Reviewerly/exHarmony huggingface dataset:
Pemodelan Konsep Augmented Reality Motif Batik Dayak Kalimantan Timur

Indonesian Batik has been designated as a Humanitarian Heritage for Oral and Intangible Culture by UNESCO since October 2, 2009. Although Batik is well known to the Indonesian people, many people are unfamiliar with batik motifs from the region. One of them is the East Kalimantan Dayak Batik motif. The introduction of information media has followed the development of existing technology. One of them is the introduction of media by utilizing AR technology. Augmented Reality (AR) is a technology that allows computers to display virtual objects precisely on natural objects in real-time. Introducing the East Kalimantan Dayak Batik Motif using Augmented Reality technology is expected to be a more interactive alternative for introducing Indonesian culture, especially in East Kalimantan. The results show that the Augmented Reality (AR) application for the East Kalimantan Dayak Batik motif has been successfully designed and built using the Vuforia SDK, Blender, and Unity3D software. Testing Distance, occlusion, and light intensity are very influential in detecting markers.
Writing Strategies Used by English Department Students with Different Self-Esteem Level to Revise Their Essay(s)

&lt;p&gt;&lt;strong&gt;Abstract:&lt;/strong&gt; This qualitative study investigated writing strategies used by English Department students with different self-esteem levels to revise their essay(s). This study involved six third-year students of the English Language Teaching study program at Universitas Negeri Malang. They have different self-esteem levels (3 students with high self-esteem and 3 students with moderate self-esteem levels). There were three types of writing strategies that the students with varying levels of self-esteem used to revise their essay(s): monitoring, getting feedback, and revising. Those strategies were considered helpful for the students to improve the quality of their essay(s).&lt;/p&gt;&lt;strong&gt;Abstrak:&lt;/strong&gt;&lt;em&gt; &lt;/em&gt;Penelitian kualitatif ini menginvestigasi strategi menulis yang digunakan oleh mahasiswa jurusan Bahasa Inggris dengan tingkat harga diri berbeda untuk merevisi esai mereka. Penelitian ini melibatkan enam siswa tahun ketiga program studi Pendidikan Bahasa Inggris di Universitas Negeri Malang yang memiliki tingkat harga diri berbeda tiga siswa dengan harga diri tinggi dan tiga siswa dengan harga diri sedang). Ada tiga jenis strategi menulis yang digunakan para siswa tersebut untuk merevisi esai mereka, yaitu &lt;em&gt;monitoring&lt;/em&gt;, &lt;em&gt;getting feedback&lt;/em&gt;, dan &lt;em&gt;revising&lt;/em&gt;. Strategi menulis tersebut dianggap membantu para siswa untuk memperbaiki kualitas esai mereka.
Efl Students’ Writing Apprehension and How It Is Related to Student’s Writing Performance

&lt;p class="Abstrak"&gt;&lt;strong&gt;Abstract:&lt;/strong&gt; Writing is an active and productive skill, students learning to write in English deal with multiple challenges and difficulties. One of the problems that might be appear on students is writing apprehension. According to several research one of the impacts caused by writing apprehension is writing performance. This study examined the level of student’s writing apprehension and the degree of relationship between student’s writing apprehension and writing performance. The Subjects of this research were 38 students in English Education Department. There were two instruments used: Writing Apprehension Test and Writing Test.&lt;/p&gt;&lt;strong&gt;Abstrak:&lt;/strong&gt; Menulis merupakan suatu aktivitas yang aktif dan produktif, mahasiswa yang belajar menulis dalam bahasa Inggris akan menghadapi berbagai rintangan dan kesulitan. Salah satu yang akan terjadi adalah kekhawatiran dalam menulis. Menurut beberapa penelitian salah satu dampak yang terjadi karena kekhawatiran tersebut adalah performa menulis seoarang mahasiswa. Penelitian ini menguji tingkat kekhawatiran dalam menulis pada mahasiswa dan tingkat hubungan antara kekhawatiran menulis mahasiswa dan performa. Subjek pada penelitian kali ini adalah 38 mahasiswa jurusan &lt;em&gt;English Education Department&lt;/em&gt;. Ada tiga instrumen yang digunakan pada penelitian ini, yaitu Tes Kekhawatiran Menulis, dan Tes Menulis.
Techniques to aid prediction of pacing dependence at 30 days in patients requiring pacemaker implantation after cardiac surgery

