Reviewerly/exHarmony huggingface dataset:
Cultivation of students’ craftsmanship identity and ability based on the theory of directed percolation

Abstract In this paper, data mining is performed based on the directed percolation theory influence mining algorithm, analyzing the maximum influence node mining proportion threshold and calculating the maximum eigenvalue of the non-return matrix. The convergence conditions of the iterative process of immobile points are analyzed to obtain the percolation threshold of the information dissemination process of directed percolation, and the initial vector is selected among the node-associated features to form the iterative formula to mine the maximum percolation theoretical influence. According to the results, students' focus on craftsmanship has risen significantly, and the percentage of students who consider persistent concentration spirit important has increased to 75%. The directed percolation theory's craftsmanship spirit is crucial for developing students' vocational skills and comprehensive literacy.
Implementing Web Applications in English as a Foreign Language Classrooms to Develop Productive Skills

Education has not been immune to the substantial changes brought about by the fast spread of digital technology, multimedia, and social networks over the last several decades. These changes have had a tremendous influence on every facet of our lives. Training was provided to teachers of English in schools and colleges on how to utilize Web 2.0 technologies, in particular a number of mobile applications, to assist their students in improving their language skills. This thesis takes a look at how Web.2 technologies may be used in English for Speakers of Other Languages (ESOL) programs taught in secondary schools with the intention of improving students' ability to communicate orally and in writing. VoiceThread and FlipGrid were the mobile applications that were used for the research study that was carried out. The purpose of the study that will be presented later on as the thesis is to conduct research on the effectiveness of using mobile apps to instruct English to speakers of other languages. The work will focus, more specifically, on the discussion of various techniques that could be helpful for students to improve their English language skills, particularly their ability to speak and write in the language. The work will also focus on the improvement of students' ability to speak and write in the language. In addition to this, the study looks at the potential downsides of using mobile apps in secondary school English lessons.
Application of Learning Vector Quantization and Trajectory Planning On a 4-DoF Robotic Arm to Move the Object

The robotic arm is a type of statistical robot with a limited range of movement. Robotic arms are generally within the scope of Cartesian coordinates, according to the specified link length. The development of robot technology leads us to continue to upgrade soft computing. Intelligent systems in robots can improve good navigation detection systems or carry out the operator's tasks. On the other hand, using a camera is an important part of finding clear information about objects or capturing the environment around the robot. In this research, we implemented an intelligent system and computer-based camera on a 4-DoF robotic arm system. This robotic arm consists of a computer as the main processor, a microcontroller to adjust the joint angle, additional electronics, and a camera to detect objects and classify them by color. The colors used are red, green, and blue. The learning process uses these colors using Learning Vector Quantization (LVQ). The implementation of LVQ also carries out pre-processing, training, and testing stages. In the experiments that have been carried out, the robotic arm successfully navigates toward the target object and moves the object using the Trajectory Planning method. This computing process is done on a computer and connected to the robot arm's microcontroller. The experiment was carried out 60 times, and the success rate was 95%. Overall, the robot successfully picked up objects and grouped them by color.
Deriving Formal Semantic Representations from Dependency Structures

We outline the essentials of a formal semantic theory for dependency grammars. This theory, which derives from Montague’s semantics, is fully compositional, allows for a treatment of quantification, and is robust in the sense that it supports the interpretation of partial dependency structures.
AI in cloud computing: Exploring how cloud providers can leverage AI to optimize resource allocation, improve scalability, and offer AI-as-a-service solutions

The integration of Artificial Intelligence (AI) in cloud computing heralds a transformative phase for the tech industry. As cloud infrastructures become more sophisticated, the potential of optimizing these services using AI has captured significant attention. This study aimed to explore how cloud providers can leverage AI to optimize resource allocation, enhance scalability, and offer innovative AI-as-a-Service (AIaaS) solutions. Through a mixed-method approach, insights were gleaned from companies that have adopted AI in their cloud architectures. The findings elucidate that AI-driven methods have led to substantial operational savings and a reduction in downtimes. Moreover, the proliferation of AIaaS models is particularly beneficial for mid-level enterprises and startups. However, concerns around data privacy, potential biases, and integration costs emerge as significant challenges. Future work in this domain promises to delve deeper into these challenges, aiming for a harmonious synergy between AI and cloud computing.
Problems and Solutions for Enhancing Awareness of Tense, Aspect, and Modality in Teaching and Learning English in Vietnam

