Reviewerly/exHarmony huggingface dataset:
A novel binary tree and pattern matching mechanism for building effective question answering system for agriculture domain

Agricultural farming has been a main source of income for approximately 58% of India's residents. However, because of the dreadful circumstances in the agriculture industry, around 10,000 farmers yearly die. These situations emerge whenever crop products may not be as planned, resulting in significant indebtedness on the farm owner's shoulders. As a result, we propose a question answering system that aids farmers by delivering answers to major agricultural inquiries. Some of the inquiries include agricultural planting, plant disease, and seasonal difficulties. As a result, farmers will profit from making better crop selections, boosting their output. The question answering system is created with a pattern-matching technology that uses an optimised binary-tree method to categorise various inquiries and deliver a solution to the farmers. The proposed question and answering system for agriculture domain (QAS-AD) model increases the efficiency of overall computation overhead by 28.33%, and reduces the time computation by 32.33% when compared with the existing system. Moreover, the proposed QAS-AD reduces the computation of the tree structure and byte overhead. Experiment outcome shows the proposed question answering system for agricultural domain achieves much higher relevance score with less time to retrieve answer in comparison with conventional QAS.
Creative Writing Assistance to Support Poetry Reading-Writing Literacy Activities for Teacher’s at Ma'arif Educational Institutions Surabaya

The low interest in reading and writing is a problem that needs to be addressed immediately, considering that reading and writing is an important means to absorb and develop knowledge, so that the Indonesian people become a quality society. The objectives of this Community Service are (1) to actively participate in improving students' competence in literacy at Ma'arif Surabaya Education in accordance with the literacy movement, (2) To increase children's literature mastery at Ma'arif Surabaya Educational Institutions (3 ) Improving students' poetry reading and writing skills (4) Supporting literacy activities of Ma'rif Surabaya Educational Institution students. The solution offered in this PKM is assistance in creative writing in supporting poetry reading and writing literacy activities.Participants consisted of 75 elementary school teachers. The training is carried out face-to-face, followed by online mentoring. The training material presented was poetry.After the poetry writing training is carried out, an evaluation will be held to identify the improvement in abilities and strengths and weaknesses as material for improvement in the next implementation. The results of the training showed that the teacher's poetry writing skills increased from being unable to compose poetry to being able to write. All the teachers who attended the training participated in the activity enthusiastically from start to finish. The material presented can be understood clearly and participants can apply it independently. This training activity has a good impact on improving the ability of teachers in literacy, especially in composing poetry. In the future, the poetry that has been created will be registered with Haki.
Statistical Machine Translation from Indonesian to Regional Languages in Indonesia

The current condition in Indonesia has 617 regional languages.There are 15 regional languages that are declared extinct and 139 others are in endangered status.Utilization of computerbased tools can be used as an effort to preserve regional languages digitally according to current technological developments, including by building digital dictionaries and translation machines.The digital dictionary has the ability to translate regional languages into Indonesian with the approach used is translating word for word, although it is not effective when done manually.An alternative solution is to create a machine translation application.Machine translation can be dictionary-based or language-parallel corpus data-based.Statistical Machine Translation (SMT) is a machine translation approach with translation results generated on the basis of a statistical model whose parameters are taken from the results of a parallel corpus analysis.The quality of the SMT translation results is influenced by several factors.The most fundamental factor is the number of parallel corpus available and the quality of the corpus used as the basis for building translation models and language models.This study aims to determine the role of parallel corpus in improving SMT accuracy, especially in regional languages in Indonesia.The research data used is parallel corpus text of 3000 pairs of sentences.Based on the results of the research that has been done, it is found that the optimization of parallel corpus can increase the value of translation accuracy.Better translation accuracy can be achieved with optimized parallel corpus.Besides that, testing with single sentences will provide higher accuracy than using compound sentences.Testing of 3000 random parallel corpus parallels can increase accuracy by 11.4%, higher than testing with 3000 random parallel corpus.
Online Recruitment Fraud Detection: A Machine Learning-based Model for Ghanaian Job Websites

