ppxscal/embedding_network huggingface dataset:
A hybrid cooperative-comprehensive learning based PSO algorithm for image segmentation using multilevel thresholding
Authors: Madhubanti Maitra, Amitava Chatterjee
Venue: Expert Systems with Applications: An International Journal (2008)

A novel optimal multilevel thresholding algorithm for histogram-based image segmentation is presented in this paper. The proposed algorithm presents an improved variant of PSO, a relatively recently introduced stochastic optimization strategy. This hybrid approach employs both cooperative learning and comprehensive learning along with some additional modifications. Cooperative learning is employed to overcome the ''curse of dimensionality'' by decomposing a high-dimensional swarm into several one-dimensional swarms. The comprehensive learning is then employed to discourage premature convergence in each one-dimensional swarm. The capability of this hybrid PSO (called HCOCLPSO) is further enhanced by cloning of fitter particles, at the expense of worst particles, determined on the basis of their fitness values. The performance of HCOCLPSO algorithm is evaluated vis-a-vis an improved GA-based algorithm [Yin, P.-Y., (1999). A fast scheme for optimal thresholding using genetic algorithms. Signal Processing 72, 85-95], Gaussian-smoothing method [Lim, Y. K., & Lee, S. U., (1990). On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques. Pattern recognition. 23, 935-952; Tsai, D. M. (1995). A fast thresholding selection procedure for multimodal and unimodal histograms. Pattern Recognition Letters 16, 653-666] and symmetry/duality method [Yin, P. Y., & Chen, L. H., (1993). New method for multilevel thresholding using the symmetry and duality of the histogram. Journal of Electronics and Imaging 2, 337-344] for several benchmark images and HCOCLPSO outperforms each of these algorithms for each such image.
Hysteretic damping of structures vibrating at resonance: An iterative complex eigensolution method based on damping-stress relation
Authors: George D. Gounaris, Eleftherios Antonakakis, Chris A. Papadopoulos
Venue: Computers and Structures (2007)

In this paper the damping is examined as an engineering property used in analysis and design of structures and machines. The design engineer needs to know not only the stresses of his structure or machine, under steady state conditions but also the stresses under resonance conditions. Then the material damping, as a function of the stress of the structure, has an important role to play and ignoring the damping the calculated stresses are far from reality. The nonlinearity here is due to the dependence of the hysteretic damping on the stress of the structure. Specifically here two problems are investigated in the following way: Firstly the direct problem is solved. The direct problem is to find the maximum bending stress at the resonance when the relation of the dissipating energy (or of the hysteretic damping) vs. the bending stress is known in advance. To perform this calculation, a useful tool for the design engineer, the structure is modelled using the continuum mechanics analytical approach or the finite elements (FE) method. Then the eigenvalues are calculated and using an iterative procedure the real stress. The procedure presented here is called iterative complex eigensolution method (ICEM). Secondly the inverse problem is solved. The inverse problem is to find the relation between the hysteretic damping and the bending stress. For this purpose the logarithmic decrement is experimentally measured, the eigenvalues and the maximum bending stress of the structure, excited at the eigenvalue, when the damping is the same as the measured one, are computed using the finite elements method. Once the bending stresses are found in each discrete element of the structure, then the mathematical expression of the relation of the dissipating energy and the stresses can be specified by minimizing a suitably formed objective function.
Reading, writing, arithmetic, and computing
Authors: Arthur Luehrmann
Venue: ACM SIGCUE Outlook (1973)

The promise of technology has always been an increase in productivity. Labor-intensive activities become machine-intensive. It seems likely that this traditional scenario applies to technology and education, probably the most labor-intensive of human activities. New communications technology, new information-processing technology and new systems for creating and validating machine-administered courses of instruction will surely lead to cost reductions and greater personal convenience and individualization.
A calculus of challenges and responses
Authors: Michael Backes, Agostino Cortesi, Riccardo Focardi, Matteo Maffei
Venue: Proceedings of the 2007 ACM workshop on Formal methods in security engineering (2007)

This paper presents a novel approach for concisely abstracting authentication protocols and for subsequently analyzing those abstractions in a sound manner, i.e., deriving authentication guarantees for protocol abstractions suffices for proving these guarantees for the actual protocols. The abstractions are formalized in a process calculus which constitutes a higher-level abstraction of the ρspi calculus and is specifically tailored towards reasoning about challenge-response mechanisms within authentication protocols. Furthermore, it allows for expressing protocols without having to include details on the specific structure of exchanged messages. This in particular entails that many authentication protocols share a common abstraction so that a single validation of this abstraction already gives rise to security guarantees for all these protocols. Such an abstract validation can be automatically performed using static analysis techniques based on an effect system proposed in this paper. Finally, extensions to additional protocol classes enjoy a soundness theorem provided that these extensions satisfy certain explicit, easily checkable conditions.
Robust global periodicity of interval bidirectional associative memory networks with finite distributed delays
Authors: Xiaofan Yang, David J. Evans, Yuan Yan Tang, Xiaofeng Liao
Venue: International Journal of Systems Science (2007)

