Miracle-dz/UniFAR-FTUs huggingface dataset:
Using SVM and Random forest for different features selection in predicting bike rental amount

Nowadays, people rely on bike renting service for transportation in short distance to replace walking. It is more convenient and faster for people to transfer from place to place. Public transportation is very popular for people to go to work or school. However, there might not be so many stations to let everyone arrive at the place where they want to go. If it takes too much time from stations to destination, it will make people have less willingness in taking public transportations. Bike renting system like U-bike solves this problem. The need for bike renting leads to a question of setting bike rental locations and the number of bikes in each place, by predicting the number of people renting bikes in each position can make it easier for governments to assign bikes to each position. When predicting the bike rent amount, there are lots of features to consider with, like the weather, time, holiday. Using more features doesn’t mean to be better, so the selection of the feature is essential. In this paper, we proposed a method which will combine random forest and support vector machine to predict the bike rental amount from the last hour. Experiments results will discuss random forest, super vector machine and the combination of the two methods results.
HawkEye: Efficient Fine-grained OS Support for Huge Pages

Effective huge page management in operating systems is necessary for mitigation of address translation overheads. However, this continues to remain a difficult area in OS design. Recent work on Ingens uncovered some interesting pitfalls in current huge page management strategies. Using both page access patterns discovered by the OS kernel and fine-grained data from hardware performance counters, we expose problematic aspects of current huge page management strategies. In our system, called HawkEye/Linux, we demonstrate alternate ways to address issues related to performance, page fault latency and memory bloat; the primary ideas behind HawkEye management algorithms are async page pre-zeroing, de-duplication of zero-filled pages, fine-grained page access tracking and measurement of address translation overheads through hardware performance counters. Our evaluation shows that HawkEye is more performant, robust and better-suited to handle diverse workloads when compared with current state-of-the-art systems.
Redistribution of heterotrophic prokaryotes through ballast water: a case study from the west coast of Canada.

Oceangoing ships contribute to the introduction of invasive, benign and pathogenic bacteria via ballast water discharge. Here we report the bacterial abundance and cell size in ballast and receiving port waters in Vancouver, British Columbia (Canada) ports during 2007 and 2008. Bacterial abundance in port water (7.5×10 8 to 3.4×10 9 cells L -1 ) was significantly (P < 0.001) higher than those in ballast water (2.5×10 8 to 2.1×10 9 cells L -1 ) and was higher in unexchanged than ballast water that was exchanged at-sea. There was no significant difference in bacterial abundances between 2007 and 2008 for each sample type. Bacterial cell volume showed a different pattern, with no significant difference among sample types and a two-fold larger average cell volume during 2007 than 2008. Bacterial abundance and cell volume in ballast water were not correlated with ballast water age, end-of-voyage temperature, salinity or pH. The absence of predictive relationships between measured physiochemical and bacterial variables in ballast water highlights the difficulty of predicting bacterial abundance or cell volume from the physiochemical factors alone. Future studies should focus on the bacterial community structure in ballast and port waters, the fate of bacteria in the new environment, and regional susceptibility to invasion by the introduced bacteria.
Valuing Customer Portfolios under Risk-Return-Aspects: A Model-based Approach and its Application in the Financial Services Industry

For identifying and selecting the most profitable customers in terms of the shareholder value, the Customer Lifetime Val-ue (CLV) gained broad attention in marketing literature. However, in this paper, the authors argue that the CLV does not take into account the risk associated with customer relationships and consequently does not conform to the principle of shareholder value. Therefore, a quantitative model based on financial portfolio selection theory is presented that considers the expected CLV of customer segments as well as their risk. The latter includes the correlation among the segments. It is shown how imperfect correlation among segments may be employed to maximize the value of the customer portfolio. Since portfolio selection theory does not allow for the consideration of fixed costs, it is extended by a heuristic method consisting of two algorithms, referred to as “subtract”- and “add”-approaches.
A Morphological Analyzer for Shipibo-Konibo

We present a fairly complete morphological analyzer for Shipibo-Konibo, a low-resourced native language spoken in the Amazonian region of Peru. We resort to the robustness of finite-state systems in order to model the complex morphosyntax of the language. Evaluation over raw corpora shows promising coverage of grammatical phenomena, limited only by the scarce lexicon. We make this tool freely available so as to aid the production of annotated corpora and impulse further research in native languages of Peru.
A Data Reconstruction Method for The Big-Data Analysis

