Reviewerly/exHarmony huggingface dataset:
A Seq2seq-based Model with Global Semantic Context for Scene Text Recognition

Scene text recognition (STR), with various applications, has become a popular research. With deep learning, many sequence to sequence (seq2seq) models have been proposed. However, the Teacher-Forcing training method used in the seq2seq models gave rise to the problem of exposure bias. Moreover, the autoregressive decoding manner limits seq2seq models ability of utilizing future semantic information. To solve these problems, a new Transformer-based network is proposed in this paper. A Re-Embedding Layer with sampling module is introduced to overcome the problem of exposure bias and a context fusion module (CFM) is designed to model global context information. Experiment results on several benchmarks have demonstrated the effectiveness of the proposed method in scene text recognition.
Curriculum Reform and Research of Airport Safety Inspection Based on Cloud Computing

With the rapid development of science and technology, the continuous innovation and progress of computer network, the use of big data in society is more and more widely, especially in recent years, cloud computing has a significant impact on the reform of college teaching. This paper mainly explores the curriculum reform of airport safety inspection major under cloud computing, and puts forward corresponding solutions. Combined with the actual work of security inspection, this paper puts forward the curriculum system of airport safety inspection major.
Generous and inviting interfaces revisited

Abstract For some decades, institutions such as galleries, libraries, archives, and museums (often abbreviated as GLAM institutions) have digitized numerous archival documents and made at least parts of these documents publicly available online. However, there is still a shortage of user-friendly and easily accessible interfaces for various user groups, especially regarding collections based on text sources. After a brief introduction to the research area and the project, this paper discusses a visualization and design study based on a selection of digitized archival material from the Ernst von Glasersfeld estate. We present a conceptual outline of this work and examples concerning discourse mapping, the analysis of argumentative structures as well as some didactical approaches. In so doing, conceptual backgrounds, educational material and design principles are outlined. The paper concludes with critical considerations as regards the limitations of the approaches discussed.
NaturalNets: Simplified Biological Neural Networks for Learning Complex Tasks

We present a new neural network architecture, called NaturalNet, which uses a simplified biological neuron model and consists of a set of nonlinear ordinary differential equations. We model the membrane potential of each neuron by integrating the in-flowing currents, but we do not consider ion channels, nor individual spikes. To keep the membrane potential within a defined value range, we introduce a suitable clipping mechanism. With our approach, we aim to develop agents solving complex tasks by providing a higher biological plausibility than commonly used neural networks for deep learning applications, while also offering low computational complexity to enable fast training. To demonstrate the learning capabilities of NaturalNets, we use the virtual robotic environments of the OpenAI Gym framework, a widely-used toolkit for developing and comparing reinforcement learning algorithms. We compared a variety of different widespread neural network architectures, including long short-term memory (LSTM), gated recurrent units (GRUs), feedforward, and Elman networks. Our experiments show that NaturalNets were able to perform well for all considered virtual robotic control tasks, where we apply the covariance matrix adaptation evolutionary strategy (CMAES) for training.
A Novel View Image Generation Network Based On Attention Mechanism Refining Features

We propose a novel view image generation network based on attention mechanism refining features. According to the prior structural model learned in the training, the network can generate multi-view images from a single-view image of an object we are interested in. The input of our network is the RGB image and pose corresponding to the source viewpoint, as well as the target pose we desired, and the output is the predicted target view image. The main innovation of our method is to exploit the attention mechanism to help us select better and more important features by assigning weight to features. Our method needs no additional 3D supervision and balances the accuracy and speed well. The final quantitative and qualitative results show that our network has achieved impressive results in the task of novel view image generation.
Potential Determination of Urban Rooftop Photovoltaics

Solar energy is one of the most promising sustainable energy, and urban environments can be considered high-potential energy producers by producing electricity from rooftop-mounted photovoltaic systems. Electricity production from solar energy and the needed parameters to define the optimal locations require an adequate study; however, effective guidelines for optimal installation of solar photovoltaics remain a challenge. This thesis aims to develop a uniform accurate multi-factor method that uses uniform open data sources to determine urban rooftop's photovoltaic potential.
Blind Decryption for Preserving Privacy in the DRM System

