Miracle-dz/UniFAR-FTUs huggingface dataset:
Text normalization and speech recognition in French

In this paper we present a quantitative investigation into the impact of text normalization on lexica and language models for speech recognition in French. The text normalization process defines what is considered to be a word by the recognition system. Depending on this definition we can measure different lexical coverages and language model perplexities, both of which are closely related to the speech recognition accuracies obtained on read newspaper texts. Different text normalizations of up to 185M words of newspaper texts are presented along with corresponding lexical coverage and perplexity measures. Some normalizations were found to be necessary to achieve good lexical coverage, while others were more or less equivalent in this regard. The choice of normalization to create language models for use in the recognition experiments with read newspaper texts was based on these findings. Our best system configuration obtained a 11.2% word error rate in the AUPELF ‘French-speaking’ speech recognizer evaluation test held in February 1997.
Concept for managing multiple semantics with AutomationML — Maturity level concept of semantic standardization

This contribution discusses the well-known paradox of today's approaches to standardizing of semantic models in automation engineering and proposes an evolutionary concept to resolve it. In this context, an overview of past and current standardization activities is presented which points out the existing barriers. Instead of aiming for a completely standardized neutral data model, the proposed approach actively supports data exchange with mixed neutral and proprietary data models. Hence, it utilizes the existing heterogeneity and the maturity of proprietary data models. In this context, the authors present a concept of maturity levels, which form milestones towards a stepwise evolution of a semantic standardization. Finally, this paper describes how this approach is technically implemented in AutomationML.
Stochastic Inversion Transduction Grammars, with Application to Segmentation, Bracketing, and Alignment of Parallel Corpora

We introduce (1) a novel stochastic inversion transduction grammar formalism for bilingual language modeling of sentence-pairs and (2) the concept of bilingual parsing with potential application to a variety of parallel corpus analysis problems The formalism combines three tactics against the constraints that render finite-state transducers less useful it skips directly to a context-free rather than finite-state base it permits a minimal extra degree of ordering flexibility and its probabilistic formulation admits an efficient maximum-likelihood bilingual parsing algorithm A convenient normal form is shown to exist and we discuss a number of examples ot how stochastic inversion transduction grammars bring bilingual constraints to bear upon problematic corpus analysis tasks.
The influence of basic tokenization on biomedical document retrieval

Tokenization is a fundamental preprocessing step in Information Retrieval systems in which text is turned into index terms. This paper quantifies and compares the influence of various simple tokenization techniques on document retrieval effectiveness in two domains: biomedicine and news. As expected, biomedical retrieval is more sensitive to small changes in the tokenization method. The tokenization strategy can make the difference between a mediocre and well performing IR system, especially in the biomedical domain.
Mel cepstral coefficient modification based on the Glimpse Proportion measure for improving the intelligibility of HMM-generated synthetic speech in noise

We propose a method that modifies the Mel cepstral coefficients of HMM-generated synthetic speech in order to increase the intelligibility of the generated speech when heard by a listener in the presence of a known noise. This method is based on an approximation we previously proposed for the Glimpse Proportion measure. Here we show how to update the Mel cepstral coefficients using this measure as an optimization criterion and how to control the amount of distortion by limiting the frequency resolution of the modifications. To evaluate the method we built eight different voices from normal read-text speech data from a male speaker. Some voices were also built from Lombard speech data produced by the same speaker. Listening experiments with speech-shaped noise and with a single competing talker indicate that our method significantly improves intelligibility when compared to unmodified synthetic speech. The voices built from Lombard speech outperformed the proposed method particularly for the competing talker case. However, compared to a voice using only the spectral parameters from Lombard speech, the proposed method obtains similar or higher performance.
Position-independent activity recognition model for smartphone based on frequency domain algorithm

There are many new issues in human activity recognition using smart phone with built-in acceleration sensors, such as variations of the location and orientation of smart phone. This paper presents a smart phone position-independent activity recognition model based on frequency domain. First, we analyzed FFT curve of Resultant Acceleration in different mobile positions and different activities. The curve shows that FFT results can be used to distinguish different actions. Furthermore, the highest recognition accuracy is achieved under the condition of 39 lower frequency FFT characteristics. In conclusion, recognition accuracy can be improved by 5% while time-consuming reduced by 12.2% in this method.
Multi-interface rendezvous in self-organizing cognitive radio networks

