Polski a amerykański dyskurs sądowy: śledcze i skargowe procedury przesłuchiwania świadków w procesach karnych

Wydział Anglistyki

Poziom wiedzy informatycznej uczniow klas młodszych

The article describes the results o f the pedagogic research o f a level o f the information science knowledge o f the pupils from the primary classes o f elementary schools in Gliwice. After taking into account the two different types of schools: governmental and private the author discusses the similarities and différencies between the information science competencies o f the pupils. The general remarks concerned the program of additional lessons of information technology, hardware and needed competencies of the teachers are added.

Medici del corpo contro terapeuti della parola: una riflessione su medicina e sofistica

The aim of the present paper is to investigate the connection between ancient medicine and sophistry at the end of 5th century B.C. Beginning with analyses of some passages from the De vetere medicina (VM), De natura hominis (NH) and De arte, the article identifies many similarities between these treatises, on the one hand, and the sophistic doctrines, on the other: these concern primarily perceptual/intellectual knowledge and the interaction between reality, knowledge and language. Among the Sophists, Gorgias was particularly followed and imitated, as he was admired not only for his tremendous rhetorical skills, but also for his philosophically significant work On not being, which probably influenced various discussions in the Hippocratic treatises. However, if Gorgias argues in favor of language as dynastēs megas, the authors of VM, NH and De arte consider knowledge to be far more relevant and reliable than logos. These Hippocratic treatises criticize the philosophical thesis and the resulting kind of reductionism. Above all they defend the supremacy of medicine over any other art. By using the same argumentative and rhetorical strategies that were employed by Gorgias, these treatises reverse the thought of those Sophists who exalted only the technē tōn logōn.

Moje teksty pisze życie – język kultury hip-hopu w polskich i niemieckich tekstach piosenek. Analiza porównawcza

Wydział Neofilologii

The ‘Faust’ or ’Lucifer’ Sonata? On Liszt’s idea of programme music as exemplifi ed by his Piano Sonata in B minor

The musicological tradition places Liszt’s Sonata in B minor within the sphere of compositions inspired by the Faustian myth. Its musical material, its structure and its narrative exhibit certain similarities to the ‘Faust’ Symphony. Yet there has appeared a diff erent and, one may say, a rival interpretation of Sonata in B minor. What is more, it is well-documented from both a musical and a historical point of view. It has been presented by Hungarian pianist and musicologist Tibor Szász. He proposes the thesis that the Sonata in B minor has been in fact inspired by Milton’s Paradise Lost, with its three protagonists: Adam, Satan and Christ. He fi nds their illustrations and even some key elements of the plot in the Sonata’s narrative. But yet Milton’s Paradise Lost and Goethe’s Faust are both stories of the Fall and Salvation, of the cosmic struggle between good and evil. The triads of their protagonists – Adam and Eve, Satan, and Christ; Faust, Mephisto and Gretchen – are homological. Thus both interpretations of the Sonata, the Goethean and the Miltonian, or, in other words, the Faustian and the Luciferian, are parallel and complementary rather than rival. It is also highly probable that both have had their impact on the genesis of the Sonata in B minor.

Análise Biográfica, do Estilo de Vinculação e da Personalidade, em Indivíduos com História de Abuso de Substâncias e Condutas Delinquentes

Tese de Doutoramento apresentada à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Doutor em Ciências Sociais.

Perceções de profissionais do Sistema de Justiça e População em geral quanto à implementação da Lei de Registo e Notificação de Ofensores Sexuais em Portugal

Dissertação apresentada à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de mestre em Psicologia Jurídica

Dynamical Instabilities and Deterministic Chaos in Ballistic Electron Motion in Semiconductor Superlattices

We consider the motion of ballistic electrons within a superlattice miniband under the influence of an alternating electric field. We show that the interaction of electrons with the self-consistent electromagnetic field generated by the electron current may lead to the transition from regular to chaotic dynamics. We estimate the conditions for the experimental observation of this deterministic chaos and discuss the similarities of the superlattice system with the other condensed matter and quantum optical systems.

BoostMap: A Method for Efficient Approximate Similarity Rankings

This paper introduces BoostMap, a method that can significantly reduce retrieval time in image and video database systems that employ computationally expensive distance measures, metric or non-metric. Database and query objects are embedded into a Euclidean space, in which similarities can be rapidly measured using a weighted Manhattan distance. Embedding construction is formulated as a machine learning task, where AdaBoost is used to combine many simple, 1D embeddings into a multidimensional embedding that preserves a significant amount of the proximity structure in the original space. Performance is evaluated in a hand pose estimation system, and a dynamic gesture recognition system, where the proposed method is used to retrieve approximate nearest neighbors under expensive image and video similarity measures. In both systems, BoostMap significantly increases efficiency, with minimal losses in accuracy. Moreover, the experiments indicate that BoostMap compares favorably with existing embedding methods that have been employed in computer vision and database applications, i.e., FastMap and Bourgain embeddings.

