Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic syllabification using segmental conditional random fields

In this paper we present a statistical approach for the automatic syllabification of phonetic word transcriptions. A syllable bigram language model forms the core of the system. Given the large number of syllables in non-syllabic languages, sparsity is the main issue, especially since the available syllabified corpora tend to be small. Traditional back-off mechanisms only give a partial solution to the sparsity problem. In this work we use a set of features for back-off purposes: on the one hand probabilities such as consonant cluster probabilities, and on the other hand a set of rules based on generic syllabification principles such as legality, sonority and maximal onset. For the combination of these highly correlated features with the baseline bigram feature we employ segmental conditional random fields (SCRFs) as statistical framework. The resulting method is very versatile and can be used for any amount of data of any language. The method was tested on various datasets in English and Dutch with dictionary sizes varying between 1 and 60 thousand words. We obtained a 97.96% word accuracy for supervised syllabification and a 91.22% word accuracy for unsupervised syllabification for English. When including the top-2 generated syllabifications for a small fraction of the words, virtual perfect syllabification is obtained in supervised mode.
Inference on Causal and Structural Parameters using Many Moment Inequalities

This article considers the problem of testing many moment inequalities where the number of moment inequalities, denoted by $p$, is possibly much larger than the sample size $n$. There is a variety of economic applications where solving this problem allows to carry out inference on causal and structural parameters; a notable example is the market structure model of Ciliberto and Tamer (2009) where $p=2^{m+1}$ with $m$ being the number of firms that could possibly enter the market. We consider the test statistic given by the maximum of $p$ Studentized (or $t$-type) inequality-specific statistics, and analyse various ways to compute critical values for the test statistic. Specifically, we consider critical values based upon (1) the union bound combined with a moderate deviation inequality for self-normalized sums, (2) the multiplier and empirical bootstraps, and (3) two-step and three-step variants of (1) and (2) by incorporating the selection of uninformative inequalities that are far from being binding and a novel selection of weakly informative inequalities that are potentially binding but do not provide first-order information. We prove validity of these methods, showing that under mild conditions, they lead to tests with the error in size decreasing polynomially in $n$ while allowing for $p$ being much larger than $n$; indeed $p$ can be of order $\exp (n^{c})$ for some $c > 0$. Importantly, all these results hold without any restriction on the correlation structure between $p$ Studentized statistics, and also hold uniformly with respect to suitably large classes of underlying distributions. Moreover, in the online supplement, we show validity of a test based on the block multiplier bootstrap in the case of dependent data under some general mixing conditions.
CBWL for sectorized cellular communications

Channel Borrowing Without Locking (CBWL) is a family of channel assignment schemes for cellular communication systems. They allow real-time borrowing of channels from adjacent cells without the need for channel locking in co-channel cells. In this paper, an analysis of co-channel interference for CBWL schemes in sectorized cellular systems is presented. Two typical configurations are analyzed. One has 1200 directional antennas and a reuse factor of 7. The other has 600 directional antennas and a reuse factor of 4. The analysis shows that CBWL can significantly enhance the traffic capacity of sectorized cellular systems at the cost of a slight increase of co-channel interference in comparison with the corresponding sectorized scheme without CBWL. The research reported in this paper was supported in part by the U.S. National Science Foundation under Grant No. NCR 90251:31and in part by 1ST/SOlO under Grant No. NOOO1491-J406:3, administered by the U.S. Office of Naval Research.
The Influence of Per-hectare Premiums on Prices for Rented Agricultural Area and on Agricultural Land Prices

Within the framework of decreasing support for agricultural incomes by market measures, the per-hectare premiums (ha-premiums) clearly have the task and the effect to stabilise farm incomes. Before direct aids were introduced, there had been periods with decreasing prices for agricultural land and for rented agricultural area in real terms, according to the Farm Accountancy Data Network (FADN) data base (specialised crop farms in 53 regions for 11 years) and EUROSTAT data (B, DK, D-W, F, NL in the period 1975 to 1999). Regression analysis is used to show the relationship between hapremiums, land rental prices and other relevant economic variables, such as land prices, rental share, livestock density, the price index of agricultural products and farm income. The analysis shows increasing price effects due to the introduction of ha-premiums on prices for agricultural land and on rental prices. Landlords received a share of about 6 % to 18 % of the ha-premiums (depending on the data source). An in-sample simulation, 1999-continuation scenario, and scenarios of further CAP (Common Agricultural Policy) reforms are used for the assessment of the ex-post and ex-ante developments. The ongoing reforms of the CAP (Agenda 2000, Mid-term Review) and EU enlargement certainly have implications for the land markets.
Protoplast production and isolation from Etlingera elatior

