Reviewerly/exHarmony huggingface dataset:
Learning Task-Relevant Features via Contrastive Input Morphing

A fundamental challenge in artificial intelligence is learning useful representations of data that yield good performance on a downstream classification task, without overfitting to spurious input features. Extracting task-relevant predictive information becomes particularly challenging for high-dimensional, noisy, real-world data. We propose Contrastive Input Morphing (CIM), a representation learning framework that learns input-space transformations of the data to mitigate the effect of irrelevant input features on downstream performance via a triplet loss. Empirically, we demonstrate the efficacy of our approach on various tasks which typically suffer from the presence of spurious correlations, and show that CIM improves the performance of other representation learning methods such as variational information bottleneck (VIB) when used in conjunction.
PENINGKATAN KEMAMPUAN MENULIS SISWA PEMULA MENGGUNAKAN METODE STRUKTUR ANALITIK SINTETIK (SAS) PADA PEMBELAJARAN BAHASA INDONESIA TINGKAT SEKOLAH DASAR (SD) DI KABUPATEN PESISIR BARAT

The purpose of this study was to improve the writing skills of novice students through the Synthetic Analytical Structures (SAS) method in Indonesian learning at the Elementary School (SD) level. The research method used is the class action method that adapts the design of Kemmis and Taggart which is carried out in two cycles with quantitative and qualitative approaches. The instruments used were the teacher and student activity observation sheets, field notes, and documentation. This research was conducted at SD Negeri 87 Krui, Pesisir Barat Regency when the research was carried out for 2 months in 2020. The results showed that the application of the Synthetic Analytical Structures (SAS) method can improve the writing skills of novice students at the elementary level in the research location. This is indicated by the writing ability of beginner students in each cycle has increased.
Business Costs Assistance Program

The Business Costs Assistance Program assists businesses in eligible sectors that have incurred direct costs as a result of the circuit breaker action.
Investigating Capabilities of Science and Information Literacy: A Critical Insight into Prevention Awareness of Corona Virus Disease (COVID-19)

Science literacy and information literacy is one form of learning outcomes. For this reason, this study aims to look at the effect of these two variables on one’s baseness to follow the government’s recommendations in order to prevent COVID-19. This research uses a quantitative approach with a survey method. Respondents involved in this study amounted to 80 people consisting of men and women with an age range between 20-50 years scattered in several cities and provinces in Indonesia. The data collection instrument consisted of 30 questions to identify and score the three variables. Data analysis was performed using a two-variable multiple linear regression test with the help of SPSS 25 software. Based on the data and discussion it was concluded that the ability of scientific literacy and information literacy affects self-awareness in the prevention of COVID-19 by 79.7%. The remaining 18.3% is influenced by other factors, such as health literacy. Because it can be said that the level of scientific literacy and information literacy has a strong enough influence on individual self-awareness in following the government’s recommendations for the prevention of COVID-19. Because science literacy and information literacy are part of learning outcomes, the government needs to train teachers and other education personnel so that they have good scientific literacy and information literacy abilities, so that in the future they can teach both variables to students through the learning process.
Plug-and-Play external and internal priors for image restoration.

Image restoration problems were traditionally formulated as the minimization of variational models, including data-fidelity and regularization terms, performed by optimization methods with well-established convergence properties. Recently, Plug-and-Play (PnP) methods for image restoration have obtained very good results and popularity by introducing, in iterative proximal algorithms, any off-the-shelf denoiser as priors. Deep Convolutional Neural Network (CNN) denoisers specify external priors (related to an outer training set) which well reflect image statistics; however they fail when dealing with unseen noise variance and image patterns in the given image. Conversely, the so-called internal denoisers induce internal priorsta ilored on the observed data, by forcing specific features on the desired image. We propose a new PnP scheme, based on the Half-Quadratic Splitting proximal algorithm, combining external and internal priors. Moreover, differently from other existing PnP methods, we propose a deep denoiser acting on the image gradient domain. Finally, we prove that a fixed point convergence is guaranteed for the proposed scheme under suitable conditions. In the experimental part, we use CNN denoisers and the Total Variation functional specifying external and internal priors, respectively. We prove the effectiveness of the proposed method in restoring blurred noisy images, both in simulated and real medical settings.
LibGuides: Ruby E. Dare Library: How To

Use this page to find books, ebooks, and other materials at the GU Library, online, or via I-Share
Research on Computer Weiqi Game based on Mathematical Model

