Reviewerly/exHarmony huggingface dataset:
Graph pattern mining on top of a domain ontology - preliminary results from a dairy production application

A domain ontology (DO) is a machine-readable knowledge repository which, whenever properly exploited, can help to discover meaningful and intelligible patterns from compatible datasets. Yet since such data is naturally graph-shaped, the corresponding task amounts to mining what we call ontologically-generalized graph patterns. We study the underlying problem within a dairy production context where a dedicated DO has been designed beforehand. Two alternative mining approaches have been designed, both representing adaptations of methods from the literature. We evaluated them on an excerpt from our dairy production dataset and report here their respective limitations. We also sketch a way to approach the design of ontology-powered graph miner.
Making Hate Visible: Online Hate Incident Reporting Tools

Given the recent number of hate-related incidents that have come to public attention, there is a significant need to collect and track these incidents in order to capture and share trends with the wider public. Outside of official hate crime data (such as annual government reports), incidents fueled by hate (but that are not crimes) often go undocumented. To address this gap, the Alberta Hate Crimes Committee – a Canadian coalition of law enforcement, government, and non-governmental organizations – developed the StopHateAB.ca website. The purpose of the StopHateAb.ca website is to fill this gap by creating a space to capture hate incidents to document and make accessible information related to hate incidents. This article will describe the development of the online hate incident reporting tool StopHateAB.ca. Through a discussion of the strengths and challenges of creating an online hate incident reporting platform, this paper will highlight the importance of innovative responses to counter hate and bias by making hate visible. As this article highlights, making hate visible forces communities to engage in joint conversations about hate and bias to support strategies that foster a public social environment of justice, equity, and human rights.
PEDAGOGICAL CONDITIONS FOR THE FORMATION OF SCHOOLCHILDREN'S READINESS FOR PROJECT ACTIVITIES THROUGH THEMATIC EDUCATIONAL WEB-QUESTS

The article describes the pedagogical conditions for the formation of schoolchildren's readiness for project activities, the expansion of this problem through thematic educational Web-quests. The definition of the process of formation of schoolchildren's readiness for project activities is given. The possibilities of the transition of schoolchildren to a higher level of readiness for project activity by means of building individual trajectories for the development of cognitive activity are described.
Innovating the Management Method of Students’ Growth Files by Using Computer Devices

With the development of information technology, computer networks have gradually penetrated into information management. However, the current college education in our country, whether it is the evaluation of talent training, the overall quality development of students, or the evaluation of school education and teaching, cannot meet the increasing needs of students. Introducing computer devices into file management can effectively solve file storage, sharing, analysis and other issues. The purpose of this article is to use computer devices to innovate the management methods of student growth files. This article first summarizes the basic theory of computing network, and then extends its core technology, and combines the management status of modern student growth files in our country, analyzes the problems and shortcomings, and combines computer devices to analyze the growth files of college students. This paper systematically elaborates the functional requirements, module design and system function design of the use of computer to solemnly design student growth archives management, and uses questionnaire surveys, field surveys and other research methods to carry out research. Experimental research shows that it is relative to traditional student growth archives management methods. The use of computer devices to innovate student growth file management system has a higher feasibility.
The riddle of time and space

Time is the most important feature or fundamental describer of normal human experience and is also the most eminent forgotten element of psychoanalysis. Freud believes that the unconscious is timeless and believes that it perceives any given moment as new and immaculate. He makes very little effort to illustrate the origin of time; however, he points to the function of the Pcpt-Cs as the key to this riddle. After him, fewer psychoanalysts can be found who have systemically researched this subject, except for André Green, Hartocolles, Arlow, and Sabbadini. In this paper, I have tried to illustrate how the sense of time is related to libidinal cathexis. I have displayed, through some pathological situations such as melancholy, mania, and schizophrenia, and a clinical vignette, that the more ego invests libido in the objects, the faster we sense the passage of time and the more it withdraws libido from the objects into itself, the slower time passes. Finally, using my previous viewpoint about transitional time objects and phenomena, I have presented some ideas about an object relation account of the origin of the sense of time.
A New Method of the Convolutional Neural Network Structure Improvement to Protect Data Based on the Obfuscation

