Reviewerly/exHarmony huggingface dataset:
PERENCANAAN STRATEGIS SISTEM INFORMASI DAN TEKNOLOGI INFORMASI UNTUK MENINGKATKAN DAYA SAING SEKOLAH PADA SMA ISLAM AL-FALAH JAMBI

According to data recorded at the end of the even semester of the 2019/2020 academic year, Al-Falah Jambi Islamic High School currently has 403 active students, 39 permanent foundation teachers, and 7 foundation contract teachers. With the increasing number of active students, which can be seen from the number of students at the beginning of its establishment, which amounted to 85 students, the services that must be provided and the academic atmosphere must remain conducive and maintained. Therefore, an information system strategic plan is needed that supports the school's business strategy and is aligned with the school's vision, mission, and goals to be able to advance Al-Falah Jambi Islamic High School and increase competitive advantage with other private SMA/SMK or with public SMA/SMK existing or future. The data collection technique that the writer used in this study was by means of interviews, observation, literature study, and documentation. The analytical methods used include the Value Chain Analysis, PEST Analysis, Porter's Five Forces, SWOT Analysis, Balanced Scorecard Analysis, the Critical Success Factor (CSF) Analysis, Gap Analysis, and McFarlan Strategic Grid Analysis. The targeted output of this research is to produce recommendations for an IS/IT strategic plan aimed at all parties, both the Principals, TU Heads, and ICT divisions, and can obtain future information system proposals and be mapped in development plans within a five-year period and mapped based on user units, namely HR, leadership, corporate secretary, treasurer, curriculum, inventory, and ICT, so that Al-Falah Jambi Islamic High School can increase competitive advantage with other private SMA/SMK or with existing or future public SMA/SMK
One-Person:Unicorn: Harnessing GPT-Based Expertise for Advancing Professional Growth in Business

This article delves into the utilization of GPT-4, an expansive generative pre-trained language model, to cultivate adept professionals in the realm of business. It explores the potential advantages and obstacles associated with leveraging GPT-4 to enhance both hard and soft skills, while also providing insights on optimizing this technology for professional development in business education.
Maintenance and Support System for Information and Communication Technology (ICT) Resources

The lack of technical support and maintenance impedes to the utilization of ICT resources in schools. As part of the government’s commitment to achieve the goals embedded in the country’s vision 2020, a number of schools have been beneficiaries of ICT facilities like computers, printers, laptops, LCD projectors, and speakers through the Department of Education Computerization Program (DCP). This descriptive-correlational research study assesses the status of the maintenance and support system of ICT resources in public elementary schools in the District of San Jose, Northern Samar, Philippines. Results revealed that school heads, ICT coordinators, and teachers were least satisfied of ICT maintenance procedure. In the same manner that the school heads, ICT coordinators and teachers support system were moderately satisfactory. There were significant differences in the maintenance procedures among the respondents particularly in replacing or repairing the faulty components and upgrading hardware and software. This concludes varied ways in which the respondents respond to the maintenance of faulty components and upgrading the hardware and software. Furthermore, difference on the technical support workload and technology integration among the three groups of respondents appeared to vary in terms of support system. The finding singles out the varied compliance of the respondents in technical support and technology integration.
Three new concepts of Pythagorean fuzzy soft UP (BCC)-filters

This paper seeks to introduce novel concepts on Pythagorean fuzzy soft sets (PFSSs) over UP (BCC)-algebras.Then, we give three different types of PFSSs over UP (BCC)-algebras and look into their generalization.We also find the results of four operations over UP (BCC)-algebras performed on two PFSSs: union, restricted union, intersection, and extended intersection.We also discuss π-level subsets of PFSSs over UP (BCC)-algebras as a final step in our inquiry into the relationships between special subsets of UP (BCC)-algebras and PFSSs.
A distantly supervised Grammatical Error Detection/Correction system for Swedish

This paper presents our submission to the first Shared Task on Multilingual Grammatical Error Detection (MultiGED-2023).Our method utilizes a transformer-based sequenceto-sequence model, which was trained on a synthetic dataset consisting of 3.2 billion words.We adopt a distantly supervised approach, with the training process relying exclusively on the distribution of language learners' errors extracted from the annotated corpus used to construct the training data.In the Swedish track, our model ranks fourth out of seven submissions in terms of the target F 0.5 metric, while achieving the highest precision.These results suggest that our model is conservative yet remarkably precise in its predictions.
On the relevance and learner dependence of co-text complexity for exercise difficulty

