Miracle-dz/UniFAR-FTUs huggingface dataset:
DengueTools: innovative tools and strategies for the surveillance and control of dengue

Dengue fever is a mosquito-borne viral disease estimated to cause about 230 million infections worldwide every year, of which 25,000 are fatal. Global incidence has risen rapidly in recent decades with some 3.6 billion people, over half of the world's population, now at risk, mainly in urban centres of the tropics and subtropics. Demographic and societal changes, in particular urbanization, globalization, and increased international travel, are major contributors to the rise in incidence and geographic expansion of dengue infections. Major research gaps continue to hamper the control of dengue. The European Commission launched a call under the 7th Framework Programme with the title of ‘Comprehensive control of Dengue fever under changing climatic conditions’. Fourteen partners from several countries in Europe, Asia, and South America formed a consortium named ‘DengueTools’ to respond to the call to achieve better diagnosis, surveillance, prevention, and predictive models and improve our understanding of the spread of dengue to previously uninfected regions (including Europe) in the context of globalization and climate change. The consortium comprises 12 work packages to address a set of research questions in three areas: Research area 1 Develop a comprehensive early warning and surveillance system that has predictive capability for epidemic dengue and benefits from novel tools for laboratory diagnosis and vector monitoring. Research area 2 Develop novel strategies to prevent dengue in children. Research area 3 Understand and predict the risk of global spread of dengue, in particular the risk of introduction and establishment in Europe, within the context of parameters of vectorial capacity, global mobility, and climate change. In this paper, we report on the rationale and specific study objectives of ‘DengueTools’. DengueTools is funded under the Health theme of the Seventh Framework Programme of the European Community, Grant Agreement Number: 282589 Dengue Tools.
Performance Engineering for a Medical Imaging Application on the Intel Xeon Phi Accelerator

We examine the Xeon Phi, which is based on Intel's Many Integrated Cores architecture, for its suitability to run the FDK algorithm--the most commonly used algorithm to perform the 3D image reconstruction in cone-beam computed tomography. We study the challenges of efficiently parallelizing the application and means to enable sensible data sharing between threads despite the lack of a shared last level cache. Apart from parallelization, SIMD vectorization is critical for good performance on the Xeon Phi; we perform various micro-benchmarks to investigate the platform's new set of vector instructions and put a special emphasis on the newly introduced vector gather capability. We refine a previous performance model for the application and adapt it for the Xeon Phi to validate the performance of our optimized hand-written assembly implementation, as well as the performance of several different auto-vectorization approaches.
Towards Managing Variability Across Business Component Development

Reusing components, which are specifically set up to suit enterprises requirements, becomes a key element of their software engineering strategy. Consequently, the ability of reusable components to be varied and appropriate to different re-users requirements is a key property in reusable component development. From this need, the concept of variability has emerged. It is defined as the ability of a software artefact to be changed or customized to be used in multiple contexts. The objective of our research is to outline variability issues through reusable component development, and in particular, in the requirements phase of a Business Component (BC) development. The main ideas of this research are (a) to address variability issues through the requirements phase of a BC development and (b) to propose a metamodel to describe variability concepts that must be classified and designed to manage this variability:
PIC2DISH: A Customized Cooking Assistant System

The art of cooking is always fascinating. Nevertheless, reproducing a delicious dish that one has never encountered before is not easy. Even if the name of dish is known and the corresponding recipe could be retrieved, the right ingredients for cooking the dish may not be available due to factors such as geography region or season. Furthermore, knowing how to cut, cook and control timing may be challenging for one whose has no cooking experience. In this paper, an all-around cooking assistant mobile app, named Pic2Dish, is developed to help users who would like to cook a dish but neither know the name of dish nor has cooking skill. Basically, by inputting a picture of the dish and the list of ingredients at hand, Pic2Dish automatically recognizes the dish name and recommends a customized recipe together with video clips to guide user on how to cook the dish. Importantly, the recommended recipe is modified from a retrieved recipe that best matches the given dish, with missing ingredients being replaced with the available ingredients that match dish context and taste. The whole process involves the recognition of dishes with convolutional neural network, classification of key and non-key ingredients, and context analysis of ingredient relationship and their cooking/cutting methods. The user studies, which recruit real users to cook dishes by using Pic2Dish, shows the usefulness of the app.
Detecting texts of arbitrary orientations in natural images

With the increasing popularity of practical vision systems and smart phones, text detection in natural scenes becomes a critical yet challenging task. Most existing methods have focused on detecting horizontal or near-horizontal texts. In this paper, we propose a system which detects texts of arbitrary orientations in natural images. Our algorithm is equipped with a two-level classification scheme and two sets of features specially designed for capturing both the intrinsic characteristics of texts. To better evaluate our algorithm and compare it with other competing algorithms, we generate a new dataset, which includes various texts in diverse real-world scenarios; we also propose a protocol for performance evaluation. Experiments on benchmark datasets and the proposed dataset demonstrate that our algorithm compares favorably with the state-of-the-art algorithms when handling horizontal texts and achieves significantly enhanced performance on texts of arbitrary orientations in complex natural scenes.
Classification using functional data analysis for temporal gene expression data

