Reviewerly/exHarmony huggingface dataset:
Research on the Professional Identity of Undergraduates Majoring in Preschool Education of Connection between Secondary Vocational Education and Undergraduate Higher Education

Questionnaire method was adopted to investigate the professional identity of 166 students majoring in preschool education of connection between secondary vocational education and undergraduate higher education in a private college in Shanghai in order to explore the factors influencing professional identity. It turned out that the professional identity is mainly affected by grade, independent choice, degree of major understanding before admission, school learning conditions, and academic achievement. Educational suggestions include incorporating professional identity education into career planning curricula, implementing double tutorial system, improving secondary vocational school and undergraduate personnel training system, and optimizing school learning conditions to improve social recognition of preschool education.
PZLAST: an ultra-fast sequence similarity search tool implemented on a MIMD processor

We have developed an ultra-fast sequence similarity search tool named PZLAST on a MIMD processor PEZY-SC2. In this paper, we show the merit of MIMD features in reducing the load imbalance among the threads. Additionally, we point out two problems related to the implementation on an ultra-parallel computation accelerator as follows: (1) Deciding the optimal amount of inputs prior to the run is extremely difficult and usually even impossible, and (2) Keeping up the parallelism efficiently throughout the whole computation is not always possible. A feedback strategy and an accumulation strategy are proposed to overcome these problems and their results are shown to be valuable in reducing the accidental memory overflow in runtime and speeding up the processing time.
A Unified Framework for Bayesian and Non-Bayesian Decision Making and Inference

After showing, by means of a series of examples, that paradigms alternative to the Bayesian one obtain by simply replacing the notion of approximation associated with the latter, the paper presents a unified framework for theories of decision making and inference. Given a statistical model, the algebra of bounded random variables on the sample space is mapped homomorphically into an algebra of operators on a certain Hilbert space. Then, the choice of a norm or a divergence function on the latter algebra produces a theory of decision making and inference. Examples include models from the Choquet expected utility class, models from robust statistics, the smooth model, maxmin and maxmax (as limiting cases) as well as a novel theory. The paper also contributes to Bayesian theory, which obtains in correspondence to a Hilbert norm. It shows that Bayes’ theorem can be derived from the fundamental concept of conditional expectation and that it is the only updating rule for which the operations of updating and of calculating the predictive commute.
Analysis on the Modeling and Image Restoration of Electromagnetic Radiation Leakage

Electromagnetic (EM) compromising leakage poses a big threat to information security. Therefore, the research on EM information leakage is of great significance and value. To address the problem of EM information leakage from computers, the radiation leakage principle of video signal is analyzed in this paper. At the same time, a radiation leakage model based on electric dipole is established. The characteristics of radiation signal and the relationship between radiation signal and excitation signal are summarized. In particular, the specific radiation waveform of the rising edge is analyzed. Furthermore, it is explained that the radiation spike pulse is caused by the rising edge and falling edge of the excitation signal. Finally, verification is carried out through simulation and experiment from the perspective of image restoration, and the cause of "hollow" strokes is analyzed.
Combining Syntactic and Position Relation for Targeted Sentiment Analysis Using Graph Neural Network

Targeted sentiment analysis aims to predict the sentiment polarity of the target in a sentence. Most traditional Graph Neural Network-based methods have focused only on the syntactic dependency information of sentences. However, they ignore the position information of words in the linear form of sentences, which leads the model to paying attention to the irrelevant syntactic dependency information to the target. To attenuate the irrelevant information, a novel model called Position-aware Dual Relational Graph Attention Network (PDRGAT) is proposed. Firstly, introducing the position-aware weight window of the syntactic dependency information to make the model pay more attention to the local syntactic information of words that neighbor the target. Secondly, a dual relational attention mechanism combining syntactic and position information is proposed. Experiments show that our model can effectively attenuate the irrelevant syntactic information and outperform state-of-the-art baselines on Accuracy and Macro-F1.
Effective Object Detection and Tracking for Holonomic Robot using Deep Neural Architecture

