Controlling Access to Personal Information

Tässä diplomityössä käsitellään henkilökohtaisen tiedon saannin kontrollointia ja tiedon kuvaamista. Työn käytännön osuudessa suunniteltiin XML –malli henkilökohtaisen tiedon kuvaamiseen. Henkilökohtaisten tietojen käyttäminen mahdollistaa henkilökohtaisen palvelun tarjoamisen ja myös palvelun automatisoinnin käyttäjälle. Henkilökohtaisen tiedon kuvaaminen on hyvin oleellista, jotta palvelut voivat kysellä ja ymmärtää tietoja. Henkilökohtaiseen tietoon vaikuttaa erilaisia tekijöitä, jotka on myös otettava huomioon tietoa kuvattaessa. Henkilökohtaisen tiedon leviäminen eri palveluiden tarjoajille tuo mukanaan myös riskejä. Henkilökohtaisen tiedon joutuminen väärän henkilön käsiin saattaa aiheuttaa vakaviakin ongelmia tiedon omistajalle. Henkilökohtaisen tiedon turvallisen ja luotettavan käytettävyyden kannalta onkin hyvin oleellista, että käyttäjällä on mahdollisuus kontrolloida kenelle hän haluaa luovuttaa mitäkin tietoa.

Wireless Authentication and Authorization, case Wireless Access Control System

Lyhyen kantaman radiotekniikoiden hyödyntäminen mahdollistaa uudenlaisten paikallisten palveluiden käytön ja vanhojen palveluiden kehittämisen. Kulunvalvonta on päivittäisenä palveluna valittu työn esimerkkisovellukseksi. Useita tunnistus- ja valtuutustapoja tutkitaan, ja julkisen avaimen infrastruktuuri on esitellään tarkemmin. Langattomat tekniikat Bluetooth, Zigbee, RFID ja IrDA esitellän yleisellä tasolla langattomat tekniikat –luvussa. Bluetooth-tekniikan rakennetta, mukaan lukien sen tietoturva-arkkitehtuuria, tutkitaan tarkemmin. Bluetooth-tekniikkaa käytetään työssä suunnitellun langattoman kulunvalvontajärjestelmän tietojen siirtoon. Kannettava päätelaite toimii käyttäjän henkilökohtaisena luotettuna laitteena, jota voi käyttää avaimena. Käyttäjän tunnistaminen ja valtuuttaminen perustuu julkisen avaimen infrastruktuuriin. Ylläpidon allekirjoittamat varmenteet sisältävät käyttäjän julkisen avaimen lisäksi tietoa hänestä ja hänen oikeuksistaan. Käyttäjän tunnistaminen kulunvalvontapisteissä tehdään julkisen ja salaisen avaimen käyttöön perustuvalla haaste-vastaus-menetelmällä. Lyhyesti, järjestelmässä käytetään Bluetooth-päätelaitteita langattomina avaimina.

Multimedia Messaging Service Center in a Component Based Architecture

Multimedia-sanomanvälityspalvelu (MMS) on matkapuhelinten väliseen viestintään kehitetty palvelu, joka mahdollistaa yhteyden Internet maailmaan. Multimedia-sanomanvälityspalvelua voidaan käyttää luomaan yhteys matkapuhelimen käyttäjän ja ulkoisen sovelluspalvelimen välille. MMS voidaan nähdä sovelluksena, joka yhdistää multimediaviestin luonnin, käsittelyn sekä toimituksen monelle eri sisältö tyypille. Multimedia-viestikeskus (MMSC) on uusi verkkoelementti, joka on vastuussa multimediaviestien varastoinnista ja toimituksesta. Multimedia-viestikeskuksella on kolme loogista elementtiä, jotka ovat välityspalvelin, sovellusrajapinnat ja matkapuhelinverkkorajapinta. Operaattorit sekä kolmannen osapuolen sovelluskehittäjät voivat kehittää lisäarvopalveluita multimedia-sanomanvälityspalvelulle hyödyntämällä sovellusrajapintoja. Sovellusrajapinnat perustuvat olemassa oleviin Internet protokolliin. Tämä diplomityö tutkii Multimedia-sanomanvälityspalvelun verkkoelementtien rajapintoja. Tulevaisuudessa on tarkoitus lisätä Multimedia-sanomanvälityspalvelun verkkoelementtejä sähköisen kaupankäynnin kehysarkkitehtuuriin, joka perustuu komponentteihin.

Advances in MRI-based computational neuroanatomy: from morphometry to in-vivo histology.

