Perceived support and AI literacy: the mediating role of psychological needs satisfaction.

Artificial Intelligence (AI) exerts significant influence on both professional and personal spheres, underscoring the necessity for college students to have a fundamental understanding of AI. Guided by self-determination theory (SDT), this study explores the influence of psychological needs satisfaction on AI literacy among university students. A cross-sectional survey involving 445 university students from diverse academic backgrounds was conducted. The survey assessed the mediation effect of students' psychological need satisfaction between two types of support-technical and teacher-and AI literacy. The results indicate that both support types positively influenced the fulfillment of autonomy and competence needs, which subsequently acted as mediators in enhancing AI literacy. However, the satisfaction of relatedness needs did not mediate the relationship between the types of support and AI literacy. Unexpectedly, no direct association was found between the two forms of support and AI literacy levels among students. The findings suggest that although technical and teacher support contribute to fulfilling specific psychological needs, only autonomy and competence needs are predictive of AI literacy. The lack of direct impact of support on AI literacy underscores the importance of addressing specific psychological needs through educational interventions. It is recommended that educators provide tailored support in AI education (AIEd) and that institutions develop specialized courses to enhance AI literacy.

[A medical visual question answering approach based on co-attention networks].

Recent studies have introduced attention models for medical visual question answering (MVQA). In medical research, not only is the modeling of "visual attention" crucial, but the modeling of "question attention" is equally significant. To facilitate bidirectional reasoning in the attention processes involving medical images and questions, a new MVQA architecture, named MCAN, has been proposed. This architecture incorporated a cross-modal co-attention network, FCAF, which identifies key words in questions and principal parts in images. Through a meta-learning channel attention module (MLCA), weights were adaptively assigned to each word and region, reflecting the model's focus on specific words and regions during reasoning. Additionally, this study specially designed and developed a medical domain-specific word embedding model, Med-GloVe, to further enhance the model's accuracy and practical value. Experimental results indicated that MCAN proposed in this study improved the accuracy by 7.7% on free-form questions in the Path-VQA dataset, and by 4.4% on closed-form questions in the VQA-RAD dataset, which effectively improves the accuracy of the medical vision question answer.

Image region duplication detection based on circular window expansion and phase correlation.

Region duplication forgery is one of the tampering techniques that are frequently used, where a part of an image is copied and pasted into another part of the same image. In this paper, a phase correlation method based on polar expansion and adaptive band limitation is proposed for region duplication forgery detection. Our method starts by calculating the Fourier transform of the polar expansion on overlapping windows pair, and then an adaptive band limitation procedure is implemented to obtain a correlation matrix in which the peak is effectively enhanced. After estimating the rotation angle of the forgery region, a searching algorithm in the sense of seed filling is executed to display the whole duplicated region. Experimental results show that the proposed approach can detect duplicated region with high accuracy and robustness to rotation, illumination adjustment, blur and JPEG compression while rotation angle is estimated precisely for further calculation.