Approximating shortest superstrings with constraints

This paper introduces a testing tool for regular expressions (regexes) named AREXT. AREXT can automatically extract regexes from C++ source files and visually represent them as DFA graphs. Given a regex, AREXT can generate a set of positive and negative strings with 100% coverage of nodes, edges, and edge pairs. We leverage prior works of synthesizing regexes from natural language to create benchmarks for evaluating AREXT. Some current tools, i.e., EGRET and MUTREX, are also being evaluated and compared. Experiments show that AREXT can outperform EGRET as AREXT can detect more unexpected synthesized regexes in almost all benchmarks. The evaluation results indicate that strings with 100% coverage metrics (generated by AREXT) or strings with maximum mutation score (generated by MUTREX) are not enough to ensure the correctness of regexes under test. Experiments also show that combining AREXT, EGRET, and MUTREX can detect a majority of unwanted synthesized regexes (87–91%).

Restricted relativizations of probabilistic polynomial time

Automatic concept learning from large scale imbalanced data sets is a key issue in video semantic analysis and retrieval, which means the number of negative examples is far more than that of positive examples for each concept in the training data. The existing methods adopt generally under-sampling for the majority negative examples or over-sampling for the minority positive examples to balance the class distribution on training data. The main drawbacks of these methods are: (1) As a key factor that affects greatly the performance, in most existing methods, the degree of re-sampling needs to be pre-fixed, which is not generally the optimal choice; (2) Many useful negative samples may be discarded in under-sampling. In addition, some works only focus on the improvement of the computational speed, rather than the accuracy. To address the above issues, we propose a new approach and algorithm named AdaOUBoost (Adaptive Over-sampling and Under-sampling Boost). The novelty of AdaOUBoost mainly lies in: adaptively over-sample the minority positive examples and under-sample the majority negative examples to form different sub-classifiers. And combine these sub-classifiers according to their accuracy to create a strong classifier, which aims to use fully the whole training data and improve the performance of the class-imbalance learning classifier. In AdaOUBoost, first, our clustering-based under-sampling method is employed to divide the majority negative examples into some disjoint subsets. Then, for each subset of negative examples, we utilize the borderline-SMOTE (synthetic minority over-sampling technique) algorithm to over-sample the positive examples with different size, train each sub-classifier using each of them, and get the classifier by fusing these sub-classifiers with different weights. Finally, we combine these classifiers in each subset of negative examples to create a strong classifier. We compare the performance between AdaOUBoost and the state-of-the-art methods on TRECVID 2008 benchmark with all 20 concepts, and the results show the AdaOUBoost can achieve the superior performance in large scale imbalanced data sets.

We study the set of incompressible strings for various resource bounded versions of Kolmogorov complexity. The resource bounded versions of Kolmogorov complexity we study are polynomial time CD complexity defined by Sipser, the nondeterministic variant CND due to Buhrman and Fortnow, and the polynomial space bounded Kolmogorov complexity CS introduced by Hartmanis. For all of these measures we define the set of random strings $\mathrm{R}^{\mathit{CD}}_t$, $\mathrm{R}^{\mathit{CND}}_t$, and $\mathrm{R}^{\mathit{CS}}_t$ as the set of strings x such that $\mathit{CD}^t(x)$, $\mathit{CND}^t(x)$, and $\mathit{CS}^s(x)$ is greater than or equal to the length of x for s and t polynomials. We show the following: $\mathrm{MA} \subseteq \mathrm{NP}^{\mathrm{R}^{\mathit{CD}}_t}$, where $\mathrm{MA}$ is the class of Merlin--Arthur games defined by Babai. $\mathrm{AM} \subseteq \mathrm{NP}^{\mathrm{R}^{\mathit{CND}}_t}$, where $\mathrm{AM}$ is the class of Arthur--Merlin games. $\mathrm{PSPACE} \subseteq \mathrm{NP}^{\mathrm{cR}^{\mathit{CS}}_s}$. In the last item $\mathrm{cR}^{\mathit{CS}}_s$ is the set of pairs $\langle x,y \rangle$ so that x is random given y. These results show that the set of random strings for various resource bounds is hard for complexity classes under nondeterministic reductions. This paper contrasts the earlier work of Buhrman and Mayordomo where they show that for polynomial time deterministic reductions the set of exponential time Kolmogorov random strings is not complete for EXP.

