Objective Measure Of Similarity Research Articles

Comparing Subjective Similarity of Automated Driving Styles to Objective Distance-Based Similarity.

This study explores subjective and objective driving style similarity to identify how similarity can be used to develop driver-compatible vehicle automation. Similarity in the ways that interaction partners perform tasks can be measured subjectively, through questionnaires, or objectively by characterizing each agent's actions. Although subjective measures have advantages in prediction, objective measures are more useful when operationalizing interventions based on these measures. Showing how objective and subjective similarity are related is therefore prudent for aligning future machine performance with human preferences. A driving simulator study was conducted with stop-and-go scenarios. Participants experienced conservative, moderate, and aggressive automated driving styles and rated the similarity between their own driving style and that of the automation. Objective similarity between the manual and automated driving speed profiles was calculated using three distance measures: dynamic time warping, Euclidean distance, and time alignment measure. Linear mixed effects models were used to examine how different components of the stopping profile and the three objective similarity measures predicted subjective similarity. Objective similarity using Euclidean distance best predicted subjective similarity. However, this was only observed for participants' approach to the intersection and not their departure. Developing driving styles that drivers perceive to be similar to their own is an important step toward driver-compatible automation. In determining what constitutes similarity, it is important to (a) use measures that reflect the driver's perception of similarity, and (b) understand what elements of the driving style govern subjective similarity.

Neuromorphic Similarity Measurement of Tactile Stimuli in Human–Machine Interface

The interpretation of tactile stimuli empowers humans to identify substances, distinguish materials, and engage in tactile communication. For stimulus design in human-computer interaction, objective similarity measures improve efficiency and save costs. Inspired by the fact that biological systems are robust in recognizing multimedia stimuli, we propose a neuromorphic method for similarity measurement. The method is divided into two steps. First, tactile information is translated into biological representations by mimicking a low-threshold mechanoreceptor through a physiological neuronal model. Then, three measures are nominated to assess the similarity of neural spike trains from the following perspectives: interval spike counting, temporal matching, and vector space embedding. Regression analysis showed that the linearity of these measures was significant, indicating that the filtering ability of the physiological neuron model is robust. One of the measures is selected for comparison with the signal-to-noise ratio, structural similarity, and hybrid metric. The results suggest that the correlation between the predictions of our method and the subjective evaluation is stable, above 0.9 for each experimental stimulus. We achieve a mutual interpretation between quantitative measures of vibrotactile similarity and subjective cognitive outcomes. Furthermore, the feasibility of this method in material classification has been substantiated through an exploratory experiment.

Mapping the evolution of accurate Batesian mimicry of social wasps in hoverflies.

Hoverflies (Diptera: Syrphidae) provide an excellent opportunity to study the evolution of Batesian mimicry, where defenseless prey avoid predation by evolving to resemble defended "model" species. Although some hoverflies beautifully resemble their hymenopteran models, others seem to be poor mimics or are apparently nonmimetic. The reasons for this variation are still enigmatic despite decades of research. Here, we address this issue by mapping social-wasp mimicry across the phylogeny of Holarctic hoverflies. Using the "distance transform" technique, we calculate an objective measure of the abdominal pattern similarity between 167 hoverfly species and a widespread putative model, the social wasp, Vespula germanica. We find that good wasp mimicry has evolved several times, and may have also been lost, leading to the presence of nonmimics deep within clades of good mimics. Body size was positively correlated with similarity to the model, supporting previous findings that smaller species are often poorer mimics. Additionally, univoltine species were less accurate wasp mimics than multivoltine and bivoltine species. Hence, variation in the accuracy of Batesian mimics may reflect variation in the opportunity for selection caused by differences in prey value or signal perception (influenced by body size) and phenology or generation time (influenced by voltinism).

Informed Clustering of Donation Service Areas for Improved Benchmarking.

