Reviewerly/exHarmony huggingface dataset:
Image Repair Based on Least Two-Way Generation Against the Network

With the rapid development of deep learning in the field of artificial intelligence, deep learning has received more and more attention in the application of computer vision technology. Image restoration is an important application in the field of image generation. The missing information in the original image can be filled in and restored through the contextual information and perceptual information of the image. Traditional physical and mathematical image restoration algorithms pay attention to context information but ignore perception information, and use information pixels around the area to be restored to restore the original image. In the face of a single natural landscape and background, the restoration effect is acceptable However, the traditional image restoration method does not perform well on images related to faces and bodies with a large amount of perceptual information. Since then, the researchers have developed image restoration based on a generative confrontation network. A large number of fake images are generated through the generator, the discriminator optimizes the discrimination, and then the part is cropped for restoration. However, the image quality generated by the traditional generative confrontation network is not high, and the phenomenon of gradient disappearance is prone to appear. Image restoration based on the improved generative confrontation network uses the least squares method of generative confrontation network to perform image restoration. The network is better, the repair effect is better, the quality of the generated image is higher, and the gradient is easier to overcome Disappearance. From the perspective of visual effects, it can make people define the results of image restoration as correct, whether it is from the contextual information of the pixel part connected by the color or the perceptual information of the object, it can be judged as the correct image, and the restoration effect is better.
A Multi-strategy Improved Fireworks Optimization Algorithm

To solve the shortcomings of traditional Fireworks Algorithm (FWA), such as slow convergence, being easy to fall into local optimum and low precision, a multi-operator improved Multi-strategy Fireworks Algorithm (MSFWA) was proposed. For initialization, the position of individual fireworks is initialized by chaos. As for the explosion operator, the explosion range is reduced nonlinearly and the explosion range of each fireworks particle is divided according to the level of fitness. It is beneficial to improve the development and exploration of the algorithm. For mutation operator, this paper adds mutation information on the basis of retaining the original information, and adopts adaptive strategy to select different mutation modes to further improve the ability to jump out of local optimum. For the selection operator, a brand-new strategy of multi-elite reservation + random / elite reservation is adopted, improving the global and local searching ability of the algorithm. Combining various strategies improves the global and local searching ability of the algorithm, and accelerates the convergence speed. Finally, 8 benchmark test functions and optimization problems of Design of Reducer are tested. The experimental results show that MSFWA has better optimization accuracy and performance than FWA and other heuristic intelligent algorithms.
Blockwise Feature-Based Registration of Deformable Medical Images

We propose a framework for feature-based registration of deformable medical images using a blockwise approach. In our approach, we apply an accelerated-KAZE (AKAZE) feature detector on the initial image frame and input image frames for feature detection, and the detected feature points in the initial image are divided into blocks based on their coordinates. Then, the best feature point in each block of the initial image is picked up by using the response values of the detected features. Tracking of feature points is performed by finding corresponding features between the blockwise features of the initial image frame and all the detected feature points of the current image frame. Our approach has good registration capability even on sparsely textured surfaces such as human organs, which allows our method to be applied for surgery assistance. We demonstrate the effectiveness of our approach using three stereo endoscopic videos.
Deep Discriminant Non-negative Matrix Factorization Method for Image Clustering

Non-negative matrix factorization (NMF) is a single-layer decomposition algorithm for image data analysis and cannot reveal the intrinsic hierarchical-structure information hidden under the data. Moreover, it extracts features with weak discriminant power due to merely using unlabeled data. These flaws cause the undesirable performance of NMF in image clustering. To address the problems of NMF, this paper presents a novel deep discriminant NMF (DDNMF) approach via neural networks (NN). Our DDNMF model is established by employing the loss function of neural networks and the supervised regularization terms. The optimization problem of DDNMF is solved utilizing gradient descent method (GDM). The proposed DDNMF algorithm is proven to be convergent and stable. Some related NMF-based algorithms are selected for comparison on two facial image datasets. The experimental results confirm the effectiveness and superior performance of our DDNMF method in image clustering tasks.
Multi-view Robustness-Enhanced Weakly Supervised Semantic Segmentation

