Reviewerly/exHarmony huggingface dataset:
Introduction to Actuarial Science and the Actuary Profession at SMA PRAMITA Tangerang

Actuarial science is a science that applies mathematical and statistical science methods to assess risk. Currently actuaries are needed with large income. But ironically, many high school students do not know the knowledge and profession of actuaries. Community service activities are carried out as an actualization of knowledge from the Statistics Study Program at Matana University through an introductory seminar on actuarial science and the actuary profession given to Class X students of PRAMITA SMA Tangerang. Community service can be carried out very well. Positive responses from all seminar participants included the level of satisfaction (satisfied 11.6%, very satisfied 46.5% and very satisfied 41.9%), the usefulness of the event (100% useful), and the quality of the program (9.3 % good, 39.5% very good, 51.2% very good). This PKM activity is an implementation of the Mathematical Finance and Insurance Mathematics courses.
Research on SLAM Path Planning of ROS Robot based on Lidar

In order to improve the autonomous navigation performance of robot, an automatic mapping and path planning method based on ROS robot is proposed, which uses two-dimensional laser radar to collect the surrounding environment. Gmapping algorithm is used for automatic optimization and mapping, Class A algorithm is used for global path planning, and DWA method is used to realize the local obstacle avoidance function of the robot. Experiments show that this method can realize the automatic generation and automatic navigation of the robot.
A Survey of Face Recognition Methods based on Deep Learning

Since the 21st century, people have put forward higher requirements for information security technology in many fields such as society and economy. The requirement has promoted the development of biometric identification technology. Face recognition technology has become a research hotspot in biometric identification technology with its unique advantages. In recent years, with the development of deep learning, face recognition technology has made breakthroughs. Face recognition technology based on deep learning has been widely used in various fields such as finance, education, security, transportation, and new retail. In the process of face recognition technology becoming popular, some comprehensive literatures are urgently needed to summarize the methods of face recognition technology. Based on this, this paper first introduces the principle and existing problems of traditional face recognition methods, and then introduces in detail two typical face recognition methods based on deep learning—face recognition methods based on convolutional neural networks and face recognition methods based on deep belief networks. Finally, we provide an overview of common face datasets.
The Application of the Intelligent Learning Assistant based on Automatic Speech Recognition in the International Learning Environment

Recently, the intelligent learning assistant is applied widely in foreign countries like America, Europe and Africa. However, the accuracy of translation and AI dialogue is not satisfied among the Chinese analogous applications due to the trait of Chinese language. To solve these problems, we propose an application which can translate and recognize user’s questions. Based on Automatic Speech Recognition, we clarify the process to make it more accurate in practical use.
Upaya Meningkatkan Keterampilan Menulis Puisi dengan Penggunaan Media Video pada Siswa Kelas II SDIT Al-Fashih

This research is a type of Classroom Action Research (CAR) which was carried out in 2 cycles, each cycle consisting of 3 meetings. The research subjects were all second grade students of SDIT AL-FASIH with a total of 13 students, consisting of 6 boys and 7 girls. The procedure for implementing each cycle includes planning, implementing, observing, and reflecting. Data collection was obtained from teacher and student observation sheets, student poetry writing skills assessment sheets and documentation. Based on the results of the study, it can be concluded that the first is that there is an increase in the results of poetry writing skills using video media in class II SDIT AL-FASHIH students, this is evidenced by the results in the initial conditions before the action, namely with an average value of 65.38 or only 4 people whose score is reach KKM. In the first cycle there was an increase with an average value of 78.08 or 9 people whose scores reached the KKM. In the second cycle, it increased with an average value of 83.38 or 11 people whose scores reached the KKM. Second, improving the quality of the learning process for poetry writing skills using video media for class II SDIT AL-FASHIH students. The improvement in the quality of the learning process can be proven from the improvement in the learning process and teacher performance. The increase in teacher performance was proven in the average value of the first cycle of 77, while the second cycle with the average value of 84. The improvement in the learning process of writing poetry was evident in the average value obtained in each cycle. Thus, video media can be used to improve poetry writing skills in second grade students of SDIT AL-FASHIH.
The Impact of Social Media use on UG students Academic Achievement

