Miracle-dz/UniFAR-FTUs huggingface dataset:
Técnicas de otimização combinatória aplicadas a um precondicionador paralelo baseado no algoritmo SPIKE

Neste trabalho, utilizamos o algoritmo paralelo hibrido SPIKE como um precondicionador para um metodo iterativo nao-estacionario combinando as arquiteturas de memoria distribuida e compartilhada MPI/OpenMP. A fim de obter um bom precondicionador resolvemos um conjunto de problemas combinatorios como reordenamento, particionamento, matching e o problema quadratico da mochila. Apresentamos os resultados avaliando o speedup e escalabilidade em sistemas resultantes de formulacoes de elementos finitos.
Multiobjective Optimization Under Uncertainty in Advanced Abrasive Machining Processes via a Fuzzy-Evolutionary Approach

This paper considers multiobjective optimization under uncertainty (MOOUC) for the selection of optimal cutting conditions in advanced abrasive machining processes. Processes considered are water-jet machining, abrasive water-jet machining and ultra-sonic machining. Decisions regarding the cutting conditions can involve optimization for multiple competing goals; such as surface finish, machining time and power consumption. In practice, there is also an issue of variations in the ability to attain the performance goals. This can be due to limitations in machine accuracy or variations in material properties of the workpiece and/or abrasive particles. The approach adopted in this work relies on a Strength Pareto Evolutionary Algorithm (SPEA2) framework, with specially tailored dominance operators to account for probabilistic aspects in the considered multiobjective problem. Deterministic benchmark problems in the literature for the considered machining processes are extended to include performance uncertainty, and then used in testing the performance of the proposed approach. Results of the study show that accounting for process variations through a simple penalty term may be detrimental for the multiobjective optimization. On the other hand, a proposed Fuzzy-tournament dominance operator appears to produce favorable results.Copyright © 2015 by ASME
Dissecting pathways with the yeast knockout collection

The yeast knockout collections provide opportunities to perform massively parallel phenotyping of deletion mutants for almost every yeast open reading frame. I used the knockout collection to screen for synthetic lethal partners, defined as alleles that cause lethality when combined but are nonlethal alone, with CTF4 and CTF18 and present the results in Chapter 2. I developed procedures for interpreting microarrays designed to compare changes in oligonucleotide TAGs specific to each knockout strain and present those methods in Chapters 3 and 4. These TAG microarrays allow thousands of experiments to screen for synthetic lethality among pairs of null alleles to be accomplished relatively quickly. In Chapter 4, I present 1410 novel predicted synthetic lethal interactions based on 707 currently completed screens. Interpretation of synthetic lethality is presented with a computational approach in Chapter 5, termed the congruence score. High congruence scores associate genes into common pathways, and I use the method to predict that YLL049W is a component of the dynein-dynactin nuclear orientation pathway. In Chapter 6, I propose a generalization of the congruence score to any phenotype, such as growth rate in the presence of various compounds, or even nonquantitative phenotypes such as cell morphology. This procedure connects genes based on similarity of multiple phenotypes using an application of information theory to produce a shared information score. Using gene ontology similarity, I show that high scores are associated with similarly annotated genes. Advisor: Forrest A. Spencer PhD Reader: Rafael A. Irizarry PhD
The (In)Visibility of Political Connections

We study the extent to which market participants are informed about firms’ political connections when the connections are not publicly disclosed and how it affects pricing efficiency. Using a sample of public announcements of government officials’ corruption investigations, we find that, on average, investors do not realize that officials under investigation are connected to firms in which they have invested and that the value of those connections has been lost. This finding suggests that the connections generally are of low visibility to investors. However, institutional investors know about at least some of the connections and abnormally sell their shares in the firms when investigations are announced. The loss of value of the connections is not fully incorporated into stock prices until the connections are subsequently disclosed, and price incorporation is slower for the connections that have been less visible to institutional investors. Our results suggest that lack of visibility significantly harms pricing efficiency of political connections.
Scalable computation of viability kernels and a viability-theoretic approach to guaranteeing safety for closed-loop medical devices

