nils-herrmann/clean_scirepeval huggingface dataset:
Cloaked Facebook pages: Exploring fake Islamist propaganda in social media

Johan Farkas, Jannick Schou & Christina Neumayer  This research analyses cloaked Facebook pages that are created to spread political propaganda by cloaking a user profile and imitating the identity of a political opponent in order to spark hateful and aggressive reactions. This inquiry is pursued through a multi-sited ethnographic case study of Danish Facebook pages disguised as radical Islamist pages, which provoked racist and anti-Muslim reactions as well as negative sentiments toward refugees, and immigrants in Denmark in general. Drawing on Jessie Daniels’ critical insights into cloaked websites, this research furthermore analyses the epistemological, methodological, and conceptual challenges of online propaganda. It enhances our understanding of disinformation and propaganda in an increasingly interactive social media environment and contributes to a critical inquiry into social media and subversive politics.
Sequencing and scheduling : algorithms and complexity

A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
Transformation from EPC to BPMN

Business process modeling is a big part in the industry, mainly to document, analyze, and optimize workflows. Currently, the EPC process modeling notation is used very wide, because of the excellent integration in the ARIS Toolset and the long existence of this process language. But as a change of time, BPMN gets popular and the interest in the industry and companies gets growing up. It is standardized, has more expressiveness than EPC and the tool support increase very rapidly. With having tons of existing EPC process models; a big need from the industry is to have an automated transformation from EPC to BPMN. This paper specified a direct approach of a transformation from EPC process model elements to BPMN. Thereby it is tried to map every construct in EPC fully automated to BPMN. But as it is described, not for every process element works this out, so in addition, some extensions and semantics rules are defined.
A Methodology for Testing Intrusion Detection Systems 1

Intrusion Detection Systems (IDSs) attempt to identify unauthorized use, misuse, and abuse of computer systems. In response to the growth in the use and development of IDSs, we have developed a methodology for testing IDSs. The methodology consists of techniques from the eld of software testing which we have adapted for the speci c purpose of testing IDSs. In this paper, we identify a set of general IDS performance objectives which is the basis for the methodology. We present the details of the methodology, including strategies for test-case selection and speci c testing procedures. We include quantitative results from testing experiments on the Network Security Monitor (NSM), an IDS developed at UC Davis. We present an overview of the software platform that we have used to create user-simulation scripts for testing experiments. The platform consists of the UNIX tool expect and enhancements that we have developed, including mechanisms for concurrent scripts and a record-and-replay feature. We also provide background information on intrusions and IDSs to motivate our work.
A Different Approach to Solving the Teacher Shortage Problem

Few educational problems have received more attention in recent times than the failure to ensure that elementary and secondary classrooms are all staffed with qualified teachers. Over the past two decades, dozens of studies, commissions, and national reports have warned of a coming crisis resulting from widespread teacher shortages. This article briefly summarizes a recent study I undertook that used national data to examine the sources of school staffing problems and teacher shortages. This research shows that although these issues are among the most important facing schools, they are also among the least understood. The data also reveal that many currently popular reforms will not solve the staffing problems of schools because they do not address some of their key causes. Disciplines Educational Administration and Supervision | Teacher Education and Professional Development Comments This brief is based on the following Center for the Study of Teaching Policy (CTP)  Teacher Turnover, Teacher Shortages, and the Organization of Schools. View on the CPRE website. This policy brief is available at ScholarlyCommons: https://repository.upenn.edu/cpre_policybriefs/21
Neural Word Embedding as Implicit Matrix Factorization

We analyze skip-gram with negative-sampling (SGNS), a word embedding method introduced by Mikolov et al., and show that it is implicitly factorizing a word-context matrix, whose cells are the pointwise mutual information (PMI) of the respective word and context pairs, shifted by a global constant. We find that another embedding method, NCE, is implicitly factorizing a similar matrix, where each cell is the (shifted) log conditional probability of a word given its context. We show that using a sparse Shifted Positive PMI word-context matrix to represent words improves results on two word similarity tasks and one of two analogy tasks. When dense low-dimensional vectors are preferred, exact factorization with SVD can achieve solutions that are at least as good as SGNS’s solutions for word similarity tasks. On analogy questions SGNS remains superior to SVD. We conjecture that this stems from the weighted nature of SGNS’s factorization.
Sizing Energy Storage to Mitigate Wind Power Forecast Error Impacts by Signal Processing Techniques

