To overcome constrained resources, firms can actively seek innovative opportunities from the external world. This innovation approach, called open innovation (Chesbrough 2003; Hippel 2005; Terwiesch and Ulrich 2009; Terwiesch and Xu 2008), is receiving more and more attention. Facilitated by the global Internet and emerging forms of information technology, it has become very easy for companies to generate large numbers of innovative solutions through the use of online open innovation contests or crowdsourcing contests (Archak and Sundararajan 2009; Terwiesch and Ulrich 2009; Terwiesch and Xu 2008; Yang et al. 2009).For an innovation project to succeed, it is necessary to generate not only a large number of good ideas or solutions, but also to identify those that are "exceptional" (Terwiesch and Ulrich 2009). This dissertation contains three studies that aim to improve our understanding of how best to use contests as a tool to aggregate external resources (collective intelligence) in the generation and evaluation of solutions. The first study views an innovation contest from the innovation seeker's perspective and provides insights on how to improve contest performance. The second study views an innovation contest from the innovation solver's perspective examining the characteristics and strategies of winners and solvers. Finally, in the third study, a new approach to the solution evaluation process is introduced, which is referred to as open evaluation. In this approach, a prediction market is used as an aggregation mechanism to coordinate the crowd in the evaluation of proposed solutions. These three studies make a number of contributions to the literature, addressing core issues in the area of online innovation contests. The analyses, which leverage large-scale empirical data, produce a number of profound results, which can help people to understand how best to use and design innovation contests in an online environment, for idea generation. Further, these studies present a variety of managerial implications associated with the aggregation of individual effort (collective intelligence) to evaluate the ideas that are generated by an innovation contest. We hope that our studies can help open innovation pioneers, such as Google, to systematically generate and identify exceptionally good ideas at much lower costs. By utilizing our findings, we expect that more firms will be able to adopt an open innovation strategy, both systematically and easily. / Business Administration/Management Information Systems

In supervised learning, it is usually assumed that true labels are readily available from a single annotator or source. However, recent advances in corroborative technology have given rise to situations where the true label of the target is unknown. In such problems, multiple sources or annotators are often available that provide noisy labels of the targets. In these multi-annotator problems, building a classifier in the traditional single-annotator manner, without regard for the annotator properties may not be effective in general. In recent years, how to make the best use of the labeling information provided by multiple annotators to approximate the hidden true concept has drawn the attention of researchers in machine learning and data mining. In our previous work, a probabilistic method (i.e., MAP-ML algorithm) of iteratively evaluating the different annotators and giving an estimate of the hidden true labels is developed. However, the method assumes the error rate of each annotator is consistent across all the input data. This is an impractical assumption in many cases since annotator knowledge can fluctuate considerably depending on the groups of input instances. In this dissertation, one of our proposed methods, GMM-MAPML algorithm, follows MAP-ML but relaxes the data-independent assumption, i.e., we assume an annotator may not be consistently accurate across the entire feature space. GMM-MAPML uses a Gaussian mixture model (GMM) and Bayesian information criterion (BIC) to find the fittest model to approximate the distribution of the instances. Then the maximum a posterior (MAP) estimation of the hidden true labels and the maximum-likelihood (ML) estimation of quality of multiple annotators at each Gaussian component are provided alternately. Recent studies show that it is not the case that employing more annotators regardless of their expertise will result in improved highest aggregating performance. In this dissertation, we also propose a novel algorithm to integrate multiple annotators by Aggregating Experts and Filtering Novices, which we call AEFN. AEFN iteratively evaluates annotators, filters the low-quality annotators, and re-estimates the labels based only on information obtained from the good annotators. The noisy annotations we integrate are from any combination of human and previously existing machine-based classifiers, and thus AEFN can be applied to many real-world problems. Emotional speech classification, CASP9 protein disorder prediction, and biomedical text annotation experiments show a significant performance improvement of the proposed methods (i.e., GMM-MAPML and AEFN) as compared to the majority voting baseline and the previous data-independent MAP-ML method. Recent experiments include predicting novel drug indications (i.e., drug repositioning) for both approved drugs and new molecules by integrating multiple chemical, biological or phenotypic data sources. / Computer and Information Science