Cognitive radio (CR) technology enables the opportunistic use of a portion of the licensed spectrum by CR users, while ensuring low interference to the primary user activity in a licensed band. It is very crucial for the CR user to rendezvous with another user on a certain channel and establish a communication link. The main focus of this paper is to design such an algorithm for blind rendezvous, i.e. rendezvous without any coordinator or common control channel, as is the case with jump-and-stay (JS) algorithm. Channels to jump have been assumed to be homogeneous in prior work; however, in the real world, channels exhibit different conditions. Therefore, by extending the JS algorithm, we designed an adaptive jumping pattern such that a better channel is jumped more often and users have a higher possibility to rendezvous on such a channel. Moreover, when multiple radio interfaces are available for CR users, the possibility of rendezvous increases but the number of channels to jump for each interface might not be the same as other users' interfaces. Consequently, we propose an enhanced adaptive rendezvous (EAR) algorithm that can work for multiple interfaces and different sizes of the channel list, and an adaptive jumping pattern for different channel conditions. Through extensive simulation, EAR is verified to work for these environments and further reduce the time-to-rendezvous compared to JS.
Evaluating data communications in natural disaster scenarios using opportunistic networks with Unmanned Aerial Vehicles

Communications between rescue teams and command center of search and rescue operations in disaster areas are frequently affected by geographical obstacles and infrastructure disruptions. One way to overcome such problems is to use Unmanned Aerial Vehicles (UAVs) as mobile nodes of a Delay/disruption Tolerant Network (DTN), in order to support communications. This paper investigates and evaluates the performance of most popular DTN routing protocols through simulations based on real flight traces of an UAV over two scenarios of natural disaster caused by heavy rains and landslides. Obtained results allow the verification of proper routing protocols and the cost/benefit relationship between number of UAVs and DTN performance.
Efficient voice activity detection algorithm based on sub-band temporal envelope and sub-band long-term signal variability

Voice activity detection (VAD) is widely used for various speech-based systems which is an important pre-processing step. This paper proposes a robust voice activity detection algorithm. In the proposed algorithm, the sub-band temporal envelope and the sub-band long-term signal variability are considered to distinguish the speech from all kinds of non-speech which include stationary noise and non-stationary noise. The two features are combined to make a robust VAD decision according to the fusion decision. The proposed algorithm also is an unsupervised low-complexity algorithm and can operate without pre-train models. The experiments results show that the proposed algorithm is prior to the different baseline algorithms and can handle a variety of noise environments over a wide range of signal-to-noise ratios. The proposed algorithm could apply to speech-based systems.
Sparse computation for large-scale data mining

Several leading data mining and clustering algorithms rely on inputs in the form of pairwise similarities. Yet, since the number of potential pairwise similarities grows quadratically in the size of the data set, it is computationally prohibitive to apply such algorithms to large data sets. This paper addresses this challenge with a novel method of sparse computation that computes only the relevant similarities instead of the complete similarity matrix. The method employs an efficient algorithm that provides an “approximate Principal Component Analysis”. In the low-dimensional space generated, the concept of grid neighborhoods is applied in order to identify groups of objects with potentially high similarity. Unlike known sparsification approaches that generate first the full set of pairwise similarities and thus take at least quadratic time, the sparse computation method generates only the relevant similarities. Sparse computation can be utilized in any data mining or clustering algorithm that requires pairwise similarities, such as the k-nearest neighbors algorithm or the spectral method. This approach is contrasted with that of grid-based clustering algorithms in that grid neighborhoods proximity is used only to determine the entries in the sparse similarity matrix, not to identify the clusters. Indeed objects can belong to the same grid neighborhood while ending up in different clusters, or conversely, belong to different neighborhoods yet get clustered jointly. The applicability of sparse computation for binary classification is demonstrated here for the recently devised supervised normalized cut (SNC). Our empirical results show that the approach achieves a significant reduction in the density of the similarity matrix, resulting in a substantial reduction in running time, while having a minimal effect (and often none) on accuracy as compared to inputs using a complete similarity matrix.
The multiple personalities of Alix

Alix is a cytosolic protein in mammalian cells that was originally identified on the basis of its association with pro-apoptotic signaling. More recent evidence has established that Alix has a hand in regulating other cellular mechanisms, including endocytic membrane trafficking and cell adhesion. Although Alix appears to participate directly in these various activities, the role it plays in each process has largely been inferred from the functions of proteins with which it interacts. For example, recruitment of Alix to endosomes is mediated by its N-terminal Bro1 domain, the structure of which was recently solved for its yeast orthologue, Bro1. The diversity of Alix functions is due to its proline-rich C-terminus, which provides multiple protein-binding sites. With this blueprint in hand, we can now ask whether Alix acts simply as an adaptor that links different proteins into networks or, instead, contributes a specific function to distinct molecular machineries.
A Study of Platelet Inhibition, Using a ‘Point of Care’ Platelet Function Test, following Primary Percutaneous Coronary Intervention for ST-Elevation Myocardial Infarction [PINPOINT-PPCI]

