Classification and contextual enhancement of remotely sensed data

The aims of the project were twofold: 1) To investigate classification procedures for remotely sensed digital data, in order to develop modifications to existing algorithms and propose novel classification procedures; and 2) To investigate and develop algorithms for contextual enhancement of classified imagery in order to increase classification accuracy. The following classifiers were examined: box, decision tree, minimum distance, maximum likelihood. In addition to these the following algorithms were developed during the course of the research: deviant distance, look up table and an automated decision tree classifier using expert systems technology. Clustering techniques for unsupervised classification were also investigated. Contextual enhancements investigated were: mode filters, small area replacement and Wharton's CONAN algorithm. Additionally methods for noise and edge based declassification and contextual reclassification, non-probabilitic relaxation and relaxation based on Markov chain theory were developed. The advantages of per-field classifiers and Geographical Information Systems were investigated. The conclusions presented suggest suitable combinations of classifier and contextual enhancement, given user accuracy requirements and time constraints. These were then tested for validity using a different data set. A brief examination of the utility of the recommended contextual algorithms for reducing the effects of data noise was also carried out.

Alternative Characterization of the Class k-UCV and Related Classes of Univalent Functions

In this paper an alternative characterization of the class of functions called k -uniformly convex is found. Various relations concerning connections with other classes of univalent functions are given. Moreover a new class of univalent functions, analogous to the ’Mocanu class’ of functions, is introduced. Some results concerning this class are derived.

Intelligent Search and Automatic Document Classification and Cataloging Based on Ontology Approach

This paper presents an approach to development of intelligent search system and automatic document classification and cataloging tools for CASE-system based on metadata. The described method uses advantages of ontology approach and traditional approach based on keywords. The method has powerful intelligent means and it can be integrated with existing document search systems.

Multidimensional classification and diagnosis of leukemia in flow cytometry

This research is to establish new optimization methods for pattern recognition and classification of different white blood cells in actual patient data to enhance the process of diagnosis. Beckman-Coulter Corporation supplied flow cytometry data of numerous patients that are used as training sets to exploit the different physiological characteristics of the different samples provided. The methods of Support Vector Machines (SVM) and Artificial Neural Networks (ANN) were used as promising pattern classification techniques to identify different white blood cell samples and provide information to medical doctors in the form of diagnostic references for the specific disease states, leukemia. The obtained results prove that when a neural network classifier is well configured and trained with cross-validation, it can perform better than support vector classifiers alone for this type of data. Furthermore, a new unsupervised learning algorithm---Density based Adaptive Window Clustering algorithm (DAWC) was designed to process large volumes of data for finding location of high data cluster in real-time. It reduces the computational load to ∼O(N) number of computations, and thus making the algorithm more attractive and faster than current hierarchical algorithms.

Solving satisfiability, *implication, and equivalence problems in database systems

Satisfiability, implication and equivalence problems are important and widely-encountered database problems that need to be efficiently and effectively solved. We provide a comprehensive and systematic study of these problems. We consider three popular types of arithmetic inequalities, (X op C), (X op Y), and (X op Y + C), where X and Y are attributes, C is a constant of the domain of X, and op $\in\ \{{<},\ {\le},\ {=},\ {\not=},\ {>},\ {\ge}\}.$ These inequalities are most frequently used in a database system, since the first type of inequalities represents $\theta$-join, the second type represents selection, and the third type is popular in deductive databases. We study the problems under the integer domain and the real domain, as well as under two different operator sets.^ Our results show that solutions under different domains and/or different operator sets are quite different. In this dissertation, we either report the first necessary and sufficient conditions as well as their efficient algorithms with complexity analysis, or provide improved algorithms. ^

Plumbing and electrical classes for women

General note: Title and date provided by Bettye Lane.

Massive inclusion of digital technologies in schools : Argentinian young adolescents? appropriation of computers and the Internet in popular and middle classes

Since the implementation of the Programa Conectar Igualdad (PCI) (Connecting Equality Program) in 2010 in Argentina, numerous Social Science specialists started to research how massive ICT introduction in schools would radically affect teaching and learning processes, knowledge building and youth behaviour. Nevertheless, there is still not much empirical evidence showing the ways in which these technologies are appropriated. This situation discloses the need of placing research questions locally situated with regard to those potential changes. What existing access methods does PCI encounter? And how does its implementation participate in the design of personal and family heterogeneous trajectories of ICTs appropriation? How do the students themselves perceive the infl uence of PCI on their own technologic abilities and competence? How do knowledge and aptitudes associated to new digital media articulate with the knowledge manners promoted by the school format and institutionalism? How does the massive introduction of netbooks affect the interaction among different school actors (students-teachers)? What happens in other sociability and socialization spaces, such as the house and cybercafé?

Degree classification and recent graduates’ ability: Is there any signalling effect?

Research across several countries has shown that degree classification (i.e. the final grade awarded to students successfully completing university) is an important determinant of graduates’ first destination outcome. Graduates leaving university with higher degree classifications have better employment opportunities and a higher likelihood of continuing education relative to those with lower degree classifications. This article investigates whether one of the reasons for this result is that employers and higher education institutions use degree classification as a signalling device for the ability that recent graduates may possess. Given the large number of applicants and the amount of time and resources typically required to assess their skills, employers and higher education institutions may decide to rely on this measure when forming beliefs about recent graduates’ abilities. Using data on two cohorts of recent graduates from a UK university, results suggest that an Upper Second degree classification may have a signalling role.

