Bulgarian Folk Songs in a Digital Library

The paper presents the main results of an ongoing project aimed at the development of technologies for digitization of Bulgarian folk music and building a heterogeneous digital library with Bulgarian folk songs presented with their music, notes and text. An initial digitization and preservation of the Bulgarian cultural heritage starts by means of digitization and insertion into the library of over 1000 songs that were recorded and written down during the 60s and 70s of XX century. Also we present a full text search engine in a collection of lyrics (text of songs) and coded notes (symbolic melody). Some perspectives for future projects are also discussed.

Automatic Classification of Bulgarian Adjectives on Emotional Semantic Axes

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2014

Majestic Gamification:a case study in the adoption of a service innovation

Purpose: A case study is presented concerning a gamified awards system designed to encourage software users to explore a suite of tools, and to share their expertise level in profile pages. Majestic is a high-tech business based in the West Midlands (UK) w hich offers a Link Intelligence database using a Software as a Service (SaaS) business model. Customers leverage the database for tasks including Search Engine Optimisation (SEO) by using a suite of web-based tools. Getting to know all the tools and how they can be deployed to good effect represents a considerable learning challenge, and Majestic were aware that. Design/methodology/approach: We present the development of Majestic Awards as a case study highlighting the most important design decisions. Then we reflect on the development process as an example of innovation adoption, thereby identifying resources and cu ltura l factors which were critical in ensuring the success of the project. Findings: The gamified awards system makes learning the tools an enjoyable, explorative experience. Success factors included identifying a clear business goal, the process/ project f it, senior management buy in, and identifying the knowledge and resources to resolve t echnical issues. Originality/value: Prior to gamification of the system, only the most expert users regu larly utilized all the tools. The user base is now more knowl edgable about the system and some users choose to use the system to publicize their expertise.

Google’s Antitrust Cases Competition Analysis in New Economy Markets

Today, global economic performance largely depends on digital ecosystems. E-commerce, cloud, social media, sharing economy are the main products of the modern innovative economic systems which are constantly raising new regulatory questions. Meanwhile the United States has an unimpeachable dominance in innovation and new technologies, as well as a large and open domestic market, the EU is only recently discovering the importance of empowering the European digital economy and aims to break down its highly fragmented cross-border online economic environment. As global economy is rapidly becoming digital, Europe’s effort to create and invest in common digital market is understandable. The comprehensive investigations launched by the European Commission into the role of social network, search engine, or sharing economy internet platforms, which are new generation technologies dominated by American firms; or the recent decision of the Court of Justice of the European Union declaring that the Commission’s US Safe Harbor Decision is invalid1 might be considered as part of an anti-American protectionist policy. However, these measures could rather be seen as part of a broader trend to foster European enterprises in technology developments.

Customer relationship management: A content analysis of issues and best practices

This dissertation is a study of customer relationship management theory and practice. Customer Relationship Management (CRM) is a business strategy whereby companies build strong relationships with existing and prospective customers with the goal of increasing organizational profitability. It is also a learning process involving managing change in processes, people, and technology. CRM implementation and its ramifications are also not completely understood as evidenced by the high number of failures in CRM implementation in organizations and the resulting disappointments. ^ The goal of this dissertation is to study emerging issues and trends in CRM, including the effect of computer software and the accompanying new management processes on organizations, and the dynamics of the alignment of marketing, sales and services, and all other functions responsible for delivering customers a satisfying experience. ^ In order to understand CRM better a content analysis of more than a hundred articles and documents from academic and industry sources was undertaken using a new methodological twist to the traditional method. An Internet domain name (http://crm.fiu.edu) was created for the purpose of this research by uploading an initial one hundred plus abstracts of articles and documents onto it to form a knowledge database. Once the database was formed a search engine was developed to enable the search of abstracts using relevant CRM keywords to reveal emergent dominant CRM topics. The ultimate aim of this website is to serve as an information hub for CRM research, as well as a search engine where interested parties can enter CRM-relevant keywords or phrases to access abstracts, as well as submit abstracts to enrich the knowledge hub. ^ Research questions were investigated and answered by content analyzing the interpretation and discussion of dominant CRM topics and then amalgamating the findings. This was supported by comparisons within and across individual, paired, and sets-of-three occurrences of CRM keywords in the article abstracts. ^ Results show that there is a lack of holistic thinking and discussion of CRM in both academics and industry which is required to understand how the people, process, and technology in CRM impact each other to affect successful implementation. Industry has to get their heads around CRM and holistically understand how these important dimensions affect each other. Only then will organizational learning occur, and overtime result in superior processes leading to strong profitable customer relationships and a hard to imitate competitive advantage. ^

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.

