Reviewerly/exHarmony huggingface dataset:
The Experience of Using Augmented Reality in the Reconstruction of the Crime Scene Committed in Transport

This work examines the forensic aspects of using augmented reality (AR) technology. It is proposed to use reality technology to reconstruct the picture of crimes committed in transport. A feature of the reconstruction of the trace picture of crimes of this type is visualizing the connections between objects that are not comparable in size to trace-perceiving objects. It is concluded that augmented reality technology can become the basis for a new stage in the development of forensic photography. AR technology provides simultaneous image of traces of crime on objects, then it becomes possible to combine in one computer application the advantages of orientation, survey, nodal and detailed photography. The technology based on markers (AR-tags) requires a minimum of equipment. For a smartphone, the article's authors have developed an application that allows visualizing a forensic scene using augmented reality. The experience of using a computer AR-application in forensic practice and training in reconstruction of the track picture of crimes in transport is analyzed. With the help of virtual objects, the trail picture of the murder in the ship's cabin was reconstructed. During reconstruction, augmented reality marks are digital pointers, which, during real investigative examinations of the scene of the incident, indicate the traces of the crime. The advantages of using an application for a smartphone in the educational process carried out in the conditions of an educational forensic training ground (AR-training ground) are considered.
Fast Graph Neural Tangent Kernel via Kronecker Sketching

Many deep learning tasks need to deal with graph data (e.g., social networks, protein structures, code ASTs). Due to the importance of these tasks, people turned to Graph Neural Networks (GNNs) as the de facto method for machine learning on graph data. GNNs have become widely applied due to their convincing performance. Unfortunately, one major barrier to using GNNs is that GNNs require substantial time and resources to train. Recently, a new method for learning on graph data is Graph Neural Tangent Kernel (GNTK). GNTK is an application of Neural Tangent Kernel (NTK) (a kernel method) on graph data, and solving NTK regression is equivalent to using gradient descent to train an infinite-wide neural network. The key benefit of using GNTK is that, similar to any kernel method, GNTK's parameters can be solved directly in a single step, avoiding time-consuming gradient descent. Meanwhile, sketching has become increasingly used in speeding up various optimization problems, including solving kernel regression. Given a kernel matrix of n graphs, using sketching in solving kernel regression can reduce the running time to o(n^3). But unfortunately such methods usually require extensive knowledge about the kernel matrix beforehand, while in the case of GNTK we find that the construction of the kernel matrix is already O(n^2N^4), assuming each graph has N nodes. The kernel matrix construction time can be a major performance bottleneck when the size of graphs N increases. A natural question to ask is thus whether we can speed up the kernel matrix construction to improve GNTK regression's end-to-end running time. This paper provides the first algorithm to construct the kernel matrix in o(n^2N^3) running time.
Pattern Recognition of Modulation Signal Classification Using Deep Neural Networks

In recent times, pattern recognition of communication modulation signals has gained significant attention in several application areas such as military, civilian field, etc.It becomes essential to design a safe and robust feature extraction (FE) approach to efficiently identify the various signal modulation types in a complex platform.Several works have derived new techniques to extract the feature parameters namely instant features, fractal features, and so on.In addition, machine learning (ML) and deep learning (DL) approaches can be commonly employed for modulation signal classification.In this view, this paper designs pattern recognition of communication signal modulation using fractal features with deep neural networks (CSM-FFDNN).The goal of the CSM-FFDNN model is to classify the different types of digitally modulated signals.The proposed CSM-FFDNN model involves two major processes namely FE and classification.The proposed model uses Sevcik Fractal Dimension (SFD) technique to extract the fractal features from the digital modulated signals.Besides, the extracted features are fed into the DNN model for modulation signal classification.To improve the classification performance of the DNN model, a barnacles mating optimizer (BMO) is used for the hyperparameter tuning of the DNN model in such a way that the DNN performance can be raised.A wide range of simulations takes place to highlight the enhanced performance of the CSM-FFDNN model.The experimental outcomes pointed out the superior recognition rate of the CSM-FFDNN model over the recent state of art methods interms of different evaluation parameters.
Hunting and Gathering in Cyberspace: Finding and Selecting Web Resources for the Library's Virtual Collection

The world of information is a world of chaos. It always has been. And the function of librarians from Alexandria and before until now and forever is to create and maintain islands of order (libraries) in that sea of chaos. Over the centuries, librarians have pretty much gotten a handle on building collections of resources in the physical media like print and film. There is one more issue which the people tend to take for granted with more traditional media, but which must be explicitly considered in the collection of Web and Internet resources: that of provenance and integrity. In cyberspace, and particularly on the World Wide Web, the world has come full circle. The distinction among public relations, advertising and vanity publishing has become blurred, making it difficult to evaluate Web sites for credibility and integrity. There are many considerations to be taken into account in making decisions about an electronic collection.
WebGE: An Open-Source Tool for Symbolic Regression Using Grammatical Evolution

