It is crucial to use contextual information to improve the recognition accuracy of Chinese script in an offline, handwritten Chinese character-recognition system. However, with the increase in the number of candidates...
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Two of the fundamental issues in message passing between mobile agents are tracking the migration of the target agent and delivering messages to it. In order to provide reliable message delivery, protocols are needed ...
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Model-based diagnostic systems (MBDSs) are increasingly being applied to real industrial problems. Often this results in the development of large models which must be partitioned into a number of subsystems in order t...
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Model-based diagnostic systems (MBDSs) are increasingly being applied to real industrial problems. Often this results in the development of large models which must be partitioned into a number of subsystems in order that the MBDSs may be applied to problems of practical size. For other MBDSs the model's equations must be causally ordered, and in dependency-based MBDSs (e.g. GDE, De Kleer and Williams. 1987) groups of simultaneous equations must be identified and solved so that a local "constraint propagation" algorithms may be employed. This paper presents the implementation, use, and extension, of a model partitioning algorithm due to Portè et al. (1988). As well as outlining its design, and its extensions, this paper also presents the results obtained by applying the algorithm to a chemical plant model.
In this paper, we show the relevance of fuzzy systems in an integrated symbolic-subsymbolic architecture, GENUES (generic neuro-expert system), for information processing. Fuzzy logic is used for modelling knowledge i...
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In this paper, we show the relevance of fuzzy systems in an integrated symbolic-subsymbolic architecture, GENUES (generic neuro-expert system), for information processing. Fuzzy logic is used for modelling knowledge in different phases of the GENUES architecture. As an illustration we show the application of fuzzy logic in the decision phase and post-processing phase of GENUES for power system fault diagnosis.
Microservice architectures are increasingly used to modularize IoT applications and deploy them in distributed and heterogeneous edge computing environments. Over time, these microservice-based IoT applications are su...
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Microservice architectures are increasingly used to modularize IoT applications and deploy them in distributed and heterogeneous edge computing environments. Over time, these microservice-based IoT applications are susceptible to performance anomalies caused by resource hogging (e.g., CPU or memory), resource contention, etc., which can negatively impact their Quality of Service and violate their Service Level Agreements. Existing research on performance anomaly detection for edge computing environments focuses on model training approaches that either achieve high accuracy at the expense of a time-consuming and resource-intensive training process or prioritize training efficiency at the cost of lower accuracy. To address this gap, while considering the resource constraints and the large number of devices in modern edge platforms, we propose two clustering-based model training approaches: (1) intra-cluster parameter transfer learning-based model training (ICPTL) and (2) cluster-level model training (CM). These approaches aim to find a trade-off between the training efficiency of anomaly detection models and their accuracy. We compared the models trained under ICPTL and CM to models trained for specific devices (most accurate, least efficient) and a single general model trained for all devices (least accurate, most efficient). Our findings show that ICPTL’s model accuracy is comparable to that of the model per device approach while requiring only 40% of the training time. In addition, CM further improves training efficiency by requiring 23% less training time and reducing the number of trained models by approximately 66% compared to ICPTL, yet achieving a higher accuracy than a single general model.
This book constitutes the refereed proceedings of the 39th International Conference on Current Trends in Theory and Practice of Computer Science, SOFSEM 2013, held in Špindlerův Mlýn, Czech Republic, in Janu...
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ISBN:
(数字)9783642358432
ISBN:
(纸本)9783642358425
This book constitutes the refereed proceedings of the 39th International Conference on Current Trends in Theory and Practice of Computer Science, SOFSEM 2013, held in Špindlerův Mlýn, Czech Republic, in January 2013. The 37 revised full papers presented in this volume were carefully reviewed and selected from 98 submissions. The book also contains 10 invited talks, 5 of which are in full-paper length. The contributions are organized in topical sections named: foundations of computer science; software and Web engineering; data, information, and knowledge engineering; and social computing and human factors.
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