This purpose of this study was to assess the relationship between the presence of classroom components of positive behavior support and student behavior outcomes. Data were collected using the Tool for Assessing Classroom Level-Positive Behavior Support (TACL-PBS) developed by Ern (2004) to assess the presence or absence of critical components of positive behavior support at the classroom level. Descriptive data on the instrument including the internal consistency, interrater agreement, and concurrent validity were analyzed and are included in this report. Forty classrooms from ten diverse elementary schools in three school districts were selected to participate in this study. Office discipline referral (ODR) information and rates of on-task behavior were collected for each classroom and were correlated with the presence of the classroom components. Data were collected using teacher interview, student interview, and direct observation methods. In all, 40 teacher intervie ws, 116 student interviews, and 39 classroom observations were conducted as part of this study and the information was used to provide evidence of the presence or absence of classroom PBS components. Given that the TACL-PBS uses three independent methods for data collection, the study was also interested in the usefulness of each method. The data revealed that the correlational structure of the instrument is strongest when scores from all three methods are combined. Results indicated low to moderate correlations between the components assessed by the TACL-PBS and student outcomes (i.e., discipline referrals, rates of on-task behavior). A significant, negative correlation was found between the consistent use of classroom management practices by teachers and ODR's. Results also indicated that as the teacher use of classroom management practices (as reported by students) increased, rates of student on-task behavior increased. The presence of preplanned and sequential procedures for respon ding to behavioral violations (i.e., consequence system) had the second highest relation to the numbers of discipline referrals in a classroom. The study also found that the fidelity with which school-wide PBS was being implemented at the building-level did not significantly relate to implementation at the classroom-level. Discussion focuses on theoretical and practical implications of the current results, limitations, and directions for future research.

The University of Arizona's Aerial Robotics Club (ARC) sponsored two senior design teams to compete in the 2011 AUVSI Student Unmanned Aerial Systems (SUAS) competition. These teams successfully design and built a UAV platform in-house that was capable of autonomous flight, capturing aerial imagery, and filtering for target recognition but required excessive computational hardware and software bugs that limited the systems capability. A new multi-discipline team of undergrads was recruited to completely redesign and optimize the system in an attempt to reach true autonomous real-time target recognition with reasonable COTS hardware.

Facial expression detection by a machine is a valuable topic for Human Computer Interaction and has been a study issue in the behavioural science for some time. Recently, significant progress has been achieved in machine analysis of facial expressions but there are still some interestes to study the area in order to extend its applications. This work investigates the theoretical concepts behind facial expressions and leads to the proposal of new algorithms in face detection and facial feature localisation, design and construction of a prototype system to test these algorithms. The overall goals and motivation of this work is to introduce vision based techniques able to detect and recognise the facial expressions. In this context, a facial expression prototype system is developed that accomplishes facial segmentation (i.e. face detection, facial features localisation), facial features extraction and features classification. To detect a face, a new simplified algorithm is developed to detect and locate its presence from the fackground by exploiting skin colour properties which are then used to distinguish between face and non-face regions. This allows facial parts to be extracted from a face using elliptical and box regions whose geometrical relationships are then utilised to determine the positions of the eyes and mouth through morphological operations. The mean and standard deviations of segmented facial parts are then computed and used as features for the face. For images belonging to the same class, thses features are applied to the K-mean algorithm to compute the controid point of each class expression. This is repeated for images in the same expression class. The Euclidean distance is computed between each feature point and its cluster centre in the same expression class. This determines how close a facial expression is to a particular class and can be used as observation vectors for a Hidden Markov Model (HMM) classifier. Thus, an HMM is built to evaluate an expression of a subject as belonging to one of the six expression classes, which are Joy, Anger, Surprise, Sadness, Fear and Disgust by an HMM using distance features. To evaluate the proposed classifier, experiments are conducted on new subjects using 100 video clips that contained a mixture of expressions. The average successful detection rate of 95.6% is measured from a total of 9142 frames contained in the video clips. The proposed prototype system processes facial features parts and presents improved results of facial expressions detection rather than using whole facial features as proposed by previous authors. This work has resulted in four contributions: the Ellipse Box Face Detection Algorithm (EBFDA), Facial Features Distance Algorithm (FFDA), Facial features extraction process, and Facial features classification. These were tested and verified using the prototype system.

