Evolution feature oriented model driven product line engineering approach for synergistic and dynamic service evolution in clouds: A04BPEL3.0 proposal.

Model Driven based approach for Service Evolution in Clouds will mainly focus on the reusable evolution patterns' advantage to solve evolution problems. During the process, evolution pattern will be driven by MDA models to pattern aspects. Weaving the aspects into service based process by using Aspect-Oriented extended BPEL engine at runtime will be the dynamic feature of the evolution.

Automatic Glaucoma Detection Based on Optic Disc Segmentation and Texture Feature Extraction

The use of digital image processing techniques is prominent in medical settings for the automatic diagnosis of diseases. Glaucoma is the second leading cause of blindness in the world and it has no cure. Currently, there are treatments to prevent vision loss, but the disease must be detected in the early stages. Thus, the objective of this work is to develop an automatic detection method of Glaucoma in retinal images. The methodology used in the study were: acquisition of image database, Optic Disc segmentation, texture feature extraction in different color models and classification of images in glaucomatous or not. We obtained results of 93% accuracy

Learning Binary Code Representations for Effective and Efficient Image Retrieval

The size of online image datasets is constantly increasing. Considering an image dataset with millions of images, image retrieval becomes a seemingly intractable problem for exhaustive similarity search algorithms. Hashing methods, which encodes high-dimensional descriptors into compact binary strings, have become very popular because of their high efficiency in search and storage capacity. In the first part, we propose a multimodal retrieval method based on latent feature models. The procedure consists of a nonparametric Bayesian framework for learning underlying semantically meaningful abstract features in a multimodal dataset, a probabilistic retrieval model that allows cross-modal queries and an extension model for relevance feedback. In the second part, we focus on supervised hashing with kernels. We describe a flexible hashing procedure that treats binary codes and pairwise semantic similarity as latent and observed variables, respectively, in a probabilistic model based on Gaussian processes for binary classification. We present a scalable inference algorithm with the sparse pseudo-input Gaussian process (SPGP) model and distributed computing. In the last part, we define an incremental hashing strategy for dynamic databases where new images are added to the databases frequently. The method is based on a two-stage classification framework using binary and multi-class SVMs. The proposed method also enforces balance in binary codes by an imbalance penalty to obtain higher quality binary codes. We learn hash functions by an efficient algorithm where the NP-hard problem of finding optimal binary codes is solved via cyclic coordinate descent and SVMs are trained in a parallelized incremental manner. For modifications like adding images from an unseen class, we propose an incremental procedure for effective and efficient updates to the previous hash functions. Experiments on three large-scale image datasets demonstrate that the incremental strategy is capable of efficiently updating hash functions to the same retrieval performance as hashing from scratch.

