ppxscal/embedding_network huggingface dataset:
Interval Methods for Processing Geometric Objects
Authors: S. Mudur, P. Koparkar
Venue: IEEE Computer Graphics and Applications (1984)

In this approach, the parametric form is applied without the usual computational nightmare. The key is to view the parametric range as an interval, relying on subdivision algorithms.
A Modified Niche Genetic Algorithm Based on Evolution Gradient and Its Simulation Analysis
Authors: Tingsong Du, Pusheng Fei, Yanjun Shen
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 04 (2007)

To solve the problems of premature convergence and local minima in standard genetic algorithm (SGA), a modified evolutionary gradient-based niche genetic algorithm (GNGA) was proposed. In the GNGA, evolutionary gradient was used to improve the ability of finding the local best; the crossover value and mutation value were adapted dynamically with the generation so that the precision was improved; the population diversity was guaranteed by the use of the niche algorithm based on crowding mechanism. Simulation results show that the proposed algorithm has its superiority in precision and convergence rate compared with SGA.
Cryptographic Security for a High-Performance Distributed File System
Authors: Roman Pletka, Christian Cachin
Venue: Proceedings of the 24th IEEE Conference on Mass Storage Systems and Technologies (2007)

Storage systems are increasingly subject to attacks. Cryptographic file systems mitigate the danger of exposing data by using encryption and integrity protection methods and guarantee end-to-end security for their clients. This paper describes a generic design for cryptographic file systems and its realization in a distributed storage-area network (SAN) file system. Key management is integrated with the meta-data service of the SAN file system. The implementation supports file encryption as well as integrity protection through hash trees. Both techniques have been implemented in the client file system driver. Benchmarks demonstrate that the overhead is noticeable for some artificially constructed use cases, but that it is very small for typical file system applications.
Visualizing Large-Scale Uncertainty in Astrophysical Data
Authors: Hongwei Li, Chi-Wing Fu, Yinggang Li, Andrew Hanson
Venue: IEEE Transactions on Visualization and Computer Graphics (2007)

Visualization of uncertainty or error in astrophysical data is seldom available in simulations of astronomical phenomena, and yet almost all rendered attributes possess some degree of uncertainty due to observational error. Uncertainties associated with spatial location typically vary signicantly with scale and thus introduce further complexity in the interpretation of a given visualization. This paper introduces effective techniques for visualizing uncertainty in large-scale virtual astrophysical environments. Building upon our previous transparently scalable visualization architecture, we develop tools that enhance the perception and comprehension of uncertainty across wide scale ranges. Our methods include a unified color-coding scheme for representing log-scale distances and percentage errors, an ellipsoid model to represent positional uncertainty, an ellipsoid envelope model to expose trajectory uncertainty, and a magic-glass design supporting the selection of ranges of log-scale distance and uncertainty parameters, as well as an overview mode and a scalable WIM tool for exposing the magnitudes of spatial context and uncertainty.
Spatial Clustering Algorithm Based on Optimized-Division
Authors: Jian-pei Zhang, Yue Yang, Jing Yang, Ze-bao Zhang, Zhuo Liu
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02 (2007)

Traditional grid-density based spatial clustering algorithms divide input data space into partitions with same width and neglect the natural distributing character of initial data space. A new robust spatial clustering algorithm based on optimized-division (OpD-Clus) is proposed in this paper. Spatial data space is divided by hyper-planes which are encertained with axis-paralleled histogram in OpD- Clus algorithm. Division of data space relies on natual distributing character of input data space to improve the accuracy and efficiency of spatial clustering. Simultaneity, the outstanding difference between density-region and spare-region makes setting of density threshold parameter easily and reduces the parameter dependence of spatial clustering algorithm. The validity, efficiency and un-sensitivity of paramenters of OpD-Clus algorithm is demonstrated by experiment results.
Reducing the Dimensionality of Feature Vectors for Texture Image Retrieval Based on Wavelet Decomposition
Authors: Junyu Dong, Muwei Jian, Dawei Gao, Shengke Wang
Venue: Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 01 (2007)

Content-based texture image retrieval based on wavelet decomposition is one of the most active research areas. Subband statistics are normally used to construct feature vectors for calculating the similarity between the example and candidate images. However, most previous methods make no further analysis of the decomposed subbands or simply remove most detail coefficients. The retrieval algorithms commonly use many features without consideration of whether the features are effective for discriminating different classes. This may produce unnecessary computation burden and even decrease the retrieval performance. This paper proposes a method for selecting effective wavelet subbands based on new feature selection functions, which are derived from a modification of Fisher's discriminant. The method can discard those subbands that are redundant or may lead to wrong retrieval results. We test our method using samples from the VisTex texture database, and evaluate the retrieval performances using Daubechies and Gabor wavelet decomposition. The experimental results indicate that, compared with traditional approaches, our method can not only reduce the dimensionality of feature vectors but also improve retrieval performance.
Prediction of Silicon Content in Hot Metal Based on Bayesian Network
Authors: Xueyi Liu, Yikang Wang, Wenhui Wang
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 05 (2007)

