Journal of Parallel and Distributed Computing 104, 19-35.

In the last years, deep learning algorithms have highly revolutionized several areas including speech, image and natural language processing. The specific field of Machine Translation (MT) has not remained invariant. Integration of deep learning in MT varies from re-modeling existing features into standard statistical systems to the development of a new architecture. Among the different neural networks, research works use feedforward neural networks, recurrent neural networks and the encoder-decoder schema. These architectures are able to tackle challenges as having low-resources or morphology variations. This manuscript focuses on describing how these neural networks have been integrated to enhance different aspects and models from statistical MT, including language modeling, word alignment, translation, reordering, and rescoring. Then, we report the new neural MT approach together with a description of the foundational related works and recent approaches on using subword, characters and training with multilingual languages, among others. Finally, we include an analysis of the corresponding challenges and future work in using deep learning in MT.

Mobile robot navigation in complex and dynamic environments is a challenging but important problem. Reinforcement learning approaches fail to solve these tasks efficiently due to reward sparsities, temporal complexities and high-dimensionality of sensorimotor spaces which are inherent in such problems. We present a novel approach to train action policies to acquire navigation skills for wheel-legged robots using deep reinforcement learning. The policy maps height-map image observations to motor commands to navigate to a target position while avoiding obstacles. We propose to acquire the multifaceted navigation skill by learning and exploiting a number of manageable navigation behaviors. We also introduce a domain randomization technique to improve the versatility of the training samples. We demonstrate experimentally a significant improvement in terms of data-efficiency, success rate, robustness against irrelevant sensory data, and also the quality of the maneuver skills.

Communication with computing machinery has become increasingly'chatty' these days: Alexa, Cortana, Siri, and many more dialogue systems have hit the consumer market on a broader basis than ever, but do any of them truly notice our emotions and react to them like a human conversational partner would? In fact, the discipline of automatically recognizing human emotion and affective states from speech, usually referred to as Speech Emotion Recognition or SER for short, has by now surpassed the "age of majority," celebrating the 22nd anniversary after the seminal work of Daellert et al. in 199610--arguably the first research paper on the topic. However, the idea has existed even longer, as the first patent dates back to the late 1970s.41 Previously, a series of studies rooted in psychology rather than in computer science investigated the role of acoustics of human emotion (see, for example, references8,16,21,34). Blanton,4 for example, wrote that "the effect of emotions upon the voice is recognized by all people. Even the most primitive can recognize the tones of love and fear and anger; and this knowledge is shared by the animals. The dog, the horse, and many other animals can understand the meaning of the human voice. The language of the tones is the oldest and most universal of all our means of communication." It appears the time has come for computing machinery to understand it as well.28 This holds true for the entire field of affective computing--Picard's field-coining book by the same name appeared around the same time29 as SER, describing the broader idea of lending machines emotional intelligence able to recognize human emotion and to synthesize emotion and emotional behavior.

In the past five years, the global financial industry has experienced major disruptions thanks to innovative technologies in AI, Machine Learning, and Blockchain. The rate at which supercomputers are taking over the financial sector is leaving no doubt that the future of finance will largely depend on computer scientists and big data experts rather than the traditional financial advisors and traders. It is no wonder that the world top financial institutions are now hiring more quantitative analysts and computer scientists than the traditional financial analysts and investment advisors. The CFA Institute, the provider of the world most prestigious professional designation for financial analysts, has realized that it is no longer business as usual in the industry and is now including AI, Big Data, and Machine Learning in its Curriculum. On the other hand, Blockchain, the technology behind cryptocurrencies, is also having its fair share in the industry with analysts predicting that it will do to the financial system what the internet did to the media.

Given the increasing affordability of equipment and growing awareness of security requirements, more and more cameras are being installed across the globe every day. While this is a good thing, the sheer volume of footages that come in makes it difficult for operators to find specific objects or people when needed. This is one area where artificial intelligence (AI) is all set to play a key role. Several security companies are already working on this. Make searching through videos as simple as using Google.

From artificial intelligence (AI) and machine learning to CCTV and big data – computer scientists at the University of East Anglia are part of an international effort to make the fishing industry more sustainable. UEA are part of a new £5 million EU-funded project to revolutionise the fishing industry, which employs over 24,000 people in the UK and contributes around £1.4 billion to our economy. It is hoped that pioneering technology will contribute to making the industry more environmentally friendly, sustainable and profitable. The'SMARTFISH-H2020' project, co-ordinated by SINTEF Ocean in Norway, draws on research from universities in Norway, Denmark, Turkey, France and Spain, along with institutes and industry partners across Europe. Other UK partners include Marine Scotland, The Centre for Environment, Fisheries and Aquaculture Science (CEFAS), and Safetynet Technologies Limited.

Tasks such as social network analysis, human behavior recognition, or modeling biochemical reactions, can be solved elegantly by using the probabilistic inference framework. However, standard probabilistic inference algorithms work at a propositional level, and thus cannot capture the symmetries and redundancies that are present in these tasks. Algorithms that exploit those symmetries have been devised in different research fields, for example by the lifted inference-, multiple object tracking-, and modeling and simulation-communities. The common idea, that we call state space abstraction, is to perform inference over compact representations of sets of symmetric states. Although they are concerned with a similar topic, the relationship between these approaches has not been investigated systematically. This survey provides the following contributions. We perform a systematic literature review to outline the state of the art in probabilistic inference methods exploiting symmetries. From an initial set of more than 4,000 papers, we identify 116 relevant papers. Furthermore, we provide new high-level categories that classify the approaches, based on the problem classes the different approaches can solve. Researchers from different fields that are confronted with a state space explosion problem in a probabilistic system can use this classification to identify possible solutions. Finally, based on this conceptualization, we identify potentials for future research, as some relevant application domains are not addressed by current approaches.

Deep learning approaches have shown remarkable performance in many areas of pattern recognition recently. In spite of their power in hierarchical feature extraction and classification, this type of neural network is computationally expensive and difficult to implement on hardware for portable devices. In an other vein of research on neural network architectures, spiking neural networks (SNNs) have been described as power-efficient models because of their sparse, spike-based communication framework. SNNs are brain-inspired such that they seek to mimic the accurate and efficient functionality of the brain. Recent studies try to take advantages of the both frameworks (deep learning and SNNs) to develop a deep architecture of SNNs to achieve high performance of recently proved deep networks while implementing bio-inspired, power-efficient platforms. Additionally, As the brain process different stimuli patterns through multi-layer SNNs that are communicating by spike trains via adaptive synapses, developing artificial deep SNNs can also be very helpful for understudying the computations done by biological neural circuits. Having both computational and experimental backgrounds, we are interested in including a comprehensive summary of recent advances in developing deep SNNs that may assist computer scientists interested in developing more advanced and efficient networks and help experimentalists to frame new hypotheses for neural information processing in the brain using a more realistic model.

End-to-end dialog systems have become very popular because they hold the promise of learning directly from human to human dialog interaction. Retrieval and Generative methods have been explored in this area with mixed results. A key element that is missing so far, is the incorporation of a-priori knowledge about the task at hand. This knowledge may exist in the form of structured or unstructured information. As a first step towards this direction, we present a novel approach, Knowledge based end-to-end memory networks (KB-memN2N), which allows special handling of named entities for goal-oriented dialog tasks. We present results on two datasets, DSTC6 challenge dataset and dialog bAbI tasks.