We present experimental results based on real world commercial data sources to show that the estimates obtained via GTM model is better than the baseline . Our experimental results also showed that the approximate match likelihood can improve the recall of the similarity function . Functioning of a biometric system in large part depends on the performance of the similarity measure function . Frequently a generalized similarity distance measure function such as Euclidian distance or Mahalanobis distance is applied to the task of matching biometric feature vectors . The edge images of the infrared and visible images are achieved by wavelet transform . The alignment metric and normalized mutual information are calculated to obtain new similarity measuring function . The transform parameters between images under registration are obtained according to the calculated result of the extreme value of the similarity measuring function . Subsequently , an orientation code based algorithm is adopted to determine the integer-pixel displacement and rotation angle estimation . The third step is to obtain the sub-pixel displacement through a similarity function based algorithm . Simulations are carried out , and the results show that the proposed method has the characteristics of translation and rotation invariance , and the matching accuracy can reach 0.01-pixel theoretically . Previous studies in finding similar users and items are based on user-defined similarity metrics such as Pearson Correlation Coefficient or Vector Space Similarity which are not adaptive and optimized for different applications and datasets . Moreover , previous studies have treated the similarity function calculation between users and items separately . In this paper , we propose a novel adaptive bidirectional similarity metricfor collaborative filtering . Report : Similarity Function Evaluation . This report presents details on and an in-depth evaluation of a similarity function used for detecting similar test steps in manual test cases , written in natural language . In this paper , we present a method which exploits this information to improve the process of learning a similarity function . Our approach is composed of an unsupervised dimensionality reduction stage and the similarity function itself . Uncertainty is taken into account throughout the whole processing pipeline during both training and testing . [ reference ] . Performance of Correlation Filter - based trackers has then been notably improved with the adoption of multi - channel formulations [ reference ][ reference ] , spatial constraints [ reference ][ reference ][ reference ][ reference ] and deep features ( e.g. [ reference ][ reference ] ) . Recently , a radically different approach has been introduced [ reference ][ reference ][ reference ] . Instead of learning a discrimative classifier online , the idea is to train ( offline ) a similarity function on pairs of video frames . At test time , this function can be simply evaluated on a new video , once per frame . Infact in multivariate normal distribution , it is used as a statistical parameter to approximate the normal density and thus also reflects information about the data distribution . This particular property along with invariance has been exploited before in for computing similarity between two set of vectors . One can also think about fitting multivariate normal distribution on but it involves computing inverse of covariance matrix which is computationally expensive . We make the following contributions : first , we propose a network architecture which aggregates and embeds the face descriptors produced by deep convolutional neural networks into a compact fixed - length representation . This compact representation requires minimal memory storage and enables efficient similarity computation . Second , we propose a novel GhostVLAD layer that includes ghost clusters , that do not contribute to the aggregation . Before the popularity of visual question answering ( VQA ) , text question answering ( QA ) had already been established as a mature research problem in the area of natural language processing . Previous QA methods include searching for the key words of the question in a search engine ; parsing the question as a knowledge base ( KB ) query ; or embedding the question and using a similarity measurement to find evidence for the answer . Recently , memory networks were proposed for solving the QA problem . The contributions of this paper can be summarized as follows . Different with the architectures of using similarity matrix , our proposed architecture directly model the strong interactions of two sentences with coupled - LSTMs , which can capture the useful local semantic relevances of two sentences . Our architecture can also capture the multiple granular interactions by several stacked coupled - LSTMs layers . If the solution method used is value - based , then states should only be considered similar if they share the features that are determinative of value . This motivates us to construct a similarity measure that exploits the feature representation that is used for value function approximation . These features are explicitly designed to be relevant for estimating value . Nodes have biased affinities , which logically partition the network into distinct parties . Nodes in the same party tend to have a positive influence on each other , but the extent to which this holds varies across nodes and depends on the chosen affinity model . This paper considers two variations on an Ising spin-glass network model that investigate opinion formation in such biased affinity systems . We use a two-stream deep neural network to extract the global appearance features and human body part semantic maps separately , and employ aggregation operations to generate final features . We argue that these features are more suitable for affinity measurement , which can be seen as the average of appearance similarity weighted by the corresponding human body part similarity . Next , our tracker adopts a hierarchical correlation clustering algorithm , which combines targets ' appearance feature similarity with motion correlation for data association . Following its initial model , we try to identify the fundamental characteristics of this family of algorithms and summarize their diversities . There exist various elements in this method , including data representation , coverage estimate , affinity measure , and matching rules , which are discussed for different variations . The various negative selection algorithms are categorized by different criteria as well . Thus , data regions have locality , or affinity , to particular threads