Reviewerly/exHarmony huggingface dataset:
Design of Customized Intelligent Manufacturing Information Interaction System Based on Virtual Technology

Abstract With the continuous development of information technology, system intelligence is leading the next round of “industrial revolution”, especially the intelligent manufacturing industry has become the core of improving industrial productivity. Intelligent manufacturing involves each link in the manufacturing industry, which is the most critical part of intelligent manufacturing is intelligent production, through intelligent manufacturing related technology to optimize the production mode of manufacturing to promote the production state more flexible and integrated. Intelligent manufacturing is based on computer simulation technology and information and communication technology, optimize the production design of the factory and simplify the production process of the factory, the purpose is to reduce the waste of resources and improve the reasonable allocation of production resources.
Remarks on System Identification Using a Quaternion Recurrent Neural Network Trained by Backpropagation through Time

This study investigates the learning capability of a quaternion recurrent neural network that is trained based on a backpropagation through time algorithm extended to quaternion numbers. Computational experiments to identify nonlinear systems, e.g. a three–dimensional chaotic system and discrete–time plant, were performed, and the simulation results confirmed the feasibility of using the quaternion recurrent neural network for a control system application.
Graph transformer-convolution network for graph classification

The Transformer has achieved tremendous success in computer vision, natural language processing, and graph representation learning. However, the transformer cannot effectively encode the topology information of the graph into the model, while it is the advantage of the graph convolution network (GCN). Therefore, we propose a model GTGC combining transformer and GCN for graph classification tasks. To this end, we take the result of graph data passing through multi-head self-attention and feed-forward blocks as the input of the graph convolution module. By increasing the number of neighbors for each node’s feature matrix, the nodes with more neighbors are more important in the attention mechanism. We validate the validity of the model on multiple data sets, including social network datasets and bioinformatics datasets. Experimental results demonstrate that our model achieves advanced accuracy
Vapor: A GPU Sharing Scheduler with Communication and Computation Pipeline for Distributed Deep Learning

Nowadays, distributed deep learning (DDL) workloads are typically trained on GPU clusters. Modern GPUs support the concurrent execution of multiple jobs to achieve efficient parallelization of computation and communication. While sharing GPU improves resource utilization and cuts down job completion time, interference among co-located jobs results in significant performance degradation. Recent studies such as interference-aware job placement carefully co-locate jobs with the least interference in a best-effort manner and are not ideal. Another challenge in GPU sharing is straggler due to unbalanced workloads in packing. When stragglers occur, GPU resources are wasted, so the training processes are delayed.We present Vapor, a GPU sharing scheduler for distributed deep learning on multi-GPUs featured by two novel scheduling policies: preemptive GPU sharing and adaptive batch redistribution to maximize GPU utilization and improve training efficiency. Firstly, Vapor uses preemptive scheduling that parallels computation and communication of co-located jobs in a pipelined manner. Secondly, Vapor uses an adaptive batch redistribution method to deal with stragglers in packing to improve resource utilization further. Vapor provides an AIMD model to predict the relationship between the batch size of the training data and the model calculation time in each iteration. We evaluate Vapor with other representative schedulers. Experiment on a Kubernetes cluster of 16 Tesla V100 GPU handling with popular DDL jobs shows that Vapor reduces job completion time by 21.8% compared with popular SOTA schedulers.
U-Net-based medical image segmentation algorithm

Before the deep learning algorithm, there are many traditional image segmentation methods. However, with the rapid development of artificial intelligence technology, the requirements for the accuracy and efficiency of image segmentation are getting higher and higher, so the image segmentation algorithm based on deep learning arises as the times require. But it is worth noting that these algorithms are natural image processing, and medical image format diversification, the difference of pixel value range, the presence of noise and artifacts, and so on, using the general image segmentation algorithms cannot meet medical demand scenarios medical image segmentation. On the basis of U-Net model, this paper improves its sub-modules, and proposes four sub-module structures: Path direct connection, Dropout direct connection, Conv direct connection, and Constant scale. Dataset uses Skin lesions of melanoma, and accuracy, sensitivity, specificity, precision, F-Measure, IOU, Dice coefficient and comprehensive score were selected as model performance evaluation indexes. After model training, validation and testing, the conclusion was drawn: In the segmentation task of melanoma skin disease image, the performance of Constant scale model was the best among all experimental models.
Experimental Research on the Mechanical Characteristics of Curved Rectangular Pipe Jacking Construction

