Miracle-dz/UniFAR-FTUs huggingface dataset:
The NDSC transcription system for the 2018 CHiME-5 Challenge

The National Digital Switching System Engineering and Technological R&D Center (NDSC) speech-to-text transcription system for the 2018 CHiME-5 is described. The time delay neural network (TDNN) and TDNN-long short term memory recurrent neural network (TDNN-LSTM) systems are trained using deep bottleneck features (BNF). Since the audio recordings from parallel worn microphone are available, the third system is trained, in which the alignments of audio recordings from Kinect device are generated from worn microphone audio recordings. At last, the minimum Bayes risk (MBR) combination was utilized to combine different systems and reduce WER further. The WER of our system on develop dataset is 74.61%, leading to a 6% absolute reduction comparing with the base-line system.
A NOTE ON RADIATION LOSS OF ZEROTH ORDER RESONATORS

A composite right/left-handed transmission line resonator with 3 unit cells is fabricated and its radiation loss is investigated. At the zeroth order resonance state, the resonator shows radiation loss, which is dominant over other conductor and dielectric losses. This paper investigates the radiation loss of zeroth order resonators from a quality-factor point of view. With the use of a metal shield, the quality factor is considerably increased through a reduction of the radiation loss. The increase in the quality factor is explained by means of the extracted parameters of the equivalent circuit model.
Using Twitter to Explore the Effect of Social Media Buzz on Sales Bachelor Thesis

9 Introduction . 10 1.1. Predictive Analytics . 11 1.2. Marketing and Predictive Analytics . 12 1.3. Traditional Marketing Research Methods for Forecasting . 12 1.4. Internet as Research Medium . 13 1.5. Twitter for Research Purposes . 14 Specifications of the Study . 16 2.1. Hypothesis 1 . 17 2.2. Hypothesis 2 . 18 2.3. YouTube as Complementary Variable . 18 2.4. Definition of Concepts . 19 2.5. Conceptual Model . 20 2.6. Contribution of our Research . 21 2.6. 1. Managerial Implications . 21 2.7. Expected Size of Effect . 22 Literature Review . 23 3.1. Historical Trends . 24 3.1.1. Historical Trend of the Research Strategy . 25 3.1.2. Historical Trend of the Population, Sample Size & Observations. 25 3.1.3. Historical Trend of the Variables . 26 3.1.4. Historical Trend of the Effect Size Parameters . 26 3.1.5. Historical Trend of the Confidence Intervals . 26 3.1. 6. Conclusion . 27 3.2. Evaluation of the Quality . 27 3.2.1. Citation Analysis . 27 3.2.2. Validity of Research Strategies . 28 3.2.3. Populations . 28
Probabilistic Collaborative Representation Learning for Personalized Item Recommendation

We present Probabilistic Collaborative Representation Learning (PCRL), a new generative model of user preferences and item contexts. The latter builds on the assumption that relationships among items within contexts (e.g., browsing session, shopping cart, etc.) may underlie various aspects that guide the choices people make. Intuitively, PCRL seeks representations of items reflecting various regularities between them that might be useful at explaining user preferences. Formally, it relies on Bayesian Poisson Factorization to model user-item interactions, and uses a multilayered latent variable architecture to learn representations of items from their contexts. PCRL seamlessly integrates both tasks within a joint framework. However, inference and learning under the proposed model are challenging due to several sources of intractability. Relying on the recent advances in approximate inference/learning, we derive an efficient variational algorithm to estimate our model from observations. We further conduct experiments on several real-world datasets to showcase the benefits of the proposed model.
Physico-chemical characteristics and quantitative structure-activity relationships of PCBs by Patrik

