Reviewerly/exHarmony huggingface dataset:
Position and Channel Attention for Image Inpainting by Semantic Structure

Image inpainting has made great progress with the emergence of deep learning. However, there are also problems of structural loss and blurry textures, which lead to the generated image has artifacts and incomplete object. When a heavily structured object is partially masked, most methods cannot complete the repair work while keeping the semantic structure intact. To solve these problems, we propose a two-stage adversarial model: introduce unsupervised semantic structure guidance. Compared with other methods, the semantic information obtained by the introduction of semantic segmentation is more accurate. Compared with the structure information of supervised semantic segmentation of manual labels, the unsupervised semantic structure information is more flexible, and the labels are more diverse. The attention model with location information and channel information strengthens the model's long-range contextual information and multi-scale context information fusion capabilities. We evaluate our model over the publicly available datasets CelebA, Places2, and Paris StreetView, our method has a higher repair quality than the existing state-of-the-art approaches, especially when repairing the image with the large missing area.
Analysis of Mediating Effect of Social Support on High School Students' Academic Burnout and Academic Engagement

The purpose of this study is to analyze the mediating effect of social support in the relationship between high school students" academic burnout and academic engagement commitment. To this end, a survey was conducted on 337 high school students from six high schools located in Busan Metropolitan City on academic burnout, academic engagement and social support. Afterwards, the results were analyzed through correlation analysis and 3-step regression analysis. The results of this study are as follows. First, high school students" academic burnout was moderate, and academic engagement and social support were high. Second, there was a negative correlation between academic burnout and academic engagement, academic burnout and social support, and a positive correlation between academic engagement and social support. Third, the academic burnout and social support of high school students significantly influenced the academic engagement. Fourth, social support influenced high school students" academic burnout and academic engagement as parameters. As a result, it was suggested that the emphasis on various social supports obtained in the interaction process with parents, teachers, and friends is needed to improve academic commitment by reducing academic burnout of high school students.
Computer-aided decision-making System of Legal Provisions of Water Ecological Compensation System based on Character Recognition

Computer-aided decision-making system for legal provisions of water ecological compensation system based on the character recognition is designed in this paper. The information of the network is realized by the interaction between neurons. The storage of knowledge and information is a distributed physical relation between the interconnection of network components. The learning and recognition of the network are determined by the dynamic evolution of the connection weights of each core neuron, hence, this core characteristics are considered to then construct the efficient compensation system. The pattern recognition models are applied to analyze the performance. The data mining and computer vision models are combined to the construct the efficient model. The experimental results have proven the overall effectiveness.
Supporting Expert Close Analysis of Historical Scientific Writings: A Case Study for Near-by Reading

Distant reading methodologies make use of computational processes to aid in the analysis of large text corpora which might not be pliable to traditional methods of scholarly analysis due to their volume. While these methods have been applied effectively to a variety of types of texts and contexts, they can leave unaddressed the needs of scholars in the humanities disciplines like history, who often engage in close reading of sources. Complementing the close analysis of texts with some of the tools of distant reading, such as visualization, can resolve some of the issues. We focus on a particular category of this intersection-which we refer to as near-by reading-wherein an expert engages in a computer-mediated analysis of a text with which they are familiar. We provide an example of this approach by developing a visual analysis application for the near-by reading of 19 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> -century scientific writings by J. W. von Goethe and A. P. de Candolle. We show that even the most formal and public texts, such as scientific treatises, can reveal unexpressed personal biases and philosophies that the authors themselves might not have recognized.
A three-dimensional measuring system with stroboscopic laser grating fringe

A three-dimensional measuring system based on a line-structured laser and a high-speed rotating multi-faced prism has been designed and described in detail. The grating fringe can be produced using stroboscopic effect in the system. The phase-height mapping model is applied to 3D measurements, and the step-by-step phase shift is used to calculate phase information. Introducing the PID control algorithm make the rotating speed of the multi-faced prism stabilize, that can improve the stability of the fringe phase shift during the measurement process. In order to verify the measurement accuracy of the system, the system is applied to the measurement of the height information of the machined part. In addition, the effectiveness of the system was verified by measuring the section information of the column gypsum. The actual experiment shows that the measurement accuracy of the measurement system can reach 0.075 mm.
FairFly: A Fair Motion Planner for Fleets of Autonomous UAVs in Urban Airspace