Learning Euclidean Embeddings for Indexing and Classification

BoostMap is a recently proposed method for efficient approximate nearest neighbor retrieval in arbitrary non-Euclidean spaces with computationally expensive and possibly non-metric distance measures. Database and query objects are embedded into a Euclidean space, in which similarities can be rapidly measured using a weighted Manhattan distance. The key idea is formulating embedding construction as a machine learning task, where AdaBoost is used to combine simple, 1D embeddings into a multidimensional embedding that preserves a large amount of the proximity structure of the original space. This paper demonstrates that, using the machine learning formulation of BoostMap, we can optimize embeddings for indexing and classification, in ways that are not possible with existing alternatives for constructive embeddings, and without additional costs in retrieval time. First, we show how to construct embeddings that are query-sensitive, in the sense that they yield a different distance measure for different queries, so as to improve nearest neighbor retrieval accuracy for each query. Second, we show how to optimize embeddings for nearest neighbor classification tasks, by tuning them to approximate a parameter space distance measure, instead of the original feature-based distance measure.

Robust Tracking of Human Motion

This technical report presents a combined solution for two problems, one: tracking objects in 3D space and estimating their trajectories and second: computing the similarity between previously estimated trajectories and clustering them using the similarities that we just computed. For the first part, trajectories are estimated using an EKF formulation that will provide the 3D trajectory up to a constant. To improve accuracy, when occlusions appear, multiple hypotheses are followed. For the second problem we compute the distances between trajectories using a similarity based on LCSS formulation. Similarities are computed between projections of trajectories on coordinate axes. Finally we group trajectories together based on previously computed distances, using a clustering algorithm. To check the validity of our approach, several experiments using real data were performed.

Extraction and Clustering of Motion Trajectories in Video

A system is described that tracks moving objects in a video dataset so as to extract a representation of the objects' 3D trajectories. The system then finds hierarchical clusters of similar trajectories in the video dataset. Objects' motion trajectories are extracted via an EKF formulation that provides each object's 3D trajectory up to a constant factor. To increase accuracy when occlusions occur, multiple tracking hypotheses are followed. For trajectory-based clustering and retrieval, a modified version of edit distance, called longest common subsequence (LCSS) is employed. Similarities are computed between projections of trajectories on coordinate axes. Trajectories are grouped based, using an agglomerative clustering algorithm. To check the validity of the approach, experiments using real data were performed.

Modal Matching for Correspondence and Recognition

Modal matching is a new method for establishing correspondences and computing canonical descriptions. The method is based on the idea of describing objects in terms of generalized symmetries, as defined by each object's eigenmodes. The resulting modal description is used for object recognition and categorization, where shape similarities are expressed as the amounts of modal deformation energy needed to align the two objects. In general, modes provide a global-to-local ordering of shape deformation and thus allow for selecting which types of deformations are used in object alignment and comparison. In contrast to previous techniques, which required correspondence to be computed with an initial or prototype shape, modal matching utilizes a new type of finite element formulation that allows for an object's eigenmodes to be computed directly from available image information. This improved formulation provides greater generality and accuracy, and is applicable to data of any dimensionality. Correspondence results with 2-D contour and point feature data are shown, and recognition experiments with 2-D images of hand tools and airplanes are described.

Hierarchal Characterization and Generation of Blogosphere Workloads

We present a thorough characterization of the access patterns in blogspace, which comprises a rich interconnected web of blog postings and comments by an increasingly prominent user community that collectively define what has become known as the blogosphere. Our characterization of over 35 million read, write, and management requests spanning a 28-day period is done at three different levels. The user view characterizes how individual users interact with blogosphere objects (blogs); the object view characterizes how individual blogs are accessed; the server view characterizes the aggregate access patterns of all users to all blogs. The more-interactive nature of the blogosphere leads to interesting traffic and communication patterns, which are different from those observed for traditional web content. We identify and characterize novel features of the blogosphere workload, and we show the similarities and differences between typical web server workloads and blogosphere server workloads. Finally, based on our main characterization results, we build a new synthetic blogosphere workload generator called GBLOT, which aims at mimicking closely a stream of requests originating from a population of blog users. Given the increasing share of blogspace traffic, realistic workload models and tools are important for capacity planning and traffic engineering purposes.

Traffic Characteristics and Communication Patterns in Blogosphere

We present a thorough characterization of the access patterns in blogspace -- a fast-growing constituent of the content available through the Internet -- which comprises a rich interconnected web of blog postings and comments by an increasingly prominent user community that collectively define what has become known as the blogosphere. Our characterization of over 35 million read, write, and administrative requests spanning a 28-day period is done from three different blogosphere perspectives. The server view characterizes the aggregate access patterns of all users to all blogs; the user view characterizes how individual users interact with blogosphere objects (blogs); the object view characterizes how individual blogs are accessed. Our findings support two important conclusions. First, we show that the nature of interactions between users and objects is fundamentally different in blogspace than that observed in traditional web content. Access to objects in blogspace could be conceived as part of an interaction between an author and its readership. As we show in our work, such interactions range from one-to-many "broadcast-type" and many-to-one "registration-type" communication between an author and its readers, to multi-way, iterative "parlor-type" dialogues among members of an interest group. This more-interactive nature of the blogosphere leads to interesting traffic and communication patterns, which are different from those observed in traditional web content. Second, we identify and characterize novel features of the blogosphere workload, and we investigate the similarities and differences between typical web server workloads and blogosphere server workloads. Given the increasing share of blogspace traffic, understanding such differences is important for capacity planning and traffic engineering purposes, for example.