Scene perception is a fundamental aspect of vision. Humans are capable of analyzing behaviorally-relevant scene properties such as spatial layouts or scene categories very quickly, even from low resolution versions of scenes. Although humans perform these tasks effortlessly, they are very challenging for machines. Developing methods that well capture the properties of the representation used by the visual system will be useful for building computational models that are more consistent with perception. While it is common to use hand-engineered features that extract information from predefined dimensions, they require careful tuning of parameters and do not generalize well to other tasks or larger datasets. This thesis is driven by the hypothesis that the perceptual representations are adapted to the statistical properties of natural visual scenes. For developing statistical features for global-scale structures (low spatial frequency information that encompasses entire scenes), I propose to train hierarchical probabilistic models on whole scene images. I first investigate statistical clusters of scene images by training a mixture model under the assumption that each image can be decoded by sparse and independent coefficients. Each cluster discovered by the unsupervised classifier is consistent with the high-level semantic categories (such as indoor, outdoor-natural and outdoor-manmade) as well as perceptual layout properties (mean depth, openness and perspective). To address the limitation of mixture models in their assumptions of a discrete number of underlying clusters, I further investigate a continuous representation for the distributions of whole scenes. The model parameters optimized for natural visual scenes reveal a compact representation that encodes their global-scale structures. I develop a probabilistic similarity measure based on the model and demonstrate its consistency with the perceptual similarities. Lastly, to learn the representations that better encode the manifold structures in general high-dimensional image space, I develop the image normalization process to find a set of canonical images that anchors the probabilistic distributions around the real data manifolds. The canonical images are employed as the centers of the conditional multivariate Gaussian distributions. This approach allows to learn more detailed structures of the local manifolds resulting in improved representation of the high level properties of scene images.

Background: Atherosclerosis is an important cause of stroke. Ultrasound offers the convenience of real-time and detailed assessment of carotid plaque features as well as arterial wall thickening and composition. Evaluation of these features is important for determining patients’ risk of suffering vascular events and also contributes to selecting the best treatment strategy. Methods: Using ultrasound data analysis we have determined plaque features in the bifurcation and internal carotid artery (ICA), including: surface plaque irregularities, calcification, echogenicity (grey scale median-GSM) and other textural plaque features (Juxtaluminal black area, entropy, coarseness). In addition, intima media thickens (IMT) and its grey scale median (IM-GSM) was measured in common carotid artery (CCA). Using Cone Beam CT (CBCT) we have quantified calcification volume of the carotid plaques extracted after carotid endarterectomy procedure. For the meta-analysis we have used comprehensive meta-analysis software version 3. Study I: We have included 39 patients and we compared carotid plaque features of the contralateral arteries with those located ipsilateral to symptomatic side and arteries of asymptomatic patients. Study II: The accuracy of US to detect atherosclerosis calcification was assessed against CBCT in 88 patients. Study III: Based on the previous vascular events in coronary, carotid and lower extremity arterial system, 87 patients were divided into three groups: asymptomatic, symptoms in one vascular system and symptoms in more that one vascular system. IMT, IM-GSM and plaque features were compared between groups. Study IV: We have meta-analyzed ten cohort prospective studies evaluating carotid plaque echogenicity for cerebrovascular symptoms prediction. Results: Study I. Plaques of the contralateral to symptomatic arteries had similar features to those in symptomatic and more vulnerable than asymptomatic arteries. Study II. Carotid ultrasound was accurate in detecting calcification volumes of ≥8mm3 with very high sensitivity but it was less accurate in detecting lower calcification volumes (<8mm3). Carotid calcification was not different between symptomatic and asymptomatic patients. Study III. Echogenicity of the intima-media complex (IM-GSM), but not its thickness (IMT), was significantly decreased with increasing number of arterial systems affected by atherosclerosis. IM-GSM was lower in patients with prior myocardial infarction and stroke. Study IV. Carotid plaque echogenicity evaluated by US could predict future cerebrovascular events in patients with asymptomatic, relative risk RR 2.72 (95% CI, 1.86 to 3.96), and recurrent symptoms in symptomatic patients, RR 2.97 (95% CI, 1.85-4.78). Conclusion: Plaques located in the contralateral to symptomatic arteries have similar features as symptomatic side and more vulnerable than asymptomatic arteries. Carotid ultrasound could accurately detect larger but not smaller carotid plaque calcification volumes (<8 mm3). Low IM- GSM could identify patients with multi-system atherosclerosis disease, suggesting a better marker for determining systemic atherosclerosis disease burden compared to conventional IMT. Finally, carotid plaque echogenicity predicts future cerebrovascular events in patients with symptomatic and asymptomatic carotid stenosis.