of execution . This affinity concept engenders a non-uniformity in shared memory accesses by a particular thread . Affinity should be considered when building data structures in algorithms and applications , but UPC provides limited tools for data locality management . In this paper we present an extension to recent works dealing with metrics for guiding the hardware-software partitioning step . This extension builds upon and complement our own work with metrics in the Design Trotter project , and is combined with the affinity metric approach . We show that the proposed extension improves the original affinity metric in terms of parallelism detection , and thus can help system designers to make wiser hardware-software partitioning decisions , which in turn reduces the time-to-market factor . Both data spaces allow to compute similarity between entities which can thereafter be used to calculate user-entity affinity . In this paper , we are interested in observing their comparative performance in the affinity assessment task . To this end , we carried out a first experiment within a travel destination recommendation scenario on a gold standard dataset . The definitions and the constructions of the components of the MMM mechanism can be found in [ 8 ] . 2.1 Affinity-Based Measures The relative affinity measures are used to indicate how frequently two URLs are accessed together . Two URL groups whose URLs are accessed together more frequently are said to have a higher relative affinity relationship . In this paper , a multiobjective modified clonal selection algorithm based on the use of the notion « Pareto set » , which can be applied for the development of the forecasting models on the base of the strictly binary trees has been offered . It is suggested to use the affinity indicator based on the average forecasting error rate , and the tendencies discrepancy indicator in the role of the objective functions . The results of experimental studies which confirm the efficiency of the offered multiobjective clonal selection algorithm have been given . In this paper , a new method is proposed based on class inequivalent side information and an evolutionary-based K-means clustering algorithm . In a distance metric learning approach , data points are transferred to a new space where the Euclidean distances between similar and dissimilar points are at their minimum and maximum , respectively . However , the evolutionary-based K-means clustering approach yields globally optimized Gaussian components for an accent classification system . First , we present the basic concepts and notations of CT . Second , we classify the research on CT into the following categories : modeling for CT , test suite generation , constraints , failure diagnosis , prioritization , metric , evaluation , testing procedure and the application of CT . For each of the categories , we survey the motivation , key issues , solutions , and the current state of research . The proposed ordering based on the new metric minimizes the SER by exploiting the instantaneous noise and considering its correlation at the linear filter output . A simpler suboptimal ordering metric is also proposed which requires much less computational complexity than that of the optimal ordering metric . We further show that there exists a simplified version of the suboptimal ordering metric for BPSK and 4QAM which achieves a significant performance gain over the conventional ordering scheme with no additional complexity . It has been successfully applied in a wide selection of problems of image processing . This success comes from two reasons , a physical one , since it computes a physical cost to transport an element of mass between two images or two histograms , and a statistical one , since it is a cross-bin metric ( as opposed to a bin-wise metric ) . In computer vision , these features are useful since small variation of illuminance can shift the histogram . The metric used for the airport zoning is DNL which is 24h metrics and is directly related with annoyance . The use of the DNL metric in airport noise zoning is based in the works of Schultz that were later improved by Fidel . The inconsistencies between metrics create urban conflicts due to lack of communication and understanding of the zoning approach used . First , we deduce the expected packet loss recovery rate from the estimation of the effective packet loss rate using loss distribution statistics . Second , layer-based distortion metric is presented including error propagation effects from hierarchical prediction structure in compressed scalable video packets . Third , the performance metric for FEC assignment is proposed from the layer-based distortion metric and the expected packet loss recovery rate . This article is an introduction to work in progress at The Aerospace Corporation and California State University , Long Beach , in the area of metrics for requirements engineering . The first in a planned series of papers on metrics for full life cycle system and software engineering , it describes the role of metrics in an integrated approach to system and software engineering , introduces the basic components of complete metric definitions , and discusses the use of metrics in comprehensively assessing objective aspects of the requirements engineering process and its products . It tells the routing algorithm that which forwarder node is having what impact on its battery life . EDF is local opportunistic routing metric , which reduces end-to-end delay in the network and also increases the network lifetime . To calculate EDF , the concept of residual energy of each node has been used . Isomap is a manifold learning approach that provides a low-dimensional embedding space that is approximately isometric to the manifold that is assumed to be formed by the high-fidelity Navier-Stokes flow solutions under smooth variations of the inflow conditions . The focus of the work at hand is the adaptive construction and refinement of the Isomap emulator : We exploit the non-Euclidean Isomap metric to detect and fill up gaps in the sampling in the embedding space . The performance of the proposed manifold filling method will be illustrated by numerical experiments , where we consider nonlinear parameter-dependent steady-state Navier-Stokes flows in the transonic regime . In the proposed algorithm , each UE only needs to report a single value of large-scale fading ( denoted as PL ) . At the central unit , use weighted PL instead of the resultant throughput to prioritize UEs , set the threshold according to the variance of all UEs ' PL , and employ a simple scalar-wise metric for grouping . Performance evaluation