Contextual Dissimilarity Research Articles

“The Feeling Is What Counts”: Fathers’ Perspectives on Child Risk and Protection within the Ultra-Orthodox Context

Context is known to have substantial influence on issues pertaining to child development. Nevertheless, the field of child well-being, risk, and protection is rooted in Western modernized research and experience, often overlooking contextual dissimilarity. The present study aimed to explore risk and protection for children in a distinct context: the Ultra-Orthodox community, which is an insular and religiously close-knit society. Fifteen in-depth interviews with Ultra-Orthodox fathers dealing with issues of child risk and protection were conducted and thematically analyzed. Analysis of the findings revealed two major areas that fathers viewed as posing potential risk for children: poverty and a lack of paternal presence. In both cases, the fathers emphasized that appropriate mediation of these circumstances can diffuse their potential harm. The discussion outlines the different ways fathers proposed mediating potential risk situations, highlighting distinct religion-related methods. It then considers specific, context-informed ramifications and recommendations and notes limitations and directions for future study.

Hybrid Regularization of Diffusion Process for Visual Re-Ranking.

To improve the retrieval result obtained from a pairwise dissimilarity, many variants of diffusion process have been applied in visual re-ranking. In the framework of diffusion process, various contextual similarities can be obtained by solving an optimization problem, and the objective function consists of a smoothness constraint and a fitting constraint. And many improvements on the smoothness constraint have been made to reveal the underlying manifold structure. However, little attention has been paid to the fitting constraint, and how to build an effective fitting constraint still remains unclear. In this article, by deeply analyzing the role of fitting constraint, we firstly propose a novel variant of diffusion process named Hybrid Regularization of Diffusion Process (HyRDP). In HyRDP, we introduce a hybrid regularization framework containing a two-part fitting constraint, and the contextual dissimilarities can be learned from either a closed-form solution or an iterative solution. Furthermore, this article indicates that the basic idea of HyRDP is closely related to the mechanism behind Generalized Mean First-passage Time (GMFPT). GMFPT denotes the mean time-steps for the state transition from one state to any one in the given state set, and is firstly introduced as the contextual dissimilarity in this article. Finally, based on the semi-supervised learning framework, an iterative re-ranking process is developed. With this approach, the relevant objects on the manifold can be iteratively retrieved and labeled within finite iterations. The proposed algorithms are validated on various challenging databases, and the experimental performances demonstrate that retrieval results obtained from different types of measures can be effectively improved by using our methods.

Fuzzy inference based contextual dissimilarity histogram equalization algorithm for image enhancement

AbstractIn order to overcome the drawback of the existing image enhancement technologies and further consider the pixel intensity expression error caused by imaging, a novel fuzzy inference‐based contextual dissimilarity histogram equalization (FICDHE) algorithm is proposed. The proposed algorithm is composed of three modules. In the first module, according to the calculated probable intensity intervals, the membership functions of intensity are generated. In the second one, fuzzy inference systems are established and the contextual dissimilarity of each pixel is calculated. In the third module, the contextual dissimilarity histograms are clipped and equalized. The fuzzy system established in this paper not only fully considers the uncertainty source of pixel gray level expression, but also has adaptability. The parameter selection of fuzzy inference system membership function in this algorithm does not need human intervention, but is automatically obtained based on the statistical information of image pixel gray. Its adaptability makes the algorithm more widely used and convenient. Experiments are conducted using four typical medical images and 800 images from the KNIX dataset and BrainWeb dataset. The performance of the proposed method was compared to a series of enhancement algorithms based on both subjective judgment and image quality measurement indexes. Experimental results demonstrate that the proposed algorithm has a better contrast enhancement ability and yields better performance.

Dynamic collaboration between small- and medium-sized enterprises from highly dissimilar markets

Emerging markets have created significant opportunities for European small- and medium-sized enterprises (SMEs). Highly dissimilar market contexts compared to traditional markets and missing resources present substantial barriers to successfully serve those markets. Collaborative partnerships with local SMEs can help to overcome the barriers. Knowledge on how SMEs can develop lasting collaborative relationships in highly dissimilar contexts is scarce. This research investigates which factors influence the stability of more informal collaborative partnerships of Central European SMEs with SMEs located in China. A qualitative analysis of four cases confirms the importance of complementarity of partner resources, working well together based on mutual trust and commitment, shared sensemaking, and the balanced consideration of partner interests. Mutual learning and adaptation in iterative steps to enhance shared sensemaking and seeing differences in interpretation and behavior arising from contextual dissimilarity as opportunities instead of threats turn out to be SME-specific factors essential for building stable informal collaborative relations.

