In this exploratory study, we examined the nuances and challenges in people’s communication experiences with GIFs through in-depth qualitative interviews. We found that participants’ communication with GIFs was impacted by technology and context a...

In this paper we have shown that individual card inference trained by supervised learning can improve the performance of PIMC-based players in trick-based card games considerably. This may not come as a surprise to seasoned Contract Bridge or Skat...

In this paper, we have introduced a Fog Robotics approach for secure and distributed deep robot learning. Secured compute and storage at the Edge of the network opens up a broad range of possibilities to meet lower-latency requirements, while prov...

In this paper, we constructed an auxiliary sentence to transform (T)ABSA from a single sentence classification task to a sentence pair classification task. We fine-tuned the pre-trained BERT model on the sentence pair classification task and obtai...

This paper compared six alternative WEMI target detection methodologies in terms of effectiveness for buried, explosive hazard discovery. As expected, high-metal targets were distinguishable by each of the algorithms compared while low and non-met...

The main conclusion of this paper is that over the last couple of decades, quantum computing has been a one-dimensional research effort focusing on understanding how to make coherent qubits and how to implement the different universal quantum gate...

We propose MC-NFSP and ANFSP in this paper. They are improvements on NFSP and can learn approximate Nash equilibrium in two-player games. MC-NFSP improve the performance of the original algorithm in two-player zero-sum games with large-scale searc...

In recent years the more traditional understanding of perceptual and motor as nearly independent processes as been put into discussion by different authors, especially in neuroscience [48, 49, 50]. It is clear that many experimental set ups are li...

In this paper, we present a data collection technique (DASS) that enables us to quickly recover, compress and distill multiple policies using supervised learning. Importantly, we demonstrate that DASS-based transfer learning can be integrated with...

We have demonstrated that GPs can play a significant role in the risk management pipeline for the power markets. Additionally, coregionalization has also shown that there is knowledge transfer between the GPs trained on spot price and load. Lastly...

In this paper, we presented Multi Event Class Synchronization (MECS), a novel algorithm for event synchronization analysis. With respect to other techniques existing in the literature, MECS introduces the possibility to measure synchronization bet...

Competing magnetic anisotropies in chiral crystals with Dzyaloshinskii Moriya exchange interactions can give rise to non-trivial chiral topological magnetisation configurations with new and interesting properties. One such configuration is the mag...

Over the last decade machine learning has radically changed the way we take a decision. Many researchers studied the performances of statistical models and human judgments, demonstrating how in some circumstances models based on machine learning h...

This work explored the optimal forecasts method for hierarchical and grouped time series proposed by [5, 13] to forecast the weekly sales of two fashion brands under HUUB’s monitoring. The data were disaggregated according to five selected attribu...

We showed that the benefits of a state-of-the-art SMT solver used to validate transition systems (as models for AI problems) can be significant. The added value of SMT solvers consists in the fact that the entire model is encoded using logical for...

In this paper, we have presented a complete RSIR pipeline capable of achieving state-of-the-art results with regards to retrieval precision and computation time. To our best knowledge, this is the first RSIR system to deploy local convolutional fe...

We have presented a method for factorising medical images into a spatial and a non-spatial latent factor, where we enforced a semantically meaningful spatial factor of the anatomy and a non-spatial factor encoding the modality information. To the ...

We borrow two fundamental label-preserving augmentation ideas from image processing: cropping and rotation. Image cropping can be defined as removal of some of the peripheral areas of an image to focus on the subject/object (e.g., focusing on the ...

In this study we have introduced 15 word-level probing tests for a total of 24 languages, where the target linguistic phenomena differ depending on the typological properties of the language, e.g., Case, Polarity for Turkish, Gender for Russian an...

We present a purely neural based end-to-end method for unsupervised text clustering. The sequence representation learning and clustering model are integrated in an unified framework. We evaluated our model on two widely used benchmarks, IMDB movie...