In recent years, the big-data approach has become important within various business operations and salesjudgment tactics. Contrarily, numerous privacy problems limit the progress of their analysis technologies. To mitigate such problems, this paper proposes several privacy-preserving methods, i.e., anonymization, extreme value record elimination, fully encrypted analysis, and so on. However, privacy-cracking fears still remain that prevent the open use of big-data by other, external organizations. We propose a big-data reconstruction method that does not intrinsically use privacy data. The method uses only the statistical features of big-data, i.e., its attribute histograms and their correlation coefficients. To verify whether valuable information can be extracted using this method, we evaluate the data by using Self Organizing Map (SOM) as one of the big-data analysis tools. The results show that the same pieces ofinformation are extracted from our data and the big-data.
Automatic Registration of Multi-Source Data Using Mutual Information

Automatic image registration is a basic step in multi-sensor data integration in remote sensing and photogrammetric applications such as data fusion. The effectiveness of Mutual Information (MI) as a technique for automated multi-sensor image registration has previously been demonstrated for medical and remote sensing applications. In this paper, a new General Weighted MI (GWMI) approach that improves the robustness of MI to local maxima, particularly in the case of registering optical imagery and 3D point clouds, is presented. Two different methods including a Gaussian Mixture Model (GMM) and Kernel Density Estimation have been used to define the weight function of joint probability, regardless of the modality of the data being registered. The Expectation Maximizing method is then used to estimate parameters of GMM, and in order to reduce the cost of computation, a multi-resolution strategy has been used. The performance of the proposed GWMI method for the registration of aerial orthotoimagery and LiDAR range and intensity information has been experimentally evaluated and the results obtained are presented.
Discrete Cosine Transform-based Image Fusion

Image fusion by multi-resolution discrete cosine transform (MDCT) algorithm has been implemented and evaluated. The performance of this algorithm is compared with that of well known image fusion technique using wavelets. It is observed that image fusion by MDCT perform almost similar to that of wavelets. It is computationally very simple and it could be well suited for real time applications. Defence Science Journal, 2010, 60(1), pp.48-54 , DOI:http://dx.doi. org/10.14429/dsj.60. 105
Analysis of Shock and High-Rate Data for Ceramics: Equation of State Properties and Fragmentation in the Ballistic Environment

Abstract : The project effort reported on here is a continuing study undertaken to collect, survey, analyze and model shock wave, and other related high-rate and supporting data, available for candidate armor ceramics with emphasis on response in the ballistic environment. Ceramics considered here include the various light metal-nonmetal compounds selected by the Army for consideration such as silicon carbide and aluminum oxide, as well as other ceramics, glass ceramics and glasses currently being pursued by TARDEC with high potential for armor applications. The TARDEC/Army Research Laboratories program in armor ceramics currently involves a number of efforts at various facilities including materials research, experimental studies and computational modeling. Thus, the present effort focuses on available data, analysis approaches and theoretical concepts, not currently under consideration at other laboratories, which best complements and furthers goals of the current TARDEC program.
A CRITICAL EXPLORATION OF THE POTENTIAL UTILITY OF RULE INDUCTION DATA MINING METHODS TO “ORTHODOX” EDUCATION RESEARCH

Despite some theoretical promise, it is unclear whether rule induction data mining approaches (e.g., classification trees and association rules) add methodological value to "orthodox" education research, i.e., research unrelated to computer-based education. To better understand whether and how rule induction methods could be useful to education researchers, I explored whether they, relative to regression approaches, (1) improve classification accuracy, and/or (2) offer new avenues of explanation. Additionally, I aimed to illustrate a practical and principled way to use the various rule induction approaches so researchers can more easily choose to use it. To these ends, I conducted an extended literature review on rule induction methods, and re-analyzed two regression studies (Byrnes & Miller, 2007; Thomas, 2006) on the National Educational Longitudinal Study of 1988 using ten rule induction approaches. Data mining happened in two rounds for each study: first, by using only the predictors used in the original study, and second by using all reasonable and available predictors. I compared results across methods and rounds to better understand whether, how, and why the rule induction may provide additional insights. I found that while rule induction approaches can be labor intensive and not necessarily more predictive than regression, they can provide unique descriptions of the sample that shows at-a-glance, how key predictors relate to each other and to the outcome. They can also help identify relationships between variables that held for some subgroups but not others. For example: (i) rulesets induced from Byrnes and Miller's dataset suggested that Algebra 2 and math self-concept were positively related to 12th grade math scores, but only for those who were higher achieving in 8th grade math; (ii) association rules mined from Thomas' dataset suggested that factors such as school safety and honors program participation were more strongly associated with 12th grade achievement for lower income and students with lower parental education. Thus, when relationships between the predictors and outcome may not be uniform across the population, rule induction can provide more information than regression in exploring those relationships. Lessons learned and recommendations on how to apply rule induction approaches are also discussed.
Product Aspect Ranking Using Semantic Oriented Sentiment Classifier

