nils-herrmann/clean_scirepeval huggingface dataset:
Automated Fact-Checking of Claims in Argumentative Parliamentary Debates

We present an automated approach to distinguish true, false, stretch, and dodge statements in questions and answers in the Canadian Parliament. We leverage the truthfulness annotations of a U.S. fact-checking corpus by training a neural net model and incorporating the prediction probabilities into our models. We find that in concert with other linguistic features, these probabilities can improve the multi-class classification results. We further show that dodge statements can be detected with an F1 measure as high as 82.57% in binary classification settings.
Evaluation of sentence embeddings in downstream and linguistic probing tasks

Despite the fast developmental pace of new sentence embedding methods, it is still challenging to find comprehensive evaluations of these different techniques. In the past years, we saw significant improvements in the field of sentence embeddings and especially towards the development of universal sentence encoders that could provide inductive transfer to a wide variety of downstream tasks. In this work, we perform a comprehensive evaluation of recent methods using a wide variety of downstream and linguistic feature probing tasks. We show that a simple approach using bag-of-words with a recently introduced language model for deep contextdependent word embeddings proved to yield better results in many tasks when compared to sentence encoders trained on entailment datasets. We also show, however, that we are still far away from a universal encoder that can perform consistently across several downstream tasks.
Facebook and Romantic Relationships: Intimacy and Couple Satisfaction Associated with Online Social Network Use

Online social networks, such as Facebook, have gained immense popularity and potentially affect the way people build and maintain interpersonal relationships. The present study sought to examine time spent on online social networks, as it relates to intimacy and relationship satisfaction experienced in romantic relationships. Results did not find relationships between an individual's usage of online social networks and his/her perception of relationship satisfaction and intimacy. However, the study found a negative relationship between intimacy and the perception of a romantic partner's use of online social networks. This finding may allude to an attributional bias in which individuals are more likely to perceive a partner's usage as negative compared to their own usage. Additionally, it was found that intimacy mediates the relationship between online social network usage and overall relationship satisfaction, which suggests that the level of intimacy experienced in a relationship may serve as a buffer that protects the overall level of satisfaction.
Self-compassion moderates the relationship between body mass index and both eating disorder pathology and body image flexibility.

The current study examined whether self-compassion, the tendency to treat oneself kindly during distress and disappointments, would attenuate the positive relationship between body mass index (BMI) and eating disorder pathology, and the negative relationship between BMI and body image flexibility. One-hundred and fifty-three female undergraduate students completed measures of self-compassion, self-esteem, eating disorder pathology, and body image flexibility, which refers to one's acceptance of negative body image experiences. Controlling for self-esteem, hierarchical regressions revealed that self-compassion moderated the relationships between BMI and the criteria. Specifically, the positive relationship between BMI and eating disorder pathology and the negative relationship between BMI and body image flexibility were weaker the higher women's levels of self-compassion. Among young women, self-compassion may help to protect against the greater eating disturbances that coincide with a higher BMI, and may facilitate the positive body image experiences that tend to be lower the higher one's BMI.
Mass Spectrometry-Based Label-Free Quantitative Proteomics

In order to study the differential protein expression in complex biological samples, strategies for rapid, highly reproducible and accurate quantification are necessary. Isotope labeling and fluorescent labeling techniques have been widely used in quantitative proteomics research. However, researchers are increasingly turning to label-free shotgun proteomics techniques for faster, cleaner, and simpler results. Mass spectrometry-based label-free quantitative proteomics falls into two general categories. In the first are the measurements of changes in chromatographic ion intensity such as peptide peak areas or peak heights. The second is based on the spectral counting of identified proteins. In this paper, we will discuss the technologies of these label-free quantitative methods, statistics, available computational software, and their applications in complex proteomics studies.
Deep learning and its application in silent sound technology

Machine learning is a branch of Artificial Intelligence that deals with making a machine Intelligent. Machine learning is being broadly used in the areas of Computer vision, Speech Recognition and Natural language Processing. Deep Learning is that area of Machine learning research which specifically deals with training Artificial Neural Networks having deep architectures. In this paper we provide an introduction to machine learning. Next we discuss the need of deep learning. Then we mention the areas in which deep learning has been successfully used, and finally we propose how deep learning can be applied or how it can be used in Silent Sound Technology.
Analysis of the Blockchain Protocol in Asynchronous Networks

