use of QR codes in financial services and authentication applications. To mitigate the risk of information leakage due to open-source QR code encoding, this paper proposes a two-level QR code scheme based on a region matrix image secret sharing algorithm. In this scheme, the first-level public information can be directly obtained by scanning with any standard QR code scanner, while the two-level secret information can only be accessed by overlaying the shared images. To enhance the robustness of joint secret information recovery using shared images, this article designs a progressive image secret sharing algorithm based on region matrices. This algorithm meticulously processes high-priority share regions and generates multiple substitute shares. In the event of attacks on key shares, substitute shares can be employed to recover the secret information. For an increased payload capacity of each QR code, an adaptive pixel depth adjustment algorithm is devised. This algorithm ensures that the recovery of two-level secret information maintains high clarity, while not affecting the scanning functionality of each shared QR code. Experimental results validate the feasibility of this scheme, which simplifies the construction matrix, reduces matrix redundancy, and exhibits priority partitioning and higher robustness. Furthermore, QR codes embedding secret shares can safeguard the two-level information, and the recovered images exhibit exceptional clarity.
Interactive complex ontology matching with local and global similarity deviations

&lt;abstract&gt;&lt;p&gt;Ontology serves as a central technique in the semantic web to elucidate domain knowledge. The challenge of dealing with the heterogeneity introduced by diverse domain ontologies necessitates ontology matching, a process designed to identify semantically interconnected entities within these ontologies. This task is inherently complex due to the broad, diverse entities and the rich semantics inherent in vocabularies. To tackle this challenge, we bring forth a new interactive ontology matching method with local and global similarity deviations (IOM-LGSD) for ontology matching, which consists of three novel components. First, a local and global similarity deviation (LGSD) metrics are presented to measure the consistency of similarity measures (SMs) and single out the less consistent SMs for user validation. Second, we present a genetic algorithm (GA) based SM selector to evolve the SM subsets. Lastly, a problem-specific induced ordered weighting aggregating (IOWA) operator based SM aggregator is proposed to assess the quality of selected SMs. The experiment evaluates IOM-LGSD with the ontology alignment evaluation initiative (OAEI) Benchmark and three real-world sensor ontologies. The evaluation underscores the effectiveness of IOM-LGSD in efficiently identifying high-quality ontology alignments, which consistently outperforms comparative methods in terms of effectiveness and efficiency.&lt;/p&gt;&lt;/abstract&gt;
PENGUATAN MATERI MENULIS TEKS PIDATO PERSUASIF MATA PELAJARAN BAHASA INDONESIA KELAS IX SMP NEGERI 2 BALIKPAPAN SELATAN

Activities "Strengthening Material Writing Persuasive Speech Texts Subjects Indonesian Class IX State Junior High School 2 South Balikpapan" is a Community Service (PKM) activity as an effort to motivate children to be skilled in writing persuasive speech texts. The activity is based on the weak understanding of learners related to the rules of language when writing persuasive speech texts. This activity is carried out with four stages, namely preparation, implementation, evaluation, and preparation of reports. This activity is carried out by carrying out learning through the Zoom Meeting application by providing persuasive speech text material, namely definitions, examples, structures, and proverbs. The implementer of this activity is a lecturer of the Faculty of Teacher Training and Education (FKIP) as a speaker and subject teacher Indonesian grades IX-1, 2, and 3 as moderators. With this activity, it is expected to motivate children to be skilled in writing persuasive speech texts.
Appealing but Potentially Biasing - Investigation of the Visual Representation of Segmentation Predictions by AI Recommender Systems for Medical Decision Making

Artificial intelligence (AI)-based recommender systems can help to improve efficiency and accuracy in medical decision making. Yet, it has been shown that a recommendation given by an algorithm can influence the human expert responsible for the decision. The strength and direction of this bias, induced by a computer-aided diagnosis workflow, can be influenced by the visual representation of the results. This study focuses on evaluating four frequently used visualization types (bounding box, segmentation mask, segmentation contour, and heatmap) for displaying segmentation results of medical data. A group of 24 medical experts specializing in pathology and radiology participated in the evaluation, assessing the subjective appeal of these visualizations. The study evaluated the pragmatic and hedonic quality of the visualizations based on a standardized questionnaire and specific criteria relevant to medical decision making. The findings indicate that the heatmap received the highest ratings for non-task-oriented aspects of the user experience. However, it exhibited significant inconsistencies among experts concerning task-oriented aspects and was perceived as the most biasing visualization type. On the other hand, the segmentation contour consistently received high ratings across various subscales. The results of the study contribute to better alignment between visualization techniques and user requirements for the development of future AI-based recommender systems.
ICT-Aided Education and Professional Development of Foreign Language Teachers