This paper proposes a new method to improve the structure of convolutional neural networks to preserve training data based on obfuscation technique. The purpose of this approach is to protect data in a distributed environment when parties want to share datasets for training while ensuring data privacy. To solve this problem, we use a technique that obfuscates the input feature matrix in each region with the size of a pooling window randomly. Based on the properties of the max pooling function and the permutation of positions in each filter window, the resulting matrix has a constant result. The proposed method could be applied to all convolutional neural network models by editing the first convolutional layer. The input matrix shuffling is done randomly so it completely protects your privacy without changing the accuracy. The proposed method has been tested according to K-fold cross validation, achieving an average accuracy of 99.11% with an average error of deletion of 0.0882%.
Recovery of Graph Signals From Sign Measurements

Sampling and interpolation of continuous graph signals have been extensively studied, in order to reconstruct or estimate the entire graph signal from the signal values on a subset of vertices. Whereas in a lot of real-world scenarios, only the signs of signals are available. For example, a rating system may only provide simple options such as "like" or "dislike". We are interested in whether it is possible to recover the original signal from such coarse information. In this paper, the reconstruction of bandlimited graph signals based on sign measurements is discussed and a greedy sampling strategy is proposed. The simulation experiments are presented, and the greedy sampling algorithm is compared with the random sampling algorithm, which verifies the feasibility of the proposed approach.
Dynamics in Coded Edge Computing for IoT: A Fractional Evolutionary Game Approach

Recently, coded distributed computing (CDC), with advantages in intensive computation and reduced latency, has attracted a lot of research interest for edge computing, in particular, IoT applications, including IoT data preprocessing and data analytics. Nevertheless, it can be challenging for edge infrastructure providers (EIPs) with limited edge resources to support IoT applications performed in a CDC approach in edge networks, given the additional computational resources required by CDC. In this article, we propose “coded edge federation” (CEF), in which different EIPs collaboratively provide edge resources for CDC tasks. To study the Nash equilibrium, when no EIP has an incentive to unilaterally alter its decision on edge resource allocation, we model the CEF based on the evolutionary game theory. Since the replicator dynamics of the classical evolutionary game are unable to model economic-aware EIPs, which memorize past decisions and utilities, we propose “fractional replicator dynamics” with a power-law fading memory via Caputo fractional derivatives. The proposed dynamics allow us to study a broad spectrum of EIP dynamic behaviors, such as EIP sensitivity and aggressiveness in strategy adaptation, which classical replicator dynamics cannot capture. Theoretical analysis and extensive numerical results justify the existence, uniqueness, and stability of the equilibrium in the fractional evolutionary game. The influence of the content and the length of the memory on the rate of convergence are also investigated.
Research On the Application of Deep Learning-Based License Plate Computer Recognition System in Traffic Scenarios

As the development of machine vision technology matures, this paper builds a set of license plate recognition algorithms based on deep learning algorithms and applies them in traffic monitoring scenarios, which can accurately identify the license plate information of passing vehicles at traffic intersections, and the detection accuracy mAP can reach more than 99%, The recognition accuracy can reach more than 94%, the detection algorithm uses YOLOV4, which can accurately detect the position information in the image where the license plate is located, the recognition algorithm uses CRNN, the detected ROI area is cut out for text recognition, and the algorithm is deployed and used Deployed with the tensorrt acceleration library and deployed end-to-end on the NVIDIA Jetson AGX Xavier embedded edge computing device. It only takes 17ms to identify all the license plate number information in an image.
An evolutionary adaptive clustering algorithm

In order to solve the problem that the number of clusters needs to be specified artificially in the partition clustering and hierarchical clustering methods, this paper proposes an adaptive clustering algorithm which can search the number of clusters independently through the self evolution pattern of samples without any prior knowledge (AdaCluster). Adacluster algorithm includes two stages: cluster number self evolution stage and label propagation stage. In the evolution stage, a small number of samples are randomly sampled from the sample space, and then the number of clusters is independently evolved according to the designed control parameters in an incremental form to obtain the initial clustering results; In the label propagation stage, the cluster labels obtained in the evolution stage are propagated to the unlabeled samples. Adacluster algorithm is completely based on self evolution pattern, and can determine the number of clusters without any prior parameters. The Adacluster algorithm is compared with other typical algorithms on a large number of datasets, and applied in image segmentation. The results show that Adacluster algorithm has excellent clustering performance and high robustness.
Small Object Detection in Complex Large Scale Spatial Image by Concatenating SRGAN and Multi-Task WGAN

