nils-herrmann/clean_scirepeval huggingface dataset:
Face attribute prediction using off the shelf CNN features
Venue: 2016 International Conference on Biometrics (ICB)

Predicting attributes from face images in the wild is a challenging computer vision problem. To automatically describe face attributes from face containing images, traditionally one needs to cascade three technical blocks face localization, facial descriptor construction, and attribute classification in a pipeline. As a typical classification problem, face attribute prediction has been addressed using deep learning. Current state of the art performance was achieved by using two cascaded Convolutional Neural Networks (CNNs) which were specifically trained to learn face localization and attribute description. In this paper, we experiment with an alternative way of employing the power of deep representations from CNNs. Combining with conventional face localization techniques, we use off the shelf architectures trained for face recognition to build facial descriptors. Recognizing that the describable face attributes are diverse, our face descriptors are constructed from different levels of the CNNs for different attributes to best facilitate face attribute prediction. Experiments on two large datasets, LFWA and CelebA, show that our approach is entirely comparable to the state of the art. Our findings not only demonstrate an efficient face attribute prediction approach, but also raise an important question: how to leverage the power of off the shelf CNN representations for novel tasks.
Face Attribute Prediction Using Off The Shelf Deep Learning Networks
Venue: ArXiv

Attribute prediction from face images in the wild is a challenging problem. To automatically describe face attributes from face containing images, traditionally one needs to cascade three technical blocks face localization, facial feature extraction, and classification in a pipeline. As a typical classification problem, face attribute prediction has been addressed by using deep learning networks. Current state of the art performance was achieved by using two cascaded CNNs, which were specifically trained to learn face localization and facial attribute prediction. In this paper we experiment in an alternative way of exploring the power of deep representations from the networks: we employ off the shelf CNNs trained for face recognition tasks to do facial feature extraction, combined with conventional face localization techniques. Recognizing that the describable face attributes are diverse, we select representations from different levels of the CNNs and investigate their utilities for attribute classification. Experiments on two large datasets, LFWA and CeleA, show that the performance is totally comparable to state of the art approach. Our findings suggest two potentially important questions in using CNNs for face attribute prediction: 1) How to maximally leverage the power of CNN representations. 2) How to best combine traditional computer vision techniques with deep learning networks.
Transferring from face recognition to face attribute prediction through adaptive selection of off the shelf CNN representations
Venue: 2016 23rd International Conference on Pattern Recognition (ICPR)

This paper addresses the problem of transferring CNNs pre trained for face recognition to a face attribute prediction task. To transfer an off the shelf CNN to a novel task, a typical solution is to fine tune the network towards the novel task. As demonstrated in the state of the art face attribute prediction approach, fine tuning the high level CNN hidden layer by using labeled attribute data leads to significant performance improvements. In this paper, however, we tackle the same problem but through a different approach. Rather than using an end to end network, we select face descriptors from off the shelf hierarchical CNN representations for recognizing different attributes. Through such an adaptive representation selection, even without any fine tuning, our results still outperform the state of the art face attribute prediction approach on the latest large scale dataset for an error rate reduction of more than 20% Moreover, by using intensive empirical probes, we have identified several key factors that are significant for achieving promising face attribute prediction performance. These results attempt to gain and update our understandings of the nature of CNN features and how they can be better applied to the transferred novel tasks.
Face Attribute Prediction with classification CNN
Venue: ArXiv

Predicting facial attributes from faces in the wild is very challenging due to pose and lighting variations in the real world. The key to this problem is to build proper feature representations to cope with these unfavorable conditions. Given the success of convolutional neural network (CNN) in image classification, the high level CNN feature as an intuitive and reasonable choice has been widely utilized for this problem. In this paper, however, we consider the mid level CNN features as an alternative to the high level ones for attribute prediction. This is based on the observation that face attributes are different: some of them are locally oriented while others are globally defined. Our investigations reveal that the mid level deep representations outperform the prediction accuracy achieved by the high level abstractions. We demonstrate that the mid level representations achieve state of the art prediction performance on CelebA and LFWA datasets. Our investigations also show that by utilizing the mid level representations one can employ a single deep network to achieve both face recognition and attribute prediction.
The Italian perspective of the right to oblivion

