Miracle-dz/UniFAR-FTUs huggingface dataset:
The role of syllable intensity in between- speaker rhythmic variability

Speech rhythm in terms of durational variability of different levels of phonetic intervals can vary between speakers. The present article examines the role of syl- labic intensity characteristics in rhythmic variability. Mean and peak intensity variability across syllables (stdevM, varcoM, stdevP, varcoP, rPVIm, nPVIm, rPVIp, nPVIp; henceforth: intensity measures) were investigated as a function of speaker in a database where within-speaker variability was strong (BonnTempo) and another database designed to examine between-speaker rhythmic variability (TEVOID). It was found that the intensity measures varied significantly between speakers in both databases. Semiautomatic speaker recognition based on duration measures (%V, ∆V(ln), ∆C(ln), ∆Peak(ln), ∆Syll(ln) and nPVISyll) and intensity measures using multinomial logistic regression and feedforward neural networks was carried out for the two databases. Results showed that intensity measures contained stronger speaker specific information compared to measures based on durational variability of phonetic intervals. In addition, effects of the recognition algorithms (speaker rec- ognition using multinomial logistic regression was significantly better than neural networks for BonnTempo) and data normalisation procedures (z-score normalised data was significantly better than non-normalised data in TEVOID) were discov- ered. This means that syllable intensity characteristics play an important role in between-speaker rhythmic differences and possibly in speech rhythm variability in general.
An EMG assessment of Front Row Rugby Union Scrummaging

The rugby union scrum comprises of two distinct phases; the engagement and second shove. The engagement phase is achieved through the interlocking of opposition forward packs through front row players of each team. The current study analysed professional and amateur front row forwards for horizontal force production. EMG patterns and muscular activation were compared between groups during two distinct time points of the engagement phase, pre engagement to first contact and first contact to maximum horizontal force to determine their effects, if any on horizontal force production. Isokinetic strength data was collected from all players to investigate any relationship this had to horizontal force production during the engagement phase. Body mass was found to be significantly different between playing groups. Maximum horizontal force during the engagement phase was significantly different between groups, with professional players generating more than both amateur groups. EMG analysis presented no significant differences between groups for muscular activation patterns. Peak muscular activation for all recorded muscles was achieved prior to first contact. A significant correlation was found between muscular activation and max horizontal force for professional players only. Junior players were found to have a significant negative correlation between isokinetic strength and max horizontal force production.
HE-PRMAC: Hop extended pipelined routing enhanced MAC protocol for wireless sensor networks

In this paper, we present a MAC protocol called Hop Extended Pipelined Routing MAC (HE-PRMAC) that enables a node to send a data packet for multiple hops in a single duty cycle similar to PR-MAC and has the ability to handle traffic overhead in an efficient way. HE-PRMAC addresses delivery latency and throughput degradation in wireless sensor networks (WSNs) using hop extended mechanism and a new message, i.e., RTR message. The main goal of this research is to reduce the overhead and latency compared to R-MAC and PR-MAC. We evaluate the performance of our protocol using ns-2 simulations. As compared to R-MAC and PR-MAC, HE-PRMAC shows 22.1% less power consumption and 13.6% less packet latency on average for 250 nodes in chain, cross, and real scenario.
Machine Learning and Visual Analytics for Consulting Business Decision Support

The application of machine learning and statistical predictive models to business problems has found success in recent years due to an exponential increase in consumer data and readily available computational power. However, visualising and interpreting this data to support business decision making in the context of consulting businesses is challenging and there is scope for advancement. The accurate prediction of hours to be spent on a project (cost) ahead of time underpins the profitability of these organisations. The aim of the research is twofold: to identify suitable techniques from the fields of machine learning and statistics for internal cost prediction in a consulting business, and to develop a user interface with visual analytics displaying results from these techniques to provide interactive decision support. The data for this project was collected from a consulting business' customer relationship management(CRM) database, which contained twelve years of past consulting projects. To date, statistical linear models and machine learning decision trees have been trialed and the research will progress into random forests, neural networks, and support vector machine (SVM) models. A prototype user interface and visualisation of the results has also been developed.
Nearest-Neighbor Based Non-Parametric Probabilistic Forecasting with Applications in Photovoltaic Systems

The present contribution offers a simple methodology for the obtainment of data-driven interval forecasting models by combining pairs of quantile regressions. Those regressions are created without the usage of the non-differentiable pinball-loss function, but through a k-nearest-neighbors based training set transformation and traditional regression approaches. By leaving the underlying training algorithms of the data mining techniques unchanged, the presented approach simplifies the creation of quantile regressions with more complex techniques (e.g. artificial neural networks). The quality of the presented methodology is tested on the usecase of photovoltaic power forecasting, for which quantile regressions using polynomial models as well as artificial neural networks and support vector regressions are created. From the resulting evaluation values it can be concluded that acceptable interval forecasting models are created.
Methodische Aspekte der Durchführung von Fokusgruppen in der Gesundheitsforschung: welche Anforderungen ergeben sich aufgrund der besonderen Zielgruppen und Fragestellungen?

