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ABSTRACT
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Bioinformatics, Genetics, Computer Science, Expert Systems, Combinatorial Optimization, and 15 moreComputational Biology, Gene regulation, Genetic Algorithms, Biology, Biological Chemistry, Gene expression, Genetic Algorithm, Genetic Engineering, Biological Sciences, Genome, CHEMICAL SCIENCES, Evolutionary Algorithm, Bayesian Network, Binding Site, and Biochemistry and cell biology
In this paper, a simple and robust watermarking algorithm is presented by using the first, second, third and the fourth Least Significant Bits (LSBs). We embed two bits in two places out of four LSBs according to the local variance value.... more
In this paper, a simple and robust watermarking algorithm is presented by using the first, second, third and the fourth Least Significant Bits (LSBs). We embed two bits in two places out of four LSBs according to the local variance value. Compared to the simple LSB algorithm where we use bits 7 and 8 to embed information, the proposed algorithm is more robust to white noise and JPEG compression. Experimental results show that the quality of the watermarked image is high in terms of Peak Signal-to-Noise (PSNR) and Structural Similarity Index (SSIM).
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Computer Science and Acm
⎯ A data warehouse is a special database used for storing business oriented information for future analysis and decision-making. In business scenarios, where some of the data or the business attributes are fuzzy, it may be useful to... more
⎯ A data warehouse is a special database used for storing business oriented information for future analysis and decision-making. In business scenarios, where some of the data or the business attributes are fuzzy, it may be useful to construct a warehouse that can support the analysis of fuzzy data. Here, we outline how Kimball‘s methodology for the design of a data warehouse can be extended to the construction of a fuzzy data warehouse. A case study demonstrates the viability of the methodology.
Most Statistical Process Control (SPC) methods are not suitable for monitoring non-linear and state-dependent processes. This paper introduces the Context-based SPC (CSPC) methodology for state-dependent data generated by a finite-memory... more
Most Statistical Process Control (SPC) methods are not suitable for monitoring non-linear and state-dependent processes. This paper introduces the Context-based SPC (CSPC) methodology for state-dependent data generated by a finite-memory source. The key idea of the CSPC is to monitor the statistical attributes of a process by comparing two context trees at any monitoring period of time. The first is a reference tree that represents the 'in control' reference behaviour of the process; the second is a monitored tree, generated periodically from a sample of sequenced observations, that represents the behaviour of the process at that period. The Kullback-Leibler (KL) statistic is used to measure the relative 'distance' between these two trees, and an analytic distribution of this statistic is derived. Monitoring the KL statistic indicates whether there has been any significant change in the process that requires intervention. An example of buffer-level monitoring in a pr...
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The Relevance Vector Machine (RVM) is a generalized linear model that can use kernel functions as basis functions. The typical RVM solution is very sparse. We present a strategy for feature ranking and selection via evaluating the... more
The Relevance Vector Machine (RVM) is a generalized linear model that can use kernel functions as basis functions. The typical RVM solution is very sparse. We present a strategy for feature ranking and selection via evaluating the influence of the features on the relevance vectors. This requires a single training of the RVM, thus, it is very efficient. Experiments on a benchmark regression problem provide evidence that it selects high-quality feature sets at a fraction of the costs of classical methods. Key-Words: Feature Selection, Relevance Vector Machine, Machine Learning
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Image understanding relies heavily on accurate multilabel classification. In recent years, deep learning (DL) algorithms have become very successful tools for multi-label classification of image objects, and various implementations of DL... more
Image understanding relies heavily on accurate multilabel classification. In recent years, deep learning (DL) algorithms have become very successful tools for multi-label classification of image objects, and various implementations of DL algorithms have been released for public use in the form of application programming interfaces (APIs). In this study, we evaluate and compare 10 of the most prominent publicly available APIs in a best-of-breed challenge. The evaluation is performed on the Visual Genome labeling benchmark dataset using 12 well-recognized similarity metrics. In addition, for the first time in this kind of comparison, we use a semantic similarity metric to evaluate the semantic similarity performance of these APIs. In this evaluation, Microsoft’s Computer Vision, TensorFlow, Imagga, and IBM’s Visual Recognition performed better than the other APIs. Furthermore, the new semantic similarity metric provided deeper insights for comparison. Keywords— multi-label classificat...
