Reviewerly/exHarmony huggingface dataset:
A Dataset for the Evaluation of Lexical Simplification in Portuguese for Children

Most research on Lexical Simplification (LS) addresses non-native speakers of English, since they are numerous and easier to recruit for evaluating resources. Target audiences that are harder to deal with, such as children, are often underrepresented in literature, although simplifying a text for children could facilitate access to knowledge in a classroom setting, for example. This paper presents an improved version of SIMPLEX-PB, a public benchmarking dataset for LS that was subject to multiple rounds of manual annotation in order for it to accurately capture the simplification needs of underprivileged children. It addresses all limitations of the old SIMPLEX-PB: incorrectly generated synonyms for complex words, low coverage of synonyms, and the absence of reliable simplicity rankings for synonyms. The dataset was subjected to an enhancement on the number of synonyms for its target complex words (7,31 synonyms on average), and the simplicity rankings introduced were manually provided by the target audience itself – children between 10 and 14 years of age studying in underprivileged public institutions.
Dynamic Web with Automatic Code Generation Using Deep Learning

The typical task assigned to a developer for any design created by a designer is very crucial when it comes to building any software. The process involves tedious and repetitive steps of adapting to rapidly changing client requirements and thus, accommodating those changes in the prototype. This poses a barrier to the software development process. As a solution to this problem, we introduce a model that can be used to automate the process of generating front-end code from hand-drawn wireframes by leveraging deep learning techniques. The image features of the GUI mock-up which is fed to the model as input are extracted to generate markup tags as tokens and rendered in the form of reusable components achieving an overall BLEU score of 0.92.
AR Augmented Reality Intelligent Image Recognition System Based on the Artificial Intelligence

With the rise of the AR technology, the effect of the virtual image enhancement has also been improved. The experimental results show that the proposed virtual reality simulation system model can effectively achieve the ideal goal of the image enhancement in the virtual reality images, and has the remarkable advantage of low memory consumption, which meets the international standards of the virtual image testing, and the effect is close to reality. Therefore, the active combination of the AR technology is helpful to improve the effect of the virtual image enhancement and has the good application value.
Attention Recurrent Neural Networks for Image-Based Sequence Text Recognition

Image-based sequence text recognition is an important research direction in the field of computer vision. In this paper, we propose a new model called Attention Recurrent Neural Networks (ARNNs) for the image-based sequence text recognition. ARNNs embed the attention mechanism seamlessly into the recurrent neural networks (RNNs) through an attention gate. The attention gate generates a gating signal that is end-to-end trainable, which empowers the ARNNs to adaptively focus on the important information. The proposed attention gate can be applied to any recurrent networks, e.g., standard RNN, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). Experimental results on several benchmark datasets demonstrate that ARNNs consistently improves previous approaches on the image-based sequence text recognition tasks.
Applying Gradual Immersion Method to Chemistry: Identification of Chemical Bonds

Understanding abstract concepts is a typical challenge when teaching Science, particularly among discipline areas that involve microscopic entities or chemical elements. However, certain digital techniques, such as augmented reality, may improve learning in such areas by using 3D modeling tools. To illustrate how transversal activities can be developed within STEAM, combining, for example, science, technology, and arts, we will show how we applied the Gradual Immersion Method (GIM) to an activity about chemical bonds. For this project, the objective was for students to identify the type of chemical bond, recognize bond characteristics based on the covalent and ionic bond model, distinguish among molecular structures created by different bonds, and relate the characteristics of everyday physical materials to chemical bond structures. The results showed that students retained about 90% more information on the topic, compared to students who did not participate in the GIM activity, even as they developed creative, collaborative, and computational-thinking skills.
Improved Feature Matching Approach for Detecting Copy-Move Forgery and Localization of Digital Images

Nowadays, with the availability of many open-source tools which help to modify digital images, with view to make false publicity. Copy-move forgery (CMF) is most frequent form of forgery technique. The methods available for detecting CMF are mainly classified as keypoint-based methods (KBM) and block-based methods (BBM). KBM found better than BBM in terms of computational complexity and sturdiness against affine transformation, but still, forgery in the flat region cannot be detected by these methods, which is detected by block-based methods. As a result, many researchers proposed a fusion of both. But these methods suffer from a high false-positive rate (FPR). This paper aims to propose a new integrated approach by combining best features of Mirror Invariant Feature Transform (MIFT) and Zernike moment (ZM). Additionally, to reduce FPR a new improved window feature matching technique is proposed which in turn improves detection accuracy.
Category «Scientific Knowledge» in the Coordinates of Different Aspects

