Miracle-dz/UniFAR-FTUs huggingface dataset:
Hollowed Out: Meaning and Authoring of High School Math and Science Identities in the Context of Neoliberal Reform

ABSTRACT Drawing on a 3-year ethnographic study in 2 urban high schools serving majority low-income students of color, we reveal how the meaning and authoring of self in mathematics and science are produced among high-achieving students in contexts profoundly influenced by neoliberal policies. We highlight the institutional practices and cultural imaginaries that students responded to as they developed identities in math and science at the schools. These processes ”hollowed out” the identity of “being good” at mathematics and science while conferring status and good moral character on those who could author themselves in these shallow terms.
Perceived Organizational ERP Benefits for SMEs: Middle Eastern Perspective

This study aims to examine the impact of organizational environment (top management support, company-wide support, business process reengineering, effective project management, and organizational culture) and enterprise resource planning (ERP) vendor environment (ERP vendor support) on ERP perceived benefits. In order to achieve the study’s aim, a questionnaire was developed based on the extant literature to collect relevant data from the research informants. The population for this research consisted of all users of Microsoft Dynamics Great Plains (a typical type of enterprise system), which is frequently used in Jordanian companies in Amman City. A random sample of 30% of the research population was selected. The results revealed that business process reengineering, effective project management, company-wide support, and organizational culture have a positive correlation with ERP perceived benefits, whereas top management support does not. In addition, there is a significant positive correlation between vendor support and ERP perceived benefits. Academic and practical recommendations are provided.
L'Agence mondiale antidopage : un nouveau régulateur des relations internationales sportives

L'Agence mondiale antidopage est un organisme mis sur pied en 1999 par les organisations sportives internationales et les autorites publiques preoccupees par le developpement du dopage dans le sport. Il constitue un partenariat public-prive original. L'organisation et les activites de ce nouvel organisme sont decrites dans cet article. Ses structures decisionnelles sont ensuite comparees avec celles du Mouvement international de la Croix-Rouge et de l'Union internationale pour la conservation de la nature qui regroupent egalement gouvernements et ONG pour accomplir leurs missions. En conclusion est evoque le possible elargissement du role de l'Agence a la regulation des derives du sport mondial.
The Importance of Online Peer Relationships During the Transition to Motherhood: Do They Decrease Stress, Alleviate Depression and Increase Parenting Competence?

This Dissertation is brought to you for free and open access. It has been accepted for inclusion in Dissertations and Theses by an authorized administrator of PDXScholar. For more information, please contact pdxscholar@pdx.edu. Recommended Citation Arias, Bobbie Sue, "The Importance of Online Peer Relationships During the Transition to Motherhood: Do They Decrease Stress, Alleviate Depression and Increase Parenting Competence? " (2016). Dissertations and Theses. Paper 2946.
Detection of Lightweight Directory Access Protocol Query Injection Attacks in Web Applications

The Lightweight Directory Access Protocol (LDAP) is a common protocol used in organizations for Directory Service. LDAP is popular because of its features such as representation of data objects in hierarchical form, being open source and relying on TCP/IP, which is necessary for Internet access. However, with LDAP being used in a large number of web applications, different types of LDAP injection attacks are becoming common. The idea behind LDAP injection attacks is to take advantage of an application not validating inputs before being used as part of LDAP queries. An attacker can provide inputs that may result in alteration of intended LDAP query structure. LDAP injection attacks can lead to various types of security breaches including (i) Login Bypass, (ii) Information Disclosure, (iii) Privilege Escalation, and (iv) Information Alteration. Despite many research efforts focused on traditional SQL Injection attacks, most of the proposed techniques cannot be suitably applied for mitigating LDAP injection attacks due to syntactic and semantic differences between LDAP and SQL queries. Many implemented web applications remain vulnerable to LDAP injection attacks. In particular, there has been little attention for testing web applications to detect the presence of LDAP query injection attacks. The aim of this thesis is two folds: First, study various types of LDAP injection attacks and vulnerabilities reported in the literature. The planned research is to critically examine and evaluate existing injection mitigation techniques using a set of open source applications reported to be vulnerable to LDAP query injection attacks. Second, propose an approach to detect LDAP injection attacks by generating test cases when developing secure web applications. In particular, the thesis focuses on specifying signatures for detecting LDAP injection attack types using Object Constraint Language (OCL) and evaluates the proposed approach using PHP web applications. We also measure the effectiveness of generated test cases using a metric named Mutation Score.
Preserving geometry and topology for fluid flows with thin obstacles and narrow gaps

