Miracle-dz/UniFAR-FTUs huggingface dataset:
Forecast Information Quality and Supply Chain Performance : An Empirical Study of Air Conditioning Industry

This paper presents an empirical study of the perceived forecast quality of a supplier and uses that quality to explain the impact of forecast information access and forecast information quality (FIQ) on supply chain performance. Forecast information quality (FIQ) was defined with 4 variables, in time, accurate, convenient to access, and reliable, derived from the theoretical framework of FIQ (English, 1999; Petersen, 1999; Moberg et al., 2002). Supply chain performance deals with 3 dimensions, corrective actions, preventive actions and customer service related metrics which reflect cost, tied-up capital and customer service (Brewer and Speh, 2000). The analysis in this research was based on a survey of the most important suppliers of Thailand air-conditioning manufactures. A t-test was used to analyze the significant differences in supply chain performance between suppliers with access to customer forecasts and suppliers without access to forecasts and Pearson correlation and linear regression were used to analyze the correlation between supply chain performance and FIQ. The findings showed that supply chain performance was positively correlated with FIQ but there was not a significant difference in supply chain performance between suppliers with access to customer forecasts compared to suppliers without access to forecasts. The study also indicated less than 40% of variability in supply chain performance was explained by perceive FIQ and more than 60% would be explained by other factors. FIQ also showed quality deficiencies on some variables, which indicates that there is room for improvement in forecasting. Customers and supply managers should consider the perceived forecast quality in order to reduce supplier costs and provide good customer service and also reduce the total cost of the supply chain. [Mohammad Asif Salam. Forecast Information Quality and Supply Chain Performance: An Empirical Study of Air Conditioning Industry. Life Sci J 2014;11(4s):49-58]. (ISSN:1097-8135). http://www.lifesciencesite. com. 6
Cloud Deep Networks for Hyperspectral Image Analysis

Advances in remote sensing hardware have led to a significantly increased capability for high-quality data acquisition, which allows the collection of remotely sensed images with very high spatial, spectral, and radiometric resolution. This trend calls for the development of new techniques to enhance the way that such unprecedented volumes of data are stored, processed, and analyzed. An important approach to deal with massive volumes of information is data compression, related to how data are compressed before their storage or transmission. For instance, hyperspectral images (HSIs) are characterized by hundreds of spectral bands. In this sense, high-performance computing (HPC) and high-throughput computing (HTC) offer interesting alternatives. Particularly, distributed solutions based on cloud computing can manage and store huge amounts of data in fault-tolerant environments, by interconnecting distributed computing nodes so that no specialized hardware is needed. This strategy greatly reduces the processing costs, making the processing of high volumes of remotely sensed data a natural and even cheap solution. In this paper, we present a new cloud-based technique for spectral analysis and compression of HSIs. Specifically, we develop a cloud implementation of a popular deep neural network for non-linear data compression, known as autoencoder (AE). Apache Spark serves as the backbone of our cloud computing environment by connecting the available processing nodes using a master–slave architecture. Our newly developed approach has been tested using two widely available HSI data sets. Experimental results indicate that cloud computing architectures offer an adequate solution for managing big remotely sensed data sets.
Analysis of eBook Lending : A Game-Theory

Retailers have seen a steady and continuous increase in eBooks sales in recent years. A new strategy to further promote eBook sales is to provide eBook lending option. For example, Amazon.com allows Kindle eBook buyers to lend their eBooks for 14 days. The free eBook lending option will add additional utility to eBook buyers such as exchanging for other eBooks or reducing uncertainties of purchasing new books. However, such strategy might cannibalize the retailers' sales of print books. Using a game theory model, we explore whether the retailer will be better off to introduce the lending program. We derive the equilibrium price and identify the conditions under which the retailer can offer eBook lending option and generate more revenue.
Are Streets Indicative of Place Types?

