中央研究院 資訊科學研究所
"Singing voice correction using canonical time warping," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), April 2018.
Authors: Yin-Jyun Luo, Ming-Tso Chen, Tai-Shih Chi, and Li Su

Expressive singing voice correction is an appealing but challenging problem. A robust time-warping algorithm which synchronizes two singing recordings can provide a promising solution. We thereby propose to address the problem by canonical time warping (CTW) which aligns amateur singing recordings to professional ones. A new pitch contour is generated given the alignment information, and a pitch-corrected singing is synthesized back through the vocoder. The objective evaluation shows that CTW is robust against pitch-shifting and time-stretching effects, and the subjective test demonstrates that CTW prevails the other methods including DTW and the commercial auto-tuning software. Finally, we demonstrate the applicability of the proposed method in a practical, real-world scenario.
"Automatic music transcription leveraging generalized cepstral features and deep learning," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), April 2018.
Authors: Yu-Te Wu, Berlin Chen, and Li Su

Spectral features are limited in modeling musical signals with multiple concurrent pitches due to the challenge to suppress the interference over the harmonic peaks from one pitch to another. In this paper, we show that using multiple features represented in both the frequency and time domains with deep learning modeling can reduce such interference. These features are derived systematically from conventional pitch detection functions that relate to one another through the Fourier transform and a nonlinear scaling function. Neural networks modeled with these features outperform state-of-the-art methods while using less training data.
"Vocal melody extraction using patch-based CNN," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), April 2018.
Authors: Li Su

A patch-based convolutional neural network (CNN) model presented in this paper for vocal melody extraction in polyphonic music is inspired from object detection in image processing. The input of the model is a novel time-frequency representation which enhances the pitch contours and suppresses the harmonic components of a signal. This succinct data representation and the patch-based CNN model enable an efficient training process with limited labeled data. Experiments on various datasets show excellent speed and competitive accuracy comparing to other deep learning approaches.
"How sampling rate affects cross-domain transfer learning for video description," IEEE International Conference on Acoustics, Speech, and Signal Processing, April 2018.
Authors: Y. S. Chou, P. H. Hsiao, S. D. Lin, and H. Y. Mark Liao

Translating video to language is very challenging due to diversified video contents originated from multiple activities and complicated integration of spatio-temporal information. There are two urgent issues associated with the video-to-language translation problem. First, how to transfer knowledge learned from a more general dataset to a specific application domain dataset? Second, how to generate stable video captioning (or description) results under different sampling rates? In this paper, we propose a novel temporal embedding method to better retain temporal representation under different video sampling rates. We present a transfer learning method that combines a stacked LSTM encoder-decoder structure and a temporal embedding learning with soft-attention (TELSA) mechanism. We evaluate the proposed approach on two public datasets, including MSR-VTT and MSVD. The promising experimental results confirm the effectiveness of the proposed approach.
"Low precision deep learning training on mobile heterogeneous platform," 26th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP 2018), March 2018.
Authors: Olivier Valery, Pangfeng Liu, Jan-Jan Wu

Recent advances in System-on-Chip architectures have made the use of deep learning suitable for a number of applications on mobile devices. Unfortunately, due to the computational cost of neural network training, it is often limited to inference task, e.g., prediction, on mobile devices. In this paper, we propose a deep learning framework that enables both deep learning training and inference tasks on mobile devices. While being able to accommodate with the heterogeneity of computing devices technology on mobile devices, it also uses OpenCL to efficiently leverages modern SoC capabilities, e.g., multi-core CPU, integrated GPU and shared memory architecture, and accelerates deep learning computation. In addition, our system encodes the arithmetic operations of deep networks down to 8-bit fixed-point on mobile devices. As a proof of concept, we trained three well-known neural networks on mobile devices and exhibits a significant performance gain, energy consumption reduction, and memory saving.
Current Research Results
"Workload Prediction and Balance for Distributed Reachability Processing for Attribute Graphs," Concurrency and Computation: Practice and Experience, To Appear.
Authors: Li-Yung Ho, Jan-Jan Wu, Pangfeng Liu