The technique of hybridization using plant protoplasts is widely used in plant breeding programs. The purpose of our study is to further characterize the process of protoplast isolation from the ornamental species Etlingera elatior (Jack) R. M. Smith. Protoplasts were isolated from different tissues: in vitro leaves, in vitro pseudostem, and leaves from plants cultivated hydroponically. We tested six enzymatic combinations, four incubation time periods, the rotary system (40 rpm) or steady in the dark, and three concentrations of mannitol (0.5, 0.6 and 0.7 M). The diameter and viability of obtained protoplasts were evaluated. The best source of explants used for protoplast isolation was the in vitro leaves, which yielded 22x105 protoplasts g-1 of fresh matter. The optimal incubation period was 15 hours. The in vitro leaves presented a greater viability (96%) and larger protoplasts (36.7 µm diameter). Greater yields were obtained using a rotatory system with protoplasts incubated in the dark. The best enzymatic combination was 3% Cellulase “Onozuca” R-10 + 2% Meicelase + 1% Driselase + 1% Dextran + 5 mM MES, followed by the addition of 0.6 M mannitol.
Learning General Features From Images and Audio With Stacked Denoising Autoencoders

One of the most impressive qualities of the brain is its neuro-plasticity. The neocortex has roughly the same structure throughout its whole surface, yet it is involved in a variety of diﬀerent tasks from vision to motor control, and regions which once performed one task can learn to perform another [ ? ]. Machine learning algorithms which aim to be plausible models of the neocortex should also display this plasticity. One such candidate is the stacked denoising autoencoder (SDA). SDA’s have shown promising results in the ﬁeld of machine perception where they have been used to learn abstract features from unlabeled data. [ ? , ? , ? ] In this thesis I develop a ﬂexible distributed implementation of an SDA and train it on images and audio spectrograms to experimentally determine properties comparable to neuro-plasticity, Speciﬁcally, I compare the visual-auditory generalization between a multi-level denoising autoen-coder trained with greedy, layer-wise pre-training (GLWPT), to one trained without. I test a hypothesis that multi-modal networks will perform better than uni-modal networks due to the greater generality of features that may be learned. Furthermore, I also test the hypothesis that the magnitude of improvement gained from this multi-modal training is greater when GLWPT is applied than when it is not. My ﬁndings indicate that these hypotheses were not conﬁrmed, but that GLWPT still helps multi-modal networks adapt to their second sensory modality.
Optimal switch configuration in software-defined networks

The emerging Software-Defined Networks (SDN) paradigm facilitates innovative applications and enables the seamless provisioning of resilient communications. Nevertheless, the installation of communication flows in SDN requires careful planning in order to avoid configuration errors and to fulfill communication requirements. In this paper we propose an approach that installs automatically and optimally static flows in SDN switches. The approach aims to select high capacity links and shortest path routing, and enforces communication link and switch capacity limitations. Experimental results demonstrate the effectiveness and scalability of the developed methodology.
Sonochemical Oxidation of Cyanide Using Potassium Peroxydisulfate as an Oxidizing Agent

In this study, the sonochemical oxidation of cyanide in the presence of peroxydisulfate (PDS) ions has been investigated. As a result of experiments, sulfate anion radicals (·SO4-) and hydroxide radicals (·OH) produced by PDS ions in acoustic cavitation could readily oxidize and even mineralize cyanide in an aqueous environment. When the frequency of ultrasound and PDS ions were applied at 450 kHz and 60 mg·L-1, the efficiencies of mineralization for free cyanide (CN-) and thiocyanate (SCN-) were 99.3 and 99%, respectively. The kinetics of total organic carbon (TOC) reduction for CN- and SCN- oxidation were interpreted using the modified pseudo-first-order kinetic model. In this study, carbonate and nitrate were found to be by-products of CN- oxidation while sulfate, carbonate and nitrate were by-products of SCN- oxidation.
Approximated Solutions to Operator Equations based on the Frame Bounds

We want to find the solution of the problem ${\L} u = f$, based on knowledge of frames. Where ${\L} : H \to H$ is a boundedly invertible and symmetric operator on a separable Hilbert space $H$. Inverting the operator can be complicated if the dimension of $H$ is large. Another option is to use an algorithm to obtain approximations of the solution. We will organize an algorithm in order to find approximated solution of the problem depends on the knowledge of some frame bounds and the guaranteed speed of convergence also depends on them.
TheGaussianMixtureMCMCParticleAlgorithm for DynamicClusterTracking

We present a novel filtering algorithm for tracking multiple clusters of coordinated objects. Based on a Markov Chain Monte Carlo (MCMC) mechanism, the new algorithm propagates a discrete approximation of the underlying filtering density. A dynamic Gaussian mixture model is utilized for representing the time-varying clustering structure. This involves point process formulations of typical behavioral moves such as birth and death of clusters as well as merging and splitting. For handling complex, possibly large scale scenarios, the sampling efficiency of the basic MCMC scheme is enhanced via the use of a Metropolis within Gibbs particle refinement step. As the proposed methodology essentially involves random set representations, a new type of estimator, termed the probability hypothesis density surface (PHDS), is derived for computing point estimates. It is further proved that this estimator is optimal in the sense of the mean relative entropy. Finally, the algorithm’s performance is assessed and demonstrated in both synthetic and realistic tracking scenarios.
Improved Learning Methodology System