Adaptive exercise sequencing in Intelligent Language Tutoring Systems (ILTS) aims to select exercises for individual learners that match their abilities.For exercises practicing forms in isolation, it may be sufficient for sequencing to consider the form being practiced.But when exercises embed the forms in a sentence or bigger language context, little is known about how the nature of this co-text influences learners in completing the exercises.To fill the gap, based on data from two large field studies conducted with an English ILTS in German secondary schools, we analyze the impact of co-text complexity on learner performance for different exercise types and learners at different proficiency levels.The results show that co-text complexity is an important predictor for a learner's performance on practice exercises, especially for gap filling and Jumbled Sentences exercises, and particularly for learners at higher proficiency levels.
Manual and Automatic Identification of Similar Arguments in EFL Learner Essays

Argument mining typically focuses on identifying argumentative units such as claim, position, evidence etc. in texts.In an educational setting, e.g. when teachers grade students' essays, they may in addition benefit from information about the content of the arguments being used.We thus present a pilot study on the identification of similar arguments in a set of essays written by English-as-a-foreignlanguage (EFL) students.In a manual annotation study, we show that human annotators are able to assign sentences to a set of 26 reference arguments with a rather high agreement of κ > .70.In a set of experiments based on (a) unsupervised clustering and (b) supervised machine learning, we find that both approaches perform rather poorly on this task, but can be moderately improved by using a set of six meta classes instead of the more finegrained argument distinction.
Enhanced Firefly Optimization Based Classifier to Diagnose Multimodality Breast Cancer Images

Breast cancer is one of the most affecting carcinoma for women from long time. Early detection is necessary to increase the lifespan of patients. In this study deep learning and machine learning approaches are applied to histopathological, mammogram and ultrasound breast cancer modalities. In- order to increase the efficacy of diagnosis of these modalities. Study has been carried out in majorly four phases. First phase involved collection of the datasets of all the three modalities mentioned earlier. Second phase consists of extracting relevant features using Resnet-18. Third phase involves feeding the extracted information to enhanced firefly or to the existing optimization techniques. Fourth phase consists of considering selected features as input to the classifiers. Then enhanced firefly based classifier compared with the existing ant colony and genetic algorithm based classifier. Enhanced firefly based classifier displays better results compared to the state of art approaches.
Speech Technology to Support Phonics Learning for Kindergarten Children at Risk of Dyslexia

We present the AiRO learning environment for kindergarten children at risk of developing dyslexia.The AiRO frontend, easy to use for pupils down to 5 years old, introduces each spelling task with pictural and auditive cues.AiRO responds to spelling attempts with phonetic renderings (synthetic voice).Below, we introduce the didactic and technical principles behind AiRO before presenting our first experiment with 49 kindergarten pupils.Our subjects were pre-and post-tested on reading an spelling.After four weeks of AiRObased training the experimental group significantly out-performed the control group, suggesting that a new CALLbased pedagogical approach to prevent dyslexia for some children may be within reach.
MultiGED-2023 shared task at NLP4CALL: Multilingual Grammatical Error Detection

This paper reports on the NLP4CALL shared task on Multilingual Grammatical Error Detection (MultiGED-2023), which included five languages: Czech, English, German, Italian and Swedish.It is the first shared task organized by the Computational SLA 1 working group, whose aim is to promote less represented languages in the fields of Grammatical Error Detection and Correction, and other related fields.The MultiGED datasets have been produced based on second language (L2) learner corpora for each particular language.In this paper we introduce the task as a whole, elaborate on the dataset generation process and the design choices made to obtain MultiGED datasets, provide details of the evaluation metrics and CodaLab setup.We further briefly describe the systems used by participants and report the results.
Automated Assessment of Task Completion in Spontaneous Speech for Finnish and Finland Swedish Language Learners

This study investigates the feasibility of automated content scoring for spontaneous spoken responses from Finnish and Finland Swedish learners.Our experiments reveal that pretrained Transformer-based models outperform the tf-idf baseline in automatic task completion grading.Furthermore, we demonstrate that pre-fine-tuning these models to differentiate between responses to distinct prompts enhances subsequent task completion finetuning.We observe that task completion classifiers exhibit accelerated learning and produce predictions with stronger correlations to human grading when accounting for task differences.Additionally, we find that employing similarity learning, as opposed to conventional classification fine-tuning, further improves the results.It is especially helpful to learn not just the similarities between the responses in one score bin, but the exact differences between the average human scores responses received.Lastly, we demonstrate that models applied to both manual and ASR transcripts yield comparable correlations to human grading.
DaLAJ-GED - a dataset for Grammatical Error Detection tasks on Swedish

