scholarly journals Concrete domains in logics

2021 ◽  
Vol 8 (3) ◽  
pp. 6-29
Author(s):  
Stéphane Demri ◽  
Karin Quaas
Keyword(s):  
Description Logics ◽  
Atomic Level ◽  
Second Order ◽  
Decision Problems ◽  
Temporal Logics ◽  
Open Problems ◽  
Proof Techniques ◽  
Decidability And Complexity ◽  
Concrete Domains ◽  
Selection Of

In this short survey, we present logical formalisms in which reasoning about concrete domains is embedded in formulae at the atomic level. These include temporal logics with concrete domains, description logics with concrete domains as well as variant formalisms. We discuss several proof techniques to solve logical decision problems for such formalisms, including those based on constrained automata or on translation into decidable second-order logics. We also present recent results mainly related to decidability and complexity as well as a selection of open problems.

scholarly journals Designing Strong Privacy Metrics Suites Using Evolutionary Optimization

2021 ◽  
Vol 24 (2) ◽  
pp. 1-35
Author(s):  
Isabel Wagner ◽  
Iryna Yevseyeva
Keyword(s):  
Optimization Problem ◽  
Evolutionary Optimization ◽  
Multiple Objectives ◽  
Shared Value ◽  
Vehicular Communications ◽  
Open Problems ◽  
Value Range ◽  
Genomic Privacy ◽  
Metaheuristic Optimization Algorithms ◽  
Selection Of

The ability to measure privacy accurately and consistently is key in the development of new privacy protections. However, recent studies have uncovered weaknesses in existing privacy metrics, as well as weaknesses caused by the use of only a single privacy metric. Metrics suites, or combinations of privacy metrics, are a promising mechanism to alleviate these weaknesses, if we can solve two open problems: which metrics should be combined and how. In this article, we tackle the first problem, i.e., the selection of metrics for strong metrics suites, by formulating it as a knapsack optimization problem with both single and multiple objectives. Because solving this problem exactly is difficult due to the large number of combinations and many qualities/objectives that need to be evaluated for each metrics suite, we apply 16 existing evolutionary and metaheuristic optimization algorithms. We solve the optimization problem for three privacy application domains: genomic privacy, graph privacy, and vehicular communications privacy. We find that the resulting metrics suites have better properties, i.e., higher monotonicity, diversity, evenness, and shared value range, than previously proposed metrics suites.

An Effective Solution to Regression Problem by RBF Neuron Network

2015 ◽  
Vol 6 (4) ◽  
pp. 57-74 ◽  
Author(s):  
Dang Thi Thu Hien ◽  
Hoang Xuan Huan ◽  
Le Xuan Minh Hoang
Keyword(s):  
Regression Function ◽  
Training Data ◽  
Neuron Network ◽  
Open Problems ◽  
Regression Problem ◽  
Rbf Network ◽  
Network Training ◽  
Hidden Layer ◽  
Definition Of ◽  
Selection Of

Radial Basis Function (RBF) neuron network is being applied widely in multivariate function regression. However, selection of neuron number for hidden layer and definition of suitable centre in order to produce a good regression network are still open problems which have been researched by many people. This article proposes to apply grid equally space nodes as the centre of hidden layer. Then, the authors use k-nearest neighbour method to define the value of regression function at the center and an interpolation RBF network training algorithm with equally spaced nodes to train the network. The experiments show the outstanding efficiency of regression function when the training data has Gauss white noise.

scholarly journals Finding the Number of Clusters in Data and Better Initial Centers for K-means Algorithm

2020 ◽  
Vol 12 (6) ◽  
pp. 1-20
Author(s):  
Ahmed Fahim ◽  
Keyword(s):  
Data Clustering ◽  
Linear Time ◽  
Original Data ◽  
Local Minima ◽  
Expected Number ◽  
Open Problems ◽  
Number Of Clusters ◽  
Benchmark Datasets ◽  
Selection Of

The k-means is the most well-known algorithm for data clustering in data mining. Its simplicity and speed of convergence to local minima are the most important advantages of it, in addition to its linear time complexity. The most important open problems in this algorithm are the selection of initial centers and the determination of the exact number of clusters in advance. This paper proposes a solution for these two problems together; by adding a preprocess step to get the expected number of clusters in data and better initial centers. There are many researches to solve each of these problems separately, but there is no research to solve both problems together. The preprocess step requires o(n log n); where n is size of the dataset. This preprocess step aims to get initial portioning of data without determining the number of clusters in advance, then computes the means of initial clusters. After that we apply k-means on original data using the resulting information from the preprocess step to get the final clusters. We use many benchmark datasets to test the proposed method. The experimental results show the efficiency of the proposed method.

scholarly journals Selection of Second Order Models’ Design Using D-, A-, E-, T- Optimality Criteria

2019 ◽  
pp. 1-15
Author(s):  
Wangui Patrick Mwangi ◽  
Ayubu Anapapa ◽  
Argwings Otieno
Keyword(s):  
Optimality Criteria ◽  
Second Order ◽  
Factorial Designs ◽  
Economical Design ◽  
Wide Range ◽  
Eigen Value ◽  
Average Variance ◽  
Rsm Technique ◽  
Central Composite ◽  
Selection Of

