Miracle-dz/UniFAR-FTUs huggingface dataset:
Collaborative Filtering versus Personal Log based Filtering: Experimental Comparison for Hotel Room Selection

In order to support decision making in e-commerce domain, recommender system has attractive features such as collaborative filtering and personal log based filtering for products/services. As experimental study, this paper compares these filtering for hotel room selection. Differently from commodity items, products/services as hotel rooms have three features: many attributes, multiformity and high-frequency update. Noting that we cannot use explicit rating data assigned by users, this paper describes how to derive implicit rating from sales records. Numerical simulation shows how accuracy between two filtering exists, where our case data consist of 10,000 users, 400,000 personal log and 160,000 room plans.
Factors Affecting Local Governments’ Public–Private Partnership Adoption in Urban China

Local governments around mainland China initiated over 14,000 public–private partnership (PPP) projects with a total investment of 18 billion RMB in recent years, but nearly half have been withdrawn since the end of 2017, which raised wide concerns about whether PPP can contribute to the sustainable infrastructure development in urban China. In this study, we empirically investigated major factors affecting local governments’ PPP adoption, especially factors that led local governments to initiate inappropriate PPP projects. Based on a unique panel dataset of 286 Chinese cities between 2014 and 2017, the empirical findings suggested that local governments’ financial pressure was the most important factor and cities with higher off-budgetary debts or lower budgetary deficits tended to initiate more PPP projects. PPP projects initiated under off-budgetary burdens were more likely to be inappropriate and subsequently withdrawn. Based on the empirical results, we provide policy suggestions to promote sustainable PPP developments in China.
APPLICATION OF NANOTECHNOLOGY IN HEALTH SCIENCES

Nanotechnology is an interdisciplinary field of science that collaborates engineering, physics, chemistry and biology. It is branch of engineering and science that involves the designing of nano tools/devices having size in the range of nano scale i.e.1-100nm. In pharmaceutical sciences a large number of nano devices have been prepared including carbon nano tubes, dendrimers, liposomes, nanoparticles and quantum dots. These nano devices have unique physical (size, shape, and strength), chemical (high catalytic activity) and biological (specificity) properties. Therefore, these devices offer many advantages over conventionally used medicines which are more toxic and more side effects. Biomedically, these nano devices have been used for the diagnosis and treatment of various diseases such as cancer, Alzheimer’s, Parkinson’s, tuberculosis, dentistry, tissue engineering etc. The present review explains the role of nano devices in pharmaceutical sciences and medical application of nanotechnology that significantly improve the health of human.
CORRELATIONS BETWEEN TEXT TOPICS AND MUSIC DIMENSIONS IN METAL MUSIC USING LATENT DIRICHLET ALLOCATION AND HIGH-LEVEL AUDIO FEATURES

Therefore, we examine the connection between audio features and the lyrical content of metal music by combining automated extraction of high-level music properties and quantitative text analysis on a large corpus of music from this genre. Sound dimensions like loudness, distortion and particularly “hardness”/“heaviness” play an essential role in defining the sound of metal music [5,10,14]. Topics typically ascribed to metal lyrics contain sadness, death, freedom, nature, occultism or unpleasant/disgusting objects and are often labeled as brutal, dystopian or satanistic [2, 13, 15].
Combine the space domain awareness and time domain astronomy with massive optical survey