The main content of the study is the analysis of the conceptual range of “science”, “scientifi c knowledge”, “multidimensionality of scientifi c knowledge”. Particular attention is paid to the role of a systemic approach that guide the learning process and ensure the formation of a knowledge system. It is shown that scientifi c knowledge diff ers from ordinary knowledge by singling out their architecture, which is a set of characteristics. A generalized characteristic of the concept of “scientifi c knowledge” from diff erent points of view — gnoseological, logical, categorical, functional and didactic.
Local Texture Features for Content-Based Image Retrieval of Interstitial Lung Disease Patterns on HRCT Lung Images

Content-basedDash, Jatindra Kumar imagePatro, Manisha retrieval (CBIR) is a techniqueMajhi, Snehasish that mayGirish, Gandham help radiologistsNancy Anurag, P. in their daily clinical practice by providing reference images against a given subject in hand for diagnosis. Several special purpose medical CBIR systems are built for the diagnosis of interstitial lung diseases (ILDs). Texture is used as a primitive feature to build such systems due to the texture-like appearance of ILD patterns. Therefore, it is necessary to evaluate the efficacy of promising texture feature descriptors proposed recently for building the CBIR system for ILDs. This paper presents an effective and exhaustive evaluation of five such recently proposed texture feature descriptors (viz. local binary pattern (LBP), orthogonal combination of local binary pattern (OC-LBP), center-symmetric local binary pattern (CS-LBP), local neighborhood difference pattern (LNDP), and combination of LNDP and LBP) for the design and development of CBIR system for ILDs. The performance of each method is compared using the most used performance metrics such as precision, recall, and F-score. The LNDP descriptor is found to be the best performer and therefore can be considered as a descriptor for ILD patterns for the design and development of CBIR system.
Understanding Key Enablers of Cloud Computing Adoption and Acceptance Over Time

Cloud computing has been increasingly implemented in various industries due to its benefits, e.g., more cost effectiveness, improved mobility and manageability, and enhanced content sharing. In Thailand, the current national Information and Communications Technology (ICT) master plan focuses to move into the era of digital economy effectively with cloud computing. However, many concerns, e.g., data security have been raised considerably in Thai cloud-based environments. To gain more understandings, this research aims to provide insights into key enablers of cloud computing adoption and acceptance and to compare identified key enablers with two different time periods. A systematic literature review and questionnaire-based surveys were performed. The review result from 46 original papers reveal 12 key enablers influencing cloud computing adoption and acceptance. These enablers were then ensured in the Thai government context by collecting data from 274 respondents during July - October 2017 and 216 practitioners during May - July 2019. The data were analyzed by stepwise multiple linear regressions. The findings indicate that two enablers (i.e., clear cloud usage policy and appropriate plan) continuously had significant correlations with cloud adoption; while three enablers (i.e., clear cloud usage policy, appropriate plan, and cost effectiveness) constantly had significant correlations with cloud acceptance, in both 2017 and 2019.
Image Compression Algorithms Based on Dual Tree-Complex Wavelet Transform

Aiming to improve the efficiency of image compression on the premise of controlling the compression time, an image compression oriented algorithm is proposed based on Dual Tree-Complex Wavelet and arithmetic coding algorithm. Methods : decompose the read-in image by Dt-cwt, then using Wellner (an adaptive binarization method) to calculate the image's threshold. Scan the image by EZW algorithm, and compared the threshold with wavelet coefficients, then output the important coefficients and compress these data by arithmetic coding. Finally, the compressed image was output by encoding inverse transform. Results :Four-level decomposition of Dual Tree-Complex Wavelet and seven-times scanning of the image have better compression result and shorter running time. Conclusion :The hardware configuration of this method is simple, and this algorithm can compress the image with high quality in a short time.
Lossless Compression Algorithm Based on Context Tree

In order to deal with the context dilution problem introduced in the lossless compression of M-ary sources, a lossless compression algorithm based on a context tree model is proposed. By making use of the principle that conditioning reduces entropy, the algorithm constructs a context tree model to make use of the correlation among adjacent image pixels. Meanwhile, the M-ary tree is transformed into a binary tree to analyze the statistical information of the source in more details. In addition, the escape symbol is introduced to deal with the zero-frequency symbol problem when the model is used by an arithmetic encoder. The increment of the description length is introduced for the merging of tree nodes. The experimental results show that the proposed algorithm can achieve better compression results.
Concept Similarity Under the Agent’s Preferences for the Description Logic $$\mathcal {A}\!\mathcal {L}\!\mathcal {E}\!\mathcal {H}$$ALEH

