How do people discuss mental health on social media? Can we train a computer program to recognize differences between discussions of depression and other topics? Can an algorithm predict that someone is depressed from their tweets alone? In this project, we collect tweets referencing “depression” and “depressed” over a seven year period, and train word embeddings to characterize linguistic structures within the corpus. We find that neural word embeddings capture the contextual differences between “depressed” and “healthy” language. We also looked at how context around words may have changed over time to get deeper understanding of contextual shifts in the word usage. Finally, we trained a deep learning network on a much smaller collection of tweets authored by individuals formally diagnosed with depression. The best performing model for the prediction task is Convolutional LSTM (CNN-LSTM) model with a F-score of 69% on test data. The results suggest social media could serve as a valuable screening tool for mental health.

Automatic morpheme segmentation as a field has been dominated by unsupervised methods since its inception. Partly due to theoretical motivations, but also due to resource constraints. Given the success neural network methods have shown on a wide variety of field in later years, it would seem compelling to apply these methods to the morpheme segmentation field. This study explores the efficacy of modern neural networks, specifically convolutional neural networks and Bi-directional LSTM networks, on the morpheme segmentation task in a resource low setting to determine their viability as contenders with previous unsupervised, minimally supervised, and semi-supervised systems in the field. One architecture of each type is implemented and trained on a new gold standard data set and the results are compared to previously established methods. A qualitative error analysis of the architectures’ segmentations is also performed. The study demonstrates that a BLSTM system can be trained with minimal effort to produce a proof of concept solution at low levels of training data and suggests that BLSTM methods may be a fruitful direction for further research in this field.

In order to achieve state-of-the-art performance for part-of-speech(POS) tagging, the traditional systems require a significant amount of hand-crafted features and data pre-processing. In this thesis, we present a discriminative word embedding, character embedding and byte pair encoding (BPE) hybrid neural network architecture to implement a true end-to-end system without feature engineering and data pre-processing. The neural network architecture is a combination of bidirectional LSTM, CNNs, and CRF, which can achieve a state-of-the-art performance for a wide range of sequence labeling tasks. We evaluate our model on Universal Dependencies (UD) dataset for English, Spanish, and German POS tagging. It outperforms other models with 95.1%, 98.15%, and 93.43% accuracy on testing datasets respectively. Moreover, the largest improvements of our model appear on out-of-vocabulary corpora for Spanish and German. According to statistical significance testing, the improvements of English on testing and out-of-vocabulary corpora are not statistically significant. However, the improvements of the other more morphological languages are statistically significant on their corresponding corpora.

Konfliktprediktering handlar om att bedöma risken för våld i ett geografiskt område vid en given tid. Uppgiften lämpar sig bra för datorer som med hjälp av matematiska modeller kan hitta mönster i stora mängder data. Att prediktera konflikthändelser går att göra med olika metoder. Syftet med studien var att utvärdera multilayer perceptron (MLP), en typ av artificiella neuronnät, som metod för konfliktprediktering i relation till två andra metoder. I studien beskrivs hur MLP-neuronnätet konstruerades och hur prestationsmått togs fram för dess prediktioner. De värdena jämfördes senare med prestationsmått från andra studier för de två andra metoderna. Prediktionerna grundade sig på data om konflikthändelser, samt ekonomiska och demografiska faktorer för länder i världen. Jämförelsen visade att MLP är användbar som metod för konfliktprediktering och hade, under de förutsättningar som rådde, i viktiga avseenden högre prediktiv förmåga än de andra metoderna. Studien presenterar även fyra faktorer som kan påverka vilken modelleringsmetod som en modellerare borde använda för konfliktprediktering.

Deep learning has proven to be a valued contributor to recent technological advancements within energy systems. This thesis project explores methods of photovoltaic (PV) system power output forecasting through the utilization of long short-term memory (LSTM) neural networks. An encoder-decoder architecture (ED-LSTM) and a stacked vector output architecture (SVO-LSTM) were compared in terms of their ability to accurately produce power output forecasts with a 24-hour forecast horizon. The datasets which were used for model training were composed of historical meteorological observations and PV system power output readings. The results indicate that the encoder-decoder model and the stacked vector output model were somewhat equally skilled at producing power output forecasts. Best results were obtained by the encoder-decoder LSTM model which achieved a 26.63% improvement over a persistence model when trained on data sequences which preceded the forecast horizon, and a 44.96% improvement over a persistence model when the model was provided meteorological data from an oracle forecaster.

