Miracle-dz/UniFAR-FTUs huggingface dataset:
Collaborations Patterns and Productivity Analysis for IEEE T-ITS Between 2010 and 2013

This paper investigates the productivity and collaborations patterns in the publications of IEEE Transactions on Intelligent Transportation Systems (T-ITS) between 2010 and 2013. Our findings show that industry is playing a more important role in research cooperation. Such a trend is particularly beneficial for expediting the commercialization of scientific and technological advances. In addition, growing international collaborations have become a norm as researchers in the U.S., China, and Europe are increasingly involved in joint research endeavors.
Modeling Recognition Memory Using the Similarity Structure of Natural Input

The natural input memory (NIM) model is a new model for recognition memory that operates on natural visual input. A biologically informed perceptual preprocessing method takes local samples (eye fixations) from a natural image and translates these into a feature-vector representation. During recognition, the model compares incoming preprocessed natural input to stored representations. By complementing the recognition memory process with a perceptual front end, the NIM model is able to make predictions about memorability based directly on individual natural stimuli. We demonstrate that the NIM model is able to simulate experimentally obtained similarity ratings and recognition memory for individual stimuli (i.e., face images).
Perturbation: An Efficient Technique for the Solution of Very Large Instances of the Euclidean TSP

In this paper we introduce a technique for developing efficient iterated local search procedures and we apply it to solve very large instances of the Euclidean Traveling Salesman Problem (TSP). This technique, which we call perturbation, uses global information on TSP instances to speed-up the computation and to improve the quality of the tours found by heuristic methods. The main idea is to escape from local optima by introducing perturbations in the problem instance rather than in the solution. The performance of our algorithms has been tested and compared with known methods. To this end, we have executed a number of experiments both on available benchmarks, for which the optimal tour length is known, and on randomly generated instances, for which the comparison is done with the Held-Karp lower bound. The experimental results, performed on up to 100,000 cities, show that our algorithms outperform the known methods for iterating local search for very large instances.
The role of parents’ educational level and centre type in parent satisfaction with early childhood care centres: a study in Greece

ABSTRACT This research examines specific facets of parent satisfaction with childcare centres, namely satisfaction with parent–centre communication and the educational services they provide, as well as respective parent beliefs. These were investigated in relation to centre type (private vs public) and parents’ education. Parents of different educational levels and with children in private or public day-care centres participated in the study. Whereas centre type played a role only concerning the satisfaction with parent–centre communication, the educational level of parents was consistently associated with parent beliefs and parent satisfaction: higher educated parents rated day-care centres less favourably as compared to lower educated parents. Furthermore, parent beliefs mediated the relationship between the educational level of parents and parent satisfaction. Our findings are discussed in the context of existing evidence and in relation to the necessity for a theoretical integration of this issue with implications for practice.
Fast reductions on a network of workstations

Reduction operations are very useful in parallel and distributed computing, with applications in barrier synchronization, distributed snapshots, termination detection, global virtual time computation, etc. In the context of parallel discrete event simulations, we have previously introduced a class of adaptive synchronization algorithms based on fast reductions. We explore the implementation of fast reductions on a popular high performance computing platform-a network of workstations. The specific platform is a set of Pentium Pro PC's running the Linux operating system interconnected by Myrinet-a Gbps network. The general reduction model on which our synchronization algorithms are based is introduced first, followed by a description of how this model can be implemented. We discuss several design trade offs that must be made in order to achieve the driving goal of high speed reductions and provide innovative algorithms to meet the correctness and performance requirements of the reduction model.
Use of Hidden Markov Models and Factored Language Models for Automatic Chord Recognition

This paper focuses on automatic extraction of acoustic chord sequences from a musical piece. Standard and factored language models are analyzed in terms of applicability to the chord recognition task. Pitch class profile vectors that represent harmonic information are extracted from the given audio signal. The resulting chord sequence is obtained by running a Viterbi decoder on trained hidden Markov models and subsequent lattice rescoring, applying the language model weight. We performed several experiments using the proposed technique. Results obtained on 175 manually-labeled songs provided an increase in accuracy of about 2%.
Fiber supplementation influences phylogenetic structure and functional capacity of the human intestinal microbiome: follow-up of a randomized controlled trial.

