Using genetic programming and decision trees for generating structural descriptions of four bar mechanisms

Four bar mechanisms are basic components of many important mechanical devices. The kinematic synthesis of four bar mechanisms is a difficult design problem. A novel method that combines the genetic programming and decision tree learning methods is presented. We give a structural description for the class of mechanisms that produce desired coupler curves. Constructive induction is used to find and characterize feasible regions of the design space. Decision trees constitute the learning engine, and the new features are created by genetic programming.

Supervised learning of graph structure

Graph-based representations have been used with considerable success in computer vision in the abstraction and recognition of object shape and scene structure. Despite this, the methodology available for learning structural representations from sets of training examples is relatively limited. In this paper we take a simple yet effective Bayesian approach to attributed graph learning. We present a naïve node-observation model, where we make the important assumption that the observation of each node and each edge is independent of the others, then we propose an EM-like approach to learn a mixture of these models and a Minimum Message Length criterion for components selection. Moreover, in order to avoid the bias that could arise with a single estimation of the node correspondences, we decide to estimate the sampling probability over all the possible matches. Finally we show the utility of the proposed approach on popular computer vision tasks such as 2D and 3D shape recognition. © 2011 Springer-Verlag.

Using structural equation modeling to assess two theoretical models that explain dropout

This study evaluated the relative fit of both Finn's (1989) Participation-Identification and Wehlage, Rutter, Smith, Lesko and Fernandez's (1989) School Membership models of high school completion to a sample of 4,597 eighth graders taken from the National Educational Longitudinal Study of 1988, (NELS:88), utilizing structural equation modeling techniques. This study found support for the importance of educational engagement as a factor in understanding academic achievement. The Participation-Identification model was particularly well fitting when applied to the sample of high school completers, dropouts (both overall and White dropouts) and African-American students. This study also confirmed the contribution of school environmental factors (i.e., size, diversity of economic and ethnic status among students) and family resources (i.e., availability of learning resources in the home and parent educational level) to students' educational engagement. Based on these findings, school social workers will need to be more attentive to utilizing macro-level interventions (i.e., community organization, interagency coordination) to achieve the organizational restructuring needed to address future challenges. The support found for the Participation-Identification model supports a shift in school social workers' attention from reactive attempts to improve the affective-interpersonal lives of students to proactive attention to their academic lives. The model concentrates school social work practices on the central mission of schools, which is educational engagement. School social workers guided by this model would be encouraged to seek changes in school policies and organization that would facilitate educational engagement. ^

Structural Health Monitoring using Index Based Reasoning for Unmanned Aerial Vehicles

Unmanned Aerial Vehicles (UAVs) may develop cracks, erosion, delamination or other damages due to aging, fatigue or extreme loads. Identifying these damages is critical for the safe and reliable operation of the systems. ^ Structural Health Monitoring (SHM) is capable of determining the conditions of systems automatically and continually through processing and interpreting the data collected from a network of sensors embedded into the systems. With the desired awareness of the systems’ health conditions, SHM can greatly reduce operational cost and speed up maintenance processes. ^ The purpose of this study is to develop an effective, low-cost, flexible and fault tolerant structural health monitoring system. The proposed Index Based Reasoning (IBR) system started as a simple look-up-table based diagnostic system. Later, Fast Fourier Transformation analysis and neural network diagnosis with self-learning capabilities were added. The current version is capable of classifying different health conditions with the learned characteristic patterns, after training with the sensory data acquired from the operating system under different status. ^ The proposed IBR systems are hierarchy and distributed networks deployed into systems to monitor their health conditions. Each IBR node processes the sensory data to extract the features of the signal. Classifying tools are then used to evaluate the local conditions with health index (HI) values. The HI values will be carried to other IBR nodes in the next level of the structured network. The overall health condition of the system can be obtained by evaluating all the local health conditions. ^ The performance of IBR systems has been evaluated by both simulation and experimental studies. The IBR system has been proven successful on simulated cases of a turbojet engine, a high displacement actuator, and a quad rotor helicopter. For its application on experimental data of a four rotor helicopter, IBR also performed acceptably accurate. The proposed IBR system is a perfect fit for the low-cost UAVs to be the onboard structural health management system. It can also be a backup system for aircraft and advanced Space Utility Vehicles. ^

Undergraduate Students’ Perceptions of Study Abroad and Their Level of Achievement of Global Learning Outcomes

