Reviewerly/exHarmony huggingface dataset:
PENGAJARAN KATA BANTU BILANGAN BAHASA MANDARIN (对外汉语量词教学浅述)

Quantifier occupies a very important position in the foreign language teaching. Chinese quantifiers are not only difficult to learn,but also difficult to teach. Therefore, the rich Chinese measure words have always been the difficult point and a troublesome issue to foreign learner. This paper will summarize the present situation of the quantifiers study in teaching Chinese as a foreign language and the current situation of students learning Chinese quantifiers and causes of errors .Then this paper will put forward some teaching strategies.
Hand Pose Estimation Based on Deep Learning

The problem of 3D hand pose estimation has aroused a lot of attention in computer vision community for long time. It has been studied in computer vision for decades, as it plays a significant role in human-computer interaction such as virtual/augmented reality applications, computer graphics and robotics. Because of the practical value associated with this topic, it regained huge research interests recently due to the emergence of commodity depth cameras. But despite the recent progress in this field, robust and accurate hand pose estimation remains a challenging task due to the large pose variations, the high dimension of hand motion, the highly articulated structure, significant self-occlusion, viewpoint changes and data noises. Besides, real time performance is often desired in many applications. In this work we have tried to make a comparative study of different methods of hand pose estimation introduced recently, we worked on the implementation of our method based on Deep Learning to solve this problem.
Development of News Text Teaching Materials for Junior High Schools Based on Journalistic Studies

The purpose of this study was to obtain news text teaching materials based on journalism and discourse studies for junior high school students.The object of this research is the selected critical discourse news texts.The method used is the method of research and development.The study begins with a descriptive analysis of the news text based on a journalistic study, namely the structure, shape, and feasibility elements.Meanwhile, the discourse study includes critical discourse analysis.From this study, news text teaching materials for junior high schools were developed through validation and due diligence.The results of this research are news text structures based on journalism and discourse which are used as the basis for developing news text teaching materials for learning.News text teaching materials can be developed into enrichment teaching materials and based on the validation test of Indonesian language teachers and experts, the teaching materials can be categorized as valid and suitable for use in learning.The results of this study are: the structure of the news text under study is based on journalism.Of the ten news texts, only two are news texts in the form of features with the headline The Success Story of Hijup Diajeng Lestari Founder and the Early Story of Mendikbud Nadiem Makarim in Founding GoJek.The other eight texts are in the form of a quote story.Meanwhile, news texts based on the results of research on discourse do not find any elements of marginalization, so that the mass media can be said to be balanced and independent from the press court.Furthermore, the study of teaching materials for student knowledge enrichment books that were made and tested by the validation of teaching material experts and practitioners based on their judgments was categorized as valid and could be used as an enrichment book for lessons.
A Framework for Establishing a Semantic Web in a University Website: A Case Study University of Gezira

The advent of technology has yielded an escalating demand for managing data, information and knowledge effectively.Apparently, the current websites represent the biggest global database that lacks the existence of a proper semantic structure.Therefore, it makes it difficult for the machine to understand the information provided by the users.Unfortunately, this shortcoming is inherited in many universities websites.Indeed, some of these websites place the information incorrectly, untidy, and incomplete, which leads to a delay in ranking the University.The aim of this paper is to design a framework for establishing a Semantic Web in a University Website.This can be accomplished by organizing the information in conceptual spaces according to its meaning by using Semantic Web and Ontology.The main objective of applying the concept of the Semantic Web is to enhance the ranking of the university.
A Pi in the Face of Tau

Once they served me pi in school, But now you offer tau. I think you take me for a fool, And that I won’t allow. Pi gives me from diameter Circumference’s bound. Tau’d force the radius on me, Sir...
KONTRIBUSI KETERAMPILAN MEMBACA PEMAHAMAN TERHADAP KETERAMPILAN MENULIS TEKS DESKRIPSI SISWA KELAS VII SMP NEGERI 7 PADANG

