ENEOLITHIC, BABYNO AND NOUA CULTURE CEMETERIES, KLEMBIVKA, SITE 1, YAMPIL REGION, VINNITSA OBLAST: ARCHAEOMETRY, TAXONOMY AND TOPOGENETICS

The paper presents excavation results and analytical studies concerning the taxonomic classification of a funerary site identified with the communities of the ‘barrow cultures’ settling the north-western Black Sea Coast in the first half of the 3rd and the middle of the 2nd millennia BC . The study focuses on the ceremonial centres of the Eneolithic communities of the Babyno and Noua cultures .

Faulkner revisited : narrating property, race, gender and history in William Faulkner's Go Down, Moses, Toni Morrison's Song of Solomon and Gloria Naylor's Mama Day

My thesis explores the formation of the subject in the novels of Faulkner’s Go Down, Moses, Toni Morrison’s Song of Solomon, and Gloria Naylor’s Mama Day. I attach the concept of property in terms of how male protagonists are obsessed with materialistic ownership and with the subordination of women who, as properties, consolidate their manhood. The three novelists despite their racial, gendered, and literary differences share the view that identity and truth are mere social and cultural constructs. I incorporate the work of Judith Butler and other poststructuralist figures, who see identity as a matter of performance rather than a natural entity. My thesis explores the theme of freedom, which I attached to the ways characters use their bodies either to confine or to emancipate themselves from the restricting world of race, class, and gender. The three novelists deconstruct any system of belief that promulgates the objectivity of truth in historical documents. History in the three novels, as with the protagonists, perception of identity, remains a social construct laden with distortions to serve particular political or ideological agendas. My thesis gives voice to African American female characters who are associated with love and racial and gender resistance. They become the reservoirs of the African American legacy in terms of their association with the oral and intuitionist mode of knowing, which subverts the male characters’ obsession with property and with the mainstream empiricist world. In this dissertation, I use the concept of hybridity as a literary and theoretical devise that African-American writers employ. In effect, I embark on the postcolonial studies of Henry Louise Gates, Paul Gilroy, W. E. B Du Bois, James Clifford, and Arjun Appadurai in order to reflect upon the fluidity of Morrison’s and Naylor’s works. I show how these two novelists subvert Faulkner’s essentialist perception of truth, and of racial and gendered identity. They associate the myth of the Flying African with the notion of hybridity by making their male protagonists criss-cross Northern and Southern regions. I refer to Mae Gwendolyn Henderson’s article on “Speaking in Tongues” in my analysis of how Naylor subverts the patriarchal text of both Faulkner and Morrison in embarking on a more feminine version of the flying African, which she relates to an ex-slave, Sapphira Wade, a volatile female character who resists fixed claim over her story and identity. In dealing with the concept of hybridity, I show that Naylor rewrites both authors’ South by making Willow Springs a more fluid space, an assumption that unsettles the scores of critics who associate the island with authenticity and exclusive rootedness.

Recognition in programmes for children with special needs

The purpose of this article is to examine the factors that affect the inclusion of pupils in programmes for children with special needs from the perspective of the theory of recognition. The concept of recognition, which includes three aspects of social justice (economic, cultural and political), argues that the institutional arrangements that prevent ‘parity of participation’ in the school social life of the children with special needs are affected not only by economic distribution but also by the patterns of cultural values. A review of the literature shows that the arrangements of education of children with special needs are influenced primarily by the patterns of cultural values of capability and inferiority, as well as stereotypical images of children with special needs. Due to the significant emphasis on learning skills for academic knowledge and grades, less attention is dedicated to factors of recognition and representational character, making it impossible to improve some meaningful elements of inclusion. Any participation of pupils in activities, the voices of the children, visibility of the children due to achievements and the problems of arbitrariness in determining boundaries between programmes are some such elements. Moreover, aided by theories, the actions that could contribute to better inclusion are reviewed. An effective approach to changes would be the creation of transformative conditions for the recognition and balancing of redistribution, recognition, and representation. (DIPF/Orig.)

