Reviewerly/exHarmony huggingface dataset:
Effectiveness of Comment Column on Facebook Group in Teaching Writing

This article was a complete report of investigation on the effectiveness of coment column in facebook group to teach writing. Communicate using Facebook in international scope or globalization era needs English skill, especially in writing skill. Writing which is clearly and acceptably is needed when writing or given a comment. The Students must have good skill writing on English. The researcher was analized the score of pre test and posttest with used computing calculated with SPSS 21.0. The researcher analized used t-test. The researcher used t-test technique because in this research all the assumptions test are full filed. The result showed that the mean of the pre-test was 61.16 and mean of the post-test was 75.23. It means that the result from the post-test was higher than pre-test. Based on the result of output paired sample test shows the result of compare analysis with used T-test shows mean pre-test and post-test is 14.065. Standard deviation is 10.577 and mean standard error is 1.900, the lower different is 10.185, while upper different is 17.944. The result t- test is 7.404 with df 30 and significance 0.000. The t-count value is 7.404 is more than t- table value 2.042. So the Ho is rejected and Ha accepted because 7.404 &gt; 2.042.The Average score of post-test is higher then the average of pre-test. It means that Facebook is an effective media in teaching writing procedure text.
Racism detection using deep learning techniques

With the pervasive role of social media in the socio-political landscape, various forms of racism have arisen on these platforms. Racism can manifest in various forms on social media, both concealed and overt. It can be hidden through the use of memes or exposed through racist comments made using fake profiles to spread social unrest, violence, and hatred. Twitter and other social media sites have become new settings in which racism and related stress appear to be thriving. Racism also spread based on characteristics including dialect, faith, and tradition. It has been determined that racial animosity on social media poses a serious threat to political, socioeconomic, and cultural equilibrium and has even put international peace at risk. Therefore, it is crucial to monitor social media as the primary source of racist opinions dissemination and to detect and block racist remarks in a timely manner. In this study, we aim to detect tweets containing racist text by performing sentiment analysis using both ML and DL algorithms. We will also build a webpage using Flask framework and SQLite for users to interact with the model.
Adapting an Unadaptable ASR System

As speech recognition model sizes and training data requirements grow, it is increasingly common for systems to only be available via APIs from online service providers rather than having direct access to models themselves. In this scenario it is challenging to adapt systems to a specific target domain. To address this problem we consider the recently released OpenAI Whisper ASR as an example of a large-scale ASR system to assess adaptation methods. An error correction based approach is adopted, as this does not require access to the model, but can be trained from either 1-best or N-best outputs that are normally available via the ASR API. LibriSpeech is used as the primary target domain for adaptation. The generalization ability of the system in two distinct dimensions are then evaluated. First, whether the form of correction model is portable to other speech recognition domains, and secondly whether it can be used for ASR models having a different architecture.
Industrial Practitioner Perspective of Mobile Applications Programming Languages and Systems

The growth of mobile application development industry made it crucial for researchers to study the industry practices of choosing mobile applications programming languages, systems, and tools.With the increased attention of cross-platform mobile applications development from both researchers and industry, this paper aims at answering the question of whether most of the industries are using crossplatform development or native development.The paper collects feedback about industry's most used mobile development systems.In addition, it provides a map of the different technologies used by mobile applications development companies according to some of the demographics like company size and location.An online questionnaire is carried out to collect the data.The survey targeted both amateur and professional mobile developers.A total of 85 participants participated in answering the survey.Qualitative analysis using descriptive statistics is done on the results of the survey.Although the results show that there is an industrial trend towards using the cross-platform languages, native development is still used by the well-established companies.More than 50% of the participants are found to be aware of the performance issues of the cross-platform development languages and frameworks.Comparison with findings of related work is discussed which raises more research questions and draws out future research in this field.
Segmentation-Reconstruction-Guided Facial Image De-occlusion

Occlusions are very common in face images in the wild, leading to the degraded performance of face-related tasks. Although much effort has been devoted to removing occlusions from face images, the varying shapes and textures of occlusions still challenge the robustness of current methods. As a result, current methods either rely on manual occlusion masks or only apply to specific occlusions. This paper proposes a novel face de-occlusion model based on face segmentation and 3D face reconstruction, which automatically removes all kinds of face occlusions with even blurred boundaries,e.g., hairs. The proposed model consists of a 3D face reconstruction module, a face segmentation module, and an image generation module. With the face prior and the occlusion mask predicted by the first two, respectively, the image generation module can faithfully recover the missing facial textures. To supervise the training, we further build a large occlusion dataset, with both manually labeled and synthetic occlusions. Qualitative and quantitative results demonstrate the effectiveness and robustness of the proposed method.
Language Report Dutch

