STRUCTURAL DYNAMIC OF THE PUBLIC SECTOR AND MULTILEVEL GOVERNANCE: BETWEEN HIERARCHIES, MARKET AND NETWORK FORMS

In the current economic, social and political context, the problem of the competitiveness reform in public sector lies in the assumption of strategic approaches focused on meeting the public interest, with the lowest cost for society. The philosophy management which governs public sector reform tends to create new paradigms and contributes to shaping a new way of thinking and behavior. Central idea of this paper is that the two dominant models of administration: bureaucracy and governance, provides a range of institutional opportunities but also raises a number of barriers to strategic approaches to emerging public sector. Bureaucracy is for example, criticized because the lack of prioritization skills and lack of goals and also because lacks to stimulate innovation in the public sector. Bureaucracy leads to uniformity, flattening of public services. Governance model contains a number of similarities with the strategic approach in the public sector, when we talk about networks, interdependence and self-organizing nature of public administration. The issue that we are trying reveal to your attention is that the current institutional conditions are more complex than two models mentioned are able to cover. These new demands require the types of organizational structures based on flexible, decentralized structure to replace the traditional centralized, which now is totally inapplicable. Institutional framework may, for example, to provide an opportunity for the New Public Management, but also create a barrier to governance.bureaucracy, governance, public policy, networks, multilevel governance.

Spectrogram classification using dissimilarity space

In this work, we combine a Siamese neural network and different clustering techniques to generate a dissimilarity space that is then used to train an SVM for automated animal audio classification. The animal audio datasets used are (i) birds and (ii) cat sounds, which are freely available. We exploit different clustering methods to reduce the spectrograms in the dataset to a number of centroids that are used to generate the dissimilarity space through the Siamese network. Once computed, we use the dissimilarity space to generate a vector space representation of each pattern, which is then fed into an support vector machine (SVM) to classify a spectrogram by its dissimilarity vector. Our study shows that the proposed approach based on dissimilarity space performs well on both classification problems without ad-hoc optimization of the clustering methods. Moreover, results show that the fusion of CNN-based approaches applied to the animal audio classification problem works better than the stand-alone CNNs

Postprocessing for skin detection

Skin detectors play a crucial role in many applications: face localization, person tracking, objectionable content screening, etc. Skin detection is a complicated process that involves not only the development of apposite classifiers but also many ancillary methods, including techniques for data preprocessing and postprocessing. In this paper, a new postprocessing method is described that learns to select whether an image needs the application of various morphological sequences or a homogeneity function. The type of postprocessing method selected is learned based on categorizing the image into one of eleven predetermined classes. The novel postprocessing method presented here is evaluated on ten datasets recommended for fair comparisons that represent many skin detection applications. The results show that the new approach enhances the performance of the base classifiers and previous works based only on learning the most appropriate morphological sequences

Animal sound classification using dissimilarity spaces

The classifier system proposed in this work combines the dissimilarity spaces produced by a set of Siamese neural networks (SNNs) designed using four different backbones with different clustering techniques for training SVMs for automated animal audio classification. The system is evaluated on two animal audio datasets: one for cat and another for bird vocalizations. The proposed approach uses clustering methods to determine a set of centroids (in both a supervised and unsupervised fashion) from the spectrograms in the dataset. Such centroids are exploited to generate the dissimilarity space through the Siamese networks. In addition to feeding the SNNs with spectrograms, experiments process the spectrograms using the heterogeneous auto-similarities of characteristics. Once the similarity spaces are computed, each pattern is \u201cprojected\u201d into the space to obtain a vector space representation; this descriptor is then coupled to a support vector machine (SVM) to classify a spectrogram by its dissimilarity vector. Results demonstrate that the proposed approach performs competitively (without ad-hoc optimization of the clustering methods) on both animal vocalization datasets. To further demonstrate the power of the proposed system, the best standalone approach is also evaluated on the challenging Dataset for Environmental Sound Classification (ESC50) dataset

Feature transforms for image data augmentation

A problem with convolutional neural networks (CNNs) is that they require large datasets to obtain adequate robustness; on small datasets, they are prone to overfitting. Many methods have been proposed to overcome this shortcoming with CNNs. In cases where additional samples cannot easily be collected, a common approach is to generate more data points from existing data using an augmentation technique. In image classification, many augmentation approaches utilize simple image manipulation algorithms. In this work, we propose some new methods for data augmentation based on several image transformations: the Fourier transform (FT), the Radon transform (RT), and the discrete cosine transform (DCT). These and other data augmentation methods are considered in order to quantify their effectiveness in creating ensembles of neural networks. The novelty of this research is to consider different strategies for data augmentation to generate training sets from which to train several classifiers which are combined into an ensemble. Specifically, the idea is to create an ensemble based on a kind of bagging of the training set, where each model is trained on a different training set obtained by augmenting the original training set with different approaches. We build ensembles on the data level by adding images generated by combining fourteen augmentation approaches, with three based on FT, RT, and DCT, proposed here for the first time. Pretrained ResNet50 networks are finetuned on training sets that include images derived from each augmentation method. These networks and several fusions are evaluated and compared across eleven benchmarks. Results show that building ensembles on the data level by combining different data augmentation methods produce classifiers that not only compete competitively against the state-of-the-art but often surpass the best approaches reported in the literature