Reachability query with label constraint in an attribute graph is one of the most fundamental and important operations in semantic network analysis. However, ever-growing graph size has resulted in intractable reachability problems on single machines. This work aims to devise efficient solutions for the reachability with label constraint problem in an attribute graph in a distributed environment. We focus on two issues in distributed processing data locality workload balancing since data locality reduces communication overhead and workload balancing improves the efficiency of cluster use. We propose three novel techniques to address the two issues: (1) a partition replication method that improves data locality while conserving community property, (2) a workload-prediction method that accurately predicts machine workloads for a given quer, and (3) a workload balancing method that uses these predictions to shift partial workloads among machines to produce a balanced workload. Experimental results suggest that these techniques significantly improve performance and reduce total execution time by 40%.
Current Research Results
"Automatic Image Cropping for Visual Aesthetic Enhancement Using Deep Neural Networks and Cascaded Regression," IEEE Transactions on Multimedia, To Appear.
Authors: Guanjun Guo, Hanzi Wang, Chunhua Shen, Yan Yan, and Hong-Yuan Mark Liao

Despite recent progress, computational visual aesthetic is still challenging. Image cropping, which refers to the removal of unwanted scene areas, is an important step to improve the aesthetic quality of an image. However, it is challenging to evaluate whether cropping leads to aesthetically pleasing results because the assessment is typically subjective. In this paper, we propose a novel cascaded cropping regression (CCR) method to perform image cropping by learning the knowledge from professional photographers. The proposed CCR method improves the convergence speed of the cascaded method, which directly uses random-ferns regressors. In addition, a two-step learning strategy is proposed and used in the CCR method to address the problem of lacking labelled cropping data. Specifically, a deep convolutional neural network (CNN) classifier is first trained on large-scale visual aesthetic datasets. The deep CNN model is then designed to extract features from several image cropping datasets, upon which the cropping bounding boxes are predicted by the proposed CCR method. Experimental results on public image cropping datasets demonstrate that the proposed ethod significantly outperforms several state-of-the-art image cropping methods
Authors: Peng-Hsuan Li, Ruo-Ping Dong, Yu-SiangWang, Ju-Chieh Chou, Wei-Yun Ma

Wei-YunMaPeng-Hsuan LiAbstract:
In this paper, we utilize the linguistic structures of texts to improve named entity recognition by BRNN-CNN, a special bidirectional recursive network attached with a convolutional network. Motivated by the observation that named entities are highly related to linguistic constituents, we propose a constituent-based BRNN-CNN for named entity recognition. In contrast to classical sequential labeling methods, the system first identifies which text chunks are possible named entities by whether they are linguistic constituents. Then it classifies these chunks with a constituency tree structure by recursively propagating syntactic and semantic information to each constituent node. This method surpasses current state-of-the-art on OntoNotes 5.0 with automatically generated parses.
Current Research Results
Authors: Cheng-Te Li, Yu-Jen Lin, and Mi-Yen Yeh

Social networking services allow users to adopt and spread information via diffusion actions, e.g., share, retweet, and reply. Real applications such as viral marketing and trending topic detection rely on information diffusion. Given past items with diffusion records on a social network, this paper aims at forecasting who will participate in the diffusion of a new item c (we use hashtags in the paper) with its k earliest adopters, without using content and profile information, i.e., finding which users will adopt c in the future. We define the Diffusion Participation Forecasting (DPF) problem, which is challenging since all users except for early adopters can be the candidates, comparing to existing studies that predict which one-layer followers will adopt a new hashtag given past diffusion observations with content and profile info. To solve the DFP problem, we propose an Adoption-based Participation Ranking (APR) model, which aims to rank the actual participants in reality at higher positions. The first is to estimate the adoption probability of a new hashtag for each user while the second is a random walk-based model that incorporates nodes with higher adoption probability values and early adopters to generate the forecasted participants. Experiments conducted on Twitter exhibit that our model can significantly outperform several competing methods in terms of Precision and Recall. Moreover, we demonstrate that an accurate DPF can be applied for effective targeted marketing using influence maximization and boosting the accuracy of popularity prediction in social media.
Current Research Results
"Non-overlapping Subsequence Matching of Stream Synopses," IEEE Tans. on Knowledge and Data Mining, January 2018.
Authors: Su-Chen Lin, Mi-Yen Yeh, and Ming-Syan Chen

