Reviewerly/exHarmony huggingface dataset:
Classroom behavior recognition based on federated network

Aiming at the problem of low recognition rate of single network due to insufficient feature extraction in classroom behavior recognition, a dual network (VGG16 and ResNet50) model architecture (Student Recognition Net, SRN) is proposed. First, collect data and preprocess it to construct a student behavior dataset. Secondly, use Yolov3 to obtain the coordinate information of the students to facilitate subsequent adjustments to the data set. Finally, the output of the dual network is weighted and retrained to increase the extraction of feature information. Experiments show that the average behavior recognition accuracy of this model on the SBR-9 data set reaches 92.3%, and the accuracy of some behavior recognition is up to 98.2%, which is better than the behavior recognition effect of a single network.
Cooperative and competitive multi-agent deep reinforcement learning

Multi-agent reinforcement learning (MARL) is an area of artificial intelligence that investigates joint behaviors of multiple individual agents and emergent patterns arising from their interactions with a common environment. Although MARL has a long history of decades, it begins to intensify recently due to the breakthrough of deep learning methods. In recent years Deep reinforcement learning (DRL) has achieved significant progress in single-agent reinforcement learning problems. Meanwhile, multi-agent systems (MASs) also benefit a lot from DRL methods. Latest advances occur in areas including video games, robot system, smart grids, etc. This article mostly focuses on recent papers on Multi-agent deep reinforcement learning (MADRL). First, some background knowledge of DRL and MARL is introduced. Both value-based and policy-based DRL algorithms are discussed. Second, representative works in both cooperative and competitive scenarios are reviewed respectively. Key ideas and main techniques in each work are discussed. Lastly, the paper draws a conclusion and some potential research directions are proposed.
Co-developing Inventive School Culture

This chapter presents an overview of the key concepts discussed in the part 3 of this book. The part explores invention pedagogy from the perspectives of developing learning environments and teachers’ transformative digital agency, as well as discusses the pathways toward the innovative school 2.0. The principles and practices of invention pedagogy can be expanded to school-level development, in which the aim is to create inventive culture for the whole school community. Invention pedagogy supports schools in co-developing such a culture through creative processes that combine research-based knowledge with teachers’ pedagogical experience and everyday school practices. Central to both inventive school culture and successful implementation of invention projects is enabling teachers’ transformative professional development in collaboration with other school communities and networks.
Automatic Anomaly Monitoring Research of Business English Literature Translation Based on Decision Tree Intelligent Analysis

As an important language tool, literature in business English defines rights and obligations in business activities from the perspective of literature translation. This article discusses business English from the perspective of literature translation, which should not only preserve the characteristics of literature, but also ensure the smooth and correct language. In order to improve the accuracy of the automatic translation of business English literature and optimize the design of the teaching platform for business English literature translation, a design method of the teaching platform for business English literature translation based on the decision tree logistic model is proposed. The platform design consists of two modules: automatic translation algorithm design and software development of the platform. Using the decision tree logistics model to analyze the semantic features of business English translation and context feature matching and adaptive semantic variable optimization method to analyze automation lexical features of business English translation and to extract the correlation between vocabulary and vocabulary characteristics, in the context of a specific business translation difference correction, the accuracy of English translation is improved. The software design of the platform is carried out under the decision tree logistics model. The platform construction is mainly divided into vocabulary database module, English information processing module, network interface module, and human-computer interaction interface module. B/S framework protocol is used for integrated development and the design of translation platform. According to the characteristics of the data business application and the particularity of data security risk monitoring, from business English requirement analysis, the study of business English translation behavior monitoring ability and analysis in the process of abnormal behavior monitoring techniques and methods, including data access, data processing, experience in engine, and model engine puts forward the future research direction. The platform test results show that this method has good accuracy and strong automatic translation ability in business English literature translation.
You don’t need 1nm contours for curvilinear shapes: pixel-based computing is the answer