BACKGROUND In our published randomized, double-blind, placebo-controlled, 3-period crossover trial, healthy adult men (n = 21) consumed bars containing no supplemental fiber (placebo; NFC), polydextrose (21 g/d), and soluble corn fiber (SCF; 21 g/d) for 21 d each. Fecal specimens were collected between days 16 and 21 for fermentative end-product analysis and 16S ribosomal RNA bacterial gene amplification for bacterial taxa identification. Fiber supplementation decreased fecal putrefaction compounds and shifted abundances of several bacterial taxa. OBJECTIVE The objective was to perform whole-genome shotgun 454 pyrosequencing on the same fecal specimens collected in that clinical trial to obtain comprehensive fecal bacterial genome sequencing coverage and explore the full range of bacterial genetic information in the fecal microbiome, thereby using a systematic approach to study the impact of dietary fiber supplementation on fecal metabolites, bacterial taxa, and bacterial metagenomes. DESIGN Fecal samples were subjected to whole-genome shotgun 454 pyrosequencing to identify both fecal bacterial populations present and their functional genetic capacity. RESULTS Whole-genome shotgun sequencing results revealed that fiber consumption shifted the Bacteroidetes:Firmicutes ratio, increasing the relative abundance of Bacteroidetes 12 ± 2% and 13 ± 2% with polydextrose and SCF, respectively, compared with NFC. Bivariate correlations showed a positive correlation between the Bacteroidetes:Firmicutes ratio and total dietary fiber intake but not body mass index. Principal coordinates analysis of Bray-Curtis distances indicated that bacterial gene composition was more similar in participants consuming fibers (polydextrose and SCF combined) in comparison with NFC. Shifts in bacterial gene abundances after polydextrose and SCF supplementation included genes associated with carbohydrate, amino acid, and lipid metabolism, as well as metabolism of cofactors and vitamins. CONCLUSION This study conveys novel information about the impact of dietary fiber supplementation on the phylogenetic structure and functional capacity of the fecal microbiome of healthy adults.
Mentha arvensis (Linn.)-mediated green silver nanoparticles trigger caspase 9-dependent cell death in MCF7 and MDA-MB-231 cells

Introduction Leaf extract of Mentha arvensis or mint plant was used as reducing agent for the synthesis of green silver nanoparticles (GSNPs) as a cost-effective, eco-friendly process compared to that of chemical synthesis. The existence of nanoparticles was characterized by ultraviolet–visible spectrophotometry, dynamic light scattering, Fourier transform infrared spectroscopy, X-ray diffraction, energy-dispersive X-ray analysis, atomic-force microscopy and transmission electron microscopy analyses, which ascertained the formation of spherical GSNPs with a size range of 3–9 nm. Anticancer activities against breast cancer cell lines (MCF7 and MDA-MB-231) were studied and compared with those of chemically synthesized (sodium borohydride [NaBH4]-mediated) silver nanoparticles (CSNPs). Materials and methods Cell survival of nanoparticle-treated and untreated cells was studied by 3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyltetrazolium bromide (MTT) assay. Cell-cycle analyses were carried out using fluorescence-activated cell sorting. Cell morphology was observed by fluorescence microscopy. Expression patterns of PARP1, P53, P21, Bcl2, Bax and cleaved caspase 9 as well as caspase 3 proteins in treated and untreated MCF7 and MDA-MB-231 cells were studied by Western blot method. Results MTT assay results showed that Mentha arvensis-mediated GSNPs exhibited significant cytotoxicity toward breast cancer cells (MCF7 and MDA-MB-231), which were at par with that of CSNPs. Cell cycle analyses of MCF7 cells revealed a significant increase in sub-G1 cell population, indicating cytotoxicity of GSNPs. On the other hand, human peripheral blood lymphocytes showed significantly less cytotoxicity compared with MCF7 and MDA-MB-231 cells when treated with the same dose. Expression patterns of proteins suggested that GSNPs triggered caspase 9-dependent cell death in both cell lines. The Ames test showed that GSNPs were nonmutagenic in nature. Conclusion GSNPs synthesized using Mentha arvensis may be considered as a promising anticancer agent in breast cancer therapy. They are less toxic and nonmutagenic and mediate caspase 9-dependent apoptosis in MCF7 and MDA-MB-231 cells.
Javacloak : Engineering Java Tm Proxy Objects Using Reeection

Java programmers need to be able to locally specialise the run-time behaviour of externally developed code in order to increase software reuse. We describe JavaCloak, a system that supports local specialisation by using proxy objects. The proxy object and the object it wraps (the wrapped object) are typed with the same full-quali ed Java class name and a specialised classloader is used by the proxy to ensure the two classes are kept separate. Many issues which arise when using proxies in object-oriented languages, such as the \self problem" and encapsulation problems, are discussed, together with Java-speci c problems. The current design and architecture of JavaCloak are described in detail.
Classifying ear disorders using support vector machines

One of the most significant causes of iatrogenic injury, death and costs in hospitals is medication errors. A medical decision-support system can help physicians to improve the safety, quality and efficiency of healthcare. In this paper we focus on development of a decision-support system for diagnosis of ear disorders. For this purpose, a dataset obtained from an otolaryngology clinic. Then two machine learning algorithms, Multi-layer perceptron neural network and support vector machine, were applied to classify ear disorders. The results show that support vector machine is considerably more accurate technique for classifying high dimensional data.
Optimization in mean field models