This paper gives a brief overview of the state of the art relating to the right to oblivion in Italy. Three major issues on the way this right is perceived, recognized and protected are presented. First, the strict link between the right to oblivion and the right to information is discussed. Then, the role of the internet revolution in enlarging the protection to different situations related to the right to oblivion is analyzed. Finally, a reflection on the distinction between the right to oblivion and the right to data protection and specifically from the right to erasure, anonymization or blocking of data that have been processed unlawfully is drafted.
Digital Accessibility: Systematic Review of Papers from the Brazilian Symposium on Human Factors in Computer Systems
Venue: IHC

The Brazilian Computer Society proposed in 2006 the Grand Research Challenges in Computing in Brazil in order to encourage and guide the research in the country. Digital Accessibility is one of the topics covered by the fourth Grand Challenge "Participative and universal access to knowledge for the Brazilian citizen" In 2012 the HCI Brazilian Community inspired by the Brazilian Computer Society Grand Challenges discussed and proposed Grand Challenges for this specific area. As the result, the second GranDIHC is Acessibility and Digital Inclusion. This paper evaluates the impact of these initiatives on Digital Accessibility in the Brazilian Symposium on Human Factors in Computing Systems. This does not only tell part of this event's history but also alerts to the need of dissemination of the importance of Digital Accessibility.
Positive penicillin allergy testing results: a systematic review and meta analysis of papers published from 2010 through 2015
Venue: Postgraduate medicine

b lactam antibiotics are the most widely used group of antibiotics, given their effectiveness for the most common bacterial pathogens and their relatively low price. Adverse reactions, mainly cutaneous, are often reported to be associated with their use and hence, less effective and usually more costly alternative antibiotics are prescribed. However, it is not clear what is the risk of immediate immune mediated (i.e. developing within one hour of administration) and potentially life threatening reactions among those using b lactam antibiotic. We conducted a systematic review to assess the prevalence of immediate adverse reactions to b lactam antibiotics, specifically penicillin derivatives, in patients with a reported adverse reaction to b lactam antibiotics. In addition, we determined the effect of age on the prevalence of immediate reactions. Assessing the true risk of using b lactam antibiotics in patients with a reported allergy could prevent physicians from unnecessarily discouraging the use of b lactam antibiotics. We conducted a systematic review and a meta analysis using the PubMed, OVID, and Embase databases of work published in English and in French in the last 5 years. Studies were only eligible if they established the prevalence of immediate penicillin reactions with skin testing or challenges in case of negative skin tests. The meta analysis was conducted using Stata version 12.0. The prevalence of immediate reactions to penicillin derivatives in patients reporting a b lactam hypersensitivity is 1.98% (95%CI; 1.35% 2.60% in the pediatric (under 18 years old) group, 7.78% (95%CI; 6.53% 9.04% in the adult group, and 2.84% (95%CI; 1.77% 3.91% in the combined group, as tested in various studies, using skin tests and oral challenges. The I2 value ranged between 87.2% and 97.0% Our results indicate that the prevalence of immediate reactions is higher in adults than in children. However, wide confidence intervals and a large study heterogeneity preclude conclusive estimates.
Regional debris flow susceptibility analysis based on principal component analysis and self organizing map: a case study in Southwest China
Venue: Arabian Journal of Geosciences

