Reviewerly/exHarmony huggingface dataset:
THE DEVELOPMENT OF ANIMATION VIDEOS FOR TEACHING ENGLISH INTEGRATED SKILLS FOR YOUNG LEARNERS

As a foreign language, English is very good to be taught from an early age because an early age is a golden age where children can understand learning material faster. However, teaching English to young learners is not easy. They are easily bored, and also easily distracted. Thus, teachers are required to provide fun and interesting learning activities. Therefore, researchers developed media in the form of animation videos to assist teachers in conveying learning to students as well as attracting their attention. This media does not only refer to vocabulary but also listening, reading, speaking, and writing. In this study, researchers used the ADDIE development model (analysis, design, development, implementation, evaluation). The data in this study were collected by observation, distributing questionnaires, and interview methods. The instruments used in collecting data are questionnaires and tests. After doing research, it is proven that by applying animation video media, the level of student learning is increasing. In conclusion, teachers are advised to apply animation videos in teaching English for young learners so that the teaching and learning process becomes effective and fun.
ON THE ISSUE OF PROFESSIONAL DEFORMATION OF THE TEACHER

The analysis of the concept of professional deformation is given; signs and factors of emotional burnout as a form of professional deformation of a teacher are considered
The Price of Autonomy: Liability Standards for Complementary and Substitutive Medical Robotics and Artificial Intelligence

When AI or robotics assist a professional, they are tools. In medicine, the doctrine of âcompetent human interventionâ has shifted liability away from those who make devices and toward the professionals who use them. However, the professional in such scenarios should not bear the entire burden of responsibility. Tools can be defective, and vendors of defective, complementary AI and robotics should be held responsible for negligence. The burden of proof will still be on the plaintiff to demonstrate that not only a skilled medical professional, but also the maker of the tools used by such a professional, should be held liable for a preventable adverse outcome.
Apache Hadoop Architecture, Applications, and Hadoop Distributed File System

The data and internet are highly growing which causes problems in management of the big-data. For these kinds of problems, there are many software frameworks used to increase the performance of the distributed system. This software is used for the availability of large data storage. One of the most beneficial software frameworks used to utilize data in distributed systems is Hadoop. This paper introduces Apache Hadoop architecture, components of Hadoop, their significance in managing vast volumes of data in a distributed system. Hadoop Distributed File System enables the storage of enormous chunks of data over a distributed network. Hadoop Framework maintains fsImage and edits files, which supports the availability and integrity of data. This paper includes cases of Hadoop implementation, such as monitoring weather, processing bioinformatics.
The Comparison of the Top 100 Cited Publications of Augmented Reality and Virtual Reality for the Last Thirty Years

The development of technology like mobile devices, computers and other smart devices is developing rapidly, especially AR and VR technologies. This study describes comparison among 100 cited papers in the field of AR and VR studies. Research data covering the top one hundred cited publications for the last thirty years were obtained from the Scopus database. Article is most publication document type. The majority of these top-cited papers were published in 2005 for VR and 2013 for AR. The journal Computer and Education is the primary source of the Elsevier, which governs the publication of the most influential AR-VR studies. Most research articles have multiple authors. Slater and Feiner are recognized as the most productive author. The United States dominates the production of highly cited AR-VR papers. Future research should focus on other disciplines, use one or more keywords, and collaborate with Google Scholar, the Web of Science data, for in-depth analysis.
PREDIKSI HARGA SAHAM MENGGUNAKAN GEOMETRIC BROWNIAN MOTION WITH JUMP DIFFUSION DAN ANALISIS RISIKO DENGAN EXPECTED SHORTFALL (Studi Kasus: Harga Penutupan Saham PT. Waskita Karya Persero Tbk.)

Investment is an activity that is quite popular among investors in recent years. One of the forms of investment in the financial sector is investing in the capital market by buying stocks in a company. The level of profit from stock investment activities can be seen from the value of stock returns. Factors that can affect the value of stock returns are stock prices. However, stock prices often experience unpredictable changes so that they experience fluctuating movements with increasing time and developing situations, therefore a stock price model is needed to predict stock prices in the future period. The Geometric Brownian Motion with Jump Diffusion’s method is more appropriate to be used in predicting stock prices if there is a jump in stock price data. Predicted stock prices can be used as a basis for measuring the value of investment risk. The results of data processing indicate that the stock return data of PT. Waskita Karya Persero Tbk has a kurtosis value &gt; 3 which means there is a jump in stock return data so that it is more accurately modeled using the Geometric Brownian Motion with Jump Diffusion’s method. The prediction results have a good level of accuracy based on the MAPE value of 18,733%. Furthermore, in order to measure the investment risk of the predicted stock price of PT. Waskita Karya Persero Tbk used the Expected Shortfall Historical Simulation’s method with a significance level of α = 5%, the results were 0,10939, and for the significance level α = 10%, the results were 0,07596. The calculation results show that the greater the trust level used, the greater the risk borne by investors.Keywords: Jump Diffusion Process, Expected Shortfall, Risk, Extreme Value
No-reference image quality assessment for confocal endoscopy images with perceptual local descriptor

