ppxscal/embedding_network huggingface dataset:
Fréchet Distance Based Approach for Searching Online Handwritten Documents
Authors: E. Sriraghavendra, Karthik K., C. Bhattacharyya
Venue: Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01 (2007)

We propose a novel, language-neutral approach for searching online handwritten text using Fréchet distance. Online handwritten data, which is available as a time series (x,y,t), is treated as representing a parameterized curve in two-dimensions and the problem of searching online hand- written text is posed as a problem of matching two curves in a two-dimensional Euclidean space. Fréchet distance is a natural measure for matching curves. The main contribu- tion of this paper is the formulation of a variant of Fréchet distance that can be used for retrieving words even when only a prefix of the word is given as query. Extensive ex- periments on UNIPEN dataset1 consisting of over 16,000 words written by 7 users show that our method outperforms the state-of-the-art DTW method. Experiments were also conducted on a multilingual dataset, generated on a PDA, with encouraging results. Our approach can be used to implement useful, exciting features like auto-completion of handwriting in PDAs.
Annotating with light for remote guidance
Authors: Doug Palmer, Matt Adcock, Jocelyn Smith, Matthew Hutchins, Chris Gunn, Duncan Stevenson, Ken Taylor
Venue: Proceedings of the 19th Australasian conference on Computer-Human Interaction: Entertaining User Interfaces (2007)

This paper presents a system that will support a remote guidance collaboration, in which a local expert guides a remotely located assistant to perform physical, three-dimensional tasks. The system supports this remote guidance by allowing the expert to annotate, point at and draw upon objects in the remote location using a pen and tablet-based interface to control a laser projection device. The specific design criteria for this system are drawn from a tele-health scenario involving remote medical examination of patients and the paper presents the software architecture and implementation details of the associated hardware. In particular, the algorithm for aligning the representation of the laser projection over the video display of the remote scene is described. Early evaluations by medical specialists are presented, the usability of the system in laboratory experiments is discussed and ideas for future developments are outlined.
Logical Networks of Flexible Cells
Authors: A. Maruoka
Venue: IEEE Transactions on Computers (1973)

The flexible network is a network composed of flexible logical multivalued cells that can be adjusted at will to perform any functions. The network is assumed to have a fixed-interconnection pattern and to have a fixed-input variable assignment. The following question is answered. What is the necessary and sufficient range of the flexibility of the cells' functions to obtain all the output functions the circuit could realize? Furthermore, the relation between the cells functions that realize the same output functions is derived and the number of different realizable output functions is counted.
Efficient keyword search over virtual XML views
Authors: Feng Shao, Lin Guo, Chavdar Botev, Anand Bhaskar, Muthiah Chettiar, Fan Yang, Jayavel Shanmugasundaram
Venue: Proceedings of the 33rd international conference on Very large data bases (2007)

Emerging applications such as personalized portals, enterprise search and web integration systems often require keyword search over semi-structured views. However, traditional information retrieval techniques are likely to be expensive in this context because they rely on the assumption that the set of documents being searched is materialized. In this paper, we present a system architecture and algorithm that can efficiently evaluate keyword search queries over virtual (unmaterialized) XML views. An interesting aspect of our approach is that it exploits indices present on the base data and thereby avoids materializing large parts of the view that are not relevant to the query results. Another feature of the algorithm is that by solely using indices, we can still score the results of queries over the virtual view, and the resulting scores are the same as if the view was materialized. Our performance evaluation using the INEX data set in the Quark [5] open-source XML database system indicates that the proposed approach is scalable and efficient.
Enhancing Role-Based Access Control Model through Fuzzy Relations
Authors: Hassan Takabi, Morteza Amini, Rasool Jalili
Venue: Proceedings of the Third International Symposium on Information Assurance and Security (2007)

Role-Based Access Control (RBAC) model is naturally suitable to organizations where users are assigned organizational roles with well-defined privileges. However, due to the large number of users in nowadays online services of organizations and enterprises, assigning users to roles is a tiresome task and maintaining user-role assignment up-to- date is costly and error-prone. Additionally, with the increasing number of users, RBAC may have problems in prohibiting cheat and changing roles of users. In order to categorize information and formulate security policies, human decision making is required which is naturally fuzzy in the real world. This leads using a fuzzy approach to address the issue in order to provide a more practical solution. In this paper, applicability of fuzzy set theory to RBAC has been investigated by identifying access control building blocks which are fuzzy in essence. An existing RBAC model is extended to allow imprecise access control policies, using the concept of trustworthiness which is fuzzy in nature. We call the extended model as Fuzzy RBAC. Applicability of the extended model has been evaluated through some case studies.
A Survey of Interconnection Networks
Authors: Tse-yun Feng
Venue: Computer (1981)