The power of Deep Learning-based Computer Vision is not only making Object Detection tasks efficient but also more interesting. It gives an ability to computers for performing complex tasks as humans by locating and distinguishing between objects. The present research paper aims to create a robust tracking algorithm based on a custom-built dataset of a rugby ball, generated to replicate industrial objects. With the rapid growth in the automation industry, the need for compact yet reliable computational algorithms is a necessity. The primary objective behind the whole system is to achieve mobile compatibility with accurate object detection and tracking. The work also states the usage of the Convolutional Neural Networks (CNN) to increase the overall performance of pattern/image detection in tracking problems. A simple yet robust object tracking algorithm accompanied with object detection on Single Shot Multi-Box Detector (SSD) MobileNet architecture is applied in this paper. The method offers a minuscule model compatible to run on embedded systems without trading off much performance.
Edge-preserving denoising using gradient-based estimation and iterative noise-aided processing

One of the major concerns while developing a good denoising algorithm is the preservation of edges. Thus, it is of prime importance to preserve the edges, corners and other sharp structures present in an image during the process of denoising. In this paper, the concept of stochastic resonance has been applied to denoise the noisy input image. In stochastic resonance, a small value of noise is added to the noisy input image iteratively to enhance the edges of the input image based on which the weight coefficient is determined. It is observed the quality of the output image improves as the number of iterations is increased up to a certain number of iteration after which the quality of the image degrades or remains constant depending on the value of noise in the input image. In order to compare the quality of the denoised output image wrt the number of iterations state of the art no-reference quality metric such as Blind Universal Quality Indices (BIQI), spatial-spectral entropy-based quality (SSEQ) have been used. When compared with conventional denoising techniques such as Anisotropic diffusion (AD), Bilateral filtering (BLT), adaptive smoothing, the proposed algorithm yields marginally better results.
Missteps in Managing the COVID-19 Crisis

The Covid-19 pandemic surprised the world with the speed of its spread and caught nations woefully unprepared to contain the virus but in hindsight its arrival was readily predictable. Why did the warning signs for Covid-19 go unnoticed? Why did so many leaders fail to plan for a pandemic? And why did they not recognize the outbreak and respond effectively even after the pandemic was at their doorstep? This paper will seek to answer these questions by exploring six stages in crisis management: 1) preventing the crisis, 2) planning for the crisis, 3) recognizing the crisis, 4) containing the crisis, 5) returning the society to normal or non-crisis state, and 6) learning from the crisis. The paper uncovers missteps to aid society to better prepare for the next crisis
A Load Power Distribution Optimization based on Improving Biogeography-Based Optimization

A popular optimizer algorithm inspired by biogeography theory BBO (Bio-geography-based optimization) has advantages, e.g., fewer parameters and easy implementation. Still, some drawbacks exist, e.g., the imbalance between the global and the local search potential optimization and dropping local optimal when dealing with complex problems. This study suggests an improving BBO (IBBO) with differential evolution (DE) for a dispatching economic load power distribution. A power loads distribution is modeled by considering the fuel cost, the valve stage's effect, the environment's cost, and its various constraints. Simulation results of the IBBO show that the proposed scheme provides more robust convergence, efficiency optimization, and superiority in solving economic load distribution problems.
RIROSE: Rational Information Retrieval in Online Search Environments

We design a benchmark algorithm that mimics the sequential behavior of users when retrieving information from the set of alternatives provided by an engine within the first page of online search results. The benchmark defined by the algorithm is designed to evaluate deviations from the rational retrieval strategies determined by the subjective preferences and beliefs of users. The algorithm accounts for the 2047 nodes composing the binary decision tree defined by the ten alternatives ranked within the first page of results. The flexibility of the algorithm allows to incorporate modifications accounting for search frictions and different degrees of impatience on the side of users, as well as testing the categorization capacities of machine learning techniques.
Monitoring Speaker Sentiment in Various Conditions Using Natural Language Processing

The identification of a speaker’s intent is the most important task in today’s customer relationship management. Each and every task in CRM is associated with a customer. An organization’s turnover will always be based on the way they satisfy their customers/clients. In order to have a summary of the services reach, the working team has to analyze the responses obtained from the customer. That way of analyzing the outcome obtained from the customer is done with artificial intelligence, a study of training the machines. One such subspace of AI is machine learning. One of the advantages of machine learning is that it is helpful in making decisions and predictions based on the data that it gets trained. The data can be of any kind, relative to the day-to-day research. Such a way of understanding, analyzing and predicting the accurate outcome is again done with natural language processing, which is a field of ML. NLP is the field of computer science that is helpful in training the human language to the machines. As seen above, our research on customer response is related to NLP. The customer’s response is taken as input for the text analysis available among the NLP algorithms. From the analysis made, the mood or intent of the customer is delivered as a result. The programming language Python is used for the entire processing of the text analysis.
Fault Detection on Bearings and Rotating Machines based on Vibration Sensors Data

