Human Computation and Convergence

Humans are the most effective integrators and producers of information, directly and through the use of information-processing inventions. As these inventions become increasingly sophisticated, the substantive role of humans in processing information will tend toward capabilities that derive from our most complex cognitive processes, e.g., abstraction, creativity, and applied world knowledge. Through the advancement of human computation - methods that leverage the respective strengths of humans and machines in distributed information-processing systems - formerly discrete processes will combine synergistically into increasingly integrated and complex information processing systems. These new, collective systems will exhibit an unprecedented degree of predictive accuracy in modeling physical and techno-social processes, and may ultimately coalesce into a single unified predictive organism, with the capacity to address societies most wicked problems and achieve planetary homeostasis.Comment: Pre-publication draft of chapter. 24 pages, 3 figures; added references to page 1 and 3, and corrected typ

Innovative business plan: a crowdsourcing medical data annotation platform company

In this business plan, a new crowdsourcing medical data annotation platform company is proposed. It is to help companies and research institutions which are developing medical artificial intelligence outsource medical data annotation work to professional workers. Crowdsourcing platform, as one of the intermediary platform, is a new Internet business model to provide service for large-scale enterprises. There is great demand for crowdsourcing service in medical AI field in China. However, there is no company in China could offer professional medical data annotation services. Since the development of medical artificial intelligence in China, most of the companies engaged in research and development of medical artificial intelligence can only rely on recruitment or give up research and development. The cost of the workforce and material resources is very high. The proposed company's services can better address these issues. On the one hand, the proposed company can provide more cost-effective and accurate annotation data quickly through outsourcing. On the other hand, the proposed company can provide medical professionals with part-time opportunities to increase their income and reduce unemployment. Through the analysis in the paper, we can predict that the proposed company can stabilise the profit by collecting commissions and advertising. It will enable medical AI companies, the proposed companies and medical professionals to achieve a win-win situation. Therefore, it is attractive for Chinese start-ups to develop and fill this niche market.Neste plano de negócios, uma nova empresa de plataforma de anotação de dados médicos de crowdsourcing é proposta. É para ajudar empresas e instituições de pesquisa que estão desenvolvendo inteligência artificial médica a terceirizar facilmente o trabalho de anotação de dados médicos para trabalhadores profissionais. A plataforma de crowdsourcing, como uma das plataformas intermediárias, é um novo modelo de negócios na Internet para fornecer serviços para empresas de grande escala. Existe uma grande demanda por serviços de crowdsourcing no campo da IA médica na China. No entanto, nenhuma empresa na China poderia oferecer serviços profissionais de anotação de dados médicos. Desde o desenvolvimento da inteligência artificial médica na China, a maioria das empresas envolvidas em pesquisa e desenvolvimento de inteligência artificial médica só pode contar com seu próprio recrutamento ou desistir de pesquisa e desenvolvimento. O custo de mão de obra e recursos materiais é muito alto. Os serviços da empresa proposta podem resolver melhor esses problemas. Por um lado, a empresa proposta pode fornecer dados de anotação mais econômicos e precisos rapidamente através da terceirização. Por outro lado, a empresa proposta pode oferecer aos profissionais médicos oportunidades de meio período para aumentar sua renda e reduzir o desemprego. Através da análise do artigo, podemos prever que a empresa proposta pode estabilizar o lucro coletando comissões e publicidade. Isso permitirá que as empresas de IA médica, as empresas propostas e os profissionais médicos alcancem uma situação em que todos saem ganhando. Portanto, é atraente para as empresas chinesas desenvolver e preencher esse nicho de mercado