shows that in contrast to the conventional methods , the proposed algorithm with twopart scheduling strategy can reduce about 20 % of the feedback overhead and improve the UEs fairness . We also compare with some set - toset similarity measurements leveraging pairwise comparison on the image level . To keep it simple , we simply use the L 2 feature distances for face recognition ( all features are normalized ) , although it is possible to combine with an extra metric learning or template adaption technique [ reference ] to further boost the performance on each dataset . In the baseline methods , CNN + Min L 2 , CNN + Max L 2 , CNN + Mean L 2 and CNN + SoftMin L 2 measure the similarity of two video faces based on the L 2 feature distances of all frame pairs . Structured losses penalize the joint configuration of the output . A large body of literature has considered losses of this kind , with methods including conditional random fields , the SSIM metric , feature matching , nonparametric losses , the convolutional pseudo - prior , and losses based on matching covariance statistics . The conditional GAN is different in that the loss is learned , and can , in theory , penalize any possible structure that differs between output and target . Our approach is most related to others that aim to train an effective metric for one - shot learning . Where they focus on the learning of the transferrable embedding and pre - define a fixed metric ( e.g. , as Euclidean ) , we further aim to learn a transferrable deep metric for comparing the relation between images ( few - shot learning ) , or between images and class descriptions ( zero - shot learning ) . By expressing the inductive bias of a deeper solution ( multiple non - linear learned stages at both embedding and relation modules ) , we make it easier to learn a generalisable solution to the problem . These studies can be seen as distance metric learning , but where all the learning occurs in the feature embedding , and a fixed metric is used given the learned embedding . Also related are conventional metric learning approaches that focus on learning a shallow ( linear ) Mahalanobis metric for a fixed feature representation . In contrast to prior work ’s fixed metric or fixed features and shallow learned metric , Relation Network can be seen as both learning a deep embedding and learning a deep non - linear metric ( similarity function ) . : We also evaluate our method by comparing it with several existing methods on VIPeR . The compared methods include distance metric learning ones : MLAPG , LOMO + XQDA , BoW , WARCA and LDNS , and deep learning based ones : IDLA , DGDropout , SI + CI , Gate S - CNN , LSTM S - CNN , MTL - LORAE and Spindle . From the results shown in Table [ reference ] , our PDC achieves the rank1 accuracy of 51.27 % . We use this ranking algorithm since our problem is naturally a ranking problem and forests of boosted decision trees have been very successful lately ( as seen , e.g. , in many recent Kaggle competitions ) . We do not use all the features of LambdaMART since we are only scoring 1 / 0 loss on the first ranked proposal , rather than using an IR - style metric to score ranked results . We use Stanford ’s neural network dependency parser to parse all our document and question text , and all other features can be extracted without additional tools . In both studies , the authors used metric regression for the age estimation subtasks . While our paper focuses on the comparison of different ordinal regression approaches , we hypothesize that such all - in - one and cascaded CNNs can be further improved by our method , since , as shown in niu2016ordinal , ordinal regression CNNs outperform metric regression CNNs in age estimation tasks . section : Proposed Method Evaluation is done using the standard Percentage of Correct Keypoints ( PCK ) metric which reports the percentage of detections that fall within a normalized distance of the ground truth . For FLIC , distance is normalized by torso size , and for MPII , by a fraction of the head size ( referred to as PCKh ) . In summary , is adaptively constructed by a CNN unit as follows : With the introduction of feature - driven distance metric , each filter of f - lcon is constructed as follows : where denotes the neighborhood containing pixels centered at position . Here , we provide a mechanism to perform f - lcon efficiently . We note pazis16efficient ’s PAC - MDP result for metric spaces and leike16thompson ’s asymptotic analysis of Thompson sampling in general environments . section : Future Directions Firstly , NNLRR used Gaussian fields and harmonic functions into the low-rank representation ( LRR ) model . Secondly , NNLRR guided the affinity matrix construction by the supervision information . Next , finding a non-negative low-rank matrix , the matrix represents each sample by some other linear combination points , and the affinity matrix is obtained by the matrix . A method for the in vitro biosynthesis of proteins in native conformation which improves the technique which is well known in the art by immobilizing on the surface of an affinity matrix at discrete locations a ligand for which the protein being synthesized has an affinity and adding to the reaction mixture said affinity matrix having said immobilized ligand so that each molecule that is in the process of folding into a functional protein molecule may bind an immobilized ligand and be kept separated from other protein molecules as the folding proceeds . The technique works with either a batch method or a continuous method . The linear correlation coefficients were all greater than 99 % , and the relative standard deviations of two binding parameters were 0.58 and 0.059 % , respectively . The experiments proved that the frontal chromatography method was accurate , reproducible , and could be used to determine the metal-binding parameters of the affinity column . The effects of buffer pH , type , and concentration on binding parameters were explored by uniform design experiment . A pointwise approximation of the pairwise affinity information may in fact be interpreted as a ‘ saliency ’ index , and the foreground of the scene may be obtained by thresholding it . An algorithm called ‘ affinity factorization ’ is thus obtained which may be used for grouping . The affinity matrix plays the central role in similarity based clustering algorithm . A recent study has shown that , conventional affinity matrices constructed using local neighborhood information are deficient to represent the overall structure of the dataset . In this paper , we propose a novel similarity measure between two points that captures the global setting