An intelligent clustering scheme for distributed intrusion detection in vehicular cloud computing

In recent years, vehicular cloud computing (VCC) has emerged as a new technology which is being used in wide range of applications in the area of multimedia-based healthcare applications. In VCC, vehicles act as the intelligent machines which can be used to collect and transfer the healthcare data to the local, or global sites for storage, and computation purposes, as vehicles are having comparatively limited storage and computation power for handling the multimedia files. However, due to the dynamic changes in topology, and lack of centralized monitoring points, this information can be altered, or misused. These security breaches can result in disastrous consequences such as-loss of life or financial frauds. Therefore, to address these issues, a learning automata-assisted distributive intrusion detection system is designed based on clustering. Although there exist a number of applications where the proposed scheme can be applied but, we have taken multimedia-based healthcare application for illustration of the proposed scheme. In the proposed scheme, learning automata (LA) are assumed to be stationed on the vehicles which take clustering decisions intelligently and select one of the members of the group as a cluster-head. The cluster-heads then assist in efficient storage and dissemination of information through a cloud-based infrastructure. To secure the proposed scheme from malicious activities, standard cryptographic technique is used in which the auotmaton learns from the environment and takes adaptive decisions for identification of any malicious activity in the network. A reward and penalty is given by the stochastic environment where an automaton performs its actions so that it updates its action probability vector after getting the reinforcement signal from the environment. The proposed scheme was evaluated using extensive simulations on ns-2 with SUMO. The results obtained indicate that the proposed scheme yields an improvement of 10 % in detection rate of malicious nodes when compared with the existing schemes.

Dynamical Analysis and Visualization of Tornadoes Time Series

In this paper we analyze the behavior of tornado time-series in the U.S. from the perspective of dynamical systems. A tornado is a violently rotating column of air extending from a cumulonimbus cloud down to the ground. Such phenomena reveal features that are well described by power law functions and unveil characteristics found in systems with long range memory effects. Tornado time series are viewed as the output of a complex system and are interpreted as a manifestation of its dynamics. Tornadoes are modeled as sequences of Dirac impulses with amplitude proportional to the events size. First, a collection of time series involving 64 years is analyzed in the frequency domain by means of the Fourier transform. The amplitude spectra are approximated by power law functions and their parameters are read as an underlying signature of the system dynamics. Second, it is adopted the concept of circular time and the collective behavior of tornadoes analyzed. Clustering techniques are then adopted to identify and visualize the emerging patterns.

Modeling Vegetable Fractals by means of Fractional-order Equations

This paper characterizes four ‘fractal vegetables’: (i) cauliflower (brassica oleracea var. Botrytis); (ii) broccoli (brassica oleracea var. italica); (iii) round cabbage (brassica oleracea var. capitata) and (iv) Brussels sprout (brassica oleracea var. gemmifera), by means of electrical impedance spectroscopy and fractional calculus tools. Experimental data is approximated using fractional-order models and the corresponding parameters are determined with a genetic algorithm. The Havriliak-Negami five-parameter model fits well into the data, demonstrating that classical formulae can constitute simple and reliable models to characterize biological structures.

Benefits & drawbacks of different means of interaction for placing objects above a video footage

Public Display Systems (PDS) increasingly have a greater presence in our cities. These systems provide information and advertising specifically tailored to audiences in spaces such as airports, train stations, and shopping centers. A large number of public displays are also being deployed for entertainment reasons. Sometimes designing and prototyping PDS come to be a laborious, complex and a costly task. This dissertation focuses on the design and evaluation of PDS at early development phases with the aim of facilitating low-effort, rapid design and the evaluation of interactive PDS. This study focuses on the IPED Toolkit. This tool proposes the design, prototype, and evaluation of public display systems, replicating real-world scenes in the lab. This research aims at identifying benefits and drawbacks on the use of different means to place overlays/virtual displays above a panoramic video footage, recorded at real-world locations. The means of interaction studied in this work are on the one hand the keyboard and mouse, and on the other hand the tablet with two different techniques of use. To carry out this study, an android application has been developed whose function is to allow users to interact with the IPED Toolkit using the tablet. Additionally, the toolkit has been modified and adapted to tablets by using different web technologies. Finally the users study makes a comparison about the different means of interaction.

