The goal of this thesis is to develop better practical methods for detecting common object classes in real world images. We present a family of object detectors that combine Histogram of Oriented Gradient (HOG), Local Binary Pattern (LBP) and Local Ternary Pattern (LTP) features with efficient Latent SVM classifiers and effective dimensionality reduction and sparsification schemes to give state-of-the-art performance on several important datasets including PASCAL VOC2006 and VOC2007, INRIA Person and ETHZ. The three main contributions are as follows. Firstly, we pioneer the use of Local Ternary Pattern features for object detection, showing that LTP gives better overall performance than HOG and LBP, because it captures both rich local texture and object shape information while being resistant to variations in lighting conditions. It thus works well both for classes that are recognized mainly by their structure and ones that are recognized mainly by their textures. We also show that HOG, LBP and LTP complement one another, so that an extended feature set that incorporates all three of them gives further improvements in performance. Secondly, in order to tackle the speed and memory usage problems associated with high-dimensional modern feature sets, we propose two effective dimensionality reduction techniques. The first, feature projection using Partial Least Squares, allows detectors to be trained more rapidly with negligible loss of accuracy and no loss of run time speed for linear detectors. The second, feature selection using SVM weight truncation, allows active feature sets to be reduced in size by almost an order of magnitude with little or no loss, and often a small gain, in detector accuracy. Despite its simplicity, this feature selection scheme outperforms all of the other sparsity enforcing methods that we have tested. Lastly, we describe work in progress on Local Quantized Patterns (LQP), a generalized form of local pattern features that uses lookup table based vector quantization to provide local pattern style pixel neighbourhood codings that have the speed of LBP/LTP and some of the flexibility and power of traditional visual word representations. Our experiments show that LQP outperforms all of the other feature sets tested including HOG, LBP and LTP.

“Tell me and I forget, show me and I remember, involve me and I understand.” - Chinese Proverb. Involvement in learning implies possessing skills and attitudes that permit students to seek resolutions to questions and issues while constructing new knowledge. Low enrollment in Plant Biology and Diversity and upper level plant science courses has been noticed at Western Kentucky University. In addition, graduating students performed below the national average on the senior assessment examination in the area of botany content knowledge offered by WKU’s Biology Department. This may be due to the fact that observation-based botany has been taught in a traditional way for biology majors at our university for many years. Traditional teaching methods include viewing prepared slides of plant sections, viewing live and herbarium specimens, and memorization of botanical terminology and illustrations. The goal of this study is to convert these existing traditional laboratories to investigative inquiry exercises without compromising the material covered by bringing observation-based labs into the twenty-first century. Various teaching strategies including inquiry, problem-based, case-based, and hands-on learning methods were implemented. Each exercise was reshaped around a central question or theme. These changes were expected to increase student learning and retention levels. Traditional teaching methods were used with the control group, while contemporary teaching strategies were used with the experimental set of students. Traditional assessments and anonymous surveys were statistically analyzed. The results of my analyses suggest that the experimental students were more challenged, interested, intellectually stimulated and less overwhelmed with contemporary teaching strategies and overall had higher learning retention demonstrated by their performance on assessments. Moreover, I predicted that an investigative approach will encourage larger numbers of students to take this restricted elective sophomore-level course for biology majors and further their study in plant biology.

Purpose: my aim was to explore ways that teachers can support students with dyslexia and what/ which tools some teachers / special education teachers use to facilitate students. Method: I used a quantitative method by interviewing some regular teachers and special education teachers Results: In my study, I learned how some teachers can support students by reading loud to them; a main task for the teachers could be to create the love of reading for the students. That task was reinforced by students' self-image.

Στη διπλωματική αυτή παρουσιάζεται μια μεθοδολογία για την ταξινόμηση των μουσικών κομματιών και τραγουδιών ανάλογα με το μουσικό είδος που ανήκουν. Συγκεκριμένα χρησιμοποιούνται τα ίδια τα μουσικά κομμάτια ως πηγή πληροφοριών, και μέσω γνωστών μεθόδων επεξεργασίας ακουστικών σημάτων (που βασίζονται στο μετασχηματισμό Fourier) εξάγονται κατάλληλα δεδομένα. Στη συνέχεια με χρήση γνωστών μεθόδων μάθησης με επίβλεψη (δέντρα αποφάσεων, σύνολα κανόνων, μπεϊσιανή μάθηση, τεχνητά νευρωνικά δίκτυα και μηχανές διανυσμάτων υποστήριξης), γίνεται η αντίστοιχη μουσική ταξινόμηση. Στο τέλος προτείνεται μια συνδυαστική μέθοδος ταξινόμησης (συνδυασμός αλγορίθμων μάθησης) για την βελτίωση των αποτελεσμάτων όπου θα βγουν και τα τελικά συμπεράσματα. / The present work describes a methodology for the automatic recognition of music genres, based exclusively on the audio content of the signal. We use proposed techniques to extract attributes (based on Fourier Transform). Then with familiar supervised classification techniques we classify seven different music genres (rock, metal, hard rock, classical music, jazz, beat, rempetika -rempetika is a Greek music genre). At the end we propose a combined technique to improve our results. This technique is based on stacking generalization. We propose an improvement of stacking generalization.

