nils-herrmann/clean_scirepeval huggingface dataset:
Some Like it Hoax: Automated Fake News Detection in Social Networks

In the recent years, the reliability of information on the Internet has emerged as a crucial issue of modern society. Social network sites (SNSs) have revolutionized the way in which information is spread by allowing users to freely share content. As a consequence, SNSs are also increasingly used as vectors for the diffusion of misinformation and hoaxes. The amount of disseminated information and the rapidity of its diffusion make it practically impossible to assess reliability in a timely manner, highlighting the need for automatic online hoax detection systems. As a contribution towards this objective, we show that Facebook posts can be classified with high accuracy as hoaxes or non-hoaxes on the basis of the users who “liked” them. We present two classification techniques, one based on logistic regression, the other on a novel adaptation of boolean crowdsourcing algorithms. On a dataset consisting of 15,500 Facebook posts and 909,236 users, we obtain classification accuracies exceeding 99% even when the training set contains less than 1% of the posts. We further show that our techniques are robust: they work even when we restrict our attention to the users who like both hoax and nonhoax posts. These results suggest that mapping the diffusion pattern of information can be a useful component of automatic hoax detection systems.
Challenges of Marketing E-banking Services in a Developing Country : The Case of Ghana

Even though there is a plethora of studies investigating the challenges of adopting ebanking services, a search through the literature indicates that prior studies have investigated either user adoption challenges or the bank implementation challenges. This study integrated both perspectives to provide a broader conceptual framework for investigating challenges banks face in marketing e-banking services in developing country such as Ghana. The results from the mixed method study indicated that institutional–based challenges as well as userbased challenges affect the marketing of e-banking products in Ghana. The strategic implications of the findings for marketing ebanking services are discussed to guide managers to implement e-banking services in Ghana.
When Online Dating Meets Nash Social Welfare: Achieving Efficiency and Fairness

Mobile dating applications such as Coffee Meets Bagel, Tantan, and Tinder, have become significant for young adults to meet new friends and discover romantic relationships. From a system designer’s perspective, in order to achieve better user experience in these applications, we should take both the efficiency and fairness of a dating market into consideration, so as to increase the overall satisfaction for all users. Towards this goal, we investigate the nature of diminishing marginal returns for online dating markets (i.e., captured by the submodularity), and trade-off between the efficiency and fairness of the market with Nash social welfare. We further design effective online algorithms to the apps. We verify our models and algorithms through sound theoretical analysis and empirical studies by using real data and show that our algorithms can significantly improve the ecosystems of the online dating applications.  Yongzheng Jia, Xue Liu, and Wei Xu. 2018. When Online Dating Meets Nash Social Welfare: Achieving Efficiency and Fairness. In WWW 2018: The 2018 Web Conference, April 23–27, 2018, Lyon, France. ACM, New York, NY, USA, 10 pages.
REST Representational State Transfer

Many modern Web-services ignore existing Web-standards and develop their own interfaces to publish their services. This reduces interoperability and increases network latency, which in turn reduces scalability of the service. The Web grew from a few thousand requests per day to million requests per hour without significant loss of performance. Applying the same architecture underlying the modern Web to Web-services could improve existing and forthcoming applications. REST is the idealized model of the interactions within an Web-application and became the foundation of the modern Web-architecture, it has been designed to meet the needs of Internet-scale distributed hypermedia systems by emphasizing scalability, generality of interfaces, independent deployment and allowing intermediary components to reduce network latency.
Continual Lifelong Learning with Neural Networks: A Review

Humans and animals have the ability to continually acquire, fine-tune, and transfer knowledge and skills throughout their lifespan. This ability, referred to as lifelong learning, is mediated by a rich set of neurocognitive mechanisms that together contribute to the development and specialization of our sensorimotor skills as well as to long-term memory consolidation and retrieval. Consequently, lifelong learning capabilities are crucial for computational systems and autonomous agents interacting in the real world and processing continuous streams of information. However, lifelong learning remains a long-standing challenge for machine learning and neural network models since the continual acquisition of incrementally available information from non-stationary data distributions generally leads to catastrophic forgetting or interference. This limitation represents a major drawback for state-of-the-art deep neural network models that typically learn representations from stationary batches of training data, thus without accounting for situations in which information becomes incrementally available over time. In this review, we critically summarize the main challenges linked to lifelong learning for artificial learning systems and compare existing neural network approaches that alleviate, to different extents, catastrophic forgetting. Although significant advances have been made in domain-specific learning with neural networks, extensive research efforts are required for the development of robust lifelong learning on autonomous agents and robots. We discuss well-established and emerging research motivated by lifelong learning factors in biological systems such as structural plasticity, memory replay, curriculum and transfer learning, intrinsic motivation, and multisensory integration.
Automatic Text Summarization