of the dataset . The experimental results show that the proposed replacement of k by k∗ slightly improves the performance of the Nyström approximation . Compared with other affinity matrix modification methods , our k∗ obtains comparable or higher clustering performance in terms of accuracy and Normalized Mutual Information ( NMI ) . The second type is uncertainty in which context segments are relevant to representing the speaker talking right before the hypothesized speaker change . We also show the robustness of affinity matrix-representation for speaker change detection . Our methods provide very significant accuracy improvements compared to several baselines including a recently published end-to-end system . We compare with the matrix factorization techniques for graph factorization . An information network can be represented as an affinity matrix , and is able to represent each vertex with a low - dimensional vector through matrix factorization . Graph factorization is optimized through stochastic gradient descent and is able to handle large networks . To address this issue , we propose a hypergraph-based matching scheme in which both pairwise and higher order relationships for the probe and gallery subjects are discovered through hypergraph learning . In this way , improved similarity scores are obtained as compared to the conventional pairwise similarity measure . We conduct experiments on two widely used person re-identification datasets and the results demonstrate that matching through hypergraph learning leads to superior performance compared with state-of-the-arts . It is therefore essential to efficiently identify these unproductive comparisons as rapidly as possible and exclude them from more time-intensive similarity calculations . The Blazing Signature Filter ( BSF ) is a highly efficient pairwise similarity algorithm which enables extensive data mining within a reasonable amount of time . The algorithm transforms datasets into binary metrics , allowing it to utilize the computationally efficient bit operators and provide a coarse measure of similarity . For shot boundary detection , our approach combines pairwise similarity analysis and supervised classification . Using primitive low-level image features , we build secondary features based on inter-frame dissimilarity . To face this threat , the literature now includes a large body of work proposing approaches for identifying repackaged apps . Unfortunately , although most research involves pairwise similarity comparison to distinguish repackaged apps from their " original " counterparts , no work has considered the threat to validity of not being able to discover the true original apps . We provide in this paper preliminary insights of an investigation into the Multi-Generation Repackaging Hypothesis : is the original in a repackaging process the outcome of a previous repackaging process ? Furthermore , to enhance the performance , a new relevance score scheme via Kernel Support Vector Machine ( KSVM ) is formulated , which efficiently make use of medical images features in order to discriminate the images accurately . This system initiated a new discriminative name called ‘ pairwise similarity measurement ’ for the discriminativeness of pairs of query and input medical database images and subsequently integrate it with the classification error for discriminativeness in classifier production to generate a unified objective function . Results : Experimentation results assessed the proposed DDL in IRMA framework on a numerous medical images like BAA images and X-Ray skull images and results confirm that DDL performs far better than the state-of the-art approaches significantly in terms of precision , recall , sensitivity , specificity and accuracy . This problem occurs when multiple authors share the same name and particularly when names are simplified as in cases where names merely contain the first initial and the last name . This paper proposes a reliable and accurate pair-wise similarities approach to disambiguate names using supervised classification on Web correlations and authorship correlations . This approach makes use of Web correlations among citations assuming citations that co-refer on publication lists on the Web should to refer to the same author .
We present experimental results based on real world commercial data sources to show that the estimates obtained via GTM model is better than the baseline . Our experimental results also showed that the approximate match likelihood can improve the recall of the similarity function .
Functioning of a biometric system in large part depends on the performance of the similarity measure function . Frequently a generalized similarity distance measure function such as Euclidian distance or Mahalanobis distance is applied to the task of matching biometric feature vectors .
The edge images of the infrared and visible images are achieved by wavelet transform . The alignment metric and normalized mutual information are calculated to obtain new similarity measuring function . The transform parameters between images under registration are obtained according to the calculated result of the extreme value of the similarity measuring function .
Subsequently , an orientation code based algorithm is adopted to determine the integer-pixel displacement and rotation angle estimation . The third step is to obtain the sub-pixel displacement through a similarity function based algorithm . Simulations are carried out , and the results show that the proposed method has the characteristics of translation and rotation invariance , and the matching accuracy can reach 0.01-pixel theoretically .
Previous studies in finding similar users and items are based on user-defined similarity metrics such as Pearson Correlation Coefficient or Vector Space Similarity which are not adaptive and optimized for different applications and datasets . Moreover , previous studies have treated the similarity function calculation between users and items separately . In this paper , we propose a novel adaptive bidirectional similarity metricfor collaborative filtering .
Report : Similarity Function Evaluation . This report presents details on and an in-depth evaluation of a similarity function used for detecting similar test steps in manual test cases , written in natural language .
In this paper , we present a method which exploits this information to improve the process of learning a similarity function . Our approach is composed of an unsupervised dimensionality reduction stage and the similarity function itself . Uncertainty is taken into account throughout the whole processing pipeline during both training and testing .
