ppxscal/embedding_network huggingface dataset:
Solving Protein Structures Using Molecular Replacement Via Protein Fragments
Authors: Jayavardhana Gubbi, Michael Parker, Marimuthu Palaniswami
Venue: Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory (2007)

The need to determine phases is a major bottleneck in a fully automated X-ray crystallography pipeline. The problem commonly called phasing can be solved by a computational method called molecular replacement (MR). With the deposition of more and more proteins into the Protein Data Bank (PDB), it has been shown that the MR yields better initial models. In this paper, ab initio first model generation is addressed. A novel scheme using PHASER is proposed which does not require any a priori information about the structure. The input to the system is the target structure factors and the sequence. We created a unique set of supersecondary structure (fragment) dataset and used them in creation of the first model. The method was evaluated with log-likelihood gain (LLG) and translational Z-score (TFZ) as defined by PHASER. The results obtained are highly encouraging with translation Z-scores of 7 and above for the first model. The proposed scheme is tested on six proteins, two each from ¿, β and ¿ + β classes with very good results.
Model and neural controller for a distributed parameter system
Authors: Bogdan Gilev
Venue: Proceedings of the 9th WSEAS International Conference on Neural Networks (2008)

This paper originates a numerical-analytical model of the electromagnetic and thermal processes in the system inverter-inductor-load. A neuron controller for this system is also developed. The mathematical model is described as follows. Initial creation the mathematical model of the electrical processes in the inverter, which is used as power supply device for the inductor. This problem is solved and the boundary condition for the electromagnetic problem in the inductor is obtained. The electromagnetic problem is described by the Maxwell equations with the boundary condition, which was found. From the task's solution we could define the density of the thermal source, which is present in the boundary problem for the equation of the thermal transfer of the load. If definitive assumptions are made we could find an analytical solution for the problem, but as parameters of the problem are variable we suggest numerical-analytical procedure by discretisation of the time domain interval. The primary electromagnetic and thermal processes flow in the inductor-load system, which has the inverter added to itself, because the system is controlled by the voltage supplying the inverter's input. During the process of developing of the controller we will use educational data, which is obtained by the mathematical model. The procedure has two stages. Furst training forward neural network modelling the system, and then using this model for training neural network controller, which maintains a specific operating mode of the system. This paper examines controller, which maintains constant amplitude of the current in the inductor. It is known that this is one of the basic operating modes in the induction hardening.
A hybrid faulty module prediction using association rule mining and logistic regression analysis
Authors: Yasutaka Kamei, Akito Monden, Shuji Morisaki, Ken-ichi Matsumoto
Venue: Proceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement (2008)

This paper proposes a fault-prone module prediction method that combines association rule mining with logistic regression analysis. In the proposed method, we focus on three key measures of interestingness of an association rule (support, confidence and lift) to select useful rules for the prediction. If a module satisfies the premise (i.e. the condition in the antecedent part) of one of the selected rules, the module is classified by the rule as either fault-prone or not. Otherwise, the module is classified by the logistic model. We experimentally evaluated the prediction performance of the proposed method with different thresholds of each rule interestingness measure (support, confidence and lift) using a module set in the Eclipse project, and compared it with three well-known fault-proneness models (logistic regression model, linear discriminant model and classification tree). The result showed that the improvement of the F1-value of the proposed method was 0.163 at maximum compared to conventional models.
Experience in the Integration of Heterogeneous Models in the Model-driven Engineering of High-Integrity Systems
Authors: Matteo Bordin, Thanassis Tsiodras, Maxime Perrotin
Venue: Proceedings of the 13th Ada-Europe international conference on Reliable Software Technologies (2008)

The development process of high-integrity systems has shifted from manual coding to designing with modeling tools that verify the correctness of the design well before production. The parallel application of several different modeling tools in the design of separate parts of the same system is now a common industrial practice. The advantage of using several, domain-specific tools is however balanced by an increasing complexity in the integration phase: it is indeed necessary to guarantee the correctness of the interaction of the several subapplications, which also includes the integration of the source code automatically generated by the different modeling tools. This constitutes a major concern for the introduction of several modeling tools in the industrial community, as well as for certification institutes. In this paper we present our practical experiences in the definition of a computer-aided sound development process to permit model-driven integration of heterogeneous models.
Finding the Right Family: Parent and Child Selection for Averaged One-Dependence Estimators
Authors: Fei Zheng, Geoffrey I. Webb
Venue: Proceedings of the 18th European conference on Machine Learning (2007)

