AGOCS – Accurate Google Cloud Simulator Framework

This paper presents the Accurate Google Cloud Simulator (AGOCS) – a novel high-fidelity Cloud workload simulator based on parsing real workload traces, which can be conveniently used on a desktop machine for day-to-day research. Our simulation is based on real-world workload traces from a Google Cluster with 12.5K nodes, over a period of a calendar month. The framework is able to reveal very precise and detailed parameters of the executed jobs, tasks and nodes as well as to provide actual resource usage statistics. The system has been implemented in Scala language with focus on parallel execution and an easy-to-extend design concept. The paper presents the detailed structural framework for AGOCS and discusses our main design decisions, whilst also suggesting alternative and possibly performance enhancing future approaches. The framework is available via the Open Source GitHub repository.

Reconfigurable Matching Networks for RF Amplifiers

This article presents applications of reconfigurable matching networks for RF amplifier design. Two possible solutions are given, one where the switching element is a PIN diode, and the other is based on graphene. Due to the fact that its conductivity depends on applied bias voltage, the graphene-based circuits can be used in microwave circuits as controllable elements. The structure of the proposed switch is very simple and it is particularly convenient for microstrip-based circuits. Because of that, a design of reconfigurable amplifier with the graphene-based switch is presented together with the one which has the PIN diode switch. Both amplifiers have the same specifications, and the one with the PIN diode switch is fabricated. The amplifier utilizing the PIN switch was used as a reference to make a comparison the two types of switches. Results of both amplifiers are very similar which indicates possible future applications of the graphene-based switch.

Recommending peers for learning: Matching on dissimilarity in interpretations to provoke breakdown

People recommenders are a widespread feature of social networking sites and educational social learning platforms alike. However, when these systems are used to extend learners’ Personal Learning Networks, they often fall short of providing recommendations of learning value to their users. This paper proposes a design of a people recommender based on content-based user profiles, and a matching method based on dissimilarity therein. It presents the results of an experiment conducted with curators of the content curation site Scoop.it!, where curators rated personalized recommendations for contacts. The study showed that matching dissimilarity of interpretations of shared interests is more successful in providing positive experiences of breakdown for the curator than is matching on similarity. The main conclusion of this paper is that people recommenders should aim to trigger constructive experiences of breakdown for their users, as the prospect and potential of such experiences encourage learners to connect to their recommended peers.

Flexibele Leerroutes voor Propedeusestudenten: Grounded Theory Onderzoek naar het Identificeren van Studentkenmerken in de Matching, ten behoeve van een Vraaggerichte, Gepersonaliseerde Leerroute in de Propedeuse Social Work

De Groot, D. (2016). Flexibele Leerroutes voor Propedeusestudenten: Grounded Theory Onderzoek naar het Identificeren van Studentkenmerken in de Matching, ten behoeve van een Vraaggerichte, Gepersonaliseerde Leerroute in de Propedeuse Social Work. Juli, 26, 2016, Heerlen, Nederland: Open Universiteit.

Simulation of Distributed Contact in String Instruments: a Modal Expansion Approach

Impactive contact between a vibrating string and a barrier is a strongly nonlinear phenomenon that presents several challenges in the design of numerical models for simulation and sound synthesis of musical string instruments. These are addressed here by applying Hamiltonian methods to incorporate distributed contact forces into a modal framework for discrete-time simulation of the dynamics of a stiff, damped string. The resulting algorithms have spectral accuracy, are unconditionally stable, and require solving a multivariate nonlinear equation that is guaranteed to have a unique solution. Exemplifying results are presented and discussed in terms of accuracy, convergence, and spurious high-frequency oscillations.

Towards a Virtual-Acoustic String Instrument

In acoustic instruments, the controller and the sound producing system often are one and the same object. If virtualacoustic instruments are to be designed to not only simulate the vibrational behaviour of a real-world counterpart but also to inherit much of its interface dynamics, it would make sense that the physical form of the controller is similar to that of the emulated instrument. The specific physical model configuration discussed here reconnects a (silent) string controller with a modal synthesis string resonator across the real and virtual domains by direct routing of excitation signals and model parameters. The excitation signals are estimated in their original force-like form via careful calibration of the sensor, making use of adaptive filtering techniques to design an appropriate inverse filter. In addition, the excitation position is estimated from sensors mounted under the legs of the bridges on either end of the prototype string controller. The proposed methodology is explained and exemplified with preliminary results obtained with a number of off-line experiments.

