ppxscal/embedding_network huggingface dataset:
Inferring Adaptive Regulation Thresholds and Association Rules from Gene Expression Data through Combinatorial Optimization Learning
Authors: Ignacio Ponzoni, Francisco Azuaje, Juan Augusto, David Glass
Venue: IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) (2007)

There is a need to design computational methods to support the prediction of gene regulatory networks. Such models should offer both biologically-meaningful and computationally-accurate predictions, which in combination with other techniques may improve large-scale, integrative studies. This paper presents a new machine learning method for the prediction of putative regulatory associations from expression data, which exhibit properties never or only partially addressed by other techniques recently published. The method was tested on a Saccharomyces cerevisiae gene expression dataset. The results were statistically validated and compared with the relationships inferred by two machine learning approaches to gene regulatory network prediction. Furthermore, the resulting predictions were assessed using domain knowledge. The proposed algorithm may be able to accurately predict relevant biological associations between genes. One of the most relevant features of this new method is the prediction of adaptive regulation thresholds for the discretization of gene expression values, which is required prior to the rule association learning process. Moreover, an important advantage consists of its low computational cost to infer association rules. The proposed system may significantly support exploratory, large-scale studies of automated identification of potentially-relevant gene expression associations.
An orthogonally filtered tree classifier based on nonlinear kernel-based optimal representation of data
Authors: Hyun-Woo Cho
Venue: Expert Systems with Applications: An International Journal (2008)

The early detection and reliable diagnosis of a fault is crucial in an on-going operation of processes. They provide early warning for a fault and identification of its assignable cause. This paper proposes a classification tree-based diagnosis scheme combined with nonlinear kernel discriminant analysis. The nonlinear kernel-based dimension reduction for the discrimination of various classes of data is performed to determine nonlinear decision boundaries. The use of the nonlinear kernel method in a classification tree is to reduce the dimension of data and to provide its lower-dimensional representation suitable for separating different classes. We also present the use of orthogonal filter as a preprocessing step. An orthogonal filter-based preprocessing is performed to remove unwanted variation of data for enhancing discrimination power and classification performance. The performance of the proposed method is demonstrated using simulation data and compared with other methods. The classification results showed that the proposed tree-based method outperforms traditional PCA-based method.
PALS: Efficient Or-Parallel execution of Prolog on Beowulf clusters
Authors: Enrico Pontelli, Karen Villaverde, Hai-Feng Guo, Gopal Gupta
Venue: Theory and Practice of Logic Programming (2007)

This paper describes the development of the PALS system, an implementation of Prolog capable of efficiently exploiting or-parallelism on distributed-memory platforms—specifically Beowulf clusters. PALS makes use of a novel technique, called incremental stack-splitting. The technique proposed builds on the stack-splitting approach, previously described by the authors and experimentally validated on shared-memory systems, which in turn is an evolution of the stack-copying method used in a variety of parallel logic and constraint systems—e.g., MUSE, YAP, and Penny. The PALS system is the first distributed or-parallel implementation of Prolog based on the stack-splitting method ever realized. The results presented confirm the superiority of this method as a simple yet effective technique to transition from shared-memory to distributed-memory systems. PALS extends stack-splitting by combining it with incremental copying; the paper provides a description of the implementation of PALS, including details of how distributed scheduling is handled. We also investigate methodologies to effectively support order-sensitive predicates (e.g., side-effects) in the context of the stack-splitting scheme. Experimental results obtained from running PALS on both Shared Memory and Beowulf systems are presented and analyzed.
An alternative dynamic binding model: deep binding with cacheing
Authors: Cyril N. Alberga
Venue: ACM SIGPLAN Lisp Pointers (1987)

The two most common dynamic binding models used in Lisp systems are shallow and deep binding. For a succinct summery of these see Padget and Fitch [1985], which includes a proposed third model. It is often claimed that shallow binding are unsuited to systems which allow context switching. We wish to describe an alternative binding model, deep binding with cacheing, which provides the efficiency of shallow binding in "ordinary" call/return control, while exhibiting the ease of context switching of deep binding. This binding model was implemented in the Lisp system built at the IBM Research Center, later elaborated into LISP/VM. In this article the term LISP/VM will be used to encompass both that program and all of its progenitors developed at IBM Research.
A Certifying Code Generation Phase
Authors: Jan Olaf Blech, Arnd Poetzsch-Heffter
Venue: Electronic Notes in Theoretical Computer Science (ENTCS) (2007)

