Reviewerly/exHarmony huggingface dataset:
Development of an architectural software solution for Internet of Thing devices

The article presents the development of an architectural software solution for Internet of Things (IoT) devices that implements the functionality of an automatic medical drug dispenser, based on the ESP32 hardware platform and utilizing the capabilities of the existing real-time operating systems (RTOS). The software architecture for IoT devices was designed with scalability and fault tolerance in mind. All components of the system interact with each other through asynchronous callback functions, which provides flexibility and extensibility to the architecture. Testing for system fault tolerance was conducted. The architecture can be implemented and used as the basis for any IoT device, allowing for support of modern security and functionality stacks, by implementing this functionality once in any of the devices. The process of designing the software architecture is presented, including the selection of suitable technologies and libraries. Particular attention was paid to ensuring the safety and reliability of the device, including protection against an unauthorized access and errors in operation. The experimental results show high efficiency and accuracy of the automatic medical drug dispenser based on the developed software. The practical part provides examples of implementing the proposed architecture in the C and C++ languages, with examples and basic interaction diagrams between the components. The rxcpp library was also used in writing the C++ implementation, which made it easier to write the code base for interacting with the operating system resources and reusing the multithreaded interaction with the system.
Image Classification on Fashion Dataset Using Inception V3

Fashion is a form of self-expression that allows us to be able to manifest our personality and identity more confidently. One of the effects of Covid 19 is the economics of the industry, especially the fashion category as the largest category in the e-commerce industry. However, A large number of categories in each fashion brand allows shop owners to be misclassifications about the placement of items that have nearly the same clothing model. The other problem is sellers uploading pictures of products on the platform for the sale and the consequent manual tagging involved. In this paper, we proposed image classification on the fashion dataset using inception V3. The methodology of this paper consists of scrapping data from the official websites of five famous fashion brands, data preprocessing, and classification with the Inception V3 method. The accuracy and F1-Score values obtained using Inception V3 are 92.86% and 92.85%. The proposed method is the highest result of the comparison method and can differentiate between knitted with a scarf that is difficult to classify when compared to other comparison methods.
Using The Canva App as A Media Horizontal Thematic Learning (Art, Science and Language) in Elementary School

This article is a development research that relies on several literacy obtained and described.Currently, the design needs are increasingly unstoppable.Of course, most people are familiar with a graphic design application called Canva.However, some people still don't know how to use Canva.Canva is an online graphic design app that allows users to easily create different types of designs, including: B. Resume design, posters, logos, etc.The graphics of the application are very suitable and are recommended even for beginners.Because the application looks very friendly and easy to use.Still, it's not uncommon for people to struggle with Canva.The reason for this is that Canva has so many features that you should be familiar with.So how do you use Canva for design purposes and more?Canva is a beginner-friendly online graphic design app.Additionally, Canva can be accessed on desktop and mobile devices.So you can work creatively anytime, anywhere.With a variety of attractive designs, the learning process will never be boring.Canva applications enable teachers to impart their knowledge, creativity, and skills to their students, enabling them to use these media in many areas of their lives.
Learning the Discovery Learning Model How to Blended Learning in Practicum Using a Simple Volumetric Gasometer to Support Scientific Literacy

Education in the 21st century requires innovation and creativity as well as skills including scientific literacy skills and numeracy literacy, one of which is experimental learning. Often experiments are constrained by insufficient learning time so that learning becomes less meaningful. The purpose of this study was to determine the effect of applying the discovery model using the blended learning method using a simple volumetric gasometer in supporting scientific literacy. This research is an experimental research conducted in February 2023 at a Bandung high school. The research population was 432 students of class X at a senior high school in Bandung, which were divided into 12 classes. The sampling technique used simple random sampling so that 70 students were obtained consisting of 35 people from one control class and 35 people from one experimental class. The instrument used is a PISA question with the theme of innovation in alcoholic fermentation technology. Instruments were in the form of multiple choice questions and true wrong choice questions to measure scientific literacy profiles. Based on the results of the research, it was found that the learning outcomes of the discovery model by means of blended learning with practical methods using a simple volumetric gasometer can support scientific literacy.
Problems of Developing Media and Information Competence of Future Journalists on the Basis of Case Technologies