This study expanded on current research on study abroad and global learning, using the Global Perspective Inventory (GPI), and conducted at Florida International University (FIU) in Miami, FL. The GPI assesses the holistic development of a global perspective in higher education within three domains and their respective FIU-determined equivalents: cognitive (global awareness), intrapersonal (global perspective), and interpersonal (global engagement). The main purpose of this study was to assess FIU’s undergraduate students’ perceptions of study abroad on their level of achievement of global awareness, global perspective, and global engagement. The secondary purpose was to determine how the students described their study abroad experience and achievement of global learning. The research design for this study consisted of parallel mixed methods. The quantitative component was an ex post facto with hypothesis design, using a pretest/posttest nonequivalent group methodology. FIU undergraduates (N=147) who studied abroad for one semester or more completed the GPI pre- and post-tests. Descriptive statistics and paired t-tests were conducted to compare the means. The interviews included 10 students, and were analyzed through Structural coding, Saldaña’s In Vivo coding, and Value coding. Quantitative analyses indicated positive changes in the students’ global awareness and global perspective. These analyses also showed that the FIU students achieved higher post-test means on all the domains of the GPI compared to other studies. Qualitative analyses showed that the students’ experiences incorporated all three global learning outcomes, most notably global awareness and perspective.

Inclusive pedagogy : from learning to action. Supporting each individual in the context of 'everybody'

Peer reviewed

Inclusive pedagogy : from learning to action. Supporting each individual in the context of 'everybody'

Peer reviewed

Sequence and Structural Determinants of Specificity Differences between Paralogous Transcription Factors

Transcription factors (TFs) control the temporal and spatial expression of target genes by interacting with DNA in a sequence-specific manner. Recent advances in high throughput experiments that measure TF-DNA interactions in vitro and in vivo have facilitated the identification of DNA binding sites for thousands of TFs. However, it remains unclear how each individual TF achieves its specificity, especially in the case of paralogous TFs that recognize distinct target genomic sites despite sharing very similar DNA binding motifs. In my work, I used a combination of high throughput in vitro protein-DNA binding assays and machine-learning algorithms to characterize and model the binding specificity of 11 paralogous TFs from 4 distinct structural families. My work proves that even very closely related paralogous TFs, with indistinguishable DNA binding motifs, oftentimes exhibit differential binding specificity for their genomic target sites, especially for sites with moderate binding affinity. Importantly, the differences I identify in vitro and through computational modeling help explain, at least in part, the differential in vivo genomic targeting by paralogous TFs. Future work will focus on in vivo factors that might also be important for specificity differences between paralogous TFs, such as DNA methylation, interactions with protein cofactors, or the chromatin environment. In this larger context, my work emphasizes the importance of intrinsic DNA binding specificity in targeting of paralogous TFs to the genome.

Structural and agentic analysis of supply-chains: a social network analysis approach

This research adds to a body of work exploring the role of Social Network Analysis (SNA) in the study of both relational and structural characteristics of supply chain networks. Two contrasting network cases (food enterprises and digital-based enterprises) are chosen in order to elicit structural differences in business networks subject to divergences in local embeddedness and the relative materiality of the goods and services produced. Our analysis and findings draw out differences in network structure as evidenced by metrics of network centralization and cohesion, the presence of components and other sub-groupings, and the position of central actors. We relate these structural features both to the nature of the networks and to the (qualitative) experiences of the actors themselves. We find, in particular, the role of customers as co-creators of knowledge (for the Food network), the central role of infrastructure and services (for the Digital network), the importance of ICT as a source of codified knowledge inputs, along with the continuing importance of geographical proximity for the development and transfer of tacit knowledge and for incremental learning.

The potential contributions of concept maps for learning website to assessment for learning practices

The purpose of this paper is to examine the promising contributions of the Concept Maps for Learning (CMfL) website to assessment for learning practices. The CMfL website generates concept maps from relatedness degree of concepts pairs through the Pathfinder Scaling Algorithm. This website also confirms the established principles of effective assessment for learning, for it is capable of automatically assessing students' higher order knowledge, simultaneously identifying strengths and weaknesses, immediately providing useful feedback and being user-friendly. According to the default assessment plan, students first create concept maps on a particular subject and then they are given individualized visual feedback followed by associated instructional material (e.g., videos, website links, examples, problems, etc.) based on a comparison of their concept map and a subject matter expert's map. After studying the feedback and instructional material, teachers can monitor their students' progress by having them create revised concept maps. Therefore, we claim that the CMfL website may reduce the workload of teachers as well as provide immediate and delayed feedback on the weaknesses of students in different forms such as graphical and multimedia. For the following study, we will examine whether these promising contributions to assessment for learning are valid in a variety of subjects.

