Discovering learning processes using inductive miner: A case study with learning management systems (LMSs)

Resumen tomado de la publicaciónDescubriendo procesos de aprendizaje aplicando Inductive Miner: un estudio de caso en Learning Management Systems (LMSs). Antecedentes: en la minería de procesos con datos educativos se utilizan diferentes algoritmos para descubrir modelos, sobremanera el Alpha Miner, el Heuristic Miner y el Evolutionary Tree Miner. En este trabajo proponemos la implementación de un nuevo algoritmo en datos educativos, el denominado Inductive Miner. Método: hemos utilizado datos de interacción de 101 estudiantes universitarios en una asignatura de grado desarrollada en la plataforma Moodle 2.0. Una vez prepocesados se ha realizado la minería de procesos sobre 21.629 eventos para descubrir los modelos que generan los diferentes algoritmos y comparar sus medidas de ajuste, precisión, simplicidad y generalización. Resultados: en las pruebas realizadas en nuestro conjunto de datos el algoritmo Inductive Miner es el que obtiene mejores resultados, especialmente para el valor de ajuste, criterio de mayor relevancia en lo que respecta al descubrimiento de modelos. Además, cuando ponderamos con pesos las diferentes métricas seguimos obteniendo la mejor medida general con el Inductive Miner. Conclusiones: la implementación de Inductive Miner en datos educativos es una nueva aplicación que, además de obtener mejores resultados que otros algoritmos con nuestro conjunto de datos, proporciona modelos válidos e interpretables en términos educativos.Universidad de Oviedo. Biblioteca de Psicología; Plaza Feijoo, s/n.; 33003 Oviedo; Tel. +34985104146; Fax +34985104126; [email protected]

Position paper on realizing smart products: challenges for Semantic Web technologies

In the rapidly developing space of novel technologies that combine sensing and semantic technologies, research on smart products has the potential of establishing a research field in itself. In this paper, we synthesize existing work in this area in order to define and characterize smart products. We then reflect on a set of challenges that semantic technologies are likely to face in this domain. Finally, in order to initiate discussion in the workshop, we sketch an initial comparison of smart products and semantic sensor networks from the perspective of knowledge technologies

Compressed Sensing - A New mode of Measurement

After introducing the concept of compressed sensing as a complementary measurement mode to the classical Shannon-Nyquist approach, I discuss some of the drivers, potential challenges and obstacles to its implementation. I end with a speculative attempt to embed compressed sensing as an enabling methodology within the emergence of data-driven discovery. As a consequence I predict the growth of non-nomological sciences where heuristic correlations will find applications but often bypass conventional pure basic and use-inspired basic research stages due to the lack of verifiable hypotheses

Discovery and Measurement of Sleptons, Binos, and Winos with a Z'

Extensions of the MSSM could significantly alter its phenomenology at the LHC. We study the case in which the MSSM is extended by an additional U(1) gauge symmetry, which is spontaneously broken at a few TeV. The production cross-section of sleptons is enhanced over that of the MSSM by the process $pp\to Z' \to \tilde{\ell} \tilde{\ell}^*$, so the discovery potential for sleptons is greatly increased. The flavor and charge information in the resulting decay, $\tilde{\ell} \to \ell + {LSP}$, provides a useful handle on the identity of the LSP. With the help of the additional kinematical constraint of an on-shell Z', we implement a novel method to measure all of the superpartner masses involved in this channel. For certain final states with two invisible particles, one can construct kinematic observables bounded above by parent particle masses. We demonstrate how output from one such observable, m_T2, can become input to a second, increasing the number of measurements one can make with a single decay chain. The method presented here represents a new class of observables which could have a much wider range of applicability.Comment: 20 pages, 15 figures; v2 references added and minor change

