nils-herrmann/clean_scirepeval huggingface dataset:
Essential oils and their principal constituents as antimicrobial agents for synthetic packaging films.

Spices and herbal plant species have been recognized to possess a broad spectrum of active constituents that exhibit antimicrobial (AM) activity. These active compounds are produced as secondary metabolites associated with the volatile essential oil (EO) fraction of these plants. A wide range of AM agents derived from EOs have the potential to be used in AM packaging systems which is one of the promising forms of active packaging systems aimed at protecting food products from microbial contamination. Many studies have evaluated the AM activity of synthetic AM and/or natural AM agents incorporated into packaging materials and have demonstrated effective AM activity by controlling the growth of microorganisms. This review examines the more common synthetic and natural AM agents incorporated into or coated onto synthetic packaging films for AM packaging applications. The focus is on the widely studied herb varieties including basil, oregano, and thyme and their EOs.
Social Network Sites: Antecedents of User Adoption and Usage

The use of social networking websites has become a current international phenomenon. Popular websites include MySpace, Facebook, and Friendster. Their rapid widespread use warrants a better understanding. However, there has been little empirical research studying the factors that determine the use of this hedonic computer-mediated communication technology This study contributes to our understanding of the antecedents that influence adoption and use of social networking websites by examining the effect of the perceptions of playfulness, critical mass, trust, and normative pressure on the use of social networking sites.. Structural equation modeling was used to examine the patterns of inter-correlations among the constructs and to empirically test the hypotheses. Each of the antecedents has a significant direct effect on intent to use social networking websites, with playfulness and critical mass the strongest indicators. Intent to use and playfulness had a significant direct effect on actual usage.
Antibiotics as selectors and accelerators of diversity in the mechanisms of resistance: from the resistome to genetic plasticity in the β-lactamases world

Antibiotics and antibiotic resistance determinants, natural molecules closely related to bacterial physiology and consistent with an ancient origin, are not only present in antibiotic-producing bacteria. Throughput sequencing technologies have revealed an unexpected reservoir of antibiotic resistance in the environment. These data suggest that co-evolution between antibiotic and antibiotic resistance genes has occurred since the beginning of time. This evolutionary race has probably been slow because of highly regulated processes and low antibiotic concentrations. Therefore to understand this global problem, a new variable must be introduced, that the antibiotic resistance is a natural event, inherent to life. However, the industrial production of natural and synthetic antibiotics has dramatically accelerated this race, selecting some of the many resistance genes present in nature and contributing to their diversification. One of the best models available to understand the biological impact of selection and diversification are β-lactamases. They constitute the most widespread mechanism of resistance, at least among pathogenic bacteria, with more than 1000 enzymes identified in the literature. In the last years, there has been growing concern about the description, spread, and diversification of β-lactamases with carbapenemase activity and AmpC-type in plasmids. Phylogenies of these enzymes help the understanding of the evolutionary forces driving their selection. Moreover, understanding the adaptive potential of β-lactamases contribute to exploration the evolutionary antagonists trajectories through the design of more efficient synthetic molecules. In this review, we attempt to analyze the antibiotic resistance problem from intrinsic and environmental resistomes to the adaptive potential of resistance genes and the driving forces involved in their diversification, in order to provide a global perspective of the resistance problem.
Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation

The encoder-decoder dialog model is one of the most prominent methods used to build dialog systems in complex domains. Yet it is limited because it cannot output interpretable actions as in traditional systems, which hinders humans from understanding its generation process. We present an unsupervised discrete sentence representation learning method that can integrate with any existing encoderdecoder dialog models for interpretable response generation. Building upon variational autoencoders (VAEs), we present two novel models, DI-VAE and DI-VST that improve VAEs and can discover interpretable semantics via either auto encoding or context predicting. Our methods have been validated on real-world dialog datasets to discover semantic representations and enhance encoder-decoder models with interpretable
A comparative study of the current technologies and approaches of relation extraction in biomedical literature using text mining