Owing to the high utilization rate of section space and the advantages of pipe jacking technology, rectangular pipe jacking engineering has been widely constructed gradually. However, in some projects, due to the restrictions of geological conditions, environmental conditions and other factors, straight pipe jacking technology cannot meet the needs of engineering construction. In this case, curved jacking construction technology can solve these problems well. However, the stress distribution on two sides of jacking pipes is uneven, and the mechanical properties of curved pipe jacking are significantly different from traditional pipe jacking. In this paper, the model test has been carried out to simulate the construction process of curved pipe jacking. The triangular wedge gaskets of different thickness are used to form deflection angle between pipes at the horizontal direction. The stress and strain on both sides of the pipe jacking with different deflection angles are analyzed, and the conclusions are as followed: (1) With the increase of the jacking force, the axial stress of pipe sections increases. The stress concentration occurs at the left side of pipes, and the stress curve is nearly linear. (2) With the increase of the deflection angle between curved pipes, the axial stress on the pipe section increases gradually. The difference of stress in the middle part of pipe segment is significant, and the stress on the left side is larger than that on the right side of jacking pipe. The research results can provide a reference for the construction of curved rectangular pipe jacking engineering.
METADATA, ARSIP, DAN INFORMASI: SUMBANGAN STANDAR- STANDAR KEARSIPAN TERHADAP KERANGKA DAN MODEL KERJASAMA KEILMUAN BIDANG-BIDANG SERUMPUN

Since the end of XIX century when for the very first time the archival principles of arrangement and description were outlined until ICA have published their first international standard commonly known as ISAD-G which defines its 26 elements of archival description and followed afterwards by series of archival standards (ISAAR- CPF, ISDIAH, and ISDF) in 1990’s and 2000’s. These elements of description are similar to metadata elements. As the metadata elements of Dublin Core, for example, that have been released in the mid of 1990’s and then later on was adopted by ISO 15836-1:2017 and ISO 15836-2:2019. This article describes the similarities among both elements, the elements of description and the elements of metadata, including the ICA’s latest archival standard (RiC) and by using paradigm of integration-interconnection and three model of discplinary cooperation (multidisciplinary, interdisciplinary, and transdisciplinary) according to Amin Abdullah. Besides reading closely towards the literary sources including the published standards released by ICA and ISO, the writer also does the same things towards various archival standards published by several national archives. These are some of the contribution of archival science and archival standards in order to open the chance of having equal cooperation and seeing the possibilities of having cooperation among its three stages.
USING A DOUBLE CONVOLUTION OF LORENTZ AND GAUSS FUNCTIONS FOR PROCESSING THE MÖSSBAUER SPECTRA OF THE SUPERSATURATED DISORDERED SOLID SOLUTIONS

An algorithm for mathematical processing of the Mössbauer spectra of supersaturated disordered solid solutions by the Tikhonov regularization method using a double convolution of the Lorentz function and two Gaussians is proposed. By the examples of spectra of supersaturated disordered solid solutions Fe100–xGex (x = 10—25 at.%) and Fe75Si15Al10, it is shown that the algorithm allows more correct processing, which provides a reliable distribution function of the hyperfine magnetic field. It is shown that to take into account the statistical ensemble of nonequivalent local atomic configurations of Fe atoms in disordered supersaturated solid solutions, it is necessary to use not only the convolution of two Gaussian functions, but also the&#x0D; projection scaling factor of the hyperfine magnetic field onto the velocity scale.
The Application of Fusion Algorithm in Automobile Machinery Manufacturing Control System

With the progress of the times, electronic technology, information technology and other industrial technologies have been developed rapidly. Automobile is an important part of the national manufacturing industry, with the continuous expansion of application field, people also put forward higher requirements for the control system of automobile machinery manufacturing. People hope that the automobile will be more automatic, intelligent and have higher safety performance. The purpose of this paper is to take the permanent magnet synchronous motor servo system as an example, to integrate all kinds of important algorithms and arrange them scientifically and reasonably, so that the servo system can achieve satisfactory control effect. This paper focuses on the research of PI control algorithm, improved sliding mode control algorithm and improved ADRC algorithm. Based on the above three algorithms, PI current controller, sliding mode speed controller and auto disturbance rejection position controller are designed. The simulation results of the current loop, speed mode and position mode of servo system are simulated by MATALAB platform. The improved algorithm fusion servo system can achieve the expected control effect. Finally, the servo control platform of PMSM is realized by taking the controller as the part. The simulation results show that the current loop can meet the steady-state accuracy and dynamic characteristics when kp = 9.735 and ti = 0.00295, and the effectiveness of the improved approach law is verified.
Development of high strength Sustainable E-Waste concrete

