Reviewerly/exHarmony huggingface dataset:
Development of geographic information system based on website in the field of education

Abstract The purpose of this study is to obtain information on the development of Geographic Information System (GIS) with various methods in GIS, namely ArcView and ARCGIS and Website-based in the world of education that can be accessed anywhere during the internet connection. The technique used in this paper is Study Literature, using examine international and national journals published in 2015 - 2019. The results of this study are information about the use of GIS in the field of education with various benefits and constraints.
Smart Fair Guide: Requirements and Design of a Smart Fair Management System

Reading is an essential part of our life, and it is a way by which we acquire new knowledge, discover ourselves and develop our skills. A book fair is an event that provides visitors with the opportunity to further enhance their knowledge, to obtain rare books and to meet their favorite authors. In this event, the staff aim to achieve visitor's satisfaction, and to increase the fair's value. Every year Kingdom of Saudi Arabia alos arrange such events to promote knowledge. These events provide readers a chance to acquire international books that may not be available in the Kingdom. The Book Fair organizers are always seeking to reach a better level of organization to gain visitors' satisfaction and avoid losing the basic value of the Book Fair. SmartFair Guide is a web- based and application-based system that will manage the Kingdom's book fair event and that will enhance the experience of all individuals involved. The reason the system is referred to as smart is its outstanding features and most specifically, the system includes implementing number of data mining techniques anticipated to have a direct effect on the books classification process and the recommendation system. In this research paper the proposed model structure will display the main procedures and the user's role behavior in the cycle of the system. The cycle includes seven of the system's processes which are the donation, ticket trade, barcode scanning, crowd deduction, recommendation, classification and section locating processes. The system analysis demonstrates the diagrams which includes the Use Case, DFDs, ER, Activity, Sequence diagrams and their description.
CARNet

Recent works in semantic segmentation have focused on exploiting richer context through attention mechanisms or multi-scale fusion. Most of them ignore the low-level details, which greatly improve the recognition of pixels in the edge of the object. In this paper, we improve the quality of feature maps from two aspects: muti-level features fusion and global context encoding. A Context Attention Unit (CAU) is proposed to aggregate context information. The CAU utilizes self-attention model to enhance the semantic representation of high-level features. To make effective use of the different characteristics of multi-level feature maps, we further present a Gated Fuse Unit (GFU). A gate is calculated to control the information transfer of the feature maps of the adjacent layers. Base on these two units, a network with encoder-decoder structure is designed to gradually refine the feature map. Then the refined feature map generates the final segmentation prediction. We conduct extensive experiments on popular semantic segmentation benchmarks including Cityscapes and PASCAL VOC, which shows the effectiveness of our method.
Optimal Gama Değeri Saptama (OGVA) Yöntemi ile Görüntü Parlaklık Seviyesinin Dinamik Optimizasyonu

In this study, the proposed Optimum Gamma Value Assignment (OGVA) method is intended to dynamically optimize the image intensity level in non-desired images due to undesired light levels. For this purpose, it is aimed to make the dark images which cannot be seen due to lack of light, while bright images are dynamically dimmed by using the optimum gamma correction value applied on the image momentarily. It has been shown that this novel method, which will only be implemented as software, without requiring any additional hardware, yields satisfying results even at different light levels.
A Multi-Navigation Data Fusion Algorithm for Vehicle Localization in Airport

Precise and robust localization is an essential factor for the guidance of vehicles driving in the airport in low visibility operation. Ground Based Augmentation System (GBAS) solution can provide satisfactory positioning in open areas, however, its performance suffered in apron area due to the effects of multipath. In order to have safe and efficient localization for vehicles, a multi-navigation data fusion algorithm is employed by using Kullback-Leibler Divergence (KLD) metric, we show how to combine GBAS and other sensory information to acquire more reliable localization. Simulation results indicate the accuracy and robustness of the proposed method.
Implementation of Y-Model GIS web development in inventory of environmental roads, drainage and septic tanks: A study case

