Reviewerly/exHarmony huggingface dataset:
Pelatihan pembuatan blog bagi guru SD/MI pada badan kerjasama sekolah (BKS) SD/MI Muhammadiyah/ Aisyiyah Kabupaten Bantul

Utilization of Information and Communication Technology (ICT) in an agency requires readiness from various parties. This readiness includes readiness in Human Resources (HR) both from the school and parents of students, facilities and infrastructure and support from the leadership of an agency. In the world of education in Elementary Schools (SD) Teachers have an important role in the use of information and communication technology in conducting the learning process and in documenting learning activities. One of the bodies in charge of school activities is the SD / MI Muhammadiyah / Aisyiyah Cooperation Agency (BKS) in Bantul Regency. There are approximately 52 Muhammadiyah / Aisyiyah Elementary Schools which are under the BKS SD / MI. Based on interviews with the BKS, most of the Muhammadiyah / Aisyiyah Elementary Schools have not used Information and Communication Technology in documenting learning activities, even though in terms of facilities and targets almost every teacher already has a personal labtop. So by looking at the condition of the teacher, in this dedication training is conducted to create a blog for teachers at the representatives of SD / MI Muhammadiyah / Aisyiyah Bantul Regency. The method in this service is pre-implementation, training, mentoring and evaluation. Pre-implementation is used to equalize the blog material needed by teachers, in which case the participants fill in the questionnaire before training. Training is a direct practice carried out to increase teacher skills in creating blogs. Assistance is carried out to assist teachers in filling blog content. Evaluation in training is done by distributing questionnaires after training to participants. From the training that has been conducted, the Blog of each trainee is generated, Increasing the competence of teachers in creating and managing Blogs from 47.15% to 81.81%. It also improves the interactive learning process that involves parents through communication using a Blog.
Semantic Segmentation of Video Sequences with Convolutional LSTMs.

Most of the semantic segmentation approaches have been developed for single image segmentation, and hence, video sequences are currently segmented by processing each frame of the video sequence separately. The disadvantage of this is that temporal image information is not considered, which improves the performance of the segmentation approach. One possibility to include temporal information is to use recurrent neural networks. However, there are only a few approaches using recurrent networks for video segmentation so far. These approaches extend the encoder-decoder network architecture of well-known segmentation approaches and place convolutional LSTM layers between encoder and decoder. However, in this paper it is shown that this position is not optimal, and that other positions in the network exhibit better performance. Nowadays, state-of-the-art segmentation approaches rarely use the classical encoder-decoder structure, but use multi-branch architectures. These architectures are more complex, and hence, it is more difficult to place the recurrent units at a proper position. In this work, the multi-branch architectures are extended by convolutional LSTM layers at different positions and evaluated on two different datasets in order to find the best one. It turned out that the proposed approach outperforms the pure CNN-based approach for up to 1.6 percent.
The Importance of Active Learning in Tetsuko Kuroyanagi’s Totto-Chan, The Little Girl at The Window (1984): A Sociological Perspective

This research focuses on the active learning issue delineated in Totto-chan novel. The objectives of the study are to know the indicators and how the active learning described in this novel. This study is qualitative research and the type is qualitative descriptive. The researcher used Sosiological theory. The researcher uses primary and secondary data source. Totto-chan: The Little Girl at The Window novel by Tetsuko Kuroyanagi is the primary data source. Other book, journal, and website that relate the issues are the secondary data source. The result of the study showed the following conclusion. First, the indicator of active learning. Second, how the active learning described in this novel. Third, the reason why the researcher addressed active learning such as to reveal the readers to concern the active learning.
학교 영어교육에 대한 기대와 현실 : 학생, 학부모, 교사의 인식 비교 연구