Semantic segmentation is the basic work in computer vision; it has been shown to achieve adequate performance in the past few years. However, owing to the inherent logical obstacles in the classification architecture, it lacks the ability to understand the long-distance dependence in the image. To address this issue, we propose a new architecture to allow the model to more expansively mine the available information for classification and segmentation tasks in a weakly supervised manner. Firstly, we raise a masking-based data enhancement approach, where images are randomly masked based on scale, forcing the model to observe other parts of the object. Secondly, a long-range correlation matrix is introduced from the image itself to make the class activation mapping (CAM) a more complete coverage on foreground objects. Finally, the experimental results on the PASCAL VOC 2012 dataset show that our method can better exploit the salient parts and non-salient regions of foreground objects in weakly labeled images comparing with other methods. On the test set, our approach achieves mIoUs of 60.9% using ResNet-based segmentation models, outperforming other methods for the weakly supervised semantic segmentation (WSSS) task.
Quasi Fourier Descriptor for Affine Invariant Features

The extraction of affine invariant features plays an important role in many computer vision tasks. Region-based methods can achieve high accuracy with expensive computation. Whereas, contour-based techniques need less computation but their performance is strongly dependant on the boundary extraction. To combine region-based and contour-based methods together for data reduction, central radial transform (CRT) is proposed in this paper. Any object is converted into a closed curve. Then, Fourier descriptor is conducted on the obtained closed curve. The derived features are invariant to affine transform, and are called quasi Fourier descriptors (QFDs). In compare with some related methods, CRT has eliminated shearing in affine transform. Consequently, parameterization and extra transforms have been avoided. Experimental results show affine invariance of the proposed QFD. In comparison with some region-based methods, the proposed QFD is more robust to additive noise.
Image-to-Video Translation Using a VAE-GAN with Refinement Network

With the development of deep learning technology, various techniques for image processing have emerged in the field of computer vision in recent years, and have excellent performance in a variety of application scenarios. In contrast to the prediction task of predicting video with multiple consecutive frames before and after the input to predict the missing images in the middle, the task of image-to-video generation proposed in this paper does not require multiple consecutive frames, but rather the directional content generation of images by inputting the first frame image with the embedding vector of motion features, and to address some of the existing problems, this paper innovates the network architecture to solve the generated video problems such as incoherence, frame loss and blurring. For multiple image-to-video translation tasks, we propose a VAE-RGAN network with a further refinement network. We add a refinement network and use new identity matching loss and connected feature matching loss to eliminate VAE and GAN's respective shortcomings and enhance the visual quality of the generated videos. Weizmann datasets have been the subject of a wide range of qualitative and quantitative experiments. We draw the following conclusions from this empirical study: (1) Compared with state-of-the-art approaches, our approach (VAE-RGAN) exhibits significant improvements in generative capability; (2) Experiments shows that our designed VAE-RGAN structure achieves better results and the refinement network significantly improves the problems of a blur.
MGVC: A Mask Voice Conversion Using Generating Adversarial Training

Recently, in the field of speech processing, voice conversion (VC) has been received more attention. However, traditional voice conversion methods require parallel utterances, which is difficult to obtain. So that a good voice conversion model should learn a converter between the source and target speakers with unparalleled utterances. Moreover, due to the limitation of non-parallel data, speech sequences are often damaged in alignment. To reduce the impact, previous works either adjust the dimension or use an attention mechanism to learn the time-frames information. These mechanisms somehow hurt the local content frame information quality. In this work, we propose MaskGAN-based-VC (MGVC), which is trained using an auxiliary task called MaskGAN to learn surrounding content frame information. The MGVC method, which is an auto-encoder-based model, can serve the purpose. The objective evaluation shows that the proposed method performs well in speaker similarity.
Tunisia: The Need for a New Development Model

The Tunisian revolution took everyone by surprise. What were the immediate causes of this historic event? What are the economic and financial challenges facing the country? How can they be addressed? The Tunisian revolution has proven the failure of the development model that has been in force since the seventies. The country is, today, facing major economic and social challenges, mainly recession, unemployment, regional disparity, a growing budget and current account deficit, a high inflation rate in addition to a heavy external public debt. The low level of national savings makes external financing the greatest challenge in the short and medium term. What development strategy should be followed? The objective of this article is to search for an appropriate development model that is capable of achieving the country’s economic and social objectives; the determination of adequate and coherent policies for its operation; and finally to provide the decision makers with practical recommendations to be implemented.
Towards Discrete Phenotypic Recombination in Cartesian Genetic Programming