Places, and here more specifically Points of Interest (POI), can be described by characteristics such as their location, names, capacity, atmosphere, accessibility, reviews, opening hours, prices of services or products they offer, and so forth. Most importantly, however, places can be categorized into place types, e.g., museum or fire station. These types are best understood as proxies for a wide range of latent characteristics that we do not typically model explicitly in an information system. For example, we would expect to hear sirens nearby fire stations, find parking restrictions nearby, etc. Nonetheless, many modern (geographic) information retrieval systems treat place types as labels, i.e., atomic tokens. The same can be said about names of places and their locations, e.g., addresses. With regards to place (type) embedding, for instance, such a view ignores the cultural structure of these types, names, and addresses, thereby loosing important information. In this work we will show that addresses, here street types, are more than just atomic tokens. They are indicative of the types of places we can expect to encounter. Both the proximity to and suffix of streets are investigated to model the interaction between place types and streets, which are thereby used as a new set of spatial signatures to characterize the semantics of place types. Rui Zhu STKO Lab, Department of Geography, University of California, Santa Barbara, e-mail: ruizhu@geog.ucsb. edu Grant McKenzie Platial Analysis Lab, Department of Geography, McGill University e-mail: grant.mckenzie@mcgill.ca Krzysztof Janowicz STKO Lab, Department of Geography, University of California, Santa Barbara, e-mail: jano@geog.ucsb. edu
Relations Between Cyclicity and Regulation in Mother-Infant Interaction at 3 and 9 Months and Cognition at 2 Years

This report addresses the relation between early interactive rhythms OS determined by microanalysis and later toddler cognition. Thirty-six mother-infant dyods were videotaped in free play at 3 and 9 months. Mother and infant attentive states were recorded on an attentive-affective scale in . 25s intervals and analyzed using timeseries techniques. Synchrony between time-series of mother ond infant wos examined with cross correlations. At 2 years children were tested with the Stanford-Binet Intelligence Scale. A stochastic-cyclic organization of the infant’s attention at 3 months (a pattern reflecting some degree of oscillation between attentive states) predicted general and verbal IQ. At 9 months, organized but not cyclic infont play predicted general IQ. Two measures of maternal regulation at 3 months, mother synchrony with the infant assessed by microanalysis and maternal regulation ossessed globally, predicted visual IQ. The temporol orgonization of infant social ottention was individually stable from 3 to 9 months and had concurrent and Ion 3 -term correlations with mother-infant synchrony. Results are discussed in terms of in ormation processing, the relations of biological, social, and cognitive regulatory mechanisms, and the associations between selfand mutual regulation during the first year and cognitive competence.
Data-driven Chance Constrained Programming based Electric Vehicle Penetration Analysis

Transportation electrification has been growing rapidly in recent years. The adoption of electric vehicles (EVs) could help to release the dependency on oil and reduce greenhouse gas emission. However, the increasing EV adoption will also impose a high demand on the power grid and may jeopardize the grid network infrastructures. For certain high EV penetration areas, the EV charging demand may lead to transformer overloading at peak hours which makes the maximal EV penetration analysis an urgent problem to solve. This paper proposes a data-driven chance constrained programming based framework for maximal EV penetration analysis. Simulation results are presented for a real-world neighborhood level network. The proposed framework could serve as a guidance for utility companies to schedule infrastructure upgrades.
Micro level factors leading to a migratory decision:
Migrant groups in the Czech Republic

This paper combines the results of research targeting several distinct migrant communities living and working in the Czech Republic. This research further develops work which documented the experience of highly educated Slovak workers in the Czech Republic by including additional linguistic groups. Preliminary findings uncovered a nuanced perspective which elucidated the tendency of young individuals to seek out opportunities in the 'near abroad' whereas experienced workers became migrants only after ensuring the economic benefits of making a move. Further work encompassing a larger cohort has indicated that Individual agency plays a key role in individual decision making. The project has elucidated the complexity of micro level causal factors in relation to migratory decision making. While this work demonstrates that a network effect exists in some cases rational choice and cultural commonalities also play a significant role leading up to migration and indeed in perpetuating flows. It has also become clear that some groups are intent on settlement in the Czech Republic. Non-Technical Summary Research focused on the experience of migrants in the Czech Republic demonstrates the importance of taking into account a variety of factors when discussing migration intentions. This paper has used information from two distinct surveys, one focused on Slovak nationals and a second including other foreigners working in the Czech Republic. The research has found that individual choice is an important factor that must be included in any discussion about why individuals choose to move. In addition existing connections with family or kin are of great importance as they provide support to migrants upon and after arrival. The work discusses the complexity of flows into the Czech Republic and how variation leads to difficulty in applying existing theoretical constructs.
A Model for Prioritization of Improvement Opportunities Based on Quality Costs in the Process Interdependency Context