This paper investigated stakeholders’ perceptions regarding English education received at public schools. For this, three groups of stakeholders – students, teachers and parents – responded to a questionnaire consisting of questions that asked about their perceptions about the following: weekly English learning hours; the ultimate goal of learning English; the expected/predicted level of English proficiency when they graduated/will graduate from high school; and their preference concerning various English teaching and learning policies offered at the schools. The resulting data findings indicated that students in general were satisfied with the current weekly hours for learning English, while teachers and parents demanded an increase in overall hours. In terms of the learning goal, the expected/predicted level of proficiency, and preference for policies, students tended to have different perceptions compared to the teachers and parents, who by contrast shared similar perceptions.
The use of ICT in teaching practice in ISUTIC

The purpose of the study was to examine the educational-technological profile of teachers to boost the use of ICT in teaching practice, in order to outline a strategy for integrating the virtual learning at the ISUTIC (Higher Institute of Information and Communication Technologies). The research design that employed the exploratory descriptive, with a quantitative methods approach was adopted for the study. Specifically, the forty five participants that taught at the ISUTIC in 2017 were used for the analyses. The results indicate that 48.89% of lecturers strongly agree that ICT makes their work more productive, 46.67% agree that ICT makes classes more interactive, 51.11% agree that better ICT knowledge results in greater preparation, 42.22% fully agree that ICTs motivate students and 48.89% agree that they need to become more and more empowered in ICT. The study recommends that, the use of technological tools in teaching-learning activities should be the vision of all teachers who now have the task of preparing intellectually, professionally and culturally future generations. The data are analysed and discussed through assess the level of preparation of teachers and their attitudes towards ICT, to design the necessary training activities.
Considerations around Transport Header Confidentiality, Network Operations, and the Evolution of Internet Transport Protocols

To protect user data and privacy, Internet transport protocols havesupported payload encryption and authentication for some time. Suchencryption and authentication is now also starting to be applied tothe transport protocol headers. This helps avoid transport protocolossification by middleboxes, while also protecting metadata about thecommunication. Current operational practice in some networks inspecttransport header information within the network, but this is no longerpossible when those transport headers are encrypted. This documentdiscusses the possible impact when network traffic uses a protocolwith an encrypted transport header. It suggests issues to considerwhen designing new transport protocols or features. Theseconsiderations arise from concerns such as network operations,prevention of network ossification, enabling transport protocolevolution and respect for user privacy.
DIAGNOSTIC ACCURACY OF RETICULOCYTE HAEMOGLOBIN EQUIVALENT (RETHE) IN DETECTING IRON DEFICIENCY ANAEMIA KEEPING SERUM FERRITIN AS GOLD STANDARD

Objective: To determine the diagnostic accuracy of Ret-He in detecting IDA, keep ingserum ferritin as goldstandardStudy Design: Cross sectional study.Place and Duration of Study: Department of Haematology, AFIP Rawalpindi, from Jan to Jul 2017.Methodology: Study involved 165 female patients who presented with anemia (hemoglobin <11g/dl). Ret-He was determined having a level of <27.8 pg/L as diagnostic of IDA. Diagnosis of IDA was confirmed on serum ferritin level <11 ng/ml taking it as gold standard. Accordingly, diagnostic accuracy of Ret-He was determined considering low serum ferritin level as gold standard.Results: The mean age of the patients was 27.52 ± 13.57 years. Mean hemoglobin level was 9.17 ± 1.59 g/dl while mean serum ferritin level was 15.20 ± 12.89 ng/ml. Ret-He ranged from 11.0 pg/L to 39.4 pg/L with a mean of 23.34 ± 8.21 pg/L. IDA on the basis of serum ferritin level was diagnosed in 75 (45.5%) patients while Ret-He labeled IDA in 85 (51.5%) patients. When cross-tabulated, there were 70 TP, 15 FP, 5 FN and 75 TN cases which yielded 93.33% sensitivity, 83.33% specificity, 87.88% accuracy, 82.35% PPV and 93.75% NPV for Ret-He in diagnosing IDA keeping serum ferritin as gold standard. Thus the optimal cut-off value of Ret-He was found to be <27.6 pg/L which gave 93.33% sensitivity and 83.33% specificity in detecting IDA keeping serum ferritin as gold standard.Conclusion: Ret-He was found as fast and reliable technique to diagnose IDA in female population. However, further studies are required for confirmation of its efficacy.
Single Network Panoptic Segmentation for Street Scene Understanding.

