Reviewerly/exHarmony huggingface dataset:
School Age Programs and Services Committee: Out of School Time: Inspiring Engagement in Children’s Programming

Programming for school age children has experienced a radical shift in the last year due to the pandemic. Out-of-school time or “after school” has taken on a different tone as some children learn at home and some are back at school.And yet, with virtual programming libraries continue to provide a bridge between home and school. Children’s librarians are digging deeper into the well of programming ideas to provide engaging library related activities. In our first column, we offer some concrete program ideas that can be utilized throughout the year when school is in session or during summer reading programming.
The Experience of Integration and Prospects for the Pedagogical Cluster Development in the System of Secondary Specialized and Higher Education of Students in the Specialty “Preschool Education”

The article analyzes the relevance and necessity of providing educational services at two levels of vocational education (secondary specialized and Ist stage of higher education) of pedagogical staff of preschool education institutions in the education system of the Republic of Belarus in the current educational situation: socio-demographic and normative-legal conditionality of vocational education; possibility of creating a pedagogical cluster in institutions of secondary specialized education and institutions of higher education; curriculum integration trends; elimination of duplication in the content and forms of the educational process organization in order to increase the motivation of students’ educational activities who study for the specialty “Preschool education”. A number of factors affect the increase in the motivation of college graduates to continue their professional education at the university: teachers with higher education have broader conditions for the implementation of professional activities in a preschool education institution; training according to integrated curricula in a shorter time frame is provided, and the cost of training is slightly lower; ability to participate in the educational process remotely, etc. The pedagogical cluster ensures not only continuity in the content of education at two levels of the education system, but also enriches the pedagogy of higher education with new forms, methods and technologies of working with students in the context of implementing the principles of cooperation pedagogy. Keywords: preschool education, secondary specialized and higher education, vocational education, pedagogical cluster, integration of curricula and programs, distance learning
A Reinforcement Learning Approach to Price Cloud Resources With Provable Convergence Guarantees

How to generate more revenues is crucial to cloud providers. Evidences from the Amazon cloud system indicate that “dynamic pricing” would be more profitable than “static pricing.” The challenges are: How to set the price in real-time so to maximize revenues? How to estimate the price dependent demand so to optimize the pricing decision? We first design a discrete-time based dynamic pricing scheme and formulate a Markov decision process to characterize the evolving dynamics of the price-dependent demand. We formulate a revenue maximization framework to determine the optimal price and theoretically characterize the “structure” of the optimal revenue and optimal price. We apply the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning to infer the optimal price from historical transaction data and derive sufficient conditions on the model to guarantee its convergence to the optimal price, but it converges slowly. To speed up the convergence, we incorporate the structure of the optimal revenue that we obtained earlier, leading to the VpQ-learning ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning with value projection) algorithm. We derive sufficient conditions, under which the VpQ-learning algorithm converges to the optimal policy. Experiments on a real-world dataset show that the VpQ-learning algorithm outperforms a variety of baselines, i.e., improves the revenue by as high as 50% over the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning, speedy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning, and adaptive real-time dynamic programming (ARTDP), and by as high as 20% over the fixed pricing scheme.
Effect of deformation of a flat plate specimen under in-plane blasting loading on 2D and 3D digital image correlation measurement

In combination with high-speed photography, a 3D digital image correlation (DIC) experimental system suitable for an in-plane blasting experiment is established to effectively explore the mechanism of blasting. The dynamic response of the specimen under in-plane blasting loading is analyzed on the basis of the model experiment, and the effect of out-of-plane deformation on the 2D-DIC measurement under a blasting stress wave is studied. First, the out-of-plane displacement and strain evolution law of the specimen under blasting load are studied by using a 3D-DIC system. Second, an analytical model of the effect of out-of-plane curved displacement on 2D-DIC measurement under the blasting load is proposed on the basis of pinhole imaging theory. Finally, the specific error value and error percentage of out-of-plane displacement to the 2D-DIC measurement results are quantitatively analyzed on the basis of the established theoretical method. Results show that the theoretical error of out-of-plane curved displacement to 2D-DIC accounts for 10.8% of the measured vertical strain. In the polycarbonate plate specimen, the effect of out-of-plane displacement caused by the blasting experiment on 2D-DIC measurements cannot be ignored. This novel, to the best of our knowledge, experimental system provides a wealth of experimental data, including in-plane full-field strain evolution and out-of-plane displacement changes.
FPGA acceleration of a quantized neural network for remote-sensed cloud detection

