Enhancing Potential Re-finding in Personalized Search with Hierarchical Memory Networks

2021 ◽  
pp. 1-1
Author(s):  
Yujia Zhou ◽  
Zhicheng Dou ◽  
Ji-Rong Wen
Keyword(s):  
Personalized Search ◽  
Hierarchical Memory

Multimodal emotion recognition with hierarchical memory networks

2021 ◽  
Vol 25 (4) ◽  
pp. 1031-1045
Author(s):  
Helang Lai ◽  
Keke Wu ◽  
Lingli Li
Keyword(s):  
Emotion Recognition ◽  
The Self ◽  
Global Memory ◽  
Human Computer Interactions ◽  
Accuracy Improvement ◽  
Local Memory ◽  
Hierarchical Memory ◽  
Multimodal Emotion Recognition ◽  
Novel Model ◽  
Computer Interactions

Emotion recognition in conversations is crucial as there is an urgent need to improve the overall experience of human-computer interactions. A promising improvement in this field is to develop a model that can effectively extract adequate contexts of a test utterance. We introduce a novel model, termed hierarchical memory networks (HMN), to address the issues of recognizing utterance level emotions. HMN divides the contexts into different aspects and employs different step lengths to represent the weights of these aspects. To model the self dependencies, HMN takes independent local memory networks to model these aspects. Further, to capture the interpersonal dependencies, HMN employs global memory networks to integrate the local outputs into global storages. Such storages can generate contextual summaries and help to find the emotional dependent utterance that is most relevant to the test utterance. With an attention-based multi-hops scheme, these storages are then merged with the test utterance using an addition operation in the iterations. Experiments on the IEMOCAP dataset show our model outperforms the compared methods with accuracy improvement.

NEURO-FUZZY BASED SEARCH ROBOT FOR SOFTWARE COMPONENTS

1999 ◽  
Vol 08 (02) ◽  
pp. 119-135
Author(s):  
YAU-HWANG KUO ◽  
JANG-PONG HSU ◽  
MONG-FONG HORNG
Keyword(s):  
Retrieval System ◽  
Software Component ◽  
Software Components ◽  
Personalized Search ◽  
Component Retrieval ◽  
Reusable Software ◽  
Major Mechanism ◽  
Fuzzy Models ◽  
Neuro Fuzzy

A personalized search robot is developed as one major mechanism of a personalized software component retrieval system. This search robot automatically finds out the Web servers providing reusable software components, extracts needed software components from servers, classifies the extracted components, and finally establishes their indexing information for local component retrieval in the future. For adaptively tuning the performance of software component extraction and classification, an adaptive thesaurus and an adaptive classifier, realized by neuro-fuzzy models, are embedded in this search robot, and their learning algorithms are also developed. A prototype of the personalized software component retrieval system including the search robot has been implemented to confirm its validity and evaluate the performance. Furthermore, the framework of proposed personalized search robot could be extended to the search and classification of other kinds of Internet documents.

Cooperative Mechanism of Local Memory and Cache in Network Processors

2013 ◽  
Vol 380-384 ◽  
pp. 1969-1972
Author(s):  
Bo Yuan ◽  
Jin Dou Fan ◽  
Bin Liu
Keyword(s):  
Packet Processing ◽  
Network Processors ◽  
Local Memory ◽  
Memory Space ◽  
Memory Mechanism ◽  
Hierarchical Memory ◽  
Packet Data ◽  
On Chip ◽  
Cache Mechanism ◽  
Cache Miss

Traditional network processors (NPs) adopt either local memory mechanism or cache mechanism as the hierarchical memory structure. The local memory mechanism usually has small on-chip memory space which is not fit for the various complicated applications. The cache mechanism is better at dealing with the temporary data which need to be read and written frequently. But in deep packet processing, cache miss occurs when reading each segment of packet. We propose a cooperative mechanism of local memory and cache. In which the packet data and temporary data are stored into local memory and cache respectively. The analysis and experimental evaluation shows that the cooperative mechanism can improve the performance of network processors and reduce processing latency with little extra resources cost.

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