Abstract This chapter provides a new state of affairs (Steurs et al. 2022) with regard to language technology (LT) for Dutch (after Odijk 2012). LT for Dutch is highly developed, and the Netherlands and Flanders have a strong and cooperative LT community. A lot of digital data is freely available through CLARIN and the Dutch Language Institute (INT). However, data and software have to be updated continuously, and there is a need for a new overarching programme to support research initiatives.
PERANCANGAN SISTEM INFORMASI MANAJEMEN BADAN AMAL MENGGUNAKAN METODE WATERFALL

Developments and advances in information technology are now increasingly advanced by providing convenience for the public in accessing information in the current era of public information. The Charity Management Information System is a service application for the community to optimize donors who want to donate in an easy and fast way. The method used in designing this service application uses the Waterfall method with the Laravel Framework. The Waterfall method consists of, Requirements and Analysis, Design, Development, Testing, and Maintenance. The results obtained using this method are Entity Relationship Diagram (ERD) models and Use Case Diagrams at the Design stage. Then, at the Testing stage, an appropriate or valid status is generated from the expected results. This system or website can increase public awareness about the importance of making donations, be it Zakat, Alms, Infaq, or Waqf. And can assist in managing donation data (Zakat, Alms, Infaq, and Waqf) effectively and efficiently, namely to BMI partners (Baitulmaal Munzalan Indonesia).
IMPLEMENTASI ALGORITMA FORWARD CHAINRING DAN CERTAINTY FACTOR PADA SISTEM PAKAR DIAGNOSA EFEK SAMPING BAHAN PEMUTIH KOSMETIK PADA KULIT

Most people, especially women, are generally not educated about what ingredients are contained in a cosmetic and only rely on social media as a reference by looking at influencers who advertise a beauty product. To consult with a dermatologist also requires a lot of money, so they choose not to care about it, this causes many cases handled by dermatologists are cases that are too severe. Therefore, there is a need for awareness and education for women, especially teenagers regarding the use of illegal cosmetics, so through this research, the authors try to propose a free, expert-based platform that can be accessed in general by people from anywhere and anytime to carry out self- assessments or self-examinations. or education that makes it easier for the public to get easy and detailed information access regarding the hazardous ingredients contained in cosmetics. So in this study, we propose an educational media and expert-based assessment of the symptoms of the disease suffered in the form of developing an expert system based on the forward chaining algorithm and certainty factor, which involves a dermatologist. based on the results of testing the accuracy of 20 data samples, 18 data were appropriate and 2 data were not appropriate so that the accuracy obtained was 90%, and from user satisfaction testing it was found that 80% were satisfied with the system created and 20% were not satisfied.
Review on: Effect of Inflation on Economic Growth in Ethiopia

One of the main objectives of macroeconomic policy of most developing countries like Ethiopia is to attain sustainable economic growth together with stable price level. Price stability is considered as a proxy for macroeconomic stability, the ongoing high level of inflation is not a good sign. Large fluctuations in inflation for firms, consumers and the public sector reduce the economy in the long run growth potential. The nations of the countries who are highly affected by the problem of inflation are those who have low level of income or fixed income and unemployed portion of the population. Higher average inflation has a negative impact on the steady state growth. This is because of the higher cost of transaction that inflation causes to the money market. It is generally accepted that instability in the general level of prices causes substantial economic distortions, leading to inefficiencies, both in aggregate employment and output. Due to different factors, the effects of inflation have significant effect on economic growth in Ethiopia.
(Im)politeness employed by multilingual Indonesian EFL learners in argumentative conversations

Politeness might lessen imposition in argumentative conversations which potentially result in conflict. However, different conventions on politeness among different cultures may cause problems for EFL (English as a Foreign Language) learners who often transfer their native communication preferences to their target language usage. This article explores the (im)politeness employed by multilingual EFL learners in casual agreeing-disagreeing exchanges among peers. Using explanatory sequential mixed method design, students’ utterances in role plays were analyzed based on Leech’s (2014) politeness theory, Kakava’s (1993) disagreement strategies, as well as Locher’s (2004) mitigating strategies on disagreement. It is found that in conversations with people of equal status and power, the observance of politeness maxims is more apparent than the violation. It indicates that maintaining others’ faces is essential irrespective of age differences, even in arguing conversations. The more frequent use of positive politeness than negative politeness strategies represents their greater orientation to others than to themselves. The prevalence of mitigated disagreement and the frequent use of an appreciation preface also show their priority to maintaining good relationships. Those findings support the view of Asians as a collectivistic group whose primary concern in communication is group membership. This suggests that awareness of politeness in argumentative conversations among EFL learners must be increased by giving them different perspectives from non-Asian cultures.
Preventing online disinformation propagation: Cost-effective dynamic budget allocation of refutation, media censorship, and social bot detection