There are numerous designs for fitting second order models that can be used in conjunction with the response surface methodology (RSM) technique in optimization processes, be it in agriculture, industries and so on. Some of the designs include the equiradial, Notz, San Cristobal, Koshal, Hoke, Central Composite and Factorial designs. However, RSM can only be applied in conjunction with a single design at a time. This research aimed at choosing a design out of the most widely employed designs for fitting 2nd order models involving 3 factors for optimization of French beans in conjunction with the RSM technique. The most commonly used designs for second order models were first identified as Box-Behnken designs, Hoke D2 and Hoke D6 designs, 3k factorial designs, CCD face centred, CCD rotatable and CCD spherical. Design matrices for these 7 designs were formed and augmented with 5 centre points (chosen through lottery methods), and information and optimal design matrices were formed. Then, for each design, the analysis of D-, A- E-, T- optimality (D-Determinant, A-Average Variance, E-Eigen Value and T-Trace) was carried out according to Pukelsheim’s definitions. The results were ranked for each criterion and the ranks corresponding to each design were averaged. The design chosen was Hoke D2 with the least average- 1.75. The Hoke D2 was found to be optimal in minimizing the variance of prediction and the most economical design among the seven. The findings are in agreement with other researchers and scientist that a design may be optimal in one criterion but fails in another criterion. Further, Hoke designs are in the class of the economical designs. It is recommended that more optimality criteria be applied and a wide range of designs be involved to see whether the results would still agree with these findings.

DECISION PROBLEMS FOR FINITELY PRESENTED AND ONE-RELATION SEMIGROUPS AND MONOIDS

2009 ◽  
Vol 19 (06) ◽  
pp. 747-770 ◽  
Author(s):  
ALAN J. CAIN ◽  
VICTOR MALTCEV
Keyword(s):  
Markov Property ◽  
Decision Problems ◽  
Open Problems ◽  
Finitely Presented

For some distinguished properties of semigroups, such as having idempotents, regularity, hopficity, etc., we study the following: whether that property or its negation is a Markov property, whether it is decidable for finitely presented semigroups and for one-relation semigroups and monoids. All the results and open problems are summarized in a table.

Decision Problems for Second-Order Linear Logic

1997 ◽  
pp. 127-143
Author(s):  
P. D. Lincoln ◽  
A. Scedrov ◽  
N. Shankar
Keyword(s):  
Linear Logic ◽  
Second Order ◽  
Decision Problems ◽  
Order Linear

The effects of two-body correlations in 16O on the bremsstrahlung weighted cross section

1980 ◽  
Vol 58 (8) ◽  
pp. 1099-1118
Author(s):  
D. Duplain ◽  
B. Goulard
Keyword(s):  
Cross Section ◽  
Cluster Expansion ◽  
Second Order ◽  
Body Density ◽  
The One ◽  
Selection Of

The bremsstrahlung weighted cross section σ−1, is calculated for,16O using the linked cluster expansion to introduce two-body correlations. All diagrams up to second order in the G-matrix are calculated. A particular choice is made among these diagrams which are grouped in order to preserve the normalization of the one-body density and of the two-body density. This selection of diagrams is shown to be consistent with an expansion in the number of hole-lines. The correlated value obtained in this way, σ−1 = 14.99 mb, is close to the experimental value σ−1 = 15.10 mb although the calculation might still be subject to improvement.

scholarly journals An Introduction to Intertask Transfer for Reinforcement Learning

AI Magazine ◽  
2011 ◽  
Vol 32 (1) ◽  
pp. 15 ◽  
Author(s):  
Matthew E. Taylor ◽  
Peter Stone
Keyword(s):  
Reinforcement Learning ◽  
Transfer Learning ◽  
Learning Problem ◽  
Open Problems ◽  
Learning Framework ◽  
Learning Domains ◽  
Multiple Tasks ◽  
Exciting Area ◽  
Generalize Information ◽  
Selection Of

Transfer learning has recently gained popularity due to the development of algorithms that can successfully generalize information across multiple tasks. This article focuses on transfer in the context of reinforcement learning domains, a general learning framework where an agent acts in an environment to maximize a reward signal. The goals of this article are to (1) familiarize readers with the transfer learning problem in reinforcement learning domains, (2) explain why the problem is both interesting and difficult, (3) present a selection of existing techniques that demonstrate different solutions, and (4) provide representative open problems in the hope of encouraging additional research in this exciting area.

Effective Modal Derivatives Based Reduction Method for Geometrically Nonlinear Structures

2011 ◽  
Author(s):  
Paolo Tiso
Keyword(s):  
Reduction Method ◽  
Order Reduction ◽  
Second Order ◽  
Geometrically Nonlinear ◽  
Vibration Modes ◽  
Model Order ◽  
Modal Derivatives ◽  
Dynamics Problems ◽  
Modal Truncation ◽  
Selection Of

Effective Model Order Reduction (MOR) for geometrically nonlinear structural dynamics problems can be achieved by projecting the Finite Element (FE) equations on a basis constituted by a set of vibration modes and associated second order modal derivatives. However, the number of modal derivatives generated by such approach is quadratic with respect to the number of chosen vibration modes, thus quickly making the dimension of the reduction basis large. We show that the selection of the most important second order modes can be based on the convergence of the underlying linear modal truncation approximation. Given a certain time dependency of the load, this method allows to select the most significant modal derivatives set before computing it.

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