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The Influence of Emotion on Keyboard Typing: An Experimental Study Using Auditory Stimuli

In recent years, a novel approach for emotion recognition has been reported, which is by keystroke dynamics. The advantages of using this approach are that the data used is rather non-intrusive and easy to obtain. However, there were only limited investigations about the phenomenon itself in previous studies. Hence, this study aimed to examine the source of variance in keyboard typing patterns caused by emotions. A controlled experiment to collect subjects’ keystroke data in different emotional states induced by International Affective Digitized Sounds (IADS) was conducted. Two-way Valence (3) x Arousal (3) ANOVAs was used to examine the collected dataset. The results of the experiment indicate that the effect of arousal is significant in keystroke duration (p < .05), keystroke latency (p < .01), but not in the accuracy rate of keyboard typing. The size of the emotional effect is small, compared to the individual variability. Our findings support the conclusion that the keystroke duration and latency are influenced by arousal. The finding about the size of the effect suggests that the accuracy rate of emotion recognition technology could be further improved if personalized models are utilized. Notably, the experiment was conducted using standard instruments and hence is expected to be highly reproducible.
An image retrieval system for distributed environments

We describe a prototype image retrieval system for distributed environments based on 2D projection interval relationships (2D-PIR). The system transforms an image to a symbolic representation that describes spatial relationships among objects in the image. The image database consists of three parts: the real image repository (which is distributed) and the symbolic and iconic image repositories (which are stored locally). We describe the result of experiments on a small image database which compare the performance of our system against a keyword-based retrieval system and also against other content-based image retrieval systems.
Product Recalls , Resource Reallocation , and Contagion along the Supply Chain

Product recall campaigns are material events for the recalling firms and their key stakeholders. In this paper, we study (i) the factors that explain the incidence of product recalls, (ii) the value implications of recalls for the recalling firms, their industry rivals, and key suppliers, and (iii) the operating consequences for the recalling firms and the actions taken by them in response to the recall. To address these issues, we compile a comprehensive dataset on recalls of a wide variety of products by publicly traded firms during the period 2006–2010 to test our hypotheses. Our final sample of 816 recall events includes consumer product, food, drug, medical devices, and automobile recalls. Our results indicate that the financial condition, competitive position, product and process innovation investments, coordination costs, incentives of workers and management, and the monitoring environment of the firm all have a significant impact on recall incidence. Further, recall events pose significant costs not only to the recalling firms, but also to their rival firms and key supplier firms. Our results show that instead of benefiting from the recalling firms’ woes, industry rivals actually suffer adverse contagion effects, perhaps due to anticipated tighter regulations for the industry and diminished perceptions about the entire product category. In addition, the key suppliers of the recalling firms are also adversely affected. Controlling for the endogeneity of the product recall event we find that a larger investment in brand capital alleviates the negative impact of the recall announcement for recalling firms. A difference-in-differences approach shows that compared to control firms, recalling firms suffer sales decreases in the years immediately following the recall, and they increase advertising expenditures as a way to repair their tarnished brand image.
Distributed Exploration of Unlabelled Graphs by Multiple Agents ∗

We consider a distributed version of the typical graph exploration problem where a mobile agent has to traverse the edges of an unlabelled (i.e., anonymous) graph and return to its starting point, building a map of the graph in the process. In our case, instead of a single agent, there are k identical (i.e., mutually indistinguishable) agents initially dispersed among the n nodes of the graph. The agents can communicate by writing to the small public bulletin boards available at each node. The objective is for each agent to build an identically labelled map of the graph; we call this the Labelled Map Construction problem. This problem is much more difficult than exploration by a single agent, because it involves achieving cooperation among multiple agents. In fact, this problem is deterministically unsolvable in some cases. We present deterministic algorithms that successfully solve the problem under the condition that the values of n and k are co-prime to each other. We also show how the problem of Labelled Map construction is related to other problems like leader election and rendezvous of agents.
On the Security of Fully Collusion Resistant Traitor Tracing Schemes