A method was developed to analyze the susceptibilities of 541 regional basins affected by debris flows at the Wudongde Dam site in southwest China. Determining susceptibility requires information on source material quantity and occurrence frequency. However, the large number of debris flows can hinder the individual field investigation in a each small basin. Factors that may trigger debris flows can be identified using remotely sensed interpretation information. Susceptibility analysis can then be conducted based on these factors. In this study, SPOT5 satellite imagery, digital elevation models (DEM) a lithology distribution map, and rainfall monitoring data were used to identify 12 debris flow trigger factors: basin relief ratio, slope gradient in the initiation zone, drainage density, downslope curvature of the main channel, vegetation coverage, main channel aspect, topographic wetness index, Melton's ruggedness number, lithology, annual rainfall, form factor, and cross slope curvature of the transportation zone. Principal component analysis was used to obtain the eight principal components of these factors that contribute to susceptibility results. Then, a self organizing map method was adopted to analyze the principal components, which resulted in a debris flow susceptibility classification. Field validation of 26 debris flow basins was used to evaluate the errors of the susceptibility classification, as well as assess the causes of such errors. The study found that principle component analysis and self organizing map methodologies are good predictors of basin susceptibility to debris flows.
The associations between body dissatisfaction, body figure, self esteem, and depressed mood in adolescents in the United States and Korea: A moderated mediation analysis.
Venue: Journal of adolescence

The perception of one's body image becomes particularly important in adolescence. Body dissatisfaction has been associated with negative psychological functioning, such as self esteem and depression. Previous findings showed that the decreased self esteem due to body dissatisfaction explained the association between negative attitude toward body and psychological well being in different cultural contexts. The present study examined adolescents from the US (N 1002) and Korea (N 3993) and replicated and extended the previous findings regarding body dissatisfaction and associated psychological outcomes. The results showed that body dissatisfaction predicted higher depressed mood and that self esteem mediated this association for both American and Korean adolescents. Notably, the indirect effect of body dissatisfaction and perceived body image on depressed mood via self esteem was greater for American adolescents than for Korean adolescents. The implications of the cultural difference in the significance of self esteem in mediating the body dissatisfaction and depressed mood are discussed.
The Effects of Social Anxiety, Self Esteem, and Depression on Suicidal Ideation in Korean Adolescents

MethodsZZThis study involved 1320 middle school students. All participants completed several self assessment questionnaires. Correlations among variables were assessed, and multiple regression analysis was performed to evaluate the relationships between variables and suicidal ideation. Finally, a structural equation model was used to determine the pathways by which the tested variables can influence suicidal ideation.
Stretching the boundaries of medical education A case of medical college embracing humanities and social sciences in medical education
Venue: Pakistan journal of medical sciences

Aga Khan University, a private medical college, had a vision of producing physicians who are not only scientifically competent, but also socially sensitive, the latter by exposure of medical students to a broad based curriculum. The objective of this study was to identify the genesis of broad based education and its integration into the undergraduate medical education program as the Humanities and Social Sciences (HASS) course.  A qualitative methodology was used for this study. Sources of data included document review and in depth key informant interviews. Nvivo software was utilized to extract themes.  The study revealed the process of operationalization of the institutional vision to produce competent and culturally sensitive physicians. The delay in the establishment of the Faculty of Arts and Sciences, which was expected to take a lead role in the delivery of a broad based education, led to the development of an innovative HASS course in the medical curriculum. The study also identified availability of faculty and resistance from students as challenges faced in the implementation and evolution of HASS.  The description of the journey and viability of integration of HASS into the medical curriculum offers a model to medical colleges seeking ways to produce socially sensitive physicians.
Opportunities and challenges of big data for the social sciences: The case of genomic data.
Venue: Social science research

In this paper, we draw attention to one unique and valuable source of big data, genomic data, by demonstrating the opportunities they provide to social scientists. We discuss different types of large scale genomic data and recent advances in statistical methods and computational infrastructure used to address challenges in managing and analyzing such data. We highlight how these data and methods can be used to benefit social science research.
Multidisciplinary design optimization of a direct drive PMSG including the power converter cost and losses
Venue: 2016 XXII International Conference on Electrical Machines (ICEM)

