Reviewerly/exHarmony huggingface dataset:
Technological Framework the Precision Teaching Based on Big Data

Firstly, this paper introduces the precise teaching method based on big data from the perspective of exploring the practical path of differentiated teaching, and introduced the technology and the practice basis of the precision teaching based on big data. Then, this paper designed the technical framework of the precision teaching based on big data, and introduced the four basic principles of the framework even compared the four principles with the views of traditional precision teaching by analyzing a set of application patterns with features like automatic recording, multidimensional observation, precise adjustment as the core. Meanwhile, combining the application practice of the precision teaching method based on big data in model schools, the best practice of the course applied in the precision teaching based on big data was presented by taking the tests explanation course and the pre-test review course as examples. Finally, the promotion strategy of the technical framework of precision teaching based on big data was proposed, expecting to promote the further development of precision teaching and the continuous deepening of teaching reform.
Multi-ColorGAN: Few-shot vehicle recoloring via memory-augmented networks

Despite the notable successes of Generative adversarial networks (GANs) achieved to date, applying them to real-world problems still poses significant challenges. In real traffic surveillance scenarios, for the task of generating images of multiple color of truck heads and cars without changing textures and license plates, conditional image generation hardly manipulate the generated images by the color attribute. Image style transfer methods inevitably produce color smearing. Even state-of-the-art methods of disentangled representation learning (e.g. MixNMatch) cannot disentangle colors individually, ensuring that irrelevant factors, such as texture remain the same. To solve this problem, we present an approach called Multi-ColorGAN based on memory-augmented networks for multi-color real vehicle coloring/generation with limited data. In particular, our model could filter out unwanted color changes in specific areas with a simple but effective method called Fusion Module, and generate more natural color images. Experiments on three vehicle image benchmarks and a new truck image dataset are conducted to evaluate the proposed Multi-ColorGAN compared to state-of-the-art.
Analysis and Application of Computer Graphics Processing and Recognition Technology

The ultimate goal of computer graphics processing is to replace people with computers to recognize images and be able to discover what people are interested in images. It is the first step in computer recognition. At this stage, it mainly simulates human vision and hearing in real life. Graphic recognition refers to the use of a machine to process and recognize graphic information (such as text, images and landscapes) to solve an important process of direct communication between the computer and reality. This article analyzes the analysis and application of computer graphics processing and recognition technology. After consulting relevant literature, it summarizes the related technology of computer graphics processing and the algorithm of computer graphics recognition. After experimenting with graphics processing technology and recognition algorithms, through experiments the accuracy of the detection algorithm provides a basis for the application of pattern recognition. The experimental results show that the recognition rate of LeNet-5 in the MNIST image library is only 93 at the beginning, and the latter two models are respectively 95 and 96 at the beginning. CNN1 only achieves 30 times the stability, while the improved CNN2 and CNN3 models achieve a relatively stable recognition rate of nearly 20 times, thereby increasing the relative learning speed. In the end, the recognition rate of the CNN1 model reached 97.8, and the improved CNN1 model achieved a maximum recognition rate of 97.82. The model can reach 98.8 and 99.5, and the improved model algorithm significantly improves the image recognition rate. And in the case of the same number of levels and different node numbers, the recognition rate of the picture first increases, then reaches a peak, and then decreases. The overall rate of return is a trend that first rises and then declines. It can be seen that when the data is 500, that is, the number of nodes is set to half of the input data, and the artificial neural network has the highest recognition rate, the data has the best performance.
Survival study on resource utilization and task scheduling in cloud

Cloud computing provides different services to the registered users because users do not need to invest in computing infrastructure. Cloud infrastructure achieved enhanced resource service using various techniques depending on user requirements. Users have the potentiality in accessing several services provided by Cloud Infrastructure via the Internet. Cloud infrastructural resource scheduling allocated the arrival and service time for every resource with the cloud service provider. Cloud infrastructural resource optimization is the method of planning one or more resources for reducing the overall cost while attaining higher performance under given constraints. Several methods were designed for resource allocation, task scheduling in cloud infrastructure. But, some challenges like load instability and low quality of service were experienced by existing techniques due to different user requests in a heterogeneous environment. To overcome such issues, various resource allocations and task scheduling are reviewed.
An image encryption based on hybrid PRESENT-SPECK algorithm