Automatic generation of natural language descriptions for images has recently become an important research topic. In this paper, we propose a frame-based algorithm for generating a composite natural language description for a given image.... more
Automatic generation of natural language descriptions for images has recently become an important research topic. In this paper, we propose a frame-based algorithm for generating a composite natural language description for a given image. The goal of this algorithm is to describe not only the objects appearing in the image but also the main activities happening in the image and the objects participating in those activities. The algorithm builds upon a pre-trained CRF (Conditional Random Field)-based structured prediction model, which generates a set of alternative frames for a given image. We use imSitu, a situation recognition dataset with 126,102 images, 504 activities, 11,538 objects, and 1,788 roles, as a test bed of our algorithm. We ask human evaluators to evaluate the quality of the descriptions for 20 images from the imSitu dataset. The results demonstrate that our composite description contains on average 16% more visual elements than the baseline method and gains a signifi...
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With the rapidly increasing number of online video resources, the ability of automatically understanding those videos becomes more and more important, since it is almost impossible for people to watch all of the videos and provide textual... more
With the rapidly increasing number of online video resources, the ability of automatically understanding those videos becomes more and more important, since it is almost impossible for people to watch all of the videos and provide textual descriptions. The duration of online videos varies in a extremely wide range, from several seconds to more than 5 h. In this paper, we focus on long videos, especially on full-length movies, and propose the first pipeline for automatically generating textual summaries of such movies. The proposed system takes an entire movie as input (including subtitles), splits it into scenes, generates a one-sentence description for each scene and summarizes those descriptions and subtitles into a final summary. In our initial experiment on a popular cinema movie (Forrest Gump), we utilize several existing algorithms and software tools for implementing the different components of our system. Most importantly, we use the S2VT (Sequence to Sequence—Video to Text) ...
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Image understanding relies heavily on accurate multi-label classification. In recent years deep learning (DL) algorithms have become very successful tools for multi-label classification of image objects. With these set of tools, various... more
Image understanding relies heavily on accurate multi-label classification. In recent years deep learning (DL) algorithms have become very successful tools for multi-label classification of image objects. With these set of tools, various implementations of DL algorithms have been released for the public use in the form of application programming interfaces (API). In this study, we evaluate and compare 10 of the most prominent publicly available APIs in a best-of-breed challenge. The evaluation is performed on the Visual Genome labeling benchmark dataset using 12 well-recognized similarity metrics. In addition, for the first time in this kind of comparison, we use a semantic similarity metric to evaluate the semantic similarity performance of these APIs. In this evaluation, Microsoft's Computer Vision, TensorFlow, Imagga, and IBM's Visual Recognition showed better performance than the other APIs. Furthermore, the new semantic similarity metric allowed deeper insights for compa...
Support Vector Machines (SVMs) are a set of related methods for supervised learning, applicable to both classification and regression problems. A SVM classifiers creates a maximum-margin hyperplane that lies in a transformed input space... more
Support Vector Machines (SVMs) are a set of related methods for supervised learning, applicable to both classification and regression problems. A SVM classifiers creates a maximum-margin hyperplane that lies in a transformed input space and splits the example ...
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Finding an appropriatetrade-off between performanceand computational complexity is an important issue in the design of adaptive algorithms. This paper introduces an algorithm for adaptive identification of Non-linear Auto-Regressive with... more
Finding an appropriatetrade-off between performanceand computational complexity is an important issue in the design of adaptive algorithms. This paper introduces an algorithm for adaptive identification of Non-linear Auto-Regressive with eXogenous inputs (NARX) ...
