Reviewerly/exHarmony huggingface dataset:
Study of the formation of teenagers’ social success in activity of student self-government

The main goal of our research is to carry out a pedagogical reflection on the results of the formation of teenagers’ social success in activity of student self-government. The tasks of the specified work are to analyse the problem of the formation of teenagers’ social success in scientific and pedagogical literature and modern educational practice; to verify the need for the formation of teenagers’ social success in activity of student self-government; to carry out the effectiveness of student self-government in the process of the formation of teenagers’ social success.A complex of various methods of pedagogical diagnostics was applied in the research: analysis and synthesis of theoretical and scientific-methodical sources; comparison and generalization of data obtained during work with pedagogical teams and teenagers of educational institutions in order to determine teenagers’ knowledge and skills, which are necessary for the formation of teenagers’ social success. Empirical methods were questionnaires (offline and online), interviews, polls, and ratings in order to clarify the role of student self-governance in the formation of teenagers’ social success.Based on analysis of the scientific and pedagogical sources and modern educational practice, the results of testing the formation of teenagers’ social success in activity of student self-government were determined. According to the summarized empirical data and clarified problem areas in the formation of teenagers’ social success, the author verifies the organizational and technological support for the formation of teenagers’ social success in activity of student self-government that focuses on the development of students’ knowledge, abilities and skills, qualities, which are necessary for their conscious life choice and self-realization, socialization, gaining social experience, increasing their motivation for real actions and deeds.Moreover, the research focuses on the need for partnership between educators and student self-government. The involvement of student self-government in the process of developing the educational policy of the educational institution was determined. The effectiveness of student self-government as a basis for the formation of a socially successful personality in the realities of the modern educational process is revealed. Based on the data obtained, the important role of student self-governance in the formation of teenagers’ social success is proved.
A new scientific achievement as a factor of education of personality

The creation of any innovation as a direct subject of scientific activity requires a special methodological discourse. Moreover, it is about an educational innovation (methodology or technology). The specificity of the creative search is determined by the fact that formulating a new educational idea is possible only at the crossing of psychological and pedagogical reality, which already creates a moment of complexity. The usual (traditional) approach to educational innovations was reduced to the restructuring of independent (external) variables and the tracing of qualitative shifts in internal variables (factors) – personal qualities, traits, and values.The research involved the discovery of a psychological phenomenon of an emotional nature, which can be established as the internal basis of the actual educational action, procedurally deployed in the sequence of operations of the educator and student. This will act as a concrete method in action.Such an emotional phenomenon was a complex desire as a form of spiritual and moral need. It sounds like a statement: “I want to desire for justice, dignity, care, etc.”This emotional duality of desire doubles its energy force that caused increased significance of the object (for example, justice). Therefore, the directed spiritual value becomes a real motive for the corresponding act. This educational idea is consistently concretized in the systematic interactions of the educator and students, which can defined as an innovative educational psycho-methodology.The developmental perspective of an individual who has gone through a system of directed spiritual and moral education is revealed.
To the 100th anniversary of the birth of рrofessor Ivan Fedorovich Khudyakov (11.25.1921–04.11.1988)

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An Improved Online Reduction from PAC Learning to Mistake-Bounded Learning

A basic result in learning theory is that mistake-bounded learnability implies PAC learnability. It was shown in [Lit89] that, if a problem can be learned with M mistakes, it can be (ε, δ)-PAC-learned from samples. However, this reduction needs to store either samples or O(M) hypotheses. A different reduction, in [KLPV87], only needs to store O(1) samples and hypotheses but was only shown to work with samples.We give a refined analysis of the KLPV reduction, showing that it only uses samples with probability 1 - M-O(1). This gives the optimal sample complexity with only O(1) space overhead, for δ > M-O(1).
Security Risk Assessment Method of High Voltage Power Communication Network Based on Fuzzy Clustering

Due to the lack of preprocessing of evaluation index data in the process of high-voltage power communication network security risk evaluation, the evaluation effect is poor. Therefore, a high-voltage power communication network security risk evaluation method based on fuzzy clustering is proposed. By determining the influencing factors and evaluation indicators, the operation data of high-voltage power communication network are collected and preprocessed. According to the preprocessing results, the similarity of the operation data of high-voltage power communication network is measured, and the fuzzy clustering method is used to realize the safety risk assessment of high-voltage power communication network. The experimental results show that the security risk assessment results of the proposed method are consistent with the actual results, and the evaluation effect is good.
Belief $$\chi ^2$$ Divergence-Based Dynamical Complexity Analysis for Biological Systems