The growth of transferring data via networks and the internet yield to safe them through communication. The image encryption process is a valuable strategy for protecting image information and it is used as an effective way of communicating several sensitive data with continuous use of images in all forms of visual media. In this paper, an efficient method of image encryption is proposed based on PRESENT-SPECK algorithms with a hybrid method for key generation called Henon and Arnold-cat map that is suggested for sequences. The proposed method petitioned the image data into the block for encryption. The total blocks joined and produced an encrypted image. The proposed method tested with various types, and the generated key also have passed various tests.
Dynamic Shadow Detection and Removal for Vehicle Tracking System

Shadow leads to failure of moving target positioning, segmentation, tracking, and classification in the video surveillance system thus shadow detection and removal is essential for further computer vision process. The existing state-of-the-art methods for dynamic shadow detection have produced a high discrimination rate but a poor detection rate (foreground pixels are classified as shadow pixels). This paper proposes an effective method for dynamic shadow detection and removal based on intensity ratio along with frame difference, gamma correction, and morphology operations. The performance of the proposed method has been tested on two outdoor ATON datasets, namely, highway-I and highway-III for vehicle tracking systems. The proposed method has produced a discrimination rate of 89.07% and a detection rate of 80.79% for highway-I video sequences. Similarly, for a highway-III video sequence, the discrimination rate of 85.60% and detection rate of 84.05% have been obtained. Investigational outcomes show that the proposed method is the simple, steadiest, and robust for dynamic shadow detection on the dataset used in this work.
Classifier learning from difficult data on the example of missing features

This paper describes a novel method for classification based on a partly incomplete and low quality image data. The method has been tested on images with low quality areas such as partially corrupted, dark, or blurry images. Image data are converted to classifier input. As a result, we are dealing with missing data describing these areas, but the classifier was designed to work in the presence of missing data. The analysis of the literature shows that missing data have a negative impact on the operation of the learning algorithms. This field is still insufficient explored. In our method, we integrate complex image processing techniques, machine learning, and statistical methods under various contexts. The novelty of the presented approach is the structure of the classifier, which works in the presence of missing data on different principles than those used so far. Evaluation was performed on the basis of realistic images, collected before experiments. Multi-variant experimental protocols, specially designed in this work, confirmed the accuracy of the method. The obtained results help to better understand the methods of recognizing objects with incomplete data structures. Analysis techniques proposed in this paper are rapidly gaining in importance, so the proposed approach can be useful in many areas where the machine learning is employed.
A Weighted Non-convex Restoration Model for Hyperspectral Image

Low-rank and sparsity are two popular regularization objects in hyperspectral image restoration. To generalize this decomposition, a weighted non-convex low-rank method is proposed by adopting non-convex \(l_p\)-norm (\(0<p<1\)) to recover the clean image more genuinely. The noise term including Gaussian noise, impulse noise, stripes, and deadlines, is assumed to be sparse, and the non-convex \(l_p\)-norm can better approach the \(l_0\)-norm when \(0<p<1\). For the clean image underlying a noised one, its low rank property is conducive to the capture of both spatial and spectral information by applying \(l_p\)-norm to the bases of the gradient of data. To remain faithful to the dissimilarity between the correlation along spatial and spectral directions, weights are assigned when computing the difference in each direction. Although being non-convex, the proposed model can be solved via ADMM algorithm. Extensive simulated experiments show that this model outperforms various existing methods visually and quantitatively.
A Study on Intelligent Obscured Face Image Completion System Based on GAN Network

Image recognition and processing is an important part of modern information technology. However, in real life, the face will inevitably be blocked by hats, masks, or face photos are damaged, scratches, and images are added mosaic and other reasons, resulting in face image masking, increasing face information recognition and processing difficulties. Image completion is a kind of technology to restore the missing information of obscured image. By supplementing the information of missing area, it can reduce the difficulty of image recognition and simplify the processing of face information.So it has important research significance for the restoration of shaded image. The traditional image completion technology produces the face of poor natural degree. At present, most of the popular completion methods combine depth learning, and the repair methods based on generating anti-network GAN are the representatives of them.But the stability of the network is poor, so it is difficult to train pictures with large sheltered area or poor image quality.Some methods are not effective in the completion of arbitrary shaded areas. Therefore, this study focuses on the improvement of the stability of the generated adversarial network and the maintenance of the global and local semantic consistency between the completion image and the real image. The main work includes: (1) Sets a special network structure of global discriminator and local discriminator. When the input is arbitrarily shaded, the completion image can maintain the consistency of global and local semantics. (2) The least square loss function is used to improve the stability of the GAN network. So it performs great when the input image has large area masking or low visualization degree. (3) ADAM algorithm is used to accelerate the training of neural network. (4) Based on the completion model, a human-computer interaction software is designed and developed, which includes data set selection, image selection, image completion, result evaluation and so on. The system is applied to the removal of facial structures such as wrinkles and spots. Compared with some existing software, the system can effectively remove the selected facial structures without image distortion. Based on the design of special network structure, the system improves the supervision ability of global and local completion, the stability of GAN network and enhances its training ability in large area masking. The system is intelligent and has strong completion ability for arbitrary masking and large area masking. The research results of this paper effectively restore the information loss caused by image masking and prepare favorable conditions for subsequent image recognition and processing.
Tackling Higher Education Adaptive Leadership Challenges