Space domain awareness is a kind of observation resource dependent task, it relies on time and space coverage, geometric distribution, observation depth of the sensors, to guarantee the completeness and rapid responding. Meanwhile, as the time domain astronomy developed, more and more large field survey telescopes have been put into use. Telescopes located on these different astronomy sites can scan the visible sky over and over during clear nights. So, it should be a great opportunity to extend the SDA ability with these sensors. Many efforts have been paid to share astronomy telescopes for certain tasks of SDA in time division mode, but this is not so efficient for both communities especially in survey. Here, we propose another observation and data reduction strategy for large field survey to take care of both space domain awareness and time domain astronomy at the same time. This will benefit both communities by improving the efficiency of the instruments. The strategy is based on exposure split and image differential, to isolate the fast-moving streaks. With the combination of long and short exposures, the orbital plane, position along the track and other information can be inferred from the position and length of the streaks and points. Afterwards, we have applied this strategy to an optical survey system CHES from Purple Mountain Observatory which consists of 12 large field refractors, whose goal is to cover the local sky every 30 minutes. With the four 10s exposures and one 1s exposure, we can make local template of static sky quickly and subtract the fastmoving objects brighter than 16 mag as dotted lines. All the objects whose transit time longer than 30min will be covered. These tracklets will be crossmatched with catalogues ASAP and any kind of unusual events will be reported to the SDA network for further follow-up.
A Supporting Method for Understanding C Programs with Grouping via Formal Concept Analysis

Formal Concept Analysis is a technique to identify possible groups sharing common properties of the target objects. With Formal Concept Analysis, we present a grouping and visualizing method for C program functions, which is applicable to software understanding. Formal Concept Analysis can cluster elements by their properties. Therefore, by choosing and analyzing various program elements and properties, we can cluster and schematize information on the programs.
Methoden zur Untersuchung der Wirtschaftlichkeit von Unternehmensportalen

Instabile ökonomische Rahmenbedingungen zwingen Unternehmen heutzutage zu Effizienzsteigerung und Rationalisierung. Prozesse müssen beschleunigt, Kunden besser bedient und den Mitarbeitern eine effizientere und produktivere Arbeitsweise ermöglicht werden. Dabei spielen Portale eine immer wichtigere Rolle. Moderne Unternehmensportale ermöglichen es Firmen, ihre Kunden direkt in Geschäftsprozesse einzubinden. Durch den Einsatz von Unternehmensportalen lassen sich Kosten einsparen, innerund zwischenbetriebliche Prozesse optimieren und die Zufriedenheit der Kunden nachhaltig steigern.
A TECHNIQUE FOR TUMOR REGION IDENTIFICATION USING CELLULAR NEURAL NETWORK

In recent times, Magnetic Resonance Imaging (MRI) has turn into a competent appliance for clinical diagnoses and research. For the recognition of different diseases through segmentation methods, this MRI has become an extremely useful medical modality. We have presented an effectual CNN based segmentation technique with lung and brain MRI images in this manuscript. This move towards the target with the aid of the following major steps, which includes, 1) Pre-processing of the brain and lung images, 2) Segmentation using cellular neural network. At first, the MRI image is pre-processed to make it fit for segmentation. At this point, in the pre-processing step, image de-noising is made using the linear smoothing filters, such as Gaussian Filter. After that, the pre-processed image is segmented according to our proposed technique, CNN-based image segmentation. We have developed a hybrid optimization algorithm (ABC+PSO) to design a template in cellular neural network. At last, the different MRI images (brain and lung) are given to the proposed approach to assess the performance of the proposed approach in segmentation process. Fuzzy C-means (FCM) and K-means classification accompanies the Comparative analysis. The accuracy of proposed segmentation approach produces healthier results (93.9% for lung and 96.4% for brain images) according to the comparative analysis, than that of existing Fuzzy C-means (FCM) and K-means classification.
Encoding Object-oriented Models in MiniZinc

Object-oriented models are omnipresent in software engineering. To use a CSP solver with an object-oriented model, the mapping between the object-oriented data model and the CSP is typically implemented manually and specific to the problem to solve. This paper discusses different generic encodings to reason about object-oriented models using the MiniZinc constraint modelling language. We focus mainly on finding instantiations of object-oriented models under domain-specific constraints. Possible applications are found in product configuration and software engineering.
Evidence from the British Education Reform"