The proliferation of online job websites has eased the difficulties in hiring and applying for jobs globally.Unfortunately, the risk of defrauding desperate job seekers exists with malicious recruiters taking advantage of the loopholes in the online recruitment process.The reactive approach to detecting online job fraud and the subsequent warnings on reputable job websites hasn't curtailed this spiteful act.The purpose of the study is to propose a machine learning model for proactive job fraud detection.In building the predictive model, a job fraud dataset from a job advertisement firm in Ghana was utilised.Using the 10-fold and the 5-fold cross-validation techniques, a job fraud detection model was built by comparing conventional and ensemble machine learning algorithms.The machine learning metrics, including accuracy, F1-score and the area under the curve (AUC) value, were reported and discussed.The findings show that the Random Forest traditional algorithm, with an accuracy of 91.86%, is best suited for the dataset.The investigation further indicates that information gain and chi-square feature selection mechanisms decreased classification accuracy marginally to 91.51%.
Artificial inventors

This brief chapter considers whether patent law doctrines as they currently exist will continue to foster human development and “progress’ once inventorship is entrusted, even only in part, to AI machines. It starts from the premise that technological change qua technological change - remembering that change and progress are not synonyms for change happens no matter what, progress, not necessarily - should not be the true aim of IP done well. The idea, the chapter suggests, is to increase the percentage of change that actually constitutes progress. It then applies doctrines such as inventorship see if patent law will lead to more human progress, not mere technological novelty, if machines are considered as inventors. In doing so, it reviews and relies on a number of important contributions made by Professor Dreyfuss.
Analyzing Innermost Runtime Complexity Through Tuple Interpretations

Time complexity in rewriting is naturally understood as the number of steps needed to reduce terms to normal forms. Establishing complexity bounds to this measure is a well-known problem in the rewriting community. A vast majority of techniques to find such bounds consist of modifying termination proofs in order to recover complexity information. This has been done for instance with semantic interpretations, recursive path orders, and dependency pairs. In this paper, we follow the same program by tailoring tuple interpretations to deal with innermost complexity analysis. A tuple interpretation interprets terms as tuples holding upper bounds to the cost of reduction and size of normal forms. In contrast with the full rewriting setting, the strongly monotonic requirement for cost components is dropped when reductions are innermost. This weakened requirement on cost tuples allows us to prove the innermost version of the compatibility result: if all rules in a term rewriting system can be strictly oriented, then the innermost rewrite relation is well-founded. We establish the necessary conditions for which tuple interpretations guarantee polynomial bounds to the runtime of compatible systems and describe a search procedure for such interpretations.
The Use of Vocabulary Learning Strategy by Undergraduate Students

Every language learner has different strategy to gain new vocabulary depending on their ability. Vocabulary learning strategy is used by undergraduate students who have vocabulary size can be found in this study. Since vocabulary as the one language aspect should be mastered, without vocabulary is nothing. Moreover, this research is conducted to investigate the vocabulary learning strategies used by undergraduate students who have high proficiency in vocabulary. The data was gained from questionnaires. In addition, the result shown that among undergraduate students who high proficiency in vocabulary used different vocabulary learning strategy based on their interest. According to three kinds oof vocabulary learning strategy (i.e., discovery, social, and consolidate strategy). By using these strategies, language learners can learn or memorize the vocabulary easily.&#x0D; Keywords: Vocabulary Learning Strategy, Learners who have high proficiency in vocabulary
Exploring the Possibilities: Reinforcement Learning and AI Innovation

Reinforcement learning is a subfield of machine learning that deals with developing algorithms that enable an agent to learn from experience through trial-and-error interactions with its environment. It is a paradigm of learning by receiving rewards or punishments based on its actions and adjusting its behavior to maximize its cumulative reward over time. Reinforcement learning has been successfully applied in a wide range of fields, including robotics, game playing, recommendation systems, and finance. It has also shown promising results in solving complex problems that are difficult to solve using traditional methods. Despite these challenges, reinforcement learning has already proven to be a powerful tool for developing intelligent systems that can learn and adapt to changing environments, and it is likely to play an increasingly important role in the development of future AI technologies.
Theoretical aspects of the problem of educational motivation development among students of higher education in foreign studies

The relevance of the research topic is due to the importance of the problem of educational motivation development among students of higher education to improve the quality and efficiency of higher education. Analysis and generalization of foreign experience makes it possible to identify and include in the domestic educational practice of developing the educational motivation of students of higher education the most successful pedagogical solutions and developments that have empirically proven their effectiveness. Purpose: based on the analysis of foreign sources on the problem of educational motivation development of students of higher education, to determine and characterize the current theories applied to the study of educational motivation of students, the factors that influence its development, methods and teaching strategies. We prove that the main condition for the positive development of students’ motivation for learning and research activities is the creation by university teachers of a motivating educational environment, including: encouraging students to focus on the goals of mastery, developing competence, granting autonomy, creating zones of proximal development, etc. It is concluded that it is expedient to include foreign experience in teaching students positive motivational strategies focused on the development of motivational competence in the domestic educational practice of higher education.
Differences Between Physical Construction and Software Development