Darbe analizuojami įvairūs metodai, tinkami mokyti(s) matematikos nuotoliniu būdu. Išnagrinėjus ŠMM dokumentus ir pedagoginę literatūrą, daroma išvada, jog nuotoliniam matematikos dalyko mokymui yra tinkamas mišrusis dalyko mokymas,kuomet tarpusavyje derinami programuotas mokymas (įgūdžių įtvirtinimui, turinio diferencijavimui) bei tradiciniai metodai (naujos medžiagos aiškinimui, konsultavimui). / In teaching mathematics it is not enough to realize the only one method. The analysis of scientific literature has opened, that for distance learning of mathematics it would be effective to realize composite method there programmed learning would be combined with traditional methods.

Remiantis nauju supratimu, mokymas(is) laikomas aktyviu procesu, kurio metu besimokantysis, remdamasis anksčiau įgytomis žiniomis ir savo unikalia patirtimi, formuoja naujas sąvokas, idėjas ar prasmes. Mokytojo vaidmuo suprantamas kaip pagalbininko, kuris turi rūpintis besimokančiojo žinių kūrimo procesu,o taip pat bendraudamas ir stebėdamas besimokančiuosius, lanksčiai ir kūrybingai įtraukti juos į mokymo(si) procesą. Šio darbo tikslas: pagrįsti aktyvaus mokymo įtaką mokinių mokymosi motyvacijai. / Nowadays theories say, that learning is an active method, where the teacher is a part of this process, like an assitent communicating with the pupils. Also nowadays theories say, that it is not enough give new information, it is important include practise. Some methods were used to evalue correlations these factors.

Sparti socialinio bei kultūrinio gyvenimo kaita, informacinės visuomenės plėtra, globalizacija kelia Lietuvos švietimui naujus iššūkius, kurie lemia jau antrą dešimtmetį vykstančius jo pokyčius. Gyvenimo pokyčiai sąlygoja ugdymo idėjų kaitą, vyksta naujų efektyvesnių mokymo ir mokymosi teorijų bei metodų paieškos. Šiandieniame švietime svarbus pedagogų papildomų kompetencijų įgijimas, todėl vis didesnis dėmesys skiriamas perkvalifikavimo studijų organizavimui. Nes nuo pedagogų kvalifikacijos bei turimos kompetencijos, priklauso švietimo kokybė ir sėkminga švietimo įstaigų veikla. Šio mokslinio darbo tikslas yra išanalizuoti pedagogų požiūrį į mokymo ir mokymosi metodų panaudojimą perkvalifikavimo studijose. Darbo uždaviniai yra panagrinėti pedagogų perkvalifikavimo sampratą ir procesą Lietuvos švietimo reformų kontekste, aptarti suaugusiųjų mokymosi organizavimo ypatumus ir ištirti pedagogų, dalyvaujančių perkvalifikavimo programose, požiūrį į mokymo ir mokymosi metodų panaudojimą perkvalifikavimo studijose. Tyrimas parodė, kad pedagogų dalyvavimą perkvalifikavimo studijose dažniausiai lemia būtinybė įgyti dalykinę kvalifikaciją bei noras tobulėti, siekis įgyti pedagoginių ir psichologinių žinių. Tačiau šių studijų paskaitose taikomi mokymo(si) metodai dažniausiai tik dalinai tenkina besimokančių poreikius. Nustatyta, kad perkvalifikavimo studijose yra naudojami įvairūs mokymo metodai. Vyraujantys du tradiciniai mokymo(si) metodai – paskaita ir aiškinimas – geriausiai... [toliau žr. visą tekstą] / The rapid alternation of social and cultural life, development of informative society, globalization challenges the education of Lithuania and condition changes of the past two decades. The changes of life determine the alteration of educational ideas, search for new and more effective theories and methods of teaching and learning. It is important in recent education for teachers to gain additional competence. Thus, greater attention is paid at the organization of retraining studies, as the quality of education and successful work of educational institutions depend on the teachers’ qualifications and competence. The aim of this research is to analyze teachers’ approach to the use of teaching and learning methods in the retraining studies. The objectives of the research are to study the conception of teachers‘ retraining and its process in the context of educational reforms in Lithuania, discuss the peculiarities of the organization of adult teaching, and survey teachers’ who participate in retraining programs approach to the use of teaching and learning methods in the retraining studies. The research clearly shows that usually teachers’ participation in the refresher course centers is due to the necessity to have the right qualification for the job and a wish for proper pedagogical and psychological knowledge. However, the teaching-learning methods used during the lectures only partly meet the requirements of the teachers. It is estimated that there are various teaching... [to full text]