Case technologies in training are an effective interactive method of teaching, the use of which is very relevant for developing the skills necessary for work in future journalists.The purpose of this work is to consider the problems of developing media-information competence using case technologies for training future journalists.This study used empirical methods involving teachers (n=8) and journalists (n=8), and journalism students (n=168) in a programme to develop students' media literacy skills.A form was used to assess the students at the end of the programme.During the programme, the teachers and journalists observed the activities and reached a consensus.The quantitative findings revealed that the least developed media information literacy skills of the future journalists are the lack of knowledge regarding the management and regulation of the media, the lack of knowledge to identify and manage inappropriate behaviour in all media and understanding how to present content (style or context) that can affect the perception of the content.Case technologies are effective for teaching journalists in general, but for the formation of media information literacy, additional classes are needed especially thematic classes using case technology to form media information literacy.The results of the study are applicable for the development of training programmes for future journalists in higher educational institutions, self-education of journalists and to improve the pedagogical activities.
Speaking Test Apps Development: English Learning Enhancement in Online Learning Complexity

Abstract. This research tried to map the strength and weaknesses and evaluate the English teaching and learning processes in Balinese EFL learners during the online class. The use of IT in online learning has become a central issue for the decreasing of students’ motivation due to the lack of students – teachers’ interaction in the online class especially in English subject. The complexities in the online learning include how the use of IT can be synchronized with teaching and learning methods in delivering the learning material, how is the role of the teacher in motivating students, how to assess students' abilities in online learning, and many more. Those are the unavoidable obstacles faced by teachers and students during the online learning. This study was aimed at evaluating and making English online teaching and learning effective and creating IT-based application to optimized the teaching and learning process and the students’ performance. This qualitative research collects the data through observation , interviews and English speaking test to 15 students and six teachers. From the empirical studies of the Balinese EFL learners, the students stated that English learning in online classroom atmosphere were ineffective and unsupportive for their English skill development. The problems mainly arise from the lack of practice and interaction which caused a decrease in student motivation and interest in online learning. To be able to overcome those problems, the researcher created learning applications called Speaking Test App. and Teacher Speaking Test App. to facilitate teachers and students in learning process. This application can be freely accessed through the Playstore and Appstore. The students and teachers use the learning applications as interactive English learning mediums in the classrooms. The results of the study showed that with the technology-based learning media, the students' performance, enthusiasm and motivation in learning could be improved and highlight a significant impact for interaction in learning especially in English online learning.Keywords: English teaching and learning, IT-based learning, Online Learning
Development and testing of a scale for examining factors affecting the learning experience in the Metaverse

The Metaverse, as it gradually becomes a reality, offers great possibilities for education, providing a new dimension of engagement, interaction, and experiences for students and educators alike. Yet, researchers lack tools that would allow them to simultaneously examine a satisfactory number of factors that shape one's experience when engaged in educational applications in the Metaverse. The study at hand is an attempt to fill this gap, as it reports the steps followed for the development and testing of the Metaverse Learning Experience Scale. The statistical analyses of data coming from 462 university students who participated in its testing, established the scale's validity and reliability. Its final version can capture the views of users using forty-three items, examining ten factors (namely, perceived quality of the virtual environment's graphics, perceived cognitive load, perceived ease of use/control of the virtual environment, immersion/presence, perceived feedback and content quality, perceived degree of interaction, motivation to learn and use the virtual environment, perceived usefulness/knowledge gains, simulator sickness, and positive feelings. The scale's factorial structure together with the implications it has for research and practice are also discussed.
Grid Satellite Rainfall Products Potential Application for Developing I-D and E-D Thresholds for Landslide Early Alert System over Bali Island

Bali has been one of the most popular tourist destinations in Indonesia. However, on the other hand, Bali has a high risk of natural disaster vulnerability. The number of landslides in Bali took the first position compared to other natural disasters. Currently, remote sensing platforms can present Grid Satellite Rainfall Products (GSRPs), which provide rainfall information that can identify rainfall conditions for landslide events. This study aims to analyze the potential GSRPs application of Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN), the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG), and Global Satellite Mapping of Precipitation (GSMaP) in determining the mean rainfall intensities and duration (I-D); accumulated rainfall and duration (E-D) thresholds for landslide occurrences over Bali Island. The method used to develop I-D and E-D thresholds is the power-law equation and frequentist sampling method in various probability levels (5%, 10%, 20%, 30%, 40%, and 50%). The result shows that I-D and E-D thresholds established by GSRPs are generally lower than the threshold defined by rain gauge observations. Among the three GSRPs, IMERG is performing the best in establishing the I-D and E-D thresholds for landslide phenomena. The level of potential that IMERG can use in developing the I-D and E-D thresholds is 59.16% and 84.06%, respectively. The E-D threshold derived from the IMERG product can be used to establish the landslide early alert system over Bali Island because it has a high spatial-temporal resolution, word-wide coverage, and near-real-time observation.
Enhancing Learning Object Analysis through Fuzzy C-Means Clustering and Web Mining Methods

