Reviewerly/exHarmony huggingface dataset:
Separability Criteria Combined with Local Filtering Transformation Based on the Bloch Representation

Quantum properties, such as entanglement and coherence, are indispensable resources in various quantum information processing tasks. However, given a quantum state, it is not a simple question to identify whether it is entangled or not. We study the entanglement of quantum systems in arbitrary dimensions using the Bloch representation of density matrix. This approach enables us to consider quantum entanglement geometrically. We first analyze the effect of local Bloch vector on determining whether a quantum state is entangled or separable, and obtain an entanglement criterion related to local Bloch vector, which is stronger than the criterion that only considers the correlation matrix. This criterion is equivalent to the optimized computable cross norm criterion in [14]. Our proof is considered from the perspective of Bloch representation, and there is no need to know the matrix relignment in advance. This criterion also be extended to the problem of the separability of tripartite quantum states. We then combine the local filtering transformation with this entanglement criterion. After applying a suitable local filtering transformation, the quantum state has at most one nonzero Bloch vector, and examples are given to illustrate that the local filtering transformation can help detect some bound states where the original entanglement criterion fails. Moreover, for a finite-dimensional quantum system in which one party is a qubit, we give an analytical form of the combined entanglement criterion.
Keypoint Context Aggregation for Human Pose Estimation

Human pose estimation has drawn much attention recently, but it remains challenging due to the deformation of human joints, the occlusion between limbs, etc. And more discriminative feature representations will bring more accurate prediction results. In this paper, we explore the importance of aggregating keypoint contextual information to strengthen the feature map representations in human pose estimation. Motivated by the fact that each keypoint is characterized by its relative contextual keypoints, we devise a simple yet effective approach, namely Keypoint Context Aggregation Module, that aggregates informative keypoint contexts for better keypoint localization. Specifically, first we obtain a rough localization result, which can be considered as soft keypoint areas. Based on these soft areas, keypoint contexts are purposefully aggregated for feature representation strengthening. Experiments show that the proposed Keypoint Context Aggregation Module can be used in various backbones to boost the performance and our best model achieves a state-of-the-art of 75.8% AP on MSCOCO test-dev split.
DEVELOPMENT OF EMOTIONAL INTELLIGENCE OF CHILDREN OF JUNIOR SCHOOL AGE BY MEANS OF MUSICAL ART

The development of psychological literacy in younger classes is an urgent task of modern school, as the development of the necessary set of psychological competencies is a guarantee of their success in various spheres of life, the ability to effectively adapt to variable living conditions of society, to assimilate the social skills of behavior, to occupy an active life position, events in their surroundings. Development of emotional sector of children is a guarantee of the formation of an active, initiative, independent, pitiful personality. The disadvantages of the development of the emotional sphere in children of junior school age are the inability to establish effective communication with both adults and peers, social adaptation problems, dissemination in training. The younger school age is a synthetic period for the development of the world of emotions. Musical art has an inexhaustible potential of emotional influences, intellectual development of children, activates emotional and sensory experiences, promotes the enrichment of emotional experience, forms aesthetic feelings, artistic culture, stimulates thinking activity, level of self-consciousness, creativity, creative activity. The need for a modern teacher of musical art to carry out not only aesthetic and cultural development of the personality of the student, but also the development of its emotional sphere, requires the formation of a number of psychological and pedagogical competencies necessary for effective management of the process of musical and aesthetic education of children. The lesson of musical art has the greatest potential in expanding emotional self-awareness of schoolchildren, the development of empathic attitude towards others. The author of the article established that the development of emotional intelligence in younger classes contributes to the growth of their educational success, the ability to concentrate attention, improves the flow of memory processes. The lesson of musical art has a great potential in the formation of psychological literacy of children. The formation of the complex of psychological and pedagogical competencies in the teacher of musical art, will allow him to use music as an important tool for developing emotional self-awareness of children, ability to identify their own emotions, to give them the verbal characteristics, to understand what they encourage them, to detect empathy to others, be able to distinguish between emotional status of the others by external features.
Reducing Label Fragmentation During Time-series Data Annotation to Reduce Annotation Costs

