Reviewerly/exHarmony huggingface dataset:
Proficiency Test Training for Mandarin Learners in Surakarta

Hanyu Shuiping Kaoshi (HSK) or the Chinese Language Proficiency Test is an international standardized test in Chinese language proficiency. It is used to assess non-native Chinese speakers’ ability in using the Chinese language in their daily, academic, and professional lives. The purpose of administering training activities for the HSK Mandarin competency test is to help Mandarin language learners know the importance and benefits of the Mandarin language proficiency test (HSK) and where the certificate is used as one of the requirements to continue to a higher education level. It is also used to assess Mandarin language proficiency when applying to better job opportunities. The participants of this research are the teachers and students of SMA Bina Widya Solo where their learning system is at the HSK 4 level. The results showed that the students and the teachers of SMA Bina Widya Solo are more open to perspectives about the benefits of the HSK competency test as an instrument of measuring language skills that are internationally certified.&#x0D; Keywords: counseling, training, HSK, Mandarin language
Augmented Reality Support for Commissioning and Monitoring of Electromechanical Multipoint Die Cushion

Commissioning die cushions of a press is a highly complex task, and errors in the process must be avoided. Augmented Reality (AR) can help the engineer to overcome the encountered problems by visualizing the current situation with a superimposed 3D model, providing necessary instruction steps derived from the actual data of the press control and monitoring system, and checking the received data for errors. This paper describes an industrial AR-based concept to support an electromechanical multipoint die cushion commissioning process. It shows how an AR application can support workers in this production use case to avoid failures and reduce the needed commissioning time, resulting in higher productivity.
MASK DETECTION ANALYSIS USING HAAR CASCADE AND NAÏVE BAYES

Coronavirus Disease (COVID-19) is a new virus variant that emerged in 2019. The World Health Organization (WHO) states that 394,381,395 people have been infected with COVID-19, and 5,735,178 have died. This epidemic has been found in Indonesia since March 2020. New cases in Indonesia are still increasing every day as a whole. The Government as a policy has imposed a policy on anyone who will be required to wear a mask and also carry out physical distancing so that they can work without the maker being exposed to the virus. In the midst of a pandemic, the use of masks has increased to prevent transmission. Various types of masks are easy to find, but not all masks are recommended to avoid transmission. Among them are the N-95 masks, which are recommended to prevent transmission. This application uses the haar cascade and naive bayes methods. The pycharm edition 2021.2 tools and python 3.8 are the detection systems used in this mask. The haar cascade method is also used in detecting objects with masks or not and naive Bayes, which is used as an accuracy calculation. This study uses a dataset of 1092, which is divided into 192 positive images and 900 negative images. Accuracy results using the haar cascade method are 100% more accurate, while the nave Bayes method is 76.6% less accurate.
MIXTURE FEATURE EXTRACTION BASED ON LOCAL BINARY PATTERN AND GREY-LEVEL CO-OCCURRENCE MATRIX TECHNIQUES FOR MOUTH EXPRESSION RECOGNITION

Some academics struggle to recognize facial emotions based on pattern recognition. In general, this recognition utilizes all facial features. However, this study was limited to identifying facial emotions in a single facial region. In this study, lips, one of the facial features that can reveal a person's expression, are utilized. Using a combination of local binary pattern feature extraction (LBP) and grey level co-occurrence matrix (GLCM) methods and a multiclass support vector machine classification approach for feature extraction in facial images. The concept begins with image segmentation to create an image of a mouth. Experiments were also conducted for various tests, and the outcomes of these experiments revealed a recognition performance of up to 95%. This result was obtained through experiments in which 10% to 40% of the data were evaluated. These findings are beneficial and can be applied to expression recognition in online learning media to monitor the audience's condition directly.
Students’ Perception of Learning Media using Londo Kampung Vlog