The donation community continuously strives to collaborate and share effective practices to further the mission of saving and healing lives. Donation service areas in which the Organ Procurement Organizations (OPOs) work are multifaceted in their demographics, inciting the Organ Procurement and Transplantation Network to consider a more holistic and objective measure of similarity rather than the size of population alone or locational proximity alone. This would allow OPOs, as a part of their quality improvement efforts, to learn from and mentor other organizations that are dealing with similar challenges. By incorporating multiple informative characteristics together, we can distinguish those likenesses only revealed by taking into account multiple factors simultaneously. We used statistical approaches that take many characteristics of interest describing a donation service area and purposely excluded performance measures that an OPO may be able to influence by their own practices. Unsupervised learning methods combined the original characteristics into a smaller number of new variables, eliminating correlation and overlap in information from the original characteristics, and clustered donation service areas based on the general characteristics and population of the area. This analysis is a first step in providing a different perspective for OPOs to learn from other organizations that may face similar challenges, as well as to share best practices and open new lines of communication.

Object similarity measures and Pawlak’s indiscernibility on decision tables

In this paper we investigate the mathematical foundations of the notion of similarity between objects in relation to the granulations on a decision table D. First of all, we compare the endogenous granulation induced by Pawlak’s indiscernibility with the exogenous granulation induced by a similarity measure ζ defined on pairs of objects and assuming values in the unit interval. To this aim, the starting point of our analysis is the introduction of the notion of refinement of the granulation induced by an attribute subset A through the object similarity measure ζ. More in detail, we say that ζrefines the granulation induced by A if ζ assumes value 1 on a pair of objects if and only if they are A-indiscernible. Next, starting from two given families ρ and ν of numerical maps defined on pairs of admissible values of D, we determine a broad class of potential similarity measures on the objects of D refining, sometimes under some specific additional hypotheses, the A-granulation on the object set of D. With regard to a such class of similarity measures, we establish several mathematical properties. Finally, we focus our attention to the analysis of specific pairs of numerical maps ρ and ν that have been classically studied in literature and, for each of them, we exhibit the main properties with respect to the aforementioned refinement of granulation.

A Unified Link Prediction Framework for Predicting Arbitrary Relations in Heterogeneous Academic Networks

Most of the existing link prediction methods for heterogeneous academic networks can only predict one or two specific relation types rather than arbitrary relation types. Although several recently proposed methods have involved multi-relational prediction problems, they do not comprehensively consider the rich semantic or temporal information of heterogeneous academic networks. Considering that researchers may have diverse requirements for different types of academic resources, in this study, we propose a new unified link prediction framework (UniLPF) for arbitrary types of academic relations. First, a weighted and directed heterogeneous academic network containing rich academic objects and relations is constructed. Then, an automatic meta-path searching method is proposed to extract the meta-paths for arbitrary prediction tasks. Two meta-path based object similarity measures combining temporal information and content relevance are also proposed to measure the features of the meta-paths. Finally, a pervasive link prediction model is built, which can be embodied based on an arbitrarily specified prediction task and the corresponding meta-path features. Extensive experiments for predicting various relation types with practical significance are conducted on a large-scale Microsoft Academic dataset. The experimental results demonstrate that our proposed UniLPF framework can predict arbitrary specified academic relations, and outperforms the comparison methods in terms of F-measure, accuracy, AUC and ROC. In addition, the time scalability experiments prove that UniLPF also achieves good performance for predicting the academic relations over time.

Brand name confusion: Subjective and objective measures of orthographic similarity.

Determining brand name similarity is vital in areas of trademark registration and brand confusion. Students rated the orthographic (spelling) similarity of word pairs (Experiments 1, 2, and 4) and brand name pairs (Experiment 5). Similarity ratings were consistently higher when words shared beginnings rather than endings, whereas shared pronunciation of the stressed vowel had small and less consistent effects on ratings. In Experiment 3 a behavioral task confirmed the similarity of shared beginnings in lexical processing. Specifically, in a task requiring participants to decide whether 2 words presented in the clear (a probe and a later target) were the same or different, a masked prime word preceding the target shortened response latencies if it shared its initial 3 letters with the target. The ratings of students for word and brand name pairs were strongly predicted by metrics of orthographic similarity from the visual word identification literature based on the number of shared letters and their relative positions. The results indicate a potential use for orthographic metrics in brand name registration and trademark law. (PsycINFO Database Record