The capture and transmission of remote-sensed imagery for Earth observation is both computationally and bandwidth expensive. In the analyses of remote-sensed imagery in the visual band, atmospheric cloud cover can obstruct up to two-thirds of observations, resulting in costly imagery being discarded. Mission objectives and satellite operational details vary; however, assuming a cloud-free observation requirement, a doubling of useful data downlinked with an associated halving of delivery cost is possible through effective cloud detection. A minimal-resource, real-time inference neural network is ideally suited to perform automatic cloud detection, both for pre-processing captured images prior to transmission and preventing unnecessary images being taken by larger payload sensors. Much of the hardware complexity of modern neural network implementations resides in high-precision floating-point calculation pipelines. In recent years, research has been conducted in identifying quantized, or low-integer precision equivalents to known deep learning models, which do not require the extensive resources of their floating-point, full-precision counterparts. Our work leverages existing research on binary and quantized neural networks to develop a real-time, remote-sensed cloud detection solution using a commodity field-programmable gate array. This follows on developments of the Forwards Looking Imager for predictive cloud detection developed by Craft Prospect, a space engineering practice based in Glasgow, UK. The synthesized cloud detection accelerator achieved an inference throughput of 358.1 images per second with a maximum power consumption of 2.4 W. This throughput is an order of magnitude faster than alternate algorithmic options for the Forwards Looking Imager at around one third reduction in classification accuracy, and approximately two orders of magnitude faster than the CloudScout deep neural network, deployed with HyperScout 2 on the European Space Agency PhiSat-1 mission. Strategies for incorporating fault tolerance mechanisms are expounded.
Learning Clause Representation from Dependency-Anchor Graph for Connective Prediction

Semantic representation that supports the choice of an appropriate connective between pairs of clauses inherently addresses discourse coherence, which is important for tasks such as narrative understanding, argumentation, and discourse parsing.We propose a novel clause embedding method that applies graph learning to a data structure we refer to as a dependencyanchor graph.The dependency anchor graph incorporates two kinds of syntactic information, constituency structure and dependency relations, to highlight the subject and verb phrase relation.This enhances coherencerelated aspects of representation.We design a neural model to learn a semantic representation for clauses from graph convolution over latent representations of the subject and verb phrase.We evaluate our method on two new datasets: a subset of a large corpus where the source texts are published novels, and a new dataset collected from students' essays.The results demonstrate a significant improvement over tree-based models, confirming the importance of emphasizing the subject and verb phrase.The performance gap between the two datasets illustrates the challenges of analyzing student's written text, plus a potential evaluation task for coherence modeling and an application for suggesting revisions to students.
Color-guided optimization model with reliable self-structure priors for depth map restoration

Depth maps captured by Kinect or time of flight (ToF) cameras have an active role in many visual applications. However, a brutal truth is that these depth maps are often contaminated with compound noise, which includes intrinsic noise and missing pixels. In addition, depth maps captured with ToF-based cameras are low in resolution. As these depth maps carry rich and critical information about 3D space, high quality post-processing is crucial for supporting subsequent visual applications. Previous works were proposed via the guiding of the registered color image and bicubic interpolation as an initialization for the up-sampling task, where challenges arose from texture coping and blurry depth discontinuities. Motivated by these challenges, in this paper, we propose a new optimization model depending on the relative structures of both depth and color images for both depth map filtering and up-sampling tasks. In our general model, two self-structure priors for depth and color images are constructed individually and used for the two tasks. For overcoming the texture coping problem, the color-based and depth-based priors are used near the depth edges and at the homogeneous regions respectively. To this end, we further propose a confidence map at every task for managing where every prior is used. Experimental results on both simulated and real datasets for Kinect and ToF cameras demonstrate that the proposed method has a superior performance than benchmarks.
Optimizing Convolutional Neural Network Using Particle Swarm Optimization For Face Recognition

Deep Convolutional neural networks are the emanating deep learning models outperforming various classic machine learning algorithms in resolving many disputes. Any deep convolutional neural networks (CNN) has hyper-parameters such as number of convolutional layer, number of filters, filter size, activation function, learning rate and number of fully connected layer. Generally, these hyper-parameters are selected manually and it varies for different models and datasets. The time consumption and computational resources are large when the structure of the neural network is complex. To avoid this, in this paper the CNN is optimized using particle swarm optimization (PSO) algorithm which converges faster than any other evolutionary algorithm and finds a better architecture for face recognition. The performance of PSO optimized CNN surpasses other algorithms. As well as the time consumption gets reduced.
GENETIC ALGORITHM APPROACH FOR OPTIMAL CYCLIC TOUR ROUND THE STATE CAPITALS IN NIGERIA’S NIGER DELTA REGION