Ο σκοπός της συγκεκριμένης έρευνας είναι η μελέτη της ηγετικής συμπεριφοράς, οι τρόποι με τους οποίους εφαρμόζεται, καθώς επίσης και ο ρόλος της ως τροφοδότης ενός ολοκληρωμένου μοντέλου διαρκούς επιτυχίας. Συγκεκριμένα, θα παρουσιαστεί το θεωρητικό υπόβαθρο των παραπάνω εννοιών, ο ορισμός τους, τα χαρακτηριστικά τους γνωρίσματα, οι συσχετιζόμενες με αυτά έννοιες. Στη συνέχεια, θα επιχειρηθεί η περαιτέρω έρευνα για την εξακρίβωση της σημαντικότητας και του ρόλου της ηγετικής συμπεριφοράς στις φαρμακευτικές εταιρείες που δραστηριοποιούνται στην Ελλάδα. Παράλληλα, η εν λόγω εργασία δύναται να προσφέρει μία διευρυμένη προσέγγιση των παραπάνω εννοιών με τα ευρήματά της. / The aim of this research is to study the leadership behaviour, the ways that this behaviour is applied and also its role as a provider of a complete model of continuous success. Moreover, there is a further research to find out when leadership behaviour is critical in shaping the performance of pharmaceutical companies that activated in Greece.

Σήμερα οι διωκτικές αρχές χρησιμοποιούν αυτόματα βιομετρικά συστήματα αναγνώρισης τα οποία αξιοποιούν βιομετρικά χαρακτηριστικά ατόμων προκειμένου να αναγνωριστούν δράστες εγκλημάτων. Στην παρούσα εργασία έγινε προσπάθεια συσχέτισης αυτής με το αντικείμενο των εγκληματολογικών εργαστηρίων των διωκτικών αρχών. Έτσι δημιουργήθηκε βάση φωνητικών δειγμάτων και κατασκευάστηκε σύστημα αναγνώρισης ομιλητή σε περιβάλλον Matlab με στόχο την μελλοντική αύξηση της βάσης δεδομένων αλλά και την μελλοντική δυνατότητα συνδυασμού: α) εξαγομένων χαρακτηριστικών, β) μεθόδων σύγκρισης των κατανομών φωνητικών δειγμάτων και γ) μεθόδων ταξινόμησης έτσι ώστε να αυξηθεί η απόδοση και να γίνει περισσότερο αξιόπιστο το σύστημα. Το σύστημα που σχεδιάσαμε έχει τα εξής χαρακτηριστικά: α) full automatic, β) open set και γ) text dependent & text in dependent. Από κάθε φωνητικό δείγμα εξάχθηκαν οι mel frequency coefficients με την εργαλειοθήκη Auditory Toolbox, Malcolm Slaney. Η σύγκριση των χαρακτηριστικών των δειγμάτων ομιλίας υλοποιήθηκε με δυο μεθόδους σύγκρισης : Α) Μια διαδικασία που την ονομάσαμε 3Μ (minimum-mean-maximum) η οποία χρησιμοποιεί την Ευκλείδεια απόσταση για την εύρεση αποστάσεων μεταξύ σημείων των κατανομών. Β) Το Wald – Wolfowitz Test (WW-Test ), που στηρίζεται στην θεωρία των γράφων. Τέλος για την ταξινόμηση χρησιμοποιήθηκε ο K-NN ταξινομητής (K – Nearest Neighbor Classifier). Από τα εξαγόμενα αποτελέσματα των μετρήσεων καταλήξαμε στα ακόλουθα συμπεράσματα. Τα όποια σφάλματα προέκυψαν οφείλονται κυρίως στον τρόπο εξαγωγής των mfcc χαρακτηριστικών και λιγότερο στην μέθοδο ταξινόμησης και στον συγκριτή που χρησιμοποιήθηκε. Με την χρήση συνδυαστικά επιπλέον χαρακτηριστικών και ταξινομητών το σύστημα θα γίνει περισσότερο αξιόπιστο. Το σύστημα με μελλοντική αύξηση της βάσης θα μας δώσει ακόμη καλύτερα αποτελέσματα. / Today the law enforcement agencies use automatic biometric identification systems, which utilize human biometric features in order to identify criminals. This thesis was correlated with the objective of forensic laboratories. Hence, a data base of human speech samples and a speaker identification system were developed using the Matlab software. The scope was to increase, in future, the number of the data base samples and to combine features, comparison and classification methods. The system is full automatic, open set, text depended and text independent. From every speech sample, the mel frequency coefficients using the Malcolm Slaney Auditory Toolbox was extracted. The comparison of the speech samples was implemented with two methods: 3M and WW-Test which are based on the graph theory. Finally, the K-NN classifier was used for the classification of the speech samples. From the system evaluation, we conclude that the feature extraction method has the main effect on the system performance. The combination of several features, comparison and classification methods improves the reliability of the system.

