nils-herrmann/clean_scirepeval huggingface dataset:
Physiological effects of melatonin: Role of melatonin receptors and signal transduction pathways

Melatonin, an endogenous signal of darkness, is an important component of the body's internal time-keeping system. As such it regulates major physiological processes including the sleep wake cycle, pubertal development and seasonal adaptation. In addition to its relevant antioxidant activity, melatonin exerts many of its physiological actions by interacting with membrane MT1 and MT2 receptors and intracellular proteins such as quinone reductase 2, calmodulin, calreticulin and tubulin. Here we review the current knowledge about the properties and signaling of melatonin receptors as well as their potential role in health and some diseases. Melatonin MT1 and MT2 receptors are G protein coupled receptors which are expressed in various parts of the CNS (suprachiasmatic nuclei, hippocampus, cerebellar cortex, prefrontal cortex, basal ganglia, substantia nigra, ventral tegmental area, nucleus accumbens and retinal horizontal, amacrine and ganglion cells) and in peripheral organs (blood vessels, mammary gland, gastrointestinal tract, liver, kidney and bladder, ovary, testis, prostate, skin and the immune system). Melatonin receptors mediate a plethora of intracellular effects depending on the cellular milieu. These effects comprise changes in intracellular cyclic nucleotides (cAMP, cGMP) and calcium levels, activation of certain protein kinase C subtypes, intracellular localization of steroid hormone receptors and regulation of G protein signaling proteins. There are circadian variations in melatonin receptors and responses. Alterations in melatonin receptor expression as well as changes in endogenous melatonin production have been shown in circadian rhythm sleep disorders, Alzheimer's and Parkinson's diseases, glaucoma, depressive disorder, breast and prostate cancer, hepatoma and melanoma. This paper reviews the evidence concerning melatonin receptors and signal transduction pathways in various organs. It further considers their relevance to circadian physiology and pathogenesis of certain human diseases, with a focus on the brain, the cardiovascular and immune systems, and cancer.
Video Games in Psychotherapy

Video games have found their way into the clinical care of youth in most medical fields, and academic interest in their use is increasing steadily. The popularity of video games among youth may qualify them as a useful tool in psychotherapy for children and adolescents. Limited literature on use of video games in mental health care suggests that they can help young patients become more cooperative and enthusiastic about psychotherapy. Recent experience suggests that video games may facilitate therapeutic relationships, complement the psychological assessment of youth by evaluating cognitive skills, and elaborate and clarify conflicts during the therapy process. Concerns about video game content, perceived effects on youth, and lack of familiarity with this medium may form a barrier in their use in therapy offices. Further research on the benefits of video game use in psychotherapy, including patient characteristics that may moderate outcomes, is needed. Finally, future collaborations between clinicians and video game developers may produce specific games to be used in psychotherapy.
Plant Disease Detection Using Image Processing

Identification of the plant diseases is the key to preventing the losses in the yield and quantity of the agricultural product. The studies of the plant diseases mean the studies of visually observable patterns seen on the plant. Health monitoring and disease detection on plant is very critical for sustainable agriculture. It is very difficult to monitor the plant diseases manually. It requires tremendous amount of work, expertize in the plant diseases, and also require the excessive processing time. Hence, image processing is used for the detection of plant diseases. Disease detection involves the steps like image acquisition, image pre-processing, image segmentation, feature extraction and classification. This paper discussed the methods used for the detection of plant diseases using their leaves images. This paper also discussed some segmentation and feature extraction algorithm used in the plant disease detection.
Unsupervised Learning by Predicting Noise

Convolutional neural networks provide visual features that perform remarkably well in many computer vision applications. However, training these networks requires significant amounts of supervision. This paper introduces a generic framework to train deep networks, end-to-end, with no supervision. We propose to fix a set of target representations, called Noise As Targets (NAT), and to constrain the deep features to align to them. This domain agnostic approach avoids the standard unsupervised learning issues of trivial solutions and collapsing of features. Thanks to a stochastic batch reassignment strategy and a separable square loss function, it scales to millions of images. The proposed approach produces representations that perform on par with state-of-the-art unsupervised methods on ImageNet and PASCAL VOC.
CitationAS: A Summary Generation Tool Based on Clustering of Retrieved Citation Content