Condivisione di dati strutturati con Google Fusion Tables

L'elaborato presenta Google Fusion Tables, un software che fa parte dei servizi messi a disposizione da Google, funzionale per la gestione di database. Il servizio gratuito e online ha quindi lo scopo di supportare i compiti di gestori di basi di dati e fornisce operazioni di manipolazione dei dati come estrazione, aggregazione, filtraggio e fusione. Il servizio utilizza dati strutturati, i quali sono estratti dalle pagine Web con appositi motori di ricerca come WebTables, trattato nell'elaborato. Google Fusion Tables è impiegato in ambito scientifico ed è nato per esplicitare le informazioni di ricerche scientifiche che spesso sono contenute in database e fogli di calcolo difficilmente condivisi nel Web. Questo servizio è molto pratico per le aziende, le quali possono integrare dati interni ed esterni all’organizzazione per ampliare la propria conoscenza e ottenere un vantaggio competitivo sui concorrenti. Vengono quindi presentate le caratteristiche distintive che potrebbero indurre numerose organizzazioni a scommettere su questo nuovo servizio.

The Pennsylvania Adoption Exchange Improves Its Matching Process

The Pennsylvania Adoption Exchange (PAE) helps case workers who represent children in state custody by recommending prospective families for adoption. We describe PAE's operational challenges using case worker surveys and analyze child outcomes through a regression analysis of data collected over multiple years. A match recommendation spreadsheet tool implemented by PAE incorporates insights from this analysis and allows PAE managers to better utilize available information. Using a discrete-event simulation of PAE, we justify the value of a statewide adoption network and demonstrate the importance of better information about family preferences for increasing the percentage of children who are successfully adopted. Finally, we detail a series of simple improvements that PAE achieved through collecting more valuable information and aligning incentives for families to provide useful preference information.

Caracterização da fosfatase String/Cdc25 como um regulador da neurotoxicidade de Tau em Drosophila

As tauopatias, grupo onde se inclui a doença de Alzheimer (AD), são caracterizadas pela deposição intracelular de emaranhados neurofibrilares (NFTs), compostos principalmente por formas hiperfosforiladas da proteína Tau, uma proteína que se associa aos microtúbulos. Os mecanismos moleculares subjacentes à neurotoxicidade induzida por Tau não são ainda claros. Drosophila melanogaster tem sido usada para modelar diversas doenças neurodegenerativas humanas, incluindo as tauopatias. Neste trabalho foi usado o sistema visual de Drosophila como modelo para identificar os passos que podem levar à acumulação de Tau em Tauopatias. Durante o desenvolvimento do olho de Drosophila, a expressão ectópica de hTau induz um olho rugoso, em consequência da neurotoxicidade, e que pode ser utilizado para identificar modificadores do fenótipo. A fosfatase codificada por string /cdc25 (stg), um regulador universal da transição G2/M, foi previamente identificada como um supressor da neurotoxicidade associada à expressão da proteina Tau. No entanto, os mecanismos moleculares que estão na base desta interação genética nunca foram estudados, desconhecendo-se também se a atividade fosfatase de Stg/Cdc25 é essencial para modular os níveis de fosforilação de Tau. O objetivo deste projeto consistiu em elucidar os mecanismos que se encontram na base da interação Stg-Tau. Para alcançar este objectivo, usou-se uma abordagem genética e bioquímica. Os resultados obtidos sugerem que Stg é um possível modulador da neurotoxicidade de Tau.

Formless being for flute, clarinet, bassoon, french horn, percussion, piano and string quartet

Thesis (D.M.A.)--University of Washington, 2016-06

Barry Lieberman & Friends present a guest artist recital by The American String Project Chamber Players May 10, 2015

Concert Program

Variación del índice de niebla usando un corpus obtenido a partir de los libros digitalizados por google

En lingüística, principalmente en el idioma inglés, se usa el Índice de Niebla de Gunning para determinar la legibilidad de un texto. El índice estima los años de educación formal necesarios para comprenderel texto en una primera lectura. Un Índice de 11 años apunta a una persona con el colegio finalizado, (Gunning, 1973). Analizamos en esta investigación la variación del Índice al cambiar la forma de obtener uno de los parámetros. En la fórmula original se consideran “palabras complejas” las que tienen tres o más sílabas. En su lugar utilizamos “palabras desconocidas” que son aquellas cuyo uso es poco familiar, según un corpus construido durante la investigación, partiendo de millones de libros digitalizados por Google y la Universidad de Harvard. Aunque la variación de los resultados dependerá del valor asignado para determinarsi una palabra es desconocida la investigación es pionera en el uso de un corpus para calcular el Índice de Niebla.