We present a solution to the problem of fairly planning a fleet of Unmanned Aerial Vehicles (UAVs) that have different missions and operators, such that no one operator unfairly gets to finish its missions early at the expense of others - unless this was explicitly negotiated. When hundreds of UAVs share an urban airspace, the relevant authorities should allocate corridors to them such that they complete their missions, but no one vehicle is accidentally given an exceptionally fast path at the expense of another, which is thus forced to wait and waste energy. Our solution, FairFly, addresses the fair planning question for general autonomous systems, including UAV fleets, subject to complex missions typical of urban applications. FairFly formalizes each mission in temporal logic. An offline search finds the fairest paths that satisfy the missions and can be flown by the UAVs, leading to lighter online control load. It allows explicit negotiation between UAVs to enable imbalanced path durations if desired. We present three fairness notions, including one that reduces energy consumption.We validate our results in simulation, and demonstrate a lighter computational load and less UAV energy consumption as a result of flying fair trajectories.
Efficiently reconstruct light field probes from light-G-buffers

In this paper, we extend the light field probe method to handle dynamic light sources. The key idea is to generate distance and material information at the light source position on-the-fly and store them in a data structure called light-G-buffer. Based on this data structure and the precomputed geometric information, an efficient method is proposed to reconstruct the (ir)radiance fields for all light field probes, from which indirect diffuse and glossy illumination effects can be achieved for dynamic light sources. Furthermore, we show the ability of our method to handle multi-bounce lighting and accommodate multi-light environments at the cost of shading accuracy. Experiments show that our approach achieves comparable effects to the precomputed light field probe method at a real-time frame rate and can further handle multiple dynamic light sources.
Incremental Streaming Graph Partitioning

Graph partitioning is an NP-hard problem whose efficient approximation has long been a subject of interest. The I/O bounds of contemporary computing environments favor incremental or streaming graph partitioning methods. Methods have sought a balance between latency, simplicity, accuracy, and memory size. In this paper, we apply an incremental approach to streaming partitioning that tracks changes with a lightweight proxy to trigger partitioning as the clustering error increases. We evaluate its performance on the DARPA/MIT Graph Challenge streaming stochastic block partition dataset, and find that it can dramatically reduce the invocation of partitioning, which can provide an order of magnitude speedup.
Institutionalization of Distance Learning Technologies in a Law School

The paper reveals the methodology of conducting a sociological study of the distance learning institutionalization in a law school, describes the main conclusions of an applied study on the effectiveness of implementing digital distance learning technologies in the educational process, traces the relationship between various educational strategies and the success of using digital technologies in new learning environments. The study reveals the factors of successful adaptation of technologies to the needs of students and teachers, analyzes the barriers to institutionalization, and provides recommendations for effective expansion of the use of distance learning technologies. The accuracy of the results of the sociological research is justified by a large sample of the survey, which included more than 1200 respondents, and the typology of respondents into three clusters: teachers, students, and support staff. In each cluster, a typology was carried out, which made it possible to identify different practices of using digital technologies during distance learning. The empirical material was collected in August 2020, which determines the relevance of the conclusions.
Dynamic Neighborhood-Based Particle Swarm Optimization for Multimodal Problems

Various black-box optimization problems in real world can be classified as multimodal optimization problems. Neighborhood information plays an important role in improving the performance of an evolutionary algorithm when dealing with such problems. In view of this, we propose a particle swarm optimization algorithm based on dynamic neighborhood to solve the multimodal optimization problem. In this paper, a dynamic ε-neighborhood selection mechanism is first defined to balance the exploration and exploitation of the algorithm. Then, based on the information provided by the neighborhoods, four different particle position updating strategies are designed to further support the algorithm’s exploration and exploitation of the search space. Finally, the proposed algorithm is compared with 7 state-of-the-art multimodal algorithms on 8 benchmark instances. The experimental results reveal that the proposed algorithm is superior to the compared ones and is an effective method to tackle multimodal optimization problems.
1.25 Gb/s Correlated Random Bit Generation Over 200 km Using Electro-Optic Hybrid Chaotic Entropy Source

1.25 Gb/s Correlated random bit sequences are generated over 200-km fiber using synchronized electro-optic hybrid chaotic entropy sources. High-quality synchronization is realized by the coordination of digital synchronization strategy and neural network.
Graph Saliency Network: Using Graph Convolution Network on Saliency Detection