Fluid animation methods based on Eulerian grids have long struggled to resolve flows involving narrow gaps and thin solid features. Past approaches have artificially inflated or voxelized boundaries, although this sacrifices the correct geometry and topology of the fluid domain and prevents flow through narrow regions. We present a boundary-respecting fluid simulator that overcomes these challenges. Our solution is to intersect the solid boundary geometry with the cells of a background regular grid to generate a topologically correct, boundary-conforming cut-cell mesh. We extend both pressure projection and velocity advection to support this enhanced grid structure. For pressure projection, we introduce a general graph-based scheme that properly preserves discrete incompressibility even in thin and topologically complex flow regions, while nevertheless yielding symmetric positive definite linear systems. For advection, we exploit polyhedral interpolation to improve the degree to which the flow conforms to irregular and possibly non-convex cell boundaries, and propose a modified PIC/FLIP advection scheme to eliminate the need to inaccurately reinitialize invalid cells that are swept over by moving boundaries. The method naturally extends the standard Eulerian fluid simulation framework, and while we focus on thin boundaries, our contributions are beneficial for volumetric solids as well. Our results demonstrate successful one-way fluid-solid coupling in the presence of thin objects and narrow flow regions even on very coarse grids.
A feedback linearization controller for trajectory tracking of the Furuta pendulum

A Furuta pendulum is a two degrees-of-freedom mechanism consisting of an arm rotating in the horizontal plane and a pendulum rotating in the vertical plane. The pendulum is attached to the arm tip. The complexity in the control of this system relies on that only the arm is actuated. The problem addressed in this paper consists in the design of a controller such that the difference between the desired joint position and the actual one is uniformly ultimately bounded. In particular, the desired arm position is a time varying signal and the desired pendulum position is zero. Roughly speaking, this has the physical meaning that the arm should be moving while the pendulum should be kept at the upward position. To satisfy that control goal, in this document a new controller based on the feedback linearization technique is introduced. The new scheme has been experimentally compared with respect to a known algorithm.
BIOTECHNOLOGICALLY RELEVANT ENZYMES AND PROTEINS A genetically engineered protein domain binding to bacterial murein, archaeal pseudomurein, and fungal chitin cell wall material

The major murein and pseudomurein cell wallbinding domains, i.e., the Lysin Motif (LysM) (Pfam PF01476) and pseudomurein cell wall-binding (PMB) (Pfam PF09373) motif, respectively, were genetically fused. The fusion protein is capable of binding to both mureinand pseudomurein-containing cell walls. In addition, it also binds to chitin, the major polymer of fungal cell walls. Binding is influenced by pH and occurs at a pH close to the pI of the binding protein. Functional studies on truncated versions of the fusion protein revealed that murein and chitin binding is provided by the LysM domain, while binding to pseudomurein is achieved through the PMB domain.
Animal Models Using Lymphocytic Choriomeningitis Virus

This unit includes protocols for inducing systemic infection and persistent infection of mice with lymphocytic choriomeningitis virus (LCMV). Methods used to measure T cell responses to LCMV are then described. A protocol to assess anti‐LCMV immunity in vivo is also included. Support protocols for preparing LCMV stocks and measuring LCMV titers using a plaque assay are also included. Finally, a support protocol for detecting anti‐LCMV antibodies by ELISA is presented.
A structure preserving crossover in grammatical evolution

Grammatical evolution is an algorithm for evolving complete programs in an arbitrary language. By utilising a Backus Naur Form grammar the advantages of typing are achieved. A separation of genotype and phenotype allows the implementation of operators that manipulate (for instance by crossover and mutation) the genotype (in grammatical evolution - a sequence of bits) irrespective of the genotype to phenotype mapping (in grammatical evolution $an arbitrary grammar). This paper introduces a new type of crossover operator for grammatical evolution. The crossover operator uses information automatically extracted from the grammar to minimise any destructive impact from the crossover. The information, which is extracted at the same time as the genome is initially decoded, allows the swapping between entities of complete expansions of non-terminals in the grammar without disrupting useful blocks of code on either side of the two point crossover. In the domains tested, results confirm that the crossover is (i) more productive than hill-climbing; (ii) enables populations to continue to evolve over considerable numbers of generations without intron bloat; and (iii) allows populations (in the domains tested) to reach higher fitness levels, quicker.
A resonant switch for LNA protection in watt-level CMOS transceivers

An integrated resonant switch designed to protect low-noise amplifier (LNA) circuits in CMOS transceivers is reported. The design implements the receive-path portion of a transmit/receive switch protecting 3-V-process transistors from 5 W (22-V peak) transmit signals while simultaneously helping to achieve a good LNA noise figure on receive and low power loss on transmit. Since the approach is to combine an LNA's matching network and switch functions, the design has no traditional insertion loss on receive. The effective loss to the transmitted signal is less than 0.5 dB using moderate quality inductors (Q>6) and 0.1 dB using Q=12 inductors achievable in most RF-aware CMOS silicon-on-insulator foundries at UHF through S-band frequencies.
Optimal incentive-driven design of participatory sensing systems