Enabled by multi-beam mask writing [1], curvilinear free-form ILT [2], and GPU acceleration [3], curvilinear masks are quickly becoming the norm in leading edge masks, whether for 193i or for EUV, particularly for contact and via layers. An industry standard for compactly representing curvilinear shapes is being developed for SEMI through an industry working group. In it, Bezier, and B-spline "Multigon" formats are proposed to augment the piecewise linear polygons that are supported today [4]. Whether these infinite-resolution curvilinear formats are used or piecewise linear polygons are used, there is a question of what constitutes a high enough vertex density to be of some pre-defined accuracy requirement. With these infinite-resolution curvilinear formats, the vertex density would be lower than with piecewise linear polygons for a particular accuracy requirement. But it is still useful to know what density is theoretically sufficient. This paper explores the concept of rasterization and the mathematical dual between contours and pixel dose arrays given a particular known resolution limit. The paper further argues that curvilinear ILT, practically speaking, is all computed in the pixel domain. And all curvilinear masks, with the notable exception of MWCO masks for 193i [5], are written with multi-beam machines using pixel dose arrays. The paper further argues that all images taken of the resulting masks, whether for inspection, disposition, or for metrology are pictures taken as pixel dose arrays of some resolution with some image processing afterwards. Information theory is a branch of computer science that, among other things, gives insight on how much data is sufficient to represent any particular information content [6,7,8]. More generally, the field covers the idea of digitizing the analog world to some known limit of resolution. Rasterization is digitalization of images that converts from contours, be it piecewise linear polygons, or some infinite resolution curves, to pixel doses of some pixel size and dose range. Contouring is the converse, going from pixel doses to geometric space. By understanding information theory, how curvilinear mask shapes are computed, and how curvilinear mask shapes are generated on
An improved tube rearrangement strategy for choice-based surveillance video synopsis generation

Video synopsis is a promising technology that offers easy browsing and indexing of surveillance videos. This article presents an improved video synopsis framework, introducing the inclusion of anomalous tube detection module. The suggested framework performs better than the existing methodologies by offering a flexibility to the user to generate a synopsis video, which is based on user's choice of interest. Traditionally, to generate a synopsis video, the object tubes are temporally shifted for achieving compression. The applied temporal shift incurs a large number of collision artifacts along with temporal chronology violation. To address this issue for producing a visually comfortable synopsis video, the collision and temporal chronology violations are amended through Acceleration/Retardation of object's motion and spatial shift. Collision oriented Acceleration/Retardation and Spatial shift strategies are embedded sequentially in the proposed combined algorithm cSAScO. The unified representation of the proposed cSAScO algorithm combines the individual strength of Simulated Annealing (SA) and Scenario Optimization (ScO) and is employed to the formulated choice-based tube rearrangement problem. The efficacy of the proposed scheme is demonstrated through extensive experiments and its performance compared with that of the benchmark schemes.
Research on the application of computer technology in music creation

With the rapid development of computer technology, it is comprehensively and deeply applied in the field of music creation. Computer technology plays an important role in the writing, practice and secondary creation of music creation by using its characteristics of rapidity, diversity and efficiency. At the same time, the integration of music software system and the use of multimedia technology can greatly realize the rapid processing of computer and sound data. So as to establish the logical relationship between music and computer software, develop a proprietary music creation system for music creation, and provide overall planning and technical support for the creation structure of music creation system by using image processing technology and multimedia technology. Based on the application of computer technology in music creation, this paper makes computer technology realize the purpose of human-computer interaction in the application process of music creation, so as to promote the good development of music creation.
Data mining technology of computer software based on association analysis

This paper mainly analyzes the specific application of the computer software data mining technology of association analysis, fully excavates the important data information in the computer software according to the principle of association analysis, and further puts forward the development rules of association analysis to improve the computer software database. With the continuous development of computer software technology, the application of computer software will produce large-scale application data, file resources and so on. Data mining technology abstracts the characteristic information needed in data analysis according to the data analysis object determined by computer software, and then selects the appropriate information collection method to store the collected information in the computer information database. It is precisely because of the in-depth application and development of computer software by data mining technology that the data processing speed and storage space of computer software have been steadily improved, so as to promote the good operation of association analysis rules of computer software.
The application analysis and development strategy of cloud computing technology in computer data processing

