The Present Situation and Future Prospect of Online Fitness in the Post-Epidemic Era

After the COVID-19 epidemic broke out around the world, large-scale home isolations have restricted the activities of ordinary residents. Therefore, online activities have become more frequent and online fitness have ushered in a new round of large-scale rise. The concept of national fitness has gradually rooted in the hearts of the people. The 14th five-year Plan of the people\u27s Republic of China for National Economic and Social Development and the outline of long-term goals for 2035 further make it clear that it is necessary to create new advantages in the digital economy and promote the digital transformation of the industry. Under the background of the epidemic, the online fitness industry promotes the digital transformation of China\u27s sports industry and provides new ideas and directions for it. Combined with the current social background, this study was to expound the development status of online fitness in the post-epidemic era and put forward prospects and suggestions for the future development of online fitness. This study took the global online fitness market as the research object and mainly used the literature method and data analysis method with two main purposes: (1) Investigating the current status of online fitness development and relevant national policies on national fitness and the promotion of industrial digital transformation through online literature platforms such as CNKI and Wanfang platform and online news platforms such as Xinhuanet, and serve as the research background of this article . (2) Collecting relevant second-hand data from the global online fitness market through online database platforms such as the Sports Information Network and the China Economic and Social Big Data Research Platform and analyzing relevant data on the online fitness market before and after the outbreak of the new crown epidemic. The findings showed the necessity of digital transformation in the sports industry. The vigorous development of emerging digital industries such as artificial intelligence, big data, and cloud computing has brought human society into a new era of digitalization. In the context of digitalization, the digital transformation of all walks of life has become an inevitable trend for the survival and development of the industry. In 2020, the global digital economy will reach US$32.6 trillion, a nominal year-on-year increase of 3.0$6.04 billion. In 2021, the scale of the global online fitness industry reached US$10.71 billion, an increase of 77.33%. Although the epidemic has restricted residents’ outing activities, more and more people have begun to choose online sports, including the use of smart wearable devices, online app guidance and recording, etc., which has brought online fitness models. In the context of the epidemic, the online fitness model has greatly promoted the implementation of the national fitness policy and is also an important path for the digital transformation of the sports industry. It is suggested that the digital transformation of the sports industry is an important direction for the future development of the sports industry. In the context of technological development and support and the new crown pneumonia epidemic, online fitness, an emerging fitness model, has emerged and has become an important fitness model, and at the same time has promoted the digital transformation of the sports industry. Online fitness should focus on the development of personalized customization of fitness courses and programs to meet the individual needs of users; strengthen the association with social platforms to increase user stickiness; dig deep into user data, identify user pain points, and then take advantage of their products make improvements with services

Field-aware Calibration: A Simple and Empirically Strong Method for Reliable Probabilistic Predictions

It is often observed that the probabilistic predictions given by a machine learning model can disagree with averaged actual outcomes on specific subsets of data, which is also known as the issue of miscalibration. It is responsible for the unreliability of practical machine learning systems. For example, in online advertising, an ad can receive a click-through rate prediction of 0.1 over some population of users where its actual click rate is 0.15. In such cases, the probabilistic predictions have to be fixed before the system can be deployed. In this paper, we first introduce a new evaluation metric named field-level calibration error that measures the bias in predictions over the sensitive input field that the decision-maker concerns. We show that existing post-hoc calibration methods have limited improvements in the new field-level metric and other non-calibration metrics such as the AUC score. To this end, we propose Neural Calibration, a simple yet powerful post-hoc calibration method that learns to calibrate by making full use of the field-aware information over the validation set. We present extensive experiments on five large-scale datasets. The results showed that Neural Calibration significantly improves against uncalibrated predictions in common metrics such as the negative log-likelihood, Brier score and AUC, as well as the proposed field-level calibration error.Comment: WWW 202

Template-Instance Loss for Offline Handwritten Chinese Character Recognition

The long-standing challenges for offline handwritten Chinese character recognition (HCCR) are twofold: Chinese characters can be very diverse and complicated while similarly looking, and cursive handwriting (due to increased writing speed and infrequent pen lifting) makes strokes and even characters connected together in a flowing manner. In this paper, we propose the template and instance loss functions for the relevant machine learning tasks in offline handwritten Chinese character recognition. First, the character template is designed to deal with the intrinsic similarities among Chinese characters. Second, the instance loss can reduce category variance according to classification difficulty, giving a large penalty to the outlier instance of handwritten Chinese character. Trained with the new loss functions using our deep network architecture HCCR14Layer model consisting of simple layers, our extensive experiments show that it yields state-of-the-art performance and beyond for offline HCCR.Comment: Accepted by ICDAR 201

Traffic Volume Forecasting Model of Freeway Toll Stations During Holidays – An SVM Model

Support vector machine (SVM) models have good performance in predicting daily traffic volume at toll stations, however, they cannot accurately predict holiday traffic volume. Therefore, an improved SVM model is proposed in this paper. The paper takes a toll station in Heilongjiang, China as an example, and uses the daily traffic volume as the learning set. The current and previous 7-day traffic volumes are used as the dependent and independent variables for model learning, respectively. This paper found that the basic SVM model is not accurate enough to forecast the traffic volume during holidays. To improve the model accuracy, this paper first used the SVM model to forecast non-holiday traffic volumes, and proposed a prediction method using quarterly conversion coefficients combined with the SVM model to construct an improved SVM model. The result of the prediction showed that the improved SVM model in this paper was able to effectively improve accuracy, making it better than in the basic SVM and GBDT model, thus proving the feasibility of the improved SVM model