Saliency detection is to detect the unique region of an image that may attract human attention. It is widely used in image/video segmentation, image enhancement, and image compression. Conventionally, saliency detection problem was solved by graph-based method cooperate with low-level features and heuristic rules. Recently, the convolutional neural networks (CNNs) based methods have been thrived in computer vision area and graph convolutional networks (GCNs), which are extended from the CNN, have been used in many graph data representations and also shown promising result in node classification problem. We proposed a novel saliency detection neural network model called the Graph Saliency Network (GSN), which use the Graph Convolutional Network as main architecture and the Jumping Knowledge Network as our backbone. For the graph creation, the Region Adjacency Graph is adopted as the image-graph transformation in the proposed architecture to propagate information through edges from the spatial boundary. We also revisit several graph-based saliency detection methods for our node feature representation. The propagation model of the GSN maintain the spatial relation of the CNN with a more flexible way and has less parameters to be optimized than the CNN from the advantage of information compression in superpixel and graph. Simulations showed that, using the proposed GCN- based model together with low-level features and heuristic rules, a saliency detection result with very less mean absolute error (MAE) can be achieved.
Optimum Tilt Angle of Solar Thermal Collectors At Sea

The use of solar-thermal collectors contributes to the increase of the amount of energy produced from renewable sources. Implementation of solar-thermal collectors in places different from the usual, like sea vehicles, may be an option in order to reduce the dependence on classical, fossil resources used by those means of transportation. In order for the best results to be obtained, in terms of collected energy, the solar-thermal collectors must first be properly oriented towards Sun, according to the position on the globe, and tilted at the optimum angle. The results of the optimum angle of tilt for the solar-thermal collectors arranged on sea vehicles are presented in this paper.
An Approach Adopting Ethereum as a Wallet Domain Name System within the Economy of Things Context

The interest in blockchain technologies has been growing in the last years. Decentralization, scalability, and data integrity are characteristics that can solve problems in different application fields. However, some issues need to be addressed, aiming to facilitate using these systems. Every day the number of users increases, and the possibility of using multiple cryptocurrencies, with several wallets in each one, also brought an issue: How to manage all these addresses? This work proposes a novel architecture, named Wallet Domain Name System (WDNS), to handle several wallets and contracts in different blockchains. Using the Ethereum network to develop a DNS approach, the WDNS uses smart contracts to store and resolve domains and enable multiple subdomains that are managed by the users. Our work provides an open and free data architecture where any person and system can connect and consume. The initial tests showed an average transaction time of almost 15 seconds and a price of 3.30 USD plus 0.0012 USD per character for the domain requests. Also, the tests showed a fixed cost of 0.71 USD plus 1 USD per each synchronized instruction. Comparing the proposed domain price and the average renewal prices for internet domains makes it possible to ensure our proposal's feasibility.
An approach for updating forks against the original project

Several software projects start from an existing project. This practice, in the VCS ecosystem, is called fork. For instance, the Bootstrap project, initially developed on Twitter, today has more than 68,000 forks, which indicates that several projects started from the Bootstrap source code at a certain moment and are being customized. The problem occurs when customized projects want to obtain updates from the original project, i.e., new features, bug fixes, etc. The merge of the source code between the original and the customized projects usually generates conflicts that need human resolution. More important, the resolution of those conflicts might not be trivial and poses an arduous task for developers. This article, therefore, proposes an approach for updating forks against the original project where features are modularized, documented, traceable, and can be reused. We claim that the such task can no longer be carried out on an ad hoc basis. In a nutshell, instead of modify the method foo from the original project, the developer implements it locally and specifies, using one of the nine instructions of the proposed DSL, something like "replace the foo method with local implementation". We have developed a tool that automates our approach and conducted an evaluation on a large-scale real-world project that is regularly updated against your original project.
An Improved Key Sentence Extraction Algorithm Based on Features Computing Oriented to Argumentative Essay

Because of the strong logical structure and narrative structure of argumentative essay, we propose an improved key sentence extraction algorithm based on features computing, which not only considers word level features, such as keywords attribute, sentiment attribute, verb attribute, conjunction attribute, summary word attribute and viewpoint word attribute, but also includes sentence level features, such as position attribute, sentence frequency attribute and title similarity attribute. In our algorithm, a scoring mechanism is set for text sentences according to above attributes extraction, then the top sentences are extracted as key sentences. Experiments show that it has good performance and proves the effectiveness of the key sentence extraction algorithm based on features computing.
Implementing e-learning with personalized trajectories