Bacterial taxonomy is the science of classifying, naming, and identifying bacteria. The scope and practice of taxonomy has evolved through history with our understanding of life and our growing and changing needs in research, medicine, and industry. As in animal and plant taxonomy, the species is the fundamental unit of taxonomy, but the genetic and phenotypic diversity that exists within a single bacterial species is substantially higher compared to animal or plant species. Therefore, the current "type"-centered classification scheme that describes a species based on a single type strain is not sufficient to classify bacterial diversity, in particular in regard to human, animal, and plant pathogens, for which it is necessary to trace disease outbreaks back to their source. Here we discuss the current needs and limitations of classic bacterial taxonomy and introduce LINbase, a Web service that not only implements current species-based bacterial taxonomy but complements its limitations by providing a new framework for genome sequence-based classification and identification independently of the type-centric species. LINbase uses a sequence similarity-based framework to cluster bacteria into hierarchical taxa, which we call LINgroups, at multiple levels of relatedness and crowdsources users' expertise by encouraging them to circumscribe these groups as taxa from the genus-level to the intraspecies-level. Circumscribing a group of bacteria as a LINgroup, adding a phenotypic description, and giving the LINgroup a name using the LINbase Web interface allows users to instantly share new taxa and complements the lengthy and laborious process of publishing a named species. Furthermore, unknown isolates can be identified immediately as members of a newly described LINgroup with fast and precise algorithms based on their genome sequences, allowing species- and intraspecies-level identification. The employed algorithms are based on a combination of the alignment-based algorithm BLASTN and the alignment-free method Sourmash, which is based on k-mers, and the MinHash algorithm. The potential of LINbase is shown by using examples of plant pathogenic bacteria. / Doctor of Philosophy / Life is always easier when people talk to each other in the same language. Taxonomy is the language that biologists use to communicate about life by 1. classifying organisms into groups, 2. giving names to these groups, and 3. identifying individuals as members of these named groups. When most scientists and the general public think of taxonomy, they think of the hierarchical structure of “Life”, “Domain”, “Kingdom”, “Phylum”, “Class”, “Order”, “Family”, “Genus” and “Species”. However, the basic goal of taxonomy is to allow the identification of an organism as a member of a group that is predictive of its characteristics and to provide a name to communicate about that group with other scientists and the public. In the world of micro-organism, taxonomy is extremely important since there are an estimated 10,000,000 to 1,000,000,000 different bacteria species. Moreover, microbiologists and pathologists need to consider differences among bacterial isolates even within the same species, a level, that the current taxonomic system does not even cover. Therefore, we developed a Web service, LINbase, which uses genome sequences to classify individual microbial isolates. The database at the backend of LINbase assigns Life Identification Numbers (LINs) that express how individual microbial isolates are related to each other above, at, and below the species level. The LINbase Web service is designed to be an interactive web-based encyclopedia of microorganisms where users can share everything they know about micro-organisms, be it individual isolates or groups of isolates, for professional and scientific purposes. To develop LINbase, efficient computer programs were developed and implemented. To show how LINbase can be used, several groups of bacteria that cause plant diseases were classified and described.

No description available.

隨著科技日新月異的發展，智慧型手機本身通訊與運算能力也隨著軟體和硬體的改善而不斷地增強，其便利性與高機動性的特色使得越來越多人持有智慧型手機，最後成為人們生活中不可或缺的部份。總觀來說，持有與使用率的上升，不知不覺的形成一種共享經濟與無所不在的行動運算網絡。 基於普及性與相對優秀的運算效能，我們設計與實作出Gnafuy，一個基於行動裝置下的分散式運算框架，希望借用世界上所有閒置行動運算裝置的資源來實行無所不在的運算。 我們發展出一套應用程式介面(API)供開發者依照自己的需求來撰寫自己的分散式運算程式，藉由遵循Gnafuy所制定的應用程式介面，開發者可只專注在演算法本身的開發，而不需要在意其演算法如何被分配到手機上以及待處理資料的分配情形。本篇文章還討論了Gnafuy所採用的分散式運算的程式模型，以及我們如何藉由一個手機應用程式將任務部署至自願者的智慧型手機中，我們發展出一套伺服器端的機制來增加訊息傳遞的成功率，以及偵測計算後回傳結果是否正確，排除被惡意程式污染的客戶端結果。

This paper analyzes and critically evaluates present music and video market functioning with emphasis to the sales of electronic format of this content which is preferred by current customers. It analyzes the widespread trend of content sharing via the Internet and specifies reasons why the trend is happening. It defines cases of behavior that are justifiable in the eyes of consumers and therefore legitimate (i.e. creation of backups, downloading and sharing legally unavailable content, downloading for personal use, etc.) and elements that reduce value of the legally distributed content for the customer (copy protection mechanisms or current licensing system). Furthermore, it analyzes new economic approaches that enable the utilization potential of the Internet (i.e. the Long Tail Theory) or of potential customers (Crowdsourcing, Word of Mouth, Gamification). The paper analyzes the functioning new services that use the Internet as a platform for their operation and offer users the experience they want. Proposal for this new system of audiovisual content distribution is proposed based on a synthesis of findings from the operation of these services, new theoretical approaches and author's own contribution. This new system rejects the idea of selling the content as goods (per items) and adopts an approach of access to the whole catalogue of content for a flat monthly fee. This newly born service has several tiers that differ qualitatively among themselves, also in terms of added value for users and finally in the price at which the layers are available. This system is very acceptable to customers as well as being an interesting long-term alternative to the current system for operators of the new system and copyright holders.