Leveraging Multiple Features for Image Retrieval and Matching

The goal of image retrieval and matching is to find and locate object instances in images from a large-scale image database. While visual features are abundant, how to combine them to improve performance by individual features remains a challenging task. In this work, we focus on leveraging multiple features for accurate and efficient image retrieval and matching. We first propose two graph-based approaches to rerank initially retrieved images for generic image retrieval. In the graph, vertices are images while edges are similarities between image pairs. Our first approach employs a mixture Markov model based on a random walk model on multiple graphs to fuse graphs. We introduce a probabilistic model to compute the importance of each feature for graph fusion under a naive Bayesian formulation, which requires statistics of similarities from a manually labeled dataset containing irrelevant images. To reduce human labeling, we further propose a fully unsupervised reranking algorithm based on a submodular objective function that can be efficiently optimized by greedy algorithm. By maximizing an information gain term over the graph, our submodular function favors a subset of database images that are similar to query images and resemble each other. The function also exploits the rank relationships of images from multiple ranked lists obtained by different features. We then study a more well-defined application, person re-identification, where the database contains labeled images of human bodies captured by multiple cameras. Re-identifications from multiple cameras are regarded as related tasks to exploit shared information. We apply a novel multi-task learning algorithm using both low level features and attributes. A low rank attribute embedding is joint learned within the multi-task learning formulation to embed original binary attributes to a continuous attribute space, where incorrect and incomplete attributes are rectified and recovered. To locate objects in images, we design an object detector based on object proposals and deep convolutional neural networks (CNN) in view of the emergence of deep networks. We improve a Fast RCNN framework and investigate two new strategies to detect objects accurately and efficiently: scale-dependent pooling (SDP) and cascaded rejection classifiers (CRC). The SDP improves detection accuracy by exploiting appropriate convolutional features depending on the scale of input object proposals. The CRC effectively utilizes convolutional features and greatly eliminates negative proposals in a cascaded manner, while maintaining a high recall for true objects. The two strategies together improve the detection accuracy and reduce the computational cost.

Applied web traffic analysis for numerical encoding of SQL Injection attack features.

SQL Injection Attack (SQLIA) remains a technique used by a computer network intruder to pilfer an organisation’s confidential data. This is done by an intruder re-crafting web form’s input and query strings used in web requests with malicious intent to compromise the security of an organisation’s confidential data stored at the back-end database. The database is the most valuable data source, and thus, intruders are unrelenting in constantly evolving new techniques to bypass the signature’s solutions currently provided in Web Application Firewalls (WAF) to mitigate SQLIA. There is therefore a need for an automated scalable methodology in the pre-processing of SQLIA features fit for a supervised learning model. However, obtaining a ready-made scalable dataset that is feature engineered with numerical attributes dataset items to train Artificial Neural Network (ANN) and Machine Leaning (ML) models is a known issue in applying artificial intelligence to effectively address ever evolving novel SQLIA signatures. This proposed approach applies numerical attributes encoding ontology to encode features (both legitimate web requests and SQLIA) to numerical data items as to extract scalable dataset for input to a supervised learning model in moving towards a ML SQLIA detection and prevention model. In numerical attributes encoding of features, the proposed model explores a hybrid of static and dynamic pattern matching by implementing a Non-Deterministic Finite Automaton (NFA). This combined with proxy and SQL parser Application Programming Interface (API) to intercept and parse web requests in transition to the back-end database. In developing a solution to address SQLIA, this model allows processed web requests at the proxy deemed to contain injected query string to be excluded from reaching the target back-end database. This paper is intended for evaluating the performance metrics of a dataset obtained by numerical encoding of features ontology in Microsoft Azure Machine Learning (MAML) studio using Two-Class Support Vector Machines (TCSVM) binary classifier. This methodology then forms the subject of the empirical evaluation.