Convex Optimization Algorithms and Statistical Bounds for Learning Structured Models

Thesis (Ph.D.)--University of Washington, 2016-08

Adapting prospective structural analysis to strengthen sustainable management and capacity building in community-based natural resource management contexts

Local communities collectively managing common pool resources can play an important role in sustainable management, but they often lack the skills and context-specific tools required for such management. The complex dynamics of social-ecological systems (SES), the need for management capacities, and communities’ limited empowerment and participation skills present challenges for community-based natural resource management (CBNRM) strategies. We analyzed the applicability of prospective structural analysis (PSA), a strategic foresight tool, to support decision making and to foster sustainable management and capacity building in CBNRM contexts and the modifications necessary to use the tool in such contexts. By testing PSA in three SES in Colombia, Mexico, and Argentina, we gathered information regarding the potential of this tool and its adaptation requirements. The results suggest that the tool can be adapted to these contexts and contribute to fostering sustainable management and capacity building. It helped identify the systems’ dynamics, thus increasing the communities’ knowledge about their SES and informing the decision-making process. Additionally, it drove a learning process that both fostered empowerment and built participation skills. The process demanded both time and effort, and required external monitoring and facilitation, but community members could be trained to master it. Thus, we suggest that the PSA technique has the potential to strengthen CBNRM and that other initiatives could use it, but they must be aware of these requirements.

A satisfação e a intenção de continuidade de uso em servços de e-learning:validação empírica de um modelo aplicado no serviço público

The present study aims to investigate the constructs of Technological Readiness Index (TRI) and the Expectancy Disconfirmation Theory (EDT) as determinants of satisfaction and continuance intention use in e-learning services. Is proposed a theoretical model that seeks to measure the phenomenon suited to the needs of public organizations that offer distance learning course with the use of virtual platforms for employees. The research was conducted from a quantitative analytical approach, via online survey in a sample of 343 employees of 2 public organizations in RN who have had e-learning experience. The strategy of data analysis used multivariate analysis techniques, including structural equation modeling (SEM), operationalized by AMOS© software. The results showed that quality, quality disconfirmation, value and value disconfirmation positively impact on satisfaction, as well as disconfirmation usability, innovativeness and optimism. Likewise, satisfaction proved to be decisive for the purpose of continuance intention use. In addition, technological readiness and performance are strongly related. Based on the structural model found by the study, public organizations can implement e-learning services for employees focusing on improving learning and improving skills practiced in the organizational environment

Structure learning of context-specific graphical models

Abstract The ultimate problem considered in this thesis is modeling a high-dimensional joint distribution over a set of discrete variables. For this purpose, we consider classes of context-specific graphical models and the main emphasis is on learning the structure of such models from data. Traditional graphical models compactly represent a joint distribution through a factorization justi ed by statements of conditional independence which are encoded by a graph structure. Context-speci c independence is a natural generalization of conditional independence that only holds in a certain context, speci ed by the conditioning variables. We introduce context-speci c generalizations of both Bayesian networks and Markov networks by including statements of context-specific independence which can be encoded as a part of the model structures. For the purpose of learning context-speci c model structures from data, we derive score functions, based on results from Bayesian statistics, by which the plausibility of a structure is assessed. To identify high-scoring structures, we construct stochastic and deterministic search algorithms designed to exploit the structural decomposition of our score functions. Numerical experiments on synthetic and real-world data show that the increased exibility of context-specific structures can more accurately emulate the dependence structure among the variables and thereby improve the predictive accuracy of the models.

To Integrate Or Not: A Comparison of the English Skills of Finnish Students in Mainstream Education and Content and Language Integrated Learning

The objective of the thesis was to study the possible linguistic differences of English of Finnish mainstream students and Finnish students following content and language integrated learning (CLIL), in terms of the given language test. The difference of test results between the test groups was further analyzed in more detail. The research was carried out by comparing the 9th grade students of the Finnish comprehensive school (the mainstream group) and CLIL students of the 9th grade of the Finnish comprehensive school (the CLIL group). The comparison was based on the national language test for the 9th grade students of the Finnish comprehensive school 2006 (A-English), produced by Sukol-Palvelu, owned by the Federation of Foreign Language Teachers in Finland SUKOL. The mainstream group of the present study consisted of 30 students, whereas the CLIL group included 27 students. Testing was carried out in spring 2007. The test results of the mainstream group (average of 64.1% out of the maximum score) were consistent with the results of the national average (63.9%). The average score of the CLIL students for the present study was 83.3% out of the maximum score. The results of the two groups in question were rather similar in the tasks measuring the skill of listening comprehension, in addition to one of the reading comprehension tasks. Moreover, a particular task with requirements of cultural and reactional skills produced results rather similar between the test groups. The differences between the results of the mainstream group and the CLIL group were most evident in three particular tasks. In general, the CLIL group performed clearly better than the mainstream group in the task measuring the knowledge of the polite conversational manners of the English-speaking world and in the tasks with requirements of lexical and structural knowledge of English. However, the writing task resulted in the most evident difference of results between the groups. In other words, the CLIL students of the present study were clearly more capable of producing English language with more varied vocabulary and more complex structures than the mainstream students. Thus, it might be argued whether the CLIL programme is to enhance the students´ performance in the productive skill of writing in particular. As a result, it might be useful to consider the possibilities of the CLIL programme in developing certain linguistic skills of the mainstream students of English as well.