ppxscal/embedding_network huggingface dataset:
Evidence-Based Insights about Issue Management Processes: An Exploratory Study
Authors: Vahid Garousi
Venue: Proceedings of the International Conference on Software Process: Trustworthy Software Development Processes (2009)

Issue (e.g., defect) repositories usually contain rich information that can be used to mine evidence about team dynamics, issue management processes, and other aspects of software development. The exploratory case study reported in this paper uses quantitative issue tracking data of three open-source projects to derive insights into how issues emerge and are handled in open-source projects. The mined information provides empirical evidence for a few beliefs in the software engineering and process communities. For example, depending on their specific context factors, projects show different degrees of responsiveness to the occurrence of defects. Software engineers can use techniques similar to those presented in this paper to mine the issue repositories of their in-house development projects. This may serve to better characterize their issue management processes, to perform self-assessment and evaluation on them, and also to identify process smells (symptoms) in those processes.
A fuzzy analysis of country-size argument for the Feldstein-Horioka puzzle
Authors: Ibrahim Ozkan, Lutfi Erden, I. B. Türkşen
Venue: Information Sciences: an International Journal (2009)

This study aims to analyze the effect of country size, represented by relative Gross National Products (GNP), on the association between domestic investment and saving, using data from a panel of 21 OECD countries. The countries are clustered into four groups with respect to their relative country sizes with an application of Fuzzy c-Means clustering technique. The novelty of this approach is that it is an unsupervised method that generates membership values between zero and one instead of binary values that take values of zero or one only. In addition to this it has the advantages of its tolerance to imprecise data and the ease of understanding. The results show that the saving retention coefficients are greater for larger countries except for the cluster which contains the largest country. Thus, this work presents only partial evidence that the country size affects the relationship of domestic saving and investment.
A hierarchical object oriented method for land cover classification of SPOT 5 imagery
Authors: Wei Su, Chao Zhang, Xiang Zhu, Daoliang Li
Venue: WSEAS Transactions on Information Science and Applications (2009)

Land cover classification with a high accuracy is necessary, especially in waste dump area, accurate land cover information is very important to eco-environment research, vegetation condition study and soil recovery destination. Funded by the international cooperation project Novel Indicator Technologies for Minesite Rehabilitation and sustainable development, a hierarchical object oriented land cover classification is produced in this study. The ample spectral information, textural information, structure and shape information of high resolution SPOT 5 imagery are used synthetically in this method. There are two steps in object oriented information extraction: image segmentation and classification. First, the image is segmented using chessboard segmentation and multi-resolution segmentation method. Second, NDVI is used to distinguish vegetation and non-vegetation; vegetation is classified as high density vegetation, middling density vegetation and low density vegetation using spectral information, object oriented image texture analysis; non-vegetation is classified as vacant land and main road using length/width. Accuracy assessment indicate that this hierarchical method can be used to do land cover classification in waste dump area, the total accuracy increases to 86.53%, and Kappa coefficient increases to 0.7907.
Proceedings of the 18th ACM international symposium on High performance distributed computing
Venue: High Performance Distributed Computing (2009)

It is our great pleasure to welcome you to the 18th ACM International Symposium on High Performance Distributed Computing. This year's symposium continues its tradition of being the premier forum for presentation of research results and experience reports on latest research findings on the design and use of parallel and distributed systems for high end computing, collaboration, data analysis, and other innovative applications. This installment takes place in Garching near Munich, Germany, June 11-13, 2009. Topics of interest include HPDC architectures, high end communications, data management and transport, software environments, operating system technologies, grid middleware, applications and algorithms, as well as fault tolerance. Co-located with HPDC 09 are six workshops. We welcome the Workshop on Challenges for Large Applications in Distributed Environments (CLADE 09), the Second International Workshop on Data-Aware Distributed Computing (DADC 09), the Workshop on Large-Scale System and Application Performance (LSAP 09), the Workshop on Monitoring, Logging and Accounting in Production Grids (MLA 09), the Workshop on Resiliency in High-Performance Computing (Resilience 09), and the 4th UPGRADE-CN Workshop on Content Management and Delivery in Large-Scale Networks (UPGRADE-CN 09). The call for papers attracted 68 submissions from Asia, Canada, Europe, Africa, and the United States. The program committee accepted 20 papers that cover a variety of topics, including Grid middleware and distributed algorithms, resource management and scheduling, data management, parallel algorithms and applications, workflow and dataflow applications, I/O and parallel computing. We hope that these proceedings will serve as a valuable reference for researchers and developers.
Learning kernels from indefinite similarities
Authors: Yihua Chen, Maya R. Gupta, Benjamin Recht
Venue: Proceedings of the 26th Annual International Conference on Machine Learning (2009)