The Department ChairVolume 32, Issue 2 p. 7-8 Articles Tackling Higher Education Adaptive Leadership Challenges Jill Channing, Jill ChanningSearch for more papers by this author Jill Channing, Jill ChanningSearch for more papers by this author First published: 01 October 2021 https://doi.org/10.1002/dch.30409Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume32, Issue2Fall 2021Pages 7-8 RelatedInformation
Vulnerability Exploiting SMS Push Notifications

SMS (Short Message Service)-based authentication is widely used as a simple and secure multi-factor authentication, where OTP (One Time Password) is sent to user’s mobile phone via SMS. However, SMS authentication is vulnerable to Password Reset Man in the Middle Attack (PRMitM). In this attack, the attacker makes a victim perform password reset OTP for sign-up verification OTP. If the victim enters OTP to a malicious man-in-the-middle site, the attacker can overtake the victim’s account.We find new smartphone useful functions may increase PR-MitM attack risks. SMS push notification informs us an arrival of message by showing only beginning of the message. Hence, those who received SMS OTP do not notice the cautionary notes and the name of the sender that are supposed to show below the code, which may lead to be compromised. Auto-fill function, which allow us to input authentication code with one touch, is also vulnerable for the same reason.In this study, we conduct a user study to investigate the effect of new smartphone functions incurring PRMitM attack.
Equivalence of Correlation Filter and Convolution Filter in Visual Tracking

(Discriminative) Correlation Filter has been successfully applied to visual tracking and has advanced the field significantly in recent years. Correlation filter-based trackers consider visual tracking as a problem of matching the feature template of the object and candidate regions in the detection sample, in which correlation filter provides the means to calculate the similarities. In contrast, convolution filter is usually used for blurring, sharpening, embossing, edge detection, etc. in image processing. On the surface, correlation filter and convolution filter are usually used for different purposes. In this paper, however, we prove, for the first time, that correlation filter and convolution filter are equivalent in the sense that their minimum mean-square errors (MMSEs) in visual tracking are equal, under the condition that the optimal solutions exist and the ideal filter response is Gaussian and centrosymmetric. This result gives researchers the freedom to choose correlation or convolution in formulating their trackers. It also suggests that the explanation of the ideal response in terms of similarities is not essential.
Asynchronous Method of Oracle: A Cost-Effective and Reliable Model for Cloud Migration Using Incremental Backups

Cloud Computing has reached a new level in flexibility to provide infrastructure. The proper migration method should be chosen for better cost management and to avoid overpayments to unused resources. So, the migrations from On-Premises to cloud infrastructure is a challenge. The migration can be done in synchronous or asynchronous modes. The synchronous method is mostly used to minimize downtime while doing the cloud migrations. The asynchronous methods can do the migrations in offline mode and very consistently. This paper addresses various issues related to the synchronous mode of Oracle while doing highly transactional database migrations. The proposed methodology provides a solution with a combination of asynchronous and incremental backups for highly transactional databases. This proposed method will be a more cost-effective and reliable model without compromising consistency and integrity.
Application Method of Deep Learning Model Trained with CG Images to Real Images

When applying deep learning to tasks, preparing training data for training a model is often a severe challenge. On the other hand, in some fields using images such as image recognition, a large amount of training data can be easily generated by using computer graphics (CG), even the target images are photos. Since target photos can be translated into CG images by using generative adversarial networks (GAN), a model can be trained with CG images to accept these photos as input for prediction. However, fake CG images translated by GANs are not always suitable for a target model resulting in an accuracy deterioration. Therefore, this study proposes a method based on cycle-consistent adversarial networks (CycleGAN) to translate photos into more suitable fake CG images for a target model, where this target model is incorporated as a discriminator. According to experimental results, it is shown that the proposed method can achieve close accuracy with CG images as it does with photos (real images).
FUNCTIONAL MATHEMATICAL LITERACY IN PRESCHOOL CHILDHOOD: COMPETENCE-BASED APPROACH