Permanent pacemaker (PPM) implantation occurs in up to 5 % of patients after cardiac surgery but there is little consensus on how long to wait between surgery and PPM insertion. Predicting the likelihood of a patient being pacing dependent 30 days after implant can aid with this timing decision and avoid unnecessary observation time waiting for intrinsic conduction to recover. In this paper, we introduce a new approach for the prediction of PPM dependency at 30 days after implant in patients who have undergone recent cardiac surgery. The aim is to create an automatic detection model able to support clinicians in the decision-making process. We first applied Synthetic Minority Oversampling Technique (SMOTE) and Bayesian Networks (BN) to the dataset, to balance the inherently imbalanced data and create additional synthetic data respectively. The six resultant datasets were then used to train four different classifiers to predict pacing dependence at 30 days, all using the same testing set. The Bagged Trees classifier achieved the best results, reaching an area under the receiver operating curve (AUC) of 90 % in the train phase, and 83 % in the test phase. The overall classification performance was clearly enhanced when using SMOTE and synthetic data created with BN to create a combined and balanced dataset. This technique could be of great use in answering clinical questions where the original dataset is imbalanced.
Human Vision and Electronic Imaging 2022 Conference Overview and Papers Program

The conference on Human Vision and Electronic Imaging explores the role of human perception and cognition in the design, analysis, and use of electronic media systems.It brings together researchers, technologists, and artists, from all over the world, for a rich and lively exchange of ideas.We believe that understanding the human observer is fundamental to the advancement of electronic media systems, and that advances in these systems and applications drive new research into the perception and cognition of the human observer.Every year, we introduce new topics through our Special Sessions, centered on areas driving innovation at the intersection of perception and emerging media technologies.The HVEI website (https://jbmulligan.github.io/HVEI/)includes additional information and updates.
Risk Management In Intensive Care Units With Artificial Intelligence Technologies: Systematic Review of Prediction Models Using Electronic Health Records

Background and aim: Clinical risk assessments should be made to protect patients from negative outcomes, and the definition, frequency and severity of the risk should be determined. The information contained in the electronic health records (EHRs) can use in different areas such as risk prediction, estimation of treatment effect ect. Many prediction models using artificial intelligence (AI) technologies that can be used in risk assessment have been developed. The aim of this study is to bring together the researches on prediction models developed with AI technologies using the EHRs of patients hospitalized in the intensive care unit (ICU) and to evaluate them in terms of risk management in healthcare. &#x0D; Methods: The study restricted the search to the Web of Science, Pubmed, Science Direct, and Medline databases to retrieve research articles published in English in 2010 and after. Studies with a prediction model using data obtained from EHRs in the ICU are included. The study focused solely on research conducted in ICU to predict a health condition that poses a significant risk to patient safety using artificial intellegence (AI) technologies. &#x0D; Results: Recognized prediction subcategories were mortality (n=6), sepsis (n=4), pressure ulcer (n=4), acute kidney injury (n=3), and other areas (n=10). It has been found that EHR-based prediction models are good risk management and decision support tools and adoption of such models in ICUs may reduce the prevalence of adverse conditions. &#x0D; Conclusions: The article results remarks that developed models was found to have higher performance and better selectivity than previously developed risk models, so they are better at predicting risks and serious adverse events in ICU. It is recommended to use AI based prediction models developed using EHRs in risk management studies. Future work is still needed to researches to predict different health conditions risks.
Global Seismic Networks: Recording the Heartbeat of the Earth

Global broadband seismographic networks have provided the science community with 30 years of data which is being used to understand the Earth.
Colonial Origins of Meritocracy: Pursuit of Personal Success and Hype over Exams as Found in Chosun Ilbo 〈Dokja Sagyoshil〉

This paper aims to trace the historical origins of meritocracy in Korea back to the perception and thoughts of the general public during the colonial period. To this end, the classified advertisement section of Chosun Ilbo titled 〈Dokja Sagyoshil (Readers’ Lounge)〉, which started in 1936, is analyzed. 〈Dokja Sagyoshil〉 printed classified ads submitted by the readers on matters ranging from job search, help-wanted, buying, selling and trading of goods to exchange of practical information.BRWith any hopes for progressive social change blocked under the colonial rule, frustrated young elites or those who aspired to become one flocked to what seemed to be the path to success, leading to power and capital. However, it was extremely difficult for the young people to actually succeed; it was almost impossible for the people of Choseon to pass various qualification exams offered by the Japanese colonial authorities and gain credentials. The young generation of Choseon deliberately ignored this reality, in which they were discriminated, and stubornly worshiped the illusionary mantra of “work hard, succeed and make a name for oneself in the world.” But the trapped and frustrated young generation of Choseon had no escape. The blind pursuit of literature and art among the young people could be understood as an expression of the same frustration, only on the other side of the spectrum. Success stories, decorated with academic credentials and passing of state qualification exams, were far out of reach for most young people of Choseon at the time. The meritocracy observed in Korea nowadays clutches on to the idea that ‘fairness’ must prevail, even though it does not translate into equality. This mirrors the popular hype over exams being seen as a panacea for success in the 1930s, where the harsh reality of the colonial rule was brushed under the carpet and the mantra of ‘success achieved through one’s hard work’ heavily hung above everyone’s heads.
Blockchain and Inter Planetary File System based Secured KYC Document Verification for Banking System