Significance: Confocal endoscopy images often suffer distortions, resulting in image quality degradation and information loss, increasing the difficulty of diagnosis and even leading to misdiagnosis. It is important to assess image quality and filter images with low diagnostic value before diagnosis. Aim: We propose a no-reference image quality assessment (IQA) method for confocal endoscopy images based on Weber’s law and local descriptors. The proposed method can detect the severity of image degradation by capturing the perceptual structure of an image. Approach: We created a new dataset of 642 confocal endoscopy images to validate the performance of the proposed method. We then conducted extensive experiments to compare the accuracy and speed of the proposed method with other state-of-the-art IQA methods. Results: Experimental results demonstrate that the proposed method achieved an SROCC of 0.85 and outperformed other IQA methods. Conclusions: Given its high consistency in subjective quality assessment, the proposed method can screen high-quality images in practical applications and contribute to diagnosis.
Robustness of Control Design via Bayesian Learning

In the realm of supervised learning, Bayesian learning has shown robust predictive capabilities under input and parameter perturbations. Inspired by these findings, we demonstrate the robustness properties of Bayesian learning in the control search task. We seek to find a linear controller that stabilizes a one-dimensional open-loop unstable stochastic system. We compare two methods to deduce the controller: the first (deterministic) one assumes perfect knowledge of system parameter and state, the second takes into account uncertainties in both and employs Bayesian learning to compute a posterior distribution for the controller.
Blockchain Applications through the Lens of Project Managers

This paper succinctly addresses the initial results of the first phase of a study aiming at the application of blockchain technology in project management. In the first stage, the perception of project managers toward the application of blockchain technology in professional areas was explored. After briefly reviewing the concepts of blockchain technology, smart contracts, and integration of the BIM and blockchain, a blockchain network is introduced as a case application in project management. This paper shows that blockchain is a viable system for governing project contracts by automating the consequences of each transaction and maintaining a tamper-proof record of project progress, which would be valuable in any kind of dispute resolution. In addition, the familiarity of project managers with potential applications of blockchain in project management was investigated through a quantitative method via a survey distributed to project managers. The results indicate that the advent of blockchain sets a platform for further integration of technology in various project management areas. The results also show that practical knowledge of project managers through blockchain-based cryptocurrencies correlates with their perceptions toward blockchain applications. The results suggest project managers can plan accordingly to embrace blockchain potentials along with other industries.
Procedural Knowledge Mining - A New Method for Extracting Best Practices by Applying Machine Learning on Data Graph

In recent years with the increase in sharing tools and sites such as Meta, Twitter, WikiHow..., the web has become a constant and permanent source of scalable knowledge where users share their know-how in the form of procedural knowledge. This procedural knowledge, which consists of a successive set of steps for achieving a specific goal, is called good practice. Extracting and formalizing these good practices is a major asset in the field of artificial intelligence. In this context we present a new method for formalizing good practices extracted from the web, and extracting the best practice for a given request by applying the techniques of artificial learning and text summary on data graphs.
Internet of Things-based Agricultural Land Monitoring

Agriculture is an industrial sector that produces raw materials such as rice, corn, and agricultural products. In the current era, there should be no problem if there is a food shortage because society, industry, and education do not make a real contribution to supporting the agricultural industry. The state also needs good agricultural land, so that the state can fulfill the needs of its people. Without good agriculture, a country will not be able to meet the needs of its people. Modern society today is not or is rarely concerned with agriculture. Agriculture is carried out only by providing fertilizer, water, and land, paying attention to the quality of the agricultural land. One of the problems of declining agricultural production is crop failure. One of the reasons island for agriculture. Soil is the most important part of the world of agriculture. If the land is not cultivated then the land is difficult to become an ideal place for agriculture. The Internet of Things can be used as a solution to problems by tilling the soil and monitoring soil conditions. In conditions in the dry season, soil moisture needs to be done by water. In the rainy season, the land should not be flooded, let alone submerged and flooded. In order to maintain the balance of moisture and waterlogged soil, the Internet of Things is a solution for monitoring and managing agricultural land. Internet of Things is a device that can communicate with each other from one device to another, such as sensors and actuators. Good land cultivation makes agricultural land fertile. Agricultural land processing is maximized by adding a monitoring system for agricultural land using a micro-controller Arduino Uno, NodeMCU ESP8266, several sensors, and integrated devices. The purpose of this research is to make a prototype that is useful for monitoring agricultural land
Computer Speech Recognition Technology and Graphic Shape Design

