Interactive Intent Modeling for Exploratory Search

Exploratory search requires the system to assist the user in comprehending the information space and expressing evolving search intents for iterative exploration and retrieval of information. We introduce interactive intent modeling, a technique that models a user’s evolving search intents and visualizes them as keywords for interaction. The user can provide feedback on the keywords, from which the system learns and visualizes an improved intent estimate and retrieves information. We report experiments comparing variants of a system implementing interactive intent modeling to a control system. Data comprising search logs, interaction logs, essay answers, and questionnaires indicate significant improvements in task performance, information retrieval performance over the session, information comprehension performance, and user experience. The improvements in retrieval effectiveness can be attributed to the intent modeling and the effect on users’ task performance, breadth of information comprehension, and user experience are shown to be dependent on a richer visualization. Our results demonstrate the utility of combining interactive modeling of search intentions with interactive visualization of the models that can benefit both directing the exploratory search process and making sense of the information space. Our findings can help design personalized systems that support exploratory information seeking and discovery of novel information.Peer reviewe

Exploring Peripheral Physiology as a Predictor of Perceived Relevance in Information Retrieval

Peripheral physiological signals, as obtained using electrodermal activity and facial electromyography over the corrugator supercilii muscle, are explored as indicators of perceived relevance in information retrieval tasks. An experiment with 40 participants is reported, in which these physiological signals are recorded while participants perform information retrieval tasks. Appropriate feature engineering is defined, and the feature space is explored. The results indicate that features in the window of 4 to 6 seconds after the relevance judgment for electrodermal activity, and from 1 second before to 2 seconds after the relevance judgment for corrugator supercilii activity, are associated with the users’ perceived relevance of information items. A classifier verified the predictive power of the features and showed up to 14% improvement predicting relevance. Our research can help the design of intelligent user interfaces for information retrieval that can detect the user’s perceived relevance from physiological signals and complement or replace conventional relevance feedback

Predicting term-relevance from brain signals (Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval)

Term-Relevance Prediction from Brain Signals (TRPB) is proposed to automatically detect relevance of text information directly from brain signals. An experiment with forty participants was conducted to record neural activity of participants while providing relevance judgments to text stimuli for a given topic. High-precision scientific equipment was used to quantify neural activity across 32 electroencephalography (EEG) channels. A classifier based on a multi-view EEG feature representation showed improvement up to 17% in relevance prediction based on brain signals alone. Relevance was also associated with brain activity with significant changes in certain brain areas. Consequently, TRPB is based on changes identified in specific brain areas and does not require user-specific training or calibration. Hence, relevance predictions can be conducted for unseen content and unseen participants. As an application of TRPB we demonstrate a high-precision variant of the classifier that constructs sets of relevant terms for a given unknown topic of interest. Our research shows that detecting relevance from brain signals is possible and allows the acquisition of relevance judgments without a need to observe any other user interaction. This suggests that TRPB could be used in combination or as an alternative for conventional implicit feedback signals, such as dwell time or click-through activity

Weighted and robust archetypal analysis

Archetypal analysis represents observations in a multivariate data set as convex combinations of a few extremal points lying on the boundary of the convex hull. Data points which vary from the majority have great influence on the solution; in fact one outlier can break down the archetype solution. The original algorithm is adapted to be a robust M-estimator and an iteratively reweighted least squares fitting algorithm is presented. As a required first step, the weighted archetypal problem is formulated and solved. The algorithm is demonstrated using an artificial example, a real world example and a detailed simulation study.Robust archetypal analysis M-estimator Breakdown point Iteratively reweighted least squares

Transverse-momentum and pseudorapidity distributions of charged hadrons in pppp collisions at s\sqrt{s} = 7 TeV

