University students information support software module based on harmonyos

This article introduce the University students information support software module based on HarmonyOS

Forecasting of Economic Value Added in Entertainment Industry

This thesis is to evaluate the past performance and predict the future financial performance on the basis of real data of the entertainment company, Ubisoft Entertainment SA. We describes financial analysis and performance measures, which includes the characteristics of the economic value added. The prediction of economic value added is based on the prediction of financial plan by using Monte Carlo simulation in Excel.Finally, we make a conclusion on the financial situation of the company in the past ten years, evaluate the feasibility of Monte Carlo simulation and the investment feasibility of the company.This thesis is to evaluate the past performance and predict the future financial performance on the basis of real data of the entertainment company, Ubisoft Entertainment SA. We describes financial analysis and performance measures, which includes the characteristics of the economic value added. The prediction of economic value added is based on the prediction of financial plan by using Monte Carlo simulation in Excel.Finally, we make a conclusion on the financial situation of the company in the past ten years, evaluate the feasibility of Monte Carlo simulation and the investment feasibility of the company.154 - Katedra financívelmi dobř

Towards Trustworthy Explanation: On Causal Rationalization

With recent advances in natural language processing, rationalization becomes an essential self-explaining diagram to disentangle the black box by selecting a subset of input texts to account for the major variation in prediction. Yet, existing association-based approaches on rationalization cannot identify true rationales when two or more snippets are highly inter-correlated and thus provide a similar contribution to prediction accuracy, so-called spuriousness. To address this limitation, we novelly leverage two causal desiderata, non-spuriousness and efficiency, into rationalization from the causal inference perspective. We formally define a series of probabilities of causation based on a newly proposed structural causal model of rationalization, with its theoretical identification established as the main component of learning necessary and sufficient rationales. The superior performance of the proposed causal rationalization is demonstrated on real-world review and medical datasets with extensive experiments compared to state-of-the-art methods.Comment: In Proceedings of the 40th International Conference on Machine Learning (ICML) GitHub Repository: https://github.com/onepounchman/Causal-Retionalizatio

Facial expression recognition method on static and dynamic image

This article is dedicated to analyze various facial expression recognition method based on different type of image, which aim at extracting feature on the image. Considering the method to optimize existing method

Localized Contrastive Learning on Graphs

Contrastive learning methods based on InfoNCE loss are popular in node representation learning tasks on graph-structured data. However, its reliance on data augmentation and its quadratic computational complexity might lead to inconsistency and inefficiency problems. To mitigate these limitations, in this paper, we introduce a simple yet effective contrastive model named Localized Graph Contrastive Learning (Local-GCL in short). Local-GCL consists of two key designs: 1) We fabricate the positive examples for each node directly using its first-order neighbors, which frees our method from the reliance on carefully-designed graph augmentations; 2) To improve the efficiency of contrastive learning on graphs, we devise a kernelized contrastive loss, which could be approximately computed in linear time and space complexity with respect to the graph size. We provide theoretical analysis to justify the effectiveness and rationality of the proposed methods. Experiments on various datasets with different scales and properties demonstrate that in spite of its simplicity, Local-GCL achieves quite competitive performance in self-supervised node representation learning tasks on graphs with various scales and properties

ET-AL: Entropy-Targeted Active Learning for Bias Mitigation in Materials Data

Growing materials data and data-driven informatics drastically promote the discovery and design of materials. While there are significant advancements in data-driven models, the quality of data resources is less studied despite its huge impact on model performance. In this work, we focus on data bias arising from uneven coverage of materials families in existing knowledge. Observing different diversities among crystal systems in common materials databases, we propose an information entropy-based metric for measuring this bias. To mitigate the bias, we develop an entropy-targeted active learning (ET-AL) framework, which guides the acquisition of new data to improve the diversity of underrepresented crystal systems. We demonstrate the capability of ET-AL for bias mitigation and the resulting improvement in downstream machine learning models. This approach is broadly applicable to data-driven materials discovery, including autonomous data acquisition and dataset trimming to reduce bias, as well as data-driven informatics in other scientific domains.Comment: 35 pages, 13 figures, under revie

将视频多模态情感分析运用在临床抑郁检测中

The current clinical diagnosis of depression in the medical community relies on selfrating scales and physician interviews, but this approach is limited by the expertise of clinicians and the uneven distribution of medical resources. This paper proposes the use of video multimodal techniques in clinical diagnosis, aiming to improve the efficiency and accuracy of depression detection in clinical settings

Uncertainty-Aware Mixed-Variable Machine Learning for Materials Design

Data-driven design shows the promise of accelerating materials discovery but is challenging due to the prohibitive cost of searching the vast design space of chemistry, structure, and synthesis methods. Bayesian Optimization (BO) employs uncertainty-aware machine learning models to select promising designs to evaluate, hence reducing the cost. However, BO with mixed numerical and categorical variables, which is of particular interest in materials design, has not been well studied. In this work, we survey frequentist and Bayesian approaches to uncertainty quantification of machine learning with mixed variables. We then conduct a systematic comparative study of their performances in BO using a popular representative model from each group, the random forest-based Lolo model (frequentist) and the latent variable Gaussian process model (Bayesian). We examine the efficacy of the two models in the optimization of mathematical functions, as well as properties of structural and functional materials, where we observe performance differences as related to problem dimensionality and complexity. By investigating the machine learning models' predictive and uncertainty estimation capabilities, we provide interpretations of the observed performance differences. Our results provide practical guidance on choosing between frequentist and Bayesian uncertainty-aware machine learning models for mixed-variable BO in materials design