Reviewerly/exHarmony huggingface dataset:
Peculiarities of international activities of Russian universities during the pandemic

Introduction . The peculiarities of international activities of Russian universities in the context of the pandemic consists of many factors, such as the number of foreign students from near or far abroad, the degree of their adaptation, IT skills and equipment, and readiness to switch to distance teaching, the peculiarities of their educational field and practices. Purpose setting . The main objective of the article is to analyze the peculiarities of international activities of Russian universities during the pandemic. Methodology and methods of the study . The materials used in the article present the analysis of semi-structured interviews with rectors, vice-rectors and persons responsible for international activities of the universities. To conduct 17 semi-structured interviews of Novosibirsk universities' representatives for international affairs (or employees in a similar position, for example, heads of international services) we developed a questionnaire. Results . The core of each semi-structured interview are several blocks of discussion of problems: features of activities of the international services of universities during the pandemic; development of quality of education and language training; problems of adaptation and extracurricular activities of foreign students during the pandemic; foreign students' feedback issues during the pandemic; mechanisms and ways to attract foreign students during the pandemic. Conclusion . The obtained results allows us to conclude that there are common problems and specific ones, typical for universities dealing with a specific educational field. We formulated the main conclusions related to the improvement of the activities of international departments in new conditions. The article will be of interest to employees of international departments, teachers working with foreign students, heads of universities.
Exploration and research on the construction of electrical and electronics curriculum group of cloud classroom+integration of theory and practice

Based on the teaching method of "cloud classroom+integration of theory and practice", the curriculum group system of electrical and electronics is constructed according to the cloud platform. According to the training goal of electromechanical talents in our school, the main courses of electrical and electronics are divided based on the cloud platform, and the practice links of electrical and electronics courses group are run through with the help of "skill competition+subject competition". By using cloud classroom, mathematical knowledge and professional knowledge can be integrated into the teaching process, and the mathematical knowledge points involved in the courses of electrical engineering and electronics can be strengthened to improve the theoretical level of students. It aims to cultivate high- quality and high-level skilled talents who can understand both theory and practice and improve teachers' professional level and teaching efficiency.
End-to-end image dehazing by joint atmospheric scattering and WGAN model

The performance of most existing dehazing methods are limited by the independency of the transmission map estimation and atmospheric light. To ameliorate this, we present an algorithm based on both GANs models and atmospheric scattering. We first enhance the accuracy of transmission map estimation by constructing a multi-scale FC-DenseNet transmission map prediction network. To effectively avoids the issue of calculation errors caused by inaccurate prediction of the transmission map, a shallow residual dehazing network is also developed, which shares the learned weights of the transmission map prediction network. A multi-task loss function is constructed that dynamically optimizes the two networks simultaneously. To augment the generation of realistic images, a classical WGAN is used to fine-tune the generated transmission maps and dehazed images. The results demonstrate that our method has achieved significant improvements compared with the state-of-the-art (SOTA) methods.
A lightweight vision transformer with symmetric modules for vision tasks

Transformer-based networks have demonstrated their powerful performance in various vision tasks. However, these transformer-based networks are heavyweight and cannot be applied to edge computing (mobile) devices. Despite that the lightweight transformer network has emerged, several problems remain, i.e., weak feature extraction ability, feature redundancy, and lack of convolutional inductive bias. To address these three problems, we propose a lightweight visual transformer (Symmetric Former, SFormer), which contains two novel modules (Symmetric Block and Symmetric FFN). Specifically, we design Symmetric Block to expand feature capacity inside the module and enhance the long-range modeling capability of attention mechanism. To increase the compactness of the model and introduce inductive bias, we introduce convolutional cheap operations to design Symmetric FFN. We compared the SFormer with existing lightweight transformers on several vision tasks. Remarkably, on the image recognition task of ImageNet [13], SFormer gains 1.2% and 1.6% accuracy improvements compared to PVTv2-b0 and Swin Transformer, respectively. On the semantic segmentation task of ADE20K [64], SFormer delivers performance improvements of 0.2% and 0.7% compared to PVTv2-b0 and Swin Transformer, respectively. On the cityscapes dataset [11], SFormer delivers performance improvements of 2.5% and 4.2% compared to PVTv2-b0 and Swin Transformer, respectively. The code is open-source and available at: https://github.com/ISCLab-Bistu/Symmetric_Former.git.
Reproductive and productive approaches to knowledge and its assimilation in the process of studying at the university

