nils-herrmann/clean_scirepeval huggingface dataset:
Clothes search in consumer photos via color matching and attribute learning

Automatic clothes search in consumer photos is not a trivial problem as photos are usually taken under completely uncontrolled realistic imaging conditions. In this paper, a novel framework is presented to tackle this issue by leveraging low-level features (e.g., color) and high-level features (attributes) of clothes. First, a content-based image retrieval(CBIR) approach based on the bag-of-visual-words (BOW) model is developed as our baseline system, in which a codebook is constructed from extracted dominant color patches. A reranking approach is then proposed to improve search quality by exploiting clothes attributes, including the type of clothes, sleeves, patterns, etc. The experiments on photo collections show that our approach is robust to large variations of images taken in unconstrained environment, and the reranking algorithm based on attribute learning significantly improves retrieval performance in combination with the proposed baseline.
Qualitative research: standards, challenges, and guidelines

Qualitative research methods could help us to improve our understanding of medicine. Rather than thinking of qualitative and quantitative strategies as incompatible, they should be seen as complementary. Although procedures for textual interpretation differ from those of statistical analysis, because of the different type of data used and questions to be answered, the underlying principles are much the same. In this article I propose relevance, validity, and reflexivity as overall standards for qualitative inquiry. I will discuss the specific challenges in relation to reflexivity, transferability, and shared assumptions of interpretation, which are met by medical researchers who do this type of research, and I will propose guidelines for qualitative inquiry.
Helpfulness of Online Product Reviews as Seen by Consumers: Source and Content Features

Online product reviews are important determinants of consumers' purchase decision. Although prior research has articulated various benefits of online product reviews, there are few investigations into whether or not they are perceived as helpful by consumers. Product review helpfulness is conceptualized as a second-order formative construct, which is manifested by perceived source credibility, perceived content diagnosticity, and perceived vicarious expression of the product review. In this study, we conduct a laboratory experiment to investigate product review helpfulness as well as its corresponding antecedents from the product review feature perspective (i.e., sourceand content-based review features). Findings from the study are threefold. First, the results of the data analysis support the theoretical conceptualization of product review helpfulness as a formative construct. Second, the results support the notion that the sourceand content-based review features have direct impact on product review helpfulness. Consumers perceive customer-written product reviews as more helpful than those written by experts; they also perceive a concrete review as more helpful than an abstract review. Third, we find an interaction effect of the sourceand content-based features of product reviews on review helpfulness. A customer-written product review with a low level of content abstractness yields the highest perceived review helpfulness.
Senior Citizens' Adoption of E-Government: In Quest of the Antecedents of Perceived Usefulness

E-Government, as a vehicle to deliver enhanced services to citizens, is now extending its reach to the elderly population through provision of targeted services. In doing so, the ideals of ubiquitous e-Government may be better achieved. However, there is a lack of studies on e-Government adoption among senior citizens, especially considering that this age group is growing in size and may be averse to new IT applications. This study aims to address this gap by investigating an innovative e- Government service specifically tailored for senior citizens, called CPF e-Withdrawal. Technology adoption model (TAM) is employed as the theoretical foundation, in which perceived usefulness is recognized as the most significant predictor of adoption intention. This study attempts to identify the antecedents of perceived usefulness by drawing from the innovation diffusion literature as well as age-related studies. Our findings agree with TAM and indicate that internet safety perception and perceived ease of use are significant predictors of perceived usefulness.
Sexual behavior and selected health measures: men and women 15-44 years of age, United States, 2002.

This report presents national estimates of several measures of sexual behavior among males and females 15-44 years of age in the United States in 2002, as collected in the 2002 National Survey of Family Growth (NSFG). These data are relevant to demographic and public health concerns, including fertility and sexually transmitted diseases among teenagers and adults. Data from the 2002 NSFG are compared with previous national surveys.



The 2002 NSFG was conducted by the Centers for Disease Control and Prevention's (CDC) National Center for Health Statistics (NCHS) and is based on in-person, face-to-face interviews with a national sample of 12,571 males and females in the household population of the United States. The measures of sexual behavior presented in this report were collected using Audio Computer-Assisted Self-Interviewing (ACASI), in which the respondent enters his or her own answers into a laptop computer without telling them to an interviewer.