Fuzzy contextual inference system for medical image enhancement

PurposeIn order to facilitate early detection and further diagnosis, enhancing contrast and preserving natural characteristics are the fundamental requirement. In MR imaging, the poor quality image specifically the low contrast image may deliver inadequate data for the visual interpretation of affected portions. MethodsFuzzy Contextual Dissimilarity Adaptive Histogram Equalization (FCDAHE) is employed to enhance the contrast of medical images. The proposed method consists of two modules. In the first module, fuzzy dissimilarity histogram is formulated by finding fuzzy neighbourhood dissimilarity for every pixel using fuzzy rules. Then, the fuzzy dissimilarity clip limit is computed based on a fuzzy inference system. Contrast (C) and discrete entropy (E) are the two parameters used by the Fuzzy Inference System (FIS) to achieve fuzzy dissimilarity clip limit. Then, the enhanced output is obtained by equalizing the fuzzy dissimilarity adaptive histogram. In the second module, Contextual Intensity Transformation (CIT) is applied to fuzzy dissimilarity adaptive histogram equalized output to get final enhanced images. ResultsExperiments are conducted and tested on a wide variety of MR images extracted from BRATS 2015 database and Magnetic Resonance – Technology Information Portal to evaluate the performance of the proposed method both qualitatively and quantitatively. Extensive quantitative measures show that the proposed technique yields better performance in terms of PSNR, entropy, contrast ratio and EME when compared to state of art enhancement methods. ConclusionThe proposed FCDAHE algorithm not only improves contrast but also preserves natural characteristics. The proposed method offers a better scope for disease analysis and diagnosis.

Learning contextual dissimilarity on tensor product graph for visual re-ranking

As the object retrieval problem cannot be well solved by pairwise distances, many algorithms have been developed for visual re-ranking as the post-processing step. As discussed in recent studies, the contextual similarity/dissimilarity based on diffusion process can be obtained by solving an optimization problem that contains a smoothness constraint and a fitting constraint. In this paper, we introduce the mean first-passage time (MFPT) as the contextual dissimilarity. By analysis of the principle behind MFPT, one can find that the corresponding cost function is generated with a hybrid fitting constraint, and the predefined values of contextual dissimilarities are limited within proper ranges. With the hybrid fitting constraint and the smoothness constraint based on a tensor product graph, we construct the objective function associated with a novel contextual dissimilarity. On that basis, we obtain the contextual dissimilarities as an iterative solution by solving the optimization problem, and the manifold structure can be effectively captured by using the proposed method. Our method is evaluated on retrieval tasks for different databases, and the re-ranking result with the proposed contextual dissimilarity outperforms other state-of-the-art algorithms.

Polarimetric SAR Despeckling by Integrating Stochastic Sampling and Contextual Patch Dissimilarity Exploration

Speckle reduction has been the longstanding task since the invention of synthetic aperture radar (SAR). For further image analysis and interpretation, it usually demands better speckle suppression to preserve spatial and polarimetric information of the polarimetric SAR (PolSAR) images. In this paper, a new PolSAR filtering algorithm which combines stochastic sampling based on nonlocal mean, random walk model, and contextual patch dissimilarity is proposed. The nonlocal mean suppresses the speckle effectively while preserving the details and polarimetric information well. However, the size of nonlocal search window is fixed without taking the homogeneous and heterogeneous areas into consideration. Thus, it usually leads to computational redundancy. Correspondingly, we propose to apply the random walk to reduce the search domain. Contextual patch is also proposed to represent the large surroundings of a patch in a compact fashion. More precisely, the random walk model is utilized to determine the sampling path. Then, the traditional center patch dissimilarity and contextual patch dissimilarity are employed to measure the transition probability of the random walk sequence. Finally, the denoised estimation of PolSAR image is obtained by weight function derived from the transition probability. Since the stochastic sampling combines spatial random walk and the polarimetric dissimilarity measurements, the proposed algorithm fully exploits both the spatial and polarimetric information. Experimental results on synthetic and real PolSAR data demonstrate the effectiveness of the proposed method in terms of spatial and polarimetric information maintenance.

The Effects of Interruption Context on Task Performance

This study examines the effects of contextual similarity or dissimilarity of interruptions during task performance. Participants engaged in a series of working memory tasks using a computer interface. While performing these tasks they were intermittently interrupted and required to perform a different task. Each participant was interrupted with four different tasks that varied in context from identically related information given during the primary task to unrelated contextual information. Performance was assessed based on the total number of errors during each task before and after an interruption. Results revealed an increase in error rate when a participant was interrupted with a contextually dissimilar task compared to the primary task. These findings suggest that a person who experiences an interruption by a task with contextually identical information to the primary task will make fewer errors following the completion of the interruption compared to a task with contextually dissimilar information.