Participatory sensing has emerged as a novel paradigm for data collection and collective knowledge formation about a state or condition of interest, sometimes linked to a geographic area. In this paper, we address the problem of incentive mechanism design for data contributors for participatory sensing applications. The service provider receives service queries in an area from service requesters and initiates an auction for user participation. Upon request, each user reports its perceived cost per unit of amount of participation, which essentially maps to a requested amount of compensation for participation. The participation cost quantifies the dissatisfaction caused to user due to participation. This cost is considered to be private information for each device, as it strongly depends on various factors inherent to it, such as the energy cost for sensing, data processing and transmission to the closest point of wireless access, the residual battery level, the number of concurrent jobs at the device processor, the required bandwidth to transmit data and the related charges of the mobile network operator, or even the user discomfort due to manual effort to submit data. Hence, participants have strong motive to mis-report their cost, i.e. declare a higher cost that the actual one, so as to obtain higher payment. We seek a mechanism for user participation level determination and payment allocation which is most viable for the provider, that is, it minimizes the total cost of compensating participants, while delivering a certain quality of experience to service requesters. We cast the problem in the context of optimal reverse auction design, and we show how the different quality of submitted information by participants can be tracked by the service provider and used in the participation level and payment selection procedures. We derive a mechanism that optimally solves the problem above, and at the same time it is individually rational (i.e., it motivates users to participate) and incentive-compatible (i.e. it motivates truthful cost reporting by participants). Finally, a representative participatory sensing case study involving parameter estimation is presented, which exemplifies the incentive mechanism above.
Home sweet home : bank lending characteristics and the impact of the recent global financial crisis

The objective is to study the evolution of the credit supply and its determinants after the collapse of Lehman Brothers. In this paper, we built an original handmade database of syndicated loans granted by banks located in four European countries, i.e. France, Germany, Italy and Spain, to jointly estimate the spread and the amount of each loan. Our conclusions highlight a significant impact of the subprime crisis on bank lending behaviour, and even more precisely, a flight-to-home effect for banks located in France and Germany. In addition, the results show a significant sectoral bias for banks located in Germany while banks located in France highlight a preference for portfolio diversification, granting loans with better terms to companies that belong to industries they are not used to lending to. Finally, we find that banks with strong balance sheets are more able to support the credit supply following the subprime crisis promoting the implementation of banking regulations such as Basel III.
Exploiting Headword Dependency and Predictive Clustering for Language Modeling

This paper presents several practical ways of incorporating linguistic structure into language models. A headword detector is first applied to detect the headword of each phrase in a sentence. A permuted headword trigram model (PHTM) is then generated from the annotated corpus. Finally, PHTM is extended to a cluster PHTM (C-PHTM) by defining clusters for similar words in the corpus. We evaluated the proposed models on the realistic application of Japanese Kana-Kanji conversion. Experiments show that C-PHTM achieves 15% error rate reduction over the word trigram model. This demonstrates that the use of simple methods such as the headword trigram and predictive clustering can effectively capture long distance word dependency, and substantially outperform a word trigram model.
The relationship between neuropsychological performance and depression in patients with traumatic brain injury

Traumatic brain injury is a multi-faceted condition that affects individuals on physical, cognitive, and emotional levels. The study investigated the relationship between depression and neuropsychological performance in a group with traumatic brain injury. A retrospective review was conducted on 75 participants who completed neuropsychological assessments. Information on clinical characteristics, sociodemographic information, neuropsychological outcomes, and Beck Depression Inventory scores were included in the analysis. Results indicated that 36% of the participants reported experiencing severe symptoms of depression, 28% moderate symptoms of depression, and 36% mild/minimal symptoms of depression. Performance on the Rey Auditory Verbal Learning Test indicated inverse relationships with depression scores suggesting that traumatic brain injury patients with lower depression scores perform better on verbal memory tasks. Similarly, findings for the written and oral versions of the Symbol Digit Modalities Test reflected inverse correlations with depression scores, indicating that lower depression scores are correlated with increased processing speed and capacity. A significant positive association between the time taken to complete the Trail Making Test Trail A and Trail B and depression scores was found, suggesting that higher depression scores in this sample were related to slower performance speed and lower executive performance. When specific clinical and sociodemographic variables were included as covariates in a partial correlational analysis, neuropsychological performance indicators and depression scores remained significant for Symbol Digit Modalities Test (oral and written), the Rey Auditory Verbal Learning Test Retrieval and Recognition trials, and Trail Making Test (Trail B). This study indicates that in a traumatic brain injury cohort, depression levels are significantly associated with specific neuropsychological performance measures. The findings of this study have implications for psychosocial treatment planning after a traumatic brain injury and contribute to our understandings of the inter-relationship between cognition and emotion.
Towards Learning From Stories: An Approach to Interactive Machine Learning