[ reference ] . Performance of Correlation Filter - based trackers has then been notably improved with the adoption of multi - channel formulations [ reference ][ reference ] , spatial constraints [ reference ][ reference ][ reference ][ reference ] and deep features ( e.g. [ reference ][ reference ] ) . Recently , a radically different approach has been introduced [ reference ][ reference ][ reference ] . Instead of learning a discrimative classifier online , the idea is to train ( offline ) a similarity function on pairs of video frames . At test time , this function can be simply evaluated on a new video , once per frame .
Infact in multivariate normal distribution , it is used as a statistical parameter to approximate the normal density and thus also reflects information about the data distribution . This particular property along with invariance has been exploited before in for computing similarity between two set of vectors . One can also think about fitting multivariate normal distribution on but it involves computing inverse of covariance matrix which is computationally expensive .
We make the following contributions : first , we propose a network architecture which aggregates and embeds the face descriptors produced by deep convolutional neural networks into a compact fixed - length representation . This compact representation requires minimal memory storage and enables efficient similarity computation . Second , we propose a novel GhostVLAD layer that includes ghost clusters , that do not contribute to the aggregation .
Before the popularity of visual question answering ( VQA ) , text question answering ( QA ) had already been established as a mature research problem in the area of natural language processing . Previous QA methods include searching for the key words of the question in a search engine ; parsing the question as a knowledge base ( KB ) query ; or embedding the question and using a similarity measurement to find evidence for the answer . Recently , memory networks were proposed for solving the QA problem .
The contributions of this paper can be summarized as follows . Different with the architectures of using similarity matrix , our proposed architecture directly model the strong interactions of two sentences with coupled - LSTMs , which can capture the useful local semantic relevances of two sentences . Our architecture can also capture the multiple granular interactions by several stacked coupled - LSTMs layers .
If the solution method used is value - based , then states should only be considered similar if they share the features that are determinative of value . This motivates us to construct a similarity measure that exploits the feature representation that is used for value function approximation . These features are explicitly designed to be relevant for estimating value .
Nodes have biased affinities , which logically partition the network into distinct parties . Nodes in the same party tend to have a positive influence on each other , but the extent to which this holds varies across nodes and depends on the chosen affinity model . This paper considers two variations on an Ising spin-glass network model that investigate opinion formation in such biased affinity systems .
We use a two-stream deep neural network to extract the global appearance features and human body part semantic maps separately , and employ aggregation operations to generate final features . We argue that these features are more suitable for affinity measurement , which can be seen as the average of appearance similarity weighted by the corresponding human body part similarity . Next , our tracker adopts a hierarchical correlation clustering algorithm , which combines targets ' appearance feature similarity with motion correlation for data association .
Following its initial model , we try to identify the fundamental characteristics of this family of algorithms and summarize their diversities . There exist various elements in this method , including data representation , coverage estimate , affinity measure , and matching rules , which are discussed for different variations . The various negative selection algorithms are categorized by different criteria as well .
Thus , data regions have locality , or affinity , to particular threads of execution . This affinity concept engenders a non-uniformity in shared memory accesses by a particular thread . Affinity should be considered when building data structures in algorithms and applications , but UPC provides limited tools for data locality management .
In this paper we present an extension to recent works dealing with metrics for guiding the hardware-software partitioning step . This extension builds upon and complement our own work with metrics in the Design Trotter project , and is combined with the affinity metric approach . We show that the proposed extension improves the original affinity metric in terms of parallelism detection , and thus can help system designers to make wiser hardware-software partitioning decisions , which in turn reduces the time-to-market factor .
Both data spaces allow to compute similarity between entities which can thereafter be used to calculate user-entity affinity . In this paper , we are interested in observing their comparative performance in the affinity assessment task . To this end , we carried out a first experiment within a travel destination recommendation scenario on a gold standard dataset .
The definitions and the constructions of the components of the MMM mechanism can be found in [ 8 ] . 2.1 Affinity-Based Measures The relative affinity measures are used to indicate how frequently two URLs are accessed together . Two URL groups whose URLs are accessed together more frequently are said to have a higher relative affinity relationship .
In this paper , a multiobjective modified clonal selection algorithm based on the use of the notion « Pareto set » , which can be applied for the development of the forecasting models on the base of the strictly binary trees has been offered . It is suggested to use the affinity indicator based on the average forecasting error rate , and the tendencies discrepancy indicator in the role of the objective functions . The results of experimental studies which confirm the efficiency of the offered multiobjective clonal selection algorithm have been given .
In this paper , a new method is proposed based on class inequivalent side information and an evolutionary-based K-means clustering algorithm . In a distance metric learning approach , data points are transferred to a new space where the Euclidean distances between similar and dissimilar points are at their minimum and maximum , respectively . However , the evolutionary-based K-means clustering approach yields globally optimized Gaussian components for an accent classification system .
First , we present the basic concepts and notations of CT . Second , we classify the research on CT into the following categories : modeling for CT , test suite generation , constraints , failure diagnosis , prioritization , metric , evaluation , testing procedure and the application of CT . For each of the categories , we survey the motivation , key issues , solutions , and the current state of research .