"A common problem shared amongst online tutoring systems is the time-consuming nature of content creation. It has been estimated that an hour of online instruction can take up to 100-300 hours to create. Several systems have created tools to expedite content creation, such as the Cognitive Tutors Authoring Tool (CTAT) and the ASSISTments builder. Although these tools make content creation more efficient, they all still depend on the efforts of a content creator and/or past historical. These tools do not take full advantage of the power of the crowd. These issues and challenges faced by online tutoring systems provide an ideal environment to implement a solution using crowdsourcing. I created the PeerASSIST system to provide a solution to the challenges faced with tutoring content creation. PeerASSIST crowdsources the work students have done on problems inside the ASSISTments online tutoring system and redistributes that work as a form of tutoring to their peers, who are in need of assistance. Multi-objective multi-armed bandit algorithms are used to distribute student work, which balance exploring which work is good and exploiting the best currently known work. These policies are customized to run in a real-world environment with multiple asynchronous reward functions and an infinite number of actions. Inspired by major companies such as Google, Facebook, and Bing, PeerASSIST is also designed as a platform for simultaneous online experimentation in real-time and at scale. Currently over 600 teachers (grades K-12) are requiring students to show their work. Over 300,000 instances of student work have been collected from over 18,000 students across 28,000 problems. From the student work collected, 2,000 instances have been redistributed to over 550 students who needed help over the past few months. I conducted a randomized controlled experiment to evaluate the effectiveness of PeerASSIST on student performance. Other contributions include representing learning maps as Bayesian networks to model student performance, creating a machine-learning algorithm to derive student incorrect processes from their incorrect answer and the inputs of the problem, and applying Bayesian hypothesis testing to A/B experiments. We showed that learning maps can be simplified without practical loss of accuracy and that time series data is necessary to simplify learning maps if the static data is highly correlated. I also created several interventions to evaluate the effectiveness of the buggy messages generated from the machine-learned incorrect processes. The null results of these experiments demonstrate the difficulty of creating a successful tutoring and suggest that other methods of tutoring content creation (i.e. PeerASSIST) should be explored."

Submitted by Mariana Dornelles Vargas (marianadv) on 2015-05-25T17:54:35Z No. of bitstreams: 1 wedode.pdf: 3627779 bytes, checksum: 55a6860ad06d61a754ba43bf34960367 (MD5) / Made available in DSpace on 2015-05-25T17:54:35Z (GMT). No. of bitstreams: 1 wedode.pdf: 3627779 bytes, checksum: 55a6860ad06d61a754ba43bf34960367 (MD5) Previous issue date: 2012 / Nenhuma / A presente pesquisa tem como proposta a estruturação de metodologia de projeto chamada de Open Strategic Design (OSD) que teve por base o acervo de conhecimento produzido pelo design estratégico, e por motivação a necessidade de que essa metodologia estivesse alinhada ao contexto social contemporâneo, marcado pela lógica de consumo, pela interação hiperconectada e pela natureza simbólica das representações. Compreende-se o design como retórica que organiza os elementos de contexto (cultura de consumo e mediação tecnológica) e de linguagem (projeto, inovação aberta e design estratégico), e que, como tal, pode formular percursos estratégicos diferenciados. Daí resultou a construção de uma plataforma digital de projeto aberto e coletivo, o wedode.com, base da realização da experiência de aplicação e teste da proposta de OSD. A coletividade que participou da experiência produziu material que possibilitou a crítica da metodologia de projeto e insumos para alteração na plataforma usada. / The present research has as purpose the structuring of design methodology called Open Strategic Design (OSD) that was based on the collection of knowledge produced by the estrategic design, and motivated by the need for this methodology to be aligned with the contemporaneous social context, marked by the logic of consumption, hyper connected interaction and the nature of symbolic representations. We understand design as rhetoric that organizes the context elements (consumer culture and technological mediation) and language (project, open innovation and strategic design), and, as such, may make differentiated strategic paths. This led to the construction of a digital platform design open and collective, wedode.com, foundation of the application experience and testing of the proposed OSD. The group that participated in the experiment produced material that allowed criticism of the project methodology and insights to be used to change the platform.