The non-rigorous cavity method of statistical mechanics, originally developed in the study of spin glasses and other disordered systems, has led to a number of purely mathematical predictions. Several of them can be found in the remarkable book [17] by Marc Mézard, Giorgio Parisi and Miguel Angel Virasoro. Establishing these predictions rigorously remains a challenge to probability theory. Important achievements in this direction are David Aldous’ proof of the ζ(2) limit in the assignment problem [4], Michel Talagrand’s proof of the correctness of the Parisi solution of the Sherrington-Kirkpatrick model [9, 24], and the algorithmic and theoretical results on phase transitions in constraint satisfaction problems [1, 2, 18]. One of the areas where the statistical mechanics view has produced a number of challenging conjectures is optimization in mean field models of distance. In the simplest of these models there are n vertices labeled 1, . , n. Each pair of vertices is connected by an edge, and the edges (i, j) are assigned i. i. d. random costs Xi,j (sometimes thought of as representing distance) from a given distribution μ on the nonnegative real numbers. In general one is interested in the large n (“thermodynamical”) limit of the cost of a solution to a combinatorial optimization problem. I will describe a mathematically rigorous method whose underlying idea resembles the cavity method. Several new results have been established with this method, some of which were anticipated by the cavity approach. For instance we have established the “mean field limit” for the traveling salesman problem, conjectured in [11, 14, 15].
A Variance–Mean Based Feature Selection in Text Classification

Feature selection is an important process to choose a subset of features relevant to a particular application in text classification. Based on the mutual information method, we designed variance-mean based feature selection (VM). After computing and ranking the variance of class discrimination value vector for each word, we can choose the most distinguishable features. This method has advantages in the case of choosing smaller number of features, especially for classes with small number of training documents. It keeps the best features, and thus improves the final performance of the classification system. The experiment results indicate the effectiveness of the proposed feature selection method in a text classification.
Parental financial support and filial responsibility in emerging adulthood: a comparative study between the United States and South Korea

ABSTRACT Why do parents provide considerable financial support to their children in college? How do college students feel about their parental financial support and how does it differ between American and Korean cultural contexts? Based on multiple group analysis, we tested the impact of family income and parents’ education on parental tuition and living expenses supports, which in turn affects college students’ perception of filial responsibility across the United States and South Korea. Participants included 179 American college (AC) students from Syracuse University and 268 Korean college (KC) students from Yonsei University Wonju. We found that family income was significantly related to an increase in parental tuition and living expenses supports for both AC and KC students. However, parents’ education was significantly related to an increase in parental tuition and living expenses supports for AC students, but not for KC students. In addition, parental tuition support was related to an increase for filial responsibility, and parental living expenses were related to a decrease in filial responsibility in KC students, but not for AC students. These results indicate that the association between parental financial support and college students’ perception of filial responsibility differs across American and Korean cultural contexts.
Performance evaluation of L-PPM links using repetition rate coding

In this paper, we evaluate the performance of infrared communication methods using repetition-rate coding over L-PPM wireless links to provide reliable infrared transmission throughout indoor environments. All receivers adapt the communication data rate by monitoring the channel, and they instruct transmitters to use highest rate if it is possible. Reducing the user data rate by a factor of RR (rate reduction) results in an RR-fold SNR improvement. We analyze how repetition-rate coding improves the capture probability of the infrared modem over L-PPM links. We also discuss the effect of RR-fold transmission in utilization using go-back-N and selective reject ARQ schemes.
Health anxiety and risk of ischaemic heart disease: a prospective cohort study linking the Hordaland Health Study (HUSK) with the Cardiovascular Diseases in Norway (CVDNOR) project

Background The risk of ischaemic heart disease (IHD) is largely influenced by lifestyle. Interestingly, cohort studies show that anxiety in general is associated with increased risk of IHD, independent of established risk factors for cardiovascular disease. Health anxiety is a specific type of anxiety characterised by preoccupation of having, acquiring or possibly avoiding illness, yet little is known about lifestyle and risk of disease development in this group. Aim Investigate whether health anxiety is prospectively associated with IHD, and whether a potential association can be explained by the presence or absence of established risk factors for cardiovascular diseases. Methods Incident IHD was studied among 7052 participants in the community-based Hordaland Health Study (HUSK) during 12 years follow-up by linkage to the Cardiovascular Diseases in Norway (CVDNOR) project. Scores above 90th centile of the Whiteley Index defined health anxiety cases. Associations were examined with the Cox proportional regression models. Results During follow-up, 6.1% of health anxiety cases developed IHD compared with 3.0% of non-cases, yielding a gender-adjusted HR of 2.12 (95% CI 1.52 to 2.95). After adjustments for established cardiovascular risk factors, about 70% increased risk of IHD was found among cases with health anxiety (HR: 1.73 (95% CI 1.21 to 2.48)). The association followed a dose–response pattern. Conclusions This finding corroborates and extends the understanding of anxiety in various forms as a risk factor for IHD. New evidence of negative consequences over time underlines the importance of proper diagnosis and treatment for health anxiety.
Analysis of the TCP/IP Flow Control in High-Speed Wide-area Networks

