Miracle-dz/UniFAR-FTUs huggingface dataset:
Speech technology for language tutoring

Language learners are known to perform best in one-on-one interactive situations in which they receive optimal corrective feedback. However, one-on-one tutoring by trained language instructors is costly and therefore not feasible for the majority of language learners. This particularly applies to oral proficiency, which requires intensive tutoring. Computer Assisted Language Learning (CALL) systems that make use of Automatic Speech Recognition (ASR) seem to offer new perspectives for language tutoring. In this paper we explain how.
Text Simplification for Reading Assistance: A Project Note

This paper describes our ongoing research project on text simplification for congenitally deaf people. Text simplification we are aiming at is the task of offering a deaf reader a syntactic and lexical paraphrase of a given text for assisting her/him to understand what it means. In this paper, we discuss the issues we should address to realize text simplification and report on the present results in three different aspects of this task: readability assessment, paraphrase representation and post-transfer error detection.
Joint dereverberation and noise reduction for binaural hearing aids and mobile phones

Bibliografische Information der Deutschen Bibliothek Die Deutsche Bibliothek verzeichnet diese Publikation in der Deutschen Nationalbibliografie; detaillierte bibliografische Da-ten sind im Internet über Acknowledgments This thesis was written during my time as research assistant at the Institute of Communication Systems and Data Processing (IND) at the Rheinisch-Westfälische Tech-nische Hochschule Aachen (RWTH Aachen University). I would like to take the opportunity to thank the people who contributed to the success of this work. At first, I am deeply grateful to my supervisor Prof. Dr.-Ing. Peter Vary for his continuous support of my work by numerous ideas and suggestions and for encouraging me in my scientific interests. I am also indebted to Prof. Dr. ir. Simon Doclo for being the co-supervisor of this thesis and showing much interest in my work. Furthermore, I want to thank all my colleagues and permanent staff of the institute for providing a pleasant and enjoyable working environment. In particular, I am grateful to Dr.-Ing. thank our former project partners for the good collaboration. Special thanks to Dr. Christophe Beaugeant and Intel Mobile Communications in Sophia Antipolis, France for the good and friendly collaboration over the last three years. I also wish to express my appreciation to Dr. Patrick Naylor and his team at Imperial College London for many fruitful discussions during my stay in their lab.
Meta-Analysis of Cytochrome P-450 2C9 Polymorphism and Colorectal Cancer Risk

Background CYP2C9 encodes a member of the cytochrome P450 superfamily of enzymes which play a central role in activating and detoxifying many carcinogens and endogenous compounds thought to be involved in the development of colorectal cancer (CRC). In the past decade, the relationship between CYP2C9 common polymorphisms (R144C and I359L) and CRC has been reported in various ethnic groups; however, these studies have yielded contradictory results. To investigate this inconsistency, we performed this meta-analysis. Methods Databases including Pubmed, EMBASE, Web of Science and China National Knowledge Infrastructure (CNKI) were searched to find relevant studies. Odds ratios (ORs) with 95% confidence intervals (CIs) were used to assess the strength of association. Results A total of 13 articles involving 9,463 cases and 11,416 controls were included. Overall, the summary odds ratio of CRC was 0.98 (95% CI: 0.89−1.06) and 0.99 (95% CI: 0.87−1.14) for CYP2C9 144C and 359L alleles, respectively. No significant results were observed using dominant or recessive genetic model for these polymorphisms. In the stratified analyses according to ethnicity and sex, no evidence of any gene-disease association was obtained. Conclusions This meta-analysis suggests that the CYP2C9 may not be associated with colorectal cancer development.
DESCRIPTION OF VEGETATION USING VISUAL RANKING OF SPECIES