A new approach is proposed to predict the silicon content in hot metal with Bayesian networks. Some key variables, affecting hot metal silicon content, were selected out and analyzed. Then a Bayesian network (BN) model was constructed according to the causal relationship of those variables. And the parameters of the model were estimated with the data selected from No.1 BF in Laiwu Iron and Steel Group Co.. Finally an improvement was made on BN method by defuzzification methods. The results show that the prediction is very successful and Bayesian network is better than BP neural network due to the visible inference and convictive results.
Durations and parametric model-checking in timed automata
Authors: Véronique Bruyère, Emmanuel Dall'olio, Jean-François Raskin
Venue: ACM Transactions on Computational Logic (TOCL) (2008)

We consider the problem of model-checking a parametric extension of the logic TCTL over timed automata and establish its decidability. Given a timed automaton, we show that the set of durations of runs starting from a region and ending in another region is definable in Presburger arithmetic (when the time domain is discrete) or in a real arithmetic (when the time domain is dense). Using this logical definition, we show that the parametric model-checking problem for the logic TCTL can be solved algorithmically; the proof of this result is simple. More generally, we are able to effectively characterize the values of the parameters that satisfy the parametric TCTL formula with respect to the given timed automaton.
Information seeking behavior of academic scientists
Authors: Bradley M. Hemminger, Dihui Lu, K. T. L. Vaughan, Stephanie J. Adams
Venue: Journal of the American Society for Information Science and Technology (2007)

The information seeking behavior of academic scientists is being transformed by the availability of electronic resources for searching, retrieving, and reading scholarly materials. A census survey was conducted of academic science researchers at the University of North Carolina at Chapel Hill to capture their current information seeking behavior. Nine hundred two subjects (26%) completed responses to a 15-minute Web-based survey. The survey questions were designed to quantify the transition to electronic communications and how this affects different aspects of information seeking. Significant changes in information seeking behavior were found, including increased reliance on web based resources, fewer visits to the library, and almost entirely electronic communication of information. The results can guide libraries and other information service organizations as they adapt to meet the needs of today's information searchers. Simple descriptive statistics are reported for the individual questions. Additionally, analysis of results is broken out by basic science and medical science departments. The survey tool and protocol used in this study have been adopted for use in a nationwide survey of the information seeking behavior of academic scientists. &copy; 2007 Wiley Periodicals, Inc.
Communities in graphs and hypergraphs
Authors: Michael Brinkmeier, Jeremias Werner, Sven Recknagel
Venue: Proceedings of the sixteenth ACM conference on Conference on information and knowledge management (2007)

In this paper we define a type of cohesive subgroups - called communities - in hypergraphs, based on the edge connectivity of subhypergraphs. We describe a simple algorithm for the construction of these sets and show, based on examples from image segmentation and information retrieval, that these groups may be useful for the analysis and accessibility of large graphs and hypergraphs.
Frame-based Image Denoising Using Local Contextual Hidden Markov Model
Authors: Xiaoyuan Yang, Xudong Zhang
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 01 (2007)

Wavelet-domain hiddenMarkov models (HMMs) provide a powerful new approach for statistical modeling and processing of wavalet coefficients. However, the local statistics of the coefficients cannot be well characterized by the HMMs. In this paper, we consider the combination of LGMM and HMMs in frame-domain and propose a Local Contextual Hidden Markov model based on frame-domain and corresponding training algorithm. This model can exploit the local statistics of coefficients so that can effectively capture correlation of wavelet frame coefficients. To illustrate the power of this new model, we apply this model for image denoising that outperforms current image denoising methods.
On a New Class of Bounds on Bayes Risk in Multihypothesis Pattern Recognition
Authors: P. A. Devijver
Venue: IEEE Transactions on Computers (1974)

An important measure concerning the use of statistical decision schemes is the error probability associated with the decision rule. Several methods giving bounds on the error probability are presently available, but, most often, the bounds are loose. Those methods generally make use of so-cailed distances between statistical distributions. In this paper a new distance is proposed which permits tighter bounds to be set on the error probability of the Bayesian decision rule and which is shown to be closely related to several certainty or separability measures. Among these are the nearest neighbor error rate and the average conditional quadratic entropy of Vajda. Moreover, our distance bears much resemblance to the information theoretic concept of equivocation. This relationship is discussed. Comparison is made between the bounds on the Bayes risk obtained with the Bhattacharyya coefficient, the equivocation, and the new measure which we have named the Bayesian distance.
On the Data Dissemination in WSNs
Authors: Jia-Liang Lu, Fabrice Valois
Venue: Proceedings of the Third IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (2007)