The development of learning objects (LO) and e-pedagogical practices has significantly influenced and changed the performance of e-learning systems. This development promotes a genuine sharing of resources and creates new opportunities for learners to explore them easily. Therefore, the need for a system of categorization for these objects becomes mandatory. In this vein, classification theories combined with web mining techniques can highlight the performance of these LOs and make them very useful for learners. This study consists of two main phases. First, we extract metadata from learning objects, using the algorithm of Web exploration techniques such as feature selection techniques, which are mainly implemented to find the best set of features that allow us to build useful models. The key role of feature selection in learning object classification is to identify pertinent features and eliminate redundant features from an excessively dimensional dataset. Second, we identify learning objects according to a particular form of similarity using Multi-Label Classification (MLC) based on Fuzzy C-Means (FCM) algorithms. As a clustering algorithm, Fuzzy C-Means is used to perform classification accuracy according to Euclidean distance metrics as similarity measurement. Finally, to assess the effectiveness of LOs with FCM, a series of experimental studies using a real-world dataset were conducted. The findings of this study indicate that the proposed approach exceeds the traditional approach and leads to viable results. Doi: 10.28991/ESJ-2023-07-03-010 Full Text: PDF
Real or Fake Text?: Investigating Human Ability to Detect Boundaries between Human-Written and Machine-Generated Text

As text generated by large language models proliferates, it becomes vital to understand how humans engage with such text, and whether or not they are able to detect when the text they are reading did not originate with a human writer. Prior work on human detection of generated text focuses on the case where an entire passage is either human-written or machine-generated. In this paper, we study a more realistic setting where text begins as human-written and transitions to being generated by state-of-the-art neural language models. We show that, while annotators often struggle at this task, there is substantial variance in annotator skill and that given proper incentives, annotators can improve at this task over time. Furthermore, we conduct a detailed comparison study and analyze how a variety of variables (model size, decoding strategy, fine-tuning, prompt genre, etc.) affect human detection performance. Finally, we collect error annotations from our participants and use them to show that certain textual genres influence models to make different types of errors and that certain sentence-level features correlate highly with annotator selection. We release the RoFT dataset: a collection of over 21,000 human annotations paired with error classifications to encourage future work in human detection and evaluation of generated text.
SSMI: Semantic Similarity and Mutual Information Maximization Based Enhancement for Chinese NER

The Chinese NER task consists of two steps, first determining entity boundaries and then labeling them. Some previous work incorporating related words from pre-trained vocabulary into character-based models has been demonstrated to be effective. However, the number of words that characters can match in the vocabulary is large, and their meanings vary widely. It is unreasonable to concatenate all the matched words into the character's representation without making semantic distinctions. This is because words with different semantics also have distinct vectors by the distributed representation. Moreover, mutual information maximization (MIM) provides a unified way to characterize the correction between different granularity of embeddings, we find it can be used to enhance the features in our task. Consequently, this paper introduces a novel Chinese NER model named SSMI based on semantic similarity and MIM. We first match all the potential word boundaries of the input characters from the pre-trained vocabulary and employ BERT to segment the input sentence to get the segmentation containing these characters. After computing their cosine similarity, we obtain the word boundary with the highest similarity and the word group with similarity score larger than a specific threshold. Then, we concatenate the most relevant word boundaries with character vectors. We further calculate the mutual information maximization of group, character and sentence, respectively. Finally, we feed the result from the above steps to our novel network. The results on four Chinese public NER datasets show that our SSMI achieves state-of-the-art performance.
Selective Knowledge Distillation for Non-Autoregressive Neural Machine Translation

Benefiting from the sequence-level knowledge distillation, the Non-Autoregressive Transformer (NAT) achieves great success in neural machine translation tasks. However, existing knowledge distillation has side effects, such as propagating errors from the teacher to NAT students, which may limit further improvements of NAT models and are rarely discussed in existing research. In this paper, we introduce selective knowledge distillation by introducing an NAT evaluator to select NAT-friendly targets that are of high quality and easy to learn. In addition, we introduce a simple yet effective progressive distillation method to boost NAT performance. Experiment results on multiple WMT language directions and several representative NAT models show that our approach can realize a flexible trade-off between the quality and complexity of training data for NAT models, achieving strong performances. Further analysis shows that distilling only 5% of the raw translations can help an NAT outperform its counterpart trained on raw data by about 2.4 BLEU.
Effective Open Intent Classification with K-center Contrastive Learning and Adjustable Decision Boundary