Among adults 25-44 years of age, 97 percent of men and 98 percent of women have had vaginal intercourse; 90 percent of men and 88 percent of women have had oral sex with an opposite-sex partner; and 40 percent of men and 35 percent of women have had anal sex with an opposite-sex partner. About 6.5 percent of men 25-44 years of age have had oral or anal sex with another man. Based on a differently worded question, 11 percent of women 25-44 years of age reported having had a sexual experience with another woman. The public health significance of the findings is described.
Validating the Physical Activity and Leisure Motivation Scale (PALMS)

Although there is abundant evidence to recommend a physically active lifestyle, adult physical activity (PA) levels have declined over the past two decades. In order to understand why this happens, numerous studies have been conducted to uncover the reasons for people's participation in PA. Often, the measures used were not broad enough to reflect all the reasons for participation in PA. The Physical Activity and Leisure Motivation Scale (PALMS) was created to be a comprehensive tool measuring motives for participating in PA. This 40-item scale related to participation in sport and PA is designed for adolescents and adults. Five items constitute each of the eight sub-scales (mastery, enjoyment, psychological condition, physical condition, appearance, other's expectations, affiliation, competition/ego) reflecting motives for participation in PA that can be categorized as features of intrinsic and extrinsic motivation based on self-determination theory. The aim of the current study was to validate the PALMS in the cultural context of Malaysia, including to assess how well the PALMS captures the same information as the Recreational Exercise Motivation Measure (REMM).



To do so, 502 Malaysian volunteer participants, aged 18 to 67 years (mean ± SD; 31.55 ± 11.87 years), from a variety of PA categories, including individual sports, team sports, martial arts and exercise, completed the study.



The hypothesized 8-factor model demonstrated a good fit with the data (CMIN/DF = 2.820, NFI = 0.90, CFI = 0.91, RMSEA = 0.06). Cronbach's alpha coefficient (α = 0.79) indicated good internal consistency for the overall measure. Internal consistency for the PALMS subscales was sound, ranging from 0.78 to 0.82. The correlations between each PALMS sub-scale and the corresponding sub-scale on the validated REMM (the 73-item questionnaire from which the PALMS was developed) were also high and varied from 0.79 to 0.95. Also, test-retest reliability for the questionnaire sub-scales was between 0.78 and 0.94 over a 4-week period.



In this sample, the PALMS demonstrated acceptable factor structure, internal consistency, test-retest reliability, and criterion validity. It was applicable to diverse physical activity contexts.
QASCA: A Quality-Aware Task Assignment System for Crowdsourcing Applications

A crowdsourcing system, such as the Amazon Mechanical Turk (AMT), provides a platform for a large number of questions to be answered by Internet workers. Such systems have been shown to be useful to solve problems that are difficult for computers, including entity resolution, sentiment analysis, and image recognition. In this paper, we investigate the online task assignment problem: Given a pool of n questions, which of the k questions should be assigned to a worker? A poor assignment may not only waste time and money, but may also hurt the quality of a crowdsourcing application that depends on the workers' answers. We propose to consider quality measures (also known as evaluation metrics) that are relevant to an application during the task assignment process. Particularly, we explore how Accuracy and F-score, two widely-used evaluation metrics for crowdsourcing applications, can facilitate task assignment. Since these two metrics assume that the ground truth of a question is known, we study their variants that make use of the probability distributions derived from workers' answers. We further investigate online assignment strategies, which enables optimal task assignments. Since these algorithms are expensive, we propose solutions that attain high quality in linear time. We develop a system called the Quality-Aware Task Assignment System for Crowdsourcing Applications (QASCA) on top of AMT. We evaluate our approaches on five real crowdsourcing applications. We find that QASCA is efficient, and attains better result quality (of more than 8% improvement) compared with existing methods.
Generic Object Detection With Dense Neural Patterns and Regionlets

This paper addresses the challenge of establlishing a bridge between deep convolutional neural networks and conventional object detection frameworks for accurate and efficient generic object detection. We introduce Dense Neural Patterns, short for DNPs, which are dense local features derived from discriminatively trained deep convolutional neural networks. DNPs can be easily plugged into conventional detection frameworks in the same way as other dense local features(like HOG or LBP). The effectiveness of the proposed approach is demonstrated with Regionlets object detection framework. It achieved 46.1% mean average precision on the PASCAL VOC 2007 dataset, and 44.1% on the PASCAL VOC 2010 dataset, which dramatically improves the originalRegionlets approach without DNPs.
Automatic stylistic manga layout

Manga layout is a core component in manga production, characterized by its unique styles. However, stylistic manga layouts are difficult for novices to produce as it requires hands-on experience and domain knowledge. In this paper, we propose an approach to automatically generate a stylistic manga layout from a set of input artworks with user-specified semantics, thus allowing less-experienced users to create high-quality manga layouts with minimal efforts. We first introduce three parametric style models that encode the unique stylistic aspects of manga layouts, including layout structure, panel importance, and panel shape. Next, we propose a two-stage approach to generate a manga layout: 1) an initial layout is created that best fits the input artworks and layout structure model, according to a generative probabilistic framework; 2) the layout and artwork geometries are jointly refined using an efficient optimization procedure, resulting in a professional-looking manga layout. Through a user study, we demonstrate that our approach enables novice users to easily and quickly produce higher-quality layouts that exhibit realistic manga styles, when compared to a commercially-available manual layout tool.
The Truth About Baby Boomer Gamers A Study of Over-Forty Computer Game Players