Efficient Image and Video Representations for Retrieval

Image (Video) retrieval is an interesting problem of retrieving images (videos) similar to the query. Images (Videos) are represented in an input (feature) space and similar images (videos) are obtained by finding nearest neighbors in the input representation space. Numerous input representations both in real valued and binary space have been proposed for conducting faster retrieval. In this thesis, we present techniques that obtain improved input representations for retrieval in both supervised and unsupervised settings for images and videos. Supervised retrieval is a well known problem of retrieving same class images of the query. We address the practical aspects of achieving faster retrieval with binary codes as input representations for the supervised setting in the first part, where binary codes are used as addresses into hash tables. In practice, using binary codes as addresses does not guarantee fast retrieval, as similar images are not mapped to the same binary code (address). We address this problem by presenting an efficient supervised hashing (binary encoding) method that aims to explicitly map all the images of the same class ideally to a unique binary code. We refer to the binary codes of the images as `Semantic Binary Codes' and the unique code for all same class images as `Class Binary Code'. We also propose a new class­ based Hamming metric that dramatically reduces the retrieval times for larger databases, where only hamming distance is computed to the class binary codes. We also propose a Deep semantic binary code model, by replacing the output layer of a popular convolutional Neural Network (AlexNet) with the class binary codes and show that the hashing functions learned in this way outperforms the state­ of ­the art, and at the same time provide fast retrieval times. In the second part, we also address the problem of supervised retrieval by taking into account the relationship between classes. For a given query image, we want to retrieve images that preserve the relative order i.e. we want to retrieve all same class images first and then, the related classes images before different class images. We learn such relationship aware binary codes by minimizing the similarity between inner product of the binary codes and the similarity between the classes. We calculate the similarity between classes using output embedding vectors, which are vector representations of classes. Our method deviates from the other supervised binary encoding schemes as it is the first to use output embeddings for learning hashing functions. We also introduce new performance metrics that take into account the related class retrieval results and show significant gains over the state­ of­ the art. High Dimensional descriptors like Fisher Vectors or Vector of Locally Aggregated Descriptors have shown to improve the performance of many computer vision applications including retrieval. In the third part, we will discuss an unsupervised technique for compressing high dimensional vectors into high dimensional binary codes, to reduce storage complexity. In this approach, we deviate from adopting traditional hyperplane hashing functions and instead learn hyperspherical hashing functions. The proposed method overcomes the computational challenges of directly applying the spherical hashing algorithm that is intractable for compressing high dimensional vectors. A practical hierarchical model that utilizes divide and conquer techniques using the Random Select and Adjust (RSA) procedure to compress such high dimensional vectors is presented. We show that our proposed high dimensional binary codes outperform the binary codes obtained using traditional hyperplane methods for higher compression ratios. In the last part of the thesis, we propose a retrieval based solution to the Zero shot event classification problem - a setting where no training videos are available for the event. To do this, we learn a generic set of concept detectors and represent both videos and query events in the concept space. We then compute similarity between the query event and the video in the concept space and videos similar to the query event are classified as the videos belonging to the event. We show that we significantly boost the performance using concept features from other modalities.

MORPHOLOGICAL DIVERSITY OF MANGO GERMPLASM FROM THE UPPER ATHI RIVER REGION OF EASTERN KENYA: AN ANALYSIS BASED ON NON- FRUIT DESCRIPTORS

Phenotypic variation in plants can be evaluated by morphological characterization using visual attributes. Fruits have been the major descriptors for identification of different varieties of fruit crops. However, even in their absence, farmers, breeders and interested stakeholders require to distinguish between different mango varieties. This study aimed at determining diversity in mango germplasm from the Upper Athi River (UAR) and providing useful alternative descriptors for the identification of different mango varieties in the absence of fruits. A total of 20 International Plant Genetic Resources Institute (IPGRI) descriptors for mango were selected for use in the visual assessment of 98 mango accessions from 15 sites of the UAR region of eastern Kenya. Purposive sampling was used to identify farmers growing diverse varieties of mangoes. Evaluation of the descriptors was performed on-site and the data collected were then subjected to multivariate analysis including Principal Component Analysis (PCA) and Cluster analysis, one- way analysis of variance (ANOVA) and Chi square tests. Results classified the accessions into two major groups corresponding to indigenous and exotic varieties. The PCA showed the first six principal components accounting for 75.12% of the total variance. A strong and highly significant correlation was observed between the color of fully grown leaves, leaf blade width, leaf blade length and petiole length and also between the leaf attitude, color of young leaf, stem circumference, tree height, leaf margin, growth habit and fragrance. Useful descriptors for morphological evaluation were 14 out of the selected 20; however, ANOVA and Chi square test revealed that diversity in the accessions was majorly as a result of variations in color of young leaves, leaf attitude, leaf texture, growth habit, leaf blade length, leaf blade width and petiole length traits. These results reveal that mango germplasm in the UAR has significant diversity and that other morphological traits apart from fruits can be useful in morphological characterization of mango.