The aim of the article was to review knowledge and research done by previous authors in terms of tense, aspect and modality. By introducing thorough classifications and examples of a range of knowledge that is less acquired and educated in Vietnam, the problems in teaching and learning tense, aspect, and modality are discussed to clarify the challenges that Vietnamese teachers and learner encountered. The measures were proposed to boost academic performance among learners, and improve teaching quality among teachers, reducing tiredness and boredom in grammar teaching and leading to alternative viewpoints for these ranges of knowledge.
Expert System for Disease Identification in Palawija Plants with the Dempster Shafer Method

Crops, including corn, are important agricultural commodities and contribute greatly to people's food needs. Diseases in crops are often the main factor that reduces crop production and quality. Maize plants are particularly susceptible to various pests and diseases, such as Leaf Blight, Leaf Spot, Rust, Stem Blight, Anthracnose, Root Rot, Cob Rot, Smut, Mosaic Virus, and Nematodes. Unfortunately, farmers often use pesticides or inappropriate control methods, resulting in suboptimal care and the emergence of new pests or diseases. This study aims to assist farmers in detecting early symptoms of pests and diseases in corn plants, so that control can be more precise and effective. In this study, 10 disease attack data were processed using the Dempster Shafer method. This method processes data based on symptoms seen in corn plants, allowing detection of types of pests and diseases and recommendations for handling them with an accuracy rate of about 80%. Therefore, the Dempster Shafer method is relevant for use in the identification of diseases in corn plants.
Full-Range Yaw Prediction: A Multi-View Approach for 3D Head Model Pose Estimation Using Convolutional Neural Networks

Head pose estimation, a crucial task in computer vision, involves determining the orientation of a person’s head in 3D space through yaw, pitch, and roll angles. While recent techniques present excellent results in estimating head pose from a single 2D RGB image when the head faces the camera directly, few methods exist for pose estimation from arbitrary viewpoints. This problem is emphasised when the input data is in 3D, such as heads reconstructed models from magnetic resonances, where an accurate estimation of the pose is necessary for diagnostic purposes. To overcome these limitations, we make a first step by proposing a method for fine-grained head pose estimation across the full-range of yaw angles using 3D head synthetic models. Our approach involves transforming the 3D pose estimation problem into a multi-class 2D image classification problem by representing 3D head models as multi-view projection images. Leveraging a fine-tuned ResNet50 convolutional neural network, we tackle the task of head pose estimation with fine granularity of 5°, effectively discretizing the 360° yaw orientations. For the evaluation of our proposal, we train and test our models with the publicly available FaceScape and 3D BIWI datasets obtaining promising results.
Peningkatan Hasil Belajar Matematika melalui Penerapan Model Pembelajaran PBL (Project Based Learning) pada Siswa Kelas IVA SDN 48 Cakranegara

This research aims to improve Mathematics learning outcomes through the application of the PBL (Project Based Learning) learning model for class IV students at SDN 48 Cakranegara for the 2022/2023 academic year. This type of research is classroom action research (PTK) which is carried out in 2 cycles consisting of planning, action implementation, observation, evaluation and reflection stages. Each cycle consists of one meeting. The results of this research show an increase from cycle to cycle. In cycle I, students' Classical Completeness reached 73% with teacher activity in the very good category and student activity in the quite active category. In cycle II, students' calculus completion reached 88.4% with teacher activity being in the very good category and student activity being in the very active category, this means that Classroom Action Research (PTK) has met the indicators to be achieved. Therefore, based on the research results, it can be concluded that the implementation of the PBL (Project Based Learning) learning model in class IVA students at SDN 48 Cakranegara for the 2022/2023 academic year.
Exploring Learning Techniques for Developing Socially-Aware Service Robots: Best Practices for Social Comfort

In the past few years, there has been an increase in commercial and research focus on service robots operating in daily surroundings. These machines are anticipated to function independently in busy settings, enhancing movement efficiency and safety parameters, as well as social acceptance. Expanding conventional path planning modules to include socially aware criteria, while sustaining speedy algorithms that can adapt to human behavior without causing distress, presents a significant challenge. To address this challenge, learning methods have gained significant relevance. Among the various techniques, deep reinforcement learning, end-to-end, and inverse reinforcement learning have been the most promising. However, it is difficult to determine which techniques are superior, and sometimes, developers may obtain poor results due to inadequate data or experimental procedures during the learning stage. Therefore, it is essential to evaluate and discuss the best practices and options for an effective training stage that can improve results. As we are specifically referring to social robots, the evaluation of results should take into consideration social comfort as a key factor.
A Real-World Dataset for Benchmarking False Alarm Rate in Keyword Spotting

