HyValue is a hybrid electronic submission system which utilizes techniques from natural language processing, neural networks and rule based systems to accept, evaluate and mark work submitted by a student for reading ...
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HyValue is a hybrid electronic submission system which utilizes techniques from natural language processing, neural networks and rule based systems to accept, evaluate and mark work submitted by a student for reading or writing. This paper describes the theory behind the system design and the development of the individual components and their interaction. Issues addressed include the definition of sentence structure, fuzzy rule construction and integration with a knowledge base containing the marking rubrics for reading and writing. An evaluation of the system is provided and conclusions drawn.
Database design has always been a challenging problem, more so if the performance of queries is the prime goal. Thus, data warehouse design driven by queries is lot more difficult because of the materialized view sele...
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The use of multivariate information visualization techniques is intrinsically difficult because the multidimensional nature of data cannot be effectively presented and understood on real-world displays, which have lim...
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Early research into electronic data interchange (EDI) stressed the greater speed, efficiencies and cost savings available from electronic document exchange. Despite EDI's cooperative focus, much of this research a...
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This book constitutes the thoroughly refereed proceedings of the 2012 ICSOC Workshops consisting of 6 scientific satellite events, organized in 3 main tracks including workshop track (ASC, DISA. PAASC, SCEB, SeMaPS an...
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ISBN:
(数字)9783642378041
ISBN:
(纸本)9783642378034
This book constitutes the thoroughly refereed proceedings of the 2012 ICSOC Workshops consisting of 6 scientific satellite events, organized in 3 main tracks including workshop track (ASC, DISA. PAASC, SCEB, SeMaPS and WESOA 2012), PhD symposium track, demonstration track; held in conjunction with the 10th International Conference on Service-Oriented computing (ICSOC), in Shanghai, China, November 2012.
The 53 revised papers presents a wide range of topics that fall into the general area of service computing such as business process management, distributed systems, computer networks, wireless and mobile computing, grid computing, networking, service science, management science, and software engineering.
EEG data classification plays a pivotal role in understanding brain activity and its applications in various domains. Deep learning has emerged as a powerful paradigm for automatically learning complex patterns from r...
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EEG data classification plays a pivotal role in understanding brain activity and its applications in various domains. Deep learning has emerged as a powerful paradigm for automatically learning complex patterns from raw data, eliminating the need for manual feature extraction. However, in the context of medical data, and in particular for EEG analysis, the use of deep learning approaching while having been very successful is not being included in medical diagnosis routines, yet. The aim of this survey is twofold. On one side, it provides a comprehensive overview of the current state-of-the-art in EEG data classification, with a specific focus on the use of deep learning techniques. On the other side, it also addresses the clinician community, explaining the power and trustfulness of such new approaches. The survey begins with an introduction highlighting the limitations of traditional model-based approaches and the potential of deep learning in EEG data classification. The fundamental principles and architectures of deep learning models are presented, including convolutional neural networks (CNNs), recurrent neural networks (RNNs) and Graph Convolution Neural Network (GCNNs) that have been successfully applied to EEG data classification tasks. A detailed review and analysis of existing literature on deep learning-based EEG data classification are provided, categorizing the studies based on the type of the input data, e.g., sequences, images, graphs or multi-modalities. We also discuss about the existing tools and technologies for EEG data classification and highlights the challenges and limitations associated with deep learning in EEG data classification, including limited data availability, interpretability of deep models and bias mitigation. Potential solutions and ongoing research efforts to overcome these challenges are explored, providing insights into the future directions of this field. This survey serves as a valuable resource for researchers, practitioners
This book constitutes the refereed proceedings of the 6th KES International Conference on Agent and Multi-Agent Systems, KES-AMSTA 2012, held in Dubrovnik, Croatia, in June 2012.;The conference attracted a substantial...
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ISBN:
(数字)9783642309472
ISBN:
(纸本)9783642309465
This book constitutes the refereed proceedings of the 6th KES International Conference on Agent and Multi-Agent Systems, KES-AMSTA 2012, held in Dubrovnik, Croatia, in June 2012.;The conference attracted a substantial number of researchers and practitioners from all over the world who submitted their papers for ten main tracks covering the methodology and applications of agent and multi-agent systems, one workshop (TRUMAS 2012) and five special sessions on specific topics within the field. The 66 revised papers presented were carefully reviewed and selected for inclusion in the book. The papers are organized in topical sections on virtual organizations, knowledge and learning agents, intelligent workflow, cloud computing and intelligent systems, self-organization, ICT-based alternative and augmentative communication, multi-agent systems, mental and holonic models, assessment methodologies in multi-agent and other paradigms, business processing agents, Trumas 2012 (first international workshop), conversational agents and agent teams, digital economy, and multi-agent systems in distributed environments.
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