Guaranteeing correctness of compilation is a vital precondition for correct software. Code generation can be one of the most error-prone tasks in a compiler. One way to achieve trusted compilation is certifying compilation. A certifying compiler generates for each run a proof that it has performed the compilation run correctly. The proof is checked in a separate theorem prover. If the theorem prover is content with the proof one can be sure that the compiler produced correct code. This paper reports on the construction of a certifying code generation phase for a compiler. It is part of a larger project aimed at guaranteeing the correctness of a complete compiler. We emphasize on demonstrating the feasibility of the certifying compilation approach to code generation and focus on the implementation and practical issues. It turns out that the checking of the certificates is the actual bottleneck of certifying compilation. We present a proof schema to overcome this bottleneck. Hence we show the applicability of the certifying compilation approach for small sized programs processed by a compiler's code generation phase.
Data Fusion based on RBF Neural Network for Error Compensation in Resistance Strain Gauge Force Transducers
Authors: Yan Chen, Benoit Boulet, Ping Chen, Mingbo Zhao
Venue: Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 03 (2007)

Many factors, such as environmental temperature and material elasticity, can affect the output of resistance strain gauge force transducers used in vehicle traction force measurements. A data fusion method based on radial basis function (RBF) neural network is proposed to reduce the negative effects and compensate the measurement error. A multiquadrics kernel is utilized as the kernel function for the RBF neural networks. It fuses the environmental temperature in the force measurement while realizing an accurate compensation of errors. Tests have been carried out within temperature ranging from -10 ..C to 60 ..C and the results show that the maximum error with load 80000N is below 0.5% after compensation while it is greater than 6% before compensation.
Ulta-Low-Power Wireless Sensor Node Design on 100 uW Scavenging Energy for Applications In Biomedical Monitoring
Authors: Mladen Berekovic
Venue: Proceedings of the 10th Euromicro Conference on Digital System Design Architectures, Methods and Tools (2007)

Recent advances in energy scavenging Technology pave the way for the design of Wireless Autonomous Transducer Systems (WATS) with a power envelope of up to 100 uW. At IMEC we are working on A set of new technologies that will enable the Design of a new class of applications that run on such autonomous transducer systems. Especially the field of health- and bio-monitoring on ECG or EEG signal looks particularly promising for such autonomous sensor nodes.
A MDA tool for the development of service-oriented component-based applications
Authors: Nestor Riba, Humberto Cervantes
Venue: Proceedings of the Eighth Mexican International Conference on Current Trends in Computer Science (2007)

This paper presents a process for the development of a Model-Driven Architecture (MDA) tool for the construction of service-oriented component-based applications. The process is used in the construction of a tool for one particular domain, but can be easily adapted to other domains. The tool in itself simplifies the development of components using a MDA approach, in which modeling is at the core of the development activities. After components are modeled, the tool validates the correctness of the design of the model based in a specification that is embedded inside the tool. Once the model has been validated, the tool is capable of creating the skeletons of the code for the components which are then executed inside the OSGi platform.
Comparison of Distance Measures in Evolutionary Time Series Segmentation
Authors: Jingwen Yu, Jian Yin, Jun Zhang
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 03 (2007)

Time series segmentation is a fundamental component in the process of analyzing and mining time series data. Given a set of pattern templates, evolutionary computation is an appropriate tool to segment time series flexibly and effectively. However, the choice of distance measure in fitness function is very important to evolutionary time series segmentation, for it will affect the convergence of the algorithm greatly. As a simple and easy method, direct point-to-point distance (DPPD) is always used as similarity measure. However, it is brittle to time phase. In this paper, we present three other distance measures for fitness evaluation, which are based on enclosed area, time warping and trend similarity respectively. Moreover, experiments are conducted to compare the performances of new distance measures with the DPPD approach. Results show that new distance measures outperform the DPPD approach in correct match, accurate segmentation.
A New Era of Performance Evaluation
Authors: Sean M. Pieper, JoAnn M. Paul, Michael J. Schulte
Venue: Computer (2007)

Computers increasingly interact with other computers, humans, and the outside world, leading to scenario-oriented computing, an emerging category of design that will enable future consumer devices and usher in a new era of performance evaluation.
Robust sensor self-initialization: Whispering to avoid intruders
Authors: Carlos Ribeiro
Venue: Proceedings of the The International Conference on Emerging Security Information, Systems, and Technologies (2007)

Wireless Sensor Networks (WSN) are becoming bigger and with this growth comes the need for new automatic mechanisms for initializations done by hand. One of those mechanisms is the assignment of addresses to nodes. Several solutions were already proposed for mobile ad-hoc networks but they either: i) do not scale well for WSN; ii) have no energy constraints; iii) have no security considerations; iv) or have no mechanisms to handle fusion of network partitions. We proposed a self-stabilizing address self-assignment protocol which: uses an improved version of a flood control mechanism to minimize the energy spent; and uses a technique named whispering to achieve robustness against malicious nodes.
Step-up and step-down procedures controlling the number and proportion of false positives
Authors: Paul N. Somerville, Claudia Hemmelmann
Venue: Computational Statistics Data Analysis (2008)

