Reviewerly/exHarmony huggingface dataset:
Canvas GAN: Bootstrapped Image-Conditional Models

Generative Adversarial Networks (GANs) learn generating functions that map a random noise distribution <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Z$</tex> to a target data distribution. Usually, little attention is paid to the form of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Z$</tex> , resulting in nearly all models assuming <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Z$</tex> follows a convenient parametric form (almost always <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Z\ \sim \ Normal (0,1)$</tex> or <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Z\ \sim\ Uniform(-1,1))$</tex> . However, we observe that image-conditional generators, such as those used in the CycleGAN, produce better quality generation than comparable single domain GANs can currently achieve. This is true even when the images being conditioned upon are substantially different from the target images. We hypothesize these models benefit from input which already has the general structure of images, even if their semantic content is different. As a result, we propose the Canvas GAN: using just a small handful of real images (“canvases”), we create random input for an image-conditional generator by randomly cropping, flipping along either or both axis, coloring, and resizing the canvas. These diverse samples allow the generator to edit an input that already exhibits natural image structure, as opposed to having to generate it from scratch from independent white noise.
Contributing more ideas to the project of developing Can Gio sea encroachment tourist urban area, Ho Chi Minh City through the experience of Smart Eco City – Forest City, Johor, Malaysia

The project of developing Can Gio sea encroachment tourist urban area, HCM has received attention from the authorities and people in the country after the Prime Minister approved to adjust the Project of developing Can Gio sea encroachment tourist urban area from 600 hectares to 2.870 hectares with the total investment of over 217.000 billion VND (Decision no. 826/QD-TTg on 12/6/2020) with the aim of turning the Can Gio sea encroachment tourist urban area into a travel tourism urban area, resort tourism, seminars, conferences, smart cities, high-tech services, housing, services, hotels … How can the project achieve the goal of urban economic growth without adversely affecting the Can Gio mangrove biosphere reserve, not narrowing the seawater supply to the mangrove forest, affecting the mangrove ecology; ensure a stable life for the people in the project area, actively contribute to the economic restructuring of Can Gio district and Ho Chi Minh City on tourism and socio-economic development, contributing to improving, improving the living standards of people in the area is not an easy problem for investors as well as management levels. The article summarizes the achievements as well as some challenges of reclaiming the sea reclamation of some countries in ASEAN, especially the model of Smart Eco City Forest City, Jothor, Malaysia, thereby contributing more ideas to the project of developing smart eco-tourism Sunbay - Can Gio, Ho Chi Minh City achieved the highest efficiency according to the roadmap and expectation.
An Unsupervised COVID-19 Report Summarizer for Developing Smart Healthcare System

Das, Ankur Pal, Debdatta Mallick, Chirantana Das, Asit KDue to the coronavirus pandemic, COVID-19 reports are being generated frequently and volume is increasing exponentially. These reports provide innovative information to healthcare and medical science. Analysis of this information helps us to extract meaningful knowledge for improving our healthcare systems. An automated report summarizer is a model whose task is to rewrite a report into a very compact form without losing any of its important content. In the paper, the COVID-19 reports collected from the new release open-source, CORD-19 dataset, are applied in our proposed automated text summarizer. Initially, we have preprocessed the reports by Natural Language Processing (NLP) and subsequently, the Unified Medical Language System (UMLS) tool has been used to bring out only the medical terms along with the concepts corresponding to that medical terms. So, a set of concepts represent each sentence in a report and with the help of a word-to-vector-based neural network model that extracted concepts are converted into fixed dimension Word2vec vectors. Next, the fixed dimension real-valued vectors replace the set of concepts corresponding to each sentence using the summation scheme. Thus, the report is described by a real-valued dataset. Finally, the unsupervised learning (i.e. clustering algorithm) has been applied for the dataset to partition every sentence into different clusters and a representative from each cluster is identified based on Pearson correlation and clustering coefficient. The representative sentences collected using clusters are arranged according to their appearances in the report, which is the desired summary of the report. In the paper, different clustering algorithms are applied, so different summaries of a report are obtained, which are compared among themselves based on some evaluation criteria to signify the efficacy of the developed summarization technique.
Multi-Branch Network for Small Human Pose Estimation

