Coin.AI: A Proof-of-Useful-Work Scheme for Blockchain-based Distributed Deep Learning

One decade ago, Bitcoin was introduced, becoming the first cryptocurrency and establishing the concept of "blockchain" as a distributed ledger. As of today, there are many different implementations of cryptocurrencies working over a blockchain, with different approaches and philosophies. However, many of them share one common feature: they require proof-of-work to support the generation of blocks (mining) and, eventually, the generation of money. This proof-of-work scheme often consists in the resolution of a cryptography problem, most commonly breaking a hash value, which can only be achieved through brute-force. The main drawback of proof-of-work is that it requires ridiculously large amounts of energy which do not have any useful outcome beyond supporting the currency. In this paper, we present a theoretical proposal that introduces a proof-of-useful-work scheme to support a cryptocurrency running over a blockchain, which we named Coin.AI. In this system, the mining scheme requires training deep learning models, and a block is only mined when the performance of such model exceeds a threshold. The distributed system allows for nodes to verify the models delivered by miners in an easy way (certainly much more efficiently than the mining process itself), determining when a block is to be generated. Additionally, this paper presents a proof-of-storage scheme for rewarding users that provide storage for the deep learning models, as well as a theoretical dissertation on how the mechanics of the system could be articulated with the ultimate goal of democratizing access to artificial intelligence.Comment: 17 pages, 5 figure

TV Series and Social Media: Powerful Engagement Factors in Mobile Video Games

The free-to-play business model has become hegemonic in the mobile video game industry, displacing the traditional paid content model that was the norm until the appearance of manufacturers’ app stores. Companies attempt to monetize these games by means of in-game micro-transactions and in-game advertising; thus, it is essential to acquire an enormous number of users because only a small percentage will ultimately make any purchases. To keep players engaged, companies typically put in place marketing and design strategies derived from behavioral telemetry, to maintain a grip on players. We propose an innovative approach, focusing our attention on the impact of having a video game based on a famous TV series. Furthermore, we analyze the effect of social networks on game metrics. The outcome indicates that developing a game based on a TV series and integrating social media with the gameplay improve and reinforce the user’s activation, retention and monetization

DataCare: Big Data Analytics Solution for Intelligent Healthcare Management

This paper presents DataCare, a solution for intelligent healthcare management. This product is able not only to retrieve and aggregate data from different key performance indicators in healthcare centers, but also to estimate future values for these key performance indicators and, as a result, fire early alerts when undesirable values are about to occur or provide recommendations to improve the quality of service. DataCare’s core processes are built over a free and open-source cross-platform document-oriented database (MongoDB), and Apache Spark, an open-source cluster-computing framework. This architecture ensures high scalability capable of processing very high data volumes coming at fast speed from a large set of sources. This article describes the architecture designed for this project and the results obtained after conducting a pilot in a healthcare center. Useful conclusions have been drawn regarding how key performance indicators change based on different situations, and how they affect patients’ satisfaction

A System for Personality and Happiness Detection

This work proposes a platform for estimating personality and happiness. Starting from Eysenck's theory about human's personality, authors seek to provide a platform for collecting text messages from social media (Whatsapp), and classifying them into different personality categories. Although there is not a clear link between personality features and happiness, some correlations between them could be found in the future. In this work, we describe the platform developed, and as a proof of concept, we have used different sources of messages to see if common machine learning algorithms can be used for classifying different personality features and happiness

Real-Time Prediction of Gamers Behavior Using Variable Order Markov and Big Data Technology: A Case of Study

This paper presents the results and conclusions found when predicting the behavior of gamers in commercial videogames datasets. In particular, it uses Variable-Order Markov (VOM) to build a probabilistic model that is able to use the historic behavior of gamers and to infer what will be their next actions. Being able to predict with accuracy the next user’s actions can be of special interest to learn from the behavior of gamers, to make them more engaged and to reduce churn rate. In order to support a big volume and velocity of data, the system is built on top of the Hadoop ecosystem, using HBase for real-time processing; and the prediction tool is provided as a service (SaaS) and accessible through a RESTful API. The prediction system is evaluated using a case of study with two commercial videogames, attaining promising results with high prediction accuracies

Longitudinal Segmented Analysis of Internet Usage and Well-Being Among Older Adults

The connection between digital literacy and the three core dimensions of psychological well-being is not yet well understood, and the evidence is controversial. We analyzed a sample of 2,314 individuals, aged 50 years and older, that participated in the English Longitudinal Study of Aging. Participants were clustered according to drivers of psychological well-being using Self-Organizing Maps. The resulting groups were subsequently studied separately using generalized estimating equations fitted on 2-year lagged repeated measures using three scales to capture the dimensions of well-being and Markov models. The clustering analysis suggested the existence of four different groups of participants. Statistical models found differences in the connection between internet use and psychological well-being depending on the group. The Markov models showed a clear association between internet use and the potential for transition among groups of the population characterized, among other things, by higher levels of psychological well-being