Abstract In recent years, computer hardware and software, even the Internet have developed rapidly, at the same time, the related disciplines are also making progress. Artificial intelligence (AI) has been a hot topic for a long time, and computer game is one of the research directions. As one of the most complex game projects, the challenging Go game can fully test the development level of artificial intelligence. Therefore, the study of computer Go game plays an important role in computer games, artificial intelligence and even computer science. Because it is inconvenient to generalize the knowledge rules of Weiqi and the search space is too large, the traditional computer game theories and methods are not applicable at present. In recent years, on the basis of the great improvement of computer performance, the computer Go game adopts the Monte Carlo method for dynamic evaluation, and introduces the upper bound confidence circle application tree algorithm, the search efficiency and evaluation performance have been greatly improved, and the level of computer Go game program has been constantly improved. At the same time, this clustering intelligent computing method is not only applicable to Go, but also applicable to planning and decision problems. Many problems, so the computer Weiqi Boben research results have important practical significance, can have a broader field of use.
BARRA LIMPA: NÃO AO LIXO NA CIDADE E NO RIO, RECICLANDO ATITUDES.

This study presents the results of the Barra Limpa project that was developed at the Julio Muller State School, in the city of Barra do Bugres-MT. The aim of the project was to bring not only the school community but also the society closer to the environment, discussing environmental education, recycling of garbage and also bringing important scientific information so that students can make their community a more pleasant and healthy place promoted the integration between organizations that work with different dimensions of citizenship, with the objective of expanding knowledge and implementing citizenship rights in the daily lives of the population. It brought schools and the community the knowledge necessary for building citizenship, involving different bodies that ensure the rights and duties of each individual in society.
Hybrid Technique: Text Detection Using a Neural Network and Boxes

Recognition is one of the challenging tasks of computer vision with considerable practical interest.This paper presents a text detector system, named NNBoxes, which detects text with both high accuracy and efficiency and move with the topic forward, NNBoxes is a hybrid technique between the Neural network (Noise reduction and letters prediction) and (Lockbox and hitbox) method created by the Author, both boxes draw grids and lines to recreate the shape of the letters and draw a path between the boxes to support the decision algorithm, in some cases the boxes technique detect text pattern in the image that the language text is not supported in the algorithm Dataset and train data.
Detection and Classification of Moving Objects

 Surveillance systems.is the process of monitoring the behavior of people, objects processes within systems for conformity to expected or desired standards in trusted systems for security. In tracking safety specific regions such as banks, roads, boundaries, forests and so on, video surveillance systems are becoming more common every day. As monitoring systems transition from analog to digital devices and numbers rise, there is a need to interpret the captured video automatically.The objective of this paper is to design a system that can automatically able to detect objects in scenes and then classify them. The proposed system consists of two stages: moving object detection and classification. The first stage consists of four steps: The first step is input video, the collection database is used in this paper where some videos are captured using fixed camera and other images are acquired from various internet sites. The second step is the pre-processing of frames using HSV color space, filtering them using Median and Gaussian filters. The third step is detection object using background subtraction method. The fourth step is feature extraction of object. In this paper, 21 features of shape features are extracted.In the second stage of proposed system is classification the object in to (human and car) using K- nearest neighbor. Two different ratios of training/testing groups which are (50% - 50%) and (70% - 30%) are applied to the classifier. K-NN gives training accuracy 100 %. The accuracy of testing is 98.6111% and 97.7% when the ratio is (50% - 50%) and (70% - 30%) respectively. MATLAB R2018 is used to design the proposed system.
Developing a Text to Speech System for Dzongkha

Text to Speech plays a vital role in imparting information to the general population who have difficulty reading text but can understand spoken language.In Bhutan, many people fall in this category in adopting the national language 'Dzongkha' and system of such kind will have advantages in the community.In addition, the language will heighten its digital evolution in narrowing the digital gap.The same is more important in helping people with visual impairment.Text to speech systems are widely used in talking BOTs to news readers and announcement systems.This paper presents an attempt towards developing a working model of Text to Speech system for Dzongkha language.It also presents the development of a transcription or grapheme table for phonetic transcription from Dzongkha text to its equivalent phone set.The transcription tables for both consonants and vowels have been prepared in such a way that it facilitates better compatibility in computing.A total of 3000 sentences have been manually transcribed and recorded with a single male voice.The speech synthesis is based on a statistical method with concatenative speech generation on FESTIVAL platform.The model is generated using the two variants CLUSTERGEN and CLUNITS of the FESTIVAL speech tools FESTVOX.The development of system prototype is of the first kind for the Dzongkha language.
PERAN GURU DALAM MENCIPTAKAN PEMBELAJARAN LITERASI MATA PELAJARAN BAHASA INDONESIA DI KELAS V SD NEGERI 050718 CEMPA

