Reviewerly/exHarmony huggingface dataset:
The Analysis of the English Use and Barriers in Front Office in Hotels in Bali

&#x0D; &#x0D; &#x0D; &#x0D; Communication is an essential element of the hospitality industry. Spoken and written communication skill is important for all hoteliers. The most important is the English skill. The significance of English skills from front office staff at hotels in Bali is a specific perception case occurring in the hospitality industry. In this case, communication skills, language skills, the willingness to understand guests' interests, and showing politeness are the leading factors in improving the quality of service. This study aimed at analyzing the function and obstacles in using English by the front office staff at hotels in Bali. Data were collected through depth interviews with the hotel front office staff. The results show that English plays an essential role in the front office for greeting and welcoming, registering, handling check-in and check-out, providing information, diplomatic agent, and problem solver. The obstacles faced in using English happening in the front office department should be minimized by providing special training to the staff. Besides, the obstacle in using English can also be solved by the collaboration between the government and educational institutions focusing on tourism study to graduate qualified students who are ready to face the real work practice and be the ones with excellent communication skills.&#x0D; &#x0D; &#x0D; &#x0D;
Wadanan as a Manifestation of Harassing Face (Study of Conversational Implicature in Osing Language)

Wadanan utterances were the research objects. The research problems were related to how the manifestation of harassing the face in wadanan utterances in the Osing language and the context of wadanan implicatures as a manifestation of harassing the face. The objectives of this study consisted of (1) to describe the manifestation of harassing face in wadanan utterances in the Osing language and (2) the context of wadanan implicatures as a manifestation of harassing face in the Osing language.The design of this study was qualitative descriptive. The data source was wadanan utterances in the Osing language. The data collection method of this study used direct observation with note-taking techniques. The procedure used in this study were: data presentation stage, data analysis, and data presentation from data analysis results. Wadanan utterances data obtained were presented descriptively.The results showed four manifestations of wadanan utterances harassing faces in the Osing language, namely with harsh words, with cynicism, ridicule, and expressions of disappointment, forbidding, and complaining.
Using Translog To Analyze Self-Corrections in Translating Advertisement Texts by Professional Translators

This study aims to determine the method and type of self-corrections done by two professional translators in translating texts from English into bahasa Indonesia. This research used a descriptive qualitative method. The data were the recording of translating advertisement texts from English into bahasa Indonesia process using Translog II and Camtasia Studio 8. The findings are (1) Meaning Correction is the most frequent type of self-correction with the frequency of 36.2%; (2) Return Correction is never used by the professional translators; (3) The professional translators apply the multidirectional method in doing self-corrections. Finally, it can be concluded that self-correction is the crucial part that the translator should not skip in producing a better quality translation. This research also proves that translators cannot rely entirely on Google Translate as it cannot ultimately deliver the meaning from the source text (ST) into the target text (TT). Consequently, the translation provided by Google Translate needs to be revised.
Expressive Acts in Euphoria Special Episode Part 1: Trouble Don’t Last Always

The research identifies the type of expressive acts uttered by the characters in a special episode entitled Euphoria Part 1: Rue – Trouble Don’t Last Always and the research implements Norrick theory to analyze the data. Then, the research designs the activity in qualitative method which shapes how the report of the analysis would be descriptively. In collecting the data, the research appropriates Observational method and non-participation technique by Sudaryanto. On the other hand, in analyzing the data, the research appropriates Pragmatic Identity Method and Matching technique by Sudaryanto. The research found 26 utterances identified completely of the types of expressive acts; (2) apologizing, (1) thanking, (1) congratulating, (4) condoling, (5) deploring, (8) lamenting, (2) welcoming, (2) forgiving, and (1) boasting. Lamenting is the most frequent expressive act as the central story of the episode where the main character shared their sorrow and regret to their friend.
Video Restoration with a Deep Plug-and-Play Prior

This paper presents a novel method for restoring digital videos via a Deep Plug-and-Play (PnP) approach. Under a Bayesian formalism, the method consists in using a deep convolutional denoising network in place of the proximal operator of the prior in an alternating optimization scheme. We distinguish ourselves from prior PnP work by directly applying that method to restore a digital video from a degraded video observation. This way, a network trained once for denoising can be repurposed for other video restoration tasks. Our experiments in video deblurring, super-resolution, and interpolation of random missing pixels all show a clear benefit to using a network specifically designed for video denoising, as it yields better restoration performance and better temporal stability than a single image network with similar denoising performance using the same PnP formulation. Moreover, our method compares favorably to applying a different state-of-the-art PnP scheme separately on each frame of the sequence. This opens new perspectives in the field of video restoration.
The Classification of Multi-Domain Samples Based on the Cooperation of Multiple Models

