Reviewerly/exHarmony huggingface dataset:
Neighbor-T: neighborhood transformer aggregation for enhancing representation of out-of-knowledge-base entities

Knowledge representation learning (KRL) aims to obtain the embedding of entities and relations from the information of knowledge graph (KG). Most existing methods can only model the entities in the training data, while failing to generalize to out-of-knowledge-base (OOKB) entities which only appear in the testing. To solve this issue, one common approach is to train an aggregator by leveraging the auxiliary knowledge such as neighbor information and entity descriptions. In this work, we propose a novel aggregation model called neighborhood transformer (Neighbor-T) to enhance the representations of OOKB entities. Compared with previous methods, Neighbor-T shows effectiveness on neighbor information aggregation because of self-attention mechanism. Experiments demonstrate that our enhanced representation outperforms the state-of-the-art on two knowledge graph completion tasks under OOKB setting: triple classification and entity prediction.
PENGGUNAAN STORYTELLING UNTUK PENDIDIKAN KARAKTER DALAM NOVEL AYAHKU (BUKAN) PEMBOHONG KARYA TERE LIYE

Storytelling is an effective way to develop cognitive (knowledge), affective (feeling), and conative (appreciation) aspects in humans. The interaction of the father and Dam characters in the novel Ayahku (Not) the Liar provides an overview of the interaction between father and son, using the storytelling method in developing positive characters in children. The researcher conducted a search on the theme of storytelling for character education in the aspects of knowledge and attitude in the novel My Father (Not) Liar? This study uses a descriptive qualitative approach by using primary data sources for the novel entitled My Father (Not) Pembelian by Tere Liye, published by Gramedia Pustaka Utama, equipped with secondary data sources from documentation materials related to the theme and the novel with content analysis as an analytical technique. The results of this study The use of storytelling in character education in the knowledge aspect in Tere Liye's Father Not a Liar Novel is through 17 uses, while in the attitude aspect it is used 12 times.
The Effect of Crypto Currency on Exchange Rate in Nigeria

In recent years, many changes in commercial activities have increased significantly. Electronic commerce continues to grow, but payment methods for services are growing, such as virtual currency. The acceptability and use of cryptocurrencies such as Bitcoin and other cryptocurrencies globally attracted the attention of economists and other financial experts in recent times. Nigeria is one of the most crypto-adopting countries globally. Still, the central bank recently issued astatement that appeared to be a ban on the usage of Bitcoin and other digital assets in the country. However, the scenario has changed as cryptocurrency grows and thrives in the country. Based on this, this study aims to examine the impact of Bitcoin on the exchange rate in Nigeria. The main independent variable is Bitcoin price volatility, specified as BTCE and controlled by the consumer price index and interest rate. The study used monthly time series data from January 2015 to December 2020 and analyzed using the Autoregressive Distributed lag model (ARDL). The result shows that Bitcoin price significantly affects the exchange rate in both the short and long run. A Higher Bitcoin price implies an appreciation of the domestic currency. Therefore, we recommend that the Nigerian government pay attention to the movement of cryptocurrency prices.
0641 Automated Detection of Isolated REM Sleep Behavior Disorder (iRBD) During Single Night In-Lab Video-Polysomnography (PSG) Using Computer Vision

Abstract Introduction At least 1% of adults aged 45 and above have isolated REM sleep behavior disorder (iRBD), a prodromal stage of a synucleinopathy, however a majority remains undiagnosed until they develop irreversible neuropathology. The gold-standard diagnostic procedure, the in-lab video-polysomnography (PSG), relies on complex rules for sleep staging and electromyography quantification which potentially lead to incorrect diagnoses. We tested a computer vision classifier for RBD versus other sleep conditions based on automated video analysis of body movements during a single-night in-lab PSG. Methods Raw PSG video recordings of 60 iRBD patients (mean 67 years, 78% males) and 100 controls (mean 65, 65% males) were selected from the Stanford Sleep Center database. Our pipeline involved three main steps: movement detection, feature extraction, and RBD recognition. The recordings between lights off and lights on were 7 to X hours in duration with some interspersed short movements. For every recording, short video segments that contain movements were first extracted using background subtraction. Additional features, such as sleep-onset time and wake-up time, were used to filter out noisy segments that did not involve patients being asleep. Next, I3D, a state-of-the-art method for action recognition, was used to derive a feature vector of 2048 features from every extracted segment. RBD diagnosis could be considered as an action recognition task in which ‘RBD’ was the action category to be classified. For every recording, the feature vectors for every segments were averaged, resulting in one feature vector per subject. With 160 PSG video recordings, this resulted in a dataset of 160 feature vectors that were used to train a multi-layer perceptron (MLP) for RBD recognition. Results Leave-one-out cross-validation (LOOC) procedure was used for evaluation achieving 91.9% accuracy, with a 78.3% sensitivity and 100% precision. Conclusion We tested the feasibility and performance of a new diagnostic paradigm for RBD solely based on single-night video analysis of PSG. Our results suggest that a machine learning model can detect and distinguish the characteristics of movements related to RBD versus other sleep disorders and normal sleep. Larger studies in heterogeneous datasets are needed to validate these findings. Support (If Any) Google
Efektifitas Penggunaan Digital Game Base Learning Menggunakan Aplikasi Spritebox untuk Meningkatkan Motivasi dan Hasil Belajar Mahasiswa