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Occupational factors have long been linked to patterns of mortality. Based on the premise that an entry in an encyclopedia tends to imply success in one's vocation, we used Wikipedia biographical entries (English version) to elucidate... more
Occupational factors have long been linked to patterns of mortality. Based on the premise that an entry in an encyclopedia tends to imply success in one's vocation, we used Wikipedia biographical entries (English version) to elucidate the relationship between career success and longevity. Analyzing 7756 Wikipedia entries for persons deceased between 2009 and 2011 in terms of gender, occupation and longevity, we found that male entries outnumbered female (6548 vs. 1208), and the mean age of death was lower for males than females (76.31 vs. 78.50 years). Younger ages of death were evident among sports players and Performing artists (73.04) and creative workers (74.68). Older deaths were seen in professionals and academics (82.63). Since these results are comparable with those found in the literature, they validate the use of Wikipedia for population studies. The gender classification procedure we developed for the biographical entries in order to obtain an occupation-by-gender com...
The last stage of any type of automatic surveillance system is the interpretation of the acquired information from the sensors. This work focuses on the interpretation of motion pictures taken from a surveillance camera, i.e.; image... more
The last stage of any type of automatic surveillance system is the interpretation of the acquired information from the sensors. This work focuses on the interpretation of motion pictures taken from a surveillance camera, i.e.; image understanding. An expert system is presented which can describe in a natural language like, simple human activity in the field of view of a
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ABSTRACT We propose to extend the use of Risannen's (1983) “tree source”-a relative of the partial hidden Markov model-to continuous signals. While the original algorithm is dedicated to modeling the context in which each symbol... more
ABSTRACT We propose to extend the use of Risannen's (1983) “tree source”-a relative of the partial hidden Markov model-to continuous signals. While the original algorithm is dedicated to modeling the context in which each symbol can occur in a discrete symbol space, we propose to match a specific ARMA model with each identified context. An example is presented
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Random exogenous environmental effects--such as radiation and virus--break the biopolymers of the cell (e.g., proteins and DNA), deteriorate its biochemical processes, and eventually cause wear-out and death. Worn-out cells are replaced... more
Random exogenous environmental effects--such as radiation and virus--break the biopolymers of the cell (e.g., proteins and DNA), deteriorate its biochemical processes, and eventually cause wear-out and death. Worn-out cells are replaced through the process of cell division. However, a limit to the number of times a cell divides has been noted in all fully differentiated human cell types, as well as in other organisms. While stem cells are an exception and may continue to regenerate cells for the entire lifespan of the organism, they are susceptible too to radiation, infection, and other forms of environmental damage. Spore-like cells are small dormant simple cell-like structures which have the ability to differentiate into mature cells of the tissue from which they are isolated or into the cell types of another tissue. They seem to be present in every tissue in the body. Spore-like cells tolerate conditions that kill differentiated or partially differentiated cells, such as complete...
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ABSTRACT A distillation column has a complex nonlinear separation process with time delays. Using the orthogonal matching pursuit algorithm, a fuzzy model is identified for the distillation column started from an empty operating... more
ABSTRACT A distillation column has a complex nonlinear separation process with time delays. Using the orthogonal matching pursuit algorithm, a fuzzy model is identified for the distillation column started from an empty operating condition. The fuzzy model could be used for predictive control of the distillation processes
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A queueing model for a distributed processing system with heterogeneous processing elements is developed. Each element can serve different processing demands. The system is controlled with a priority matrix, which determines the routing... more
A queueing model for a distributed processing system with heterogeneous processing elements is developed. Each element can serve different processing demands. The system is controlled with a priority matrix, which determines the routing of processing demands to processing elements. The systems performance is estimated using priority queueing models
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Most statistical process control (SPC) methods are not suitable for monitoring nonlinear and state- dependent processes. This article introduces the context-based SPC (CSPC) methodology for state- dependent data generated by a... more
Most statistical process control (SPC) methods are not suitable for monitoring nonlinear and state- dependent processes. This article introduces the context-based SPC (CSPC) methodology for state- dependent data generated by a finite-memory source. The key idea of the CSPC is to monitor the statistical attributes of a process by comparing two context trees at any monitoring period of time. The first is a ref- erence tree that represents the "in control" reference behavior of the process; the second is a monitored tree, generated periodically from a sample of sequenced observations, that represents the behavior of the process at that period. The Kullback-Leibler (KL) statistic is used to measure the relative "distance" be- tween these two trees, and an analytic distribution of this statistic is derived. Monitoring the KL statistic indicates whether there has been any significant change in the process that requires intervention. An ex- ample of buffer-level monitor...