Closing the performance gap between siamese networks for dissimilarity image classification and convolutional neural networks

In this paper, we examine two strategies for boosting the performance of ensembles of Siamese networks (SNNs) for image classification using two loss functions (Triplet and Binary Cross Entropy) and two methods for building the dissimilarity spaces (FULLY and DEEPER). With FULLY, the distance between a pattern and a prototype is calculated by comparing two images using the fully connected layer of the Siamese network. With DEEPER, each pattern is described using a deeper layer combined with dimensionality reduction. The basic design of the SNNs takes advantage of supervised k-means clustering for building the dissimilarity spaces that train a set of support vector machines, which are then combined by sum rule for a final decision. The robustness and versatility of this approach are demonstrated on several cross-domain image data sets, including a portrait data set, two bioimage and two animal vocalization data sets. Results show that the strategies employed in this work to increase the performance of dissimilarity image classification using SNN are closing the gap with standalone CNNs. Moreover, when our best system is combined with an ensemble of CNNs, the resulting performance is superior to an ensemble of CNNs, demonstrating that our new strategy is extracting additional information

Neonatal pain detection in videos using the iCOPEvid dataset and an ensemble of descriptors extracted from Gaussian of Local Descriptors

Diagnosing pain in neonates is difficult but critical. Although approximately thirty manual pain instruments have been developed for neonatal pain diagnosis, most are complex, multifactorial, and geared toward research. The goals of this work are twofold: 1) to develop a new video dataset for automatic neonatal pain detection called iCOPEvid (infant Classification Of Pain Expressions videos), and 2) to present a classification system that sets a challenging comparison performance on this dataset. The iCOPEvid dataset contains 234 videos of 49 neonates experiencing a set of noxious stimuli, a period of rest, and an acute pain stimulus. From these videos 20 s segments are extracted and grouped into two classes: pain (49) and nopain (185), with the nopain video segments handpicked to produce a highly challenging dataset. An ensemble of twelve global and local descriptors with a Bag-of-Features approach is utilized to improve the performance of some new descriptors based on Gaussian of Local Descriptors (GOLD). The basic classifier used in the ensembles is the Support Vector Machine, and decisions are combined by sum rule. These results are compared with standard methods, some deep learning approaches, and 185 human assessments. Our best machine learning methods are shown to outperform the human judges

Synergistic impact of endurance training and intermittent hypobaric hypoxia on cardiac function and mitochondrial energetic and signaling

Background Intermittent hypobaric-hypoxia (IHH) and endurance-training (ET) are cardioprotective strategies against stress-stimuli. Mitochondrial modulation appears to be an important step of the process. This study aimed to analyze whether a combination of these approaches provides additive or synergistic effects improving heart-mitochondrial and cardiac-function. Methods Two-sets of rats were divided into normoxic-sedentary (NS), normoxic-exercised (NE, 1 h/day/5 weeks treadmill-running), hypoxic-sedentary (HS, 6000 m, 5 h/day/5 weeks) and hypoxic-exercised (HE) to study overall cardiac and mitochondrial function. In vitro cardiac mitochondrial oxygen consumption and transmembrane potential were evaluated. OXPHOS subunits and ANT protein content were semi-quantified by Western blotting. HIF-1α, VEGF, VEGF-R1 VEGF-R2, BNP, SERCA2a and PLB expressions were measured by qRT-PCR and cardiac function was characterized by echocardiography and hemodynamic parameters. Results Respiratory control ratio (RCR) increased in NE, HS and HE vs. NS. Susceptibility to anoxia/reoxygenation-induced dysfunction decreased in NE, HS and HE vs. NS. HS decreased mitochondrial complex-I and -II subunits; however HE completely reverted the decreased content in complex-II subunits. ANT increased in HE. HE presented normalized ventricular–arterial coupling (Ea) and BNP myocardial levels and significantly improved myocardial performance as evaluated by increased cardiac output and normalization of the Tei index vs. HS. Conclusion Data demonstrates that IHH and ET confer cardiac mitochondria with a more resistant phenotype although without visible addictive effects at least under basal conditions. It is suggested that the combination of both strategies, although not additive, results into improved cardiac function