Miracle-dz/UniFAR-FTUs huggingface dataset:
Knowledge, attitudes and behaviour about Sexually Transmitted Infections: a survey among Italian university female students

Aims: Improvements have been made in terms of STIs mortality, but young women still have a lack of attention to the STIs prevention. We investigated the knowledge and attitudes about STIs and their prevention among a sample of Italian university female students. Methods: We designed a questionnaire about knowledge and attitudes toward STIs, Papanicolau test (Pap test) and Human Papilloma Virus (HPV) and had it filled out by 285 young women. Chi-square test, Mann-Whitney test and a multivariable analysis were performed in order to identify associated factors. Results: Knowledge about STIs seems weak, oppositely to knowledge about preventive measures. In line with the results of the univariate analyses, the multivariable one confirmed a higher probability to answer rightly for women who knew the effective behaviours for preventing STIs, women who took information about STIs from Internet, books and TV and those using natural methods (OR 2.77 - 95%CI 1.47-5.23, OR 2.99 - 95%CI 1.62-5.53 and OR 2.55 - 95%CI 1.05-6.18, respectively) and a lower one for students from Cassino and those who knew is possible to recover from STIs (OR 0.38, 95%CI 0.17-0.87 and 0.42, 95%CI 0.20-0.88, respectively). Furthermore, about Pap test it also confirmed the role of a medium-high STIs level of knowledge (OR 2.11, 95%CI 1.02-4.37) and a lower probability to know about it for women from Cassino (OR 0.25, 95%CI 0.09-0.73). Conclusions: Young women are not completely aware of STIs risks and preventive measures. Informational and educational campaigns should be implemented to reach this target group and lighten the current and future burden of STIs.
SAR image classification method based on Gabor feature and K‐NN

Synthetic aperture radar (SAR) image target classification is a hot issue in remote-sensing image application. Fast and accurate target classification is important in both military and civilian fields. Consequently, this study proposes a novel SAR image target classification method based on Gabor feature extraction and K-NN classifier. First, the multi-scale Gabor features of SAR image are extracted. Then, a k-nearest neighbour (k-NN) classifier with principle component analysis is trained by the extracted Gabor features. Finally, the classifier is used to realise the multi-types SAR image targets classification. MSTAR database is used to validate the classification ability. Experimental results demonstrate that the proposed method has superior performance in term of efficiency and accuracy.
Viewpoint Estimation in Images by a Key-Point Based Deep Neural Network

Viewpoint estimation in a 2D image is a challenging task due to the great variations in the object’s shape, appearance, visible parts, etc. To overcome the above difficulties, a new deep neural network is proposed, which employs the key-points of the object as a regularization term and a semantic bridge connecting the raw pixels with the object’s viewpoint. A series of Hourglass structures are adopted for key-point extraction. With the extracted key-points, an LSTM based network is designed to model both the intrinsic relationship among the key-points and the underlying connections between the key-points and the object’s viewpoint. A multitasks learning scheme is designed to optimize the key-point detection and the viewpoint estimation performance simultaneously. The experiment results on the PASCAL 3D+ dataset have demonstrated the effectiveness of the proposed approach.
How do interruptions affect user contributions on social commerce?

Social commerce is an extension of e‐commerce, in which social media is leveraged to promote user contributions. Our study asks how interruptions in relation to interface design influence two types of user contributions: creating shared content and appreciating others' content. We use two interface designs, pagination and infinite scrolling, to manipulate the extent of interruptions to social commerce users. On the basis of the capacity theory of attention, we develop five hypotheses. We empirically test our model using a lab experiment and a field study to show that interruptions reduce users' content appreciation but increase their content creation and that user characteristics moderate these effects. The theoretical and practical contributions of our study are discussed.
Region Mutual Information Loss for Semantic Segmentation

Semantic segmentation is a fundamental problem in computer vision. It is considered as a pixel-wise classification problem in practice, and most segmentation models use a pixel-wise loss as their optimization criterion. However, the pixel-wise loss ignores the dependencies between pixels in an image. Several ways to exploit the relationship between pixels have been investigated, \eg, conditional random fields (CRF) and pixel affinity based methods. Nevertheless, these methods usually require additional model branches, large extra memories, or more inference time. In this paper, we develop a region mutual information (RMI) loss to model the dependencies among pixels more simply and efficiently. In contrast to the pixel-wise loss which treats the pixels as independent samples, RMI uses one pixel and its neighbour pixels to represent this pixel. Then for each pixel in an image, we get a multi-dimensional point that encodes the relationship between pixels, and the image is cast into a multi-dimensional distribution of these high-dimensional points. The prediction and ground truth thus can achieve high order consistency through maximizing the mutual information (MI) between their multi-dimensional distributions. Moreover, as the actual value of the MI is hard to calculate, we derive a lower bound of the MI and maximize the lower bound to maximize the real value of the MI. RMI only requires a few extra computational resources in the training stage, and there is no overhead during testing. Experimental results demonstrate that RMI can achieve substantial and consistent improvements in performance on PASCAL VOC 2012 and CamVid datasets. The code is available at \url{https://github.com/ZJULearning/RMI}.
Do Nuclear Submarines Have Nuclear Captains? A Challenge Dataset for Commonsense Reasoning over Adjectives and Objects