The tree-based representation model of Genetic Programming (GP) is largely used with subtree crossover for genetic variation. Unlike Cartesian Genetic Programming (CGP) which is commonly used merely with mutation. Compared to comprehensive knowledge about recombination in the field of tree-based GP, the state of knowledge in CGP appears to be comparatively poor. Even if CGP was officially introduced over twenty years ago, the role of recombination in CGP has been recently considered an open issue. Several promising steps have been taken in recent years, but more research is needed to develop towards a more comprehensive and holistic perspective on crossover in CGP. In this work, we propose a phenotypic variation method for discrete recombination in CGP. We compare our method to the traditional mutation-only CGP approach on a set of well-known symbolic regression problems. The initial results presented in this work demonstrate that the use of our proposed discrete recombination method performs significantly better than the traditional mutation-only approach.
Efid: Edge-Focused Image Denoising Using a Convolutional Neural Network

In this paper, we propose an edge-focused image denoising convolutional neural network for the restoration of noisy images corrupted with additive Gaussian noise. First, the edge information for an input image is obtained using the simple Laplacian edge operator. Second, residual convolutional blocks, where each block comprises two parallel depth layers, perform image and edge processing separately. Finally, the processed image and edge features are mixed and mapped using a single convolutional layer into the restored image. Our proposed strategy can recover edges and fine structures while removing noise. Additionally, our proposed network is efficiently designed such that the total number of weight parameters can be considerably reduced compared with conventional methods. Experimental results show that the proposed model is more effective in preserving textures and edges while removing noise and achieves up to 0.8 dB higher PSNR and 0.05 higher SSIM on a real image dataset than conventional methods.
Cbs: A Deep Learning Approach for Encrypted Traffic Classification with a Mixed Spatio-Temporal and Statistical Features Classification

With the rapid development of the internet and online applications, traffic classification not only is an attractive topic in the field of computer networks but also plays a critical and vital role in managing network resources, enhancing the quality of network service and cyber-security. Internet traffic encryption has recently received significant attention because of the growing number of applications and the necessity for privacy. Traffic encryption techniques have caused conventional traffic classification approaches to become inefficient and inaccurate. Due to the limitations of conventional traffic classification methods, such as port-based, payload-based, and machine learning-based techniques, the scientific community currently regards deep learning as a high-performance approach to classifying encrypted traffic. In this paper, an encrypted traffic classification approach based on a deep learning technique, CBS, is proposed. CBS can classify encrypted traffic at two levels using CNN, attention-based Bi-LSTM, and SAE deep network models. The proposed model classifies the types of traffic and applications based on a comprehensive set of session and packet-level features. After traffic preprocessing, the session and packet features are fed into the proposed framework. In addition, a traffic data augmentation technique based on a GAN network is applied to alleviate the impact of imbalanced data on particular traffic classes. The performance of the proposed framework was evaluated on the public ISCX VPN-Non VPN 2016 dataset. The results demonstrate that the framework accurately and efficiently identifies the application and classifies encrypted traffic. Compared to the state-of-the-art methods, the proposed traffic classification model improved precision by 1.58% to 15.08%, recall by 1.40% to 17.56%, and F1 by 1.39% to 17.56%.
A Security-Enhanced Query Result Verification Scheme for Outsourced Data in Location-Based Services

Location-based services (LBSs) facilitate people’s lives; location-based service providers (LBSPs) usually outsource services to third parties to provide better services. However, the third party is a dishonest entity that might return incorrect or incomplete query results under the consideration of saving storage space and computation resources. In this paper, we propose a security-enhanced query result verification scheme (SEQRVS) for the outsourced data in a LBS. Specifically, while retaining fine-grained query result verification, we improve the construction process of verification objects to enhance the security of the outsourced data. To prevent the third party from deducing the knowledge of the outsourced data stored in itself (statistically), our scheme designs a novel storage structure to enhance the ability of privacy preservation for the outsourced data. Furthermore, based on the secure keyword search and query result verification mode proposed in our scheme, the user cannot only verify the correctness and completeness of the query result but also achieve consistency verification by the blockchain. Finally, the security analysis and extensive simulation results show the security and practicality of the proposed scheme.
Geo-Location Method for Images of Damaged Roads