The proposed ordering based on the new metric minimizes the SER by exploiting the instantaneous noise and considering its correlation at the linear filter output . A simpler suboptimal ordering metric is also proposed which requires much less computational complexity than that of the optimal ordering metric . We further show that there exists a simplified version of the suboptimal ordering metric for BPSK and 4QAM which achieves a significant performance gain over the conventional ordering scheme with no additional complexity .
It has been successfully applied in a wide selection of problems of image processing . This success comes from two reasons , a physical one , since it computes a physical cost to transport an element of mass between two images or two histograms , and a statistical one , since it is a cross-bin metric ( as opposed to a bin-wise metric ) . In computer vision , these features are useful since small variation of illuminance can shift the histogram .
The metric used for the airport zoning is DNL which is 24h metrics and is directly related with annoyance . The use of the DNL metric in airport noise zoning is based in the works of Schultz that were later improved by Fidel . The inconsistencies between metrics create urban conflicts due to lack of communication and understanding of the zoning approach used .
First , we deduce the expected packet loss recovery rate from the estimation of the effective packet loss rate using loss distribution statistics . Second , layer-based distortion metric is presented including error propagation effects from hierarchical prediction structure in compressed scalable video packets . Third , the performance metric for FEC assignment is proposed from the layer-based distortion metric and the expected packet loss recovery rate .
This article is an introduction to work in progress at The Aerospace Corporation and California State University , Long Beach , in the area of metrics for requirements engineering . The first in a planned series of papers on metrics for full life cycle system and software engineering , it describes the role of metrics in an integrated approach to system and software engineering , introduces the basic components of complete metric definitions , and discusses the use of metrics in comprehensively assessing objective aspects of the requirements engineering process and its products .
It tells the routing algorithm that which forwarder node is having what impact on its battery life . EDF is local opportunistic routing metric , which reduces end-to-end delay in the network and also increases the network lifetime . To calculate EDF , the concept of residual energy of each node has been used .
Isomap is a manifold learning approach that provides a low-dimensional embedding space that is approximately isometric to the manifold that is assumed to be formed by the high-fidelity Navier-Stokes flow solutions under smooth variations of the inflow conditions . The focus of the work at hand is the adaptive construction and refinement of the Isomap emulator : We exploit the non-Euclidean Isomap metric to detect and fill up gaps in the sampling in the embedding space . The performance of the proposed manifold filling method will be illustrated by numerical experiments , where we consider nonlinear parameter-dependent steady-state Navier-Stokes flows in the transonic regime .
In the proposed algorithm , each UE only needs to report a single value of large-scale fading ( denoted as PL ) . At the central unit , use weighted PL instead of the resultant throughput to prioritize UEs , set the threshold according to the variance of all UEs ' PL , and employ a simple scalar-wise metric for grouping . Performance evaluation shows that in contrast to the conventional methods , the proposed algorithm with twopart scheduling strategy can reduce about 20 % of the feedback overhead and improve the UEs fairness .
We also compare with some set - toset similarity measurements leveraging pairwise comparison on the image level . To keep it simple , we simply use the L 2 feature distances for face recognition ( all features are normalized ) , although it is possible to combine with an extra metric learning or template adaption technique [ reference ] to further boost the performance on each dataset . In the baseline methods , CNN + Min L 2 , CNN + Max L 2 , CNN + Mean L 2 and CNN + SoftMin L 2 measure the similarity of two video faces based on the L 2 feature distances of all frame pairs .
Structured losses penalize the joint configuration of the output . A large body of literature has considered losses of this kind , with methods including conditional random fields , the SSIM metric , feature matching , nonparametric losses , the convolutional pseudo - prior , and losses based on matching covariance statistics . The conditional GAN is different in that the loss is learned , and can , in theory , penalize any possible structure that differs between output and target .
Our approach is most related to others that aim to train an effective metric for one - shot learning . Where they focus on the learning of the transferrable embedding and pre - define a fixed metric ( e.g. , as Euclidean ) , we further aim to learn a transferrable deep metric for comparing the relation between images ( few - shot learning ) , or between images and class descriptions ( zero - shot learning ) . By expressing the inductive bias of a deeper solution ( multiple non - linear learned stages at both embedding and relation modules ) , we make it easier to learn a generalisable solution to the problem .
These studies can be seen as distance metric learning , but where all the learning occurs in the feature embedding , and a fixed metric is used given the learned embedding . Also related are conventional metric learning approaches that focus on learning a shallow ( linear ) Mahalanobis metric for a fixed feature representation . In contrast to prior work ’s fixed metric or fixed features and shallow learned metric , Relation Network can be seen as both learning a deep embedding and learning a deep non - linear metric ( similarity function ) .
: We also evaluate our method by comparing it with several existing methods on VIPeR . The compared methods include distance metric learning ones : MLAPG , LOMO + XQDA , BoW , WARCA and LDNS , and deep learning based ones : IDLA , DGDropout , SI + CI , Gate S - CNN , LSTM S - CNN , MTL - LORAE and Spindle . From the results shown in Table [ reference ] , our PDC achieves the rank1 accuracy of 51.27 % .
