ppxscal/embedding_network huggingface dataset:
Empowerment zones? women, internet cafés, and life transformations in egypt
Authors: Deborah L. Wheeler
Venue: Information Technologies and International Development (2008)

Information technology is said to provide paths to empowerment, yet hard data about how this occurs are scant, especially concerning women's IT practices in the Middle East and North Africa. This article uses 25 interviews with female Internet café users in Cairo as a small step toward bridging this gap. In general, this study found three main empowerment narratives among females who regularly use Internet cafés in Egypt (the average number of hours online per week was 9.87 for those interviewed). The Internet is said by participants in this study to 1) increase information access/professional development, 2) expand or maintain social networks and social capital, and 3) transform social and political awareness. While this study is based on a small sample size that cannot be generalized at this stage, the analysis does reveal subtle ways in which Egyptian society is changing---one Internet-enabled person at a time.
Programming the Mark I: Early Programming Activity at the University of Manchester
Authors: Martin Campbell-Kelly
Venue: IEEE Annals of the History of Computing (1980)

Computer activity at Manchester University began in 1946 with the construction of a CRT-based memory, which was followed by a series of prototype computers. The work culminated in the Ferranti Mark I, completed in early 1951. This paper describes the programming systems devised, first for the prototype and then for the production Mark I, and includes an account of two novel automatic coding schemes developed during 1952 and 1954.The paper concludes with an assessment of the programming activity.
Integration of configuration management into the IT curriculum
Authors: Keyuan Jiang, Reza Kamali
Venue: Proceedings of the 9th ACM SIGITE conference on Information technology education (2008)

The discipline of information technology deals with the selection, creation, application, integration and administration of computing technologies. With the prevailing adoption of information technology, growing sophistication and complexity of today's technology solutions, and increased security threats to the existing systems, it becomes more and more challenging to maintain and support IT operations with the highest availability possible. One of the important and useful practices is to design and implement the appropriate configuration management strategies in the operations, administration and development of IT applications and systems. Configuration management (CM) is a discipline of technical management that ensures the consistency of a product's performance, its quality, functional and physical attributes with its intended requirements, design, and operational information throughout its life. The practice of configuration management applies to every aspect of information technology, ranging from application development, to operating system setup, and to security management. Despite its importance, many practitioners, especially those fresh out of college, consider configuration management as an after-thought. Among all the computing related disciplines defined by ACM Computing Curricula, software engineering is the only one that offers configuration management in its curriculum but with a limited scope. In developing our new IT curriculum based upon the IT Curriculum 2005, we integrated configuration management into our curriculum by developing a course titled "Application Configuration and Management". In the course, students are exposed to various configuration management standards, best practices, techniques, and tools. A series of laboratory exercises were also developed to enhance students' understanding and learning of what is actually involved in the practice of configuration management.
Computing Frequent Elements Using Gossip
Authors: Bibudh Lahiri, Srikanta Tirthapura
Venue: Proceedings of the 15th international colloquium on Structural Information and Communication Complexity (2008)

We present algorithms for identifying frequently occurring elements in a large distributed data set using gossip. Our algorithms do not rely on any central control, or on an underlying network structure, such as a spanning tree. Instead, nodes repeatedly select a random partner and exchange data with the partner --- if this process continues for a (short) period of time, the desired results are computed, with probabilistic guarantees on the accuracy. Our algorithm for frequent elements is built by layering a novel small space "sketch" of data over a gossip-based data dissemination mechanism. We prove that the algorithm converges to the approximate frequent elements with high probability, and provide bounds on the time till convergence. To our knowledge, this is the first work on computing frequent elements using gossip.
Using OBDDs for Efficient Query Evaluation on Probabilistic Databases
Authors: Dan Olteanu, Jiewen Huang
Venue: Proceedings of the 2nd international conference on Scalable Uncertainty Management (2008)

We consider the problem of query evaluation for tuple independent probabilistic databases and Boolean conjunctive queries with inequalities but without self-joins. We approach this problem as a construction problem for ordered binary decision diagrams (OBDDs): Given a query q and a probabilistic database D, we construct in polynomial time an OBDD such that the probability of q(D) can be computed linearly in the size of that OBDD. This approach is applicable to a large class of queries, including the hierarchical queries, i.e., the Boolean conjunctive queries without self-joins that admit PTIME evaluation on any tuple-independent probabilistic database, hierarchical queries extended with inequalities, and non-hierarchical queries on restricted databases.
A three-layered model for expressive speech perception
Authors: Chun-Fang Huang, Masato Akagi
Venue: Speech Communication (2008)

