Neighborhood Intervention Consistency: Measuring Confidence for Knowledge Graph Link Prediction

Link prediction based on knowledge graph embeddings (KGE) has recently drawn a considerable momentum. However, existing KGE models suffer from insufficient accuracy and hardly evaluate the confidence probability of each predicted triple. To fill this critical gap, we propose a novel confidence measurement method based on causal intervention, called Neighborhood Intervention Consistency (NIC). Unlike previous confidence measurement methods that focus on the optimal score in a prediction, NIC actively intervenes in the input entity vector to measure the robustness of the prediction result. The experimental results on ten popular KGE models show that our NIC method can effectively estimate the confidence score of each predicted triple. The top 10% triples with high NIC confidence can achieve 30% higher accuracy in the state-of-the-art KGE models.

Properties of Cryptocurrency as Antifiduciary Money as a Tool for Measuring Public Confidence in Power

The use of the cryptocurrency share indicator in the total money supply as a tool for measuring public confidence in authority, allowing you to conduct an objective analysis using economic, financial indicators in order to give them a sociological assessment and develop recommendations for creating confidence management technologies. Cryptocurrency properties as antifiduciary money are considered. It is proved why an increase in the share of cryptocurrency in the money supply can be considered as a sign of a decrease in public confidence in the state. The prospects of statistics that allow tracking the dynamics of the cryptocurrency share in the money supply are analyzed. The advantages of the proposed confidence measurement tool compared to sociological survey, sociological experiment and introduction of indirect institutional indicators are demonstrated. Properties of Cryptocurrency as Antifiduciary Money as a Tool for Measuring Public Confidence in Power

Strategi Restructuring Cognitive Dalam Konseling Kelompok CBT Untuk Meningkatkan Kepercayaan Diri Siswa Kelas VIII G di SMP PGRI 1 Buduran Sidoarjo

This study aims to determine the effect of the use of cognitive restructuring strategies in group counseling on class VIII G students' confidence in SMP PGRI 1 BUDURAN SIDOARJO. The research design used was a Pre-Experimental study with a One Group Pretest-Postest Design. The population of class VIII G students at SMP PGRI 1 BUDURAN SIDOARJO was 30 students, then 4 students were taken as research samples using purposive sampling techniques. The selection of this sample was chosen based on the results of students' low self-confidence scores through filling in the scale of student confidence measurement. The results of this study was found that there was no significance changed in the use of cognitive restructuring in counseling group on the confidence of students of class VIII G in SMP PGRI 1 BUDURAN SIDOARJO.

No-Reference Depth Map Quality Evaluation Model Based on Depth Map Edge Confidence Measurement in Immersive Video Applications

When it comes to evaluating perceptual quality of digital media for overall quality of experience assessment in immersive video applications, typically two main approaches stand out: Subjective and objective quality evaluation. On one hand, subjective quality evaluation offers the best representation of perceived video quality assessed by the real viewers. On the other hand, it consumes a significant amount of time and effort, due to the involvement of real users with lengthy and laborious assessment procedures. Thus, it is essential that an objective quality evaluation model is developed. The speed-up advantage offered by an objective quality evaluation model, which can predict the quality of rendered virtual views based on the depth maps used in the rendering process, allows for faster quality assessments for immersive video applications. This is particularly important given the lack of a suitable reference or ground truth for comparing the available depth maps, especially when live content services are offered in those applications. This paper presents a no-reference depth map quality evaluation model based on a proposed depth map edge confidence measurement technique to assist with accurately estimating the quality of rendered (virtual) views in immersive multi-view video content. The model is applied for depth image-based rendering in multi-view video format, providing comparable evaluation results to those existing in the literature, and often exceeding their performance.