In this paper, we propose SUbsequence Matching framework with cell MERgence (SUMMER) for online subsequence matching between histogram-based stream synopsis structures under the dynamic time warping distance. Given a query synopsis pattern, SUMMER continuously identifies all the matching subsequences for a stream as the bins are generated. To effectively reduce the computation time, we design a Weighted Dynamic Time Warping (WDTW) algorithm, which computes the warping distance directly between two histogram-based synopses. Furthermore, a Stack-based Overlapping Filter Algorithm (SOFA) is provided to remove the overlapping subsequences to avoid the redundant information. Finally, we design an optional refinement module to relax the subsequence range limit and improve the matching accuracy. Our experiments on real datasets show that the proposed method significantly speeds up the pattern matching without compromising the accuracy required when compared with other approaches.
"PRUNE: Preserving Proximity and Global Ranking for Node Embedding," The 31st Annual Conference on Neural Information Processing Systems (NIPS-2017), December 2017.
Authors: Yi-An Lai, Chin-Chi Hsu, Chin-Chi Hsu, Mi-Yen Yeh, and Shou-De Lin

We investigate an unsupervised generative approach for network embedding. A multi-task Siamese neural network structure is formulated to connect embedding vectors and our objective to preserve the global node ranking and local proximity of nodes. We provide deeper analysis to connect the proposed proximity objective to link prediction and community detection in the network. We show our model can satisfy the following design properties: scalability, asymmetry, unity and simplicity. Experiment results not only verify the above design properties but also demonstrate the superior performance in learning-to-rank, classification, regression, and link prediction tasks.
Current Research Results
Authors: Wai-Kok Choong, T. Mamie Lih, Yu-Ju Chen, Ting-Yi Sung

To confirm the existence of missing proteins, we need to identify at least two unique peptides with length of 9–40 amino acids of a missing protein in bottom-up mass-spectrometry-based proteomic experiments. However, an identified unique peptide of the missing protein, even identified with high level of confidence, could possibly coincide with a peptide of a commonly observed protein due to isobaric substitutions, mass modifications, alternative splice isoforms, or single amino acid variants (SAAVs). Besides unique peptides of missing proteins, identified variant peptides (SAAV-containing peptides) could also alternatively map to peptides of other proteins due to the aforementioned issues. Therefore, we conducted a thorough comparative analysis on data sets in PeptideAtlas Tiered Human Integrated Search Proteome (THISP, 2017-03 release), including neXtProt (2017-01 release), to systematically investigate the possibility of unique peptides in missing proteins (PE2–4), unique peptides in dubious proteins, and variant peptides affected by isobaric substitutions, causing doubtful identification results. In this study, we considered 11 isobaric substitutions. From our analysis, we found <5% of the unique peptides of missing proteins and >6% of variant peptides became shared with peptides of PE1 proteins after isobaric substitutions.
Current Research Results
Authors: Hui-Yin Chang, Ching-Tai Chen, Chu-Ling Ko, Yi-Ju Chen, Yu-Ju Chen, Wen-Lian Hsu, Chiun-Gung Juo, Ting-Yi Sung

Top-down proteomics using liquid chromatogram coupled with mass spectrometry has been increasingly applied for analyzing intact proteins to study genetic variation, alternative splicing, and post-translational modifications (PTMs) of the proteins (proteoforms). However, only a few tools have been developed for charge state deconvolution, monoisotopic/average molecular weight determination and quantitation of proteoforms from LC-MS1 spectra. Though Decon2LS and MASH Suite Pro have been available to provide intra-spectrum charge state deconvolution and quantitation, manual processing is still required to quantify proteoforms across multiple MS1 spectra. An automated tool for inter-spectrum quantitation is a pressing need. Thus in this paper, we present a user-friendly tool, called iTop-Q (intelligent Top-down Proteomics Quantitation), that automatically performs large-scale proteoform quantitation based on inter-spectrum abundance in top-down proteomics. Instead of utilizing single spectrum for proteoform quantitation, iTop-Q constructs extracted ion chromatograms (XICs) of possible proteoform peaks across adjacent MS1 spectra to calculate abundances for accurate quantitation. Notably, iTop-Q is implemented with a newly proposed algorithm, called DYAMOND, using dynamic programming for charge state deconvolution. In addition, iTop-Q performs proteoform alignment to support quantitation analysis across replicates/samples. The performance evaluations on an in-house standard data set and a public large-scale yeast lysate data set show that iTop-Q achieves highly accurate quantitation, more consistent quantitation than using intra-spectrum quantitation. Furthermore, the DYAMOND algorithm is suitable for high charge state deconvolution and can distinguish shared peaks in co-eluting proteoforms. iTop-Q is publicly available for download at http://ms.iis.sinica.edu.tw/COmics/Software_iTop-Q.
"Aesthetic Critiques Generation for Photos," International Conference on Computer Vision, ICCV 2017, October 2017.
Authors: Kuang-Yu Chang, Kung-Hung Lu, and Chu-Song Chen