Abstract Inventory of GIS Web Applications for Environmental Roads, Drainage, and Septic Tanks is an information system built to support spatial requirements, where these applications are built to address the problem of investing in environmental roads, drainage, and septic tanks according to maps. In developing a web-based GIS application, it usually uses the software development methodology but in this study, the application implementation uses the Y-Model Webgis Development (YWDM) which is a combination of web engineering and the context of developing web-based app with a study case on inventorying spatial requirements on roads, drainage, and septic tanks. This methodology was implemented as a map visualization for city planning and development and is needed applications that are capable and appropriate. By using the YWDM methodology, the needs and accuracy of application development can be done well, using several tools. The data process uses tools, including ArcGIS ESRI, to create spatial data and PHP and JavaScript programming languages to visualize the results. The results show that application development is built more easily because requirements can be well defined because a collection of requirements from the context of software development and spatial specifications are met and the application is completed. When creating a GIS web application, some methodologies can be used besides waterfalls or ordinary SDLCs, because spatial requirements are not well defined. With the implementation of this methodology, it is proven that the development of GIS web applications can be easier and by the needs desired by the user.
A Shared Representation for Object Tracking and Classification using Siamese Networks

Recently, Siamese neural networks have been employed to build several high performance object trackers capable of operating in real time. To further improve the tracking performance, one can train one network on the tracking task and another network on the task of object classification. One can then use the feature representations of both networks to obtain a tracker which performs better than each network on its own. This approach, however, has the downside that two networks have to be evaluated instead of one, resulting in runtime degradation. We demonstrate that it is feasible to train one Siamese network on the tracking and the classifications tasks simultaneously. Specifically, we achieve a tracking performance similar to the performance of two networks trained on tracking and classification separately. Since our approach does not depend on two separate networks though, it allows one to improve the performance of a Siamese network tracker without any runtime penalty.
Deep Learning Features for Modeling Perceptual Similarity in Microcalcification Lesion Retrieval

Retrieving cases with similar image features has been found to be effective for improving the diagnostic accuracy of microcalcification (MC) lesions in mammograms. However, a major challenge in such an image-retrieval approach is how to determine a retrieved lesion image has diagnostically similar features to that of a query case. We investigate the feasibility of modeling perceptually similar MC lesions by using deep learning features extracted from two types of deep neural networks, of which one is a supervised-learning network developed for the task of MC detection and the other is a denoising autoencoder network. In the experiments, the deep learning features were compared against the perceptual similarity scores collected from a reader study on 1,000 MC lesion image pairs. The results indicate that the deep learning features can potentially be more effective for modelling the notion of perceptual similarity of MC lesions than traditional handcrafted texture features.
Exploring and Designing of Data Warehouse in Coal

Design of multi-dimensional data is required for data warehouse and it is important for establishing a coal mining data warehouse and then individual study and planning of coal industry data warehouse. Characteristics of the coal mining companies, the database is built on coal plants. Based on the system requirements for storing data in carbon minerals, the star structures are accepted in the data warehouse of the Coal Enterprise. Finally, OLAP technology and data warehouse are studied in a coal mine.
Еffect of insect infestation on the economic value of smoked fish sold in selected markets in Nigeria

Abstract. In the present study a multistage sampling technique using semi-structured questionnaire was used to obtain information on socio- economic characteristics of the respondents and on economic loss of smoked fish resulting from insect pest infestation. Smoked fishes were bought from 10 markets in Oyo state, South West of Nigeria and checked in the laboratory for the insect pests. Data were analyzed using descriptive statistics and Chi-square. Results indicated that insects at their larvae stage cause more economic loss to smoked fish than adult insects while Necrobia rufipes and Dermestes spp. are the insects found to infest the smoked fishes in this study. The insect infestation of the smoked fish leads to huge economic loss. Of all respondent groups the women dominate smoked fish business.
Design on Image-based Visual Servo Controllers for Uncalibrated Target Tracking of Quadrotor

Aiming at the shortcoming of uncalibrated visual servo, Image-based visual servo controllers are designed for uncalibrated target tracking of quadrotor. First, the models of quadrotor and image dynamics are respectively established, and the virtual camera method is used to decouple the system into linear motion and yaw motion, so that the state equation and observation equation of the mapping of image and motion space are introduced, the mapping is estimated online according to the recursive relation, and the rate of position change is obtained. Secondly, the position controller reversely calculates the velocity control law according to the estimated mapping matrix, and directly obtains the desired attitude by means of dynamic expansion. In the meantime, the sliding mode controller is designed for attitude and altitude tracking control. Finally, static and dynamic simulation results show that the designed controllers can effectively serve the uncalibrated visual servo control for target tracking of quadrotor.
Color Naming for Describing Object in Image using Different Classification Algorithms and Color Spaces

In this paper, we study to name a color of an object in the given image, where the number of color names is 11 English basic color names, including pink, purple, red, orange, yellow, green, blue, gray, brown, black, and white. Our goal is to apply machine learning algorithms for assigning a color name to a principal object in images. Here, we evaluate three basic classification algorithms, namely Support Vector Machine (SVM), naïve Bayes, and k-Nearest Neighbor (k-NN). The feature vector of the object in an image is the pixel intensities represented in different color spaces including RGB, HSV, Lab and CYMK. We randomly collect the training pixel intensities with their corresponding color names from the images obtained from the Internet. We then evaluate three classification algorithms using the e-Bay dataset. The experimental results show that the linear SVM with Lab color space is the best choice for solving this task.
A Recommender System for Linear Satellite TV: Is It Possible?