In recent years, a considerable amount of work has been devoted to the task of automatic frame-semantic analysis. Given the relative maturity of syntactic parsing technology, which is an important prerequisite, frame-semantic analysis represents a realistic next step towards broad-coverage natural language understanding and has been shown to benefit a range of natural language processing applications such as information extraction and question answering. Due to the complexity which arises from variations in syntactic realization, data-driven models based on supervised learning have become the method of choice for this task. However, the reliance on large amounts of semantically labeled data which is costly to produce for every language, genre and domain, presents a major barrier to the widespread application of the supervised approach. This thesis therefore develops unsupervised machine learning methods, which automatically induce frame-semantic representations without making use of semantically labeled data. If successful, unsupervised methods would render manual data annotation unnecessary and therefore greatly benefit the applicability of automatic framesemantic analysis. We focus on the problem of semantic role induction, in which all the argument instances occurring together with a specific predicate in a corpus are grouped into clusters according to their semantic role. Our hypothesis is that semantic roles can be induced without human supervision from a corpus of syntactically parsed sentences, by leveraging the syntactic relations conveyed through parse trees with lexical-semantic information. We argue that semantic role induction can be guided by three linguistic principles. The first is the well-known constraint that semantic roles are unique within a particular frame. The second is that the arguments occurring in a specific syntactic position within a specific linking all bear the same semantic role. The third principle is that the (asymptotic) distribution over argument heads is the same for two clusters which represent the same semantic role. We consider two approaches to semantic role induction based on two fundamentally different perspectives on the problem. Firstly, we develop feature-based probabilistic latent structure models which capture the statistical relationships that hold between the semantic role and other features of an argument instance. Secondly, we conceptualize role induction as the problem of partitioning a graph whose vertices represent argument instances and whose edges express similarities between these instances. The graph thus represents all the argument instances for a particular predicate occurring in the corpus. The similarities with respect to different features are represented on different edge layers and accordingly we develop algorithms for partitioning such multi-layer graphs. We empirically validate our models and the principles they are based on and show that our graph partitioning models have several advantages over the feature-based models. In a series of experiments on both English and German the graph partitioning models outperform the feature-based models and yield significantly better scores over a strong baseline which directly identifies semantic roles with syntactic positions. In sum, we demonstrate that relatively high-quality shallow semantic representations can be induced without human supervision and foreground a promising direction of future research aimed at overcoming the problem of acquiring large amounts of lexicalsemantic knowledge.

Šiandieninėje bendrojo lavinimo mokykloje labai svarbu ugdyti sąmoningą, naujoms technologijoms atvirą, žinias su praktika gebantį sieti žmogų. Tam neabejotinai prireiks matematinių žinių ir įgūdžių. Matematika – svarbus gamtos mokslų ir technologijų įrankis. Visi mokiniai, nepriklausomai nuo jų gabumų, polinkių ar mokymosi ypatumų turėtų pajusti matematikos praktinę naudą. Mokydamasis matematikos kiekvienas mokinys turi patirti sėkmę, o pačios matematikos ugdymo turinys, jo perteikimo būdai ir tam naudojami metodai turi padėti mokiniui susikoncentruoti į mokymosi sėkmę. Taigi vienas iš svarbiausių mokytojo uždavinių yra surasti tokius mokymo metodus ir priemones, kurie būtų įdomūs, tikslingi įvairių gebėjimų bei poreikių mokiniams ir leistų visiems pasiekti gerų mokymosi rezultatų. Vienas iš tokių būdų galėtų būti nuotolinis mokymasis. Nuotolinis mokymasis – tai nuo laiko ir erdvės apribojimo, mokytoją ir mokinį, išlaisvinanti mokymosi forma. Pasitelkiant kompiuterį ir interneto ryšį, įvairių poreikių, bei skirtingų gabumų mokiniai nevaržomi vietos ir laiko gali mokytis savarankiškai – pakartoti, pagilinti, pritaikyti ar pasitikrinti savo žinias ne tik per pamokas, bet ir po pamokų. Vis dėlto, nuotolinis mokymasis daugiau įprastas suaugusiųjų mokymesi. Daugeliu atvejų mokymuisi naudojami informacijos perdavimą užtikrinantys mokymosi metodai. O matematikos dalykui įsisavinti reikia ne tik teorinių žinių, bet ir praktinių įgūdžių. Šiame darbe tiriamos nuotolinio mokymosi... [toliau žr. visą tekstą] / In today's secondary school it is very essential to develop a conscious, open to new technologies person, who will be capable to link knowledge and practices. This will undoubtedly require mathematical knowledge and skills. Mathematics - an important science and technology tool. All students, regardless of their abilities, aptitudes and learning features should feel mathematics practical benefits. Learning mathematics each student has to experience success in mathematics education and the content of the rendering techniques and the methods should help the student to concentrate on learning success. So one of the most important tasks of the teacher is to find such teaching methods and tools that are interesting, targeted to different abilities and needs of students and to enable them to attain academic success. One such way could be distance learning. Distance learning - this is the unlocking form that helps to separate the teacher and the pupil from time and space limitation. With the help of a computer and an Internet connection, students with different needs and different abilities can learn self without the restrictions to place and time - repeat, deepen, adapt, or test their knowledge not only in the classroom but also after school. However, remote learning is more common in adult learning. In most cases, the transfer of learning is used to ensure learning methods. In order to master the things of math it is essential to have not only theoretical but also practical... [to full text]