Genetic programming in data mining for drug discovery

Genetic programming (GP) is used to extract from rat oral bioavailability (OB) measurements simple, interpretable and predictive QSAR models which both generalise to rats and to marketed drugs in humans. Receiver Operating Characteristics (ROC) curves for the binary classier produced by machine learning show no statistical dierence between rats (albeit without known clearance dierences) and man. Thus evolutionary computing oers the prospect of in silico ADME screening, e.g. for \virtual" chemicals, for pharmaceutical drug discovery

Enhanced No-Go Theorem for Quantum Position Verification

Based on the instantaneous nonlocal quantum computation (INQC), Buhrman et al. proposed an excellent attack strategy to quantum position verification (QPV) protocols in 2011, and showed that, if the colluding adversaries are allowed to previously share unlimited entangled states, it is impossible to design an unconditionally secure QPV protocol in the previous model. Here, trying to overcome this no-go theorem, we find some assumptions in the INQC attack, which are implicit but essential for the success of this attack, and present three different QPV protocols where these assumptions are not satisfied. We show that for the general adversaries, who execute the attack operations at every common time slot or the time when they detect the arrival of the challenge signals from the verifiers, secure QPV is achievable. This implies practically secure QPV can be obtained even if the adversaries is allowed to share unlimited entanglement previously. Here by "practically" we mean that in a successful attack the adversaries need launch a new round of attack on the coming qubits with extremely high frequency so that none of the possible qubits, which may be sent at random time, will be missed. On the other side, using such Superdense INQC (SINQC) attack, the adversaries can still attack the proposed protocols successfully in theory. The particular attack strategies to our protocols are presented respectively. On this basis, we demonstrate the impossibility of secure QPV with looser assumptions, i.e. the enhanced no-go theorem for QPV.Comment: 19 pages, single column, 3 tables, 6 figure

On Cognitive Preferences and the Plausibility of Rule-based Models

It is conventional wisdom in machine learning and data mining that logical models such as rule sets are more interpretable than other models, and that among such rule-based models, simpler models are more interpretable than more complex ones. In this position paper, we question this latter assumption by focusing on one particular aspect of interpretability, namely the plausibility of models. Roughly speaking, we equate the plausibility of a model with the likeliness that a user accepts it as an explanation for a prediction. In particular, we argue that, all other things being equal, longer explanations may be more convincing than shorter ones, and that the predominant bias for shorter models, which is typically necessary for learning powerful discriminative models, may not be suitable when it comes to user acceptance of the learned models. To that end, we first recapitulate evidence for and against this postulate, and then report the results of an evaluation in a crowd-sourcing study based on about 3.000 judgments. The results do not reveal a strong preference for simple rules, whereas we can observe a weak preference for longer rules in some domains. We then relate these results to well-known cognitive biases such as the conjunction fallacy, the representative heuristic, or the recogition heuristic, and investigate their relation to rule length and plausibility.Comment: V4: Another rewrite of section on interpretability to clarify focus on plausibility and relation to interpretability, comprehensibility, and justifiabilit

ATLAS Detector Status and Physics Startup Plans

The status of the ATLAS detector shortly before the start-up of the LHC is presented. The progress in the commissioning of the detector, as well as plans for early physics analysis are outlined.Comment: Plenary talk at ICHEP08, Philadelphia, USA, July 2008. 10 pages PDF fil

Symbolic regression of generative network models

Networks are a powerful abstraction with applicability to a variety of scientific fields. Models explaining their morphology and growth processes permit a wide range of phenomena to be more systematically analysed and understood. At the same time, creating such models is often challenging and requires insights that may be counter-intuitive. Yet there currently exists no general method to arrive at better models. We have developed an approach to automatically detect realistic decentralised network growth models from empirical data, employing a machine learning technique inspired by natural selection and defining a unified formalism to describe such models as computer programs. As the proposed method is completely general and does not assume any pre-existing models, it can be applied "out of the box" to any given network. To validate our approach empirically, we systematically rediscover pre-defined growth laws underlying several canonical network generation models and credible laws for diverse real-world networks. We were able to find programs that are simple enough to lead to an actual understanding of the mechanisms proposed, namely for a simple brain and a social network