Introduction . The article shows that the attitude to reproductive assimilation of knowledge, which is often used in education (both at school and at university, although to a lesser extent in the latter), is in principle erroneous, since it is associated with a misconception about the very essence of knowledge. In this case, the thought is directed not at the truth, but at the product of the process of cognition carried out earlier by someone else. Purpose setting . The problem lies in the contradiction between the idea that knowledge is separable from personality, and the fact that truth is comprehended only in the direct experience of personality. Methodology and methods of the study . The phenomenological method is used (to clarify the essence of knowledge as a phenomenon), hermeneutical (to identify the existential source of the phenomenon of knowledge itself), the method of ascent from the abstract to the concrete (when considering the problem of the correlation of historical and logical in cognition). Results . It is shown that the personal principle cannot be removed from the cognitive act, since for the thinker the truth appears only as a form of self-realization of his being. Therefore, a work of culture created by a thinker (text, treatise, etc.) does not act as a means of storing knowledge (from where they could be extracted and made the property of one's own thought), but plays the role of a tool with which the author of this work carried out the act of establishing a direct connection with the true essence of the thing or phenomenon he comprehends. Conclusion . So, the use of reproductive methods in teaching leads not only to the impossibility of educating creators, but even consumers, since in this case the student is not able to fully assimilate the existing knowledge in order to use it effectively.
Multi-sensor system fusion estimation based on improved Kalman filtering

For the sampling points of sensors at different moments, a sequential asynchronous algorithm to improve multi-sensor fusion is proposed after using the idea of sequential discretization to achieve fusion estimation of position states. It is assumed that the system uses N sensors with different sampling rates for the observed position states. Among them, a multi-rate Kalman filter is designed to estimate the target position at multiple scales with multiple sampling rates. The equation of state between the different sampling points during each sampling moment of the fusion cycle can be obtained as well as the measurement equation at this point in the cycle. Using an optimized linear Kalman filtering algorithm, a sequential fusion process of signals observed by asynchronous sampling is investigated in one cycle to achieve a good estimation of the target position. The effectiveness of the improved algorithm is verified by a numerical example simulation, and the advantages of the algorithm in terms of less computation, good real-time performance and high estimation of fusion accuracy are evident.
Operation of an Electrical-Only-Contact Photonic Integrated Chip for Quantum Random Number Generation Using Laser Gain-Switching

We present the results of the detected voltage distribution of a quantum random number generator (QRNG) based on a photonic integrated circuit comprising a semiconductor laser, delay interferometer and photodetector. We find that the integrated QRNG system behaves as expected for a QRNG from discrete gain-switched laser sources, especially exhibiting all of the peculiarities of the random voltage distribution and behaving as previously demonstrated for a discrete optical component setup. The biggest advantage of having all of the components integrated into a single chip is that only electrical connections are needed to operate the system, without the need for tricky and expensive optical alignment to external circuitry. We supply results showing that a random bit stream created from the random numbers passes the NIST statistical test suite tests, thus demonstrating the feasibility to generate random numbers via quantum means at gigabit/s rates from a single photonic integrated circuit. All of our results are backed by numerical simulations.
Improving the Performance of Object Detection by Preserving Balanced Class Distribution

Object detection is a task that performs position identification and label classification of objects in images or videos. The information obtained through this process plays an essential role in various tasks in the field of computer vision. In object detection, the data utilized for training and validation typically originate from public datasets that are well-balanced in terms of the number of objects ascribed to each class in an image. However, in real-world scenarios, handling datasets with much greater class imbalance, i.e., very different numbers of objects for each class, is much more common, and this imbalance may reduce the performance of object detection when predicting unseen test images. In our study, thus, we propose a method that evenly distributes the classes in an image for training and validation, solving the class imbalance problem in object detection. Our proposed method aims to maintain a uniform class distribution through multi-label stratification. We tested our proposed method not only on public datasets that typically exhibit balanced class distribution but also on private datasets that may have imbalanced class distribution. We found that our proposed method was more effective on datasets containing severe imbalance and less data. Our findings indicate that the proposed method can be effectively used on datasets with substantially imbalanced class distribution.
Will artificial intelligence replace the human mind?