Over the last few decades, social media use has grown significantly. Due to its expansion, it has become highly popular for UG students to communicate with one another. These social media platforms can be a useful way for kids to communicate with their teachers and one another. But excessive social media use can harm students' academic performance and cast doubt on this practice. By surveying UG students, this study examines the advantages and disadvantages of social media use on academic achievement. The poll looked at which social network UG students use the most and which is best for their academic abilities. There were 120 replies to the survey, and descriptive data demonstrate the correlation between the student's academic achievement and the number of hours spent on social media sites. This study's findings may help develop a practical strategy for raising students' academic performance through better use of social media platforms.
COMPUTER AIDED DIAGNOSTIC SYSTEM FOR WHOLE SLIDE IMAGE OF LIQUID BASED CERVICAL CYTOLOGY SAMPLE CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK

Cervical cancer screening with Papanicolaou test and liquid based cytology relies on the expertise of the pathologist. Liquid based cytology is proven to be more efficient than conventional Papanicolaou test when it comes to sample preparation and possibility of conducting several tests on the same sample. However, specificity and sensitivity of the test are in the range of the Papanicolaou test accuracy metrics, with false negative results still being the main drawback of these manually performed tests. Advances in technology and availability of digital data have enabled succesfull application of machine learning models in diagnostics. Images of cervical cells are now used as input to different deep learning models currently tested in studies concerning computer aided diagnostic systems. This study explores different architectures of convolutional neural network for cervical cancer detection based on Optomagnetic imaging spectroscopy and liquid based cytology samples. The proposed VGG16 based model achieved 93.3% sensitivity and 67.8% specificity in the binary classification problem. Results highlight the need for more balanced dataset in order for suggested deep model to achieve better performance.
Application of Big Data in Counter-Terrorism Intelligence Analysis and Early Warning

Counter-terrorism intelligence plays a critical role in counter-terrorism operations.This paper analyzes the inherent requirements of counter-terrorism intelligence work in the information age and proposes the application of Big Data in counter-terrorism intelligence from four aspects: the rapid collection of intelligence, storage and processing of intelligence, analysis and extraction of intelligence, and assistance in counter-terrorism combat decision-making.The application of Big Data technology to counter-terrorism intelligence analysis can solve the problem of complicated sources of counter-terrorism intelligence information and the ineffectiveness of traditional collection and analysis methods, thereby improving the level of counter-terrorism early warning capabilities.
Research on Chinese Named Entity Recognition Based on RoBERTa-BIGRU-MRC Model

Nested entity is the focus and difficulty of Chinese named entity recognition.The existing methods regard nested NER as two subtasks of Chinese word segmentation and sequence annotation.This method depends very much on the quality of input word vector, and low-quality word vector will lead to the error propagation of the model.To solve the above problems, a Chinese named entity recognition model based on RoBERTa-BiGRU-MRC is proposed.Firstly, RoBERTa is used to embed the entity type description and sentences to obtain the dynamic word vector.Secondly, BiGRU is used to extract contextual semantic features for further understanding of semantic information.Then two binary classifiers are constructed to better predict the probability value of the index at the beginning and end of the entity.Finally, the accuracy of the model is improved by constructing the loss function optimizer of predicted value and real value.Experiments were conducted on Chinese MSRA and onto4 data sets, and the accuracy, recall and F1 value were used as evaluation indexes.The experimental results show that the F1 value of the optimized model is 0.41 and 0.36 higher than that of the traditional sequence annotation model, respectively.
Multi-module Accounting Sharing Model from the Perspective of Regional Network

In order to improve the multi-module accounting sharing ability and the accuracy of shared data query, a multi-module accounting sharing model under the regional network perspective is proposed.The big data fusion and fuzzy association mining method are used to extract information and construct features of multi-module accounting big data.The adaptive learning algorithm is used to optimize the learning of multi-module accounting sharing big data.The effective feature quantity of multi-module accounting sharing big data is mined through efficient item set mining method, and the fuzzy feature distribution of multi-module accounting sharing big data is obtained.Based on big data technology, the application model of multi-module accounting sharing information platform is established.The distributed grid computing method is used for multi-module accounting sharing big data information fusion and hierarchical clustering.According to the clustering results, combined with the regional network networking design method, a multi-module accounting sharing platform model is constructed to realize multi-module accounting sharing.The experimental results show that the proposed method has a high precision ratio and a small root mean squared error, which can effectively improve the multi-module accounting sharing ability and the accuracy of shared data query.
Investment Strategy Based on LSTM Network and PSO Model