DaLAJ-GED is a dataset for linguistic acceptability judgments for Swedish, covering five head classes: lexical, morphological, syntactical, orthographical and punctuation.DaLAJ-GED is an extension of DaLAJ.v1dataset (Volodina et al., 2021a,b).Both DaLAJ datasets are based on the SweLL-gold corpus (Volodina et al., 2019) and its correction annotation categories.DaLAJ-GED presented here contains 44,654 sentences, distributed (almost) equally between correct and incorrect ones and is primarily aimed at linguistic acceptability judgment task, but can also be used for other tasks related to grammatical error detection (GED) on a sentence level.DaLAJ-GED is included into the Swedish SuperLim 2.0 collection, 1 an extension of SuperLim (Adesam et al., 2020), a benchmark for Natural Language Understanding (NLU) tasks for Swedish.This paper gives a concise overview of the dataset and presents a few benchmark results for the task of linguistic acceptability, i.e.
Human Action Behavior Recognition in Still Images with Proposed Frames Selection Using Transfer Learning

One of the most difficult challenges is recognizing human actions., especially in still images where there isn't much movement. Therefore, Using the transfer learning strategy, we suggested a technique for identifying human action., which consists of training some of the layers of deep learning techniques while freezing others. Also presented a way for data split, which is to choose some frames because we are working on a large dataset such as ucf-101, and this method is summarized by discovering the features for each frame, then clustering the elements, and then choosing a percentage of each cluster for training and test data. We used three techniques. They are vgg16, inceptionv3, and xception. The proposed models have been implemented on UCF-101 Dataset. Depending on three data split methods with the dataset, the random split method, and the proposed split method, the inceptionv3 achieved the highest accuracy. In contrast, the vgg16 achieved the least accuracy, and the accuracy of the xception was close to that of the inceptionv3. By comparing the size of the dataset, the proposed methods achieved good results: the vgg16 in the proposed split attained an accuracy of 92.5%, the inception v3 in the proposed split attained an accuracy of 98.12%, and the xception in the proposed split attained an accuracy of 95.16%. The VGG16 network is simple, so the VGG16 is less accurate. While the network in inceptionv3, xception, is more extensive and complex, the learning space is more significant, although the network size is more prominent in inceptionv3, xception. We only trained some blocks in the top layer&#x0D;
Experiments on Automatic Error Detection and Correction for Uruguayan Learners of English

This paper presents an initial experiment on Grammatical Error Correction and Automatic Grading for short texts written by Uruguayan students that are learning English.We present a set of error detection and correction heuristics, and some experiments on using these heuristics for predicting the grade.Although our experiments are limited due to the nature of the dataset, they are a good proof of concept with promising results that might be extended in the future.
EliCoDe at MultiGED2023: fine-tuning XLM-RoBERTa for multilingual grammatical error detection

In this paper we describe the participation of our team, ELICODE, to the first shared task on Multilingual Grammatical Error Detection, MultiGED, organised within the workshop series on Natural Language Processing for Computer-Assisted Language Learning (NLP4CALL).The multilingual shared task includes five languages: Czech, English, German, Italian and Swedish.The shared task is tackled as a binary classification task at token level aiming at identifying correct or incorrect tokens in the provided sentences.The submitted system is a token classifier based on XLM-RoBERTa language model.We fine-tuned five different models-one per each language in the shared task.We devised two different experimental settings: first, we trained the models only on the provided training set, using the development set to select the model achieving the best performance across the training epochs; second, we trained each model jointly on training and development sets for 10 epochs, retaining the 10-epoch fine-tuned model.Our submitted systems, evaluated using F0.5 score, achieved the best performance in all evaluated test sets, except for the English REALEC data set (second classified).Code and models are publicly available at
NTNU-TRH system at the MultiGED-2023 Shared on Multilingual Grammatical Error Detection

The paper presents a monolithic approach to grammatical error detection, which uses one model for all languages, in contrast to the individual approach, which creates separate models for each language.For both approaches, pre-trained embeddings are the only external knowledge sources.Two sets of embeddings (Flair and BERT) are compared as well as two approaches to the problem of multilingual rammar detection, building individual and monolithic systems for multilingual grammar error detection.The system submitted to the test phase of the MultiGED-2023 shared task ranked 5th of 6 systems.In the subsequent open phase, more experiments were conducted, improving results.These results show the individual models to perform better than the monolithic ones and BERT embeddings working better than Flair embeddings for the individual models, while the picture is more mixed for the monolithic models.
Sequence Tagging in EFL Email Texts as Feedback for Language Learners

When predicting scores for different aspects of a learner text, automated scoring algorithms usually cannot provide information about which part of text a score is referring to.We therefore propose a method to automatically segment learner texts as a way towards providing visual feedback.We train a neural sequence tagging model and use it to segment EFL email texts into functional segments.Our algorithm reaches a token-based accuracy of 90% when trained per prompt and between 83 and 87% in a cross-prompt scenario.
Information System Development for Receiving and Handling Customer Complaints in Public Services

