Miracle-dz/UniFAR-FTUs huggingface dataset:
SWEDISH "VIBY-I": ACOUSTICS, ARTICULATION, AND VARIATION

The paper investigates acoustic and articulatory variation in Swedish “Viby-i”, an /i:/ variant with an unusual vowel quality, characterised by a low F2. Viby-i appears to be part of a vowel shift, but its articulation is poorly understood, as speakers may be able to achieve it through multiple articulatory strategies. This study uses audio and ultrasound tongue imaging recordings of 34 speakers from Stockholm, Gothenburg, and Uppsala to explore variation in Viby-i acoustics, and its relation to socio-geographic and linguistic factors. It also examines the possibility of articulatory trade-off, comparing the relationship between tongue gesture and acoustic output. Viby-i is found to have a lower F2 in highly educated speakers, shorter vowel durations, and specific consonant environments. There is also a variable mapping between acoustics and articulation, such that F1 and F2 are partly disassociated from tongue height and backness. Possible compensatory behaviours are considered that could explain this phenomenon.
Classifying Short Text for the Harmonized System with Convolutional Neural Networks

Classifying goods in the hierarchical Harmonized System is a challenging problem for both humans and machines. To combat this, we propose a Convolutional Neural Network (CNN) architecture to label descriptions on the basis of short text descriptions. In order to obtain domain-specific word embeddings we identify and collect text data from several online resources, including DBpedia. We evaluate our approach on two data data sets, and find we can classify the HS-2 code (chapter) with an f1-score of 0.92 and 0.90, respectively. On the much more complex HS-4 (heading) level we find we can still classify among over 1200 classes with an accuracy of 0.88 and 0.84. We believe our gains are partly due to our architecture, which includes convolutional layers with filters of 2 to 5 words, batch-normalization, and more fully-connected layers than previous work. We theorize that this approach can also be applied to classify sub-headings and beyond and provides a strong baseline in this domain.
Chapter 9 from : Prions Current Progress in Advanced Research

Chronic wasting disease (CWD) is a prion disease of cervids characterized by clinical symptoms of progressive weight loss, abnormal behaviour and excessive salivation. Incidents have been reported in North America and Korea as well as in Europe. Current knowledge, based on in vitro and in vivo experiments, suggests direct CWD transmission to humans is unlikely. Nonetheless, humans may consume CWD-infected materials, which presents a potential risk. Studies indicate that transmission by horizontal infection of cervids probably occurs via saliva, faeces, and urine as well as from environmental reservoirs of prions found in soil and water. In addition, infectivity in the skeletal muscle of infected deer has been observed. These findings suggest that direct contact with infected animals and indirect contact with prion-contaminated materials are potential sources of infection. However, recent studies on the detection of pregnancy-related prion infectivity imply the potential transmission of CWD from mother to offspring. In this chapter, fundamental aspects of CWD are reviewed. History of chronic wasting disease (CWD) and its current status Chronic wasting disease (CWD) is a fatal neurodegenerative prion disease that affects cervids. CWD is transmitted between a range of cervid species such as mule deer (Odocoileus hemionus hemionus), black-tailed deer (Odocoileus hemionus columbianus), white-tailed deer (Odocoileus virginianus), Rocky Mountain elk (Cervus elaphus nelsoni), red deer (Cervus elaphus elaphus), elk (Cervus Canadensis), reindeer (Rangifer tarandus), and Shira’s moose (Alces alces shirasi), Reeves’ muntjac deer (Muntiacus reevesi) (Williams et al., 1980, 1982; Baeten et al., 2007; Nalls et al., 2013; Haley et al., 2015; Benestad et al., 2016). The first report of CWD described a wasting disorder in mule deer caught for nutrition
Ocena wiedzy żywieniowej poznańskich kajakarzy o bogatych źródłach witamin i składników mineralnych