This article proposed a novel classification framework that can classify the samples of multiple domains based on the outputs of multiple models. Different from the existing methods that train single model on all domains, our framework trains multiple models on each domain. On a testing sample, the outputs of all trained models are used to predict the domain of this sample. Then, this sample is classified by the output of models that belong to the predicted domain. Experiments show that our framework achieved higher accuracy than the existing methods. Furthermore, our framework achieves good scalability on multiple domains.
METHODS OF TEACHING ENGLISH TO YOUNG LEARNERS

It is fundamentally essential for children to learn English from a young age in this rapidly globalizing world. English knowledge will help to open many opportunities for them in the future and it will be invaluable in their future careers. However, teaching English to children is not an easy job. But it is also not difficult, if we already know how to do it. Many teaching positions involve teaching children - a unique experience that is both challenging and fun. Compared to adults, children are more energetic, have shorter attention spans, and learn language according to specific stages of development; these present planning challenges for the teacher. The key to teaching English to children is to understand the principles of language acquisition and apply it in ways that keep children motivated to learn. Children's world is playing and imitating. The present paper deals with the following subjects: what are the principles of teaching English to children, what are the characteristics of a language teacher, why do we teach children a foreign language, teachers social and psychological preparation , the emotional and physical aspects of young learners , the teachers main roles in class , a review of TPR , and finally some practical tips and teaching techniques for beginner teachers of English language.
Nursing Telemedicine and Multipliers Training During the SARS-CoV-2 Pandemic – An Experience Report

The worldwide pandemic due to SARS-CoV 2 demanded an exponential increase in intensive care. The deficit between the training of professionals specialized in intensive care and the increase of ICUs exposed the need to expand specialized action in the care of critically ill patients. In this scenario, a tele-ICU project was implemented in two philanthropic hospitals in the metropolitan region of Rio Grande do Sul. Objective: The purpose of this article is to report on the experience in implementing telemedicine among nurses and training multipliers during the pandemic resulting from COVID-19. Materials &amp; methods: The nursing telemedicine occurred weekly and lasted around 30 to 50 minutes, focusing on developing the professional in the evaluation of the critically ill patient, electing priorities, as well as in the management and education of the technical team for key elements on the patient's status and the care to be provided. The training of professionals was based on a training matrix developed together with the nursing leadership, listing the main demands for each unit. Results/Discussion: Making the trained professional a multiplier brings responsibility and engagement in replicating the knowledge and, concomitantly, in changing the institutional culture. By developing and implementing the tele-ICU project, it was possible to notice an improvement in the quality of care, reflected in the indicators and the nurses' assessment, as the discussions matured. Furthermore, it was possible to emphasize with the management team the importance of continuing education with a managed matrix to develop critical sense and continuous quality of care even despite the completion of the project.
CURRICULUM AND EDUCATIONAL PROCESS

The purpose is projected into the future as a Vision of the finished product of the process of schooling namely the Ideal Person we envision at the end of the process. The vision gives us clear concept of the kind of person we hope our students would become. The school should specify the kind of knowledge, skills and competencies, attitudes and values that our graduates will possess in keeping with this Vision. Hopefully in concert with other schools, these graduates will form a “critical mass” that will act as a leaven in transforming society into the “Ideal Society” of the future. Principles of educational process are values that shape and form the basis of our pedagogy. By this I mean the “strategy” the teacher adapts to forward learning among students. These “principles” are the standards and values embedded in the very process of education. The teacher is a model of one who is open-minded and critical of prejudiced views. In short, he or she teaches to enhance multicultural understanding and acceptance of and respect for various groups in society. The idea here is to specify a teaching strategy that requests the teacher to subject his or her teaching and the effects of that teaching to research as the basis for evaluation and improvement.
Modeling Multi-Granularity Hierarchical Features for Relation Extraction

Relation extraction is a key task in Natural Language Processing (NLP), which aims to extract relations between entity pairs from given texts. Recently, relation extraction (RE) has achieved remarkable progress with the development of deep neural networks. Most existing research focuses on constructing explicit structured features using external knowledge such as knowledge graph and dependency tree. In this paper, we propose a novel method to extract multi-granularity features based solely on the original input sentences. We show that effective structured features can be attained even without external knowledge. Three kinds of features based on the input sentences are fully exploited, which are in entity mention level, segment level, and sentence level. All the three are jointly and hierarchically modeled. We evaluate our method on three public benchmarks: SemEval 2010 Task 8, Tacred, and Tacred Revisited. To verify the effectiveness, we apply our method to different encoders such as LSTM and BERT. Experimental results show that our method significantly outperforms existing state-of-the-art models that even use external knowledge. Extensive analyses demonstrate that the performance of our model is contributed by the capture of multi-granularity features and the model of their hierarchical structure. Code and data are available at \url{https://github.com/xnliang98/sms}.
Automatic Qubit Characterization and Gate Optimization with QubiC