&lt;p&gt;Purpose of this study to determine the effect of using SpriteBox educational game applications on student motivation and learning outcomes in basic programming subjects, especially looping material. This research was conducted at JPTK UNS Informatics Engineering Education Study Program. The research method used quantitative research with a pre-experimental design One Group Pretest-Posttest. The population was all students of PTIK UNS class of 2020 which consisted of 76 students. All the members the of the population are sampled, so the sampling technique used total sampling. Data collection techniques used questionnaires and tests. Questionnaires and tests were piloted first and then tested for validity and tested for reliability. The data analysis technique used the t test (difference test) after previously was tested by normality and homogeneity. The results showed that: 1) Learning-based SpriteBox game media had an effect on students' learning motivation. Proved by the difference significantly on learning motivation before and after experimentation. There was an increased average learning motivation after using SpriteBox game; 2) Learning-based SpriteBox game media affected students’ learning outcomes. Proved by the difference significantly on learning outcomes between before and after experimentation. There was an increased average learning outcome after using SpriteBox game.&lt;/p&gt;
An Efficient Anchor-Based Face Alignment Network With Transformer

Despite significant advances have been made in facial alignment recently, face alignment remains a challenging problem due to the existence of issues like occlusion and large pose. Besides, small attention has been paid to the algorithm's performance, efficient face landmark localization algorithm with high robustness still has room to enhance. In this work, we propose an efficient face alignment network that combines the Transformer with an anchor-based prediction method. First, we extract features of the input image by CNNs, then capture long-range relationships efficiently using Transformer encoders, at last, we use anchor points to predict landmark coordinates. We test our algorithm through experiments on WFLW, the popular face alignment benchmark. The experiments show that our algorithm can reach high accuracy with satisfactory robustness while also enjoying the high speed.
Disentangled Face Identity Representations for Joint 3D Face Recognition and Neutralisation

In this paper, we propose a new deep learning based approach for disentangling face identity representations from expressive 3D faces. Given a 3D face, our approach not only extracts a disentangled identity representation, but also generates a realistic 3D face with a neutral expression while predicting its identity. The proposed network consists of three components; (1) a Graph Convolutional Autoencoder (GCA) to encode the 3D faces into latent representations, (2) a Generative Adversarial Network (GAN) that translates the latent representations of expressive faces into those of neutral faces, (3) and an identity recognition sub-network taking advantage of the neutralized latent representations for 3D face recognition. The whole network is trained in an end-to-end manner. Experiments are conducted on three publicly available datasets showing the effectiveness of the proposed approach.
Persepsi Pustakawan Terhadap Diklat Daring (Studi Kasus Pustakawan PDDI-LIPI Peserta Diklat in Passing Pustakawan)

This research was aimed to study librarians' perceptions towards online library training during the COVID-19 pandemic.This study used a descriptive-qualitative and a triangulation method.A survey was conducted on eight librarians of PDDI-LIPI who had joined an in passing online librarian training via Eldika, a web-based application program developed by the National Library.The recorded perceptions were perceptions towards trainers, learning media/ platform, learning materials, and online training.The results were summarized into tables and narrative descriptions, then triangulated through direct interviews to explore the causes and solutions to problems related to the online training.The result of this research indicated that librarians' perceptions towards all four online training indicators were positive.However, weakness was found regarding the online training platform.Based on the findings, this study suggested that the National Library and other institutions provide online librarian training to develop some more friendly platforms accessible for librarians with various IT skills, and an IT support service quickly responded during training.
ZERO-KNOWLEDGE PROOF APPLICATION IN ECOMMERCE PAYMENT