Electronic waste (e-waste) is generated from various sources like televisions, personal computers, laptops, mobile phones, and tablets, etc., which are not in working condition and have very few resale values. The disposal of these wastes poses a huge threat to the environment as this e-waste contains harmful metals like lead, arsenic, mercury, etc. When disposed to open barren lands where the leachates percolate into groundwater and make it harmful for use. The usage of these electronic waste goods in concrete is a solution for its improper disposal. In the present research, concrete (M60) was prepared with partially replacing filler material with E-waste in the form of printed circuit boards which were shredded into small pieces of size up to 4.75 mm. This replacement is in the order of 5%, 10%, 15%, and 20 % of fine aggregate. For determining the workability of concrete Slump test was used. The strengths in compression, tensile, flexure was performed on M60 grade concrete after 28 days in water. To determine the durability of concrete, an acid attack test using H2SO4 and HCl was conducted. The results obtained are showing that an optimum 15% replacement of filler material with shredded e-waste yielded better results.
Efficient Capsule Network with Multi-Subspace Learning

The capsule networks (CapsNet) is a promising domain in deep learning, which can effectively solve the fatal shortcomings of Convolutional Neural Networks (CNNs), that is, the lack of the capability to represent the spatial relationship within feature map and the ability to encode corresponding positions. Yet its true potential is not fully realized thus far. Almost all kinds of CapsNet models, even if they only contain a few capsule layers, the model parameters have risen to the order of several million, which means that huge computer resources and training time are required. In this paper, we propose Multi-Head Self-Attention Capsule Network (MA-CapsNet) to address these drawbacks above. We design a lateral inhibition scheme to highlight the target capsule. Compared with other CapsNet, we use multi-head self-attention to route capsules, instead of the recently popular tensor convolution routing and the old-fashion iterative routing, which reduces the parameters of the entire CapsNet to about 19k, less than 0.3% of the original CapsNet proposed by Hinton. Our routing mechanism strengthens the model's ability to focus on different positions in feature maps and expands the representation subspace of the attention layer. With MA-CapsNet, we surpass the state-of-the-art results in the field of CapsNet on MNIST, FashionMNIST, SmallNORB with least model parameters.
Methodological substantiation of the spheral approach to the training of specialists in the social sphere

The activities of specialists in the social sphere are aimed at creating conditions for the successful development of society as a whole and the welfare and prosperity of each of its members. At the same time, the quality of training of such specialists does not always correlate with the expected results. The reason for this discrepancy lies in the absence of a clearly expressed methodological strategy for training specialists in the social sphere. This article is devoted to the substantiation of the spheral approach to the training of specialists in the social sphere, which serves as a kind of methodological guideline, since the social sphere itself is a kind of springboard that launches this process. The study confirmed the assumption of the spheral approach as a global strategy, following which it is possible to predict with a certain accuracy the results aimed at transforming both the social sphere itself and the social institutions that make up it.
Fast Small Object Detection Algorithm Based on Feature Enhancement and Reconstruction

Small object detection is one of the research hotspots and challenges in the field of machine vision. Because small targets have fewer pixels in the image, the number of features captured by the first few layers of the target detection network is very small, and the high-level semantic information such as feature classification disappears quickly, making the accuracy of target detection relatively low. The paper uses sub-pixel convolution to reconstruct feature information, uses SE (Squeeze-and-Excitation) attention mechanism to enhance features, and uses U-Net network to capture feature context information and has the advantages of faster detection speed. The algorithm realizes the fast and accurate identification of small targets. Numerical experimental results on the COCO data set and the SUN data set show that the algorithm improved performance by more than 12% in the case of the bounding box AP compared with mainstream algorithms such as YOLOv4.
Methodology for solving the task no. 8 of the unified state exam in informatics and ICT by a combinatory method and method of programming on python

The article discusses the metodology for solving the task No. 8 of the Unified State Exam in informatics and ICT in two ways: by mathematical combinatorial calculation and writing a program in the Python programming language. The purpose of this methodology is the successful completion of task No. 8 (until 2021 — No. 10) in the Unified State Exam in informatics and ICT by graduates. The article is of an interdisciplinary nature, touches upon issues at the intersection of mathematics and informatics. The relevance of the work is due to the fact that tasks of this type are annually present in the Unified State Exam in informatics and ICT, but the success of this task is too low for tasks of the basic level of complexity. The use of programming tools in the Unified State Exam in informatics and ICT is available starting in 2021. The scientific novelty of the work lies in the use of the Python programming language to solve tasks of this type. The peculiarity of the metodology lies in the gradual increase in the complexity of the algorithms and the "modular" application of parts of the code, which allows using the "modules" of previous tasks to solve subsequent ones. Specific versions of the programs are proposed, a comparative analysis of methods for various prototypes of the corresponding tasks is given. As a result, it was determined that task No. 8 can be effectively solved by the programming method.
Crowdsourcing Recognized Image Objects In Mobile Devices Through Machine Learning