As the size and complexity of a quantum computer increases, quantum bit (qubit) characterization and gate optimization become complex and time-consuming tasks. Current calibration techniques require complicated and verbose measurements to tune up qubits and gates, which cannot easily expand to the large-scale quantum systems. We develop a concise and automatic calibration protocol to characterize qubits and optimize gates using QubiC, which is an open source FPGA (field-programmable gate array) based control and measurement system for superconducting quantum information processors. We propose mutli-dimensional loss-based optimization of single-qubit gates and full XY-plane measurement method for the two-qubit CNOT gate calibration. We demonstrate the QubiC automatic calibration protocols are capable of delivering high-fidelity gates on the state-of-the-art transmon-type processor operating at the Advanced Quantum Testbed at Lawrence Berkeley National Laboratory. The single-qubit and two-qubit Clifford gate infidelities measured by randomized benchmarking are of $4.9(1.1) \times 10^{-4}$ and $1.4(3) \times 10^{-2}$, respectively.
FPM: A Collection of Large-scale Foundation Pre-trained Language Models

Large-scale Transformer models have significantly promoted the recent development of natural language processing applications. However, little effort has been made to unify the effective models. In this paper, driven by providing a new set of baseline models in the future, we adopt various novel transformer architectures and launch a model set with the help of recent mainstream technologies. We focus the discussions on optimizing the depth of the networks based on the existing powerful encode-decoder structures. We show that by properly avoiding training defects such as non-convergence and degradation, scaling up off-the-shelf transformer architectures consistently delivers better performance. To stimulate future research on large-scale language model pretraining, we present extensive results and detailed discussions on network performance improvements with respect to the network depth and confirm the existence of the optimal number of layers under specific tasks. To the best of our knowledge, we provide the largest Chinese generative model and the largest Chinese encoding model. The BERT language models we trained on English datasets deliver a 14.45% higher F1 score than the Turing-NLR.
On the marginal likelihood and cross-validation

In Bayesian statistics, the marginal likelihood, also known as the evidence, is used to evaluate model fit as it quantifies the joint probability of the data under the prior. In contrast, non-Bayesian models are typically compared using cross-validation on held-out data, either through $k$-fold partitioning or leave-$p$-out subsampling. We show that the marginal likelihood is formally equivalent to exhaustive leave-$p$-out cross-validation averaged over all values of $p$ and all held-out test sets when using the log posterior predictive probability as the scoring rule. Moreover, the log posterior predictive is the only coherent scoring rule under data exchangeability. This offers new insight into the marginal likelihood and cross-validation and highlights the potential sensitivity of the marginal likelihood to the choice of the prior. We suggest an alternative approach using cumulative cross-validation following a preparatory training phase. Our work has connections to prequential analysis and intrinsic Bayes factors but is motivated through a different course.
Analisis Pengaruh Limbah Domestik terhadap Kualitas Air Anak Sungai Asam

The Asam River is one of the tributaries located along the RT 15 area of Paal V Village, the Asam River crossing the residential area of this river has the potential to be polluted by domestic waste. Based on data on the water quality of the Asam River according to the Jambi City Environmental Service in 2020, the water quality of the Asam River has exceeded the surface water quality standard on the parameters of TSS, turbidity, pH, BOD, COD and ammonia. The purpose of the study was to determine the disposal of domestic wastewater and to determine the quality status of the acid river in terms of the pollution index method and to know the plan for controlling the pollution of the acid river with the influence of domestic waste. Based on the calculation of the status of the Asam River water quality, which was determined using the Pollution Index (IP) method, the points AP-1 (Upstream) and AP-2 (Downstream) were in a “Lightly Polluted” condition. This is because the results of the ammonia and turbidity parameter tests at the AP-1 (Upstream) and AP-2 (Downstream) points that exceed the established quality standards. Efforts to control river pollution can be carried out by making riparian buffer strips or planting vegetation on the banks of the Asam River. , the manufacture of communal WWTPs in every existing sub-district along the Asam River and tightening of wastewater quality standards for industrial and domestic activities which must not exceed the water quality standard according to its class.
Blockchain-Enabled Secure and Efficient Data Sharing Scheme for Trust Management in Healthcare Smartphone Network