Over the past few years, Keyword Spotting (KWS) has emerged as a popular area of research. Although numerous open-source KWS datasets have been recently released, there is a general lack of realism in benchmarking the false alarm rate (FAR) in real environments. This can produce models that achieve great accuracies but are not able to work on real-world conditions due to a high number of false triggers. In this work, we demonstrate that two recent KWS models report state-of-the-art accuracies on Google Speech Command dataset but suffer from high false alarm rates in presence of noisy environments. To this end, we propose an extensive benchmark dataset comprising various real-world noises and sounds to evaluate specifically the FAR across different acoustic environments.
Unlocking the Power of LLMs: Navigating Challenges for Industry in the AI Renaissance

During the last year, AI has undergone its most disruptive transformation in half a century, largely due to OpenAI's innovations and cloud democratization.ChatGPT, stable diffusion, and Dall-E have reshaped our AI understanding and applications across society.Despite ethical concerns, it's vital to examine this shift's impact on resourceconstrained entities like small and medium-sized enterprises and research groups.At its core, this transformation relies on Large Language Models (LLMs) built upon Transformer and GPT architectures.Yet, it's essential to recognize that training these models demands significant hardware budgets and access to curated datasets.Equally crucial is the labor-intensive data curation, supervision, and refinement often overlooked.Beyond training challenges, we must address inference cost, latency, concurrency, and scalability-affecting real-world viability and the quality decline in ChatGPT due to high request loads and its business model while maintaining profitability.In this keynote, we introduce LLM fundamentals and explore challenges.We delve into how this AI revolution impacts medium-sized enterprises, providing speech technology as a use case, focusing on technical and business aspects.We'll emphasize customercentric customization, domain specialization, and value addition in process automation.We will try to understand the profound implications of this AI renaissance for enterprises of all sizes.
Cognitive Similarity Through Bibliometric Analysis

Cognitive similarity is a fundamental concept in the peer review process and the research funding programs. It can help to find qualified experts in a field in the refereeing or evaluation process. The main objective of this work is to find an automatic way to determine cognitive similarity based on bibliographic information retrieval using the Scopus database. In this paper, we will compare different measures of cognitive similarity between pairs of authors through the whole publication portfolio of both. These measures are based on the authors’ publications and citations, at different levels of depth of authorship and citation networks. We have applied bibliographic coupling and text-based techniques. We have expressed these measures as cosine similarity. For this work, we use a small empirical case study with authors in the fields related to artificial intelligence to compare the measures of cognitive similarity. The results of this study show that the use of combined measures using bibliographic coupling and text-based can provide more information on the cognitive similarity between a pair of authors.
Recommenders for Improved Lesson Planning in Formal Education

This paper presents a system that combines a multi-recommender mechanism and an iterative algorithm to assist teachers in formal education in planning learning activities for students. It aligns with the latest educational trends, emphasising the central role of competencies. The system allows for initial planning based on specified competencies and knowledge, among other preferences. After that, it helps with alternative suggestions during the course when deficiencies in the learning of competencies are detected.
Reversal Error in Mathematics: Training Against It Using ε⃔ coach

This paper presents ε⃔ coach, a serious game prototype which aims at training players to detect and avoid reversal error when representing word problems of the type “N times as many S as P” algebraically as an equation. The ε⃔ coach visualizes the relative size of the entities in the relation following a qualitative model based on containers. This serious game also provides a quantitative visualization of the entities related using graphics, it helps players to resolve the formulated equation using discrete values and it shows players’ progress.
An Application to Measure Consensus on Customers’ Ratings Using Hesitant Fuzzy Linguistic Term Sets

In this study, we propose a methodology using hesitant fuzzy linguistic term sets to aggregate rating values. The main idea is to define an integrated methodology for collaborative platforms involving individuals that use the same set of linguistic terms with different semantics. The objective is to fuse the opinions of different profiles of reviewers or customers when assessing an alternative. The proposed approach allows considering previous assessments to give personalized meaning to a set of linguistic terms. To this end, the concepts of linguistic perceptual maps and projections among perceptual maps are introduced to model and aggregate reviewers’ different profiles. Finally, we compute a measure of consensus to capture the agreement among reviewers’ opinions. All these concepts are applied in a real case example of rating books from Amazon to demonstrate the potential of the methodology in facilitating the evaluation and comparison of different alternatives. Using our methodology, the degree of consensus among customers can be used to rate in a more precise way the books before recommendation.
A General Model for the Metainformation of Complex Questionnaires for Automatic Preprocessing and Reporting Under INSESS Methodology