The polychlorinated biphenyls (PCBs) comprise a group of 209 congeners varying in the number of chlorine atoms and substitution patterns. These compounds tend to be biomagnified in foodwebs and have been shown to induce an array of effects in exposed organisms. The structural characteristics of the PCBs influence their potency as well as mechanism of action. In order to assess the biological potency of these compounds a multi-step quantitative structure-activity relationship (QSAR) procedure was used in the project described in this thesis. The ultraviolet absorption (UV) spectra were measured for all 209 PCBs, and digitised for use as physico-chemical descriptors. Interpretations of the spectra using principal component analysis (PCA) showed the number of ortho chlorine atoms and para-para substitution patterns to be significant. Additional physico-chemical descriptors were derived from semi-empirical calculations. These included various molecular energies, the ionisation potential, electron affinity, dipole moments, and the internal barrier of rotation. The internal barrier of rotation was especially useful for describing the conformation of the PCBs on a continuous scale. In total 52 physico-chemical descriptors were compiled and analysed by PCA for the tetrato hepta-chlorinated congeners. The structural variation within these compounds was condensed into four principal properties derived from a PCA for use as design variables in a statistical design to select congeners representative for these homologue-groups. The 20 selected PCBs have been applied to study structure-specific biochemical responses in a number of bioassays, and to study the biomagnification of the PCBs in various fish species. QSARs were established using partial least squares projections to latent structures (PLS) for the PCBs potency to inhibit intercellular communication, activate respiratory burst, inhibit dopamine uptake in synaptic vesicles, compete with estradiol for binding to estrogen receptors, and induce cytochrome P4501A (CYP1A) related activities. By the systematic use of the designed set of PCBs the biological potency was screened over the chemical domain of the class of compounds. Further, sub-regions of highly potent PCBs were identified for each response measured. For risk assessment of the PCBs potency to induce dioxin-like activities the predicted induction potencies (PIPs) were calculated. In addition, two sets of PCBs were presented that specifically represent congeners of environmental relevance in combination with predicted potency to induce estrogenic and CYP1A related activities.
PHOTOCATALYTIC DEGRADATION OF FORMALDEHYDE IN INDOOR AIR BY GRAPHENE / S , N-TiO 2 NANOCOMPOSITE PHOTOCATALYSTS USING A FLUORESCENT LAMP

Nowadays, people spend more than 80% of time indoor. Formaldehyde often appears in the indoor environment where can be escaped from furnishing, cleaning agents and paints. And it has been confirmed to be carcinogenic to the human body. Indoor air pollutants, often with low concentration, are not easy to be removed. Fortunately, photocatalytic oxidation is a suitable technology with low-cost and low energy consumption. In this study, the nanocomposite photocatalysts were prepared through doping sulfur, nitrogen and graphene (rGO) in titanium dioxide by a solvothermal method. The XRD and TEM results show that crystallite sizes of rGO/S0.05N0.1TiO2 are reduced by introduction of graphene sheets. The FTIR spectra show that the rGO/S0.05N0.1TiO2 composites produce new chemical defect and bonding by introducing oxygencontaining functional groups, further enhance the photocatalytic effect. rGO can enhance the overall photocatalytic efficiency due to its excellent electron transport capacity and high specific surface area, but excess rGO could shield the light. In this study, 0.1 wt% rGO is the best doping amount. The reaction rate of formaldehyde is mainly affected by water vapor concentration. Langmuir-Hinshelwood model 4 is best fitted to this study. The reaction mechanism is also verified by FTIR. Formaldehyde may be first converted to formic acid, then formate, carbon monoxide, and finally carbon dioxide.
A COMPREHENSIVE THERMAL-AWARE POWER MANAGEMENT SYSTEM WITH BLOCK-LEVEL OPTIMIZATION IN 100NM CMOS TECHNOLOGY

