Miracle-dz/UniFAR-FTUs huggingface dataset:
Study on the Factors that Determine the Success of ERP Implementation

Abstract The world boundaries are broken with the advancement of technology and the emergence of digitized industries where economies are connected via the cloud. As a result, effective management of information internally and externally is needed to cope with the quick changes in markets. For a long time, Enterprise Resource Planning (ERP) systems have been used to streamline company processes data, and hierarchy. ERP’s are capable of managing big data, integrating business processes and providing effective communication channels within the entire organization. ERP’s provide many benefits to businesses if implemented right. However, an alarming 70% of ERP implementations fail. The failure can very well bankrupt the organization and at the same time affect the vendors very negatively. The aim of this paper is to discuss the critical success factors in ERP implementations from the employees, management, consultants and vendors perspectives. Prior research has identified critical success factors for ERP implementations but none classified them according to the above perspectives (employees, management, consultants and vendors). Surveys were conducted with 60 ERP project participants including senior managers, employees and vendors. A regression analysis was made to investigate the relation between the success of ERP implementations (dependent variable) and the contributions of employees, management, consultants and vendors (independent variables). The study shows that there exists a positive relation between successful ERP implementation and the following: management support, employees’ motivation, training, management, vision, and professional consultants. Add to that, the study aids managers and ERP participants to identify the major success factors that must be implemented for the objective of having successful ERP deployment.
Can They and Will They? Exploring Proxy Response of Sexual Orientation and Gender Identity in the Current Population Survey

Abstract Within the United States Federal Statistical System, there has been interest in capturing sexual orientation (SO) and gender identity (GI), collectively known as SOGI, on surveys to allow researchers to estimate the size and distribution of sexual and gender minority populations. SOGI measurement in federal surveys may also help to identify disparities between people who identify as lesbian, gay, bisexual, and transgender (LGBT) and those who do not in domains such as health, crime, or employment. Although research has been conducted on best practices for SOGI measurement in surveys, it has largely been limited to examination of self-reports. Many federal surveys use proxy reports, when one person generally responds for all household members. This research used cognitive interviews and focus groups to explore proxy response to SOGI questions. We explored potential sources of measurement error in proxy responses to SOGI questions, including sensitivity, difficulty, as well as the willingness and ability of respondents to answer SOGI questions about other household members. We also conducted paired interviews with members of the same household to assess level of agreement for SOGI questions. Findings suggest that measuring SOGI by proxy may be feasible in federal large-scale, general population surveys.
Mineral dust altering cloud microphysics and precipitation

Evidence of mineral dust altering cloud microphysics and precipitation Q. -L. Min, R. Li, B. Lin, E. Joseph, S. Wang, Y. Hu, V. Morris, and F. Chang Atmospheric Sciences Research Center, State University of New York, USA Science Directorate, NASA Langley Research Center, USA NOAA Center for Atmospheric Sciences, Howard University, USA Received: 2 June 2008 – Accepted: 15 September 2008 – Published: 3 November 2008 Correspondence to: Q. -L. Min (min@asrc.cestm.albany.edu) Published by Copernicus Publications on behalf of the European Geosciences Union.
Statistical and Cognitive Spatial Mapping Applied to Face Analysis

The natural process of pattern recognition has always played a key role in the species survival. For human beings the face recognition has become an essential tool in interpersonal relations and this subject always received attention in the computer vision area due to its applicability. It has been proposed recently, with promising results, a spatial mapping technique that highlights the most discriminating facial features employing some task driven information. The aim of this paper is to compare two distinct methods to define such spatial maps related to some recognition tasks. The first relies on statistical significant differences from the samples and the second on gaze data calculated from eye tracking experiments carried out. Keywords-task driven; statistical; cognitive; LBP; face analysis;
A Representative Consumer Model in Data-Driven Multi-Product Pricing Optimization