The traditional computer has low computing efficiency, and the data processing mode is relatively single. However, the cloud computing technology has the ability of multiple information to accelerate data processing and can process the data in a short time. Therefore, by applying the cloud computing technology to the computer data processing, the speed and dynamic optimization technology of the cloud computing technology can effectively control the multi-source information data flow. At the same time, the cloud computing data processing service mode is built, which can integrate and reconstruct information resources, and solve the problem of computer resource scheduling. However, to prevent technology leakage, architectural security protocols are needed, and new encryption technologies are developed to reduce the cost of data operation as much as possible while ensuring the application security of cloud computing technology.
Research on the workpiece sorting system based on visual recognition

Aiming at the characteristics of high intensity and low efficiency of manual sorting of irregular shaped workpieces, a workpiece sorting system based on visual recognition was studied in this paper. A new sorting system based on ROS robot system in Linux environment is proposed. Machine vision technology and ROS method are used in the intelligent sorting of common shape geometry. Firstly, considering the characteristics of large amount of operation and high flexibility of workpiece sorting, the Baxter dual arm robot was selected as the hardware device, an experimental platform for workpiece sorting was constructed. Secondly, the template matching method based on edge information detection realizes image acquisition and processes the collected image to solve the difficult problem of workpiece recognition caused by large background noise and low image feature contrast, Open cv combined with VC + + was used to realize the design of visual program and the motion control function of actuator. The simulation results show that the workpiece sorting method based on visual recognition technology has the characteristics of fast timeliness, strong flexibility and high stability, and can be applied to the rapid sorting of irregular parts.
Research on digital music course teaching assistant system based on computer application technology

The development of computers has made people's lives and work faster and more efficient. Now the application of computer technology has gradually entered various industries, and the education field is also constantly introducing computers into the classroom to help teachers achieve better teaching results. The digital music course teaching assistant system can dig deeper into student information, save the student's specific training process and results in the database, and quantify it. The digital music course teaching assistant system makes full use of WPF image presentation technology, multi-touch interactive technology, MIDI technology, and other computer technologies, which can present teachers with more intuitive teaching information and richer teaching resources and improve the efficiency of music training.
Fusion application of big data technology and computer software technology

Cloud storage technology and big data technology, as the two major branches of virtual reality technology, are applied to computer software technology, which can reprocess and modify the data resources of cloud space, and also make users feel the virtual three-dimensional space. With the support of computer software technology, the system virtual technology and cloud computing technology improve the computing level of computer equipment and the use efficiency of computer software and software by dividing the computing processing program into basic computing units.
Application research of RRT algorithm path planning based on reinforcement learning

In order to solve the problems of the traditional fast search random tree (RRT) algorithm in complex environment, such as low planning efficiency, zigzagging generation path and many inflection points, a Q-RRT algorithm combined with reinforcement learning method was proposed. By introducing q-value table and reward function in Q-learning algorithm to influence the expansion process of RRT random tree, the randomness problem of tree node expansion is overcome. By defining reward function, the number of invalid nodes is reduced and the search efficiency of the algorithm is improved. At the same time, a path optimization method is proposed, which uses the pruning method and cubic Bezier curve to shorten the planned path length, realize the smooth processing of the path, and satisfy the motion constraints of the mobile robot. Experimental results show that the planning efficiency of Q-RRT algorithm is greatly improved compared with traditional RRT algorithm in complex environment, and the planned path is smoother, which can better adapt to the operation requirements of mobile robot.
Privacy-preserving linear regression on distributed data with multiple keys

Linear regression is widely used as a tool for statistical analysis and is very popular as a technique to build predictive models in data mining field. Due to the fear of leakage of their privacy, data owners are reluctant to share data with others, while they wish to perform statistical analysis cooperatively. To solve the privacy issues, one of most popular approaches is to encrypt user’s data with public keys. However, this technique inevitably leads to another challenge that how to train linear regression based on encrypted data with multiple keys. In this work, we propose a privacy-preserving linear regression scheme base on a public-key cryptosystem with distributed two trapdoors to protect the privacy of the training data. We consider the problem of linear regression where the data are split up and held by different parties. We give details of how this scheme is implemented. Finally, we provide the theoretical analysis of the scheme.
Accurate super-resolution with residual network and cross stage partial network for sonar images