With the rapid development of economy, many people are keen to buy and sell unstable financial products to maximize their interests.This paper proposes an investment strategy based on Sharp ratio and neural network particle swarm optimization, which is able to predict the best time to buy, hold and sell various financial products through artificial intelligence based on the price flow data of the products over the past period of time.Taking cash, gold and bitcoin as examples, this paper conducts empirical research on the algorithm and obtains the final profit of the optimal investment scheme.Then, through the sensitivity analysis, we found that as the transaction fee increases, the number of transactions of gold and Bitcoin decreases significantly, and the value decreases.On the contrary, there is the same theory, which proves that our model is very good.However, the model proposed in this paper still has some shortcomings.In summary, although the model proposed in this paper has some shortcomings, its accuracy and stability are enough to solve this problem, so it can be explained again the accuracy of the model proposed in this paper.
A Study on the Combination Strategy of Quantitative Investment Trend Tracking EMA Triple Averages

In this paper, we use python to code implementation of the classic moving average double mean model in quantitative investment strategies and improve the original model.The improved algorithm generates trading signals on specific breakouts of the EMA triple averages for automatic trading and sets floating stoploss and take-profit points.The experimental results show that taking CSI 300 as an example, the moving average model can achieve 150% excess return in the decade from 2010 to 2020, and the improved triple-mean model can achieve 555% excess return, which has great investment potential in the experimental sense.
Organization of independent work teaching at the lessons of mathematics for support of the Classtime platform

In the article we analyzed the possibilities of the Classtime platform for all participants in the educational process. We considered the expediency of using this platform in the organization of independent work of students in mathematics lessons. Described the importance of introducing information and communication technologies in the educational process. Developed a fragment of a geometry lesson in the 7th grade using the Classtime platform.
Algorithmic competence of mathematics teachers as a component of professional competence

The article examines the concept of algorithmic competence, considers its role in the structure of professional competence of mathematics teachers. Ways of forming the algorithmic competence of future mathematics teachers are considered.
The use of Maple system in solving the problems of balance analysis

The article describes the current state of the use of computer mathematics systems and the advantages of using the Maple computer mathematics system. The possibility and relevance of using the Maple computer mathematics system in solving problems of balance analysis of the course of further mathematics for economic specialties is proved, and the promising aspects of further research are determined.
Problems and possibilities of e-learning "Philosophy"

The article presents a meaningful analysis of strategies, problems and challenges of e-learning of philosophy. It has been found that electronic teaching of philosophy is a natural step in the technologicalization of education, caused by the general informatization of society and specific challenges of today, which make traditional education impossible. It is shown that e-Learning, on the one hand, provides a whole range of new opportunities for acquiring new knowledge of philosophy and ways of creative realization of students, but, on the other hand, is accompanied by threats of a significant change in communication, the dominance of formal and technological aspects, which increases the need for professional ability teacher to motivate and interest students.&#x0D; The paper examines the problem of disposition of e-Learning philosophy.
Application of cloud technologies in mathematics learning process

The article highlights the main possibilities of using cloud technologies in the process of teaching mathematics; popular cloud services that are used in the teacher’s work.
The use of the Maple computer mathematics system in solving problems of physical content

The article describes the current state of the use of computer mathematics systems, the advantages of using the Maple computer mathematics system, the possibility and relevance of using the Maple computer mathematics system in solving physical problems are proved, and the promising areas for further research are identified.
Factors of Motivation of Students to Physical Culture Classes

The article deals with the issues of educational motivation of students in physical education classes in the implementation of programs of mixed type of education. The problem of motivational learning and sense-forming motives is investigated. The means and methods that influence the increase in the motivation of students are analyzed. The role of the application of information and communication technologies for interest in the discipline “Physical cultureˮ is noted. In conclusion, the factors contributing to the increase in the educational motivation of students for physical education classes are noted.
Research Training and Machine Learning: from Matching to Convergence