Creation of a web application for smart park system and parking prediction study for system integration

In the last few years, we have observed an exponential increasing of the information systems, and parking information is one more example of them. The needs of obtaining reliable and updated information of parking slots availability are very important in the goal of traffic reduction. Also parking slot prediction is a new topic that has already started to be applied. San Francisco in America and Santander in Spain are examples of such projects carried out to obtain this kind of information. The aim of this thesis is the study and evaluation of methodologies for parking slot prediction and the integration in a web application, where all kind of users will be able to know the current parking status and also future status according to parking model predictions. The source of the data is ancillary in this work but it needs to be understood anyway to understand the parking behaviour. Actually, there are many modelling techniques used for this purpose such as time series analysis, decision trees, neural networks and clustering. In this work, the author explains the best techniques at this work, analyzes the result and points out the advantages and disadvantages of each one. The model will learn the periodic and seasonal patterns of the parking status behaviour, and with this knowledge it can predict future status values given a date. The data used comes from the Smart Park Ontinyent and it is about parking occupancy status together with timestamps and it is stored in a database. After data acquisition, data analysis and pre-processing was needed for model implementations. The first test done was with the boosting ensemble classifier, employed over a set of decision trees, created with C5.0 algorithm from a set of training samples, to assign a prediction value to each object. In addition to the predictions, this work has got measurements error that indicates the reliability of the outcome predictions being correct. The second test was done using the function fitting seasonal exponential smoothing tbats model. Finally as the last test, it has been tried a model that is actually a combination of the previous two models, just to see the result of this combination. The results were quite good for all of them, having error averages of 6.2, 6.6 and 5.4 in vacancies predictions for the three models respectively. This means from a parking of 47 places a 10% average error in parking slot predictions. This result could be even better with longer data available. In order to make this kind of information visible and reachable from everyone having a device with internet connection, a web application was made for this purpose. Beside the data displaying, this application also offers different functions to improve the task of searching for parking. The new functions, apart from parking prediction, were: - Park distances from user location. It provides all the distances to user current location to the different parks in the city. - Geocoding. The service for matching a literal description or an address to a concrete location. - Geolocation. The service for positioning the user. - Parking list panel. This is not a service neither a function, is just a better visualization and better handling of the information.

Detecção de padrões de desempenho académico no ensino superior

O paradigma de avaliação do ensino superior foi alterado em 2005 para ter em conta, para além do número de entradas, o número de alunos diplomados. Esta alteração pressiona as instituições académicas a melhorar o desempenho dos alunos. Um fenómeno perceptível ao analisar esse desempenho é que a performance registada não é nem uniforme nem constante ao longo da estadia do aluno no curso. Estas variações não estão a ser consideradas no esforço de melhorar o desempenho académico e surge motivação para detectar os diferentes perfis de desempenho e utilizar esse conhecimento para melhorar a o desempenho das instituições académicas. Este documento descreve o trabalho realizado no sentido de propor uma metodologia para detectar padrões de desempenho académico, num curso do ensino superior. Como ferramenta de análise são usadas técnicas de data mining, mais precisamente algoritmos de agrupamento. O caso de estudo para este trabalho é a população estudantil da licenciatura em Eng. Informática da FCT-UNL. Propõe-se dois modelos para o aluno, que servem de base para a análise. Um modelo analisa os alunos tendo em conta a sua performance num ano lectivo e o segundo analisa os alunos tendo em conta o seu percurso académico pelo curso, desde que entrou até se diplomar, transferir ou desistir. Esta análise é realizada recorrendo aos algoritmos de agrupamento: algoritmo aglomerativo hierárquico, k-means, SOM e SNN, entre outros.

Pair trading: Clustering based on principal component analysis

This study focuses on the implementation of several pair trading strategies across three emerging markets, with the objective of comparing the results obtained from the different strategies and assessing if pair trading benefits from a more volatile environment. The results show that, indeed, there are higher potential profits arising from emerging markets. However, the higher excess return will be partially offset by higher transaction costs, which will be a determinant factor to the profitability of pair trading strategies. Also, a new clustering approach based on the Principal Component Analysis was tested as an alternative to the more standard clustering by Industry Groups. The new clustering approach delivers promising results, consistently reducing volatility to a greater extent than the Industry Group approach, with no significant harm to the excess returns.

Students' choice of a master in management in Portugal: A means-end chain approach

In the increasingly competitive market of higher education introduced by the Bologna Declaration, understanding the decision-making of master in management students is at the center of institutional management and marketing efforts on its mission to attract prospective students in a less costly, more efficient manner. The means-end chain approach, applied to the choice of a Portuguese institution in which to pursue a master in management, points to the position in rankings and to the non-specificity of the program as the most important attributes. Additionally, results show that students with distinct demographic, household, or background characteristics choose in significantly different manners.

Segmentation and Clustering of Textual Sequences: a Typological Approach

The long term goal of this research is to develop a program able to produce an automatic segmentation and categorization of textual sequences into discourse types. In this preliminary contribution, we present the construction of an algorithm which takes a segmented text as input and attempts to produce a categorization of sequences, such as narrative, argumentative, descriptive and so on. Also, this work aims at investigating a possible convergence between the typological approach developed in particular in the field of text and discourse analysis in French by Adam (2008) and Bronckart (1997) and unsupervised statistical learning.