It is said that a picture is worth a thousand words. Thus, there are various ways to describe an image, especially in aesthetic quality analysis. Although aesthetic quality assessment has generated a great deal of interest in the last decade, most studies focus on providing a quality rating of good or bad for an image. In this work, we extend the task to produce captions related to photo aesthetics and/or photography skills. To the best of our knowledge, this is the first study that deals with aesthetics captioning instead of AQ scoring. In contrast to common image captioning tasks that depict the objects or their relations in a picture, our approach can select a particular aesthetics aspect and generate captions with respect to the aspect chosen. Meanwhile, the proposed aspect-fusion method further uses an attention mechanism to generate more abundant aesthetics captions. We also introduce a new dataset for aesthetics captioning called the Photo Critique Captioning Dataset (PCCD), which contains pair-wise image-comment data from professional photographers. The results of experiments on PCCD demonstrate that our approaches outperform existing methods for generating aesthetic-oriented captions for images.
Current Research Results
Authors: Ng, I.M. , Huang, J.H., Tsai, S.C., and Tsai, H.K.*


Alternative splicing (AS), a mechanism by which different forms of mature messenger RNAs (mRNAs) are generated from the same gene, widely occurs in the metazoan genomes. Knowledge about isoform variants and abundance is crucial for understanding the functional context in the molecular diversity of the species. With increasing transcriptome data of model and non-model species, a database for visualization and comparison of AS events with up-to-date information is needed for further research. IsoPlot is a publicly available database with visualization tools for exploration of AS events, including three major species of mosquitoes, Aedes aegyptiAnopheles gambiae, and Culex quinquefasciatus, and fruit fly Drosophila melanogaster, the model insect species. IsoPlot includes not only 88,663 annotated transcripts but also 17,037 newly predicted transcripts from massive transcriptome data at different developmental stages of mosquitoes. The web interface enables users to explore the patterns and abundance of isoforms in different experimental conditions as well as cross-species sequence comparison of orthologous transcripts. IsoPlot provides a platform for researchers to access comprehensive information about AS events in mosquitoes and fruit fly. Our database is available on the web via an interactive user interface with an intuitive graphical design, which is applicable for the comparison of complex isoforms within or between species.

Database URLhttp://isoplot.iis.sinica.edu.tw/

Current Research Results
Authors: Hsin-Nan Lin and Wen-Lian Hsu

Motivation: Next-generation sequencing (NGS) provides a great opportunity to investigate genome-wide variation at nucleotide resolution. Due to the huge amount of data, NGS applications require very fast and accurate alignment algorithms. Most existing algorithms for read mapping basically adopt seed-and-extend strategy, which is sequential in nature and takes much longer time on longer reads. Results: We develop a divide-and-conquer algorithm, called Kart, which can process long reads as fast as short reads by dividing a read into small fragments that can be aligned independently. Our experiment result indicates that the average size of fragments requiring the more time-consuming gapped alignment is around 20 bp regardless of the original read length. Furthermore, it can tolerate much higher error rates. The experiments show that Kart spends much less time on longer reads than other aligners and still produce reliable alignments even when the error rate is as high as 15%.
Current Research Results
Authors: Yi-Ting Wang, Szu-Hua Pan, Chia-Feng Tsai, Ting-Chun Kuo, Yuan-Ling Hsu, Hsin-Yung Yen, Wai-Kok Choong, Hsin-Yi Wu, Yen-Chen Liao, Tse-Ming Hong, Ting-Yi Sung, Pan-Chyr Yang, and Yu-Ju Chen