In this work a comparative study among the known fault detection techniques Local Outlier Factor and Isolation Forest as well as a proposed methodology called Standardised Mahalanobis Distance is presented. The study is focusing on the challenging problem of fault detection on bearings and rotating machines using vibration sensors’ data. During the first phase of the experiments, all models are applied and evaluated using cross-validation on a dataset created in lab by obtaining vibration signals of a rotating machine. In the second phase, the outlier detection techniques including the proposed one, are applied and evaluated on a popular, public dataset. In both phases, various parameters’ combinations are tested in order to find the most efficient set for each technique. As can been derived by the evaluation results, the Standardised Mahalanobis Distance methodology outperforms Local Outlier Factor and Isolation Forest on fault detection on voltage drop down of rotating machines in the case the voltage value of the abnormal condition is not close to the nominal. In addition, the evaluation results from the public dataset indicate that Standardised Mahalanobis Distance is able to identify outliers before an outer race fault on a bearing occurs, in a more efficient and solid way than Local Outlier Factor and Isolation Forest models. The proposed approach is applied also on a real world scenario in the premises of major lift manufacturer, using custom vibration sensors and it is currently under further evaluation.
Research on computer aided design and algorithm optimization of intelligent home care operation system

In recent years, the aging of China’s population has become more and more serious, and the issue of old-age care, old-age resources, and old-age care cause more and more concern. The previous way of providing old-age care services can no longer meet the needs of the current aging demographic structure of society. At the present time of networked and information-based social development pattern, explore the intelligent home retirement community operation mode, design and develop intelligent home retirement operation system based on computer network technology, analyze the construction path and demand of intelligent home retirement community operation system by combining the perspective of volunteer service, and carry out the architecture, function design and development of intelligent home retirement community operation system, so as to integrate the community elderly personal physiological data, The system will process and analyze data and information such as health status, living needs, spiritual needs, medical consultation, etc. through a unified intelligent management platform, thus promoting the healthy and stable development of the community elderly at home.
Learning Channel Attention in Frequency Domain for Visual Tracking

Visual tracking based on attention mechanism, especially channel attention, has achieved great success. However, they do not make full use of the correlation between the feature map channels and use the feature information to model the target appearance. In this paper, under the Siamese network framework, we propose a simple and effective tracking algorithm based on channel attention mechanism in the frequency domain, referred to as SiamFCA. The frequency channel attention preprocesses the feature map obtained through the fully convolutional network (FCN). It divides and weights the feature maps in the frequency domain, and recalibrates the channel feature response by modeling the correlation between the feature channels. The proposed model further enhances the feature information of the extracted target image, reduces the model complexity and computational burden. The results of the experiment on the two benchmark datasets of OTB2015 and VOT2016 show that the proposed tracker based on the frequency channel attention mechanism is better than many state-of-the-art trackers.
CONetV2: Efficient Auto-Channel Size Optimization for CNNs

Neural Architecture Search (NAS) has been pivotal in finding optimal network configurations for Convolution Neural Networks (CNNs). While many methods explore NAS from a global search-space perspective, the employed optimization schemes typically require heavy computational resources. This work introduces a method that is efficient in computationally constrained environments by examining the micro-search space of channel size. In tackling channel-size optimization, we design an automated algorithm to extract the dependencies within different connected layers of the network. In addition, we introduce the idea of knowledge distillation, which enables preservation of trained weights, admist trials where the channel sizes are changing. Further, since the standard performance indicators (accuracy, loss) fail to capture the performance of individual network components (providing an overall network evaluation), we introduce a novel metric that highly correlates with test accuracy and enables analysis of individual network layers. Combining dependency extraction, metrics, and knowledge distillation, we introduce an efficient searching algorithm, with simulated annealing inspired stochasticity, and demonstrate its effectiveness in finding optimal architectures that outperform baselines by a large margin.
Deep Learning based Facial Attendance System using Convolutional Neural Network

