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The paper is devoted to development of intelligent context-aware energy management system of power semiconductor converters inside a SmartGrid using the principles of cognitive, object-oriented analysis. Mathematical software for decision-making system of power semiconductor converters was developed using combination of different converter control strategies. The proposed solution includes processing...
Sequence to sequence (seq2seq) prediction is a key to many tasks of machine learning. Personal computer software sequence, as one of these tasks, was regarded as stochastic and unpredictable in the past. However, the deep neural networks (DNNs) have achieved excellent performance recently in sequence to sequence tasks, especially in the field of natural language process (NLP) such as language model,...
In this work, we propose contextual language models that incorporate dialog level discourse information into language modeling. Previous works on contextual language model treat preceding utterances as a sequence of inputs, without considering dialog interactions. We design recurrent neural network (RNN) based contextual language models that specially track the interactions between speakers in a dialog...
Monitoring user interaction activities provides the basis for creating a user model that can be used to predict user behaviour and enable user assistant services. The BaranC framework provides components that perform UI monitoring (and collect all associated context data), builds a user model, and supports services that make use of the user model. In this case study, a Next-App prediction service...
Human drivers continuously attend to important scene elements in order to safely and smoothly navigate in intricate environments and under uncertainty. This paper develops a human-centric framework for object recognition by analyzing a notion of object importance, as measured in a spatio-temporal context of driving a vehicle. Given a video, a main research question in this paper is - which of the...
We propose an attention-enabled encoder-decoder model for the problem of grapheme-to-phoneme conversion. Most previous work has tackled the problem via joint sequence models that require explicit alignments for training. In contrast, the attention-enabled encoder-decoder model allows for jointly learning to align and convert characters to phonemes. We explore different types of attention models, including...
In the recent years, sentiment analysis has emerged as a major research problem in the field of Natural Language Processing. Here, the problem is to identify the sentiment/emotion in given sentence/paragraph. Usually it is positive, negative and neutral. Here, we consider only binary classification task (positive and negative). We have considered the best performing sentiment analysis model which...
Data-trained predictive models see widespread use, but for the most part they are used as black boxes which output a prediction or score. It is therefore hard to acquire a deeper understanding of model behavior, and in particular how different features influence the model prediction. This is important when interpreting the behavior of complex models, or asserting that certain problematic attributes...
Traditional affective lexicons are mainly based on discrete classes, such as positive, happiness, sadness, which may limit its expressive power compared to the dimensional representation in which affective meanings are expressed through continuous numerical values on multiple dimensions, such as valence-arousal. Traditional methods for acquiring dimensional lexicons are mainly based on time-consuming...
The paper describes a heuristic method for the ultra-short-term computation of prediction intervals (PIs) for photovoltaic (PV) power generation. The method allows for directly forecasting the AC active power output of a PV system by simply extracting information from past time series. Two main approaches are investigated. The former relies on experimentally observed correlations between the time...
The use of cloud resources for processing and analysing medical data has the potential to revolutionise the treatment of a number of chronic conditions. For example, it has been shown that it is possible to manage conditions such as diabetes, obesity and cardiovascular disease by increasing the right forms of physical activity for the patient. Typically, movement data is collected for a patient over...
We present a neural network based punctuation prediction method using Long Short-Term Memory (LSTM) network. The proposed method uses bidirectional LSTM to encode both the past and future observation as its inputs. It models the dependency between input features and output labels through multiple layers. We also empirically study the impacts of modeling the dependency between output labels. Our results...
Code smells are symptoms of poor design and implementation choices. Previous studies empirically assessed the impact of smells on code quality and clearly indicate their negative impact on maintainability, including a higher bug-proneness of components affected by code smells. In this paper we capture previous findings on bug-proneness to build a specialized bug prediction model for smelly classes...
Utilizing a language model in a brain-computer-interface-based (BCI-based) speller has been proven helpful in improving the performance of the system. Since it is important to evaluate the effect of the language model on the system, it is necessary to choose the words in a way that they can represent different levels of difficulty based on the language model. In this paper, we will give a brief introduction...
Predictive Processing (PP) [1], [2], [3] is becoming an influential account in cognitive neuroscience, including developmental neuroscience [4]. According to PP, human brains interpret their sensory inputs by predicting them, based on a hierarchy of generative models. These predictions are then compared to the actual, observed inputs, and the difference between predictions and observations (so-called...
In this paper we present a model that, based on the principle of total energy balance (similar to energy conservation in Physics), bridges the gap between Darwinian fitness theories and reward-driven theories of behaviour. Results show that it is possible to accommodate the reward maximization principle underlying modern approaches in behavioural reinforcement learning and traditional fitness approaches...
Deep Convolutional Neural Networks(DCNNs) have recently shown great performance in many high-level vision tasks, such as image classification, object detection and more recently outdoor semantic segmentation. However, the convolutional layer only process the local regions in the image, ignoring the global context information. To overcome this poor localization property of Convolutional Neural Networks(CNNs),...
By predicting where humans look in natural scenes, we can understand how they perceive complex natural scenes and prioritize information for further high-level visual processing. Several models have been proposed for this purpose, yet there is a gap between best existing saliency models and human performance. While many researchers have developed purely computational models for fixation prediction,...
Statistical language models are central to many applications that use semantics. Recurrent Neural Networks (RNN) are known to produce state of the art results for language modelling, outperforming their traditional n-gram counterparts in many cases. To generate a probability distribution across a vocabulary, these models require a softmax output layer that linearly increases in size with the size...
Spelling errors are very common in various electronic documents and it leads to serious influence sometimes. To solve this problem, methods based on the n-gram language model are the most commonly used. CSLM (continuous space language model) which represents a word as a vector is different from traditional models. In this paper, we experimented with a specific CSLM, namely, the CBOW (Continuous Bag-of-Words)...
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