Author’s Note: Special Thanks for their support to Emily Morganti and Heather Logas of Telltale Games; Becky Waxman, Jan Christoe and Marita Robinson of GameBoomers; Stefaan de Keersmaeker, a.k.a. “Father,” of The Older Gamers; Jen at FourFatChicks.com; Cindy at MysteryManor.com; Tami at Spyglassguides.com; Mina at grrlgamer.com; the team at Seasoned Gamers; Chris Morris; Betsy Book; and all those who participated in the study. Games and Culture Volume XX Number X Month XXXX xx-xx  http://gac.sagepub.com hosted at http://online.sagepub.com The Truth About Baby Boomer Gamers
A Supervised Time Series Feature Extraction Technique Using DCT and DWT

The increased availability of time series datasets prompts the development of new tools and methods that allow machine learning classifiers to better cope with time series data. Time series data are usually characterized by a high space dimensionality and a very strong correlation among features. This special nature makes the development of effective time series classifiers a challenging task. This work proposes and analyzes methods combining spectral decomposition and feature selection for time series classification problems and compares them against methods that work with original time series and time-dependent features. Briefly, our approach first applies discrete cosine transform (DCT) or discrete wavelet transform (DWT) on time series data. Then, it performs supervised feature selection/reduction by selecting only the most discriminative set of coefficients to represent the data. Experimental evaluations, carried out on multiple datasets, demonstrate the benefits of our approach in learning efficient and accurate time series classifiers.
The role of the thalamus in the flow of information to the cortex.

The lateral geniculate nucleus is the best understood thalamic relay and serves as a model for all thalamic relays. Only 5-10% of the input to geniculate relay cells derives from the retina, which is the driving input. The rest is modulatory and derives from local inhibitory inputs, descending inputs from layer 6 of the visual cortex, and ascending inputs from the brainstem. These modulatory inputs control many features of retinogeniculate transmission. One such feature is the response mode, burst or tonic, of relay cells, which relates to the attentional demands at the moment. This response mode depends on membrane potential, which is controlled effectively by the modulator inputs. The lateral geniculate nucleus is a first-order relay, because it relays subcortical (i.e. retinal) information to the cortex for the first time. By contrast, the other main thalamic relay of visual information, the pulvinar region, is largely a higher-order relay, since much of it relays information from layer 5 of one cortical area to another. All thalamic relays receive a layer-6 modulatory input from cortex, but higher-order relays in addition receive a layer-5 driver input. Corticocortical processing may involve these corticothalamocortical 're-entry' routes to a far greater extent than previously appreciated. If so, the thalamus sits at an indispensable position for the modulation of messages involved in corticocortical processing.
Portfolio Allocation for Bayesian Optimization

Bayesian optimization with Gaussian processes has become an increasingly popular tool in the machine learning community. It is efficient and can be used when very little is known about the objective function, making it popular in expensive black-box optimization scenarios. It uses Bayesian methods to sample the objective efficiently using an acquisition function which incorporates the model’s estimate of the objective and the uncertainty at any given point. However, there are several different parameterized acquisition functions in the literature, and it is often unclear which one to use. Instead of using a single acquisition function, we adopt a portfolio of acquisition functions governed by an online multi-armed bandit strategy. We propose several portfolio strategies, the best of which we call GP-Hedge, and show that this method outperforms the best individual acquisition function. We also provide a theoretical bound on the algorithm’s performance.
Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation

A flexible statistical framework is developed for the analysis of read counts from RNA-Seq gene expression studies. It provides the ability to analyse complex experiments involving multiple treatment conditions and blocking variables while still taking full account of biological variation. Biological variation between RNA samples is estimated separately from the technical variation associated with sequencing technologies. Novel empirical Bayes methods allow each gene to have its own specific variability, even when there are relatively few biological replicates from which to estimate such variability. The pipeline is implemented in the edgeR package of the Bioconductor project. A case study analysis of carcinoma data demonstrates the ability of generalized linear model methods (GLMs) to detect differential expression in a paired design, and even to detect tumour-specific expression changes. The case study demonstrates the need to allow for gene-specific variability, rather than assuming a common dispersion across genes or a fixed relationship between abundance and variability. Genewise dispersions de-prioritize genes with inconsistent results and allow the main analysis to focus on changes that are consistent between biological replicates. Parallel computational approaches are developed to make non-linear model fitting faster and more reliable, making the application of GLMs to genomic data more convenient and practical. Simulations demonstrate the ability of adjusted profile likelihood estimators to return accurate estimators of biological variability in complex situations. When variation is gene-specific, empirical Bayes estimators provide an advantageous compromise between the extremes of assuming common dispersion or separate genewise dispersion. The methods developed here can also be applied to count data arising from DNA-Seq applications, including ChIP-Seq for epigenetic marks and DNA methylation analyses.
Implicit Learning and Tacit Knowledge