The study aims to know the role of the teacher in creating literacy in Bahasa Indonesia lesson. This research uses the interview technique and observation. The respondent of the study is the teacher that teaches in the 5th grade of SD Negeri 050718 Cempa. The method of data analysis in this research is descriptive qualitative, in which the kind of data is explanation and documentation. The data is gained and analyzed by using descriptive qualitative. Based on the interview shows that there are obstacles in the school, such as school planning in creating literacy is not implementable and the source is not complete as the material in Bahasa Indonesia lesson. The teacher spends all his energy by creating and posting the book at the edge of the classroom so that the students may read books every time. Meanwhile, based on the observation at the classroom, the researcher finds some books are posted by the teacher in the classroom whiteboard that are full of books related to the knowledge.
The Argentine digital education curriculum: an analysis of the “critical” dimension of digital skills

In this article we analyze a corpus of documents belonging to the Digital Education curriculum launched in Argentina between years 2015 and 2019 as part of a broader reform, aimed at digital literacy and training in digital skills at school. The object of analysis of our study focuses in particular on the way in which the curricular texts conceptualize the critical dimension of digital skills. The methodology we have adopted articulates the content analysis approach with historical-anthropological educational ethnography, the latter being the theoretical-methodological framework that, in general, guides our study. As a theoretical framework, we recover the contributions made by the critical pedagogies on the competences curriculum and other developments developed by the field of educational technology and digital literacies. In the analysis, we stablish a dialogue between the curricular texts of the corpus and a series of broader technoeducational, pedagogical and cultural processes. Finally, we project an agenda of problems that is an alternative to the one dominant in the field of digital literacy.
Should all UI/UX web designers require knowledge ...

Should all UI/UX web designers require knowledge of how CSS Grid and CSS Flexbox actually work before making specifications and layouts?
SWIS - Shared Weight bIt Sparsity for Efficient Neural Network Acceleration

Quantization is spearheading the increase in performance and efficiency of neural network computing systems making headway into commodity hardware. We present SWIS - Shared Weight bIt Sparsity, a quantization framework for efficient neural network inference acceleration delivering improved performance and storage compression through an offline weight decomposition and scheduling algorithm. SWIS can achieve up to 54.3% (19.8%) point accuracy improvement compared to weight truncation when quantizing MobileNet-v2 to 4 (2) bits post-training (with retraining) showing the strength of leveraging shared bit-sparsity in weights. SWIS accelerator gives up to 6x speedup and 1.9x energy improvement overstate of the art bit-serial architectures.
Penggunaan Konjungsi pada Tesis Mahasiswa Program Studi Pendidikan Matematika dan Pendidikan Bahasa, Kekhususan Pendidikan Bahasa Indonesia Pps Universitas Negeri Makassar

Abstract. This type of research is a qualitative descriptive study using the analysis model of Miles and Huberman. The data source in this study was the student's thesis of the Mathematics Education and Language Education Study Program, the specialization of Indonesian Language Education PPs, Makassar State University. The data collection techniques in this study were documentation, read-reading, recording / understanding, and data classification. The results showed that (1) all types and forms of conjunctions were used in the student thesis of the PPs Mathematics Education Study Program, Makassar State University. (2) all types and forms of conjunctions are used in the thesis of students of the Language Education Study Program, the specificity of Indonesian Language Education PPs Makassar State University. (3) variations in the use of conjunctions in the Mathematics Education Study Program students' theses include errors in using subordinate conjunctions, use of coordinative conjunctions, use of correlative conjunctions, and conjunctions between sentences. (4) variations in the use of conjunctions in the student thesis of the Language Education Education Study Program, the specificity of Indonesian Language Education PPs Makassar State University includes errors in using subordinate conjunctions, coordinative conjunctions, correlative conjunctions, and conjunctions between sentences. (5) the comparison of the misuse of conjunctions in the thesis of students of the Mathematics Education Study Program and the Language Education Study Program, the specificity of Indonesian Language Education PPs Makassar State University shows that the misuse of conjunctions in the thesis is more dominated by students of the PPs Mathematics Education Study Program, Makassar State University. Keywords : conjunctions, variations, errors, and comparisons
The challenge of feature engineering in programming for moving bodies

The design of bespoke human movement analysis and control systems by end users and other people without programming or signal processing expertise presents great opportunities for the arts, accessible interface design, games, and other domains. In this paper, we describe the challenge of feature engineering that confronts many people wishing to build such systems. We have conducted three studies exploring approaches to supporting feature engineering and investigated how such approaches may impact on system accuracy, user experience, and design outcomes. We briefly outline study outcomes that are most relevant to the workshop themes.
Constraining Latent Space to Improve Deep Self-Supervised e-Commerce Products Embeddings for Downstream Tasks

