Reviewerly/exHarmony huggingface dataset:
Multidirection Features Based Image Inpainting with Color-Direction Patch-Sparsity

To preserve the texture clarity, coherence of structure and surrounding consistence of the inpainted areas of an image in a better way, patch sparsity based image inpainting approach through color direction is proposed. To approximate the multi direction characteristics of a degraded image, super- wavelet transform is utilized in this method. It combines with color details to build the weighted color-direction distance (WCDD) for the determining the difference among two patches. WCDD is used to interpret the color direction structure sparsity which gives a more satisfying order of filling and more matching candidate patches for selection. Then, the target regions are sparsely characterized by the numerous candidate patches under surrounding consistency by considering the multi direction and color spaces constrictions together. Experimental results prove that the removal of scratch, text and objects are performed effectively through the proposed inpainting method.
Implementation of Machine Learning for CAPTCHAs Authentication Using Facial Recognition

Web-based technologies are evolving day by day and becoming more interactive and secure. Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) is one of the security features that help detect automated bots on the Web. Earlier captcha was complex designed text-based, but some optical recognition-based algorithms can be used to crack it. That is why now the captcha system is image-based. But after the arrival of strong image recognition algorithms, image-based captchas can also be cracked nowadays. In this paper, we propose a new captcha system that can be used to differentiate real humans and bots on the Web. We use advanced deep layers with pre-trained machine learning models for captchas authentication using a facial recognition system.
FGA-NAS: Fast Resource-Constrained Architecture Search by Greedy-ADMM Algorithm

Differentiable architecture search has demonstrated promising results in automatically designing neural network architectures with desired properties, such as high accuracy and low FLOPs. However, it suffers from a cumbersome training process, and the injection of constraints in the search phase often relies on some hand-crafted heuristic regularizers, the design of which typically requires tremendous human effort. In this paper, to address these critical challenges, we present FGA-NAS, an efficient method for resource-constrained architecture search. First, to reduce the computational cost and improve search flexibility, we propose a novel condensed search space that merges multiple parallel-placed candidates into a single one. Second, to enable the gradient-based optimization for neural architecture search (NAS) under multiple combinatorial constraints, we decompose the constrained NAS into a few simple sub-problems without introducing any heuristics by using the ADMM algorithm [1]. Then, the constrained NAS can be resolved by alternately solving the simple sub-problems. Experimental results on ImageNet show that our method can discover efficient and accurate neural network architectures that achieve the state-of-the-art by only using 0.2 GPU days.
Machine Learning-Based Approach to Nonlinear Functional Data Analysis for Photovoltaic Power Forecasting

Despite high-resolution data being available and providing more comprehensive information, existing forecast models fail to utilize the available high-resolution data due to the lack of methods for processing such type of data and thus use the average values over a specific time interval instead as input, resulting in a loss of information. Functional Data Analysis (FDA) offers a pathway to modelling such data by fitting discrete data into functional curves. However, the existing statistics-based FDA methods lack nonlinear functional modelling capabilities as the explicit expression of the mapping between predictor and response in infinite function space is unknown. Further, there is little research using FDA for photovoltaic power forecasting. For these reasons, this paper proposes a novel machine learning-based approach to nonlinear FDA and introduces this approach as a new method for Day Ahead Photovoltaic Power Forecasting (PVPF). The proposed method utilizes machine learning techniques to reveal the nonlinear relationship between input and output without searching for the explicit expression. Moreover, the difficulty of traditional machine learning when dealing with infinite-dimensional data is avoided by mapping the prediction task from infinite functional space into finite parameter space and learning the prediction model in the parameter space. Based on a publicly available data set from NREL Solar Radiation Research Laboratory, the effectiveness and improved accuracy of the proposed method are evaluated and illustrated compared to currently used machine learning approaches, including the state-of-the-art Long-Short-Term Memory approach.
Experimental Study of Russian-Language News Text Categorization Performance Using Machine Learning Algorithms

Text categorization is one of the fields of natural language processing. The relevance of the research is due to the really large demand for automatic text categorization means for real-time processing of the content in news resources and social networks. It is necessary to note that the volume of such content grows at a high rate. The article considers the problem of Russian-language news text categorization. An algorithm of text preprocessing is proposed. The feature generation was performed using vector space model FastText. The following machine learning methods were used: Naive Bayes, Random Forest, Logistic Regression and Artificial Neural Network. The research was carried out on the Russian-language corpus of documents related to the nine following topics: Home, Internet and Media, Culture, Science and Technology, Politics, Journeys, Military Structures, Sport, Economics and Business. The classification quality of the trained classification models has been evaluated using a number of performance measures: precision, recall and F1-score.
Generative Research in the Context of Academic Extended Reality Research

