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4th International Visual Informatics Conference, IVIC 2015

作者： Zaman, Halimah Badioze Robinson, Peter Smeaton, Alan F. Shih, Timothy K. Velastin, Sergio Jaafar, Azizah Ali, Nazlena Mohamad Institute of Visual Informatics Universiti Kebangsaan Malaysia Bangi Malaysia University of Cambridge Cambridge United Kingdom Insight Centre for Data Analytics Dublin City University Dublin Ireland Computer Science and Information Engineering National Central University Jhongli Taiwan Kingston University Kingston upon Thames United Kingdom

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Improving latent factor models via personalized feature projection for one class recommendation 15

Improving latent factor models via personalized feature proj...

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24th ACM International Conference on Information and Knowledge Management, CIKM 2015

作者： Zhao, Tong McAuley, Julian King, Irwin Shenzhen Key Laboratory of Rich Media Big Data Analytics and Applications Shenzhen Research Institute Chinese University of Hong Kong Shenzhen China Department of Computer Science and Engineering Chinese University of Hong Kong Shatin N.T. Hong Kong Department of Computer Science and Engineering UC San Diego San DiegoCA United States

ISBN: (纸本)9781450337946

Latent Factor models, which transform both users and items into the same latent feature space, are one of the most successful and ubiquitous models in recommender systems. Most existing models in this paradigm define both users' and items' latent factors to be of the same size and use an inner product to represent a user's 'compatibility' with an item. Intuitively, users' factors encode 'preferences' while item factors encode 'properties', so that the inner product encodes how well an item matches a user's preferences. However, a user's opinion of an item may be more complex, for example each dimension of each user's opinion may depend on a combination of multiple item factors simultaneously. Thus it may be better to view each dimension of a user's preference as a personalized projection of an item's properties so that the preference model can capture complex relationships between items' properties and users' preferences. Therefore, in this paper we propose a novel personalized feature projection method to model users' preferences over items. Specifically, for each user, we define a personalized projection matrix, which takes the place of user-specific factors from existing models. This matrix describes a mapping between items' factors and users' preferences in order to build personalized preference models for each user and item. The proposed personalized feature projection method is quite general and existing latent factor models, for example, can be cast as a special case. We present three objective functions to optimize predictions in the form of ranked lists of users' preferences over items, and demonstrate how each can be used to improve one-class recommendation performance. Experiments are conducted on four real-world datasets and our results show that our personalized feature projection method outperforms several state-of-the-art methods on various evaluation metrics. © 2015 ACM.

关键词： Collaborative filtering

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Linear and nonlinear precoding based dynamic spectrum management for downstream vectored *** transmission

Linear and nonlinear precoding based dynamic spectrum manage...

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IEEE Global Communications Conference

作者： Wouter Lanneer Marc Moonen Paschalis Tsiaflakis Jochen Maes KU Leuven Dept. of Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems Signal Processing and Data Analytics Bell Labs Alcatel-Lucent

ISBN: (纸本)9781479959532

In the *** frequency range (up to 212 MHz), the diagonal dominance structure of the channel matrix is no longer valid at the higher frequencies. As a result, the linear Zero Forcing (ZF) precoder in combination with dynamic spectrum management (DSM) is no longer near-optimal for downstream vectored *** transmission. To boost performance, we develop a novel low-complexity algorithm for both linear and non-linear precoding based DSM that maximizes the weighted line sum-rate under realistic per-line total power and per-tone spectral mask constraints. The algorithm relies on a Lagrange multiplier based transformation of the downstream dual decomposition approach formulation into its dual upstream formulation, together with a low-complexity iterative fixed-point formula to solve the resulting upstream problems. Simulations with measured *** channel data up to both 106 and 212 MHz are provided revealing a significantly increased performance of this algorithm over linear ZF precoding.