In order to solve the problem of lack of multimodal emotional database, a computer speech recognition technology and graphic form design research were proposed. Using the hidden Markov model (HMM) to recognize speech emotion is mainly to provide emotion type for the subsequent expression fusion. When the emotion category in the speech is obtained, the facial animation parameter (FAP) corresponding to the speech emotion can be combined with the lip movement FAP based on the Moving Pictures Experts Group (MPEG-4) face animation standard to obtain a comprehensive FAP. The results show that the recognition effect of speech emotion recognition in the method used in this paper is relatively better than that obtained by other literature methods, and the average recognition rate reaches 70.24%, which is higher than the other three methods. It is verified that this method can present a better recognition effect.
Pelatihan Dasar-Dasar Komputer Sebagai Persiapan Menyambut Pelaksanaan Asesmen Nasional Berbasis Komputer Pada Siswa SD Negeri 218 Congkoe

This basic computer basic training aims to facilitate students in preparing themselves to welcome the implementation of the Computer-Based National Assessment (ANBK). There are several obstacles from this research because students still do not understand about computers. This implementation method is carried out by direct practicum method and lecture method. The training participants were 12 grade 5 students. The training took place in the class room of SD Negeri 218 Congkoe. Based on the training carried out, the desired results are achieved well, namely students become more skilled in operating computers.
White-box Testing of NLP models with Mask Neuron Coverage

Recent literature has seen growing interest in using black-box strategies like CHECKLIST for testing the behavior of NLP models.Research on white-box testing has developed a number of methods for evaluating how thoroughly the internal behavior of deep models is tested, but they are not applicable to NLP models.We propose a set of white-box testing methods that are customized for transformerbased NLP models.These include MASK NEU-RON COVERAGE (MNCOVER) that measures how thoroughly the attention layers in models are exercised during testing.We show that MN-COVER can refine testing suites generated by CHECKLIST by substantially reduce them in size, for more than 60% on average, while retaining failing tests -thereby concentrating the fault detection power of the test suite.Further we show how MNCOVER can be used to guide CHECKLIST input generation, evaluate alternative NLP testing methods, and drive data augmentation to improve accuracy.'We like this food.''I liked the cabin crew.' 'I liked this aircraft.'The customer service was exceptional
Methods for Eliciting Feedback about AI and Racial Equity: How Black and Latinx Youth Interact with Digital Assistants

Digital assistants (DAs) such as Siri, Google Assistant, or Alexa are among most accessible forms of artificial intelligence (AI) in the lives of youth.As such, it is important to ensure that these technologies are designed to be culturally inclusive, especially in relation to youth from groups underrepresented in STEM.The goal of this paper is to develop and evaluate methods to elicit feedback about AI and racial equity in the context of how Black and Latinx youth (aged 8-12) interact with DAs.The study design includes two iterations of instrument design, deployment, and evaluation.The first approach, narrative interviews, tended to uncover the roles played by the devices in the participants' everyday lives, but obscured the actual interactions with the device.The second approach, observational interviews, exposed reasoning about the device and what it is doing but tended to obscure aspects of family history and culture due to device-level frictions, as the devices did not always behave as expected during the interviews.The paper illustrates approaches for eliciting feedback about AI and racial equity, including how online data collection can be used during a pandemic.The paper also explores potential future steps for engaging youth in the process of developing more equitable technology.
The Relationship between Indonesian Language Learning and the Environment

Environmentally based Indonesian language learning is known to reduce the boredom experienced by students during teaching and learning activities. When students experience this, educators or teachers can invite students to take advantage of the surrounding environment as a source and fun learning media. This learning base can also improve students' social relationships which include values and norms. This helps students rethink the relationship between humans and the environment and is aware of and considers environmental issues. The environment is an aspect that is related to many things, including learning. In learning especially Indonesian, the environment can affect the quality of student learning.
Pelatihan Menulis Kreatif Berbasis Media Populer Pada Alumni Sastra Indonesia di Lingkungan Universitas Pamulang