Labeling training data for human activity recognition systems is a time consuming task that is prone to errors. In this paper, we build on prior works that have proposed semi-automatic labeling techniques to improve the labeling process by proposing a novel technique for reducing label fragmentation in time-series data that can reduce annotation costs by improving how well the automatically generated labels model the time intervals of the underlying activities. We perform temporal clustering of the classifier output using an image segmentation algorithm, with the time-series output of the classifier fed to the algorithm as a 1D image with the class probabilities in place of color channels. During an evaluation of the proposed method on the task of labeling 233 minutes of time-series data, we achieved a 56% average reduction in annotation time when compared to the use of raw classifier output for semi-automatic labeling.
A Fast Implementation of Image Rotation with Bresenham’s Line-Scanning Algorithm

Image rotation is a fundamental task in image processing. It has two primary stages: rasterization of the rotated domain and evaluation of these pixel values. This paper mainly proposes a novel strategy for the first stage with Bresenham’s line description, and further a natural way for the second stage. Two neighbouring sides of the boundary of the rotated image are expressed by a variation of Bresenham scanning lines, one taken as the supporting side and the other as the moving side. Then the rotated region can be expressed as stacking the copies of the moving side along the supporting side. We re-implement Bresenham’s line algorithm in such a scheme that a line is expressed as successive runs of line segments, so as to rapidly realize the rasterization of the defined domain of the rotated image. At the same time, an incremental method is used to assign values to the pixels. Our method avoids lots of computation that must be conducted in previous works for determining whether a point is in the rotated domain and therefore, according to experiments, significantly decreases running time of image rotation.
Analyzing Masked Ciphers Against Transition and Coupling Effects

This paper discusses how to analyze the probing security of masked symmetric primitives against the leakage effects from Faust et al. in CHES 2018; glitches, transitions, and coupling effects. This is illustrated on several architectures of ciphers like PRESENT, AES, and ASCON where we transform glitch-extended probing secure maskings into transition and/or coupling secure ones. The analysis uses linear cryptanalytic methods and the diffusion layers of the cipher to efficiently protect against the advanced leakage effects.
INDIVIDUAL DIFFERENCES IN STUDENTS IN THE ELEMENTARY SCHOOL ENVIRONMENT

Every human being was created by the Creator with all the uniqueness and characteristics of each. This arises as individual differences (individual differences). Individual differences are differences in abilities and characteristics (cognitive, personality, physical skills, and so on) between students at a certain age level and in each particular group. Through educational practices and activities, we can accommodate the individual differences of students. Likewise, the learning styles of each other are also different. There are visual, auditory, and kinesthetic learning styles. It is important to recognize the learning styles of students so that teachers can vary their teaching styles, learning methods that are suitable to be applied in the learning process. As a technique, we must be able to choose various ways that can accommodate these differences personally and optimize the improvement and interpersonal skills of each student in the long term, especially when viewed from the learning style so that it can be carried out optimally. Individual differences that need to be considered in the implementation of classroom teaching are factors related to the readiness of children to receive teaching because these differences will determine the education system as a whole. Factors that influence individual differences are self-concept (self-concept), locus of control, student anxiety, learning motivation.
Robust Graph Learning for Multi-view Clustering

The multi-view algorithm based on graph learning pays attention to the manifold structure of data and shows the good performance in clustering task. However, multi-view data usually contains noise, which reduces the robustness of multi-view clustering algorithm. In order to solve this problem, we propose a novel multi-view clustering model, namely robust graph learning for multi-view clustering (RGLMC). RGLMC eliminates noise and errors from the original data and employs the adaptive graph, which characterizes the relationship between clusters, as the new input of the algorithm. Our model can be optimized efficiently by utilizing the Augmented Lagrangian Multiplier with Alternating Direction Minimization (ALM-ADM) algorithm. Extensive experimental results on six benchmark datasets verify the superiority of the proposed method in clustering task.
3D Security User Identification in Banking

Biometric systems may be divided into 2 categories betting on the characteristics used. One category uses physical characteristics that area unit associated with the form and presence of the body and body components, like fingerprint, finger knuckles, face (2-D and 3-D), DNA, hand and palm pure mathematics, iris texture, and retinal vasculature. Systems belong to the second category use activity characteristics, like gait, handwriting, keyboard writing, and speech. Analysis in face recognition has endlessly been challenged by alien (head create, lighting conditions) and intrinsic (facial expression, aging) sources of variability. During this system is employed to several organizations and lots of applications for security purpose.Many approaches area unit face recognition exists, during this project, specialize in a comparative study of 3D face recognition beneath expression variations. First 3D face databases with expressions area unit listed, and therefore the most significant ones area unit conferred and their complexness is quantified victimisation principal part analysis, linear discriminate analysis and native binary patterns. The project to be real time enforced datasets to reason the assorted varieties of expressions. Pictures in terms of the popularity performance are evaluated with 3 completely different techniques (principal component analysis, linear discriminant analysis, and native binary patterns) on face recognition grand challenge and strait 3D face databases.
Information Fusion of Topological Structure and Node Features in Graph Neural Network