Summary: A vegetation sampling technique is described where the prime measurement is only the listing of the relative order of abundance (rank) of species at a series of sample points and is estimated visually. Such data are appropriate for ordinal methods of statistical analysis. Repeated measurements show that the observer is only a small component of variation in rank values. The analysis of rank data for individual species is shown for deriving summary parameters, deriving association between species, and determining significance of changes in ranking with respect to qualitative or quantitative treatment variables. Vegetation types can be defined by the rank order of constituent species, and a hierarchial procedure is described for subdivision of sample data. Repeated measurements at regular intervals allow the application of the transition matrix approach to the study of vegetation trends.
Enhanced language modeling for extractive speech summarization with sentence relatedness information

Extractive summarization is intended to automatically select a set of representative sentences from a text or spoken document that can concisely express the most important topics of the document. Language modeling (LM) has been proven to be a promising framework for performing extractive summarization in an unsupervised manner. However, there remain two fundamental challenges facing existing LM-based methods. One is how to construct sentence models involved in the LM framework more accurately without resorting to external information sources. The other is how to additionally take into account the sentence-level structural relationships embedded in a document for important sentence selection. To address these two challenges, in this paper we explore a novel approach that generates overlapped clusters to extract sentence relatedness information from the document to be summarized, which can be used not only to enhance the estimation of various sentence models but also to allow for the sentencelevel structural relationships for better summarization performance. Further, the utilities of our proposed methods and several state-of-the-art unsupervised methods are analyzed and compared extensively. A series of experiments conducted on a Mandarin broadcast news summarization task demonstrate the effectiveness and viability of our method.
Subbanded DSP Architectures Based on Underdecimated Filter Banks for Coherent OFDM Receivers: Overview and recent advances

Subbanded digital signal processing (DSP) with underdecimated (Udeci) filter banks (FBs), is a recent DSP technique whereby the optical channel bandwidth is digitally sliced into multiple spectrally disjoint subbands (SBs) to be processed in parallel. In terms of DSP hardware (HW) architecture, digital subbanding amounts to an alternative mode of parallelizing the receiver signal processing task to multiple slower processors, whereby the parallelization performed in the frequency domain (FD) rather than in the time domain (TD). We show that FD parallelization is especially suited to the long-haul optical fiber channel and present novel receiver DSP structures based on Udeci FBs, providing substantial complexity savings for ultra-high-speed optically coherent transmission.
A 3D Porous Gelatin-Alginate-Based-IPN Acts as an Efficient Promoter of Chondrogenesis from Human Adipose-Derived Stem Cells

Cartilage has limited regeneration potential. Thus, there is an imperative need to develop new strategies for cartilage tissue engineering (CTE) amenable for clinical use. Recent CTE approaches rely on optimal cell-scaffold interactions, which require a great deal of optimization. In this study we attempt to build a novel gelatin- (G-) alginate- (A-) polyacrylamide (PAA) 3D interpenetrating network (IPN) with superior performance in promoting chondrogenesis from human adipose-derived stem cells (hADSCs). We show that our G-A-PAA scaffold is capable of supporting hADSCs proliferation and survival, with no apparent cytotoxic effect. Moreover, we find that after exposure to prochondrogenic conditions a key transcription factor known to induce chondrogenesis, namely, Sox9, is highly expressed in our hADSCs/G-A-PAA bioconstruct, along with cartilage specific markers such as collagen type II, CEP68, and COMP extracellular matrix (ECM) components. These data suggest that our G-A-PAA structural properties and formulation might enable hADSCs conversion towards functional chondrocytes. We conclude that our novel G-A-PAA biomatrix is a good candidate for prospective in vivo CTE applications.
Viral Infection of Human Lung Macrophages Increases PDL1 Expression via IFNβ