: Huge collections of consumer reviews are available on the Web expressing various opinions on multiple aspects of products . The important reviews are mostly not organized properly thereby creating problems in information navigation and knowledge acquisition. To address this problem, product aspect ranking is explored to automatically identify important product aspects or features from online consumer reviews. Probabilistic aspect ranking algorithm is proposed using K-NN based sentiment classifier. In this proposed system, semantic-oriented subjective information is first extracted and ranking is calculated based on the aspect frequency. This algorithm is experimented using T-Mobile dataset to show the effectiveness of the ranking approach. The scope of the proposed system is to organize the consumer review in appropriate way so that the product promotion can be done effectively based on reviews.
NUMERICAL STUDY OF EFFECT OF WALL HEATING CONDITIONS ON HEAT TRANSFER PERFORMANCE OF ROTATING INTERNAL COOLING CHANNEL

This paper investigates the impacts of wall heating conditions on heat transfer performace for a rotating channel with one side smooth and one side roughened by 45 degree inclined ribs. Previous experimental and numerical studies for only ribbed wall heated case showed that rotation has significantly negative impact on heat transfer performance. In order to investigate this uncommon behaviour, RANS simulations were conducted under three different wall heating conditions in present study: ribbed wall heated, all walls heated and adiabatic conditions. Numerical results show that the uneven wall heating condtions has negligible impact on stationary case, however, it has a large influence on rotational cases, in both the heat transfer and the fluid field. This is because in rotating cases, uneven heatings result in different buoyancy effects on trailing and leading walls that alter the main flow velocity profile. As a consequence, also secondary flows and heat transfer performance are affected.
Tourism and Poverty Reduction: Evidence from Tanzania

The paper examines the contribution of tourism towards poverty reduction in Tanzania for the period 1985 to 2015. Time series analytical method has been used in the analysis of data, by using vector error correction model and Granger causality tests, to examine the long run and causal relationship between tourism development and poverty reduction in Tanzania. The empirical results indicated a long-run relationship between tourism and poverty reduction also the Granger causality tests suggest unidirectional causal relation running from tourism earning to poverty reduction. Tourism as an industry can play very important roles in economic development including improved livelihoods and socio-cultural development that are critical for poverty alleviation. Therefore, in order to alleviate poverty in Tanzania through tourism, there is a need for more government regulating mechanism, aggressive promotion strategies, enhancement of skills and knowledge of the tourism sector, increasing effort in conserving and preserving heritage sites, and improvement of infrastructures and facilities.
Early Detection of Mild Alzheimer's Disease in Filipino Elderly: Validation of the Montreal Cognitive Assessment-Philippines (MoCA-P)

Background: The Montreal Cognitive Assessment (MoCA) is a useful instrument employed by clinicians to detect cognitive impairment and diagnose probable Alzheimer’s Disease (AD) while in its early stages. Methods: A cross-sectional study was conducted to determine the diagnostic validity of the Philippine version of the MoCA (MoCA-P) among 1385 community-dwelling Filipino elderly from Marikina City, Metro Manila. Results: 509 controls and 97 elderly with probable AD and a Clinical Dementia Rating (CDR) global score of 0.5 were included in the analysis. Analysis of variance showed that the AD group was older (p < 0.05) and had fewer years of education (p < 0.05). The optimal cut-off score to differentiate controls from those with probable AD was 20/21, with a sensitivity of 0.835 and a specificity of 0.723, and area under the curve (AUC) of 0.89 (p < 0.001). The positive and negative likelihood ratios were 3.01 and 0.23, respectively; and pre- and post-test odds were 0.0951 and 0.2224, respectively. Logistic regression showed that the odds of scoring < 20 on the MoCA-P increased with advancing age and with education at ≤7 years (p < 0.05). Two points are added to the MoCA-P score for those with ≤7 years of education. Conclusion: The MoCA-P is a valid instrument for the early detection of mild AD among the Filipino elderly.
The Propagation of Regional Shocks in Housing Markets: Evidence from Oil Price Shocks in Canada