This paper proposes to use discrete Fourier transform (DFT) and discrete wavelet transform (DWT) methods to schedule grid-scale energy storage systems to mitigate wind power forecast error impacts while considering energy storage properties. This is accomplished by decomposing the wind forecast error signal to different time-varying periodic components to schedule sodium sulfur (NaS) batteries, compressed air energy storage (CAES), and conventional generators. The advantage of signal processing techniques is that the resultant decomposed components are appropriate for cycling of each energy storage technology. It is also beneficial for conventional generators, which are more efficient to operate close to rated capacity. The tradeoff between installing more energy storage units and decreasing the wind spillage, back-up energy, and the standard deviation of residual forecast error signal is analyzed. The NaS battery life cycle analysis and CAES contribution on increasing NaS battery lifetime are studied. The impact of considering the frequency bias constant to allow small frequency deviations is also investigated. To showcase the applicability of the proposed approach, a simulation case study based on a real-world 5-min interval wind data from Bonneville Power Administration (BPA) in 2013 is presented.
EXTRACTIVE QUESTION ANSWERING

The reading comprehension task, that asks questions about a given evidence document, is a central problem in natural language understanding. Recent formulations of this task have typically focused on answer selection from a set of candidates pre-defined manually or through the use of an external NLP pipeline. However, Rajpurkar et al. (2016) recently released the SQUAD dataset in which the answers can be arbitrary strings from the supplied text. In this paper, we focus on this answer extraction task, presenting a novel model architecture that efficiently builds fixed length representations of all spans in the evidence document with a recurrent network. We show that scoring explicit span representations significantly improves performance over other approaches that factor the prediction into separate predictions about words or start and end markers. Our approach improves upon the best published results of Wang & Jiang (2016) by 5% and decreases the error of Rajpurkar et al.’s baseline by > 50%.
Graph Regularized Nonnegative Matrix Factorization for Data Representation

Matrix factorization techniques have been frequently applied in information retrieval, computer vision, and pattern recognition. Among them, Nonnegative Matrix Factorization (NMF) has received considerable attention due to its psychological and physiological interpretation of naturally occurring data whose representation may be parts based in the human brain. On the other hand, from the geometric perspective, the data is usually sampled from a low-dimensional manifold embedded in a high-dimensional ambient space. One then hopes to find a compact representation,which uncovers the hidden semantics and simultaneously respects the intrinsic geometric structure. In this paper, we propose a novel algorithm, called Graph Regularized Nonnegative Matrix Factorization (GNMF), for this purpose. In GNMF, an affinity graph is constructed to encode the geometrical information and we seek a matrix factorization, which respects the graph structure. Our empirical study shows encouraging results of the proposed algorithm in comparison to the state-of-the-art algorithms on real-world problems.
DENSER: deep evolutionary network structured representation

Deep evolutionary network structured representation (DENSER) is a novel evolutionary approach for the automatic generation of deep neural networks (DNNs) which combines the principles of genetic algorithms (GAs) with those of dynamic structured grammatical evolution (DSGE). The GA-level encodes the macro structure of evolution, i.e., the layers, learning, and/or data augmentation methods (among others); the DSGE-level specifies the parameters of each GA evolutionary unit and the valid range of the parameters. The use of a grammar makes DENSER a general purpose framework for generating DNNs: one just needs to adapt the grammar to be able to deal with different network and layer types, problems, or even to change the range of the parameters. DENSER is tested on the automatic generation of convolutional neural networks (CNNs) for the CIFAR-10 dataset, with the best performing networks reaching accuracies of up to 95.22%. Furthermore, we take the fittest networks evolved on the CIFAR-10, and apply them to classify MNIST, Fashion-MNIST, SVHN, Rectangles, and CIFAR-100. The results show that the DNNs discovered by DENSER during evolution generalise, are robust, and scale. The most impressive result is the 78.75% classification accuracy on the CIFAR-100 dataset, which, to the best of our knowledge, sets a new state-of-the-art on methods that seek to automatically design CNNs.
Culture, language, and the doctor-patient relationship.

This review's goal was to determine how differences between physicians and patients in race, ethnicity and language influence the quality of the physician-patient relationship.



We performed a literature review to assess existing evidence for ethnic and racial disparities in the quality of doctor-patient communication and the doctor-patient relationship.