Modern SoC integrations and mobile systems have emphasized low power techniques due to shortage of battery life. Conventional power management designs focused on the reduction of dynamic power consumption, recent designs begin to take leakage power into consideration since it becomes an important factor in nano-scale CMOS technology. Latest development has taken advantage of modularity in SoC design methodology to develop the block-level control technique for power reductions. However, thermal gradient over the system and its impacts to SoC designs are barely discussed. In this research, a block-level optimization of comprehensive thermal aware power management is presented. The proposed design applies several low power techniques to control different power sources and handles thermal impacts to provide performance coherence. As a result, optimal powerreductions and performance coherence can be guaranteed within the whole system. The simulation results show a significant improvement in stability and leakage power reduction for most circuitries. The results are based on TSMC 100nm CMOS technology.
Fast and accurate population structure prediction using a greedy support vector machine clustering algorithm

Identification of population structure is a central problem in population and medical genetics. The model-based approach implemented in STRUCTURE and the k-means clustering on the principal component projection are two techniques that have been shown to accurately separate admixed populations. We introduce a greedy support vector machine clustering algorithm for solving this problem. Support vector machines are discriminative classification methods with strong theoretical guarantees. We demonstrate its performance on several real datasets. We show that our method obtains comparable accuracies than STRUCTURE and k-means applied to the principal component projection. Further, our algorithm is fast and can incorporate known ancestry of individuals.
A ROBUST FAULT TOLERANT CONTROL FRAMEWORK: APPLICATION TO A SOLAR POWER PLANT

A robust fault-tolerant control framework is proposed and applied to a Distributed Solar Collector field. The framework considers a model-based approach to estimate the system’s state variables for supervision and fault diagnosis purposes and a model -based predictive control combined with a recurrent neural network. The supervisor system includes a fault detection filter to generate residual signals, which are used to is olate and identify the faults. The control system is based on a nonlinear adaptive constrained model-based predictive control scheme with steady-state offset compensation. To assess the controller’s robustness and fault tolerance, some faults on the actuator, sensors and system parameters were considered.
Comparing performance of HyperV and VMware considering network isolation in virtual machines

Cloud computing is the set of resources and services which are offered through the internet. These resources are delivered globally from data centers located throughout the world. Cloud services use virtualisation technology to provide better resource management. The biggest challenge facing cloud computing is security as resources are shared between various users. In addition, recent technological improvements in virtualisation are also the reason for breaches in the security of cloud services. To overcome this problem to some extent, isolation of virtual machines is provided. This will ensure security and protection of applications and cloud services from malicious attacks that arises due to resource sharing in virtualisation. Virtual machines are enabled with VLANs and their performance is measured using benchmarking tools. Network isolation is crucial to enhance the performance of virtual machines present in any hypervisors. Isolation of virtual machines will greatly remove the overheads of one VM influencing the performance of another virtual machine. Results delivered from the tests indicate that there is little difference between the two hypervisors in terms of performance, when they are considered in individual VMs. However, VMware proves to deliver better performance than HyperV. Virtual machine isolation at hardware-level and network isolation is considered suitable to resource allocation for virtual machines and to improve their performances.
Title of Thesis: Quantitative Detection of Cold Flow in Transdermal Drug Delivery System

Title of Thesis: Quantitative Detection of Cold Flow in Transdermal Drug Delivery System Yuxiang Zhao,Master of Science, 2014 Thesis Directed By: Stephen W. Hoag, PhD, Professor, Department of Pharmaceutical Sciences School of Pharmacy Cold flow, by definition is the migration of adhesive beyond the boundaries of the transdermal drug delivery system (TDDS). It can result in poor transdermal product performance on the skin, decrease in the drug flux across the skin and undesirable drug exposure. These problems usually show up on storage and can only be detected by stability studies. However, once a transdermal product is approved, a company typically does not test the stability of every production batch. Hence, the first aim of this study was to investigate the relationship between the cold flow degree and the physical-mechanical properties of the TDDSs. The second aim was to investigate the feasibility of near infrared spectroscopy (NIRS) to quantitatively predict cold flow. For this study, Durotak87-4098 ® was selected as a model pressure sensitive adhesive (PSA). Triacetin and triethyl citrate were used as plasticizers, respectively, mixing with Durotak87-4098 ® to formulate the prototype transdermal products. Thermally induced cold flow studies, static/dynamic shear adhesion tests and a pressure induced cold flow study were conducted to understand the relationship between the product shear adhesion and cold flow degree. A near infrared (NIR) model was developed; shear adhesion testing data and the amount of induced cold flow were used as the reference values to calibrate the model. This study shows that plasticizer content in a PSA can affect the product shear adhesion; the product shear adhesion is highly related to the cold flow degree. The NIR data was analyzed by using a series of chemometric methods like principal components analysis (PCA) and partial least square regression (PLS) showing that the NIR spectroscopy is able to quantitatively predict the shear adhesion of the transdermal product at the time of production. Quantitative Detection of Cold Flow in Transdermal Drug Delivery System
An Exploratory Study into the Causes of Conflict and the Effect of Conflict Management Style on Outcome in a Competitive Workplace

