Title: Handwritten Text Segmentation using Average Longest Path Algorithm
Authors: Dhaval Salvi, Jun Zhou, Jarrell Waggoner, and Song Wang
Abstract: Offline handwritten text recognition is a very challenging problem. Aside from the large variation of different hand-writing styles, neighboring characters within a word are usually connected, and we may need to segment a word into individual characters for accurate character recognition. Many existing methods achieve text segmentation by evaluating the local stroke geometry and imposing constraint on the size of each resulting character, such as the character width, height and aspect ratio. These constraints are well suited for printed texts, but may not hold for handwritten texts. Other methods apply holistic approach by using setof lexicons to guide and correct the segmentation and recognition. This approach may fail when the lexicon domain is insufficient. In this paper, we present a new global non-holistic method for handwritten text segmentation, which does not make any limiting assumptions on the characterize and the number of characters in a word. Specifically, the proposed method finds the text segmentation with the maximum average likeliness for the resulting characters. For this purpose, we use a graph model that describes the possible locations for segmenting neighboring characters, and we then develop an average longest path algorithm to identify the globally optimal segmentation. We conduct experiments on real images of handwritten texts taken from the IAM handwriting database and compare the performance of the proposed method against an existing text segmentation algorithm that uses dynamic programming.
Publish Year: 2013
Published in: WACV – IEEE
موضوع: تشخصی دست خط (Handwritten Text Recognition)
Title: Influence of Sci-Fi Films on Artificial Intelligence and Vice-Versa
Authors: D Lorenk, M Tarhaniov and P Sincak
Abstract: Sci-fi technological movie domain is an important part of human culture. The paper focus on comparison study of selected robotics sci-fi movie domain from a technological point of view. It is necessary to accomplish technological analysis of studied sci-fi movies and able to distinguish about possible current technology and future direction of the artificial intelligence in the domain of Robot intelligence. The review of existing movies which are in fact influencing thinking of humans is essential since it can influence a future research direction in AI. This information is interesting for inspiration of students and research associates in theory and applications. In conclusion, we envision potential problems of social networks and impact of Internet of things facts which is becoming a reality with IPv6 protocol. The goal of the paper is also to underline the importance of such cultural phenomena as sci-fi movies for the future of humanity.
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Title: Tell Me More? The Effects of Mental Model Soundness on Personalizing an Intelligent Agent
Authors: Todd Kulesza, Simone Stumpf, Margaret Burnett, Irwin Kwan
Abstract: What does a user need to know to productively work with an intelligent agent? Intelligent agents and recommender systems are gaining widespread use, potentially creating a need for end users to understand how these systems operate in order to fix their agents personalized behavior. This paper explores the effects of mental model soundness on such personalization by providing structural knowledge of a music recommender system in an empirical study. Our findings show that participants were able to quickly build sound mental models of the recommender system’s reasoning, and that participants who most improved their mental models during the study were significantly more likely to make the recommender operate to their satisfaction. These results suggest that by helping end users understand a system’s reasoning, intelligent agents may elicit more and better feedback, thus more closely aligning their output with each user’s intentions.
Title: A survey of skyline processing in highly distributed environments
Authors: Katja Hose Akrivi Vlachou
Abstract: During the last decades, data management and storage have become increasingly distributed. Advanced query operators, such as skyline queries, are necessary in order to help users to handle the huge amount of available data by identifying a set of interesting data objects. Skyline query processing in highly distributed environments poses inherent challenges and demands and requires non-traditional techniques due to the distribution of content and the lack of global knowledge. This paper surveys this interesting and still evolving research area, so that readers can easily obtain an overview of the state-of-the-art. We outline the objectives and the main principles that any distributed skyline approach have to fulfill, leading to useful guidelines for developing algorithms for distributed skyline processing. We review in detail existing approaches that are applicable for highly distributed environments, clarify the assumptions of each approach, and provide a comparative performance analysis. Moreover, we study the skyline variants each approach supports. Our analysis leads to taxonomy of existing approaches. Finally, we present interesting research topics on distributed skyline computation that have not yet been explored.
Title: Efficient stochastic algorithms for document clustering
Authors: Rana Forsati, Mehrdad Mahdav, Mehrnoush Shamsfard, Mohammad Reza Meybodi
Abstract: Clustering has become an increasingly important and highly complicated research area for targeting useful and relevant information in modern application domains such as the World Wide Web. Recent studies have shown that the most commonly used partitioning-based clustering algorithm, the K-means algorithm, is more suitable for large datasets. However, the K-means algorithm may generate a local optimal clustering. In this paper, we present novel document clustering algorithms based on the Harmony Search (HS) optimization method. By modeling clustering as an optimization problem, we first propose a pure HS based clustering algorithm that finds near-optimal clusters within a reasonable time. Then, harmony clustering is integrated with the K-means algorithm in three ways to achieve better clustering by combining the explorative power of HS with the refining power of the K-means. Contrary to the localized searching property of K-means algorithm, the proposed algorithms perform a globalized search in the entire solution space. Addition- ally, the proposed algorithms improve K-means by making it less dependent on the initial parameters such as randomly chosen initial cluster centers, therefore, making it more stable. The behavior of the proposed algorithm is theoretically analyzed by modeling its population variance as a Markov chain. We also conduct an empirical study to determine the impacts of various parameters on the quality of clusters and convergence behavior of the algorithms. In the experiments, we apply the proposed algorithms along with K-means and a Genetic Algorithm (GA) based clustering algorithm on five different document data- sets. Experimental results reveal that the proposed algorithms can find better clusters and the quality of clusters is comparable based on F-measure, Entropy, Purity, and Average Distance of Documents to the Cluster Centroid (ADDC).