As an educational institution, Indonesian Literature Study Program, Pamulang University is a study program that has good integrity in the South Tangerang City area. This is in accordance with the ranking of the percentage of Indonesian literature alumni users, both in government and non-government. Based on this background, the Indonesian Literature Study Program is committed to producing superior graduates who are ready to compete, therefore it is necessary to have special abilities or competencies that are mastered by alumni according to the characteristics of the Indonesian Literature Study Program, Pamulang University. One of the competencies expected is to be able to write fiction and scientific works. In connection with this commitment, the implementation of the tridarma of community service colleges this semester has the theme of creative writing based on popular media for Indonesian literature alumni at Pamulang University. In this writing training, the alumni were reminded again about writing techniques and the structure of creative writing texts. The speakers in this training are writers in popular media who have very qualified competencies, therefore the participants gain experience to practice directly editing and submitting manuscripts for publication. It is hoped that this training will be useful in the aspect of strengthening the competence of alumni in creative writing skills based on popular media.
Verification of Bitcoin Script in Agda using Weakest Preconditions for Access Control

This paper contributes to the verification of programs written in Bitcoin's smart contract language SCRIPT in the interactive theorem prover Agda. It focuses on the security property of access control for SCRIPT programs that govern the distribution of Bitcoins. It advocates that weakest preconditions in the context of Hoare triples are the appropriate notion for verifying access control. It aims at obtaining human-readable descriptions of weakest preconditions in order to close the validation gap between user requirements and formal specification of smart contracts. As examples for the proposed approach, the paper focuses on two standard SCRIPT programs that govern the distribution of Bitcoins, Pay to Public Key Hash (P2PKH) and Pay to Multisig (P2MS). The paper introduces an operational semantics of the SCRIPT commands used in P2PKH and P2MS, which is formalised in the Agda proof assistant and reasoned about using Hoare triples. Two methodologies for obtaining human-readable descriptions of weakest preconditions are discussed: (1) a step-by-step approach, which works backwards instruction by instruction through a script, sometimes grouping several instructions together; (2) symbolic execution of the code and translation into a nested case distinction, which allows to read off weakest preconditions as the disjunction of conjunctions of conditions along accepting paths. A syntax for equational reasoning with Hoare Triples is defined in order to formalise those approaches in Agda. Keywords and phrases: Blockchain; Cryptocurrency; Bitcoin; Agda; Verification; Hoare logic; Bitcoin script; P2PKH; P2MS; Access control; Weakest precondition; Predicate transformer semantics; Provable correctness; Symbolic execution; Smart contracts
Challenges on the Implementation of the 3-Year Diploma Curriculum in TESDA Region IX

The study aimed to find out the challenges of teachers and students during the implementation of TESDA's 3-year diploma program. In accordance, it determined whether there is a significant difference in the implementation of the 3- year diploma program when categorized into program handled by teachers and the courses taken by students. To address the challenges, this study used a quantitative research approach. Furthermore, the study employed a sampling technique based on Slovin's formula to obtain comments from 220 students and 44 teachers. The data for this study came from two TESDA-administered schools in Region IX, and the statistical tools utilized to confirm the accuracy of the replies were weighted mean and one-way ANOVA. Results revealed that teacher respondents are less challenged in addressing issues in the diploma program's execution. The student respondents believed that the challenges they had in implementing the diploma program were challenging. Similarly, there is no significant difference in the implementation of the 3-year diploma program whether it was categorized into program handled by the teachers and student courses taken. Thus, the result of this study can assist to gain a better grasp of what they need to know and do in order to overcome the TESDA challenges in a three-year Diploma curriculum.
Collaborative Attention Memory Network for Video Object Segmentation

Semi-supervised video object segmentation is a fundamental yet Challenging task in computer vision. Embedding matching based CFBI series networks have achieved promising results by foreground-background integration approach. Despite its superior performance, these works exhibit distinct shortcomings, especially the false predictions caused by little appearance instances in first frame, even they could easily be recognized by previous frame. Moreover, they suffer from object's occlusion and error drifts. In order to overcome the shortcomings , we propose Collaborative Attention Memory Network with an enhanced segmentation head. We introduce a object context scheme that explicitly enhances the object information, which aims at only gathering the pixels that belong to the same category as a given pixel as its context. Additionally, a segmentation head with Feature Pyramid Attention(FPA) module is adopted to perform spatial pyramid attention structure on high-level output. Furthermore, we propose an ensemble network to combine STM network with all these new refined CFBI network. Finally, we evaluated our approach on the 2021 Youtube-VOS challenge where we obtain 6th place with an overall score of 83.5\%.