The rapid progress in the deep learning field and high-performance computing, has contributed significantly to the growth of object detection in images techniques. This paper presents the development of an innovative smartphone application, which recognizes and counts objects in images that users offer. As we all know, object detection, is a complicated algorithm that consumes high-performance hardware to be executed in real time. Therefore, because of the limited computational power of smartphones, it is necessary to develop a methodology, so the process of recognizing and counting items, will be achieved effectively in terms of time and resources.
Mutual information estimation in Distributed data fusion

A crucial problem of data fusion in distributed sensor networks mainly results from the unknown correlation in the fusion process. With unknown correlation, the common process noises may cause double-counting errors in fusion results. To optimize the fusion results, the common process noises should be eliminated in fusion results. In this paper, the correlation from common process noises are modeled as the mutual part of two estimates. The size of the mutual part is qualified with information entropy, and then the value range of the mutual part is also analyzed with the joint entropy. Finally, the mutual part is estimated to optimize the fusion results and eliminated with covariance intersection. The simulation of target position verifies the improvement of the proposed fusion method compared to the current alternatives.
Exploring 3D Playblast-To-2D Animation Rotoscoping Techniques

Rotoscoping can produce believable 2D animation and movements when the process is done correctly. However, not all live-action footage can be easily rotoscoped due to scenes or fictional designs that are impossible to shoot. To overcome this technical challenge, 3D playblast can be used as footage for an alternative source for rotoscoping. This research aims to explore and evaluate two rotoscoping techniques of transforming 3D playblast into 2D animation. Two different animation software (Adobe Animate CC and Toon Boom Harmony) were used to explore those rotoscoping techniques. The method began by designing a storyboard with dynamic camera angles. Then, animated 3D models were developed based on the storyboard as footage for rotoscoping. Then, those animated 3D models were rotoscoped frame by frame using ‘Toon Boon Harmony’ and ‘Adobe Animated CC’. The rotoscope outputs were evaluated for their effectiveness and weaknesses. Findings show that rotoscoping frame by frame is viable for both software with some nuances. Nevertheless, the proposed rotoscope techniques can maintain consistency between each frame without losing the proportion of anatomy and style when animating in an extreme and distorted scene.
Digital Audio Watermarking: Techniques, Applications, and Challenges

Increased use of Internet has led to increased sharing, storing, and distribution of the digital media across the globe. In the current era, huge amount of digital media, i.e., text documents, image, audio, and video is being populated and distributed at very fast rate. Therefore, it is very important to maintain the authenticity and copyright information of the digital media. Also, security of the digital media during transmission and receipt is an important issue. It is observed that the digital media is prone to malicious attack and being pirated. Watermarking is one of the solutions for providing the security, authenticity, and copyright to the digital media. Watermarking has been used in many applications in the area of image processing, speech processing, broadcasting of digital media, etc. The basic entities on which watermarking is applied are text documents, images, audio, and video. It becomes important to achieve robustness, security, and imperceptibility while transmitting the data with watermark. Lot of research is already been done in text and image watermarking as compared to audio and video. In this paper, we discussed about important concepts of digital audio watermarking and explain few important applications through the literature survey of various techniques used for it.
Design of coding device based on color characteristics and K-means separable

Aiming at the current courier companies' security inspection coding generally adopts manual coding operation, the coding speed is low, and the coding quality is guaranteed, a fast coding device based on color characteristics and rapid segmentation by K-method is designed. System, and image processing are composed of two image systems. The image processing system is used to obtain the coordinates of the express location, and the machine executes the operation of fast and high-speed coding. The express positioning algorithm first performs image color feature processing on the collected express, performs Gaussian filtering of noise, uses K-means to shoot the target, performs critical value segmentation of the target, morphological processing and minimum contour detection to extract the diagnosis center coordinates, and finally the extracted coordinates are passed to the mechanical result agency for coding. Guarantee: Using the inkjet code designed in this article to carry out the courier security inspection coding experiment, the accuracy rate reaches 98%, which greatly improves the express security inspection device.