Mind the Mining

In this paper we revisit the mining strategies in proof of work based cryptocurrencies and propose two strategies, we call smart and smarter mining, that in many cases strictly dominate honest mining. In contrast to other known attacks, like selfish mining, which induce zero-sum games among the miners, the strategies proposed in this paper increase miners' profit by reducing their variable costs (i.e., electricity). Moreover, the proposed strategies are viable for much smaller miners than previously known attacks, and surprisingly, an attack performed by one miner is profitable for all other miners as well. While saving electricity power is very encouraging for the environment, it is less so for the coin's security. The smart/smarter strategies expose the coin to under 50\% attacks and this vulnerability might only grow when new miners join the coin as a response to the increase in profit margins induced by these strategies

Scalable mining for classification rules in relational databases

doi:10.1214/lnms/1196285404Data mining is a process of discovering useful patterns (knowledge) hidden in extremely large datasets. Classification is a fundamental data mining function, and some other functions can be reduced to it. In this paper we propose a novel classification algorithm (classifier) called MIND (MINing in Databases). MIND can be phrased in such a way that its implementation is very easy using the extended relational calculus SQL, and this in turn allows the classifier to be built into a relational database system directly. MIND is truly scalable with respect to I/O efficiency, which is important since scalability is a key requirement for any data mining algorithm. We have built a prototype of MIND in the relational database management system DB2 and have benchmarked its performance. We describe the working prototype and report the measured performance with respect to the previous method of choice. MIND scales not only with the size of datasets but also with the number of processors on an IBM SP2 computer system. Even on uniprocessors, MIND scales well beyond dataset sizes previously published for classifiers.We also give some insights that may have an impact on the evolution of the extended relational calculus SQL

Future energy

Energy resources have been a major focus for BGS over our 175 year history. In the past, our geologists searched for coal to keep the UK supplied with energy crucial for economic development. Coal mining subsequently declined and by the 1980s we were studying abandoned mines to try and resolve problems of subsidence, flooding as the dewatering pumps were switched off, and contaminated water discharging into rivers. More recently we have returned to our geological maps and archives of coal mine plans with a new energy source in mind — geothermal energy

Mining HCI Data for Theory of Mind Induction

Human-computer interaction (HCI) results in enormous amounts of data-bearing potentials for understanding a human user’s intentions, goals, and desires. Knowing what users want and need is a key to intelligent system assistance. The theory of mind concept known from studies in animal behavior is adopted and adapted for expressive user modeling. Theories of mind are hypothetical user models representing, to some extent, a human user’s thoughts. A theory of mind may even reveal tacit knowledge. In this way, user modeling becomes knowledge discovery going beyond the human’s knowledge and covering domain-specific insights. Theories of mind are induced by mining HCI data. Data mining turns out to be inductive modeling. Intelligent assistant systems inductively modeling a human user’s intentions, goals, and the like, as well as domain knowledge are, by nature, learning systems. To cope with the risk of getting it wrong, learning systems are equipped with the skill of reflection

Probabilistic Perspectives on Collecting Human Uncertainty in Predictive Data Mining

In many areas of data mining, data is collected from humans beings. In this contribution, we ask the question of how people actually respond to ordinal scales. The main problem observed is that users tend to be volatile in their choices, i.e. complex cognitions do not always lead to the same decisions, but to distributions of possible decision outputs. This human uncertainty may sometimes have quite an impact on common data mining approaches and thus, the question of effective modelling this so called human uncertainty emerges naturally. Our contribution introduces two different approaches for modelling the human uncertainty of user responses. In doing so, we develop techniques in order to measure this uncertainty at the level of user inputs as well as the level of user cognition. With support of comprehensive user experiments and large-scale simulations, we systematically compare both methodologies along with their implications for personalisation approaches. Our findings demonstrate that significant amounts of users do submit something completely different (action) than they really have in mind (cognition). Moreover, we demonstrate that statistically sound evidence with respect to algorithm assessment becomes quite hard to realise, especially when explicit rankings shall be built

Galois Connections between Semimodules and Applications in Data Mining

In [1] a generalisation of Formal Concept Analysis was introduced with data mining applications in mind, K-Formal Concept Analysis, where incidences take values in certain kinds of semirings, instead of the standard Boolean carrier set. A fundamental result was missing there, namely the second half of the equivalent of the main theorem of Formal Concept Analysis. In this continuation we introduce the structural lattice of such generalised contexts, providing a limited equivalent to the main theorem of K-Formal Concept Analysis which allows to interpret the standard version as a privileged case in yet another direction. We motivate our results by providing instances of their use to analyse the confusion matrices of multiple-input multiple-output classifiers

Data Mining the Brain to Decode the Mind

In recent years, neuroscience has begun to transform itself into a “big data” enterprise with the importation of computational and statistical techniques from machine learning and informatics. In addition to their translational applications such as brain-computer interfaces and early diagnosis of neuropathology, these tools promise to advance new solutions to longstanding theoretical quandaries. Here I critically assess whether these promises will pay off, focusing on the application of multivariate pattern analysis (MVPA) to the problem of reverse inference. I argue that MVPA does not inherently provide a new answer to classical worries about reverse inference, and that the method faces pervasive interpretive problems of its own. Further, the epistemic setting of MVPA and other decoding methods contributes to a potentially worrisome shift towards prediction and away from explanation in fundamental neuroscience

Evaluation of Bord and Pillar Mining System in MCL Coal Mines

The importance of mining is definitely significant to human civilization. In fact, as one of the earliest of human enterprises, mining and its development correlate closely with cultural progress Mining is the mother industry for other industries. For effectiveness in mining, different methods have been approached keeping in mind the production and safety. One of such methods is the Bord and Pillar method of mining. Bord and Pillar method of mining is one of the oldest methods. The key to the successful Bord and Pillar mining is selecting the optimum pillar size. If the pillars are too small the mine will collapse. If the pillars are too large then significant quantities of valuable material will be left behind reducing the profitability of the mine. The issues relating to the stability of pillars and effective extraction from it is a major concern now-a-days. The most important parameter before designing a pillar is the Safety factor. The main purpose of this project is to increase the extraction ratio of Bord and Pillar workings without compromising the safety facto

Test, Teachers, Quorum (Pure Populations)

The “trial and error” method is fundamental for Master Minddecision algorithms. On the basis of Master Mind games and strategies weconsider some data mining methods for tests using students as teachers.Voting, twins, opposite, simulate and observer methods are investigated.For a pure data base these combinatorial algorithms are faster then manyAI and Master Mind methods. The complexities of these algorithms arecompared with basic combinatorial methods in AI. ACM Computing Classification System (1998): F.3.2, G.2.1, H.2.1, H.2.8, I.2.6