Reviewerly/exHarmony huggingface dataset:
AP-Perf: Incorporating Generic Performance Metrics in Differentiable Learning

We propose a method that enables practitioners to conveniently incorporate custom non-decomposable performance metrics into differentiable learning pipelines, notably those based upon neural network architectures. Our approach is based on the recently developed adversarial prediction framework, a distributionally robust approach that optimizes a metric in the worst case given the statistical summary of the empirical distribution. We formulate a marginal distribution technique to reduce the complexity of optimizing the adversarial prediction formulation over a vast range of non-decomposable metrics. We demonstrate how easy it is to write and incorporate complex custom metrics using our provided tool. Finally, we show the effectiveness of our approach various classification tasks on tabular datasets from the UCI repository and benchmark datasets, as well as image classification tasks. The code for our proposed method is available at this https URL.
Learnable Bernoulli Dropout for Bayesian Deep Learning

In this work, we propose learnable Bernoulli dropout (LBD), a new model-agnostic dropout scheme that considers the dropout rates as parameters jointly optimized with other model parameters. By probabilistic modeling of Bernoulli dropout, our method enables more robust prediction and uncertainty quantification in deep models. Especially, when combined with variational auto-encoders (VAEs), LBD enables flexible semi-implicit posterior representations, leading to new semi-implicit VAE~(SIVAE) models. We solve the optimization for training with respect to the dropout parameters using Augment-REINFORCE-Merge (ARM), an unbiased and low-variance gradient estimator. Our experiments on a range of tasks show the superior performance of our approach compared with other commonly used dropout schemes. Overall, LBD leads to improved accuracy and uncertainty estimates in image classification and semantic segmentation. Moreover, using SIVAE, we can achieve state-of-the-art performance on collaborative filtering for implicit feedback on several public datasets.
B-Learning in Basic Vocational Training Students for the Development of the Module of Applied Sciences I

Information and communication technologies are a step forward in education, as they have given rise to innovative methodologies, such as blended learning. This type of training can be applied at any stage or educational typology such as basic vocational training. The main objective of this article is to know the degree of effectiveness of this methodology in this stage, specifically in an applied science module. For this purpose, a quasi-experimental design has been applied with a control group and an experimental group with a total of 147 participants. The results show how those students who have worked through b-learning have experienced better results in all the dimensions of the study. In conclusion, the implementation of this methodology in basic vocational training brings benefits, such as motivation and autonomy in the teaching–learning processes of all students.
The history of Standard ML

The ML family of strict functional languages, which includes F#, OCaml, and Standard ML, evolved from the Meta Language of the LCF theorem proving system developed by Robin Milner and his research group at the University of Edinburgh in the 1970s. This paper focuses on the history of Standard ML, which plays a central role in this family of languages, as it was the first to include the complete set of features that we now associate with the name “ML” (i.e., polymorphic type inference, datatypes with pattern matching, modules, exceptions, and mutable state). Standard ML, and the ML family of languages, have had enormous influence on the world of programming language design and theory. ML is the foremost exemplar of a functional programming language with strict evaluation (call-by-value) and static typing. The use of parametric polymorphism in its type system, together with the automatic inference of such types, has influenced a wide variety of modern languages (where polymorphism is often referred to as generics ). It has popularized the idea of datatypes with associated case analysis by pattern matching. The module system of Standard ML extends the notion of type-level parameterization to large-scale programming with the notion of parametric modules, or functors . Standard ML also set a precedent by being a language whose design included a formal definition with an associated metatheory of mathematical proofs (such as soundness of the type system). A formal definition was one of the explicit goals from the beginning of the project. While some previous languages had rigorous definitions, these definitions were not integral to the design process, and the formal part was limited to the language syntax and possibly dynamic semantics or static semantics, but not both. The paper covers the early history of ML, the subsequent efforts to define a standard ML language, and the development of its major features and its formal definition. We also review the impact that the language had on programming-language research.
An Assessment of Educators’ Level of Concern on the Adoption of Education 5.0: A Case of One University in Zimbabwe