Individual risk attitudes are frequently used to predict decisions regarding education. However, using risk attitudes as a control variable for education decisions has been criticized because of potential issues related to reverse causality. Causality between risk aversion and education is unclear, and disentangling the different directions it may run is difficult. In this study, we make the first attempt to investigate the causal effects of education on risk aversion by examining the British education reform of 1972, which increased the termination age of compulsory schooling from age 15 to 16. We find that this additional year of schooling increases the level of risk aversion using IV2SLS and Regression Discontinuity Design which is contrary to previous findings in the literature and we also find that this result is particularly strong for less-educated individuals. This positive causal effect of education on risk aversion could relieve our concerns regarding the endogeneity/reverse causality issue when using risk aversion as an explanatory variable for education decisions; the sign would still be credible because the coefficients are underestimated. JEL Classification: C36 ; I21 ; I28 ; J24
Active Methods Of Teaching-Learning In The Health Area: The Problems In Nursing Education

Introduction: Currently, educational institutions are inadequate in preparing health professionals for the social demands of the Brazilian health system. Because of this, educational institutions have been encouraged to reform their didactic and pedagogical practices and develop processes that, among other things, have equity, recognise the health needs of the population and foster multidisciplinary teamwork. These education reforms are seen as necessary steps for changing the health care model. Objective: To describe the applicability of active methodologies, such as the learning strategy of the Charles Maguerez Arc, in nursing education. Method: This is an experience report, conducted in a State School of Professional Education in the macro-region of Cariri, of Ceara state, Brazil. The participants were 38 students from the nursing course integrated to the high school, aged between 13 and 15 years. The problematisation methodology through the Charles Maguerez Arc was used. The activity was divided into four parts. First, we gave a presentation to the participants, followed by an explanation of the problematisation strategy and the Charles Maguerez Arc. Then a text was read to the students to get them thinking about an issue and the class was divided into small groups for the implementation of the Arc. Finally, there was a presentation by the groups on these Arcs. Results: The application of the Charles Maguerez Arc as an active methodology of teaching and learning enhanced the development of group discussions. Starting from the elected problem of high school dropout, the keywords identified were vocation, motivation, family adaptation, bullying, self-esteem and interpersonal relationships. Final considerations: The class showed collaboration while engaging in this work, proving that the implementation of active methods in health education is feasible at the technical level. The previous organisation and systematisation of the meeting facilitated the development of the proposal, and the explanation of the problematisation method through the Charles Maguerez Arc facilitated the activity process and reflection.
Effect of Chitosan/Nano-Titanium Dioxide/Thymol and Tween Films on Ready-to-Eat Cantaloupe Fruit Quality

The effect of chitosan/nano-titanium dioxide coating with antimicrobial agents on ready to eat cantaloupe fruit by chilling was investigated. In comparison with uncoated samples, ascorbic acid and juice leakage in chitosan/nano-titanium dioxide (CH/TiO2) treated fruit were significantly maintained. Likewise, the decrease of malondialdehyde (MDA) and total soluble solids (TSS) in chitosan/nano-titanium dioxide/thymol/tween (CH/TiO2/TT) coated fruits was also inhibited. Total mold and yeast population counts decreased from 2.60 to 1.60 log CFU/g, respectively. Moreover, activities of water (AW) and polyphenol oxidase (PPO) were also much lower than those in control sample fruit. The results indicated that (CH/TiO2/TT) coating was effective in enhancing the shelf life with acceptable in the internal and the external cantaloupe fruit quality.
Allocative Efficiency Change and Total Factor Productivity Growth Based on Trans-logarithm Production Function Model

In this study, total factor productivity change is decomposed into four components, namely, technical change, pure technical efficiency change, scale efficiency change and allocative efficiency change, using trans-logarithm production function model and stochastic frontier production function, and empirical analyses are carried out on the total factor productivity change in China’s agriculture and its decomposition between 1991 and 2012. The conclusions are as follows: the total factor productivity in China’s agriculture grew at 5.73% per year, with an overall declining trend in growth rate and a sharp decreasing in 2010-2012; there was significant regional difference in the total factor productivity growth in China’s agriculture, with Northeast Region and North China being the main region and distributive efficiency change being the main factor.
LOOKING FOR HEALTH INFORMATION ON THE INTERNET: CAN SOCIAL BOOKMARKING SYSTEMS REPLACE EXPERT GATEWAYS?

