Miracle-dz/UniFAR-FTUs huggingface dataset:
Sixth International Conference on Emerging trends in Engineering and Technology ( ICETET ' 16 )

The main objective of this paper is co-extracting opinion targets and opinion words by using a modified word alignment model. This process mainly used for bad comments removal from VIP facebook account. Nowadays facebook is one of the popular social networking used by everyone. The main purpose of the facebook is to share communicate with the whole friends at a time. But today facebook account is used to spread the bad thoughts and opinion in other facebook accounts. It may lead very serious problems and affect the people's privacy. So this paper focuses to identify the bad comments and remove that. To do this, the paper is to focus on detecting opinion relations between opinion targets and opinion words. To detect the opinion relations this project uses the word alignment model. This method captures opinion relations more precisely and therefore is more effective for opinion target and opinion word extraction. Next, construct an Opinion Relation Graph to model all candidates and the detected opinion relations among them, along with a graph co-ranking algorithm to estimate the confidence of each candidate. In this co-ranking algorithm the items with higher ranks are extracted. Finally the Semantic Labeling is used for finding the synonyms of the given word. This is also compared with bad words to block the comments. This method provides best result. To analyse the performance of the precision rate, recall rate, sensitivity, specificity and f-measure rate are used. The proposed method is compared with the several existing methods. From the comparison it is shown that the proposed method provides better performance than the existing methods.
Embedded Algorithm of Fingerprint Recognition Using Novel Approach Based on Euclidian Distance

Fingerprint is the technique to identify the identity of an individual. Fingerprint recognition system is the best technique for the security purpose, because of its unique characteristics like: core point, delta points, and bifurcation points for every individual. This paper presents a method for fingerprint recognition by using Novel Approach based on Euclidian Distance between core and minutiae points. Hong's enhancement is used to improve the quality of fingerprints and Otsu segmentation technique has been used to segment out the ridges and valleys from fingerprints. After segmenting out the ridges and valleys, feature have been extracted based on Euclidian distance. Artificial Neural Network is employed for classification and then the performance has been evaluated on the basis of Sensitivity, Specificity and Accuracy.
Applying human-centered design to the construction of a cirrhosis management clinical decision support system

Background: Electronic health record (EHR)-based clinical decision support is a scalable way to help standardize clinical care. Clinical decision support systems have not been extensively investigated in cirrhosis management. Human-centered design (HCD) is an approach that engages with potential users in intervention development. In this study, we applied HCD to design the features and interface for a clinical decision support system for cirrhosis management, called CirrhosisRx. Methods: We conducted technical feasibility assessments to construct a visual blueprint that outlines the basic features of the interface. We then convened collaborative-design workshops with generalist and specialist clinicians. We elicited current workflows for cirrhosis management, assessed gaps in existing EHR systems, evaluated potential features, and refined the design prototype for CirrhosisRx. At the conclusion of each workshop, we analyzed recordings and transcripts. Results: Workshop feedback showed that the aggregation of relevant clinical data into 6 cirrhosis decompensation domains (defined as common inpatient clinical scenarios) was the most important feature. Automatic inference of clinical events from EHR data, such as gastrointestinal bleeding from hemoglobin changes, was not accepted due to accuracy concerns. Visualizations for risk stratification scores were deemed not necessary. Lastly, the HCD co-design workshops allowed us to identify the target user population (generalists). Conclusions: This is one of the first applications of HCD to design the features and interface for an electronic intervention for cirrhosis management. The HCD process altered features, modified the design interface, and likely improved CirrhosisRx’s overall usability. The finalized design for CirrhosisRx proceeded to development and production and will be tested for effectiveness in a pragmatic randomized controlled trial. This work provides a model for the creation of other EHR-based interventions in hepatology care.
Flux-corrected transport for full-waveform inversion

S U M M A R Y Conventional full-waveform inversion (FWI) often fails to retrieve the unknown model parameters from noisy seismic data. A successful FWI implementation usually requires to follow a multistage recovery approach, starting from the retrieval of the lower model wavenumbers (tomography) to those with the higher resolutions (migration). Here, we propose a new method based on the flux-corrected transport (FCT) technique often used in computational fluid dynamics for the removal of instabilities in a shock profile. FCT involves three finite-difference steps: a transport, a diffusion and an antidiffusion process. This third step involves non-linear operators such as maximum and minimum, which are non-differentiable in a classic sense. However, since the seismic source wavelet and the corresponding wavefield are relatively smooth and continuous in nature without any strong ripples like shock waves, we exclude the non-linear step from FCT, which allows us to evaluate the novel FWI gradient efficiently. As a result, we achieve a converging FWI model by gradually reducing the diffusive fluxcorrection. We demonstrate the versatility of FCT-based FWI on a noisy synthetic data set from the Marmousi II model and a marine field data set from offshore Australia.
Rapid northward shift of the Indian Monsoon on the Tibetan Plateau at the end of the Little Ice Age