Computing the degree of concept similarity is an essential problem in description logic ontologies as it has contributions in various applications. However, many computational approaches to concept similarity do not take into account the logical relationships defined in an ontology. Moreover, they cannot be personalized to subjective factors (i.e. the agent’s preferences). This work introduces a computational approach to concept similarity for the description logic $$\mathcal {A}\!\mathcal {L}\!\mathcal {E}\!\mathcal {H}$$. Our approach computes the degree of similarity between two concept descriptions structurally under the agent’s preferences. Hence, the derived degree is analyzed based on the logical definitions defined in an ontology. We also illustrate its applicability in rice disease detection, in which a farmer queries for relevant disease based on an agricultural observation.
Electronic see-through head mounted display with minimal peripheral obscuration

SA Photonics and SL Process have developed a new type of electronic see-through augmented reality head mounted display. Our display utilizes a very compact freeform prism eyepiece that has almost no peripheral obscuration. This provides the wearer with the advantages of an electronic see-through system (occlusion, operation in high ambient environments, high contrast imagery, image enhancement, image export) but in a system that also allows natural vision of the real world outside the field of view of the eyepiece. As such, the user sees a seamless view of the entire real world, with augmentation in the central 62-degree diagonal field of view. The cameras for the see-through imagery are “hidden” behind the eyepieces, so they do not block the view of the real world, providing less than a 10% obscuration of the total visual field, much less than any other electronic see-through system and even less than many optical see-through systems. In addition to providing the see-through imagery, these cameras and SL Process’ innovative video processing architecture provide six degrees of freedom head tracking as well as hand tracking.
ІНФОРМАЦІЙНО-ЦИФРОВА КОМПЕТЕНТНІСТЬ МАЙБУТНІХ УЧИТЕЛІВ ІНОЗЕМНОЇ МОВИ ЯК КЛЮЧОВА ВИМОГА НОВОЇ УКРАЇНСЬКОЇ ШКОЛИ

The use of information and communication technologies in the educational process today is one of the main tools for ensuring the success of New Ukrainian School. The importance of introducing modern ICT into the educational process is due to the need to the formation of the information and digital competence of foreign language teachers, which is recognized as one of the key competencies. It reflects the research topicality. The article is devoted to the problem of forming one of the important competences of foreign language teachers - information and digital.The purpose of the article is an analysis of the use of information and communication technologies by foreign language teacher and their influence on the formation of information and digital competence. To achieve the purpose the general theoretical scientific methods are used such as analysis, synthesis and systematization of researchers’ works where the problem of the information and digital competence is described; logical-systemic, problem-targeted, comparative analyses as for theoretical basics of the peculiarities of ICT using. There are some research results. The competence approach concept in the educational process at a current stage of educational sphere reformation was revealed. The main functions of information and communication technology using in the educational process and possibilities of its successful using were mentioned. The ICT tools classification according to their application in teachers’ activity was proposed. The formation of information and digital competence in foreign languages future teachers, as a core component in current educational process was indicated. The article contains the characteristics of skills necessary for the successful formation of information-digital competence and a set of activities for teaching foreign languages with the help of ICT. The particular attention was paid to the ICT using at foreign languages classes that gives new opportunities to pupils, as well as supports teachers to solve some didactic tasks. The ways of ICT using according to the need of the specific lesson were presented. Also the advantages and disadvantages of ICT using at foreign language lessons were discussed.
An In-depth Analysis of Graph Neural Networks for Semi-supervised Learning

Graph Neural Networks have experienced a rapid development in the last few years and become powerful tools for many machine learning tasks in graph domain. Graph Convolution Network is a breakthrough and become a strong baseline for node classification task. To this end, we perform a thorough experiment for several prominent GCN-related models, including GAT, AGNN, Co-Training GCN and Stochastic GCN. We found that different models take their advantages in different scenarios, depending on training set size, graph structure and datasets. Through our in-depth analysis of attention mechanism, dataset splits and the preprocessing for knowledge graphs, we report some interesting findings. And we look into GCNs for knowledge graphs carefully, then propose a new scheme for data processing, which achieves a better performance compared to traditional methods.
SRM-Net

Recently, the great development of deep learning has prompted many neural networks for single image dehazing to occur. How-ever, due to the ill-posed nature of haze, an excellent charac-teristics representation capacity is still challenging. In this paper, we propose a lightweight yet effective senet-residual (SE-Res) multiscale end-to-end neural network named SRM-Net. Inspired by the remarkable performance of residual networks, we intro-duce a SE-Res structure which is an improved residual framework with an embedded SE unit to obtain feature maps. These maps pass through a multiscale mapping layer which can aggregate characteristics in different receptive fields. Notably, the utilization of all point-wise convolutions in the SRM-Net leads to fewer parameters for training, and the reuse of feature maps makes it more lightweight. Through extensive numerical experiments on three datasets including real hazy images, synthetic indoor and outdoor hazy images, the proposed SRM-Net achieves superior performances on subjective visual results and objective evaluation metrics compared to the state-of-the-art methods.
Parallel Algorithms for Solving Sparse Binary Subset Sums Using Random Mappings