Data dissemination has been put forward as an impor- tant mechanism for WSN. The dissemination schemes al- low sink nodes to diffuse information in the network. It is known as an energy-consuming phase. In this paper, we propose to resolve this problem in two steps: firstly us- ing a localized self-organization scheme which generates a non-directed backbone; secondly, sink node sends a re- quest through this backbone to create a directed dissemi- nation structure in the network. Our scheme effectively re- duces the data traffic during the dissemination phase and saves energy when compared to other schemes. It is also extended to run properly in multiple sinks and mobile sink scenarios. The results also put forward the benefits of using a self-organization scheme for data dissemination.
Towards a Theory of Universal Speed-Independent Modules
Authors: R. M. Keller
Venue: IEEE Transactions on Computers (1974)

Of concern here are asynchronous modules, i.e., those whose activity is regulated by initiation and completion signals with no clocks being present. First a number of operating conditions are described that are deemed essential or useful in a system of asynchronous modules, while retaining an air of independence of particular hardware implementations as much as possible. Second, some results are presented concerning sets of modules that are universal with respect to these conditions. That is, from these sets any arbitrarily complex module may be constructed as a network. It is stipulated that such constructions be speed independent, i.e., independent of the delay time involved in any constituent modules. Furthermore it is required that the constructions be delay insensitive in the sense that an arbitrary number of delay elements may be inserted into or removed from connecting lines without effecting the external behavior of the network.
Bayesian Analysis of Lidar Signals with Multiple Returns
Authors: Sergio Hernandez-Marin, Andrew M. Wallace, Gavin J. Gibson
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence (2007)

Time-Correlated Single Photon Counting and Burst Illumination Laser data can be used for range profiling and target classification. In general, the problem is to analyse the response from a histogram of either photon counts or integrated intensities to assess the number, positions and amplitudes of the reflected returns from object surfaces. The goal of our work is a complete characterisation of the 3D surfaces viewed by the laser imaging system. The authors present a unified theory of pixel processing that is applicable to both approaches based on a Bayesian framework which allows for careful and thorough treatment of all types of uncertainties associated with the data. We use reversible jump Markov chain Monte Carlo (RJMCMC) techniques to evaluate the posterior distribution of the parameters and to explore spaces with different dimensionality. Further, we use a delayed rejection step to allow the generated Markov chain to mix better through the use of different proposal distributions. The approach is demonstrated on simulated and real data, showing that the return parameters can be estimated to a high degree of accuracy. We also show some practical examples from both near and far range depth imaging.
Problems of creating intelligent autonomous robotic underwater vehicles and their application
Authors: V. V. Volosov, V. F. Gubarev, V. M. Kuntsevich
Venue: Cybernetics and Systems Analysis (2007)

Basic scientific and applied tasks to be accomplished by autonomous robotic underwater vehicles are considered, and distinctive features of their control system are analyzed. Some unsolved fundamental problems in this subject are formulated.
A Case for GUI Testing Using Symbolic Execution Poster Abstract
Authors: Svetoslav Ganov, Sarfraz Khurshid, Dewayne Perry
Venue: Proceedings of the Testing: Academic and Industrial Conference Practice and Research Techniques - MUTATION (2007)

A Graphical User Interface (GUI) consists of virtual objects (widgets) that are more intuitive to use, for example buttons, edit boxes, etc. While GUIs have become ubiquitous, testing them remains largely adhoc. In contrast with console applications where there is only one point of interaction (the command line), GUIs provide multiple points each of which might have different states. This structure makes GUI testing especially challenging because of its large input space.
Research on RTOS-Integrated TMR for Fault Tolerant Systems
Authors: Hao Fu, Ming Cai, Liu Fang, Peng Liu, Jinxiang Dong
Venue: Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 03 (2007)

Safety and availability are issues of major importance in many critical systems. A RTOS (realtime operating system) -Integrated fault-tolerant system using TMR technology is presented in this paper. The system incorporates three homogeneous microcomputers and provides the fault-tolerant function through System-APIs to applications. As it is integrated with RTOS, the system is more general-purpose, and programmers need not pay too much attention to the fault tolerance technology. This system works in normal and degraded (duple or even single modular) modes, and can tolerate transient or permanent faults. The system also provides MultiTask- Support Fault-Tolerant function, and reconfiguration after a fault occurs is transparent to applications. Meanwhile, a novel seamless software upgrade method through intelligent state-transition-control is brought forward.
A Cognitive Model for the Forensic Recovery of End-User Passwords
Authors: Grigorios Fragkos, Theodore Tryfonas
Venue: Proceedings of the Second International Workshop on Digital Forensics and Incident Analysis (2007)