Similarity measures in many real applications generate indefinite similarity matrices. In this paper, we consider the problem of classification based on such indefinite similarities. These indefinite kernels can be problematic for standard kernel-based algorithms as the optimization problems become non-convex and the underlying theory is invalidated. In order to adapt kernel methods for similarity-based learning, we introduce a method that aims to simultaneously find a reproducing kernel Hilbert space based on the given similarities and train a classifier with good generalization in that space. The method is formulated as a convex optimization problem. We propose a simplified version that can reduce overfitting and whose associated convex conic program can be solved efficiently. We compare the proposed simplified version with six other methods on a collection of real data sets.
SOA decision making - what do we need to know
Authors: Qing Gu, Hans van Vliet
Venue: Proceedings of the 2009 ICSE Workshop on Sharing and Reusing Architectural Knowledge (2009)

The SOA decision making process addresses specific concerns, which requires (its own) specific information. Currently, SOA design decisions are often documented (if at all) using generic architectural design decision models. The information that is specifically relevant to SOA design decisions remains hidden in the description of the existing entities of architectural decision models. As a result, this information could be overlooked during the decision making process and result in unsound decisions. In this paper, we highlight a set of information items that aids SOA decision making.
A Note on Non-Alethic Temporal Logics
Authors: Jair Minoro Abe, Kazumi Nakamatsu
Venue: Proceeding of the 2005 conference on Advances in Logic Based Intelligent Systems: Selected Papers of LAPTEC 2005 (2005)

In this paper we discuss time in paraconsistency and paracompleteness. We introduce briefly the temporal logical system Δ1 which may constitute, for instance, a framework for non-alethic temporal reasoning.
Hierarchical approximate matching for retrieval of chinese historical calligraphy character
Authors: Xia-Fen Zhang, Yue-Ting Zhuang, Jiang-Qin Wu, Fei Wu
Venue: Journal of Computer Science and Technology (2007)

As historical Chinese calligraphy works are being digitized, the problem of retrieval becomes a new challenge. But, currently no OCR technique can convert calligraphy character images into text, nor can the existing Handwriting Character Recognition approach does not work for it. This paper proposes a novel approach to efficiently retrieving Chinese calligraphy characters on the basis of similarity: calligraphy character image is represented by a collection of discriminative features, and high retrieval speed with reasonable effectiveness is achieved. First, calligraphy characters that have no possibility similar to the query are filtered out step by step by comparing the character complexity, stroke density and stroke protrusion. Then, similar calligraphy characters are retrieved and ranked according to their matching cost produced by approximate shape match. In order to speed up the retrieval, we employed high dimensional data structure -- PK-tree. Finally, the efficiency of the algorithm is demonstrated by a preliminary experiment with 3012 calligraphy character images.
A pragmatic approach to summary extraction in clinical trials
Authors: Graciela Rosemblat, Laurel Graham
Venue: Proceedings of the Workshop on Linking Natural Language Processing and Biology: Towards Deeper Biological Literature Analysis (2006)

ClinicalTrials.gov, the National Library of Medicine clinical trials registry, is a monolingual clinical research website with over 29,000 records at present. The information is presented in static and free-text fields. Static fields contain high-level informational text, descriptors, and controlled vocabularies that remain constant across all clinical studies (headings, general information). Free-text data are detailed and trial-specific, such as the Purpose section, which presents each trial's goal, with large inter-trial variability in length as well as in technical difficulty. The crux of the trial purpose is generally found in 1--3 sentences, often introduced by clearly identified natural language markers.
Serial and parallel methods for i/o efficient suffix tree construction
Authors: Amol Ghoting, Konstantin Makarychev
Venue: Proceedings of the 35th SIGMOD international conference on Management of data (2009)

