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This paper investigates how data mining can be applied in functional debug, which is formulated as the problem of explaining a functional simulation error based on human-understandable machine states. We present a rule discovery methodology comprising two steps. The first step selects relevant state variables for constructing the mining dataset. The second step applies rule learning to extract rules...
Digital libraries on distance learning platforms are ways that allow learners to consult and enrich their knowledge on the content that are studied in their course. In this paper, we are interested in the semantic analysis of the content of the resources visited in the digital libraries (eBooks) by learners using domain ontology. The purpose of this analysis is to identify the domain concepts that...
The interest point (IP) matching algorithms match the points either locally or spatially. We propose a local-spatial IP matching algorithm usable for articulated human body tracking. The local-based stage finds matched IP pairs of two reference and target IP lists using a local-feature-descriptors-based matching method. Then, the spatial-based stage recovers more matched pairs from the remaining unmatched...
Humans as well as humanoid robots can use a large number of degrees of freedom to solve very complex motor tasks. The high-dimensionality of these motor tasks adds difficulties to the control problem and machine learning algorithms. However, it is well known that the intrinsic dimensionality of many human movements is small in comparison to the number of employed DoFs, and hence, the movements can...
In order to describe a structured region of memory, the routines in the MPI standard use a (count, datatype) pair. The C specification for this convention uses an int type for the count. Since C int types are nearly always 32 bits large and signed, counting more than 231 elements poses a challenge. Instead of changing the existing MPI routines, and all consumers of those routines, the MPI Forum asserts...
Our aim is to better understand the action selection process of intelligent systems by looking at their ability of internal prediction. In robotic systems, one problem is to generate meaningful robot behaviour with a very small and simple set of trained motions. An additional problem is to compensate for incomplete sensory data while generating behaviour. We propose a new predictive action selector...
One of the most interesting topics in social network research is opinion formation. In this paper we have introduced a new dependent multi-dimensional opinion formation method. This method models agents with several dependent opinions so that modifying the agent's opinion about one issue can affect its opinion about another issue. Agents share their opinion with agents which have trusted them. A directed...
In this paper, we propose an improved table-based white-box implementation of AES which is able to resist different types of attack, including the BGE attack and De Mulder et al.'s cryptanalysis, to protect information under “white-box attack context”. The notion of white-box attack context, introduced by Chow et al., describes a general setting in which cryptographic algorithms are executed in untrusted...
In this paper, we present the construction of a multilevel focus context visualization framework for the navigation and exploration of large-scale 2D and 3D images. The presented framework utilizes a balanced multiresolution (BMR) technique supported by a balanced wavelet transform (BWT). This devised framework extends the mode of focus context visualization, where spatially separate magnification...
Lemmas on demand is an abstraction/refinement technique for procedures deciding Satisfiability Modulo Theories (SMT), which iteratively refines full candidate models of the formula abstraction until convergence. In this paper, we introduce a dual propagation-based technique for optimizing lemmas on demand by extracting partial candidate models via don't care reasoning on full candidate models. Further,...
In the past, fingerprinting algorithms have been suggested as a practical and cost-effective means for deploying localisation services. Previous research, however, often assumes an (idealised) laboratory environment rather than a realistic set-up. In our work we analyse challenges occurring from a university environment which is characterised by hundreds of access points deployed and by heterogeneous...
This paper introduces two new categories of players, an equalization player, with the goal to minimize the score gap between the opponents, and a variation enhancement player, with the goal to maximize the same. In addition, a complementary player type is added to the classic max-player game, called the min-player, along with a mathematical formalization of basic strategies, yielding a new algorithm...
Radar resource management (RRM) is an active research field that has a strong practical importance and attracts the attention of both the scientists and industry experts. A discussion of the various approaches to RRM became essentially a discussion on how to formulate the optimization problem. Although much work has been done on optimal performance-based RRM, the approaches that take mission objectives...
This paper proposes a new composition method to represent semantic compositionality of sentences. Using the unfolding recursive autoencoders, we build sentence representing trees from the original sentences of words. We utilize trained word embeddings and sentence parser to train the model, and we can build sentence representing trees from the trained model. We further propose to use dynamic average...
The online anomaly detection has been propounded as the the key idea of monitoring fault of large-scale sensor nodes in Internet of Things. Although the exciting progresses of research have been made in online anomaly detection area, the highly dynamic distribution makes the anomaly detection scheme difficult to be used in online manner. This paper presents an online anomaly learning and forecasting...
We present an approach for object class learning using a part-based shape categorization in RGB-augmented 3D point clouds captured from cluttered indoor scenes with a Kinect-like sensor. We propose an unsupervised hierarchical learning procedure which allows to symbolically classify shape parts by different specificity levels of detailedness of their surface-structural appearance. Further, a hierarchical...
State abstraction [1] is one of solutions to the curse of dimensionality [2] problem, and possibly allows real-life application of AI algorithms. We present a new state abstraction algorithm inspired by stimulus discrimination theory from behavioral psychology [3], [4] and by current work on bisimulation theory as applied to reinforcement learning [5], [6], [7]. The new way of comparing state abstractions...
In the process of medicine information extraction, there are many Named Entity (NE) need to be recognized But currently the research on identification of NE in the field of medicine, such as physician, hospital, disease, medicine NE etc. is rarely. So in this paper we present an new approach for Named Entity Recognition (NER) in the field of medicine based on Bootstrapping method This method primarily...
Although dynamically reconfigurable processor arrays (DRPAs) are advantageous for embedded devices because of their high energy efficiency, many of the recent mobile devices are required to execute increasingly performance-centric jobs. One fairly straingtfoward way of increasing the clock frequency is introducing a pipelined structure into each PE. However, this results in frequent pipeline stalls...
In this paper we evaluate the influence of the selection of key points and the associated features in the performance of word spotting processes. In general, features can be extracted from a number of characteristic points like corners, contours, skeletons, maxima, minima, crossings, etc. A number of descriptors exist in the literature using different interest point detectors. But the intrinsic variability...
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