So far, we’ve focused on the similarities between these two types of work—but it’s also important to understand the limitations of these analogies. In software development, the construction metaphor is sometimes criticized for overemphasizing upfront planning. For the most part, building construction will not begin until the architectural plans are complete and building permission has been granted.
Multimodal Fusion Framework and Multiresolution Analysis

This chapter presents a multi-modal fusion framework for emotion recognition using multiresolution analysis. The proposed framework consists of three significant steps: (1) feature extraction and selection, (2) feature level fusion, and (3) mapping of emotions in three-dimensional VAD space. The proposed framework considers subject-independent features that can incorporate many more emotions. It is possible to handle many channel features, especially synchronous EEG channels and feature-level fusion works. This framework of representing emotions in 3D space can be extended for mapping emotion in three-dimensional spaces with three specific coordinates for a particular emotion. In addition to the fusion framework, we have explained multiresolution approaches, such as wavelet and curvelet transform, to classify and predict emotions.
Digital Transformation in Education: Emerging Markets and Opportunities

The widespread digitalization of all aspects of life, including the disruption caused by the Internet and the invasion by artificial intelligence, necessitates rethinking educational transformation processes. The broader purpose of the digital transformation of education is to develop an effective educational system that fits the needs of the digital economy and guarantees easy access to information for stakeholders in this sector. Digital Transformation in Education: Emerging Markets and Opportunities provides a comprehensive understanding of the emergence and evolution of digital technologies and artificial intelligence in the field of education. It presents 12 reviews that shed light on the various advantages and drawbacks of digital technology along with the opportunities and markets that are emerging because of such a digital education transition. Each chapter is written by a group of distinguished contributors and presents a detailed literature review, methodology (where appropriate), discussion and reference list. The book content equips educators at all levels with essential information required to adapt to educational technology in order to improve teaching, student support and learning outcomes. This book is an informative resource for post-graduate scholars, academics, policymakers, educators, and researchers in pedagogy, learning theory, digital learning, communication and education research.
Emotion Recognition From Facial Expression In A Noisy Environment

This study presents emotion recognition from facial expressions in a noisy environment. The challenges addressed in this study are noise in the images and illumination changes. Wavelets have been extensively used for noise reduction; therefore, we have applied wavelet and curvelet analysis from noisy images. The experiments are performed with different values of Gaussian noise (mean: 0.01, 0.03) and (variance: 0.01, 0.03). Similarly, for experimentation with illumination changes, we have considered different dynamic ranges (0.1, 0.9). Three benchmark databases, Cohn-Kanade, JAFFE, and In-house, are used for all experimentation. The five best machine learning algorithms are used for classification purposes. Experimental results show that SVM and MLP classifiers with wavelet and curvelet-based coefficients yield better results for emotion recognition. We can conclude that Wavelet coefficients-based features perform well, especially in the presence of Gaussian noise and illumination changes for facial expression recognition.
Emerging Use of Technologies in Education

The use of technology in the classroom makes learning more pleasant and simpler. The utilization of technology in the classroom is becoming more prevalent in these modern times, especially in this COVID-19 era. In this chapter, we discuss the emerging trends in education including the incorporation of new technologies. Among the technologies covered in no particular order of importance are virtual reality, augmented reality, mobile learning devices, and the internet of things. We evaluate the potential impact of emerging technologies on university teaching and learning. The technology education tools, including headsets, handsets, computers/laptops, electronic readers and online books, were identified in the literature.
Affective Information Extraction, Processing and Evaluation

This chapter presents a state-of-the-art review of existing affective information extraction and processing approaches. Various evaluation criteria, such as Evaluation matrices like ROC, F1 measure, Mean Square Error, Mean Average Error, Threshold criteria, and Performance criteria are also reported in this chapter.
Influence of time scale of lightning discharge in ionosphere

In this paper, a complete 3-D EMP model is established to simulate the nonlinear effects of the ionosphere based on the finite difference time domain (FDTD) method. Then the influence of time scale of lightning current discharge on ionospheric atmospheric discharge is innovatively analyzed by using this model.
Development of an Embodied Avatar-Agent Communication System to Support Communication Education for Clinical Engineer Students