Lung macrophages are an important defence against respiratory viral infection and recent work has demonstrated that influenza-induced macrophage PDL1 expression in the murine lung leads to rapid modulation of CD8+ T cell responses via the PD1 receptor. This PD1/PDL1 pathway may downregulate acute inflammatory responses to prevent tissue damage. The aim of this study was to investigate the mechanisms of PDL1 regulation by human macrophages in response to viral infection. Ex-vivo viral infection models using influenza and RSV were established in human lung explants, isolated lung macrophages and monocyte-derived macrophages (MDM) and analysed by flow cytometry and RT-PCR. Incubation of lung explants, lung macrophages and MDM with X31 resulted in mean cellular infection rates of 18%, 18% and 29% respectively. Viral infection significantly increased cell surface expression of PDL1 on explant macrophages, lung macrophages and MDM but not explant epithelial cells. Infected MDM induced IFNγ release from autologous CD8+ T cells, an effect enhanced by PDL1 blockade. We observed increases in PDL1 mRNA and IFNβ mRNA and protein release by MDM in response to influenza infection. Knockdown of IFNβ by siRNA, resulted in a 37.5% reduction in IFNβ gene expression in response to infection, and a significant decrease in PDL1 mRNA. Furthermore, when MDM were incubated with IFNβ, this cytokine caused increased expression of PDL1 mRNA. These data indicate that human macrophage PDL1 expression modulates CD8+ cell IFNγ release in response to virus and that this expression is regulated by autologous IFNβ production.
Classification of Rotated and Scaled Textured Images Using Spectral Moments

This paper describes a classification method for rotated and scaled textured images using invariant parameters based on spectral-moments. Although it is well known that rotation invariants can be derived from moments of grey-level images, the use is limited to binary images because of its computational unstableness. In order to obtain computationally stable moments, we use power spectrum instead of the grey levels to compute moments and adjust an integral region to scale change. Rotation and scale invariants are obtained as the ratio of the rotation invariants on the basis of a spectral-moment property with respect to scale. The effectiveness of the approach is illustrated through experiments on natural textures from the Brodatz album. The stability of the invariants with respect to change of scale is discussed theoretically and confirmed experimentally.
K factor estimation for MIMO multipath channels

The channel capacity of a MIMO system depends on a Rician K factor. In the fields of a cognitive radio and link adaptation, it is required to estimate the K factor accurately. This paper proposes K factor estimation for a MIMO multipath channel. The conventional K factor estimation assumes that the MIMO channel is flat fading. Thus, the conventional method cannot estimate the K factor for MIMO multipath channel. In order to improve the estimation accuracy of a K factor, this paper adopts a multipath channel model as a MIMO Channel. In addition, since the proposed method selects high SNR paths, the estimation accuracy is more improved.
Stochastic Variance-Reduced ADMM

The alternating direction method of multipliers (ADMM) is a powerful optimization solver in machine learning. Recently, stochastic ADMM has been integrated with variance reduction methods for stochastic gradient, leading to SAG-ADMM and SDCA-ADMM that have fast convergence rates and low iteration complexities. However, their space requirements can still be high. In this paper, we propose an integration of ADMM with the method of stochastic variance reduced gradient (SVRG). Unlike another recent integration attempt called SCAS-ADMM, the proposed algorithm retains the fast convergence benefits of SAG-ADMM and SDCA-ADMM, but is more advantageous in that its storage requirement is very low, even independent of the sample size $n$. We also extend the proposed method for nonconvex problems, and obtain a convergence rate of $O(1/T)$. Experimental results demonstrate that it is as fast as SAG-ADMM and SDCA-ADMM, much faster than SCAS-ADMM, and can be used on much bigger data sets.
Continuous Distance-Dependent Level of Detail for Rendering Heightmaps

This paper presents a technique for GPU-based rendering of heightmap terrains, which is a refinement of several existing methods with some new ideas. It is similar to the terrain clipmap approaches [Tanner et al. 98, Losasso 04], as it draws the terrain directly from the source heightmap data. However, instead of using a set of regular nested grids, it is structured around a quadtree of regular grids, more similar to [Ulrich 02], which provides it with better level-of-detail distribution. The algorithm's main improvement over previous techniques is that the LOD function is the same across the whole rendered mesh and is based on the precise three-dimensional distance between the observer and the terrain. To accomplish this, a novel technique for handling transition between LOD levels is used, which gives smooth and accurate results. For these reasons the system is more predictable and reliable, with better screen-triangle distribution, cleaner transitions between levels, and no need for stitching meshes. This also simplifies integration with other LOD systems that are common in games and simulation applications. With regard to the performance, it remains favorable compared to similar GPU-based approaches and works on all graphics hardware supporting Shader Model 3.0 and above. The example (demo and complete source code) is available online.
An Algorithm for Fatigue Driving Face Detection and Location