This paper investigates the security of FTT (fully collusion resistant traitor tracing) schemes in terms of DOT (Denial Of Tracing) and framing. With DOT attack, a decoder is able to detect tracing activity, and then prolongs the tracing process such that the tracer is unable to complete tracing job in a realistic time duration and hence has to abort his effort. On the other hand, by merely embedding several bytes of non-volatile memory in the decoder, we demonstrate, for the FTT schemes, how the decoder can frame innocent users at will. Furthermore, we propose a countermeasure on the framing attack.
Sparse representation using nonnegative curds and whey

It has been of great interest to find sparse and/or nonnegative representations in computer vision literature. In this paper we propose a novel method to such a purpose and refer to it as nonnegative curds and whey (NNCW). The NNCW procedure consists of two stages. In the first stage we consider a set of sparse and nonnegative representations of a test image, each of which is a linear combination of the images within a certain class, by solving a set of regressiontype nonnegative matrix factorization problems. In the second stage we incorporate these representations into a new sparse and nonnegative representation by using the group nonnegative garrote. This procedure is particularly appropriate for discriminant analysis owing to its supervised and nonnegativity nature in sparsity pursuing. Experiments on several benchmark face databases and Caltech 101 image dataset demonstrate the efficiency and effectiveness of our nonnegative curds and whey method.
Neuronal Substrates Underlying Performance Variability in Well-Trained Skillful Motor Task in Humans

Motor performance fluctuates trial by trial even in a well-trained motor skill. Here we show neural substrates underlying such behavioral fluctuation in humans. We first scanned brain activity with functional magnetic resonance imaging while healthy participants repeatedly performed a 10 s skillful sequential finger-tapping task. Before starting the experiment, the participants had completed intensive training. We evaluated task performance per trial (number of correct sequences in 10 s) and depicted brain regions where the activity changes in association with the fluctuation of the task performance across trials. We found that the activity in a broader range of frontoparietocerebellar network, including the bilateral dorsolateral prefrontal cortex (DLPFC), anterior cingulate and anterior insular cortices, and left cerebellar hemisphere, was negatively correlated with the task performance. We further showed in another transcranial direct current stimulation (tDCS) experiment that task performance deteriorated, when we applied anodal tDCS to the right DLPFC. These results indicate that fluctuation of brain activity in the nonmotor frontoparietocerebellar network may underlie trial-by-trial performance variability even in a well-trained motor skill, and its neuromodulation with tDCS may affect the task performance.
DDPSL: An easy way of defining properties

The paper proposes DDPSL (Drag and Drop PSL) a template library and a tool which simplifies the definition of PSL (Property Specification Language) formal properties by exploiting PSL-based templates. DDPSL allows users not expert in formal methods to define PSL properties by dragging and dropping logical and temporal operators, and variables from the design under verification (DUV) into predefined templates. Moreover, confident users or experts can extend the set of templates, reducing the effort required for formalizing complex properties. From the methodological point of view, DDPSL combines the advantages of both Open Verification Library (OVL) and PSL. Note that the templates are characterized by a parametric interface that separates the formal definition from its semantics, as provided by OVL. Moreover, the adoption of PSL as reference language guarantees the expressiveness of popular temporal logics such as Linear Temporal Logic (LTL) and Computational Tree Logic (CTL), which, on the contrary, are not fully supported by OVL. DDPSL has been successfully used to define properties for verifying an embedded application running on the microcontroller of an industrial oven.
EM-based recursive estimation of channel parameters

Recursive (online) expectation-maximization (EM) algorithm along with stochastic approximation is employed in this paper to estimate unknown time-invariant/variant parameters. The impulse response of a linear system (channel) is modeled as an unknown deterministic vector/process and as a Gaussian vector/process with unknown stochastic characteristics. Using these models which are embedded in white or colored Gaussian noise, different types of recursive least squares (RLS), Kalman filtering and smoothing and combined RLS and Kalman-type algorithms are derived directly from the recursive EM algorithm. The estimation of unknown parameters also generates new recursive algorithms for situations, such as additive colored noise modeled by an autoregressive process. The recursive EM algorithm is shown as a powerful tool which unifies the derivations of many adaptive estimation methods.
Fisher classifier and fuzzy logic based multi-focus image fusion

In this paper, we propose a new method of spatially registered multi-focus images fusion. Image fusion based on wavelet transform is the most commonly fusion method, which fuses the source images information in wavelet domain according to some fusion rules. We formulate image fusion process as a two class problem: in focus and out of focus classes. Two-class fisher classifier is used for this purpose and six dimensional feature vectors, which is obtained via dual-tree discrete wavelet transform sub-bands are used for training classifier. We use classifier output as a decision map for selecting wavelet coefficients between two images in the different directions and level of decomposition, equally. Also there is an uncertainty about selecting wavelet coefficients in the smooth regions of two images, which causes some misclassified regions. In order to solve this uncertainty and integrate as much information of each source image as possible into the fused image, we propose an algorithm based on fuzzy logic, which combines output of three fusion rules. This new method provides improved subjective and objectives results compared to the previous fusion methods.
La «production réflexive» : une alternative à la «production de masse» et à la «production au plus juste» ?