Currently, there are few opportunities to develop communication skills in clinical engineering training schools in Japan. Therefore, we developed a system to interact with an agent equipped with a rule-based chatbot, which was created with reference to the objective clinical competence examination (OSCE). In a previous study, a virtual hospital room system was developed for nursing students to interact with the rule-based chatbot agent. In this study, we added an avatar that reflects the user's embodied movements to the system. In the virtual space created, the agent playing the patient, equipped with a rule-based chatbot, responds to the user's voice, and the avatar uses a webcam to project the user's body movements in real time. This system allows for repetitive learning, which is difficult with face-to-face training. In addition, the avatar, which reflects the user's body movements, exists in the same space as the agent, allowing the user to view his or her own body movements objectively.
The Impact of the Implementation of Student Tuition Payment Information Systems on the Quality of Financial Reports: A Case Study at Universitas Komputer Indonesia

The purpose of this research is to see the impact of implementing a student tuition payment system on the quality of financial reports at the Universitas Komputer Indonesia. This is done because students are customers whose loyalty must be maintained. Efforts are being made to provide satisfaction to students by providing transparent and accountable financial report information. The research method used is a survey method using a questionnaire via google form. Respondents were 28 study programs at Universitas Komputer Indonesia as users of the system. The data analysis was carried out through descriptive and verification analysis. Data processing using SEM PLS. The results showed the level of closeness (correlation coefficient) between the student tuition payment system and the quality of financial reports. From these results it can be said that the student tuition payment system is very helpful for the Universitas Komputer Indonesia in producing quality financial reports so that it can make it easier for users to make decisions. Universitas Komputer Indonesia continues to innovate in developing an integrated student tuition payment information system so that the resulting financial reports can provide useful information for the user. The student tuition payment information system has been proven to have improved the quality of information that is accurate, relevant, timely and complete. In addition, the student tuition payment information system created by the Universitas Komputer Indonesia has made it easier for system users to carry out their work so as to cut time more efficiently.
A Brief Discussion on the Approaches to Improve High School English Listening Instruction

English listening is an essential skill for high school students to master. However, the current status of English listening teaching in high schools is not optimistic. This paper aims to analyze the current situation and problems of English listening teaching in high schools from the perspectives of teachers, students, and teaching materials. Moreover, some suggestions are also proposed to improve the English listening teaching in high schools.
Benefits Perceived by Vietnamese EFL Learners and Their Engagement in Online Collaborative Learning during the Covid-19 Pandemic

The benefits of collaborative learning have been proven by previous research across disciplines, contexts, and levels of education. The present study aims to validate these benefits perceived by 76 English-as-a-foreign-language (EFL) learners who experienced online-collaborative learning via group work activities in English translation classes at a local university, South Vietnam during the Covid-19 pandemic. Surveying these learners at the end of the course via a questionnaire and analyzing their reciprocal assignment discussion record ings were carried out. The survey results uncover ed that the learners highly appreciated online collaborative learning because they perceived that it was beneficial to their future life, career, cognition, and social communication strategies. Their positive view on collaborative learning benefits was deemed to engage them in online group work activities while interacting with partners to complete shared assignments. Furthermore, the empirical discussion recordings analysis document ed their sound engagement via a range of group work interactive acts. It is believed that these online purposeful interactions make learning occur, knowledge acquired, and social skills reinforced by the learners. As a result, the present study not only validates the robust benefits reported in previous studies , but also it could be used as a guidance to implement collaborative learning in teaching other subjects other than English translation. Keywords: benefits ; collaborative learning ; EFL learner ; engagement ; group work
Naïve Realism, Cognitive Bias, and the Benefits and Risks of AI

In this short piece I comment on Orly Lobel's book on artificial intelligence (AI) and society "The Equality Machine." Here, I reflect on the complex topic of aI and its impact on society, and the importance of acknowledging both its positive and negative aspects. More broadly, I discuss the various cognitive biases, such as naïve realism, epistemic bubbles, negativity bias, extremity bias, and the availability heuristic, that influence individuals' perceptions of AI, often leading to polarized viewpoints. Technology can both exacerbate and ameliorate these biases, and I commend Lobel's balanced approach to AI analysis as an example to emulate.Although AI is changing at an unprecedented rate, as exemplified by recent advances in Large Language Model (LLM) technology such as ChatGPT/GPT4, humans are adaptable, and society can actively steer toward a desirable future. By acknowledging the potential benefits and risks of AI, and by striving to overcome inherent cognitive biases, individuals can achieve a more balanced understanding of the technology and its impact on society.