This article addresses the robust periodicity properties of a class of interval bidirectional associative memory networks with finite distributed delays. A set of criteria are proposed for the robust global exponential periodicity of such networks. Our results depend neither on differentiability nor on monotonicity of the activation functions of the neurons. In addition, these criteria are easily checkable.
Some nonlinear/adaptive methods for fast recovery of the missing samples of signals
Authors: Mahmoud Ghandi, Mohammad Mahdi Jahani Yekta, Farokh Marvasti
Venue: Signal Processing (2008)

In this paper an iterative method for recovery of the missing samples of signals is investigated in detail, and some novel linear, nonlinear, and adaptive extrapolation techniques are proposed to be used along with it to increase the convergence rate of the recovery system. The proposed methods would remarkably speed up the convergence rate, save processing power, and reduce the delay of the system compared to some well known accelerated versions of the aforementioned iterative algorithm.
Single Word Term Extraction Using a Bilingual Semantic Lexicon-Based Approach
Authors: Hongying Zan, Guocheng Duan, Ming Fan
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 05 (2007)

The existing approaches to automatic term recognition include these types: dictionary-based, rule-based, statistical, etc. First, we discuss the dictionary-based methods briefly in this paper. Then we propose an approach for Chinese single word term extraction combining the dictionary-based method with seed knowledge-based method. Our method is based on two resources. One is the Chinese Concept Dictionary which is a general bilingual semantic lexicon and the other one is the bilingual seeds set extracted from a bilingual glossary of HK law. The approach is to recognize the legal domain-specific term. Our approach is applying general semantic lexicon for domain-specific term extraction. The experimental results show that our approach can get high precision in legal field.
Statistical Structure Analysis in MRI Brain Tumor Segmentation
Authors: Xiao Xuan, Qingmin Liao
Venue: Proceedings of the Fourth International Conference on Image and Graphics (2007)

Automated MRI (Magnetic Resonance Imaging) brain tumor segmentation is a difficult task due to the variance and complexity of tumors. In this paper, a statistical structure analysis based tumor segmentation scheme is presented, which focuses on the structural analysis on both tumorous and normal tissues. Firstly, 3 kinds of features including intensity-based, symmetry-based and texture-based are extracted from structural elements. Then a classification technique using AdaBoost that learns by selecting the most discriminative features is proposed to classify the structural elements into normal tissues and abnormal tissues. Experimental results on 140 tumor-contained brain MR images achieve an average accuracy of 96.82% on tumor segmentation.
Variations-aware low-power design and block clustering with voltage scaling
Authors: Navid Azizi, Muhammad M. Khellah, Vivek K. De, Farid N. Najm
Venue: IEEE Transactions on Very Large Scale Integration (VLSI) Systems (2007)

We present a new methodology which takes into consideration the effect of within-die (WID) process variations on a low-voltage parallel system. We show that in the presence of process variations one should use a higher supply voltage than would otherwise be predicted to minimize the power consumption of a parallel systems. Previous analyses, which ignored WID process variations, provide a lower nonoptimal supply voltage which can underestimate the energy/operation by 8.2×. We also present a novel technique to limit the effect of temperature variations in a parallel system. As temperatures increases, the scheme reduces the power increase by 43% allowing the system to remain at it's optimal supply voltage across different temperatures. To further limit the effect of variations, and allow for a reduced power consumption, we analyzed the effects of clustering. It was shown that providing different voltages to each cluster can provide a further 10% reduction in energy/operation to a low-voltage parallel system, and that the savings by clustering increase as technology scales.
A Functional Form Approach to Test Set Coverage in Tree Networks
Authors: V. K. Agarwal
Venue: IEEE Transactions on Computers (1979)

To efficiently perform the fault analysis of digital networks it is necessary that pertinent fault interrelationships be utilized. However, to determine these fault interrelationships can entail an analysis which is quite complex and thereby reduces the overall advantage of utilizing the gained insights in a fault analysis process. In this paper we suggest an approach to establishing the existence of a certain fault interrelationship relative to test set coverage in tree networks which is based only on the form of the output function. A procedure is given for generating a form expression (called an L-expression) corresponding to that function. A theorem is stated regarding the interpretation of these form expressions relative to test set coverage.
Fuzzy Clustering Graph for Graded-Evaluation of Hydrocarbon Reserves Value
Authors: Jinlan Liu, Yuan Li, Xiaojun Zhou
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 04 (2007)