E-commerce plays a significant role in a country's economic condition. Since the COVID-19 outbreak, it has become more popular, along with concerns about its ability to handle information security. The Zero-Knowledge Proof (ZKP) method could be a possible solution to the e-commerce payment security issue that hampers customer trust. This paper investigates the viability of an online payment framework based on the ZPK method. This method is an upgrade for authentication during the payment process in online shopping. Experiments on customers' perspectives of the payment framework based on the ZKP method were conducted and supported the perceived usefulness, ease of use, trust, control, satisfaction, and loyalty aspects of a better e-commerce website. It allows advantages for both customers and e-commerce and prevents fraud, which will increase the trust level for both sides. zkSNARK speeds up and lowers the cost of the process, but there is a risk of DOS. Future work needs to be done to handle DOS in this method.
Using Instagram and Social Media Engagement Techniques Based Programme to Develop Pre-service Teachers’ EFL Speaking Skills and Intercultural Awareness

The current study aimed to investigate the effect of using Instagram and social media engagement techniques on developing EFL students' speaking skills (accuracy, fluency, pronunciation, and presentation). Forty students enrolled in Third year Faculty of education, Shebin Elkom, Menoufia University were the participants of this study. A sequential mixed methods design was used to collect data, thus in the first phase of the study, the researcher designed a pre-posttest for measuring EFL pre-service teachers' speaking skills (accuracy, fluency, pronunciation, and presentation), a scoring rubric to be used for grading students' performance on the test, a programme based on Instagram and social media engagement techniques and an intercultural awareness test. In Phase two, semi-structured interviews conducted with ten EFL pre-service teachers to elicit their perceptions of the study programme. Data were analyzed quantitatively and qualitatively. Findings revealed that using a program based on Instagram and social media engagement techniques was effective in developing EFL pre-service teachers' speaking skills and intercultural awareness.
Digital Educational Technologies: Market Ends or Means at the Service of Critical Learning?

This essay intends to show that digital technologies have their own goals since their creation and that some possible consequences of their indiscriminate use in education are misinformation, solipsism, loss of privacy and labor rights, the instrumentalization of reason and life, generating benefits for a few companies and business people that promote them to accumulate riches concentrated in few hands. From this reality, we propose that their use be revised so that their application does not follow parameters issued by big companies, financial entities, or neoliberal governments, that they be used critically, ethically, and politically to benefit those that have fewer opportunities, nature itself, those that are unprivileged, and social justice to reach an ethical, aesthetic, and transformative learning.
Evaluasi Prosedur Waktu Pengembalian Dokumen Rekam Medis Rawat Inap Di Rumah Sakit Umum X Surabaya

Management of medical record documents must be monitored, both the time of their provision and the timeliness of their return. The establishment of standard operating procedures (SOP) at General Hospital X Surabaya aims to maintain administrative order and improve service quality. This includes the return of medical record documents, which the standard is 2x24 hours after the patient is declared home. However, there are still discrepancies in the implementation of the time for returning inpatient medical record documents to the storage room. The purpose was to determine the suitability of the procedure for returning inpatient medical record documents in the fourth quarter of 2021. The method was qualitative, with a retrospective approach. The population and sample are inpatient medical record documents in the fourth quarter of 2021 as many as 2,995 medical record documents and 5 officers. Used purposive sampling technique, with observation sheets and interviews. The results found were that 57,3% (1692 documents) didn’t comply with the procedure for returning medical record documents, and 42,7% (1263 documents) complied. Conclusion, the time for returning inpatient medical record documents for the fourth quarter of 2021 hasn’t been in accordance with existing procedures, because many medical record document returns exceed the 2x24 hour limit. The reason is disorganized administration of officers and there isn’t continuous evaluation to improve system for returning medical record documents with regulations. Suggestions monitoring of staff performance, as well as optimizing the use of computers to make it easier to find medical record documents that haven’t been returned.
Image Captioning using Convolutional Neural Networks and Long Short Term Memory Cells

This paper discusses an efficient approach to captioning a given image using a combination of Convolutional Neural Network (CNN) and Recurrent Neural Networks (RNN) with Long Short Term Memory Cells (LSTM). Image captioning is a realm of deep learning and computer vision which deals with generating relevant captions for a given input image. The research in this area includes the hyperparameter tuning of Convolutional Neural Networks and Recurrent Neural Networks to generate captions which are as accurate as possible. The basic outline of the process includes giving an image as input to the CNN which outputs a feature map. This feature map is passed as input to the RNN which outputs a sentence describing the image. The research in image captioning is relevant because this method demonstrates the true power of the encoder-decoder network made up of Convolutional Neural Network and Recurrent Neural Network and potentially will open many pathways for further interesting research on different types of neural networks.
Using the service quality instrument to assess the quality of the professional certification programme