In the field of analytics and intelligent data analytics one of the open challenges regards to the metainformation model that allows an intelligent reporting system to preprocess and process correctly data coming from any kind of questionnaire, independently of number of variables, types of variables involved, including multiple response variables or other complex structures like compositional variables or temporal variables among others. INSESS is an intelligent system designed and used for the first time in INSESS-COVID19 project, specialized in automatic intelligent reporting for a parameterized questionnaire that includes different types of variables. From one INSESS-consultation to another, the questionnaire can change completely and the consultation may be oriented as well to different types of target populations going from citizens, to associates to an NGO or to special nonpublic populations and the pre and processing still works. The flexibility required to do this, relies on a well-founded and sufficiently general conceptual model for the metainformation associated to the questionnaire, so that the analytical module can properly reason on top of the metainformation and make intelligent decisions about the kind of preprocessing required or the kind of descriptive analysis to be applied. The metainformation model has been developed from a knowledge representation perspective.The paper presents the context of the research, the conceptual model defined and 4 real use cases where the INSESS methodology was used for different kinds of private/public consultations proving the flexibility of the tool.
Membangun Kreativitas dan Minat Baca Puisi Melalui Musikalisasi Puisi pada Siswa SMA Muhammadiyah 1 Pekanbaru

This service activity aims to stimulate students' interest in reading poetry at SMA Muhammadiyah I Pekanbaru through a poetry musicalisation approach. The service team conducted a situational analysis of the partners and found that the students' interest in reading poetry was low due to the lack of understanding and appreciation of poetry and the lack of students' creativity in presenting poetry. This service activity was also carried out as an effort to develop students' creativity in arts and culture. Creativity can help students develop critical and creative thinking skills, problem solving and innovation in various fields. Poetry musicalisation was chosen as a method in this activity because it can increase the appreciation and understanding of literary works, especially poetry. Poetry musicalisation can make poetry more interesting and easier for students to understand, and improve students' language and artistic skills. This method is effective in increasing the appreciation of poetry and helping students to develop their creative skills.
Perception on global economy through world trade report: A corpus and computation-driven approach

This research aimed to investigate the lexical trends and sentiment shifts in the World Trade Report spanning 2018-2020 using corpus and sentiment analysis tools. Data of the World Trade Report from 2018 to 2020 was analyzed. We employed the Words tool, Whelk tool, and GraphColl tool within the LancsBox corpus tool to count high-frequency nouns and verbs, scrutinize the distribution characteristics of key words, and assess their semantic collocation features. Furthermore, sentiment analysis was conducted using the VADER algorithm. The results indicated that the 2020 World Trade Report prominently featured high-frequency nouns such as policy, innovation, and government, as well as verbs like support, mirroring the challenging global economic climate in that year. Semantic collocation analysis of key words from the 2020 report highlighted the significant challenges COVID-19 posed to global economic stability. Additionally, the sentiment scores from 2019 to 2020 exhibited notable differences, with a consistent decline in average scores annually and a more pronounced negative sentiment in the 2020 report compared to the previous years. Recognizing these linguistic and sentiment trends can aid policymakers and businesses in understanding the nuanced shifts in global economic narratives, especially in response to significant events like the COVID-19 pandemic.
TPACK in In-service Secondary Education Teachers: A Systematic Review of the Literature

The TPACK model is an essential framework for appropriately integrating technology in the classroom. The present study aimed to categorize and establish the main research findings around TPACK between 2017 and 2022, specifically in practicing teachers at the secondary education level. A systematic literature review guided by the PRISMA statement was conducted. The search for papers was carried out in the Scopus, Web of Science, Sage Journal, and SciELO databases in September 2022. 37 articles were selected for review. The review of the studies made it possible to establish that the papers were mainly grouped into two categories: the first related to teachers' perception of TPACK, which includes research with self-perception questionnaires, case studies and relationships of self-perception with other variables such as barriers to technology integration in the classroom, pedagogical beliefs, and online education; and the second to developing training courses based on TPACK, complemented with self-perception questionnaires in the form of pretest-posts, classroom observations, and interviews. The results of the first category are dissimilar since TPACK levels differ from one study to another in variables such as gender and teaching experience. From the second, it is established that teacher professional development courses favor improving technological integration in the classroom.