&lt;abstract&gt;&lt;p&gt;Disinformation refers to false rumors deliberately fabricated for certain political or economic conspiracies. So far, how to prevent online disinformation propagation is still a severe challenge. Refutation, media censorship, and social bot detection are three popular approaches to stopping disinformation, which aim to clarify facts, intercept the spread of existing disinformation, and quarantine the source of disinformation, respectively. In this paper, we study the collaboration of the above three countermeasures in defending disinformation. Specifically, considering an online social network, we study the most cost-effective dynamic budget allocation (DBA) strategy for the three methods to minimize the proportion of disinformation-supportive accounts on the network with the lowest expenditure. For convenience, we refer to the search for the optimal DBA strategy as the DBA problem. Our contributions are as follows. First, we propose a disinformation propagation model to characterize the effects of different DBA strategies on curbing disinformation. On this basis, we establish a trade-off model for DBA strategies and reduce the DBA problem to an optimal control model. Second, we derive an optimality system for the optimal control model and develop a heuristic numerical algorithm called the DBA algorithm to solve the optimality system. With the DBA algorithm, we can find possible optimal DBA strategies. Third, through numerical experiments, we estimate key model parameters, examine the obtained DBA strategy, and verify the effectiveness of the DBA algorithm. Results show that the DBA algorithm is effective.&lt;/p&gt;&lt;/abstract&gt;
PENERAPAN MODEL PROBLEM BASED LEARNING DENGAN MEDIA WORDWALL UNTUK MENINGKATKAN HASIL BELAJAR SISWA TENTANG SIKLUS AIR PADA MATA PELAJARAN IPA DI KELAS V SD NEGERI 3 PANGURAGAN WETAN

The purpose of this study was to find out and describe the steps for implementing PBL in improving science learning outcomes with material on the water cycle process at SD Negeri 3 Panguragan Wetam in class V which had not yet reached (KKM). This type of research is classroom action research (PTK) carried out in 2 cycles. Collecting information using test and non-test techniques, data were analyzed using descriptive techniques. The research subjects were class V SD Negeri 3 Panguragan Wetan totaling 34 students. The results showed that cognitive learning outcomes from pre-cycle 15 students (45%) increased to 26 students (77%) in cycle II increased to 34 students (100%) in cycle III. Affective learning outcomes in cycle II and cycle III showed an average politeness of 87 increased to 98, caring 76 to 83 and responsibility 84 to 93. Psychomotor learning in cycles II and III averaged aspects of practical skills 89 to 96, accuracy 77 to 84, representing 75 to 87. It can be concluded that the application of the problem based learning (PBL) model can improve learning outcomes in the water cycle material both from cognitive, affective and psychomotor.
A Unified Logical Framework for Reasoning about Deontic Properties of Actions and States

This paper studies some normative relations that hold between actions, their preconditions and their effects, with particular attention to connecting what are often called ‘ought to be’ norms with ‘ought to do’ norms. We use a formal model based on a form of transition system called a ‘coloured labelled transition system’ (coloured LTS) introduced in a series of papers by Sergot and Craven. Those works have variously presented a formalism (an ‘action language’) nC+ for defining and computing with a (coloured) LTS, and another, separate formalism, a modal language interpreted on a (coloured) LTS used to express its properties. We consolidate these two strands. Instead of specifying the obligatory and prohibited states and transitions as part of the construction of a coloured LTS as in nC+, we represent norms in the modal language and use those to construct a coloured LTS from a given regular (uncoloured) one. We also show how connections between norms on states and norms on transitions previously treated as fixed constraints of a coloured LTS can instead be defined within the modal language used for representing norms.
Twitter Data Analysis and Text Normalization in Collecting Standard Word

is one of the most important data sources in social data analysis. However, the text contained on Twitter is often unstructured, resulting in difficulties in collecting standard words. Therefore, in this research, we analyze Twitter data and normalize text to produce standard words that can be used in social data analysis. The purpose of this research is to improve the quality of data collection on standard words on social media from Twitter and facilitate the analysis of social data that is more accurate and valid. The method used is natural language processing techniques using classification algorithms and text normalization techniques. The result of this study is a set of standard words that can be used for social data analysis with a total of 11430 words, then 4075 words with structural or formal words and 7355 informal words. Informal words are corrected by trusted sources to create a corpus of formal and informal words obtained from social media tweet data @fullSenyum. The contribution to this research is that the method developed can improve the quality of social data collection from Twitter by ensuring the words used are standard and accurate and the text normalization method used in this study can be used as a reference for text normalization in other social data, thus facilitating collection. and better-quality social data analysis. This research can assist researchers or practitioners in understanding natural language processing techniques and their application in social data analysis. This research is expected to assist in collecting social data more effectively and efficiently.
Exploring Indonesian EFL teacher-student interactions in online learning