A little attention has been made to Recommender Systems (RS) for TV services broadcast through satellites due to the one-way data flow nature of the linear satellite TV. Nevertheless, the emergence of new Internet enabled TV Set-Top-Box (STB) with an embedded open source operating system has partially overcame this shortage and has encouraged implementing new kinds of applications for those STBs. In this context, we discuss in this paper the feasability of a Recommender System for the linear satellite based TV which suggests a dynamic list of services that may interest the TV viewer based on his profile (age, gender, etc) with regards to the ongoing audience rate of users having the same profile as him.
Perceptual Generative Adversarial Network for Image Inpainting with Regular and Irregular Patch

Traditional image inpainting algorithms aim to obtain satisfactory results for the relatively small and regular missing regions. However, the problem of the image with the large and irregular holes has always been challenging. Our work focus is not on filling holes in the corrupted image, but on the more difficult task of semantic repair, which aims to predict the details of the large regions according to the context of surrounding pixels. In this paper, we propose a perceptual generative adversarial network for image generation and inpainting method. In the training stage, VGG pre-trained model is introduced to GAN network to make the generated image have more high-frequency features. This framework can not only synthesize images with perceptual reality but also make a better prediction of the corrupted images by using the encoding of images. The weighted context loss is adopted to repair the missing regions in image inpainting, and the adversarial loss is used to punish the perceptually unrealistic image. Experiments on natural scene data set CelebA show that our proposed method can produce higher quality repair results.
Temporally anchored spatial knowledge: Corpora and experiments

Abstract This article presents a two-step methodology to annotate temporally anchored spatial knowledge on top of OntoNotes. We first generate potential knowledge using semantic roles or syntactic dependencies and then crowdsource annotations to validate the potential knowledge. The resulting annotations indicate how long entities are or are not located somewhere and temporally anchor this spatial information. We present an in-depth corpus analysis comparing the spatial knowledge generated by manipulating roles or dependencies. Experiments show that working with syntactic dependencies instead of semantic roles allows us to generate more potential entity-related spatial knowledge and obtain better results in a realistic scenario, that is, with predicted linguistic information.
Maneuverability Range Prediction of Gliding Vehicle Based on Structure-optimized GA -BP Neural Network

To meet the requirement for future volatile battlefield, gliding vehicles' complex mission and multi- constraint trajectory planning call for efficient maneuverability range prediction method. This paper proposes a novel gliding vehicle maneuverability range prediction method based on structure-optimized GA-BP neural network. The sample trajectories are obtained by hp-adaptive Gauss pseudospectral method. To obtain the global optimum network, the network's initial weights, thresholds and structure are all optimized. Different variables are separately encoded with hybrid encoding method. During the genetic process, GA's training performance is further improved by adaptive adjustment of crossover and permutation operation probability. The training and test results are compared to verify the method's accuracy and efficiency advantage over the BP and structure-invariant GA-BP. The results show that the proposed method can avoid to be stuck in local optimum value and effectively improve the maneuverability range prediction's accuracy and reliability.
Shape Recovery by Point Illumination using a Rotating Point Spread Function Imager

Shape Recovery for a three-dimensional (3D) extended object finds many applications. From imaging perspective, 3D extended objects can be modeled as consisting of densely packed point sources, each of which has its own independent brightness and depth parameter. Rotating point spread function (RPSF) imagers are capable to encode the depth of point sources, but the ability to decode that information for a crowded object becomes difficult. Here we present a novel technique that will allow for a robust reconstruction of the shape of an extended 3D object using point illumination via RPSF imager. Simulation results are obtained for obtaining depth map of extended objects using data frames with and without read noise.
Description and Analyzing of Web Services Composition based on Coloured Petri Nets