Background Rapid coronary recanalization following ST-elevation myocardial infarction (STEMI) requires effective anti-platelet and anti-thrombotic therapies. This study tested the impact of door to end of procedure (‘door-to-end’) time and baseline platelet activity on platelet inhibition within 24hours post-STEMI. Methods and Findings 108 patients, treated with prasugrel and procedural bivalirudin, underwent Multiplate® platelet function testing at baseline, 0, 1, 2 and 24hours post-procedure. Major adverse cardiac events (MACE), bleeding and stent thrombosis (ST) were recorded. Baseline ADP activity was high (88.3U [71.8–109.0]), procedural time and consequently bivalirudin infusion duration were short (median door-to-end time 55minutes [40–70] and infusion duration 30minutes [20–42]). Baseline ADP was observed to influence all subsequent measurements of ADP activity, whereas door-to-end time only influenced ADP immediately post-procedure. High residual platelet reactivity (HRPR ADP>46.8U) was observed in 75% of patients immediately post-procedure and persisted in 24% of patients at 2hours. Five patients suffered in-hospital MACE (4.6%). Acute ST occurred in 4 patients, all were <120mins post-procedure and had HRPR. No significant bleeding was observed. In a post-hoc analysis, pre-procedural morphine use was associated with significantly higher ADP activity following intervention. Conclusions Baseline platelet function, time to STEMI treatment and opiate use all significantly influence immediate post-procedural platelet activity.
ASSESSMENT OF FOREIGN OBJECT DAMAGE ( FOD ) IN AERO ENGINE BLADES

Aircraft gas turbine engines are subjected to a range of HCF-LCF cycles in service and may also suffer Foreign Object Damage (FOD) from ingestion of small hard particles. There is increasing pressure to reduce maintenance costs by design of more ‘FOD-tolerant’ blades, but it is difficult for the designer to achieve an acceptable compromise between integrity and performance whilst design rules for FOD tolerance remain largely empirical. This paper describes progress onto the work undertaken by the University of Oxford in collaboration with Rolls-Royce plc. A design methodology for compressor blades to resist FOD damage is the ultimate purpose of this research. Experimental results on ‘blade-like’ specimens have been conducted using a ballistic facility, which is considered the best method of reproducing realistic damage. The specimens were subsequently fatigue tested by a step loading method. Earlier work in the programme used a short crack arrest methodology to predict the loss of fatigue strength and it was shown that this gave a lower bound to the experimental results and also reproduced the effect of the notch size. The current paper will report a development of this approach. The basic method using the Kitagawa-Takahashi has been compared with the El-Haddad approach. Furthermore, a method has been developed to include the effect of residual stresses in the fatigue strength prediction. In particular different levels of constant residual stress are incorporated, using the distribution of residual stress given by a numerical simulation of the impact as a reference. The modified curves are then compared to the experimental results. It is shown that prediction and experiment are consistent and that residual stress considerations provide an explanation for the observed variation of fatigue strength with impact angle.
Deep Kernel: Learning Kernel Function from Data Using Deep Neural Network

Kernel function implicitly maps data from its original space to a higher dimensional feature space. Kernel based machine learning algorithms are typically applied to data that is not linearly separable in its original space. Although kernel methods are among the most elegant part of machine learning, it is challenging for users to define or select a proper kernel function with optimized parameter settings for their data. In this paper, we propose a novel method called Deep Kernel that can automatically learn a kernel function from data using deep learning. The deep kernel is currently utilized in classification, and dimension reduction and visualization. For the classification task, we evaluate the deep kernel method by comparing its performance with the optimized Gaussian kernels, both using support vector machines as the decision model, on different types of datasets. The experimental results show that the proposed deep kernel method outperforms the traditional methods with Gaussian kernels on most of the data sets. For the dimension reduction and visualization task, the deep kernel is used along with kernel PCA. The results are also compared and contrasted with using the RBF kernel with multiple parameters. The deep kernel is shown to be more powerful in dimension reduction and visualization than the RBF kernel.
Application of global-extreme-learning to law-discovery neural networks

The problem of improving efficiency of training special-type neural networks (SNN) used to create symbolic description of rules governing a set of empirical data is considered. Values of the description parameters are determined by the network training. Difficulties with the SNN learning appear mainly due to a great number of local minima encountered in this process. The learning methods we applied so far were based on a modified version the Back Propagation algorithm called BP-CM-BFGS. It turned out, however, that this approach is not always effective, especially when the number of input variables of the SNN increases. In this paper, we propose to use as training technique an evaluation algorithm called Differential Evolution (DE). To illustrate effectiveness of this technique we present results of learning a reciprocal-function-based SNN [15] implementing a fifth order polynomial.
Evaluation of performance enhancing proxies in internet over satellite