Averaged One-Dependence Estimators (AODE) classifies by uniformly aggregating all qualified one-dependence estimators (ODEs). Its capacity to significantly improve naive Bayes' accuracy without undue time complexity has attracted substantial interest. Forward Sequential Selection and Backwards Sequential Elimination are effective wrapper techniques to identify and repair harmful interdependencies which have been profitably applied to naive Bayes. However, their straightforward application to AODE has previously proved ineffective. We investigate novel variants of these strategies. Our extensive experiments show that elimination of child attributes from within the constituent ODEs results in a significant improvement in probability estimate and reductions in bias and error relative to unmodified AODE. In contrast, elimination of complete constituent ODEs and the four types of attribute addition are found to be less effective and do not demonstrate any strong advantage over AODE. These surprising results lead to effective techniques for improving AODE's prediction accuracy.
The Parameterized Complexity of the Rectangle Stabbing Problem and Its Variants
Authors: Michael Dom, Somnath Sikdar
Venue: Proceedings of the 2nd annual international workshop on Frontiers in Algorithmics (2008)

We study an NP-complete geometric covering problem called d -Dimensional Rectangle Stabbing, where, given a set of axis-parallel d-dimensional hyperrectangles, a set of axis-parallel (d ¿ 1)-dimensional hyperplanes and a positive integer k, the question is whether one can select at most k of the hyperplanes such that every hyperrectangle is intersected by at least one of these hyperplanes. This problem is well-studied from the approximation point of view, while its parameterized complexity remained unexplored so far. Here we show, by giving a nontrivial reduction from a problem called Multicolored Clique, that for d ¿ 3 the problem is W[1]-hard with respect to the parameter k. For the case d = 2, whose parameterized complexity is still open, we consider several natural restrictions and show them to be fixed-parameter tractable.
Attacks on CKK Family of RFID Authentication Protocols
Authors: Zbigniew Gołębiewski, Krzysztof Majcher, Filip Zagórski
Venue: Proceedings of the 7th international conference on Ad-hoc, Mobile and Wireless Networks (2008)

At Pervasive 2008, Cichon, Klonowski, Kutylowski proposed a family of shared-key authentication protocols ($\mathcal CKK$). Small computational and communication cost, together with possibility of efficient hardware implementation makes $\mathcal CKK$ attractive for low-cost devices such as RFID tags. In this paper we present a couple of attacks on $\mathcal CKK$ protocols, both passive and active.
Sweeping Points
Authors: Adrian Dumitrescu, Minghui Jiang
Venue: Proceedings of the 11th international workshop, APPROX 2008, and 12th international workshop, RANDOM 2008 on Approximation, Randomization and Combinatorial Optimization: Algorithms and Techniques (2008)

Given a set of points in the plane, and a sweep-line as a tool, what is best way to move the points to a target point using a sequence of sweeps? In a sweep, the sweep-line is placed at a start position somewhere in the plane, then moved orthogonally and continuously to another parallel end position, and then lifted from the plane. The cost of a sequence of sweeps is the total length of the sweeps. Another parameter of interest is the number of sweeps. Four variants are discussed, depending whether the target is a hole or a pile, and whether the target is specified or freely selected by the algorithm. Here we present a ratio 4/¿ ≈ 1.27 approximation algorithm in the length measure, which performs at most four sweeps. We also prove that, for the two constrained variants, there are sets of n points for which any sequence of minimum cost requires 3n/2 ¿ O(1) sweeps.
Neural Network Training with Extended Kalman Filter Using Graphics Processing Unit
Authors: Peter Trebatický, Jiří Pospíchal
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part II (2008)

The graphics processing unit has evolved through the years into the powerful resource for general purpose computing. We present in this article the implementation of Extended Kalman filter used for recurrent neural networks training, which most computational intensive tasks are performed on the GPU. This approach achieves significant speedup of neural network training process for larger networks.
A Model and Rule Driven Approach to Service Integration with Eclipse Modeling Framework
Authors: Isaac Cheng, Neil Boyette, Joel Bethea, Vikas Krishna
Venue: Proceedings of the 5th international conference on Service-Oriented Computing (2007)

BPEL is fast becoming the most widely-adopted standard for business processes involving web services; however BPEL is geared mainly at the higher level processes and is not well suited for the lightweight, short-lived "micro-processes" that share the same service space. Such processes require the advantages of interoperability and asynchronicity offered by an SOA approach but at a more granular logical level. This paper details a way to use a declarative approach to define the micro-processes that occur in the services called by an SOA based application. Using the context of a global call center workflow application framework named CCF, for Custom Call Flows, this paper describes how micro-processes (call flows) can be defined, and how declaratively defined rules can be used to integrate these micro-processes with other services to build a flexible service system.
Plenary lecture 4: data mining through data visualisation: computational intelligence approaches
Authors: Colin Fyfe
Venue: Proceedings of the 7th WSEAS International Conference on Electronics, Hardware, Wireless and Optical Communications (2008)

