Action Recognition Using Visual-Neuron Feature of Motion-Salience Region

This paper proposes a shape-based neurobiological approach for action recognition. Our work is motivated by the successful quantitative model for the organization of the shape pathways in primate visual cortex. In our approach the motion-salience region (MSR) is firstly extracted from the sequential silhouettes of an action. Then, the MSR is represented by simulating the static object representation in the ventral stream of primate visual cortex. Finally, a linear multi-class classifier is used to classify the action. Experiments on publicly available action datasets demonstrate the proposed approach is robust to partial occlusion and deformation of actors and has lower computational cost than the neurobiological models that simulate the motion representation in primate dorsal stream

Large Language Models can be Guided to Evade AI-Generated Text Detection

Large Language Models (LLMs) have demonstrated exceptional performance in a variety of tasks, including essay writing and question answering. However, it is crucial to address the potential misuse of these models, which can lead to detrimental outcomes such as plagiarism and spamming. Recently, several detectors have been proposed, including fine-tuned classifiers and various statistical methods. In this study, we reveal that with the aid of carefully crafted prompts, LLMs can effectively evade these detection systems. We propose a novel Substitution-based In-Context example Optimization method (SICO) to automatically generate such prompts. On three real-world tasks where LLMs can be misused, SICO successfully enables ChatGPT to evade six existing detectors, causing a significant 0.54 AUC drop on average. Surprisingly, in most cases these detectors perform even worse than random classifiers. These results firmly reveal the vulnerability of existing detectors. Finally, the strong performance of SICO suggests itself as a reliable evaluation protocol for any new detector in this field

研究生心理健康教育课程生活化模式探索——基于沟通分析理论*

The traditional mental health course focus on the dissemination of knowledge and less experience of promoting student awareness in the classroom. It is difficult to promote the improvement of students' self-awareness and social adaptability. It is necessary to adjust the mental health course contents and form. In the teaching content, it is necessary to establish a close student life and has coherence and consistency of the theoretical system. In the form it should break the traditional mode of teaching and focus on student-centered, student participation and experience. This study attempts to traditional mental health course to graduate students can be adjusted to Transactional Analysis（TA）as the main theoretical framework. In the teaching content to students' self consciousness development as the core, covering all living subjects of graduate students, learning, interpersonal, emotion, love, parent-child communication, career development and other, to help graduate students improve self-awareness and strengthen social adaptability. Taking an interactive, experiential learning, student-centered, to stimulate students' enthusiasm, and enhance the effectiveness of teaching and improve the practicality of mental health education in teaching methods. The course to improve graduate students' interpersonal attitudes have a significant effect.传统的心理健康课程集中于知识的传播，较少在课堂上促进学生的体验性觉察，难以促进学生自我意识的完善和社会适应性，从内容与形式上对心理健康课程进行调整势在必行。在教学内容上，有必要建立贴近研究生生活并具有连贯性与一致性的理论体系，在形式上则应突破传统的授课模式，以学生为主体，注重学生的参与和体验。以研究生为授课对象，尝试对传统心理健康课程进行调整，以沟通分析理论(Transactional Analysis, TA)为主要的理论框架，在教学内容上以研究生自我意识发展为核心，涵盖学习适应、人际交往、情绪调控、恋爱关系、亲子沟通、职业发展等研究生各生活主题，以帮助研究生完善自我意识及增强社会适应性。在教学方法上采取互动式、体验式教学，以学生为主体，激发学生的参与热情，增强教学效能，提高心理健康教育的实用性。该课程对研究生人际态度的改善具有明显效果