The representation of products in a e-commerce marketplace is a key aspect to be exploited when trying to improve the user experience on the site. A well known example of the importance of a good product representation are tasks such as product search or product recommendation. There is however a multitude of lesser known tasks relevant to the business, examples are the detection of counterfeit items, the estimation of package sizes or the categorization of products, among others. It is in this setting that good vector representations of products that can be reused on different tasks are very valuable. Past years have seen a major increase in research in the area of latent representations for products in e-Commerce. Examples of this are models like Prod2Vec or Meta-Prod2Vec which leverage from the information of a user session in order to generate vectors of the products that can be used in product recommendations. This work proposes a novel deep encoder model for learning product embeddings to be applied in several downstream tasks. The model uses pairs of products that appear together in a browsing session of the users and adds a proximity constraint to the final latent space in order to project the embeddings of similar products close to each other. This has a regularization effect which gives better features representations to use across multiple downstream tasks, we explore such effect in our experimentation by assessing its impact on the performance of the tasks. Our experiments show effectiveness in transfer learning scenarios comparable to several industrial baselines.
E-Graphologist for Personality Profile

Deep learning of a person’s signature can revealhis personality profile like fear, honesty, emotional state, thinkingstyle and much more. Analyzing signature can help in predictingsocial skills, thinking style, work habits, achievements, etc. of thewriter. Type and style of signature can be judged by graphologistbased on curved start, single line, dot on letter, etc. Similarlypattern recognition and image processing are used to analyzesignature and handwriting in our system. Here the signature isconsidered as image and then prediction is performed throughdifferent stages such as gray level conversion, calculatingthreshold value, binary conversion etc. The most preferredtechnique by researchers for personality prediction is 'ArtificialNeural Network'.
Impact-driven Exploration with Contrastive Unsupervised Representations

Procedurally-generated sparse reward environments pose significant challenges for many RL algorithms. The recently proposed impact-driven exploration method (RIDE) by Raileanu & Rocktaschel (2020), which rewards actions that lead to large changes (measured by l2-distance) in the observation embedding, achieves state-of-the-art performance on such procedurally-generated MiniGrid tasks. Yet, the definition of in RIDE is not conceptually clear because its learned embedding space is not inherently equipped with any similarity measure, let alone l2-distance. We resolve this issue in RIDE via contrastive learning. That is, we train the embedding with respect to cosine similarity, where we define two observations to be similar if the agent can reach one observation from the other within a few steps, and define impact in terms of this similarity measure. Experimental results show that our method performs similarly to RIDE on the MiniGrid benchmarks while learning a conceptually clear embedding space equipped with the cosine similarity measure. Our modification of RIDE also provides a new perspective which connects RIDE and episodic curiosity (Savinov et al., 2019), a different exploration method which rewards the agent for visiting states that are unfamiliar to the agent's episodic memory. By incorporating episodic memory into our method, we outperform RIDE on the MiniGrid benchmarks.
RETRACTED: Psychological Characteristics and Influencing Factors of Students with Learning Difficulties based on the Analysis of Big Data

Abstract Learning difficulty is a universal global problem and one of the main problems in adolescence. In addition to intelligence factors, some non-intelligence factors also have a certain impact on learning according to the analysis of big data. Because the students with learning difficulties are a large group, it is of great significance to study the influencing factors of learning difficulties and how to intervene them. This paper first expounds the analysis of students’ personality, psychological state and family environment, and then introduces the influencing factors of students’ learning difficulties. Finally, this paper puts forward some countermeasures and thoughts for the education of students with learning difficulties.
INTEGRATION OF INTERNATIONAL EDUCATIONAL PROJECTS INTO NATIONAL EDUCATIONAL SYSTEM

Educational environment and education system in particular are the key institutions participating in human resources quality formation and development. Reforms undertaken in these spheres are aimed to enrich and improve qualitative characteristics of population regarding its educational, cultural and professional components. National educational system has developed its own rich traditions and methodological instruments. However, global processes taking place in the world require international trends in educational sphere consideration and analysis. The purpose of this article is to research the degree of educational services market subjects involvement in international educational projects implementation as well as to analyze the effectiveness of international cooperation expansion of in the field of education. The research methodology includes bibliographic, analytical and statistical research methods. The findings are based on statistical data, collected and provided by official statistics bodies, research centres and agencies, analysis. The results of this research have made it possible to identify factors that reduce the effectiveness of international educational projects realization. Consequently, these factors elimination will lead to an increase in professional and academic mobility, quality of education and the competitiveness of Russian educational organizations improvement