This book constitutes the refereed proceedings of the 18th International Multimedia Modeling Conference, MMM 2012, held in Klagenfurt, Austria, in January 2012. The 38 revised regular papers, 12 special session papers...
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ISBN:
(数字)9783642273551
ISBN:
(纸本)9783642273544
This book constitutes the refereed proceedings of the 18th International Multimedia Modeling Conference, MMM 2012, held in Klagenfurt, Austria, in January 2012. The 38 revised regular papers, 12 special session papers, 15 poster session papers, and 6 demo session papers were carefully reviewed and selected from 142 submissions. The papers are organized in the following topical sections: annotation, annotation and interactive multimedia applications, event and activity, mining and mobile multimedia applications, search, summarization and visualization, visualization and advanced multimedia systems, and the special sessions: interactive and immersive entertainment and communication, multimedia preservation: how to ensure multimedia access over time, multi-modal and cross-modal search, and video surveillance.
This book presents selected research papers on current developments in the fields of soft computing and signal processing from the Seventh International Conference on Soft Computing and Signal Processing (ICSCSP 2024)...
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ISBN:
(数字)9789819609246
ISBN:
(纸本)9789819609239
This book presents selected research papers on current developments in the fields of soft computing and signal processing from the Seventh International Conference on Soft Computing and Signal Processing (ICSCSP 2024), organized by Malla Reddy college of engineering & Technology, Hyderabad, India. The book covers topics such as soft sets, rough sets, fuzzy logic, neural networks, genetic algorithms, and machine learning and discusses various aspects of these topics, e.g., technological considerations, product implementation, and application issues.
Recently, supervised cross-modal hashing methods have gained considerable attention due to their ability to mine credible semantic relationships between multi-modal data. These methods typically rely on labels to expl...
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Recently, supervised cross-modal hashing methods have gained considerable attention due to their ability to mine credible semantic relationships between multi-modal data. These methods typically rely on labels to explore semantic relationships provided that labels are always reliable, which, however, may not be true from the practical perspective. In fact, labels may be incomplete, i.e., true-label incomplete and fine-grained incomplete, which makes the performance of the existing methods deteriorated. To this end, we propose a method called Discrete Elective Hashing with Incomplete Labels (DEH-IL), which is designed to alleviate the impact of incomplete labels. Specifically, we introduce a relaxed label scheme that allows the algorithm to automatically mine potential missing information from incomplete labels, which is beneficial for exploring intra-class relationships. Moreover, we propose a novel elective loss that aggregates all estimations from incomplete labels to mine inter-class relationships. Since elective loss does not rely on any single estimation, it can effectively mitigate estimation errors arising from incomplete labels. By combining these two components, DEH-IL can effectively explore both intra-class and inter-class relationships through incomplete labels. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed method.
This book and its companion volume, LNCS vols. 7331 and 7332, constitute the proceedings of the Third International Conference on Swarm Intelligence, ICSI 2012, held in Shenzhen, China in June 2012. The 145 revised fu...
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ISBN:
(数字)9783642309762
ISBN:
(纸本)9783642309755
This book and its companion volume, LNCS vols. 7331 and 7332, constitute the proceedings of the Third International Conference on Swarm Intelligence, ICSI 2012, held in Shenzhen, China in June 2012. The 145 revised full papers presented were carefully reviewed and selected from 247 submissions. The papers are organized in 27 cohesive sections covering all major topics of swarm intelligence research and developments.
There is no doubt that the popularity of smart devices and the development of deep learning models bring individuals too much convenience. However, some rancorous attackers can also implement unexpected privacy infere...
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There is no doubt that the popularity of smart devices and the development of deep learning models bring individuals too much convenience. However, some rancorous attackers can also implement unexpected privacy inferences on sensed data from smart devices via advanced deep-learning tools. Nonetheless, up to now, no work has investigated the possibility of riskier overheard, referring to inferring an integral event about humans by analyzing polyphonic audios. To this end, we propose an Audio-based integraL evenT infERence (ALTER) model and two upgraded models (ALTER-p and ALTER-pp) to achieve the integral event inference. Specifically, ALTER applies a link-like multi-label inference scheme to consider the short-term co-occurrence dependency among multiple labels for the event inference. Moreover, ALTER-p uses a newly designed attention mechanism, which fully exploits audio information and the importance of all data points, to mitigate information loss in audio data feature learning for the event inference performance improvement. Furthermore, ALTER-pp takes into account the long-term co-occurrence dependency among labels to infer an event with more diverse elements, where another devised attention mechanism is utilized to conduct a graph-like multi-label inference. Finally, extensive real-data experiments demonstrate that our models are effective in integral event inference and also outperform the state-of-the-art models.
In today's techno world,teachers worldwide can learn the world-famous masterclasses and access all sorts of courses for school teachers. Learners have more learning opportunities based on the excellent sharing of ...
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In today's techno world,teachers worldwide can learn the world-famous masterclasses and access all sorts of courses for school teachers. Learners have more learning opportunities based on the excellent sharing of resources. Technical support is provided in developing the Fuzzy Integrated Cloud Computing Framework (FICCF) to develop Online Courses. This paper explores the educational capabilities of cloud computing, the real-time contact between teachers and students, studying, and heterogeneous terminal access to track evaluation-based fuzzy variables. It addresses the benefits of open-ended modes, creating an English course design system in a cloud-based environment, modifying lecture mode, and reforming the English course design mode. The experimental result shows that the proposed cloud computing framework has better efficiency in developing and promoting English online *** self-learning systems make it possible to provide the best possible learning environment. It ensures that sensitive data generated by an application is deleted, and this feature ensures accuracy.
This book focuses on the modelling and optimization aspects of the feature selection problem through computational intelligence methods in complex, high-dimensional supervised machine learning. To aid&#...
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ISBN:
(数字)9789819626878
ISBN:
(纸本)9789819626861
This book focuses on the modelling and optimization aspects of the feature selection problem through computational intelligence methods in complex, high-dimensional supervised machine learning. To aid readers in conducting research in this field, it covers fundamental concepts and state-of-the-art algorithms. This book also provides a detailed insight into applying these algorithms into real-world applications. The authors begin by introducing the definition high-dimensional machine learning (ML) problems and the challenges they pose. Subsequently, they delve into dimension reduction methods for high-dimensional ML, including global and local feature selection (FS) techniques. This book also comprehensively presents computational intelligence methods such as evolutionary computation and deep neural networks for FS, supported by both theoretical and empirical evidence. Furthermore, this book explores real-world scenario applications involving high-dimensional ML, particularly in the context of smart cities, bioinformatics and industrial informatics.
This book is a suitable read for postgraduates and researchers who are interested in the research areas of computational intelligence, soft computing, machine learning and deep learning. Professionals and practitioners within these related fields will also benefit from this book.
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