Petroleum and natural gas are important substance for economical and social development and their reserves play a key role in the exploration. As an important index, graded evaluation of hydrocarbon reserves value is significant for enterprise management. This paper introduces the method of fuzzy clustering graph to evaluate hydrocarbon reserves value, then a case is implemented in detail to depict the method, which can be used to evaluate multiple oil fields, achieving reasonable and applicable decision-making measures. Key Words: hydrocarbon reserves, graded evaluation, fuzzy clustering graph
Evolutionary induction of sparse neural trees
Authors: Byoung-Tak Zhang, Peter Ohm, Heinz Mühlenbein
Venue: Evolutionary Computation (1997)

This paper is concerned with the automatic induction of parsimonious neural networks. In contrast to other program induction situations, network induction entails parametric learning as well as structural adaptation. We present a novel representation scheme called neural trees that allows efficient learning of both network architectures and parameters by genetic search. A hybrid evolutionary method is developed for neural tree induction that combines genetic programming and the breeder genetic algorithm under the unified framework of the minimum description length principle. The method is successfully applied to the induction of higher order neural trees while still keeping the resulting structures sparse to ensure good generalization performance. Empirical results are provided on two chaotic time series prediction problems of practical interest.
WALe: a problem-oriented computer-based learning environment to foster quality of interpretation and argumentation
Authors: Robin Stark, Babette Koch
Venue: Proceedings of the sixth conference on IASTED International Conference Web-Based Education - Volume 2 (2007)

Even advanced students of education often have serious problems with interpreting empirical data and constructing valid and convincing scientific arguments in educational discourses. Starting from an analysis of these problems, a problem-oriented, computer-based learning environment (WALe) was conceptualized. WALe is a hypertext that displays a complex and authentic educational discourse in the context of a fictitious workshop situation. In this hypertext, various means to induce intensive self-directed learning (e.g., open questions with feedback) to support the students understanding of statistical concepts and procedures and educational theories are integrated (e.g., explication of arguments, tables). In order to evaluate WALe, a field study was carried out with a sample of 43 advanced students of education. Beforehand, cognitive and motivational learning prerequisites were recorded. Then, the participants were instructed to work with WALe at home. First results indicate that the prior knowledge of the students was very low. By learning with WALe, learning progress was fostered substantially. However, the quality of knowledge application in general and specifically of interpretation and argumentation still was dissatisfying. Although a lot of students were overtaxed by learning with WALe, its potential as a rich learning tool was acknowledged. Consequences for instructional practice and further research are drawn.
Facility Location Optimization Methods Based on Aggregate and Disperse Moves
Authors: Hong-Zhen Zheng, Jun-Heng Huang, De-Chen Zhan
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 04 (2007)

The hierarchical facility costs are a special case of the setting in which the facility cost is a more complex function of the set of clients assigned to a single facility, and the algorithm for the problem independent of the number of levels in the hierarchy tree, for the case of identical costs on all facilities does not simply depend on their number. We use the aggregate and disperse moves to show that a constant factor approximation algorithm for the facility location problem with hierarchical facility costs, which a locally optimal solution is a 5-approximation for the problem. Using scaling we improve the bound to 4.236, and accepting only sufficiently large improvements, we can turn this into a polynomial time (4.236+ )-approximation algorithm for the hierarchical facility location problem. A more general class of such problems be defined by using a facility cost that is an arbitrary sub-modular function cost(S) of the set of clients S assigned to the facility. Sub-modularity represents a natural economy of scale in handling extra clients at an existing facility.
Automatic Vulnerability Checking of IEEE 802.16 WiMAX Protocols through TLA+
Authors: Prasad Narayana, Ruiming Chen, Yao Zhao, Yan Chen, Zhi Fu, Hai Zhou
Venue: Proceedings of the 2006 2nd IEEE Workshop on Secure Network Protocols (2006)

Vulnerability analysis is indispensably the first step towards securing a network protocol, but currently remains mostly a best effort manual process with no completeness guarantee. Formal methods are proposed for vulnerability analysis and most existing work focus on security properties such as perfect forwarding secrecy and correctness of authentication. However, it remains unclear how to apply these methods to analyze more subtle vulnerabilities such as denial-of-service (DoS) attacks. To address this challenge, in this paper, we propose use of TLA+ to automatically check DoS vulnerability of network protocols with completeness guarantee. In particular, we develop new schemes to avoid state space explosion in property checking and to model attackers' capabilities for finding realistic attacks. As a case study, we successfully identify threats to IEEE 802.16 air interface protocols.
An Enhanced Text Analysis Approach in Text-to-Speech Synthesis for Mandarin Chinese
Authors: Wei Jiang, Xiao-Long Wang, Yi Guan, Xiu-Li Pang
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 05 (2007)