This paper addresses the user’s privacy problem in the DRM system. Focusing on achieving optimal security for content provider, DRM system often neglect user’s privacy. We propose solutions to this problem in a new perspective: providing balance protection on privacy and security. Preserving user’s privacy is approached by minimizing user’s data acquisition. The implementation of this privacy protection has to be controlled so that the security of the content provider is preserved. All solutions presented in this paper are based on the blind decryption scheme. To demonstrate the advantages of a blind decryption based solution, we also compared it to an anonymous cash scheme.
A Dynamic Scaling Approach in Hadoop YARN

In Cloud based Big Data applications, Hadoop has been widely adopted for distributed processing large scale data sets. However, the wastage of energy consumption of data centers still constitutes an important axis of research due to overuse of resources and extra overhead costs. As a solution to overcome this challenge, a dynamic scaling of resources in Hadoop YARN Cluster is a practical solution. This paper proposes a dynamic scaling approach in Hadoop YARN (DSHYARN) to add or remove nodes automatically based on workload. It is based on two algorithms (scaling up/down) which are implemented to automate the scaling process in the cluster. This article aims to assure energy efficiency and performance of Hadoop YARN’ clusters. To validate the effectiveness of DSHYARN, a case study with sentiment analysis on tweets about covid-19 vaccine is provided. the goal is to analyze tweets of the people posted on Twitter application. The results showed improvement in CPU utilization, RAM utilization and Job Completion time. In addition, the energy has been reduced of 16% under average workload.
Chronotopologic krigology

Various types of chronotopologic Kriging are presented, including ordinary, simple and indicator Kriging of natural attribute distributions, as well as chronoblock and functional Kriging. Their specific properties, links to chronotopologic mapping and real-world application ranges are reviewed. Interpolation accuracy indicators and cross-validation tests are analyzed. The benefits and concerns of applied Krigology are outlined.
Analyzing the Tail Latency in TailBench Image Recognition Application IMG-DNN

In intelligence image recognition application, tail latency becomes a core factor to affect user experience. Although the tail latency is only a high-percentile latency of the total latency, reducing the tail latency can significantly improve the user experience caused by waiting for the server to respond to the tail latency. In order to increase user experience, we chose IMG-DNN image recognition application from TailBench benchmark suite. The aim of this study is finding which parts of program causes tail latency. So we analyzed the source code of IMG-DNN and profiled the performance distribution in its runtime period. We built some models to demonstrate the relationship between source code and configuration. Through the model, we analyzed the source code to find out which models have tail latency. And by the help of program analysis, we found that most of the tail latency is caused by cache misses which happened at the configuration of producing requests.
Detection of Pancreatic Tumor using Particle Swarm Optimization based Image Enhancement

Pancreatic cancer is one of the most lethal cancers, with a five-year survival rate of about 9% for all stages. Pancreatic cancer may be difficult to detect and is usually only discovered when the disease has progressed to an advanced stage. Medical imaging provides key data for diagnosing patients, analysing diseases, developing and monitoring new medications further enhancing human health care through the use of various complimentary imaging modalities. Nature inspired optimization techniques play an essential role in the field of medical imaging. This paper presents an approach to visually enhance the pancreatic cancer CT scans using particle swarm optimization and filtering techniques. The algorithm has been tested on 3D CT scans of pancreatic cancer and evaluated on validation parameter-Entropy, which gives improved results.
Multi-instance Classification Based on Random Point Pattern

Aiming at the problem that the performance of bag data classification based on Naive Bayesian model is limited by the similar feature distribution and lack of prior information. A method for multiple-instance classification based on random point pattern is proposed. It is based on random point pattern framework. Firstly, several random point pattern models with different complexity are constructed according to the training set. Then the maximum likelihood estimate (MLE) of model parameters is equivalent to the parameter estimate of the cardinality distribution and the feature distribution. The expectation-maximization (EM) algorithm is used to estimate the feature distribution parameters, and the EM algorithm parameters are initialized by the fuzzy c-means clustering (FCM) algorithm. Finally, the best model is given by calculating the bayesian information criterion (BIC) value of each model. Through two data set classification experiments, the results verify the improvement of the classification performance of the random point pattern model compared with the traditional model, as well as the robustness of the method.
Other forms of leadership in education

I observe a split in the field of education today between two academic sub-tribes: those who champion ‘practice’ and are suspicious of ‘theories’ on the one hand, and those who insist on ‘theory and philosophy’ on the other. But philosophical commitments are implicit in our use of language and all our ways of being and acting in the world. This recognition points towards other concepts and forms of educational leadership. Below, I explore if and how philosophy and writing lead to another kind of educational leadership.
A Design of Historical Database Used in Ship Platform Monitoring System