Shocks to the demand for housing that originate in one region may seem important only for that regional housing market. We provide evidence that such shocks can also affect housing markets in other regions. Our analysis focuses on the response of Canadian housing markets to oil price shocks. Oil price shocks constitute an important source of exogenous regional variation in income in Canada because oil production is highly geographically concentrated. We document that, at the national level, real oil price shocks account for 11% of the variability in real house price growth over time. At the regional level, we find that unexpected increases in the real price of oil raise housing demand and real house prices not only in oil-producing regions, but also in other regions. We develop a theoretical model of the propagation of real oil price shocks across regions that helps understand this finding. The model differentiates between oil-producing and non-oil-producing regions and incorporates multiple sectors, trade between provinces, government redistribution, and consumer spending on fuel. We empirically confirm the model prediction that oil price shocks are propagated to housing markets in non-oil-producing regions by the government redistribution of oil revenue and by increased interprovincial trade.
ON THE SEDIMENTATION PROCESS OF POLYDISPERSE COLLOIDAL PARTICULATE SYSTEMS

Understanding the settling behaviour of suspensions of small particles, and how such suspensions can be manipulated to maximize a desired effect, is important for the successful formulation of chemical and biological products. In e.g. solid liquid separation flocculants are used to destabilize suspensions and to promote flocculation which yields a higher overall settling velocity of a suspension. The effects of present or prevented flocculation/aggregation can be studied experimentally, but such experiments are often time consuming and costly. This drawback can be overcome by means of numerical simulations, either in addition to experiments or replacing them. There are a number of approaches to solve particulate flow problems numerically. Each of them has its pros and cons, but a discussion would lead beyond the scope of this work. Yet we want to mention the method of our choice. Differing from most other numerical schemes, the Stokesian dynamics (SD) method (Durlofsky et. al., 1987) does not rely on a numerical grid. The fluid motion is implicitly incorporated into the system which is solved. This makes it an efficient method for the numerical simulation of particulate flows at low Reynolds number. SD and related methods which are based on the Oseen equations have successfully been applied to the simulation of clouds of non-colloidal particles settling in a quiescent fluid (Subramanian and Koch, 2008; Pignatel et. al., 2011), as well as to the simulation of colloidal suspensions (Wagner and Brady, 2009; Harshe and Lattuada, 2012). (Binder et. al., 2006) have compared a Lattice Boltzmann method and SD on the example of agglomerates and aggregates. (Binder et. al., 2009) have investigated the drag force acting on settling aggregates of monodisperse particles. As a lot of works on aggregates, the mentioned works by Binder et al. do not consider inter-particle forces which lead to the build-up of aggregates. Widely accepted models for such forces arise from the DLVO theory (Derjaguin and Landau, 1941; Verwey, 1948), which we use for the simulations giving the results presented in this work.
Production of gastrodin through biotransformation of p-hydroxybenzyl alcohol using hairy root cultures of Datura tatula L.

Inoculation of leaf sections of Datura tatula L. plants with A4 strain of Agrobacterium rhizogenes, induced transformed roots. The rol C (root loci) gene of TL region of A. rhizogenes was detected by PCR analyses in hair roots. The conversion of exogenous p-hydroxybenzyl alcohol into p-hydroxymethyl-phenol--D-glucoside (gastrodin) was studied using hairy root cultures of D. tatula L. The chemical structure of the synthesized gastrodin was identified on the basis of spectral analysis and chemical evidence. The procedure of conversion of p-hydroxybenzyl alcohol into gastrodin by D. tatula L. hairy root cultures was established. The synthesized gastrodin was isolated from the ferment liquor and identified by spectral analysis. The biotransformation of exogenous p-hydroxybenzyl alcohol to gastrodin using hairy root cultures of D. tatula L. is a promising approach.
CORPORATE SUSTAINABILITY REPORTING BY PHARMACEUTICAL COMPANIES: IS IT WHAT IT SEEMS TO BE?

A well-functioning pharmaceutical industry can contribute directly to social wellbeing. Corporate sustainability is an important precondition for the further development and growth of the industry. In this research multi methods are used to provide a complete, holistic and contextual portrait of the level of CSR by pharmaceutical companies in a developing country - Bangladesh. Firstly, we used content analysis to investigate corporate social reporting by listed pharmaceutical companies. Secondly, we conducted surveys to document management responses. Thirdly, we sought stakeholders' views on the extent to which they believe CSR is being implemented in the industry. Analysis of annual reports published in 2009- 2010 shows that only 26.67% of listed pharmaceutical companies made some CSR disclosure. However, more than seventy-five per cent of these disclosures are sweeping qualitative statements without any attempt at quantification. Most managers believe social reporting should strike a balance between meeting stakeholders' reasonable expectations and running a successful business. The majority of stakeholders appear to favour mandatory requirements for CSR disclosure.