Exploring Hidden Coherent Feature Groups and Temporal Semantics for Multimedia Big Data Analysis

Thanks to the advanced technologies and social networks that allow the data to be widely shared among the Internet, there is an explosion of pervasive multimedia data, generating high demands of multimedia services and applications in various areas for people to easily access and manage multimedia data. Towards such demands, multimedia big data analysis has become an emerging hot topic in both industry and academia, which ranges from basic infrastructure, management, search, and mining to security, privacy, and applications. Within the scope of this dissertation, a multimedia big data analysis framework is proposed for semantic information management and retrieval with a focus on rare event detection in videos. The proposed framework is able to explore hidden semantic feature groups in multimedia data and incorporate temporal semantics, especially for video event detection. First, a hierarchical semantic data representation is presented to alleviate the semantic gap issue, and the Hidden Coherent Feature Group (HCFG) analysis method is proposed to capture the correlation between features and separate the original feature set into semantic groups, seamlessly integrating multimedia data in multiple modalities. Next, an Importance Factor based Temporal Multiple Correspondence Analysis (i.e., IF-TMCA) approach is presented for effective event detection. Specifically, the HCFG algorithm is integrated with the Hierarchical Information Gain Analysis (HIGA) method to generate the Importance Factor (IF) for producing the initial detection results. Then, the TMCA algorithm is proposed to efficiently incorporate temporal semantics for re-ranking and improving the final performance. At last, a sampling-based ensemble learning mechanism is applied to further accommodate the imbalanced datasets. In addition to the multimedia semantic representation and class imbalance problems, lack of organization is another critical issue for multimedia big data analysis. In this framework, an affinity propagation-based summarization method is also proposed to transform the unorganized data into a better structure with clean and well-organized information. The whole framework has been thoroughly evaluated across multiple domains, such as soccer goal event detection and disaster information management.

Feature-based tracking of urethral motion in low-resolution trans-perineal ultrasound

This paper describes a novel algorithm for tracking the motion of the urethra from trans-perineal ultrasound. Our work is based on the structure-from-motion paradigm and therefore handles well structures with ill-defined and partially missing boundaries. The proposed approach is particularly well-suited for video sequences of low resolution and variable levels of blurriness introduced by anatomical motion of variable speed. Our tracking method identifies feature points on a frame by frame basis using the SURF detector/descriptor. Inter-frame correspondence is achieved using nearest-neighbor matching in the feature space. The motion is estimated using a non-linear bi-quadratic model, which adequately describes the deformable motion of the urethra. Experimental results are promising and show that our algorithm performs well when compared to manual tracking.

Feature-based tracking of urethral motion in low-resolution trans-perineal ultrasound

This paper describes a novel algorithm for tracking the motion of the urethra from trans-perineal ultrasound. Our work is based on the structure-from-motion paradigm and therefore handles well structures with ill-defined and partially missing boundaries. The proposed approach is particularly well-suited for video sequences of low resolution and variable levels of blurriness introduced by anatomical motion of variable speed. Our tracking method identifies feature points on a frame by frame basis using the SURF detector/descriptor. Inter-frame correspondence is achieved using nearest-neighbor matching in the feature space. The motion is estimated using a non-linear bi-quadratic model, which adequately describes the deformable motion of the urethra. Experimental results are promising and show that our algorithm performs well when compared to manual tracking.

An examination of verbal descriptors of cancer-induced bone pain and neuropathic cancer pain