Usually, if researchers want to understand research status of any field, they need to browse a great number of related academic literatures. Luckily, in order to work more efficiently, automatic documents summarization can be applied for taking a glance at specific scientific topics. In this paper, we focus on summary generation of citation content. An automatic tool named CitationAS is built, whose three core components are clustering algorithms, label generation and important sentences extraction methods. In experiments, we use bisecting Kmeans, Lingo and STC to cluster retrieved citation content. Then Word2Vec, WordNet and combination of them are applied to generate cluster label. Next, we employ two methods, TF-IDF and MMR, to extract important sentences, which are used to generate summaries. Finally, we adopt gold standard to evaluate summaries obtained from CitationAS. According to evaluations, we find the best label generation method for each clustering algorithm. We also discover that combination of Word2Vec and WordNet doesn’t have good performance compared with using them separately on three clustering algorithms. Combination of Ling algorithm, Word2Vec label generation method and TF-IDF sentences extraction approach will acquire the highest summary quality. Conference Topic Text mining and information extraction
Determinants of Leadership Style in Big Five Personality Dimensions

This study addresses the inconsistency of contemporary literature on defining the link between leadership styles and personality traits. The plethora of literature on personality traits has culminated into symbolic big five personality dimensions but there is still a dearth of research on developing representative leadership styles despite the perennial fascination with the subject. Absence of an unequivocal model for developing representative styles in conjunction with the use of several non-mutually exclusive existing leadership styles has created a discrepancy in developing a coherent link between leadership and personality. This study sums up 39 different styles of leadership into five distinct representative styles on the basis of similar theoretical underpinnings and common characteristics to explore how each of these five representative leadership style relates to personality dimensions proposed by big five model.
Hello World Deep Learning in Medical Imaging

There is recent popularity in applying machine learning to medical imaging, notably deep learning, which has achieved state-of-the-art performance in image analysis and processing. The rapid adoption of deep learning may be attributed to the availability of machine learning frameworks and libraries to simplify their use. In this tutorial, we provide a high-level overview of how to build a deep neural network for medical image classification, and provide code that can help those new to the field begin their informatics projects.
Systems Biology in the Context of Big Data and Networks

Science is going through two rapidly changing phenomena: one is the increasing capabilities of the computers and software tools from terabytes to petabytes and beyond, and the other is the advancement in high-throughput molecular biology producing piles of data related to genomes, transcriptomes, proteomes, metabolomes, interactomes, and so on. Biology has become a data intensive science and as a consequence biology and computer science have become complementary to each other bridged by other branches of science such as statistics, mathematics, physics, and chemistry. The combination of versatile knowledge has caused the advent of big-data biology, network biology, and other new branches of biology. Network biology for instance facilitates the system-level understanding of the cell or cellular components and subprocesses. It is often also referred to as systems biology. The purpose of this field is to understand organisms or cells as a whole at various levels of functions and mechanisms. Systems biology is now facing the challenges of analyzing big molecular biological data and huge biological networks. This review gives an overview of the progress in big-data biology, and data handling and also introduces some applications of networks and multivariate analysis in systems biology.
Does Happiness Promote Career Success ? Revisiting the Evidence

Empirical research demonstrates a relationship between happiness and career success. For example, happy people receive higher earnings, exhibit better performance, and obtain more favorable supervisor evaluations than their less happy peers. Researchers have posited that success leads to happiness, but Boehm and Lyubomirsky reviewed the relevant research in 2008 and argued that the alternative hypothesis—that happiness causes success—may be equally plausible. A decade later, we return to the literature to supplement studies we previously cited with new research and to determine whether the results of cross-sectional, longitudinal, and experimental investigations provide additional support for this hypothesis. We conclude that the evidence continues to persuasively suggest that happiness is correlated with and often precedes career success and that experimentally enhancing positive emotions leads to improved outcomes in the workplace.
Could autoimmunity be induced by vaccination?

Autoimmune reactions to vaccinations may rarely be induced in predisposed individuals by molecular mimicry or bystander activation mechanisms. Autoimmune reactions reliably considered vaccine-associated, include Guillain-Barré syndrome after 1976 swine influenza vaccine, immune thrombocytopenic purpura after measles/mumps/rubella vaccine, and myopericarditis after smallpox vaccination, whereas the suspected association between hepatitis B vaccine and multiple sclerosis has not been further confirmed, even though it has been recently reconsidered, and the one between childhood immunization and type 1 diabetes seems by now to be definitively gone down. Larger epidemiological studies are needed to obtain more reliable data in most suggested associations.
An Introduction to Subtitling: Challenges and Strategies Bilal