Education is one of the systems that have been always upgrading. During past years the teaching process has been always modified and upgraded in order to enable more efficient learning. The use of new ICT technologies enables innovative ideas to make the learning process more efficient. The students gain with better skills and obtained knowledge by these innovations. We established an e-learning system that supports the education process. This system offers not just content available on-line by using web technologies, but also a wide variety of simulators, animations and films as multimedia approach to the sys- tem. In addition to this system we use a system of interactive quizzes and question- naires as part of the self testing tool that help students understand basic concepts and acquire skills. The system of e-testing helps the professor to assess the knowledgeand grade the students. The on-line learning tool is the interactive system that supports the homework assignment and grading tool and helps both the students to learn and the professors to check the knowledge obtained. In this paper we report the results from using these innovationsas improvedlearn- ing methodology, and how it affects the level and degree of obtained knowledge and skills, students' interest and average score in total knowledge. We present indicators to measure the results gained by introducing new method- ology. These indicators concern quantity and quality measures, obtained by analyzing the pass rate and average scores in the class.
Bearing Fault Detection by One-Dimensional Convolutional Neural Networks

Bearing faults are the biggest single source of motor failures. Artificial Neural Networks (ANNs) and other decision support systems are widely used for early detection of bearing faults. The typical decision support systems require feature extraction and classification as two distinct phases. Extracting fixed features each time may require a significant computational cost preventing their use in real-time applications. Furthermore, the selected features for the classification phase may not represent the most optimal choice. In this paper, the use of 1D Convolutional Neural Networks (CNNs) is proposed for a fast and accurate bearing fault detection system. The feature extraction and classification phases of the bearing fault detection are combined into a single learning body with the implementation of 1D CNN. The raw vibration data (signal) is fed into the proposed system as input eliminating the need for running a separate feature extraction algorithm each time vibration data is analyzed for classification. Implementation of 1D CNNs results in more efficient systems in terms of computational complexity. The classification performance of the proposed system with real bearing data demonstrates that the reduced computational complexity is achieved without a compromise in fault detection accuracy.
Increasing the efficiency of microfluidic micromixer with gaps and baffles using design of experiments based on Taguchi method

In this study, the geometrical parameters of a micromixer with gaps and baffles are optimized by using design of experiments based on the Taguchi method. Design of micromixers with gaps and baffles to increase the mixing efficiency is crucial in various biological applications such as DNA analysis. The arrangement of gaps and baffles significantly affects the fluid flow structure and mixing. In some cases, arrangement of obstacles outweigh design of framework. The effects of four design parameters are investigated on two important characteristics of a mixer namely mixing efficiency and pressure drop. A weighted profit function is defined to combine these two characteristics. The computational fluid dynamics (CFD) is used to simulate the fluid flow and mixing in three-dimensional models. For this numerical study, the commercial CFD package, COMSOL, is used. The analyses of the numerical results are then shown in signal to noise (S/N) ratio plots indicating which combination of parameters leads to a maximum profit. It is found that a micromixer with the optimized geometry compared to its initial design increases the mixing index by 15% on average in the Reynolds numbers ranging from 1 to 10.
The Moroccan Association of Agricultural Economics (AMAECO)

Climate variability, the short-term fluctuations in the weather and agriculture affect each other. Climate variability affects the agroecological and growing conditions of crops and livestock, and is recently believed to be the greatest impediment to the realisation of the first Millennium Development Goal of reducing poverty and food insecurity in arid and semi-arid regions of developing countries. Conversely, agriculture is a major contributor to climate variability and change by emitting greenhouse gases and reducing the agroecology’s potential for carbon sequestration. What however, is the empirical evidence of this inter-dependence of climate variability and agriculture in Sub-Sahara Africa? In this paper, I provide some insight into the long run relationship between inter-annual variations in temperature and rainfall, and annual yields of the most important staple food crops in Northern Ghana. Applying pooled panel data of rainfall, temperature and yields of the selected crops from 1976 to 2010 to cointegration and Granger causality models, there is cogent evidence of cointegration between seasonal, total rainfall and crop yields; and causality from rainfall to crop yields in the Sudano-Guinea Savannah and Guinea Savannah zones of Northern Ghana. This suggests that inter-annual yields of the crops have been influenced by the total mounts of rainfall in the planting season. Temperature variability over the study period is however stationary, and is suspected to have minimal if any effect on crop yields. Overall, the results confirm the appropriateness of my attempt in modelling long-term relationships between the climate and crop yield variables.
The post-rumen digestion model The late