Does pre‐entry licensing undermine the performance of subsequent independent activities? Evidence from the global aerospace industry, 1944–2000

AbstractWe study how firms' use of in‐licensing for their initial entry to a business domain can detract from the performance of their subsequent autonomous endeavors in the domain. We argue that in‐licensing produces high levels of causal ambiguity about factors that drive the performance achieved with the licensed product. In turn, the experience that firms gather through pre‐entry licensing is likely to generate superstitious learning and overconfidence that undermine the performance of licensees' subsequent independent operations. The biases will be particularly strong in the face of contextual dissimilarity. We find consistent evidence in a study of firms that entered the global aircraft industry between 1944 and 2000. The research helps advance the understanding of the benefits and costs of markets for technology. Copyright © 2012 John Wiley & Sons, Ltd.

ProDis-ContSHC: learning protein dissimilarity measures and hierarchical context coherently for protein-protein comparison in protein database retrieval

BackgroundThe need to retrieve or classify protein molecules using structure or sequence-based similarity measures underlies a wide range of biomedical applications. Traditional protein search methods rely on a pairwise dissimilarity/similarity measure for comparing a pair of proteins. This kind of pairwise measures suffer from the limitation of neglecting the distribution of other proteins and thus cannot satisfy the need for high accuracy of the retrieval systems. Recent work in the machine learning community has shown that exploiting the global structure of the database and learning the contextual dissimilarity/similarity measures can improve the retrieval performance significantly. However, most existing contextual dissimilarity/similarity learning algorithms work in an unsupervised manner, which does not utilize the information of the known class labels of proteins in the database.ResultsIn this paper, we propose a novel protein-protein dissimilarity learning algorithm, ProDis-ContSHC. ProDis-ContSHC regularizes an existing dissimilarity measure dij by considering the contextual information of the proteins. The context of a protein is defined by its neighboring proteins. The basic idea is, for a pair of proteins (i, j), if their context and is similar to each other, the two proteins should also have a high similarity. We implement this idea by regularizing dij by a factor learned from the context and .Moreover, we divide the context to hierarchial sub-context and get the contextual dissimilarity vector for each protein pair. Using the class label information of the proteins, we select the relevant (a pair of proteins that has the same class labels) and irrelevant (with different labels) protein pairs, and train an SVM model to distinguish between their contextual dissimilarity vectors. The SVM model is further used to learn a supervised regularizing factor. Finally, with the new Supervised learned Dissimilarity measure, we update the Protein Hierarchial Context Coherently in an iterative algorithm--ProDis-ContSHC.We test the performance of ProDis-ContSHC on two benchmark sets, i.e., the ASTRAL 1.73 database and the FSSP/DALI database. Experimental results demonstrate that plugging our supervised contextual dissimilarity measures into the retrieval systems significantly outperforms the context-free dissimilarity/similarity measures and other unsupervised contextual dissimilarity measures that do not use the class label information.ConclusionsUsing the contextual proteins with their class labels in the database, we can improve the accuracy of the pairwise dissimilarity/similarity measures dramatically for the protein retrieval tasks. In this work, for the first time, we propose the idea of supervised contextual dissimilarity learning, resulting in the ProDis-ContSHC algorithm. Among different contextual dissimilarity learning approaches that can be used to compare a pair of proteins, ProDis-ContSHC provides the highest accuracy. Finally, ProDis-ContSHC compares favorably with other methods reported in the recent literature.

Accurate Image Search Using the Contextual Dissimilarity Measure

This paper introduces the contextual dissimilarity measure, which significantly improves the accuracy of bag-of-features-based image search. Our measure takes into account the local distribution of the vectors and iteratively estimates distance update terms in the spirit of Sinkhorn's scaling algorithm, thereby modifying the neighborhood structure. Experimental results show that our approach gives significantly better results than a standard distance and outperforms the state of the art in terms of accuracy on the Nistér-Stewénius and Lola data sets. This paper also evaluates the impact of a large number of parameters, including the number of descriptors, the clustering method, the visual vocabulary size, and the distance measure. The optimal parameter choice is shown to be quite context-dependent. In particular, using a large number of descriptors is interesting only when using our dissimilarity measure. We have also evaluated two novel variants: multiple assignment and rank aggregation. They are shown to further improve accuracy at the cost of higher memory usage and lower efficiency.