The trendy integration of educational information communication technology (ICT) with foreign language teaching and learning in China, which breeds a new ecology of foreign language education at different levels, and also calls for new professional development approaches for foreign language teachers. This paper will clarify the new connotation of foreign language teachers’ professional ability, analyze the difficulties of foreign language teachers’ professional development, and propose the optimal approaches of foreign language teachers’ professional development from the aspects of cultivating teachers’ information literacy, developing teachers’ ability to acquire learning resources, improving teachers’ scientific research ability, and building a good relationship between teachers and students. It aims to build a team of high-quality professional and innovative teachers and quicken the pace of educational modernization.
ANALYSIS OF CYBER-SECURITY CONSCIOUSNESS AND EDUCATION STRATEGY IN NIGERIA

Cybersecurity consciousness and education are critical to a cyber user's safety when utilizing the internet.As a result, several governments have implemented rules and programs to educate their population about cybersecurity, as no one is immune to cyber-attacks.The aim of this research is to determine the level of user's cybersecurity Consciousness and Education and make a comparative analysis between Indonesians and Nigerians, and develop a sustainable strategy to close the knowledge gap and, as a result, advance better cyberhygiene and cybersecurity.This study focused to decide the level of client's network safety cognizance and training, and make a near examination between the two nations in Nigeria, likewise foster a maintainable technique to close the hole and thus, advance better digital cleanliness and online protection.This research uses Partial least square (PLS) as a statistical techniques that allows comparison between multiple response variable by incorporating qualitative measures with data collected by means of a survey questionnaire.The survey sample of 355 respondents respectively includes gender, age, education level, employment status, preferred device, and years of using internet.The results shows that Nigerians has a high percent consciousness about cyber-security and cyber-crimes than Indonesians.The Impact of Government Cybersecurity consciousness and Education has been more effective in Indonesia than in Nigeria.
KLASIFIKASI DATA MINING UNTUK MENENTUKAN KUALITAS UDARA DI PROVINSI DKI JAKARTA MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBORS (K-NN)

Air plays an important role in maintaining the life of living things on earth. Metabolic processes that occur in the bodies of living things cannot take place without oxygen from the air. The air pollution problem in DKI Jakarta is very serious and can cause health problems such as irritation of the respiratory tract, respiratory diseases, and long-term health problems such as cardiovascular disease and lung cancer. Air pollution can also affect environmental quality, reduce visibility, and damage ecosystems. The purpose of this study is to determine the accuracy of classifying air quality in DKI Jakarta province. The data mining method that the author uses is the K-Nearest Neighbors (K-NN) algorithm. From the results of the evaluation process of the K-Nearest Neighbors (K-NN) algorithm using the K-5 fold that has been carried out using the RapidMiner tool, the results of K-2 fold accuracy of 73.97%, K-3 fold accuracy of 72.60%, K-4 fold accuracy of 72.60%, and K-5 fold accuracy of 75.35%.
PDEODE Strategy Assisted by GeoGebra: Improving Students’ Critical Thinking and Mathematical Analysis

This study aimed to determine the effect of the PDEODE learning strategy on students' critical thinking and mathematical analysis skills. This research is quantitative research with a quasi-Experimental posttest-only control group design. The population of this study was all students of the eighth grade of MTs Negeri 2 Bandar Lampung, with a total population of 256 students. The sampling technique employed was cluster random sampling. The data analysis techniques performed were the normality test and the homogeneity test. The hypothesis testing performed was a Multivariate Analysis of Variance (MANOVA) test. Based on the results of research and the MANOVA test, the PDEODE learning strategy assisted by the GeoGebra application has an effect on students’ critical thinking and mathematical analysis skills. Further research is recommended to use the PDEODE strategy and GeoGebra media.
A Study on the Convergent Liberal Arts and Lerners’ Opinions

Objectives The purpose of this study is to examine the direction and possibility of the convergent liberal arts curriculum of humanities and arts. For this end, this research attempted to combine liberal English and Western art history and examine the opinions of learners.&#x0D; Methods The participants were 40 college students with different majors and grades who took this course. A survey was employed with two types of questions: one for their satisfaction was presented as multiple-choice questions, and the other for their various opinions of the strengths and weaknesses of the subject.was presented as descriptive questions.&#x0D; Results According to the results of the survey, the satisfaction of the subject was 4.2 out of 5, which was very similar to the opinion on the balance between English and Western art history. The advantage of the subject was that it was possible to take two courses at the same time, while the disadvantage was that it did not help English learning enough because it focused on Western art history.&#x0D; Conclusions The convergent liberal arts curriculum of West art history and liberal arts English has shown a possibility to foster learners' understanding of other cultures through art history and improve their global communication ability through English as a great tool. The future convergent liberal arts curriculum, therefore, needs to provide students the cultural background and balance between integrated subjects.
A Framework for Open World Object Detection