关键词： Spectrum management Linear channel matrix prediction coding Downstream zero frequency Diagnostic and Statistical Manual based

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Effects of Feedback Latency on P300-based Brain-computer Interface

Effects of Feedback Latency on P300-based Brain-computer Int...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society

作者： Mahnaz Arvaneh Tomas E. Ward Ian H. Robertson Trinity College Institute of Neuroscience and Insight Centre for Data Analytics Dublin Ireland Dept. of Electronic Engineering National University of Ireland Maynooth Ireland

ISBN: (纸本)9781424492695

Feedback has been shown to affect performance when using a Brain-Computer Interface (BCI) based on sensorimotor rhythms. In contrast, little is known about the influence of feedback on P300-based BCIs. There is still an open question whether feedback affects the regulation of P300 and consequently the operation of P300-based BCIs. In this paper, for the first time, the influence of feedback on the P300-based BCI speller task is systematically assessed. For this purpose, 24 healthy participants performed the classic P300-based BCI speller task, while only half of them received feedback. Importantly, the number of flashes per letter was reduced on a regular basis in order to increase the frequency of providing feedback. Experimental results showed that feedback could significantly improve the P300-based BCI speller performance, if it was provided in short time intervals (e.g. in sequences as short as 4 to 6 flashes per row/column). Moreover, our offline analysis showed that providing feedback remarkably enhanced the relevant ERP patterns and attenuated the irrelevant ERP patterns, such that the discrimination between target and nontarget EEG trials increased.

关键词： increase frequency providing feedback Feedback Brain-Computer Interfaces Enterprise resource planning

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Author Correction: Genomic footprints of activated telomere maintenance mechanisms in cancer (Dec, 10.1038/s41467-019-13824-9, 2022)

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NATURE COMMUNICATIONS 2022年第1期13卷 1-1页

作者： Sieverling, Lina Hong, Chen Koser, Sandra D. Ginsbach, Philip Kleinheinz, Kortine Hutter, Barbara Braun, Delia M. Cortes-Ciriano, Isidro Xi, Ruibin Kabbe, Rolf Park, Peter J. Eils, Roland Schlesner, Matthias Brors, Benedikt Rippe, Karsten Jones, David T. W. Feuerbach, Lars Division of Applied Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Faculty of Biosciences Heidelberg University 69120 Heidelberg Germany Division of Applied Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Biosciences Heidelberg University Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Department for Bioinformatics and Functional Genomics Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant 69120 Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Institute of Pharmacy and Molecular Biotechnology and BioQuant Heidelberg University Heidelberg Germany German Cancer Consortium (DKTK) Heidelberg Germany National Center for Tumor Diseases (NCT) Heidelberg Heidelberg Germany Heidelberg Center for Personalized Oncology (DKFZ-HIPO) German Cancer Research Center (DKFZ) Heidelberg Germany German Cancer Consortium (DKTK) German Cancer Research Center (DKFZ) Heidelberg Germany Division of Chromatin Networks German Cancer Research Center (DKFZ) and BioQuant 69120 Heidelberg Germany Department of Biomedical Informatics Harvard Medical School Boston Massachusetts 02115 USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge CB2 1EW UK Ludwig Center at Harvard Medical School Boston Massachusetts 02115 USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge UK Ludwig Center at Harvard Medical School Boston MA USA School of Mathematical Sciences and Center for Statistical Science Peking University Beijing 100871 China Heidelberg University Heidelberg Germany New BIH Digital Health Center Berlin Institute of Health (BIH) and Charité - Universitätsmedizin Berlin Berlin Germany Bioi

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Analysis of dysarthric speech using distinctive feature recognition 6

Analysis of dysarthric speech using distinctive feature reco...