Although EGFR tyrosine kinase inhibitors (TKIs) have demonstrated good efficacy in non-small-cell lung cancer (NSCLC) patients harboring EGFR mutations, most patients develop intrinsic and acquired resistance. We quantitatively profiled the phosphoproteome and proteome of drug-sensitive and drug-resistant NSCLC cells under gefitinib treatment. The construction of a dose-dependent responsive kinase-substrate network of 1548 phosphoproteins and 3834 proteins revealed CK2-centric modules as the dominant core network for the potential gefitinib resistance-associated proteins. CK2 knockdown decreased cell survival in gefitinib-resistant NSCLCs. Using motif analysis to identify the CK2 core sub-network, we verified that elevated phosphorylation level of a CK2 substrate, HMGA1 was a critical node contributing to EGFR-TKI resistance in NSCLC cell. Both HMGA1 knockdown or mutation of the CK2 phosphorylation site, S102, of HMGA1 reinforced the efficacy of gefitinib in resistant NSCLC cells through reactivation of the downstream signaling of EGFR. Our results delineate the TKI resistance-associated kinase-substrate network, suggesting a potential therapeutic strategy for overcoming TKI-induced resistance in NSCLC.
Current Research Results
"ADF: an Anomaly Detection Framework for Large-scale PM2.5 Sensing Systems," IEEE Internet of Things Journal, To Appear.
Authors: Ling-Jyh Chen, Yao-Hua Ho, Hsin-Hung Hsieh, Shih-Ting Huang, Hu-Cheng Lee, and Sachit Mahajan

As the population density continues to grow in the urban settings, air quality is degrading and becoming a serious issue. Air pollution, especially fine particulate matter (PM2.5), has raised a series of concerns for public health. As a result, a number of large-scale, low cost PM2.5 monitoring systems have been deployed in several international smart city projects. One of the major challenges for such environmental sensing systems is ensuring the data quality. In this paper, we propose an Anomaly Detection Framework (ADF) for large-scale, real-world environmental sensing systems. The framework is comprised of four modules: 1) Time-Sliced Anomaly Detection (TSAD), which detects Spatial, Temporal, and Spatio-temporal anomalies in the real-time sensor measurement data stream; 2) Real-time Emission Detection (RED), which detects potential regional emission sources; 3) Device Ranking (DR), which provides a ranking for each sensing device; and 4) Malfunction Detection (MD), which identifies malfunctioning devices. Using real world measurement data from the AirBox project, we demonstrate that the proposed framework can effectively identify outliers in the raw measurement data as well as infer anomalous events that are perceivable by the general public and government authorities. Because of its simple design, ADF is highly extensible to other advanced applications, and it can be exploited to support various large-scale environmental sensing systems.
Current Research Results
Authors: Shu-Hwa Chen, Wen-Yu Kuo, Sheng-Yao Su, Wei-Chun Chung, Jen-Ming Ho, Henry Horng-Shing Lu, Chung-Yen Lin

A new emerged cancer treatment utilizes intrinsic immune surveillance mechanism that is silenced by those malicious cells. Hence, studies of tumor infiltrating lymphocyte populations (TILs) are key to the success of advanced treatments. In addition to laboratory methods such as immunohistochemistry and flow cytometry, in silico gene expression deconvolution methods are available for analyses of relative proportions of immune cell types.
Herein, we used microarray data from the public domain to profile gene expression pattern of twenty-two immune cell types. Initially, outliers were detected based on the consistency of gene profiling clustering results and the original cell phenotype notation. Subsequently, we filtered out genes that are expressed in non-hematopoietic normal tissues and cancer cells. For every pair of immune cell types, we ran t-tests for each gene, and defined differentially expressed genes (DEGs) from this comparison. Equal numbers of DEGs were then collected as candidate lists and numbers of conditions and minimal values for building signature matrixes were calculated. Finally, we used 𝛎-Support Vector Regression to construct a deconvolution model. The performance of our system was finally evaluated using blood biopsies from 20 adults, in which 9 immune cell types were identified using flow cytometry. The present computations performed better than current state-of-the-art deconvolution methods.
Finally, we implemented the proposed method into R and tested extensibility and usability on Windows, MacOS, and Linux operating systems. The method, MySort, is wrapped as the Galaxy platform pluggable tool and usage details are available at https://testtoolshed.g2.bx.psu.edu/view/moneycat/mysort/e3afe097e80a.
"Identifying Protein-protein Interactions in Biomedical Literature using Recurrent Neural Networks with Long Short-Term Memory," The 8th International Joint Conference on Natural Language Processing (IJCNLP 2017), November 2017.
Authors: Yu-Lun Hsieh, Yung-Chun Chang, Nai-Wen Chang and Wen-Lian Hsu