This study was conducted to explore the causes of conflict among coworkers in a competitive workplace. Data were collected from a local campus of an International Private Schooling System using participant observation and semi-structured interviews. The data analysis identified five major causes of conflict. The impact of conflict management style and approach on outcome were also explored. Finally, the usefulness of research findings has been discussed.
Infectious Bronchitis Virus Detection and Isolation from Broiler Chicken

Infectious bronchitis virus was detected in tissue samples of 16.39% (n=10) non-vaccinated commercial broiler flocks showing respiratory and or nephrotic lesions by real time RT-PCR and RT-PCR amplification of 5’-UTR and N gene. Tissue/ swab suspensions from all 10 flocks were inoculated in 9-11 day old SPF embryonated chicken eggs by allantoic route. Nephropathogenic IBV was isolated from samples of 2 farms from Vijaywada region of Andhra Pradesh. Necropsy of birds from above two farms revealed lesions of swollen and mottled kidneys in many birds and visceral gout in few birds. Histopathological examination confirmed nephropathogenicity of the isolates. S1 gene amplification from two IBV isolates viz. BVC/2017/VSD/ IBV/South/1 and BVC/2017/VSD/South/2 and subsequent sequencing revealed 92.7 to 96% amino acid sequence identity with the sequences of Indian nephropathogenic isolates published earlier. Amino acid sequences of two isolates shared 73.8%, 75.3% and 72.1% identity with M41, H120 and 4-91 type as respectively, which indicated substantial genotypic divergence from M41 and H120 (major respiratory and vaccine strain) and 4-91 strain. Of the samples from 8 flocks with respiratory lesions (positive for IBV by RT-PCR however, negative on isolation); 6 were found positive for Mycoplasma gallisepticum and or Avian influenza (H9) by PCR and RT-PCR test respectively.
Modified Pagerank Algorithm Based Real-Time Metropolitan Vehicular Traffic Routing Using GPS Crowdsourcing Data

Abstract: This paper aims at providing a theoretical framework to find an optimized route from any source to destination considering the real-time traffic congestion issues. The distance of various possible routes from the source and destination are calculated and a PathRank is allocated in the descending order of distance to each possible path. Each intermediate locations are considered as nodes of a graph and the edges are represented by real-time traffic flow monitored using GoogleMaps GPS crowdsourcing data. The Page Rank is calculated for each intermediate node. From the values of PageRank and PathRank, the minimum sum term is used to find an optimized route with minimal trade-off between shortest path and real-time traffic.
Colorants: General survey

Abstract This survey shows how colorants are not only ubiquitous in modern life, but also pervade many fields of science and technology. It reveals their diversity with respect to properties, origin of color, applications, composition and usage. After opening with a technical definition of “colorant”, this survey exemplifies the huge assortment of uses to which colorants are put. It highlights the breadth of characteristics, other than color, that are of importance to the roles of “functional colorants”. Following a description of the technical distinction between the terms “dye” and “pigment”, the survey discusses the differences between these two colorant types from the perspective of how their color arises and the ways in which they are exploited, then goes on to demonstrate that particular colorants may be applied in certain instances as a dye and in others as a pigment. It outlines the wide ranges of composition, properties and economics of commercial colorants. This survey closes with a look at the nomenclature of colorants and the means by which they are classified into practically useful categories. It is intended to prepare the reader for the enormous technical and commercial variety that one encounters when dealing generally with colorants.
Exploring E2E speech recognition systems for new languages

