People-Powered Music: Using User-Generated Tags and Structure in Recommendations

Music recommenders often rely on experts to classify song facets like genre and mood, but user-generated folksonomies hold some advantages over expert classifications—folksonomies can reflect the same real-world vocabularies and categorizations that end users employ. We present an approach for using crowd-sourced common sense knowledge to structure user-generated music tags into a folksonomy, and describe how to use this approach to make music recommendations. We then empirically evaluate our “people-powered” structured content recommender against a more traditional recommender. Our results show that participants slightly preferred the unstructured recommender, rating more of its recommendations as “perfect” than they did for our approach. An exploration of the reasons behind participants’ ratings revealed that users behaved differently when tagging songs than when evaluating recommendations, and we discuss the implications of our results for future tagging and recommendation approaches

Auxiliary State Variables for Rotor Crack Detection

In the present study, a new model-based method for rotor crack detection and crack location is proposed. The finiteelement model of the rotor-bearing system accounts for the breathing mechanism of the crack. The model of the rotor system is augmented with an auxiliary single-degree-of-freedom oscillator. The observer is designed and the estimates of its two auxiliary state variables are proposed as crack indicators. The crack location along the shaft is determined by designing a set of observers, which calculate the values of these indicators for different possible crack locations along the shaft. The proposed method is validated numerically and the results prove its capability to detect and locate the crack. Further study will include experimental and numerical investigations to make the approach more robust

Controlled Deflection Approach for Rotor Crack Detection

Atransverse shaft crack is a serious malfunction that can occurdue to cyclic loading, creep, stress corrosion, and other mechanismsto which rotating machines are subjected. Though studied for manyyears, the problems of early crack detection and warning arestill in the limelight of many researchers. This is dueto the fact that the crack has subtle influence onthe dynamic response of the machine and still there areno widely accepted, reliable methods of its early detection. Thispaper presents a new approach to these problems. The methodutilizes the coupling mechanism between the bending and torsional vibrationsof the cracked, nonrotating shaft. By applying an external lateralforce of constant amplitude, a small shaft deflection is induced.Simultaneously, a harmonic torque is applied to the shaft inducingits torsional vibrations. By changing the angular position of thelateral force application, the position of the deflection also changesopening or closing of the crack. This changes the waythe bending and torsional vibrations are being coupled. By studyingthe coupled lateral vibration response for each angular position ofthe lateral force one can assess the possible presence ofthe crack. The approach is demonstrated with a numerical modelof a rotor. The model is based on the rigidfinite element method (RFE), which has previously been successfully appliedfor the dynamic analysis of many complicated, mechanical structures. TheRFE method is extended and adopted for the modeling ofthe cracked shafts. An original concept of crack modeling utilizingthe RFE method is presented. The crack is modeled asa set of spring-damping elements (SDEs) of variable stiffness connectingtwo sections of the shaft. By calculating the axial deformationsof the SDEs, the opening/closing mechanism of the crack isintroduced. The results of numerical analysis demonstrate the potential ofthe suggested approach for effective shaft crack detection

Where Are My Intelligent Assistant's Mistakes? A Systematic Testing Approach

Intelligent assistants are handling increasingly critical tasks, but until now, end users have had no way to systematically assess where their assistants make mistakes. For some intelligent assistants, this is a serious problem: if the assistant is doing work that is important, such as assisting with qualitative research or monitoring an elderly parent’s safety, the user may pay a high cost for unnoticed mistakes. This paper addresses the problem with WYSIWYT/ML (What You See Is What You Test for Machine Learning), a human/computer partnership that enables end users to systematically test intelligent assistants. Our empirical evaluation shows that WYSIWYT/ML helped end users find assistants’ mistakes significantly more effectively than ad hoc testing. Not only did it allow users to assess an assistant’s work on an average of 117 predictions in only 10 minutes, it also scaled to a much larger data set, assessing an assistant’s work on 623 out of 1,448 predictions using only the users’ original 10 minutes’ testing effort

Tell me more?: the effects of mental model soundness on personalizing an intelligent agent

What does a user need to know to productively work with an intelligent agent? Intelligent agents and recommender systems are gaining widespread use, potentially creating a need for end users to understand how these systems operate in order to fix their agent's personalized behavior. This paper explores the effects of mental model soundness on such personalization by providing structural knowledge of a music recommender system in an empirical study. Our findings show that participants were able to quickly build sound mental models of the recommender system's reasoning, and that participants who most improved their mental models during the study were significantly more likely to make the recommender operate to their satisfaction. These results suggest that by helping end users understand a system's reasoning, intelligent agents may elicit more and better feedback, thus more closely aligning their output with each user's intentions

Explanatory debugging: Supporting end-user debugging of machine-learned programs

Many machine-learning algorithms learn rules of behavior from individual end users, such as task-oriented desktop organizers and handwriting recognizers. These rules form a “program” that tells the computer what to do when future inputs arrive. Little research has explored how an end user can debug these programs when they make mistakes. We present our progress toward enabling end users to debug these learned programs via a Natural Programming methodology. We began with a formative study exploring how users reason about and correct a text-classification program. From the results, we derived and prototyped a concept based on “explanatory debugging”, then empirically evaluated it. Our results contribute methods for exposing a learned program's logic to end users and for eliciting user corrections to improve the program's predictions