Concurrent processing depends on interconnection networks for communication among processors and memory modules. Various network topologies and switching strategies are covered here.
Social media, participatory design and cultural engagement
Authors: Jerry Watkins
Venue: Proceedings of the 19th Australasian conference on Computer-Human Interaction: Entertaining User Interfaces (2007)

This paper reports on the application of Participatory Design methodology to an experiment in social media production. Staff at the Australian Museum are developing new content genres, creative tools and techniques in order to produce original cultural multimedia based on -- or inspired by -- the Museum's extensive collections. The ultimate aim of the project is for the Museum to act as a social media hub for external communities of interest to co-create their own narrative-based interpretations of the Museum's content, leading to an individualized cultural experience for physical and online visitors alike. A participatory content creation method has been developed for this project, which features iterative design cycles marked by social prototyping, evaluation and strategic formulation. These cycles are repeated until desired performance is achieved.
A fuzzy generalized predictive control using affine fuzzy predictors for nonlinear systems
Authors: Sang Chul Ahn, Yong Ho Kim, Wook Hyun Kwon
Venue: Journal of Intelligent Fuzzy Systems: Applications in Engineering and Technology (1998)

In this paper a fuzzy generalized predictive control (FGPC) for nonlinear plants is proposed. In the proposed method, the receding horizon control is applied to the control part, while fuzzy systems are used for the predictor part. It is suggested that the fuzzy predictor is time-varying affine with respect to input variables for easy computation of control inputs. Since the receding horizon control can be obtained only with a predictor instead of a plant model, the fuzzy predictor is obtained directly from input-output data without identifying a plant model. A modified parameter estimation algorithm is used for identifying the fuzzy predictor. The control inputs of the FGPC are computed by minimizing a receding horizon cost function with predicted plant outputs. The proposed controller has a similar architecture to the generalized predictive control (GPC) except for the predictor synthesis method, and thus could possess inherent good properties of the GPC. It is shown by computer simulation that the performance of the FGPC is satisfactory.
Threats on building models from CVS and Bugzilla repositories: the Mozilla case study
Authors: K. Ayari, P. Meshkinfam, G. Antoniol, M. Di Penta
Venue: Proceedings of the 2007 conference of the center for advanced studies on Collaborative research (2007)

Information obtained by merging data extracted from problem reporting systems - such as Bugzilla - and versioning systems - such as Concurrent Version System (CVS) - is widely used in quality assessment approaches. This paper attempts to shed some light on threats and difficulties faced when trying to integrate information extracted from Mozilla CVS and bug repositories. Indeed, the heterogeneity of Mozilla bug reports, often dealing with non-defect issues, and lacking of traceable information may undermine validity of quality assessment approaches relying on repositories integration. In the reported Mozilla case study, we observed that available integration heuristics are unable to recover thousands of traceability links. Furthermore, Bugzilla classification mechanisms do not enforce a distinction between different kinds of maintenance activities. Obtained evidence suggests that a large amount of information is lost; we conjecture that to benefit from CVS and problem reporting systems, more systematic issue classification and more reliable traceability mechanisms are needed.
C-Focus-3: a C-Focus with a New Heuristic Search Strategy
Authors: Daniel M. Santoro, Maria do Carmo Nicoletti, Estevan R. Hruschka
Venue: Proceedings of the Seventh International Conference on Intelligent Systems Design and Applications (2007)

The problem of feature selection is particularly important in areas such as machine learning and data mining. Given a training set which generally is described as a set of instances, each of them represented as a vector of feature-value pairs and an associated class, a feature selection method tries to identify features that are relevant for describing the concept embedded in the training set. The Focus algorithm is a very popular feature subset approach used in discrete data; its version for continuous data is known as C-Focus. The selection process implemented by Focus searches for the smallest feature subset that does not produce inconsistency in the training set. Due to its search mechanism, however, the use of the Focus (or C-Focus) becomes restrictive when the number of features is very large. This paper proposes a heuristic search method to be used by Focus (C-Focus), aiming at improving its performance. Empirical results of a system implementing C-Focus with the new search strategy (C-Focus-3) show that the proposed heuristic performs very well when the relevant subset of features has more than half of the number of features present in the original training set.
The Matrix* Data Base Management System
Authors: R. M. Sneider
Venue: Computer (1979)

This system is designed for the health professional with no previous DP experience herewith a description, from a user's perspective, of MATRIX's use, architecture, and capabilities.
Guest Editors' Introduction: IR Drop in Very Deep-Submicron Designs
Authors: Mohammad Tehranipoor, Kenneth M. Butler
Venue: IEEE Design Test (2007)