Accurate identification of protein-protein interaction (PPI) helps biomedical researchers to quickly capture crucial information in literatures. This work proposes a recurrent neural network (RNN) model to identify PPIs. Experiments on two largest public benchmark datasets, AIMed and BioInfer, demonstrate that RNN outperforms state-of-the-art methods with relative improvements of 10% and 18%, respectively. Cross-corpus evaluation also indicates that RNN is robust even when trained on data from different domains. These results suggest that RNN effectively captures semantic relationships among proteins without any feature engineering.
Current Research Results
Authors: Liu, W.H., Tsai, Z. T., and Tsai, H. K.*

The regulatory roles of long intergenic noncoding RNAs (lincRNAs) in humans have been revealed through the use of advanced sequencing technology. Recently, three possible scenarios of lincRNA origins have been proposed: de novo origination from intergenic regions, duplication from other long noncoding RNAs, and pseudogenization from protein-coding genes. The first two scenarios are largely studied and supported, yet few studies focused on the evolution from pseudogenized protein-coding sequence to lincRNA. Due to the non-mutually exclusive nature of these three scenarios and the need of systematic investigation of lincRNA origination, we conducted a comparative genomics study to investigate the evolution of human lincRNAs. Results
Combining with syntenic analysis and stringent Blastn e-value cutoff, we found that the majority of lincRNAs are aligned to intergenic regions of other species. Interestingly, 193 human lincRNAs could have protein-coding orthologs in at least two of nine vertebrates. Transposable elements in these conserved regions in human genome are much less than expectation. Moreover, 19% of these lincRNAs have overlaps with or are close to pseudogenes in the human genome. Conclusions
We suggest that a notable portion of lincRNAs could be derived from pseudogenized protein-coding genes. Furthermore, based on our computational analysis, we hypothesize that a subset of these lincRNAs could have potential to regulate their paralogs by functioning as competing endogenous RNAs. Our results provide evolutionary evidence of the relationship between human lincRNAs and protein-coding genes.
Current Research Results
Authors: Ming-Chin Chuang, Meng Chang Chen

Meng ChangChenAbstract:
In the vehicular network, the smart car provides variously network services via user equipment (UE). However, the services might be interrupted since the handover procedure of the UE is performed frequently. The existing handover decision schemes in cellular networks are mostly based on received signal strength (RSS) to make the handover decision. However, these schemes suffer from a ping-pong effect and, as a result, perform unnecessary handovers incurring extra signaling overhead and packet loss, which is even more exaggerated in the ultra-dense environment. The failure probability of the handover procedure will increase when the velocity of the car is fast. Therefore, in this paper we propose a navigation-assisted seamless handover (NASH) scheme for reducing unnecessary handovers and the ping-pong effect. NASH uses the massive Multiple Input Multiple Output (MIMO) technique to perform the bicasting scheme and then it utilizes coordination multi-point (CoMP) and carrier aggregation (CA) mechanisms in the handover procedure to avoid the packet loss problem and enhance throughput. Moreover, a dynamic time to trigger (TTT) mechanism is proposed according to the velocity of the car. The simulation results show that the proposed scheme has a better performance in terms of handover latency, packet loss, network throughput, handover failure probability, and the number of unnecessary handovers.
"Accurate audio-to-score alignment for expressive violin recordings," International Society of Music Information Retrieval Conference (ISMIR), October 2017.
Authors: Jia-Ling Syue, Li Su, Yi-Ju Lin, Yen-Kuang Lu, Yu-Lin Wang and Alvin W. Y. Su