ABSTRACTThis study revealed the contribution of reading comprehension skills toward writing skill related to the description texts of that made by grade VII students of SMP Negeri 7 Padang. The purpose of this study was categorized into three-part. First, to describe the writing skills of the description text of grade VII students of SMP Negeri 7 Padang. Second, to describe the reading skills of students in grade VII of SMP Negeri 7 Padang.. Third, to describe the contribution of reading comprehension skill to the writing skill of the description texts of grade VII students of SMP Negeri 7 Padang. This type of study was quantitative with descriptive methods. The design used in this study was the contribution design. The population of this study was students of class VII SMP Negeri 7 Padang in the 2018/2019 academic year about 251 students. The sample in this study was determined by proportional random sampling 15% of the population that was 40 students. The data of this study were in the form of scores on reading comprehension skill test scores and scores on writing description text skill. The data were obtained through two types of tests namely objective tests to measure the comprehension of reading skill and performance tests to measure description text writing skill. There were three results of this study. First, the description writing skill of grade VII students of SMP Negeri 7 Padang was in good qualifications (B) with an average of 76.80. Second, reading comprehension skill of grade VII students of SMP Negeri 7 Padang was in good qualifications (B) with an average of 82.50. Third, there was a contribution to reading comprehension text skill on the ability to write description texts of grade VII students of SMP Negeri 7 Padang with percentage 42, 51%. Kata kunci: Kontribusi, Keterampilan, Membaca Pemahaman, Menulis, Teks Deskripsi
Meta-Learned Confidence for Transductive Few-shot Learning

Transductive inference is an effective means of tackling the data deficiency problem in few-shot learning settings. A popular transductive inference technique for few-shot metric-based approaches, is to update the prototype of each class with the mean of the most confident query examples, or confidence-weighted average of all the query samples. However, a caveat here is that the model confidence may be unreliable, which may lead to incorrect predictions. To tackle this issue, we propose to meta-learn the confidence for each query sample, to assign optimal weights to unlabeled queries such that they improve the model’s transductive inference performance on unseen tasks. We achieve this by meta-learning an input-adaptive distance metric over a task distribution under various model and data perturbations, which will enforce consistency on the model predictions under diverse uncertainties for unseen tasks. We validate our few-shot learning model with meta-learned confidence on four benchmark datasets, on which it largely outperforms strong recent baselines and obtains new state-of-the-art results. Further application on semi-supervised few shot learning tasks also yields significant performance improvements over the baselines.
How Student Worksheets Based Metacognitive can Improve Learning Outcomes on Material Price Index and Inflation?

The purpose of this paper is to improve student learning outcomes. This study is a Research and Development (R&amp;D) using the 4D model Thiagarajan which includes stages Define, Design, Develop, and Disseminate but the Disseminate stage is not carried out. The findings showed that the feasibility of the material, language, evaluation, and graphics of 87%, 85%, 85%, 87% with a very decent category. The average N-gain score got a score of 0.6 categorized as moderate and classical completeness of student learning outcomes is 90%. While the results of the responses of students get an average percentage of 90,4% categorized as very feasible. Improved learning outcomes can be obtained using MMPII worksheets which include metacognitive features namely planning, monitoring, and evaluating. This study uses limited trials that were conducted on 20 students of XI IPS class Kanjeng Sepuh Senior High School.
Name that Synth Patch (Advanced)

When looking through sound presets on keyboards, guitars, synths, or any sound generating device, it is common for the title of the preset to verbally foreshadow the sound. In this game-based activity designed for undergraduate students, learners reverse-engineer sounds determined by patch names created by their peers. Students must play their sounds and have their peers guess which name matches their sound. This activity is a useful exercise because it gets learners to more closely analyze why certain words are used to describe certain sounds. The collaborative naming process also gets students thinking about how others perceive things and how to reconcile differences in interpretation.
How to select training data to segment mammary gland region using a deep-learning approach for reliable individualized screening mammography

In individualized screening mammography, a breast density is important to predict potential risks of breast cancer incidence and missing lesions in mammographic diagnosis. Segmentation of the mammary gland region is required when focusing on missing lesions. A deep-learning method was recently developed to segment the mammary gland region. A large amount of ground truth (prepared by mammary experts) is required for highly accurate deep-learning practice; however, this work is time- and labor-intensive. To streamline the ground truth in deep learning, we investigated a difference in acquired mammary gland regions among multiple radiological technologists having various experience and reading levels, who shared the criteria on segmentation. If we can ignore a skill level for image reading, we can increase a number of training images. Three certified radiological technologists segmented the mammary gland region in 195 mammograms. The degree of coincidence among them was assessed with respect to seven factors which indicated the feature of segmented regions including the breast density and mean glandular dose, using Student’s t-test and Bland-Altman analysis. The assessments made by the three radiological technologists were consistent considering all factors, except the mean pixel value. Thus, we concluded that the ground truths prepared by multiple practitioners with different experiences can be accepted for the segmentation of the mammary gland region and they are applicable for training images if they stringently share the criteria on the segmentation.
Efficient randomized smoothing by denoising with learned score function