Social bookmarking systems allow Web users to store and organize their bookmarks of online content by assigning them metadata in the form of tags (natural language terms). The process of adding and sharing tags is called collaborative tagging and leads to the construction of folksonomies. Recently, collaborative tagging has been described as an alternative method for creating classification systems. In this paper, we conduct a quantitative and qualitative analysis of online resources and their associated tags assigned by Web users compared with descriptors provided by librarians for the same content. Our study is based on a data set of 113 online health resources listed in both the social bookmarking system Delicious (formerly Del.icio.us) and the expert gateway CISMeF. The aim of our study is to measure the overlap between healthcare resources listed in CISMeF and Delicious, and especially between the metadata that both have assigned to those resources. In other words, the study tries to determine the extent to which tools such as CISMeF and Delicious are redundant.
Deliberative Perception for Warehouse Automation

We describe the application of a recently developed deliberative perception framework to the task of multiobject instance recognition in warehouse environments. Traditional object recognition pipelines based exclusively on discriminative feature-matching and/or statistical learners are often sensitive to inter-object occlusions and the training data used. Deliberative approaches such as PERCH treat multi-object pose estimation as a generative global optimization over possible configurations of objects, thereby predicting and accounting for occlusions. Further, D2P—an extension of PERCH, leverages guidance from modern learning-based techniques to combine the efficiency of discriminative approaches with the robustness provided by global reasoning. We conclude with a discussion of how these approaches were used by Carnegie Mellon University’s team in the 2016 Amazon Picking Challenge, and their role in the upcoming 2017 Amazon Robotics Challenge.
Semi-supervised Open Domain Information Extraction with Conditional VAE

Open information extraction (OpenIE) is the task of extracting open-domain assertions from natural language sentences. However, the lack of annotated data hurts the performance of current model and made it barely satisfactory. In this paper, we aim to improve the OpenIE model with the help of the semantic role labeling (SRL) data, which has a very similar goal of identifying predicateargument structure from natural language sentences, but with more labeled instances available. We propose a semi-supervised OpenIE model, which jointly optimizes supervised loss and unsupervised loss by treating OpenIE labels as hidden variables to reconstruct observed SRL labels. Conditional variantional autoencoder (CVAE) is used to optimize the lower bound of the data loglikelihood. Different from traditional multitask or transfer learning, we apply a more direct way to exploit the correlation between OpenIE and SRL. We compare our model with transfer and multitask learning, and the results corroborate that our framework is able to better utilize such correlation information.
An Intervention for Parents of Children With Epilepsy and Significant Learning Problems: Lessons Learned From a Feasibility Study

Children with epilepsy often experience significant learning, mental health, and behavior problems that affect their quality of life. Moreover, their parents report high rates of stress. The objectives of this article are to describe the development of a theory-based cognitive behavioral intervention for parents of these children, changes made to the program based on content validity testing, and the lessons learned from implementation of a feasibility trial. The intervention was designed to foster parents' skills and confidence in advocating for their children when interacting with health care professionals and school personnel and to help parents identify community resources. Four mothers participated in the program to establish feasibility and usability. Using qualitative analysis of discussions following each phase of the intervention, we learned that parents needed more time for epilepsy education and interacting with one another and that the intervention should emphasize forming partnerships with health care and school professionals. J Am Psychiatr Nurses Assoc, 2007; 13(3), 146-152. DOI: 10.1177/1078390307303747
FLOWSYNTH: SEMANTIC AND VOCAL SYNTHESIS CONTROL

