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Peter A. Gloor

Bio: Peter A. Gloor is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Social network analysis & Social network. The author has an hindex of 37, co-authored 211 publications receiving 4918 citations. Previous affiliations of Peter A. Gloor include University of Cologne & Union Bank of Switzerland.


Papers
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Journal Article
TL;DR: For example, the authors found that creative people work more effectively when they have strong leaders, and that customer satisfaction was higher when a few designated leaders communicated regularly with customers, rather than having a large number of employees communicate with the customer.
Abstract: By sifting through data from such sources as email archives, Twitter feeds, and Facebook group pages, managers can in fact learn a lot about how to manage these and other areas of their organizations. They can then use this information to generate better results. By studying data from various sources (such as email archives, tweets, and blog links), social network researchers have identified several indicators of how effective collaborative communication works. The indicators can guide managers in decisions about how groups should be organized and led, recommended levels of participation for group members, how quickly members should be expected to respond, the tone of the language that team members should use, and how technical the language should be. The research found that creative people work more effectively when they have strong leaders. For account management teams at a global services company, researchers found that customer satisfaction was higher when a few designated leaders communicated regularly with customers, rather than having a large number of employees communicate with the customer.

16 citations

Journal ArticleDOI
TL;DR: In this paper, the authors analyzed the communication at Swiss House for Advanced Research and Technology (now Consulate of Switzerland/Swissnex Boston; formerly SHARE), an organisation whose mission is to foster collaboration networks between the scientific and entrepreneurial communities of Switzerland and the greater Boston area.
Abstract: This paper contributes to measuring the efficiency of business social networking events and organisations. In particular, we analysed the communication at Swiss House for Advanced Research and Technology (now Consulate of Switzerland/Swissnex Boston; formerly SHARE), an organisation whose mission is to foster collaboration networks between the scientific and entrepreneurial communities of Switzerland and the greater Boston area. The study consists of two parts. In the first part, SHARE's social network growth over more than a year was measured through an analysis of its e-mail traffic. In the second part, growth of social networks of individuals participating in a set of networking events during a collaboration programme over one week was measured through a web survey. Comparing individual social network growth through attendance and individual follow-up at events organised in Boston and San Francisco demonstrated creation of a much denser network in Boston – with an almost even split between academic and industrial participants in Boston, while the majority of participants in the Silicon Valley came from industry. Boston's academic participants acted as information brokers, building bridges between industrial participants from Boston and Switzerland.

15 citations

Patent
06 Mar 2007
TL;DR: In this paper, a method and system is disclosed for producing a combined analysis and visualization that calculates both the ties between actors based on a weighted average of the number of messages exchanged and the weighted number of common terms in the messages exchanged.
Abstract: A method and system is disclosed for producing a combined analysis and visualization that calculates both the ties between actors based on a weighted average of the number of messages exchanged and the weighted number of common terms in the messages exchanged. In addition, another link is calculated based on the number of common terms collected from all of the messages sent by either of the two actors to or from any other actor. Within this framework, a link weight is defined ranging from 0 to 1 and a term weight is inversely defined ranging from 1 to 0. As this weighting value is changed, the visualization is dynamically shifted between placing emphasis on common communication links or common terms.

15 citations

Journal ArticleDOI
TL;DR: In this paper, the authors investigated how team coordination mechanisms can influence the likelihood of surgical glitches during routine surgery in a large Italian university hospital and found that team cohesion appears positively related with the surgical performance.
Abstract: This study investigates the relationships between team dynamics and performance in healthcare operations. Specifically, it explores, through wearable sensors, how team coordination mechanisms can influence the likelihood of surgical glitches during routine surgery.,Breast surgeries of a large Italian university hospital were monitored using Sociometric Badges – wearable sensors developed at MIT Media Lab – for collecting objective and systematic measures of individual and group behaviors in real time. Data retrieved were used to analyze team coordination mechanisms, as it evolved in the real settings, and finally to test the research hypotheses.,Findings highlight that a relevant portion of glitches in routine surgery is caused by improper team coordination practices. In particular, results show that the likelihood of glitches decreases when practitioners adopt implicit coordination mechanisms rather than explicit ones. In addition, team cohesion appears to be positively related with the surgical performance.,For the first time, direct, objective and real time measurements of team behaviors have enabled an in-depth evaluation of the team coordination mechanisms in surgery and the impact on surgical glitches. From a methodological perspective, this research also represents an early attempt to investigate coordination behaviors in dynamic and complex operating environments using wearable sensor tools.

14 citations

Journal ArticleDOI
TL;DR: The results reveal that the proposed approaches for classifying emotions from speech by combining conventional mel-frequency cepstral coefficients (MFCCs) with image features extracted from spectrograms by a pretrained convolutional neural network (CNN) lead to an improvement in prediction accuracy.
Abstract: Speech is one of the most natural communication channels for expressing human emotions. Therefore, speech emotion recognition (SER) has been an active area of research with an extensive range of applications that can be found in several domains, such as biomedical diagnostics in healthcare and human–machine interactions. Recent works in SER have been focused on end-to-end deep neural networks (DNNs). However, the scarcity of emotion-labeled speech datasets inhibits the full potential of training a deep network from scratch. In this paper, we propose new approaches for classifying emotions from speech by combining conventional mel-frequency cepstral coefficients (MFCCs) with image features extracted from spectrograms by a pretrained convolutional neural network (CNN). Unlike prior studies that employ end-to-end DNNs, our methods eliminate the resource-intensive network training process. By using the best prediction model obtained, we also build an SER application that predicts emotions in real time. Among the proposed methods, the hybrid feature set fed into a support vector machine (SVM) achieves an accuracy of 0.713 in a 6-class prediction problem evaluated on the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset, which is higher than the previously published results. Interestingly, MFCCs taken as unique input into a long short-term memory (LSTM) network achieve a slightly higher accuracy of 0.735. Our results reveal that the proposed approaches lead to an improvement in prediction accuracy. The empirical findings also demonstrate the effectiveness of using a pretrained CNN as an automatic feature extractor for the task of emotion prediction. Moreover, the success of the MFCC-LSTM model is evidence that, despite being conventional features, MFCCs can still outperform more sophisticated deep-learning feature sets.

14 citations


Cited by
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01 Jan 2016
TL;DR: The using multivariate statistics is universally compatible with any devices to read, allowing you to get the most less latency time to download any of the authors' books like this one.
Abstract: Thank you for downloading using multivariate statistics. As you may know, people have look hundreds times for their favorite novels like this using multivariate statistics, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some harmful bugs inside their laptop. using multivariate statistics is available in our digital library an online access to it is set as public so you can download it instantly. Our books collection saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the using multivariate statistics is universally compatible with any devices to read.

14,604 citations

Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

Journal ArticleDOI
06 Jun 1986-JAMA
TL;DR: The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or her own research.
Abstract: I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

7,563 citations

Book
01 Jan 1995
TL;DR: In this article, Nonaka and Takeuchi argue that Japanese firms are successful precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies, and they reveal how Japanese companies translate tacit to explicit knowledge.
Abstract: How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.

7,448 citations

Journal ArticleDOI
TL;DR: This work investigates whether measurements of collective mood states derived from large-scale Twitter feeds are correlated to the value of the Dow Jones Industrial Average (DJIA) over time and indicates that the accuracy of DJIA predictions can be significantly improved by the inclusion of specific public mood dimensions but not others.

4,453 citations