This paper proposes a multi-layer approach to modeling perception of expressive speech. Many earlier studies of expressive speech focused on statistical correlations between expressive speech and acoustic features without taking into account the fact that human perception is vague rather than precise. This paper introduces a three-layer model: five categories of expressive speech constitute the top layer, semantic primitives constitute the middle layer, and acoustic features, the bottom layer. Three experiments followed by multidimensional scaling analysis revealed suitable semantic primitives. Then, fuzzy inference systems were built to map the vagueness of the relationship between expressive speech and the semantic primitives. Acoustic features in terms of F0 contour, time duration, power envelope, and spectrum were analyzed. Regression analysis revealed correlation between the semantic primitives and the acoustic features. Parameterized rules based on the analysis results were created to morph neutral utterances to those perceived as having different semantic primitives and expressive speech categories. Experiments to verify the relationships of the model showed significant relationships between expressive speech, semantic primitives, and acoustic features.
From Data Reverence to Data Relevance: Model-Mediated Wireless Sensing of the Physical Environment
Authors: Paul G. Flikkema, Pankaj K. Agarwal, James S. Clark, Carla Ellis, Alan Gelfand, Kamesh Munagala, Jun Yang
Venue: Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007 (2007)

Wireless sensor networks can be viewed as the integration of three subsystems: a low-impact in situ data acquisition and collection system, a system for inference of process models from observed data and a priori information, and a system that controls the observation and collection. Each of these systems is connected by feedforward and feedback signals from the others; moreover, each subsystem is formed from behavioral components that are distributed among the sensors and out-of-network computational resources. Crucially, the overall performance of the system is constrained by the costs of energy, time, and computational complexity. We are addressing these design issues in the context of monitoring forest environments with the objective of inferring ecosystem process models. We describe here our framework of treating data and models jointly, and its application to soil moisture processes.
Establishing Trust Beliefs Based on a Uniform Disposition to Trust
Authors: Rachid Saadi, Omar Hasan, Jean Marc Pierson, Lionel Brunie
Venue: Proceedings of the 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System (2007)

Trust based access control models have recently attracted significant interest in the area of pervasive computing. In several trust based models, organizations are required to establish a set of trust beliefs regarding their neighboring nodes. In an organization there may be multiple administrators or the administrators may change with time. When multiple administrators contribute to the creation of the set of trust beliefs of an organization, inconsistencies may occur due to variations in their disposition to trust.In this paper we introduce a novel method for establishing the set of trust beliefs, which is likely to be more consistent. The proposed solution attempts to tie the quantification of trust not to the multiple dispositions to trust of the administrators but to a uniform disposition to trust of the organization. Having administrators evaluate the trustworthiness of neighboring nodes in relation to other nodes and using a mapping function for assigning quantitative values are the highlights of the method.
Extended Null-Move Reductions
Authors: Omid David-Tabibi, Nathan S. Netanyahu
Venue: Proceedings of the 6th international conference on Computers and Games (2008)

In this paper we review the conventional versions of null-move pruning, and present our enhancements which allow for a deeper search with greater accuracy. While the conventional versions of null-move pruning use reduction values of R ≤ 3, we use an aggressive reduction value of R = 4 within a verified adaptive configuration which maximizes the benefit from the more aggressive pruning, while limiting its tactical liabilities. Our experimental results using our grandmaster-level chess program, Falcon, show that our null-move reductions (NMR) outperform the conventional methods, with the tactical benefits of the deeper search dominating the deficiencies. Moreover, unlike standard null-move pruning, which fails badly in zugzwang positions, NMR is impervious to zugzwangs. Finally, the implementation of NMR in any program already using null-move pruning requires a modification of only a few lines of code.
Image Restoration Using Piecewise Iterative Curve Fitting and Texture Synthesis
Authors: Ke Sun, Yingyun Yang, Long Ye, Qin Zhang
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues (2008)

In this paper, we present a novel scheme for parsing images into medium level vision representation: contour curve and region texture. This scheme is integrated with piecewise iterative curve fitting and texture synthesis, in which an original image is analyzed at the representation side so as to obtain contours and local texture exemplars. The contour of each region will be processed piecewise by iterative curve-fitting procedure. At the reconstruction side, transmitted coefficients denoted to structural information of regions will be used to reconstruct contours. Texture synthesis algorithm is adopted to restore the rest. Experimental results indicate that our scheme can be done successfully for images with massive structural and stochastic textures. Compared with JPEG, our method can get more coding gain but remain vast information people care.
E.G. Andrews, 1898-l980
Authors: Deirdre La Porte, George R. Stibitz
Venue: IEEE Annals of the History of Computing (1982)