Graph neural networks(GNNs) have shown great popularity and achieved promising performance on various graph-based tasks in the past years. However, there is little work that explores the information fusion mechanism, which plays an import role in GNNs. Besides, datasets in the real world often have noises, which make the information fusion difficult. In this paper, we give an information-theoretic explanation. Specifically, we focus on how the information from topological structures and node features fuses and how different information contributes to the downstream task. Furthermore, we propose a general framework named M-GCN to express the fusion process in GNNs. Graph embeddings and feature graph are introduced to extract the information from topological structure and node features separately in M-GCN. Extensive experiments are conducted on several benchmark datasets and experimental results show that our proposed models are more robust and outperform state-of-the-art methods.
Pedestrian detection and positioning for component hoisting based on millimeter wave radar and monocular vision sensor

Aiming at the problem that it is difficult to accurately detect and locate pedestrians due to high air dust concentration and low visibility at the construction site lifting equipment, a pedestrian detection and positioning method based on monocular vision combined with millimeter wave radar is proposed. This method inputs the RGB image obtained by the monocular camera into the YOLOv4 algorithm to achieve pedestrian target detection. The distance information of the obstacle obtained by the millimeter wave radar and the pixel coordinate information of the pedestrian obstacle target obtained by the monocular camera are used for information fusion, and then it based on the positioning principle of monocular vision realizes the spatial positioning of pedestrian targets. Experimental results show that the accuracy and recall rates of pedestrian detection reach 94.09% and 93.52%, respectively, which are higher than 90.79% and 89.13% of the YOLOv3 algorithm, and the detection speed reaches 65 FPS; the relative error of pedestrian positioning in the lateral distance is 3.52%. The method is accurate and rapid, and can better realize real-time pedestrian detection and positioning in the hoisting equipment site.
2-D Neural Network Based on M-neurons and its Learning

A matrix neuron and algorithm for its weights tuning are conceived. This neuron processes matrix input directly without its transformation to vector form. Neural networks based on it allow decreasing the number of tunable weights that increases the speed of information processing.
Discrepancy between the Labor market and the system of vocational training in Russia

The article is devoted to the problem of mismatch between the labor market and the education system. There is a serious shortage of skilled workers and a surplus of unclaimed specialists on the Russian labor market. According to the latest Boston Consulting s research, 80% of working-age population in Russia don’t have the skills and competencies to work in the modern markets. Many experts predict that in the near future a lot of people will find themselves unemployed due to automatization of processes. Driven by this fear, people tend to misguide their own children, when it comes to the choice of the future profession. This tendency may lead to a massive threat to the Russian economy, which runs the risk of having a top-heavy state sector on the global market. Thus, it is becoming increasingly difficult to ignore the discrepancy between the labor market and the education system. The study seeks to find out whether the vocational training organized in Russian schools correspond to the real needs for employees in the modern market. The study was conducted by the method of secondary data analysis. We have analyzed 120 different sources ranging from scientific data and studies to analysis of media publications, blog posts and twits, experts opinion, that allows us to get a whole picture of the public sentiment concerning the labor market and the education system in Russia. The main conclusion of the study is, that the Russian labor market and education system don’t cooperate at all. In fact, there is vocational training in schools for unclaimed professions, and the demand of the labor market remains unsatisfied. If the situation doesn’t change in the near future, we will face large-scale unemployment, flourishing ignorance and rising crime in the society.
Cyberbullying Detection by Including Emotion Model using Stacking Ensemble Method