As user experience (UX) research has become more formalized in industry research settings, discrepancies in approach and jargon have arisen between industry and academic research, particularly in the field of extended reality (XR). To begin elucidating some of these discrepancies, we conducted a brief investigation into aca-demic XR's acknowledgment of generative research, a type of user research that is exploratory and focuses on understanding users and the problems they face. For our investigation, we searched the digital catalogs of three XR conferences - IEEE ISMAR, IEEE VR, and ACM VRST - with two types of queries: 1) keywords based on generative research and its various names and 2) keywords representing research methods that are often associated with the generative research phase. The results of our searches revealed that the jargon of generative research is rarely explicitly used and that certain generative research methods are under-used by the academic XR community. Based on these results, we recommend that future academic XR researchers explicitly acknowledge the generative research stage. Currently, it is unclear to what degree users of XR are included at various stages of the academic XR user research process. We also recommend that academic XR researchers consider the application of the currently under-utilized research methods. By acknowledging generative research explicitly in the human-centered design process and considering new research methods, we suggest that academic XR researchers may be able to uncover hidden and/or “new” problems related to XR use that may have been otherwise difficult to identify.
A Novel Distilled Generative Essay Polish System via Hierarchical Pre-Training

In language processing tasks, the most important process in automated text polishment always consists of text correction and text supplementation. Finding that text polishment is a necessary step in the field of English essay reviewing, we are motivated to be the first of building an end-to-end automated English essay polish system, to support writing instruction. There were independent methods for text correction tasks and text supplementation tasks, but when combining them for essay polishment tasks, conflicts arise from their interplay. In this paper, we propose a polish system that elegantly performs text correction and text supplementation at the same time, achieving an improved revision quality. Furthermore, we design a closed-loop essay polishing process, made up of a Rewriting Model and a Scoring Model, which refers to modified GPT2 and ensembled Bert respectively. The rewriting process targets the deficiencies of the essays by a threshold controlled mechanism. Lastly, the performance of our proposed system is further enhanced by an optimization method. Intensive experiments on both real data and simulated data have shown score improvements on full essays by Scoring Model, as well as higher text correction accuracy and longer text supplementation length.
Ground Obstacle Detection Technology Based on Fusion of RGB-D and Inertial Sensors

Aiming at the real-time detection of ground obstacles in the passable area for the visually impaired, a ground obstacle detection technology based on the fusion of RGB-D and inertial sensor is proposed. Firstly, the spatial model of the ground obstacle is established, and the inertial sensor parameters are fused to calculate the camera inclination to correct the world coordinates of the ground obstacle. Secondly, according to the actual scenes and needs of the visually impaired, the remote detection pixels in the depth image are removed by threshold segmentation algorithm, and the depth map is divided into 4 regions, and the dynamic division of ROI is realized by fusing inertial sensor data. Finally, an obstacle detection algorithm is designed by improved RANSAC algorithm based on the growth of ground area algorithm, and collected real data for experimental verification. The experimental results show that the accuracy and recall rates of the proposed technology reach 90.87% and 89.33%, and better than the existing detection algorithms in the time efficiency of execution, which meets the real-time requirements of the algorithm for the visually impaired.
Rendering Personalized Real-Time Expressions While Speaking Under a Mask

During COVID-19, people often wear masks in daily activities or communication. To solve the problem of generating faces with expressions under masks, we propose a framework of methods, including detecting the shape or locations of masked faces, generating the facial expressions under masks. Further, due to synthesizing quality facial expressions, we propose to optimize the merging of sub-results with useful face information such as key points of face. Further, we propose a framework for customization or personalization of user-preferring AI-generation results. We showed the system capable of running real-time and discussed the development in multiple aspects of research, interface, and applications.
A unified trajectory tracking control for of N-dimensional closed quantum systems

A Lyapunov-based unified control method for quantum state trajectory tracking control is proposed in this paper. By inducing the error between the controlled state and the target state as error state, the trajectory tracking control problem of the quantum system is transformed into the regular control problem of systematic error state. Through an interaction picture transformation, the spin effect is eliminated. The unified control method is designed in the trajectory tracking control for the quantum system which can also solve the problems caused by the non-ideal quantum systems, where the free Hamiltonian of the system is not strongly regular and (or) not commutes with the target state, and (or) the control Hamiltonian is not fully connected. The unified trajectory tracking control proposed can drive the system from an arbitrary initial state to an arbitrary target state and achieve the purpose of tracking. The numerical simulation experiments and results analyses are given.
Metadata-Based Quality-Estimation Model for Tile-Based Omnidirectional Video Streaming