In this article we consider using random mappings to solve sparse binary subset sums via collision search. A mapping is constructed that suits our purpose and two parallel algorithms are proposed based on known collision-finding techniques. Following the applicability of binary subset sums, results of this paper are relevant to learning parities with noise, decoding random codes and related problems.
Operation of Transport and Logistics in a Time of (Cyber)Insecurity

All of transport is becoming increasingly automated and computerized. Consider the recent Boeing 737 Max 8 accidents in Indonesia and shortly thereafter. All indications point to a software error in code adopted following a redesign of the aircraft in which larger engines were installed than on previous Boeing 737 models as the likely culprit in both accidents. A computer software error likely overwhelmed both doomed flight crews as they fought the computerized instructions that were being given to the aircraft shortly after takeoff. A total of 346 persons died in the two accidents. Their loss should give stark warning about the safety of computer code emplaced in transportation systems: land, maritime, and air. There is little evidence of liability in commodity software. Since the infancy of modern operating systems, computer bugs, crashes, and hacks have plagued the Information Technology (IT) industry. While countless business plans, academic paper drafts, and personal correspondence have been lost to software bugs and crashes, the idea of filing suit on commodity productivity and operating system software manufacturers for damages has always been and remains a preposterous idea. What should concern us in transport is a new phenomenon—the merging of automation or process control software with network interconnectivity. Whether an aircraft autopilot, a shipboard navigation system, or a self-driving semi-truck, there remains a drive to interconnect these systems with others, often via the same technology and protocols that encompass the Internet. Unfortunately, the Internet remains a rickety ship with regard to security from unauthorized manipulation or subversion. Establishing the ideational footing for a risk and remediation strategy for this problem in the transport sector is the purpose of this paper.
Feature selection via kernel sparse representation

To solve the "dimension disaster" problem caused by high-dimensional data, this paper proposes a new feature selection algorithm that combines kernel functions with sparse learning. Firstly, each dimension feature of the data set is mapped to a high-dimensional kernel space by using a kernel function, and each dimension of the data set performs linear feature selection in the high-dimensional kernel space to achieve nonlinear feature selection in low-dimensional space. Secondly, we sparsely reconstruct the features mapped to the kernel space to get a sparse representation of the original data set. Meanwhile, we use l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm to construct feature scoring selection mechanism and select the optimal feature subset. Finally, this data subset is used for classification experiments. Experimental results on public datasets showed that the proposed algorithm can conduct feature selection better, and the accuracy of classification can be increased by about 4%.
Perennial, Permuted, and Pervasive Search in Ambient Intelligence

Searching in ambient intelligence is complex be- cause it requires fine-grained tracking of human intentions, adapting to varying interests and intentions, and maintaining search accuracy in the context of fast content changes and increasing diversity of information. The paper describes details on some of the computational challenges by framing them at three levels: 1) a human interacting with a machine, 2) a small group of humans and machines collaborating, and 3) decision- support based on diverse and fast evolving information in the context of a large community of users and machines. Along with the challenges, some early glimpses of potential solutions are also discussed.
Variational Inference of Finite Asymmetric Gaussian Mixture Models

Mixture models are a popular unsupervised learning technique useful for discovering homogeneous clusters in unlabeled data. A key research problem lies in the accurate and efficient determination of their associated parameters. Variational inference has recently risen as a prominent parameter learning approach. Hence, in this research, we propose a variational Bayes learning framework for asymmetric Gaussian mixture model. Unlike Gaussian mixture models, these models incorporate the asymmetric shape of data and are adaptive to different conditions in real-word image processing domains. Experimental results show the merit of the proposed approach.
Custom code generation for a graph DSL

We present challenges faced in making a domain-specific language (DSL) for graph algorithms adapt to varying requirements of generating a spectrum of efficient parallel codes. Graph algorithms are at the heart of several applications, and achieving high performance on graph applications has become critical due to the tremendous growth of irregular data. However, irregular algorithms are quite challenging to auto-parallelize, due to access patterns influenced by the input graph - which is unavailable until execution. Former research has addressed this issue by designing DSLs for graph algorithms, which restrict generality but allow efficient codegeneration for various backends. Such DSLs are, however, too rigid, and do not adapt to changes. For instance, these DSLs are incapable of changing the way of processing if the underlying graph changes. As another instance, most of the DSLs do not support more than one backends. We narrate our experiences in making an existing DSL, named Falcon, adaptive. The biggest challenge in the process is to retain the DSL code for specifying the underlying algorithm, and still generate different backend codes. We illustrate the effectiveness of our proposal by auto-generating codes for vertex-based versus edge-based graph processing, synchronous versus asynchronous execution, and CPU versus GPU backends from the same specification.