Over the past three decades, the suffix tree has served as a fundamental data structure in string processing. However, its widespread applicability has been hindered due to the fact that suffix tree construction does not scale well with the size of the input string. With advances in data collection and storage technologies, large strings have become ubiquitous, especially across emerging applications involving text, time series, and biological sequence data. To benefit from these advances, it is imperative that we realize a scalable suffix tree construction algorithm. To deal with the aforementioned challenge, the past few years have seen the emergence of several disk-based suffix tree construction algorithms. However, construction times continue to be daunting -- for e.g., indexing the entire Human genome still takes over 30 hours on a system with 2 gigabytes of physical memory. In this paper, first, we empirically demonstrate and argue that all existing suffix tree construction algorithms have a severe limitation -- to glean reasonable disk I/O efficiency, the input string being indexed must fit in main memory. This limitation is attributed to the poor locality properties of existing suffix tree construction algorithms and inhibits both sequential and parallel scalability. To deal with this limitation, second, we show that through careful algorithm design, one of the simplest suffix tree construction algorithms can be re-architected to build a suffix tree in a tiled fashion, allowing the implementation to maintain a constant working set size and fixed memory footprint when indexing strings of any size. Third, we show how improved locality of reference coupled with effective collective communication facilitates an efficient parallelization on massively parallel systems like the IBM Blue Gene/L. Finally, we empirically show that the proposed approach affords improvements of several orders of magnitude when indexing large strings. Furthermore, we demonstrate that the proposed parallelization is scalable and allows one to index the entire Human genome on a 1024 processor system in under 15 minutes.
Intermediate Probability: A Computational Approach
Authors: Marc Paolella
Venue:  (2007)

Intermediate Probability is the natural extension of the author's Fundamental Probability. It details several highly important topics, from standard ones such as order statistics, multivariate normal, and convergence concepts, to more advanced ones which are usually not addressed at this mathematical level, or have never previously appeared in textbook form. The author adopts a computational approach throughout, allowing the reader to directly implement the methods, thus greatly enhancing the learning experience and clearly illustrating the applicability, strengths, and weaknesses of the theory. The book: Places great emphasis on the numeric computation of convolutions of random variables, via numeric integration, inversion theorems, fast Fourier transforms, saddlepoint approximations, and simulation. Provides introductory material to required mathematical topics such as complex numbers, Laplace and Fourier transforms, matrix algebra, confluent hypergeometric functions, digamma functions, and Bessel functions. Presents full derivation and numerous computational methods of the stable Paretian and the singly and doubly non-central distributions. A whole chapter is dedicated to mean-variance mixtures, NIG, GIG, generalized hyperbolic and numerous related distributions. A whole chapter is dedicated to nesting, generalizing, and asymmetric extensions of popular distributions, as have become popular in empirical finance and other applications. Provides all essential programming code in Matlab and R. The user-friendly style of writing and attention to detail means that self-study is easily possible, making the book ideal for senior undergraduate and graduate students of mathematics, statistics, econometrics, finance, insurance, and computer science, as well as researchers and professional statisticians working in these fields. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Electronic-Healthcare Information Security, 1st edition
Authors: Kudakwashe Dube, Fredrick Mtenzi, Charles A. Shoniregun
Venue: Advances In Information Security (2009)

The ever-increasing healthcare expenditure and pressing demand for improved quality and efficiency of patient care services are driving innovation in healthcare information management. The domain of healthcare has become a challenging testing ground for information security due to the complex nature of healthcare information and individual privacy. Electronic Healthcare Information Security explores the challenges of e-healthcare information and security policy technologies. It evaluates the effectiveness of security and privacy implementation systems for anonymization methods and techniques. The first comprehensive book dedicated to electronic healthcare information security also introduces new standards. Designed for practitioners and researchers in the healthcare sectors, this book is also suitable for advanced-level students in computer science and medicine, as a secondary textbook or reference.
Knowledge delivery mechanism for autonomic overlay network management
Authors: Dominic Jones, John Keeney, Dave Lewis, Declan O'Sullivan
Venue: Proceedings of the 6th international conference on Autonomic computing (2009)

The complexity of current computing networks and the required fluidity of their operation requires autonomy to be central to the design and implementation of a managed overlay network. This paper presents a system in which a Knowledge-based Network (KBN) is used as the delivery mechanism for management information. This allows multiple autonomous managers to co-exist, playing separate roles in the management of multiple parallel overlay networks using a single dynamic and flexible knowledge delivery mechanism.
Scalable pseudo-likelihood estimation in hybrid random fields
Authors: Antonino Freno, Edmondo Trentin, Marco Gori
Venue: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (2009)

