Analysis of the Bitcoin Blockchain [26] is an area of intense activity [20, 1], and one which has witnessed an explosion of interest as the value of the Bitcoin cryptocurrency hasskyrocketed. Research areas include explorations of address clustering techniques toidentify logical agents [11, 21, 11, 7], de-anonymization using side-channel attacks [8, 13]. An understanding of the properties of Bitcoin transactions is paramount to the legitimation ofthe cryptocurrency economy; it constitutes a building block to the conception of effective and adequate regulations [9], and to the design of novel and integrated services benefiting society as a whole. As of 2018, with more than 500 million address nodes, the Bitcoin graph is comparable insize to a large social network. Yet while probabilistic models of social networks have received considerable attention, from community detection [19] to diffusion models andinfluence maximization [34], to probabilistic graph modeling [17], probabilistic models of the Bitcoin Blockchain network have not. 1 Bitcoin transactions are tantamount to a partially observed social network, within which participants can have multiple seemingly independent aliases.

This research aims to identify how Bitcoin-related news publications and online discourse are expressed in Bitcoin exchange movements of price and volume. Being inherently digital, all Bitcoin-related fundamental data (from exchanges, as well as transactional data directly from the blockchain) is available online, something that is not true for traditional businesses or currencies traded on exchanges. This makes Bitcoin an interesting subject for such research, as it enables the mapping of sentiment to fundamental events that might otherwise be inaccessible. Furthermore, Bitcoin discussion largely takes place on online forums and chat channels. In stock trading, the value of sentiment data in trading decisions has been demonstrated numerous times [1] [2] [3], and this research aims to determine whether there is value in such data for Bitcoin trading models. To achieve this, data over the year 2015 has been collected from Bitcointalk.org, (the biggest Bitcoin forum in post volume), established news sources such as Bloomberg and the Wall Street Journal, the complete /r/btc and /r/Bitcoin subreddits, and the bitcoin-otc and bitcoin-dev IRC channels. By analyzing this data on sentiment and volume, we find weak to moderate correlations between forum, news, and Reddit sentiment and movements in price and volume from 1 to 5 days after the sentiment was expressed. A Granger causality test confirms the predictive causality of the sentiment on the daily percentage price and volume movements, and at the same time underscores the predictive causality of market movements on sentiment expressions in online communities

In multi-view learning, features are organized into multiple sets called views. Multi-view stacking (MVS) is an ensemble learning framework which learns a prediction function from each view separately, and then learns a meta-function which optimally combines the view-specific predictions. In case studies, MVS has been shown to increase prediction accuracy. However, the framework can also be used for selecting a subset of important views. We propose a method for selecting views based on MVS, which we call stacked penalized logistic regression (StaPLR). Compared to existing view-selection methods like the group lasso, StaPLR can make use of faster optimization algorithms and is easily parallelized. We show that nonnegativity constraints on the parameters of the function which combines the views are important for preventing unimportant views from entering the model. We investigate the view selection and classification performance of StaPLR and the group lasso through simulations, and consider two real data examples. We observe that StaPLR is less likely to select irrelevant views, leading to models that are sparser at the view level, but which have comparable or increased predictive performance.

Classification, the process of assigning a label (or class) to an observation given its features, is a common task in many applications. Nonetheless in most real-life applications, the labels can not be fully explained by the observed features. Indeed there can be many factors hidden to the modellers. The unexplained variation is then treated as some random noise which is handled differently depending on the method retained by the practitioner. This work focuses on two simple and widely used supervised classification algorithms: discrete choice models and artificial neural networks in the context of binary classification. Through various numerical experiments involving continuous or discrete explanatory features, we present a comparison of the retained methods' performance in presence of missing variables. The impact of the distribution of the two classes in the training data is also investigated. The outcomes of those experiments highlight the fact that artificial neural networks outperforms the discrete choice models, except when the distribution of the classes in the training data is highly unbalanced. Finally, this work provides some guidelines for choosing the right classifier with respect to the training data.