We develop a data-driven approach for the multiproduct pricing problem, using the theory of a representative consumer in discrete choice. We establish a set of mathematical relationships between product prices and demand for each product in the system, including that of the outside option. We provide identification conditions to recover the underlying representative consumer model and show that, with sufficient pricing experiments, the approach can identify the underlying demand model (more precisely, the associated perturbation function in the representative consumer model) accurately up to a constant shift and a given tolerance level. This holds even when the demand data obtained are a noisy realization of the theoretical demand. We use this approach to solve the multiproduct pricing problem using a (mixed integer) linear optimization method. Extensive tests using both synthetic and industry data clearly demonstrate the benefits of this approach, which addresses the issue of model misspecification in traditional pricing methods using discrete choice models and circumvents the computational issues associated with pricing methods that assume a known consumer valuation of each product. This paper was accepted by George Shanthikumar and Hamid Nazerzadeh, big data analytics.
Umbra: A Disk-Based System with In-Memory Performance

The increases in main-memory sizes over the last decade have made pure in-memory database systems feasible, and in-memory systems offer unprecedented performance. However, DRAM is still relatively expensive, and the growth of main-memory sizes has slowed down. In contrast, the prices for SSDs have fallen substantially in the last years, and their read bandwidth has increased to gigabytes per second. This makes it attractive to combine a large in-memory buffer with fast SSDs as storage devices, combining the excellent performance for the in-memory working set with the scalability of a disk-based system. In this paper we present the Umbra system, an evolution of the pure in-memory HyPer system towards a disk-based, or rather SSD-based, system. We show that by introducing a novel low-overhead buffer manager with variable-size pages we can achieve comparable performance to an in-memory database system for the cached working set, while handling accesses to uncached data gracefully. We discuss the changes and techniques that were necessary to handle the out-of-memory case gracefully and with low overhead, offering insights into the design of a memory optimized disk-based system.
CONSTRUCTION AND VERIFICATION OF AN EXPERIMENTAL CHAMBER DRYER FOR DRYING HOPS

Hop drying takes a significant part in growers’ costs of the final product processing. The current drying technology is based on drying at the drying air temperature of 55-60°C for 6-9 hours to the final moisture content of about 10%. However, the process results in irreversible transformations and losses of, inter alia, heat labile substances contained in hops. The paper describes the design and builds an experimental chamber dryer. The experimental chamber drier was tested at harvest 2015. Assays hop drying were carried out at a temperature of the drying medium 40°C. The research results in the form of an experimental new experimental chamber dryer will be used for testing of drying technologies at lower temperatures of the drying medium. This is what will make it possible to preserve the quality of aroma as well as other characteristics of the components contained in hops.
Making Parameter Dependencies of Time‐Series Segmentation Visually Understandable

This work presents an approach to support the visual analysis of parameter dependencies of time‐series segmentation. The goal is to help analysts understand which parameters have high influence and which segmentation properties are highly sensitive to parameter changes. Our approach first derives features from the segmentation output and then calculates correlations between the features and the parameters, more precisely, in parameter subranges to capture global and local dependencies. Dedicated overviews visualize the correlations to help users understand parameter impact and recognize distinct regions of influence in the parameter space. A detailed inspection of the segmentations is supported by means of visually emphasizing parameter ranges and segments participating in a dependency. This involves linking and highlighting, and also a special sorting mechanism that adjusts the visualization dynamically as users interactively explore individual dependencies. The approach is applied in the context of segmenting time series for activity recognition. Informal feedback from a domain expert suggests that our approach is a useful addition to the analyst's toolbox for time‐series segmentation.
Effect of Increasing the Plasma Phospholipase A2 
Mass on the Risk of Masked Hypertension in Humans