122 BILAL KHALID KHALAF ABSTRACT This study attempts to identify the main challenges for the translators during subtitling process, and what are the presented solutions ‘strategies’ by theorists to overcome them. In order to have full understanding of subtitling, it will start with defining what is subtitling? its types, classifications and then exploring the challenges which translators encounter during the subtitling process. Later on, it will show briefly how the subtitling related to Skopos functional theory. After all, the suggested strategies in the field of subtitling to overcome these challenges will be discussed like, Gottlieb (1992) and Schjoldager (2008) with related concepts.
Big Sensing Data Curation in Cloud Data Center for Next Generation IoT and WSN

Modern sensing devices play a pivotal role in achieving data acquisition, communication and dissemination for the Internet-of-Things (IoT). Naturally, IoT applications and intelligent sensing systems supported by sensing devices, such as wireless sensor networks (WSN), are closely coupled. Modern intelligent sensing systems generate huge volumes of sensing data, well beyond the processing capabilities of common techniques and tools. Hence, to collect, manage, and process IoT big sensing data within an acceptable time duration is a new challenge for both research and industrial applications. The massive size, extreme complexity and high speed of big sensing data bring new technical requirements including data collection, data storage, data organization, data analysis and data publishing in real time when deploying real world IoT applications. To better facilitate these IoT applications, the convergent research of WSN, big data, IoT and Cloud Computing is a natural scientific development trend. In this paper, we concentrate on big sensing data curation and preparation issues with Cloud Computing under the theme of IoT. There are three especially critical issues that need to be addressed, namely scalable big sensing data cleaning, scalable big sensing data compression and Cloud based data curation response for IoT device optimization. Viewed from the IoT side, all IoT sensing devices are integrated together in an adaptive solution and upload their data onto the Cloud. The automatic responses from both the Cloud and intelligent sensors will change the status or behavior of sensing devices, hence the status of the IoT itself.
Personalized Academic Paper Recommendation System

Recommendation systems can take advantage of social media in various ways. One common example is combining social relationship into neighborhood-based recommendation systems, under the assumption that social relationship affects individuals’ interest or preference. Although this assumption may not be always true, this paper presents a realistic application, personalized academic paper recommendation system, which social relationship can be closely related to taste. There is an increasing number of academic papers being published each year, but most researchers rely on keyword-based search or browsing through proceedings of top conferences and journals to find their related work. Personalized academic paper recommendation system is designed to reduce their workload. With a collaborative-filteringbased approach, it recommends potentially preferred articles for each researcher in personalized manner. Both computational evaluation and user study demonstrate that our system recommends a useful set of research papers.
Decision-making with the AHP: Why is the principal eigenvector necessary

In this paper it is shown that the principal eigenvector is a necessary representation of the priorities derived from a positive reciprocal pairwise comparison judgment matrix A 1⁄4 ðaijÞ when A is a small perturbation of a consistent matrix. When providing numerical judgments, an individual attempts to estimate sequentially an underlying ratio scale and its equivalent consistent matrix of ratios. Near consistent matrices are essential because when dealing with intangibles, human judgment is of necessity inconsistent, and if with new information one is able to improve inconsistency to near consistency, then that could improve the validity of the priorities of a decision. In addition, judgment is much more sensitive and responsive to large rather than to small perturbations, and hence once near consistency is attained, it becomes uncertain which coefficients should be perturbed by small amounts to transform a near consistent matrix to a consistent one. If such perturbations were forced, they could be arbitrary and thus distort the validity of the derived priority vector in representing the underlying decision.
QuickNet: Maximizing Efficiency and Efficacy in Deep Architectures

We present QuickNet, a fast and accurate network architecture that is both faster and significantly more accurate than other “fast” deep architectures like SqueezeNet. Furthermore, it uses less parameters than previous networks, making it more memory efficient. We do this by making two major modifications to the reference “Darknet” model (Redmon et al, 2015): 1) The use of depthwise separable convolutions and 2) The use of parametric rectified linear units. We make the observation that parametric rectified linear units are computationally equivalent to leaky rectified linear units at test time and the observation that separable convolutions can be interpreted as a compressed Inception network (Chollet, 2016). Using these observations, we derive a network architecture, which we call QuickNet, that is both faster and more accurate than previous models. Our architecture provides at least four major advantages: (1) A smaller model size, which is more tenable on memory constrained systems; (2) A significantly faster network which is more tenable on computationally constrained systems; (3) A high accuracy of 95.7% on the CIFAR-10 Dataset which outperforms all but one result published so far, although we note that our works are orthogonal approaches and can be combined (4) Orthogonality to previous model compression approaches allowing for further speed gains to be realized.
A semantics-based measure of emoji similarity