Nakamoto’s famous blockchain protocol enables achieving consensus in a so-called permissionless setting—anyone can join (or leave) the protocol execution, and the protocol instructions do not depend on the identities of the players. His ingenious protocol prevents “sybil attacks” (where an adversary spawns any number of new players) by relying on computational puzzles (a.k.a. “moderately hard functions”) introduced by Dwork and Naor (Crypto’92). The analysis of the blockchain consensus protocol (a.k.a. Nakamoto consensus) has been a notoriously difficult task. Prior works that analyze it either make the simplifying assumption that network channels are fully synchronous (i.e. messages are instantly delivered without delays) (Garay et al, Eurocrypt’15) or only consider specific attacks (Nakamoto’08; Sampolinsky and Zohar, FinancialCrypt’15); additionally, as far as we know, none of them deal with players joining or leaving the protocol. In this work we prove that the blockchain consensus mechanism satisfies a strong forms of consistency and liveness in an asynchronous network with adversarial delays that are a-priori bounded, within a formal model allowing for adaptive corruption and spawning of new players, assuming that the computational puzzle is modeled as a random oracle. We complement this result by showing a simple attack against the blockchain protocol in a fully asynchronous setting, showing that the puzzle-hardness needs to be appropriately set as a function of the maximum network delay; this attack applies even for static corruption.) As an independent contribution, we define an abstract notion of a blockchain protocol and identify appropriate security properties of such protocols; we prove that Nakamoto’s blockchain protocol satisfies them and that these properties are sufficient for typical applications; we hope that this abstraction may simplify further applications of blockchains. ∗Supported in part by NSF Award CNS-1217821, AFOSR Award FA9550-15-1-0262, a Microsoft Faculty Fellowship, and a Google Faculty Research Award. †Supported in part by NSF grants CNS-0845811, TC-1111781, TC-0939718, a Microsoft Faculty Fellowship, an SAIC Faculty Award, and a Google Faculty Research Award.
Attempting to Define IT Governance: Wisdom or Folly?

This paper describes the first phase of an ongoing program of research into theory and practice of IT governance. It conceptually explores existing IT governance literature and reveals diverse definitions of IT governance, that acknowledge its structures, control frameworks and/or processes. The definitions applied within the literature and the nature and breadth of discussion demonstrate a lack of a clear shared understanding of the term IT governance. This lack of clarity has the potential to confuse and possibly impede useful research in the field and limit valid cross-study comparisons of results. Using a content analysis approach, a number of existing diverse definitions are moulded into a "definitive" definition of IT governance and its usefulness is critically examined. It is hoped that this exercise will heighten awareness of the "broad reach" of the IT governance concept to assist researchers in the development of research projects and more effectively guide practitioners in the overall assessment of IT governance.
Automated Hyperparameter Tuning for Effective Machine Learning

Machine learning predictive modeling algorithms are governed by “hyperparameters” that have no clear defaults agreeable to a wide range of applications. The depth of a decision tree, number of trees in a forest, number of hidden layers and neurons in each layer in a neural network, and degree of regularization to prevent overfitting are a few examples of quantities that must be prescribed for these algorithms. Not only do ideal settings for the hyperparameters dictate the performance of the training process, but more importantly they govern the quality of the resulting predictive models. Recent efforts to move from a manual or random adjustment of these parameters include rough grid search and intelligent numerical optimization strategies. This paper presents an automatic tuning implementation that uses local search optimization for tuning hyperparameters of modeling algorithms in SAS® Visual Data Mining and Machine Learning. The AUTOTUNE statement in the TREESPLIT, FOREST, GRADBOOST, NNET, SVMACHINE, and FACTMAC procedures defines tunable parameters, default ranges, user overrides, and validation schemes to avoid overfitting. Given the inherent expense of training numerous candidate models, the paper addresses efficient distributed and parallel paradigms for training and tuning models on the SAS® ViyaTM platform. It also presents sample tuning results that demonstrate improved model accuracy and offers recommendations for efficient and effective model tuning.
Understanding Machine Learning: From Theory to Algorithms

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and nonexpert readers in statistics, computer science, mathematics, and engineering.
How the motor system handles nouns: a behavioral study.