Know your client (KYC) is a requirement for the banking system to validate a customer using identity, appropriateness, and risk assessment in establishing a banking relationship. With the developed concern for security, the KYC process is complex and involves a high cost for completing for a single customer. In this paper, we propose a reasonable, abrupt, secure, and transparent platform for KYC document verification for the Banking system through Inter Planetary File System (IPFS) and blockchain technology. The proposed system allows a customer to open an account at any Bank, complete the KYC process there, generate a hash value using the IPFS network and share it with other banks using the blockchain technique. Using a private key, any Bank/financial organization can retrieve, and store customer data (i.e., KYC) securely using the IPFS network if the customer wants to open another account in that Bank/financial organization. The proposed system can avoid repetitive work and save time, and money during the KYC process when someone tries to open an account at multiple Banks. In this paper, we have focused on how the present banking industry, especially the KYC document verification process, can be impacted after using blockchain to store and track the records.
A Novel Framework for Trustworthy Privacy Preserving Machine Learning Model for Industrial IoT Systems Using Blockchain Techniques

Industrial Internet of Things (IIoT) is changing many driving enterprises like transportation, mining, horticulture, energy and medical care. Machine Learning calculations are utilized for getting stages for IT frameworks. The IoT network unit hubs typically asset in a strange manner by making them more responsible to digital assaults. IIoT frameworks requests various situations in genuine one among them is giving security and the causes that encompass them in true viewpoints. It incorporates a system called PriModChain causes security and reliability on IIoT information by joining differential protection, Ethereum block chain and unified Machine learning. Consequently, security will be compromised and we use PriMod chain for giving protection and different compliances and created utilizing Python with attachment programming on essential PC.
A Study on the Librarians’ Perception about the Creation and Operation of Tween Space in Public Libraries

The provision of spaces and services that reflect the characteristics and needs of the tween generation has important meaning as an opportunity to revitalize youth services in public libraries and expand the role of public libraries as a space for youth' leisure and activities. Therefore, this study intended to investigate the awareness of the public librarian on the creation and operation of the tween space in public libraries. We collected survey response data from 63 librarians in charge of youth services at public libraries located in Seoul, and the analysis results are as follows. First, the recognition of the tweens of librarians was below the normal level, while the perception of necessity of providing tween space appeared to be more than normal. Second, it was expected that the tween’s library usage rate and satisfaction would improve when the tween space and services were provided. Third, it was appropriate to create the tween space as a dedicated space, and securing space and budget were important prerequisites. The tasks to revitalizing the creation of tween spaces in public libraries are as follows: efforts to understand and properly recognize the tweens, develop a promotional strategy to secure a sufficient budget, and find ways to create tween spaces suitable for library conditions.
The Relationship Between Learning Self-Efficacy and Online Class Satisfaction of Middle School Students in the COVID-19 Situation : Mediating Effect of Digital Literacy

This study confirmed the effect of middle school students' learning self-efficacy on online class satisfaction in the COVID-19 situation, and verified the mediating effect of digital literacy in the relationship between learning self-efficacy and online class satisfaction. For this purpose, data from 323 middle school students were used for regression analysis using SPSS 26 and PROCESS macro. As a result of the analysis, first, it was found that learning self-efficacy had a positive effect on digital literacy, and that digital literacy was found to have a positive effect on online class satisfaction. However, learning self-efficacy did not significantly affect online class satisfaction. Second, as a result of verifying the mediating effect of digital literacy between learning self-efficacy and online class satisfaction, it was confirmed that the direct effect was not significant, but the indirect effect was significant. In other words, it was found that digital literacy mediates indirect-only mediation in the relationship between learning self-efficacy and online class satisfaction. Using the results of this study, practical measures were presented to improve the satisfaction of middle school students with online classes in the context of COVID-19.
Peran BPBD dalam Pemberdayaan Penanganan Banjir Bandang di Kelurahan Lodoyong, Ambarawa, Semarang

The Lodoyong Village area is an area that is prone to flooding. Therefore, handling is needed to protect the community from the threat of disaster. This study aims to determine the role of BPBD Semarang in empowering banjir bandang management in Lodoyong Village, Ambarawa District, Semarang Regency. The research method uses descriptive qualitative. The method of data collection using observation and interviews. Observations were carried out by observing directly at the location affected by the flood disaster. Meanwhile, interviews were conducted with BPBD Semarang and affected communities. The results showed that the role of BPBD in disaster management was carried out at the pre-disaster stage by establishing community preparedness and post-flood evacuation, logistical assistance and repair of public infrastructure. These various actions were taken to build community capacity in restoring socio-economic conditions
Model AI Assignments 2021