Fermee depuis 1993, l'usine Volvo d'Uddevalla reouvrira ses portes debut 1996, remettant ainsi en question les diverses argumentations deployees jusque-la pour expliquer sa fermeture. Cet article propose une comparaison terme a terme du travail sur chaine et en stations fixes. Il montre que, le toyotisme n'est finalement qu'un ensemble de remedes visant a pallier certains maux du fordisme sans s'ecarter radicalement de ce dernier. L'experience uddevallienne en revanche releve d'une conception du travail qui s'appuie sur les dimensions cognitives et cooperatives de l'action et qui remet en cause l'apparente necessite des operations de decomposition/recomposition et la fluidification du processus productif.
Wideband Doherty Power Amplifier for WiMAX Application

In this work, an integrated Doherty power amplifier in 0.18 im CMOS process is presented for WiMax applications at 2.5 GHz. Doherty configuration with cascode structure is adopted to achieve high efficiency and output power. The scaling and transistor size of main and peaking amplifier is iteratively determined to result optimum power splitting between main and peaking amplifiers. The uneven power splitting helps the peaking amplifier to generate enough current so that proper load modulation is achieved. The proposed power amplifier provides a maximum output power of 24 dBm and a power added efficiency (PAE) as high as 41% at 2.5 GHz operation frequency. At 1-dB compression point this PA exhibits 22 dBm of output power with 33% PAE.
An Extremely Large Vocabulary Approach to Named Entity Extraction from Speech

This paper describes an approach to named entity (NE) extraction from speech data, in which an extremely large vocabulary lexicon including all NEs occurring in a large text corpus is used for automatic speech recognition (ASR). Accordingly, NEs appear in the recognition results just as they are. Our approach is implemented by the following steps: (1) run an NE-tagger for a whole text corpus and make an NE-tagged corpus in which each NE is padded with its category, (2) construct a lexicon and a language model for ASR using the tagged corpus where each NE is considered as a regular word, and (3) run the speech recognizer in one pass. Although a very large vocabulary is necessary to ensure a high coverage of NEs, that is no longer a major problem since we recently achieved real-time extremely large vocabulary ASR using a WEST framework. In experiments on NE extraction from spoken queries for an open-domain question-answering system, our approach yielded higher F-measure values than a conventional approach
On the all-pairs Euclidean short path problem

Given a set of polygonal obstacles of n vertices in the plane’, the problem of processing the all-pairs Euclidean short path queries is that of reporting an obstacle-avoiding path P (or its length) between two arbitrary query points p and q in the plane, such that the length of P is within a small factor of the length of a Euclidean shortest obstacle-avoiding path between p and q. The goal is to answer each short path query quickly by constructing data structures that capture path information in the obstacle-scattered plane. For the related all-pairs Euclidean shortest path problem, the best known algorithms for even very simple cases (e.g., rectilinear shortest paths among disjoint rectangular obstacles in the plane) require at least quadratic space and time to construct a data structure, so that a length query can be answered in polylogarithmic time. The previously best known solution to the all-pairs Euclidean short path problem also uses a data structure of quadratic space and superquadratic construction time, in order to answer a length query in polylogarithmic time. In this paper, we present a data structure that requires nearly linear space and takes subquadratic time to construct. Precisely, for any given E satisfying 0 < E < 1, our data structure can be built in o(q3j2) iO((nlogn)/c) time and O(n log n + n/e) space, where q, 1 5 Q 5 n, is the minimum number of faces needed to cover all the vertices of a certain planar graph we use. This data structure enables us to report the length of a short path between two arbitrary query points in O((logn)/e + 1/c2) time and the actual path in O((logn)/E + l/~~ + L) time, where L is the number of edges of the output path. The constant approximation factor, 6 + E, for the short paths that we compute is quite small. Our techniques are parallelizable and can also be used to improve the previously best known results on several related graphic and geometric problems.