Many frameworks and libraries are available for researchers working on optimization. However, the majority of them require programming knowledge, lack of a friendly user interface and cannot be run on different operating systems. WebGE is a new optimization tool which provides a web-based graphical user interface allowing any researcher to use Grammatical Evolution and Differential Evolution on symbolic regression problems. In addition, the fact that it can be deployed on any server as a web service also incorporating user authentication, makes it a versatile and portable tool that can be shared by multiple researchers. Finally, the modular software architecture allows to easily extend WebGE to other algorithms and types of problems.
The role of modern educational technologies in the teaching of legal sciences

The article covers the importance of using modern pedagogical technologies in the teaching of Legal Sciences, the role and ways of using innovative techniques in the formation of legal consciousness, thinking and legal culture of students.
Dense multi-object 3D glomerular reconstruction and quantification on 2D serial section whole slide images

There has been a long pursuit for precise and reproducible glomerular quantification in the field of renal pathology in both research and clinical practice. Currently, 3D glomerular identification and reconstruction of large-scale glomeruli are labor-intensive tasks, and time-consuming by manual analysis on whole slide imaging (WSI) in 2D serial sectioning representation. The accuracy of serial section analysis is also limited in the 2D serial context. Moreover, there are no approaches to present 3D glomerular visualization for human examination (volume calculation, 3D phenotype analysis, etc.). In this paper, we introduce an end-to-end holistic deep-learning-based method that achieves automatic detection, segmentation and multi-object tracking (MOT) of individual glomeruli with large-scale glomerular-registered assessment in a 3D context on WSIs. The high-resolution WSIs are the inputs, while the outputs are the 3D glomerular reconstruction and volume estimation. This pipeline achieves 81.8 in IDF1 and 69.1 in MOTA as MOT performance, while the proposed volume estimation achieves 0.84 Spearman correlation coefficient with manual annotation. The end-to-end MAP3D+ pipeline provides an approach for extensive 3D glomerular reconstruction and volume quantification from 2D serial section WSIs.
Research on Graph Structure Data Adversarial Examples Based on Graph Theory Metrics

Graph neural networks can learn graph structure data directly and mine its information, which can be used in drug research and development, financial fraud prevention, and other fields. The existing research shows that the graph neural network is lacking robustness and is vulnerable to attack by adversarial examples. At present, there are two problems in the generation of confrontation examples for graph neural networks. One is that the properties of graph structure are not fully used to describe the antagonistic examples, the other is that the gradient calculation is linked with the loss function and not directly linked with the properties of graph structure, which leads to excessive search space. To solve these two problems, this paper proposes a graph structure data confrontation example generation scheme based on graph theory measurement. In this paper, the average distance and clustering coefficient is used as the basis for each step of disturbance, and the counterexamples are generated under the premise of keeping the data characteristics. Experimental results on small-world networks and random graphs show that, compared with the previous methods, the proposed method makes full use of the nature of graph structure, does not need complex derivation, and takes less time to generate confrontation examples, which can meet the needs of iterative development.
Face recognition based attendance management system by using machine learning

Attendance is an important need of every organization. Maintaining daily attendance register is a tough and time consuming task. There are many alternative methods for marking the attendence like Biometric, RFID, eye detection, voice recognition, and many more. But Facial Recognition is an efficient and smart method for marking attendance. As it is known that primary identification for any human is its face, face recognition provides an accurate system which deals with the ambiguities like proxy in attendance, high cost, and time consumption. This system uses face recognizer library for facial recognition and storing attendance. It has a camera that takes an input image, an algorithm to detect a face from the input image, encode it and recognize the face and mark the attendance in a spreadsheet. The system camera of an android phone/laptop clicks the image and sends it to the server where faces are recognized from the database and attendance is calculated on basis of it. The aim of reducing the errors that occur in the older attendance marking system has been achieved by implementing the automated attendance system using deep learning. Face recognition system has been presented using deep learning which shows robustness towards recognition of the users with accuracy of 98.3% and result is converted into a PDF File. Index Terms- Android application, Biometric, Recognition system, Face Recognition, Deep Learning, PDF.
Channel Attention Module for Efficient Action Recognition