Multifractal portrayal of the Swiss population

Fractal geometry is a fundamental approach for describing the complex irregularities of the spatial structure of point patterns. The present research characterizes the spatial structure of the Swiss population distribution in the three Swiss geographical regions (Alps, Plateau and Jura) and at the entire country level. These analyses were carried out using fractal and multifractal measures for point patterns, which enabled the estimation of the spatial degree of clustering of a distribution at different scales. The Swiss population dataset is presented on a grid of points and thus it can be modelled as a "point process" where each point is characterized by its spatial location (geometrical support) and a number of inhabitants (measured variable). The fractal characterization was performed by means of the box-counting dimension and the multifractal analysis was conducted through the Renyi's generalized dimensions and the multifractal spectrum. Results showed that the four population patterns are all multifractals and present different clustering behaviours. Applying multifractal and fractal methods at different geographical regions and at different scales allowed us to quantify and describe the dissimilarities between the four structures and their underlying processes. This paper is the first Swiss geodemographic study applying multifractal methods using high resolution data.

Mentorships and co-op placements as a means of heightening the need for a secondary school diploma to the education of at-risk students.--

Both educators and politicians appear to be quite concerned about a dropout rate in Ontario's public schools of some 30 percent. With the basic understanding that a high dropout rate is costly both in economic terms and in human terms, something quite obviously needs to be done to reduce the dropout rate in Ontario schools and, in doing so, ensuring Ontario and its graduates an active role in a growing global economy. This study is an exploratory pilot study in that it examined mentoring and the role that mentoring can play in assisting a student in staying in school and graduating from secondary school. Also incorporated in this is co-operative education and the role it can play, through mentoring, in making students aware of lifestyle level of employment, and of the skills necessary to obtain gainful, meaningful employment. In order to gain information on student attitudes, needs and expectations of a mentoring situation, a series of three questionnaires was used. Also, a questionnaire was distributed to the various co-operative education employers. The intent of this questionnaire was to probe the attitudes, needs and expectations of a mentoring situation from the perspective of an employer. The findings of this study indicated that co-operative education and mentoring are a very valuable and useful component in education. There exist certain factors in a co-operative education setting that serve to enhance and to augment the traditional or "theoretical" setting of the classroom. In addition, a mentoring situation tends to add a sense of relevance to education that students seem to require. Also, an opportunity is offered that allows a student to practice and further refine the skills that have been taught over the course of the student's academic life. Results from this study suggested that a mentoring situation, occurring through a co-operative education situation, adds relevance and a sense of "application" to the traditional or classroom schooling situation. The whole idea of mentoring bodes well for the future of education and of the student. Many advantages are identified in a mentoring situation. One of the advantages is that the schools are able to work quite closely with the community and business in order to stay current and informed on the needs and expected needs of the business community. Co-operative education has now gone beyond being an "experimental" mode of education. All students can benefit from being involved in the program. Certainly at-risk students are aided with staying in school. Those students who are said to be not at-risk can also benefit from being enrolled in the program by gaining hands-on work experience and some of the necessary skills to ensure a place in a growing world economy.

Letters from the Secretary of War to the Committee of Ways and Means, in relation to the number of Militia called into the public service in 1813, to a provision for paying the bounties and premiums to soldiers lately authorized, and to the strength of the army.--

At head of title: [78].

Letter from the Chairman of the Committee of Ways and Means to the Secretary of the Treasurer on the subject of a system of revenue to revive and maintain unimpaired the public credit, with the answer of the Secretary thereto. (1814)

October 18, 1814. Read, and committed to the Committee of the whole House on the report of the Committee of Ways and Means on so much of the President's message as relates to the finances of the United States.

Interprofessional Collaboration: The Experience of Nursing and Medical Students’ Interprofessional Education

In this hermeneutic phenomenological study, we examined the experience of interprofessional collaboration from the perspective of nursing and medical students. Seventeen medical and nursing students from two different universities participated in the study. We used guiding questions in face-to-face, conversational interviews to explore students’ experience and expectations of interprofessional collaboration within learning situations. Three themes emerged from the data: the great divide, learning means content, and breaking the ice. The findings suggest that the experience of interprofessional collaboration within learning events is influenced by the natural clustering of shared interests among students. Furthermore, the carry-forward of impressions about physician–nurse relationships prior to the educational programs and during clinical placements dominate the formation of new relationships and acquisition of new knowledge about roles, which might have implications for future practice.