In this paper, a metadata-based quality-estimation model is proposed for tile-based omnidirectional video streaming services, aiming to realize quality monitoring during service provision. In the tile-based omnidirectional video (ODV) streaming services, the ODV is divided into tiles, and the high-quality tiles and the low-quality tiles are distributed in accordance with the user's viewing direction. When the user changes the viewing direction, the user temporarily watches video with the low-quality tiles. In addition, the longer the time (delay time) until the high-quality tile for the new viewing direction is downloaded, the longer the viewing time of video with the low-quality tile, and thus the delay time affects quality. From the above, the video quality of the low-quality tiles and the delay time significantly impact quality, and these factors need to be considered in the quality-estimation model. We develop quality-estimation models by extending the conventional quality-estimation models for 2D adaptive streaming. We also show that the quality-estimation model using the bitrate, resolution, and frame rate of high- and low-quality tiles and that the delay time has sufficient estimation accuracy based on the results of subjective quality evaluation experiments.
Diffusion-Based Approach to Style Modeling in Expressive TTS

In this article, we propose an aggregation of denoising diffusion probabilistic models (DDPMs) onto an end-to-end text-to-speech system to learn a distribution of reference speaking styles in an unsupervised manner. By applying a few steps of a forward noising process to an embedding extracted from a reference mel spectrogram, we make profit of its information to reduce the diffusion chain and reconstruct an improved style embedding with only a few reverse steps, performing style transfer. Additionally, a proposed combination of spectrogram reconstruction and denoising losses allows for conditioning of the acoustic model on the synthesized style embeddings. A subjective perceptual evaluation is conducted to evaluate naturalness and style transfer capability of the proposed approach. The results show a 5-point increment on the mean of naturalness ratings and a preference of the raters (43%) of our proposed approach over state-of-the-art models (29%) in the style transfer scenario.
Cloud-based backup and data recovery

As the field of cloud computing keeps progressing day by day, it is noticeable that how much it is important to have our data safe in cloud as we are almost dependant on it nowadays. Data and information is important in almost every sector. The data loss at any point is loss to the process and cost to an organization. The cloud based high speed solution is more useful for high speed data communication. The data security is main issue in high speed communication and it needs to be addressed. The data recovery process is important in online process and utility in cloud. With the advancement in technology the DR needs to be robust for secured utility in cloud platform.
VQEG Column: New topics

Welcome to the fourth column on the ACM SIGMM Records from the Video Quality Experts Group (VQEG). During the last VQEG plenary meeting (14-18 Dec. 2020) various interesting discussions arose regarding new topics not addressed up to then by VQEG groups, which led to launching three new sub-projects and a new project related to: 1) clarifying the computation of spatial and temporal information (SI and TI), 2) including video quality metrics as metadata in compressed bitstreams, 3) Quality of Experience (QoE) metrics for live video streaming applications, and 4) providing guidelines on implementing objective video quality metrics to the video compression community. The following sections provide more details about these new activities and try to encourage interested readers to follow and get involved in any of them by subscribing to the corresponding reflectors.
Multiple Mask Enhanced Transformer for Robust Visual Tracking

In visual object tracking tasks, scenarios such as deformation and scale variation are still challenging. In this work, we proposed a new tracking architecture with transformer and multiple masks as its key components. The transformer structure models the spatial and temporal connections among frames. The transformer encoder learns the target via attention mechanism, while the decoder utilizes the information of pervious frames to better track the current frame. To make sure that transformer pays attention to the exact target area, we propose multiple masks. Multiple masks suppress the background while leaving the target area unchanged. Multiple masks consist of spatial masks and temporal masks. Spatial masks focus on the current information while temporal masks make use of the historical information. Multiple masks further enhance the transformer, making it more focused on the target and more robust under extreme scenarios. With the transformer and multiple masks, our proposed tracker achieves the state-of-the-art level performance.
Coherence of Quantum States Based on Mutually Unbiased Bases in $$\mathbb {C}^4$$