Background: Masked Hypertension (MHT) is associated with an increased risk for Cardiovascular Disease (CVD). The etiopathogenesis of MHT is thought to be affected by oxidative stress and vascular inflammation. This study aimed to analyze the relationships between Lipoprotein-Associated Phospholipase A2 (Lp-PLA2), a unique vascular inflammation marker, with blood pressure variation and traditional risk factors in patients with MHT, and to determine the clinical significance. Methods: One hundred eighty-three patients without any prior therapeutic medications were included and divided into the following three groups: MHT (n=82); True Hypertension (THT) [n=52]); and normotensive (n=59). An Ambulatory Blood Pressure Monitor (ABPM) was used. Clinical biochemical parameters and the Lp-PLA2 mass in each group were measured, and the related clinical characteristics and risk factors for CVD were statistically analyzed. Results: The level of Lp-PLA2 in MHT group was significantly higher than the normotensive (191.8 ± 62.58 vs.108.3 ± 44.74 ng/ml, p<0.01) and true hypertension groups (191.8 ± 62.58 vs. 169.3 ± 54.55 ng/ml, p<0.05). Furthermore, the incidence of MHT was correlated with the increase in Lp-PLA2, around 65% of MHT patients with a Lp-PLA2 level ≥ 225 μ mol/L. The Lp-PLA2 level had a positive correlation with ABPM measurements, office-measured systolic blood pressure, and serum Uric Acid (UA) and Low-Density Lipoprotein Cholesterol (LDL-C) levels, but a negative correlation with the High-Density Lipoprotein Cholesterol (HDL-C) level. Conclusion: An increased LP-PLA2 level was closely associated with the metabolic stress and incidence of MHT, thus exhibit an important role in the pathophysiology and diagnostic assessment of MHT.
Influence of Cashew Apple (Anacardium occidentale) on the Stress Level, Productivity, Organoleptic Property and Profitability of Broiler Chickens

The study aims to determine how feeding dried ground cashew apple pulp (DGCAP) and fermented cashew apple juice (FCAJ) affects the stress level, productivity, organoleptic property and profitability of broiler chickens. A total of 240 day old chicks were used and data was analyzed following 2x4 factorial CRD and significant differences between treatments was analyzed using Scheffe’s Test and Friedman’s Test. Broilers fed with 50 grams DGCAP significantly reduced the blood cortisol, lipid profile, enhanced the organoleptic property of cooked broiler meat in terms of texture, flavor, tenderness and general acceptability, improved the ADG, weight gain and FCR, and increase net income, income per bird and ROI. Fermented cashew apple showed significant effects on the blood cortisol, blood lipid profile, enhanced all the organoleptic property of cooked meat, increase dressing percentage and reduced abdominal fat pad. Moreover, FCA improves the growth rate and feed efficiency, net income, income per bird and ROI. Providing DGCAP and FCAJ significantly improved the blood cortisol and blood lipid profile, ADG, abdominal fat pad, net income, income per bird and ROI. Results suggest that feeding DGCAP and 30ml FCAJ were potential CP, energy and Vitamin supplement and potential alternative and natural feedstuff to reduced feed cost and consequently maximized profit in broiler production.
University of Birmingham Dysfunction of the mTOR pathway is a risk factor for Alzheimer’s disease

Background: The development of disease-modifying therapies for Alzheimer ’ s disease is hampered by our lack of understanding of the early pathogenic mechanisms and the lack of early biomarkers and risk factors. We have documented the expression pattern of mTOR regulated genes in the frontal cortex of Alzheimer ’ s disease patients. We have also examined the functional integrity of mTOR signaling in peripheral lymphocytes in Alzheimer ’ s disease patients relative to healthy controls. Results: In the brain mTOR is seen to control molecular functions related to cell cycle regulation, cell death and several metabolic pathways. These downstream elements of the mTOR signaling cascade are deregulated in the brain of Alzheimer ’ s disease patients well before the development of pathology. This dysregulation of the mTOR downstream signaling cascade is not restricted to the brain but appears to be systemic and can be detected in peripheral lymphocytes as a reduced Rapamycin response. Conclusions: The dysfunction of the signaling pathways downstream of mTOR may represent a risk factor for Alzheimer ’ s disease and is independent of the ApoE status of the patients. We have also identified the molecular substrates of the beneficial effects of Rapamycin on the nervous system. We believe that these results can further inform the development of clinical predictive tests for the risk of Alzheimer ’ s disease in patients with mild cognitive impairment.
Effective Speech Processing for Various Impaired Listeners