As closed-loop controllers become increasingly prevalent in medical technology, increasing emphasis is being placed on ensuring that such systems operate in a safe manner. In our approach to guaranteeing the safe operation of a physiologic closed-loop control system, we wish to provide a mathematical guarantee that, despite limited control authority, the system’s state can be confined to a region designated as safe. The largest subset of the safe region for which there exists an admissible control input that keeps the state within the safe region is known as the viability kernel, or maximal controlled invariant set. Many methods are known for computing viability kernels in low-dimensional systems, but these existing methods rely on gridding the state space and hence their time complexity increases exponentially with the state dimension. In this thesis we describe a new connection between reachability and viability theory that enables us to approximate the viability kernel using Lagrangian methods which scale well with the state dimension. We present four new viability kernel approximation algorithms using polytope-, ellipsoidand support vector-based set representations and we compare their performances in terms of accuracy and scalability with the state dimension. Using the support vector and ellipsoidal techniques, we are able to accurately approximate the viability kernel for systems of much larger state dimension than was previously feasible using existing Eulerian methods. We also present a viability theoretic solution to the problem of determining when a physiologic closed-loop control system should initiate a fallback
Developing a Financial Statement-Based Effectiveness Measure of Interorganizational Systems' Contribution

Measuring the contribution of interorganizational systems (IOS) is challenging, and mainly based on subjective surveys. The purpose of this study is to objectively examine the effectiveness of IOS, through data from corporate financial statements. We developed a novel performance measure: an effectiveness ratio that reflects changes in average inventory level in comparison with change in annual sales. This measure reflects a certain aspect of improvement in supply chain management achieved by the evident developments of IOS, while considering external events that affected the companies. Two samples of American and Israeli companies were used for developing and empirically examining the suggested effectiveness ratio over 20 years (1993-2012). The results show considerable improvement as measured by the average effectiveness ratio of randomly selected American international companies in the cosmetics and toiletry products sector. However, the Israeli sample of food suppliers and importers, showed average marginal improvement, and major variance among the companies.
Exploring spatio-temporal patterns in emergency department use for mental health reasons from children and adolescents in Alberta, Canada

This project analyses mental health related emergency department visits from children and adolescents in Alberta, Canada to understand the spatio-temporal patterns and identify risk factors. The data are extracted for the period 2002-2011 from the provincial health administrative data systems of Alberta. A descriptive data analysis is presented and then generalized linear models are explored to model the spatio-temporal pattern of the emergency department visit counts. The seasonal effect is examined using seasonal factors, sine and cosine functions and cyclic cubic smoothing splines. The spatial and temporal correlation structures are modelled using autoregressive model of order 1 and conditionally autoregressive model random effects. Demographic risk factors and their association with the frequency of mental health related emergency department visits is examined. Estimates of the model parameters are obtained and model diagnostics are performed to assess the fit of the model. Age, gender and proxy for socio-economic status are found to be important risk factors. The proposed model can be used as a predictive model to help identify regions and groups at a higher risk for mental health related emergency department visits.
Intrusion tolerant software architectures

The complexity of the software systems built today virtually guarantees the existence of security vulnerabilities. When the existence of specific vulnerabilities becomes known - typically as a result of detecting a successful attack - intrusion prevention techniques such as firewalls and anti-virus software seek to prevent future attackers from exploiting these vulnerabilities. However, vulnerabilities cannot be totally eliminated, their existence is not always known and preventing mechanisms cannot always be built. Intrusion tolerance is a new concept, a new design paradigm, and potentially a new capability for dealing with residual security vulnerabilities. In this article, we describe our initial exploration of the hypothesis that intrusion tolerance is best designed and enforced at the software architecture level.
The Role of Private Universities in Building Knowledge Society: A Case Study of Private Universities in United Arab Emirates

This study examines the role of private universities in building knowledge society in UAE using quantitative approach. We distributed 30 questionnaires to three selected private universities. We found that the association between creating knowledge, storing knowledge, capturing knowledge, faculty resources and infrastructure and knowledge sharing are positively strengthened sustainability of higher education to build knowledge community in UAE. The creating knowledge, storing knowledge, capturing knowledge, faculty resources and infrastructure and knowledge sharing are significant variables in explaining building knowledge community in UAE due to their positive interconnectivity. We suggest that education sector managers should extend the quality enhancement scope to student satisfaction because satisfaction of student is highly essential in sustaining private universities to compete within and outside the system since part of the objectives of knowledge economy is to provide a quality educational services that will arrest the attention of students residing in UAE and abroad. HEIs should improve overall service for a longterm revenue generation. To meet the international standards of education, special attention is required to upgrade infrastructure and other allied facilities, as well as to improve the communication skills of the students, to establish efficient quality enhancement cells and job placement cells, and to arrange industrial visits and study tours to enhance practical skills of the students. Keynotes: Higher Education, Sustainability and Knowledge community.
Storage does not Guarantee Access: The Problem of Organizing and Accessing Words in a Speaker's Lexicon