Cyberbullying is a serious problem and caused an immense impact to the victim.To prevent the cyberbullying, the solution is to develop an automatic detection system.In this research, we propose a combined model for cyberbullying detection and emotion detection by using stacking method.The experiment is to create a better model for cyberbullying detection using SVM (support-vector machine), KNN (K-Nearest Neighbors), and Naive Bayes method, then combine the best model with the emotion model.The result conducted that using SVM classifier give the best accuracy for both emotion and cyberbullying detection.Emotion detection yields an accuracy of 96.7%.Cyberbullying detection using SVM classifier yields an accuracy of 72.73%.Then, both model are combined using the stacking ensemble method and yield an average accuracy of 77.8%.It concluded that including the emotion model would improve the accuracy of detection.
Key Technologies of Collaborative Design and Manufacturing of Mechanical and Electrical Products Based on Internet of Things

An electric motor driven by the electromechanical system of the Internet of Things is attractive because of its long life capability of the propulsion system. In this paper, the application of collaborative design and manufacturing in the design automation of IOT electromechanical system is reviewed, and the application of collaborative design and manufacturing in robots, a typical IOT electromechanical system, is described in detail. In this paper, we explain five aspects including the construction of a multiangle unified modeling method for the electromechanical system of the Internet of Things; the constraint processing mechanism for the optimization problem of the electromechanical system of the Internet of Things; the constraint multiobjective optimization methods; design methods that integrate constraint multipurpose evolutionary algorithms and knowledge extraction; and design automation of visual perception systems for electromechanical systems based on the Internet of Things and deep neural networks. The research shows that under the control of a conventional radial basis function neural network controller and the control of a radial basis function neural network controller based on the electromechanical system of the Internet of Things, the system will be affected to a certain extent when there is interference. Under the control of a traditional RBF neural network controller, the system requires 0.18 seconds to restore stability. When using the RBF neural network controller based on the electromechanical system of the Internet of Things, the system returns to a stable state after 0.09 s, and the peak time is reduced by 59% compared with the conventional RBF neural network controller.
Infrared and Low Light Image Registration from Coarse-to-Fine Matching

At present, due to the different imaging characteristics of infrared and low light bands, they are complementary, and are widely used for multi-modal image registration and fusion. Image registration is a precondition for image fusion. For infrared and low light image registration, this paper first performs rough matching of image features based on the grid motion statistics method. Then, precision matching algorithm based on the combination of distance constraint and slope consistency is proposed, and the coarse matching feature points are initially screened for precision matching. Finally, the coarse matching after screening is selected by the random sampling consensus algorithm for the secondary screening of fine matching, and the final feature matching is obtained. The image registration strategy in this paper performs well in the evaluation indexes of accuracy and recall, which improve the accuracy of image registration.
A New Reward System Based on Human Demonstrations for Hard Exploration Games

The main idea of reinforcement learning is evaluating the chosen action depending on the current reward. According to this concept, many algorithms achieved proper performance on classic Atari 2600 games. The main challenge is when the reward is sparse or missing. Such environments are complex exploration environments like Montezuma’s Revenge, Pitfall, and Private Eye games. Approaches built to deal with such challenges were very demanding. This work introduced a different reward system that enables the simple classical algorithm to learn fast and achieve high performance in hard exploration environments. Moreover, we added some simple enhancements to several hyperparameters, such as the number of actions and the sampling ratio that helped improve performance. We include the extra reward within the human demonstrations. After that, we used Prioritized Double Deep Q-Networks (Prioritized DDQN) to learning from these demonstrations. Our approach enabled the Prioritized DDQN with a short learning time to finish the first level of Montezuma’s Revenge game and to perform well in both Pitfall and Private Eye. We used the same games to compare our results with several baselines, such as the Rainbow and Deep Q-learning from demonstrations (DQfD) algorithm. The results showed that the new rewards system enabled Prioritized DDQN to out-perform the baselines in the hard exploration games with short learning time.
Multi-homography Estimation and Inference Driven by Contour Alignment

Recently, multiple homography estimation is preferable for image stitching to handle the parallax problem, by estimating homographies from the feature correspondence in each local region. However, correspondence outliers and insufficient feature coverage will lead to unreliable local homography fitting. In this paper, we propose a novel method of multi-homography estimation and inference, driven by contour alignment. Our method uses explicit structural verification through contour alignment to eliminate incorrectly fitted homographies in some regions, and to select a better homography from other regions if current homography is rejected or with worse accuracy. With the guidance of the contour alignment, dense image alignment result is obtained by further inferring the local homography per superpixel. Quantitative and qualitative comparisons demonstrate the effectiveness of our method, especially for scenes with large parallax and viewpoint changes.