Submitted by PPG Ci?ncia da Computa??o (ppgcc@pucrs.br) on 2018-10-09T13:24:12Z No. of bitstreams: 1 LUIS FERNANDES VAZ.DIS.pdf: 17076970 bytes, checksum: 6f8adcfdc62d9c6204d43c0aaaace7e5 (MD5) / Approved for entry into archive by Caroline Xavier (caroline.xavier@pucrs.br) on 2018-10-09T17:12:07Z (GMT) No. of bitstreams: 1 LUIS FERNANDES VAZ.DIS.pdf: 17076970 bytes, checksum: 6f8adcfdc62d9c6204d43c0aaaace7e5 (MD5) / Made available in DSpace on 2018-10-09T17:16:23Z (GMT). No. of bitstreams: 1 LUIS FERNANDES VAZ.DIS.pdf: 17076970 bytes, checksum: 6f8adcfdc62d9c6204d43c0aaaace7e5 (MD5) Previous issue date: 2018-03-27 / This research aimed to investigate task documentation in Software Crowdsourcing, more specifically, in the TopCoder platform. It also aimed to identify the elements that should be considered in the documentation of a task in this kind of software development. This research is of importance when considering that a Task is the component that links the other components of the software crowdsourcing model, which are: the Buyer, the Platform, and the Crowd. It is the task that expresses the Buyer?s need to the crowd members. We followed a qualitative research approach and conducted a Case Study with newcomers in Software Crowdsourcing and a Field Study with industry professionals. Data was analyzed using the Content Analysis technique. We found that, for the Case Study novices, the documentation of the task had a secondary role in the task selection. However, the need of a clear documentation become more relevant during the development of the task given that this is the moment that the instructions within the documentation need to be decoded by the developer and turned into a solution to be later submitted to the platform. For the Field Study participants, the most relevant elements related to the documentation of a task were how clear the description of a task is and their prior knowledge about the task content in order to influence its selection. Inspired on our studies? results, we propose a model for task documentation in TopCoder. We believe this model will likely aid the description of tasks in software crowdsourcing and will, as a consequence, help crowd members in their task development journey. / A presente pesquisa teve como objetivo investigar a documenta??o das tarefas disponibilizadas na plataforma TopCoder e os elementos que devem ser considerados na documenta??o de uma tarefa em Software Crowdsourcing. Esta investiga??o torna-se relevante na medida em que a Tarefa ? o elemento fundamental de liga??o entre os demais elementos do modelo de Software Crowdsourcing (Contratante, Plataforma e Multid?o). ? a Tarefa que expressa a necessidade do Contratante para os membros da multid?o. Assim, para o desenvolvimento desta investiga??o foi adotada a abordagem qualitativa, por meio de um Estudo de Caso com novatos em Software Crowdsourcing e de um Estudo de Campo, com profissionais da ind?stria. Para a an?lise e interpreta??o dos dados foi aplicada a t?cnica de An?lise de Conte?do. Como resultado desta pesquisa, constatou-se que no Estudo de Caso a documenta??o da tarefa teve um papel secund?rio quando os participantes selecionavam as tarefas. Entretanto, o papel da clareza da documenta??o surge com maior for?a durante a execu??o da tarefa, uma vez que ? neste momento que deve ser decodificada a instru??o da documenta??o a fim de realizar efetivamente a tarefa e submet?-la ? plataforma. Para os participantes do Estudo de Campo, os elementos mais relevantes referentes ? documenta??o das tarefas foram a clareza na descri??o da tarefa e o conhecimento sobre o assunto tratado pela tarefa. A partir dos resultados obtidos ? proposto um modelo de documenta??o de tarefa a ser utilizado na plataforma TopCoder. Acredita-se que com o mapeamento dos elementos identificados na pesquisa e a proposta de um modelo de documenta??o para a tarefa ser? poss?vel aprimorar a descri??o das tarefas e consequentemente as entregas realizadas pelos membros da multid?o.