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Finding an appropriatetrade-off between performanceand computational complexity is an important issue in the design of adaptive algorithms. This paper introduces an algorithm for adaptive identification of Non-linear Auto-Regressive with... more
Finding an appropriatetrade-off between performanceand computational complexity is an important issue in the design of adaptive algorithms. This paper introduces an algorithm for adaptive identification of Non-linear Auto-Regressive with eXogenous inputs (NARX) ...
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Based on the fact that fuzzy systems can be represented as a linear combination of fuzzy basis functions (FBF), it is proposed that spline wavelets scaling functions be used as FBF. A fuzzy system built from dilations and translations of... more
Based on the fact that fuzzy systems can be represented as a linear combination of fuzzy basis functions (FBF), it is proposed that spline wavelets scaling functions be used as FBF. A fuzzy system built from dilations and translations of a wavelet scaling function can universally ...
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Performance of Copper and Aluminum Electrodes During EDM of Stainless Steel and Carbide AA Khan and S. Mridha ... Mathematical Modeling for Study of Parametric Influence on Bead Height, Bead Width and Depth of Heat Affected Zone of... more
Performance of Copper and Aluminum Electrodes During EDM of Stainless Steel and Carbide AA Khan and S. Mridha ... Mathematical Modeling for Study of Parametric Influence on Bead Height, Bead Width and Depth of Heat Affected Zone of Submerged Arc Bead-on-Plate ...
Direct marketing involves offering a product or service to a carefully selected group of customers, the ones expected to render the most profits. Active learning is a data mining policy which actively selects unlabeled instances for... more
Direct marketing involves offering a product or service to a carefully selected group of customers, the ones expected to render the most profits. Active learning is a data mining policy which actively selects unlabeled instances for labeling. In this research our goal is to ...
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System Identification, Radial Basis Function, Adaptive Control, Neural Networks, Neural Network, and 16 moreAdaptive Signal Processing, Parameter estimation, Adaptive Systems, Identification, Nonlinear Systems, Dynamic Simulation, Lecture notes, Adaptive Filter, Dictionaries, Adaptive Filters, Unscented Kalman Filter, Vectors, Nonlinear system, Neural Net, Time varying, and Non Linear System
Kernels for the Relevance Vector Machine - An Empirical Study 255 where r(v) is the gamma function and Kv(x) is the modified Bessel function of the second kind1 of order v, and a in this case is the width scaling parameter of the Matern... more
Kernels for the Relevance Vector Machine - An Empirical Study 255 where r(v) is the gamma function and Kv(x) is the modified Bessel function of the second kind1 of order v, and a in this case is the width scaling parameter of the Matern function. When v > oo the Matern ...
We present a new approach for gene finding based on a Variable-Order Markov (VOM) Model. The VOM model is a generalization of the traditional Markov model, which is more efficient in terms of its parameterization, thus, can be trained on... more
We present a new approach for gene finding based on a Variable-Order Markov (VOM) Model. The VOM model is a generalization of the traditional Markov model, which is more efficient in terms of its parameterization, thus, can be trained on relatively short sequences. As a ...