Open intent classification, which aims to correctly classify the known intents into their corresponding classes while identifying the new unknown (open) intents, is an essential but challenging task in dialogue systems. In this paper, we introduce novel K-center contrastive learning and adjustable decision boundary learning (CLAB) to improve the effectiveness of open intent classification. First, we pre-train a feature encoder on the labeled training instances, which transfers knowledge from known intents to unknown intents. Specifically, we devise a K-center contrastive learning algorithm to learn discriminative and balanced intent features, improving the generalization of the model for recognizing open intents. Second, we devise an adjustable decision boundary learning method with expanding and shrinking (ADBES) to determine the suitable decision conditions. Concretely, we learn a decision boundary for each known intent class, which consists of a decision center and the radius of the decision boundary. We then expand the radius of the decision boundary to accommodate more in-class instances if the out-of-class instances are far from the decision boundary; otherwise, we shrink the radius of the decision boundary. Extensive experiments on three benchmark datasets clearly demonstrate the effectiveness of our method for open intent classification.For reproducibility, we submit the code at: https://github.com/lxk00/CLAP
A Scope Sensitive and Result Attentive Model for Multi-Intent Spoken Language Understanding

Multi-Intent Spoken Language Understanding (SLU), a novel and more complex scenario of SLU, is attracting increasing attention. Unlike traditional SLU, each intent in this scenario has its specific scope. Semantic information outside the scope even hinders the prediction, which tremendously increases the difficulty of intent detection. More seriously, guiding slot filling with these inaccurate intent labels suffers error propagation problems, resulting in unsatisfied overall performance. To solve these challenges, in this paper, we propose a novel Scope-Sensitive Result Attention Network (SSRAN) based on Transformer, which contains a Scope Recognizer (SR) and a Result Attention Network (RAN). SR assignments scope information to each token, reducing the distraction of out-of-scope tokens. RAN effectively utilizes the bidirectional interaction between SF and ID results, mitigating the error propagation problem. Experiments on two public datasets indicate that our model significantly improves SLU performance (5.4% and 2.1% on Overall accuracy) over the state-of-the-art baseline.
Self-Supervised Logic Induction for Explainable Fuzzy Temporal Commonsense Reasoning

Understanding temporal commonsense concepts, such as times of occurrence and durations is crucial for event-centric language understanding. Reasoning about such temporal concepts in a complex context requires reasoning over both the stated context and the world knowledge that underlines it. A recent study shows massive pre-trained LM still struggle with such temporal reasoning under complex contexts (e.g., dialog) because they only implicitly encode the relevant contexts and fail to explicitly uncover the underlying logical compositions for complex inference, thus may not be robust enough. In this work, we propose to augment LMs with the temporal logic induction ability, which frames the temporal reasoning by defining three modular components: temporal dependency inducer and temporal concept defuzzifier and logic validator. The former two components disentangle the explicit/implicit dependency between temporal concepts across context (before, after, ...) and the specific meaning of fuzzy temporal concepts, respectively, while the validator combines the intermediate reasoning clues for robust contextual reasoning about the temporal concepts. Extensive experimental results on TIMEDIAL, a challenging dataset for temporal reasoning over dialog, show that our method, Logic Induction Enhanced Contextualized TEmporal Reasoning (LECTER), can yield great improvements over the traditional language model for temporal reasoning.
Denoising Pre-training for Machine Translation Quality Estimation with Curriculum Learning

Quality estimation (QE) aims to assess the quality of machine translations when reference translations are unavailable. QE plays a crucial role in many real-world applications of machine translation. Because labeled QE data are usually limited in scale, recent research, such as DirectQE, pre-trains QE models with pseudo QE data and obtains remarkable performance. However, there tends to be inevitable noise in the pseudo data, hindering models from learning QE accurately. Our study shows that the noise mainly comes from the differences between pseudo and real translation outputs. To handle this problem, we propose CLQE, a denoising pre-training framework for QE based on curriculum learning. More specifically, we propose to measure the degree of noise in the pseudo QE data with some metrics based on statistical or distributional features. With the guidance of these metrics, CLQE gradually pre-trains the QE model using data from cleaner to noisier. Experiments on various benchmarks reveal that CLQE outperforms DirectQE and other strong baselines. We also show that with our framework, pre-training converges faster than directly using the pseudo data. We make our CLQE code available (https://github.com/NJUNLP/njuqe).