E. G. Andrews, a pioneer in the development of binary computers, died on October 13, 1980, in Hanover, New Hampshire. Ernest Galen Andrews was born in Topeka, Kansas, on January 10, 1898. Following service aboard the USS Kansas during World War I, Andrews attended William Jewell College in Liberty, Missouri, where he received a bachelor's degree in mathematics in 1922. In that same year, he accepted a position with the installation department of the Western Electric Company in Kansas City, whence he was transferred to Atlanta and then to New York City.
Underpinning /nailon/: Automatic Estimation of Pitch Range and Speaker Relative Pitch
Authors: Jens Edlund, Mattias Heldner
Venue: Speaker Classification II: Selected Projects (2007)

In this study, we explore what is needed to get an automatic estimation of speaker relative pitch that is good enough for many practical tasks in speech technology. We present analyses of fundamental frequency (F0) distributions from eight speakers with a view to examine (i) the effect of semitone transform on the shape of these distributions; (ii) the errors resulting from calculation of percentiles from the means and standard deviations of the distributions; and (iii) the amount of voiced speech required to obtain a robust estimation of speaker relative pitch. In addition, we provide a hands-on description of how such an estimation can be obtained under real-time online conditions using /nailon/ --- our software for online analysis of prosody.
Mining Mass Spectrometry Database Search Results--A Rough Set Approach
Authors: Jianwen Fang, Jerzy W. Grzymala-Busse
Venue: Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms (2007)

This paper reports results of experiments on mass spectrometry database search results produced by Keller et al. This data set describes human proteins. Data mining was conducted using the LERS system. First, the data set was discretized by a cluster analysis algorithm based on agglomerative approach. Then the basic rule set was induced by the LEM2 algorithm. Finally, the rule set was refined using changing rule strength methodology and truncation of the rule set. Our results reach the level of sensitivity and specificity of competing methods. However, our results are explainable since they are in a form of rules and, additionally, we can interpret the role of important features.
Data Reduction Algorithm for Machine Learning and Data Mining
Authors: Ireneusz Czarnowski, Piotr Jedrzejowicz
Venue: Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence (2008)

The paper proposes an approach to data reduction. The data reduction procedures are of vital importance to machine learning and data mining. To solve the data reduction problems the agent-based population learning algorithm was used. The proposed approach has been used to reduce the original dataset in two dimensions including selection of reference instances and removal of irrelevant attributes. To validate the approach the computational experiment has been carried out. Presentation and discussion of experiment results conclude the paper.
Automatic Recognition of Emotions from Speech: A Review of the Literature and Recommendations for Practical Realisation
Authors: Thurid Vogt, Elisabeth André, Johannes Wagner
Venue: Affect and Emotion in Human-Computer Interaction: From Theory to Applications (2008)

In this article we give guidelines on how to address the major technical challenges of automatic emotion recognition from speech in human-computer interfaces, which include audio segmentation to find appropriate units for emotions, extraction of emotion relevant features, classification of emotions, and training databases with emotional speech. Research so far has mostly dealt with offline evaluation of vocal emotions, and online processing has hardly been addressed. Online processing is, however, a necessary prerequisite for the realization of human-computer interfaces that analyze and respond to the user's emotions while he or she is interacting with an application. By means of a sample application, we demonstrate how the challenges arising from online processing may be solved. The overall objective of the paper is to help readers to assess the feasibility of human-computer interfaces that are sensitive to the user's emotional voice and to provide them with guidelines of how to technically realize such interfaces.
A Server Based ASR Approach to Automated Cryptanalysis of Two Time Pads in Case of Speech
Authors: Liaqat Ali Khan, Muhammad Shamim Baig
Venue: Proceedings of the 2008 The Fourth International Conference on Information Assurance and Security (2008)

Keystream reuse in stream ciphers in case of textual data has been the focus of cryptanalysts for quite some time. The first ever use of hidden Markov models based speech recognition approach to cryptanalysis of encrypted digitized speech signals in a keystream reuse situation was presented by us in [1]. In this paper, we extend the idea presented in [1] and show the applicability of different speech recognition architectures in mobile environment to automatically recover the digitized speech signals encrypted under the same keystream. The server based automatic speech recognition (ASR) approach and its associated architectures are adapted to make them applicable in our attack. The two main implementation architectures of Network Speech Recognition (NSR) from the acoustic front-end point of view are compared with respect to automated cryptanalysis of the two time pads of stream ciphered digitized speech. The simulation experiments performed on conventional speech recognition tools are presented for both the NSR architectures.
Comparing the Impact of Two Different Designs for Online Discussion
Authors: Yuankun Yao
Venue: Proceedings of the 7th international conference on Advances in Web Based Learning (2008)