In this work, we propose a single deep neural network for panoptic segmentation, for which the goal is to provide each individual pixel of an input image with a class label, as in semantic segmentation, as well as a unique identifier for specific objects in an image, following instance segmentation. Our network makes joint semantic and instance segmentation predictions and combines these to form an output in the panoptic format. This has two main benefits: firstly, the entire panoptic prediction is made in one pass, reducing the required computation time and resources; secondly, by learning the tasks jointly, information is shared between the two tasks, thereby improving performance. Our network is evaluated on two street scene datasets: Cityscapes and Mapillary Vistas. By leveraging information exchange and improving the merging heuristics, we increase the performance of the single network, and achieve a score of 23.9 on the Panoptic Quality (PQ) metric on Mapillary Vistas validation, with an input resolution of 640 × 900 pixels. On Cityscapes validation, our method achieves a PQ score of 45.9 with an input resolution of 512 × 1024 pixels. Moreover, our method decreases the prediction time by a factor of 2 with respect to separate networks.
benwerdBut on a first date instead of ...

@benwerdBut on a first date instead of Singin' I love rock and rollSo put another dime in the jukebox babyalgorithms have to text to voice:I
Meta Answering for Machine Reading

We investigate a framework for machine reading, inspired by real world information-seeking problems, where a meta question answering system interacts with a black box environment. The environment encapsulates a competitive machine reader based on BERT, providing candidate answers to questions, and possibly some context. To validate the realism of our formulation, we ask humans to play the role of a meta-answerer. With just a small snippet of text around an answer, humans can outperform the machine reader, improving recall. Similarly, a simple machine meta-answerer outperforms the environment, improving both precision and recall on the Natural Questions dataset. The system relies on joint training of answer scoring and the selection of conditioning information.
Multi-Temporal Recurrent Neural Networks For Progressive Non-Uniform Single Image Deblurring With Incremental Temporal Training

Multi-scale (MS) approaches have been widely investigated for blind single image / video deblurring that sequentially recovers deblurred images in low spatial scale first and then in high spatial scale later with the output of lower scales. MS approaches have been effective especially for severe blurs induced by large motions in high spatial scale since those can be seen as small blurs in low spatial scale. In this work, we investigate alternative approach to MS, called multi-temporal (MT) approach, for non-uniform single image deblurring. We propose incremental temporal training with constructed MT level dataset from time-resolved dataset, develop novel MT-RNNs with recurrent feature maps, and investigate progressive single image deblurring over iterations. Our proposed MT methods outperform state-of-the-art MS methods on the GoPro dataset in PSNR with the smallest number of parameters.
Semantic Reanalysis of Scene Words in Visual Question Answering

Visual Question Answering (VQA) is a joint task that aims to answer questions based on the given images. The correct analysis of multiple album aggregate issues to remain a key issue in the VQA case, especially when answering question from multiple albums, how to correctly understand album images and corresponding question is an urgent problem. Under the influence of multiple photo albums and the presence of scene words in the question, it may lead to understanding the wrong scene and outputting the wrong answer, resulting in a decrease in VQA performance. In order to solve this problem, this paper proposes a new image and sentence similarity matching model, which outputs the correct image representation by learning the semantic concept. Due to the scene word is not an entity, sometimes the information which the model extracted may be incorrect. Therefore, we can try to reanalyse the question in another different way and give the answer by the similarity between the question and the visual-text. Our model was tested on the MemexQA dataset. The experimental results show that our model not only produces meaningful text sentences to prove the correctness of the answer, but also improves the accuracy by nearly 10%.
Learning from Google : About A Computational EXFOR Database for Efficient Data Retrieval and Analysis