The purpose of the article is to obtain new knowledge about artificial intelligence and to form priorities in the framework of scientific research. Contains a generalization of the experience of intensive development of artificial intelligence technologies. Scientists note that as artificial intelligence develops, its potential in education as a teacher, partner, human-machine system is increasing more and more. In the process of implementing artificial intelligence in education, technological, information, methodological and psychological problems arise. There are no methodological foundations and methodological recommendations for the use of artificial intelligence in education.
A Study on Generative Models for Visual Recognition of Unknown Scenes Using a Textual Description

In this study, we investigate the application of generative models to assist artificial agents, such as delivery drones or service robots, in visualising unfamiliar destinations solely based on textual descriptions. We explore the use of generative models, such as Stable Diffusion, and embedding representations, such as CLIP and VisualBERT, to compare generated images obtained from textual descriptions of target scenes with images of those scenes. Our research encompasses three key strategies: image generation, text generation, and text enhancement, the latter involving tools such as ChatGPT to create concise textual descriptions for evaluation. The findings of this study contribute to an understanding of the impact of combining generative tools with multi-modal embedding representations to enhance the artificial agent's ability to recognise unknown scenes. Consequently, we assert that this research holds broad applications, particularly in drone parcel delivery, where an aerial robot can employ text descriptions to identify a destination. Furthermore, this concept can also be applied to other service robots tasked with delivering to unfamiliar locations, relying exclusively on user-provided textual descriptions.
Emotion analysis and design of internet users based on big data

In the era of rapid development of the Internet, interactive platforms such as Weibo, Zhihu and Douban have developed very rapidly. The appearance of each hot event will bring the heated discussion of netizens, showing all kinds of netizens' emotions. By analyzing the emotional data, we can quickly obtain the positive and negative emotions of netizens, which can provide technical support for the marketing of commodities and the prevention of cyber crimes. This paper designs a netizen emotion analysis and visualization system based on social data by using platforms such as Douban and Weibo. This model takes the novel coronavirus outbreak event as the case for analysis, uses data mining, machine learning, data visualization and other methods to mine potential important information in the data, and uses the clustering method to analyze and classify emotions. Quickly obtain the positive and negative emotions of Internet users, study the emotional changes of Internet users under different states of the epidemic, and conduct correlation analysis between the epidemic events and the emotions of Internet users. It will help governments at all levels to accurately grasp the situation of online public opinion and conduct public opinion guidance and epidemic prevention and control work.
Design of bridge crack detection algorithm based on wireless sensor network

The quality and safety of bridges are related to the national economy and people's livelihood, and to thousands of families. The reason for bridge collapse is often due to the lack of scientific and timely bridge disease detection. However, when the bridge is completed and put into use, it will be repeatedly crushed by external forces and affected by natural factors such as rain, snow, flood, freezing and earthquake. As one of the most important bridge diseases, cracks seriously affect the safe operation of bridges, and even lead to accidents of bridge destruction and death. In view of this situation, this paper studies the bridge crack detection based on wireless sensor network. By setting appropriate threshold, the background and target are divided and the relevant feature information of cracks is extracted. It is composed of an off-line data processing system and a data acquisition vehicle. Finally, through the evaluation and comparison, the favorable rate of this algorithm is higher than the other two algorithms, and the favorable rate will be as high as 88.42%. In the test of running time, the running time of this algorithm is shorter than that of the other two algorithms, and the average running time is only 6.02 seconds. It can be seen that this algorithm is more suitable for bridge crack detection.
Super-Resolution Imaging Enhancement through a 2D Scanning Galvanometer: Algorithm Formulation and Application in Aerial Optoelectronic Systems