We study in this thesis a particular machine learning approach to represent signals that that consists of modelling data as linear combinations of a few elements from a learned dictionary. It can be viewed as an extension of the classical wavelet framework, whose goal is to design such dictionaries (often orthonormal basis) that are adapted to natural signals. An important success of dictionary learning methods has been their ability to model natural image patches and the performance of image denoising algorithms that it has yielded. We address several open questions related to this framework: How to efficiently optimize the dictionary? How can the model be enriched by adding a structure to the dictionary? Can current image processing tools based on this method be further improved? How should one learn the dictionary when it is used for a different task than signal reconstruction? How can it be used for solving computer vision problems? We answer these questions with a multidisciplinarity approach, using tools from statistical machine learning, convex and stochastic optimization, image and signal processing, computer vision, but also optimization on graphs.

Gana dažnai nusilpusiai imuninei sistemai stabilizuoti vartojami augaliniai imunostimuliuojantys preparatai, nes jų poveikyje pasireiškia mažiau pašalinių reakcijų, jie saugesni vartojant didesnėmis dozėmis, todėl yra pranašesni už sintetinius cheminius preparatus. Herpes virusų sukeltoms ligoms gydyti vartojami ne tik cheminiai preparatai, bet ir augaliniai vaistai savo sudėtyje turintys flavonoidų ir fenolkarboksirūgščių. Šiuos junginius kaupia kanadinė jakšūnė (Desmodium canadense (L.) DC.). Darbo tikslas – ištirti ketvirtųjų – penktųjų auginimo metų kanadinės jakšūnės (Desmodium canadense (L.) DC.) biologinius savumus Lietuvos klimatinėmis sąlygomis ir nustatyti besikaupian��ių veikliųjų junginių dinamiką vegetacijos eigoje. Buvo nustatyti kanadinės jakšūnės antžeminės dalies augimo ypatumai ketvirtaisiais ir penktaisiais auginimo metais. Ištirta Desmodium canadense (L.) DC. požeminės dalies vystymosi dinamika vaizdinės analizės metodu. Nustatytas kanadinės jakšūnės sėklų daigumas pritaikant skarifikavimo ir stratifikavimo metodus. Atlikta Desmodium canadense (L.) DC. fitocheminė analizė efektyviosios skysčių chromatografijos metodu (ESC) ir nustatyti besikaupiančių veikliųjų junginių kiekiai bei jų dinamika vegetacijos eigoje. Gausiausia veikliųjų junginių susikaupia žydėjimo pradžioje. Kanadinės jakšūnės žolėje tuo laikotarpiu penktaisiais vegetacijos metais rasta 1,38  flavonoidų ir fenolkarboksirūgščių, o lapuose – 2,1  šių junginių. Pagrindiniai veiklieji... [toliau žr. visą tekstą] / Nowadays a lot of people use herbal drugs as immunity stimulating preparations, because they cause less side-effects and are safer in high doses. That’s why they are superior to chemical drugs. Plants have many useful substances – and one of them are flavonoids with other phenolic compounds. One of possible sources of flavonoids and phenolic acids – Showy tick trefoil – Desmodium canadense (L.) DC. The aim of work is to determine biological features of Showy tick trefoil of fourth and fifth vegetation year in Lithuanian climate conditions and to estimate dynamics of the active compounds in different phases of vegetation. There were determined biological features of overground part of plant of fourth and fifth vegetation year. The underground part of Desmodium canadense (L.) was examined by visual analysis method. Seeds of Showy tick trefoil were investigated using scarification and stratification methods. Phytochemical analysis was done by using High pressure liquid chromatography (HPLC). There were determined flavonoids, phenolic acids and their amounts in plant material, collected in different phases of vegetation. The most active compounds plant accumulates in the beginning of flowering. In this period in herb of Desmodium canadense (L.) was found 1,38 flavonoids and phenolic acids, while in leaves – 2,1. The most important active compounds are apigenin, luteolin and their glycosides – orientin, vitexin, isovitexin, also rutin.