Background: This study was an assessment of educators’ levels of concern on the adoption of Education 5.0: A case of a State University in Zimbabwe. Education 5.0 being a new curriculum reform in Zimbabwe which focuses on five pillars namely, research, teaching, community service, innovation and industrialisation. Educators are directly involved in implementing education reforms hence the importance of an assessment of their stages of concern towards the adoption of Education 5.0.&#x0D; Methodology: A survey using 35 questionnaire items adapted from the Concerns-Based Adoption model was used to collect data from a sample of 28 lecturers derived from a population of 30. The researcher completed a score sheet using the responses from the questionnaires. Data on stages of concern was coded using Likert scale of 0 to 3. Data was analysed to measure stages of concern among the educators who are implementors of Education 5.0. by averaging the five items per stage to determine the level of concern among the educators. Standard deviation for each stage of concern was also calculated to determine variations among the educators’ level of concern.&#x0D; Results: The study revealed that the educators had their highest intensity of concern under stage 2 (personal), followed by stage 5 (collaboration), and their least concern on stage 6 (refocusing), followed by stage 3 (management). These findings indicate that these educators are on the self-level of concern, much concerned about how they would personally be affected by Education 5.0 and least concerned about the task at hand, which is the implementation of Education 5.0.&#x0D; Conclusion: The high intensity scores of educators’ concern at the personal and collaboration stages suggest that educators are more worried about how Education 5.0 adoption would affect them personally and also their significant others, students being part of this group. It is expected of educators to be concerned about how their students will be affected because students are part of lecturers’ most valued clients.
Effectiveness of Practicing Social Distancing in Museums and Art Galleries for Visitors Using Mobile Augmented Reality (MAR)

After the tragic Coronavirus (COVID-19) pandemic was declared by the WHO (World Health Organization) in March 2020, social distancing and sanitization were recommended by top medical experts and health officials to help stop the virus spread. Research has sought to help people practice social distancing in public attractions such as museums and art galleries using touchless technologies. In the modern circle of innovation and technology, mobile augmented reality (MAR) is a touchless technology that adds layers of virtual information on top of real-world images. An individual can view 3D images and videos by pointing an AR-enabled device towards a piece of digital information. This paper describes the constructive use of the major design elements of MAR, which can directly be applied for impaired and non-impaired visitors to practice social distancing in museums and art galleries.
Accurate three-dimensional measurement for small objects based on the thin-lens model

Both accuracy and depth of field (DOF) are required for small objects’ measurement in advanced manufacture and accurate robotics industries. In this paper, a stereo vision system with structured light based on the thin-lens model is developed to measure small objects with high accuracy and extended DOF at high magnification. The model of the proposed stereo vision system is built. The new system calibration scheme and measurement procedures are proposed. The DOF of the used thin-lens model is extended by utilizing autofocus capability. With the thin-lens model, accurate calibration and extended DOF at high magnification can be achieved. Three measurement experiments are conducted with the proposed system to evaluate its effectiveness and accuracy. The error of the protrusions’ height on the reconstructed aluminum part is lower than 0.55%, and the standard deviation (STD) of a fitting plane reaches 1.7 µm. Low cost, high accuracy, and extended DOF can be simultaneously achieved for measuring small objects with the proposed system based on the thin-lens model.
Moment-Guided Discriminative Manifold Correlation Learning on Ordinal Data

Canonical correlation analysis (CCA) is a typical and useful learning paradigm in big data analysis for capturing correlation across multiple views of the same objects. When dealing with data with additional ordinal information, traditional CCA suffers from poor performance due to ignoring the ordinal relationships within the data. Such data is becoming increasingly common, as either temporal or sequential information is often associated with the data collection process. To incorporate the ordinal information into the objective function of CCA, the so-called ordinal discriminative CCA has been presented in the literature. Although ordinal discriminative CCA can yield better ordinal regression results, its performance deteriorates when data is corrupted with noise and outliers, as it tends to smear the order information contained in class centers. To address this issue, in this article we construct a robust manifold-preserved ordinal discriminative correlation regression (rmODCR). The robustness is achieved by replacing the traditional ( l 2 -norm) class centers with l p -norm centers, where p is efficiently estimated according to the moments of the data distributions, as well as by incorporating the manifold distribution information of the data in the objective optimization. In addition, we further extend the robust manifold-preserved ordinal discriminative correlation regression to deep convolutional architectures. Extensive experimental evaluations have demonstrated the superiority of the proposed methods.
An Improved CMA-ES for Solving Large Scale Optimization Problem

In solving large scale optimization problems, CMA-ES has the disadvantages of high complexity and premature stagnation. To solve this problem, this paper proposes an improved CMA-ES, called GI-ES, for large-scale optimization problems. GI-ES uses all the historical information of the previous generation of individuals to evaluate the parameters of the distribution of the next generation. These estimates can be considered as approximate gradient information, which complete covariance information is not required. Thus GI-ES is friendly to large scale optimization problems. Comparative experiments have been done on state-of-the-art algorithms. The results proved the effectiveness and efficiency of GI-ES for large scale optimization problems.
Question Classification and Answer Extraction for Developing a Cooking QA System

