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Computer Vision, Imaging and Computer Graphics Theory and Applications : 14th International Joint Conference, VISIGRAPP 2019, Prague, Czech Republic, February 25–27, 2019, Revised Selected Papers
- 2020
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Editorial collection (peer-reviewed)abstract
- This book constitutes thoroughly revised and selected papers from the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2019, held in Prague, Czech Republic, in February 2019. The 25 thoroughly revised and extended papers presented in this volume were carefully reviewed and selected from 395 submissions. The papers contribute to the understanding of relevant trends of current research on computer graphics; human computer interaction; information visualization; computer vision.
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Computer Vision, Imaging and Computer Graphics Theory and Applications : 15th International Joint Conference, VISIGRAPP 2020 Valletta, Malta, February 27–29, 2020, Revised Selected Papers
- 2022. - 1
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Editorial collection (peer-reviewed)abstract
- This book constitutes thoroughly revised and selected papers from the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2020, held in Valletta, Malta, in February 2020. The 25 thoroughly revised and extended papers presented in this volume were carefully reviewed and selected from 455 submissions. The papers contribute to the understanding of relevant trends of current research on computer graphics; human computer interaction; information visualization; computer vision.
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Best Practices for the Knowledge Society. Knowledge, Learning, Development and Technology for All : Second World Summit on the Knowledge Society, WSKS 2009, Chania, Crete, Greece, September 2009. Proceedings
- 2009
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Editorial proceedings (peer-reviewed)abstract
- This book, in conjunction with the volume LNAI 5736, constitutes the refereed proceedings of the Second World Summit, WSKS 2009, held in Chania, Crete, Greece, in September 2009. The 61 revised full papers presented were carefully reviewed and selected from 256 submissions. The papers deal with information technologies - knowledge management systems - e-business and business, organizational and inter-organizational information systems for the Knowledge Society, knowledge, learning, education, learning technologies and e-learning for the Knowledge Society, social and humanistic computing for the Knowledge Society – emerging technologies for the society and the humanity, culture and cultural heritage - technology for culture management - management of tourism and entertainment - tourism networks in the Knowledge Society, e-government and e-democracy in the Knowledge Society, innovation, sustainable development and strategic management for the Knowledge Society, service science, management, engineering, and technology, intellectual and human capital development in the Knowledge Society, advanced applications for environmental protection and green economy management, future prospects for the Knowledge Society: from foresight studies to projects and public policies, technologies and business models for the creative industries.
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- Cheng, Lingyun, et al.
(author)
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Interactive Anomaly Detection Based on Clustering and Online Mirror Descent
- 2020
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Conference paper (peer-reviewed)abstract
- In several applications, when anomalies are detected, human experts have to investigate or verify them one by one. As they investigate, they unwittingly produce a label - true positive (TP) or false positive (FP). In this paper, we propose a method (called OMD-Clustering) that exploits this label feedback to minimize the FP rate and detect more relevant anomalies, while minimizing the expert effort required to inves- tigate them. The OMD-Clustering method iteratively suggests the top-1 anomalous instance to a human expert and receives feedback. Before suggesting the next anomaly, the method re-ranks instances so that the top anomalous instances are similar to the TP instances and dissimi- lar to the FP instances. This is achieved by learning to score anomalies differently in various regions of the feature space. An experimental eval- uation on several real-world datasets is conducted. The results show that OMD-Clustering achieves significant improvement in both detection pre- cision and expert effort compared to state-of-the-art interactive anomaly detection methods.
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