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of six AI assignments from the 2021 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu.
Key Phrases Extraction Method Based on a New Ranking Function

A new key phrases extraction method based on statistical approaches and contextual embedding models is proposed. The proposed method identifies the main candidate keywords for a document based on their frequencies and then weights them based on their similarity values to the document. The frequency of each type of n-gram is calculated independently to ensure fair competition between them at the ranking stage. In addition, to assess the importance of a candidate keyword in a document, context-based embeddings are used to represent both the document and its candidate keywords in preparation for assessing their similarity. To prevent bias in similarity values in long keywords, a function has been introduced to normalize similarity scores of each candidate based on its length. The final score for the importance of a keyword is determined by a score function that returns the harmonic mean of the frequency and the similarity. The proposed method is tested using five different benchmark datasets against several state-of-art baselines including TF-IDF, TextRank, and Yake. The proposed method outperforms all baselines in terms of the MAP@K ranking metric.
Cloud Based Secure File Sharing Using Access Control

In the current environment, data security is paramount, and any confidential files we must have with us everywhere we go raise the chance of losing such files. To prevent this inconvenient method of transporting private information, our suggested solution uses the cloud to store users' myriad of files of any size in encrypted form, encrypting them using the AES-256 version technique to ensure that your private data remains secret. The user no longer has to be concerned about security breaches when sharing his file with the intended recipient, since he may now provide permission to access his document to only the people he chooses, and it will only be accessible to him. With this approach, the saving and exchange of data is made possible. Our encryption algorithm uses the AES-256 version, in which all data is grouped into a string of bits and is subsequently converted into 128-bit blocks.
Model to Detect and Correct the Grammatical Error in a Sentence Using Pre-trained BERT

Language learners can utilize an automatic grammatical error detection system to see if their own compositions include errors. In research, many models for dealing with this problem have gotten increased attention, Various ways have been suggested, and better outcomes have been attained when compared to rules-based systems. It is well recognized that intentionally created erroneous texts can enhance grammatical mistake correction performance, and that paired training is required for many recommendation systems. We combine these two approaches in this study to solve the error detection task utilizing Bidirectional Encoder Representations from Transformer’s (BERT) pretrained words embeddings. Extensive testing has been done to demonstrate the efficacy of the paired training process. According on the findings of the experiments, the proposed method can perform well on four distinct standard benchmarks. For effective prediction of whether phrases are grammatically acceptable or unsuitable, a learning rate of 2e-5, a batch size of 32, and epochs 4 were used. This system is critical for language learners to determine if their phrases are correct or incorrect.
How Much Deep Is Deep Enough?

Typical deep learning models defined in terms of multiple layers are based on the assumption that a better representation is obtained with a hierarchical model rather than with a shallow one. Nevertheless, increasing the depth of the model by increasing the number of layers can lead to the model being lost or stuck during the optimization process.This paper investigates the impact of linguistic complexity characteristics from text on a deep learning model defined in terms of a stacked architecture. As the optimal number of stacked recurrent neural layers is specific to each application, we examine the optimal number of stacked recurrent layers corresponding to each linguistic characteristic. Last but not least, we also analyze the computational cost demanded by increasing the depth of a stacked recurrent architecture implemented for a linguistic characteristic.
Adaptation of Number of Filters in the Convolution Layer of a Convolutional Neural Network Using the Fuzzy Gravitational Search Algorithm Method and Type-1 Fuzzy Logic

This paper presents a model of the search for adaptation of parameters and the creation of the membership functions of various fuzzy systems created using the fuzzy gravitational algorithm (FGSA). These fuzzy systems were created to find the optimal number of filters to enter a convolutional neural network (CNN) with an architecture of two convolution layers, as well as two pooling layers respectively and a classification layer, which is responsible for recognizing images. With this model, the results obtained by optimizing this CNN with the FGSA algorithm and the adaptation of parameters using this same algorithm are compared to form the membership functions of fuzzy systems. Both methods and their results are comparing with each other.
What’s Your Style? Automatic Genre Identification with Neural Network

Genre identification is an important task in natural language processing that can be useful for many practical and research purposes. The challenge of this task is that genre is not a homogeneous and unequivocal property of the texts and it is often hard to separate from the topic. In this paper we compare the performance of two different automatic genre identification methods. We classified six text types: literary, academic, legal, press, spoken and personal. In one part of our research we did experiments with traditional machine learning methods using linguistic, n-gram and error features. In the other part we tested the same task with a word embedding based neural network. In this part we did experiments with different training data (words only, POS-tags only, words and POS-tags etc.). Our results revealed that neural network is a suitable method for this task while traditional machine learning showed significantly lower performance. We gained high (around 70%) accuracy with our word embedding based method. The results of the different text categories seemed to depend on the stylistic properties of the studied genres.