Air pollution is becoming a rising and serious environmental problem, especially in urban areas affected by an increasing migration rate. The large availability of sensor data enables the adoption of analytical tools to provide decision support capabilities. Employing sensors facilitates air pollution monitoring, but the lack of predictive capability limits such systems’ potential in practical scenarios. On the other hand, forecasting methods offer the opportunity to predict the future pollution in specific areas, potentially suggesting useful preventive measures. To date, many works tackled the problem of air pollution forecasting, most of which are based on sequence models. These models are trained with raw pollution data and are subsequently utilized to make predictions. This paper proposes a novel approach evaluating four different architectures that utilize camera images to estimate the air pollution in those areas. These images are further enhanced with weather data to boost the classification accuracy. The proposed approach exploits generative adversarial networks combined with data augmentation techniques to mitigate the class imbalance problem. The experiments show that the proposed method achieves robust accuracy of up to 0.88, which is comparable to sequence models and conventional models that utilize air pollution data. This is a remarkable result considering that the historic air pollution data is directly related to the output—future air pollution data, whereas the proposed architecture uses camera images to recognize the air pollution—which is an inherently much more difficult problem.

Forecasting air pollution is crucial for understanding the phenomenological and contextual variety of mechanisms underlying pollution in a particular area or region. Analyzing high-dimensional data with spatial and temporal dependencies is pointed out as a major challenge for traditional machine learning approaches for air pollution forecasting. The unprecedented advances in deep learning employed on massive quantities of IoT sensor data raise high hopes for the future of air pollution forecasting. Drawing on past and current experiences and solutions advanced in a growing body of research on the topic, we proposed and evaluated four encoder-decoder architectures with attention for forecasting particulate matter (PM) levels, with a general applicability that is both location- and season-independent. The attention mechanism was employed to better learn representation of time series data. By selectively focusing the attention on elements that contribute the most, superior performance gains have been obtained by the proposed models. Sensor measurements obtained in the past 7 or 14 days as well a set of external factors known to affect the pollutant levels, such as weather conditions, location and time frame (e.g., season, time of day) were taken into account. In this research, we carried out a case study on PM _2.5 forecasting for the city of Skopje, allowing us to discuss the relevance of the results obtained by the proposed solutions. The ability to draw valid inferences from data has critical importance when forecasting air pollution. In this respect, any air pollution forecasting model based on deep learning would require a component for generation of realistic data samples to augment the time series dataset. We address the challenges in missing data by proposing and evaluating two adversarial networks for data augmentation. We have conducted a number of experiments to investigate the performance of the predictive models with and without augmenting the training datasets and obtain superior performance gains by the proposed adversarial models for data augmentation. The deep neural architectures advanced in this research are general enough to be used for predictive and generative tasks for other pollutants; moreover, they could be adopted for related tasks handling time series data in other domains.

Natural language is hypothetically the best user interface for many domains. However, general models that provide an interface between natural language and any other domain still do not exist. Providing natural language interface to relational databases could possibly attract a vast majority of users that are or are not proficient with query languages. With the rise of deep learning techniques, there is extensive ongoing research in designing a suitable natural language interface to relational databases. This survey aims to overview some of the latest methods and models proposed in the area of SQL query generation from natural language. We describe models with various architectures such as convolutional neural networks, recurrent neural networks, pointer networks, reinforcement learning, etc. Several datasets intended to address the problem of SQL query generation are interpreted and briefly overviewed. In the end, evaluation metrics utilized in the field are presented mainly as a combination of execution accuracy and logical form accuracy.

Distributed language representation has become the most widely used technique for language representation in various natural language processing tasks. Most of the natural language processing models that are based on deep learning techniques use already pre-trained distributed word representations, commonly called word embeddings. Determining the most qualitative word embeddings is of crucial importance for such models. However, selecting the appropriate word embeddings is a perplexing task since the projected embedding space is not intuitive to humans. In this paper, we explore different approaches for creating distributed word representations. We perform an intrinsic evaluation of several state-of-the-art word embedding methods. Their performance on capturing word similarities is analysed with existing benchmark datasets for word pairs similarities. The research in this paper conducts a correlation analysis between ground truth word similarities and similarities obtained by different word embedding methods.