Reviewerly/exHarmony huggingface dataset:
Implementasi Framework Yii dalam Membangun Sistem Inventaris pada SMK Negeri 2 Prabumulih dengan Menggunakan Model User Centered Design (UCD)

The Yii framework is a high-performance, component-based php framework for developing large-scale web applications. In schools the system implemented at SMK Negeri 2 Prabumulih still uses a semi-manual method, namely using MS-Excel as there is no data storage media, inventory data has not been integrated. This is the background for this research. Inventory of assets is an activity of recording assets or goods as well as managing asset data owned by the organization in a professional manner for the sake of smooth operations. The results of this study are in the form of an inventory system design with the Yii framework using the UCD (user centered design) concept which is expected to help solve the problems that occur. By using an inventory system that can help infrastructure, school principals, and teachers in obtaining and managing inventory of goods correctly and in a faster time. In designing this system using the user centered design method with the Unified Modeling Language (UML) system modeling tool, which consists of use case diagrams, activity diagrams, and class diagrams.
Prospects for cybersecurity in 2024

Significance These include the impact of generative artificial intelligence (GenAI) on cyberattacks, espionage and influencer campaigns; supply-chain risks; and the emergence of a software liability regime. Each of these trends will place additional pressure on firms to adapt their security postures to emerging threats and regulations in light of the rapidly changing technology landscape.
Use of Social Media to Instigate Violence: Users’ Role and Challenges in Prevention

In Bangladesh, there have been many incidents of violence linked to social media platforms, particularly Facebook. These incidents have been caused by posts that have either intentionally or unintentionally provoked hatred and instigated violence. In this study, we examine seven such incidents that were triggered by religious differences in Bangladesh. By reviewing previous research and case studies, we aim to identify the factors that contribute to the spread of hate speech and misinformation on social media. We also discuss the challenges involved in preventing violent incidents. To reduce the likelihood of such incidents, we propose three recommendations: prioritize news for fact-checking, promote fact-checking among users, and involve users in the fact-checking process. These recommendations can be implemented using existing technology to limit the spread of false information and hate speech on social media platforms. This could ultimately reduce the risk of violent incidents triggered by social media.
Image-text fusion sentiment analysis with textual attention

With the development of information technology and big data, large amount of multimodal data with opinion biases has exploded. Compared to unimodal data, multimodal data can accurately identify users' emotional tendencies and enrich emotional attitudes from a variety of perspectives. To effectively identify the consistency of sentiment representations between image and text semantics, as well as the variability in the degree of contribution in sentiment analysis, an imagetext fusion sentiment analysis method based on text attention is proposed. The method constructs attention-based models for extracting text and images' features, emphasizes the words containing sentiment information, and builds a sentiment classification model using multimodal feature fusion. Experimental results show fusion method proposed has achieved better performance in both accuracy and F1 values compared with baseline models.
ESP32: QEMU Emulation Within a Docker Container

The ESP32 is a popular microcontroller from Espressif that can be used in many embedded applications. Robotic joints, smart car chargers, beer vat agitators and automated bread mixers are a few examples where this system-on-a-chip excels. It is cheap to buy and has a number of vendors providing low-cost development board kits that come with the microcontroller and many external connection points with peripherals. There is a large software ecosystem for the ESP32. Espressif maintains an SDK containing many C-language sample projects providing a starting point for a huge variety of software services and I/O needs. Third party projects provide additional sample code as well as support for other programming languages. For example, MicroPython is a mature project with sample code and officially supported by Espressif. The SDK provides tools to not just build an application but also merge a flash image, flash to the microcontroller and monitor the output. Is it possible to build the ESP32 load and emulate on another host OS? This paper qualitatively explores the QEMU emulator and its ability to emulate the ethernet interface for the guest OS. Additionally, we look into the concept of containerizing the entire emulator and ESP32 load package such that a microcontroller flash image can successfully run with a one-step deployment of a Docker container.
Evaluation of Teaching Strategies for Humanities Education in Higher Vocational Education in the Digital Age