It is an open question whether the motor system is involved during understanding of concrete nouns, as it is for concrete verbs. To clarify this issue, we carried out a behavioral experiment using a go-no go paradigm with an early and delayed go-signal delivery. Italian nouns referring to concrete objects (hand-related or foot-related) and abstract entities served as stimuli. Right-handed participants read the stimuli and responded when the presented word was concrete using the left or right hand. At the early go-signal, slower right-hand responses were found for hand-related nouns compared to foot-related nouns. The opposite pattern was found for the left hand. These findings demonstrate an early lateralized modulation of the motor system during noun processing, most likely crucial for noun comprehension.
The effect of training volume and intensity on improvements in muscular strength and size in resistance-trained men

This investigation compared the effect of high-volume (VOL) versus high-intensity (INT) resistance training on stimulating changes in muscle size and strength in resistance-trained men. Following a 2-week preparatory phase, participants were randomly assigned to either a high-volume (VOL; n = 14, 4 × 10-12 repetitions with ~70% of one repetition maximum [1RM], 1-min rest intervals) or a high-intensity (INT; n = 15, 4 × 3-5 repetitions with ~90% of 1RM, 3-min rest intervals) training group for 8 weeks. Pre- and posttraining assessments included lean tissue mass via dual energy x-ray absorptiometry, muscle cross-sectional area and thickness of the vastus lateralis (VL), rectus femoris (RF), pectoralis major, and triceps brachii muscles via ultrasound images, and 1RM strength in the back squat and bench press (BP) exercises. Blood samples were collected at baseline, immediately post, 30 min post, and 60 min postexercise at week 3 (WK3) and week 10 (WK10) to assess the serum testosterone, growth hormone (GH), insulin-like growth factor-1 (IGF1), cortisol, and insulin concentrations. Compared to VOL, greater improvements (P < 0.05) in lean arm mass (5.2 ± 2.9% vs. 2.2 ± 5.6%) and 1RM BP (14.8 ± 9.7% vs. 6.9 ± 9.0%) were observed for INT. Compared to INT, area under the curve analysis revealed greater (P < 0.05) GH and cortisol responses for VOL at WK3 and cortisol only at WK10. Compared to WK3, the GH and cortisol responses were attenuated (P < 0.05) for VOL at WK10, while the IGF1 response was reduced (P < 0.05) for INT. It appears that high-intensity resistance training stimulates greater improvements in some measures of strength and hypertrophy in resistance-trained men during a short-term training period.
Analysis and Assessment of Situational Awareness Models for National Cyber Security Centers

National cyber security centers (NCSCs) are gaining more and more importance to ensure the security and proper operations of critical infrastructures (CIs). As a prerequisite, NCSCs need to collect, analyze, process, assess and share security-relevant information from infrastructure operators. A vital capability of mentioned NCSCs is to establish Cyber Situational Awareness (CSA) as a precondition for understanding the security situation of critical infrastructures. This is important for proper risk assessment and subsequent reduction of potential attack surfaces at national level. In this paper, we therefore survey theoretical models relevant for Situational Awareness (SA) and present a collaborative CSA model for NCSCs in order to enhance the protection of CIs at national level. Additionally, we provide an application scenario to illustrate a handson case of utilizing a CSA model in a NCSC, especially focusing on information sharing. We foresee this illustrative scenario to aid decision makers and practitioners who are involved in establishing NCSCs and cyber security processes on national level to better understand the specific implications regarding the application of the CSA model for NCSCs.
Supervised deep learning embeddings for the prediction of cervical cancer diagnosis

Cervical cancer remains a significant cause of mortality all around the world, even if it can be prevented and cured by removing affected tissues in early stages. Providing universal and efficient access to cervical screening programs is a challenge that requires identifying vulnerable individuals in the population, among other steps. In this work, we present a computationally automated strategy for predicting the outcome of the patient biopsy, given risk patterns from individual medical records. We propose a machine learning technique that allows a joint and fully supervised optimization of dimensionality reduction and classification models. We also build a model able to highlight relevant properties in the low dimensional space, to ease the classification of patients. We instantiated the proposed approach with deep learning architectures, and achieved accurate prediction results (top area under the curve AUC = 0.6875) which outperform previously developed methods, such as denoising autoencoders. Additionally, we explored some clinical findings from the embedding spaces, and we validated them through the medical literature, making them reliable for physicians and biomedical researchers. Subjects Bioinformatics, Computational Biology, Artificial Intelligence, Data Mining and Machine Learning
Biofuels from microalgae — A review of technologies for production , processing , and extractions of biofuels and co-products