MOTIVATION Temporal gene expression profiles provide an important characterization of gene function, as biological systems are predominantly developmental and dynamic. We propose a method of classifying collections of temporal gene expression curves in which individual expression profiles are modeled as independent realizations of a stochastic process. The method uses a recently developed functional logistic regression tool based on functional principal components, aimed at classifying gene expression curves into known gene groups. The number of eigenfunctions in the classifier can be chosen by leave-one-out cross-validation with the aim of minimizing the classification error. RESULTS We demonstrate that this methodology provides low-error-rate classification for both yeast cell-cycle gene expression profiles and Dictyostelium cell-type specific gene expression patterns. It also works well in simulations. We compare our functional principal components approach with a B-spline implementation of functional discriminant analysis for the yeast cell-cycle data and simulations. This indicates comparative advantages of our approach which uses fewer eigenfunctions/base functions. The proposed methodology is promising for the analysis of temporal gene expression data and beyond. AVAILABILITY MATLAB programs are available upon request.
WebBox: Supporting Decentralised and Privacy−respecting Micro−sharing with Existing Web Standards

The popularity and plurality of social sharing and collaboration platforms today demonstrates the demand for sharing information at a wide variety of granularities and scale — from public blogging of micro-data (e.g., microblogging posts, page “Likes”, and place-“check-ins”) to specific, full-on collaborative sharing of documents and projects. The centralised and fragmented nature of today’s sharing platforms, however, creates several problems, among which include issues of seamless access, ownership, and control. In this paper, we present an alternative approach that puts the user back in control over their data, and supports secure, private sharing and collaboration in a distributed, user-centric manner “out of the box”. This approach represents a novel configuration of Web-standard, non-novel components, which we demonstrate are sufficiently generic to support types of sharing offered by the most popular sharing platforms today. By offering a standards-compliant method of globally sharing triples, with known semantics about how they are received, our approach enables social applications to utilise the Web as the distributed system it has always promised to be. Beyond such sharing, we discuss what other capabilities such a platform enables, and its implication for future Web architectures and tools.
DESIGN OF TEMPLE SHAPE SLOT ANTENNA FOR ULTRA WIDEBAND APPLICATIONS

A novel design of a temple shaped printed slot antenna for circular polarization applications is presented in this paper. The slot, half trapezoidal and half semi-circular in shape, is excited by a 50› CPW feed terminated with a tuning stub. Modiflcations of the initial design for improving return loss and circular polarization characteristics are proposed and discussed. The antenna is very compact (40mm £ 35mm) in size and simple to design. The flnal version of the antenna ofiers an experimentally measured impedance bandwidth of 108%, i.e., from 2.75GHz to 9.25GHz. The 3-dB axial ratio bandwidth achieved is 51.6%, i.e., from 4.6GHz to 7.8GHz. The antenna is further characterized by a peak gain of about 5dB and a relatively stable radiation pattern in the useful band.
DEPARTEMENT TOEGEPASTE ECONOMISCHE WETENSCHAPPEN COMPLEMENTARITY BETWEEN TECHNOLOGY MAKE AND BUY IN INNOVATION STRATEGIES: EVIDENCE FROM BELGIAN MANUFACTURING FIRMS

This paper characterizes the innovation strategy of manufacturing finns and exa.rpjnes the relation between the innovation strategy and important industry-, firm- and innovation-specific characteristics using Belgian data from the Eurostat Community Innovation Survey. In addition to important size effects explaining innovation, we fmd that high perceived risks and costs and low appropriability of innovations do not necessarily discourage innovation, but rather determine how the innovation strategy is formulated. The paper then focuses on the determinants of the decision of the firm to produce technology itself (Make) or to source technology externally (Buy). One striking observation is that most firms use a combination of Make and Buy strategies. Small firms more likely restrict their innovation strategy to an exclusive make or buy strategy, while large firms are more likely to combine both internal and external knowledge acquisition in their innovation strategy. An interesting result that highlights the complementary nature of the Make and Buy decisions, is that firms for which internal information is an important information source for innovation are more likely to combine internal and external sources of technology. We find this to be evidence of the fact that in-house R&D generates the necessary absorptive capacity to profit from external knowledge acquisition. We also find that the effectiveness of different mechanisms to appropriate the benefits of innovations and the internal organizational resistance against change are important determinants of the firm's technology sourcing strategy.
Learning from positively deviant wards to improve patient safety: an observational study protocol