N-opcode analysis for android malware classification and categorization

Malware detection is a growing problem particularly on the Android mobile platform due to its increasing popularity and accessibility to numerous third party app markets. This has also been made worse by the increasingly sophisticated detection avoidance techniques employed by emerging malware families. This calls for more effective techniques for detection and classification of Android malware. Hence, in this paper we present an n-opcode analysis based approach that utilizes machine learning to classify and categorize Android malware. This approach enables automated feature discovery that eliminates the need for applying expert or domain knowledge to define the needed features. Our experiments on 2520 samples that were performed using up to 10-gram opcode features showed that an f-measure of 98% is achievable using this approach.

Classification and clinical behavior of blastic plasmacytoid dendritic cell neoplasms according to their maturation-associated immunophenotypic profile

Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is a rare subtype of leukemia/lymphoma, whose diagnosis can be difficult to achieve due to its clinical and biological heterogeneity, as well as its overlapping features with other hematologic malignancies. In this study we investigated whether the association between the maturational stage of tumor cells and the clinico-biological and prognostic features of the disease, based on the analysis of 46 BPDCN cases classified into three maturation-associated subgroups on immunophenotypic grounds. Our results show that blasts from cases with an immature plasmacytoid dendritic cell (pDC) phenotype exhibit an uncommon CD56- phenotype, coexisting with CD34+ non-pDC tumor cells, typically in the absence of extramedullary (e.g. skin) disease at presentation. Conversely, patients with a more mature blast cell phenotype more frequently displayed skin/extramedullary involvement and spread into secondary lymphoid tissues. Despite the dismal outcome, acute lymphoblastic leukemia-type therapy (with central nervous system prophylaxis) and/or allogeneic stem cell transplantation appeared to be the only effective therapies. Overall, our findings indicate that the maturational profile of pDC blasts in BPDCN is highly heterogeneous and translates into a wide clinical spectrum -from acute leukemia to mature lymphoma-like behavior-, which may also lead to variable diagnosis and treatment.

Comparative Functional Analysis of Boundary Infrastructures, Library Classification, and Folksonomies

This paper outlines three information organization frameworks: library classification, social tagging, and boundary infrastructures. It then outlines functionality of these frameworks. The paper takes a neo-pragmatic approach. The paper finds that these frameworks are complementary, and by understanding the differences and similarities that obtain between them, researchers and developers can begin to craft a vocabulary of evaluation.

Digital 3D reconstruction as a research environment in art and architecture history: uncertainty classification and visualisation

The dissertation addresses the still not solved challenges concerned with the source-based digital 3D reconstruction, visualisation and documentation in the domain of archaeology, art and architecture history. The emerging BIM methodology and the exchange data format IFC are changing the way of collaboration, visualisation and documentation in the planning, construction and facility management process. The introduction and development of the Semantic Web (Web 3.0), spreading the idea of structured, formalised and linked data, offers semantically enriched human- and machine-readable data. In contrast to civil engineering and cultural heritage, academic object-oriented disciplines, like archaeology, art and architecture history, are acting as outside spectators. Since the 1990s, it has been argued that a 3D model is not likely to be considered a scientific reconstruction unless it is grounded on accurate documentation and visualisation. However, these standards are still missing and the validation of the outcomes is not fulfilled. Meanwhile, the digital research data remain ephemeral and continue to fill the growing digital cemeteries. This study focuses, therefore, on the evaluation of the source-based digital 3D reconstructions and, especially, on uncertainty assessment in the case of hypothetical reconstructions of destroyed or never built artefacts according to scientific principles, making the models shareable and reusable by a potentially wide audience. The work initially focuses on terminology and on the definition of a workflow especially related to the classification and visualisation of uncertainty. The workflow is then applied to specific cases of 3D models uploaded to the DFG repository of the AI Mainz. In this way, the available methods of documenting, visualising and communicating uncertainty are analysed. In the end, this process will lead to a validation or a correction of the workflow and the initial assumptions, but also (dealing with different hypotheses) to a better definition of the levels of uncertainty.

Identification of potential miRNAs related to classification and treatment response of actinic keratosis

The Workflow activity was the following: Preliminary phase: Identification of 18 Formalin-fixed paraffin embedded (FFPE) samples (9 patients) («matched» 9 AK lesions and 9 SCC lesions). Working on biopsies samples we perform an extraction and RNA analysis with droplet Digital PCR (ddPCR) and we perform the data analysis. Second and final step phase: Evaluation of additional 39 subjects (36 men and 3 women). Results: We perform an evaluation and comparison of the following miRNA: miR-320 (a miRNA involved in apoptosis and cell proliferation control; miR-204, a miRNA involved in cell proliferation in and miRNA-16-5p, a miRNA involved in apoptosis).Conclusion: Our data suggest that there is no significant variation in the expression of the three tested microRNAs between adjacent AK lesions and squamous-cell carcinoma. However, a relevant trend has been observed Furthermore, by evaluating the miRNA expression trend between keratosis and carcinoma of the same patient, it is observed that there is no "uniform trend": for some samples the expression rises for the transition from AK to SCC and viceversa.