We use this ranking algorithm since our problem is naturally a ranking problem and forests of boosted decision trees have been very successful lately ( as seen , e.g. , in many recent Kaggle competitions ) . We do not use all the features of LambdaMART since we are only scoring 1 / 0 loss on the first ranked proposal , rather than using an IR - style metric to score ranked results . We use Stanford ’s neural network dependency parser to parse all our document and question text , and all other features can be extracted without additional tools .
In both studies , the authors used metric regression for the age estimation subtasks . While our paper focuses on the comparison of different ordinal regression approaches , we hypothesize that such all - in - one and cascaded CNNs can be further improved by our method , since , as shown in niu2016ordinal , ordinal regression CNNs outperform metric regression CNNs in age estimation tasks . section : Proposed Method
Evaluation is done using the standard Percentage of Correct Keypoints ( PCK ) metric which reports the percentage of detections that fall within a normalized distance of the ground truth . For FLIC , distance is normalized by torso size , and for MPII , by a fraction of the head size ( referred to as PCKh ) .
In summary , is adaptively constructed by a CNN unit as follows : With the introduction of feature - driven distance metric , each filter of f - lcon is constructed as follows : where denotes the neighborhood containing pixels centered at position . Here , we provide a mechanism to perform f - lcon efficiently .
We note pazis16efficient ’s PAC - MDP result for metric spaces and leike16thompson ’s asymptotic analysis of Thompson sampling in general environments . section : Future Directions
Firstly , NNLRR used Gaussian fields and harmonic functions into the low-rank representation ( LRR ) model . Secondly , NNLRR guided the affinity matrix construction by the supervision information . Next , finding a non-negative low-rank matrix , the matrix represents each sample by some other linear combination points , and the affinity matrix is obtained by the matrix .
A method for the in vitro biosynthesis of proteins in native conformation which improves the technique which is well known in the art by immobilizing on the surface of an affinity matrix at discrete locations a ligand for which the protein being synthesized has an affinity and adding to the reaction mixture said affinity matrix having said immobilized ligand so that each molecule that is in the process of folding into a functional protein molecule may bind an immobilized ligand and be kept separated from other protein molecules as the folding proceeds . The technique works with either a batch method or a continuous method .
The linear correlation coefficients were all greater than 99 % , and the relative standard deviations of two binding parameters were 0.58 and 0.059 % , respectively . The experiments proved that the frontal chromatography method was accurate , reproducible , and could be used to determine the metal-binding parameters of the affinity column . The effects of buffer pH , type , and concentration on binding parameters were explored by uniform design experiment .
A pointwise approximation of the pairwise affinity information may in fact be interpreted as a ‘ saliency ’ index , and the foreground of the scene may be obtained by thresholding it . An algorithm called ‘ affinity factorization ’ is thus obtained which may be used for grouping .
The affinity matrix plays the central role in similarity based clustering algorithm . A recent study has shown that , conventional affinity matrices constructed using local neighborhood information are deficient to represent the overall structure of the dataset . In this paper , we propose a novel similarity measure between two points that captures the global setting of the dataset .
The experimental results show that the proposed replacement of k by k∗ slightly improves the performance of the Nyström approximation . Compared with other affinity matrix modification methods , our k∗ obtains comparable or higher clustering performance in terms of accuracy and Normalized Mutual Information ( NMI ) .
The second type is uncertainty in which context segments are relevant to representing the speaker talking right before the hypothesized speaker change . We also show the robustness of affinity matrix-representation for speaker change detection . Our methods provide very significant accuracy improvements compared to several baselines including a recently published end-to-end system .
We compare with the matrix factorization techniques for graph factorization . An information network can be represented as an affinity matrix , and is able to represent each vertex with a low - dimensional vector through matrix factorization . Graph factorization is optimized through stochastic gradient descent and is able to handle large networks .
To address this issue , we propose a hypergraph-based matching scheme in which both pairwise and higher order relationships for the probe and gallery subjects are discovered through hypergraph learning . In this way , improved similarity scores are obtained as compared to the conventional pairwise similarity measure . We conduct experiments on two widely used person re-identification datasets and the results demonstrate that matching through hypergraph learning leads to superior performance compared with state-of-the-arts .
It is therefore essential to efficiently identify these unproductive comparisons as rapidly as possible and exclude them from more time-intensive similarity calculations . The Blazing Signature Filter ( BSF ) is a highly efficient pairwise similarity algorithm which enables extensive data mining within a reasonable amount of time . The algorithm transforms datasets into binary metrics , allowing it to utilize the computationally efficient bit operators and provide a coarse measure of similarity .
For shot boundary detection , our approach combines pairwise similarity analysis and supervised classification . Using primitive low-level image features , we build secondary features based on inter-frame dissimilarity .