Vid flertalet studier har det framkommit att ordförrådets storlek och förmågan till nonordsrepetition påvisar samband med nyordsinlärning. De prosodiska egenskapernas inverkan vid nyordsinlärning är emellertid inte lika studerad. Syftet med föreliggande studie var att undersöka hur barn mellan 4:5 och 6:0 år med typisk språkutveckling presterar på nyordsinlärning i relation till ordförråd och repetition av nonord samt att utforska vilken inverkan prosodiska egenskaper har på förmågan till nyordsinlärning. I studien deltog 15 barn vilkas resultat på nyorden, ordförrådstestningen och nonordsrepetitionen uträknades. Nyordsinlärningsuppgiften bestod av sex ord vilka sammankopplades med sex olika föremål. Nyorden matchades i par med avseende på en åtskiljande prosodisk egenskap mellan dem: antingen antal stavelser, betoning eller ordaccent. Inga signifikanta korrelationer mellan nyordsinlärning, ordförråd och nonordsrepetition kunde påvisas. Det framkom dock att korrelationen mellan ålder och nonordsrepetition var nära signifikans och indikerade att ökad ålder gav ett högre resultat på nonordsrepetitionen. Gällande de prosodiska egenskapernas relation till nyordsinlärning upptäcktes en signifikant skillnad i betoningsplacering, nyord med betoning på den finala stavelsen fick högst resultat. Barnen lärde sig också trestaviga ord i större utsträckning än tvåstaviga ord. Studien implicerar att betoning och ordlängd verkar ha viss betydelse för nyordsinlärning i kontrast till ordaccent, medan det inte kan påvisas några föreliggande korrelationer mellan nyordsinlärning, ordförråd och nonordsrepetition. / Several studies have shown that vocabulary size and nonword repetition ability correlate with novel word learning. The impact of prosodic features on novel word learning has, however, not been studied extensively. The purpose of this study was to examine how children aged 4:5–6:0 with typical language development perform on novel word learning, vocabulary and nonword repetition and to explore what impact prosodic features have on the ability to learn novel words. The study involved 15 children whose performance on the novel word learning task, vocabulary testing and nonword repetition was calculated. The novel word learning task consisted of six words which were connected to six different items. The novel words were matched in pairs differing by only one prosodic feature: either the number of syllables, stress or tonal word accent. No significant correlations between the novel word learning, vocabulary and nonword repetition were found. However, the correlation between age and nonword repetition reached near significance, which indicated that increased age gave a higher result on the nonword repetition. Regarding how the prosodic features related to the novel word learning, a significant difference between stress placements was detected. Novel words with stress on the final syllable were easier to learn. The children also achieved a higher result on the three syllable words than the two syllable words. The study implies that stress and word length seem to play a somewhat important role for novel word learning in contrast to tonal word accent, while it appears to be no relation between novel word learning, vocabulary and nonword repetition.