Recently, many measures have been put forward to quantify the coherence of quantum states relative to a given basis. We extend the relationship between mutually unbiased basis (MUBs) and quantum coherence to a higher dimension. Results include arbitrary complete sets of MUBs from $$\mathbb {C}^2$$ to $$\mathbb {C}^4$$ , and the form of arbitrary $$2 \times 2$$ Unitary matrix and any density matrix of qubit states in respect of complete sets of MUBs. We construct a set of three MUBs by tensor product and further think of complete sets of five MUBs in $$\mathbb {C}^4$$ . Taking the Bell diagonal state as an example, we analyze the coherence of quantum states under MUBs and calculate the corresponding upper and lower bounds. The results show that in addition to selecting the unbiased basis which is often used, we can consider more sets of MUBs, which may be helpful to the analysis of quantum states.
THE EFFECT OF RUNNING DICTATION TECHNIQUE TOWARDS STUDENTS’ READING COMPREHENSION AT THE SECONDGRADE STUDENTS OF SMPN 4 BATUKLIANG UTARAIN ACADEMIC YEAR 2017/2018

This research is aimed at finding out whether or not Running Dictation technique effective towards students’ reading comprehension at the second grade students of SMPN 4 Batukliang Utara in academic year 2018/2019. The research was quasi experimental design with non-equivalent control group design. The instrument used was multiple choice test (narrative text) which consisted of 25 questions and to collect the data used for pre-test and post-test. The population of the study was the second grade students of SMP Negeri 4 Batukliang Utara, which consisted of two classes with the total number of the students were 40 students. The researcher taken the sample by using total population sampling technique.The data was analysis using statistical formula of t-test. The statistical of the data gathered has been already counted. In this term, the result of the value, it is needed consult t-table by using N-1 as the degree freedom (df). The df is N-1 = 20-1= 19, having known the value of df, the researcher needed to cheeked up the t-table of df19. Theresearcher found that the value t-table was 2.093 for confidence level of 0.05 (95%) and the value t-test was 2.168. t-test was higher than t-table (2.168 &gt; 2.093). In which t-test value was significant. Therefore, it can be concluded that Running Dictation technique is effective towards students’ reading comprehension at the second grade students of SMPN 4 Batukliang Utara in academic year 2018/2019.
Dual Learning Incremental Dimension Reduction for Single-View Human Motion Estimation

Estimating 3 dimensional (3D) human motion from monocular image sequence is a hot researching topic. This technique requires a lot of complex algorithms and models, and needs numerous samples to train. Furthermore, the 3D human motion will cost a lot time to be estimated, when some complex algorithms and models are utilized, such as convolutional neural network (CNN) and other networks. Thus, it is difficult to achieve more accurate results of the estimation. For such a problem, a new model called dual learning incremental dimension reduction model (DLIDRM) is proposed to achieve the 3D human motion estimation. With the learning of small-scale samples, it can estimate the 3D human motion more efficiently from monocular view, reduce the cost of running time, and get more accurate results.
Survey on blockchain for sharing of students' credentials in education ecosystem

In recent years, blockchain technology has experienced tremendous growth in application development and gained a lot of attention from people. Blockchain technology-based applications are rapidly expanding in a variety of domains, including government, education, the energy sector, and the internet of things (IoT), among many others. Blockchain is a decentralised and peer-to-peer system that permits the recording of digital transactions in a distributed, encrypted, and secure manner. Even though blockchain technology has the potential to provide more efficient and reliable applications, challenges and obstacles are also present. This paper gives a rigorous overview of the underlying concepts of blockchain: distributed ledger technology, smart contracts, and other features; with the challenges and the corresponding solutions also discussed at the end. This paper also surveyed the blockchain applications in the education ecosystem that are tied to student credentials.
STRDD: Scene Text Removal with Diffusion Probabilistic Models

Scene text removal (STR), which aims to erase text in the wild and fill with visually plausible content. Because text in the wild is often in a complex background, existing methods fail to replace the text regions with a visually plausible background. To tackle this challenge, it requires the model to learn the distribution of a large amount of data. Inspired by the successful adoptions of score-based diffusion models in image generation task, we proposed a new two-stage text erasing approach termed as STRDD. STRDD contains two modules: an autoencoder and an SDE. The autoencoder encodes the image into the features and reconstruct the image from the features. SDE learns the distribution of features encoded by the encoder and uses non-text regions in the image as conditions to turn text regions into background. The results of experiment on real-world dataset demonstrate that STRDD can remove the text in the wild well and achieve improvements on STR as compared to all baselines.