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6th Workshop on Speech and Language Processing for Assistive Technologies, SLPAT 2015

作者： Wong, Ka Ho Yeung, Yu Ting Wong, Patrick C. M. Levow, Gina Anne Meng, H. Human-Computer Communications Laboratory Department of Systems Engineering and Engineering Management United States Stanley Ho Big Data Decision Analytics Research Centre CUHK-Utrecht University Centre for Language Mind and Brain Netherlands Department of Linguistics and Modern Languages Chinese University of Hong Kong Hong Kong Hong Kong Department of Linguistics University of Washington SeattleWA United States

ISBN: (纸本)9781941643792

Imprecise articulatory breakdown is one of the characteristics of dysarthric speech. This work attempts to develop a framework to automatically identify problematic articulatory patterns of dysarthric speakers in terms of distinctive features (DFs), which are effective for describing speech production. The identification of problematic articulatory patterns aims to assist speech therapists in developing intervention strategies. A multilayer perceptron (MLP) system is trained with nondysarthric speech data for DF recognition. Agreement rates between the recognized DF values and the canonical values based on phonetic transcriptions are computed. For nondysarthric speech, our system achieves an average agreement rate of 85.7%. The agreement rate of dysarthric speech declines, ranging between 1% to 3% in mild cases, 4% to 7% in moderate cases, and 7% to 12% in severe cases, when compared with non-dysarthric speech. We observe that the DF disagreement patterns are consistent with the analysis of a speech therapist. © SLPAT 2015 - 6th Workshop on Speech and Language Processing for Assistive Technologies, Proceedings.

关键词： Speech recognition

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data mining of a remote behavioral tracking system for type 2 diabetes patients: A prospective cohort study

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JMIR Diabetes 2016年第1期1卷 e1页

作者： Wayne, Noah Cercone, Nick Li, Jiye Zohar, Ariel Katz, Joel Brown, Patrick Ritvo, Paul Health Behaviour Change Lab School of Kinesiology and Health Science York University Toronto ON Canada Data Mining Lab Lassonde School of Engineering York University Toronto ON Canada Pain Mechanisms Lab Department of Psychology York University Toronto ON Canada Division of Biostatistics Dalla Lana School of Public Health University of Toronto Toronto ON Canada Analytics and Informatics Prevention and Cancer Control Cancer Care Ontario Toronto ON Canada

Background: Complications from type 2 diabetes mellitus can be prevented when patients perform health behaviors such as vigorous exercise and glucose-regulated diet. The use of smartphones for tracking such behaviors has demonstrated success in type 2 diabetes management while generating repositories of analyzable digital data, which, when better understood, may help improve care. data mining methods were used in this study to better understand self-monitoring patterns using smartphone tracking software. Objective: Associations were evaluated between the smartphone monitoring of health behaviors and HbA1c reductions in a patient subsample with type 2 diabetes who demonstrated clinically significant benefits after participation in a randomized controlled trial. Methods: A priori association-rule algorithms, implemented in the C language, were applied to app-discretized use data involving three primary health behavior trackers (exercise, diet, and glucose monitoring) from 29 participants who achieved clinically significant HbA1c reductions. Use was evaluated in relation to improved HbA1c outcomes. Results: Analyses indicated that nearly a third (9/29, 31%) of participants used a single tracker, half (14/29, 48%) used two primary trackers, and the remainder (6/29, 21%) of the participants used three primary trackers. Decreases in HbA1c were observed across all groups (0.97-1.95%), but clinically significant reductions were more likely with use of one or two trackers rather than use of three trackers (OR 0.18, P=.04). Conclusions: data mining techniques can reveal relevant coherent behavior patterns useful in guiding future intervention structure. It appears that focusing on using one or two trackers, in a symbolic function, was more effective (in this sample) than regular use of all three trackers. © Noah Wayne, Nick Cercone, Jiye Li, Ariel Zohar, Joel Katz, Patrick Brown, Paul Ritvo. Originally published in JMIR Diabetes (http://***), 06.04.2016. This is

关键词： data mining Diabetes mellitus Health coaching Mhealth Telehealth Type 2

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Playing Immersive Games on the REVERIE platform

Playing Immersive Games on the REVERIE platform

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IEEE International Conference on Ubiquitous Computing and Communications

作者： Ioannis Doumanis David S. Monaghan Julie Wall CTVC Ltd London UK Insight Centre for Data Analytics Dublin City University University of East London (UEL) The School of Architecture Computing and Engineering London UK