Historical database is a key technology in the field of ship platform monitoring, which is widely used in ship platform monitoring system. It is of great significance for the development of ship platform monitoring technology to establish a historical database system with easy expansion, good openness, high access efficiency and standardized data model. Aiming at the application requirements of data acquisition and process control in real-time monitoring system, this paper puts forward a design idea of historical database system, designs the overall structure of historical database system, and analyzes and designs some important working mechanisms, such as historical data storage mechanism, historical data query mechanism, historical database concurrency control mechanism, historical data synchronization mechanism, etc. The historical database system implemented by this scheme has been running stably in a certain type of ship platform monitoring system, and the practice has proved that it has good storage and query efficiency.
Roads to What We Want: A Game Generator based on Reverse Design

It is difficult for existing approaches of procedural content generation (PCG) to balance automatically and purposely generating ideal game levels. Inspired by the idea of reverse design, we give ways of modelling the optimal solutions or the rough ones into our framework. We build the framework based on a multi-objective genetic algorithm NSGA-II, taking the interacts with different game elements in a game as the objectives for maintaining diversity during generation. Experiments demonstrate the effectiveness of our approach in producing ideal game levels purposely and the ability of being a general game generator that generates interesting levels which require the players to think in depth.
Eyes Move, Drones Move Explore the Feasibility of Various Eye Movement Control Intelligent Drones

Nearly all drones on the market are controlled by joystick controllers. The advantage of this traditional control method is that the operator can accurately control or adjust the flight angle and distance of the drone, but there also exists a disadvantage: without practice, the operator will easily mix up the corresponding function of each joystick and hardly tell the direction of flight. Therefore, drones need a new type of control method. As a device invented at the end of the 20th century, eye trackers are not a completely new concept. Eye trackers are currently limited to expensive psychological research, but this type of instrument has little practical value for ordinary consumers. However, the drones can be effectively combined with the eye tracker and take full advantage of both equipment. This article explores the possibility of combining eye trackers with drones, and in particular, explores whether eye trackers can easily and accurately control drones. The experiments will be performed to see if the accuracy and speed of the drone will be guaranteed when using the eye tracker to control the drone, and if the eye movement control method can help the user faster To achieve the instructions they want to accomplish when compared with the original control method of the drone, Image process algorithms, which belong to a branch of Artificial intelligence (AI), the most potential computer science tool in the 21st century, can complete numerous equation learning tasks. It is a low-cost and efficient exploration to apply the human eye recognition framework (Dlib and OpenCV) to real-time detection of eye movements to control the drones.
A Study on Specific Emitter Identification Using Modified Allan Variance and Correlation Coefficient

With the development of information technology, security issues have also emerged. This work finds out the electronic fingerprints of wireless devices in the field of specific emitter identification (SEI). In this study, signals of each wireless communication device are received and and Allan variance is employed to calculate the standard deviation. Then we check whether a specific individual device can be distinguished from the same type of wireless communication device by using the Pearson correlation coefficient.
Fabrication of Energy Potent Data Centre Using Energy Efficiency Metrics

For estimating the yielding capacity and rate of performance metrics for every important power consumption sub-procedure in the data centre, a few metrics are used. For augmenting such metrics and controlling better data centre energy potency for trades all over the globe, this research presents two metrics and those metrics’ fruitful utilization for creating a green data centre. They are power usage effectiveness (PUE) and data centre efficiency (DCE) which support to offer an impartial outlook of the accurate condition of data centre executions in regard to power consumption. PUE and DCE estimate the procedure of fruitful energy prices that are distributed to IT components. The use of whole power obtainable for a data centre is specified by the PUE. With the modification of the potency of the cooling procedure of power allocation, reducing PUE, permits more power to be exploited for IT components and lingers the requirement for the constitution of novel data centres. DCE is nothing but the correlative of PUE. This research allows us to get offered with a substitute standard named data centre efficiency (DCE), the correlative of the PUE. Such points may be more beneficial in a few conditions as this illustrates data centre power consumption potency in the mode of percentage.
A Secured Network Layer and Information Security for Financial Institutions: A Case Study