The use of online platforms has been shown to help students engage in learning writing. The student’s engagement in revising their writing draft can be seen from the interactions between the teacher and the students or among the students. This study aims to determine teacher-student interactions in a writing class conducted synchronously and asynchronously via Google Classroom, Google Meet, and WhatsApp. The data were derived from the teacher’s and students’ discussions posted in written and oral modes in the applications. A qualitative approach in designing and gathering the data was used in this study. The findings show that the interactions between the teacher and the students raised the students’ understanding of the teacher’s instruction so that the students were engaged in writing their drafts and revising them properly. The patterns of the teacher’s interactions can be categorised into giving feedback (39%) followed respectively by prescribing the editor role (17.7%), promoting individual contributions (13.3%), promoting joint construction of meaning and form (11%), thanking and praising (10%), guiding through the writing steps (5.7%) and stimulating students (3.3%). Students’ responses toward the teacher’s patterns of interaction depend on the teacher’s talk. There is no initiative from the students to start the discussion. The student’s background as freshmen can cause this; the first-year students may not be brave enough to start the discussion. This suggests that exploring the interactions between the teacher and students of different backgrounds is necessary.
Improving The Understanding of Science Concept Through Guided Discovery Learning Model in Azzahra Preschool Kindergarten

Understanding simple concepts (scientific concepts) was one of important understanding in aspects of cognitive development for early childhood. Children were trained to think actively and critically in order to understand they activities, namely by applying a constructivist approach or guided discovey learning. However in Azzahra Preschool Kindergarten has not applied a guided discovey learning. It considered to be a factor in childrenâ€™s low understanding of science concepts. Therefore, this research aimed to improve the children's understanding of science concepts by applying guided discovery learning. This research used a classroom action research with two cycles. Subject of this research is all of children group B in Azzahra Preschool Kindergarten. And the results show that the guided discovery learning can improved childrenâ€™s understanding of science concept. This is based on percentage score of childrenâ€™s understanding of science concept which is increasing in each cycle. Other findings of this research showed that children were eager to learn, curiosity was increasing, and active to conduct experiments to discover various simple concepts. So, this research was recommended that apply the guided discovery learning model to develop various aspects of the child.
NLPositionality: Characterizing Design Biases of Datasets and Models

Design biases in NLP systems, such as performance differences for different populations, often stem from their creator's positionality, i.e., views and lived experiences shaped by identity and background. Despite the prevalence and risks of design biases, they are hard to quantify because researcher, system, and dataset positionality is often unobserved. We introduce NLPositionality, a framework for characterizing design biases and quantifying the positionality of NLP datasets and models. Our framework continuously collects annotations from a diverse pool of volunteer participants on LabintheWild, and statistically quantifies alignment with dataset labels and model predictions. We apply NLPositionality to existing datasets and models for two tasks -- social acceptability and hate speech detection. To date, we have collected 16,299 annotations in over a year for 600 instances from 1,096 annotators across 87 countries. We find that datasets and models align predominantly with Western, White, college-educated, and younger populations. Additionally, certain groups, such as non-binary people and non-native English speakers, are further marginalized by datasets and models as they rank least in alignment across all tasks. Finally, we draw from prior literature to discuss how researchers can examine their own positionality and that of their datasets and models, opening the door for more inclusive NLP systems.
LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

We present LLM-Blender, an ensembling framework designed to attain consistently superior performance by leveraging the diverse strengths of multiple open-source large language models (LLMs). Our framework consists of two modules: PairRanker and GenFuser, addressing the observation that optimal LLMs for different examples can significantly vary. PairRanker employs a specialized pairwise comparison method to distinguish subtle differences between candidate outputs. It jointly encodes the input text and a pair of candidates, using cross-attention encoders to determine the superior one. Our results demonstrate that PairRanker exhibits the highest correlation with ChatGPT-based ranking. Then, GenFuser aims to merge the top-ranked candidates, generating an improved output by capitalizing on their strengths and mitigating their weaknesses. To facilitate large-scale evaluation, we introduce a benchmark dataset, MixInstruct, which is a mixture of multiple instruction datasets featuring oracle pairwise comparisons. Our LLM-Blender significantly outperform individual LLMs and baseline methods across various metrics, establishing a substantial performance gap.