Set Of Similarity Metrics Research Articles

L2T-DFM: Learning to Teach with Dynamic Fused Metric

The loss function plays a crucial role in the construction of machine learning algorithms. Employing a teacher model to set loss functions dynamically for student models has attracted attention. In existing works, (1) the characterization of the dynamic loss suffers from some inherent limitations, ie, the computational cost of loss networks and the restricted similarity measurement handcrafted loss functions; and (2) the states of the student model are provided to the teacher model directly without integration, causing the teacher model to underperform when trained on insufficient amounts of data. To alleviate the above-mentioned issues, in this paper, we select and weigh a set of similarity metrics by a confidence-based selection algorithm and a temporal teacher model to enhance the dynamic loss functions. Subsequently, to integrate the states of the student model, we employ statistics to quantify the information loss of the student model. Extensive experiments demonstrate that our approach can enhance student learning and improve the performance of various deep models on real-world tasks, including classification, object detection, and semantic segmentation scenarios.

Model-based diversity-driven learn-to-rank test case prioritization

Model-based Test Case Prioritization utilizing similarity metrics has proved effective in software testing. However, the utility of similarity metrics in it varies with test scenarios, hindering its universal effectiveness and performance optimization. To tackle this problem, we propose a Diversity-driven Learn-to-rank model-based TCP approach, named DLTCP, for optimizing early fault detection performance. Our method first employs the whale optimization algorithm to search for a suitable set of similarity metrics from a pool of existing candidates. This search process determines which metrics should be used. According to each selected metric, test cases are then prioritized. The resulting test case rankings are used as the training data for DLTCP. Finally, the proposed method incorporates random forest to train a ranking model for test case prioritization. As such, it can fuse multiple similarity metrics to improve the TCP performance. We conduct extensive experiments to evaluate our method’s performance using the average percentage fault detected (APFD) as metric. The experimental results show that DLTCP achieve an average APFD value of 0.953 for seven classic benchmark models, which is 11.37% higher than that of the state-of-the-art algorithms. It can well select a set of similarity metrics for effective fusion, demonstrating competitive performance in early fault detection.

Similarity Metrics-Based Uncertainty Analysis of River Water Quality Models

Uncertainty analysis (UA) is essential to reinforce the decisions made by water resource engineers and managers. In this study, the stepwise multiple linear regression procedure assessed the relationship between water quality parameters and physical characteristics of 48 catchments in the southwestern basin of the Caspian Sea, Iran. The results of the modeling showed that the coefficient of determination ranged between 0.47 and 0.68 and indicated a positive relationship between the area (%) of agricultural lands and the sodium adsorption ratio (SAR), potassium (K) and total dissolved solids (TDS). A negative relationship was also found between bicarbonate (HCO3−) and the area (%) of the intermediate-density forest. In contrast to previous studies focusing on analyzing the uncertainty of the model parameters, we addressed the uncertainty of the model variables. The results of the GLUE-based uncertainty analysis (UA) performed on the model’s variables indicated that the measures of the R-factor for all models were between 0.13 and 0.98. The lowest R-factor was obtained for the HCO3− model (0.13) suggesting it performed well when predicting HCO3−. To increase the degree of objectivity in the GLUE-UA method, a set of similarity metrics, including Czekanowski, Motyka, Ruzicka, Cosine, Kumar-Hassebrook, Jaccard and Dice was applied to determine the degree of proximity and or similarity between the probability density functions of the measured and simulated water quality parameters. The measures of the similarity metrics for the HCO3− model were generally close to 1, indicating good performance and low uncertainty, while it showed higher uncertainty (between 0.2487 and 0.897) for the other three models (SAR, K, and TDS).

THE ROLE OF TASTE AFFINITY IN AGENT-BASED MODELS FOR SOCIAL RECOMMENDATION

In the Internet era, online social media emerged as the main tool for sharing opinions and information among individuals. In this work, we study an adaptive model of a social network where directed links connect users with similar tastes, and over which information propagates through social recommendation. Agent-based simulations of two different artificial settings for modeling user tastes are compared with patterns seen in real data, suggesting that users differing in their scope of interests is a more realistic assumption than users differing only in their particular interests. We further introduce an extensive set of similarity metrics based on users' past assessments, and evaluate their use in the given social recommendation model with both artificial simulations and real data. Superior recommendation performance is observed for similarity metrics that give preference to users with small scope — who thus act as selective filters in social recommendation.

Location-aware music recommendation

The increasing amount of online music content has opened opportunities for implementing new effective information access services—commonly known as music recommender systems—that support music navigation, discovery, sharing, and formation of user communities. In recent years, a new research area of contextual music recommendation and retrieval has emerged. Context-aware music recommender systems are capable of suggesting music items taking into consideration contextual conditions, such as the user’s mood or location, that may influence the user’s preferences at a particular moment. In this work, we consider a particular kind of context-aware recommendation task—selecting music content that fits a place of interest (POI). To address this problem we have used emotional tags assigned to both music tracks and POIs, and we have considered a set of similarity metrics for tagged resources to establish a match between music tracks and POIs. Following an initial web-based evaluation of the core matching technique, we have developed a mobile application that suggests an itinerary and plays recommended music for each visited POI, and evaluated it in a live user study. The results of the study show that users judge the recommended music as suited for the POIs, and that the music is rated higher when it is played in this usage scenario.

Similarity metrics for surgical process models

The objective of this work is to introduce a set of similarity metrics for comparing surgical process models (SPMs). SPMs are progression models of surgical interventions that support quantitative analyses of surgical activities, supporting systems engineering or process optimization. Five different similarity metrics are presented and proven. These metrics deal with several dimensions of process compliance in surgery, including granularity, content, time, order, and frequency of surgical activities. The metrics were experimentally validated using 20 clinical data sets each for cataract interventions, craniotomy interventions, and supratentorial tumor resections. The clinical data sets were controllably modified in simulations, which were iterated ten times, resulting in a total of 600 simulated data sets. The simulated data sets were subsequently compared to the original data sets to empirically assess the predictive validity of the metrics. We show that the results of the metrics for the surgical process models correlate significantly (p<0.001) with the induced modifications and that all metrics meet predictive validity. The clinical use of the metrics was exemplarily, as demonstrated by assessment of the learning curves of observers during surgical process model acquisition. Measuring similarity between surgical processes is a complex task. However, metrics for computing the similarity between surgical process models are needed in many uses in the field of medical engineering. These metrics are essential whenever two SPMs need to be compared, such as during the evaluation of technical systems, the education of observers, or the determination of surgical strategies. These metrics are key figures that provide a solid base for medical decisions, such as during validation of sensor systems for use in operating rooms in the future.