Submitted by Silvana Teresinha Dornelles Studzinski (sstudzinski) on 2016-12-21T15:44:57Z No. of bitstreams: 1 Rodrigo Silva Pereira_.pdf: 951778 bytes, checksum: 90c6af826318df7c8204565678dff935 (MD5) / Made available in DSpace on 2016-12-21T15:44:57Z (GMT). No. of bitstreams: 1 Rodrigo Silva Pereira_.pdf: 951778 bytes, checksum: 90c6af826318df7c8204565678dff935 (MD5) Previous issue date: 2016-08-28 / Nenhuma / Com a emergência do crowdsourcing junto a difusão mundial de smartphones esforços recentes e pesquisas importantes sobre o uso de crowdsourcing na área da saúde ou ainda smarthealth visam auxiliar na melhoria hábitos de saúde, construção de históricos médicos pessoais de longo prazo, análise e revisão de dados médica, controle de dietas alimentares, gerenciamento do estresse, analise e comparação de informações e assistência em tempo real para catástrofes. Porém, nenhum deles usou de crowdsourcing para recomendação de centros clínicos de saúde. Segundo Chatzimilioudis crowdsourcing refere-se "a um modelo distribuído de solução de problemas em que uma multidão de tamanho indefinido é contratada para resolver um problema complexo através de um convite aberto". Neste âmbito, este trabalho apresenta um modelo de sistema de recomendação de centros clínicos de saúde, chamado CrowdHealth. A principal contribuição do modelo de sistema de recomendação de centros clínicos é possibilitar a criação de uma relação ganha-ganha entre seus usuários que podem ser cidadãos, médicos ou ainda entidades ligadas ao governo. Na literatura encontramos alguns trabalhos que carecem a abordagem do uso de crowdsourcing como fonte de dados para recomendação de centros clínicos de saúde. Nós desenvolvemos um protótipo de aplicação baseada no modelo de sistema de recomendação de centros clínicos de saúde para proporcionar uma visão do que seria uma aplicação baseada no modelo de sistema de recomendação de centros clínicos de saúde. Para avaliar o nosso modelo, apresentamos um cenário hipotético baseado numa possível aplicação para mensurar a percepção dos usuários quanto a utilidade dos centros clínicos de saúde. Os cenários descritos levavam em consideração os seguintes critérios: (1) a distância entre do usuário ao centro clinico, (2) a avaliação dos usuários em relação ao atendimento recebido nos centros clínicos e (3) o tempo de atendimento informado pelos usuários. Desta forma realizamos uma simulação de requisições de recomendações de usuários usando um dataset real contendo informações do Foursquare. O arquivo do dataset possuia 227428 check-in’s na cidade de Nova Iorque, EUA. O arquivo, foi dividido em duas partes, onde a primeira representava os check-in’s realizados pelos usuários nos centros clínicos, e a segunda representava usuários requisitando por recomendações de centros clínicos em outros locais. Assim, foram criadas funções para simular os processos de cálculo do tempo de atendimento e avaliação dos centros clínicos por parte dos usuários. Também simulou-se usuários requisitando por recomendações de centros clínicos em outros locais. Então, medimos precisão e recuperação dos centros clínicos de saúde sugeridos para cada usuário. Obtivemos valores médios de 57,5% e 61,33% para precisão e recuperação, respectivamente. Com isso, nossa avaliação retrata que centros clínicos de saúde recomendados por uma aplicação baseada no CrowdHealth poderiam aumentar beneficamente a utilidade de centros clínicos de saúde recomendados para os usuários. / With the emergence of crowdsourcing with the worldwide spread of smartphones recent efforts and important research on the use of crowdsourcing in health or smart-health are intended to assist in improving health habits, construction of historical long-term medical personnel, medical analysis and data review, control diets, stress management, analysis and comparison of information and real-time assistance for disasters. However, none of them used the crowdsourcing for recommendation clinical health centers. In this context, this paper presents a model of clinical health centers recommendation system called CrowdHealth. The main contribution of clinical health centers recommendation system model is possible to create a win-win relationship between its users that can be citizens, doctors or entities linked to the government. In the literature we find some papers that require the use of crowdsourcing as a data source for recommendation clinical health centers approach. We have developed a prototype application based on clinical health centers recommendation system model to provide a vision of what would be an application based on the clinical health centers recommendation system model. To evaluate our model, we present a hypothetical scenario based on a possible application to measure the perception of users and the utility of clinical health centers. The scenarios described took into consideration the following criteria: (1) the distance from the user to the clinical center, (2) the evaluation of other users on the service received in the clinical centers and (3) the time of service reported by users. Thus we performed a simulation of user requests recommendations using a real dataset containing information of Foursquare. The file dataset haved 227428 check in’s in New York City, USA. The file was divided into two parts, where the first represented the textit check in ’s performed by users in clinical centers, and the second represented by requesting users polyclinics recommendations elsewhere. Thus, functions were created to simulate service time calculation and evaluation processes of polyclinics by users. Also users was simulated by ordering polyclinics recommendations elsewhere. So we measure precision and recall of health clinical centers suggested for each user. Average values obtained from 57.5 % and 61.33 % for precision and recall, respectively. Thus, our assessment that portrays clinical health centers recommended by an application based on CrowdHealth could increase beneficially the usefulness of clinical health centers recommended for users.