How do adjectives project from a noun to its parts? If a motorcycle is red, are its wheels red? Is a nuclear submarine’s captain nuclear? These questions are easy for humans to judge using our commonsense understanding of the world, but are difficult for computers. To attack this challenge, we crowdsource a set of human judgments that answer the English-language question “Given a whole described by an adjective, does the adjective also describe a given part? ” We build strong baselines for this task with a classification approach. Our findings indicate that, despite the recent successes of large language models on tasks aimed to assess commonsense knowledge, these models do not greatly outperform simple word-level models based on pre-trained word embeddings. This provides evidence that the amount of commonsense knowledge encoded in these language models does not extend far beyond that already baked into the word embeddings. Our dataset will serve as a useful testbed for future research in commonsense reasoning, especially as it relates to adjectives and objects
Learning Representations for Time Series Clustering

Time series clustering is an essential unsupervised technique in cases when category information is not available. It has been widely applied to genome data, anomaly detection, and in general, in any domain where pattern detection is important. Although feature-based time series clustering methods are robust to noise and outliers, and can reduce the dimensionality of the data, they typically rely on domain knowledge to manually construct high-quality features. Sequence to sequence (seq2seq) models can learn representations from sequence data in an unsupervised manner by designing appropriate learning objectives, such as reconstruction and context prediction. When applying seq2seq to time series clustering, obtaining a representation that effectively represents the temporal dynamics of the sequence, multi-scale features, and good clustering properties remains a challenge. How to best improve the ability of the encoder is still an open question. Here we propose a novel unsupervised temporal representation learning model, named Deep Temporal Clustering Representation (DTCR), which integrates the temporal reconstruction and K-means objective into the seq2seq model. This approach leads to improved cluster structures and thus obtains cluster-specific temporal representations. Also, to enhance the ability of encoder, we propose a fake-sample generation strategy and auxiliary classification task. Experiments conducted on extensive time series datasets show that DTCR is state-of-the-art compared to existing methods. The visualization analysis not only shows the effectiveness of cluster-specific representation but also shows the learning process is robust, even if K-means makes mistakes.
Adversarial Removal of Demographic Attributes Revisited

Elazar and Goldberg (2018) showed that protected attributes can be extracted from the representations of a debiased neural network for mention detection at above-chance levels, by evaluating a diagnostic classifier on a held-out subsample of the data it was trained on. We revisit their experiments and conduct a series of follow-up experiments showing that, in fact, the diagnostic classifier generalizes poorly to both new in-domain samples and new domains, indicating that it relies on correlations specific to their particular data sample. We further show that a diagnostic classifier trained on the biased baseline neural network also does not generalize to new samples. In other words, the biases detected in Elazar and Goldberg (2018) seem restricted to their particular data sample, and would therefore not bias the decisions of the model on new samples, whether in-domain or out-of-domain. In light of this, we discuss better methodologies for detecting bias in our models.
Optimal pipe dimensioning in water distribution networks using Gravitational Search Algorithm

ABSTRACT An effective method for optimal design of water distribution network (WDN) can significantly benefit to develop commercial software for component sizing. This research investigates Gravitational Search Algorithm (GSA) for pipe cost optimization model problems. GSA is a meta-heuristic (MH) algorithm which makes ease of its applicability to the design of WDNs due to its minimum number of algorithm parameters and requiring least effort in fine-tuning the parameters. Three well-known benchmark networks (Hanoi network, Two-Reservoir network and New York tunnels network) and a real-world WDN located Khorramshahr city in Iran were used. The GSA results were compared with the solutions obtained through various Evolutionary Algorithms. Experimental results show success of GSA in arriving minimum cost solution. GSA achieved to the best so far solution reported for one case (Hanoi network), and it could find the least cost for three other networks compared to the best results of other optimization algorithms. In addition, for two case studies (Hanoi network and Khorramshahr city network) number of function evaluations were less than other algorithms. Further, the study reveals that GSA achieved the maximum number of times the best so far solution and confirming rapid convergence without struck up at local optimum.
“Heating Is More Important Than Food”