Natural language production requires both a grammar and a lexicon. In this article, we deal only with the latter, trying to enhance an existing electronic resource to allow for search via navigation in a huge associative network. Our primary focus is on the structure of the lexicon (i.e. its indexing scheme). This issue has often been overlooked, yet it is crucial, as it determines to a large extent the chances of finding the word a language user (speaker/writer) is looking for. While researchers working on natural language generation (NLG) have given a lot of thought to lexicalization (i.e. the mapping of meanings to forms), lexical access has received no attention at all. Lexicalization is generally considered to be only a choice problem, the assumption being that stored data can always be accessed. While this may hold for machines, it does not always hold for people, as is well attested by the “tip-of-the-tongue” problem. A speaker may know a word, yet still be unable to access it. However, even machines may experience access problems. We illustrate this last point via a small experiment, showing how a well-known lexical resource (WordNet) may fail to reveal information (words) it contains. Additionally, in this article we show how a lexicon might be organized or indexed to allow language users to find the words they are looking for quickly and naturally.
A modal approach to model computational trust

The concept of trust is a socio-cognitive concept that plays an important role in representing interactions within concurrent systems. When the complexity of a computational system and its unpredictability makes standard security solutions (commonly called hard security solutions) inapplicable, computational trust is one of the most useful concepts to design protocols of interaction. In this work, our main objective is to present a prospective survey of the field of study of computational trust. We will also present two trust models, based on logical formalisms, and show how they can be studied and used. While trying to stay general in our study, we use service-oriented architecture paradigm as a context of study when examples are needed. Our work is subdivided into three chapters. The first chapter presents a general view of the computational trust studies. Our approach is to present trust studies in three main steps. Introducing trust theories as first attempts to grasp notions linked to the concept of trust, fields of application, that explicit the uses that are traditionally associated to computational trust, and finally trust models, as an instantiation of a trust theory, w.r. t. some formal framework. Our survey ends with a set of issues that we deem important to deal with in priority in order to help the advancement of the field. The next two chapters present two models of trust. Our first model is an instantiation of Castelfranchi & Falcone's socio-cognitive trust theory. Our model is implemented using a Dynamic Epistemic Logic that we propose. The main originality of our solution is the fact that our trust definition extends the original model to complex action (programs, composed services, etc.) and the use of authored assignment as a special kind of atomic actions. The use of our model is then illustrated in a case study related to service-oriented architecture. Our second model extends our socio-cognitive definition to an abductive framework that allows us to associate trust to explanations. Our framework is an adaptation of Bochman's production relations to the epistemic case. Since Bochman approach was initially proposed to study causality, our definition of trust in this second model presents trust as a special case of causal reasoning, applied to a social context. We end our manuscript with a conclusion that presents how we would like to extend our work.
Shape classification using hidden Markov model

A planar shape recognition approach is presented which is based on a hidden Markov model and autoregressive parameters. This approach segments closed shapes into segments and explores the characteristic relations between consecutive segments to make classification at a finer level. The algorithm can tolerate a lot of shape contour perturbation and a moderate amount of occlusion. Also, the overall classification scheme is independent of shape orientation. Excellent recognition results have been reported. A distinct advantage of the approach is that the classifier does not have to be trained all over again when a new class of shapes is added. <<ETX>>
Cooperative evolutive concept learning: an empirical study

An investigation of the results produced by two cooperative learning strategies exploited in the system REGAL is reported. The objective is to produce a more efficient learning system. An extensive description about how to setup suitable experiments is included. It is worthwhile to note that, in principle, these cooperative learning strategies could be applied to a pool of different learning systems.
An Augmented 3D iPad Mobile Application for Communication, Collaboration, and Learning (CCL) of Building MEP Systems

The goal of this research is to gain better understanding of how BIM models can be used to create mobile 3D game environments for some serious purpose other than entertainment. This project was motivated by the perspective that the capability of BIM creating realistic 3D virtual game environments for collaboration, communication and learning (CCL). The wide dissemination of mobile devices is driving many computer applications toward the direction of ubiquitous mobile accessibility. Considering the popularity of the iPad mobile device, a BIM model of an institutional building was created for this 3D mobile game application on iPad. This game application allows users to visualize and interact with the HVAC BIM components. In this paper, we reported our preliminary findings in developing a BIM-based iPad 3D game application for CCL activities in architecture, engineering, and construction (AEC). Technical barriers of using BIM models to create mobile 3D CCL applications were also discussed. The detailed system architecture and implementation framework were presented in this paper. Differences between the developed program and the existing BIM mobile programs were elaborated to distinguish the developed program from the off-the-shelf BIM mobile programs, such as Vella and Autodesk Inventor mobile viewer. The preliminary results indicated that fairly complex BIM models including multiple building systems can be imported and created through 3D virtual-built environments to conducted CCL activities in the AEC domain.
Towards a Dynamic, Generative Computer Art