Normal hearing listeners are able to understand speech with different types of degradation, because speech has redundancy in the spectro-temporal domains. On the other hand, hearing impaired listeners have less such capability. Because of this, speech signal processing for hearing impairment needs to preserve important landmarks when enhancing a speech signal. The hearing impairments are characterized by high-frequency hearing loss, increase in the threshold of hearing, compression in the dynamic range, severity of temporal masking, and loss of spectral resolution due to the spread of masking. Thus, many studies on spectral and temporal enhancement have been proposed. In this paper we discussed effective speech processing techniques that we have developed to spectrally and temporally enhance speech signals for various types of impaired listeners. For the spectral enhancement of speech, we have proposed two approaches: critical-band based frequency compression and formant enhancement. In critical-band based frequency compression, band-limited signals in each critical band are compressed along the frequency axis. For formant enhancement, we proposed a technique based on FFT and linear predictive coding. These techniques reduce the interference between adjacent subbands and augment the distance between the peak and valley in a speech spectrum. Also, the suppression in spectral power in the lower frequencies results in a reduction of the masking effect in higher frequencies. For the temporal enhancement of speech, we proposed two approaches: modulation filtering and steady-state suppression. Both techniques emphasize the temporal dynamics of speech in the time domain. We previously found that the important frequency of temporal dynamics, or modulation frequency, for speech perception lie between 1-16 Hz, especially 4 Hz (Arai et al., JASA, 1999). Therefore, we proposed modulation filtering to emphasize these modulation frequencies. We further discussed steady-sate suppression (Arai et al., Acoust., Sci. & Tech., 2002) for improving speech intelligibility for hearing impaired listeners.
SOIL PHYSICAL PROPERTIES AS AFFECTED BY REPEATED WHEELING

A field experiment was carried out in the spring of 2015 to analyse the effect of field traffic on soil physical properties. A tractor used for wheeling and the experiment had a randomized block design with four replicate plots. Control and four repeated wheeling were used as treatments. Intact cores of 100 cm 3 were collected from and used for determination of soil mechanical and physical properties. The inflicted compaction was significantly increased bulk density at 10 and 20 cm depths, whereas the saturated hydraulic conductivity was reduced. At -6 and -30 hPa, the air-filled porosities were consistently lower for compacted soil than for control soil; whereas for most soil the values were higher than value proposed as the critical lower limit for plant growth. Our study hence documents that commonly used agricultural machinery may compact the soil to 0.3 m and even deeper with effect on important soil functions.
An Inclusive Study on Substantial Need for Healthcare IoT Architecture: An Architectural Review

In recent years Internet of Thing (IoT) devices have set a thrilling new trend in Modern Life Style. Also advances in wireless communication has led to an increased usage of IoT devices, particularly in healthcare field. IoT devices like sensors, wearable plays an important role in Healthcare IoT environment. Due to its benefit, an incredible number of IoT devices will be connected to the network and led to huge accumulation of data that makes complexity in data analysis and maintenance. To manage such an enormous number of IoT devices with an increased amount of multimodal data produced by them, an improved IoT Architecture is required. This paper mainly reviews some issues and significant needs for healthcare IoT architecture on the key considerations of Quality of Service (QoS), Big Data Technology, Analytic Techniques and Computing Level Paradigms in field of Health Informatics. Managing this role of data analytics and some computing architypes has turn out to be vital. Further, some Healthcare IoT based challenges and open issues are enumerated and outlined. KeywordsInternet of Things, Wearable Sensors, Wireless Communications, Health Informatics.
Understanding Uncertainty in Bayesian Neural Networks