Many techniques in Relation Extraction (RE) require an understanding of the concept and association of relations between entities. This paper presents advanced approaches that combined many methods to extract simple and complex relations between different entities from the biomedical literature. The literature comparative study includes a wide variety of techniques for RE all of which fall into one of the following approaches such as supervised, semi-supervised, higher order relation and self-supervised approaches. Supervised approach needs very detailed data, while in semi-supervised approach only a small knowledge base to automatically annotates features. This approach does not need an initial set of labeled pages for extraction rules. Self-supervised is an integration of these two approaches. On the other hand, the term higher order relation indicates a relation that connects different biomedical relations together. The F-score performance differs from relation to relation when it is extracted. Finally, an extensive performance evaluations with real datasets for both supervised, semi-supervised, higher order relation and self-supervised approaches are described for entity — RE tasks.
Opportunities and obstacles for deep learning in biology and medicine

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.
Phish Phactors: Offensive and Defensive Strategies

Phishing attacks attempt to fraudulently solicit sensitive information from a user by masquerading as a known trustworthy agent. They commonly use spoofed emails in association with fake websites in order to coerce a user into revealing personal financial data. Phishing is now a serious problem with criminals adopting the well-developed and well-known techniques to exploit Internet users with sophisticated attacks. Phishers are known to have successfully attacked an estimated 1.2 million users and stolen an estimated US$929 million in the twelve
Sentiment Analysis in Social Media Texts

This paper presents a method for sentiment analysis specifically designed to work with Twitter data (tweets), taking into account their structure, length and specific language. The approach employed makes it easily extendible to other languages and makes it able to process tweets in near real time. The main contributions of this work are: a) the pre-processing of tweets to normalize the language and generalize the vocabulary employed to express sentiment; b) the use minimal linguistic processing, which makes the approach easily portable to other languages; c) the inclusion of higher order n-grams to spot modifications in the polarity of the sentiment expressed; d) the use of simple heuristics to select features to be employed; e) the application of supervised learning using a simple Support Vector Machines linear classifier on a set of realistic data. We show that using the training models generated with the method described we can improve the sentiment classification performance, irrespective of the domain and distribution of the test sets.
Action potential initiation and backpropagation in neurons of the mammalian CNS

Most neurons in the mammalian CNS encode and transmit information via action potentials. Knowledge of where these electrical events are initiated and how they propagate within neurons is therefore fundamental to an understanding of neuronal function. While work from the 1950s suggested that action potentials are initiated in the axon, many subsequent investigations have suggested that action potentials can also be initiated in the dendrites. Recently, experiments using simultaneous patch-pipette recordings from different locations on the same neuron have been used to address this issue directly. These studies show that the site of action potential initiation is in the axon, even when synaptic activation is powerful enough to elicit dendritic electrogenesis. Furthermore, these and other studies also show that following initiation, action potentials actively backpropagate into the dendrites of many neuronal types, providing a retrograde signal of neuronal output to the dendritic tree.
Subjective well-being and social media use: Do personality traits moderate the impact of social comparison on Facebook?

The purpose of the study was to explore whether personality traits moderate the association between social comparison on Facebook and subjective well-being, measured as both life satisfaction and eudaimonic well-being. Data were collected via an online questionnaire which measured Facebook use, social comparison behavior and personality traits for 337 respondents. The results showed positive associations between Facebook intensity and both measures of subjective well-being, and negative associations between Facebook social comparison and both measures of subjective well-being. Personality traits were assessed by the Reinforcement Sensitivity Theory personality questionnaire, which revealed that Reward Interest was positively associated with eudaimonic well-being, and Goal-Drive Persistence was positively associated with both measures of subjective well-being. Impulsivity was negatively associated with eudaimonic well-being and the Behavioral Inhibition System was negatively associated with both measures of subjective well-being. Interactions between personality traits and social comparison on Facebook indicated that for respondents with high Goal-Drive Persistence, Facebook social comparison had a positive association with eudaimonic well-being, thus confirming that some personality traits moderate the association between Facebook social comparison and subjective well-being. The results of this study highlight how individual differences in personality may impact how social comparison on Facebook affects individuals’ subjective well-being.
Shoulder muscle recruitment patterns and related biomechanics during upper extremity sports.