High-level languages, such as Python and R, find broad adoption for data science and machine learning due to their expressive power and the many community-contributed packages to apply sophisticate ...
Detection of Parkinson's disease symptoms based on wearable sensors

A time series is a sequence of points ordered in time.Time series analysis can often reveal useful patterns for describing certain behavior or for predicting future events.In this thesis, we experimentally express the relationship between the symptoms severity scores of the patients and their gait signals defined as time series.We used different deep neural networks for time series classification andinvestigated the ability of deep neural networks to automatically extract discriminatory features from raw sensory data.We show how transferred features from the bottom, middle, or top layer of the neural network for human activity recognition affect the models' performance for detection of the symptoms.We empirically assess the accuracy of deep neural networks in a practical scenario where we try to automatically predict the patients' symptoms based on their gait signals.
Light Weight Online Signature Verification Framework by Compound Feature Selection and Few-shot Separable Convolution Based Deep Learning

Online Signature Verification (OSV) is an extensively used biometric trait aims to verify genuineness of a test signature by computing unique features of the signature. The advancements in mobile and communication technologies resulted in usage of computationally sparse mobile devices in critical applications like m-commerce etc., demands for OSV frameworks which are able to classify the dynamic test signature with fewer number of training signature samples and lesser number of features. The recent advancements in Deep Learning (DL) technologies, resulted in exponential improvements of accuracy in traditional tasks like Object Detection, Scene Text Detection etc. The main disrupt in usage of DL based frameworks for OSV is the requirement of extensive number of training samples and larger number of parameters to learn. To overcome the above pitfalls, we propose a novel dimensionality reduction technique which reduces the dimensionality of a feature set from 100 to 3 in case of MCYT-100 and 47 to 3 in case of SVC, SUSIG datasets respectively. In addition to it, we propose a depth wise separable (DWS) convolution based OSV framework which enables one/few shot learning for test signature verification. To inspect the robustness of our proposed dimensionality reduction technique and DWS OSV framework, exhaustive experiments are conducted with three widely used datasets i.e. MCYT-100, SUSIG and SVC. We have attained state of the art EER in majority of experimentation categories compared to many recent and state-of-the art OSV models.
Decision - Update of Policies relating to Highways Inspections of the Civil Enforcement Policy

Full details of the decision Update of Policies relating to Highways Inspections of the Civil Enforcement Policy
일본 강점기 일본인 교사 야스다 야스노리(安田保則)의 교육저술과 교육활동

This study examined educational writings and activity of Yasda Yasnori a Japanese teacher in Gwagju during the Japanese colonial rule(安田保則, 1885∼?). He was born in Nomi-gun, Isikawa, Japan and was graduated from a teacher school in Isikawa. He came to Korea and worked as a teacher of a public s...
Ontology-Based Computing of Sentence Similarity

Sentence similarity is the basis for many natural language processing tasks and it is studied in this paper by the tools of ontology and Wikipedia-based Wiktionary. If a word appears in the definition of another word in Wiktionary, the two words can be said to be related to each other. Based on this kind of knowledge from Wiktionary, a graph-based ontology is built. In the graph, nodes represent words, and if one word appears in the definition of the other, there is a line between them in the graph. And the line or degree as it is called is used to compute word similarity. Accordingly, word similarity is used to compute sentence similarity. In the paper, content words such as nouns, verbs, adjectives and adverbs are used to computer sentence similarity. Sentence similarity computed in this way is effective for natural language processing tasks such as question answering, information extraction, etc. And it is used in online chat robot “FreeTalker”.
Fuzzy Inference System Model from Non-Fuzzy Clustering Output