This paper presents the multidisciplinary design optimization (MDO) of a WECS permanent magnet synchronous generator (PMSG) The MDO is applied to a medium power WECS (55 kW) with the objective of reducing its cost. The WECS model includes, in addition to the model of the PMSG, cost and losses models of the power converter. The wind turbine cost is not included in the model. Although numerous papers discuss the optimal design of the PMSG, few examine the influence of power converter cost and losses in WECS total cost. The optimal design methodology proposed here produced three different WECS, each to a different phase angle of the current. The results show that the current phase angle affects the costs of the PMSG and of the power converter differently. The result of the optimization allowed to define the phase angle of the current which minimizes the total cost of the WECS.
Sperm Capacitation and Acrosome Reaction in Mammalian Sperm.
Venue: Advances in anatomy, embryology, and cell biology

Physiological changes that endow mammalian sperm with fertilizing capacity are known as sperm capacitation. As part of capacitation, sperm develop an asymmetrical flagellar beating known as hyperactivation and acquire the ability to undergo the acrosome reaction. Together, these processes promote fertilizing competence in sperm. At the molecular level, capacitation involves a series of signal transduction events which include activation of cAMP dependent phosphorylation pathways, removal of cholesterol, hyperpolarization of the sperm plasma membrane, and changes in ion permeability. In recent years, new technologies have aided in the study of sperm signaling molecules with better resolution, at both spatial and temporal levels, unraveling how different cascades integrate and cooperate to render a fertilizing sperm. Despite this new information, the molecular mechanisms connecting capacitation with acrosomal exocytosis and hyperactivation are not well understood. This review brings together results obtained in mammalian species in the field of sperm capacitation with special focus on those pathways involved in the preparation to undergo the acrosomal reaction.
Regulation of Sperm Capacitation by the 26S Proteasome: An Emerging New Paradigm in Spermatology1
Venue: Biology of reproduction

The ubiquitin proteasome system (UPS) participates in many biological processes ranging from cell cycle and antigen processing to cellular defense and signaling. Work of the last decade has made it evident that the UPS is involved in many sperm related processes leading up to and as part of fertilization. The current knowledge of UPS involvement and changes during sperm capacitation are reviewed together with a list of known proteasome associated sperm proteins and a discussion of the relationships between these proteins and the proteasome. Proteasomal inhibitors such as MG 132 and epoxomicin significantly alter capacitation and prevent acrosome reaction. The 26S proteasome degrades AKAP3, an A kinase anchoring protein, partially regulating the release of protein kinase A (PKA) a vital component necessary for the steps leading up to capacitation. Further, changes occur in 20S core subunit localization and abundance throughout capacitation. Proteasome interacting valosine containing protein (VCP) undergoes tyrosine phosphorylation; however, its physiological roles in capacitation and fertilization remain unknown. The E1 type ubiquitin activating enzyme (UBA1) inhibitor PYR 41 also alters acrosomal membrane remodeling during capacitation. Furthermore, after capacitation, the acrosomal proteasomes facilitate the degradation of zona pellucida glycoproteins leading up to fertilization. Methods to modulate the sperm proteasome activity during sperm storage and capacitation may translate to increased reproductive efficiency in livestock animals. Human male infertility diagnostics may benefit from incorporation of research outcomes built upon relationships between UPS and capacitation. Altogether, the studies reviewed here support the involvement of UPS in sperm capacitation and present opportunities for new discoveries.
The vehicle routing problem: State of the art classification and review
Venue: Comput. Ind. Eng.