Variations in the Indian Monsoon (IM) and Westerlies (WS) significantly affect the climate on the Tibetan Plateau (TP) and have widespread ecological and socioeconomic impacts on the whole of Asian society. So far, however, the rate and magnitude of changes in the IM have still remained unclear. Here we report for the first time that the IM rapidly shifted northward at the end of the Little Ice Age (LIA). We used sediment proxies for humidity and moisture sources from the Taro Co Lake, which is located in the transition zone between the WS and IM. Our comprehensive survey of climate records for the TP and its peripheral mountain ranges revealed that the northern boundary of the IM (i.e., the southern boundary of the WS) lay along the southern slope of the Gandise Range (~29.5°N) in the late LIA. In contrast, it passed quickly over the Gandise Range by at least 1.5° in latitude at the end of the LIA. Our results suggest that this rapid climatic shift was potentially triggered by the counteracting effects of blocking by the TP and its marginal orography, which hindered the northward movement of the IM, and the pulling thermal gradient of the TP.
3D Face recognition by ICP-based shape matching

In this paper, we propose a novel face recognition approach based on 2.5D/3D shape matching. While most of existing methods use facial intensity image, we aim to develop a method using three-dimensional information of the human face. This is the main innovation of our technology. In our approach, the 3D dimensional information is introduced in order to overcome classical face recognition problems which are pose, illumination and facial expression variations. The paradigm is to build a 3D face gallery using a laser-based scanner: the off-line stage. In the on-line stage, the recognition, we capture one 2.5D face model at any view point and with any facial expressions. Our processing allows the identification of the presented person by performing the captured model with all faces from the database. Here, the Iterative Closest Point-based matching algorithm provides the pose of the probe whereas the region-based metric provides a spatial deviation between the probe and each face from the gallery. In this metric, we calculate the global recognition score as a weighted sum of region-based distances already labelled as mimic or static regions. For automatic 3D face segmentation, we use an immersion version of watershed segmentation algorithm. This paper also presents some experiments in order to shown illumination, pose and facial expression compensations.
New challenges in CSCL: towards adaptive script support

Scaffolding learners, i.e. helping learners to attain tasks they could not accomplish without support, entails the notion of fading, i.e. reducing the scaffolding for learners to become more and more self-regulated. Fading implies to tailor support for collaboration, such as collaboration scripts, to the particular needs of the specific collaborators. In computer supported collaborative learning (CSCL) settings, support can be designed in a very restrictive and inflexible fashion; at the same time computerized settings open new possibilities for the realization of adaptive support as they enable automation of analysis and feedback mechanisms. In this symposium we present new technical approaches and latest empirical research on possibilities and limitations of adaptive support for learners in CSCL settings.
Merits of a 108-Member Ensemble System in ENSO and IOD Predictions

This paper explores merits of 100-ensemble simulations from a single dynamical seasonal prediction system by evaluating differences in skill scores between ensembles predictions with few (~10) and many (~100) ensemble members. A 100-ensemble retrospective seasonal forecast experiment for 1983–2015 is beyond current operational capability. Prediction of extremely strong ENSO and the Indian Ocean dipole (IOD) events is significantly improved in the larger ensemble. It indicates that the ensemble size of 10 members, used in some operational systems, is not adequate for the occurrence of 15% tails of extreme climate events, because only about 1 or 2 members (approximately 15% of 12) will agree with the observations. We also showed an ensemble size of about 50 members may be adequate for the extreme El Niño and positive IOD predictions at least in the present prediction system. Even if running a large-ensemble prediction system is quite costly, improved prediction of disastrous extreme events is useful for minimizing risks of possible human and economic losses.
NORM software review : handling missing values with multiple imputation methods

Evaluation studies often lack sophistication in their statistical analyses, particularly where there are small data sets or missing data. Until recently, the methods used for analysing incomplete data focused on removing the missing values, either by deleting records with incomplete information or by substituting the missing values with estimated mean scores. These methods, though simple to implement, are problematic. However, recent advances in theoretical and computational statistics have led to more flexible techniques with sound statistical bases. These procedures involve multiple imputation (MI), a technique in which the missing values are replaced by m > 1 estimated values, where m is typically small (e.g. 3–10). Each of the resultant m data sets is then analysed by standard methods, and the results are combined to produce estimates and confidence intervals that incorporate missing data uncertainty. This paper reviews the key ideas of multiple imputation, discusses the currently available software programs relevant to evaluation studies, and demonstrates their use with data from a study of the adoption and implementation of information technology in Bali, Indonesia.
A lifetime of the Variscan orogenic plateau from uplift to collapse as recorded by the Prague Basin, Bohemian Massif