A Novel and Fast SimRank Algorithm

SimRank is a widely adopted similarity measure for objects modeled as nodes in a graph, based on the intuition that two objects are similar if they are referenced by similar objects. The recursive nature of SimRank definition makes it expensive to compute the similarity score even for a single pair of nodes. This defect limits the applications of SimRank. To speed up the computation, some existing works replace the original model with an approximate model to seek only rough solution of SimRank scores. In this work, we propose a novel solution for computing all-pair SimRank scores. In particular, we propose to convert SimRank to the problem of solving a linear system in matrix form, and further prove that the system is non-singular, diagonally dominate, and symmetric definite positive (for undirected graphs). Those features immediately lead to the adoption of Conjugate Gradient (CG) and Bi-Conjugate Gradient (BiCG) techniques for efficiently computing SimRank scores. As a result, a significant improvement on the convergence rate can be achieved; meanwhile, the sparsity of the adjacency matrix is not damaged all the time. Inspired by the existing common neighbor sharing strategy, we further reduce the computational complexity of the matrix multiplication and resolve the scalable issues. The experimental results show our proposed algorithms significantly outperform the state-of-the-art algorithms.

A full reference Morphological Edge Similarity Index to account processing induced edge artefacts in magnetic resonance images

An objective measure of edge similarity between the original and processed images to quantify the processing induced artefacts in medical image computing is proposed in this article. Globally accepted Image Quality Analysis (IQA) indices such as Peak Signal Noise Ratio (PSNR) and Structural Similarity Index Metric (SSIM) measure the structural similarity between the original and processed image and do not specifically reflect the resemblance of the edge content. Most of the IQA indices either do not comply with the subjective quality ratings or they are prone to noise level. In the proposed Morphological Edge Similarity Index (MESI), the binary edge maps of the reference and processed images are generated via gradient based threshold and these edge maps are objectively compared to yield a reliable edge quality metric. The index is found superior to Edge Preservation Index (EPI), Edge Strength Similarity based Image quality Metric (ESSIM) and SSIM in terms of dynamic variability, correlation with subjective quality ratings, robustness to noise and sensitivity to degradation in edge quality caused by blockiness artefacts in image compression. MESI exhibits a correlation of 0.9985, very close to unity, with the subjective quality ratings. It is useful for objectively evaluating the performance of denoising, sharpening and enhancement schemes and for the selection of optimum value of the arbitrary parameters used in them.

Coupled attribute similarity learning on categorical data.

Attribute independence has been taken as a major assumption in the limited research that has been conducted on similarity analysis for categorical data, especially unsupervised learning. However, in real-world data sources, attributes are more or less associated with each other in terms of certain coupling relationships. Accordingly, recent works on attribute dependency aggregation have introduced the co-occurrence of attribute values to explore attribute coupling, but they only present a local picture in analyzing categorical data similarity. This is inadequate for deep analysis, and the computational complexity grows exponentially when the data scale increases. This paper proposes an efficient data-driven similarity learning approach that generates a coupled attribute similarity measure for nominal objects with attribute couplings to capture a global picture of attribute similarity. It involves the frequency-based intra-coupled similarity within an attribute and the inter-coupled similarity upon value co-occurrences between attributes, as well as their integration on the object level. In particular, four measures are designed for the inter-coupled similarity to calculate the similarity between two categorical values by considering their relationships with other attributes in terms of power set, universal set, joint set, and intersection set. The theoretical analysis reveals the equivalent accuracy and superior efficiency of the measure based on the intersection set, particularly for large-scale data sets. Intensive experiments of data structure and clustering algorithms incorporating the coupled dissimilarity metric achieve a significant performance improvement on state-of-the-art measures and algorithms on 13 UCI data sets, which is confirmed by the statistical analysis. The experiment results show that the proposed coupled attribute similarity is generic, and can effectively and efficiently capture the intrinsic and global interactions within and between attributes for especially large-scale categorical data sets. In addition, two new coupled categorical clustering algorithms, i.e., CROCK and CLIMBO are proposed, and they both outperform the original ones in terms of clustering quality on UCI data sets and bibliographic data.