Text-based Sentiment Analysis and Music Emotion Recognition

Nowadays, with the expansion of social media, large amounts of user-generated texts like tweets, blog posts or product reviews are shared online. Sentiment polarity analysis of such texts has become highly attractive and is utilized in recommender systems, market predictions, business intelligence and more. We also witness deep learning techniques becoming top performers on those types of tasks. There are however several problems that need to be solved for efficient use of deep neural networks on text mining and text polarity analysis. First of all, deep neural networks are data hungry. They need to be fed with datasets that are big in size, cleaned and preprocessed as well as properly labeled. Second, the modern natural language processing concept of word embeddings as a dense and distributed text feature representation solves sparsity and dimensionality problems of the traditional bag-of-words model. Still, there are various uncertainties regarding the use of word vectors: should they be generated from the same dataset that is used to train the model or it is better to source them from big and popular collections that work as generic text feature representations? Third, it is not easy for practitioners to find a simple and highly effective deep learning setup for various document lengths and types. Recurrent neural networks are weak with longer texts and optimal convolution-pooling combinations are not easily conceived. It is thus convenient to have generic neural network architectures that are effective and can adapt to various texts, encapsulating much of design complexity. This thesis addresses the above problems to provide methodological and practical insights for utilizing neural networks on sentiment analysis of texts and achieving state of the art results. Regarding the first problem, the effectiveness of various crowdsourcing alternatives is explored and two medium-sized and emotion-labeled song datasets are created utilizing social tags. One of the research interests of Telecom Italia was the exploration of relations between music emotional stimulation and driving style. Consequently, a context-aware music recommender system that aims to enhance driving comfort and safety was also designed. To address the second problem, a series of experiments with large text collections of various contents and domains were conducted. Word embeddings of different parameters were exercised and results revealed that their quality is influenced (mostly but not only) by the size of texts they were created from. When working with small text datasets, it is thus important to source word features from popular and generic word embedding collections. Regarding the third problem, a series of experiments involving convolutional and max-pooling neural layers were conducted. Various patterns relating text properties and network parameters with optimal classification accuracy were observed. Combining convolutions of words, bigrams, and trigrams with regional max-pooling layers in a couple of stacks produced the best results. The derived architecture achieves competitive performance on sentiment polarity analysis of movie, business and product reviews. Given that labeled data are becoming the bottleneck of the current deep learning systems, a future research direction could be the exploration of various data programming possibilities for constructing even bigger labeled datasets. Investigation of feature-level or decision-level ensemble techniques in the context of deep neural networks could also be fruitful. Different feature types do usually represent complementary characteristics of data. Combining word embedding and traditional text features or utilizing recurrent networks on document splits and then aggregating the predictions could further increase prediction accuracy of such models

From Traditional to Online Methods for Generating Business Ideas

The traditional methods for generating venture ideas are in use for more than 70 years in the business, while the development of information and communication technologies (ICT) opened new opportunities for generating and harvesting business ideas, available to entrepreneurs of any kind. Our initial research discovered that there is a missing link in the academic literature between the traditional and the emerging online methods for generating business ideas and therefore, in this paper, we study the traditional and online sources and methods in parallel. The purpose of our study is to improve the venture idea creation process from an applicable perspective, and to add to the existing ideation literature by (1) identifying and classifying the sources of ideas to create the necessary link between the onsite and online access to idea sources; (2) explaining the traditional methods for generating business idea trough their dominant features in order to (3) further identify and elaborate the online sources and ideation methods trough these features and link them to the known traditional approaches. The sources, methods, and tools we examine and elaborate in this paper could be used for both, generating ideas for traditional and online business models. Hence, our findings have practical and applicable value for the first step in the entrepreneurial process. Additionally, our study could be used as a starting point for further research in the field of online ideation, a field that needs to be yet, more extensively, addressed by practitioners and research scholars

Augmenting the performance of image similarity search through crowdsourcing

Crowdsourcing is defined as “outsourcing a task that is traditionally performed by an employee to a large group of people in the form of an open call” (Howe 2006). Many platforms designed to perform several types of crowdsourcing and studies have shown that results produced by crowds in crowdsourcing platforms are generally accurate and reliable. Crowdsourcing can provide a fast and efficient way to use the power of human computation to solve problems that are difficult for machines to perform. From several different microtasking crowdsourcing platforms available, we decided to perform our study using Amazon Mechanical Turk. In the context of our research we studied the effect of user interface design and its corresponding cognitive load on the performance of crowd-produced results. Our results highlighted the importance of a well-designed user interface on crowdsourcing performance. Using crowdsourcing platforms such as Amazon Mechanical Turk, we can utilize humans to solve problems that are difficult for computers, such as image similarity search. However, in tasks like image similarity search, it is more efficient to design a hybrid human–machine system. In the context of our research, we studied the effect of involving the crowd on the performance of an image similarity search system and proposed a hybrid human–machine image similarity search system. Our proposed system uses machine power to perform heavy computations and to search for similar images within the image dataset and uses crowdsourcing to refine results. We designed our content-based image retrieval (CBIR) system using SIFT, SURF, SURF128 and ORB feature detector/descriptors and compared the performance of the system using each feature detector/descriptor. Our experiment confirmed that crowdsourcing can dramatically improve the CBIR system performance