The aim of this work was to evaluate the nutritional knowledge of Poznan kayakers and canoers on rich sources of vitamins and minerals. A questionnare on the knowledge about rich sources of vitamins and minerals were filled in by a group of 108 Poznan canoers and kayakers of both gender. The statistical analysis of the results was conducted with the use of the computer program SPSS 11,5. for Windows. The population was divided according to gender. The sportsmen questioned differed in a statistically significant way in regard to their sports class, type of boat (kayak or canoe) and the distance on which they usually swam during competitions. Gender had statistically significant influence on the answers to two questions, namely whether fruits are a rich source of B vitamins and if poultry is a rich source of iron. The male kayakers and canoers answered both of the questions better than the female kayakers. The nutritional knowledge of Poznan kayakers and canoers on rich sources of vitamins and minerals should be described as extensive, but not systematic enough.
Adapting Term Recognition to an Under-Resourced Language: the Case of Irish

Automatic Term Recognition (ATR) is an important method for the summarization and analysis of large corpora, and normally requires a signiﬁcant amount of linguistic input, in particular the use of part-of-speech taggers. For an under-resourced language such as Irish, the resources necessary for this may be scarce or entirely absent. We evaluate two methods for the automatic extraction of terms, based on the small part-of-speech-tagged corpora that are available for Irish and on a large terminology list, and show that both meth-ods can produce viable term extractors. We evaluate this with a newly constructed corpus that is the ﬁrst available corpus for term extraction in Irish. Our results shine some light on the challenge of adapting natural language processing systems to under-resourced scenarios.
THE EFFECT OF PITCH ON EMOTIONAL PROSODY PERCEPTION: EVIDENCE FROM BEHAVIORAL EXPERIMENTS ON AUTISTIC CHILDREN AND TYPICALLY DEVELOPING ADOLESCENTS

Acoustic cues, such as pitch, intensity and duration can be used as indicators of emotional states of speakers, among which pitch has been regarded as one of the most salient cues in reflecting different levels of emotional arousal. The current study explores the relationship between pitch and the ability of emotional prosody perception in individuals with high-functioning autism (HFA) and typically developing (TD) adolescents. An identification task was conducted using Mandarin emotional stimuli that varied in pitch level. Results showed that HFA individuals had much worse performance than TD individuals in emotional prosody identification. TD group was more sensitive to the change of pitch level than HFA group. The effect of pitch in identifying happiness is higher than that in identifying sadness.
Distributed Unit Clustering

Given a set of points in the plane, the unit clustering problem asks for finding a minimum-size set of unit disks that cover the whole input set. We study the unit clustering problem in a distributed setting, where input data is partitioned among several machines. We present a (3 + ε)-approximation algorithm for the problem in the Euclidean plane, and a (4 + ε)-approximation algorithm for the problem under general Lp metric (p ≥ 1). We also study the capacitated version of the problem, where each cluster has a limited capacity for covering the points. We present a distributed algorithm for the capacitated version of the problem that achieves an approximation factor of 4+ε in the L2 plane, and a factor of 5 + ε in general Lp metric. We also provide some complementary lower bounds.
Interface Technology Based on LabVIEW

The research adopts the classical visual evoked potentials paradigm, designs the stimulation program and experimental scheme base on the labview platform,and the way of visual stimulate is studied in this paper.In this way we can produce event-related potentials more effectively . On the platform in labview,achieve the event-related potential signal acquisition and processing.Explore how extract the weak signal event-related potentials in the background of strong noise rapidly and accurately,and structures based on event-related potentials of the brain machine interface platform.
Rate-optimal estimation of a high-dimensional semiparametric time series model

An emerging literature in high-dimensional statistics focuses on studying high-dimensional time series. However, there is little fundamental study on optimal estimation. This paper founds, for the first time in the literature, such a result. For this, we focus on the copula-based time series model. It includes the Gaussian vector autoregressive model as a sub-class. The temporal dependence of this model is fully characterized by the transition matrix A, which we aim to estimate. Under a low-rank assumption on A, we derive sharp upper and lower bounds for estimation. A key step to establishing the lower bound is through a novel analysis of the log determinant term in calculating the Kullback-Leibler divergence. For this, we observe a clear distinction from the analysis of the independent data, where the log determinant term is typically ignorable.
SRN: Stacked Regression Network for Real-time 3D Hand Pose Estimation