This paper investigates on sentence extraction based single Document summarization. It saves time in our daily work once we get summarized data. Today there are so many Documents, articles, papers and reports available in digital form, but most of them lack summaries. Automatic text Summarization is a technique where a computer summarizes a text. A text is given to the computer and the computer returns a required extract of the original text document. Our methods on the sentence extraction-based text summarization task use the graph based algorithm to calculate importance of each sentence in document and most important sentences are extracted to generate document summary. These extraction based text summarization methods give an indexing weight to the document terms to compute the similarity values between sentences
Asking 'Why' in AI: Explainability of intelligent systems - perspectives and challenges

This researchwas sponsored by theU.S. ArmyResearch Laboratory and theUKMinistry of Defence under Agreement Number W911NF-16-3-0001. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Army Research Laboratory, the U.S. Government, the UK Ministry of Defence or the UK Government. The U.S. and UK Governments are authorized to reproduce and distribute reprints forGovernment purposes notwithstanding any copyright notation hereon.
Assessment of older people: self-maintaining and instrumental activities of daily living.

THE use of formal devices for assessing function is becoming standard in agencies serving the elderly. In the Gerontological Society's recent contract study on functional assessment (Howell, 1968), a large assortment of rating scales, checklists, and other techniques in use in applied settings was easily assembled. The present state of the trade seems to be one in which each investigator or practitioner feels an inner compusion to make his own scale and to cry that other existent scales cannot possibly fit his own setting. The authors join this company in presenting two scales first standardized on their own population (Lawton, 1969). They take some comfort, however, in the fact that one scale, the Physical Self-Maintenance Scale (PSMS), is largely a scale developed and used by other investigators (Lowenthal, 1964), which was adapted for use in our own institution. The second of the scales, the Instrumental Activities of Daily Living Scale (IADL), taps a level of functioning heretofore inadequately represented in attempts to assess everyday functional competence. Both of the scales have been tested further for their usefulness in a variety of types of institutions and other facilities serving community-resident older people. Before describing in detail the behavior measured by these two scales, we shall briefly describe the schema of competence into which these behaviors fit (Lawton, 1969). Human behavior is viewed as varying in the degree of complexity required for functioning in a variety of tasks. The lowest level is called life maintenance, followed by the successively more complex levels of func-
Hate crimes and stigma-related experiences among sexual minority adults in the United States: prevalence estimates from a national probability sample.

Using survey responses collected via the Internet from a U.S. national probability sample of gay, lesbian, and bisexual adults (N = 662), this article reports prevalence estimates of criminal victimization and related experiences based on the target's sexual orientation. Approximately 20% of respondents reported having experienced a person or property crime based on their sexual orientation; about half had experienced verbal harassment, and more than 1 in 10 reported having experienced employment or housing discrimination. Gay men were significantly more likely than lesbians or bisexuals to experience violence and property crimes. Employment and housing discrimination were significantly more likely among gay men and lesbians than among bisexual men and women. Implications for future research and policy are discussed.
The Impact of Corporate Social Responsibility on Investment Recommendations

Using a large sample of publicly traded US firms over 16 years, we investigate the impact of corporate socially responsible (CSR) strategies on security analysts’ recommendations. Socially responsible firms receive more favorable recommendations in recent years relative to earlier ones, documenting a changing perception of the value of such strategies by the analysts. Moreover, we find that firms with higher visibility receive more favorable recommendations for their CSR strategies and that analysts with more experience, broader CSR awareness or those with more resources at their disposal, are more likely to perceive the value of CSR strategies more favorably. Our results document how CSR strategies can affect value creation in public equity markets through analyst recommendations. 1 Assistant Professor of Strategic and International Management, London Business School, Regent’s Park, NW1 4SA, London, United Kingdom.  Assistant Professor of Business Administration, Harvard Business School, Soldiers’ Field Road, Morgan Hall 381, 02163 Boston, MA, USA. Email:gserafeim@hbs.edu, Ph: +1 617 495 6548, Fx: +1 617 496 7387. We are grateful to Constantinos Markides, and seminar participants at the research brown bag (SIM area) of the London Business School, the academic conference on Social Responsibility at University of Washington Tacoma, the 2010 European Academy of Management Conference, and the 2010 Academy of Management Conference. Ioannou acknowledges financial support from the Research and Materials Development Fund (RAMD) at the London Business School. All remaining errors are our own.
An Effective Way to Improve YouTube-8M Classification Accuracy in Google Cloud Platform