Charged-hadron transverse-momentum and pseudorapidity distributions in proton-proton collisions at $\sqrt{s} = 7$~TeV are measured with the inner tracking system of the CMS detector at the LHC. The charged-hadron yield is obtained by counting the number of reconstructed hits, hit-pairs, and fully reconstructed charged-particle tracks. The combination of the three methods gives a charged-particle multiplicity per unit of pseudorapidity \dnchdeta|_{|\eta| < 0.5} = 5.78\pm 0.01\stat\pm 0.23\syst for non-single-diffractive events, higher than predicted by commonly used models. The relative increase in charged-particle multiplicity from $\sqrt{s} = 0.9$ to 7~TeV is 66.1\%\pm 1.0\%\stat\pm 4.2\%\syst. The mean transverse momentum is measured to be 0.545\pm 0.005\stat\pm 0.015\syst\GeVc. The results are compared with similar measurements at lower energies.Charged-hadron transverse-momentum and pseudorapidity distributions in proton-proton collisions at sqrt(s) = 7 TeV are measured with the inner tracking system of the CMS detector at the LHC. The charged-hadron yield is obtained by counting the number of reconstructed hits, hit-pairs, and fully reconstructed charged-particle tracks. The combination of the three methods gives a charged-particle multiplicity per unit of pseudorapidity, dN(charged)/d(eta), for |eta| < 0.5, of 5.78 +/- 0.01 (stat) +/- 0.23 (syst) for non-single-diffractive events, higher than predicted by commonly used models. The relative increase in charged-particle multiplicity from sqrt(s) = 0.9 to 7 TeV is 66.1% +/- 1.0% (stat) +/- 4.2% (syst). The mean transverse momentum is measured to be 0.545 +/- 0.005 (stat) +/- 0.015 (syst) GeV/c. The results are compared with similar measurements at lower energies

Measurement of the charge ratio of atmospheric muons with the CMS detector

We present a measurement of the ratio of positive to negative muon fluxes from cosmic ray interactions in the atmosphere, using data collected by the CMS detector both at ground level and in the underground experimental cavern at the CERN LHC. Muons were detected in the momentum range from 5 GeV/ c to 1 TeV/ c . The surface flux ratio is measured to be 1.2766±0.0032(stat.)±0.0032(syst.) , independent of the muon momentum, below 100 GeV/ c . This is the most precise measurement to date. At higher momenta the data are consistent with an increase of the charge ratio, in agreement with cosmic ray shower models and compatible with previous measurements by deep-underground experiments.We present a measurement of the ratio of positive to negative muon fluxes from cosmic ray interactions in the atmosphere, using data collected by the CMS detector both at ground level and in the underground experimental cavern at the CERN LHC. Muons were detected in the momentum range from 5 GeV/c to 1 TeV/c. The surface flux ratio is measured to be 1.2766 \pm 0.0032(stat.) \pm 0.0032 (syst.), independent of the muon momentum, below 100 GeV/c. This is the most precise measurement to date. At higher momenta the data are consistent with an increase of the charge ratio, in agreement with cosmic ray shower models and compatible with previous measurements by deep-underground experiments

Search for Pair Production of Second-Generation Scalar Leptoquarks in pp Collisions at sqrt(s) = 7 TeV

A search for pair production of second-generation scalar leptoquarks in the final state with two muons and two jets is performed using proton-proton collision data at sqrt(s) = 7 TeV collected by the CMS detector at the LHC. The data sample used corresponds to an integrated luminosity of 34 inverse picobarns. The number of observed events is in good agreement with the predictions from the standard model processes. An upper limit is set on the second-generation leptoquark cross section times beta^2 as a function of the leptoquark mass, and leptoquarks with masses below 394 GeV are excluded at a 95% confidence level for beta = 1, where beta is the leptoquark branching fraction into a muon and a quark. These limits are the most stringent to date

Measurement of the lepton charge asymmetry in inclusive WW production in pp collisions at s=7\sqrt{s} = 7 TeV

A measurement of the lepton charge asymmetry in inclusive pp to WX production at sqrt(s)= 7 TeV is presented based on data recorded by the CMS detector at the LHC and corresponding to an integrated luminosity of 36 inverse picobarns. This high precision measurement of the lepton charge asymmetry, performed in both the W to e nu and W to mu nu channels, provides new insights into parton distribution functions.A measurement of the lepton charge asymmetry in inclusive pp to WX production at sqrt(s)= 7 TeV is presented based on data recorded by the CMS detector at the LHC and corresponding to an integrated luminosity of 36 inverse picobarns. This high precision measurement of the lepton charge asymmetry, performed in both the W to e nu and W to mu nu channels, provides new insights into parton distribution functions.A measurement of the lepton charge asymmetry in inclusive pp to WX production at sqrt(s)= 7 TeV is presented based on data recorded by the CMS detector at the LHC and corresponding to an integrated luminosity of 36 inverse picobarns. This high precision measurement of the lepton charge asymmetry, performed in both the W to e nu and W to mu nu channels, provides new insights into parton distribution functions