A Two-Stage Real-time Prediction Method for Multiplayer Shooting E-Sports

E-sports is an industry with a huge base and the number of people who pay attention to it continues to rise. The research results of E-sports prediction play an important role in many aspects. In the past game prediction algorithms, there are mainly three kinds: neural network algorithm, AdaBoost algorithm based on Naïve Bayesian (NB) classifier and decision tree algorithm. These three algorithms have their own advantages and disadvantages, but they cannot predict the match ranking in real time. Therefore, we propose a real-time prediction algorithm based on random forest model. This method is divided into two stages. In the first stage, the weights are trained to obtain the optimal model for the second stage. In the second stage, each influencing factor in the data set is corresponded to and transformed with the data items in the competition log. The accuracy of the prediction results and its change trend with time are observed. Finally, the model is evaluated. The results show that the accuracy of real-time prediction reaches 92.29%, which makes up for the shortage of real-time in traditional prediction algorithm

The influence of online Danmu on users\u27 reward behavior: Based on the data of Douyu live broadcast

In live streaming, the Danmu is a crucial technique of interaction, and the reward is the interaction\u27s feedback. The audience receives more input through the reward the more frequently they interact. The effect of the bullet screen in the live broadcast on the audience\u27s reward behavior was investigated by gathering data from the live broadcast room 5720533 on Douyu, a domestic Danmu live-streaming website, from February 14 to February 24, 2021. Based on empirical research, the following conclusions can be drawn: the number of user Danmu, the proportion of fan Danmu, the number of user entry Danmu, and the number of super Danmu will all significantly improve users\u27 reward, while personal experience attenuates the positive impact of the number of user access Danmu and the number of super Danmu on the impact of user reward. The study\u27s findings will offer theoretical justification for the creation of live broadcast platforms, the upkeep of anchors\u27 notoriety, and users\u27 rational consumption

Jigsaw: Learning to Assemble Multiple Fractured Objects

Automated assembly of 3D fractures is essential in orthopedics, archaeology, and our daily life. This paper presents Jigsaw, a novel framework for assembling physically broken 3D objects from multiple pieces. Our approach leverages hierarchical features of global and local geometry to match and align the fracture surfaces. Our framework consists of three components: (1) surface segmentation to separate fracture and original parts, (2) multi-parts matching to find correspondences among fracture surface points, and (3) robust global alignment to recover the global poses of the pieces. We show how to jointly learn segmentation and matching and seamlessly integrate feature matching and rigidity constraints. We evaluate Jigsaw on the Breaking Bad dataset and achieve superior performance compared to state-of-the-art methods. Our method also generalizes well to diverse fracture modes, objects, and unseen instances. To the best of our knowledge, this is the first learning-based method designed specifically for 3D fracture assembly over multiple pieces.Comment: 17 pages, 9 figure

Ethicist: Targeted Training Data Extraction Through Loss Smoothed Soft Prompting and Calibrated Confidence Estimation

Large pre-trained language models achieve impressive results across many tasks. However, recent works point out that pre-trained language models may memorize a considerable fraction of their training data, leading to the privacy risk of information leakage. In this paper, we propose a method named Ethicist for targeted training data extraction through loss smoothed soft prompting and calibrated confidence estimation, investigating how to recover the suffix in the training data when given a prefix. To elicit memorization in the attacked model, we tune soft prompt embeddings while keeping the model fixed. We further propose a smoothing loss that smooths the loss distribution of the suffix tokens to make it easier to sample the correct suffix. In order to select the most probable suffix from a collection of sampled suffixes and estimate the prediction confidence, we propose a calibrated confidence estimation method, which normalizes the confidence of the generated suffixes with a local estimation. We show that Ethicist significantly improves the extraction performance on a recently proposed public benchmark. We also investigate several factors influencing the data extraction performance, including decoding strategy, model scale, prefix length, and suffix length. Our code is available at https://github.com/thu-coai/Targeted-Data-Extraction.Comment: ACL 2023 Long Paper (Main Conference