To face this threat , the literature now includes a large body of work proposing approaches for identifying repackaged apps . Unfortunately , although most research involves pairwise similarity comparison to distinguish repackaged apps from their " original " counterparts , no work has considered the threat to validity of not being able to discover the true original apps . We provide in this paper preliminary insights of an investigation into the Multi-Generation Repackaging Hypothesis : is the original in a repackaging process the outcome of a previous repackaging process ?
Furthermore , to enhance the performance , a new relevance score scheme via Kernel Support Vector Machine ( KSVM ) is formulated , which efficiently make use of medical images features in order to discriminate the images accurately . This system initiated a new discriminative name called ‘ pairwise similarity measurement ’ for the discriminativeness of pairs of query and input medical database images and subsequently integrate it with the classification error for discriminativeness in classifier production to generate a unified objective function . Results : Experimentation results assessed the proposed DDL in IRMA framework on a numerous medical images like BAA images and X-Ray skull images and results confirm that DDL performs far better than the state-of the-art approaches significantly in terms of precision , recall , sensitivity , specificity and accuracy .
This problem occurs when multiple authors share the same name and particularly when names are simplified as in cases where names merely contain the first initial and the last name . This paper proposes a reliable and accurate pair-wise similarities approach to disambiguate names using supervised classification on Web correlations and authorship correlations . This approach makes use of Web correlations among citations assuming citations that co-refer on publication lists on the Web should to refer to the same author .
Probabilistic estimates of attribute statistics and match likelihood for people entity resolution Similarity Measure Functions for Strategy-Based Biometrics Infrared and visible image registration based on alignment and mutual information A three-step displacement measurement method for a 3-DOF macro-micro positioning stage. Learning Bidirectional Similarity for Collaborative Filtering Tech. Report: Similarity Function Evaluation Similarity Function Learning with Data Uncertainty Fast Online Object Tracking and Segmentation: A Unifying Approach Graph Capsule Convolutional Neural Networks GhostVLAD for set-based face recognition Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering Modelling Interaction of Sentence Pair with Coupled-LSTMs Count-Based Exploration in Feature Space for Reinforcement Learning Opinion formation in Ising networks Multi-Target Multi-Camera Tracking with Human Body Part Semantic Features Revisiting Negative Selection Algorithms Toward an Application Support Layer: Numerical Computation in Unified Parallel C Algorithm-Architecture Affinity - Parallelism Changes the Picture Crowdsourced Affinity: A Matter of Fact or Experience Disjoint Web Document Clustering and Management in Electronic Commerce Multiobjective Clonal Selection Algorithm for the Forecasting Models on the Base of the Strictly Binary Trees An evolutionary approach for accent classification in IVR systems A survey of combinatorial testing Optimal Detection Ordering for V-BLAST Earth Mover Distance on superpixels Airport zoning based on a logical condition on LAeqD and LAeqN noise metrics Scalable Video Transmission Over Wireless Networks Based on Loss Distribution and Layer Information Metrics for requirements engineering An Energy Efficient Opportunistic Routing Metric for Wireless Sensor Networks Adaptive sampling for nonlinear dimensionality reduction based on manifold learning Dominant Users Grouping Algorithm for Multiple RAUs-UEs Coordination in DAS System Neural Aggregation Network for Video Face Recognition Image-to-Image Translation with Conditional Adversarial Networks Learning to Compare: Relation Network for Few-Shot Learning Pose-Driven Deep Convolutional Model for Person Re-identification A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task Consistent Rank Logits for Ordinal Regression with Convolutional Neural Networks Stacked Hourglass Networks for Human Pose Estimation LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation Unifying Count-Based Exploration and Intrinsic Motivation Semi-Supervised Subspace Clustering via Non-Negative Low-Rank Representation for Hyperspectral Images Logistische Kennzahlen in der grünen Logistik Evaluation and optimization of the metal-binding properties of a complex ligand for immobilized metal affinity chromatography. A Factorization Approach to Grouping A voting approach to learn affinity matrix for robust clustering Projected Affinity Values for Nyström Spectral Clustering Context and Uncertainty Modeling for Online Speaker Change Detection LINE: Large-scale Information Network Embedding Person re-identification via hypergraph-based matching Blazing Signature Filter: a library for fast pairwise similarity comparisons FXPAL Experiments for TRECVID 2004 The Multi-Generation Repackaging Hypothesis A Novel Pre-processing and Kernel Based Support Vector Machine Classifier with Discriminative Dictionary Learning for Bone Age Assessment Disambiguating authors in citations on the web and authorship correlations
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BigData Conference ECML/PKDD (1) ICPRAM 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) ArXiv ACCV ECCV EMNLP IJCAI ITA CIKM PPAM ESWC (1) SMC ICC ICIP VTC Fall 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2017 IEEE International Conference on Computer Vision (ICCV) ACL ArXiv ECCV 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition NIPS RCAR PBFT ECCV (1) ICASSP WWW '15 TRECVID ICSE (Companion Volume)
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