12,905 research outputs found
Fast DCTTS: Efficient Deep Convolutional Text-to-Speech
No full textWe propose an end-to-end speech synthesizer, Fast DCTTS, that synthesizes speech in real time on a single CPU thread. The proposed model is composed of a carefully-tuned lightweight network designed by applying multiple network reduction and fidelity improvement techniques. In addition, we propose a novel group highway activation that can compromise between computational efficiency and the regularization effect of the gating mechanism. As well, we introduce a new metric called elastic mel-cepstral distortion (EMCD) to measure the fidelity of the output mel-spectrogram. In experiments, we analyze the effect of the acceleration techniques on speed and speech quality. Compared with the baseline model, the proposed model exhibits improved MOS from 2.62 to 2.74 with only 1.76% computation and 2.75% parameters. The speed on a single CPU thread was improved by 7.45 times, which is fast enough to produce mel-spectrogram in real time without GPU
The Expansion of Virtual Landscape in Digital Games Classification of Virtual Landscapes through Five principles
No full textAuthor Correction: Evaluation of skin cancer resection guide using hyper‑realistic in‑vitro phantom fabricated by 3D printing
No full textThe original version of this Article contained an error in the spelling of the author Taehun Kim which was incorrectly given as Teahun Kim. The original Article has been corrected
Service placement for detecting and localizing failures Using End-to-End Observations
No full textWe consider the problem of placing services in a telecommunication network in the presence of failures. In
contrast to existing service placement algorithms that focus on optimizing the quality of service (QoS), we consider the performance of monitoring failures from end-to-end connection states between clients and servers, and investigate service placement algorithms that optimize the monitoring performance subject to
QoS constraints. Based on novel performance measures capturing the coverage, the identifiability, and the distinguishability in monitoring failures, we formulate the service placement problem as a set of combinatorial optimizations with these measures as objective functions. In particular, we show that maximizing the distinguishability is equivalent to minimizing the uncertainty in failure localization. We prove that all these optimizations are NP-hard. However, we show that the objectives of coverage and distinguishability have a desirable property that allows them to be approximated to a constant factor by a greedy algorithm.
We further show that while the identifiability objective does not have this property, it can be approximated by the maximumdistinguishability placement in the high-identifiability regime.
Our evaluations based on real network topologies verify the effectiveness of the proposed algorithms in improving the monitoring
performance compared with QoS-based service placement
Interactive Data Acquisition for CBR System Based Smart Home Assistant
No full textThis research aims to develop a Case-Based Reasoning (CBR) system that recommends services to users in IoT environment. To develop this system, we establish a framework that designs raw data into analyzable information using Function-Behavior-Structure properties. Also, we develop an interactive flow of data acquisition that builds up cases gradually by gathering data through conversational interactions between the system and its user. This research develop a prototype of this system based on simulated cases. Finally, the prototype of this system was evaluated by experts in the field of system design to verify how the service (solution) recommended by system is similar with them. The results of this evaluation showed an agreement of average 54%, but found that there was a big difference from the experts in the specific context. This result implies that it is necessary to improve the context awareness in the reasoning process of this system
DBLP-derived labeled data for author name disambiguation
No full textThis is a DBLP-derived labeled data originally created by Dr. C. Lee Giles at Penn State University and filtered for duplicate removal and error correction by Dr. Jinseok Kim at University of Michigan. For more details, see references below.1. Kim, Jinseok (2018). Evaluating author name disambiguation for digital libraries: a case of DBLP. Scientometrics. doi:10.1007/s11192-018-2824-5 2. Kim, Jinseok & Kim, Jenna (2018). The impact of imbalanced training data on machine learning for author name disambiguation. Scientometrics. doi: 10.1007/s11192-018-2865-9Each row refers to an author name instance with following feature information separated by tab.author name: full name string extracted from DBLPunique author id: labels assigned manually by Dr. C. Lee Giles's teampaper id: assigned by Dr. Jinseok Kimauthor list: names of authors in the byline of the paperyear: publication yearvenue: conference or journal namestitle: stopwords removed and stemmed by the Porter's stemmerIf you want to use this dataset, please consider to cite papers below.For the original dataset: Han, H., Giles, L., Zha, H., Li, C., & Tsioutsiouliklis, K. (2004). Two Supervised Learning Approaches for Name Disambiguation in Author Citations. JCDL 2004: Proceedings of the Fourth ACM/IEEE Joint Conference on Digital Libraries, 296-305. doi:10.1145/996350.996419For the filtered dataset: 1. Kim, Jinseok (2018). Evaluating author name disambiguation for digital libraries: a case of DBLP. Scientometrics. doi:10.1007/s11192-018-2824-5 or2. Kim, Jinseok & Kim, Jenna (2018). The impact of imbalanced training data on machine learning for author name disambiguation. Scientometrics. doi: 10.1007/s11192-018-2865-9</div
Khoo Kay Kim, professor of Malaysian history : a biobibliometric study
No full textPresents an analysis of the publication productivity, authorship pattern, channels of communication, journal preference and language preference of Professor Dato' Khoo Kay Kim, Professor of Malaysian History in the University of Malaya, Kuala Lumpur. The results of this biobibliometric study indicate that he can be a role model for future Malaysian historians to emulate his various achievements especially in the field of history education
Anxious, Dismal, Giddy, Aggressive: Seth Kim-Cohen interviewed by Mark Peter Wright for Ear Room.
No full textA conversation with author Seth Kim-Cohen
Kim Gordon - no icon
No full textAs cofounder of legendary rock band Sonic Youth, best-selling author, and celebrated artist, Kim Gordon is one of the most singular and influential figures of the modern era. This personally curated scrapbook is an edgy and evocative portrait of Gordon s life, art, and style. Spanning from her childhood on Californian surf beaches in the 60s and 70s to New York s downtown art and music scene in the 80s and 90s where Sonic Youth was born. Through unpublished personal photographs, magazine and newspaper clippings, fashion editorials, and advertising campaigns, interspersed with Gordon s song lyrics, writings, artworks, private objects, and ephemera, this book demonstrates how Kim Gordon has been a role model for generations of women and me
Overview of Recent Progress in Fire Suppression
No full textthis document is published in / Une version de ce document se trouve dans : Invited Keynote Lecture at the 2 nd NRIFD Symposium, Proceedings, Tokyo, Japan, July 17-19, 2002, pp. 1-13 www.nrc.ca/irc/ircpubs NRCC-45690 Title: OVERVIEW OF RECENT PROGRESS IN FIRE SUPPRESSION TECHNOLOGY Author(s): Andrew KIM Corresponding (first) author: Andrew Kim Academic degree: Ph.
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