Introduction Positive deviance is an asset-based approach to improvement which has recently been adopted to improve quality and safety within healthcare. The approach assumes that solutions to problems already exist within communities. Certain groups or individuals identify these solutions and succeed despite having the same resources as others. Within healthcare, positive deviance has previously been applied at individual or organisational levels to improve specific clinical outcomes or processes of care. This study explores whether the positive deviance approach can be applied to multidisciplinary ward teams to address the broad issue of patient safety among elderly patients. Methods and analysis Preliminary work analysed National Health Service (NHS) Safety Thermometer data from 34 elderly medical wards to identify 5 ‘positively deviant’ and 5 matched ‘comparison’ wards. Researchers are blinded to ward status. This protocol describes a multimethod, observational study which will (1) assess the concurrent validity of identifying positively deviant elderly medical wards using NHS Safety Thermometer data and (2) generate hypotheses about how positively deviant wards succeed. Patient and staff perceptions of safety will be assessed on each ward using validated surveys. Correlation and ranking analyses will explore whether this survey data aligns with the routinely collected NHS Safety Thermometer data. Staff focus groups and researcher fieldwork diaries will be completed and qualitative thematic content analysis will be used to generate hypotheses about the strategies, behaviours, team cultures and dynamics that facilitate the delivery of safe patient care. The acceptability and sustainability of strategies identified will also be explored. Ethics and dissemination The South East Scotland Research Ethics Committee 01 approved this study (reference: 14/SS/1085) and NHS Permissions were granted from all trusts. Findings will be published in peer-reviewed, scientific journals, and presented at academic conferences. Trial registration number This study is registered on the UK Clinical Research Network Study Portfolio (reference number—18050).
TOPP - the OpenMS proteomics pipeline

MOTIVATION Experimental techniques in proteomics have seen rapid development over the last few years. Volume and complexity of the data have both been growing at a similar rate. Accordingly, data management and analysis are one of the major challenges in proteomics. Flexible algorithms are required to handle changing experimental setups and to assist in developing and validating new methods. In order to facilitate these studies, it would be desirable to have a flexible 'toolbox' of versatile and user-friendly applications allowing for rapid construction of computational workflows in proteomics. RESULTS We describe a set of tools for proteomics data analysis-TOPP, The OpenMS Proteomics Pipeline. TOPP provides a set of computational tools which can be easily combined into analysis pipelines even by non-experts and can be used in proteomics workflows. These applications range from useful utilities (file format conversion, peak picking) over wrapper applications for known applications (e.g. Mascot) to completely new algorithmic techniques for data reduction and data analysis. We anticipate that TOPP will greatly facilitate rapid prototyping of proteomics data evaluation pipelines. As such, we describe the basic concepts and the current abilities of TOPP and illustrate these concepts in the context of two example applications: the identification of peptides from a raw dataset through database search and the complex analysis of a standard addition experiment for the absolute quantitation of biomarkers. The latter example demonstrates TOPP's ability to construct flexible analysis pipelines in support of complex experimental setups. AVAILABILITY The TOPP components are available as open-source software under the lesser GNU public license (LGPL). Source code is available from the project website at www.OpenMS. de
Dedicated computational models for the electromagnetic emissions of integrated circuits

To model the electromagnetic emissions of integrated circuits, a full chip simulation is the most accurate method. Full chip simulations are very complex, time consuming, and do not protect the intellectual property. Therefore, other models were developed that are fast, protect confidential information and have a lower complexity. In this paper, we give a state-of-the-art overview of these models.
Design of Human-Friendly Powered Lower Limb Rehabilitation Orthosis

Many patients with spinal injures are confined to whee lchairs, leading to a sedentary lifestyle with secondar y pathologies and increased dependence on a carer. Increasi g evidence has shown that training using devices such as Reciprocating Gait Orthoses (RGO) reduces the incidence of these secondary pathologies, but the physical effort involved in this training is such that there is poor compliance. This paper reports on the design of a new “human friendl y” orthosis powered by high power pneumatic Muscle Actuators. The combination of a highly compliant actua tion system with an intelligent embedded control mechan ism which senses hip, knee and ankle position, velocity, accelerat ion nd force produces a powerful yet inherently safe oper ation for paraplegic patients. The application of this technology wil l greatly improve the rehabilitative protocols for pa aplegic patients.
The priority computation of image inpainting

In the image inpainting based on the sample block, the quality of image restoration is directly related to the repair order, and the existing algorithms ignore the respective proportions of the image texture features and the structural characteristics. The priority computation of the damaged image is completed by means of P-Laplace algorithms and D-S Evidence Theory in this paper. Experimental results show that the algorithm given in this paper can effectively improve the quality of image restoration.
Distributed-Memory Parallel Algorithms for Distance-2 Coloring and Related Problems in Derivative Computation