We found consistent evidence that race, ethnicity; and language have substantial influence on the quality of the doctor-patient relationship. Minority patients, especially those not proficient in English, are less likely to engender empathic response from physicians, establish rapport with physicians, receive sufficient information, and be encouraged to participate in medical decision making.



The literature calls for a more diverse physician work force since minority patients are more likely to choose minority physicians, to be more satisfied by language-concordant relationships, and to feel more connected and involved in decision making with racially concordant physicians. The literature upholds the recommendation for professional interpreters to bridge the gaps in access experienced by non-English speaking physicians. Further evidence supports the admonition that "majority" physicians need to be more effective in developing relationships and in their communication with ethnic and racial minority patients.
The growing use of herbal medicines: issues relating to adverse reactions and challenges in monitoring safety

The use of herbal medicinal products and supplements has increased tremendously over the past three decades with not less than 80% of people worldwide relying on them for some part of primary healthcare. Although therapies involving these agents have shown promising potential with the efficacy of a good number of herbal products clearly established, many of them remain untested and their use are either poorly monitored or not even monitored at all. The consequence of this is an inadequate knowledge of their mode of action, potential adverse reactions, contraindications, and interactions with existing orthodox pharmaceuticals and functional foods to promote both safe and rational use of these agents. Since safety continues to be a major issue with the use of herbal remedies, it becomes imperative, therefore, that relevant regulatory authorities put in place appropriate measures to protect public health by ensuring that all herbal medicines are safe and of suitable quality. This review discusses toxicity-related issues and major safety concerns arising from the use of herbal medicinal products and also highlights some important challenges associated with effective monitoring of their safety.
Visual Clustering in Parallel Coordinates

Parallel coordinates have been widely applied to visualize high-dimensional and multivariate data, discerning patterns within the data through visual clustering. However, the effectiveness of this technique on large data is reduced by edge clutter. In this paper, we present a novel framework to reduce edge clutter, consequently improving the effectiveness of visual clustering. We exploit curved edges and optimize the arrangement of these curved edges by minimizing their curvature and maximizing the parallelism of adjacent edges. The overall visual clustering is improved by adjusting the shape of the edges while keeping their relative order. The experiments on several representative datasets demonstrate the effectiveness of our approach.
The cognitive organization of music knowledge: a clinical analysis

Despite much recent interest in the clinical neuroscience of music processing, the cognitive organization of music as a domain of non-verbal knowledge has been little studied. Here we addressed this issue systematically in two expert musicians with clinical diagnoses of semantic dementia and Alzheimer's disease, in comparison with a control group of healthy expert musicians. In a series of neuropsychological experiments, we investigated associative knowledge of musical compositions (musical objects), musical emotions, musical instruments (musical sources) and music notation (musical symbols). These aspects of music knowledge were assessed in relation to musical perceptual abilities and extra-musical neuropsychological functions. The patient with semantic dementia showed relatively preserved recognition of musical compositions and musical symbols despite severely impaired recognition of musical emotions and musical instruments from sound. In contrast, the patient with Alzheimer's disease showed impaired recognition of compositions, with somewhat better recognition of composer and musical era, and impaired comprehension of musical symbols, but normal recognition of musical emotions and musical instruments from sound. The findings suggest that music knowledge is fractionated, and superordinate musical knowledge is relatively more robust than knowledge of particular music. We propose that music constitutes a distinct domain of non-verbal knowledge but shares certain cognitive organizational features with other brain knowledge systems. Within the domain of music knowledge, dissociable cognitive mechanisms process knowledge derived from physical sources and the knowledge of abstract musical entities.
Sequence-Level Knowledge Distillation

Neural machine translation (NMT) offers a novel alternative formulation of translation that is potentially simpler than statistical approaches. However to reach competitive performance, NMT models need to be exceedingly large. In this paper we consider applying knowledge distillation approaches (Bucila et al., 2006; Hinton et al., 2015) that have proven successful for reducing the size of neural models in other domains to the problem of NMT. We demonstrate that standard knowledge distillation applied to word-level prediction can be effective for NMT, and also introduce two novel sequence-level versions of knowledge distillation that further improve performance, and somewhat surprisingly, seem to eliminate the need for beam search (even when applied on the original teacher model). Our best student model runs 10 times faster than its state-of-the-art teacher with little loss in performance. It is also significantly better than a baseline model trained without knowledge distillation: by 4.2/1.7 BLEU with greedy decoding/beam search. Applying weight pruning on top of knowledge distillation results in a student model that has 13× fewer parameters than the original teacher model, with a decrease of 0.4 BLEU.
A field study of API learning obstacles

