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In a connected world, spare CPU cycles are up for grabs, if you only make its obtention easy enough. In this paper we present a distributed evolutionary computation system that uses the computational capabilities of the ubiquituous web browser. Using Asynchronous Javascript and JSON (Javascript Object Notation, a serialization protocol) allows anybody with a web browser (that is, mostly everybody connected to the Internet) to participate in a genetic algorithm experiment with little effort, or none at all. Since, in this case, computing becomes a social activity and is inherently impredictable, in this paper we will explore the performance of this kind of virtual computer by solving simple problems such as the Royal Road function and analyzing how many machines and evaluations it yields. We will also examine possible performance bottlenecks and how to solve them, and, finally, issue some advice on how to set up this kind of experiments to maximize turnout and, thus, performance.

The aim of this paper is to develop hybrid non-orthogonal multiple access (NOMA) assisted downlink transmission. First, for the single-input single-output (SISO) scenario, i.e., each node is equipped with a single antenna, a novel hybrid NOMA scheme is introduced, where NOMA is implemented as an add-on of a legacy time division multiple access (TDMA) network. Because of the simplicity of the SISO scenario, analytical results can be developed to reveal important properties of downlink hybrid NOMA. For example, in the case that the users' channel gains are ordered and the durations of their time slots are the same, downlink hybrid NOMA is shown to always outperform TDMA, which is different from the existing conclusion for uplink hybrid NOMA. Second, the proposed downlink SISO hybrid NOMA scheme is extended to the multiple-input single-output (MISO) scenario, i.e., the base station has multiple antennas. For the MISO scenario, near-field communication is considered to illustrate how NOMA can be used as an add-on in legacy networks based on space division multiple access and TDMA. Simulation results verify the developed analytical results and demonstrate the superior performance of downlink hybrid NOMA compared to conventional orthogonal multiple access.

This paper presents a novel solution concept, called BAR Nash Equilibrium (BARNE) and apply it to analyse the Verifier's dilemma, a fundamental problem in blockchain. Our solution concept adapts the Nash equilibrium (NE) to accommodate interactions among Byzantine, altruistic and rational agents, which became known as the BAR setting in the literature. We prove the existence of BARNE in a large class of games and introduce two natural refinements, global and local stability. Using this equilibrium and its refinement, we analyse the free-rider problem in the context of byzantine consensus. We demonstrate that by incorporating fines and forced errors into a standard quorum-based blockchain protocol, we can effectively reestablish honest behavior as a globally stable BARNE.

In this paper we study the expectation maximization (EM) technique for one-bit MIMO-OFDM detection (OMOD). Arising from the recent interest in massive MIMO with one-bit analog-to-digital converters, OMOD is a massive-scale problem. EM is an iterative method that can exploit the OFDM structure to process the problem in a per-iteration efficient fashion. In this study we analyze the convergence rate of EM for a class of approximate maximum-likelihood OMOD formulations, or, in a broader sense, a class of problems involving regression from quantized data. We show how the SNR and channel conditions can have an impact on the convergence rate. We do so by making a connection between the EM and the proximal gradient methods in the context of OMOD. This connection also gives us insight to build new accelerated and/or inexact EM schemes. The accelerated scheme has faster convergence in theory, and the inexact scheme provides us with the flexibility to implement EM more efficiently, with convergence guarantee. Furthermore we develop a deep EM algorithm, wherein we take the structure of our inexact EM algorithm and apply deep unfolding to train an efficient structured deep net. Simulation results show that our accelerated exact/inexact EM algorithms run much faster than their standard EM counterparts, and that the deep EM algorithm gives promising detection and runtime performances.

It is common practice for researchers to join public WhatsApp chats and scrape their contents for analysis. However, research shows collecting data this way contradicts user expectations and preferences, even if the data is effectively public. To overcome these issues, we outline design considerations for collecting WhatsApp chat data with improved user privacy by heightening user control and oversight of data collection and taking care to minimize the data researchers collect and process off a user's device. We refer to these design principles as User-Centered Data Sharing (UCDS). To evaluate our UCDS principles, we implemented a mobile application representing one possible instance of these improved data collection techniques and evaluated the viability of using the app to collect WhatsApp chat data. Second, we surveyed WhatsApp users to gather user perceptions on common existing WhatsApp data collection methods as well as UCDS methods. Our results show that we were able to glean similar informative insights into WhatsApp chats using UCDS principles in our prototype app to common, less privacy-preserving methods. Our survey showed that methods following the UCDS principles are preferred by users because they offered users more control over the data collection process. Future user studies could further expand upon UCDS principles to overcome complications of researcher-to-group communication in research on WhatsApp chats and evaluate these principles in other data sharing contexts.