In an automated Question Answering (QA) system, Question Classification (QC) is an essential module. The aim of QC is to identify the type of questions and classify them based on the expected answer type. Although the machine-learning approach overcomes the limitation of rules as is the case with the conventional rule-based approach but is restricted to the predefined class of questions. The existing approaches are too specific for the users. To address this challenge, we have developed a cooking QA system in which a recipe question is contextually classified into a particular category using deep learning techniques. The question class is then used to extract the requisite details from the recipe obtained via the rule-based approach to provide a precise answer. The main contribution of this paper is the description of the QC module of the cooking QA system. The obtained intermediate classification accuracy over the unseen data is 90% and the human evaluation accuracy of the final system output is 39.33%.
Refleksi Hasil PISA (The Programme For International Student Assesment): Upaya Perbaikan Bertumpu Pada Pendidikan Anak Usia Dini)

Abstract The results of the program for international student assessment (PISA) in 2018 in the category of reading ability, Indonesia ranked 74th out of 79 countries, while for the assessment of mathematical ability and scientific ability, Indonesia ranked 73rd and 71st out of 79 participating countries PISA Achievement of Indonesia's ranking in the PISA assessment has always been constant since the beginning of Indonesia's participation in the assessment from 2000 to 2018. With consistent results that are ranked below bring the consequence of thinking that the quality of Indonesian education is not in accordance with global community standards and below the countries other in the world. Government efforts to make improvements to the results of the PISA assessment are changes in the curriculum, but in reality the results of PISA have not experienced significant changes. This paper is an effort to provide a view on the improvement of PISA results through improving the quality of early childhood education, among others for several reasons, first, the opinions of experts based on the results of their research which found that the development of intellectual abilities reached 80% at an early age so the term which is often used is the golden age (golden age); second, all indicators in the program for international student assessment (PISA) begin to be studied and developed by children in early childhood education institutions ranging from literacy reading (language), mathematical literacy and scientific literacy to be the focus in early childhood learning in institutions PAUD is holistically integrative; third, PAUD access which has not yet covered the whole territory of the Republic of Indonesia, as well as the quality and quality of early childhood education institutions that have not been maximized.
Lessons from the Training and Support of Teachers in the Development of Digital Skills: A case study of @prende 2.0

Since 1997, Mexico - like many Latin American countries - has seen significant public and private investment aimed at incorporating information technology in the classroom. In-class information technology holds the great promise of revolutionizing the teaching-learning process. The reality is different, often showing negligible impact from such efforts. One common criticism has been the failure to properly train and support teachers before rolling out technology for use in their classrooms. This article looks at a recent effort of the Mexican government to address the issue of teacher training and support. @prende 2.0 was a program of the Mexican federal government that involved 2,700 digital trainers who trained more than 63,000 teachers in the use of technological equipment that they would be provided. Analyzing administration information and hard data from @prende, this article analyzes the program’s successes and challenges to fashion a series of recommendations regarding similar training and support efforts.
Self-supervised Bernoulli Autoencoders for Semi-supervised Hashing

Semantic hashing is a technique to represent high-dimensional data using similarity-preserving binary codes for efficient indexing and search. Recently, variational autoencoders with Bernoulli latent representations achieved remarkable success in learning such codes in supervised and unsupervised scenarios, outperforming traditional methods thanks to their ability to handle the binary constraints architecturally.In this paper, we propose a novel method for supervision (self-supervised) of variational autoencoders where the model uses its own predictions of the label distribution to implement the pairwise objective function. Also, we investigate the robustness of hashing methods based on variational autoencoders to the lack of supervision, focusing on two semi-supervised approaches currently in use. Our experiments on text and image retrieval tasks show that, as expected, both methods can significantly increase the quality of the hash codes as the number of labelled observations increases, but deteriorates when the amount of labelled samples decreases. In this scenario, the proposed self-supervised approach outperforms the classical approaches and yields similar performance in fully-supervised settings.
Task Scheduling Algorithm Based on Reliability Perception in Cloud Computing