Abstract The Ordovician to Middle Devonian Prague Basin, Bohemian Massif, represents the shallowest crust of the Variscan orogen corresponding to c. 1–4 km palaeodepth. The basin was inverted and multiply deformed during the Late Devonian to early Carboniferous Variscan orogeny, and its structural inventory provides an intriguing record of complex geodynamic processes that led to growth and collapse of a Tibetan-type orogenic plateau. The northeastern part of the Prague Basin is a simple syncline cross-cut by reverse/thrust faults and represents a doubly vergent compressional fan accommodating c. 10–19 % ~NW–SE shortening, only minor syncline axis-parallel extension and significant crustal thickening. The compressional structures were locally overprinted by vertical shortening, kinematically compatible with ductile normal shear zones that exhumed deep crust in the orogen's interior at c. 346–337 Ma. On a larger scale, the deformation history of the Prague Syncline is consistent with building significant palaeoelevation during Variscan plate convergence. Based on a synthesis of finite deformation parameters observed across the upper crust in the centre of the Bohemian Massif, we argue for a differentiated within-plateau palaeotopography consisting of domains of local thickening alternating with topographic depressions over lateral extrusion zones. The plateau growth, involving such complex three-dimensional internal deformations, was terminated by its collapse driven by multiple interlinked processes including gravity, voluminous magma emplacement and thermal softening in the hinterland, and far-field plate-boundary forces resulting from the relative dextral motion of Gondwana and Laurussia.
Training of HMM Training part Synthesis part Label Mel-cepstrumF 0 Parameter generation from HMM TEXT Label Text analysis SYNTHESIZED SPEECH Excitation generation MLSA filter

In the present paper, hidden Markov model (HMM) based speech synthesis system developed in Nagoya Institute of Technology (Nitech-HTS) for a competition of text-to-speech synthesis systems using the same speech databases, named Blizzard Challenge 2005, is described. We show an overview of the basic HMM-based speech synthesis system and then recent developments to the latest one such as STRAIGHT-based vocoding, hidden semi-Markov model (HSMM) based acoustic modeling, and parameter generation considering global variance are illustrated. Constructed voices can synthesize speech around 0.3 xRT (real time ratio) and their footprints are less than 2 MB. The listening test results show that performances of our systems are much better than we expected.
An Interior Point Algorithm for Linearly Constrained Optimization

We describe an algorithm for optimization of a smooth function subject to general linear constraints. An algorithm of the gradient projection class is used, with the important feature that the \projection" at each iteration is performed using a primal-dual interior point method for convex quadratic programming. Convergence properties can be maintained even if the projection is done inexactly in a well-deened way. Higher-order derivative information on the manifold deened by the apparently active constraints can be used to increase the rate of local convergence.
Symbolic analysis of large analog integrated circuits by approximation during expression generation

A novel algorithm is presented that generates approximate symbolic expressions for small-signal characteristics of large analog integrated circuits. The method is based upon the approximation of an expression while it is being computed. The CPU time and memory requirements are reduced drastically with regard to previous approaches, as only those terms are calculated which will remain in the final expression. As a consequence, the maximum circuit size amenable to symbolic analysis has largely increased. The simplification procedure explicitly takes into account variation ranges of the symbolic parameters to avoid inaccuracies of conventional approaches which use a single value. The new approach is also able to take into account mismatches between the symbolic parameters.
Feature Preserving Point Set Surfaces based on Non-Linear Kernel regression