An audio-to-score alignment system adaptive to various playing styles and techniques, and also with high accuracy for onset/offset annotation is the key step toward advanced research on automatic music expression analysis. Technical barriers include the processing of overlapped notes, repeated note sequences, and silence. Most of these characteristics vary with expressions. In this paper, the audio-to-score alignment problem of expressive violin performance is addressed. We propose a two-stage alignment system composed of the dynamic time warping (DTW) algorithm, simulation of overlapped sustain notes, background noise model, silence detection, and refinement process, to better capture the onset. More importantly, we utilize the non-negative matrix factorization (NMF) method for synthesis of the reference signal in order to deal with highly diverse timbre in real-world performance. A dataset of annotated expressive violin recordings in which each piece is played with various expressive musical terms is used. The optimal choice of basic parameters considered in conventional alignment systems, such as features, distance functions in DTW, synthesis methods for the reference signal, and energy ratios, is analyzed. Different settings on different expressions are compared and discussed. Results show that the proposed methods notably improve the conventional DTW-based alignment method.
Current Research Results
"Building/environment Data/information Enabled Location Specificity and Indoor Positioning," IEEE Internet of Things Journal, September 2017.
Authors: C. C. Li, J. Su, E. T. H. Chu and J. W. S. Liu

Jane Win ShihLiuAbstract:

The building/environment data and information system (BeDIS) described here is a part of infrastructure needed to support location-specific, active emergency preparedness and responses within large buildings. BeDIPS (Building/environment Data and information based Indoor Positioning System) is one of its components. BeDIPS can provide people in large buildings with sufficiently accurate location data. It is scalable, disaster resilient and is easy to configure, deploy and maintain. BeDIPS works without Internet and serves both smart phones and most legacy Bluetooth devices. The other component of BeDIS is BeDi mist, a virtual repository of data and information on the building, interior layouts and facilities. The mist uses micro data servers and smart gateways to deliver fine-scale, location-specific decision support data on a timely basis to hundreds and thousands of active devices and mobile applications.

Current Research Results
"A Position-Aware Language Modeling Framework for Extractive Broadcast News Speech Summarization," ACM Transactions on Asian and Low-Resource Language Information Processing, August 2017.
Authors: Shih-Hung Liu, Kuan-Yu Chen, Yu-Lun Hsieh, Berlin Chen, Hsin-Min Wang, Hsu-Chun Yen, and Wen-Lian Hsu

Extractive summarization, a process that automatically picks exemplary sentences from a text (or spoken) document with the goal of concisely conveying key information therein, has seen a surge of attention from scholars and practitioners recently. Using a language modeling (LM) approach for sentence selection has been proven effective for performing unsupervised extractive summarization. However, one of the major difficulties facing the LM approach is to model sentences and estimate their parameters more accurately for each text (or spoken) document. We extend this line of research and make the following contributions in this work. First, we propose a position-aware language modeling framework using various granularities of position-specific information to better estimate the sentence models involved in the summarization process. Second, we explore disparate ways to integrate the positional cues into relevance models through a pseudo-relevance feedback procedure. Third, we extensively evaluate various models originated from our proposed framework and several well-established unsupervised methods. Empirical evaluation conducted on a broadcast news summarization task further demonstrates performance merits of the proposed summarization methods.
Current Research Results
Authors: Ling-Jyh Chen, Yao-Hua Ho, Hu-Cheng Lee, Hsuan-Cho Wu, Hao-Min Liu, Hsin-Hung Hsieh, Yu-Te Huang, and Shih-Chun Candice Lung

As the population in cities continues to increase rapidly, air pollution become a serious issue from public health to social economy.  Among all pollutants, fine particulate matters (PM2.5) directly related to various serious health concerns, e.g., lung cancer, premature death, asthma, cardiovascular and respiratory diseases. To enhance the quality of urban living, sensors are deployed to create smart cities. In this paper, we present a participatory urban sensing framework for PM2.5 monitoring with more than 2,500 devices deployed in Taiwan and 29 other countries.  It is one of the largest deployment project for PM2.5 monitor in the world as we know until May 2017. The key feature of the framework is its open system architecture, which is based on the principles of open hardware, open source software, and open data. To facilitate the deployment of the framework, we investigate the accuracy issue of low-cost particle sensors with a comprehensive set of comparison evaluations to identify the most reliable sensor. By working closely with government authorities, industry partners, and maker communities, we can construct an effective eco-system for participatory urban sensing of PM2.5 particles. Based on our deployment achievements to date, we provide a number of data services to improve environmental awareness, trigger on-demand responses, and assist future government policymaking. The proposed framework is highly scalable and sustainable with the potential to facilitate the Internet of Things, smart cities and citizen science in the future.