In order to detect and locate face region accurately, improve real-time, accuracy, and reliability of face detection in the fatigue driving warning system, according to the theory put forward by Yang, combined with skin color segmentation and edge detection technology, we mixed Gaussian Model and Oval Clustering Model in this paper, and improved hybrid detection and model algorithm. Experimental results show that the effect of mixed detection is more advanced and perfect than that of single detection, and the algorithm presented can improve face detect accuracy.
Every 4-regular graph is acyclically edge-6-colorable

An acyclic edge coloring of a graph $G$ is a proper edge coloring such that no bichromatic cycles are produced. The acyclic chromatic index $a'(G)$ of $G$ is the smallest integer $k$ such that $G$ has an acyclic edge coloring using $k$ colors. Fiam${\rm \check{c}}$ik (1978) and later Alon, Sudakov and Zaks (2001) conjectured that $a'(G)\le \Delta + 2$ for any simple graph $G$ with maximum degree $\Delta$. Basavaraju and Chandran (2009) showed that every graph $G$ with $\Delta=4$, which is not 4-regular, satisfies the conjecture. In this paper, we settle the 4-regular case, i.e., we show that every 4-regular graph $G$ has $a'(G)\le 6$.
Robotic face milling path correction and vibration reduction

In this paper the developed method for off-line compensation of tool deflections and vibration reduction when milling aluminum with an industrial robot is presented. The efficiency of this approach is verified with high precision measurements of deflections using a laser tracker. The compensation method is based on the static milling process model which can predict the mean value components of the tool forces and the passive damping system mounted on the spindle to reduce vibrations. With a process model such as the one presented in this paper and estimates of the robot's joint stiffness values, the tool path can be adjusted to counteract deflections of the tool during milling operations. The presented method increases accuracy when using an industrial robot for milling in relatively hard materials.
ABRAXAS 1 gene mutatiOnS in the grOup Of BRCA 1-negative patientS

In the present study, we analysed the association of mutations of a BRCA1-associated gene, ABRAXAS1, with the risk of development of breast cancer (BC) in BRCA1-negative women from North-Central Poland. A hundred women with consecutively diagnosed BC and 100 women belonging to the control group were screened for new mutations predisposing to breast cancer. The first step was a test carried out in order to find one of the three Polish founder mutations in the BRCA1 gene. In 96 BRCA1-negative patients two missense variants: c.422C>T and c.1042G>A as well as two intronic variants: IVS3-34G>A, IVS3-44T>C were detected in the ABRAXAS1 gene. The c.422C>T mutation was detected in one of 96 women diagnosed with breast cancer (1.04%); it was not associated with increased risk of disease in this group, compared to the controls (p = 0.49), but the odds ratio was 3.314; 95% CI: 0.122-75.352. IVS3-44T>C was found more frequently in the control group (15/93) than in the tested group (1/85), OR 0.062; 95% CI: 0.008-0.480, p = 0.007, which may suggest protective properties of this variant against tumorigenicity. The data obtained from the present study suggest the necessity for further research to be conducted on the ABRAXAS1 gene in relation to hereditary predisposition to breast cancer.
Isolated malay speech recognition using Hidden Markov Models