Understanding when and how much shoulder muscles are active during upper extremity sports is helpful to physicians, therapists, trainers and coaches in providing appropriate treatment, training and rehabilitation protocols to these athletes. This review focuses on shoulder muscle activity (rotator cuff, deltoids, pectoralis major, latissimus dorsi, triceps and biceps brachii, and scapular muscles) during the baseball pitch, the American football throw, the windmill softball pitch, the volleyball serve and spike, the tennis serve and volley, baseball hitting, and the golf swing. Because shoulder electromyography (EMG) data are far more extensive for overhead throwing activities compared with non-throwing upper extremity sports, much of this review focuses on shoulder EMG during the overhead throwing motion. Throughout this review shoulder kinematic and kinetic data (when available) are integrated with shoulder EMG data to help better understand why certain muscles are active during different phases of an activity, what type of muscle action (eccentric or concentric) occurs, and to provide insight into the shoulder injury mechanism. Kinematic, kinetic and EMG data have been reported extensively during overhead throwing, such as baseball pitching and football passing. Because shoulder forces, torques and muscle activity are generally greatest during the arm cocking and arm deceleration phases of overhead throwing, it is believed that most shoulder injuries occur during these phases. During overhead throwing, high rotator cuff muscle activity is generated to help resist the high shoulder distractive forces approximately 80-120% bodyweight during the arm cocking and deceleration phases. During arm cocking, peak rotator cuff activity is 49-99% of a maximum voluntary isometric contraction (MVIC) in baseball pitching and 41-67% MVIC in football throwing. During arm deceleration, peak rotator cuff activity is 37-84% MVIC in baseball pitching and 86-95% MVIC in football throwing. Peak rotator cuff activity is also high is the windmill softball pitch (75-93% MVIC), the volleyball serve and spike (54-71% MVIC), the tennis serve and volley (40-113% MVIC), baseball hitting (28-39% MVIC), and the golf swing (28-68% MVIC). Peak scapular muscle activity is also high during the arm cocking and arm deceleration phases of baseball pitching, with peak serratus anterior activity 69-106% MVIC, peak upper, middle and lower trapezius activity 51-78% MVIC, peak rhomboids activity 41-45% MVIC, and peak levator scapulae activity 33-72% MVIC. Moreover, peak serratus anterior activity was approximately 60% MVIC during the windmill softball pitch, approximately 75% MVIC during the tennis serve and forehand and backhand volley, approximately 30-40% MVIC during baseball hitting, and approximately 70% MVIC during the golf swing. In addition, during the golf swing, peak upper, middle and lower trapezius activity was 42-52% MVIC, peak rhomboids activity was approximately 60% MVIC, and peak levator scapulae activity was approximately 60% MVIC.
Combining flat and structured representations for fingerprint classification with recursive neural networks and support vector machines

We present new fingerprint classification algorithms based on two machine learning approaches: support vector machines (SVM), and recursive neural networks (RNN). RNN are trained on a structured representation of the fingerprint image. They are also used to extract a set of distributed features of the fingerprint which can be integrated in the SVM. SVM are combined with a new error correcting code scheme. This approach has two main advantages: (a) It can tolerate the presence of ambiguous fingerprint images in the training set, and (b) It can effectively identify the most difficult fingerprint images in the test set. By rejecting these images the accuracy of the system improves significantly. We report experiments on the fingerprint database NIST-4. Our best classification accuracy is of 95.6 percent at 20 percent rejection rate and is obtained by training SVM on both FingerCode and RNN-extracted features. This result indicates the benefit of integrating global and structured representations and suggests that SVM are a promising approach for fingerprint classification.
Gamification: What It Is and Why It Matters to Digital Health Behavior Change Developers

This editorial provides a behavioral science view on gamification and health behavior change, describes its principles and mechanisms, and reviews some of the evidence for its efficacy. Furthermore, this editorial explores the relation between gamification and behavior change frameworks used in the health sciences and shows how gamification principles are closely related to principles that have been proven to work in health behavior change technology. Finally, this editorial provides criteria that can be used to assess when gamification provides a potentially promising framework for digital health interventions.
Multi-Task Learning for Stock Selection

Artificial Neural Networks can be used to predict future returns of stocks in order to take financial decisions . Should one build a separate network for each stock or share the same network for all the stocks? In this paper we also explore other alternatives, in which some layers are shared and others are not shared. When the prediction of future returns for different stocks are viewed as different tasks, sharing some parameters across stocks is a form of multi-task learning. In a series of experiments with Canadian stocks, we obtain yearly returns that are more than 14% above various benchmarks.
Advanced study of SDN/OpenFlow controllers

This paper presents an independent comprehensive analysis of the efficiency indexes of popular open source SDN/OpenFlow controllers (NOX, POX, Beacon, Floodlight, MuL, Maestro, Ryu). The analysed indexes include performance, scalability, reliability, and security. For testing purposes we developed the new framework called hcprobe. The test bed and the methodology we used are discussed in detail so that everyone could reproduce our experiments. The result of the evaluation show that modern SDN/OpenFlow controllers are not ready to be used in production and have to be improved in order to increase all above mentioned characteristics.
The biological basis of human sexual orientation: is there a role for epigenetics?