ISBN: (纸本)9781509001552

REVERIE (REal and Virtual Engagement in Realistic Immersive Environments) [1] is a multimedia and multimodal framework, which supports the creation of immersive games. The framework supports the creation of games integrating technologies such as 3D spatial audio, detection of the player's body movement using Kinect and WIMO sensors, NPCs (Non-Playable Characters) with advanced AI capabilities featuring various levels of representation and gameplay into an immersive 3D environment. A demonstration game was developed for REVERIE, which is an adapted version of the popular Simon Says game. In the REVERIE version, a player tries to follow physical instructions issued by two autonomous agents with different degrees of realism. If a player follows a physical instruction correctly, they are awarded one point. If not, they are deducted one point. This paper presents a technical overview of the game technologies integrated in the Simon Says demo and its evaluation by players with variable computer literacy skills. Finally the potential of REVERIE as an immersive framework for gaming is discussed, followed by recommendations for improvements in future versions of the framework.

关键词： REVERIE framework Immersive games Non-Playable Characters (NPCs) Empirical evaluation of games

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A data Capturing Platform in the Cloud for Behavioral Analysis among Smokers: An Application Platform for Public Health Research

A Data Capturing Platform in the Cloud for Behavioral Analys...

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IEEE International Conference on Services Economics (SE)

作者： Kelvin K.F. Tsoi Yong-Hong Kuo Helen M. Meng School of Public Health and Primary Care Stanley Ho Big Data Decision Analytics Research Centre Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong China

Technology in the Cloud frameworks for healthcare data management and analytics has opened new horizons for public health research. Smoking is an addictive behavior and increases risk of death from different diseases, such as heart attacks or lung cancer. Nowadays, electronic cigarettes (e-cigarettes) are becoming popular in western countries and it has been recommended as an effective tool for smoking abstinence. However, smoking behavior and the efficacy of e-cigarette applications are insufficiently studied. This work presents a novel, Cloud-based infrastructure for data collection and storage to capture smoking behavior with e-cigarette. A user's smoking data generated by the daily use of e-cigarettes is uploaded to the cloud through mobile internet and a Bluetooth connection between a smart phone and the e-cigarette. All personal identity can be encrypted and a study identity number will be assigned to each subject for data privacy protection. The remote platform in the cloud can provide efficient analytic performance on a huge volume of data with high velocity of data creation. data mining on smoking behavior will help to better understand the ways of using the e-cigarette. This data infrastructure will also be potentially used in other epidemiological studies in public health.

关键词： Smart phones Cancer Big data Cloud computing Mobile communication Servers Public healthcare

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Building an IoT Framework for Connected Dairy

Building an IoT Framework for Connected Dairy

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IEEE International Conference on Big data Computing Service and Applications (BigdataService)

作者： Anitha Ilapakurti Chandrasekar Vuppalapati IoT & Data Analytics Group Hanumayamma Innovations and Technologies Inc. Fremont CA Computer Engineering Department San Jose State University San Jose CA

Heat stress (HS) causes cows to produce less milk with the same nutritional input, which effectively increases farmers' production costs. The economic toll due to higher-temperature, heat stress is a $1 billion annual problems. Not only in the United States, but also around the globe heat stress causes an adverse impact on dairy productivity. The opportunities, however, for the dairy industry is to electronically monitor cattle temperature and implement appropriate measures so that the impact of HS can be minimized. The U.S. Department of Agriculture estimates nearly $2.4 billion a year in losses from animal illnesses that lead to death can be prevented by electronically checking on cattle's' vital signs. This research paper recommends the most innovative electronic monitor framework, the 'Smart Connected Objects", aka, 'the Internet of Things (IoT)', that enables dairies to minimize the economic impact of HS and, at the same, capture the higher Return on Assets (ROA) & Return on Investment (ROI) by improving operational efficiencies. Happy Cow, more importantly, means happier, more profitable, dairy industry and richer and creamer dairy products. The proposed framework supports both offline and online dairy IoT. This paper presents a prototyping solution design as well as its application and certain experimental results.

关键词： Bluetooth Cows Temperature sensors Temperature measurement Decision trees Heating Stress

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