The study aims to develop an automated isolated word speech recognition for Malay language that relies heavily on the well known and widely used statistical method in characterizing the speech pattern, the Hidden Markov Model (HMM). This paper discusses the development and implementation of an isolated Malay word speech recognition system using HMM as the acoustic model. This research focuses on isolated 5 phonemes word structure such as empat (four), lapan (eight), rekod (record), tidak (no), tujuh (seven) and tutup (close). The proposed system is relatively successful where it can identify spoken word at 88% recognition rate which is an acceptable rate of accuracy for speech recognition.
New Method Based on Support Vector Machine in Classification for Hyperspectral Data

Cross-validation is a normal method for parameter selection of support vector machine (SVM) which is a novel machine learning method for hyperspectral data classification. Because of the high dimensionality of hyperspectral data, the process of cross-validation will cost more time. For reducing the time of cross-validation and improving classification accuracy, a new combination method of improving sequential minimal optimization (SMO), independent component analysis (ICA) and mixture kernels is proposed. It can be described as follows: first use the improving SMO method to optimize the model of SVM, and then use ICA method to do dimensionality reduction before cross-validation, at last use mixture kernels to do classification of unknown samples. By the experiments, it is proved that this method can guarantee the accuracy of unknown samples classification while reducing the time of cross-validation.
A Comparative Study of Deterministic and Stochastic Sum-of-Sinusoids Models of Rayleigh-Fading Wireless Channels

We compare eleven sum-of-sinusoids simulation models for generating a Rayleigh-fading envelope in this paper. We consider the correlation properties and level-crossing rates of the simulation models, as these properties influence the error rates of many communication systems. The paper concludes with the selection of the most suitable simulation models for use in flat fading and frequency selective fading channel simulators.
Computation time study in biomedical signal processing with Empirical Mode Decomposition: The case of electrocardiogram

In this paper, a study of the Empirical Mode Decomposition (EMD) performance is presented in terms of computation time. Smart resource allocation and management in embedded systems are facilitated by signal processing techniques modeling for time scheduling of tasks. Empirical Mode Decomposition computation time is mainly determined by the number of iterations and the size of Intrinsic Mode Functions (IMF) set which are unknown at the beginning of the process. A metric is introduced to include these factors into a single variable of a linear model developed to a priori estimate method's computation time. In the same framework of Empirical Mode Decomposition computation time study the effects of noisy components and the application of preprocessing techniques are evaluated.
The QLP Approximation to the Singular Value Decomposition

In this paper we introduce a new decomposition called the pivoted QLP decomposition. It is computed by applying pivoted orthogonal triangularization to the columns of the matrix X in question to get an upper triangular factor R and then applying the same procedure to the rows of R to get a lower triangular matrix L. The diagonal elements of R are called the R-values of X; those of L are called the L-values. Numerical examples show that the L-values track the singular values of X with considerable fidelity---far better than the R-values. At a gap in the L-values the decomposition provides orthonormal bases of analogues of row, column, and null spaces provided of X. The decomposition requires no more than twice the work required for a pivoted QR decomposition. The computation of R and L can be interleaved, so that the computation can be terminated at any suitable point, which makes the decomposition especially suitable for low-rank determination problems. The interleaved algorithm also suggests a new, efficient 2-norm estimator.
A Framework for Extremum Seeking Control of Systems With Parameter Uncertainties

Traditionally, the design of extremum seeking algorithm treats the system as essentially a black-box, which for many applications means disregarding known information about the model structure. In contrast to this approach, there have been recent examples where a known plant structure with uncertain parameters has been used in the online optimization of plant operation. However, the results for these approaches have been restricted to specific classes of plants and optimization algorithms. This paper seeks to provide general results and a framework for the design of extremum seekers applied to systems with parameter uncertainties. General conditions for an optimization method and a parameter estimator are presented so that their combination guarantees convergence of the extremum seeker for both static and dynamic plants. Tuning guidelines for the closed loop scheme are also presented. The generality and flexibility of the proposed framework is demonstrated through a number of parameter estimators and optimization algorithms that can be combined to obtain extremum seeking. Examples of anti-lock braking and model reference adaptive control are used to illustrate the effectiveness of the proposed framework.