The classical traveling salesman problem(TSP) is simple to state but difficult a problem to solve. TSP seeks to determine the total distance or cost of visiting (n-1) cities or points and returning to the starting city or point. In this research, the Genetic Algorithm (GA) technique is utilized for solving the problem of finding the optimal tour around the nine Niger Delta state capitals in Nigeria which is an example of a traveling salesman problem. The partially mapped(PMX) crossover operator and the inversion mutation operator techniques were employed due to their simplicity. Genetic algorithms are evolutionary techniques used in solving optimization problems according to the survival of the fittest. The method does not provide an optimal exact solution, rather, it gives an approximated result in time. Data required for the tour were obtained from an online google map website where the distances between the state capitals and their coordinates (longitude and latitudes) were obtained. The MATLAB software which is suitable for scientific computations was used in coding the results show that the BB algorithm yielded an optimal tour of 1351km with a cyclic tour of (X3,1), (X1,9), (X9,6), (X6,8), (X8,4), (X4,7), (X7,5), (X5,2), (X2,3) and then (X3,1) after nine (9) iterations. Solving using the genetic algorithm with the four genetic parameters population size(N), maximum generation(G), crossover probability (Pc), and mutation probability(Pn) were used and set to 30; 10; 0.8; and 0.1 respectively yielded an optimal path of (8476125398) which is with an optimal tour of 1124.0KMs. genetic algorithm yielded an improved result.
Aggregating From Multiple Target-Shifted Sources

Multi-source domain adaptation aims at leveraging the knowledge from multiple tasks for predicting a related target domain. Hence, a crucial aspect is to properly combine different sources based on their relations. In this paper, we analyzed the problem for aggregating source domains with different label distributions, where most recent source selection approaches fail. Our proposed algorithm differs from previous approaches in two key ways: the model aggregates multiple sources mainly through the similarity of semantic conditional distribution rather than marginal distribution; the model proposes a \emph{unified} framework to select relevant sources for three popular scenarios, i.e., domain adaptation with limited label on target domain, unsupervised domain adaptation and label partial unsupervised domain adaption. We evaluate the proposed method through extensive experiments. The empirical results significantly outperform the baselines.
Warm-starting quantum optimization

There is an increasing interest in quantum algorithms for problems of integer programming and combinatorial optimization. Classical solvers for such problems employ relaxations, which replace binary variables with continuous ones, for instance in the form of higher-dimensional matrix-valued problems (semidefinite programming). Under the Unique Games Conjecture, these relaxations often provide the best performance ratios available classically in polynomial time. Here, we discuss how to warm-start quantum optimization with an initial state corresponding to the solution of a relaxation of a combinatorial optimization problem and how to analyze properties of the associated quantum algorithms. In particular, this allows the quantum algorithm to inherit the performance guarantees of the classical algorithm. We illustrate this in the context of portfolio optimization, where our results indicate that warm-starting the Quantum Approximate Optimization Algorithm (QAOA) is particularly beneficial at low depth. Likewise, Recursive QAOA for MAXCUT problems shows a systematic increase in the size of the obtained cut for fully connected graphs with random weights, when Goemans-Williamson randomized rounding is utilized in a warm start. It is straightforward to apply the same ideas to other randomized-rounding schemes and optimization problems.
Web 2.0 tools and information literacy instruction in UK university libraries

The literature reveals a clear debate around the use of Web 2.0 tools in information literacy (IL) instruction, with some commentators arguing that they effectively support pedagogy and others arguing that there is no sustained evidence for this. Instead, they argue that many librarians are reluctant to use the tools, hindering their overall adoption. This mixed-methods study incorporated a survey and interview to explore this debate. The aim of the study was to analyse the adoption and perception of Web 2.0 tools for IL teaching specifically within university libraries in the United Kingdom. The results revealed that there is initial evidence to suggest that a large proportion of librarians are actively using Web 2.0 tools to support IL pedagogy, but that there is also a smaller group that has a negative reaction to them and do not consider them beneficial. This study provides new knowledge for researchers around the use of technology in IL teaching and librarians’ perception of it, addressing a key gap in the literature around the UK university sector. Additionally, it is particularly useful for practitioners, as the issues it raises can improve the use of technology in IL teaching.
How Leaders Get the Worst Out of People: The Threat of Hate-based Populism