Learning probabilistic graphical models from high-dimensional datasets is a computationally challenging task. In many interesting applications, the domain dimensionality is such as to prevent state-of-the-art statistical learning techniques from delivering accurate models in reasonable time. This paper presents a hybrid random field model for pseudo-likelihood estimation in high-dimensional domains. A theoretical analysis proves that the class of pseudo-likelihood distributions representable by hybrid random fields strictly includes the class of joint probability distributions representable by Bayesian networks. In order to learn hybrid random fields from data, we develop the Markov Blanket Merging algorithm. Theoretical and experimental evidence shows that Markov Blanket Merging scales up very well to high-dimensional datasets. As compared to other widely used statistical learning techniques, Markov Blanket Merging delivers accurate results in a number of link prediction tasks, while achieving also significant improvements in terms of computational efficiency. Our software implementation of the models investigated in this paper is publicly available at http://www.dii.unisi.it/~freno/. The same website also hosts the datasets used in this work that are not available elsewhere in the same preprocessing used for our experiments.
A Computer Vision based Whiteboard Capture System
Authors: Richard Yi da Xu
Venue: Proceedings of the 2008 IEEE Workshop on Applications of Computer Vision (2008)

Conventional whiteboard video capture using a static camera usually results in a poor quality. In this paper, we present an autonomous whiteboard scan and capture prototype system, which consist a pair of static and Pan-Tilt-Zoom (PTZ) cameras. The PTZ camera is used to scan the newly-updated whiteboard regions without interrupting the instructor. We will illustrate several computer vision techniques used in our system: Firstly, we present our unique camera calibration method using rough hand-drawn gridlines. Secondly, we present the image processing methods used to determine where the newly updated whiteboard region to be scanned is. Our method also accounts for the whiteboard region occlusion from the instructor. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Statistical properties of inter-arrival times distribution in social tagging systems
Authors: Andrea Capocci, Andrea Baldassarri, Vito D.P. Servedio, Vittorio Loreto
Venue: Proceedings of the 20th ACM conference on Hypertext and hypermedia (2009)

Folksonomies provide a rich source of data to study social patterns taking place on the World Wide Web. Here we study the temporal patterns of users' tagging activity. We show that the statistical properties of inter-arrival times between subsequent tagging events cannot be explained without taking into account correlation in users' behaviors. This shows that social interaction in collaborative tagging communities shapes the evolution of folksonomies. A consensus formation process involving the usage of a small number of tags for a given resources is observed through a numerical and theoretical analysis of some well-known folksonomy datasets.
Dynamic code footprint optimization for the IBM Cell Broadband Engine
Authors: Tobias Werth, Tobias Flossmann, Michael Klemm, Dominic Schell, Ulrich Weigand, Michael Philippsen
Venue: Proceedings of the 2009 ICSE Workshop on Multicore Software Engineering (2009)

Multicore designers often add a small local memory close to each core to speed up access and to reduce off-chip IO. But this approach puts a burden on the programmer, the compiler, and the runtime system, since this memory lacks hardware support (cache logic, MMU, …) and hence needs to be managed in software to exploit its performance potential. The IBM Cell Broadband Engine (Cell B. E.) is extreme in this respect, since each of the parallel cores can only address code and data in its own local memory directly. Overlay techniques from the 70ies solve this problem with the well-known drawbacks: The programmer must manually divide the program into overlays and the largest overlay determines how much data the application can work with. In our approach, programmers do no longer need to cut overlays. Instead, we automatically and at runtime fragment and load small code snippets into a code cache located in the local stores and supervised by a garbage collector. Since our loader does not load code that is not needed for execution, the code cache can be much smaller (up to 70%) than the original program size. Applications can therefore work on larger data sets, i. e., bigger problems. Our loader is highly efficient and slows down applications by less than 5% on average. It can load any native code without pre-processing or changes in the software tool chain.
An Improved Support Vector Machine Classifier for EEG-Based Motor Imagery Classification
Authors: Hui Zhou, Qi Xu, Yongji Wang, Jian Huang, Jun Wu
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II (2009)

Electroencephalogram (EEG) recordings during motor imagery tasks are often used as input signals for brain-computer interfaces (BCIs). We analyze the EEG signals with Daubechies order 4 (db4) wavelets in 10 Hz and 21Hz at C3 channel, and in 10 Hz and 20 Hz at C4 channel, for these frequencies are prominent in discrimination of left and right motor imagery tasks according to EEG frequency spectral. We apply the improved support vector machines (SVMs) for classifying motor imagery tasks. First, a SVM is trained on all the training samples, then removes the support vectors which contribute less to the decision function from the training samples, finally the SVM is re-trained on the remaining samples. The classification error rate of the presented approach was as low as 9.29 % and the mutual information could be 0.7 above based on the Graz BCI 2003 data set.