As technology scales to 22 nm and functional density continues to rise, many factors and parameters have a direct impact on the design and test of chips. Among such challenges, IR-drop and power supply noise (PSN) effects have become more significant in recent years. This special issue addresses some of the key issues in this area, focusing on the impact of PSN on design and test of very deep-submicron designs, and highlighting the importance of PSN and IR drop to design and test engineers in the semiconductor industry and academic researchers.
Ada-Based support for programming-in-the-Large
Authors: A. L. Wolf
Venue: IEEE Software (1985)

Specialized language features and an integrated set of tools can help developers of a large software system describe, analyze, organize, and manage its many modules.
Computer-Aided Geometric Design: A bibliography with Keywords and Classified Index
Authors: B. A. Barsky
Venue: IEEE Computer Graphics and Applications (1981)

Computer-aided geometric design is a subject which involves the representation, specification, design, manipulation, display, and analysis of free-form curves and surfaces. This eclectic field draws on techniques and principles from numerical analysis, approximation theory, computer graphics, interactive computer systems, mechanical and geometric design, and many other areas. Nevertheless, computer-aided geometric design is an endeavor with a character, emphasis, and purpose of its own, combining techniques and principles from other fields with unique approaches that are often somewhat enigmatic and confusing to the novice. Many things sound and seem familiar, yet the directions and motivations are distinct to this discipline. This decade is proving to be an era of intense interest in computer-aided geometric design, an activity attracting people from many sectors including computing and manufacturing. As the discipline more firmly establishes valid principles and standard techniques, it will undoubtedly be of increasing importance in computer-aided manufacturing and a wide range of other applications.
The Prediction of RNA Secondary Structure Using Multiple Unaligned Sequences
Authors: Xiaoyong Fang, Zhigang Luo, Bo Yuan
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 02 (2007)

Comparative analysis of homologous sequences has been used to predict RNA secondary structure. However, most of existing comparative approaches are vulnerable to alignment errors and thus are not quite suitable for practical application. Here we devise a new method for predicting RNA secondary structure using multiple unaligned sequences. Our method completes the prediction in four major steps: 1) to detect all possible stems in each sequence using the so-called position matrix which indicates paired or unpaired information for each position in the sequence; 2) to find conserved stems across all sequences by multiplying the position matrices; 3) to assess the conserved stems and select some of them as the constraint for RNA folding; 4) to perform final structure prediction using RNAalifold, which is a popular program for secondary structure prediction. We tested our method on data sets composed of RNA sequences with known secondary structures. Our method has average accuracy 73.21% for two-sequence tests, 74.18% for three-sequence tests, and 79.75% for four-sequence tests. The results show that our method can predict RNA secondary structure with a higher accuracy than RNAalifold.
Mining competent case bases for case-based reasoning
Authors: Rong Pan, Qiang Yang, Sinno Jialin Pan
Venue: Artificial Intelligence (2007)

Case-based reasoning relies heavily on the availability of a highly competent case base to make high-quality decisions. However, good case bases are difficult to come by. In this paper, we present a novel algorithm for automatically mining a high-quality case base from a raw case set that can preserve and sometimes even improve the competence of case-based reasoning. In this paper, we analyze two major problems in previous case-mining algorithms. The first problem is caused by noisy cases such that the nearest neighbor cases of a problem may not provide correct solutions. The second problem is caused by uneven case distribution, such that similar problems may have dissimilar solutions. To solve these problems, we develop a theoretical framework for the error bound in case-based reasoning, and propose a novel case-base mining algorithm guided by the theoretical results that returns a high-quality case base from raw data efficiently. We support our theory and algorithm with extensive empirical evaluation using different benchmark data sets.
Towards Trace Based Model Synthesis for Program Understanding and Test Automation
Authors: Teemu Kanstren
Venue: Proceedings of the International Conference on Software Engineering Advances (2007)

Effective maintenance and evolution of complex, software intensive systems requires understanding how the system works and having tests available to verify the effects of changes. Understanding complex systems is difficult, and testability of these systems is often low due to design constraints, system complexity and longterm evolution. Thus understanding the system and adding new tests is difficult. Yet, especially in these cases, the understanding and tests are important to verify the system correctness over long-term evolution. This paper discusses synthesizing models from system traces and using these models to facilitate program understanding and test automation. Basing the models on execution traces allows generation of automated tests even for low testability systems. Generating and visualizing abstracted models facilitates program understanding, which helps in system maintenance.
Managing business intelligence in a virtual enterprise: A case study and knowledge management lessons learned
Authors: Mitja Jermol, Nada Lavraç, Tanja Urbançiç
Venue: Journal of Intelligent Fuzzy Systems: Applications in Engineering and Technology (2003)