Current formalized description is a key issue for semantic web services. Related works attempt at analyzing and verifying for web service composition with traditional Petri nets. However, Petri nets can only show the flow of process, in this paper we specify basic definitions of CPNs and 4 basic structures of web service described by CPN model. Finally, the author presented CPNs to model web service composition by CPN tools. The feasibility and effectiveness on the CPNs has been verified by simulation result.
A Vision-based Slow-Fast Target-Positioning Framework for Person-Following

The person-following technique has a promising prospect in living and industrial applications, but it encounters several practical issues such as scene changes and pose variations, especially when deployed on a mobile robot with limited computing power. In order to address the challenges in the person-following scenario, this study formulates the visual target-positioning procedure and proposes a Slow-fast Target-Positioning Framework (STPF) to provide robust target positions in real time. Within this framework, the fast branch enables the real-time capability, while the slow branch corrects the cumulative error and improves the robustness. A dataset is collected and setup to evaluate the impact of STPF on the performance of long-term person-following. Extensive experiments demonstrate that STPF reduces 75% interruptions compared to the KCF tracker baseline, and is well adapted to the long-term person-following in real scenarios.
Multi-scale Fusion for Arbitrary-scale Image Super-resolution

Convolutional neural network (CNN)-based single image super-resolution (SR) methods have achieved superior performance on some discrete scale factors. Majority CNN-base methods only work for some integer scale factors, such as 2, 3, and 4. Meta-SR method manages to work for more discrete scale factors, such as 1.1, 1.2, etc. However, the scale factors for SR should be arbitrary in practical applications. To extend CNN-based SR methods to an arbitrary scale, in this paper, we propose a multi-scale fusion method for arbitrary-scale SR (MSFASR). In our MSFASR, we first input a low-resolution (LR) image into an existing SR model to generate two different high-resolution (HR) images. Then we downscale/upscale HR images to predict the SR results with target resolution. By exploring scale factor preference, we combine the SR results to get the final SR image. Experimental results with Meta-SR method demonstrated that the MSFASR method could extend CNN-based SR method from discrete to continuous scale and achieve good SR performance on arbitrary scales.
Fast Markovian Images Segmentation

This paper presents a solution to speed up the execution time of Hidden Markov Chain with Independent Noise model used in images segmentation. This solution is a traditional solution that divides the input image into a number of data blocks. Each block is treated independently to another following the same steps of estimation than the standard approach before dividing follows. The treatement of these blocks is sequentially. After the treatement of all blocks, we combine the result blocks to build the image result of segmentation. We compare our approach with the initial approach in term of complexity, segmentation quality and execution time. From the values of evaluated measures, we can confirm that our proposition provides the same results of segmentation than the initial approach but, our solution reduces the real execution time before dividing approximately by 40%.
Web Service QoS Prediction via Exploiting Locatoin and Trustworthy Information

When historical matrix is very sparse and matrix data is unreliable due to untrustworthy service users, the QoS prediction results is inaccurate. In this study, a method to prediction Web service QoS is presented through exploiting location and trustworthy information. Firstly, for obtaining candidate untrustworthy users, QoS similar users and geographically similar users, the QoS information and user geographic information are divided by K-means clustering algorithm. And then, the unknown service QoS values are predicted by using the QoS information of similar users only from trustworthy users. Comprehensive experiments are conducted on a real-world Web service QoS dataset. The experimental results indicate that our method outperforms the other QoS prediction methods.
ВНЕДРЕНИЕ ЭЛЕКТРОННОГО МЕНЕДЖМЕНТА В ПРАКТИКУ ОБРАЗОВАНИЯ

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A Method for User Credibility Evaluation on Online Business Review Services

The review mechanism provided by e-commerce services plays a critical role in the decision-making process of online consumers. Due to its importance, the review mechanism has also been misused for malicious intents, such as spamming and posting fake reviews. Although many innovative techniques have been developed to filter spam reviews and spammers, these techniques could not help users further pick out high-credible information. In this paper, we proposed a machine-learning-based approach to evaluate the credibility of reviewers. Typically, high-credible reviewers are more likely to post reliable reviews. Therefore, our approach can help users discover high-credible information more efficiently. We propose three groups of features that measure reviewer credibility in terms of user behavior, social relationships, and review quality. Then, we learn a classifier with a resampling mechanism to decide whether a user is high-credible or ordinary. Different from existing studies, which mostly focus on online shopping websites, our targets are users on business review services, such as Yelp and Dianping.com. Experimental results have demonstrated the effectiveness and advance of our approach.