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Compute-intensive applications are emerging in intelligent home, retail store and automotive industries. These applications are becoming more sophisticated with new features rich in audio, video, image, and machine learning capabilities that demand heavy computations. We present the EMERALD (EMERging Applications and algorithms for Low power Device) workload suite. We profile the workloads to show...
Advances in neuroscience have enabled researchers to develop computational models of auditory, visual and learning perceptions in the human brain. HMAX, which is a biologically inspired model of the visual cortex, has been shown to outperform standard computer vision approaches for multi-class object recognition. HMAX, while computationally demanding, can be potentially applied in various applications...
Recently significant advances have been achieved in understanding the visual information processing in the human brain. The focus of this work is on the design of an architecture to support HMAX, a widely accepted model of the human visual pathway. The computationally intensive nature of HMAX and wide applicability in real-time visual analysis application makes the design of hardware accelerators...
In this paper, a generic summarization method that uses cluster refinement and NMF is introduced to extract meaningful sentences from documents. The proposed method uses cluster refinement to improve the quality of document clustering since it helps us to remove dissimilarity information easily and avoid biased inherent semantics of documents to be reflected in clusters by NMF. In addition, it uses...
This paper proposes a new query based personalized summarization agent using non-negative matrix factorization (NMF) and relevance feedback (RF) to extract meaningful sentences from to retrieve documents in Internet. The proposed method can improve the quality of personalized summaries because the inherent semantics of the documents are well reflected by using the semantic features calculated by NMF...
This paper proposes a new document summarization method using relevance feedback (RF) and non-negative matrix factorization (NMF) to distill the contents of the documents with respect to a given query. The proposed method expands the query through relevance feedback to reflect user's requirement and extract meaningful sentences using the cosine similarity measure between the expanded query and the...
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