Performance enhancing proxies (PEPs) are widely used to improve the performance of TCP over high delay‐bandwidth product links and links with high error probability. In this paper we analyse the performance of using TCP connection splitting in combination with web caching via traces obtained from a commercial satellite system. We examine the resulting performance gain under different scenarios, including the effect of caching, congestion, random loss and file sizes. We show, via analysing our measurements, that the performance gain from using splitting is highly sensitive to random losses and the number of simultaneous connections, and that such sensitivity is alleviated by caching. On the other hand, the use of a splitting proxy enhances the value of web caching in that cache hits result in much more significant performance improvement over cache misses when TCP splitting is used. We also compare the performance of using different versions of HTTP in such a system. Copyright © 2003 John Wiley & Sons, Ltd.
Sensory navigation of autonomous cleaning robots

This paper addresses a general problem of sensory navigation for autonomous cleaning robots (ACR) in unknown environments. Although absolute self-localization and accurate environment modeling still remain unsolved, a delicately designed mobile robot still can carry out a task practically. Under this strategy, we propose a cleaning robot system, which works without any environment map and global self-localization. It has a three-layer structure. The lower layer is composed of general hardware: ultrasonic sensors, infrared sensors, incremental encoders, DC motors, vacuum, etc. Upon these sensors is the sensory behavior layer, which includes several motion templates. These intelligent motion templates can deal with most situations, seldom making the robot trapped. The upper layer is the task-based navigation layer, which carries out the tasks of environment learning, cleaning, and homing. Finally the experiment results show that this strategy works well, even if the robot knows only a little about the environment.
Iris recognition performance enhancement using weighted majority voting

Biometric authentication is a convenient and increasingly reliable way to prove one's identity. Iris scanning in particular is among the most accurate biometric authentication technologies currently available. However, despite their extremely high accuracy under ideal imaging conditions, existing iris recognition methods degrade when the iris images are noisy or the enrollment and verification imaging conditions are substantially different. To address this issue and enable iris recognition on less-than-ideal images, we introduce a weighted majority voting technique applicable to any biometric authentication system using bitwise comparison of enrollment-time and verification-time biometric templates. In a series of experiments with the CASIA iris database, we find that the method outperforms existing majority voting and reliable bit selection techniques. Our method is a simple and efficient means to improve upon the accuracy of existing iris recognition systems.
A highly divergent archaeo-eukaryotic primase from the Thermococcus nautilus plasmid, pTN2

We report the characterization of a DNA primase/polymerase protein (PolpTN2) encoded by the pTN2 plasmid from Thermococcus nautilus. Sequence analysis revealed that this protein corresponds to a fusion between an N-terminal domain homologous to the small catalytic subunit PriS of heterodimeric archaeal and eukaryotic primases (AEP) and a C-terminal domain related to their large regulatory subunit PriL. This unique domain configuration is not found in other virus- and plasmid-encoded primases in which PriS-like domains are typically fused to different types of helicases. PolpTN2 exhibited primase, polymerase and nucleotidyl transferase activities and specifically incorporates dNTPs, to the exclusion of rNTPs. PolpTN2 could efficiently prime DNA synthesis by the T. nautilus PolB DNA polymerase, suggesting that it is used in vivo as a primase for pTN2 plasmid replication. The N-terminal PriS-like domain of PolpTN2 exhibited all activities of the full-length enzyme but was much less efficient in priming cellular DNA polymerases. Surprisingly, the N-terminal domain possesses reverse transcriptase activity. We speculate that this activity could reflect an ancestral function of AEP proteins in the transition from the RNA to the DNA world.
Kind of Quick Ray Tracing

Adaptive ray tracing is a way to accelerate the traditional ray tracing we implemented in homework three. The idea, at its most fundamental, is to apply a test to each pixel and point in the scene which determines if the final color of the pixel/point would be altered if a distributed ray tracing technique is applied to it. In this project, I implemented such tests for antialiasing, soft shadows, glossy reflections, and motion blur, along with alternative methods of testing and subsequently shooting the distributed rays once/if said tests are passed. Some of these methods is adaptive on the other end—it dynamically adjusts how many rays are shot into the scene after a pixel or point passes its corresponding test. This paper is basically a description, analysis, and comparison—of both efficiency and quality—of the original methods for these techniques and my newly implemented adaptive methods. CR Categories: I.3. 3 [Computer Graphics]: Three-Dimensional Graphics and Realism—Display Algorithms I.3. 7 [Computer Graphics]: Three-Dimensional Graphics and Realism—Ray Tracing;