One of the major tasks today is to create information from data. We do not mean to define information in terms of Shannon or indeed any other mathematical definition but information in terms of the subjective experience of a viewer of the data. People (and probably animals) are very good at pattern recognition; we are far more robust pattern matchers than any current computer programs. Increasingly however, we are dealing with high dimensional (and often high volume) data so to gain intuitions about a data set, we often project data onto low dimensional manifolds. One question which arises then, is what projections to low dimensional manifolds are best in order to present the projected data to a human user. We illustrate several projections which have been found by artificial neural network extensions of Hebbian learning. We then show examples of similar projections found by reinforcement learning; our rationale in this case is that we have agents interacting proactively with a database examining different projections and, without human intervention, getting rewards when the projections reveal some interesting structure. We then give examples of the same projections found by other computational intelligence methods such as the cross entropy method and artificial immune systems. We then examine projections to nonlinear manifolds and show that with a particular model of an underlying latent space, we may identify clusters in data sets when such clusters are not visible in any low dimensional linear projection. Finally we review different data representation techniques: we begin with parallel coordinates and point out some difficulties with this method before reviewing Andrews' Curves, a method from the 1970s which has only become truly practicable with the advent of modern desktop computers. An extension to this method came from Wegman and his colleagues in the 1990s. We also discuss a more recent extension and illustrate three dimensional projections of data samples dancing together.
The XtreemOS jScheduler: using self-scheduling techniques in large computing architectures
Authors: F. Guim, I. Rodero, M. Garcia, J. Corbalan
Venue: First USENIX Workshop on Large-Scale Computing (2008)

Several centralized scheduling solutions have been proposed in the literature for environments composed of several independent computational resources, such as centralized schedulers, centralized queues and global controllers. These approaches use a unique scheduling entity responsible for scheduling all jobs submitted by users. In our previous work we proposed the use of self-scheduling techniques to dispatch jobs which are submitted to a set of distributed computational hosts, which are in turn managed by independent schedulers (such as MOAB or LoadLeveler). In the ISIS-Dispatcher, scheduling decisions are made independently for each job instead of using a global policy where all jobs are considered. In this paper we present how the ISIS-Dispatcher techniques can be used in the XtreemOS architecture for manage the jobs. This system is designed to be deployed in large scenarios that potentially involve thousands of resources. In such systems it is not feasible to make the dispatcher contact to all the systems. It is not realistic to suppose that the dispatcher stores the information about all the resources and where they are located. Obviously, this approach would imply problems of scalability. In this paper we also evaluate the impact about the amount of resource information that the dispatcher can collect during the job scheduling.
Proposal of the Stretch Detection Hypothesis of the Meissner Corpuscle
Authors: Shinobu Kuroki, Hiroyuki Kajimoto, Hideaki Nii, Naoki Kawakami, Susumu Tachi
Venue: Proceedings of the 6th international conference on Haptics: Perception, Devices and Scenarios (2008)

In order to realize artificial tactile sensation, we are researching the natural nerve activity timing of mechanoreceptors. Considering the energy conversion system of the Meissner corpuscle, we insist that the corpuscles encode the normal strain, particularly detecting horizontal normal strain. We observed a clear difference in the thresholds between the pushing and pulling cases. This finding suggests that horizontal stretch preferentially induces nerve activity in the Meissner corpuscle.
Deep Lexical Semantics
Authors: Jerry Hobbs
Venue: Proceedings of the 11th international conference on Text, Speech and Dialogue (2008)

The link between words and the world is made easier if we have conceptualized the world in a way that language indicates. In the effort I will describe, we have constructed a number of core formal theories, trying to capture the abstract structure that underlies language and enable literal and metaphorical readings to be seen as specializations of the abstract structures. In the core theories, we have axiomatized composite entities (or things made out of other things), the figure-ground relation, scalar notions (of which space, time and number are specializations), change of state, causality, and the structure of complex events and processes. These theories explicate the basic predicates in terms of which the most common word senses need to be defined or characterized. We are now encoding axioms that link the word senses to the core theories, focusing on 450 of words senses in Core WordNet that are primarily concerned with events and their structure. This may be thought of as a kind of "advanced lexical decomposition", where the "primitives" into which words are "decomposed" are elements in coherently worked-out theories.
Guest Editor's Introduction: Middleware Works in Progress Papers
Venue: IEEE Distributed Systems Online (2003)