Abstract For action recognition, 3D CNNs can achieve satisfactory performance, but they are parameter-rich and computationally intensive. Although conventional 2D CNNs have a small computational burden, they do not capture the channel relationships well. To mitigate this issue, we propose a plug-and-play channel attention module that is simple yet effective. Our CA module can perform adaptive refinement of the feature for the given input feature map by capturing channel-wise dependencies. Since the CA module is generic and lightweight, it can be easily integrated into regular convolutional neural networks. We embedded the CA module into TSM and evaluated it by conducting extensive experiments on the commonly used action recognition dataset: Kinetics-400 and gesture recognition dataset: Jester. For Kinetics-400, our CA module was able to improve the top-1 accuracy by 1.24%, 1.46%, 1.43% and 1.53% when using one crop, three crops, five crops and ten crops, respectively. Our experimental results showed that the CA module brings consistent performance improvements, proving the effectiveness of the CA module.
Application of the Iot Network and the Deep Learning Concept in Music Teaching

In order to realize students’ in-depth understanding of music teaching content, it is necessary to reasonably allocate teaching materials according to the teaching content. Therefore, this paper puts forward the application of the concept of the Iot network and deep learning in music teaching. After the teaching resources are vectorized, the distinguishing local registration method of the DLA model is used to extract their features. Based on the dimension of the teaching content, the features of the teaching content are output in the DLA model, the music teaching resources are allocated according to the minimum matching error criterion, the hyperbolic tangent function is taken as the activation function, and the feature error is filtered by maximum aggregation. The experimental results show that the design method can, according to the music teaching content, have more than 80% accuracy in the shallow distribution and deep distribution of teaching materials, and play a positive role in promoting students’ in-depth learning.
Using local activity encoding for dynamic graph pooling in stuctural-dynamic graphs

Graphs have recently become more popular for modeling real-world as well as theoretical problems, and several Machine Learning algorithms for learning on them have developed. For graphs that change over time, however, research is still at an early stage. This article proposes a framework DynHeatmaP that generates a constant-size graph sequence representation of graphs whose structure changes over time (structural-dynamic, SDG) in linear time. The framework uses a representation of additions and deletions of nodes and edges (events) to perform dynamic graph pooling. Our algorithm translates the dynamic changes of the nodes and edges to a heatmap representation of the current node activity, regarding additions and deletions in the neighborhood, respectively. The created activity heatmap is utilized as probabilities of the nodes to be sampled in the subsequent pooling step. In addition, the neighborhood of selected nodes has been sampled such that the ratio of additions and deletions is represented accordingly. The obtained downsampled graph sequence can then be used representatively as input to a GNN for learning on the original stream. Focussing on the active regions in a graph, the fast pooling process can reduce the training time significantly and addresses the problem of unbalanced datasets in real-world applications modeled as sparse graphs.
JueWu-MC: Playing Minecraft with Sample-efficient Hierarchical Reinforcement Learning

Learning rational behaviors in open-world games like Minecraft remains to be challenging for Reinforcement Learning (RL) research due to the compound challenge of partial observability, high-dimensional visual perception and delayed reward. To address this, we propose JueWu-MC, a sample-efficient hierarchical RL approach equipped with representation learning and imitation learning to deal with perception and exploration. Specifically, our approach includes two levels of hierarchy, where the high-level controller learns a policy to control over options and the low-level workers learn to solve each sub-task. To boost the learning of sub-tasks, we propose a combination of techniques including 1) action-aware representation learning which captures underlying relations between action and representation, 2) discriminator-based self-imitation learning for efficient exploration, and 3) ensemble behavior cloning with consistency filtering for policy robustness. Extensive experiments show that JueWu-MC significantly improves sample efficiency and outperforms a set of baselines by a large margin. Notably, we won the championship of the NeurIPS MineRL 2021 research competition and achieved the highest performance score ever.
An Efficient Online Trajectory Generation Method Based on Kinodynamic Path Search and Trajectory Optimization for Human-Robot Interaction Safety

With the rapid development of robot perception and planning technology, robots are gradually getting rid of fixed fences and working closely with humans in shared workspaces. The safety of human-robot coexistence has become critical. Traditional motion planning methods perform poorly in dynamic environments where obstacles motion is highly uncertain. In this paper, we propose an efficient online trajectory generation method to help manipulator autonomous planning in dynamic environments. Our approach starts with an efficient kinodynamic path search algorithm that considers the links constraints and finds a safe and feasible initial trajectory with minimal control effort and time. To increase the clearance between the trajectory and obstacles and improve the smoothness, a trajectory optimization method using the B-spline convex hull property is adopted to minimize the penalty of collision cost, smoothness, and dynamical feasibility. To avoid the collisions between the links and obstacles and the collisions of the links themselves, a constraint-relaxed links collision avoidance method is developed by solving a quadratic programming problem. Compared with the existing state-of-the-art planning method for dynamic environments and advanced trajectory optimization method, our method can generate a smoother, collision-free trajectory in less time with a higher success rate. Detailed simulation comparison experiments, as well as real-world experiments, are reported to verify the effectiveness of our method.
Multifeature Fusion Human Pose Tracking Algorithm Based on Motion Image Analysis