In this paper, the PAF (preventive-appraisal-failure) quality costs model is used on the process level, in order to identify those processes which are critical as a top-priority for improvement. The elements of quality costs are considered in relation to the outputs of the processes, where the principle that one process affects the quality of another process by its outputs is taken into account. That is why the failure costs of the affected processes should also be assigned to the quality costs of the observed process. Using the factors proposed in the Process cost model, costs are identified and calculated for each process for two moments: the current (before improvements) and desired (after improvements). Improvements are implemented on the basis of the causes of nonconformity analysis and according to the PAF model assumption that preventive and appraisal activities reduce failure costs and the total costs of quality. The difference between the total costs of quality in two observed moments is seen as the greatest possible quality cost reduction for the observed process. This value is used as a criterion for ranking the processes according to their priority for improvements, taking into account the assumption of the PAF model that greater costs mean lower quality and hence a higher priority for improvement.
MOdality and THought Unified-Mothú

Mothú translates to the Irish word for feeling, and it is this intrinsically complex cognitive process which we aim to understand through the program which was created as part of this research project. A large percentage of human communication consists of non-verbal communication, although in comparison with its verbal counterpart, non-verbal sentiment analysis remains a relatively new field. The non-verbal sentiment analysis systems currently available, although they are at the cutting edge of technology, they have not been widely adopted due to usability issues. The program presented in this dissertation aims to analyse facial-expression and speech sentiment data in order to produce results which will allow the user to gain an insight into the sentiment felt by the subject, reducing the burden of interpretation. Previous studies which apply sentiment analysis techniques to the study of leaders have often focused on the male sex. This research aims to expand these prior studies by conducting an extensive investigation on the sentiment of a sample of female leaders. This study will consider the sample of leaders in both emergency and non-emergency situations to determine if a deviance within their expression of emotions can be determined. From the statistical analysis performed and the graphical representations produced, a deviance within the behaviour of the leaders was found through unimodal analysis and then validated via multimodal analysis. As the validity of results presented in this project are a consequence of the accuracy of the sentiment analysis systems used, an evaluation of the performance of these systems was conducted. FACET, used for facial expression analysis, was found to be an accurate system whereas openSMILE used for speech analysis was found to have some inaccuracies. Therefore, the precision of the audio sentiment analysis must be improved for multimodal analysis such as this to be used for practical applications.
The I 2 R-NWPU-NTU Text-to-Speech System at Blizzard Challenge 2016

In this paper, we introduce a trajectory tiling method guided by deep neural networks (DNNs) for text-to-speech (TTS), which is the entry to Blizzard Challenge 2016 by I2R-NWPU-NTU team. We build a deep bidirectional LSTM (DBLSTM) based network to predict the phoneme level duration and frame level acoustic parameters. After the acoustic parameters are predicted, the best units are selected from the database using a trajectory tiling method. Experiments demonstrate that, under the DBLSTM framework, the context information of a phoneme extracted in text processing will help the duration prediction, while not help the acoustic modeling. The results of subjective evaluation are also discussed.
Three-Dimensional Reconstruction Using the Depth Map

This paper presents an approach to reconstructing 3D objects based on the generation of dense depth map. From a two 2D images (a pair of images) of the same 3D object, taken from different points of view, a new grayscale image is estimated. It is an intermediate image between a purely 2D image and a 3D image where each pixel of this image represents a z-height according to its gray level value. Our objective therefore is to play on the precision of this map in order to prove the interest and effectiveness of this map on the quality of the reconstruction.
Goodness of Fit Tests of Laplace Distribution Using Selective Order Statistics

In this paper, a comparison for the power and efficiency of a set empirical distribution function(EDF) goodness of fit tests for the Laplace distribution is studied. Moreover, a new method to improve the power of these tests under the ranked set sampling is introduced. Using simulation, under ranked set sampling (RSS), the test are more powerful than their counterparts in simple random sampling (SRS) is shown. Also, the percentage points of these tests under the null hypotheses is obtained.
AKBL at NTCIR-14 QA Lab-PoliInfo Task