Development of a new client -server architecture for context aware mobile computing

This dissertation studies the context-aware application with its proposed algorithms at client side. The required context-aware infrastructure is discussed in depth to illustrate that such an infrastructure collects the mobile user’s context information, registers service providers, derives mobile user’s current context, distributes user context among context-aware applications, and provides tailored services. The approach proposed tries to strike a balance between the context server and mobile devices. The context acquisition is centralized at the server to ensure the reusability of context information among mobile devices, while context reasoning remains at the application level. Hence, a centralized context acquisition and distributed context reasoning are viewed as a better solution overall. The context-aware search application is designed and implemented at the server side. A new algorithm is proposed to take into consideration the user context profiles. By promoting feedback on the dynamics of the system, any prior user selection is now saved for further analysis such that it may contribute to help the results of a subsequent search. On the basis of these developments at the server side, various solutions are consequently provided at the client side. A proxy software-based component is set up for the purpose of data collection. This research endorses the belief that the proxy at the client side should contain the context reasoning component. Implementation of such a component provides credence to this belief in that the context applications are able to derive the user context profiles. Furthermore, a context cache scheme is implemented to manage the cache on the client device in order to minimize processing requirements and other resources (bandwidth, CPU cycle, power). Java and MySQL platforms are used to implement the proposed architecture and to test scenarios derived from user’s daily activities. To meet the practical demands required of a testing environment without the impositions of a heavy cost for establishing such a comprehensive infrastructure, a software simulation using a free Yahoo search API is provided as a means to evaluate the effectiveness of the design approach in a most realistic way. The integration of Yahoo search engine into the context-aware architecture design proves how context aware application can meet user demands for tailored services and products in and around the user’s environment. The test results show that the overall design is highly effective, providing new features and enriching the mobile user’s experience through a broad scope of potential applications.

Information Discovery on Electronic Health Records Using Authority Flow Techniques

Background As the use of electronic health records (EHRs) becomes more widespread, so does the need to search and provide effective information discovery within them. Querying by keyword has emerged as one of the most effective paradigms for searching. Most work in this area is based on traditional Information Retrieval (IR) techniques, where each document is compared individually against the query. We compare the effectiveness of two fundamentally different techniques for keyword search of EHRs. Methods We built two ranking systems. The traditional BM25 system exploits the EHRs' content without regard to association among entities within. The Clinical ObjectRank (CO) system exploits the entities' associations in EHRs using an authority-flow algorithm to discover the most relevant entities. BM25 and CO were deployed on an EHR dataset of the cardiovascular division of Miami Children's Hospital. Using sequences of keywords as queries, sensitivity and specificity were measured by two physicians for a set of 11 queries related to congenital cardiac disease. Results Our pilot evaluation showed that CO outperforms BM25 in terms of sensitivity (65% vs. 38%) by 71% on average, while maintaining the specificity (64% vs. 61%). The evaluation was done by two physicians. Conclusions Authority-flow techniques can greatly improve the detection of relevant information in EHRs and hence deserve further study.

Novas abordagens para configurações automáticas dos parâmetros de controle em comitês de classificadores

Significant advances have emerged in research related to the topic of Classifier Committees. The models that receive the most attention in the literature are those of the static nature, also known as ensembles. The algorithms that are part of this class, we highlight the methods that using techniques of resampling of the training data: Bagging, Boosting and Multiboosting. The choice of the architecture and base components to be recruited is not a trivial task and has motivated new proposals in an attempt to build such models automatically, and many of them are based on optimization methods. Many of these contributions have not shown satisfactory results when applied to more complex problems with different nature. In contrast, the thesis presented here, proposes three new hybrid approaches for automatic construction for ensembles: Increment of Diversity, Adaptive-fitness Function and Meta-learning for the development of systems for automatic configuration of parameters for models of ensemble. In the first one approach, we propose a solution that combines different diversity techniques in a single conceptual framework, in attempt to achieve higher levels of diversity in ensembles, and with it, the better the performance of such systems. In the second one approach, using a genetic algorithm for automatic design of ensembles. The contribution is to combine the techniques of filter and wrapper adaptively to evolve a better distribution of the feature space to be presented for the components of ensemble. Finally, the last one approach, which proposes new techniques for recommendation of architecture and based components on ensemble, by techniques of traditional meta-learning and multi-label meta-learning. In general, the results are encouraging and corroborate with the thesis that hybrid tools are a powerful solution in building effective ensembles for pattern classification problems.