Large APIs can be hard to learn, and this can lead to decreased programmer productivity. But what makes APIs hard to learn? We conducted a mixed approach, multi-phased study of the obstacles faced by Microsoft developers learning a wide variety of new APIs. The study involved a combination of surveys and in-person interviews, and collected the opinions and experiences of over 440 professional developers. We found that some of the most severe obstacles faced by developers learning new APIs pertained to the documentation and other learning resources. We report on the obstacles developers face when learning new APIs, with a special focus on obstacles related to API documentation. Our qualitative analysis elicited five important factors to consider when designing API documentation: documentation of intent; code examples; matching APIs with scenarios; penetrability of the API; and format and presentation. We analyzed how these factors can be interpreted to prioritize API documentation development efforts
The Variational Fair Autoencoder

We investigate the problem of learning representations that are invariant to certain nuisance or sensitive factors of variation in the data while retaining as much of the remaining information as possible. Our model is based on a variational autoencoding architecture (Kingma & Welling, 2014; Rezende et al., 2014) with priors that encourage independence between sensitive and latent factors of variation. Any subsequent processing, such as classification, can then be performed on this purged latent representation. To remove any remaining dependencies we incorporate an additional penalty term based on the “Maximum Mean Discrepancy” (MMD) (Gretton et al., 2006) measure. We discuss how these architectures can be efficiently trained on data and show in experiments that this method is more effective than previous work in removing unwanted sources of variation while maintaining informative latent representations.
Keyframe-based visual-inertial online SLAM with relocalization

Complementing images with inertial measurements has become one of the most popular approaches to achieve highly accurate and robust real-time camera pose tracking. In this paper, we present a keyframe-based approach to visual-inertial simultaneous localization and mapping (SLAM) for monocular and stereo cameras. Our visual-inertial SLAM system is based on a real-time capable visual-inertial odometry method that provides locally consistent trajectory and map estimates. We achieve global consistency in the estimate through online loop-closing and non-linear optimization. Furthermore, our system supports relocalization in a map that has been previously obtained and allows for continued SLAM operation. We evaluate our approach in terms of accuracy, relocalization capability and run-time efficiency on public indoor benchmark datasets and on newly recorded outdoor sequences. We demonstrate state-of-the-art performance of our system compared to a visual-inertial odometry method and baseline visual SLAM approaches in recovering the trajectory of the camera.
Multi-Step Regression Learning for Compositional Distributional Semantics

We present a model for compositional distributional semantics related to the framework of Coecke et al. (2010), and emulating formal semantics by representing functions as tensors and arguments as vectors. We introduce a new learning method for tensors, generalising the approach of Baroni and Zamparelli (2010). We evaluate it on two benchmark data sets, and find it to outperform existing leading methods. We argue in our analysis that the nature of this learning method also renders it suitable for solving more subtle problems compositional distributional models might face.
Champollion: A Robust Parallel Text Sentence Aligner

This paper describes Champollion, a lexicon-based sentence aligner designed for robust alignment of potential noisy parallel text. Champollion increases the robustness of the alignment by assigning greater weights to less frequent translated words. Experiments on a manually aligned Chinese – English parallel corpus show that Champollion achieves high precision and recall on noisy data. Champollion can be easily ported to new language pairs. It’s freely available to the public.
A Physically-Based Earthquake Recurrence Model for Estimation of Long-Term Earthquake Probabilities

A physically-motivated model for earthquake recurrence based on the Brownian relaxation oscillator is introduced. The renewal process defining this point process model can be described by the steady rise of a state variable from the ground state to failure threshold as modulated by Brownian motion. Failure times in this model follow the Brownian passage time (BPT) distribution, which is specified by the mean time to failure, μ, and the aperiodicity of the mean, α (equivalent to the familiar coefficient of variation). Analysis of 37 series of recurrent earthquakes, M -0.7 to 9.2, suggests a provisional generic value of α = 0.5. For this value of α, the hazard function (instantaneous failure rate of survivors) exceeds the mean rate for times > μ⁄2, and is ∼∼ 2 ⁄ μ for all times > μ. Application of this model to the next M 6 earthquake on the San Andreas fault at Parkfield, California suggests that the annual probability of the earthquake is between 1:10 and 1:13.