Effects of script similarity on bilingual advantages in executive control are likely to be negligible or null.

Effects of script similarity on bilingual advantages in executive control are likely to be negligible or null.

English

Pharmaceutical equivalence is an important issue in the regulatory approval of generic and similar drug products, particularly for those that will not be tested for bioequivalence. However, there is no scientific approach that provides us an objective measure of quality and similarity of the results obtained for testing (generic or similar) and branding drug products simultaneously. This paper describes a new multivariate similarity factor for the assessment of in vitro therapeutic equivalence between two medicines by using pharmaceutical equivalence study. We performed pharmaceutical equivalence studies for acyclovir cream, metronidazole injection, meropenem for injection and atropine sulfate injection. All tests and assays results were standardized using an appropriate desirability function. Multivariate similarity factors for pharmaceutical studies were calculated based on individual acceptance factors and similarity deviations for brand, generic, and similar drugs. We found a perfect correlation among multivariate similarity factor and regulatory requirements. The multivariate similarity factor is a useful tool for in vitro therapeutic equivalence assessment, and may be used for regulatory approval of generic and similar drugs. Key words: Therapeutic equivalency, in vitro, multivariate analysis.

Validation Techniques for Geological Patterns Simulations Based on Variogram and Multiple-Point Statistics

Traditional simulation methods that are based on some form of kriging are not sensitive to the presence of strings of connectivity of low or high values. They are particularly inappropriate in many earth sciences applications, where the geological structures to be simulated are curvilinear. In such cases, techniques allowing the reproduction of multiple-point statistics are required. The aim of this paper is to point out the advantages of integrating such multiple-statistics in a model in order to allow shape reproduction, as well as heterogeneity structures, of complex geological patterns to emerge. A comparison between a traditional variogram-based simulation algorithm, such as the sequential indicator simulation, and a multiple-point statistics algorithm (e.g., the single normal equation simulation) is presented. In particular, it is shown that the spatial distribution of limestone with meandering channels in Lecce, Italy is better reproduced by using the latter algorithm. The strengths of this study are, first, the use of a training image that is not a fluvial system and, more importantly, the quantitative comparison between the two algorithms. The paper focuses on different metrics that facilitate the comparison of the methods used for limestone spatial distribution simulation: both objective measures of similarity of facies realizations and high-order spatial cumulants based on different third- and fourth-order spatial templates are considered.

Evaluation of Objective Similarity Measures for Selecting Similar Images of Mammographic Lesions

The purpose of this study was to investigate four objective similarity measures as an image retrieval tool for selecting lesions similar to unknown lesions on mammograms. Measures A and B were based on the Euclidean distance in feature space and the psychophysical similarity measure, respectively. Measure C was the sequential combination of B and A, whereas measure D was the sequential combination of A and B. In this study, we selected 100 lesions each for masses and clustered microcalcifications randomly from our database, and we selected five pairs of lesions from 4,950 pairs based on all combinations of the 100 lesions by use of each measure. In two observer studies for 20 mass pairs and 20 calcification pairs, six radiologists compared all combinations of 20 pairs by using a two-alternative forced-choice method to determine the subjective similarity ranking score which was obtained from the frequency with which a pair was considered as more similar than the other 19 pairs. In both mass and calcification pairs, pairs selected by use of measure D had the highest mean value of the average subjective similarity ranking scores. The difference between measures D and A (P = 0.008 and 0.024), as well as that between measures D and B (P = 0.018 and 0.028) were statistically significant for masses and microcalcifications, respectively. The sequential combination of the objective similarity measure based on the Euclidean distance and the psychophysical similarity measure would be useful in the selection of images similar to those of unknown lesions.