Higher vocational institutions should focus on students' whole development and strengthen humanistic education in the current environment of higher education reform and development in order to meet the needs of social and economic development. In the context of the information age, there is also an urgent need to use digital technology to carry out more in-depth changes in teaching strategies. This paper starts from the development of humanistic education in higher vocational education, studies the content of humanistic education and the characteristics of teaching strategies, and analyzes the classroom environment and strategy design of digital smart teaching based on digital technology. Using the Analytic Hierarchy Process (AHP) to determine the weight of each factor, a multi-level fuzzy comprehensive evaluation model is constructed, which makes the comprehensive evaluation of teaching more objective. The research showed that teachers' demands for the improvement of digital intelligent classrooms accounted for the largest proportion of the improvement of the situation diagnosis and student data analysis systems in the middle school, as high as 83%. This appeal is conducive to improving digital teaching strategies. Second, after adopting the digital-based interactive teaching strategy, the proportion of teacher-led classrooms decreased from 34% to 27%, the proportion of student-led classrooms increased from 16% to 25%, and the proportion of classrooms in which students evaluated each other from 14% increased to 22%. This finding demonstrated that in digital teaching, teachers give students' initiative more and more consideration, increase interactive learning in the classroom, and permit more students to actively participate in class activities. Students' enthusiasm for learning and the number of classroom interactions have been improved.
Status, Challenges, Andstrategies of Phonics Instruction in Thecentral Regionofchina

Phonics is a teaching method used to guide students to directly learn the pronunciation rules of 26 letters and letter combinations in words by emphasizing the name sounds of letters to establish the connection between letters and letter combinations with pronunciation to achieve the ability to read words when they see them and understand the words when they hear them. This method can reduce the difficulty of English learning for elementary students. To explore relationship among the status, challenges and strategies, the research made a questionnaire which was conducted in the central region of China-Henna Province, and 457 English teachers from 20 public primary schools and ten private elementary schools were randomly selected as participants. It was found that status of teachers' overall perception with efficient phonics instruction is relatively high, but challenges resulted from traditional teaching methods and the lacking of updated teaching resources led to students’ inefficient learning results. A highly significant correlation has been illustrated among the three variables When Grouped According to Profile. The better the status is, the less challenges teachers encountered, the more challenges exist, the more strategies are accepted.
A Cross-Scale Embedding Based Fusion Transformer for Automatic Modulation Recognition

A novel Cross-Scale Embedding Based Fusion Transformer, called CE-FuFormer for automatic modulation recognition was proposed in this paper. In CE-FuFormer, a cross-scale embedding layer is introduced to convert the input signal into an embedding with multiple scale features. To reduce the computational complexity of the model, we allocate fewer channel dimensions for large convolution kernels. A fusion transformer encoder (FTE) module is proposed for simultaneous local and global information extraction of the input modulated signal features. In addition, we rationally allocate the channel dimensions of the CNN part in the FTE module to reduce the generation of parameters while maintaining network performance. The proposed CE-FuFormer outperforms other state-of-the-art (SOTA) models on the datasets RadioML2016.10a and RadioML2018.01a.
Retraction Notice: A Machine Learning Method for Detecting Sarcasm in Negative Sentiment Tweets using Hyperboles

The use of sarcasm as an expression is a common literary device that involves someone purposefully expressing the opposite of what is being meant. The accurate identification of sarcasm in a text may boost other natural language processing activities, particularly ones involving sentiment analysis, and make it easier to comprehend the genuine intents of the speakers in the text. It is difficult to determine if someone is being sarcastic only based on their written words since sarcasm is a kind of implicitly expressing one's feelings and sarcastic speakers purposefully confound their audiences. Existing methods based on machine learning and deep learning obtained an insufficient level of performance when processing sarcastic material that included complicated expressions or required a certain set of prior knowledge to comprehend. The purpose of this research is to examine how sarcasm may be detected in tweets that have a negative mood and include hyperboles. For the purpose of sarcasm identification, the hashtags #Chinesevirus, #Kungflu, #COVID19, #Hantavirus, and #Coronavirus were included in the collection of 6,600 pre-processed negative sentiment tweets. The three well-known machine learning algorithms known as Support Vector Machine, Random Forest, and Random Forest with Bagging were utilised in order to conduct an analysis of the five hyperbole characteristics known as interjection, intensifier, capital letter, punctuation mark, and elongated word. The suggested model with the extended term attained an accuracy of 78.74% and an F-score of 71%, respectively, despite the inclusion of hyperbolic words in the tweets included in an unbiased dataset. It was discovered that intensifier was the exaggeration with the highest level of significance (p. 0001). The results of the experiments and analyses c
The New Systemic Risks

Since the 1960s, finance has undergone a long process of digital transformation and is today probably the most globalised segment of the world’s economy and among the most digitised and datafied. This process is evident across four major axes: the emergence of global wholesale markets, an explosion of financial technology (FinTech) start-ups since 2008, an unprecedented digital financial transformation in developing countries (particularly China), and the increasing role of large technology companies (BigTechs) in financial services. This process of digital financial transformation brings structural changes with both benefits and risks. This chapter considers new risks, particularly new systemic risks which have emerged, focusing on cybersecurity and data.
Countering Democratic Disruption Amid The Disinformation Phenomenon Through Artificial Intelligence (Ai) In Public Sector