With the advent of the computer we now have the technical means for developing a dynamic, generative visual art form that emulates the condition and principles of music. In discussing the premises upon which the emergence of such an art rests, the author gives a broad overview of the approaches being used in search of a structural format and language, and he reviews his search for a formal imaging system to produce dynamic computer compositions of his paintings.
A high performance FPGA implementation of DES

FPGAs have proven to be very effective and efficient devices on which to implement encryption algorithms. They perform at much faster data-rates and provide better security than equivalent software implementations. They also provide more flexibility than ASIC implementations. This paper presents a high performance silicon intellectual property (IP) core for the data encryption standard (DES) encryption algorithm. The 16-stage pipelined DES design runs at an encryption rate of 3.87 Gbits/s using Xilinx Virtex FPGA technology making this the fastest single-chip DES FPGA implementation reported to date. This result is a factor 28 times faster than software implementations.
Adaptive Control of Robot Manipulators Including Motor Dynamics

An adaptive control scheme for robot manipulators including motor dynamics is proposed in this paper. The proposed scheme avoids the assumption that the values of motor parameters are known which is required in reference (13). An exponential control law is first developed under the assumption of no uncertainty. This forms a controller structure for the adaptive control. Using this control structure, a full-order adaptive control law is proposed to overcome parameter uncertainty for both robot link and motor. The stability analysis is in the Lyapunov stability sense. The method is further extended to the task space. Extensive simulations are performed to compare the different control schemes.
Providing Identification Services to External Entities using SAML

This project investigates approaches for implementing a method for providing identification services to external entities using SAML. SAML attempts to provide standardized means of exchanging authentication data between multiple parties. By implementing support for SAML both so-called service providers and identity providers can reach a large number of other entities that have done the same. An evaluation of different SAML-features and SAML-libraries was performed in order to produce a prototype that is described in this report. The result of the prototype is a system that allows a user to log in to external websites using existing identification infrastructure such as the user's online bank, with the use of SAML between the service and the identity provider.
Visual Analysis Algorithms for Embedded Systems

The main contribution of this thesis is the design and development of an optimized framework to realize the deep neural classifiers on the embedded platforms. Deep convolutional networks exhibit unmatched performance in image classification. However, these deep classifiers demand huge computational power and memory storage. That is an issue on embedded devices due to limited onboard resources. The computational demand of neural networks mainly stems from the convolutional layers. A significant improvement in performance can be obtained by reducing the computational complexity of these convolutional layers, making them realizable on embedded platforms. In this thesis, we proposed a CUDA (Compute Unified Device Architecture)based accelerated scheme to realize the deep architectures on the embedded platforms by exploiting the already trained networks. All required functions and layers to replicate the trained neural networks were implemented and accelerated using concurrent resources of embedded GPU. Performance of our CUDA-based proposed scheme was significantly improved by performing convolutions in the transform domain. This matrix multiplication based convolution was also compared with the traditional approach to analyze the improvement in inference performance. The second part of this thesis focused on the optimization of the proposed framework. The flow of our CUDA-based framework was optimized using unified memory scheme and hardware-dependent utilization of computational resources. The proposed flow was evaluated over three different image classification networks on Jetson TX1 embedded board and Nvidia Shield K1 tablet. The performance of proposed GPU-only flow was compared with its sequential and heterogeneous versions. The results showed that the proposed scheme brought the higher performance and enabled the real-time image classification on the embedded platforms with lesser storage
Approaches for monitoring benthic and water column biophysical properties in Australian coastal environments

Outlines the rationale for a program of monitoring of water quality and benthic and inter-tidal flora/fauna in different types of coastal environment: Moreton Bay serves as a multi-use resource for a large population in the greater Brisbane urban area; Port Curtis is the largest industrial port in Queensland; and the Fitzroy River primarily drains a catchment dominated by agricultural and rangeland activities. The objectives guiding the remote sensing project are (1) a national survey on remote sensing applications for resource management; (2) data collection campaigns; and (3) mapping of a harmful algal bloom.