With rapid development of IOT, especially in the field of geological exploration, areal images obtained through optical sensors with large spatial coverage are becoming an effective material for earth understanding. As such that research on object detection among large scale spatial image is of great significance for resource exploration, natural disaster assessment, and military target detection and recognition. Compared with common images, object detection in spatial image is still a big challenge because of the spatial heterogeneity, scale dependency and with complex context. In order to solve this problem, we propose a novel framework by concatenating a super resolution GAN (SRGAN) and a multitask Wasserstein GAN (WGAN) to increase the performance of a backbone detection model, like RetinaNet. Here, SRGAN and WGAN act as image restoration module attempt to increase global and local resolution respectively. To be exactly, the SRGAN is applied to the global image before detection, while the WGAN is applied to the anchor regions after detection. Unlike previous method with unique generative task, our WGAN is designed to complete generation, classification and regression tasks, with which can increase the detection performance comprehensively by back propagating classification loss and regression loss to the generator to remove false positive anchors and regulate the region box. In order to verify the effectiveness of the proposed model, we conducted a large number of ablation and comparative experiments on the public areal image dataset DOTA, and obtained superior average precision for small object detection.
Robust Face Recognition Using Multi-scale Feature Pattern Sparse Representation with Extreme Learning Machine

Robust vision-assisted facial recognition is the top stimulating job in automation machines. Most of the face recognition techniques introduced till now considered sparse coding as an effective method, whereas the sparse coding prototypes are being hindered through the tremendously expensive evaluation cost in terms of execution. However, these sparse coding models could tolerate robust face recognition having high dimensionality. Therefore, it is essential to develop new face recognition approaches with reduced complexity and less computational time. Hence, novel robust face recognition approaches are presented in this paper. The recognition of robust facial images is performed through ELM classifier which is priory preprocessed by extracting the multi-scale sparse coding feature patterns from the sample images. Then finally the feature patterns dictionaries are obtained for every sample image that is employed with the primary function to work with the SLFN neural network. The experimental outcomes revealed that the suggested methodology has higher recognition rates under all three circumstances compared to other algorithms such as SRC, RSC, and MPRSC-ELM classification algorithms and has less recognition time in seconds.
spcl++: overall improvement of spcl algorithm

Spcl algorithm introduces a unified contrast loss function with the help of mixed memory model, combined with adaptive clustering screening criteria, and achieves the best results of cross-domain pedestrian re-identification task. However, the algorithm ignores many basic problems in pedestrian re-identification task, which limits the performance of the algorithm. In this work, the algorithm is improved in all aspects, and we call the improved algorithm spcl++. Experiments show that spcl++ performs much better than spcl in pedestrian re-identification task, and achieves the best results in current cross-domain pedestrian re-identification task.
An Application of Transfer Learning: Fine-Tuning BERT for Spam Email Classification

The increased use of cyberspace and social media has resulted in a rise in the number of unsolicited bulk e-mails, necessitating the implementation of a reliable system for filtering out such anomalies. In recent years several deep learning based word representation techniques are devised. These advances in the field of word representation can provide a robust solution to such problems. In this paper, we applied a transfer learning technique, i.e., a pre-trained Bidirectional Encoder Representations from Transformer (BERT) model is fine-tuned on the required datasets for spam email classification. The classification results are compared with other state-of-the-art classification techniques such as logistic regression, SVM, Naïve Bayes, Random Forest, and LSTM. To evaluate the performance of a proposed technique, experiments are carried out on two well-known datasets viz. Enron spam dataset with 33,716 email messages and Kaggle’s SMSSpamcollection dataset containing 5574 messages. Significant improvements are observed in results generated by the proposed model over other models.
Semi-supervised Domain Adaptation via adversarial training

Whilst convolutional neural networks (CNN) offer state-of-the-art performance for classification and detection tasks in computer vision, their successful adoption in defence applications is limited by the cost of labelled data and the inability to use crowd sourcing due to classification issues. Popular approaches to solve this problem use the expansive labelled data for training. It would be more cost-efficient to learn representations from the unlabelled data whilst leveraging labelled data from existing datasets, as empirically the performance of supervised learning is far greater than unsupervised-learning. In this paper we investigate the benefits of mixing Domain Adaptation and semi-supervised learning to train CNNs and showcase using adversarial training to tackle this issue.
Taylor mapping method for solving and learning of dynamic processes

In this paper, we discuss the so-called Taylor map approach to solve systems of ordinary differential equations. We demonstrate its capabilities of solving the corresponding inverse problems including parameter identification. The method applies even if the underlying ordinary differential equation is not explicitly known. This procedure is interpreted in terms of Polynomial Neural Networks. Physical knowledge is incorporated into the neural network since its architecture is designed directly on top of the Taylor map approach. This does not only improve the data efficiency but also reduce the effort of the included training procedure. We prove an asymptotic convergence result of polynomial neural networks. On this basis, we demonstrate validation examples to highlight the capabilities of this method in practice.
Secure Image Authentication Scheme Using Double Random-Phase Encoding and Compressive Sensing