Aims: The aim of the thesis was to identify verbal descriptors of cancer induced bone pain (CIBP) and neuropathic cancer pain (NCP). An examination of the verbal descriptors associated with these two pain syndromes further considered the relationship between common verbal descriptors, cancer type, performance status and analgesia. Methods: The project was conducted in two phases; Phase one was a systematic review of the literature to examine current evidence of verbal descriptors in CIBP and NCP. Phase two utilised secondary data analysis methodology. Data from 120 patients with confirmed CIBP and 61 patients with confirmed NCP were deemed eligible for entry into a de novo database for secondary analysis. Key descriptive data were considered such as gender, ECOG and pain scores to characterise the patient population. Verbal descriptors of CIBP and NCP were considered in detail across the secondary de novo database. Results: Gender was not identified as a diagnostic characteristic of CIBP and NCP with similar distribution across prevalence of pain reporting and also pain severity. Patients with breast (n=52,43.3%), prostate (n=35,29.2%) and lung (n=14,11.7%) cancer were found to be at an increased risk of CIBP. Those with NCP more was found more commonly among patients with breast cancer (n=21,34.4%). Patients with CIBP were found to have an ECOG performance of 1 (n=49, 40.8%) or 2 (n=43, 35.8%) which was lower than those with NCP with an ECOG of 0 (n=32, 52.5%) or 2 (n=18, 29.5%). Comparisons were made across analgesia and treatment options for CIBP and NCP. Patients with CIBP received a greater variety of treatment options including bisphosphonates and radiotherapy while patients with NCP were more commonly treated with analgesia alone. Patients with CIBP and NCP were taking strong opioids, however those with NCP (n=45, 73.8%) were more likely to utilise strong opioids than those with CIBP (n=61, 50.8%). It was noted that those with NCP required a daily morphine equivalence of almost 50% higher than those with CIBP. Average consumption of opioids was 155.6mg, for patients with NCP, compared to 76mg in patients with CIBP. Common verbal descriptors of CIBP and NCP were identified. The most common verbal descriptors for CIBP were aching, gnawing and throbbing and the most common verbal descriptors of NCP were aching, tender and sharp. Of the most common 6 descriptors for CIBP and NCP only one descriptor was unique to each pain type, gnawing for CIBP and stabbing for NCP. Conclusions: Patients with CIBP and NCP use similar verbal descriptors to characterise their pain with gnawing being unique to CIBP and stabbing being unique to NCP in the data considered within project. Further research is required to explore verbal descriptors which are both common and unique to CIBP and NCP. Further exploration of verbal descriptors would assist development of a comprehensive pain assessment tool which would enhance pain assessment for nurses, clinicians and patients.

TARDI VARISCAN DEFORMATION IN THE IBERIAN PENINSULA; A LATE FEATURE IN THE LAURASIA-GONDWANA DEXTRAL COLLISION

The Late Variscan deformation event in Iberia, is characterized by an intraplate deformation regime induced by the oblique collision between Laurentia and Gondwan. This episode in Iberia is characterized by NNE-SSW strike-slip faults, which are considered by the classic works as sinistral strike-slips. However, the absence of Mesozoic formations constraining the age of this sinistral kinematics, led some authors to consider it as the result of Alpine reworking. Structural studies in Almograve and Ponta Ruiva sectors (SW Portugal), not only shows that NNE-SSW faults presents a clear sinistral kinematics and are occasionally associated with E-W dextral shears, but also that this kinematics is related to the late deformation episodes of Variscan Orogeny. In Almograve sector, the late Variscan structures are characterized by NNE-SSW sinistral kink-bands, spatially associated with E-W dextral faults. These structures are contemporaneous and affect the previously deformed Carboniferous units. The Ponta Ruiva Sector constrains the age of deformation because the E-W dextral shears affect the Late Carboniferous (late Moscovian) units, but not the overlying Triassic series. The new exposed data shows that the NNE-SSW and the E-W faults are dynamically associated and results from the same deformation event. The NNE-SSW sinistral faults could be considered as second order dominoes structures related with first order E-W dextral shears, related with Laurasia-Gondwana collision during Late Carboniferous-Permian Times.

Genetic diversity of mango accessions (Mangifera indica) using new microsatellite markers and morphological descriptors.

