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The performances of the automatic speaker verification (ASV) systems degrade due to the reduction in the amount of speech used for enrollment and verification. Combining multiple systems based on different features and classifiers considerably reduces speaker verification error rate with short utterances. This work attempts to incorporate supplementary information during the system combination process. We use quality of the estimated model parameters as supplementary information. We introduce a class of novel quality measures formulated using the zero-order sufficient statistics used during the i-vector extraction process. We have used the proposed quality measures as side information for combining ASV systems based on Gaussian mixture model-universal background model (GMM-UBM) and i-vector. The proposed methods demonstrate considerable improvement in speaker recognition performance on NIST SRE corpora, especially in short duration conditions. We have also observed improvement over existing systems based on different duration-based quality measures., Comment: Accepted for publication in Digital Signal Processing: A Review Journal

Visual quality evaluation is one of the challenging basic problems in image processing. It also plays a central role in the shaping, implementation, optimization, and testing of many methods. The existing image quality assessment methods focused on images corrupted by common degradation types while little attention was paid to color quantization. This in spite there is a wide range of applications requiring color quantization assessment being used as a preprocessing step when color-based tasks are more efficiently accomplished on a reduced number of colors. In this paper, we propose and carry-out a quantitative performance evaluation of nine well-known and commonly used full-reference image quality assessment measures. The evaluation is done by using two publicly available and subjectively rated image quality databases for color quantization degradation and by considering suitable combinations or subparts of them. The results indicate the quality measures that have closer performances in terms of their correlation to the subjective human rating and show that the evaluation of the statistical performance of the quality measures for color quantization is significantly impacted by the selected image quality database while maintaining a similar trend on each database. The detected strong similarity both on individual databases and on databases obtained by integration provides the ability to validate the integration process and to consider the quantitative performance evaluation on each database as an indicator for performance on the other databases. The experimental results are useful to address the choice of suitable quality measures for color quantization and to improve their future employment., Comment: Preprint

The use of quality measures to adjust health care payments and to rank providers is growing rapidly, but there are many problems with the quality measures that are currently being used. This article discusses some of these problems and then lays out some principles and procedures that should be used in the development and combination of quality measures. Many of the problems with existing quality measures would have been avoided had these principles been applied as they were developed.

Equivalent mutants are usually seen as an inconvenience in mutation analysis.We claim that equivalent mutants can be useful to detect and remove static anomalies.A process for detecting static anomalies is proposed.The process is based on mutation, equivalence checking, and quality measurement.The process is applicable to different kinds of software artifacts. Context: In mutation analysis, a mutant of a software artifact, either a program or a model, is said equivalent if it leaves the artifact meaning unchanged. Equivalent mutants are usually seen as an inconvenience and they reduce the applicability of mutation analysis.Objective: Instead, we here claim that equivalent mutants can be useful to define, detect, and remove static anomalies, i.e., deficiencies of given qualities: If an equivalent mutant has a better quality value than the original artifact, then an anomaly has been found and removed.Method: We present a process for detecting static anomalies based on mutation, equivalence checking, and quality measurement.Results: Our proposal and the originating technique are applicable to different kinds of software artifacts. We present anomalies and conduct several experiments in different contexts, at specification, design, and implementation level.Conclusion: We claim that in mutation analysis a new research direction should be followed, in which equivalent mutants and operators generating them are welcome.

The presence of stochastic and systematic inconsistencies is a concern for the precision and interpretability of Interferometric Synthetic Aperture Radar (InSAR) when distributed scatterers are exploited for SAR time series analysis [1]. Multitemporal phase estimators aim at retrieving a consistent common-master interferometric time series, thereby reducing the effect of stochastic inconsistencies. Exploiting data redundancy the latter estimators are theoretically expected to decrease the susceptibility to systematic inconsistencies as well. In this contribution we seek a computationally efficient quality measure to show the effectiveness of multitemporal phase estimation in the reduction of inconsistencies. Choosing the ensemble coherence as a candidate, we firstly seek a constant false alarm rate detector for initial detection of signal-bearing areas. Furthermore the impact of phase inconsistencies on the ensemble coherence will be brought into attention.

Background: Triclustering has shown to be a valuable tool for the analysis of microarray data since its appearance as an improvement of classical clustering and biclustering techniques. The standard for validation of triclustering is based on three different measures: correlation, graphic similarity of the patterns and functional annotations for the genes extracted from the Gene Ontology project (GO). Results: We propose TRIQ, a single evaluation measure that combines the three measures previously described: correlation, graphic validation and functional annotation, providing a single value as result of the validation of a tricluster solution and therefore simplifying the steps inherent to research of comparison and selection of solutions. TRIQ has been applied to three datasets already studied and evaluated with single measures based on correlation, graphic similarity and GO terms. Triclusters have been extracted from this three datasets using two different algorithms: TriGen and OPTricluster. Conclusions: TRIQ has successfully provided the same results as a the three single evaluation measures. Furthermore, we have applied TRIQ to results from another algorithm, OPTRicluster, and we have shown how TRIQ has been a valid tool to compare results from different algorithms in a quantitative straightforward manner. Therefore, it appears as a valid measure to represent and summarize the quality of tricluster solutions. It is also feasible for evaluation of non biological triclusters, due to the parametrization of each component of TRIQ. Ministerio de Ciencia y Tecnología TIN2014-55894-C2-1-R