In multiple hypotheses testing, it is important to control the probability of rejecting ''true'' null hypotheses. A standard procedure has been to control the family-wise error rate (FWER), the probability of rejecting at least one true null hypothesis. For large numbers of hypotheses, using FWER can result in very low power for testing single hypotheses. Recently, powerful multiple step FDR procedures have been proposed which control the ''false discovery rate'' (expected proportion of Type I errors). More recently, van der Laan et al. [Augmentation procedures for control of the generalized family-wise error rate and tail probabilities for the proportion of false positives. Statist. Appl. in Genetics and Molecular Biol. 3, 1-25] proposed controlling a generalized family-wise error rate k-FWER (also called gFWER(k)), defined as the probability of at least (k+1) Type I errors (k=0 for the usual FWER). Lehmann and Romano [Generalizations of the familywise error rate. Ann. Statist. 33(3), 1138-1154] suggested both a single-step and a step-down procedure for controlling the generalized FWER. They make no assumptions concerning the p-values of the individual tests. The step-down procedure is simple to apply, and cannot be improved without violation of control of the k-FWER. In this paper, by limiting the number of steps in step-down or step-up procedures, new procedures are developed to control k-FWER (and the proportion of false positives) PFP. Using data from the literature, the procedures are compared with those of Lehmann and Romano [Generalizations of the familywise error rate. Ann. Statist. 33(3), 1138-1154], and, under the assumption of a multivariate normal distribution of the test statistics, show considerable improvement in the reduction of the number and PFP.
Hybrid MATLAB and LabVIEW with neural network to implement a SCADA system of AC servo motor
Authors: Jui-Hong Horng
Venue: Advances in Engineering Software (2008)

The AC servo motor system is a non-linear system with uncertainty of parameters. Therefore, it is not easy to identify the system with traditional mathematical model. In this paper, there are four algorithms, that is, SDBP, MOBP, VLBP and LMBP of back-propagation network are trained, and choose better one to identify the system. The method of designing controller is easy and can be applied conveniently in engineering. Simulation results show that LMBP has more fast convergence and it is suitable for system identification and controller design. In the proposed scheme, we successfully integrate MATLAB/Simulink and LabVIEW with neural network to develop a SCADA system of AC servo motor. Numerical simulation results are provided to confirm the performance and effectiveness of the proposed control approach.
Existence of equilibrium solution for a non-cooperative game with fuzzy goals and parameters
Authors: F. Kacher, M. Larbani
Venue: Fuzzy Sets and Systems (2008)

In this paper we introduce a concept of equilibrium for a non-cooperative game with fuzzy goals involving fuzzy parameters. This equilibrium is based on Zimmermann's approach for solving linear multiobjective problems with fuzzy goals and the concept of N-S equilibrium introduced by Zhukovskii for a non-cooperative game with payoffs involving unknown parameters in the case of complete ignorance of their behavior. We also provide a theorem of existence of this equilibrium based on the Fan inequality.
Using Three Layer Neural Networks to Compute Discrete Real Functions
Authors: Jian Wang, Yixian Yang, Nan Jiang, Zhaozhi Zhang, Xiaomin Ma
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 01 (2007)

This paper concerns how to compute discrete real functions using three-layer feedforward neural networks with one hidden layer. Firstly, we define strongly and weakly symmetric real functions. Then we give a network to compute a specific strongly symmetric real function. The number of the hidden neurons is given and the weights of hidden neurons are 1 or -1. Algorithm 1 modifies the weights to real numbers to compute arbitrary strongly symmetric real functions. Theorem 3 extends the results to compute any discrete real functions. Finally, we give an example to indicate our results.
Optimized enterprise risk management
Authors: C. Abrams, J. von Känel, S. Müller, B. Pfitzmann, S. Ruschka-Taylor
Venue: IBM Systems Journal (2007)

As the result of the increasing costs of risk and compliance activities, enterprises are beginning to integrate compliance and risk management into a comprehensive enterprise risk management function and thus proactively address all sorts of risk, including operational risk and the risk of noncompliance. We present the IBM Research enterprise risk management framework, designed to address risk and compliance management in a strategic, integrated, and comprehensive manner. We demonstrate how enterprises evolve along an enterprise-risk-management maturity continuum from a state of mere penalty avoidance through a state of improvement until they finally reach a state of continuous, risk-based transformation. We then explain our high-level model of the enterprise and its environment and describe the central issues, systems, models, and technologies involved. We conclude by presenting the tactical steps necessary to successfully launch enterprise risk management in accordance with our framework.
Association Rule Retrieved from Web Log Based on Rough Set Theory
Authors: Sen Guo, Yongsheng Liang, Zhili Zhang, Wei Liu
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 03 (2007)