The distance-2 graph coloring problem aims at partitioning the vertex set of a graph into the fewest sets consisting of vertices pairwise at distance greater than 2 from each other. Its applications include derivative computation in numerical optimization and channel assignment in radio networks. We present efficient, distributed-memory, parallel heuristic algorithms for this NP-hard problem as well as for two related problems used in the computation of Jacobians and Hessians. Parallel speedup is achieved through graph partitioning, speculative (iterative) coloring, and a bulk synchronous parallel-like organization of parallel computation. Results from experiments conducted on a PC cluster employing up to 96 processors and using large-size real-world as well as synthetically generated test graphs show that the algorithms are scalable. In terms of quality of solution, the algorithms perform remarkably well—the numbers of colors used by the parallel algorithms are observed to be very close to the numbers used by their sequential counterparts, which in turn are quite often near optimal. Moreover, the experimental results show that the parallel distance-2 coloring algorithm compares favorably with the alternative approach of solving the distance-2 coloring problem on a graph $\mathcal{G}$ by first constructing the square graph $\mathcal{G}^2$ and then applying a parallel distance-1 coloring algorithm on $\mathcal{G}^2$. Implementations of the algorithms are made available via the Zoltan toolkit.
CXC Chemokine Receptor 7 (CXCR7) Affects the Migration of GnRH Neurons by Regulating CXCL12 Availability

Gonadotropin-releasing hormone (GnRH) neurons are neuroendocrine cells, located in the hypothalamus, that play an essential role in mammalian reproduction. These neurons originate in the nasal placode and migrate during embryonic development, in association with olfactory/vomeronasal nerves, first in the nose, then through the cribriform plate to enter the forebrain, before settling in the hypothalamus. One of the molecules required for their early migration in the nose is the chemokine CXCL12, which is expressed in the embryonic nasal mesenchyme in an increasing ventral to dorsal gradient, presumably guiding GnRH neurons toward the forebrain. Mice lacking CXCR4, the receptor for CXCL12, exhibit defective GnRH cell movement and a significant reduction in their number, suggesting that CXCL12/CXCR4 signaling is important in the migration and survival of these neurons. Here, we investigated the role of the more recently identified second CXCL12 receptor, CXCR7, in GnRH neuron development. We demonstrate that CXCR7 is expressed along the migratory path of GnRH neurons in the nasal cavity and, although not expressed by GnRH neurons, it affects their migration as indicated by the ectopic accumulation of these cells in the nasal compartment in CXCR7−/− mice. Absence of CXCR7 caused abnormal accumulation of CXCL12-RFP at CXCR4-positive sites in the nasal area of CXCL12-RFP-transgenic mice and excessive CXCL12-dependent intracellular clustering of CXCR4 in GnRH neurons, suggesting internalization. These findings imply that CXCR7 regulates CXCL12 availability by acting as a scavenger along the migratory path of GnRH neurons and, thus, influences the migration of these cells in a noncell-autonomous manner.
Pro , Con , and Affinity Tagging of Product Reviews

1 Introduction As internet use becomes more prevalent, an increasing number of consumers are turning to product reviews to guide their search for the best product. This is especially true for products that the consumer may not have extensive knowledge about, such as cameras and GPS devices. According to a 2006 eTailing Group and JC Williams Consultancy study, 70% of online shoppers examine customer reviews before they buy and 92% find customer reviews to be "very helpful" in making a decision, which makes product reviews an important facet of consumer decision making and an important aspect of retailing and e-commerce. While plain text reviews are helpful, most consumers do not have enough time to read many reviews about multiple products. As a result, reviews are much more helpful if they contain tags for the pros and cons. Most consumers would agree that reviews are even more helpful if the product's main page that contains all of its reviews contains the aggregate information of these tags. In this paper, we consider the task of assigning pros, cons, and affinities (more on these later) to product reviews given information such as the review's title, comment text, and rating. We begin in Section 2 with background research in extracting pros and cons from reviews and describe our problem definition in Section 3. We then present our baseline systems that use a bag of word approach with a Naïve Bayes classifier (Section 4). In Section 4 we also examine the implications of various preprocessing techniques such as removing punctuation, lowercasing words, and stemming. In Section 5 we present a maximum entropy classifier that shows considerable performance increases over the baseline system. We move to making joint decisions on tag sets in Section 6, and finish with a brief listing of the many ideas and techniques that we did not have sufficient time to incorporate into our systems. 2 Background There has been many successful previous works pertaining to analyzing reviews. For example, Bo Pang, Lillian Lee, and others have had much success in predicting the rating , or sentiment, expressed in review text [1, 2, 3]. In their work, they found that sentiment classification was by-and-large more difficult than standard topic-based categorization, with many successful techniques in topic classification such as incorporating frequency information into n-grams actually decreasing performance in the sentiment classification setting. Additionally, they found that unigram presence features were the most …