Sexual orientation is one of the largest sex differences in humans. The vast majority of the population is heterosexual, that is, they are attracted to members of the opposite sex. However, a small but significant proportion of people are bisexual or homosexual and experience attraction to members of the same sex. The origins of the phenomenon have long been the subject of scientific study. In this chapter, we will review the evidence that sexual orientation has biological underpinnings and consider the involvement of epigenetic mechanisms. We will first discuss studies that show that sexual orientation has a genetic component. These studies show that sexual orientation is more concordant in monozygotic twins than in dizygotic ones and that male sexual orientation is linked to several regions of the genome. We will then highlight findings that suggest a link between sexual orientation and epigenetic mechanisms. In particular, we will consider the case of women with congenital adrenal hyperplasia (CAH). These women were exposed to high levels of testosterone in utero and have much higher rates of nonheterosexual orientation compared to non-CAH women. Studies in animal models strongly suggest that the long-term effects of hormonal exposure (such as those experienced by CAH women) are mediated by epigenetic mechanisms. We conclude by describing a hypothetical framework that unifies genetic and epigenetic explanations of sexual orientation and the continued challenges facing sexual orientation research.
KINEMATIC MODEL AND CONTROL OF MOBILE ROBOT FOR TRAJECTORYTRACKING

ABSTACT: Trajectory tracking has been an extremely active research area in robotics in the past decade.In this paper, a kinematic model of two wheel mobile robot for reference trajectory tracking is analyzed and simulated. For controlling the wheeled mobile robot PID controllers are used. For finding the optimal parameters of PID controllers, in this work particle swarm optimization (PSO) is used. The proposed methodology is shown to be a successful solutionfor solving the problem.
Simple Broadband Planar CPW-Fed Quasi-Yagi Antenna

In this letter, we present a novel coplanar waveguide fed quasi-Yagi antenna with broad bandwidth. The uniqueness of this design is due to its simple feed selection and despite this, its achievable bandwidth. The 10 dB return loss bandwidth of the antenna is 44% covering X-band. The antenna is realized on a high dielectric constant substrate and is compatible with microstrip circuitry and active devices. The gain of the antenna is 7.4 dBi, the front-to-back ratio is 15 dB and the nominal efficiency of the radiator is 95%.
Living with p values: resurrecting a Bayesian perspective on frequentist statistics.

In response to the widespread abuse and misinterpretation of significance tests of null hypotheses, some editors and authors have strongly discouraged P values. However, null P values still thrive in most journals and are routinely misinterpreted as probabilities of a "chance finding" or of the null, when they are no such thing. This misuse may be lessened by recognizing correct Bayesian interpretations. For example, under weak priors, 95% confidence intervals approximate 95% posterior probability intervals, one-sided P values approximate directional posterior probabilities, and point estimates approximate posterior medians. Furthermore, under certain conditions, a one-sided P value for a prior median provides an approximate lower bound on the posterior probability that the point estimate is on the wrong side of that median. More generally, P values can be incorporated into a modern analysis framework that emphasizes measurement of fit, distance, and posterior probability in place of "statistical significance" and accept/reject decisions.
Survey of Probabilistic Graphical Models

Probabilistic graphical model (PGM) is a generic model that represents the probability-based relationships among random variables by a graph, and is a general method for knowledge representation and inference involving uncertainty. In recent years, PGM provides an important means for solving the uncertainty of intelligent information field, and becomes research focus in the fields of machine learning and artificial intelligence etc. In the paper, PGM and its three types of basic models are reviewed, including the learning and inference theory, research status, application and promotion.
Recommender Systems Based on Evolutionary Computing: A Survey

Data mining techniques and information personalization have made significant growth in the past decade. Enormous volume of data is generated every day. Recommender systems can help users to find their specific information in the extensive volume of information. Several techniques have been presented for development of Recommender System (RS). One of these techniques is the Evolutionary Computing (EC), which can optimize and improve RS in the various applications. This study investigates the number of publications, focusing on some aspects such as the recommendation techniques, the evaluation methods and the datasets which are used.