The professional teacher certification programme is a programme that has been implemented online for both in-service teachers and pre-service teachers since 2019. The change in the pattern of implementing the professional teacher programme from offline to online will provide a different programme experience for teachers, participants and tutors. The aim of this research is to measure the quality of service provided to the professional teacher certification programme participants using the SERVQUAL instrument. The instrument used was developed and analysed using confirmatory factor analysis (CFA). Subjects involved were 354 teacher respondents. Data analysis was carried out by SmartPLS version 3. The CFA results show that the model has met the fit index. Convergent and discriminant validity meet the criteria. The CR ranges between 0.948 and 0.989 and CA ranges between 0.917 and 0.985. Finally, the level of service quality provided to the professional teacher certification programme participants at Universitas Negeri Yogyakarta is in the very high level category.&#x0D; Keywords: Confirmatory factor analysis, service quality, professional teacher certification programme.
IMPROVING CNN-BASED BUILDING SEMANTIC SEGMENTATION USING OBJECT BOUNDARIES

Abstract. Semantic segmentation is an active area of research with a wide range of applications including autonomous driving, digital mapping, urban monitoring, land use analysis and disaster management. For the past few years approaches based on Convolutional Neural Networks, especially end-to-end approaches based on architectures like the Fully Convolutional Networks (FCN) and UNet, have made great progress and are considered the current state-of-the-art. Nevertheless, there is still room for improvement as CNN-based supervised-learning models require a very large amount of labelled data in order to generalize effectively to new data and the segmentation results often lack detail, mostly in areas near the boundaries between objects. In this work we leverage the semantic information provided by the objects’ boundaries to improve the quality and detail of an encoder-decoder model’s semantic segmentation output. We use a UNet-based model with ResNet as an encoder for our backbone architecture in which we incorporate a decoupling module that separates the boundaries from the main body of the objects and thus learns explicit representations for both body and edges of each object. We evaluate our proposed approach on the Inria Aerial Image Labelling dataset and compare the results to a more traditional Unet-based architecture. We show that the proposed approach marginally outperforms the baseline on the mean precision, F1-score and IoU metrics by 1.1 to 1.6%. Finally, we examine certain cases of misclassification in the ground truth data and discuss how the trained models perform in such cases.
Upaya Peningkatan Minat Belajar Pada Masa Pandemi Covid 19 di Desa Kutomulyo

Shaping and redeveloping a sense of interest in learning in every child during the covid-19 pandemic which is one of the main problems. During the Covid-19 Pandemic, learning hours have also reduced so that children are lazy to study and prefer to play. This causes laziness in children and does not provide enthusiasm for learning. The purpose of implementing community service is intended for children to realize the importance of learning for their education, not only children but also parents to monitor their children's learning more and Community Service is intended to provide more lessons in their free time. The target achieved in this community service is to increase children's interest in learning during the COVID-19 pandemic and increase their language knowledge. The method used is to provide an explanation of how to read and count and speak English correctly, a socialization session in the form of questions and answers about the material presented, and evaluating the ability of village children by holding a competition at the end of the meeting. Kutomulyo Dusun 1, Sibiru-biru District, is to see how far the children in Kotamulyo village understand the lessons that have been conveyed by their teachers through online learning, and we can also improve the learning abilities of children in Kutomulyo village.
Process Automation for instant Procurement of Crypto currencies

Cryptocurrencies have been a global trend since inception and have emerged as important pecuniary software systems.Cryptocurrencies lack a central authority to mediate transactions because they were designed as peer-to-peer systems, and they rely on users' activities to validate transactions and require strong, secure mining algorithms to maintain its block chain.This research project presents an overview of cryptocurrencies and process automation for instant procurement using influencer's tweets.Tweets are culled from twitter and used to streamline the procurement process and predict its market volatility based on available data.
El aula de ingreso como lugar curricular donde mirar la comunicación no verbal del discurso docente en la Universidad de la República, Uruguay

The text presents the theoretical discussion towards the notion of the entrance classroom as a curricular place and the value of non-verbal teacher communication in communicational interactions at the beginning of training. It thus refers to the construction of a singular discursive, didactic-pedagogical and physical-temporal space for the promotion of communicational interactions towards student permanence and persistence at the beginning of university education. This view is of special interest in university models of free access and gratuity in which the expansion of enrollment becomes a situation of profound relevance in the right to education. The documentary analysis carried out consolidates the entrance classroom as a unique curricular and communicational place for each discipline in particular and for the field of Higher Education in general. Entrance teaching can make available and model experiences that affect student persistence in the early days of institutional accommodation. Among those doings, non-verbal communicational interactions make up 93% of communication. Its interactive didactic value promotes classroom climates that facilitate learning according to communication styles. The findings deepen, from the field of the curriculum and from communication theories, the value of non-verbal communication (NVC) in the teaching discourse at the beginning of university education. They propose to understand and deepen their particularities in the communicational pedagogical positions towards the awareness of a differentiated doing in the initiating period as a subjective student process and admission as a curricular place of teaching.