PURPOSE OF REVIEW: Current computational neuroanatomy based on MRI focuses on morphological measures of the brain. We present recent methodological developments in quantitative MRI (qMRI) that provide standardized measures of the brain, which go beyond morphology. We show how biophysical modelling of qMRI data can provide quantitative histological measures of brain tissue, leading to the emerging field of in-vivo histology using MRI (hMRI). RECENT FINDINGS: qMRI has greatly improved the sensitivity and specificity of computational neuroanatomy studies. qMRI metrics can also be used as direct indicators of the mechanisms driving observed morphological findings. For hMRI, biophysical models of the MRI signal are being developed to directly access histological information such as cortical myelination, axonal diameters or axonal g-ratio in white matter. Emerging results indicate promising prospects for the combined study of brain microstructure and function. SUMMARY: Non-invasive brain tissue characterization using qMRI or hMRI has significant implications for both research and clinics. Both approaches improve comparability across sites and time points, facilitating multicentre/longitudinal studies and standardized diagnostics. hMRI is expected to shed new light on the relationship between brain microstructure, function and behaviour, both in health and disease, and become an indispensable addition to computational neuroanatomy.

Search Interfaces on the Web: Querying and Characterizing

Current-day web search engines (e.g., Google) do not crawl and index a significant portion of theWeb and, hence, web users relying on search engines only are unable to discover and access a large amount of information from the non-indexable part of the Web. Specifically, dynamic pages generated based on parameters provided by a user via web search forms (or search interfaces) are not indexed by search engines and cannot be found in searchers’ results. Such search interfaces provide web users with an online access to myriads of databases on the Web. In order to obtain some information from a web database of interest, a user issues his/her query by specifying query terms in a search form and receives the query results, a set of dynamic pages that embed required information from a database. At the same time, issuing a query via an arbitrary search interface is an extremely complex task for any kind of automatic agents including web crawlers, which, at least up to the present day, do not even attempt to pass through web forms on a large scale. In this thesis, our primary and key object of study is a huge portion of the Web (hereafter referred as the deep Web) hidden behind web search interfaces. We concentrate on three classes of problems around the deep Web: characterization of deep Web, finding and classifying deep web resources, and querying web databases. Characterizing deep Web: Though the term deep Web was coined in 2000, which is sufficiently long ago for any web-related concept/technology, we still do not know many important characteristics of the deep Web. Another matter of concern is that surveys of the deep Web existing so far are predominantly based on study of deep web sites in English. One can then expect that findings from these surveys may be biased, especially owing to a steady increase in non-English web content. In this way, surveying of national segments of the deep Web is of interest not only to national communities but to the whole web community as well. In this thesis, we propose two new methods for estimating the main parameters of deep Web. We use the suggested methods to estimate the scale of one specific national segment of the Web and report our findings. We also build and make publicly available a dataset describing more than 200 web databases from the national segment of the Web. Finding deep web resources: The deep Web has been growing at a very fast pace. It has been estimated that there are hundred thousands of deep web sites. Due to the huge volume of information in the deep Web, there has been a significant interest to approaches that allow users and computer applications to leverage this information. Most approaches assumed that search interfaces to web databases of interest are already discovered and known to query systems. However, such assumptions do not hold true mostly because of the large scale of the deep Web – indeed, for any given domain of interest there are too many web databases with relevant content. Thus, the ability to locate search interfaces to web databases becomes a key requirement for any application accessing the deep Web. In this thesis, we describe the architecture of the I-Crawler, a system for finding and classifying search interfaces. Specifically, the I-Crawler is intentionally designed to be used in deepWeb characterization studies and for constructing directories of deep web resources. Unlike almost all other approaches to the deep Web existing so far, the I-Crawler is able to recognize and analyze JavaScript-rich and non-HTML searchable forms. Querying web databases: Retrieving information by filling out web search forms is a typical task for a web user. This is all the more so as interfaces of conventional search engines are also web forms. At present, a user needs to manually provide input values to search interfaces and then extract required data from the pages with results. The manual filling out forms is not feasible and cumbersome in cases of complex queries but such kind of queries are essential for many web searches especially in the area of e-commerce. In this way, the automation of querying and retrieving data behind search interfaces is desirable and essential for such tasks as building domain-independent deep web crawlers and automated web agents, searching for domain-specific information (vertical search engines), and for extraction and integration of information from various deep web resources. We present a data model for representing search interfaces and discuss techniques for extracting field labels, client-side scripts and structured data from HTML pages. We also describe a representation of result pages and discuss how to extract and store results of form queries. Besides, we present a user-friendly and expressive form query language that allows one to retrieve information behind search interfaces and extract useful data from the result pages based on specified conditions. We implement a prototype system for querying web databases and describe its architecture and components design.