Double random-phase encoding- (DRPE-) based compressive sensing (CS) systems support image authentication for noisy images. When extending such systems to resource-constrained applications, how to ensure the authentication strength for noisy images becomes challenging. To tackle the issue, an efficient and secure image authentication scheme is presented. The phase information of the plain image is generated using DRPE and quantized into a binary image as the authentication information. Meanwhile, a sparser error matrix generated by the same plain image and vector quantization (VQ) image works as the input of CS. The authentication information and VQ indexes are self-hidden into the quantized measurements to construct the combined image. Then, it is permutated and diffused with the chaotic sequences generated from a modified Henon map. After decryption at the receiver side, the verifier can implement the blind authentication between the noisy decoded image and the reconstructed image. Supported by the detailed numerical simulations and theoretical analyses, the DRPE-CSVQ exhibits more powerful compression and authentication capability than its counterpart.
Humanistic foundations of a teacher's work as a condition for the effectiveness of a person's spiritual and moral education

The problem of spiritual and moral education has recently become urgent and requires its solution with negative processes occurring in public consciousness and behavior. Therefore, school needs thoughtful and focused work on the development and formation of the spiritual and moral qualities of schoolchildren.
Enhanced image encryption algorithm by quantum key distribution

Abstract Quantum Key Distribution (QKD) is one type of Quantum Cryptography (QC) which is based on quantum mechanics fundamentals such as Heisenberg’s uncertainty principle and No-cloning theory. The usage of QKD warns the legitimate communicated parties to any attack attempt and this is the most interesting security parameter. Therefore, QKD provides unconditional secure communication method and supports a powerful encryption scheme. The combination between classical communication and QKD creates a new technique called semi quantum key distribution SQKD. Unfortunately, SQKD increases the schemes complexity and requires two steps for ciphering, scramble and encryption. In this paper an enhance image encryption algorithm is proposed based on QKD that eliminates most of the drawbacks of SQKD. The proposed algorithm is simpler than other encryption schemes as it exploits only one encryption step based on the power and the randomness of the generated secret key, which decreases the chance to be cracked. The correctness and efficiency of the proposed algorithm are validated by numerical simulations.
Recommending the Best Product Based on User Requirements Using Opinion Mining

Today's modern technologies have paved the way for multi-functional devices like smart refrigerators, washing machines, watches, mobiles, and computers, etc., which are available at a wide range of cost leads to the development of faster, portable, and larger capacity products than ever before. Selecting the best performance and cost-effectiveness in the given budget is a time-consuming task for the client. Today, customers do not rely solely on print and communication media, they also surf the Internet, and they may be misled by the rating and end up with dissatisfaction. Through our application, the customer requirements are collected and then the best electronic product among these multiple brands will be recommended by applying the analysis on the collected feedback data. The proposed model is derived by using the logistic regression algorithm for sentiment analysis that is classifying the possibility of number of positive and negative reviews in a given dataset.
Comparative Study of Various Dehazing Algorithms

Haze in images acts as a hindrance in various research activities in the field of image processing, mainly in object detection, object recognition, and computer vision applications. In this paper, we aim to compare various ‘single image dehazing algorithms’ based on mean square error (MSE) [1], peak signal to noise ratio (PSNR) [1], and structural similarity index measure (SSIM) [1]. Single image dehazing algorithms see more applications as no reference images are required for the processing. The algorithms, which will be discussed and covered in this paper, are ‘dark channel prior dehazing algorithm (DCP)’ [2–4], ‘multi-scale fusion dehazing algorithm’ [5], ‘color attenuation prior dehazing algorithm (CAP)’ [6–8].
A Segmentation Network for CT Image of Hepatocellular Carcinoma Based on Attention Block

Hepatocellular carcinoma is one of the most diagnosed cancers in the world today. Accurate image segmentation based on CT image of liver and tumors plays an essential role in the treatment of hepatocellular carcinoma. This paper proposed an improved U-Net segmentation network based on attention mechanism for automatic segmentation of liver and tumor in CT images of the abdomen. The channel attention mechanism used in this paper not only considers the different importance of different channels for segmentation results but also considers the interdependence between various channels. At the same time, in order to prevent feature loss, we add a deep supervision module in the network. The proposed method in this paper is tested on the public dataset LiTS, and the results of the segmentation show that our method has better segmentation performance compared with the existing main methods.