The randomized smoothing with various noise distributions is a promising approach to protect classifiers from lp adversarial attacks. However, it requires an ensemble of classifiers trained with different noise types and magnitudes, which is computationally expensive. In this work, we present an efficient method for randomized smoothing that does not require any re-training of classifiers. We built upon denoised smoothing, which prepends denoiser to the pre-trained classifier. We investigate two approaches to the image denoising problem for randomized smoothing and show that using the score function suits for both. Moreover, we present an efficient algorithm that can scale to randomized smoothing and can be applied regardless of noise types or levels. To validate, we demonstrate the effectiveness of our methods through extensive experiments on CIFAR-10 and ImageNet, under various lp adversaries.
Adversarial Privacy Preservation in MRI Scans of the Brain

De-identification of magnetic resonance imagery (MRI) is intrinsically difficult since, even with all metadata removed, a person's face can easily be rendered and matched against a database. Existing de-identification methods tackle this task by obfuscating or removing parts of the face, but they either fail to reliably hide the patient's identity or they remove so much information that they adversely affect further analyses in the 3D space surrounding the face. In this work, we describe a new class of MRI de-identification techniques that remodel privacy-sensitive facial features as opposed to removing them.To accomplish this, we propose a conditional, multi-scale, 3D GAN architecture that takes a patient's MRI scan as input and generates a 3D volume in which the brain is not modified but the face has been de-identified. Compared to the classical removal-based techniques, our deep learning framework preserves privacy more reliably without adversely affecting downstream medical analyses on the brain, including segmentation and age prediction.
Long Distance Method to Prepare Matura Students to Write a Matura Essay - No problem

Long distance learning brought a huge challenge to students as well as professors in times of pandemics and many were not ready for it. But my 20-year practice (method) of preparing students to be ready to write a matura essay proved to be less stressful as personal approach of dealing with the text did not bring considerate changes nor difficulties. The method of placing questions and active approach of students working in small groups and group evaluation of answers as well as style and topic analysis showed that students developed all the skills of thought processing of critical thinking: they proved to be very responsible and at the same time experiencing literature on intellectually cognitive and emotionally mature critical thinking and emotionally competent level. In distance learning, most students kept their social connections in order to provide for debate and checking. In doing so, the social frame showed up and connection of students from different classes - social and heterogeneous specifics of the class (art class from geographically distant places) proved that students coming from distant places were less connected in long distance learning while on the other hand the group who lived closer to one another while in school proved more solid in e-learning. The questionnaire showed a greater amount of independence and less fear in view of the on-coming matura exam. The important part is a result which speaks for distrust in evaluating and grading of the external essay which is a never ending story in our field.
Interpretable Meta-Reinforcement Learning with Actor-Critic Method

Meta-reinforcement learning (meta-RL) algorithms have successfully trained agent systems to perform well on different tasks within only few updates. However, in gradient-based meta-RL algorithms, the Q-function at adaptation step is mainly estimated by the return of few trajectories, which can lead to high variance in Q-value and biased meta-gradient estimation, and the adaptation uses a large number of batched trajectories. To address these challenges, we propose a new meta-RL algorithm that can reduce the variance and bias of the meta-gradient estimation and perform few-shot task data sampling, which makes the meta-policy more interpretable. We reformulate the meta-RL objective, and introduce contextual Q-function as a meta-policy critic during task adaptation step and learn the Q-function under a soft actor-critic (SAC) framework. The experimental results on 2D navigation task and meta-RL benchmarks show that our approach can learn an more interpretable meta-policy to explore unknown environment and the performance are comparable to previous gradient-based algorithms.
LibGuides: Capability Framework [V1]: Digital Scholarship tasks

Supporting researcher-facing librarians to successfully deliver inside-out services and support. What capabilities are strongly associated with these tasks and at what level
Person tracking methodologies and algorithms in service robotic applications

The vital statistics of the last century highlight a sharply increasement of the average life of the world population with a consequent growth of the number of elderly people. This scenario has caused new social needs that the research in the service robotics field is trying to fulfill. Particularly, the idea of this thesis is born at the PIC4SeR (PoliTo interdepartmental centre for service robotics) with the purpose of creating complex service robotics applications to support the autonomous and self-sufficient old people into their house in everyday life, avoiding the task of monitoring them by third parties. This work represents the first steps of a broad project in which many other service tasks will be integrated. The main argument of this thesis is to develop algorithms and methodologies to detect, track and follow a person in an indoor environment using a small wheeled rover and low cost and available sensors to monitor the target person. Several techniques are explored showing the evolution of these methods along the years: from the classical Machine Learning algorithms to the Deep Neural Network ones. Since the main requirement to be respected is the necessity of real-time results, only few of the analysed algorithms are developed for this project scope and at the end are compared in order to find the best solution with optimal outcomes. The detection and localization are the base of the person tracking application, done by the robot on which it has been implemented a movement control algorithm and at last it has been introduced an obstacle avoidance algorithm to prevent collisions.
Scienza, tecnologia e forme di produzione giuridica. Qualche rilievo d’insieme