Caracterização de superfícies seletivas de frequência de periódicas e não-periódicas

The past years have seen a great interest in the use of frequency selective surfaces (FSS), as spatial filters, in many microwave applications. Among these, we highlight applications in telecommunication systems (such as satellite communications and radar), high gain antennas (combined with planar antennas) and (home and industrial) microwave ovens. The FSS is usually composed of two-dimensional periodic arrays, with equally spaced elements, which may be metallic patches (printed on dielectric substrates) or aperture (holes in thin metal surfaces). Using periodic arrays, the FSS have been able to meet the demands of the telecommunications industry. However, new demands are finding technological limitations. In this context, adverse filtering requirements have forced designers to use FSS optimization methods to find specific formats of FSS elements. Another alternative that has been used to increase the selectivity of the FSS is the cascaded FSS, a simple technique that has as main drawback the increased dimensions of the structure, as well as its weight. This work proposes the development of a new class of selective surfaces frequency (FSS) composed of quasi-periodic (or non-periodic) arrangements. The proposed FSS have no array periodicity, in relation with the spatial position of their elements. The frequency responses of these structures were simulated using commercial softwares that implement full-wave methods. For the purpose of validation of this study, FSS prototypes were built and measured, being possible to observe a good agreement between simulated and measured results. The main conclusions of this work are presented, as well as suggestions for future works.

Otimização do controle do diagrama de radiação de radares de varredura para rastreio de foguetes usando o método GAMMC para o Caso Planar (GAMMC-P)

Launching centers are designed for scientific and commercial activities with aerospace vehicles. Rockets Tracking Systems (RTS) are part of the infrastructure of these centers and they are responsible for collecting and processing the data trajectory of vehicles. Generally, Parabolic Reflector Radars (PRRs) are used in RTS. However, it is possible to use radars with antenna arrays, or Phased Arrays (PAs), so called Phased Arrays Radars (PARs). Thus, the excitation signal of each radiating element of the array can be adjusted to perform electronic control of the radiation pattern in order to improve functionality and maintenance of the system. Therefore, in the implementation and reuse projects of PARs, modeling is subject to various combinations of excitation signals, producing a complex optimization problem due to the large number of available solutions. In this case, it is possible to use offline optimization methods, such as Genetic Algorithms (GAs), to calculate the problem solutions, which are stored for online applications. Hence, the Genetic Algorithm with Maximum-Minimum Crossover (GAMMC) optimization method was used to develop the GAMMC-P algorithm that optimizes the modeling step of radiation pattern control from planar PAs. Compared with a conventional crossover GA, the GAMMC has a different approach from the conventional one, because it performs the crossover of the fittest individuals with the least fit individuals in order to enhance the genetic diversity. Thus, the GAMMC prevents premature convergence, increases population fitness and reduces the processing time. Therefore, the GAMMC-P uses a reconfigurable algorithm with multiple objectives, different coding and genetic operator MMC. The test results show that GAMMC-P reached the proposed requirements for different operating conditions of a planar RAV.

Development of a New Client-Server Architecture for Context Aware Mobile Computing

This dissertation studies the context-aware application with its proposed algorithms at client side. The required context-aware infrastructure is discussed in depth to illustrate that such an infrastructure collects the mobile user’s context information, registers service providers, derives mobile user’s current context, distributes user context among context-aware applications, and provides tailored services. The approach proposed tries to strike a balance between the context server and mobile devices. The context acquisition is centralized at the server to ensure the usability of context information among mobile devices, while context reasoning remains at the application level. Hence, a centralized context acquisition and distributed context reasoning are viewed as a better solution overall. The context-aware search application is designed and implemented at the server side. A new algorithm is proposed to take into consideration the user context profiles. By promoting feedback on the dynamics of the system, any prior user selection is now saved for further analysis such that it may contribute to help the results of a subsequent search. On the basis of these developments at the server side, various solutions are consequently provided at the client side. A proxy software-based component is set up for the purpose of data collection. This research endorses the belief that the proxy at the client side should contain the context reasoning component. Implementation of such a component provides credence to this belief in that the context applications are able to derive the user context profiles. Furthermore, a context cache scheme is implemented to manage the cache on the client device in order to minimize processing requirements and other resources (bandwidth, CPU cycle, power). Java and MySQL platforms are used to implement the proposed architecture and to test scenarios derived from user’s daily activities. To meet the practical demands required of a testing environment without the impositions of a heavy cost for establishing such a comprehensive infrastructure, a software simulation using a free Yahoo search API is provided as a means to evaluate the effectiveness of the design approach in a most realistic way. The integration of Yahoo search engine into the context-aware architecture design proves how context aware application can meet user demands for tailored services and products in and around the user’s environment. The test results show that the overall design is highly effective,providing new features and enriching the mobile user’s experience through a broad scope of potential applications.