Taking Jair Bolsonaro, the President of Brazil, as case example, this article explores various manifestations of hatred, viewed from both an individual and societal perspective. Distinguishing between interpersonal and intergroup hatred, it is pointed out that hatred is like a contagious disease that has contributed to many atrocities. The article also discusses how hate-based populism uses ideologies as a bonding mechanism, describing the ways in which populist leaders use hatred to create ingroups and outgroups, uniting their constituencies through the use of the defense mechanisms of projection and splitting, and using outgroups as scapegoats. The dynamics of dehumanization are also explored. In addition, the article shows how the rise of social networking has made hatred more pernicious. It ends with suggestions about how hatred can be transcended and ways to overcome destructive feelings.
Signer-Independent Sign Language Recognition with Adversarial Neural Networks

Sign Language Recognition (SLR) has become an appealing topic in modern societies because such technology can ideally be used to bridge the gap between deaf and hearing people. Although important steps have been made towards the development of real-world SLR systems, signer-independent SLR is still one of the bottleneck problems of this research field. In this regard, we propose a deep neural network along with an adversarial training objective, specifically designed to address the signer-independent problem. Specifically, the proposed model consists of an encoder, mapping from input images to latent representations, and two classifiers operating on these underlying representations: (i) the sign-classifier, for predicting the class/sign labels, and (ii) the signer-classifier, for predicting their signer identities. During the learning stage, the encoder is simultaneously trained to help the sign-classifier as much as possible while trying to fool the signer-classifier. This adversarial training procedure allows learning signer-invariant latent representations that are in fact highly discriminative for sign recognition. Experimental results demonstrate the effectiveness of the proposed model and its capability of dealing with the large inter-signer variations.
Factors of Interest of Children to Art in Artistic Activity

In the article the actuality of the role of fine art in the society, its importance in the education of young repentance is discussed, the problems of visual activity in the initial stage of education in preschool institutions and their implementation in life are considered.
Rent Seeking in Blockchain Governance: The Awkward Transition From Market Decision Making to Non-market Decision Making

Blockchains and applications built on blockchains are decentralised ecosystems that are nonetheless built by centralised firms. The typical launch and maturity of a blockchain ecosystem involves the transition from an entrepreneurial institutional arrangement characterised by market decisionmaking to a decentralised one characterised by non-market decisionmaking. This paper considers how to assess rentseeking in the context of blockchain governance. Rentseeking in blockchain implementations and ecosystems occurs when participants seek rewards or privileges in excess what would be considered a market contribution after a certain threshold of decentralisation. The paper considering two controversies in blockchain governance – the Zcash founders’ reward and the SushiSwap developer fund – which involved the intertwining of mechanisms to fund public goods with mechanisms to compensate founders for their entrepreneurial effort. The paper finds that the normative ideal of decentralisation in blockchain governance has a parallel function to the normative ideal of liberal governance in political systems.
A sentiment analysis study for twitter using the various model of convolutional neural network

Abstract Sentiment analysis about natural language processing had been developed by various methods such as machine learning. In Indonesia, fewer studies on sentiment analysis have been studied using machine learning with specific datasets. The use of machine learning results in low accuracy that is applied in a wider scope. Therefore, the deep learning method developing to improve the accuracy of sentiment analysis. This paper presents different configuration parameters based on deep learning using the Convolutional Neural Network (CNN) algorithm to improve accuracy performance. The CNN models are presented by various parameters such as the number of convolutional layers, number of filters, and filter size to analyze the model’s performance. Indonesian-Sentiment-Analysis-Dataset which consists of 10.806 tweets has been used with the Word2Vec model for Indonesian as a word vector representation. The CNN models are trained on 80% of the dataset and tested on the remaining 20% of the dataset. The proposed CNN models’ results are compared with machine learning algorithms such as SVM, KNN, and SGD. The CNN models performed better than machine learning and got the best accuracy of 81.4% for general sentiment analysis in Indonesian.
Generating images with sparse representations

The high dimensionality of images presents architecture and sampling-efficiency challenges for likelihood-based generative models. Previous approaches such as VQ-VAE use deep autoencoders to obtain compact representations, which are more practical as inputs for likelihood-based models. We present an alternative approach, inspired by common image compression methods like JPEG, and convert images to quantized discrete cosine transform (DCT) blocks, which are represented sparsely as a sequence of DCT channel, spatial location, and DCT coefficient triples. We propose a Transformer-based autoregressive architecture, which is trained to sequentially predict the conditional distribution of the next element in such sequences, and which scales effectively to high resolution images. On a range of image datasets, we demonstrate that our approach can generate high quality, diverse images, with sample metric scores competitive with state of the art methods. We additionally show that simple modifications to our method yield effective image colorization and super-resolution models.