We study a decentralized cooperative multi-agent multi-armed bandit problem with $K$ arms and $N$ agents connected over a network. In our model, each arm's reward distribution is same for all agents, and rewards are drawn independently across agents and over time steps. In each round, agents choose an arm to play and subsequently send a message to their neighbors. The goal is to minimize cumulative regret averaged over the entire network. We propose a decentralized Bayesian multi-armed bandit framework that extends single-agent Bayesian bandit algorithms to the decentralized setting. Specifically, we study an information assimilation algorithm that can be combined with existing Bayesian algorithms, and using this, we propose a decentralized Thompson Sampling algorithm and decentralized Bayes-UCB algorithm. We analyze the decentralized Thompson Sampling algorithm under Bernoulli rewards and establish a problem-dependent upper bound on the cumulative regret. We show that regret incurred scales logarithmically over the time horizon with constants that match those of an optimal centralized agent with access to all observations across the network. Our analysis also characterizes the cumulative regret in terms of the network structure. Through extensive numerical studies, we show that our extensions of Thompson Sampling and Bayes-UCB incur lesser cumulative regret than the state-of-art algorithms inspired by the Upper Confidence Bound algorithm. We implement our proposed decentralized Thompson Sampling under gossip protocol, and over time-varying networks, where each communication link has a fixed probability of failure.

Artificial Intelligence (AI) has been improving the performance of systems for a diverse set of tasks and introduced a more interactive generation of personal agents. Despite the current trend of applying AI for a great amount of areas, we have not seen the same quantity of work being developed for the tourism sector. This paper reports on the main applications of AI systems developed for tourism and the current state of the art for this sector. The paper also provides an up-to-date survey of this field regarding several key works and systems that are applied to tourism, like Personal Agents, for providing a more interactive experience. We also carried out an in-depth research on systems for predicting traffic human flow, more accurate recommendation systems and even how geospatial is trying to display tourism data in a more informative way and prevent problems before they arise.

We propose a new approach for learning contextualised cross-lingual word embeddings based only on a small parallel corpus (e.g. a few hundred sentence pairs). Our method obtains word embeddings via an LSTM-based encoder-decoder model that performs bidirectional translation and reconstruction of the input sentence. Through sharing model parameters among different languages, our model jointly trains the word embeddings in a common multilingual space. We also propose a simple method to combine word and subword embeddings to make use of orthographic similarities across different languages. We base our experiments on real-world data from endangered languages, namely Yongning Na, Shipibo-Konibo and Griko. Our experiments on bilingual lexicon induction and word alignment tasks show that our model outperforms existing methods by a large margin for most language pairs. These results demonstrate that, contrary to common belief, an encoder-decoder translation model is beneficial for learning cross-lingual representations, even in extremely low-resource scenarios.

In the realm of planning, this approach was pioneered by Howey, Long, and Fox As witnessed by the different planning competitions (Long who developed VAL (Howey, Long, and Fox 2004) that, 2000; Coles et al. 2012; Vallati et al. 2015), planning algorithms given a planning problem and potential solution, certifies and systems are becoming more and more scalable that the solution actually solves the given problem. Also, and efficient, which makes them suited for more realistic certifying unsolvability for planning was tackled by Eriksson, applications. Given that many applications of planning Röger, and Helmert (2017) who provided unsolvability are safety-critical, increasing the trustworthiness of certificates and checkers for state-space search algorithms planning algorithms and systems--i.e. the likelihood that and by Eriksson and Helmert (2020) for property they compute correct results--could be instrumental in their directed SATbased planning.

The Dead Sea Scrolls are tangible evidence of the Bible's ancient scribal culture. Palaeography - the study of ancient handwriting - can provide access to this scribal culture. However, one of the problems of traditional palaeography is to determine writer identity when the writing style is near uniform. This is exemplified by the Great Isaiah Scroll (1QIsaa). To this end, we used pattern recognition and artificial intelligence techniques to innovate the palaeography of the scrolls regarding writer identification and to pioneer the microlevel of individual scribes to open access to the Bible's ancient scribal culture. Although many scholars believe that 1QIsaa was written by one scribe, we report new evidence for a breaking point in the series of columns in this scroll. Without prior assumption of writer identity, based on point clouds of the reduced-dimensionality feature-space, we found that columns from the first and second halves of the manuscript ended up in two distinct zones of such scatter plots, notably for a range of digital palaeography tools, each addressing very different featural aspects of the script samples. In a secondary, independent, analysis, now assuming writer difference and using yet another independent feature method and several different types of statistical testing, a switching point was found in the column series. A clear phase transition is apparent around column 27. Given the statistically significant differences between the two halves, a tertiary, post-hoc analysis was performed. Demonstrating that two main scribes were responsible for the Great Isaiah Scroll, this study sheds new light on the Bible's ancient scribal culture by providing new, tangible evidence that ancient biblical texts were not copied by a single scribe only but that multiple scribes could closely collaborate on one particular manuscript.