With the development of deep learning, deep neural network can be consumed to many kinds of tasks, such as target detection, super resolution and other image processing applications. However, super resolution for sonar images adopts traditional ways without neural network. Due to the characteristic of sonar sensors, which shoot images in turbid underwater environment and have low resolution, objects taken by sonar sensors are difficult to recognize. This paper proposes an efficient method called Multi-stage Residual Network (MRN) which combines neural network to achieve super resolution for sonar images. Adding pixel shuffle to the medium of the structure, the number of layers and blocks in every part is different. In addition, we trained the network with no modification from underwater sonar images. Experimental results show that low-resolution fuzzy images can acquire a clear super resolution by using our model, and furthermore, PSNR of our resultant images is higher than that of the interpolation algorithms and residual network.
Application of SaaS cloud computing technology in the whole process management of project cost

This paper analyzes the application of SaaS cloud computing technology in the whole process of project cost management, and specifically studies the application of cloud computing technology in project cost. Through the comprehensive analysis of the advantages of cloud technology, the obvious role of project cost after the use of cloud computing technology is obtained. Cloud computing technology can provide large-scale and high-quality management and development platform for project management. Project cost can obtain high-level and highly extensible storage and data operation functions through computer browser, realize fully optimized allocation of project cost resources, and use cloud computing technology for large-scale data management. Process, analyze and retrieve massive amounts of data information. Through the comprehensive application of cloud computing technology, a complete set of engineering cost construction process management system has been formed. The application of cloud computing technology enriches the system structure, promotes the stable operation of the management system, and provides a new development path for the engineering cost industry.
Automatic learning explicit and implicit feature interactions for click-through rate prediction

Click-through rate prediction, which aims to predict the probability of a user clicking on an advertisement or product, is widely used and important in advertising and recommendation systems. Various click-through prediction models proposed in recent years enumerate all cross features at a predefined maximum order and then train the model to identify useful feature interactions, but there are a number of problems with this approach. First, the complexity of the model is proportional to the order, so there is a trade-off between expressiveness and computational cost. Second, at the maximum order, the introduction of some noisy crossover features can degrade the model performance. Third, implicit higher-order feature interactions are poorly interpretable and lack convincing reasons to explain the model results. In this paper, we propose MAFN, which explicitly models feature combinations of different orders using multi-headed self-attentive networks with different levels of residual connectivity. At the same time, we introduce an adaptive factorization network to learn crossover features of arbitrary order. Extensive experiment evaluations show that MAFN performs well compared to existing state-of-the-art models.
Language identification based on multi-scale and multi-dimensional convolution

Aiming at the problem of insufficient feature learning of time-delay neural networks, which is widely used in the field of language identification, a new architecture called multi-scale and multi-dimensional convolution is proposed. The structure includes a global inter-frame correlation network, local and global multi-scale network, global channel correlation network, and multi-head attention statistics pooling layer. The global inter-frame correlation network models the global context at the initial frame layer to obtain the dependency characteristics of the global context, which makes up for the natural deficiency of time-delay neural network based on limited context; local and global multi-scale networks aggregate the information within and between layers to extract features on a finer and more complex scale; the global channel correlation network is explicitly modeled from the channel dimension to realize the adaptive correction of the channel dimension characteristics; The attention statistics pool layer is extended to multiple heads so that features can be distinguished from multiple aspects. Through the training of the AP17-OLR data set, it has been improved by 41% compared with the previous excellent model.
Research on the application of multifaceted Rasch model analysis software facets in English test

At present, most oral English tests use manual scoring, which has many drawbacks. In the past English tests, the scores of the subjects often depend on the subjective impression of the rater. Because of the strong subjectivity in the English test, the reliability of the English test is not high. In order to improve this situation, you can use the multi-faceted Rasch The model is improved. The multi-faceted Rasch model can use a rigorous mathematical statistical probability model to standardize test scoring through various data generated in the test, and correct deviations in the scoring, so as to achieve the fairness of the scoring of the spoken English test. The multi-faceted Rasch model can be completed by the analysis software Facets. This software can complete the deviation analysis of examinees, graders, grading standards and other aspects by relatively simple setting of the parameters of the required model.