This study aims at determining students' perceptions of learning English using Londo Kampung vlog as a learning media to develop the speaking skill. The theoretical framework focuses on the concept of vlogs and English-speaking competence. It is a qualitative case study with statements as the data, and the data source is interviews with 14 of 11th grade students, SMA Sejahtera Surabaya, the data collection technique is structural interviews, and the data analysis technique is descriptive. The results showed that students have a positive perception of using Londo Kampung vlogs as learning media to improve speaking skills in English.
PADDY WETLAND PRODUCTIVITY ANALYSIS WITH LINEAR REGRESSION OF MACHINE LEARNING APPROACH

Paddy is one of the priority crops in agricultural production. South Kalimantan is an area that produces Paddy. In paddy productivity in the southern Kalimantan region, there are paddy wetlands and paddy dryland. The need for paddy production in the southern Kalimantan region can increase or decrease every year. The method used in this study is a linear regression algorithm with a machine learning approach. Linear regression analysis basically predicts a variable's value based on its free variables. Linear regression only predicts variables whose data nature is intervals or ratios. Linear regression analysis can be used to examine the relationship between two or more variables. Linear regression can also make additional assumptions between variables through the most suitable lines of straight-line data points. This study is to determine the relationship between harvest area and productivity. As a result of trials using the machine learning approach, linear regression algorithms show a relationship between harvest and production area. The correlation test results can find relationships between data points so that linear regression can be used to predict. From the relationship between harvest area and productivity, a prediction accuracy of 95% was obtained.
A DEIXIS ANALYSIS IN JOKO WIDODO’S STATEMENT ON INDONESIA’S G20 PRESIDENCY

Deixis is an important element in pragmatic studies to avoid misunderstandings between speakers and listeners. People will be misunderstanding a conversation when they don't understand the context. The purpose of this research was to determine the type of deixis contained in Joko Widodo's statement in the Indonesia’s G20 presidency and to determine the dominant type of deixis in Joko Widodo's statement. Yule's (1996) theory of deixis is used to justify the data or findings in this study. This research was designed using qualitative methods. Data was taken from the English version of the script from President Joko Widodo's statement. Following data collection, researchers found and selected data that showed that most phrases or words contain deixis types. The researchers reached conclusions after thoroughly identifying and evaluating many types of deixis detected in statements. From the data analysis, three types of deixis were found in Joko Widodo's statement in the Indonesia’s G20 presidency, namely person deixis, spatial deixis, and temporal deixis. The total deixis contained are 35 data. Persona Deiksis is the deixis that most often appears in Joko Widodo's statement in the Indonesia’s G20 presidency with a total of 26 data. In addition, spatial deixis found 2 data and temporal deixis found 7 data. Based on previous research, the researchers concluded that person deixis always dominates every moment when Joko Widodo delivers his statement.
MYTHOLOGY IN SEXTING EMOJI: REVEALING CHANGES IN MEANING

&lt;p&gt;&lt;em&gt;The development of digital-based communication and the widespread use of social media allows the emergence of various variants of communication modes. The use of emojis to express oneself stemmed from the past when people still used pictures or pictographs to communicate through writing to express themselves. However, in modern times, with the advance of the internet and technology, people are increasingly using emojis to amplify messages conveyed to others. In using social media platforms or messaging applications such as WhatsApp and Instagram, people often use emojis to express themselves, including sexuality. &lt;/em&gt;&lt;em&gt;The e&lt;/em&gt;&lt;em&gt;mojis, commonly known as sexting (emojis), are used implicitly to convey meaning because, in social construction, matters related to sexuality tend to be taboo&lt;/em&gt;&lt;em&gt; and&lt;/em&gt;&lt;em&gt; &lt;/em&gt;&lt;em&gt;i&lt;/em&gt;&lt;em&gt;ts existence allows the emergence of various interpretations&lt;/em&gt;&lt;em&gt;.&lt;/em&gt;&lt;em&gt; &lt;/em&gt;&lt;em&gt;T&lt;/em&gt;&lt;em&gt;his study aims to reconstruct the development of the meaning of sexting emoji from a semiotic perspective. This study is directed to interpret the change in meaning by focusing on the reconstruction of meaning across texts in intertextual relations and the personal interpretation of the informants as a basis for argumentation. This qualitative research uses visual sexting emoji as primary data, which is further interpreted personally by non-native English informants in the interview process and elaborated in an intertextual relation with other sources. This study shows that there is a mythology in the process of interpreting emojis. Connotative meaning allows various kinds of interpretation for each recipient of the message. On the other hand, the development of meaning also impacts the emergence of other linguistic expressions associated with sex and sexual activity.&lt;/em&gt;&lt;/p&gt;
Non-linear Adaptive Wiener Filter for Time Series Smoothing