The task of 2D human pose estimation aims to obtain the position of the body’s articulation points in picture, it is the basis for many other tasks, but the current human pose estimation network has shortcomings when dealing with small objects. Due to the ambiguity caused by inadequate expansion, a small human object contains insufficient semantic information. Therefore, the prediction of coordinate points becomes imprecise. In this paper, to address the problem of small human pose estimation, we present a novel network structure called Multi-Branch Network (MBN), consisting of three modules: Multi-Branch Expansion Module (MBEM), Multi-Branch Downsample Module (MBDM), and Refine Module (RM). MBEM reduces the input bias before the image enters the backbone network. MBDM adds an extra downsampling branch to obtain richer semantic information. RM locates hard joints by the refined operation. The experimental studies on COCO benchmark show that our approach gains noteworthy enhancements on the state-of-the-art single-stage models of ResNet and RSN.
온라인 과학수업에서 초등예비교사의 학습몰입에 영향을 미치는 요인 연구

This study is a study of factors affecting pre-service teachers' learning immersion in online science classes. The results of a survey on the responses of 88 pre-primary teachers to online science classes were interpreted. In online science class, independent variables were set as ease, usefulness, and social presence, and dependent variables were set as learning immersion. Online science classes were conducted based on the university's LMS system. The results of the study were interpreted by regression analysis for t-test, correlation between factors, and multicollinearity test in pre-post-responses of pre-service teachers for ease, usefulness, and social presence. The results of this study are as follows. First, there was a significant effect in the before-and-after tests of factors in the online science class. Second, the correlation coefficient between factors in online science class is .306 for sense of community and mutual support and concentration at the significance level of .01, and .354 for learning immersion and open communication, indicating that there is a correlation. Third, considering the effective results of the pre-post test of each factor in the online science class, it cannot be interpreted that a particular factor had an effect, but it is interpreted that learning was immersed in ease, usefulness, and a sense of social presence.
Research on QR code image processing on the LED screen

Nowadays, Quick Response (QR) code technology is used in all aspects of our lives due to the widespread use of smartphones. However, when the Light Emitting Diode (LED) display is suspended from the ceiling, the actual camera is not facing the LED and must have a certain pitch angle when shooting, so the captured image must have a three-dimensional spatial rotation effect, which leads to low decoding rate. Therefore, to address this phenomenon, this paper proposes an image processing method based on the combination of Hough transform and affine transform, which solves the problems of image rotation in the recognition of QR code images at the receiving end.
A Novel Encryption System Based on PGP Using Elliptic Curve Cryptosystem and Bézier Curve for Secure Information Exchange

Abstract This paper presents an approach to generating a secure ciphertext. Quadratic Bézier curve equation has been used in this paper to add more secure and complexity to elliptic curve cryptosystem. Proposed method using quadratic Bézier curve with ElGamal elliptic curve cryptosystem (ElGamal ECC). The idea of proposed method is encrypt the key that has been generated by public key in ElGamal elliptic curve cryptosystem by using quadratic Bézier curve equation based on PGP behavior. The proposed method have been more efficiency and complex. This method have been compared with different keys size to computing the randomness tests National Institute of Standards and Technology (NIST) and running time for the encryption and decryption process.
Methods for Automatic Machine-Learning Workflow Analysis

Developing real-world Machine Learning-based Systems goes beyond algorithm development. ML algorithms are usually embedded in complex pre-processing steps and consider different stages like development, testing or deployment. Managing workflows poses several challenges, such as workflow versioning, sharing pipeline elements or optimizing individual workflow elements - tasks which are usually conducted manually by data scientists. A dataset containing 16 035 real-world Machine Learning and Data Science Workflows extracted from the ONE DATA platform (https://onelogic.de/en/one-data/) is explored and made available. Based on our analysis, we develop a representation learning algorithm using a graph-level Graph Convolutional Network with explicit residuals which exploits workflow versioning history. Moreover, this method can easily be adapted to supervised tasks and outperforms state-of-the-art approaches in NAS-bench-101 performance prediction. Another interesting application is the suggestion of component types, for which a classification baseline is presented. A slightly adapted GCN using both graph- and node-level information further improves upon this baseline. The used codebase as well as all experimental setups with results are available at https://github.com/wendli01/workflow_analysis.
Target-aware for Few-shot Segmentation

Few-shot segmentation refers to learn a segmentation model that can be generalized to novel classes with limited labeled images. Establishing the correspondence between support images and query images effectively has a considerable effect on guiding the segmentation of query images. Most existing methods mainly adopt a trained classification network as the backbone, nevertheless, the classification tasks only focus on the most discriminate regions of the target rather than the targets' integrity and the most discriminate regions may not be part of the target we need to segment while multiple classes object included in images. Besides, there exists another question that the most discriminate regions of the target in support image also do not necessarily appear in query images because of occlusion or incomplete object. All these may cause the correspondence between two images inaccurately. To tackle these problems, we propose a Target-aware Network(TaNet). Our network has two objectives: (1) increasing both intra-object similarity and inter-object dissimilarity for query image and support image to make each object more complete rather than highlight the most discriminate regions; (2) adaptively generating target-aware correspondence between support images and query images. Experiments on PASCAL-5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> and COCO-20 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> show that our method achieves state-of-the-art performance.
Sparsity Augmented Restoration of Underwater Images using Compressive Sensing