Data filtering in spreadsheets is a common problem faced by millions of end-users. The task of data filtering requires a computational model that can separate intended positive and negative string instances. We present a system, FIDEX, that can efficiently learn desired data filtering expressions from a small set of positive and negative string examples.

This article is devoted to a comparative legal analysis of the constitutional and legislative foundations of public control in the countries of modern Asia on the example of the Lebanese Republic and the Sultanate of Oman. The relevance of the research topic is due to a number of circumstances: these countries have a different history of national constitutionalism; their legal systems are distinguished, on the one hand, by a powerful national flavor, and on the other hand, they have experienced significant foreign influence in the process of their formation and development; they relate to various forms of government and political regime; they are distinguished by the complex structure of civil society due to the influence of confessional, ethnic, and communal factors; national constitutions and legislation of these States define the possibility of participation of citizens of the country and their public associations in the political life of the state in different ways. The article uses a number of scientific research methods: formal-logical; historical-legal; comparative-legal; method of analysis of specific legal situations; statistical; sociological. The constitutional and legal foundations of public control in the Lebanese Republic and the Sultanate of Oman are distinguished by a number of negative examples of legal regulation, in particular: this institution of civil society is not formalized in national constitutions; there are no separate laws on the legal foundations of public control; the mechanism for forming subjects of public control is not entirely democratic; its effective functioning is hampered by strong pressure from religious, ethnic and community factors that defragment the system of public control. At the same time, in these countries there are a number of positive examples in the organization of this institution of civil society, for example: a number of subjects of public control in the Lebanese Republic have a set of real powers (in particular, the right to veto political decisions that contradict the interests of regional and local communities, the right to control the activities of the police, courts, and prosecutor's offices); The Consultative Assembly (Majlis al-Shura) in the Sultanate of Oman, being, in fact, a public council in front of the monarch, has not only advisory powers, but also the right to legislative initiative after public discussion of draft laws; the Coordinating Council for Public Control over Voting in the Lebanese Republic controls not only the voting process on the day of its holding, but also the entire election procedure.

Customer Churn Prediction is an increasingly pressing issue in today's ever-competitive commercial arena. Although there are several researches in churn prediction, but the accuracy rate, which is very important to business, is not high enough. Recently, Support Vector Machines (SVMs), based on statistical learning theory, are gaining applications in the areas of data mining, machine learning, computer vision and pattern recognition because of high accuracy and good generalization capability. But there has no report about using SVM to Customer Churn Prediction. According to churn data set characteristic, the number of negative examples is very small, we introduce an improved one-class SVM. And we have tested our method on the wireless industry customer churn data set. Our method has been shown to perform very well compared with other traditional methods, ANN, Decision Tree, and Naive Bays.

We introduce a new two-side approximation method for the channel scheduling problem, which controls the accuracy of approximation in two sides by a pair of parameters f , g . We present a series of simple and practical-for-implementation greedy algorithms which give constant factor approximation in both sides. First, we propose four approximation algorithms for the weighted channel allocation problem: 1. a greedy algorithm for the multichannel with fixed interference radius scheduling problem is proposed and an one side O 1 -IS-approximation is obtained; 2. a greedy O 1 , O 1 -approximation algorithm for single channel with fixed interference radius scheduling problem is presented; 3. we improve the existing algorithm for the multichannel scheduling and show an E O d / ε time 1 − ϵ -approximation algorithm; 4. we speed up the polynomial time approximation scheme for single-channel scheduling through merging two algorithms and show a 1 − ϵ , O 1 -approximation algorithm. Next, we study two polynomial time constant factor greedy approximation algorithms for the unweighted channel allocation with variate interference radius. A greedy O 1 -approximation algorithm for the multichannel scheduling problem and an O 1 , O 1 -approximation algorithm for single-channel scheduling problem are developed. At last, we do some experiments to verify the effectiveness of our proposed methods.

Feature selection is an important step in data analysis to address the curse of dimensionality. Such dimensionality reduction techniques are particularly important when if a classification is required and the model scales in polynomial time with the size of the feature (e.g., some applications include genomics, life sciences, cyber-security, etc.). Feature selection is the process of finding the minimum subset of features that allows for the maximum predictive power. Many of the state-of-the-art information-theoretic feature selection approaches use a greedy forward search; however, there are concerns with the search in regards to the efficiency and optimality. A unified framework was recently presented for information-theoretic feature selection that tied together many of the works in over the past twenty years. The work showed that joint mutual information maximization (JMI) is generally the best options; however, the complexity of greedy search for JMI scales quadratically and it is infeasible on high dimensional datasets. In this contribution, we propose a fast approximation of JMI based on information theory. Our approach takes advantage of decomposing the calculations within JMI to speed up a typical greedy search. We benchmarked the proposed approach against JMI on several UCI datasets, and we demonstrate that the proposed approach returns feature sets that are highly consistent with JMI, while decreasing the run time required to perform feature selection.