I examine the phenomenon of implicit learning, the process by which knowledge about the ralegoverned complexities of the stimulus environment is acquired independently of conscious attempts to do so. Our research with the two, seemingly disparate experimental paradigms of synthetic grammar learning and probability learning is reviewed and integrated with other approaches to the general problem of unconscious cognition. The conclusions reached are as follows: (a) Implicit learning produces a tacit knowledge base that is abstract and representative of the structure of the environment; (b) such knowledge is optimally acquired independently of conscious efforts to learn; and (c) it can be used implicitly to solve problems and make accurate decisions about novel stimulus circumstances. Various epistemological issues and related prob1 lems such as intuition, neuroclinical disorders of learning and memory, and the relationship of evolutionary processes to cognitive science are also discussed.
Passage Retrieval Revisited

Ranking based on passages addresses some of the shortcomings ofwhole-document ranking. It provides convenient units of text toreturn to the user, avoids the difficulties of comparing documentsof different length, and enables identification of short blocks ofrelevant material amongst otherwise irrelevant text. In this paperwe explore the potential of passage retrieval, based on anexperimental evaluation of the ability of passages to identifyrelevant documents. We compare our scheme of arbitrary passageretrieval to several other document retrieval and passage retrievalmethods; we show experimentally that, compared to these methods,ranking via fixed-length passages is robust and effective. Ourexperiments also show that, compared to whole-document ranking,ranking via fixed-length arbitrary passages significantly improvesretrieval effectiveness, by 8% for TREC disks 2 and 4 and by18%-37% for the Federal Register collection.
A Unified Framework for Multi-Oriented Text Detection and Recognition

High level semantics embodied in scene texts are both rich and clear and thus can serve as important cues for a wide range of vision applications, for instance, image understanding, image indexing, video search, geolocation and automatic navigation. In this paper, we present a unified framework for text detection and recognition in natural images. The contributions of this work are three-fold: (1) Text detection and recognition are accomplished concurrently using exactly the same features and classification scheme. (2) In contrast to methods in the literature, which mainly focus on horizontal or near-horizontal texts, the proposed system is capable of localizing and reading texts of varying orientations. (3) A new dictionary search method is proposed, to correct the recognition errors usually caused by confusions among similar yet different characters. As an additional contribution, a novel image database with texts of different scales, colors, fonts and orientations in diverse realworld scenarios, is generated and released. Extensive experiments on standard benchmarks as well as the proposed database demonstrate that the proposed system achieves highly competitive performance, especially on multi-oriented texts.
Measurement of sexual arousal in postoperative male-to-female transsexuals using vaginal photoplethysmography.

Men's sexual arousal patterns are category-specific: Men typically display significantly greater physiological responses to sexual stimuli depicting members of their preferred gender category. Category-specific patterns of sexual arousal have not been consistently reported in natal women. We used vaginal photoplethysmography to examine patterns of sexual arousal in 11 male-to-female (MtF) transsexuals following sex reassignment surgery (SRS) and in 72 natal women. Subjective arousal was measured with a continuous response lever. Video clips depicting sexual activity between two males, two females, or one male and one female were used as erotic stimuli. All transsexual participants displayed category-specific sexual arousal. Five homosexual transsexual participants (attracted exclusively to males before sex reassignment) showed greater genital and subjective responses to male than to female stimuli, while six nonhomosexual transsexual participants showed the opposite pattern. Vaginal pulse amplitude (VPA) was lower in transsexual participants than in natal women. The mean correlation between VPA and subjective responses was high in nonhomosexual transsexuals, but was significantly lower in homosexual transsexuals and in natal women. One transsexual participant who reported a change in sexual orientation following sex reassignment displayed VPA and subjective responses consistent with her pre-reassignment sexual orientation. We conclude that male-to-female transsexuals display male-typical category-specific sexual arousal following SRS, and that vaginal photoplethysmography is a promising methodology for studying patterns of sexual arousal in postoperative transsexuals.