In this work, we introduce a technique that uses stories to train virtual agents to exhibit believable behavior. This technique uses a compact representation of a story to define the space of acceptable behaviors and then uses this space to assign rewards to certain world states. We show the effectiveness of our technique with a case study in a modified gridworld environment called Pharmacy World. The results show that a reinforcement learning agent using Q-learning was able to learn a policy that results in believable behavior.
Improving single-trial detection of event-related potentials through artificial deformed signals

To propose a reliable and robust Brain-Computer Interface (BCI), efficient machine learning and signal processing methods have to be used. However, it is often necessary to have a sufficient number of labeled brain responses to create a model. A large database that would represent all of the possible variabilities of the signal is not always possible to obtain, because calibration sessions have to be short. In the case of BCIs based on the detection of event-related potentials (ERPs), we propose to tackle this problem by including additional deformed patterns in the training database to increase the number of labeled brain responses. The creation of the additional deformed patterns is based on two approaches: (i) smooth deformation fields, and (ii) right and left shifted signals. The evaluation is performed with data from 10 healthy subjects participating in a P300 speller experiment. The results show that small shifts of the signal allow a better estimation of both spatial filters, and a linear classifier. The best performance, AUC=0.828 ± 0.061, is obtained by combining the smooth deformation fields and the shifts, after spatial filtering, compared to AUC=0.543 ± 0.025, without additional deformed patterns. The results support the conclusion that adding signals with small deformations can significantly improve the performance of single-trial detection when the amount of training data is limited.
Benchmarking the graphulo processing framework

Graph algorithms have wide applicablity to a variety of domains and are often used on massive datasets. Recent standardization efforts such as the GraphBLAS specify a set of key computational kernels that hardware and software developers can adhere to. Graphulo is a processing framework that enables GraphBLAS kernels in the Apache Accumulo database. In our previous work, we have demonstrated a core Graphulo operation called TableMult that performs large-scale multiplication operations of database tables. In this article, we present the results of scaling the Graphulo engine to larger problems and scalablity when a greater number of resources is used. Specifically, we present two experiments that demonstrate Graphulo scaling performance is linear with the number of available resources. The first experiment demonstrates cluster processing rates through Graphulo's TableMult operator on two large graphs, scaled between 217 and 219 vertices. The second experiment uses TableMult to extract a random set of rows from a large graph (219 nodes) to simulate a cued graph analytic. These benchmarking results are of relevance to Graphulo users who wish to apply Graphulo to their graph problems.
Graph-Based Lexicon Regularization for PCFG With Latent Annotations

This paper aims at learning a better probabilistic context-free grammar with latent annotations (PCFG-LA) by using a graph propagation (GP) technique. We propose leveraging the GP to regularize the lexical model of the grammar. The proposed approach constructs k-nearest neighbor ( k-NN) similarity graphs over words with identical pre-terminal (part-of-speech) tags, for propagating the probabilities of latent annotations given the words. The graphs demonstrate the relationship between words in syntactic and semantic levels, estimated by using a neural word representation method based on Recursive autoencoder (RAE). We modify the conventional PCFG-LA parameter estimation algorithm, expectation maximization (EM), by incorporating a GP process subsequent to the M-step. The GP encourages the smoothness among the graph vertices, where different words under similar syntactic and semantic environments should have approximate posterior distributions of nonterminal subcategories. The proposed PCFG-LA learning approach was evaluated together with a hierarchical split-and-merge training strategy, on parsing tasks for English, Chinese and Portuguese. The empirical results reveal two crucial findings: 1) regularizing the lexicons with GP results in positive effects to parsing accuracy; and 2) learning with unlabeled data can also expand the PCFG-LA lexicons.
Molecular Differentiation of Schistosoma japonicum and Schistosoma mekongi by Real-Time PCR with High Resolution Melting Analysis

Human schistosomiasis caused by Schistosoma japonicum and Schistosoma mekongi is a chronic and debilitating helminthic disease still prevalent in several countries of Asia. Due to morphological similarities of cercariae and eggs of these 2 species, microscopic differentiation is difficult. High resolution melting (HRM) real-time PCR is developed as an alternative tool for the detection and differentiation of these 2 species. A primer pair was designed for targeting the 18S ribosomal RNA gene to generate PCR products of 156 base pairs for both species. The melting points of S. japonicum and S. mekongi PCR products were 84.5±0.07℃ and 85.7±0.07℃, respectively. The method permits amplification from a single cercaria or an egg. The HRM real-time PCR is a rapid and simple tool for differentiation of S. japonicum and S. mekongi in the intermediate and final hosts.