Abstract Fuel poverty is estimated to affect millions of people in the UK, and older people are especially vulnerable. Most research into fuel poverty has drawn upon quantitative paradigms. The aim of this study, therefore, was to investigate the perceptions and experiences of older women in relation to fuel issues. Ten qualitative semi-structured interviews were undertaken during the winter of 2005. The interviews were tape-recorded and transcribed verbatim. A phenomenological approach was used for analysis. Four main themes emerged: causes of fuel poverty; managing money; heating is a priority;and government initiatives. The results from this study show that fuel poverty is an important health and financial concern for older women.
Isolation of Teaching Effectiveness Factors from Nigerian Senior Secondary School Chemistry Students' Point of View

Establishing the specific criteria by which effective teaching can be evaluated is a vital step in the teaching learning process. Students’ evaluation is commonly used in developed countries to provide information that could be used by teacher to improve his/her teaching and by administrators to make personnel decisions like promotion . Although students’ evaluation of teaching is one area of educational research that is becoming increasingly emphasized in recent times in Nigeria, but limited attention is still given to the identification of teaching effectiveness factors from students’ point of view, in order to develop a valid and reliable instrument. This study therefore analysed secondary school students’ evaluation of teaching effectiveness factors in chemistry. The study adopted survey research design of ex-post facto type. Two thousand nine hundred and eighty eight SSII chemistry students participated, using multistage and stratified random sampling techniques from the six states in the Southwest geopolitical zone of Nigeria. Data collected through validated and reliable “Students’ Evaluation of Teaching Effectiveness Scale (0.78)” were analysed using factor analysis. The major findings of the study was the isolation of nine teaching effectiveness factors from chemistry students’ point of view. Chemistry students regard teacher-student interaction as the most important teaching effectiveness factor, while giving assignment is the least important. Educational planners, policy makers and administrators are therefore urged to work out modalities for the development and implementation of students’ evaluation of teaching effectiveness instrument for use at the secondary level of education.
Fingerprint-Based Localization for Massive MIMO-OFDM System With Deep Convolutional Neural Networks

Fingerprint technique is a promising enabler for mobile terminals (MTs) localization in rich scattering environments, such as urban areas and indoor corridors. In this paper, we investigate fingerprint based location for massive multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems with deep convolutional neural networks(DCNNs). By taking full advantage of the high resolution in the angle domain and the delay domain in massive MIMO-OFDM systems, we first propose an efficient angle-delay channel amplitude matrix (ADCAM) fingerprint extraction method. Then a DCNN enabled localization method is proposed, in which the modeling error for fingerprint similarity calculation can be overcome. Both DCNN classification and DCNN regression are considered. For practical implementation, a hierarchical DCNN architecture is proposed. The performance of the proposed DCNN localization method is evaluated via simulation performed in a geometry-based ray tracing signal propagation scene. Numerical results demonstrate that DCNN performs well in achieving high localization accuracy as well as reducing storage overhead and computational complexity.
Dual-Domain Single Image De-Raining Using Conditional Generative Adversarial Network

This paper presents a novel method for a single image rain streak removal problem which exploits the spatial as well as wavelet transformed coefficients of the rainy images. The proposed method adopts the Conditional Generative Adversarial Network [1] framework and consists of two following networks: Generator and Discriminator. The generator model receives the input from both spatial, frequency domain of the rainy image and yields five de-rained image candidates. A Deep Residual Network [2] has been used to merge these de-rained candidates and predict a single de-rained image. To ensure the visual quality of the de-rained image, Perceptual loss function [3] in addition to adversarial training has been incorporated. Extensive experiments on the synthetic and real-world rainy images dataset reveal an improvement over the existing state-of-the-art methods [4], [5] by $\sim 1.08\% ,2.57\% $ in Structural Similarity Index [6] and $\sim 7.39\% ,9.95\% $ in Peak signal-to-noise ratio respectively.
Neural Topic Model with Reinforcement Learning

In recent years, advances in neural variational inference have achieved many successes in text processing. Examples include neural topic models which are typically built upon variational autoencoder (VAE) with an objective of minimising the error of reconstructing original documents based on the learned latent topic vectors. However, minimising reconstruction errors does not necessarily lead to high quality topics. In this paper, we borrow the idea of reinforcement learning and incorporate topic coherence measures as reward signals to guide the learning of a VAE-based topic model. Furthermore, our proposed model is able to automatically separating background words dynamically from topic words, thus eliminating the pre-processing step of filtering infrequent and/or top frequent words, typically required for learning traditional topic models. Experimental results on the 20 Newsgroups and the NIPS datasets show superior performance both on perplexity and topic coherence measure compared to state-of-the-art neural topic models.
Job Stress and Burnout: Research, Theory, and Intervention Perspectives