Contested Play and Clean Water: McMillan Park, Race, and the Built Environment in Washington, D.C., 1900-1941

This study focuses on the intersection of the politics and culture of open public space with race relations in the United States from 1900 to 1941. The history of McMillan Park in Washington, D.C. serves as a lens to examine these themes. Ultimately, the park’s history, as documented in newspapers, interviews, reports, and photographs, reveals how white residents attempted to protect their dominance in a racial hierarchy through the control of both the physical and cultural elements of public recreation space. White use of discrimination through seemingly neutral desires to protect health, safety, and property values, establishes a congruence with their defense of residential property. Without similar access to legal methods, African Americans acted through direct action in gaps of governmental control. Their use of this space demonstrates how African-American residents of Washington and the United States contested their race, recreation, and spatial privileges in the pre-World War II era.

Contributions to 3D Data Registration and Representation

Nowadays, new computers generation provides a high performance that enables to build computationally expensive computer vision applications applied to mobile robotics. Building a map of the environment is a common task of a robot and is an essential part to allow the robots to move through these environments. Traditionally, mobile robots used a combination of several sensors from different technologies. Lasers, sonars and contact sensors have been typically used in any mobile robotic architecture, however color cameras are an important sensor due to we want the robots to use the same information that humans to sense and move through the different environments. Color cameras are cheap and flexible but a lot of work need to be done to give robots enough visual understanding of the scenes. Computer vision algorithms are computational complex problems but nowadays robots have access to different and powerful architectures that can be used for mobile robotics purposes. The advent of low-cost RGB-D sensors like Microsoft Kinect which provide 3D colored point clouds at high frame rates made the computer vision even more relevant in the mobile robotics field. The combination of visual and 3D data allows the systems to use both computer vision and 3D processing and therefore to be aware of more details of the surrounding environment. The research described in this thesis was motivated by the need of scene mapping. Being aware of the surrounding environment is a key feature in many mobile robotics applications from simple robotic navigation to complex surveillance applications. In addition, the acquisition of a 3D model of the scenes is useful in many areas as video games scene modeling where well-known places are reconstructed and added to game systems or advertising where once you get the 3D model of one room the system can add furniture pieces using augmented reality techniques. In this thesis we perform an experimental study of the state-of-the-art registration methods to find which one fits better to our scene mapping purposes. Different methods are tested and analyzed on different scene distributions of visual and geometry appearance. In addition, this thesis proposes two methods for 3d data compression and representation of 3D maps. Our 3D representation proposal is based on the use of Growing Neural Gas (GNG) method. This Self-Organizing Maps (SOMs) has been successfully used for clustering, pattern recognition and topology representation of various kind of data. Until now, Self-Organizing Maps have been primarily computed offline and their application in 3D data has mainly focused on free noise models without considering time constraints. Self-organising neural models have the ability to provide a good representation of the input space. In particular, the Growing Neural Gas (GNG) is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation. However, this type of learning is time consuming, specially for high-dimensional input data. Since real applications often work under time constraints, it is necessary to adapt the learning process in order to complete it in a predefined time. This thesis proposes a hardware implementation leveraging the computing power of modern GPUs which takes advantage of a new paradigm coined as General-Purpose Computing on Graphics Processing Units (GPGPU). Our proposed geometrical 3D compression method seeks to reduce the 3D information using plane detection as basic structure to compress the data. This is due to our target environments are man-made and therefore there are a lot of points that belong to a plane surface. Our proposed method is able to get good compression results in those man-made scenarios. The detected and compressed planes can be also used in other applications as surface reconstruction or plane-based registration algorithms. Finally, we have also demonstrated the goodness of the GPU technologies getting a high performance implementation of a CAD/CAM common technique called Virtual Digitizing.

