Web 3.0 and Crowdservicing

The World Wide Web (WWW) has undergone significant evolution in the past decade. The emerging web 3.0 is characterized by the vision of achieving a balanced integration of services provided by machines and human agents. This is also the logic of ‘crowdservicing’ which has led to the creation of platforms on which new applications and even enterprises can be created, and complex, web-scale problem solving endeavors undertaken by flexibly connecting billions of loosely coupled computational agents or web services as well as human, service provider agents. In this paper, we build on research and development in the growing area of crowdsourcing to develop the concept of crowdservicing. We also present a novel crowdservicing application prototype, OntoAssist, to facilitate ontology evolution as an illustration of the concept. OntoAssist integrates the computational features of an existing search engine with the human computation provided by the crowd of users to find desirable search results

Crowdsourcing, Cognitive Load, and User Interface Design

Harnessing human computation through crowdsourcing offers a new approach to solving complex problems, especially those that are easy for humans but difficult for computers. Micro-tasking platforms such as Amazon Mechanical Turk have attracted a large on-demand workforce of millions of workers as well as hundreds of thousands of requesters. Achieving high quality results and minimizing the total task execution times are the two main goals of these crowdsourcing systems. In this paper we study the effects of cognitive load and complexity of user interface design on work quality and the latency of system. Our results indicate that complex and poorly designed user interfaces contributed to lower worker performance and increased latency

Legal crowdsourcing and relational law : what the semantic web can do for legal education

Crowdsourcing and Relational Law are interrelated concepts that can be successfully applied to the legal domain and, more specifically, to the field of legal education. 'Crowdsourcing' means 'participation of people (crowds)' and refers theoretically to the aggregated production of a common knowledge in a global data space. 'Relational law' refers to the regulatory link between Web 2.0 and 3.0, based on trust and dialogue, which emerges from the intertwining of top-down existing legal systems and bottom-up participation (the Web of People). Legal education today has a major role to play in the broad space opened up in terms of future potential of the Semantic Web. The following paper places a lens on the educational value of crowdsourcing and the relational approach to governance and law

3D-LIVE : live interactions through 3D visual environments

This paper explores Future Internet (FI) 3D-Media technologies and Internet of Things (IoT) in real and virtual environments in order to sense and experiment Real-Time interaction within live situations. The combination of FI testbeds and Living Labs (LL) would enable both researchers and users to explore capacities to enter the 3D Tele-Immersive (TI) application market and to establish new requirements for FI technology and infrastructure. It is expected that combining both FI technology pull and TI market pull would promote and accelerate the creation and adoption, by user communities such as sport practitioners, of innovative TI Services within sport events

The Value of Crowdsourcing for Complex Problems: Comparative Evidence from Software Developed By the Crowd And Professionals

Crowdsourcing is a problem solving model. In the context of complex problems, conventional theory suggests that solving complex problems is a province of professionals, that is, people with sufficient knowledge about the domain. Prior literature has indicated that the crowd, in addition to professionals, is also a great source for solving problems such as product innovation and idea generation. However, this assumption has yet to be tested. Adopting a quasi-experimental approach, this study uses a two-phase process to investigate this question. In the first phase we compare the development of a software by the crowd and professionals. In the second phase we evaluate the software developed by the crowdsourcing business model and professionals in terms of key perceived quality dimensions assessed by users of the systems. Quality is measured in terms of pragmatic quality, hedonic quality stimulation, and hedonic quality identification. Our study results suggest that there is a statistically significant difference between the software developed by a crowdsourcing business model and professionals in terms of hedonic quality stimulation and hedonic quality identification but there is no difference in terms of pragmatic quality. This research offers a first assessment of whether a crowdsourcing business model can be used to develop software with better user experience than professionallydeveloped software

3D-LIVE : live interactions through 3D visual environments

This paper explores Future Internet (FI) 3D-Media technologies and Internet of Things (IoT) in real and virtual environments in order to sense and experiment Real-Time interaction within live situations. The combination of FI testbeds and Living Labs (LL) would enable both researchers and users to explore capacities to enter the 3D Tele-Immersive (TI) application market and to establish new requirements for FI technology and infrastructure. It is expected that combining both FI technology pull and TI market pull would promote and accelerate the creation and adoption, by user communities such as sport practitioners, of innovative TI Services within sport events

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

Agreement and relational justice : a perspective from philosophy and sociology of law

Relationships between empirical and philosophical approaches to the law have not been always peaceful. Agreement seems the most natural way to build up and implementing regulations and justice within human-machine inter-faces (natural and artificial societies), and might help to bridge the gap between both theoretical approaches. Recent researches on relational law, relational jus-tice, crowdsourcing, regulatory systems and regulatory models are introduced. These concepts need further clarification, but they stand as political companions to more standard conceptions of law in the Semantic We

UNDERSTANDING ONLINE KNOWLEDGE CONTRIBUTION IN SOCIAL LEARNING PERSPECTIVE

Online open knowledge sharing is the idea that the Internet can promote the aggregation and dissemination of useful knowledge between a potentially large number of people. Starting from the knowledge sharing idea, various types of online open knowledge sharing services have provided the central platform for users to interact with each other, share their knowledge, and even jointly create new knowledge. In this study, we derive two research questions: 1) what framework can better explain online knowledge contribution? and 2) what factors influence online knowledge contribution? The study draws on both social learning theory and the social model of knowledge creation to investigate the overall antecedents of knowledge contribution and to examine three facets, user-oriented, service-oriented, and community-oriented knowledge contribution behaviour. In the study, we examine which knowledge sharing antecedents motivate people to contribute to knowledge sharing in the framework based on the social model of knowledge creation. We then verify each variable and hypothesis using a survey and the PLS analysis. This study uses social learning perspective to include all three aspects of knowledge sharing behaviour: personal, community-related, and service-related antecedents. With this new perspective, while previous studies have focused on personal cognitive factors in this area, this study examines the integrative influence of factors from social learning and social knowledge creation antecedents. In addition, our findings offer guidance and insights for knowledge sharing service practitioners and managers who are trying to encourage users? contributions