The analysis of the social and communicative conditions of maturation of young children actualizes the need for them to develop a special kind of personal qualities in the course of education, formed as a result of acquiring the ability to "think mathematically". The problem of teaching a growing personality to apply mathematical content to solving a wide range of problems is considered in the context of putting forward a new paradigm of the quality of education. According to it, the effectiveness of the functioning of the education system is established in accordance with the change of educational guidelines "From knowledge, skills, skills - to functional literacy". The practical solution to the problem of improving the quality of education, considered from the perspective of the formation of functional literacy, is due to the development of positive behavioral strategies in students in various life problem situations, the resolution of which can occur in the absence of a fully deterministic solution. The current stage of development of education encourages teachers in their professional activities to proceed from the targeted development of the students of the educational system of skills that allow them to resolve emerging problem situations on the basis of the functional literacy formed in the course of conducting an initiative research search. The problem of developing such skills is relevant at all stages of the educational development of a growing personality. The readiness of preschool teachers to develop functional mathematical literacy in their pupils deserves close attention from the system of additional professional education of teachers. The phenomenon of the lack of special developments aimed at implementing the strategy of education development on the basis of the designated paradigm directs the study to the problem of developing competently oriented tasks for the formation of functional mathematical literacy in pre-school students.
Airway Anomaly Detection by Prototype-Based Graph Neural Network

Detecting the airway anomaly can be an essential part to aid the lung disease diagnosis. Since normal human airways share an anatomical structure, we design a graph prototype whose structure follows the normal airway anatomy. Then, we learn the prototype and a graph neural network from a weakly-supervised airway dataset, i.e., only the holistic label is available, indicating if the airway has anomaly or not, but which bronchus node has the anomaly is unknown. During inference, the graph neural network predicts the anomaly score at both the holistic level and node-level of an airway. We initialize the airway anomaly detection problem by creating a large airway dataset with 2589 samples, and our prototype-based graph neural network shows high sensitivity and specificity on this new benchmark dataset. The code is available at https://github.com/tznbz/Airway-Anomaly-Detection-by-Prototype-based-Graph-Neural-Network.
A Non-heuristic Approach to Time-Space Tradeoffs and Optimizations for BKW

Blum, Kalai and Wasserman (JACM 2003) gave the first sub-exponential algorithm to solve the Learning Parity with Noise (LPN) problem. In particular, consider the LPN problem with constant noise and dimension n. The BKW solves it with space complexity $$2^{\frac{(1+\epsilon )n}{\log (n)}}$$ and time/sample complexity $$2^{\frac{(1+\epsilon )n}{\log (n)}}\cdot 2^{\varOmega (n^{\frac{1}{1+\epsilon }})}$$ for small constant $$\epsilon \rightarrow 0^+$$ . We propose a variant of the BKW by tweaking Wagner’s generalized birthday problem (Crypto 2002) and adapting the technique to a c-ary tree structure. In summary, our algorithm achieves the following: In particular, the time/sample optimization benefits from a careful analysis of the error distribution among the correlated candidates, which was not studied by previous rigorous approaches such as the analysis of Minder and Sinclair (J.Cryptology 2012) or Devadas et al. (TCC 2017).
Predicting the User Intention in Web Search

The search for information is inherent to the human being and with the popularization of computers, search systems have become the main source of obtaining knowledge. However, they do not support greater user interaction when they want to do a complex search. It is important to evaluate the user and infer information about him, to know his intention to search to increase the performance of these systems under the principles of SaL. In this paper, we propose a model for classifying the search intention using Transformers. It was used an available dataset and we obtained 97% accuracy in the validation set and 99% f-measure in the test set, achieving better results when comparing to traditional methods.
News Recommendations by Combining Intra-session with Inter-session and Content-Based Probabilistic Modelling

Recommender systems in news industry use the time dimension to reveal users’ preferences over time, but they miss to exploit adequately the information encapsulated inside user sessions. Here, we combine intra- with inter-session item transition probabilities to reveal the short- and long-term intentions of individuals along with the public preference over news topic categories. Thus, we are able to better capture the similarities among items that are co-selected inside a session but also within any two consecutive sessions. We have evaluated experimentally our method and compare it against state-of-the-art algorithms on two real-life datasets. We show the superiority of our method over its competitors.