Ontology-Based Workflow Design for the Coordination of Homecare Interventions

Part 20: Health and Care Networks

Generating Discriminative Objective Proposals via Submodular Ranking

Object recognition has long been a core problem in computer vision. To improve object spatial support and speed up object localization for object recognition, generating high-quality category-independent object proposals as the input for object recognition system has drawn attention recently. Given an image, we generate a limited number of high-quality and category-independent object proposals in advance and used as inputs for many computer vision tasks. We present an efficient dictionary-based model for image classification task. We further extend the work to a discriminative dictionary learning method for tensor sparse coding. In the first part, a multi-scale greedy-based object proposal generation approach is presented. Based on the multi-scale nature of objects in images, our approach is built on top of a hierarchical segmentation. We first identify the representative and diverse exemplar clusters within each scale. Object proposals are obtained by selecting a subset from the multi-scale segment pool via maximizing a submodular objective function, which consists of a weighted coverage term, a single-scale diversity term and a multi-scale reward term. The weighted coverage term forces the selected set of object proposals to be representative and compact; the single-scale diversity term encourages choosing segments from different exemplar clusters so that they will cover as many object patterns as possible; the multi-scale reward term encourages the selected proposals to be discriminative and selected from multiple layers generated by the hierarchical image segmentation. The experimental results on the Berkeley Segmentation Dataset and PASCAL VOC2012 segmentation dataset demonstrate the accuracy and efficiency of our object proposal model. Additionally, we validate our object proposals in simultaneous segmentation and detection and outperform the state-of-art performance. To classify the object in the image, we design a discriminative, structural low-rank framework for image classification. We use a supervised learning method to construct a discriminative and reconstructive dictionary. By introducing an ideal regularization term, we perform low-rank matrix recovery for contaminated training data from all categories simultaneously without losing structural information. A discriminative low-rank representation for images with respect to the constructed dictionary is obtained. With semantic structure information and strong identification capability, this representation is good for classification tasks even using a simple linear multi-classifier.

Avoiding staff removal stage in optical music recognition: application to scores written in white mensural notation

Staff detection and removal is one of the most important issues in optical music recognition (OMR) tasks since common approaches for symbol detection and classification are based on this process. Due to its complexity, staff detection and removal is often inaccurate, leading to a great number of errors in posterior stages. For this reason, a new approach that avoids this stage is proposed in this paper, which is expected to overcome these drawbacks. Our approach is put into practice in a case of study focused on scores written in white mensural notation. Symbol detection is performed by using the vertical projection of the staves. The cross-correlation operator for template matching is used at the classification stage. The goodness of our proposal is shown in an experiment in which our proposal attains an extraction rate of 96 % and a classification rate of 92 %, on average. The results found have reinforced the idea of pursuing a new research line in OMR systems without the need of the removal of staff lines.

ViDRILO: The Visual and Depth Robot Indoor Localization with Objects information dataset

In this article we describe a semantic localization dataset for indoor environments named ViDRILO. The dataset provides five sequences of frames acquired with a mobile robot in two similar office buildings under different lighting conditions. Each frame consists of a point cloud representation of the scene and a perspective image. The frames in the dataset are annotated with the semantic category of the scene, but also with the presence or absence of a list of predefined objects appearing in the scene. In addition to the frames and annotations, the dataset is distributed with a set of tools for its use in both place classification and object recognition tasks. The large number of labeled frames in conjunction with the annotation scheme make this dataset different from existing ones. The ViDRILO dataset is released for use as a benchmark for different problems such as multimodal place classification and object recognition, 3D reconstruction or point cloud data compression.

Probability and Common-Sense: Tandem Towards Robust Robotic Object Recognition in Ambient Assisted Living

The suitable operation of mobile robots when providing Ambient Assisted Living (AAL) services calls for robust object recognition capabilities. Probabilistic Graphical Models (PGMs) have become the de-facto choice in recognition systems aiming to e ciently exploit contextual relations among objects, also dealing with the uncertainty inherent to the robot workspace. However, these models can perform in an inco herent way when operating in a long-term fashion out of the laboratory, e.g. while recognizing objects in peculiar con gurations or belonging to new types. In this work we propose a recognition system that resorts to PGMs and common-sense knowledge, represented in the form of an ontology, to detect those inconsistencies and learn from them. The utilization of the ontology carries additional advantages, e.g. the possibility to verbalize the robot's knowledge. A primary demonstration of the system capabilities has been carried out with very promising results.