This short paper introduces the monographic section that contains the documents relating to a day of study concerning the role of legislation and political decision-maker in the face of scientific progress, especially in three fields of investigation: biolaw, artificial intelligence and environmental protection. In particular, it focuses on the more general context in which the analysis conducted by the several contributions is developed.Keywords: Law and sciences; Scientific progress; Biolaw; Artificial intelligence; Environmental protection.
Phoneme Embeddings on Predicting Fundamental Frequency Pattern for Electrolaryngeal Speech

Electrolaryngeal (EL) speech has robotic quality owing to constant fundamental frequency (F 0 ) patterns. In existing F 0 pattern prediction frameworks, acoustic models are trained on spectral features of a large corpus of healthy speech. However, EL speech does not embed any useful information about F 0 into spectrogram. Moreover, creating datasets with reasonably large number of EL utterances for training neural networks is very time-consuming. Hence, F 0 prediction based on other features with sharing capability between EL and normal speech must be investigated. In this study, we investigate F 0 prediction based on clustering of the phoneme embeddings. For a dataset consisting of utterances of both speech types, phoneme labels are extracted. These phoneme labels are then used to learn phoneme embeddings in a common 2-D space. Through clustering of the learned phoneme embeddings, new onehot features are created for F 0 prediction. Experimental results show that when considering training sets consisting mixed utterances of EL and normal speech, by using new features, improvements in F 0 prediction accuracy can be achieved. Moreover, accurate F 0 patterns can be predicted even based on lower-dimensional features corresponding to small values for the number of clusters. This could simplify the structure of the recognition system required to extract phoneme labels from EL speech.
Language Identification Performance Evaluation Using Spectral Processing

Language Identification (LID) is a method by which the language is identified from an utterance communicated by an unidentified person. Present work develops an effective baseline method with the Gaussian Mixture Model (GMM) and Mel Frequency Cepstral Coefficients (MFCC) for language identification and the performance of the LID system is evaluated for an unknown speaker. The LID framework is designed utilizing a user-defined database in 4 different languages Tamil, Malayalam, Hindi, and English. This work is based on some optimization approaches such as Minimum Mean Square Error (MMSE) and Spectral Subtraction (SS) to improve the LID performance with background noise. Moreover, the LID system performance is also investigated by changing the size of data used to train the system, duration of test data as well as the amount of noise.
Critical Study on Disseminating Information on COVID-19 to Rural Dwellers through Translation: A Panacea to COVID-19 Spread

The ravaging coronavirus has been declared a pandemic by the World Health Organization (WHO). Its rate of spread and mortality is alarming. To worsen matters, there is no cure yet, though clinical trials are on-going. For developing countries like Nigeria, the only safeguard to mitigate the impact of the pandemic is to adhere to the precautionary measures outlined by health experts – physical distancing, wearing of facemasks, use of sanitizers, maintaining personal hygiene and avoiding social gatherings. Information on COVID-19 needs to reach all Nigerians no matter their location or level of literacy if the impact would be minimized or contained. Nigeria is a multilingual society so the COVID-19 information needs to be available to all linguistic groups especially those in the rural areas. This paper discusses the dissemination of information on COVID-19 to rural dwellers through translation. The focus here is the Igbo people in the South Eastern part of Nigeria. The paper examines translation and terminology, then goes on to give the Igbo version of some COVID-19 related terms, employing different methods of term creation like equivalence, composition, naturalization, idiomatic expressions and explicative modulation. With these, the rural Igbo speakers can understand what COVID-19 is all about, the inherent dangers and the precautionary measures to take in order to reduce mortality. Some recommendations were also made to help the containment of the pandemic.
The Optimization Strategy Analysis of the MobileTeaching Model Based on WeChat Public Platform

Taking WeChat public platform as the research object, this paper mainly studies the improvement and optimization strategy of mobile teaching mode in WeChat public platform. Combining with the actual situation of current teaching, this paper expounds the problems existing in the traditional teaching mode. This paper analyzes the advancement of mobile teaching model based on WeChat public platform in current teaching. Finally, this paper analyzes how to carry out mobile teaching model through WeChat public platform, and puts forward corresponding optimization and improvement strategies.