As the fields of aviation and aerospace optics continue to evolve, there is an increasing demand for enhanced detection capabilities in equipment. Nonetheless, in applications where both optical and mechanical constraints are stringent, the continuous expansion of optical aperture and focal length is impractical. Given the existing technological landscape, employing super-resolution algorithms to enhance the imaging capability of optical systems is both practical and highly relevant. This study capitalizes on using a 2D scanning galvanometer in optical systems to acquire micro-displacement information. Initially, an imaging model for optical systems equipped with a 2D scanning galvanometer was established, and the displacement vectors for both forward and sweep image motions were defined. On this foundation, we incorporated micro-displacement information that can induce high-frequency aliasing. Subsequently, the motion paths of the galvanometer were planned and modeled. To align image sequences with micro-displacement correlations, the Lucas–Kanade (L-K) optical flow method was employed with multi-layer pyramid iteration. Then, super-resolution reconstruction was performed using kernel regression techniques. Ultimately, we tested the algorithm on an aeronautical optoelectronic pod to evaluate its impact on optical resolution and imaging quality. Compared with the original images, the 16-frame image demonstrated a 39% improvement in optical resolution under laboratory conditions. Moreover, the algorithm exhibited satisfactory performance under both nighttime and daytime conditions, as well as during aerial tests.
Multi-Target Tracking AA Fusion Method for Asynchronous Multi-Sensor Networks

Aiming at the problem of asynchronous multi-target tracking, this paper studies the AA fusion optimization problem of multi-sensor networks. Firstly, each sensor node runs a PHD filter, and the measurement information obtained from different sensor nodes in the fusion interval is flood communicated into composite measurement information. The Gaussian component representing the same target is associated with a subset by distance correlation. Then, the Bayesian Cramér–Rao Lower Bound of the asynchronous multi-target-tracking error, including radar node selection, is derived by combining the composite measurement information representing the same target. On this basis, a multi-sensor-network-optimization model for asynchronous multi-target tracking is established. That is, to minimize the asynchronous multi-target-tracking error as the optimization objective, the adaptive optimization design of the selection method of the sensor nodes in the sensor network is carried out, and the sequential quadratic programming (SQP) algorithm is used to select the most suitable sensor nodes for the AA fusion of the Gaussian components representing the same target. The simulation results show that compared with the existing algorithms, the proposed algorithm can effectively improve the asynchronous multi-target-tracking accuracy of multi-sensor networks.
Is Africa ready to join the blockchain revolution: a feasibility study

This study assesses the readiness of African governments to adopt blockchain technology, which has gained global recognition since the emergence of bitcoin in 2008. The paper examines whether African governments, given their political, economic, and social challenges, are prepared to embrace the blockchain revolution. The research focuses on a feasibility study of several African countries, including Nigeria, South Africa, and the Central African Republic, using a mixed-methods approach that combines quantitative and qualitative data analysis methods. The author draws on Jennifer Bridges' four interdependent elements of a feasibility study, which include political feasibility, economic feasibility, technical capacities, and legal and regulatory compliance.&#x0D; The study examines how blockchain technology can provide transparent and reliable systems to combat corruption and mismanagement in government structures, which contribute to poor service delivery in the region. Furthermore, the research evaluates the potential positive and negative outcomes of blockchain implementation on African development, such as an increase in foreign direct investment and productivity, and a reduction in wasteful expenditure.
Abnormal data detection method of smart substation based on K-means-SVM

The safe operation of smart substation is an important guarantee to ensure the stability of power supply. Aiming at the problems of substation data anomaly identification caused by large amount of substation data, complex data and high dimension, a smart substation anomaly data detection method based on K-means-SVM is proposed in this paper. Firstly, the unlabeled substation data is transformed into labeled data by K-means method, and then the unsupervised classification results provided by K-means are used as the training data of SVM. After SVM training, use the trained plane to test the two types of data obtained by K-means clustering, use the accurate data predicted by SVM to retrain the SVM segmentation plane, and iteratively update the SVM segmentation plane according to this method until the error number of SVM prediction data is not changed. This method can effectively improve the accuracy and efficiency of substation abnormal data identification, so as to improve the operation and maintenance safety and equipment maintenance ability.
Design of university student information management system based on teaching resource sharing

Many universities adopt information management systems based on C/S architecture. Although this kind of management system can realize information processing and sharing in a certain range, it cannot share information effectively with the expansion of the scope, which has great limitations. In this paper, a mixed architecture of C/S and B/S is adopted to design and implement a college student information management system based on teaching resource sharing, which is realized based on Web technology, and an improved server flow scheduling algorithm combined with proxy cache prefix is used to maximize the utilization of system resources. In the design and implementation of this system, many efforts have been made in reusability, maintainability, upgradeability and stability. In addition, all links should get enough documents and technical information, which can make the handling of matters more convenient. The function and standard of this system can meet the reality.