This study examined the impact of two different designs of online discussion on student participation in the discussions and the type of learning that was generated. The results showed that when students were given an opportunity to respond to each other with the instructor mostly absent in the discussion process until the end of the discussions, they would visit the discussion forums more frequently. They were also more likely to pose follow-up questions for their classmates and engage in meaningful inquires. On the other hand, when the instructor posted frequently in the discussion forums, students would respond to him instead of their peers, and the discussions were seldom carried forth in much depth.
A Coinduction Rule for Entailment of Recursively Defined Properties
Authors: Joxan Jaffar, Andrew E. Santosa, Răzvan Voicu
Venue: Proceedings of the 14th international conference on Principles and Practice of Constraint Programming (2008)

Recursively defined properties are ubiquitous. We present a proof method for establishing entailment $\mathcal{G} \models \mathcal{H}$ of such properties $\mathcal{G}$ and $\mathcal{H}$ over a set of common variables. The main contribution is a particular proof rule based intuitively upon the concept of coinduction. This rule allows the inductive step of assuming that an entailment holds during the proof the entailment. In general, the proof method is based on an unfolding (and no folding) algorithm that reduces recursive definitions to a point where only constraint solving is necessary. The constraint-based proof obligation is then discharged with available solvers. The algorithm executes the proof by a search-based method which automatically discovers the opportunity of applying induction instead of the user having to specify some induction schema, and which does not require any base case.
Filtering Methods for Feature Selection in Web-Document Clustering
Authors: Heum Park, Hyuk-Chul Kwon
Venue: Proceedings of the 7th international conference on Computational Science, Part II (2007)

This paper presents the results of a comparative study of filtering methods for feature selection in web document clustering. First, we focused on feature selection methods based on Mutual Information (MI) and Information Gain (IG). With those features and feature values, and using MI and IG, we extracted from documents representative max-value features as well as a representative cluster for a feature and a representative cluster for a document. Second, we tested the Max Feature Selection Method (MFSM) with those representative features and clusters, and evaluated the web-document clustering performance. However, when document sets yield poor clustering results by term frequency, we cannot obtain good features using the MFSM with the MI and IG values. Therefore, we propose new filtering methods, Min Count of Representative Cluster for a Feature (MCRCF) and Min Count of Representative Cluster for a Document (MCRCD). In the experimental results, the MFSM showed better performance than was achieved using only term frequency, MI and IG. And when we applied the new filtering methods for feature selection (MCRCF, MCRCD), the clustering performance improved notably. Thus we can assert that those filtering methods are effective means of feature selection and offer good performance in web document clustering.
Selection of Gaussian Kernel Parameter for SVM Based on Convex Estimation
Authors: Changqian Men, Wenjian Wang
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks (2008)

The selection of the kernel and the corresponding parameter is one of the key problems for support vector machine (SVM). This paper presents an approach to select the optimal Gaussian kernel parameter for support vector classification through the estimation of approximate convex hull of the sample set. The presented approach can overcome some disadvantages such as high computation cost existing in the traditional optimization-based methods, and it can be used no matter whether the dataset is dense or whether the distribution is uniform. The simulation experiments demonstrate the feasibility and the effectiveness of the presented method.
The Fox's H-function and unsteady Hydromagnetic boundary - layer in a rotating medium
Authors: Stelian Turbatu
Venue: Proceedings of the 10th WSEAS International Conference on Mathematical Methods and Computational Techniques in Electrical Engineering (2008)

Motivated essentially by the work of P. Puri and P. K. Kulshrestha [7] the author investigate and derive exact analytical solutions for the three-dimensional flow of a viscous fluid in the presence of the transverse magnetic field past an infinite porous flat plate with uniform suction or injection, moving with a time-dependent velocity in a rotating medium. Assuming the normal velocity w to be a constant (uniform suction/injection), several different cases of the velocity -U0 f(t) along the plate are treated here in a unified manner.
Communication avoiding Gaussian elimination
Authors: Laura Grigori, James W. Demmel, Hua Xiang
Venue: Proceedings of the 2008 ACM/IEEE conference on Supercomputing (2008)

We present CALU, a Communication Avoiding algorithm for the LU factorization of dense matrices distributed in a two-dimensional cyclic layout. The algorithm is based on a new pivoting strategy, which is stable in practice. The new algorithm is optimal (up to polylogarithmic factors) in the amount of communication it performs. Our experiments show that CALU leads to a reduction in the parallel time, in particular when the latency time is an important factor of the overall time. The factorization of a block-column, a subroutime of CALU, outperforms the corresponding routine PDGETF2 from ScaLAPACK up to a factor of 4.37 on an IBM POWER 5 system and up to a factor of 5.58 on a Cray XT4 system. On square matrices of order 104, CALU outperforms the corresponding routine PDGETRF from ScaLAPACK by a factor of 1.24 on IBM POWER 5 and by a factor of 1.31 on Cray XT4.