Since their commercial beginnings, audio synthesizers have flourished in music production, and now even entirely defining new music genres. While becoming widely accessible and pervasive in music, their increasing complexity and number of parameters also rendered their use concomitantly difficult. Hence, the development of methods allowing to easily create and explore with synthesizers is a crucial need. Unfortunately, no synthesizer provides intuitive or semantic controls related to the perception of the synthesized audio. A solution proposed by some manufacturers is macro-parameters, which control multiple parameters through a single knob, but still need to be programmed manually. An alternative would be to infer the set of parameters that could best reproduce a given target sound. This parameter inference task has been studied in the past years using various optimization techniques such as genetic programming [2] or deep learning [4]. However, all previous approaches share the same flaws that they do not account for non-linear relationships between parameters, do not learn explanatory parameters, nor provide semantic controls over synthesizers.
A Novel Homozygous Nonsense Variant in BICD2 Underlies Hereditary Spastic Paraplegia Complex Type

Sajida Rasool1, Saba Irshad1*, Neelam Saba1, Mehak Fiaz1, Muhammad Sajid Hussain2, Muhammad Wajid Hussain3 and Peter Nürnberg2 1Institute of Biochemistry and Biotechnology, University of the Punjab, Quaid-I-Azam Campus, Lahore, 54590 2Cologne Center for Genomics, University of Cologne, D-50931 Cologne, Germany 3Department of Zoology, Okara University, Okara, Pakistan Article Information Received 22 January 2019 Revised 22 May 2019 Accepted 18 June 2019 Available online 22 November 2019
HyGraph: Fast Graph Processing on Hybrid CPU-GPU Platforms by Dynamic Load-Balancing

Graph analysis is becoming increasingly important in many domains, including biology, sociology, and data-mining. Many different large-scale graph-processing systems have been proposed for different platforms. However, little effort has been placed on hybrid CPU-GPU platforms. In this work, we present the design and evaluation of HyGraph, a novel graph-processing systems for hybrid platforms which delivers performance by using CPU and GPU concurrently. Its core feature is a specialized data structure which enables dynamic scheduling of jobs onto both the CPU and the GPUs, thus (1) supersedes the need for static workload distribution, (2) provides load balancing, and (3) minimizes inter-process communication overhead by overlapping computation and communication. We demonstrate the capabilities of HyGraph using 3 types of GPUs, 4 graph-processing algorithms, and 7 datasets. Performance-wise, we observe speedups of up to 9.2x over CPUonly execution and 1.5x over GPU-only execution. Moreover, we show how HyGraph’s load balancing and low overhead lead to a close-to-optimal performance. Finally, we show how dynamic scheduling enables significant GPU acceleration even when a dataset does not fit entirely into GPU memory.
M ar 2 01 0 Status of the Cylindical-GEM project for the KLOE-2 Inner Tracker

The status of the R&D on the Cylindrical-GEM (CGEM) detector f eseen as Inner Tracker for KLOE-2, the upgrade of the KLOE experiment at the DA ΦNE φ–factory , will be presented. The R&D includes several activ ities: i) the construction and complete characterization of the full-size CGEM prototype , equipped with 650μm pitch 1-D longitudinal strips; ii) the study of the 2-D readout with XV patterned strips and operation in mag netic field (up to 1.5T), performed with small planar prototy pes in a dedicated test at the H4-SPS beam facility; iii) the charac te ization of the single-mask GEM technology for the realiz ation of large-area GEM foils.
Comparison of the standards of air kerma of the LNMRI and the BIPM for 60 Co ' Y rays

A second comparison of the standards of air kerma of the Laborat6rio Nacional de Metrologia das Radiayoes Ionizantes, Rio de Janeiro, Brazil and of the Bureau International des Poids et Mesures has been carried out in 60Co radiation. It shows that the LNMRI and BIPM standards agree closely, as was the case for the first comparison in 1986.