Abstract Often, in the analysis of time series and digital signals, a smoothing procedure is required to filter undesired random perturbations as noise and outliers in data. Among the most widely known techniques for time series smoothing, we have convolutional filters, simple exponential smoothing, triple exponential smoothing (Holt-Winters method) and linear adaptive filters, such as the Wiener filter. In this paper, we propose the NoLAW filter (Non-Linear Adaptive Wiener filter), a higher-order nonlinear extension for the adaptive Wiener filter, which is a linear statistical smoothing technique that assumes the hypothesis that the underlying series is corrupted by a zero mean, additive and independent Gaussian noise. Numerical experiments show that the proposed method is a computationally efficient and viable approach for filtering time series. Quantitative metrics show that the NoLAW filter is capable of producing better results than the usual linear Wiener filter, simple exponential smoothing and Holt-Winters method. Moreover, the computational cost of the proposed NoLAW is linear in the number of samples, which means that, asymptotically, it is equivalent to the regular Wiener filter.
Chinese Proficiency Test-based Teaching Scheme in Xth and XIth Grades of Bina Widya National Trilingual School Solo

The Chinese Proficiency Test or Hanyu Shuiping Kaoshi (HSK) is an international standardized tool to measure the Chinese language competence of non-native learners. Bina Widya Trilingual National School Solo is a school that emphasizes foreign language teaching, Chinese language in particular. The school always promotes learners to pursue higher education in China, where one of the requirements to enter a Chinese College is the HSK certificate. Thus, it is important to create a Chinese Proficiency Test-based teaching scheme to provide a general picture of Chinese language teaching that focuses on the outcomes according to the levels of HSK for the teachers. The modified Development Method of Sugiyono was employed in this study. The research included identifying the potential and problem, collecting the data, validating the design, designing the product, revising the design, testing the product, revising the product, and finally producing the final product. Through the Chinese Proficiency Test, the teachers could actively create the Chinese language teaching concept for the best outcomes.&#x0D; Keywords: scheme, pedagogy, Chinese language, HSK
The Eco Office Approach to Achieving Enviromentally Friendly Offices

The current level of environmental damage has become a global problem. Office buildings have many activities that require a lot of energy consumption, including water and electricity. They also produced a lot of waste which harms the environment. Overcoming this environmental problem requires cooperation from all elements of society, especially academics in universities who act as the scientific community. The Eco Campus/Eco-Office program is the solution that is applicable in overcoming problems in the campus’s environment. This study aims to develop a design for implementing the Eco-Office at the Faculty of Engineering, Universitas Islam Bandung (UNISBA). The method used is a descriptive qualitative method using observations, in-depth interviews, and documentation studies. The results of this study were in the form of alternative and technical strategies. These alternative strategies included policy documents, standard operating procedures (SOP) for Eco-Office, and the formation of a special team to handle the Eco Office and routine outreach at the Faculty of Engineering, UNISBA. The technical strategies based on the existing conditions were energy efficiency (water and electricity), waste management, reducing the usage of papers, and maintaining office sanitation.&#x0D; Keywords: Environmental, eco-office, campus, standard operating procedures, technical strategies
Yield and Fluctuation of Cryptocurrency under the Covid-19 Pandemic: Empirical Research based on Ethereum