In today’s era, artificial intelligence (AI) has emerged as a groundbreaking technology that plays a vital role in shaping societies and revolutionizing various sectors. Many democratic countries are currently exposed to a variety of false information spread through social media. In politics, disinformation is often associated with the efforts of a particular movement or party to mobilize supporters against the moral order. The spread of disinformation causes the emergence of legitimacy problems in many democratic countries. Citizen confidence in the credibility of official information in the news started to decline and shifts to alternative sources of information such as social media. Several countries that adhere to a democratic system make various efforts to fight the phenomenon of disinformation. The origins of the problems research want to examine the use of AI technology as an effort to overcome the spread of disinformation on social media. Furthermore, this research examines the challenges posed by AI disinformation and highlights several measures that democratic governments should adopt to mitigate its impacts. In summary, prioritizing the fight against disinformation in democratic countries is imperative to safeguard the integrity of democratic processes, enhance societal resilience, and foster an informed citizenry capable of making critical decisions for the collective benefit of the nation.
Verifying Compiler Optimisations

Compilers are a vital tool but errors in a compiler can lead to errors in the myriad of programs it compiles. Our research focuses on verifying the optimisation phase because it is a common source of errors within compilers. In programming language semantics, expressions (or terms) are represented by abstract syntax trees, and their semantics is expressed over their (recursive) structure. Optimisations can then be represented by conditional term rewriting rules. The correctness of these rules is verified in Isabelle/HOL. In the GraalVM compiler, the intermediate representation is a sea-of-nodes graph structure that combines data flow and control flow in the one graph. The data flow sub-graphs correspond to term graphs, and the term rewriting rules apply equally to this representation.
Introduction to Machine Learning

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Peningkatan Partisipasi dan Penguasaan Vocabulary Siswa Kelas VII A SMP 1 Kabat Menggunakan Media Flashcard

In the primary school level, English is no longer a compulsory subject to be taught. In some schools, English is only a local subject. As a result, when students enter secondary school, they have very little knowledge of the English language. This is also the case in SMP 1 Kabat. Students' knowledge of English vocabulary is very low. Therefore, teaching vocabulary becomes crucial because vocabulary mastery is the foundation of learning the other four English language competencies. This research aims to enhance the participation and vocabulary mastery of 7th-grade students at SMP 1 Kabat using Flashcards as a media. This study employs the Class Action Research (CAR) method, consisting of two cycles, each with four stages: planning, acting, observing, and reflecting. Collaborative action research (CAR) is conducted, meaning students work together with the English teacher. The researcher collects data using tests, observations, and questionnaires. To analyze the data, the researcher uses both quantitative and qualitative descriptive analysis. Based on the research conducted, it was found that the percentage of student participation in the learning process in cycles 1 and 2 was 76.67% and 86.67%, respectively. As for the learning outcomes in the first cycle, it was 57%, which did not meet the passing target. However, in the second cycle, there was an increase to 80%, surpassing the minimum target of ≥75%. Based on the results obtained from the research, it can be concluded that the use of Flashcards as a media is highly effective in improving the participation and vocabulary mastery of 7th-grade students at SMP 1 Kabat.
GEnI-FR: Granularity to Ensure Interpretability of the Fuzzy Rules

While Fuzzy Rule-Based System (FRBS) is a valuable tool for mapping knowledge from datasets, its balance between accuracy and interpretability remains a challenge. In this paper, we propose a method that utilizes FRBS to enhance interpretability while maintaining accuracy by managing uncertainty and adjusting fuzzy sets based on data characteristics. The results demonstrate the effectiveness of our approach in achieving the balance between interpretability and accuracy by applying the principle of justifiable granularity.
An expandable voice user interface as lab assistant based on an improved version of Google’s speech recognition

Abstract Voice assistants are potentially helpful when working in a scientific laboratory. A big challenge is the extremely specific use of language in every laboratory. As with any voice assistant, another concern is data security. Here, we present Rainbow—an open source voice user interface (VUI) for scientific laboratories, that is adaptable to any Windows PC with Internet access. We used Google Translate Site (GTS) as a voice input and output system to ensure communication to the user. The scripting language AutoIt controls GTS, executes all actions and builds the VUI. Rainbow performs tasks from three different areas—general Microsoft Windows tasks, lab-specific tasks, and device-specific tasks. We achieved significantly higher speech recognition accuracy with our VUI than with GTS alone (91.3% versus 85.1%). Because of Rainbow's architecture, it is possible to improve the voice assistant in terms of functionality and accuracy, allowing each laboratory to optimize its own Rainbow system in a user-friendly way. In a test setup, this led to a speech recognition accuracy of 98.6%. Taken together, Rainbow provides an opportunity for every scientist to implement highly specific scientific terms and tasks to this open source voice assistant system in a very user-friendly way.
A Nonvolatile Bipolar Optical Fiber Memory Based on Phase Change Materials