Prototype of a web-based participative decision support platform in natural hazards and risk management

This paper presents the current state and development of a prototype web-GIS (Geographic Information System) decision support platform intended for application in natural hazards and risk management, mainly for floods and landslides. This web platform uses open-source geospatial software and technologies, particularly the Boundless (formerly OpenGeo) framework and its client side software development kit (SDK). The main purpose of the platform is to assist the experts and stakeholders in the decision-making process for evaluation and selection of different risk management strategies through an interactive participation approach, integrating web-GIS interface with decision support tool based on a compromise programming approach. The access rights and functionality of the platform are varied depending on the roles and responsibilities of stakeholders in managing the risk. The application of the prototype platform is demonstrated based on an example case study site: Malborghetto Valbruna municipality of North-Eastern Italy where flash floods and landslides are frequent with major events having occurred in 2003. The preliminary feedback collected from the stakeholders in the region is discussed to understand the perspectives of stakeholders on the proposed prototype platform.

Construction of an index of information from clinical practice in Radiology and Imaging Diagnosis based on text mining and thesaurus

Objective To construct a Portuguese language index of information on the practice of diagnostic radiology in order to improve the standardization of the medical language and terminology. Materials and Methods A total of 61,461 definitive reports were collected from the database of the Radiology Information System at Hospital das Clínicas – Faculdade de Medicina de Ribeirão Preto (RIS/HCFMRP) as follows: 30,000 chest x-ray reports; 27,000 mammography reports; and 4,461 thyroid ultrasonography reports. The text mining technique was applied for the selection of terms, and the ANSI/NISO Z39.19-2005 standard was utilized to construct the index based on a thesaurus structure. The system was created in *html. Results The text mining resulted in a set of 358,236 (n = 100%) words. Out of this total, 76,347 (n = 21%) terms were selected to form the index. Such terms refer to anatomical pathology description, imaging techniques, equipment, type of study and some other composite terms. The index system was developed with 78,538 *html web pages. Conclusion The utilization of text mining on a radiological reports database has allowed the construction of a lexical system in Portuguese language consistent with the clinical practice in Radiology.

A Hierarchical Approach for Multi-task Logistic Regression

Peer-reviewed

Collaborative group membership and access control for JXTA

Peer-reviewed

Management of Exceptions on Access Control Policies

Peer-reviewed

Fair value versus historical cost-based valuation for biological assets: Predictability of financial information

There is an intense debate on the convenience of moving from historical cost (HC) toward the fair value (FV) principle. The debate and academic research is usually concerned with financial instruments, but the IAS 41 requirement of fair valuation for biological assets brings it into the agricultural domain. This paper performs an empirical study with a sample of Spanish farms valuing biological assets at HC and a sample applying FV, finding no significant differences between both valuation methods to assess future cash flows. However, most tests reveal more predictive power of future earnings under fair valuation of biological assets, which is not explained by differences in volatility of earnings and profitability. The study also evidences the existence of flawed HC accounting practices for biological assets in agriculture, which suggests scarce information content of this valuation method in the predominant small business units existing in the agricultural sector in advanced Western countries

Learning Preferences with Kernel-Based Methods

Learning of preference relations has recently received significant attention in machine learning community. It is closely related to the classification and regression analysis and can be reduced to these tasks. However, preference learning involves prediction of ordering of the data points rather than prediction of a single numerical value as in case of regression or a class label as in case of classification. Therefore, studying preference relations within a separate framework facilitates not only better theoretical understanding of the problem, but also motivates development of the efficient algorithms for the task. Preference learning has many applications in domains such as information retrieval, bioinformatics, natural language processing, etc. For example, algorithms that learn to rank are frequently used in search engines for ordering documents retrieved by the query. Preference learning methods have been also applied to collaborative filtering problems for predicting individual customer choices from the vast amount of user generated feedback. In this thesis we propose several algorithms for learning preference relations. These algorithms stem from well founded and robust class of regularized least-squares methods and have many attractive computational properties. In order to improve the performance of our methods, we introduce several non-linear kernel functions. Thus, contribution of this thesis is twofold: kernel functions for structured data that are used to take advantage of various non-vectorial data representations and the preference learning algorithms that are suitable for different tasks, namely efficient learning of preference relations, learning with large amount of training data, and semi-supervised preference learning. Proposed kernel-based algorithms and kernels are applied to the parse ranking task in natural language processing, document ranking in information retrieval, and remote homology detection in bioinformatics domain. Training of kernel-based ranking algorithms can be infeasible when the size of the training set is large. This problem is addressed by proposing a preference learning algorithm whose computation complexity scales linearly with the number of training data points. We also introduce sparse approximation of the algorithm that can be efficiently trained with large amount of data. For situations when small amount of labeled data but a large amount of unlabeled data is available, we propose a co-regularized preference learning algorithm. To conclude, the methods presented in this thesis address not only the problem of the efficient training of the algorithms but also fast regularization parameter selection, multiple output prediction, and cross-validation. Furthermore, proposed algorithms lead to notably better performance in many preference learning tasks considered.