Physiological signals contain the information of physical state in healthy systems. Especially, the complexity of dynamical time series data is a valid indicator to measure the pathological states. However, how to quantify the complexity of physical signals is still an open issue. In this paper, a novel complexity analysis algorithm based on divergence, called DCA, is proposed to figure out the complexity of biological system time series. Specifically, DCA algorithm splits biological systems time series data into different slices with boundaries. In addition, the feature of each time series data is extracted by converted into the basic probability assignments (BPAs) based on the Dempster-Shafer (D-S) evidence theory. DCA algorithm considers that the average divergence of BPAs indicates the complexity in a piece of time series. Moreover, an application in cardiac inter-beat interval time series is carried out to demonstrate the effectiveness of the proposed algorithm, which performs well in a pathological states analysis issue.
Security Scheduling Method of Cloud Network Big Data Cluster Based on Association Rule Algorithm

In order to avoid unreasonable deployment and realize the orderly deployment of big data cluster nodes, a cloud network big data cluster security scheduling method based on association rule algorithm is proposed. According to the execution steps of association rule mining algorithm, the connection form of hadoop/mapreduce framework is determined, and then the security mapping conditions are jointly solved to complete the security performance analysis of cloud network based on association rule algorithm. Combine the obtained big data information parameters, set the link bandwidth time list structure, and solve the specific value of packet routing index according to the connection form of SDN scheduling system. The experimental results show that the cloud network big data information migration amount of the method in this paper reaches 6.50 × 107 MB, and the continuous occupation time of cluster nodes is less than 0.7 ms, which increases the unit migration amount of big data information and reduces the continuous occupation time of cluster nodes. It can solve the problem of unreasonable allocation of information parameters and realize the orderly deployment of big data cluster nodes.
Next Generation Leadership: The Rise of the Robot Servant-Leaders?

People have always dreamed of a selfless servant, a machine that serves humans (Domaine, 2008; Mayor, 2020), a helper who frees people from heavy labor. This dream has inspired the common notion and understanding of the role which robots take in our society: The robot is humans’ servant. The origin of the English word robot lies in the Czech word robota, which can be translated as “servitude” or “compulsory labor”, describing the compulsory service in the context of a feudal system (Launius &amp; McCurdy, 2012). Technological change progressed rapidly in the last few decades (Avadikyan et al., 2016). We have become accustomed to the role of the robot as a loyal servant (Helper et al., 2018). Today, machines autonomously mow our lawns, construct cars or even transport people in self-driving trains or buses. In order to regulate the coexistence between robots and humans, Isaac Asimov (2004) established three basic rules. Rule number 2 of his catalog corresponds to the notion of the robot as a servant of humans, “A robot must obey the orders given it by human beings.” But is Asimov’s principle still universally valid today? Now, in an ever-increasing number of cases, the principle seems to be reversed. Our research objective is to explore the theoretical feasibility of servant-leadership through machines. We deliberately choose the term robot leadership because we not only analyze AI Leadership, but also conventionally programmed Leadership software/programs. In other words, we examine whether machines may completely replace humans as servant-leaders. To summarize we can say that the core question of our paper is whether robots/machines can be programmed to lead not only transactionally but also in an inspirational and servant-led way.
Organizing Committee: The 5th International Conference on Mathematics and Science Education (ICoMSE) 2021

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Organizing Committee: The 5th International Conference on Mathematics and Science Education (ICoMSE) 2021. AIP Conf. Proc. 10 January 2023; 2569 (1): 010002. https://doi.org/10.1063/12.0015067 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
A Fall Detection Algorithm for Thigh mounted Smartphones using Random Forest and Feature Selection Techniques