Good public service is one indicator of the success of a government in providing satisfaction for the community. Along with the development of information technology, the government is also trying to improve public services by developing information systems that make it easier for people to make complaints or complaints about public services. This research aims to develop an information system that can improve the receipt and handling of customer complaints on public services. The method used in this research is the waterfall method. Data will be collected through interviews with relevant parties in public services, direct observation of the existing complaint system, and literature study related to the development of information systems. The results of this research are expected to produce an effective and efficient information system in receiving and handling customer complaints in public services. In this case, the information system can facilitate customers in making complaints, as well as speeding up the handling process and responses from related parties. So that it is expected to increase customer satisfaction with the public services provided.
Wujud Aspek Psikologis dalam Sajak Penyair Jambi Ari Setya Ardhi

The purpose of this study is to describe the form of conveying the psychological aspects of the psychiatric form contained in the poems of the Jambi poet Ari Setya Ardhi. Jambi poet means a poet who lives in the Jambi area, actively works, and always documents his rhymes in individual anthologies and joint anthologies. This research includes descriptive research with a content analysis approach to documents in the form of literary works. The data in this study are rhyme texts that refer to the psychological aspects of the psychiatric form of depression and the psychiatric form of anxiety. The results showed that in the poems of the Jambi poet Ari Setya Ardhi depression was identified through the use of sounds, word choices, complete text, typography, and the use of imagery
Penerapan Model Pembelajaran Discovery Learning untuk Meningkatkan Kemampuan Mengindentifikasi Teks Laporan Hasil Observasi Pada Peserta Didik Kelas X SMAN 1 Muara Sugihan

This study aims to apply the discovery learning learning model in improving the ability to identify the text of the observation report for students of SMAN 1 Muara Sugihan. To achieve this goal, the classroom action research was conducted with three cycles. Based on the results of the study, it can be concluded that the application of the discovery learning model can improve the ability to identify the text of the observation report of class XI students of SMAN 1 Muara Sugihan. In cycle I, the action of the first cycle has not been achieved regarding the observation aspect, then in the second cycle, the observed aspects have increased but cannot be said to have reached the KKM, so the researcher takes action in cycle III. In cycle III, all aspects observed experienced an increase from the previous two cycles. The average increase from the results of observations in the learning process cycle I and cycle II increased to thirtysix students or around 51.42%. In this cycle there was an increase to 27.14%. In cycle III there was an increase from the previous two cycles, the proportion achieved was 73.86% (seventythree point eighty six percent) and there were sixty seven students who achieved scores above the KKM. So, the increase achieved was 43.58% (fortythree point five eight percent).
Peningkatan Keterampilan Menulis Teks Eksplanasi Melalui Media Audio Visual pada Siswa Kelas XI SMK Negeri 1 Palembang

Based on the results of improving learning carried out through Classroom Action Research (CAR), it can be concluded that the use of audio-visual media can improve the skills of writing explanatory texts at SMK Negeri 1 Palembang class XI AKL 1 in Indonesian subjects with the sub-subjects of explanatory texts as follows. The results obtained by students during the learning improvement activities in the first cycle of students who have obtained a score of 70 reach 17 students with an average score of 59, the student activity score is 44% and the learning completeness is 50%. Then the second cycle of students who get a score of 70 reaches 31 students with an average score of 83, the student activity rate is 91% and the learning completeness has reached 91%. Thus the desired target has been achieved and student learning outcomes have reached the KKM
The Effect of Cooperative Integrated Reading and Composition Strategy towards Reading Comprehension at Tenth Grade Students of Vocational High School 1 Muaro Jambi

The purpose of this research is to find out the effect of Cooperative and Integrated Reading and Composition Strategy toward student reading comprehension at tenth grade students of Vocational High School 1 Muaro Jambi. Th research design in this research was quantitative research design with experimental research approach. The population of this research was tenth grade student and the sample were class XTKJ 1 and XTKJ 2 with simple random technique. The research instrument was test with multiple choice test. The data collection was pre-test, treatment and post-test. The data analysis in this research by using ttest formula. The result of the research was found that the students’ ability in reading comprehension in experimental class got mean score 43,85 in pre-test with the maximum score 55 and the minimum score was 30. While in post-test the students got mean 70,14 with the maximum score 60 and the minimum score 85. Then, in control class the students got mean 46,42 in pre-test with the maximum score 55 and the minimum score was 30. While in post-test the students got mean 76,00 with the maximum score 65 and the minimum score was 55. Based on the statistical computation t–test was found that the coefficient of observation = 2,270, where the coefficient of t-table = 1,995. It means that there was significant effect of using Cooperative Integrated Reading and Composition (CIRC) technique. It was indicated that Ha was accepted and H0 was rejected. In addition, there is effect of using Cooperative Integrated Reading and Composition (CIRC) technique towards student reading comprehension mastery