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Wireless network (radio) virtualization and its synergy with ML/AI-based technologies is a novel concept that can efficiently address problems of legacy networks, such as flash crowds. This paper discusses the integration aspects of intelligence-based technologies with Sate-of-the-Art end-to-end reconfigurable, flexible and scalable network architecture, capable of handling demands in flash crowd scenarios. The presented results, demonstrate that advanced solutions based on ML can significantly improve the network proactivity and adaptivity by reliably predicting flash crowd scenarios. The results also show that in case of low dataset fidelity, conventional statistical models are a more suitable option.

Human falls are common source of injury among the elderly, because often the elderly person is injured and cannot call for help. In the literature this is addressed by various fall-detection systems, of which most common are the ones that use wearable sensors. This paper describes the winning method developed for the Challenge Up: Multimodal Fall Detection competition. It is a multi-sensor data-fusion machine-learning method that recognizes human activities and falls using 5 wearable inertial sensors: accelerometers and gyroscopes. The method was evaluated on a dataset collected by 12 subjects of which 3 were used as a test-data for the challenge. In order to optimally adapt the method to the 3 test subjects, we performed an unsupervised similarity search—that finds the three most similar users to the three users in the test data. This helped us to tune the method and its parameters to the 3 most similar users as the ones used for the test. The internal evaluation on the 9 users showed that with this optimized configuration the method achieves 98% accuracy. During the final evaluation for the challenge, our method achieved the highest results (82.5% F1-score, and 98% accuracy) and won the competition.

The paper presents a Machine Learning (ML) approach to household Electrical Energy (EE) consumption prediction. It includes: data preprocessing, feature engineering, learning a classification model, and experimental evaluation on one of the largest datasets for household EE consumption – DataPort dataset. Beside the features extracted on the historical EE consumption, we additionally analyze weather and contextual-calendar related features. We believe that the combination of multiple sources of data (calendar, weather, historical EE consumption) provides more information to the model in order to learn better performing model. The experimental results showed that in all the cases the ML algorithms outperform the baselines, with the best performing the XGBoost - achieved 0.69 RMSE score, 0.41 MAE score and 0.67 R2 score which is significantly better than the best performing baseline model (the value from 24 h ago). Additionally, the results show that the largest errors are made for the weekends, which was expected due to the irregularities in the schedule - trips, vacations, etc.

The Sussex-Huawei Transportation-Locomotion (SHL) Recognition Challenge 2018 aims to recognize eight transportation activities (Still, Walk, Run, Bike, Bus, Car, Train, Subway) from the inertial and pressure sensor data of a smartphone. In this chapter, we, as part of competition organizing team, present reference recognition performance obtained by applying various classical and deep-learning classifiers to the testing dataset. The classical classifiers include naive Bayes, decision tree, random forest, K-nearest neighbours and support vector machine, while the deep-learning classifiers include fully-connected and convolutional deep neural networks. We feed different types of input to the classifier, including hand-crafted features, raw sensor data in the time domain, and in the frequency domain. We additionally employ a post-processing scheme, which smoothens the predictions in order and improves the recognition performance. Results show that convolutional neural network operating on frequency-domain raw data achieves the best performance among all the classifiers. Finally, we achieve a benchmark result with F1 score 92.9%, which is comparable to the best result from the team that won the competition (achieving F1 score 93.9%). The competition dataset and the benchmark implementation is made available online (http://www.shl-dataset.org/).

Neural text generation is the process of a training neural network to generate a human understandable text (poem, story, article). Recurrent Neural Networks and Long-Short Term Memory are powerful sequence models that are suitable for this kind of task. In this paper, we have developed two types of language models, one generating news articles and the other generating poems in Macedonian language. We developed and tested several different model architectures, among which we also tried transfer-learning model, since text generation requires a lot of processing time. As evaluation metric we used ROUGE-N metric (Recall-Oriented Understudy for Gisting Evaluation), where the generated text was tested against a reference text written by an expert. The results showed that even though the generate text had flaws, it was human understandable, and it was consistent throughout the sentences. To the best of our knowledge this is a first attempt in automatic text generation (poems and articles) in Macedonian language using Deep Learning.

The paper describes an approach for recognizing a person entering a room using only door accelerations. The approach analyzes the acceleration signal in time and frequency domain. For each domain two types of methods were developed: (i) feature-based – use features to describe the acceleration and then uses classification method to identify the person; (ii) signal-based – use the acceleration signal as input and finds the most similar ones in order to identify the person. The four methods were evaluated on a dataset of 1005 entrances recorded by 12 people. The results show that the time-domain methods achieve significantly higher accuracy compared to the frequency-domain methods, with signal-based method achieving 86 % accuracy. Additionally, the four methods were combined and all 15 combinations were examined. The best performing combined method increased the accuracy to 90 %. The results confirm that it is possible to identify a person entering a room using the door’s acceleration.