Falls are sudden incidents that cause the body to go down onto the ground or toward the ground without intention or by accident. Falls can result in physical injuries and mental distress, especially in the elderly. Such results can cause serious disabilities and in the worst cases even death. Consequently, falls among the elderly are a widespread concern. That is one of the reasons why research on fall detection and prevention systems has gained considerable attention. Recently, smartphone (SP) based fall detection systems have attracted a lot of research efforts. Although there are several works using SPs for fall detection systems, most of them need to use additional external devices in addition to the SP and/or require the SPs to be positioned with a fixed orientation limiting their applicability. In daily life, people, most of the time, carry the SP in thigh locations such as the pockets of the pants. Therefore, this study focuses on the development of a reliable and highly accurate fall detection algorithm using thigh position SPs signals. The proposed algorithm employs a random forest classifier that incorporates feature selection techniques for an adequate selection of the best features. The proposed algorithm is trained and tested on the SP dataset used in a recent algorithm called FallDroid. Our simulation results show that the proposed approach achieves excellent fall detection scores outperforming the FallDroid.
Multi-Granularity Feature Distillation Learning Network for Fine-Grained Visual Classification

Fine-grained visual classification (FGVC) aims to identify objects belonging to multiple sub-categories of the same super-category (such as species of birds, models of cars and aircraft). The key to solving fine-grained classification problems is to learn discriminative visual feature representation with only subtle differences. Although previous work based on refined feature learning has made great progress, however, high-level semantic features often lack key information for fine-grained visual object nuances. How to efficiently integrate semantic information of different granularities from classification networks is a Critical. In this paper, we propose Multi-Granularity Feature Distillation Learning Network (MGFDL-Net). Our solution integrates multi-granularity hierarchical information through a multi-granularity fusion learning strategy to enhance feature representation. In view of the inherent challenges of large intraclass differences in FGVC, a cross-layer self-distillation regularization is proposed to strengthen the connection between high-level semantics and low-level semantics for robust multi-granularity feature learning. Comprehensive experiments show that our method achieves state-of-the-art performance on three challenging fine-grained visual classification FGVC datasets (CUB-200-2011, Stanford Cars and FGVC-Aircraft).
Robust image-based CAPTCHA generation using adversarial attack

CAPTCHA (Completely Automated Public Truing test to tell Computers and Humans Apart) is a technology used to protect network security, distinguishing humans and machines by tests that humans can easily pass but machines fail. However, with the wide application of CAPTCHAs, many cracking methods have emerged. Specifically, Deep Learning (DL) have played an important role in in destroying CAPTCHA, which brings a great threat to the security of CAPTCHAs. Therefore, it is of great significance to improve the security of the CAPTCHAs against cracking methods based on deep learning technology. This paper focuses on image-based CAPTCHAs which is the most widely used type, and proposes an image-based CAPTCHA generation method based on adversarial examples. Starting from the actual application scenario of CAPTCHA, we improve it on the basis of I-FGSM (Iterative Fast Gradient Sign Method) by randomly transforming the input samples and performing secondary processing to adversarial noise. Experiments show that our method can effectively improve the robustness and transferability of the CAPTCHA.
Cryptanalysis and improvement of the measurement-device-independent quantum key distribution with hyper-encoding

Recently, Cui et al. [Sci. China Phys. 62, 110311 (2019)] proposed a measurement-device-independent quantum key distribution (MDI-QKD) scheme based on high-dimensional encoding to increase the channel capacity of each individual photon. A secret key is shared among two legitimate parties via the help of a dishonest third-party (TP). They claimed that the proposed protocol can improve the drawback of the low-key generation rate that existed in the past MDI-QKD schemes. However, this study pointed out a loophole during the security check of TP. That is, the two legitimate participants merely use the corresponding qubit pairs that are both generated in the diagonal basis for the discussion, which leads to a potential attack that not only allows TP to avoid the security check but also creates partial inconsistency of key bits for both communicating sides. Fortunately, this problem can be fixed by a minor correction of the protocol.
Analysis of the mechanism of generative adversarial networks learning based on gray image

Machine learning has a crucial role in people's lives. Machine learning can be divided into four parts: supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. It can help people with image recognition, stock prediction, etc. In addition, machine learning has a subset called deep learning (DL). DL in machine learning can help people optimize and learn models better. DL in machine learning can help people optimize and learn models better. One of them is generative adversarial networks (GANs). The other one is Convolutional Neural Networks (CNNs). First, GANs have achieved impressive results in image generation, data enhancement, etc. However, effective intervention and control of the results of generative adversarial networks is a challenging problem. Based on grayscale image data, this paper investigates the change patterns of the generation results of generative adversarial networks in the face of different input data. There are three groups in the experiment. The first group is to explore the effect of the size of the dataset on the generated images. The second group is to explore the effect of the diversity of the book dataset on the results. The third group was to explore the effect of the overall dataset on the generative images. The experimentally generated images revealed that data size and diversity are important factors that affect the quality of the results generated by generative adversarial networks. Among other things, the increase in the amount of data does not always positively affect the generative results.
During the COVID-19 Pandemic, Everyone Had a Head-up Display