This section includes the second batch of six papers that will be presented as Works in Progress papers at the ACM/IFIP/USENIX International Middleware Conference (Middleware 2003) (http://middleware2003.inf.puc-rio.br). These papers, selected by the review committee from a large selection of excellent submissions, are short summaries intended to describe ongoing work and interim results. Please contact the authors for more detailed descriptions of the work described. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Document Clustering by Semantic Smoothing and Dynamic Growing Cell Structure (DynGCS) for Biomedical Literature
Authors: Min Song, Xiaohua Hu, Illhoi Yoo, Eric Koppel
Venue: Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery (2008)

The general goal of clustering is to group data elements such that the intra-group similarities are high and the inter-group similarities are low. In this paper, we propose a novel hybrid clustering technique that incorporates semantic smoothing of document models into a neural network framework. Recently it has been reported that the semantic smoothing model enhances the retrieval quality in Information Retrieval (IR). Inspired by that, we apply the context-sensitive semantic smoothing model to boost accuracy of clustering that is generated by a dynamic growing cell structure algorithm, a variation of the neural network technique. We evaluated the proposed technique on article sets from MEDLINE, the largest biomedical digital library in Biomedicine. Our experimental evaluations show that the proposed algorithm significantly improves the clustering quality over the traditional clustering techniques.
Specification and Checking of Software Contracts for Conditional Information Flow
Authors: Torben Amtoft, John Hatcliff, Edwin Rodríguez, Robby, Jonathan Hoag, David Greve
Venue: Proceedings of the 15th international symposium on Formal Methods (2008)

Information assurance applications providing Multi-Level Secure (MLS) solutions must often implement information flow policies that are conditional in the sense that data is allowed to flow between system components only when the system satisfies certain state predicates. However, existing specification and verification environments, such as SPARK, used to develop such applications, are capable of capturing only unconditional information flows. Motivated by the need to better formally specify and certify MLS applications in industrial contexts, we present an enhancement of the SPARK system that enables specification, inference, and compositional checking of conditional information flow contracts. We report on the use of this framework for a collection of SPARK examples.
Micro-Cobol: A Data Processing Language for Microprocessor Systems
Authors: Yaohan Chu, Paul Schapiro
Venue: IEEE Micro (1981)

New constructs and the essentials of Cobol are combined to Create Micro-Cobol: a new, simple language for small business systems.
A New Hybrid Approach for Analysis of Factors Affecting Crude Oil Price
Authors: Wei Xu, Jue Wang, Xun Zhang, Wen Zhang, Shouyang Wang
Venue: Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007 (2007)

In this paper, a new hybrid approach is presented to analyze factors affecting crude oil price using rough set and wavelet neural network. Related factors that affect crude oil price are found using text mining technique and Brent oil price is chosen as the decision price because it plays an important role in world crude oil markets. The relevant subsets of the factors are discovered by rough set module and the main factors are got, and then the important degrees of these are measured using wavelet neural network. Based on the novel hybrid approach, the predictability of crude oil price is discussed.
Self checking systolic FIFO stack
Authors: Huda B. Abugharsa, Ali H. Maamar
Venue: Proceedings of the 7th WSEAS International Conference on Instrumentation, Measurement, Circuits and Systems (2008)

The advances in VLSI technology have made possible many changes not only in the amount of hardware that can be integrated into a die permitting the implementation of single chip processor, but also in processor architecture. This creates a need for algorithms that can exploit a high degree of pipelining and parallelism. The algorithms that are the best at this time, for being able to incorporate a high degree of parallelism are the systolic arrays. The systolic systems have balanced uniform architectures which typically look like grids where each line indicates a communication path and each intersection represents a cell or a systolic element. Unfortunately as the scale of integration has increased so also has the occurrence of intermittent faults. The characteristics of these types of faults render them undetectable by standard test strategies. This is particularly problematic with the wide use of complex circuits in safety-critical applications. Ensuring the reliability of these systems is a major testing challenge. The detection of intermittent faults requires the use of Concurrent Error Detection (coding) techniques. This paper investigates the use of Berger code as a means of incorporating CED into a self checking systolic FIFO stack.
Effectiveness of highlighting as a prompt in text reading on a computer monitor
Authors: Yuka Kawasaki, Hironori Sasaki, Haruhisa Yamaguchi, Yumi Yamaguchi
Venue: Proceedings of the 8th WSEAS International Conference on Multimedia systems and signal processing (2008)

In this paper, we examine the influence of highlighting as a prompt to memory on a computer monitor. We conducted an experiment, in which the participants in the experimental group read the text with highlighting provided on a computer monitor, and those in the control group read the text without highlighting, then, they took a test on the content of the text. We found clear evidence that highlighting as a prompt is effective in reading and memorizing the text on a computer monitor. That is to say, prompts are effective in enhancing learning on a computer monitor. Therefore, we recommend to pre-highlight important words, phrases, and sentences in the text of the learning materials when teachers provide them to students on a computer monitor.