Mapeamento das Instituições Federais de Ensino Superior da Região Nordeste do Brasil na Web

VANTI, Nadia. Mapeamento das Instituições Federais de Ensino Superior da Região Nordeste do Brasil na Web. Informação & informação, Londrina, v. 15, p. 55-67, 2010

Identification of protein targets of nevirapine reactive metabolites using click chemistry and mass spectrometry-based differential proteomics

Abstract : Adverse drug reactions (ADRs) are undesirable effects caused after administration of a single dose or prolonged administration of drug or result from the combination of two or more drugs. Idiosyncratic drug reaction (IDR) is an adverse reaction that does not occur in most patients treated with a drug and does not involve the therapeutic effect of the drug. IDRs are unpredictable and often life-threatening. Idiosyncratic reaction is dependent on drug chemical characteristics or individual immunological response. IDRs are a major problem for drug development because they are usually not detected during clinical trials. In this study we focused on IDRs of Nevirapine (NVP), which is a non-nucleoside reverse transcriptase inhibitor used for the treatment of Human Immunodeficiency Virus (HIV) infections. The use of NVP is limited by a relatively high incidence of skin rash. NVP also causes a rash in female Brown Norway (BN) rats, which we use as animal model for this study. Our hypothesis is that idiosyncratic skin reactions associated with NVP treatment are due to post-translational modifications of proteins (e.g., glutathionylation) detectable by MS. The main objective of this study was to identify the proteins that are targeted by a reactive metabolite of Nevirapine in the skin. The specific objectives derived from the general objective were as follow: 1) To implement the click chemistry approach to detect proteins modified by a reactive NVP-Alkyne (NVP-ALK) metabolite. The purpose of using NVP-ALK was to couple it with Biotin using cycloaddition Click Chemistry reaction. 2) To detect protein modification using Western blotting and Mass Spectrometry techniques, which is important to understand the mechanism of NVP induced toxicity. 3) To identify the proteins using MASCOT search engine for protein identification, by comparing obtained spectrum from Mass Spectrometry with theoretical spectrum to find a matching peptide sequence. 4) To test if the drug or drug metabolites can cause harmful effects, as the induction of oxidative stress in cells (via protein glutathionylation). Oxidative stress causes cell damage that mediates signals, which likely induces the immune response. The results showed that Nevirapine is metabolized to a reactive metabolite, which causes protein modification. The extracted protein from the treated BN rats matched 10% of keratin, which implies that keratin was the protein targeted by the NVP-ALK.

Contextual text mining

With the dramatic growth of text information, there is an increasing need for powerful text mining systems that can automatically discover useful knowledge from text. Text is generally associated with all kinds of contextual information. Those contexts can be explicit, such as the time and the location where a blog article is written, and the author(s) of a biomedical publication, or implicit, such as the positive or negative sentiment that an author had when she wrote a product review; there may also be complex context such as the social network of the authors. Many applications require analysis of topic patterns over different contexts. For instance, analysis of search logs in the context of the user can reveal how we can improve the quality of a search engine by optimizing the search results according to particular users; analysis of customer reviews in the context of positive and negative sentiments can help the user summarize public opinions about a product; analysis of blogs or scientific publications in the context of a social network can facilitate discovery of more meaningful topical communities. Since context information significantly affects the choices of topics and language made by authors, in general, it is very important to incorporate it into analyzing and mining text data. In general, modeling the context in text, discovering contextual patterns of language units and topics from text, a general task which we refer to as Contextual Text Mining, has widespread applications in text mining. In this thesis, we provide a novel and systematic study of contextual text mining, which is a new paradigm of text mining treating context information as the ``first-class citizen.'' We formally define the problem of contextual text mining and its basic tasks, and propose a general framework for contextual text mining based on generative modeling of text. This conceptual framework provides general guidance on text mining problems with context information and can be instantiated into many real tasks, including the general problem of contextual topic analysis. We formally present a functional framework for contextual topic analysis, with a general contextual topic model and its various versions, which can effectively solve the text mining problems in a lot of real world applications. We further introduce general components of contextual topic analysis, by adding priors to contextual topic models to incorporate prior knowledge, regularizing contextual topic models with dependency structure of context, and postprocessing contextual patterns to extract refined patterns. The refinements on the general contextual topic model naturally lead to a variety of probabilistic models which incorporate different types of context and various assumptions and constraints. These special versions of the contextual topic model are proved effective in a variety of real applications involving topics and explicit contexts, implicit contexts, and complex contexts. We then introduce a postprocessing procedure for contextual patterns, by generating meaningful labels for multinomial context models. This method provides a general way to interpret text mining results for real users. By applying contextual text mining in the ``context'' of other text information management tasks, including ad hoc text retrieval and web search, we further prove the effectiveness of contextual text mining techniques in a quantitative way with large scale datasets. The framework of contextual text mining not only unifies many explorations of text analysis with context information, but also opens up many new possibilities for future research directions in text mining.