Despite the existence of a number of advanced authentication mechanisms such as twofactor tokens, biometrics etc., the use of passwords is still the most popular means of authenticating users in a computing system. Consequently, we need to generate and remember a large number of passwords, and these passwords need to be as strong as the assets they protect. During the course of a forensic examination a computer forensics analyst may come across a number of situations where the recovery of passwords is required, either in order to access a particular user account, or to unlock encrypted or otherwise obfuscated digital content. In this paper we create a cognitive model to describe the creation of end-user generated passwords that may be applied particularly during an attempt to forensically recover such passwords. We propose that it may be feasible to recover a password by reversing the logic of its creation, taking into account contextual and other parameters, instead of applying computationally expensive brute force.
Statistical Metamorphic Testing Testing Programs with Random Output by Means of Statistical Hypothesis Tests and Metamorphic Testing
Authors: Ralph Guderlei, Johannes Mayer
Venue: Proceedings of the Seventh International Conference on Quality Software (2007)

Testing software with random output is a challenging task as the output corresponding to a given input dif- fers from execution to execution. Therefore, the usual ap- proaches to software testing are not applicable to random- ized software. Instead, statistical hypothesis tests have been proposed for testing those applications. To apply these sta- tistical hypothesis tests, either knowledge about the theo- retical values of statistical characteristics of the program output (e. g. the mean) or a reference implementation (e. g. a legacy system) are required to apply statistical hypothesis tests. But often, both are not available. In the present paper, it is discussed how a testing method called Metamorphic Testing can be used to construct sta- tistical hypothesis tests without knowing exact theoretical characteristics or having a reference implementation. For that purpose, two or more independent output sequences are generated by the implementation under test (IUT). Then, these sequences are compared according to the metamor- phic relation using statistical hypothesis tests.
A Certified Infinite Norm for the Implementation of Elementary Functions
Authors: Sylvain Chevillard, Christoph Lauter
Venue: Proceedings of the Seventh International Conference on Quality Software (2007)

The high-quality floating-point implementation of use- ful functions f : R R, such as exp, sin, erf requires bounding the error = p-f f of an approximation p with regard to the function f . This involves bounding the infi- nite norm of the error function. Its value must not be underestimated when implementations must be safe. Previous approaches for computing infinite norm are shown to be either unsafe, not sufficiently tight or too te- dious in manual work. We present a safe and self-validating algorithm for auto- matically upper- and lower-bounding infinite norms of er- ror functions. The algorithm is based on enhanced inter- val arithmetic. It can overcome high cancellation and high condition number around points where the error function is defined only by continuous extension. The given algorithm is implemented in a software tool. It can generate a proof of correctness for each instance on which it is run.
Finding Prototypes For Nearest Neighbor Classifiers
Authors: Chin-Liang Chang
Venue: IEEE Transactions on Computers (1974)

A nearest neighbor classifier is one which assigns a pattern to the class of the nearest prototype. An algorithm is given to find prototypes for a nearest neighbor classifier. The idea is to start with every sample in a training set as a prototype, and then successively merge any two nearest prototypes of the same class so long as the recognition rate is not downgraded. The algorithm is very effective. For example, when it was applied to a training set of 514 cases of liver disease, only 34 prototypes were found necessary to achieve the same recognition rate as the one using the 514 samples of the training set as prototypes. Furthermore, the number of prototypes in the algorithm need not be specified beforehand.
Illustrative Design Space Studies with Microarchitectural Regression Models
Authors: Benjamin C. Lee, David M. Brooks
Venue: Proceedings of the 2007 IEEE 13th International Symposium on High Performance Computer Architecture (2007)

We apply a scalable approach for practical, comprehensive design space evaluation and optimization. This approach combines design space sampling and statistical inference to identify trends from a sparse simulation of the space. The computational efficiency of sampling and inference enables new capabilities in design space exploration. We illustrate these capabilities using performance and power models for three studies of a 260,000 point design space: (1) pareto frontier analysis, (2) pipeline depth analysis, and (3) multiprocessor heterogeneity analysis. For each study, we provide an assessment of predictive error and sensitivity of observed trends to such error. We construct pareto frontiers and find predictions for pareto optima are no less accurate than those for the broader design space. We reproduce and enhance prior pipeline depth studies, demonstrating constrained sensitivity studies may not generalize when many other design parameters are held at constant values. Lastly, we identify efficient heterogeneous core designs by clustering per bench-mark optimal architectures. Collectively, these studies motivate the application of techniques in statistical inference for more effective use of modern simulator infrastructure.