The Covid-19 pandemic has caused one of the most severe systematic shocks to global financial markets as investors discovered the sudden slumps in major global stocks indexes in March 2020. However, at the same time, the pandemic also accelerated the rise of decentralized finance and cryptocurrencies as the public began to shift their investments from traditional stock markets to the newly emerged fintech markets as the decentralized financial market’s risks hedging ability are believed to be better during global emergencies. Although it’s tempting to attribute these observed phenomena solely to the Covid-19 pandemic, other political shocks such as the US 2020 election and China’s crypto crackdown in 2021 also exacerbated the uncertainties and thus should be considered as potential reasons for the observations. Through theoretical analysis on financial and political economics as well as empirical modeling utilizing Stata17, this report has constructed a time series ARMA-GARCH model quantifying the relationship between Ethereum’s investment return and potential factors including the daily new confirmed cases of Covid-19 and other policy changes, and discovered for the first time that the rise of Ethereum’s investment return is majorly caused by the two aforementioned policy changes, and the rapid infection of Covid-19 only caused a short-term rise in Ethereum’s investment return whereas the daily new infection numbers of the later stage only caused fluctuations to the Ethereum trading market. Based on the findings, the article made recommendations for both policy makers and investors on crypto investments during the fintech era.
Influences of Emotion and Negative Online Word-Of-Mouth (WoM) on Nissan Motor Co., Ltd.

Emotion, defined as one’s conscious mental reaction to an object or event, is heavily responsible for the majority of our actions, it also leads to the sharing of information that one has strong feelings for, hence resulting in either positive or negative word-of-mouth. This research aims to assess the impact of emotion and how it generates word-of-mouth online and its impacts on study, the automobile manufacturer, Nissan Motor Co., Ltd. was examined, more specifically, one advertisement of the Infiniti brand under it. Both financial data and marketing sales data were analyzed to observe the extent to which negative word-of-mouth impacts a company. In addition to this, previous research articles on the topics of ‘emotion’ and ‘advertisement’ were also gathered and studied.
Study and Analysis of Copy-move Forgery Detection with Local Binary Pattern Method

With the advancement of technology, new problems and challenges also follow, among which the more serious problem is media forgery. Forged media information brings a lot of inconvenience to life. It is difficult to distinguish the truth from the false. In this article, various types of forgery are listed and elaborated with a focus on the copy-move forgery category, the study and research involving copy-move forgery(CMF) detection techniques using the Local Binary Pattern (LBP) are presented. The technical review of recent state-of-the-art LBP-based is provided.
On the Accessibility of a PoN-Enabled Use Case Notation

The Physics of Notations (PoN) is a framework to design cognitively effective notations. The term cognitive effectiveness here refers to the ease (speed and accuracy) by which model readers read the models. In 2019 a PoN-Enabled notation for use case diagrams was proposed. It has been empirically proven that the cognitive effectiveness of this new notation is superior to the original use case notation. The new notation however relies in part on the use of color. The accessibility of this new design however has not been validated for use by the color-blind community, or any users who need to use a greyscale version of the models created. The PoN framework was not designed to explicitly account for the color-blind community or users who can only access greyscale versions of the models. Inclusivity should not be an afterthought in software engineering practice or research. To this end, this paper presents an empirical evaluation of the cognitive effectiveness of the PoN-enabled use case notation in the situation it was viewed in greyscale.
KDBI special issue: MapIntel: A Visual Analytics Platform for Competitive Intelligence

