13,596 research outputs found
COLLABORATIVE TAGGING USING CAPTCHA
No full textTagging is most widely used feature in online networks. There are no of tags are available mainly offline resources based on their feedback, expressed in the form of free-text labels (i.e., tags). Recently there is a problem based on the tagging of feedback, free-text labels etc. Without user permission tags are automatically generated spam scripts. So, users are facing many sensitive problems like privacy. In the existing system, a privacy-preserving collaborative tagging service, by showing how a specific privacy-enhancing technology, namely tag suppression, can be used to protect end-user privacy. Some problems identified in the existing system. To overcome these problems captcha based security in introduced in the proposed system to provide better security for the tagging information. Results will show the performance of the proposed system
Distributed human computation framework for linked data co-reference resolution
No full textDistributed Human Computation (DHC) is a technique used to solve computational problems by incorporating the collaborative effort of a large number of humans. It is also a solution to AI-complete problems such as natural language processing. The Semantic Web with its root in AI is envisioned to be a decentralised world-wide information space for sharing machine-readable data with minimal integration costs. There are many research problems in the Semantic Web that are considered as AI-complete problems. An example is co-reference resolution, which involves determining whether different URIs refer to the same entity. This is considered to be a significant hurdle to overcome in the realisation of large-scale Semantic Web applications. In this paper, we propose a framework for building a DHC system on top of the Linked Data Cloud to solve various computational problems. To demonstrate the concept, we are focusing on handling the co-reference resolution in the Semantic Web when integrating distributed datasets. The traditional way to solve this problem is to design machine-learning algorithms. However, they are often computationally expensive, error-prone and do not scale. We designed a DHC system named iamResearcher, which solves the scientific publication author identity co-reference problem when integrating distributed bibliographic datasets. In our system, we aggregated 6 million bibliographic data from various publication repositories. Users can sign up to the system to audit and align their own publications, thus solving the co-reference problem in a distributed manner. The aggregated results are published to the Linked Data Cloud
Exploratory talk within collaborative small groups in mathematics
No full textThis report describes one aspect of a wider research study on exploratory talk within collaborative small groups in secondary mathematics lessons. It outlines students’ views of using collaborative activity to learn mathematics. The fuller research study explores the extent to which exploratory talk occurs in collaborative peer groups in secondary mathematics classrooms
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Collaborative Educational Systems in the Virtual Environment
No full textThe work leads to an original approach to the construction of collaborative systems metrics. The approach is based both on research already conducted by the author, on the experimental results obtained, and the foundation taken from the specific literature. The collaborative systems in knowledgebased economy are formalized and their characteristics are identified. The virtual campus structure is described and a comparison with the classical university is achieved. The architecture of virtual is designed and the categories of agents in virtual campus are analyzed.
Collaborative gym: A simulation benchmark for multi-robotic tasks
No full textThe design of multi-robot systems has gained increasing attention in recent years. The field of cooperative Multi-Agent Robot Systems (MARS) has shown the potential to provide reliable and cost-effective solutions to a wide range of automated applications. Communication and coordination between autonomous agents require robust and intelligent control systems in order to achieve high-quality performance. This paper presents Collaborative Gym, an open-source, physics-based simulation framework for multi-robot interaction. This simulation environment differs from existing robotic simulation environments in that it is designed to model the interaction between multiple robots. Despite the presence of a large number of single robotic environments, multi-robotic simulation environments for reinforcement learning are rare. Collaborative Gym contains four simulated tasks in which different commercial robots work in collaboration: poking, lifting, balancing, and passing. For each of the four tasks, baseline policies are presented for various combinations of commercial robots which have been trained using reinforcement learning. The study demonstrated that Collaborative Gym is a promising open-source framework for the development of multi-robotic collaborative robotic tasks.https://github.com/gabriansa/collaborative-gymMechanical Engineering | Multi-Machine Engineerin
Bayesian latent variable models for collaborative item rating prediction
No full textCollaborative filtering systems based on ratings make it easier for users to find content of interest on the Web and as such they constitute an area of much research. In this paper we first present a Bayesian latent variable model for rating prediction that models ratings over each user's latent interests and also each item's latent topics. We describe a Gibbs sampling procedure that can be used to estimate its parameters and show by experiment that it is competitive with the gradient descent SVD methods commonly used in state-of-the-art systems. We then proceed to make an important and novel extension to this model, enhancing it with user-dependent and item-dependant biases to significantly improve rating estimation. We show by experiment on a large set of real ratings data that these models are able to outperform 3 common baselines, including a very competitive and modern SVD-based model. Furthermore we illustrate other advantages of our approach beyond simply its ability to provide more accurate ratings and show that it is able to perform better on the common and important case where the user profile is short
Collaborative Systems – Finite State Machines
No full textIn this paper the finite state machines are defined and formalized. There are presented the collaborative banking systems and their correspondence is done with finite state machines. It highlights the role of finite state machines in the complexity analysis and performs operations on very large virtual databases as finite state machines. It builds the state diagram and presents the commands and documents transition between the collaborative systems states. The paper analyzes the data sets from Collaborative Multicash Servicedesk application and performs a combined analysis in order to determine certain statistics. Indicators are obtained, such as the number of requests by category and the load degree of an agent in the collaborative system.Collaborative System, Finite State Machine, Inputs, States, Outputs
Assessment of (computer-supported) collaborative learning
No full textWithin the Computer-Supported Collaborative Learning (CS)CL research community there has been an extensive dialogue on theories and perspectives on learning from collaboration, approaches to scaffold (script) the collaborative process, and most recently research methodology. In contrast, the issue of assessment of collaborative learning has received much less attention. This article discusses how assessment of collaborative learning has been addressed, provides a perspective on what could be assessed, and highlights limitations of current approaches. Since assessment of collaborative learning is a demanding experience for teachers and students alike, they require adequate computer-supported and intelligent tools for monitoring and assessment. A roadmap for the role and application of intelligent tools for assessment of (CS)CL is presented
iBingo mobile collaborative search
No full textThis paper describes a collaborative video search system for mobile devices, 'iBingo'. It supports division of labour among users, providing search results to colocated iPod Touch devices
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