We investigate the applicability of the formalism of quantum mechanics to everyday life. It seems to be directly relevant for situations in which the very act of coming to a conclusion or decision on one issue affects one's confidence about conclusions or decisions on another issue. Lie algebra theory is argued to be a very useful tool in guiding the construction of quantum descriptions of such situations. Tests, extensions and speculative applications and implications, including for the encoding of thoughts in neural networks, are discussed. It is suggested that the recognition and incorporation of such mathematical structure into machine learning and artificial intelligence might lead to significant efficiency and generality gains in addition to ensuring probabilistic reasoning at a fundamental level.

-- a group of intelligent agents which fulfill a set of tasks in parallel is represented first by the tensor multiplication of corresponding processes in a linear logic game category. An optimal itinerary in the configuration space of the group states is defined as a play with maximal total reward in the category. New moments also are: the reward is represented as a degree of certainty (visibility) of an agent goal, and the system goals are chosen by the greatest value corresponding to these processes in the system goal lattice. The artificial intelligence is represented in the Artificial General Intelligence (AGI) approach as an information processor which consumes and gives out information. Investigations in the field are focused on systems which act rationally. A formal description of the most intelligent agent (AIXI) behavior, in the sense of some intelligence measure, is suggested in AGI framework [1].

In the context of capacity planning, forecasting the evolution of informatics servers usage enables companies to better manage their computational resources. We address this problem by collecting key indicator time series and propose to forecast their evolution a day-ahead. Our method assumes that data is structured by a daily seasonality, but also that there is typical evolution of indicators within a day. Then, it uses the combination of a clustering algorithm and Markov Models to produce day-ahead forecasts. Our experiments on real datasets show that the data satisfies our assumption and that, in the case study, our method outperforms classical approaches (AR, Holt-Winters).

Answer set programming (ASP) is a successful declarative formalism for knowledge representation and reasoning. The evaluation of ASP programs is nowadays based on the Conflict-Driven Clause Learning (CDCL) backtracking search algorithm. Recent work suggested that the performance of CDCL-based implementations can be considerably improved on specific benchmarks by extending their solving capabilities with custom heuristics and propagators. However, embedding such algorithms into existing systems requires expert knowledge of the internals of ASP implementations. The development of effective solver extensions can be made easier by providing suitable programming interfaces. In this paper, we present the interface for extending the CDCL-based ASP solver WASP. The interface is both general, i.e. it can be used for providing either new branching heuristics and propagators, and external, i.e. the implementation of new algorithms requires no internal modifications of WASP. Moreover, we review the applications of the interface witnessing it can be successfully used to extend WASP for solving effectively hard instances of both real-world and synthetic problems. Under consideration in Theory and Practice of Logic Programming (TPLP).

With the growth in sustainable investing, there's been a surge in data on environmental, social and governance (ESG) factors over the past few years. Demand for ESG data is rising as asset managers look to incorporate ESG factors such as low-carbon emissions or gender diversity on boards into their investment analysis and decision-making processes. Fund managers, including BlackRock and Vanguard, are offering sustainable funds and exchange-traded funds (ETFs) based on sustainable indexes to capture assets from millennials and women. But the uptake has moved beyond specialty funds and has spread to pension funds, particularly in Europe, looking for long-term returns, reported Bloomberg Intelligence in April. "The financial cost of environmental, social and governance (ESG) performance and better disclosure is spurring uptake," wrote Bloomberg Intelligence in "Sustainable Investing Grows on Pensions, Millennials."

Years from now, artificial intelligence (AI), predictive algorithms and biometric sensors might provide the poorest people in society with far better healthcare than the richest people currently have access to today, and nearly all aspects of society will benefit from this imminent technological boom. Governments all over the world are becoming aware of this trend and similarities are already being drawn to the industrial revolution of the late 18th to early 19th century. Experts are predicting that whoever leads the world in AI will most likely dominate the entire world, and effectively threaten liberal democratic principles globally. Taking a different look at the differences between communism and liberalism, it can be deduced that their dissimilarities did not just emanate from their fundamental core principles but also in the way both political systems process data and make decisions. The liberal democratic system is essentially a distributed system -- it distributes information and the power to make decisions between several individuals and organizations.