Genetic diversity estimates based on morphological and molecular data can provide different information on the relationship between cultivars of a species. This study aimed to develop new microsatellite markers as additional tools in genetic studies on mangoes (Mangifera indica L.), and to analyze the genetic variability of 20 mango cultivars based on morphological descriptors and microsatellite markers. We aimed to better understand the cultivars enhanced breeding histories and to support crossbreeding planning. Positive clones were selected from a DNA library enriched for microsatellite regions for sequencing and primer design. Four plants of each of the 20 accessions were used for observations, based on 48 morphological descriptors. Twenty accessions were analyzed using 27 microsatellite markers, of which 16 were developed during this study. The clusters, based on the morphological descriptors by Ward - MLM strategy and the microsatellite markers, suggested that Brazilian mango cultivars have extensive genetic diversity and are related to cultivars with different provenances, demonstrating their different enhanced breeding histories.

Genes involved in germline regulative pathways in metazoa: an integrative approach to investigate a key feature of animal biology

In Metazoa, the germline represents the cell lineage devoted to transmission of genetic heredity across generations. Its functions intuitively evoke the crucial roles that it plays in the development of a new organism and in the evolution of the species. Germline establishment is tightly tied to animal multicellularity itself, in which the complex differentiation of cell lineages is favoured by the confinement of totipotency in specific cell populations. In the present thesis, I addressed the subject of germline characterization in animals through different approaches, in an attempt to cover different sides and scales. First, I investigated the extent and nature of shared differentially transcribed molecular factors in 10 different species germline-related lineages. I observed that newly evolved genes are less likely to be involved in germline-related mechanisms and that the mostly shared transcriptional signal across the species considered was the upregulation of genes associated to proper DNA replication, instead of the expected transcriptional and post-transcriptional regulation, that apparently have a higher level of lineage-specificity. I then focused on the evolutionary history of Tudor domain containing proteins, a gene family that underwent germline-associated expansions in animals. Using data from 24 holozoan phyla, I could confirm the previously proposed evolution of the Tudor domain secondary structure. Also, I associated lineage-specific family reductions and expansions to peculiar genomic dynamics and to the evolution of germline-associated piRNA pathway of retrotransposon silencing. Lastly, I characterized and investigated the expression of the Tudor protein TDRD7 in the clam Ruditapes philippinarum. Through immunolocalization, I could compare its expression profiles in gametogenic specimens to the previously characterized germline marker vasa. Combining results with literature, I proposed that, in this species, TDRD7 is involved in the assembly of germ granules, i.e. cytoplasmic structures associated to germline differentiation in virtually all animals, but whose assemblers can be taxon specific.

Machining feature recognition

La tesi ricade nell'ambito della modellazione dei solidi ed in particolare nel riconoscimento automatico di lavorazioni su di esso presenti con l'obiettivo principale di risolvere alcune delle problematiche presenti nel software SolidPlus. Quest'ultimo ha come scopo quello di acquisire un solido progettato con un software commerciale di progettazione Cad, di rilevare le possibili lavorazioni presenti sulla parte assimilata e generare un file in un formato riconoscibile dalle macchine a controllo numerico per poterlo lavorare. Nel primo capitolo si introduce la rivoluzione industriale scaturita dall'attuazione dei principi dell'industria 4.0. Nel secondo capitolo si esegue un'analisi di alcune delle soluzioni attualmente presenti in letteratura per rilevare e classificare le lavorazioni, con particolare riferimento a quelle soluzioni che si basano sull'analisi del BRep, modello alla base del Cad. Il terzo capitolo riguarda invece il software oggetto di analisi definendo quelle che sono le funzionalità attualmente presenti e le problematiche riscontrate. Il quarto capitolo consiste nell'esame di alcuni casi di fallimento nel riconoscimento delle lavorazioni e delle relative strategie attuate per la risoluzione delle problematiche. In appendice sono stati introdotti i concetti relativi all'iso 10303, standard alla base dei file d'interscambio tra il software di progettazione Cad e le proprietà dei linguaggi a marcatori, fondamentale per lo scambio dati tra software ufficio e macchina