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The following article is created as a result of the AAIA'17 Data Mining Challenge: Helping AI to Play Hearthstone. The Challenge goal was to correctly predict which bot would win a bot-vs-bot Hearthstone match based on what was known at the given time. Hearthstone is an online two-players card game with imperfect information (unlike chess and go, and like poker), where the goal of one player is to...
Our research is initially motivated by a conversation we had with a group of cyber security analysts that are responsible for monitoring enterprise security at a large corporation who were experiencing day-to-day operational burdens. As a result, this paper focuses on the design and implementation of an Intelligent Cyber Security Assistant (ICSA) architecture that would provide intelligent assistance...
Existing work on identifying security requirements relies on training binary classification models using domain-specific data sets to achieve a high accuracy. Considering that domain-specific data sets are often not readily available, we propose a domain-independent model for classifying security requirements based on two key ideas. First, we train our model on the description of weaknesses from the...
In this work, we investigate the robustness of 1-transistor-1-resistor (1T1R) synaptic array to implement a low-precision neural network. The experimental results on 1 kb HfOx-based RRAM array show a large on/off ratio (i.e. > 105×) and 5 stable resistance states can be reliably achieved with 10× window between adjacent two states. As the RRAM has the resistance drift over time under read voltage...
In this work, we present a performance comparison of the Multi Layer Perceptron (MLP), Support Vector Machines (SVM) and Voted Perceptron (VP) when applied to a social signal processing task. The signal processing task is in the field of computational politics where the aim is to predict the political parties of American congress members based on their response to certain questions. Using this dataset...
This paper presents a knee torque estimation in non-pathological gait cycle at stance phase. Comparative modelling by using dynamics model and neural network model is discussed. Dynamics modelling is constructed by using simple two degree of freedom dynamics with Newtonian calculation approach and more complex four degree of freedom dynamics with Lagrangian calculation approach. Neural network based...
Recent meta-learning approaches are oriented towards algorithm selection, optimization or recommendation of existing algorithms. In this paper we show how Inductive algorithms constructed from building blocks on small data sub-sample can be scaled up to model large data sets. We demonstrate how one particular template (simple ensemble of fast sigmoidal regression models) outperforms state-of-the-art...
One of the key tasks of mobile robotics is navigation, which for Outdoor-type robots is exacerbated by the functioning in a priori of an unknown environment. In this paper, for the first time, the learning navigation system for mobile robot based on inductive modeling approach is presented. This approach is based on the principles of the group method of data handling (GMDH), which is one of the first...
In order to train neural networks (NN) for text-to-speech synthesis (TTS), phonetic segmentation must be performed. The most accurate segmentation is performed manually, but the process of creating manual alignments is costly and time-consuming, so automatic procedures are preferable. In this paper, a simple alignment method based on models trained during hidden Markov Model (HMM) based TTS system...
This paper presents the time series cluster kernel (TCK) for multivariate time series with missing data. Our approach leverages the missing data handling properties of Gaussian mixture models (GMM) augmented with empirical prior distributions. Further, we exploit an ensemble learning approach to ensure robustness to parameters by combining the clustering results of many GMM to form the final kernel...
Individuals utilize online networking sites like Facebook and Twitter to express their interests, opinions or reviews. The users used English language as their medium for communication in earlier days. Despite the fact that content can be written in Unicode characters now, people find it easier to communicate by mixing two or more languages together or lean toward writing their native language in...
Step change is a key factor affecting the user trajectory and distance, to determine the trajectory of the user is a common indoor positioning method based on inertial navigation line calculation model, the prediction step is mainly based on the linear sensor, acceleration sensor data and the movement of the periodic estimation of pedestrians every step of the displacement distance. In order to improve...
Generative models are widely used for unsupervised learning with various applications, including data compression and signal restoration. Training methods for such systems focus on the generality of the network given limited amount of training data. A less researched type of techniques concerns generation of only a single type of input. This is useful for applications such as constraint handling,...
Network security has become a very important issue and attracted a lot of study and practice. To detect or prevent network attacks, a network intrusion detection (NID) system may be equipped with machine learning algorithms to achieve better accuracy and faster detection speed. One of the major advantages of applying machine learning to network intrusion detection is that we don't need expert knowledge...
Due to the influence of indoor signal multipath effect and human disturbance, the indoor positioning technology of WiFi fingerprint based on deep learning is poor stability. The large sample and accurate data in the room is very difficult to collect for weights training of deep learning, so it is difficult to be widely used. Firstly, the innovative algorithm with multi-sensor fingerprint and deep...
We study the task of unsupervised domain adaptation, where no labeled data from the target domain is provided during training time. To deal with the potential discrepancy between the source and target distributions, both in features and labels, we exploit a copula-based regression framework. The benefits of this approach are two-fold: (a) it allows us to model a broader range of conditional predictive...
We present a conditional generative method that maps low-dimensional embeddings of image and natural language to a common latent space hence extracting semantic relationships between them. The embedding specific to a modality is first extracted and subsequently a constrained optimization procedure is performed to project the two embedding spaces to a common manifold. Based on this, we present a method...
Flight parameters record the flight state and performance of the each flight phase. The precise division of the aircraft flight process using flight parameters can not only perform the stage quality evaluation of the whole flight process, but also can detect the aircraft faults. In this paper, the decision tree classifier is used to divide the flight parameters. The parameter reduction is carried...
A machine learning approach for the operational situational awareness (OSA) in flight operations is presented. The spacecraft health and safety telemetry are generally time dependent and periodical. The machine learning algorithms, such as neural networks, are used to capture the time dependent trend of the telemetry datasets characterized by their data patterns and noise level, which provides a direct...
Radio frequency interference (RFI) is electromagnetic interference (EMI) from signals in the radio frequencies of the electromagnetic spectrum. RFI reduces the sensitivity of radio telescope and produces artefacts in the observed data. We present the result of applying machine learning techniques to detect confidently man made RFI. We confirm that not all the features selected to characterise RFI...
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