Sustainability is a key principle in natural resource management, and it involves operational efficiency, minimisation of environmental impact and socio-economic considerations; all of which are interdependent. It has become increasingly obvious that continued reliance on fossil fuel energy resources is unsustainable, owing to both depleting world reserves and the green house gas emissions associated with their use. Therefore, there are vigorous research initiatives aimed at developing alternative renewable and potentially carbon neutral solid, liquid and gaseous biofuels as alternative energy resources. However, alternate energy resources akin to first generation biofuels derived from terrestrial crops such as sugarcane, sugar beet, maize and rapeseed place an enormous strain on world food markets, contribute to water shortages and precipitate the destruction of the world’s forests. Second generation biofuels derived from lignocellulosic agriculture and forest residues and from nonfood crop feedstocks address some of the above problems; however there is concern over competing land use or required land use changes. Therefore, based on current knowledge and technology projections, third generation biofuels specifically derived from microalgae are considered to be a technically viable alternative energy resource that is devoid of the major drawbacks associated with first and second generation biofuels. Microalgae are photosynthetic microorganisms with simple growing requirements (light, sugars, CO2, N, P, and K) that can produce lipids, proteins and carbohydrates in large amounts over short periods of time. These products can be processed into both biofuels and valuable co-products. This study reviewed the technologies underpinning microalgae-to-biofuels systems, focusing on the biomass production, harvesting, conversion technologies, and the extraction of useful co-products. It also reviewed the synergistic coupling of microalgae propagation with carbon sequestration and wastewater treatment potential for mitigation of environmental impacts associated with energy conversion and utilisation. It was found that, whereas there are outstanding issues related to photosynthetic efficiencies and biomass output, microalgae-derived biofuels could progressively substitute a significant proportion of the fossil fuels required to meet the growing energy demand.
Student Satisfaction, Performance, and Knowledge Construction in Online Collaborative Learning

A growing amount of research focuses on learning in group settings and more specifically on learning in computersupported collaborative learning (CSCL) settings. Studies on western students indicate that online collaboration enhances student learning achievement; however, few empirical studies have examined student satisfaction, performance, and knowledge construction through online collaboration from a cross-cultural perspective. This study examines satisfaction, performance, and knowledge construction via online group discussions of students in two different cultural contexts. Students were both first-year university students majoring in educational sciences at a Flemish university and a Chinese university. Differences and similarities of the two groups of students with regard to satisfaction, learning process, and achievement were analyzed.
The Ostomy-Q: Development and Psychometric Validation of an Instrument to Evaluate Outcomes Associated with Ostomy Appliances.

Using an ostomy appliance can affect many aspects of a person's health-related quality of life (HRQL). A 2-part, descrip- tive study was designed to develop and validate an instrument to assess quality-of-life outcomes related to ostomy ap- pliance use. Study inclusion/exclusion criteria stipulated participants should be 18 to 85 years of age, have an ileostomy or colostomy, used an appliance for a minimum of 3 months without assistance, and able to complete an online survey. All participants provided sociodemographic and clinical information. In phase 1, a literature search was conducted and existing instruments used to measure HRQL in persons with an ostomy were assessed. Subsequently, the Ostomy-Q, a 23-item, Likert-response type questionnaire, divided into 4 domains (Discreetness, Comfort, Confidence, and Social Life), was developed based on published evidence and existing ostomy-related HRQL tools. Seven (7) participants re- cruited from a manufacturer user panel took part in exploratory/cognitive qualitative interviews to refine the new quality- of-life questionnaire. In phase 2, the instrument was tested to assess item variability and conceptual structure, item-total correlation, internal consistency, test-retest reliability, sensitivity, and minimal important difference (MID) in an online validation study among 200 participants from the manufacturer's user panel (equally divided by gender, 125 [62.5%] >50 years old, 128 [64%] with an ileostomy). This exercise also included completion of the Stoma Quality of Life Question- naire and 2 domains from the Ostomy Adjustment Inventory-23 to assess convergent validity. Eighty-two (82) participants recompleted these study instruments 2 weeks later to assess test-retest reliability. Sociodemographic and clinical data were assessed using descriptive statistics; Cronbach's alpha was used for internal consistency (minimum 0.70), principle component analysis for item variability/conceptual structure, and item-total correlation; intraclass correlation coefficient was used for test-retest reliability; and standard error of measurement was applied to MID. All domains demonstrated good internal consistency (between 0.69 and 0.78). All scales showed stability, with a minimum intraclass correlation coefficient of 0.743 (P <.001). The Ostomy-Q showed good convergent validity with other instruments to which it was compared (P <.01). In this study, the Ostomy-Q was found to be a reliable and valid outcome measure that can enhance understanding of the impact of ostomy appliances on users. Some items for social relationships and discreetness may need more exploring in the future with other patient groups.