1. Briefly summarize the paper's contributions. Does it address a new problem? Does it present a new approach? Does it show new types of results?  [AS] This paper presents a new point set surface representation by combining implicit moving least squares (IMLS) with robust statistics. In particular, they explicitly derive IMLS in the local kernel regression (LKR) framework, forming the basis of a new robust moving least squares (MLS) surface representation that can handle sparse sampling, preserves details, and handles sharp features.  [DS] The paper presents a method that applies a robust statistics methods to MLS implicit surface. They use a robust kernel regression, which results in a representation that can handle sparse sampling, generates a continuous surface that preserves fine details better, and can handle sharp features with controllable sharpness. It can handle outliers and high frequency features, it is efficient and easy to implement, and it uses local computations so no preprocessing is needed. Improves representation of sharp features and details of any frequency, and can deal with high order corners and peaks.  [FP] The authors implicitly define the surface using a Robust Local Kernel Regression technique. The approach uses a first order estimation of the Local Kernel, which is reduced to a zero order estimation by approximating the gradient with the normal at each sample point. The obtained Local Kernel is equivalent to Kolluri's IMLS implicit function. The main contribution is the introduction of an efficient and robust technique which is resistant to both spatial and normal outliers. This robust approach allows the reconstruction of sharp features without needing to decompose or tag a local neighborhood.  [JD]  [LF]  [MK] The paper proposes interpreting the (implicit) moving least squares approach in the context of local kernel regression. This allows for designing a more robust algorithm that minimizes an energy that is less sensitive to outliers. Additionally, the authors show that by effectively incorporating a bilateral term that penalizes normal variation, their method can be made to better adapt to sharp features. 2. What is the key insight of the paper? (in 1-2 sentences)  [AS] The key insight of this paper is that MLS surfaces can be expressed in terms of LKR, which is a method in statistics to estimate the conditional expectation of a random variable.  [DS] The key insight is the fact that MLS surfaces can be expressed in …
Representing the Lexicon: Identifying Meaning in Use via Underspecification∗

∗We wish to express our thanks to audiences at Szklarska Poreba, the Workshop Bridging Formal and Conceptual Semantics, and Formal Semantics Meets Cognitive Semantics as well as to Katrin Erk, to Louise McNally and, especially, to an anonymous reviewer of these proceedings for many useful comments, more, in fact, than we could integrate into this preliminary report. In addition, we would like to thank the the Netherlands Organization for Scientific Research (NWO) for their financial support to Lotte Hogeweg. All errors are our own. Authors after the first author are listed in alphabetical order.
Triangulating Statistical Manifolds

Triangulating Statistical Manifolds Jean-Daniel Boissonnat 1 David Cohen-Steiner 1 Pooran Memari 2 Arnaud Poinas 1 1 INRIA Sophia Antipolis - Mediterranee, 2 CNRS - LTCI, Telecom ParisTech In this paper, we describe an algorithm to construct an intrinsic Delaunay triangulation of a smooth closed statistical manifold, i.e. a manifold of probability density functions (PDFs). In differ- ent applications (namely in data analysis, information geometry, biology, shape analysis, compression, scientific visualization), statistical data are often seen as points in a metric space, which is most fre- quently high dimensional. The generally accepted hypothesis is that, although it is embedded in spaces of high dimensions, data lives usually close to a much smaller structure with a low intrinsic dimension. Making use of this intrinsic geometric structure, this paper will develop a certified algo- rithm that can reconstruct statistical manifolds with a complexity that depends only linearly on the ambient dimension. We consider the Fisher information metric defined as a particular Riemannian metric on smooth statistical manifolds to calculate the informational difference between measureme
Industrial Management & Data Systems Mobile Applications in Tourism : The Future of the Tourism Industry ?

Purpose – The unified theory of acceptance and use of technology (UTAUT) was extended with psychological constructs namely perceived playfulness (PP), mobile innovativeness in information technology (MIIT) and perceived expressiveness (PEX) to examine on the consumers’ intention to adopt mobile applications (m-apps) as another mean in purchasing tourism related product and services via their mobile devices. The study also included gender as moderating variable. Design/methodology/approach – Partial Least Square-Structural Equation Modeling (PLSSEM) approach was adopted to test the research framework using 474 valid respondents. Findings –The results revealed that only performance expectancy (PE) and PEX are nonsignificant with the intention to adopt. In addition, gender moderates the effect between social influence (SI) and intention. Practical Implications – The study has several useful implications for researchers, m-apps developers, travel related organizations and even government agencies when rendering m-apps services and disseminating information to their potential consumers. Originality/value –The study contributes to the growing literature on m-apps in tourism from a developing country’s perspective.
Improving Lip-reading with Feature Spac Audio-Visual Speech

In this paper we investigate feature space transforms to improve lip-reading performance for multi-stream HMM based audio-visual speech recognition (AVSR). The feature space transforms include non-linear Gaussianization transform and feature space maximum likelihood linear regression (fMLLR). We apply Gaussianization at the various stages of visual front-end. The results show that Gaussianizing the final visual features achieves the best performance: gain on lip-reading and gain on AVSR. We also compare performance of speakerbased Gaussianization and global Gaussianization. Without fMLLR adaptation, speaker-based Gaussianization improves more on lip-reading and multi-stream AVSR performance. However, with fMLLR adaptation, global Gaussianization shows better results, and achieves over baseline fMLLR adaptation for AVSR.