Cross-Over between Discrete and Continuous Protein Structure Space: Insights into Automatic Classification and Networks of Protein Structures

Structural classifications of proteins assume the existence of the fold, which is an intrinsic equivalence class of protein domains. Here, we test in which conditions such an equivalence class is compatible with objective similarity measures. We base our analysis on the transitive property of the equivalence relationship, requiring that similarity of A with B and B with C implies that A and C are also similar. Divergent gene evolution leads us to expect that the transitive property should approximately hold. However, if protein domains are a combination of recurrent short polypeptide fragments, as proposed by several authors, then similarity of partial fragments may violate the transitive property, favouring the continuous view of the protein structure space. We propose a measure to quantify the violations of the transitive property when a clustering algorithm joins elements into clusters, and we find out that such violations present a well defined and detectable cross-over point, from an approximately transitive regime at high structure similarity to a regime with large transitivity violations and large differences in length at low similarity. We argue that protein structure space is discrete and hierarchic classification is justified up to this cross-over point, whereas at lower similarities the structure space is continuous and it should be represented as a network. We have tested the qualitative behaviour of this measure, varying all the choices involved in the automatic classification procedure, i.e., domain decomposition, alignment algorithm, similarity score, and clustering algorithm, and we have found out that this behaviour is quite robust. The final classification depends on the chosen algorithms. We used the values of the clustering coefficient and the transitivity violations to select the optimal choices among those that we tested. Interestingly, this criterion also favours the agreement between automatic and expert classifications. As a domain set, we have selected a consensus set of 2,890 domains decomposed very similarly in SCOP and CATH. As an alignment algorithm, we used a global version of MAMMOTH developed in our group, which is both rapid and accurate. As a similarity measure, we used the size-normalized contact overlap, and as a clustering algorithm, we used average linkage. The resulting automatic classification at the cross-over point was more consistent than expert ones with respect to the structure similarity measure, with 86% of the clusters corresponding to subsets of either SCOP or CATH superfamilies and fewer than 5% containing domains in distinct folds according to both SCOP and CATH. Almost 15% of SCOP superfamilies and 10% of CATH superfamilies were split, consistent with the notion of fold change in protein evolution. These results were qualitatively robust for all choices that we tested, although we did not try to use alignment algorithms developed by other groups. Folds defined in SCOP and CATH would be completely joined in the regime of large transitivity violations where clustering is more arbitrary. Consistently, the agreement between SCOP and CATH at fold level was lower than their agreement with the automatic classification obtained using as a clustering algorithm, respectively, average linkage (for SCOP) or single linkage (for CATH). The networks representing significant evolutionary and structural relationships between clusters beyond the cross-over point may allow us to perform evolutionary, structural, or functional analyses beyond the limits of classification schemes. These networks and the underlying clusters are available at http://ub.cbm.uam.es/research/ProtNet.php

Comparison of human face matching behavior and computational image similarity measure

Computational similarity measures have been evaluated in a variety of ways, but few of the validated computational measures are based on a high-level, cognitive criterion of objective similarity. In this paper, we evaluate two popular objective similarity measures by comparing them with face matching performance in human observers. The results suggest that these measures are still limited in predicting human behavior, especially in rejection behavior, but objective measure taking advantage of global and local face characteristics may improve the prediction. It is also suggested that human may set different criterions for "hit" and "rejection" and this may provide implications for biologically-inspired computational systems.

Tensor Framework and Combined Symmetry for Hypertext Mining

We have made a case here for utilizing tensor framework for hypertext mining. Tensor is a generalization of vector and tensor framework discussed here is a generalization of vector space model which is widely used in the information retrieval and web mining literature. Most hypertext documents have an inherent internal tag structure and external link structure that render the desirable use of multidimensional representations such as those offered by tensor objects. We have focused on the advantages of Tensor Space Model, in which documents are represented using sixth-order tensors. We have exploited the local-structure and neighborhood recommendation encapsulated by the proposed representation. We have defined a similarity measure for tensor objects corresponding to hypertext documents, and evaluated the proposed measure for mining tasks. The superior performance of the proposed methodology for clustering and classification tasks of hypertext documents have been demonstrated here. The experiment using different types of similarity measure in the different components of hypertext documents provides the main advantage of the proposed model. It has been shown theoretically that, the computational complexity of an algorithm performing on tensor framework using tensor similarity measure as distance is at most the computational complexity of the same algorithmperforming on vector space model using vector similarity measure as distance.