Developing an optical fiber storage platform that integrates storage and computation poses a challenge. This article presents a bipolar memory that incorporates phase change materials by combining two tapered fibers with a microsphere. The state of Ge2Sb2Te5 is altered by modulating an external laser, resulting in repeatable or randomly accessible five-level data storage. Multistage writing is achieved using a 532 nm pump laser and laser energy levels ranging from 0.423 to 1.206 mJ, while a 793 nm continuous wave laser with an average power of 4 to 11 mW completes the multistage reset. The bipolar memory demonstrates nonvolatility, high signal-to-noise ratio, excellent repeatability, 75 ns write response time, 180 ns reset response time, and 18 dB contrast. Furthermore, we develop a continuous programming platform using quaternary ASCII coding as an alternative to English letters. The bipolar memory can implement the synaptic weight update mechanism within the neural network system’s synapses, thereby showcasing excellent data storage, programming, and neural network computing potential.
A SUMMARY OF RESEARCH ON THE LEADERSHIP OF PRINCIPALS IN RURAL SMALL-SCALE SCHOOLS

This study uses the literature analysis method to conduct a review. Enter the keywords "small rural schools" and "principal leadership" on China HowNet and Baidu Academics to obtain literature. The perspectives of the literature review on small rural schools are time dimension, value dimension, and element dimension; the perspective of principal leadership literature collation is the individual level and the organizational level; finally, it clarifies the literature context of top leadership in rural small-scale schools from three aspects. Comments on this research: First, most of the studies did not highlight the impact of "distance from the city" and "size" of rural small-scale schools on principal leadership; second, in small-scale rural schools, principals' "multi-role" seriously dilutes They are just "practices" of school leaders; third, they have not analyzed the leadership of small-scale rural school principals from the perspective of leadership theory.
Natural Language Generation (NLG) for Automated Report Generation

Automated report generation plays a vital role in numerous industries, enabling efficient and accurate communication of complex data. Natural Language Generation (NLG) is a powerful technique that leverages artificial intelligence (AI) to transform structured data into human-readable narratives. This white paper provides an overview of NLG and its applications in automated report generation. We explore the benefits, challenges, and best practices associated with NLG-based report generation, along with real-world examples. Furthermore, we discuss the current state of the technology, future prospects, and potential ethical considerations.
COVID-19 crisis era; Engineering interventions in Sub-Saharan Africa

Despite numerous notable contributions by Africans during the Corona Virus Disease (COVID-19) outbreak, little effort has been made to document these contributions in a comprehensive publication. Herein this work, an attempt was made to bring together African-based technological interventions proffered in Sub-Saharan Africa during this period. Across Africa generally, different engineering methods were used to develop several contrivances such as ventilators as well as deploying Artificial Intelligence to facilitate contact-free human- human and human-object interactions. Broadly, these approaches center on two key areas which are efforts to mitigate the spread of the infectious disease, and management of the infected, which are the focus of this present work. Indeed, a comprehensive report of these efforts is essential to appraise the contributions of the home-based researchers and to create an archive for future research and decision making across the African continent, thus necessitating the need for this systematic appraisal.
Sample Compression Schemes for Balls in Graphs

One of the open problems in machine learning is whether any set-family of VC-dimension admits a sample compression scheme of size . In this paper, we study this problem for balls in graphs. For a ball of a graph , a realizable sample for is a signed subset of such that contains and is disjoint from . A proper sample compression scheme of size consists of a compressor and a reconstructor. The compressor maps any realizable sample to a subsample of size at most . The reconstructor maps each such subsample to a ball of such that includes and is disjoint from . For balls of arbitrary radius , we design proper labeled sample compression schemes of size 2 for trees, of size 3 for cycles, of size 4 for interval graphs, of size 6 for trees of cycles, and of size 22 for cube-free median graphs. For balls of a given radius, we design proper labeled sample compression schemes of size 2 for trees and of size 4 for interval graphs. We also design approximate sample compression schemes of size 2 for balls of -hyperbolic graphs.