Large-scale datasets have played a significant role in progress of neural network and deep learning areas. YouTube-8M is such a benchmark dataset for general multilabel video classification. It was created from over 7 million YouTube videos (450,000 hours of video) and includes video labels from a vocabulary of 4716 classes (3.4 labels/video on average). It also comes with pre-extracted audio & visual features from every second of video (3.2 billion feature vectors in total). Google cloud recently released the datasets and organized ‘Google Cloud & YouTube-8M Video Understanding Challenge’ on Kaggle. Competitors are challenged to develop classification algorithms that assign video-level labels using the new and improved Youtube-8M V2 dataset. Inspired by the competition, we started exploration of audio understanding and classification using deep learning algorithms and ensemble methods. We built several baseline predictions according to the benchmark paper  and public github tensorflow code. Furthermore, we improved global prediction accuracy (GAP) from base level 77% to 80.7% through approaches of ensemble.
Metabolic engineering with systems biology tools to optimize production of prokaryotic secondary metabolites.

Covering: 2012 to 2016Metabolic engineering using systems biology tools is increasingly applied to overproduce secondary metabolites for their potential industrial production. In this Highlight, recent relevant metabolic engineering studies are analyzed with emphasis on host selection and engineering approaches for the optimal production of various prokaryotic secondary metabolites: native versus heterologous hosts (e.g., Escherichia coli) and rational versus random approaches. This comparative analysis is followed by discussions on systems biology tools deployed in optimizing the production of secondary metabolites. The potential contributions of additional systems biology tools are also discussed in the context of current challenges encountered during optimization of secondary metabolite production.
The bright and dark sides of leader traits : A review and theoretical extension of the leader trait paradigm

a r t i c l e i n f o a b s t r a c t The leader trait perspective is perhaps the most venerable intellectual tradition in leadership research. Despite its early prominence in leadership research, it quickly fell out of favor among leadership scholars. Thus, despite recent empirical support for the perspective, conceptual work in the area lags behind other theoretical perspectives. Accordingly, the present review attempts to place the leader trait perspective in the context of supporting intellectual traditions, including evolutionary psychology and behavioral genetics. We present a conceptual model that considers the source of leader traits, mediators and moderators of their effects on leader emergence and leadership effectiveness, and distinguish between perceived and actual leadership effectiveness. We consider both the positive and negative effects of specific " bright side " personality traits: the Big Five traits, core self-evaluations, intelligence, and charisma. We also consider the positive and negative effects of " dark side " leader traits: Narcissism, hubris, dominance, and Machiavellianism. If one sought to find singular conditions that existed across species, one might find few universals. One universal that does exist, at least those species that have brains and nervous systems, is leadership. From insects to reptiles to mammals, leadership exists as surely as collective activity exists. There is the queen bee, and there is the alpha male. Though the centrality of leadership may vary by species (it seems more important to mammals than, say, to avians and reptiles), it is fair to surmise that whenever there is social activity, a social structure develops, and one (perhaps the) defining characteristic of that structure is the emergence of a leader or leaders. The universality of leadership, however, does not deny the importance of individual differences — indeed the emergence of leadership itself is proof of individual differences. Moreover, even casual observation of animal (including human) collective behavior shows the existence of a leader. Among a herd of 100 cattle or a pride of 20 lions, one is able to detect a leadership structure (especially at times of eating, mating, and attack). One quickly wonders: What has caused this leadership structure to emerge? Why has one animal (the alpha) emerged to lead the collective? And how does this leadership cause this collective to flourish — or founder? Given these questions, it is of no surprise that the earliest conceptions of leadership focused on individual …
Brain Tumor Type Classification via Capsule Networks

Brain tumor is considered as one of the deadliest and most common form of cancer both in children and in adults. Consequently, determining the correct type of brain tumor in early stages is of significant importance to devise a precise treatment plan and predict patient's response to the adopted treatment. In this regard, there has been a recent surge of interest in designing Convolutional Neural Networks (CNNs) for the problem of brain tumor type classification. However, CNNs typically require large amount of training data and can not properly handle input transformations. Capsule networks (referred to as CapsNets) are brand new machine learning architectures proposed very recently to overcome these shortcomings of CNNs, and posed to revolutionize deep learning solutions. Of particular interest to this work is that Capsule networks are robust to rotation and affine transformation, and require far less training data, which is the case for processing medical image datasets including brain Magnetic Resonance Imaging (MRI) images. In this paper, we focus to achieve the following four objectives: (i) Adopt and incorporate CapsNets for the problem of brain tumor classification to design an improved architecture which maximizes the accuracy of the classification problem at hand; (ii) Investigate the over-fitting problem of CapsNets based on a real set of MRI images; (iii) Explore whether or not CapsNets are capable of providing better fit for the whole brain images or just the segmented tumor, and; (iv) Develop a visualization paradigm for the output of the CapsNet to better explain the learned features. Our results show that the proposed approach can successfully overcome CNNs for the brain tumor classification problem.
Mining Mobile User Preferences for Personalized Context-Aware Recommendation