This paper is concerned with the problem of finding a hypothesis in \mathcal{TP}^2 consistent with given positive and negative examples. The hypothesis class \mathcal{TP}^2 consists of all sets of at most two tree patterns and represents the class of unions of at most two tree pattern languages. Especially, we consider the problem from the point of view of the consistency problem for \mathcal{TP}^2. The consistency problem is a problem for deciding whether there exists a consistent hypothesis with given positive and negative examples within some fixed hypothesis space. Efficient solvability of that problem is closely related to the possibility of efficient machine learning or machine discovery. Unfortunately, however, the consistency problem is known to be NPdcomplete for many hypothesis spaces. In this paper, the problem for the class \mathcal{TP}^2 is also shown to be NPdcomplete. In order to overcome this computational hardness, we try to use additional information obtained by making queries. First, we give an algorithm that, using restricted subset queries, solves the consistency problem for \mathcal{TP}^2 in time polynomial in the total size of given positive and negative examples. Next, we show that each subset query made by the algorithm can be replaced by several membership queries under some condition on a set of function symbols. As a result, we have that the consistency problem for \mathcal{TP}^2 is solved in polynomial time using membership queries.

Optimization algorithms for the solution of the frictionless normal contact between rough surfaces

In this paper, we investigate the problem of placing sensors in a mobile social network to get quickly informed about contagious outbreaks, i.e., placing k sensors in a network in order to minimize the time until a contaminant - starting from a random node in the network - is detected. We aim to optimize the Sensor Placement from two complementary directions. One is to improve the original greedy algorithm and its extensions [13] to reduce sensor selection time, and the other is to propose a new Quickest Path heuristic that can shorten the detection time. We test and compare our algorithms with previous algorithms on four real data sets. Experimental results show that 1) the new greedy algorithm is more efficient than existing greedy algorithms in terms of selection time, 2) the quickest path heuristic obtains less detection time than centrality-based heuristics, and is as effective as the greedy algorithms, and 3) the new heuristic has the potential to scale well to large networks, having low detection time and selection time.

Sorting strings and constructing digital search trees in parallel

Optimizing strategies for post-disaster reconstruction of school systems

BackgroundThe increasing production of genomic data has led to an intensified need for models that can cope efficiently with the lossless compression of DNA sequences. Important applications include long-term storage and compression-based data analysis. In the literature, only a few recent articles propose the use of neural networks for DNA sequence compression. However, they fall short when compared with specific DNA compression tools, such as GeCo2. This limitation is due to the absence of models specifically designed for DNA sequences. In this work, we combine the power of neural networks with specific DNA models. For this purpose, we created GeCo3, a new genomic sequence compressor that uses neural networks for mixing multiple context and substitution-tolerant context models.FindingsWe benchmark GeCo3 as a reference-free DNA compressor in 5 datasets, including a balanced and comprehensive dataset of DNA sequences, the Y-chromosome and human mitogenome, 2 compilations of archaeal and virus genomes, 4 whole genomes, and 2 collections of FASTQ data of a human virome and ancient DNA. GeCo3 achieves a solid improvement in compression over the previous version (GeCo2) of 2.4%, 7.1%, 6.1%, 5.8%, and 6.0%, respectively. To test its performance as a reference-based DNA compressor, we benchmark GeCo3 in 4 datasets constituted by the pairwise compression of the chromosomes of the genomes of several primates. GeCo3 improves the compression in 12.4%, 11.7%, 10.8%, and 10.1% over the state of the art. The cost of this compression improvement is some additional computational time (1.7–3 times slower than GeCo2). The RAM use is constant, and the tool scales efficiently, independently of the sequence size. Overall, these values outperform the state of the art.ConclusionsGeCo3 is a genomic sequence compressor with a neural network mixing approach that provides additional gains over top specific genomic compressors. The proposed mixing method is portable, requiring only the probabilities of the models as inputs, providing easy adaptation to other data compressors or compression-based data analysis tools. GeCo3 is released under GPLv3 and is available for free download at https://github.com/cobilab/geco3.