The local council's proceedings are useful as research materials and as a basis for selecting one among candidates during elections, but they are large in volume. For this reason, it would be helpful for many people to be able to identify the source from a summary published on the Web and automatically summarize the utterance.Therefore, in this paper, using the dataset of the Tokyo Metropolitan Conference proceedings provided by NTCIR-14 QA Lab-PoliInfo, we worked on the automatic specification of the original utterance extent (Segmentation Task) and the summary of the utterance (Summarization Task) . In the Segmentation Task, the extraction extent is specified by the number of lines in the conference proceedings, and the result is evaluated by its Precision, Recall, and F-measure. In the Summarization Task, the ROUGE and human evaluation are performed. In addition, it is difficult for individuals to conduct fact-checking to cope with fake news that is disseminated to a large amount of information on the Internet. Therefore, we use the dataset of the Tokyo Metropolitan Congress Proceedings, which is also provided by NTCIR-14 QA Lab-PoliInfo, and work on estimation of fact-checkability against utterances and classification of utterances when it is possible to check facts (Classification Task). In the Classification Task, labels support, against and other are assigned to an utterance and the results are evaluated by its Precision, Recall, F-measure. We also made a decision on the relevance of the target policy.
Model Predictive Control for Multi-Phase Buck Converter with Variable Output Voltage

13 Model Predictive Control for Multi-Phase Buck Converter with Variable Output Voltage Abstract Nowadays most of the power converters are controlled by analog regulators but the consistent reduction of the price of electronic components and their increasing performance over the past years are favoring the use of digital controllers in this area. Among the advantages of digital controllers we can mention greater flexibility of use, the implementation of advanced control techniques, more integration, less sensitivity to temperature changes and the possibility of remote communication. For these reasons, digital controllers have become a major focus of research in the area of power converter control. Following these research trends, this work attempts to implement a linear control for a Multi-Phase Buck converter to take advantage of future information of the output voltage reference and to improve its tracking. In most cases this information is not available but there are applications in which variations in the reference are known in advance. Examples include power amplifiers for radio frequency transmitters and microprocessors. Starting from this idea, a control able to effectively use such information based on the predictive control technique is proposed. The first step was to define several models of the converter used in different steps of the control design. A linear discrete-time model of the converter is used to obtain a prediction of the output voltage in the number control intervals specified by the prediction horizon. The prediction is compared on-line with the future values of the reference and the error is properly weighed to determine the current value of the control variable, in this case the duty cycle. A second model based on the MultiFrequency Small-Signal approach is used during the design process. The parameters of the controller are chosen in order to minimize the error between output voltage and a specified reference. A third Switched-Model is used to obtain the validation of the controller in simulations. Several control architectures are suggested in this work. The main difference among them lies in the definition of the error and in the use of an explicit integral action. The definition of control variable can depend only on the error between output voltage and its reference, but it can also be defined as the error between a proper reference and all the state variables. In the second case there is a problem of how to define a reference for all the state variables. A new simpler definition is proposed, which is simpler than the only met in the current literature. A second problem concerns the feasibility of control and the instability of the overall system due to delays in the microcontroller. This was solved by introducing a delay compensator, based on the technique of the Kalman prediction.
Immunocompetent adult infected with Epstein-Barr virus presenting as severe respiratory insufficiency : a case report

Epstein-Barr virus (EBV) is an ubiquitous human herpes virus with worldwide distribution. Severe EBV infections are mostly reported in immunocompromised patients, but not in immunocompetent individuals. Here, we reported an immunocompetent female adult suffering from an acute EBV pulmonary infection, leading to bilateral pneumonitis with respiratory failure. This case indicated combined therapy (steroids, immunoglobulin and antiviral agents) is recommended in EBV-induced severe respiratory insufficiency.
DISPLAY OF OUTLINES , CONTOURS AND SURFACE FEATURES FROM VOLUME DATA

Volume visualization is an ever-growing field of computer graphics with multiple applications in the representation and analysis of 3D scalar data. Such data often comes with a high degree of complexity, making it a challenging task to render a proper 2D image which highlights relevant information while discarding other less significant data. In this paper, we present techniques for the rendering, identification and representation of various features which are meaningful to the human vision system, such as contours, outlines or various surface shapes. We use several approaches, based on the orientation of gradient vectors or on image processing techniques, and show how they can be successfully employed to highlight relevant details from volume data. We illustrate the differences between images obtained through 3D rendering directly and those subjected to contour and feature identification techniques and show how these provide a better visual understanding of the underlying data.