Language carries implicit human biases, functioning both as a reflection and a perpetuation of stereotypes that people carry with them. Recently, ML-based NLP methods such as word embeddings have been shown to learn such language biases with striking accuracy. This capability of word embeddings has been successfully exploited as a tool to quantify and study human biases. However, previous studies only consider a predefined set of conceptual biases to attest (e.g., whether gender is more or less associated with particular jobs), or just discover biased words without helping to understand their meaning at the conceptual level. As such, these approaches are either unable to find conceptual biases that have not been defined in advance, or the biases they find are difficult to interpret and study. This makes existing approaches unsuitable to discover and interpret biases in online communities, as such communities may carry different biases than those in mainstream culture. This paper proposes a general, data-driven approach to automatically discover and help interpret conceptual biases encoded in word embeddings. We apply this approach to study the conceptual biases present in the language used in online communities and experimentally show the validity and stability of our method.

It has been shown that word embeddings can exhibit gender bias, and various methods have been proposed to quantify this. However, the extent to which the methods are capturing social stereotypes inherited from the data has been debated. Bias is a complex concept and there exist multiple ways to define it. Previous work has leveraged gender word pairs to measure bias and extract biased analogies. We show that the reliance on these gendered pairs has strong limitations: bias measures based off of them are not robust and cannot identify common types of real-world bias, whilst analogies utilising them are unsuitable indicators of bias. In particular, the well-known analogy "man is to computer-programmer as woman is to homemaker" is due to word similarity rather than societal bias. This has important implications for work on measuring bias in embeddings and related work debiasing embeddings.

DSP Group, Inc., a leading global provider of wireless and voice-processing chipset solutions for converged communications, announced software development kit (SDK) support for TensorFlow Lite for Microcontrollers for the DBMD7 family of low-cost, high-performance, multi-core AI and DSP processors. The SDK for the DBMD7 allows developers of AI IoT devices to cost-effectively deploy high-performance machine learning (ML) inference at the edge to avoid network latencies, minimize power consumption, ensure end-user privacy, and free up scarce network bandwidth. "DSP Group offers compelling hardware for many low-power applications, so we're excited to collaborate to offer machine learning software to help enable developers create products that wouldn't be possible otherwise," said Peter Warden, Staff Research Engineer at Google. TensorFlow Lite for Microcontrollers is an extension of TensorFlow Lite that addresses the need to run ML on memory-constrained devices with only kilobytes of memory. It comes with a specific set of optimized operations to allow the execution of ML models for applications such as wake-word detection, sound detection, and image wake-up. In addition, developers can add their own ML algorithms.

Broadvoice, an award-winning provider of hosted voice, unified communications (UC), and SIP trunking services for businesses, is reinforcing its commitment to its indirect sales partners with the addition of two new regional sales professionals to its channel management team. Robert Sanchez joins Broadvoice as Regional Channel Manager – Mountain West and Joseph Galluzzi is now Regional Channel Manager – Northeast. Broadvoice also has regional channel managers in California, Southwest, Midwest, and Southeast. "As a channel-first organization, Broadvoice continues to expand its partner community, so we're adding resources and expertise to stay ahead of growing demand," said Kim McLachlan, Senior Vice President of Sales and Marketing. "We're pleased to welcome two veteran telecom sales professionals to the Broadvoice team to provide in-region sales support."

"With the emergence of the Internet it has become easy and quick for anyone who creates something to get their work to the target audience. Now it takes only a few minutes for our work to have universal reach. The Internet comes not only accompanied by advantages. As everything in life it has its is advantages and entails of attention. As it provides quick reach, it also allows for copying and misusing the works a creator makes available. In our work we will address Intellectual Property in the national context, of which we will carry out a scientific study, analyzing how its protection is done and whether there is concern from both the competent authorities and our artists and authors.