Abstract The study of underwater environments is a difficult task because both laws of reflection and refraction are influenced in the visualization of underwater medium. Other variables, such as aquatic flora and fauna, also have a major effect on the lighting conditions. While optical processing of such images is regarded, degradation can take place due to various environmental and capturing device defects. Also, due to over hitting of samples, the algorithm based on traditional Nyquist algorithm can prove computationally sensitive and produce lower accuracy. Compressive Sensing (CS) provides an alternative approach to overcome the over-hitting problem. The efficiency of underwater algorithms can be improved by incoherent sampling combined with sparsity. The proposed model is applied to the underwater blurred image and the algorithm is evaluated for restoration using directional gradient priors. The contribution of the proposed algorithm can be characterized as: a) Incoherent sampling reduces the problem of hitting (b) Enhancement of sparsity coefficients for restoration algorithms. The proposed contribution to the application of underwater imaging is mainly because there are less random samples in the compressive sensing that produce better sparsity prior to the initial stage, which makes the solution to the ill-posed problem highly convergent with high accuracy. Therefore, we propose a new Temporal Sparse Bayesian Learning (TSBL) using compressive sensing that leads to higher resolution image enhancement in underwater conditions.
Denoising AutoEncoder Based Delete and Generate Approach for Text Style Transfer

Text style transfer task is transferring sentences to other styles while preserving the semantics as much as possible. In this work, we study a two-step text style transfer method on non-parallel datasets. In the first step, the style-relevant words are detected and deleted from the sentences in the source style corpus. In the second step, the remaining style-devoid contents are fed into a Natural Language Generation model to produce sentences in the target style. The model consists of a style encoder and a pre-trained DenoisingAutoEncoder. The former extracts style features of each style corpus and the latter reconstructs source sentences during training and generates sentences in the target style during inference from given contents. We conduct experiments on two text sentiment transfer datasets and comprehensive comparisons with other relevant methods in terms of several evaluation aspects. Evaluation results show that our method outperforms others in terms of sentence fluency and achieves a decent tradeoff between content preservation and style transfer intensity. The superior performance on the Caption dataset illustrates our method’s potential advantage on occasions of limited data.
Deep Reinforcement Learning with New-Field Exploration for Navigation in Detour Environment

Deep Reinforcement Learning (DRL) has made a great progress in recent years with the development of many relative researching areas, such as Deep Learning. Researchers have trained agents to achieve human-level and even beyond human-level scores in video games by using DRL. In the field of robotics, DRL can also achieve satisfactory performance for the navigation task when the environment is relatively simple. However, when environments become complex, e.g., the detour ones, the DRL system often fails to attain good results. To tackle this problem, we propose an internal reward obtaining method called New-Field-Explore (NFE) mechanism which can navigate a robot from initial position to target position without collision in detour environments. We also present a benchmark suite based on the AI2-Thor environment for robot navigation in complex detour environments. The proposed method is evaluated in these environments by comparing the performance of state-of-the-art algorithms with or without the NFE mechanism <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . Experimental results show the above reward is effective for mobile robot navigation tasks in detour indoor environments.
Portable Gesture Recognition Based on Flexible Stretchable Electronic Skin

In response to the large size and high price of traditional wearable gesture recognition systems, we presented a gesture recognition system based on self-developed flexible electronic skin (e-skin) with the advantages of easy portability, low cost and high recognition rate. Fourteen flexible sensors were integrated into the e-skin gesture recognition glove, which could monitor the movement of each finger joint. Meanwhile, an accelerometer was integrated, which could collect rollover information of the wrist. Furthermore, we innovatively proposed an improved dynamic time warping (DTW) algorithm to build a gesture template library, which is used to recognize complex and continuously changing gestures. Finally, the recognition results are displayed in the form of text, pictures and even the voice broadcast. Continuous recognition experiments on eight daily gestures were carried out, and the segmentation accuracy and recognition accuracy of continuous gestures is 97.49% and 93.73% respectively, which can meet the needs of deaf-mute people for normal communication with the outside world.
Layer Flexible Adaptive Computation Time