Recent advances in mobile devices and their sensing capabilities have enabled the collection of rich contextual information and mobile device usage records through the device logs. These context-rich logs open a venue for mining the personal preferences of mobile users under varying contexts and thus enabling the development of personalized context-aware recommendation and other related services, such as mobile online advertising. In this article, we illustrate how to extract personal context-aware preferences from the context-rich device logs, or context logs for short, and exploit these identified preferences for building personalized context-aware recommender systems. A critical challenge along this line is that the context log of each individual user may not contain sufficient data for mining his or her context-aware preferences. Therefore, we propose to first learn common context-aware preferences from the context logs of many users. Then, the preference of each user can be represented as a distribution of these common context-aware preferences. Specifically, we develop two approaches for mining common context-aware preferences based on two different assumptions, namely, context-independent and context-dependent assumptions, which can fit into different application scenarios. Finally, extensive experiments on a real-world dataset show that both approaches are effective and outperform baselines with respect to mining personal context-aware preferences for mobile users.
Developing an IT Maturity Model for Sustainable Supply Chain Management Implementation

Many organisations are currently involved in implementing Sustainable Supply Chain Management (SSCM) initiatives to address societal expectations and government regulations. Implementation of these initiatives has in turn created complexity due to the involvement of collection, management, control, and monitoring of a wide range of additional information exchanges among trading partners, which was not necessary in the past. Organisations thus would rely more on meaningful support from their IT function to help them implement and operate SSCM practices. Given the growing global recognition of the importance of sustainable supply chain (SSC) practices, existing corporate IT strategy and plans need to be revisited for IT to remain supportive and aligned with new sustainability aspirations of their organisations. Towards this goal, in this paper we report on the development of an IT maturity model specifically designed for SSCM context. The model is built based on four dimensions derived from software process maturity and IS/IT planning literatures. Our proposed model defines four progressive IT maturity stages for corporate IT function to support SSCM implementation initiatives. Some implications of the study finding and several challenges that may potentially hinder acceptance of the model by organisations are discussed.
Cross-Lingual Word Embeddings for Low-Resource Language Modeling

Most languages have no formal writing system and at best a limited written record. However, textual data is critical to natural language processing and particularly important for the training of language models that would facilitate speech recognition of such languages. Bilingual phonetic dictionaries are often available in some form, since lexicon creation is a fundamental task of documentary linguistics. We investigate the use of such dictionaries to improve language models when textual training data is limited to as few as 1k sentences. The method involves learning cross-lingual word embeddings as a pretraining step in the training of monolingual language models. Results across a number of languages show that language models are improved by such pre-training.
Autopoiesis and cognition maturana pdf Autopoiesis and cognition maturana pdf

Data EB. Boston studies in the philosophy of science.The concept of autopoiesis is due to Maturana and Varela 8, 9. The aim of this article is to revisit the concepts of autopoiesis and cognition in the hope of.Amazon.com: Autopoiesis and Cognition: The Realization of the Living Boston Studies in the Philosophy of Science, Autopoiesis, The Santiago School of Cognition, and. In their early work together Maturana and Varela developed the idea of autopoiesis.Autopoiesis and Cognition: The Realization of the Living Dordecht.
Semi-supervised Learning with Deep Generative Models for Asset Failure Prediction

This work presents a novel semi-supervised learning approach for data-driven modeling of asset failures when health status is only partially known in historical data. We combine a generative model parameterized by deep neural networks with non-linear embedding technique. It allows us to build prognostic models with the limited amount of health status information for the precise prediction of future asset reliability. The proposed method is evaluated on a publicly available dataset for remaining useful life (RUL) estimation, which shows significant improvement even when a fraction of the data with known health status is as sparse as 1% of the total. Our study suggests that the non-linear embedding based on a deep generative model can efficiently regularize a complex model with deep architectures while achieving high prediction accuracy that is far less sensitive to the availability of health status information.