An enhanced text analysis approach for Chinese text- to-speech (TTS) systems is presented in this paper, as the basic understanding process, the text analysis need provide a fine and effective linguistic information, which is marked explicitly with the corresponding notation. Two kinds of work are done to improve the TTS performance. Firstly, the shallow parsing information is introduced, which is processed by the conditional random fields, accordingly, the label bias problem is overcome; Secondly, considering the dictionary is very important not only in the Chinese Word Segmentation, but also in the Pinyin-to-Character Conversion, we present a semi- automatic word extraction approach for general dictionary and the specialty dictionaries based on Information Entropy. The experiments show that CRF achieved 1.09% improvement in POS tagging task, and 0.67% in shallow parsing task in terms of F-measure. The specialty words can increases the precision by 1.80% to the word segmentation.
Weighted and probabilistic context-free grammars are equally expressive
Authors: Noah A. Smith, Mark Johnson
Venue: Computational Linguistics (2007)

This article studies the relationship between weighted context-free grammars (WCFGs), where each production is associated with a positive real-valued weight, and probabilistic context-free grammars (PCFGs), where the weights of the productions associated with a nonterminal are constrained to sum to one. Because the class of WCFGs properly includes the PCFGs, one might expect that WCFGs can describe distributions that PCFGs cannot. However, Z. Chi (1999, Computational Linguistics, 25(1):131--160) and S. P. Abney, D. A. McAllester, and P. Pereira (1999, In Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics, pages 542--549, College Park, MD) proved that every WCFG distribution is equivalent to some PCFG distribution. We extend their results to conditional distributions, and show that every WCFG conditional distribution of parses given strings is also the conditional distribution defined by some PCFG, even when the WCFG's partition function diverges. This shows that any parsing or labeling accuracy improvement from conditional estimation of WCFGs or conditional random fields (CRFs) over joint estimation of PCFGs or hidden Markov models (HMMs) is due to the estimation procedure rather than the change in model class, because PCFGs and HMMs are exactly as expressive as WCFGs and chain-structured CRFs, respectively.
Component-based face detection and verification
Authors: Kyoung-Mi Lee
Venue: Pattern Recognition Letters (2008)

For face detection, while pose and illumination significantly change the global facial appearance, components of a face are less affected by these changes. Component detectors can accurately locate facial components, and component-based approaches can be used to check whether the geometric locations of the components comply with a face. This paper proposes a face detection and verification method using component-based online learning, which is based on using unsupervised clustering to find a set of templates specific to faces consisting of face component and their relations. The main difference from previously reported component-based face detection methods is the use of online learning, which is ideal for highly repetitive tasks. This results in faster and more accurate face detection, because system performance improves with continued use. Further, uncertainty is added by calculating the standard deviation of face components and their relations. A component-based method with uncertainty provides flexibility to allow variability to describe an object in appearance and geometry.
On the construction of restricted-denominator exponential W-methods
Authors: Paolo Novati
Venue: Journal of Computational and Applied Mathematics (2008)

In this paper we consider the practical construction of exponential W-methods for the solution of large stiff nonlinear initial value problems, based on the restricted-denominator rational approach for the computation of the functions of matrices required. This approach is employed together with the Krylov subspace method based on the Arnoldi algorithm. Two integrators are constructed and tested on some classical stiff equations arising from the semidiscretization of parabolic problems.
Small Disjuncts Grouping by Rule Coverage and Accuracy Measures
Authors: Alan Keller Gomes
Venue: Proceedings of the Seventh International Conference on Intelligent Systems Design and Applications (2007)

Supervised Machine Learning systems can induce knowledge from a set of examples. It knowledge can be de- scribed as one set of rules in the form B H, where B is the rule body and H the rule class. In this form, a rule can be evaluated taking contingency table as standard base, of which can be calculated absolute values (cardinalities) and covering and accuracy rules measures. Small Dis- junct is a rule that cover a small number of examples. It correctly classify individually only few examples but, col- lectively, cover a significant percentage of the set of exam- ples. In this way, discover small disjuncts groups can be important to identify some interesting points like if differ- ent small disjuncts can have the same rule class. In this paper, covering and accuracy measures are used to iden- tify small disjuncts groups.
Team Edit Automata for Testing Security Property
Authors: Zhenrong Yang, Aiman Hanna, Mourad Debbabi
Venue: Proceedings of the Third International Symposium on Information Assurance and Security (2007)

This paper introduces a mathematical model, called Team Edit Automata, for evaluating software security properties. We use the model to describe security properties and their correlation in the software programs. The component automata can suppress and insert actions and report possible flaws. They are used to specify individual security properties. The team is composed of multiple component automata interacting through shared actions. It models the situation where some program events are concerned by multiple security properties jointly. The paper concludes by a case study of detecting memory management and pointer manipulation flaws in C/C++ programs.