Through the development and implementation of modified learning contexts, the current study encouraged undergraduate teacher education students to modify their approaches to learning by reducing their reliance on surface approaches and progressively adopting deeper approaches. This outcome was considered desirable because students who employed deep approaches would exit the course having achieved higher quality learning than those who relied primarily on surface approaches. It was expected that higher quality learning in a preservice teacher education program would also translate into greater self-confidence in the management of teaching tasks, leading to improvements in students� teaching self-efficacy beliefs. Altered learning contexts were developed through the application of action research methodology involving core members of the teaching team. Learning activities were designed with a focus on co-operative small-group problem-based learning, which included multiple subtasks requiring variable outcome presentation modes. Linked individual reflection was encouraged by personal learning journals and learning portfolios. Students also provided critical analyses of their own learning during the completion of tasks, from both individual and group perspectives. Assessment methods included lecturer, peer and self-assessment, depending on the nature of the learning task. Often these were integrated, so that subtasks within larger ones were assessed using combinations of methods. Learning approach theorists (Biggs, 1993a, 1999; Entwistle, 1986, 1998; Prosser & Trigwell, 1999; Ramsden, 1992, 1997) contend that learning outcomes are directly related to the learning approaches used in their development. They further contend that the approach adopted is largely a result of students� intent, which in turn, is influenced by their perception of the learning context. The present study therefore aimed to develop an integrated and pervasive course-based learning context, constructively aligned (after: Biggs, 1993a, 1996), achievable within the normal constraints of a university program, that would influence students� adoption of deep learning approaches. The cognitive processes students used in response to the altered contexts were interpreted in accordance with self-regulatory internal logic (after: Bandura, 1986, 1991b; Zimmerman, 1989, 1998b). Longitudinal quasi-experimental methods with repeated measures on non-equivalent dependent variables were applied to three cohorts of students. Cohort 1 represented the contrast group who followed a traditional program. Cohort 2 was the main treatment group to whom the modified program was presented. Cohort 3 represented a comparison group that was also presented with the modified program over a shorter period. Student data on learning approach, teaching efficacy and academic attributions were gathered from repeated administrations of the Study Process Questionnaire (Biggs, 1987b), Teacher Efficacy Scale (Gibson & Dembo, 1984) and Multidimensional-Multiattributional Causality Scale (Lefcourt, 1991). In addition, reflective journals, field observations and transcripts of interviews undertaken at the beginning and conclusion of the course, were used to clarify students� approaches to learning and their responses to program modifications. Analyses of learning approaches adopted by Cohorts 1 and 2 revealed that they both began their course predominantly using surface approaches. While students in Cohort 1 completed the course with approximately equal reliance on deep and surface approaches, students in Cohort 2 reported a predominant use of deep approaches on course completion. The relative impact of the modified learning context on students with differing approaches to learning in this cohort were further explained through qualitative data and cluster analyses. The partial replication of the study with Cohort 3, across the first three semesters of their program, produced similar effects to those obtained with Cohort 2. The analyses conducted with teaching efficacy data indicated a similar pattern of development for all cohorts. Little change in either personal or general dimensions was noted in the first half of the program, followed by strong growth in both, in the latter half. While a relationship between learning approach usage and teaching efficacy was not apparent in Cohort 1, developmental path and mediation analyses indicated that the use of deep learning approaches considerably influenced the development of personal teaching efficacy in Cohort 2. The current research suggests that value lies in the construction of learning environments, in teacher education, that enhance students� adoption of deep learning approaches. The nature of the task is complex, multifaceted and context specific, most likely requiring the development of unique solutions in each environment. Nevertheless, this research demonstrates that such solutions can be developed and applied within the prevailing constraints of pre-existing course structures.