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Title: Optimal Bangla Keyboard Layout using Association Rule of Data Mining |
Abstract: In this paper we present an optimal Bangla Keyboard Layout, which distributes the load equally on both hands so that maximizing the ease and minimizing the effort. Bangla alphabet has a large number of letters, for this it is difficult to type faster using Bangla keyboard. Our proposed keyboard will maximize ... |
Title: A Fast Audio Clustering Using Vector Quantization and Second Order Statistics |
Abstract: This paper describes an effective unsupervised speaker indexing approach. We suggest a two stage algorithm to speed-up the state-of-the-art algorithm based on the Bayesian Information Criterion (BIC). In the first stage of the merging process a computationally cheap method based on the vector quantization (VQ... |
Title: Balancing clusters to reduce response time variability in large scale image search |
Abstract: Many algorithms for approximate nearest neighbor search in high-dimensional spaces partition the data into clusters. At query time, in order to avoid exhaustive search, an index selects the few (or a single) clusters nearest to the query point. Clusters are often produced by the well-known $k$-means approach ... |
Title: Modeling Instantaneous Changes In Natural Scenes |
Abstract: This project aims to create 3d model of the natural world and model changes in it instantaneously. A framework for modeling instantaneous changes natural scenes in real time using Lagrangian Particle Framework and a fluid-particle grid approach is presented. This project is presented in the form of a proof-ba... |
Title: Efficient L1/Lq Norm Regularization |
Abstract: Sparse learning has recently received increasing attention in many areas including machine learning, statistics, and applied mathematics. The mixed-norm regularization based on the L1/Lq norm with q > 1 is attractive in many applications of regression and classification in that it facilitates group sparsity i... |
Title: Improving the Quality of Non-Holonomic Motion by Hybridizing C-PRM Paths |
Abstract: Sampling-based motion planners are an effective means for generating collision-free motion paths. However, the quality of these motion paths, with respect to different quality measures such as path length, clearance, smoothness or energy, is often notoriously low. This problem is accentuated in the case of no... |
Title: Multi-parametric Solution-path Algorithm for Instance-weighted Support Vector Machines |
Abstract: An instance-weighted variant of the support vector machine (SVM) has attracted considerable attention recently since they are useful in various machine learning tasks such as non-stationary data analysis, heteroscedastic data modeling, transfer learning, learning to rank, and transduction. An important challe... |
Title: Image Segmentation by Discounted Cumulative Ranking on Maximal Cliques |
Abstract: We propose a mid-level image segmentation framework that combines multiple figure-ground hypothesis (FG) constrained at different locations and scales, into interpretations that tile the entire image. The problem is cast as optimization over sets of maximal cliques sampled from the graph connecting non-overla... |
Title: Towards Quality of Service and Resource Aware Robotic Systems through Model-Driven Software Development |
Abstract: Engineering the software development process in robotics is one of the basic necessities towards industrial-strength service robotic systems. A major challenge is to make the step from code-driven to model-driven systems. This is essential to replace hand-crafted single-unit systems by systems composed out of... |
Title: Bayesian Tracking of Emerging Epidemics Using Ensemble Optimal Statistical Interpolation (EnOSI) |
Abstract: We explore the use of the optimal statistical interpolation (OSI) data assimilation method for the statistical tracking of emerging epidemics and to study the spatial dynamics of a disease. The epidemic models that we used for this study are spatial variants of the common susceptible-infectious-removed (S-I-R... |
Title: Speaker Identification using MFCC-Domain Support Vector Machine |
Abstract: Speech recognition and speaker identification are important for authentication and verification in security purpose, but they are difficult to achieve. Speaker identification methods can be divided into text-independent and text-dependent. This paper presents a technique of text-dependent speaker identificati... |
Title: Rotation Invariant Face Detection Using Wavelet, PCA and Radial Basis Function Networks |
Abstract: This paper introduces a novel method for human face detection with its orientation by using wavelet, principle component analysis (PCA) and redial basis networks. The input image is analyzed by two-dimensional wavelet and a two-dimensional stationary wavelet. The common goals concern are the image clearance a... |
Title: Text Classification using Association Rule with a Hybrid Concept of Naive Bayes Classifier and Genetic Algorithm |
Abstract: Text classification is the automated assignment of natural language texts to predefined categories based on their content. Text classification is the primary requirement of text retrieval systems, which retrieve texts in response to a user query, and text understanding systems, which transform text in some wa... |
Title: Optimal Bangla Keyboard Layout using Data Mining Technique |
Abstract: This paper presents an optimal Bangla Keyboard Layout, which distributes the load equally on both hands so that maximizing the ease and minimizing the effort. Bangla alphabet has a large number of letters, for this it is difficult to type faster using Bangla keyboard. Our proposed keyboard will maximize the s... |
Title: On reverse-engineering the KUKA Robot Language |
Abstract: Most commercial manufacturers of industrial robots require their robots to be programmed in a proprietary language tailored to the domain - a typical domain-specific language (DSL). However, these languages oftentimes suffer from shortcomings such as controller-specific design, limited expressiveness and a la... |
Title: On the complexity of the multiple stack TSP, kSTSP |
Abstract: The multiple Stack Travelling Salesman Problem, STSP, deals with the collect and the deliverance of n commodities in two distinct cities. The two cities are represented by means of two edge-valued graphs (G1,d2) and (G2,d2). During the pick-up tour, the commodities are stored into a container whose rows are s... |
Title: Approximability of the Multiple Stack TSP |