Abstract The article presents the author’s approach to building adaptive e-learning courses, an example of the Moodle distance learning system. The introduction of adaptive e-learning courses into the educational process improves the quality of the educational process. The main advantage is the individualization of the educational process for each student. In addition, students’ independent work will acquire a directional character with minimal teacher involvement. The developed course allows for statistical processing of learning outcomes and tracking the dynamics students’ performance as they study the discipline.
Exploring Individual Variation in Learner Corpus Research: Methodological Suggestions

Second Language Acquisition is a complex process, and Learner Corpus Research is increasingly turning to complex statistical methods. While traditional approaches mostly relied on simple frequency counts and monofactorial analyses, some recent studies employ more sophisticated statistics that permit the inclusion of more than one predictor variable as well as more varied kinds of probabilistic/distributional information such as association strengths and dispersion values. One such recent approach is called MuPDAR (Multifactorial Prediction and Deviation Analysis Using Regression; Gries & Adelman 2014, Gries & Deshors 2014). One intriguing aspect of MuPDAR is its extensibility: in its current form, the exploration of the learner data can be L1-specific, but in the present paper we extend this approach towards also including speaker-specific effects. As such, this method should appeal to the growing number of researchers who are interested in investigating individual variation. We present a case study of genitive alternation in the Chinese and German sections of the International Corpus of Learner English alongside English native speaker data obtained from the International Corpus of English, and we illustrate different ways in which the MuPDAR approach could be extended to obtain models that license deeper interpretation at the individual speaker level.
Efficient Vehicle Counting by Eliminating Identical Vehicles in UAV aerial videos

Traffic surveillance using Unmanned Aerial Vehicles (UAV`s) has gained a lot of attraction in civilian applications and remote sensing tasks. Thanks to its high mobility and large field of view and ability to cover regions at different altitudes UAVs have made a mark in recent years for surveillance. The primary purpose of UAV in traffic surveillance is to monitor the daily activities in the busy traffic areas and report the abnormal activities which may take place. In recent years, many gated campuses such as educational institutions, organizations, shopping malls, etc. have taken steps to keep a track of vehicles trespassing within its vicinity. Vehicle counting is one of the monitoring tasks performed in surveillance to estimate the density of vehicles in an event or areas where traffic congestion is common. In this paper, a vehicle counting framework is proposed to eliminate the problem of redundant vehicle information count when a vehicle has appeared in successive frames of UAV videos. This work demonstrates that the comparison of concatenated three features vectors (Histogram of Oriented Gradients, Local Binary Pattern, and mean RGB value) can be used to recognize identical vehicles in UAV aerial videos.
Evaluation of Curtailment Associated with PV System Design Considerations

Distributed photovoltaic (PV) systems equipped with advanced inverters can control real and reactive power output based on grid and atmospheric conditions. The Volt-Var control method allows inverters to regulate local grid voltages by producing or consuming reactive power. Based on their power ratings, the inverters may need to curtail real power to meet the reactive power requirements, which decreases their total energy production. To evaluate the expected curtailment associated with Volt-Var control, yearlong quasi-static time-series (QSTS) simulations were conducted on a realistic distribution feeder under a variety of PV system design considerations. Overall, this paper found that the amount of curtailed energy is low (<; 0.55%) compared to the total PV energy production in a year but is affected by several PV system design considerations.
WITHDRAWN: High performance classification algorithm for analyzing medical images through computer aided diagnosis

Lung cancer is the utmost severe diseases in worldwide. After analysis, a huge growth with respect to count of deaths every year. Lung cancer detection is straightly linked to the progress based on finding period. Primary finding and curing of lung cancer may significantly increase the existence period. Imaging techniques are universally used in the medical field for initial finding of lung cancer. This study intentions to evaluate parameters of primary finding of lung cancer through imaging methods. The lung image is pre-processed and the region is extracted, retained and multiresolution analysis is done using DWT. Additionally parameters are extracted from DWT images using a GLCM with classification rate achieved by SVM technique. On the whole, SVM classifier has achieved an Accuracy of 90%, Precision of 91.993%, Sensitivity of 95.778% and Specificity of 75.1333%.