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This paper describes a system for phone segmentation using phonetic features, where context information influences the performance of Automatic Speech Recognition (ASR). Current Hidden Markov Model (HMM) based ASR systems have solved this problem by using context-sensitive triphone models. However, these models need a large number of speech parameters and a large volume of speech corpus. In this paper,...
The design of visual feature extraction with scale invariant feature transform (SIFT) is widely used for recognition of object in logos. However, the real-time implementation suffers from heavy computation, and high memory storage, long latency because of its frame level computation, so we propose PCSIFT(principal component analysis SIFT) to over come all the drawback by recognition and matching multiple...
In this work, a Multi-Level Artificial Bee Colony (called MLABC) is presented. In MLABC two species are used. The first species employs n colonies in which each of the them optimizes the complete solution vector. The cooperation between these colonies is carried out by exchanging information through a leader colony, which contains a set of elite bees. The second species uses a cooperative approach...
Craig interpolation has turned out to be an essential method for many applications in formal verification. In this paper we focus on the computation of simple interpolants for the theory of linear arithmetic with rational coefficients. We successfully minimize the number of linear constraints in the final interpolant by several methods including proof transformations, linear programming, and SMT solving...
With the growing needs of dimension reduction for term selection and recommendation and the up to date trends in natural language processing modules integrated in existing architectures and multiple semantic web system such as search engine. The existence of multiples tokenization techniques of the same text represents a persistent problem in current semantic search engine practice and create a non-trivial...
Double Patterning Technology (DPT) conflicts express themselves as odd cycles of spacing between layout shapes. One way of resolving these is by imposing a large spacing constraint between a pair of shapes participant in an odd cycle. However, this may shrink spacing in other parts of the layout and introduce DRC violations or new DPT conflicts. In this work, we model DPT conflict resolution as a...
Formal verification has increased efficiency by detecting corner case design bugs but it has also introduced new challenges when failures are detected. Once a counter-example is returned by a formal tool, the user typically does not know if the failure is caused by a design bug, an incorrectly written assertion, or a missing assumption. Previous work in debug automation has focused on the former two...
Processor design tools integrate in their workflows generators for instruction set simulators (ISS) from architecture descriptions. However, it is difficult to validate the correctness of these simu-lators. ISA coverage analysis is insufficient to isolate modelling faults, which might only be exposed in corner cases. We present a novel ISA branch coverage analysis, which considers every possible execution...
In the last few years, Transactional Memories (TMs) have been shown to be a parallel programming model that can effectively combine performance improvement with ease of programming. Moreover, the recent introduction of TM-based ISA extensions, by major microprocessor manufacturers, also seems to endorse TM as a programming model for today's parallel applications. One of the central issues in designing...
In this paper we introduce an algorithm for affective reasoning based on Bent ham's Felific Calculus known also as the hedonic calculus. Knowledge recquired for the task is retrived from a blog corpus by means of sentiment analysis on sentences containing an action or state input. This approach allows a machine to gather information on how usually other people feel when something happens, why people...
Detection and recognition of collective human activities are important modules of any system devoted to high level social behavior analysis. In this paper, we present a novel semantic-based spatio-temporal descriptor which can cope with several interacting people at different scales and multiple activities in a video. Our descriptor is suitable for modelling the human motion interaction in crowded...
This paper proposed a novel probabilistic topic model based on word senses. Different from the classic topic model exploring word form, this model generated the word form and at the same generated the word sense in a specific context. There are totally four layers in this model compared with the three layers in transitional probability topic models. We further illustrated how to solve the parameters...
We propose a framework for speech emotion detection that maps acoustic features into high level descriptors that integrates time context. Our framework uses three different algorithms to integrate the temporal context. The first method is based on temporal averaging of the original features. The second algorithm derives the descriptors by clustering the data using self-organizing maps (SOMs) and computing...
Cross-lingual document clustering is the task of automatically organizing a large collection of cross-lingual documents into a few groups according to their content or topic. It is well known that language barrier and translation ambiguity are two challenging issues for cross-lingual document representation. To address such issues, we propose to represent cross-lingual documents through statistical...
We propose a generalized approach to human gesture recognition based on multiple data modalities such as depth video, articulated pose and speech. In our system, each gesture is decomposed into large-scale body motion and local subtle movements such as hand articulation. The idea of learning at multiple scales is also applied to the temporal dimension, such that a gesture is considered as a set of...
A robust algorithm for non-negative matrix factorization (NMF) is presented in this paper with the purpose of dealing with large-scale data, where the separability assumption is satisfied. In particular, we modify the Linear Programming (LP) algorithm of [6] by introducing a reduced set of constraints for exact NMF. In contrast to the previous approaches, the proposed algorithm does not require the...
Sequence prediction is a key task in machine learning and data mining. It involves predicting the next symbol in a sequence given its previous symbols. Our motivating application is predicting the execution path of a process on an operating system in real-time. In this case, each symbol in the sequence represents a system call accompanied with arguments and a return value. We propose a novel online...
In multi-instance multi-label (MIML) instance annotation, the goal is to learn an instance classifier while training on a MIML dataset, which consists of bags of instances paired with label sets, instance labels are not provided in the training data. The MIML formulation can be applied in many domains. For example, in an image domain, bags are images, instances are feature vectors representing segments...
Choosing descriptive keywords to best describe digital media content is crucial for many applications, especially those involving content-based indexing or retrieval. Traditionally such keywords are selected manually, which is labor intensive, restrictive to a limited set of words and inherently subjective to the annotator. Therefore, in this paper, we propose an automatic and objective keyword selection...
In this paper we present a super-resolution (SR) method for upscaling low-resolution (LR) video sequences, that relies on the presence of periodic high-resolution (HR) key frames, and validate it in the context of video compression. For a given LR intermediate frame, the HR details are retrieved patch-by-patch by taking sparse linear combinations of patches found in the neighbor key frames. The performance...
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