Fuzzy Inference System (FIS) is a process of mapping input into the desired output using fuzzy logictheory where decisions can be made or pattern are dscerned. Ths study aims to discuss on how non-fuzzyclustering output can be used to constmct a model of FIS. Here, the proposed idea is to show the efficient useof the FIS as a predction model for the data classification. In this study, employment income, self-employmentincome, property and tramfer received are taken into account for clustering the household income data. Then,the FIS prediction model is built using the center values of clusters formed and the output of FIS is comparedto the original cluster in whch the best fit predction model to the data is determined. In conclusion, the bestpredction model in identify~ngin come class is dscovered based on the Root Mean Square Error (RMSE) valuecomputed.
The Influence of The Use of Direct Method in Teaching Reading Comprehension and The Students' Achievement

ABSTRACT: Direct method of teaching is a systematic instructional method that first and foremost requires the teacher to have a command of the subject matter at as close to mastery level as possible. This means that whether subject matter is at the elementary level, middle school level, high school level, college level or adult education level, the teacher thoroughly understands the content. The main purpose of direct method of teaching is to provide information within a structure that enables all students to attain the stated objectives at a level of mastery. The aim of the research was whether there was any influence between the use of direct method in teaching reading comprehension and the students' achievement in reading comprehension or not. The methods of collecting data were questionnaire, test for reading, and documentary study. The data of this research are analyzed by using Product Moment Correlation Person method. The result of calculation of the correlation coefficient between the use of direct method in teaching reading comprehension and the students' achievement up to 0.93. The value of product moment in the table with N = 30 or df = N-2 = 28 in significant 5% is 0.361 and 1 % is 0.463. If this value or r xy is compared with the value of product moment, so 0.361 0.463 or rxy > rxyt, it means the value found is bigger than the value in the table. It is a significant correlation between the use of direct method in teaching reading comprehension and the students' achievement.
Extreme Speech and Global Digital Cultures — Introduction

In this article, we introduce the Special Section on Extreme Speech and Global Digital Cultures by developing the concept of “extreme speech.” In addressing the growing cultures of online vitriol and extremism, this concept advances a critical ethnographic sensibility to situated online speech cultures and a comparative global conversation that moves beyond the legal-normative debates that have been dominant in North America and Europe. We demonstrate this intervention by highlighting three interlinked arguments: Extreme speech inhabits a spectrum of practices rather than a binary opposition between acceptable and unacceptable speech; the sociotechnological aspects of new media embody a context in itself; and the violence of extreme speech acts is productive of identity in historically specific ways. This approach entails a methodological move that takes account of the meanings online users attach to vitriol as historical actors. It thus allows for critical frameworks to emerge from emic terms of action rather than moral concepts superimposed from the outside. Ethnographic explorations of extreme speech, we suggest, open up a new avenue to critique the contemporary global conjuncture of exclusionary politics.
HYPERREALITY CONCEPTS IN TOURISM AND RECREATION

Today, along with the changes in consumption patterns, societies are oriented towards more active and more innovative products and services that touch more to their emotions. With this orientation the touristic paradigm of sunbathing by the sea left its place to new elements and activities with excursion, struggle, and even risk, which have become the paradigm of tourism, for tourist destinations. In this context, destinations and various businesses, have highlighted the hyper realities and they have built up from the beginning to the end of the experience with an integrative approach. Hyperreality is a balance element and the main element of tourism in today's conditions. The main purpose of this study is to carry out an in-depth research on hyperreality and related concepts. The data were compiled by using secondary sources in the study. Therefore, in addition to hyper realism, concepts such as simulation, simulacrum, images, enhanced reality and phantasmagoria are discussed in the context of data which is obtained from previous studies. These consepts are also discussed in terms of Universal Studios theme park.
스마트시티/스마트에너지시티 기술과 표준화 국내외 동향

Over several years, the term Smart City(SC) has become a very popular concept around the world. The city is growing increasingly larger, more complex and more important as the population ranks of urban areas swell with ever increasing speed. The smart grid and multi-Microgrid play important roles as the core infrastructure in the smart city. New concepts are required to configure the multi-microgrid as smart energy city and futhermore to implement citizen-friendly services. This conceptual paper discusses smart energy city in the smart city.