Combinatorial versus sequential auctions to allocate PPP highway projects

This article models a procurement process for allocating multiple related public-private partnership (PPP) highway projects. Traditionally, public infrastructure procurement processes have used a sequential allocation mechanism, despite the potential benefits of allocating all projects at once. The main contribution of this research is to address the question whether these projects should be auctioned individually, in sequential auctions, or at the same time, in a combinatorial auction. Our goal is to understand the impact of the allocation process in terms of efficiency and social welfare. In sequential auctions, bidders submit their offers for each project independently. However, in combinatorial auctions, contractors have the ability to bid for their preferred packages (combinations of projects), reflecting synergies or entry costs, if any, in their valuations. We have compared the impact in terms of efficient allocation and social welfare of both mechanisms in order to help policymakers to take future decisions when facing these processes. The methodology used to address these core questions in the multidisciplinary environment described is based on social simulations, which involves conducting analysis by means of computational simulations. For this work we have created a sophisticated valuation model adapted to the public infrastructure sector and we have developed a simulator which includes multiple types of bidders, projects and several scenarios. The experimental setup is based on the second wave of the Colombian 4G program, a case involving the allocation of 9 highway construction projects across the country. We have also included references to multiple examples of real markets in which these mechanisms could be implemented. Therefore, this research provides a valuable reference for policymakers chasing to enhance market design that could be applied in many real-world scenarios. The results reveal that the combinatorial mechanism improves the process in terms of optimal allocation and efficiency, yielding significant savings for all parties

TV Series and Social Media: Powerful Engagement Factors in Mobile Video Games

The free-to-play business model has become hegemonic in the mobile video game industry, displacing the traditional paid content model that was the norm until the appearance of manufacturers' app stores. Companies attempt to monetize these games by means of in-game micro-transactions and in-game advertising; thus, it is essential to acquire an enormous number of users because only a small percentage will ultimately make any purchases. To keep players engaged, companies typically put in place marketing and design strategies derived from behavioral telemetry, to maintain a grip on players. We propose an innovative approach, focusing our attention on the impact of having a video game based on a famous TV series. Furthermore, we analyze the effect of social networks on game metrics. The outcome indicates that developing a game based on a TV series and integrating social media with the gameplay improve and reinforce the user's activation, retention and monetization

Mechanobiology of Platelets: Techniques to Study the Role of Fluid Flow and Platelet Retraction Forces at the Micro- and Nano-Scale

Coagulation involves a complex set of events that are important in maintaining hemostasis. Biochemical interactions are classically known to regulate the hemostatic process, but recent evidence has revealed that mechanical interactions between platelets and their surroundings can also play a substantial role. Investigations into platelet mechanobiology have been challenging however, due to the small dimensions of platelets and their glycoprotein receptors. Platelet researchers have recently turned to microfabricated devices to control these physical, nanometer-scale interactions with a higher degree of precision. These approaches have enabled exciting, new insights into the molecular and biomechanical factors that affect platelets in clot formation. In this review, we highlight the new tools used to understand platelet mechanobiology and the roles of adhesion, shear flow, and retraction forces in clot formation

Hybridizing Evolutionary Computation and Deep Neural Networks: An Approach to Handwriting Recognition Using Committees and Transfer Learning

Neuroevolution is the field of study that uses evolutionary computation in order to optimize certain aspect of the design of neural networks, most often its topology and hyperparameters. The field was introduced in the late-1980s, but only in the latest years the field has become mature enough to enable the optimization of deep learning models, such as convolutional neural networks. In this paper, we rely on previous work to apply neuroevolution in order to optimize the topology of deep neural networks that can be used to solve the problem of handwritten character recognition. Moreover, we take advantage of the fact that evolutionary algorithms optimize a population of candidate solutions, by combining a set of the best evolved models resulting in a committee of convolutional neural networks. This process is enhanced by using specific mechanisms to preserve the diversity of the population. Additionally, in this paper, we address one of the disadvantages of neuroevolution: the process is very expensive in terms of computational time. To lessen this issue, we explore the performance of topology transfer learning: whether the best topology obtained using neuroevolution for a certain domain can be successfully applied to a different domain. By doing so, the expensive process of neuroevolution can be reused to tackle different problems, turning it into a more appealing approach for optimizing the design of neural networks topologies. After evaluating our proposal, results show that both the use of neuroevolved committees and the application of topology transfer learning are successful: committees of convolutional neural networks are able to improve classification results when compared to single models, and topologies learned for one problem can be reused for a different problem and data with a good performance. Additionally, both approaches can be combined by building committees of transferred topologies, and this combination attains results that combine the best of both approaches