With the development of Internet, data mining in web log has been a hot spot of research work. Many data mining methods based on association rule have been proposed. In this paper, a novel association rule prediction model based on rough set theory is advanced, it can deal with no-contiguous access sequence and get explicit expression of rules without prior knowledge, the result of experiment prove that it can achieve more prediction accuracy than existing methods.
Modification of Backpropagation Algorithm and Its Application for Neural Networks with Threshold Activation Function
Authors: V. A. Ptitchkin
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 01 (2007)

The method for determination of gradient of quadratic quality index of multi-layer neural network (MLNN) in one forward passage is proposed. Here, dependence of the gradient on the derivatives of the activation functions (AF) shall become obvious. Replacing the derivatives by the linearization coefficients of the activation functions shall make it possible to determine the coefficients of linearization of the quadratic quality index and to use these coefficients for determination of new values of the synaptic matrices in the supervisory learning procedure. As a result, extension of Backpropagation Algorithm (BPA) application to the networks with nondifferentiable and even discontinuous activation functions shall become possible. As an example, simple algorithm is proposed for determining the coefficients of linearization of the threshold-type activation function.
A Software Architecture For Simplifying Verification of System Timing Properties
Authors: Jia Xu
Venue: Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 03 (2007)

When real-time embedded systems are constructed using conventional software architectures, it is often practically impossible to verify their timing properties. This paper describes a software architecture based on pre-run-time scheduling that greatly simplifies the task of verifying a real-time embedded system's timing properties.
Inventory budget optimization: meeting system-wide service levels in practice
Authors: P. Korevaar, U. Schimpel, R. Boedi
Venue: IBM Journal of Research and Development (2007)

The work described in the literature on inventory and supply chain management has advanced greatly over the last few decades and now covers many aspects and challenges of applied supply chain management. In this paper we describe an approach that combines many of these academic aspects in a practical way to manage the spare parts logistics at a German automobile manufacturer. The basic problem is a single-echelon inventor), problem with a system-wide service-level requirement and the possibility of issuing emergency orders. There exist two related optimization problems: One is to maximize the system-wide service level under the constraint of a given budget; the other is to minimize the budget for a given system-wide service level. The most important requirements and constraints considered are a detailed cost structure, different packaging sizes, capacity constraints, several storage zones, the decision whether or not to stock a product, stochastic lead times, highly sporadic demands, and the stability of the optimization result over time. Our approach has been implemented successfully in an automotive spare parts planning environment. The complete solution package integrates into the mySAP ERP®, the SAP Enterprise Resource Planning system, and APO 4.0, the SAP Advanced Planning and Optimization system. A detailed description of the model is given and results are presented.
A New Priority Method for Triangular Fuzzy Number Complementary Judgment Matrix
Authors: Xuelan Zeng, Fanyong Meng, Meihua Chen
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 05 (2007)

In this paper, firstly, the existing problems of the solution of weight vector of triangular fuzzy number complementary judgment matrix,which are proposed by Xu [3] and Gu [4], are discussed. Secondly, on the basis of the consistency defi- nition for triangular fuzzy number complementary judgment matrix (TFNCJM) proposed by Gu [4], a consistency sufficient condition of TFNCJM is proposed, and a linear goal programming of TFNCJM is established. Then the weight vector of TFNCJM is obtained by solving the model.Thirdly, the paper gives a new priority formula for triangular fuzzy number, and the priority of alternatives are obtained by using the formula. Finally, a numerical example is given to show their feasibility and effectiveness.
On Winograd's Algorithm for Inner Products
Authors: A. Waksman
Venue: IEEE Transactions on Computers (1970)

This correspondence demonstrates an improvement on Winogard's algorithm for inner products as applied to the multiplication of two matrices.
Incorporating Modular Imperfect Coverage into Dynamic Hierarchical Systems Analysis
Authors: Prashanthi Boddu, Liudong Xing
Venue: Proceedings of the Third IEEE International Symposium on Dependable, Autonomic and Secure Computing (2007)

This paper deals with the evaluation of the reliability of a dynamic hierarchical system. Reliability of the system is calculated precisely by incorporating modular imperfect coverage model for both static and dynamic subsystems. Modular imperfect coverage model is analyzed in detail using formal methods to debug the specification errors and remove any ambiguity. A modular and hierarchical decomposition method that combines Markov Analysis and a separable binary decision diagrams based combinatorial method is applied to the reliability analysis of dynamic hierarchical systems subject to modular imperfect coverage. We illustrate our approach by analyzing an example hierarchical computer system.