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<div> <div> <div> <p>For pedestrians and autonomous vehicles (AVs) to co-exist harmoniously and safely in the real-world, AVs will need to not only react to pedestrian actions, but also anticipate their intentions. In this thesis, we propose to use rich visual and pedestrian-environment interaction features to improve pedestrian crossing intention prediction from the ego-view. We do so by combining visual feature extraction, graph modeling of scene objects and their relationships, and feature encoding as comprehensive inputs for an LSTM encoder-decoder network. </p> <p>Pedestrians react and make decisions based on their surrounding environment, and the behaviors of other road users around them. The human-human social relationship has already been explored for pedestrian trajectory prediction from the bird’s eye view in stationary cameras. However, context and pedestrian-environment relationships are often missing in current research into pedestrian trajectory, and intention prediction from the ego-view. To map the pedestrian’s relationship to its surrounding objects we use a star graph with the pedestrian in the center connected to all other road objects/agents in the scene. The pedestrian and road objects/agents are represented in the graph through visual features extracted using state of the art deep learning algorithms. We use graph convolutional networks, and graph autoencoders to encode the star graphs in a lower dimension. Using the graph en- codings, pedestrian bounding boxes, and human pose estimation, we propose a novel model that predicts pedestrian crossing intention using not only the pedestrian’s action behaviors (bounding box and pose estimation), but also their relationship to their environment. </p> <p>Through tuning hyperparameters, and experimenting with different graph convolutions for our graph autoencoder, we are able to improve on the state of the art results. Our context- driven method is able to outperform current state of the art results on benchmark dataset Pedestrian Intention Estimation (PIE). The state of the art is able to predict pedestrian crossing intention with a balanced accuracy (to account for dataset imbalance) score of 0.61, while our best performing model has a balanced accuracy score of 0.79. Our model especially outperforms in no crossing intention scenarios with an F1 score of 0.56 compared to the state of the art’s score of 0.36. Additionally, we also experiment with training the state of the art model and our model to predict pedestrian crossing action, and intention jointly. While jointly predicting crossing action does not help improve crossing intention prediction, it is an important distinction to make between predicting crossing action versus intention.</p> </div> </div> </div>

Táto práca sa zameriava na analýzu vybraných častí GPON rámca pomocou algoritmov strojového učenia implementovaných pomocou knižnice TensorFlow. Vzhľadom na to, že GPON protokol je definovaný ako sada odporúčaní, implementácia naprieč spoločnosťami sa môže líšiť od navrhnutého protokolu. Preto analýza pomocou zásobníkového automatu nie je dostatočná. Hlavnou myšlienkou je vytvoriť systém modelov za použitia knižnice TensorFlow v Python3, ktoré sú schopné detekovať abnormality v komunikácií. Tieto modely používajú viaceré architektúry neuronových sietí (napr. LSTM, autoencoder) a zameriavajú sa na rôzne typy analýzy. Tento systém sa naučí na vzorovej vzorke dát a upozorní na nájdené odlišnosti v novozachytenej komunikácií. Výstupom systému odhad podobnosti aktuálnej komunikácie v porovnaní so vzorovou komunikáciou.

In this thesis, two different types of bus passengers are identified. These two types, namely current passengers and passengers-to-be have different needs in terms of arrival time predictions. A set of machine learning models based on recurrent neural networks and long short-term memory units were developed to meet these needs. Furthermore, bus data from the public transport in Östergötland county, Sweden, were collected and used for training new machine learning models. These new models are compared with the current prediction system that is used today to provide passengers with arrival time information. The models proposed in this thesis uses a sequence of time steps as input and the observed arrival time as output. Each input time step contains information about the current state such as the time of arrival, the departure time from thevery first stop and the current position in Cartesian coordinates. The targeted value for each input is the arrival time at the next time step. To predict the rest of the trip, the prediction for the next step is simply used as input in the next time step. The result shows that the proposed models can improve the mean absolute error per stop between 7.2% to 40.9% compared to the system used today on all eight routes tested. Furthermore, the choice of loss function introduces models thatcan meet the identified passengers need by trading average prediction accuracy for a certainty that predictions do not overestimate or underestimate the target time in approximately 95% of the cases.

Humanizing Technologies GmbH develops Intelligent software for the humanoid robots from Softbank Robotics. The main objective of this thesis is to develop and deploy Conversational Artificial Intelligence software into the humanoid robots using deep learning techniques. Development of conversational agents using Machine Learning or Artificial Intelligence is an intriguing issue with regards to Natural Language Processing. Great research and experimentation is being conducted in this area. Currently most of the chatbots are developed with rule based programming that cannot hold conversation which replicates real human interaction. This issue is addressed in this thesis with the development of Deep learning conversational AI based on Sequence to sequence, Attention mechanism, Transfer learning, Active learning and Beam search decoding which emulates human like conversation. The complete end to end conversational AI software is designed, implemented and deployed in this thesis work according to the conceptual specifications. The research objectives are successfully accomplished and results of the proposed concept are dis- cussed in detail.