Recently, most of state-of-the-art methods are based on 3D input data, because 3D data capture more spatial information than the depth image. However, these methods either require a complex network structure or time-consuming data preprocessing and post-processing. We present a simple and accurate method for 3D hand pose estimation from a 2D depth image. This is achieved by a differentiable re-parameterization module, which constructs 3D heatmaps and unit vector ﬁelds from joint coordinates directly. Taking the spatial-aware representations as intermediate features, we can easily stack multiple regression modules to capture spatial structures of depth data for accurate and robust estimation. Furthermore, we explore multiple good practices to improve the performance of the 2D CNN for 3D hand pose estimation. Experiments on four challenging hand pose datasets show that our proposed method outperforms all state-of-the-art meth-ods with faster inference speed.
Recoverability and Expectations-Driven Fluctuations

Time series methods for identifying structural economic disturbances often require disturbances to satisfy technical conditions that can be inconsistent with economic theory. We propose replacing these conditions with a less restrictive condition called recoverability, which only requires that the disturbances can be inferred from the observable variables. We show how to check recoverability in any linear model, and use this condition to construct new identifying restrictions for technological and expectational disturbances. Using these restrictions in a vector-autoregressive analysis of postwar U.S. data, we find that independent disturbances to expectations about future technology are a major driver of business cycles. JEL classification: E32, D84, C32
Aalborg Universitet Designing on-site Facilitating Participatory Contextual Architecture with Mobile

Some movements within modern architecture particularly emphasise the importance of matching buildings to their surroundings. However, practicing such ‘‘contextual architecture’’ is highly challenging and typically not something the future inhabitants of a building are well equipped for participating in. This paper explores the potentials of using mobile phone technology for facilitating such client participation in the parts of an architecture process that take place on the building site. For this we introduce ArchiLens, a mobile system for interactive on-site 3D visualisation of houses, and findings from a field study with 40 participants in the process of building or modifying their home. The study showed that using the system helped evoke people’s imagination of the look and feel of their future house, and envision it in context. This enabled them to participatemore closely in the design process on-site by iteratively reviewing design alternatives and exploring, for example, other placements and materials. © 2012 Elsevier B.V. All rights reserved.
Robust Video Content Authentication using Video Binary Pattern and Extreme Learning Machine

Recently, due to easy accessibility of smartphones, digital cameras and other video recording devices, a radical enhancement has been experienced in the field of digital video technology. Digital videos have become very vital in court of law and media (print, electronic and social). On the other hand, a widely-spread availability of Video Editing Tools (VETs) have made video tampering very easy. Detection of this tampering is very important, because it may affect the understanding and interpretation of video contents. Existing techniques used for detection of forgery in video contents can be broadly categorized into active and passive. In this research a passive technique for video tampering detection in spatial domain is proposed. The technique comprises of two phases: 1) Extraction of features with proposed Video Binary Pattern (VBP) descriptor, and 2) Extreme Learning Machine (ELM) based classification. Experimental results on different datasets reveal that the proposed technique achieved accuracy 98.47%.
1 The General Class of Fréchet Risk Measures

We consider the concept of convex risk measures under the presence of ambiguity regarding the probability model of the risk. A number of plausible probability models, possibly diverging, are provided to the decision maker by various information sources or experts, however it is not a priori known which of these sources are valid. We employ the concept of Fréchet mean in order to define a new class of convex risk measures, the Fréchet risk measures, which are appropriate for this framework, and provide closed form expressions for them which clarify the interaction between risk and uncertainty. We also investigate the robustness of these measures after applying them in some standard applications, such as (a) the total claim amount estimation and (b) the selection of the static hedging strategy for contingent claims, both of these applications considered under the concept of uncertainty. The proposed risk measures are shown to be very effective in aggregating models in the set of priors, thus filtering out the effects of uncertainty and leading to robust decision making.
A Secured Approach for ID Authentication System using RFID and Fingerprint Technology