Techniques for generating multichannel audio from stereo audio signals are supposed to enhance and extend the listening experience of the listener. To assess the quality of such upmix algorithms, subjective evaluations have been carried out. In this paper, we propose an objective evaluation test for stereo-to-multichannel upmix algorithms. Based on defined objective criteria and special test signals, an objective comparative evaluation is enabled in order to obtain a quantifiable measure for the quality of stereo-to-multichannel upmix algorithms. Therefore, the basic functional principle of the evaluation test is demonstrated, and it is illustrated how possible results can be visualized. In addition, the proposed issues are introduced for the optimization of upmix algorithms and also for the clarification and illustration of the impacts and influences of different modes and parameters.

With the rapid development of video surveillance and broadcast systems monitoring the video quality becomes an important aspect to assure a better quality of service. Error detection is an important technique to measure the quality of videos transmitted over the unreliable network. With the advent of HDTV previously subtle errors in videos are becoming more prominent. In this paper, we want to propose a next generation cloud-based video error detection system using image/video processing technology for making the detection process real-time. At first, we introduce an automatic video error detection method and then propose a cloud computing platform. Finally, we integrate error detection method with cloud computing platform named Hawkeye to achieve a real-time video error detection system which ensures contents integrity and minimizes testing time and efforts required to keep ahead of other conventional quality check system. Extensive experiments on prominent datasets and telecasted videos show that the proposed algorithm is very much efficient to detect errors for video broadcast and surveillance applications in terms of computation time and the detection of distorted frames. Refereed/Peer-reviewed

This paper proposes a new criterion, called absolute transparency, to design control schemes applied to bilateral teleoperation of mobile robots with time-varying delay. The absolute transparency measures how and how fast the human operator and the remote system interact with each other through a teleoperation system. The absolute transparency of different control schemes is analyzed and tested through teleoperation experiments where a human operator drives a mobile robot and receives both visual and force feedback.

International audience; Many studies use community detection algorithms in order to understand complex networks. Most papers study node communities, i.e. groups of nodes, which may or may not overlap. A widely used measure to evaluate the quality of a community structure is the modularity. However, sometimes it is also relevant to study link partitions rather than node partitions. In order to evaluate a link partition, we propose a new quality function: Expected Nodes. Our function is based on the same inspiration as the modularity and compares, for a given link group, the number of incident nodes to the expected one. In this short note, we discuss the advantages and drawbacks of our quality function compared to other ones on synthetics graphs. We show that Expected Nodes is able to pass some fundamental sanity criteria and is the one that best identifies the most relevant partition in a more realistic context.

The quality of pan-sharpened image is usually quantified separately by various spectral and spatial quality measures mostly originating from image processing. This quantity and diversity of quality measures makes it quite difficult to rank different image fusion methods. A new Joint Quality Measure (JQM) is proposed which is based on the combination of two Structural SIMilarity (SSIM) indices (one for spectral quality and another one for spatial quality) allowing comparison of different methods using a sole measure. Quality assessment of four fusion methods: Component Substitution (CS), General Fusion Filtering (GFF), variant of GFF and ATWT (one of ARSIS implementations) is performed for IKONOS and WorldView-2 satellite optical remote sensing data. Experiments and results showed the superiority of the proposed JQM when compared with already known joint measure - Quality with No Reference (QNR) index. Moreover, the results are fully supported by a visual analysis of imagery.

This paper presents a new comparative objective method for image quality evaluation. This method relies on two keys points: a local objective evaluation and a perceptual gathering. The local evaluation concerns the dissimilarities between the degraded image and the reference image; it is based on a gray-level local Hausdorff distance. This local Hausdorff distance uses a generalized distance transform which is studied here. The evaluation result is a local dissimilarity map (LDMap). In order to include perceptual information, a perceptual map based on the image properties is then proposed. The coefficients of this map are used to weight and to gather the LDMap measures into a single quality measure. The perceptual map is tunable and it gives encouraging quality measures even with naive parameters.

Using our multi-user model, a community of users provides feedback in a pay-as-you-go fashion to the ontology matching process by validating the mappings found by automatic methods, with the following advantages over having a single user: the effort required from each user is reduced, user errors are corrected, and consensus is reached. We propose strategies that dynamically determine the order in which the candidate mappings are presented to the users for validation. These strategies are based on mapping quality measures that we define. Further, we use a propagation method to leverage the validation of one mapping to other mappings. We use an extension of the AgreementMaker ontology matching system and the Ontology Alignment Evaluation Initiative (OAEI) Benchmarks track to evaluate our approach. Our results show how Fmeasure and robustness vary as a function of the number of user validations. We consider different user error and revalidation rates (the latter measures the number of times that the same mapping is validated). Our results highlight complex trade-offs and point to the benefits of dynamically adjusting the revalidation rate.