Abstract: STSP seeks a pair of pickup and delivery tours in two distinct networks, where the two tours are related by LIFO contraints. We address here the problem approximability. We notably establish that asymmetric MaxSTSP and MinSTSP12 are APX, and propose a heuristic that yields to a 1/2, 3/4 and 3/2 standard appro... |
Title: The Most Advantageous Bangla Keyboard Layout Using Data Mining Technique |
Abstract: Bangla alphabet has a large number of letters, for this it is complicated to type faster using Bangla keyboard. The proposed keyboard will maximize the speed of operator as they can type with both hands parallel. Association rule of data mining to distribute the Bangla characters in the keyboard is used here.... |
Title: The Time Machine: A Simulation Approach for Stochastic Trees |
Abstract: In the following paper we consider a simulation technique for stochastic trees. One of the most important areas in computational genetics is the calculation and subsequent maximization of the likelihood function associated to such models. This typically consists of using importance sampling (IS) and sequentia... |
Title: Sequential design of computer experiments for the estimation of a probability of failure |
Abstract: This paper deals with the problem of estimating the volume of the excursion set of a function $f:^d \to $ above a given threshold, under a probability measure on $^d$ that is assumed to be known. In the industrial world, this corresponds to the problem of estimating a probability of failure of a system. When ... |
Title: Defining and Generating Axial Lines from Street Center Lines for better Understanding of Urban Morphologies |
Abstract: Axial lines are defined as the longest visibility lines for representing individual linear spaces in urban environments. The least number of axial lines that cover the free space of an urban environment or the space between buildings constitute what is often called an axial map. This is a fundamental tool in ... |
Title: General Scaled Support Vector Machines |
Abstract: Support Vector Machines (SVMs) are popular tools for data mining tasks such as classification, regression, and density estimation. However, original SVM (C-SVM) only considers local information of data points on or over the margin. Therefore, C-SVM loses robustness. To solve this problem, one approach is to t... |
Title: Measuring Similarity of Graphs and their Nodes by Neighbor Matching |
Abstract: The problem of measuring similarity of graphs and their nodes is important in a range of practical problems. There is a number of proposed measures, some of them being based on iterative calculation of similarity between two graphs and the principle that two nodes are as similar as their neighbors are. In our... |
Title: Robust Shrinkage Estimation of High-dimensional Covariance Matrices |
Abstract: We address high dimensional covariance estimation for elliptical distributed samples, which are also known as spherically invariant random vectors (SIRV) or compound-Gaussian processes. Specifically we consider shrinkage methods that are suitable for high dimensional problems with a small number of samples (l... |
Title: A Novel Approach for Cardiac Disease Prediction and Classification Using Intelligent Agents |
Abstract: The goal is to develop a novel approach for cardiac disease prediction and diagnosis using intelligent agents. Initially the symptoms are preprocessed using filter and wrapper based agents. The filter removes the missing or irrelevant symptoms. Wrapper is used to extract the data in the data set according to ... |
Title: Task-Driven Dictionary Learning |
Abstract: Modeling data with linear combinations of a few elements from a learned dictionary has been the focus of much recent research in machine learning, neuroscience and signal processing. For signals such as natural images that admit such sparse representations, it is now well established that these models are wel... |
Title: A Scenario-Based Mobile Application for Robot-Assisted Smart Digital Homes |
Abstract: Smart homes are becoming more popular, as every day a new home appliance can be digitally controlled. Smart Digital Homes are using a server to make interaction with all the possible devices in one place, on a computer or webpage. In this paper we designed and implemented a mobile application using Windows Mo... |
Title: Portfolio Allocation for Bayesian Optimization |
Abstract: Bayesian optimization with Gaussian processes has become an increasingly popular tool in the machine learning community. It is efficient and can be used when very little is known about the objective function, making it popular in expensive black-box optimization scenarios. It uses Bayesian methods to sample t... |
Title: The thermodynamic temperature of a rhythmic spiking network |
Abstract: Artificial neural networks built from two-state neurons are powerful computational substrates, whose computational ability is well understood by analogy with statistical mechanics. In this work, we introduce similar analogies in the context of spiking neurons in a fixed time window, where excitatory and inhib... |
Title: Square-Root Lasso: Pivotal Recovery of Sparse Signals via Conic Programming |
Abstract: We propose a pivotal method for estimating high-dimensional sparse linear regression models, where the overall number of regressors $p$ is large, possibly much larger than $n$, but only $s$ regressors are significant. The method is a modification of the lasso, called the square-root lasso. The method is pivot... |
Title: Kernel Bayes' rule |
Abstract: A nonparametric kernel-based method for realizing Bayes' rule is proposed, based on representations of probabilities in reproducing kernel Hilbert spaces. Probabilities are uniquely characterized by the mean of the canonical map to the RKHS. The prior and conditional probabilities are expressed in terms of RK... |
Title: Use of multiple singular value decompositions to analyze complex intracellular calcium ion signals |
Abstract: We compare calcium ion signaling ($\mathrm Ca^2+$) between two exposures; the data are present as movies, or, more prosaically, time series of images. This paper describes novel uses of singular value decompositions (SVD) and weighted versions of them (WSVD) to extract the signals from such movies, in a way t... |
Title: Face Detection with Effective Feature Extraction |
Abstract: There is an abundant literature on face detection due to its important role in many vision applications. Since Viola and Jones proposed the first real-time AdaBoost based face detector, Haar-like features have been adopted as the method of choice for frontal face detection. In this work, we show that simple f... |
Title: Approximate Maximum A Posteriori Inference with Entropic Priors |
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