Authentication is a fundamental issue to any trustoriented computing system and also a critical part in many security protocols. In addition, authentication also serves as the first step for many other security purposes, such as key management and secure group communication [5]. Passwords or smartcards have been the most widely used authentication methods due to easy implementation and replacement; however, memorizing a password or carrying a smartcard, or managing multiple passwords/smartcards for different systems (one for each system),is a significant overhead to users. In addition, they are artificially associated with users and cannot truly identify individuals Performing authentication is notoriously difficult. Biometrics has been widely used and adopted as a promising authentication [8] method due to its advantages over some existing methods, particularly, its resistance to losses incurred by theft of passwords and smart cards. However, biometrics introduces its own challenges, Such as being irreplaceable once compromised. Biometrics can be used to prevent unauthorized access to ATMs, cellular phones, smart cards, workstations, and computer networks. It can be used during transactions conducted via telephone and internet (electronic commerce and electronic banking).
Exploring the Dynamics of Women Entrepreneurship : A Case Study of UAE

This study explores the dynamics of women entrepreneurs in the context of the United Arab Emirates. A review of the entrepreneurial environment and female entrepreneurship in UAE was first carried out through an analysis of extant literature and the exploration of personal characteristics and motivations with data drawn from 178 female entrepreneurs. The results of factor analysis show that self-determination, desire for change, crave for family life balance, occupational f lexibility, work responsibility, career advancement, and independence were the most important attributes for women entrepreneurs. Further, the result shows that majority of female entrepreneurs are less likely to settle down with their family before they decide to become entrepreneurs. In terms of education, majority have at least secondary education and some even post-graduate qualifications. In conclusion, women entrepreneurs should not be treated as a monolithic category: rather, policies and programs to support them should begin with a diagnosis of their personal characteristics and motives aimed at strengthening pull motives that comprise a base for more viable and innovative entrepreneurial activity.
A brief inspiration on poverty and climate change mitigation

Poverty and climate change mitigation are two concepts, which trouble policy makers and climate policy analysts, because they perceive them as conflicting public policy objectives. Poverty remains a pressing problem in many middleincome countries, where efforts for mitigating climate change become more urgent. Poverty alleviation often disappears as an explicit priority in the wider concepts like socio-economic, low-carbon or sustainable development. Mitigation strategies rarely directly address the fact that large parts of the population live below the poverty line. Development remains the main rationale for policy intervention, whereas poverty and mitigation need explicit prioritization. How can actions for poverty alleviation and climate mitigation be effectively combined? Three main concepts attempt to link climate change objectives and development policies in the negotiations. Firstly, so called ‘sustainable development policies and measures’ (SDPAM) address ‘entire socio-technical systems’ in order to identify institutional mechanisms that link climate change objectives with policies for sustainable development (Bradley & Baumert 2005; Roman 2008; Winkler et al. 2002 cited in Roman 2012). This South African proposal was first introduced to the convention in 2006 (UNFCCC 2006). The systemic concept of SD-PAM differs from the projectbased Clean Development Mechanism (CDM). Secondly, the CDM attempts to promote development through mitigation projects, which generate internationally-tradable carbon credits. These climate interventions are obviously no silver bullet for promoting development (Michaelowa & Michaelowa 2007). Further literature finds that renewable energy projects alleviate poverty best in rural areas (Crowe 2012; Holm Olsen et al. & Fenhann 2008) and residential energy efficiency improvements (Uerge-Vorsatz & Tirado Herrero 2012). Thirdly, the negotiations are concerned with Nationally Appropriate Mitigation Actions (NAMA). The CDM allows projects to generate carbon credits, but not policies. The NAMA approach might change this rule (Okubo et al. 2011) and makes the support through bilateral cooperation more flexible. NAMA is an emerging institutional mechanism, which tries to capture the priority to promote development as a vehicle to address climate change (Roman 2012). The framework of Poverty Alleviating Mitigation Actions (PAMA) allows for a more conceptual and practical debate about how to combine efforts to reduce emissions and improve people’s livelihoods at the same time. PAMAs need
Replacing Starch with Non-Forage Fiber Sources in Dairy Cow Diets