ContinuousMultimodal Global Optimization with Differential Evolution-Based Methods

Metaheuristic methods have become increasingly popular approaches in solving global optimization problems. From a practical viewpoint, it is often desirable to perform multimodal optimization which, enables the search of more than one optimal solution to the task at hand. Population-based metaheuristic methods offer a natural basis for multimodal optimization. The topic has received increasing interest especially in the evolutionary computation community. Several niching approaches have been suggested to allow multimodal optimization using evolutionary algorithms. Most global optimization approaches, including metaheuristics, contain global and local search phases. The requirement to locate several optima sets additional requirements for the design of algorithms to be effective in both respects in the context of multimodal optimization. In this thesis, several different multimodal optimization algorithms are studied in regard to how their implementation in the global and local search phases affect their performance in different problems. The study concentrates especially on variations of the Differential Evolution algorithm and their capabilities in multimodal optimization. To separate the global and local search search phases, three multimodal optimization algorithms are proposed, two of which hybridize the Differential Evolution with a local search method. As the theoretical background behind the operation of metaheuristics is not generally thoroughly understood, the research relies heavily on experimental studies in finding out the properties of different approaches. To achieve reliable experimental information, the experimental environment must be carefully chosen to contain appropriate and adequately varying problems. The available selection of multimodal test problems is, however, rather limited, and no general framework exists. As a part of this thesis, such a framework for generating tunable test functions for evaluating different methods of multimodal optimization experimentally is provided and used for testing the algorithms. The results demonstrate that an efficient local phase is essential for creating efficient multimodal optimization algorithms. Adding a suitable global phase has the potential to boost the performance significantly, but the weak local phase may invalidate the advantages gained from the global phase.

Machine Learning and Clinical Text. Supporting Health Information Flow

Fluent health information flow is critical for clinical decision-making. However, a considerable part of this information is free-form text and inabilities to utilize it create risks to patient safety and cost-­effective hospital administration. Methods for automated processing of clinical text are emerging. The aim in this doctoral dissertation is to study machine learning and clinical text in order to support health information flow.First, by analyzing the content of authentic patient records, the aim is to specify clinical needs in order to guide the development of machine learning applications.The contributions are a model of the ideal information flow,a model of the problems and challenges in reality, and a road map for the technology development. Second, by developing applications for practical cases,the aim is to concretize ways to support health information flow. Altogether five machine learning applications for three practical cases are described: The first two applications are binary classification and regression related to the practical case of topic labeling and relevance ranking.The third and fourth application are supervised and unsupervised multi-class classification for the practical case of topic segmentation and labeling.These four applications are tested with Finnish intensive care patient records.The fifth application is multi-label classification for the practical task of diagnosis coding. It is tested with English radiology reports.The performance of all these applications is promising. Third, the aim is to study how the quality of machine learning applications can be reliably evaluated.The associations between performance evaluation measures and methods are addressed,and a new hold-out method is introduced.This method contributes not only to processing time but also to the evaluation diversity and quality. The main conclusion is that developing machine learning applications for text requires interdisciplinary, international collaboration. Practical cases are very different, and hence the development must begin from genuine user needs and domain expertise. The technological expertise must cover linguistics,machine learning, and information systems. Finally, the methods must be evaluated both statistically and through authentic user-feedback.

Analyzing the Background Information of an Activity-Based Costing System

The aim of this thesis was to analyze the background information of an activity-based costing system, which is being used in a domestic forest industry company. The reports produced by the system have not been reliable, and this has caused the utilization of the system to diminish. The study was initiated by examining the theory of activity-based costing. It was also discovered, that the system produces management accounting information and therefore also that theory was introduced briefly. Next the possible sources of errors were examined. The significance of these errors was evaluated and waste handling was chosen as a subject of further study. The problem regarding waste handling was that there is no waste compensation in current model. When paper or board machine produces waste, it can be used as raw material in the process. However, at the moment the product, which is being produced, at the time does not get any compensation. The use of compensation has not been possible due to not knowing the quantity of process waste. As a result of the study a calculatory model, which enables calculating the quantity of process waste based on the data from the mill system, was introduced. This, for one, enables starting to use waste compensation in the future.