The Cornell Net Carbohydrate and Protein System (CNCPS) post-rumen digestion model calculates nutrient absorption in the small intestine from microbial cell, feed residue and endogenous secretion composition, but thereafter it only makes use of one class of nutrient, the absorbed amino acids (AA). This is because apparently digested protein, carbohydrate and fat are amalgamated in the calculation of total digested nutrients (TDN) as a measure of energy supply, which is then converted to digested energy (DE), metabolizable energy (ME) and net energy for lactation (NEL). At the maintenance level of feeding, this results in TDN values for feeds 0.5 to 2.5% below those listed in NRC (1988). ME values predicted at a typical production feeding level (L = 4) are about 6% below the listed maintenance level values, when a reduction of only about 5% would be predicted. The model makes no direct use of NRC (1988) tabulated values for the TDN, DE, ME or NEL of feeds. A sensitivity test of the supply model revealed that variations of ±10% in many of the input parameters were without significant effect upon the nutrient supply measured as TDN or metabolizable protein (MP) supply. Variations in dietary crude protein (CP) concentration affected rumen N and peptide supply, and MP supply from undegraded intake protein (UIP). Plasma urea N and milk urea N varied ±20% because of a multiplier effect within the model, twice the imposed CP% variation. Variations in neutral detergent fibre concentration (NDF%) affected effective NDF (eNDF) and microbial protein synthesis. Variations in the rate of degradation of cell walls (NDF) caused a mean change of ±7.7% in microbial efficiency, whereas variations in the degradation rates for the A and B1 carbohydrate (CHO) fractions were without effect on microbial efficiency. Variations in starch content (B l fraction of CHO) quantitatively affected microbial production, but this was offset by a consequential reduction in the size of the A fraction (sugars and soluble CHO) of CHO, i f all other 204 CORNELL SYSTEM FOR DAIRY CATTLE parameters were held constant. Variations in the fat content of the diet produced significant effects upon predicted TDN and ME values, because of the high gross energy of fat and the multiplier (2.25) used on fat percentage in the calculation of TDN values. Published tests of the CNCPS claim that it can predict non-ammonia nitrogen (NAN) outflows from the rumen of dairy cattle adequately. There are weaknesses in the statistical analysis of the NAN data, particularly the pooling of data from growing cattle with that from dairy cows. Later work has shown that NAN supply in the CNCPS is over predicted with high undegraded protein diets, since any shortage of rumen degraded protein does not reduce microbial protein synthesis.
Development of a customised scale to measure service quality in the luxury cruise liner industry

The services marketing literature focuses on the measurement of service quality and its impact and consequences on businesses. This is important as service quality affects behavioural outcomes such as repeat purchases and loyalty. Research in this respect has been concentrated in the banking, financial, insurance and health care sectors. However in recent times several quantitative and qualitative studies pertaining to service quality have been undertaken in the hospitality industry. This study examines how service quality operates in the cruise liner industry, an industry that is now facing overcapacity. Based on data collected from travellers of a luxury cruise liner we analysed their perceptions of service quality using a customised scale which had seventy five attributes. Factor analysis of these attributes revealed a four factor solution. Liner Service Personnel was the most important factor. This is not surprising as cruise liners are highly service-oriented. Luxury cruise operators would be well advised to review our customised scale and possibly use the findings to enhance their service quality and hence their competitive edge.
The Effect of Audit Team Norms on Auditor Objectivity

Legislators have enacted laws and standards to develop the ethical conduct among auditors. However, unethical behaviours among auditors still take place. Audit standards require auditors to understand objectivity risks and do something to avoid contact with these risks. The suggested reason behind constrained auditor objectivity has been devoted to auditors' financial incentives and long audit tenure. Regulators propose auditor rotation against constrained auditor judgment induced by the auditor-client relationship. Recent research has challenged those assumptions and questioned the potency of auditor rotation to counteract shorttenure risks to auditor objectivity. This study examines whether audit team norms improve the objectivity of auditor judgment. A cross-sectional design was used and responses from Iranian auditors were analyzed statistically. The findings indicate that audit team norms are associated with improved auditor objectivity.
CCD and HI observations of LSB dwarf galaxies in the general field

CCD B and V observations of 16 low-surface-brightness galaxies and HI data for a subsample of 4 of them are presented. Both paired and isolated dwarf galaxies are observed to study possible environmental eects on their characteristics. Most of the observed galaxies are true dwarfs with 8.0 MB 16.0, blue colours (0.16 B V 0.76) and low central surface brightnesses 22.2 B 24.0 mag arcsec 2 . Most of them show exponential light proles with a central light depression. About half of them have bright generally blue knots or peculiar loops.