Communication networks are used to transfer valuable and confidential information for a variety of purposes. As a result, they attract the attention of those who intend to steal or misuse information or disrupt or destroy storage or communication systems. In this paper, the authors try to explore some of the major issues related to achieving a reasonable level of resilience against network attacks. Some attacks are planned and specifically targeted, while others may be opportunistic, resulting in thefts. In this contemporary world, security plays a significant part. And when it is about a financial industry, it becomes an emergency case. Keeping proper protection is challenging. Though security contains various fields of the section, but the network layer is one of the most critical sections for online systems. Cyber security is changing and developing rapidly; no system can claim to be a hundred percent safe in this world. But we can measure the majority risk and overcome this. We are offering mixed nodes and a decentralized protocol to make transactions more secure. And lastly, we want to make sure that if the system is compromised with any other security sections, then our alarming system of honeypots will notify the administrator and keep the system up and running.
Research on Character Computer Intelligent Recognition of Automobile Front Bar Bracket Based on Machine Vision

Abstract Aiming at the problem that the characters in the superimposed character area on the surface of the front bumper bracket of the car are difficult to recognize, a machine vision-based automobile front bumper bracket character recognition method is proposed, use Python, OpenCV and Halcon computer vision library for software system design. For eight images collected from different angles of light source directions, an innovative method of polynomial fitting gray value can reducethe uneven illumination of the images. The photometric stereo algorithm is used to obtain a high-contrast character image, and the separation of the two types of characters is simultaneously achieved. After the image filtering, opening and closing processes canremove background interference, use a morphological improvement algorithm based on scanning algorithm to complete character positioning, and then the improved algorithm of projection dichotomy based on the connected domain size is used to complete the character segmentation, and finally the support vector machine is used for character feature recognition. The experimental results claim that these methods can quickly separate superimposed characters and complete the recognition of non-color difference convex characters and inkjet characters, and the average recognition accuracy rate is over 96%, which meets the expected recognition requirements.
Effective Educational Technologies for Professional and Personal Formation of the Future Teacher at the University

Professional mobility is largely based on the ability to varying professional correlation of university knowledge, skills and abilities with the requirements of the labor market and centuries-old established experience of the life of society. All this presupposes the objective necessity of introducing modern educational technologies into professional training carried out in universities. The question is to determine the most rational and effective educational technologies for the professional and personal formation of a future teacher at a university.
New face recognition technologies based on 3DMM

Face recognition system includes face detection, face positioning, and face identification. ith the advent of the information age, identity authentication has become more and more important, and face recognition has become the mainstream method of identity verification due to its non-invasiveness, easy availability, and high reliability. With the current rapid development of artificial intelligence, it is of practical significance to introduce deep learning methods into face recognition. My work uses the caffe framework to design an 18-layer network model. At the same time, 448,808 pictures of 1,583 objects in the YouTube Face data set were used as training set, and 111,403 pictures of 1583 objects were used as verification sets. After preprocessing. These images, we entered them into the network for training our model. The facerecognition accuracy of the final model reached 99.7%. Next, my work did a forward-looking work for 3D face verification: single-view 3D face reconstruction. At present, the lack of open source 3D face database can be said to be the biggest obstacle to 3D face verification if you want to use deep learning algorithms. In order to solve this problem, my work will focus on the reconstruction of 3D human faces. Traditional 3D face reconstruction methods are either unstable or over-regularized. So I tried to apply the deep learning method to the 3D face reconstruction. First of all, in order to solve the problem of lacking 3D model data, we improved the current Multi-view 3D face reconstruction methods, using the 3D Morphable Models (3DMM) to generate huge numbers of labeled examples. Next, design the network and train it, the network finally implemented the function of constructing a 3D model of the object from a 2D picture of itself.
A graph neural network fused with multi-head attention for text classification

Abstract Graph neural network (GNN) has done a good job of processing intricate architecture and fusion of global messages, research has explored GNN technology for text classification. However, the model that fixed the entire corpus as a graph in the past faced many problems such as high memory consumption and the inability to modify the construction of the graph. We propose an improved model based on GNN to solve these problems. The model no longer fixes the entire corpus as a graph but constructs different graphs for each text. This method reduces memory consumption, but still retains global information. We conduct experiments on the R8, R52, and 20newsgroups data sets, and use accuracy as the experimental standard. Experiments show that even if it consumes less memory, our model accomplish higher than existing models on multiple text classification data sets.