This paper updates the survey of AI accelerators and processors from past three years. This paper collects and summarizes the current commercial accelerators that have been publicly announced with peak performance and power consumption numbers. The performance and power values are plotted on a scatter graph, and a number of dimensions and observations from the trends on this plot are again discussed and analyzed. Two new trends plots based on accelerator release dates are included in this year's paper, along with the additional trends of some neuromorphic, photonic, and memristor-based inference accelerators.

This paper offers a comprehensive review of the research on Natural Language Generation (NLG) over the past two decades, especially in relation to data-to-text generation and text-to-text generation deep learning methods, as well as new applications of NLG technology. This survey aims to (a) give the latest synthesis of deep learning research on the NLG core tasks, as well as the architectures adopted in the field; (b) detail meticulously and comprehensively various NLG tasks and datasets, and draw attention to the challenges in NLG evaluation, focusing on different evaluation methods and their relationships; (c) highlight some future emphasis and relatively recent research issues that arise due to the increasing synergy between NLG and other artificial intelligence areas, such as computer vision, text and computational creativity.

In this paper, we propose a novel Feature Decomposition and Reconstruction Learning (FDRL) method for effective facial expression recognition. We view the expression information as the combination of the shared information (expression similarities) across different expressions and the unique information (expression-specific variations) for each expression. More specifically, FDRL mainly consists of two crucial networks: a Feature Decomposition Network (FDN) and a Feature Reconstruction Network (FRN). In particular, FDN first decomposes the basic features extracted from a backbone network into a set of facial action-aware latent features to model expression similarities. Then, FRN captures the intra-feature and inter-feature relationships for latent features to characterize expression-specific variations, and reconstructs the expression feature. To this end, two modules including an intra-feature relation modeling module and an inter-feature relation modeling module are developed in FRN. Experimental results on both the in-the-lab databases (including CK+, MMI, and Oulu-CASIA) and the in-the-wild databases (including RAF-DB and SFEW) show that the proposed FDRL method consistently achieves higher recognition accuracy than several state-of-the-art methods. This clearly highlights the benefit of feature decomposition and reconstruction for classifying expressions.

In this paper, we introduce the Reinforced Mnemonic Reader for machine reading comprehension tasks, which enhances previous attentive readers in two aspects. First, a reattention mechanism is proposed to refine current attentions by directly accessing to past attentions that are temporally memorized in a multi-round alignment architecture, so as to avoid the problems of attention redundancy and attention deficiency. Second, a new optimization approach, called dynamic-critical reinforcement learning, is introduced to extend the standard supervised method. It always encourages to predict a more acceptable answer so as to address the convergence suppression problem occurred in traditional reinforcement learning algorithms. Extensive experiments on the Stanford Question Answering Dataset (SQuAD) show that our model achieves state-of-the-art results. Meanwhile, our model outperforms previous systems by over 6% in terms of both Exact Match and F1 metrics on two adversarial SQuAD datasets.

Inferring missing links in knowledge graphs (KG) has attracted a lot of attention from the research community. In this paper, we tackle a practical query answering task involving predicting the relation of a given entity pair. We frame this prediction problem as an inference problem in a probabilistic graphical model and aim at resolving it from a variational inference perspective. In order to model the relation between the query entity pair, we assume that there exists an underlying latent variable (paths connecting two nodes) in the KG, which carries the equivalent semantics of their relations. However, due to the intractability of connections in large KGs, we propose to use variation inference to maximize the evidence lower bound. More specifically, our framework (\textsc{Diva}) is composed of three modules, i.e. a posterior approximator, a prior (path finder), and a likelihood (path reasoner). By using variational inference, we are able to incorporate them closely into a unified architecture and jointly optimize them to perform KG reasoning. With active interactions among these sub-modules, \textsc{Diva} is better at handling noise and coping with more complex reasoning scenarios. In order to evaluate our method, we conduct the experiment of the link prediction task on multiple datasets and achieve state-of-the-art performances on both datasets.