This paper presents a virtual enterprise model used in networking international expert teams from academia and business in the area of data mining and decision support. The knowledge management aspects of business intelligence as implemented in the virtual enterprise model are analyzed in terms of (a) the appropriate business, organizational and management models, (b) the need for knowledge management and the construction of a knowledge map of the available tools, expertise and collaborative work procedures, (c) the cognitive authority in collaborative work management, as well as (d) the network intelligence aspect of the virtual enterprise endeavor. The project Data Mining and Decision Support for Business Competitiveness: A European Virtual Enterprise (SolEuNet) is used as a case study and the source of lessons learned.
Performance of balanced two-stage empirical predictors of realized cluster latent values from finite populations: A simulation study
Authors: Silvina San Martino, Julio M. Singer, Edward J. Stanek,  III
Venue: Computational Statistics Data Analysis (2008)

Predictors of random effects are usually based on the popular mixed effects (ME) model developed under the assumption that the sample is obtained from a conceptual infinite population; such predictors are employed even when the actual population is finite. Two alternatives that incorporate the finite nature of the population are obtained from the superpopulation model proposed by Scott and Smith (1969. Estimation in multi-stage surveys. J. Amer. Statist. Assoc. 64, 830-840) or from the finite population mixed model recently proposed by Stanek and Singer (2004. Predicting random effects from finite population clustered samples with response error. J. Amer. Statist. Assoc. 99, 1119-1130). Predictors derived under the latter model with the additional assumptions that all variance components are known and that within-cluster variances are equal have smaller mean squared error (MSE) than the competitors based on either the ME or Scott and Smith's models. As population variances are rarely known, we propose method of moment estimators to obtain empirical predictors and conduct a simulation study to evaluate their performance. The results suggest that the finite population mixed model empirical predictor is more stable than its competitors since, in terms of MSE, it is either the best or the second best and when second best, its performance lies within acceptable limits. When both cluster and unit intra-class correlation coefficients are very high (e.g., 0.95 or more), the performance of the empirical predictors derived under the three models is similar.
Relational link-based ranking
Authors: Floris Geerts, Heikki Mannila, Evimaria Terzi
Venue: Proceedings of the Thirtieth international conference on Very large data bases - Volume 30 (2004)

Link analysis methods show that the interconnections between web pages have lots of valuable information. The link analysis methods are, however, inherently oriented towards analyzing binary relations. We consider the question of generalizing link analysis methods for analyzing relational databases. To this aim, we provide a generalized ranking framework and address its practical implications. More specically, we associate with each relational database and set of queries a unique weighted directed graph, which we call the database graph. We explore the properties of database graphs. In analogy to link analysis algorithms, which use the Web graph to rank web pages, we use the database graph to rank partial tuples. In this way we can, e.g., extend the PageRank link analysis algorithm to relational databases and give this extension a random querier interpretation. Similarly, we extend the HITS link analysis algorithm to relational databases. We conclude with some preliminary experimental results.
RBF Neural Network Based on Genetic Algorithm Used in Line Loss Calculation for Distribution Network
Authors: Huilan Jiang, Yunzhou Yuan, Yi Huang, Guixin Li
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 03 (2007)

In this paper, RBF neural network (RBFNN) based on Genetic Algorithm (GA) optimization is presented to deal with line losses calculation for power distribution networks. The centers and widths of hidden layer and the weights of the output layer are coded into one chromosome. It strengthens the cooperation between the hidden layer and the output layer, and avoids the risk of getting stuck into a local minimum. The simulation results indicate that the method presented has the advantages of a simple model, speedy learning and high precision.
Developing learning strategies for topic-based summarization
Authors: You Ouyang, Sujian Li, Wenjie Li
Venue: Proceedings of the sixteenth ACM conference on Conference on information and knowledge management (2007)

Most up-to-date well-behaved topic-based summarization systems are built upon the extractive framework. They score the sentences based on the associated features by manually assigning or experimentally tuning the weights of the features. In this paper, we discuss how to develop learning strategies in order to obtain the optimal feature weights automatically, which can be used for assigning a sound score to a sentence characterized with a set of features. The two fundamental issues are about training data and learning models. To save the costly manual annotation time and effort, we construct the training data by labeling the sentence with a "true" score calculated according to human summaries. The Support Vector Regression (SVR) model is then used to learn how to relate the "true" score of the sentence to its features. Once the relations have been mathematically modeled, SVR is able to predict the "estimated" score for any given sentence. The evaluations by ROUGE-2 criterion on DUC 2006 and DUC 2005 document sets demonstrate the competitiveness and the adaptability of the proposed approaches.