Deep recurrent neural networks perform well on sequence data and are the model of choice. However, it is a daunting task to decide the structure of the networks, i.e. the number of layers, especially considering different computational needs of a sequence. We propose a layer flexible recurrent neural network with adaptive computation time, and expand it to a sequence to sequence model. Different from the adaptive computation time model, our model has a dynamic number of transmission states which vary by step and sequence. We evaluate the model on a financial data set and Wikipedia language modeling. Experimental results show the performance improvement of 7 % to 12 % and indicate the model's ability to dynamically change the number of layers along with the computational steps.
Online Multi-Object Tracking with United Siamese Network and Candidate-Refreshing Model

Current mainstream multi-object tracking (MOT) algorithms aim to maintain the identities of targets by data-association. However, the accuracy of multi-object tracking would be affected by the unreliability results of detectors. Simultaneously, the separation of motion module and appearance module cannot fully exploit affinity features that will increase computational complexity. To address these issues, we propose a multi-task tracking framework including the United Siamese Network (USN) and Candidate-Refreshing (CR) model. The USN integrates motion affinity and appearance affinity into an end-to-end network. Such design can pay attention to joint learning of similarities between motion patterns and appearance features and enhance the ability to distinguish similar targets in complex environments. The CR model aims to combine detection candidates with tracking candidates, and compensate for unreliable detection results by scoring and regressing the bounding boxes. In addition, we equip our framework with a tracklet confidence function, which is based on the spatialtemporal information. This function can determine the vitality of unmatched tracklets and further alleviate the influence of detectors. Experiments on the MOT16 and MOT17 benchmark datasets show that our framework has achieved outstanding performance on online MOT algorithms.
Anamorphic imaging spectrometers

A model for standard line-scan imaging spectrometers with circularly symmetric optics is presented. Using reasonable approximations and special cases to maintain clarity, straight-forward analysis demonstrates design options are constrained due to lack of parameters that can readily be engineered. As a result, compromises must be made. This motivates line-scan imaging spectrometers that utilize anamorphic optics. The presented anamorphic design provides an additional degree of engineering freedom and eliminates the need for compromises required in standard systems. Results from a prototype anamorphic line-scan imaging spectrometer are provided, including Signal-to-Noise Ratios and sample imagery.
CMVCG: Non-autoregressive Conditional Masked Live Video Comments Generation Model

The blooming of live comment videos leads to the need of automatic live video comment generating task. Previous works focus on autoregressive live video comments generation and can only generate comments by giving the first word of the target comment. However, in some scenes, users need to generate comments by their given prompt keywords, which can't be solved by the traditional live video comment generation methods. In this paper, we propose a Transformer based non-autoregressive conditional masked live video comments generation model called CMVCG model. Our model considers not only the visual and textual context of the comments, but also time and color information. To predict the position of the given prompt keywords, we also introduce a keywords position predicting module. By leveraging the conditional masked language model, our model achieves non-autoregressive live video comment generation. Furthermore, we collect and introduce a large-scale real-world live video comment dataset called Bili-22 dataset. We evaluate our model in two live comment datasets and the experiment results present that our model outperforms the state-of-the-art models in most of the metrics.
A DESCRIPTIVE ANALYSIS OF PRESERVICE TEACHERS’ OPPORTUNITIES TO LEARN TO TEACH SCIENCE USING ICTS IN SOUTH AFRICA

Research suggests that there is not enough integration of Information and Communication Technologies (ICTs) into subject teaching by graduate teachers across a variety of school settings. This points in part to the inadequacy of preservice teacher preparation. Hence, this research explores the question of how preservice teachers develop the necessary competence to teach, particularly science subjects, using ICTs, by examining the opportunities to learn (OTLs) that are provided at three different South African universities. Keywords: ICT, opportunities to learn, preservice teachers, science teaching, teacher education.
Naïve Bayes and Named Entity Recognition for Requirements Mining in Job Postings

This paper analyses how the required skills in a job post can be extracted. With an automated extraction of skills from unstructured text, applicants could be more accurately matched and search engines could provide better recommendations. The problem is optimized by classifying the relevant parts of the description with a multinomial naïve Bayes model. The model identifies the section of the unstructured text in which the requirements are stated. Subsequently, a named entity recognition (NER) model extracts the required skills from the classified text. This approach minimizes the false positives since the data which is analyzed is already filtered. The results show that the naïve Bayes model classifies up to 99% of the sections correctly, and the NER model extracts 65% of the skills required for a position. The accuracy of the NER model is not sufficient to be used in production. On the validation set, the performance was insufficient. A more consistent labelling guideline would be needed and more data should be annotated to increase the performance.