Open World Object Detection (OWOD) is a computer vision task that focuses on real-world scenarios where object detection algorithms need to not only detect known and labeled objects but also handle novel and unknown objects that were not seen during training. This distinguishes OWOD from traditional object detection benchmarks, where the scope is limited to detecting only known object classes. The main challenge in OWOD lies in detecting and classifying unknown objects, which were not part of the training data. In standard object detection, objects not overlapping with labeled objects are automatically classified as background. However, these approaches are not suitable for OWOD, as unknown objects may be wrongly predicted as background due to the lack of specific supervision for distinguishing unknown objects from the background. The paper proposes a novel framework for Open World Object Detection called Open World Object Detection based on Non-Parametric classification (OWOD-NP). This method aims to address the challenges of identifying unknown objects and extending the knowledge base by incrementally introducing new object categories. OWOD-NP incorporates a non-parametric learning approach based on mean prototypes and rejection criteria into a standard detector model. The non-parametric learning model allows the system to detect whether the perceived region contains an unknown object and perform incremental learning in an end-to-end manner. The extensive experiments conducted on the benchmark dataset of Pascal Visual Object Classes (VOC) validate the effectiveness of OWOD-NP. Compared to the standard faster RCNN model, OWOD-NP achieves approximately 14% higher mean Average Precision (mAP) in class incremental scenarios. This improvement showcases the capability of OWOD-NP to handle open-world object detection tasks more efficiently. By combining non-parametric learning with object detection, OWOD-NP provides a promising solution for open-world scenarios, where the environment is dynamic and new objects may appear over time. The ability to detect and classify both known and unknown objects makes OWOD-NP a valuable approach for real-world applications in robotics, autonomous systems, and other computer vision tasks. It allows for continuous adaptation and learning, enabling the system to extend its knowledge and cope with ever-changing environments effectively.
Research Trends of the Infant and Young Child Observation using Keyword Network Analysis

Objectives This study analyzed research trends of the infant and young child observation using keyword network analysis.&#x0D; Methods To this end, keywords were extracted from 71 academic papers on the infant and young child observation which were preserved in the Korea Educational Information Service Database (RISS) under the theme of “infant-young children (infant, young children) + observation.” After refining the collected keywords, 161 keywords were analyzed by UCINET 6, and then visualized using Netdraw.&#x0D; Results First, in research related to infant and young child observation, “observation,” “infants,” “pre-service teachers,” “rating scales,” “behavior,” “recording,” “play,” and “research” appeared as major keywords with high frequency of appearance. Visualized results showed that “observation,” “pre-service teachers,” “infants,” and “recording” were complicatedly correlated. This results also showed that all the keywords including “observation,” “infants,” “rating scales,” “pre-service teachers,” “recording,” and “behavior” had high levels of degree centrality, closeness centrality, and betweenness centrality. Second, results of examining the keywords derived from the infant and young child observation studies over the past 30 years revealed that major keywords of three periods were as follows: The first period (1994-2011) - “behavior,” “rating scales,” “development,” the second period (2012-2018) - “preservice teachers,” “rating scales,” and “music activities,” and the third period (2019-2023) - “recording,” “play observation,” and “observation capabilities.”&#x0D; Conclusions This study is meaningful in that it investigated areas, connectedness, and periodical emphases of the infant and young child observation studies using keyword network analysis and sought ways of diversifying future research directions. It would be necessary to seek ways to support educational practice in the day-care centers and kindergartens through actively conducting the infant and young child observation.
Development of Teaching-Learning Materials for the Clustering in &lt;Mathematics for Artificial Intelligence&gt; Applying Python

Objectives The purpose of this study is to develop teaching-learning materials for the subject &lt;Mathematics for Artificial Intelligence&gt;.&#x0D; Methods After setting the criteria to develop objective and professional teaching-learning materials, we developed 4 sessions of teaching-learning materials for &lt;Mathematics for Artificial Intelligence&gt;. After that, we edited materials based on teachers’ review and field application.&#x0D; Results As a criterion for the development, the goal of the 2015 revised mathematics curriculum &lt;Mathematics for Artificial Intelligence&gt; teaching-learning methods and notes were referred to, processed huge data was provided, and tasks were presented according to data scientists' actual problem-solving procedures. In addition, the task of promoting communication and collaboration was presented without overemphasizing Python grammar. We developed 4-sessions of teaching-learning for &lt;Mathematics for Artificial Intelligence&gt; using Python. And we selected ‘News clustering through natural language processing’ as the theme because it can show process of AI using mathematics to solve the problem and also it’s appropricate for the level of the students.&#x0D; Conclusions This teaching-learning materials using Python helped students recognize the usefulness of mathematics by practicing the process of using mathematics to process data by AI. In addition, this teaching-learning material allows students to experience the actual problem-solving process of data scientists, and stimulate communication and collaboration.