Over the last few years, advances in both machine learning algorithms and computer hardware have led to signiﬁcant improvements in speech recognition technology, mainly through the use of Deep Learning paradigms. As it was amply demonstrated in different studies, Deep Neural Networks (DNNs) have already outperformed traditional Gaussian Mixture Models (GMMs) at acoustic modeling in combination with Hidden Markov Models (HMMs). More recently, new attempts have focused on building end-to-end (E2E) speech recognition architectures, especially in languages with many resources like English and Chinese, with the aim of overcoming the performance of LSTM-HMM and more conventional systems. The aim of this work is ﬁrst to present the different techniques that have been applied to enhance state-of-the-art E2E systems for American English using publicly available datasets. Secondly, we describe the construction of E2E systems for Spanish and Basque, and explain the strategies applied to overcome the problem of the limited availability of training data, especially for Basque as a low-resource language. At the evaluation phase, the three E2E systems are also compared with LSTM-HMM based recognition engines built and tested with the same datasets.
A multivariate analysis of the causal flow between renewable energy consumption and GDP in Tunisia

This paper examines the causality linkages between economic growth, renewable energy consumption, CO2 emissions and domestic investment in Tunisia between 1971 and 2010. Using the ARDL bounds testing approach to cointegration, long-run relationships between the variables are identified. The Granger causality analysis, on the other hand, indicates that there is bi-directional causality between renewable energy consumption and economic growth, which supports the feedback hypothesis in Tunisia. In addition, the quantity of CO2 emissions collapses as a reaction to an increase in renewable energy consumption. These findings remain robust even when controlling for the presence of structural break. We conclude that more efforts should be undertaken to further develop a suitable infrastructure to the renewable energy sector, given its enhancing-effects on economic growth and reducing-effects on CO2 emissions.
Microclimatic Volatility and Elasticity of Glacier Skiing Demand

The effects of climate conditions and weather on ski tourism have become a hot topic as the negative impacts of climate change on ski tourism become increasingly visible. This study aims at measuring the significance and magnitude of long-term diurnal and daily weather conditions on lift frequentation at an Austrian glacier ski area in terms of the winter and the summer skiing offers. In doing so, it utilizes an autoregressive conditional heteroskedasticity (ARCH) model to reveal and quantify any volatility associated with ski area visitation and then employs regression models to account for any microclimatic elasticity of glacier skiing demand. The main findings reveal a significant volatility in ski area visitation, especially during the summer seasons. While this study does not aim for an ultimate determination on the reasons for such volatility, skiing demand models illustrate the importance of thermal comfort, especially wind chill factor (WCF), as a major determinant of demand sensitivity for glacier skiing as well as non-skier visits during the summer season. Significant, albeit inelastic, relationships between other microclimatic characteristics, such as snow depth and relative humidity, with visitation and lift frequentation are also identified. Based on these findings, implications according to a changing climate and practical suggestions on the sustainability of winter and summer skiing activities are provided.
How Biased Is Your NLG Evaluation? (short paper)

Human assessments by either experts or crowdworkers are used extensively for the evaluation of systems employed on a variety of text generative tasks. In this paper, we focus on the human evaluation of textual summaries from knowledge base triple-facts. More specifically, we investigate possible similarities between the evaluation that is performed by experts and crowdworkers. We generate a set of summaries from DBpedia triples using a state-of-the-art neural network architecture. These summaries are evaluated against a set of criteria by both experts and crowdworkers. Our results highlight significant differences between the scores that are provided by the two groups.