Background: In order to obtain reliable cloud resources, reduce the impact of resource node faults in cloud computing environment and reduce the fault time perceived by the application layer, a task scheduling model based on reliability perception is proposed. Methods: The model combines the two-parameter weibull distribution and analyzes various interaction relations between parallel tasks to describe the local characteristics of the failure rules of resource nodes and communication links in different periods. The model is added into the particle swarm optimization (PSO) algorithm, and an adaptive inertial weighted PSO resource scheduling algorithm based on reliability perception is obtained. Results: Simulation results show that when A increases to 0.3, the average scheduling length of the task increases rapidly. When it is 0.4-0.6, the growth rate is relatively slow. When greater than 0.8, the average scheduling length increases sharply, it can be seen that the r-PSO algorithm proposed in this paper can accurately estimate the relevant parameters of cloud resource failure rule, and the generated resource scheduling scheme has better fitness, and the optimization effect is more significant with the increase in the number of tasks. Conclusion: With only a small amount of time added, the reliability of cloud services is greatly improved.
Deep Learning-Based Ship Detection in Remote Sensing Imagery Using TensorFlow

Ship detection has been a very significant proposal that is recently being researched extensively due to its very wide application and help. Ship detection is the one which helps in handling several issues that endangers the ocean security based on of several grounds. Ship detection is included in the object segmentation and detection problem that requires the aid of computer vision to preprocess and process the satellite images. The whole process is treated differently by humans and computers which makes the process complex. It is because the later treats the images as number matrix. Hence, this paper tries to propose steps to solve the issue using deep learning and convolutional neural network that works toward achieving excellent accuracy as compared to the state-of-the-art method. This not only helps us detect an object in the homogenous background but also aids us immensely in object detection and processing among the heterogeneous background. This paper has achieved this using the Keras model which is a good accurate satellite image analysis tool. It can precisely detect the ships in the mid-seas. Datasets are collected from the Kaggle Ship Detection Challenge. Here we take the dataset which consists of image chips extracted from Planet satellite imagery collected over the San Francisco Bay area. This analysis would help us control the exploitation of the sea’s resources. The need of guaranteeing sustainable sea exploitation with the likes of fishing, coral extractions, artifact explorations is essential.
Machine-learned super-resolution analysis of three-dimensional turbulent channel flow

We use machine-learning-based super-resolution analysis to reconstruct high-resolution flow field data from grossly coarse low-resolution data, for three-dimensional fully developed turbulent channel flow at Reτ = 180. The training data is obtained by three-dimensional direct numerical simulation (DNS). We use an average pooling operation used commonly in image tasks, to prepare the coarse input data set. As a machine learning model, the hybrid downsampled skip-connection multi-scale (DSC/MS) model based on convolutional neural network is utilized in this study. Remarkable about this model are its robustness against rotation/translation of the flow images and its ability to consider multi-scale property of turbulence. The super-resolved flow fields recovered through the proposed machine learning model are in agreement with the reference DNS data in terms of velocity color distributions, root mean squared values of velocity fluctuations and L2 error norm defined as the difference between the reference DNS data and super-resolved flow field. The maximum wavenumbers of streamwise and spanwise energy spectrum recovered by machine learning are increased by the super-resolution reconstruction. The proposed method holds great potential for various applications in experimental and numerical situations to handle the fluid big data efficiently, e.g., PIV measurements and subgrid-scale modeling of large-eddy simulation.
Multi-Frame Super-Resolution Reconstruction Algorithm of Optical Remote Sensing Images Based on Double Regularization Terms and Unsupervised Learning

High-resolution images have always been in urgent need in the fields of surveying, mapping, military and civilian. In this paper, first, based on anisotropic nonlinear diffusion tensor, a diffusion tensor regularization term which can make full use of direction selection smoothing property was constructed. Based on the improved gradient vector field (GVF), a regularization term which can constrain the continuity of gradient vectors for high-resolution and low-resolution images was constructed. On the basis of these, a multi-frame super-resolution reconstruction algorithm based on double regularization terms was proposed and verified by simulation. Second, combining PCA with adaptive dictionary learning, two constraints of reconstruction regularity based on improved nonlocal means and kernel regression were proposed for experimental verification, and an improved K-means clustering algorithm for initial centre selection of spatial characteristic measure clustering was proposed to enhance the stability of the algorithm. Then high-resolution image generated by learning method was used as the initial input of multi-frame reconstruction of optical remote sensing images. The experimental results show that the reconstruction algorithm based on partial differential equation and unsupervised learning achieves both subjective and objective results for the realization of super-resolution reconstruction of optical remote sensing images.