Due to the large difference between normal conditions and damaged road images, geo-location in damaged areas often fails due to occlusion or damage to buildings and iconic signage in the image. In order to study the influence of post-war building and landmark damage conditions on the geolocation results of localization algorithms, and to improve the geolocation effect of such algorithms under damaged conditions, this paper used informative reference images and key point selection. Aiming at the negative effects of occlusion and landmark building damage in the retrieval process, a retrieval method called reliability- and repeatability-based deep learning feature points is proposed. In order to verify the effectiveness of the above algorithm, this paper constructed a data set consisting of urban, rural and technological parks and other road segments as a training set to generate a database. It consists of 11,896 reference images. Considering the cost of damaged landmarks, an artificially generated method is used to construct images of damaged landmarks with different damage ratios as a test set. Experiments show that the database optimization method can effectively compress the storage capacity of the feature index and can also speed up the positioning speed without affecting the accuracy rate. The proposed image retrieval method optimizes feature points and feature indices to make them reliable against damaged terrain and images. The improved algorithm improved the accuracy of geo-location for damaged roads, and the method based on deep learning has a higher effect on the geo-location of damaged roads than the traditional algorithm. Furthermore, we fully demonstrated the effectiveness of our proposed method by constructing a multi-segment road image dataset.
Application of the Blowfish Algorithm in securing patient data in the database

Patient data is one of the datasets managed by the hospital. Patient data in the form of examination results and other data is important data that is private and confidential. Therefore, patient data needs to be secured so that there is no misuse of data by parties who are not responsible for things that can harm the data owner. One of the several methods that can be used to secure data is cryptography. Cryptography itself has several algorithms, one of which is blowfish. This study applies the blowfish algorithm to secure patient data in the database to reduce the possibility of data misuse by irresponsible parties. This study succeeded in implementing the blowfish algorithm for securing patient data. The data stored in the database is the result of encryption using the blowfish algorithm, the results of which are difficult to understand because there is a combination of symbols and text. The application of the algorithm affects the data storage time in the database, which originally took 0.12 seconds to save data and now takes 0.28 seconds to store data.
PENGARUH MINAT DAN PEMANFAATAN SUMBER BELAJAR TERHADAP PRESTASI BELAJAR SISWA

This study aims to prove whether there is an influence of interest and utilization of learning resources on the achievement of vocational high school students in Turen, Malang Regency.This type of research is quantitative with descriptive correlational method.The entire population was used as the research sample, namely 101 students of class X Office Governance Automation at SMKN 1 Turen.The data analysis technique used SPSS 25.00 for multiple linear regression analysis, t test and chi-square.The results of descriptive data show that students have sufficient interest and use of learning resources.Based on the hypothesis test, partial results were obtained, namely the interest and utilization of student learning resources had a sig value.> 0.05.The results of the study prove that interest and utilization of learning resources have no significant effect on student achievement.
Spiral Model Sebagai SDLC Method Pada Sistem Informasi Desa (SIMDES)

Village Administrative Services are done in the village office, village information is also done by way of door-to-door system. This resulted in the process of delivering information fairly long. In addition, residents who want to make a submission must come to the village office and wait on the spot or will return with a period of more than one day. The purpose of this study is to provide a container in the form of online information system in the form of web-based village administration services, where residents can get Village information by opening the website. In addition, residents can submit online without having to come to the village submission place. In the design of Village information system using spiral model as Software Development Life Cycle (SDLC) method. Needs analysis made has three pages, namely the main page, admin page and community page. In its design consists of the design of the database, namely ERD, input and output document design and navigation structure as the design of the admin and community web page menu. For the programming language used is PHP by using the Framework. Applications created in trials with unit testing using blackbox testing as an evaluation of the application is successful or not.
The use of information technology in the career guidance work of a university

Considerable attention is paid to career guidance work in higher educational institutions. This is typical not only for universities in Russia, but also abroad. Information technology makes it possible to expand career guidance opportunities. They allow organizing interaction with potential applicants and educational institutions from different regions on the issues of informing and vocational guidance for students; the inclusion of university teachers in educational and consulting work with schoolchildren, involving them in research and project work on the basis of the university, “attending” university classes and educational events. The strengths of online career guidance work include: the interaction of students from different regions of Russia and other countries; choosing a convenient time for consultation; saving resources and time; comfortable environment for the subjects of work and other.
Analysis of digital literacy of teachers on the use of distance learning technologies

The work is based on the requirements to a teacher of higher education, according to the model of digital competence of educators, on the essence of the components for the formation of three blocks of digital competencies. To assess the degree of formation of digital competencies among teachers, a survey among 19 teachers of the Institute of Optics and Information Security Technologies was conducted. Taking into account the development of teaching activities, a high degree of development of digital pedagogical technologies was established.