Taxonomic review of vehicle routing literature published between 2009 and mid 2015.An adapted version of an existing comprehensive taxonomy is presented.277 VRP articles are classified based on problem characteristics and assumptions.The classification table allows researchers to quickly find relevant literature.Recent trends in VRP literature are discussed. Over the past decades, the Vehicle Routing Problem (VRP) and its variants have grown ever more popular in the academic literature. Yet, the problem characteristics and assumptions vary widely and few literature reviews have made an effort to classify the existing articles accordingly. In this article, we present a taxonomic review of the VRP literature published between 2009 and June 2015. Based on an adapted version of an existing comprehensive taxonomy, we classify 277 articles and analyze the trends in the VRP literature. This classification is the first to categorize the articles to this level of detail.
The combined role of biomarkers and interim PET scan in prediction of treatment outcome in classical Hodgkin's lymphoma: a retrospective, European, multicentre cohort study.
Venue: The Lancet. Haematology

Early interim fluorodeoxyglucose (FDG) PET scan after two ABVD (doxorubicin, bleomycin, vinblastine, and dacarbazine) chemotherapy courses (PET 2) represents the most effective predictor of treatment outcome in classical Hodgkin's lymphoma. We aimed to assess the predictive value of PET 2 combined with tissue biomarkers in neoplastic and microenvironmental cells for this disease.  We enrolled 208 patients with classical Hodgkin's lymphoma and treated with ABVD (training set) from Jan 1, 2002, to Dec 31, 2009, and validated the results in a fully matched independent cohort of 102 patients with classical Hodgkin's lymphoma (validation set) enrolled from Jan 1, 2008, to Dec 31, 2012. The inclusion criteria for both the training and validation sets were: the availability of a representative formalin fixed, paraffin embedded tissue sample collected at diagnosis; treatment with ABVD with or without radiotherapy; baseline staging and interim restaging after two ABVD courses with FDG PET; no treatment change based solely on interim PET result; and HIV negative status. We used Cox multivariate analysis classification and regression tree (CART) to compare the predictive values of these markers with that of PET 2 and to assess the biomarkers' ability to correctly classify patients whose outcome was incorrectly predicted by PET 2.  In multivariate analysis, PET 2 was the only factor able to predict both progression free survival (hazard ratio [HR] 33*3 [95% CI 13*6 83*3] p<0*0001) and overall survival (HR 31*3 [95% CI 3*7 58*9] p=0*002) In the training set, no factor had a stronger adverse predictive value than a positive PET 2 scan and none was able to correctly reclassify PET 2 positive patients. In PET 2 negative patients, expression of CD68 =25% and PD1 (diffuse or rosetting pattern) in microenvironmental cells, and STAT1 negativity in Hodgkin Reed Sternberg cells identified a subset of PET 2 negative patients with a 3 year progression free survival significantly lower than that of the remaining PET 2 negative population (21 [64% of 33 [95% CI 45*2 79*0] vs 130 [95% of 137 [95% CI 89*4 97*7] p<0*0001) These findings were reproduced in the validation set.  The CART algorithm correctly predicted the response to treatment in more than a half of patients who had a relapse or disease progression despite a negative PET 2 scan, thus increasing the negative predictive value of PET 2. In keeping with preliminary results from interim PET response adapted clinical trials of patients with advanced Hodgkin's lymphoma, there might be a non negligible proportion of treatment failures in the interim PET negative group treated with standard ABVD.
BIG DATA TECHNIQUES HADOOP AND MAP REDUCE FOR WEATHER FORECASTING

Nowadays analyzing large amounts of data has become a big challenge. Data could be scientific, medical, meteorological, climatically, financial or marketing. Data mining techniques is used to extract meaningful information from large data set. Weather forecasting can be used to support many important sectors that are affected by climate like agriculture, water resources, air traffic, and tourism. Weather forecasting is an area of meteorology that is done by collecting data from the different stations related to the current state of the weather like temperate, rainfall, wind, and fog. Weather forecasting is the most challenging problem for scientists. Hadoop and MapReduce are the most models used to analysis a huge data set. This paper explains a system that uses the historical weather data of a region and apply the MapReduce and Hadoop techniques to analysis these historical data.