In applications with strict safety or fairness requirements, such as self-driving cars, drug toxicity prediction or loan default prediction, understanding why a model made a decision can be as crucial as the prediction’s accuracy. Critical applications are also behind the surging mainstream interest in Bayesian deep learning. Bayesian neural networks are able to provide reliable uncertainty estimates together with their predictions. These can prevent automated systems from behaving erratically when faced with unforeseen circumstances. Surprisingly, there has been very little work in leveraging the uncertainty estimates provided by probabilistic models to make automated decisions more interpretable. This work develops CLUE, a new method for interpreting predictive uncertainty estimates using counterfactual generation. By searching for low uncertainty points that are near the input we wish to explain, CLUE answers the question: "How should we change an input such that it makes our model less uncertain? " A generative model is used to ensure that CLUE’s explanations consist of plausible input configurations. Unlike traditional interpretability methods, which are often limited to asking questions in terms of class probabilities, an uncertainty-based approach can provide insight into both regression and classification predictions. We also propose a clustering algorithm that groups similar CLUE explanations, allowing for the identification of the main sources of uncertainty within datasets. A novel functionally-grounded framework for the quantitative evaluation of uncertainty explanations is proposed. It uses a conditional generative model as a ground-truth generative process. This allows for the generation of data with which to train models as well as the evaluation of explanations’ uncertainty. CLUE is found to perform favourably compared to baseline methods using the aforementioned framework. Our qualitative assessment matches our numerical results, finding that CLUE generates cleaner and more intuitive saliency maps than the baselines.
Running head: BIRACIAL FACES REDUCE COLORBLINDNESS

Across six studies, we demonstrate that exposure to biracial individuals significantly reduces endorsement of colorblindness as a racial ideology among White individuals. Real-world exposure to biracial individuals predicts lower levels of colorblindness compared to White and Black exposure (Study 1). Brief manipulated exposure to images of biracial faces reduces colorblindness compared to exposure to White faces, Black faces, a set of diverse monoracial faces, or abstract images (Studies 2-5). Additionally, these effects occur only when a biracial label is paired with the face rather than resulting from the novelty of the mixed-race faces themselves (Study 4). Finally, we show that the shift in White participants’ colorblindness attitudes is driven by social tuning, based on participants’ expectations that biracial individuals are lower in colorblindness than monoracial individuals (Studies 5-6). These studies suggest that the multiracial population’s increasing size and visibility has the potential to positively shift racial attitudes.
Demo Abstract: Object Recognition in Visual Sensor Networks. A comparison of two different operative paradigms

This demo features a multi-hop Visual Sensor Network, capable of recognizing objects using two different visual paradigms. In the compress-then-analyze (CTA) paradigm, JPEG compressed images are transmitted through the network from a camera node to a central controller, where the analysis takes place. Conversely, in the analyze-then-compress (ATC) paradigm, the camera node extracts and compress local visual features from the acquired images and transmits them to the central controller, where they are used to perform object recognition. We show that, in a bandwidth constrained scenario, the latter paradigm allows to reach higher application frame-rates, still ensuring excellent recognition results.
Comparative analysis of the three editions of the national technical and working standards for AIDS testing

Objective To help HIV-related laboratory staff quickly understand and implement the new "National Technical Standard for HIV/AIDS Testing "(2009 revised edition). Method Variations were summarized by comparing three editions of the standards for HIV/AIDS testing,namely "the National Technical Standards for HIV/AIDS Testing(2009 revised edition and 2004 edition)",the National Working Standards for AIDS Testing(September 1997 Edition) and the "Guidelines for Quality Management of HIV Testing "(1997 edition). Results The new edition of the standards for HIV/AIDS testing more closely meets current developing technological requirements and contributes to the improvement of safety protection and quality management in HIV/AIDS testing laboratories and so is more practical and operational.