Competitive Intelligence allows an organization to keep up with market trends and foresee business opportunities. This practice is mainly performed by analysts scanning for any piece of valuable information in a myriad of dispersed and unstructured sources. Here we present MapIntel, a system for acquiring intelligence from vast collections of text data by representing each document as a multidimensional vector that captures its own semantics. The system is designed to handle complex Natural Language queries and visual exploration of the corpus, potentially aiding overburdened analysts in finding meaningful insights to help decision-making. The system searching module uses a retriever and re-ranker engine that first finds the closest neighbors to the query embedding and then sifts the results through a cross-encoder model that identifies the most relevant documents. The browsing or visualization module also leverages the embeddings by projecting them onto two dimensions while preserving the multidimensional landscape, resulting in a map where semantically related documents form topical clusters which we capture using topic modeling. This map aims at promoting a fast overview of the corpus while allowing a more detailed exploration and interactive information encountering process. We evaluate the system and its components on the 20 newsgroups dataset, using the semantic document labels provided, and demonstrate the superiority of Transformer-based components. Finally, we present a prototype of the system in Python and show how some of its features can be used to acquire intelligence from a news article corpus we collected during a period of 8 months.
Parameter estimation based on novel enhanced self-learning particle swarm optimization algorithm with Levy flight for PMSG

A novel parameter estimation method is proposed for the permanent magnet synchronous generator (PMSG), which is implemented by an enhanced self-learning particle swarm optimization algorithm with Levy flight (SLPSO), and the problem of lower parameter estimation precision of standard PSO is obviated. This method injects currents of different intensities into the d-axis in a time-sharing manner to solve the problem of equation under-ranking, and the mathematical model for full-rank parameter estimation is developed. The speed term of PSO is simplified to expedite the convergence of PSO, and a strategy with Chaotic decline for the inertia weight of PSO is adopted to strengthen its ability to jump out of the local optimum. Moreover, the self-learning dense fleeing strategy (SLDF) is proposed where particles perform diffusion learning based on population density information and Levy flight, the evolutionary unitary problem and human intervention in the evolutionary process is averted. Furthermore, the memory tempering annealing algorithm (MTA) and greedy algorithm (GA) is integrated into the algorithm, MTA can facilitate the exploration of potentially better regions, and GA for local optimization enhances the convergence speed and accuracy in late stage of the algorithm. Comparing the proposed method with several existing PSO algorithms through simulation and experiments, the experimental data show that the proposed method can effectively track variable parameters under different working conditions and has better robustness.
A meta-analysis of the effectiveness of internet-based cognitive-behavioral therapy on social phobia

This meta-analysis was aimed at evaluating the evidence related to the effectiveness of Internet-Based Cognitive-Behavioral Therapy (ICBT) in patients with social phobia.A systematic review of Randomized Controlled Trials (RCTs) with regard to the effectiveness of ICBT for patients with social phobia was sought across multiple databases.The outcome variable in clinical trials was decline in symptoms of social phobia.The measures used included LSAS, SPS, SP-12, BAI and SAD.Twenty clinical trials met the inclusion criteria (1211 patients overall).The results of the analysis revealed that the ICBT is more effective compared to other treatments or control conditions based on patients' evaluation.The results of the present meta-analysis provide preliminary evidence of the effectiveness of ICBT as a clinical method for social phobia and suggest that more RCTs are needed to compare ICBT with common treatments such as medication, neurological methods, and behavioral interventions.
Combination of Oversampling and Undersampling Techniques on Imbalanced Datasets

Many practical classification datasets are unbalanced, meaning that one of the classes is in the majority when compared to the others. In various real-world circumstances, class-imbalanced datasets arise, where the number of data samples in a class is not equal to the other class. To develop good classification models based on present level calculations, using these datasets is difficult, particularly for separating minority classes from the majority class. To address the issue of class imbalance, under/oversampling procedures are used to minimize and enhance the quantities of data examined in minority and majority class. This paper explores the utilization of combination of both undersampling and oversampling techniques mainly synthetic minority oversampling technique (SMOTE) and neighborhood cleaning rule (NCL) to balance the datasets. The performance has been evaluated using two machine learning algorithms. The results are then classified using recall measure and geometric mean which showed improved performance of the algorithms.