Investigation of psychophysical similarity measures for selection of similar images in the diagnosis of clustered microcalcifications on mammograms

The presentation of images with lesions of known pathology that are similar to an unknown lesion may be helpful to radiologists in the diagnosis of challenging cases for improving the diagnostic accuracy and also for reducing variation among different radiologists. The authors have been developing a computerized scheme for automatically selecting similar images with clustered microcalcifications on mammograms from a large database. For similar images to be useful, they must be similar from the point of view of the diagnosing radiologists. In order to select such images, subjective similarity ratings were obtained for a number of pairs of clustered microcalcifications by breast radiologists for establishment of a "gold standard" of image similarity, and the gold standard was employed for determination and evaluation of the selection of similar images. The images used in this study were obtained from the Digital Database for Screening Mammography developed by the University of South Florida. The subjective similarity ratings for 300 pairs of images with clustered microcalcifications were determined by ten breast radiologists. The authors determined a number of image features which represent the characteristics of clustered microcalcifications that radiologists would use in their diagnosis. For determination of objective similarity measures, an artificial neural network (ANN) was employed. The ANN was trained with the average subjective similarity ratings as teacher and selected image features as input data. The ANN was trained to learn the relationship between the image features and the radiologists' similarity ratings; therefore, once the training was completed, the ANN was able to determine the similarity, called a psychophysical similarity measure, which was expected to be close to radiologists' impressions, for an unknown pair of clustered microcalcifications. By use of a leave-one-out test method, the best combination of features was selected. The correlation coefficient between the gold standard and the psychophysical similarity measure through the use of seven features was relatively high (r=0.71) and was comparable to the correlation coefficients between the ratings by one radiologist and the average ratings by nine radiologists (r=0.69 +/- 0.07). The correlation coefficient was improved compared to that of a distance-based method (r=0.58). The result indicated that similar images selected by the psychophysical similarity measure may be useful to radiologists in the diagnosis of clustered microcalcifications on mammograms.

SU-FF-I-03: Investigation of Similarity Measures for Selection of Similar Images for Breast Lesions On Mammograms

Purpose: Similar known images may be helpful for radiologists in the diagnosis of breast lesions on mammograms. However, selected images may not be really similar if a similarity measure is not properly determined. We determined the radiologists' subjective similarity ratings for pairs of lesions and investigated the objective measures that would agree with the radiologists' ratings. Material and Methods: We selected 300 pairs of masses and 300 pairs of clustered microcalcifications for determination of subjective ratings to establish a “gold standard.” These ratings would be useful for determination and evaluation of objective similarity measures. The 300 pairs were randomly grouped into two groups; one group was used as a training set, whereas the other group was used as a test set. This process was repeated for three times. The objective measures based on the distance in the feature space and by use of an artificial neural network (ANN) were compared by a cross validation method. Results: For distance-based objective measure, the correlations between the subjective ratings and objective measures were 0.50, 0.53, and 0.51 for the mass pairs. When the radiologists' ratings were used as teacher in training of the ANN, the similarity measures were improved, and the correlations were improved to 0.66, 0.70, and 0.67. Conclusion: The results indicate that similar images selected by the ANN-based similarity measure may be more useful than the images selected by the distance-based similarity measure in feature space.

An electrophysiological investigation of indirect semantic priming

In two experiments, direct and indirect semantic priming were measured using event-related potentials. In Experiment 1, participants rated the relatedness between prime and target on a seven-point scale. In Experiment 2, participants simply read the primes and targets as they monitored for a semantic category in probe filler items. Significant direct and indirect N400 priming effects were observed in both experiments. In Experiment 1, the indirect N400 priming effect remained significant when indirectly related and unrelated word pairs were matched for participants' explicit relatedness judgments. In both experiments, the indirect N400 priming effects were preserved when indirectly related and unrelated word pairs were matched on more global and objective measures of semantic similarity. These findings are discussed in the context of current theoretical models of semantic memory and semantic priming.