Emoji have grown to become one of the most important forms of communication on the web. With its widespread use, measuring the similarity of emoji has become an important problem for contemporary text processing since it lies at the heart of sentiment analysis, search, and interface design tasks. This paper presents a comprehensive analysis of the semantic similarity of emoji through embedding models that are learned over machine-readable emoji meanings in the EmojiNet knowledge base. Using emoji descriptions, emoji sense labels and emoji sense definitions, and with different training corpora obtained from Twitter and Google News, we develop and test multiple embedding models to measure emoji similarity. To evaluate our work, we create a new dataset called EmoSim508, which assigns human-annotated semantic similarity scores to a set of 508 carefully selected emoji pairs. After validation with EmoSim508, we present a real-world use-case of our emoji embedding models using a sentiment analysis task and show that our models outperform the previous best-performing emoji embedding model on this task. The EmoSim508 dataset and our emoji embedding models are publicly released with this paper and can be downloaded from http://emojinet.knoesis.org/.
The Relative Importance of Perceived Ease of Use in IS Adoption: A Study of E-Commerce Adoption

The technology acceptance model (Davis 1989) is one of the most widely used models of IT adoption. According to TAM, IT adoption is influenced by two perceptions: usefulness and ease-of-use. Research has shown that perceived usefulness (PU) affects intended adoption of IT, but has mostly failed to do so regarding perceived ease of use (PEOU). The basic proposition of this study is that this varying importance of PEOU may be related to the nature of the task. PEOU relates to assessments of the intrinsic characteristics of IT, such as the ease of use, ease of learning, flexibility, and clarity of its interface. PU, on the other hand, is a response to user assessment of its extrinsic, i.e., task-oriented, outcomes: how IT
A practical overview of quantitative structure-activity relationship

Quantitative structure-activity relationship (QSAR) modeling pertains to the construction of predictive models of biological activities as a function of structural and molecular information of a compound library. The concept of QSAR has typically been used for drug discovery and development and has gained wide applicability for correlating molecular information with not only biological activities but also with other physicochemical properties, which has therefore been termed quantitative structure-property relationship (QSPR). Typical molecular parameters that are used to account for electronic properties, hydrophobicity, steric effects, and topology can be determined empirically through experimentation or theoretically via computational chemistry. A given compilation of data sets is then subjected to data pre-processing and data modeling through the use of statistical and/or machine learning techniques. This review aims to cover the essential concepts and techniques that are relevant for performing QSAR/QSPR studies through the use of selected examples from our previous work.
Predicting distributions with Linearizing Belief Networks

Conditional belief networks introduce stochastic binary variables in neural networks. Contrary to a classical neural network, a belief network can predict more than the expected value of the output Y given the input X . It can predict a distribution of outputs Y which is useful when an input can admit multiple outputs whose average is not necessarily a valid answer. Such networks are particularly relevant to inverse problems such as image prediction for denoising, or text to speech. However, traditional sigmoid belief networks are hard to train and are not suited to continuous problems. This work introduces a new family of networks called linearizing belief nets or LBNs. A LBN decomposes into a deep linear network where each linear unit can be turned on or off by non-deterministic binary latent units. It is a universal approximator of real-valued conditional distributions and can be trained using gradient descent. Moreover, the linear pathways efficiently propagate continuous information and they act as multiplicative skip-connections that help optimization by removing gradient diffusion. This yields a model which trains efficiently and improves the state-of-the-art on image denoising and facial expression generation with the Toronto faces dataset.
Classes for Fast Maximum Entropy Training

Maximum entropy models are considered by many to be one of the most promising avenues of language modeling research. Unfortunately, long training times make maximum entropy research difficult. We present a novel speedup technique: we change the form of the model to use classes. Our speedup works by creating two maximum entropy models, the first of which predicts the class of each word, and the second of which predicts the word itself. This factoring of the model leads to fewer nonzero indicator functions, and faster normalization, achieving speedups of up to a factor of 35 over one of the best previous techniques. It also results in typically slightly lower perplexities. The same trick can be used to speed training of other machine learning techniques, e.g. neural networks, applied to any problem with a large number of outputs, such as language modeling.