Experts on burnout discuss in this volume its symptoms and processes, and ways of understanding the syndrome. The causes of job stress, its relationship to broader social trends, and the economic costs of burnout are also defined and analyzed. The second half of the book describes effective interventions, both with individuals and organizations. `The book is well written and easy to read. Recommended for both lower-division and advanced undergraduates as wll as graduate or professional students interested in the topic. ' -- Choice, October 1983 `. most of the papers in this collection are informative, clearly written, and stimulating. There are significant theoretical contributions as well as many practical guidelines for educators, trainers, administrators, and consultants who are interested in preventing or limiting burnout and treating its victims. ' -- Personnel Psychology, Vol 36 No 4, Winter 1983 `This is an excellent resource for the professional's bookshelf. For the student who has not been extensively exposed to the literature previously, the volume should be of even greater value. ' -- Contemporary Psychology, Vol 29 No 6, 1984 `The book is recommended if one wishes to know the state of the art with burnout. ' -- Journal of Occupational Psychology, Vol 57 No 2, 1984
Handbook of Volatility Models and Their Applications

A complete guide to the theory and practice of volatility models in financial engineering Volatility has become a hot topic in this era of instant communications, spawning a great deal of research in empirical finance and time series econometrics. Providing an overview of the most recent advances, Handbook of Volatility Models and Their Applications explores key concepts and topics essential for modeling the volatility of financial time series, both univariate and multivariate, parametric and non-parametric, high-frequency and low-frequency. Featuring contributions from international experts in the field, the book features numerous examples and applications from real-world projects and cutting-edge research, showing step by step how to use various methods accurately and efficiently when assessing volatility rates. Following a comprehensive introduction to the topic, readers are provided with three distinct sections that unify the statistical and practical aspects of volatility: Autoregressive Conditional Heteroskedasticity and Stochastic Volatility presents ARCH and stochastic volatility models, with a focus on recent research topics including mean, volatility, and skewness spillovers in equity markets Other Models and Methods presents alternative approaches, such as multiplicative error models, nonparametric and semi-parametric models, and copula-based models of (co)volatilities Realized Volatility explores issues of the measurement of volatility by realized variances and covariances, guiding readers on how to successfully model and forecast these measures Handbook of Volatility Models and Their Applications is an essential reference for academics and practitioners in finance, business, and econometrics who work with volatility models in their everyday work. The book also serves as a supplement for courses on risk management and volatility at the upper-undergraduate and graduate levels.
The Practical Side of Culinary Arts Education: The Role of Social Ability and Durable Knowledge in Culinary Arts Externships

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Effects of the liquid yogurts containing "lactotripeptide (VPP, IPP)" on high-normal blood pressure

A placebo-controlled, double-blind study was conducted to investigate antihypertensive effects of the daily intake of liquid yogurt containing “lactotripeptide (Val-Pro-Pro, Ile-Pro-Pro)” in individuals with high-normal blood pressure. Subjects were randomly divided into two groups (test group; male/female=17/36, mean 39±12 years of age, systolic blood pressure (SBP）/diastolic blood pressure （DBP）=134±5/79±5 mmHg, placebo group; male/female =17/36, mean 38±9 years of age, SBP/DBP=135±5/78±5 mmHg). The test group ingested 150g liquid yogurt containing “lactotripeptide” in both the morning and evening for 12 weeks. The placebo group ingested the same amount of placebo drink. The decrease in SBP and DBP was significantly greater in the test group than that in the placebo group. In the test group, SBP significantly decreased by 5.5±7.1 mmHg (p<0.05) and 6.1±5.7 mmHg (p<0.01), at week 6 and 12 of intake, respectively. DBP also decreased significantly by 3.6±5.6 mmHg(p<0.05) and 3.8±6.3 mmHg (p<0.01), at week 6 and 12 of intake, respectively, compared to measurements before intake of liquid yogurt. Adverse effects such as dry cough or digestive tract symptoms caused by test drinks were not observed. In addition, no adverse reactions were observed by blood and uric analysis. These results suggest that liquid yogurt drink containing “lactotripeptides” is useful for subjects with high-normal blood pressure to suitably reduce their blood pressure without causing adverse events.
Intrusion Survivability for Commodity Operating Systems and Services: A Work in Progress

This paper presents a work-in-progress of our approach for intrusion survivability in commodity operating systems. Our approach relies on an orchestration of recovery and mitigation actions. We rollback infected services (i.e., their processes) and infected files to a previous known safe state, and we apply per-service mitigations (i.e., privileges removal) before unfreezing the restored processes. Such approach effectively puts the previously compromised service into a degraded mode, allowing the system to withstand ongoing intrusions and ensures the availability of core functions to the users. A prototype for Linux-based systems is currently in development.