&lt;p&gt;Empowerment of human capabilities with new technologies that can increase labor productivity is an ever-increasing trend. The exponential rate of progress is demonstrated by artificial intelligence technologies. Existing applied solutions with the use of machine learning in pedagogy are analyzed, ways of its extrapolation to the convergence model of research training and machine learning are shown. As a basic idea, there are ideas generally accepted in the scientific community about the structure of education. The addition of this concept with the possibilities of quantitative content analysis made it possible to clarify the essence of &amp;ldquo;machine learning&amp;rdquo;, to substantiate its place among such related semantic concepts as &amp;ldquo;artificial intelligence&amp;rdquo; and &amp;ldquo;neural networks&amp;rdquo;. The applied systematic approach contributed to the identification of latent links between research training and machine learning, including the importance of a variety of structured and unstructured data on the subjects and objects of research training, the reliability of the data sources used. The SWOT analysis made it possible to substantiate the expediency of introducing and further developing the concept of &amp;ldquo;digital research profile&amp;rdquo; as one of the possible options for the convergence of man and machine, as well as to identify promising areas for the development of traditional pedagogical systems based on artificial intelligence.&lt;/p&gt;
Efficient comparison of independence structures of log-linear models

Log-linear models are a family of probability distributions which capture relationships between variables. They have been proven useful in a wide variety of fields such as epidemiology, economics and sociology. The interest in using these models is that they are able to capture context-specific independencies, relationships that provide richer structure to the model. Many approaches exist for automatic learning of the independence structure of log-linear models from data. The methods for evaluating these approaches, however, are limited, and are mostly based on indirect measures of the complete density of the probability distribution. Such computation requires additional learning of the numerical parameters of the distribution, which introduces distortions when used for comparing structures. This work addresses this issue by presenting the first measure for the direct and efficient comparison of independence structures of log-linear models. Our method relies only on the independence structure of the models, which is useful when the interest lies in obtaining knowledge from said structure, or when comparing the performance of structure learning algorithms, among other possible uses. We present proof that the measure is a metric, and a method for its computation that is efficient in the number of variables of the domain.
Representation Separation for Semantic Segmentation with Vision Transformers

Vision transformers (ViTs) encoding an image as a sequence of patches bring new paradigms for semantic segmentation.We present an efficient framework of representation separation in local-patch level and global-region level for semantic segmentation with ViTs. It is targeted for the peculiar over-smoothness of ViTs in semantic segmentation, and therefore differs from current popular paradigms of context modeling and most existing related methods reinforcing the advantage of attention. We first deliver the decoupled two-pathway network in which another pathway enhances and passes down local-patch discrepancy complementary to global representations of transformers. We then propose the spatially adaptive separation module to obtain more separate deep representations and the discriminative cross-attention which yields more discriminative region representations through novel auxiliary supervisions. The proposed methods achieve some impressive results: 1) incorporated with large-scale plain ViTs, our methods achieve new state-of-the-art performances on five widely used benchmarks; 2) using masked pre-trained plain ViTs, we achieve 68.9% mIoU on Pascal Context, setting a new record; 3) pyramid ViTs integrated with the decoupled two-pathway network even surpass the well-designed high-resolution ViTs on Cityscapes; 4) the improved representations by our framework have favorable transferability in images with natural corruptions. The codes will be released publicly.
OVO: One-shot Vision Transformer Search with Online distillation

Pure transformers have shown great potential for vision tasks recently. However, their accuracy in small or medium datasets is not satisfactory. Although some existing methods introduce a CNN as a teacher to guide the training process by distillation, the gap between teacher and student networks would lead to sub-optimal performance. In this work, we propose a new One-shot Vision transformer search framework with Online distillation, namely OVO. OVO samples sub-nets for both teacher and student networks for better distillation results. Benefiting from the online distillation, thousands of subnets in the supernet are well-trained without extra finetuning or retraining. In experiments, OVO-Ti achieves 73.32% top-1 accuracy on ImageNet and 75.2% on CIFAR-100, respectively.
Swin MAE: Masked Autoencoders for Small Datasets

The development of deep learning models in medical image analysis is majorly limited by the lack of large-sized and well-annotated datasets. Unsupervised learning does not require labels and is more suitable for solving medical image analysis problems. However, most of the current unsupervised learning methods need to be applied to large datasets. To make unsupervised learning applicable to small datasets, we proposed Swin MAE, which is a masked autoencoder with Swin Transformer as its backbone. Even on a dataset of only a few thousand medical images and without using any pre-trained models, Swin MAE is still able to learn useful semantic features purely from images. It can equal or even slightly outperform the supervised model obtained by Swin Transformer trained on ImageNet in terms of the transfer learning results of downstream tasks. The code is publicly available at https://github.com/Zian-Xu/Swin-MAE.