we use measurements taken over the Internet to provide a basis for the chosen an­ alytic model: a TCP connection is modeled by a single node shared with other connections. Non bottleneck nodes are modeled by fixed delays. Sec­ ond, we consider a single TCP connection shar­ ing a bottleneck node with other connections. The traffic generated by these connections is assumed to be independent from the behavior of the TCP connection under study. We refer to this traf­ fic as exogenous traffic, and to these connections as non-controlled connections. Third, we consider two TCP connections sharing a bottleneck node. We use fluid models to analyze the behavior of the TCP connections.
Analyzing Human Movements from Silhouettes via Fourier Descriptor

Human movement analysis has recently gained growing interest for computer vision researchers. In this paper, a simple but efficient algorithm using Fourier descriptor to represent spatial-temporal silhouette for human movement analysis is proposed. For each image sequence, motion detection and segmentation methods are used to segment and extract the moving silhouettes of people. Then, Fourier descriptor(FD)is used to describe the moving silhouettes. HMMs and Haudorff distance are applied to the time-varying distance signals described by FD for the activity classification and gait recognition. Experimental results have demonstrated that the proposed algorithm greatly improve the recognition rates.
Standardization approaches within Cloud Computing: Evaluation of infrastructure as a service architecture

Cloud Computing is becoming increasingly established and offers several opportunities to obtain IT services in an on-demand manner. Especially infrastructure services, like storage and scalable computing resources, are gaining relevance and provide alternatives to conventional sourcing models. Despite the Cloud paradigm of flexible and limitless scalability the lack of standardization presents a big challenge in this context. Due to many providers, which are using different Cloud software including proprietary interfaces, the interoperability in the Cloud remains a theoretical construct. In this paper we examine standardization approaches within Cloud Computing and provide a comparison to practical implementations of interfaces of relevant Cloud software on the market. Finally, characteristics for a potential Cloud standard on the infrastructure level will be postulated.
Switching State-Space ModelsZoubin

We introduce a statistical model for non-linear time series which iteratively segments the data into regimes with approximately linear dynamics and learns the parameters of each of these linear regimes. This model combines and generalizes two of the most widely used stochastic time series models|the hidden Markov model and the linear dynamical system|and is related to models that are widely used in the control and econometrics literatures. It can also be derived by extending the mixture of experts neural network model (Jacobs et al., 1991) to its fully dynamical version, in which both expert and gating networks are recurrent. Inferring the posterior probabilities of the hidden states of this model is computationally intractable, and therefore the exact Expectation Maximization (EM) alogithm cannot be applied. However, we present a variational approximation which maximizes a lower bound on the log likelihood and makes use of both the forward{backward recursions for hidden Markov models and the Kalman lter recursions for linear dynamical systems.
Music therapy and percussion for persons with dementia: A systematic literature review

Dementia is a progressive deterioration in cognitive ability that affects many people worldwide. As cognitive and verbal skills decline, persons from this population who are involved in music therapy require more diverse music therapy interventions. Persons with dementia may gradually lose the ability to sing as the disease progresses and the use of percussion may allow for continued musical participation. This research is a systematic literature review of the use of percussion with persons with dementia in music therapy and non music therapy contexts. From 2005 to 2014, many resources were published on the use of percussion in music therapy and non music therapy contexts for persons with dementia. This review examines the literature in terms of the professionals, the stages of dementia, the prevalence of group versus individual settings, the percussion instruments described, the experiences used, the goals targeted, and the results of experimental studies on the use of percussion for persons with dementia. The findings may serve as a resource for clinicians, students, and researchers, and has the potential to increase knowledgeable use of diverse percussion interventions in music therapy and non music therapy contexts for persons with dementia.
Generation of Organic Textures with Controlled Anisotropy and Directionality via Packing Rectangular and Elliptical Cells

This paper presents a computational method for generating organic textures. The method first tessellates a region into a set of pseudo-Voronoi polygons using a particle model and then generates the detailed geometry of each of the polygons using Loop’s subdivision surface with fractal noise. Unlike previous particle models, which are designed for creating hexagonal cell arrangements, this particle model can also create rectangular cell arrangements, often observed in organic textures. In either cell arrangement, the method allows a user to control the anisotropy of the cell geometry and the directionality of the cell arrangements. A detailed three-dimensional cell geometry is then created by adjusting a set of parameters that control the cells’ height and degree of skewing and tapering. A user can create various types of realistic looking organic textures with ease, by simply choosing a cell arrangement type, anisotropy, and directionality, along with the geometry control parameters.