A study of culture specific items and translation strategies in Bahram Beyza'i’s play “Marionettes”

L'objectif de cette recherche est de fournir une analyse de la pièce de théâtre perse ''Marionettes'' de Bahrām Beyzā'ī (1963) ainsi que de sa traduction anglaise (1989) afin de comparer et de mettre en contraste les traits propres à la culture «Culture-specific items» (CSI) et des stratégies de traduction. Les formes problématiques pertinentes des différences culturelles seront étudiées et les procédés suggérés par Newmark (1988) seront examinés afin de déterminer dans quelle mesure ils sont pertinents dans la traduction des différences culturelles du perse à l'anglais. La pièce a été traduite par une équipe de traducteurs: Sujata G.Bhatt, Jacquelin Hoats, Imran A. Nyazee et Kamiar K. Oskouee. (Parvin Loloi et Glyn Pursglove 2002:66). Les oeuvres théâtrales de Beyzā'ī sont basées sur les traditions ainsi que sur le folklore iranien. L'auteur aborde la réalité sous une perspective philosophique. « (Un point de vue) enveloppé dans une cape de comparaisons complexes à tel point que nombre des personnages de son oeuvre errent entre des symboles de la mythologie et de l’histoire, ou sociaux» (M.R. Ghanoonparvar, John Green 1989, p.xxii notre traduction). La classification des éléments culturels de Newmark (1988) va comme suit: «Écologie, culture matérielle, culture sociale, organisations, coutumes / moeurs, gestes et habitudes» (Newmark 1988:95). La recherche mettra l’accent sur les procédés suggérés pour traduire les CSI ainsi que sur les stratégies de traduction selon Newmark. Ces procédés comprennent : «traduction littérale, transfert, équivalent culturel, neutralisation, équivalent fonctionnel, équivalent descriptif, synonymie, par le biais de la traduction, transposition, modulation, traduction reconnue, étiquette de traduction, compensation, analyse componentielle, réduction et expansion, paraphraser, distique, notes, additions, gloses» (Newmark 1988:81-93). L'objectif ici est de déterminer si les procédés suggérés sont applicables à la traduction des CSIs du perse à l'anglais, et quels sont les procédés les plus fréquemment utilisés par les traducteurs.

Comparison of Computer-Based and Optical Face Recognition Paradigms

The main objectives of this thesis are to validate an improved principal components analysis (IPCA) algorithm on images; designing and simulating a digital model for image compression, face recognition and image detection by using a principal components analysis (PCA) algorithm and the IPCA algorithm; designing and simulating an optical model for face recognition and object detection by using the joint transform correlator (JTC); establishing detection and recognition thresholds for each model; comparing between the performance of the PCA algorithm and the performance of the IPCA algorithm in compression, recognition and, detection; and comparing between the performance of the digital model and the performance of the optical model in recognition and detection. The MATLAB © software was used for simulating the models. PCA is a technique used for identifying patterns in data and representing the data in order to highlight any similarities or differences. The identification of patterns in data of high dimensions (more than three dimensions) is too difficult because the graphical representation of data is impossible. Therefore, PCA is a powerful method for analyzing data. IPCA is another statistical tool for identifying patterns in data. It uses information theory for improving PCA. The joint transform correlator (JTC) is an optical correlator used for synthesizing a frequency plane filter for coherent optical systems. The IPCA algorithm, in general, behaves better than the PCA algorithm in the most of the applications. It is better than the PCA algorithm in image compression because it obtains higher compression, more accurate reconstruction, and faster processing speed with acceptable errors; in addition, it is better than the PCA algorithm in real-time image detection due to the fact that it achieves the smallest error rate as well as remarkable speed. On the other hand, the PCA algorithm performs better than the IPCA algorithm in face recognition because it offers an acceptable error rate, easy calculation, and a reasonable speed. Finally, in detection and recognition, the performance of the digital model is better than the performance of the optical model.