Publish Year: 2013
Published in: Information Sciences - Science Direct
Title: Increasing the efficiency of quicksort using a neural network based algorithm selection model
Author: Ugur Erkin Kocamaz
Abstract: Quicksort is one of the most popular sorting algorithms it is based on a divide-and-conquer technique and has a wide acceptance as the fastest general-purpose sorting technique. Though it is successful in separating large partitions into small ones, quicksort runs slowly when it processes its small partitions, for which completing the sorting through using a different sorting algorithm is much plausible solution. This variant minimizes the overall execution time but it switches to a constant sorting algorithm at a constant cut-off point. To cope with this constancy problem, it has been suggested that a dynamic model which can choose the fastest sorting algorithm for the small partitions. The model includes continuation with quicksort so that the cut-off point is also more flexible. To implement this with an intelligent algorithm selection model, artificial neural net- works are preferred due to their non-comparison, constant-time and low-cost architecture features. In spite of the fact that finding the best sorting algorithm by using a neural net- work causes some extra computational time, the gain in overall execution time is greater. As a result, a faster variant of quicksort has been implemented by using artificial neural network based algorithm selection approach. Experimental results of the proposed algorithm and the several other fast sorting algorithms have been presented, compared and discussed.
Publish Year: 2013
Published in: Information Sciences - Science Direct
موضوع: شبکه های عصبی مصنوعی (Artificial Neural Networks)
Title: Dynamic router node placement in wireless mesh networks: A PSO approach with constriction coefficient and its convergence analysis
Author: Chun Cheng Lin
Abstract: Different from previous works, this paper considers the router node placement of wireless mesh networks (WMNs) in a dynamic network scenario in which both mesh clients and mesh routers have mobility, and mesh clients can switch on or off their network access at different times. We investigate how to determine the dynamic placement of mesh routers in a geographical area to adapt to the network topology changes at different times while maximizing two main network performance measures: network connectivity and client coverage, i.e., the size of the greatest component of the WMN topology and the number of the clients within radio coverage of mesh routers, respectively. In general, it is computationally intractable to solve the optimization problem for the above two performance measures. As a result, this paper first models a mathematical form for our concerned problem, then proposes a particle swarm optimization (PSO) approach, and, from a theoretical aspect, provides the convergence and stability analysis of the PSO with constriction coefficient, which is much simpler than the previous analysis. Experimental results show the quality of the proposed approach through sensitivity analysis, as well as the adaptability to the topology changes at different times.
Publish Year: 2013
Published in: Information Sciences - Science Direct
Title: Comparative Study of Traditional Requirement Engineering and Agile Requirement Engineering
Authors: Asma Batool , Yasir Hafeez Motla , Bushra Hamid , Sohail Asghar, Muhammad Riaz , Mehwish Mukhtar, Mehmood Ahmed
Abstract: Traditional RE and Agile RE are two different approaches on the basis of their planning and control mechanism. This Paper distinguishes the Traditional RE and Agile RE. Furthermore it investigates the reasons for which software industries shifted from Traditional RE to Agile RE. Research is carried out by conducting a literature study and finally a case study of software development to evaluate which approach has better success rate than other. With the help of our finding and results we have evaluated that Agile RE performs better than Traditional RE in large organizations where changes evolve throughout the development phase of software life cycle. Keywords- Requirement Engineering , Traditional Requirement Engineering, Agile Requirement Engineering, Scrum, Extreme Programming, Obj ect Oriented Development, Requirement Elicitation, Requirement Analysis, Requirement Management, Software Requirement Specification.
Abstract: In this paper we demonstrate highly dense immobilization of bacteria into nanoimprinted holes. Nanoimprinting enables micro holes smaller than 2 mm in diameter with a high accuracy, which cannot be patterned using conventional UV photolithography. In our prior work, we developed a microbial reactor immobilizing bacteria into micro holes, which facilitated collection and evaluation of reaction products while the number of bacteria involved in the reaction could be quantified. However, the holes were made by photolithography and the minimum size was limited to be 3 mm in diameter. Large holes allow multiple bacteria to be immobilized in a hole, which resulted in errors in quantification. The number of bacteria immobilized in a nanoimprinted hole was found to have smaller deviation than in photolithographically formed holes. In addition, density of the immobilized bacteria was experimentally found to be largest in case of 2-mm-holes. The proposed processes will be of great help for precise evaluation of bacteria reaction.
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