Abstract Internet of Medical Things (IoMT) is an extended genre of Internet of Things (IoT) where the Things collaborate to provide remote patient health monitoring, also known as the Internet of Health (IoH). Smartphones and IoMTs' are expected to maintain a secure and trusted confidential patient-records exchange while managing the patient remotely. Healthcare organizations deploy Healthcare Smartphone Networks (HSN) for confidential patient data collection and sharing among smartphone users and IoMT nodes. However, the attackers gain access to confidential patient data via compromised/malicious IoMT nodes from the HSN. Additionally, attackers can compromise the entire network via the compromised nodes. This article proposes a Hyperledger blockchain-based technique to identify the compromised IoMT nodes and safeguard sensitive patient records. Furthermore, the paper proposes a Clustered Hierarchical Trust Management System (CHTMS) to block malicious nodes. In addition, the proposal employs Elliptic Curve Cryptography (ECC) to protect sensitive health records. Similarly, the proposed approach is resilient against denial of service, eclipse attacks, and terminal device failure. Finally, the evaluation results show that integrating blockchains into the HSN system improved the detection performance compared to the existing state-of-the-art. Therefore, the simulation results indicate better security and reliability when compared to conventional databases.
Tuberculosis Modeling in East Java Based on Geographically Weighted Regression Approach

Tuberculosis is the 13th trigger of death causes around the world. Even after Covid-19, tuberculosis ranks 2nd as a contagious killer disease. In 2020, Indonesia ranks 2nd out of 8 countries with the highest contributor to tuberculosis sufferers after India. East Java Province is the region with the largest number of tuberculosis cases in order of 8. Tuberculosis cases in East Java in 2020 have decreased, but when viewed from the success rate of treatment of tuberculosis cases per district/city in East Java, it was found that 53% still did not meet the target of 90%. According to (World Health Organization), gender affects the occurrence of tuberculosis disease, where men are more susceptible than women. In finding treatment for all tuberculosis incidents in East Java, the highest patient was male. This study was conducted to model tuberculosis in men in the East Java area. The results of the study prove that the modeling of male tuberculosis in East Java used linear regression and GWR (Geographically Weighted Regression) obtained the best model was GWR with Fixed Gaussian Kernel weighting, CV value of 5.68, and R2 86.47%. Variables that have a significant effect on male tuberculosis in East Java are BCG immunization for male infants, public places meeting health requirements, youth who smoke tobacco every day, sex ratio, and households with access to proper sanitation facilities.
Forecasting the Number of Foreign Tourist Visits to Indonesia Used Intervention Analysis with Step Function

Intervention analysis is a method for processing time series data that can be used to explain the effect of an intervention that is influenced by external and internal factors. One application of this method is the data on the number of foreign tourist visits. Since the emergence of COVID-19 in Indonesia, especially in March 2020, Indonesia has begun to implement a lockdown policy and restrict foreign tourists from entering Indonesia. Lockdown policy caused the number of foreign tourist arrivals to decreased drastically. The purpose of this study was obtained a model and forecast results for the number of foreign tourist arrivals for the period November 2021 to November 2022 used a step function intervention analysis. The results of the analysis was shown that the ARIMA intervention model (0,1,1) with a step function with an intervention orde of b=0, s=0, and r=0 was the best model. The results of forecasting the number of foreign tourist visits to Indonesia will increase slowly from November 2021 to November 2022 with a MAPE value 9.91%.
Methods for Detoxification of Texts for the Russian Language

We introduce the first study of automatic detoxification of Russian texts to combat offensive language. Such a kind of textual style transfer can be used, for instance, for processing toxic content in social media. While much work has been done for the English language in this field, it has never been solved for the Russian language yet. We test two types of models - unsupervised approach based on BERT architecture that performs local corrections and supervised approach based on pretrained language GPT-2 model - and compare them with several baselines. In addition, we describe evaluation setup providing training datasets and metrics for automatic evaluation. The results show that the tested approaches can be successfully used for detoxification, although there is room for improvement.
Inventive Design Solutions for the Complex Socio-technical Problems in Preserving Indigenous Symbolic Visual Communication

Visual symbolic communication systems such as emojis are increasingly important to facilitate casual communications and spontaneous information exchange in our daily lives. However, the use of such systems poses dangers to the preservation of local visual symbolic languages as practised by many indigenous and culturally rich local communities. This research aims at developing a local cultural value-based visual communication system for indigenous people in the Malaysian Borneo states of Sabah and Sarawak. The design of such systems requires systematic analysis and identifying the core issue in solving complex socio-technical system problems. Meaningful engagement with different community levels, the sustainability of local knowledge, and cultural values were the primary considerations in designing a culture-preserving model. By utilising the Law of System Completeness of TRIZ, and the engagement of the interaction of supersystems, a conceptual model that can map and analyse indigenous symbolic visual communication systems was developed. This modelling approach has provided numerous insightful ideas for transforming global communication approaches to be sensitive to the cultural needs of indigenous communities.