Lower dietary inclusion of costly grains can improve income over feed costs and potentially spare cereal grains for other more profitable uses. Non-forage fiber sources (NFFS) have been traditionally recommended to decrease cereal grain starch in the diet since they contain monosaccharides (Miron et al., 2001) and highly digestible fiber that can maintain or even improve the performance of dairy cattle (Bradford and Mullins, 2012). However, the effect of decreasing dietary starch concentration by partially replacing cereal grains with NFFS on the productivity of lactating dairy cows remains debatable.
Electromagnetic Analysis of a Novel Outer Rotor Permanent Magnet Generator Electromagnetic Analysis of a Novel Outer Rotor Permanent Magnet Generator

Electromagnetic Analysis of a Novel Outer Rotor Permanent Magnet Generator Kamalinni Mohan Raj, Aravind CV, R.N. Firdaus CIARG, Taylor’s University, Malaysia, Universiti Tecknical Malaysia, Melaka *Corresponding email: aravindcv@ieee.org Abstract— Newer types of synchronous generators were being developed for typical applications in the past decade due to the advancements in high power density permanent magnets. An outer rotor generator which can typically be applied for wind power and electric vehicle applications are proposed in this paper. The design of the structure is done through analytical calculations and the analysis to optimize the machine parameters are done through numerical analysis. This paper proposes the electromagnetic analysis of a novel outer rotor permanent magnet generator. The designed machine which has 18 slots and 20 poles generates a voltage of 150V when running at 1200 rpm.
Speech Organ Contour Extraction Using Real-Time MRI and Machine Learning Method

Real-time MRI can be used to obtain videos that describe articulatory movements during running speech. For detailed analysis based on a large number of video frames, it is necessary to extract the contours of speech organs, such as the tongue, semi-automatically. The present study attempted to extract the contours of speech organs from videos using a machine learning method. First, an expert operator manually extracted the contours from the frames of a video to build training data sets. The learning operators, or learners, then extracted the contours from each frame of the video. Finally, the errors representing the geometrical distance between the extracted contours and the ground truth, which were the contours excluded from the training data sets, were examined. The results showed that the contours extracted using machine learning were closer to the ground truth than the contours traced by other expert and non-expert operators. In addition, using the same learners, the contours were extracted from other naive videos obtained during different speech tasks of the same subject. As a result, the errors in those videos were similar to those in the video in which the learners were trained.
Are IP Initial Vowels Acoustically More Distinct? Results from LDA and CNN Classifications

Past results have suggested that initial strengthening (IS) effects target the contrastive phonetic properties of segments, with a maximization of acoustic contrasts in initial position of strong prosodic domains. Here, we investigate whether IS effects translate into a better acoustic discriminability within the French oral vowels system. Discriminability is assessed on the basis of classification results of two types of classifiers: a linear discriminant analysis (LDA) based on the four formants frequencies, and a deep convolutional neural network (CNN) based on spectrograms. The test set includes 720 exemplars of /i, y, e, ɛ, a, x, u, o, ɔ/ (with /x/=/ø, œ/) produced in a labial context, either in intonational phrase initial (IPi) or word initial (Wi) position. Classifiers were trained using a set of 4500 vowels extracted from a large read speech corpus. Results show a better discriminability of vowels (overall better classification rate) in IPi than in Wi with the two methods. Less confusion in IPi is found between rounded and unrounded, and between back and front vowels, but not between the vowels along the four-way height contrast. Less confusion between peripheral and central vowels also expresses a maximization of contrasts within the acoustic space in IPi position.