A head-up display has been part of my daily life for more than two decades, and people often ask why I wear one. This article makes explicit some of the benefits of video conferencing and shows how a head-worn display provides similar benefits when physically co-present in a conversation.
Researches advanced in object detection based on deep learning

Object detection is a popular direction in the field of computer research, and the main task is to accurately classify and localize object instances in images. Since the advent of deep learning technology, it has advanced significantly in both speed and accuracy. However, there is still potential for improvement in the areas of multi-scale object detection, weakly supervised object detection, and unsupervised object detection. In this research, we examine and describe the current mainstream object identification algorithms based on deep learning from the viewpoints of process, network structure, common data sets, and model training approaches, based on detailed reference to a vast quantity of existing literature. We also give improvement suggestions according to the main problems and look forward to the future research focus in the field of object detection.
Discussion on programming language teaching of information major based on Python language

This paper first analyzes the data visualization teaching in the university classroom, then draws the conclusion that the Matplotlib in Python language is suitable for data visualization teaching and data visualization teaching will be the inevitable trend of the development of information technology. Finally, an example is given to discuss the teaching practice of Matplotlib in data visualization teaching.
An Efficient Pre-processing Technique using HOLF for 2D Image Interpolation

A pre-processing interpolation method is suggested for producing edge preserved high resolution (HR) images of greater size from low resolution (LR) images. There are several interpolation techniques that cause non-uniform blurring in the upscaled image. Upscaling, like low pass filtering, produces blurring in the image’s edge region when compared to the flat region. Prior to upscaling, a pre-processing scheme is applied to mitigate blurring artifacts caused by interpolation in order to recover fine details and high frequency (HF). Using a combination of low pass and high pass filters, the LR is decomposed into various frequency sub-bands. The degraded HF bands are extracted and sharpened using higher order laplacian filters (HOLF). This method restores the HF details prior to interpolation to produce an HR image with no blurring artifacts. According to objective and subjective measures, the proposed algorithm outperforms other state-of-the-art methods.
Research on Explainable Recommendation Model Based on Knowledge Graph

In order to further obtain the best explanation paths and recommendation items that satisfy the preferences of users accurately and efficiently, a many-objective explainable recommendation model GrEA-ERS based on preferences of users is proposed, which designs pruning strategies to delete unsatisfied paths in knowledge graph based on on preferences of users, so as to reduce the search paths. Combined with the many-objective optimization algorithm GrEA, the paths in knowledge graph are used as decision variables, at which different explained paths correspond to different recommended items, and accuracy, diversity, novelty and explainability are optimized to obtain explained paths and their corresponding recommended items. Through extensive experiments, the model GrEA-ERS always outperforms other algorithms in terms of accuracy, novelty, diversity, and explainability.
Research on stream processing engine and benchmarking framework

Stream computing engine is an important part of big data system, and benchmarking is one of the main means to measure the engine's performance. In this paper, we compare the differences between two engines, Spark Streaming and Flink, in stream processing technologies. Then the open source benchmarking frameworks supporting stream processing and their respective characteristics are studied, and the HiBench testing framework is selected to test the two stream processing engines. The test results show that Flink is better than Spark Streaming in terms of performance in shuffle, stateful computation and windowed computation.
Multi-Object Tracking Based on Anchor-Free Detection and Message Passing Association

Due to the current multi-target tracking work, in the scene of dense and staggered targets, the tracked targets are prone to ID switch, which greatly affects the tracking effect. Aiming at this problem, this paper proposed a new multi-target tracking algorithm: using an improved anchor-free detection method and extracting features from multiple aspects, combined with the message passing graph neural network to participate in the data association process to further improve the exchange of feature information, so as to improve the algorithm's ability to associate target information data between frames and improve the tracking accuracy. After conducting quantitative analysis and comparison experiments on datasets such as MOT17 and MOT20, the experimental results show that the algorithm in this paper can effectively improve the problem of target ID switching and provide better tracking accuracy and performance.