Same-sign W pair production as a probe of double parton scattering at the LHC

We study the production of same-sign W boson pairs at the LHC in double parton interactions. Compared with simple factorised double parton distributions (dPDFs), we show that the recently developed dPDFs, GS09, lead to non-trivial kinematic correlations between the W bosons. A numerical study of the prospects for observing this process using same-sign dilepton signatures, including same-sign WWjj, di-boson and heavy flavour backgrounds, at 14 TeV centre-of-mass energy is then performed. It is shown that a small excess of same-sign dilepton events from double parton scattering over a background dominated by single scattering WZ(gamma*) production could be observed at the LHC.Comment: 14 pages, 8 figures. Added references, slight changes in the text

Principles of Explanatory Debugging to personalize interactive machine learning

How can end users efficiently influence the predictions that machine learning systems make on their behalf? This paper presents Explanatory Debugging, an approach in which the system explains to users how it made each of its predictions, and the user then explains any necessary corrections back to the learning system. We present the principles underlying this approach and a prototype instantiating it. An empirical evaluation shows that Explanatory Debugging increased participants' understanding of the learning system by 52% and allowed participants to correct its mistakes up to twice as efficiently as participants using a traditional learning system

Direct Democracy Institutions in Authoritarian States in Pre-War Europe

The aim of the paper is to present direct democracy institutions provided for in the constitutions of authoritarian states in pre-war Europe, or applied in practice by authoritarian states. Such institutions were included in the constitutions created by authoritarian governments in Lithuania (1928), Austria (1934) and Estonia (1937) or used in practice: in Portugal (1933), Greece (1935), Estonia (1936) and Romania (1938). Two authoritarian states included in their constitutions provisions on opinion–giving and law–giving referenda. These were Austria (1934) and Estonia (1937). However, these provisions had never been applied in practice. Seeking the genesis of the phenomenon described in the paper, it is necessary to go back to the start of the 19th century and point to the reign of Napoleon Bonaparte, initially as First Consul and then Emperor of France. The nature of these republican and subsequently imperial plebiscytes was clearly anti-parlamentary and [email protected] WarszawskiBonarek J., Czekalski T., Sprawski S., Turlej S., Historia Grecji, Kraków 2005.Baszkiewicz J., Powszechna historia ustrojów państwowych, wyd. 2, Gdańsk 2001.Demel J., Historia Rumunii, wyd. 2 popr. i uzup., Warszawa 1986.Gembarzewski L., Nowe konstytucje. Konstytucja austriacka, „Biuletyn Urzędniczy” 1934, nr 5-6 i nr 7-8.Gembarzewski L., Nowe konstytucje. Konstytucja Estonii, „Biuletyn Urzędniczy” 1938, nr 7-8.Gembarzewski L., Nowe konstytucje. Konstytucja litewska, „Biuletyn Urzędniczy” 1938, nr 5-6.Gembarzewski L., Nowe konstytucje. Konstytucja Portugalii, „Biuletyn Urzędniczy” 1935, nr 5-6.Hermano Soraiva J., Krótka historia Portugalii, Kraków 2000.Konstytucja Litwy, red. L. Garlicki, Warszawa 2006.Konstytucje Finlandii, Włoch, Niemieckiej Republiki Federalnej i Francji, red. A. Burda, M. Rybicki, Wrocław 1971.Kozeński J., Historia Austrii 1918-1968, Poznań 1970.Makowski J., Nowe konstytucje, Warszawa 1925.Miller A., Nowa konstytucja państwa litewskiego, Warszawa 1930.Ochmański J., Historia Litwy, Warszawa 1982.Oliveira Marques A. de, Historia Portugalii, Warszawa 1987.Romer M., Organizacja władzy politycznej w rozwoju konstytucyjnym Republiki Litewskiej, Wydawnictwa Grup Polskich Porozumień Prawniczych z Zagranicą, Warszawa 1939, zeszyt 4.Tanty M., Bałkany w XX wieku Dzieje polityczne, Warszawa 2003.Uluots J., Rozwój konstytucyjny Estonii ze specjalnym uwzględnieniem roli prezydenta republiki [w:] Państwa bałtyckie, Wydawnictwa Grup Polskich Porozumień Prawniczych z Zagranicą, Warszawa 1939, z. 4.Wereszycki H., Historia Austrii, Wrocław 1986.127-14

Too much, too little, or just right? Ways explanations impact end users' mental models

Research is emerging on how end users can correct mistakes their intelligent agents make, but before users can correctly "debug" an intelligent agent, they need some degree of understanding of how it works. In this paper we consider ways intelligent agents should explain themselves to end users, especially focusing on how the soundness and completeness of the explanations impacts the fidelity of end users' mental models. Our findings suggest that completeness is more important than soundness: increasing completeness via certain information types helped participants' mental models and, surprisingly, their perception of the cost/benefit tradeoff of attending to the explanations. We also found that oversimplification, as per many commercial agents, can be a problem: when soundness was very low, participants experienced more mental demand and lost trust in the explanations, thereby reducing the likelihood that users will pay attention to such explanations at all