Pattern recognition methods for EMG prosthetic control

In this work we focus on pattern recognition methods related to EMG upper-limb prosthetic control. After giving a detailed review of the most widely used classification methods, we propose a new classification approach. It comes as a result of comparison in the Fourier analysis between able-bodied and trans-radial amputee subjects. We thus suggest a different classification method which considers each surface electrodes contribute separately, together with five time domain features, obtaining an average classification accuracy equals to 75% on a sample of trans-radial amputees. We propose an automatic feature selection procedure as a minimization problem in order to improve the method and its robustness.

Off-line and On-line Affective Recognition of a Computer User through A Biosignal Processing Approach

Physiological signals, which are controlled by the autonomic nervous system (ANS), could be used to detect the affective state of computer users and therefore find applications in medicine and engineering. The Pupil Diameter (PD) seems to provide a strong indication of the affective state, as found by previous research, but it has not been investigated fully yet. In this study, new approaches based on monitoring and processing the PD signal for off-line and on-line affective assessment (“relaxation” vs. “stress”) are proposed. Wavelet denoising and Kalman filtering methods are first used to remove abrupt changes in the raw Pupil Diameter (PD) signal. Then three features (PDmean, PDmax and PDWalsh) are extracted from the preprocessed PD signal for the affective state classification. In order to select more relevant and reliable physiological data for further analysis, two types of data selection methods are applied, which are based on the paired t-test and subject self-evaluation, respectively. In addition, five different kinds of the classifiers are implemented on the selected data, which achieve average accuracies up to 86.43% and 87.20%, respectively. Finally, the receiver operating characteristic (ROC) curve is utilized to investigate the discriminating potential of each individual feature by evaluation of the area under the ROC curve, which reaches values above 0.90. For the on-line affective assessment, a hard threshold is implemented first in order to remove the eye blinks from the PD signal and then a moving average window is utilized to obtain the representative value PDr for every one-second time interval of PD. There are three main steps for the on-line affective assessment algorithm, which are preparation, feature-based decision voting and affective determination. The final results show that the accuracies are 72.30% and 73.55% for the data subsets, which were respectively chosen using two types of data selection methods (paired t-test and subject self-evaluation). In order to further analyze the efficiency of affective recognition through the PD signal, the Galvanic Skin Response (GSR) was also monitored and processed. The highest affective assessment classification rate obtained from GSR processing is only 63.57% (based on the off-line processing algorithm). The overall results confirm that the PD signal should be considered as one of the most powerful physiological signals to involve in future automated real-time affective recognition systems, especially for detecting the “relaxation” vs. “stress” states.

Lucumí (Yoruba) Culture in Cuba: A Reevaluation (1830S -1940s)

The status, roles, and interactions of three dominant African ethnic groups and their descendants in Cuba significantly influenced the island’s cubanidad (national identity): the Lucumís (Yoruba), the Congos (Bantú speakers from Central West Africa), and the Carabalís (from the region of Calabar). These three groups, enslaved on the island, coexisted, each group confronting obstacles that threatened their way of life and cultural identities. Through covert resistance, cultural appropriation, and accommodation, all three, but especially the Lucumís, laid deep roots in the nineteenth century that came to fruition in the twentieth. During the early 1900s, Cuba confronted numerous pressures, internal and external. Under the pretense of a quest for national identity and modernity, Afro-Cubans and African cultures and religion came under political, social, and intellectual attack. Race was an undeniable element in these conflicts. While all three groups were oppressed equally, only the Lucumís fought back, contesting accusations of backwardness, human sacrifice, cannibalism, and brujería (witchcraft), exaggerated by the sensationalistic media, often with the police’s and legal system’s complicity. Unlike the covert character of earlier epochs’ responses to oppression, in the twentieth century Lucumí resistance was overt and outspoken, publically refuting the accusations levied against African religions. Although these struggles had unintended consequences for the Lucumís, they gave birth to cubanidad’s African component. With the help of Fernando Ortiz, the Lucumí were situated at the pinnacle of a hierarchical pyramid, stratifying African religious complexes based on civilizational advancement, but at a costly price. Social ascent denigrated Lucumí religion to the status of folklore, depriving it of its status as a bona fide religious complex. To the present, Lucumí religious descendants, in Cuba and, after 1959, in many other areas of the world, are still contesting this contradiction in terms: an elevated downgrade.