This undertaking expects to perceive faces in a picture, video, or through a live camera utilizing a profound learning-based Convolutional Neural Network model that is quick, just as precise. Face salutation is a cycle of distinguishing faces in a picture and has viable applications in various spaces, including data security, biometrics, access control, law authorization, smart cards, and reconnaissance framework. Transfer learning utilizes multiple layers to find translations of information at various extraction levels. It has worked on the scene for performing research in facial salutation. The cutting-edge execution has been better by the presence of profound learning in face salutation and has invigorated accomplishment in practical applications. Convolutional neural networks, a sort of deep neural organization model, have been demonstrated to progress in the face salutation space. For continuous frameworks, inspecting be done before utilizing CNNs. Then again, complete pictures (all the pixel esteems) are passed to contribute to Convolutional Neural Networks. The overall accuracy is 99%. The accompanying advances: highlight determination, include extraction, and preparing are acted in each progression. This may prompt the supposition, where convolutional neural organization execution gets an opportunity to get muddled and tedious.
On the Properties of Algebraic Geometric Codes as Copy Protection Codes

Traceability schemes that are applied to broadcast encryption can prevent unauthorized parties from accessing the distributed data. In a traceability scheme, a distributor broadcasts the encrypted data and gives each authorized user a unique key and identifying word from the selected error-correcting code for decrypting. The following attack is possible in these schemes: groups of c malicious users join into coalitions and gain illegal access to the data by combining their keys and identifying codewords to obtain a pirate key and codeword. To prevent this type of attack, classes of error-correcting codes with special c-FP and c-TA properties are used. In particular, c-FP codes are codes that make direct compromise of scrupulous users impossible and c-TA codes are codes that make it possible to identify one of the attackers. We are considering the problem of evaluating the lower and the upper boundaries on c, within which the L-construction algebraic geometric codes have the corresponding properties. In the case of codes on an arbitrary curve, the lower bound for the c-TA property was obtained earlier; in this paper, the lower bound for the c-FP property was constructed. In the case of curves with one infinite point, the upper bounds for the value of c are obtained for both c-FP and c-TA properties. During our work, we have proven an auxiliary lemma and the proof contains an explicit way to build a coalition and a pirate-identifying vector. Methods and principles presented in the lemma can be important for analyzing broadcast encryption schemes’ robustness. Also, the c-FP and c-TA boundaries’ monotonicity by subcodes are proven.
Camouflage Target Detection Based on SSD Algorithm

Camouflage targets are highly integrated with the background, and there are problems of low detection accuracy and high detection difficulty with traditional detection methods. In this paper, the SSD network in one stage detection algorithm is applied to the detection of camouflage targets to explore the detection effect of using deep learning on camouflage targets, and it is validated on the military camouflage personnel dataset, and the experimental results show that the SSD algorithm can be well applied to the detection of camouflage targets with an average detection accuracy of 63.16%.
Educational activity of the Irkutsk branch of Krasnoyarsk Special Secondary School of Militia of the Ministry of Internal Affairs of the USSR: historical-pedagogical aspect

The subject of this research is the process of establishment and development of the Irkutsk branch of Krasnoyarsk Special Secondary School of Militia of the Ministry of Internal Affairs of the USSR from 1989 to 1991 and its subsequent professional pedagogical transformation. Detailed analysis is conducted on the historical-pedagogical peculiarities of the recruitment of academic staff of the branch at the initial stage. The author examines the question of professional characteristics in implementation of educational, line training, service and combat activity of nonpermanent and permanent staff of the school. Research methodology leans on the specific historical approach (M. V. Astakhov, I. D. Kovalchenko, V. F. Kolomiytsev, A. P. Pronstein, etc.), as well as generalization, comparison, synthesis, classification, concretization; analysis of archival documents, legislation and scientific literature; retrospective analysis; and method of historical analogies. In the course of research, the author determines and analyzes the factors that affected the establishment and development of the Irkutsk branch of Krasnoyarsk Special Secondary School of Militia of the Ministry of Internal Affairs of the USSR during the transformation of the political system in Russia. The peculiarities of implementation of pedagogical activity at the initial stage of educational institution are analyzed. The article provides the examples of interaction between the cadets and staff members of the branch with practical divisions. This topic draws the attention of wide audience due to the fact that in the XX century, the establishment and development of departmental education of system of the Ministry of Internal Affairs of the USSR in the territory of Baikal region has become a seminal event for the educational system of the entire Siberian region.