Human pose and motion detection is an important area of computer vision research, covering the interplay of different fields such as image processing, pattern recognition, and artificial intelligence. Due to the complexity of human motion, existing 3D recognition and pose detection methods based on low-quality depth images are not very accurate and reliable. Due to the time-sensitive nature of features, a single feature cannot adapt to the dynamic changes of the scene, so it is difficult for the target tracking algorithm based on a single feature to achieve robust tracking results. If multiple features are fused and applied in the tracking algorithm, the complementarity between different features can be used to better adapt to the scene changes and achieve robust tracking results. In order to solve the problem of human pose and human motion recognition in low-quality depth images, this paper uses the Kinect somatosensory camera to obtain 20 human skeleton joint points through the Kinect skeleton tracking technology. This paper studies the typical human posture and interactive action recognition technology in daily life. On the basis of understanding the characteristics of skeletal data, this paper proposes a distance feature and angle feature model combined with human body structure. Through the experimental results, it is found that the distance feature and the angle feature value are basically not affected by the distance change. When the subjects turned 45° to the left and 45° to the right, the distance characteristics changed, which was different from the characteristic data when the subjects turned around.
Wireless Control Industrial Robot Processing Irradiation System Based on Artificial Intelligence Technology

Radiation processing technology has been more and more widely used in light industry, chemical industry, food preservation, medical care, and other industries. Therefore, this paper designs a kind of wireless control system based on artificial intelligence technology γ radiation method, to strengthen the application of radiation processing equipment, and γ radiation monitoring can strengthen the radiation monitoring of its surrounding environment. The experimental results show that the C/Si atomic ratio of oxidized SiC fiber in air treated by this scheme is about 1.75, while the C/Si atomic ratio of SiC(Be) fiber treated by electron irradiation is close to 1 : 1.
Facilitating English Learners Competence in Listening through Flowerdew and Miller’s Listening Strategies

This study aims to examine the use of listening strategies to facilitate the students’ listening competence and aims to examine the students’ perception about the roles of listening strategies. Using a qualitative research as the design of the research, the researcher collects the data through observation in listening classroom and interview to the university students. The result reveals that the lecturer used Flowerdew and Miller’ listening strategies, such as metacognitive strategies, cognitive strategies, and socioaffective strategies to facilitate the students in improving their listening competence, Moreover, the students perceive that the roles of listening strategies can help them to enhance their competence and help them become more skillful in listening.&#x0D; Keywords: Listening Strategies, Cognitive, Metacognitive, Socioaffective
Multi-AUV collaborative search for multiple targets in a dynamic obstacle environment

Aiming at the problem of dynamic obstacles encountered in the path of multi-AUV collaborative search for multiple targets under the two-dimensional grid map, a multi-AUV search algorithm based Glasius Bio-Inspired Neural Network (GBNN) is proposed. First, a two-dimensional grid map based on the AUV search environment is built, and a corresponding two-dimensional biologically inspired neural network model based on the two-dimensional grid map is realized. Each neuron in the neural network corresponds to each location unit of the grid maps one by one; Then, dynamic obstacles are introduced into the search path, and the AUV path is independently planned according to the activity output value of GBNN. Finally, the experiments show that the target search algorithm proposed in this article can not only avoid static obstacles in the raster map, but also avoid dynamic obstacles to search for multiple targets. It compares with the traditional biologically inspired neural network algorithm (BNN). In contrast, the algorithm has a shorter search path, shorter time-consuming, and stronger real-time performance.
Face Tracking And Recognition Using Matlab And Arduino

In this paper, we propose topologyprotectivediagram coordinative (VIOLA/JONES) strategy for halfway face acknowledgment. In any case, faces in true free is also stopped up by objects or absolutely fully completely different faces, that cannot provide the whole face footage to depiction, Key purpose primarily based fragmentary face acknowledgment ways in which to handle this, our VIOLA/JONES technique evaluates a non inflexible amendment secret writing the second organize geometric structure of the diagram, with the goal that more precise put together, powerful correspondence are visiting be dotted with the topological information so camera capture the face whether or nor matched or unmatched the image, if the matched the mortal you get the buzzer otherwise you can get semiconductor contraption light-weight watch the Arduino uno. We tend to propose a topology safe guarding helper coming up with (viola jones) strategy near develop the subsequent request structure for every face.