Automatização de administração e segurança em redes Windows NT

Orientadores: Paulo Licio de Geus, Celio Cardoso GuimarãesDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: A administração, a manutenção da segurança e o gerenciamento de grandes redes de computadores baseados em Windows NT são tarefas desafiadoras e trabalhosas. Algumas tarefas podem se tornar extremamente laboriosas para os administradores dessas redes, como por exemplo: instalação remota de programas, auditoria e modificação de uma configuração de segurança remota ou melhorar o desempenho de cada máquina. Este trabalho tem como objetivo desenvolver técnicas para automatizar as tarefas de administração de redes Windows NT, tornando-as menos complexas. Além disso, este trabalho apresenta e descreve DoIt4Me ("do it for me"), uma ferramenta de gerenciamento remoto capaz de melhorar a segurança, a administração e o desempenho de cada máquina dessas redesAbstract: The administration, the maintenance of the security and the management of large Windows NT networks are challenging tasks. Some tasks can be extremely laborious, such as: software remote install action, auditing and updating the security configurations or to improve the performance of each network machine. The goal of this work is to develop techniques to automate network administra tive tasks, turning them less complexo Besides that, this work presents DoIt4Me ("do it for me"), a network management tool to improve the security, the administration and the performance of each network machineMestradoMestre em Ciência da Computaçã

Gapped Two-Body Hamiltonian for continuous-variable quantum computation

We introduce a family of Hamiltonian systems for measurement-based quantum computation with continuous variables. The Hamiltonians (i) are quadratic, and therefore two body, (ii) are of short range, (iii) are frustration-free, and (iv) possess a constant energy gap proportional to the squared inverse of the squeezing. Their ground states are the celebrated Gaussian graph states, which are universal resources for quantum computation in the limit of infinite squeezing. These Hamiltonians constitute the basic ingredient for the adiabatic preparation of graph states and thus open new venues for the physical realization of continuous-variable quantum computing beyond the standard optical approaches. We characterize the correlations in these systems at thermal equilibrium. In particular, we prove that the correlations across any multipartition are contained exactly in its boundary, automatically yielding a correlation area law.Comment: 4 pages, one figure. New version: typos corrected, one reference added. To appear in PR

The algebro-geometric study of range maps

Localizing a radiant source is a widespread problem to many scientific and technological research areas. E.g. localization based on range measurements stays at the core of technologies like radar, sonar and wireless sensors networks. In this manuscript we study in depth the model for source localization based on range measurements obtained from the source signal, from the point of view of algebraic geometry. In the case of three receivers, we find unexpected connections between this problem and the geometry of Kummer's and Cayley's surfaces. Our work gives new insights also on the localization based on range differences.Comment: 38 pages, 18 figure

Design study of a photon beamline for a soft X-ray FEL driven by high gradient acceleration at EuPRAXIA@SPARC_LAB

We are proposing a facility based on high gradient acceleration via x-band RF structures and plasma acceleration. We plan to reach an electron energy of the order of 1 GeV, suitable to drive a Free Electron Laser for applications in the so called "water window" (2 - 4 nm). A conceptual design of the beamline, from the photon beam from the undulators to the user experimental chamber, mainly focusing on diagnostic, manipulation and transport of the radiation is presented and discussed. We also briefly outline a user end station for coherent imaging, laser ablation and pump-probe experiments

A latent rhythm complexity model for attribute-controlled drum pattern generation

AbstractMost music listeners have an intuitive understanding of the notion of rhythm complexity. Musicologists and scientists, however, have long sought objective ways to measure and model such a distinctively perceptual attribute of music. Whereas previous research has mainly focused on monophonic patterns, this article presents a novel perceptually-informed rhythm complexity measure specifically designed for polyphonic rhythms, i.e., patterns in which multiple simultaneous voices cooperate toward creating a coherent musical phrase. We focus on drum rhythms relating to the Western musical tradition and validate the proposed measure through a perceptual test where users were asked to rate the complexity of real-life drumming performances. Hence, we propose a latent vector model for rhythm complexity based on a recurrent variational autoencoder tasked with learning the complexity of input samples and embedding it along one latent dimension. Aided by an auxiliary adversarial loss term promoting disentanglement, this effectively regularizes the latent space, thus enabling explicit control over the complexity of newly generated patterns. Trained on a large corpus of MIDI files of polyphonic drum recordings, the proposed method proved capable of generating coherent and realistic samples at the desired complexity value. In our experiments, output and target complexities show a high correlation, and the latent space appears interpretable and continuously navigable. On the one hand, this model can readily contribute to a wide range of creative applications, including, for instance, assisted music composition and automatic music generation. On the other hand, it brings us one step closer toward achieving the ambitious goal of equipping machines with a human-like understanding of perceptual features of music

A Study on Convergence, Divergence and Maintenance in OCD Patients

The aim of this study was to verify if patients with Obsessive-Compulsive Disorder (OCD) adopted self-referential and non-adherent interactive modalities, during face-to-face conversation, to a higher extent as compared with subjects who did not have a diagnosis of OCD. For this purpose, four subjects with OCD and four age, sex and education matched Normal Controls (NC) underwent a semi-structured interview. The video-taped records have been evaluated and coded by means of the Initiative-Response Analysis system (I-R). The interview allowed us to obtain indexes of interactive strategies, namely, convergence, divergence and maintenance, which have been interpreted in the Communication Accommodation Theory (CAT) framework. Subjects with OCD, as compared with both NC and interviewers, mainly used Maintenance strategy, indicating a non-adherence to turns and an over-reliance on their own turns, thus neglecting the partners' contribution. This kind of strategy exclusively correlated with the scores of the Y-BOCS compulsion subscale. Results are consistent with the hypothesis that communication in subjects with OCD relies on particular strategies and support the view that communication is strongly correlated with "personological" variables. This hypothesis may be incorporated (not being incompatible with) in the CAT framework

Toward deep drum source separation

In the past, the field of drum source separation faced significant challenges due to limited data availability, hindering the adoption of cutting-edge deep learning methods that have found success in other related audio applications. In this letter, we introduce StemGMD, a large-scale audio dataset of isolated single-instrument drum stems. Each audio clip is synthesized from MIDI recordings of expressive drum performances using ten real-sounding acoustic drum kits. Totaling 1224 h, StemGMD is the largest audio dataset of drums to date and the first to comprise isolated audio clips for every instrument in a canonical nine-piece drum kit. We leverage StemGMD to develop LarsNet, a novel deep drum source separation model. Through a bank of dedicated U-Nets, LarsNet can separate five stems from a stereo drum mixture faster than real-time and is shown to considerably outperform state-of-the-art nonnegative spectro-temporal factorization methods

Hybrid Packet Loss Concealment for Real-Time Networked Music Applications

Real-time audio communications over IP have become essential to our daily lives. Packet-switched networks, however, are inherently prone to jitter and data losses, thus creating a strong need for effective packet loss concealment (PLC) techniques. Though solutions based on deep learning have made significant progress in that direction as far as speech is concerned, extending the use of such methods to applications of Networked Music Performance (NMP) presents significant challenges, including high fidelity requirements, higher sampling rates, and stringent temporal constraints associated to the simultaneous interaction between remote musicians. In this article, we present PARCnet, a hybrid PLC method that utilizes a feed-forward neural network to estimate the time-domain residual signal of a parallel linear autoregressive model. Objective metrics and a listening test show that PARCnet provides state-of-the-art results while enabling real-time operation on CPU