GENDIS: Genetic Discovery of Shapelets

In the time series classification domain, shapelets are subsequences that are discriminative of a certain class. It has been shown that classifiers are able to achieve state-of-the-art results by taking the distances from the input time series to different discriminative shapelets as the input. Additionally, these shapelets can be visualized and thus possess an interpretable characteristic, making them appealing in critical domains, where longitudinal data are ubiquitous. In this study, a new paradigm for shapelet discovery is proposed, which is based on evolutionary computation. The advantages of the proposed approach are that: (i) it is gradient-free, which could allow escaping from local optima more easily and supports non-differentiable objectives; (ii) no brute-force search is required, making the algorithm scalable; (iii) the total amount of shapelets and the length of each of these shapelets are evolved jointly with the shapelets themselves, alleviating the need to specify this beforehand; (iv) entire sets are evaluated at once as opposed to single shapelets, which results in smaller final sets with fewer similar shapelets that result in similar predictive performances; and (v) the discovered shapelets do not need to be a subsequence of the input time series. We present the results of the experiments, which validate the enumerated advantages.

Exact Method for Generating Strategy-Solvable Sudoku Clues

A Sudoku puzzle often has a regular pattern in the arrangement of initial digits and it is typically made solvable with known solving techniques called strategies. In this paper, we consider the problem of generating such Sudoku instances. We introduce a rigorous framework to discuss solvability for Sudoku instances with respect to strategies. This allows us to handle not only known strategies but also general strategies under a few reasonable assumptions. We propose an exact method for determining Sudoku clues for a given set of clue positions that is solvable with a given set of strategies. This is the first exact method except for a trivial brute-force search. Besides the clue generation, we present an application of our method to the problem of determining the minimum number of strategy-solvable Sudoku clues. We conduct experiments to evaluate our method, varying the position and the number of clues at random. Our method terminates within 1 min for many grids. However, as the number of clues gets closer to 20, the running time rapidly increases and exceeds the time limit set to 600 s. We also evaluate our method for several instances with 17 clue positions taken from known minimum Sudokus to see the efficiency for deciding unsolvability.

On the calculation of the optimal value of the loading force in the conditions of a slow creep in the vicinity of a single microdefect

В работе рассматривается проблема расчета величин оптимального нагружающего усилия при деформировании материала в условиях медленного крипа. Решение краевой задачи строится в рамках модели больших упругоползучих деформаций. Полученное численное решение используется для поиска оптимального процесса нагружения необходимого для достиженения заданного размера одиночного микродефекта. Поиск оптимального решения проводится на множестве полиномов сетепни 5 методо полного перебора. Полученное решение анализируется графически и сравнивается с ранее полученныи результатами. In the paper the problem of calculating of the optimal loading pressure during material deformation under creep is considered. The solution of the boundary-value problem is constructed within the frameworks of the model of large elastic-creep deformations. The obtained numerical solution is used to find the optimal loading process necessary to achieve a given size of a single microdefect. The search for the optimal solution is carried out on the set of 5-order polynomials by a brute-force search. The resulting solution is analyzed graphically and compared with previously obtained results.

Brute Force Search Method for Cyberbullying Detection

In this chapter, the authors present a method for automatic detection of malicious internet contents, based on a combinatorial approach resembling brute force search algorithms, with application to language classification. The method automatically extracts sophisticated patterns from sentences and applies them in classification. The experiments performed on actual cyberbullying data showed advantage of this method to previous methods, including the one described in Chapter 4. Pros and cons of this method when compared to previous ones are also discussed in this chapter.

Secure Remote User Authentication using Pass Script & Pass Text

Authenticating users to secure systems is a crucial task for security experts to solve a password problem, where user should able to memorize a password or secret and password should be hard to guess and crack by adversaries. In general, Most of the secure systems were designed with text passwords along with additional factors such as tokens like smart card, mobile device. Text passwords are not resistant to dictionary, brute-force and guessing attacks. This paper proposes a novel graphical password method, which solves the password problem and secure against all password vulnerabilities. Theoretically, graphical passwords are easy to memorize and recall them easily for long term and resistant to dictionary and brute-force search attacks

Cyberbullying Blocker Test Application for Android Devices

In this chapter, the authors present an application for Android smartphones to automatically detect possible harmful content in input text. The developed application is aimed to test in practice the performance of the developed cyberbullying detection methods described in previous chapters. The final goal of the developed application will be to help mitigate the problem of cyberbullying by quickly detecting possibly harmful contents in user's entry and warning the user of the possible negative influence. The test application was prepared to use one of two methods for detection of harmful messages: a method inspired by a brute force search algorithm applied to language modelling and a method which uses seed words from three categories to calculate semantic orientation score SO-PMI-IR and then maximize the relevance of categories to specify harmfulness of a message (both methods were described in previous chapters). First tests showed that both methods are working properly under the Android environment.

Traditional Classifiers vs. Deep Learning for Cyberbullying Detection

In this chapter, the authors present their approach to cyberbullying detection with the use of various traditional classifiers, including a deep learning approach. Research has tackled the problem of cyberbullying detection during recent years. However, due to complexity of language used in cyberbullying, the results obtained with traditional classifiers has remained only mildly satisfying. In this chapter, the authors apply a number of traditional classifiers, used also in previous research, to obtain an objective view on to what extent each of them is suitable to the task. They also propose a novel method to automatic cyberbullying detection based on convolutional neural networks and increased feature density. The experiments performed on actual cyberbullying data showed a major advantage of the presented approach to all previous methods, including the two best performing methods so far based on SO-PMI-IR and brute-force search algorithm, presented in previous two chapters.

Brute Force Search Method for Cyberbullying Detection

In this chapter, the authors present a method for automatic detection of malicious internet contents, based on a combinatorial approach resembling brute force search algorithms, with application to language classification. The method automatically extracts sophisticated patterns from sentences and applies them in classification. The experiments performed on actual cyberbullying data showed advantage of this method to previous methods, including the one described in Chapter 4. Pros and cons of this method when compared to previous ones are also discussed in this chapter.