Forexample, age estimation aims toestimate the age ofagivenface image while image aesthetic assessment predicts the aesthetic score for an image.

By design, COLP is a parsimonious classifier, which gives rise to a provably identifiable causal model.

Phonotrauma refers to vocal fold tissue damage resulting from exposure to forces during voicing. It occurs on a continuum from mild to severe, and treatment options can vary based on severity. Assessment of severity involves a clinician's expert judgment, which is costly and can vary widely in reliability. In this work, we present the first method for automatically classifying phonotrauma severity from vocal fold images. To account for the ordinal nature of the labels, we adopt a widely used ordinal regression framework. To account for label uncertainty, we propose a novel modification to ordinal regression loss functions that enables them to operate on soft labels reflecting annotator rating distributions. Our proposed soft ordinal regression method achieves predictive performance approaching that of clinical experts, while producing well-calibrated uncertainty estimates. By providing an automated tool for phonotrauma severity assessment, our work can enable large-scale studies of phonotrauma, ultimately leading to improved clinical understanding and patient care.

Readability assessment aims to evaluate the reading di ffi culty of a text. In recent years, while deep learning technol - ogy has been gradually applied to readability assessment, m ost approaches fail to consider either the length of the text or the ordinal relationship of readability labels. This pap er proposes a bidirectional readability assessment mechan ism that captures contextual information to identify regions w ith rich semantic information in the text, thereby predicti ng the readability level of individual sentences. These sente nce-level labels are then used to assist in predicting the ov erall readability level of the document. Additionally, a pairwis e sorting algorithm is introduced to model the ordinal relationship between readability levels through label subtrac tion. Experimental results on Chinese and English datasets demonstrate that the proposed model achieves competitive p erformance and outperforms other baseline models. Introduction Automatic Text Readability (ARA) research originated in th e early 20th century, aiming to evaluate text reading di ffi culty and assist educators in recommending appropriate rea ding materials for learners [ 1 ]. Readability assessment approaches are generally classified into three paradig ms: human evaluation, co-selection-based analysis, and content-based analysis. Human evaluation involves expert annotation or reader surveys; co-selection methods leverage user interaction data such as reading time or choices [ 2 ]; and content-based approaches infer readability using linguistic, syntactic, or semantic features extracted fro m the text itself. Early studies predominantly relied on experts' subjective evaluations and simple statistical feat ures, such as sentence length and word complexity.

Ordinal regression seeks class label predictions when the penalty incurred for mistakes increases according to an ordering over the labels. The absolute error is a canonical example. Many existing methods for this task reduce to binary classification problems and employ surrogate losses, such as the hinge loss. We instead derive uniquely defined surrogate ordinal regression loss functions by seeking the predictor that is robust to the worst-case approximations of training data labels, subject to matching certain provided training data statistics. We demonstrate the advantages of our approach over other surrogate losses based on hinge loss approximations using UCI ordinal prediction tasks.

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Predicting an individual's personalities from their generated texts is a challenging task, especially when the text volume is large. In this paper, we introduce a straightforward yet effective novel strategy called targeted preselection of texts (TPoT). This method semantically filters the texts as input to a deep learning model, specifically designed to predict a Big Five personality trait, facet, or item, referred to as the BIG5-TPoT model. By selecting texts that are semantically relevant to a particular trait, facet, or item, this strategy not only addresses the issue of input text limits in large language models but also improves the Mean Absolute Error and accuracy metrics in predictions for the Stream of Consciousness Essays dataset.

In recent years, the pen and paper RPG market has experienced significant growth. As a result, companies are increasingly exploring the integration of AI technologies to enhance player experience and gain a competitive edge. One of the key challenges faced by publishers is designing new opponents and estimating their challenge level. Currently, there are no automated methods for determining a monster's level; the only approaches used are based on manual testing and expert evaluation. Although these manual methods can provide reasonably accurate estimates, they are time-consuming and resource-intensive. Level prediction can be approached using ordinal regression techniques. This thesis presents an overview and evaluation of state-of-the-art methods for this task. It also details the construction of a dedicated dataset for level estimation. Furthermore, a human-inspired model was developed to serve as a benchmark, allowing comparison between machine learning algorithms and the approach typically employed by pen and paper RPG publishers. In addition, a specialized evaluation procedure, grounded in domain knowledge, was designed to assess model performance and facilitate meaningful comparisons.