nils-herrmann/clean_scirepeval huggingface dataset:
Business Model Formation within the Online News Market: The Core + Complement Business Model Framework

The business model literature is both rich and rapidly-growing. Authors identify special-purpose business and eBusiness models – and, increasingly, develop taxonomies of business models types. But, in searching for a comparatively simple way to understand the components of a “typical” internet business model, as part of our work for the EC research project SimWeb, we found that these taxonomies had little overlap and offered only a modest assistance to smaller companies trying to identify their own business identity. In this paper, therefore, we present the preliminary results of a three-year research project into appropriate business models for the online news and music industries. Having identified the problems, we describe the general taxonomies and components of Internet business models found in the literature, and explain our own core + component framework for developing an internet business model – using the online news industry as our example. We show how a combination of core and complementary components can be combined by any news-providing organisation for its Internet business model on the basis to its specific needs, resources and changing circumstances – and illustrate the usefulness of our this framework by means of “mini-case” examples of regional online newspapers in Germany. Cornelia C. Krüger, Paula M.C. Swatman, Kornelia van der Beek
Tourism Marketing through Virtual Environment Experience

This article proposes the develop of a dynamic virtual environment that with the consumption of real time data about the state of a place, offer an immersion to the tourist like be at the desired location. The development implements a communication and loader structure from many information sources, manual information data loaded from mobile devices and data loader from collecting equipment that get environmental and atmospheric data. The virtual reality application use Google Maps, and worldwide heightmap to get 3D geographic map models; HTC VIVE and Oculus SDK for support virtual reality experience; and weather API to show the weather information from the desired location in real time. In addition, the proposed virtual reality application emphasizes user interaction on the virtual environment by displaying dynamic and up-to-date information about tourism services.
Cranial aneurysmal bone cyst: a diagnostic problem With a review of the literature

Aneurysmal bone cysts (ABC) are benign osteolytic lesions that are more common in young people than in adults and involve the skull only exceptionally. The origin of ABC is still debated; indeed, some authors consider ABC to be an anomalous bony reaction that is secondary to traumas or tumours. Conversely, others consider ABC to be a distinct entity. A case of a healthy young female affected by a left frontal ABC is reported here. The clinical onset was characterised by the sudden appearance of a tender and mildly painful frontal mass. Neuroradiological assessment showed a well-circumscribed lytic lesion of the frontal bone with predominantly outward extension. En bloc surgical removal of the lesion was successfully achieved; a reconstructive cranioplasty was also performed to repair the cranial defect. The rarity of the condition described, together with the absence of clear guidelines, prompted us to review the more recent literature with the twin goals of identifying radiological features and becoming able to address the diagnosis and rules for treatment of such a rare entity.
Extracting paraphrase patterns from bilingual parallel corpora

Paraphrase patterns are semantically equivalent patterns, which are useful in both paraphrase recognition and generation. This paper presents a pivot approach for extracting paraphrase patterns from bilingual parallel corpora, whereby the paraphrase patterns in English are extracted using the patterns in another language as pivots. We make use of log-linear models for computing the paraphrase likelihood between pattern pairs and exploit feature functions based on maximum likelihood estimation (MLE), lexical weighting (LW), and monolingual word alignment (MWA). Using the presented method, we extract more than 1 million pairs of paraphrase patterns from about 2 million pairs of bilingual parallel sentences. The precision of the extracted paraphrase patterns is above 78%. Experimental results show that the presented method significantly outperforms a well-known method called discovery of inference rules from text (DIRT). Additionally, the log-linear model with the proposed feature functions are effective. The extracted paraphrase patterns are fully analyzed. Especially, we found that the extracted paraphrase patterns can be classified into five types, which are useful in multiple natural language processing (NLP) applications.
Universal Value Function Approximators

Value functions are a core component of reinforcement learning systems. The main idea is to to construct a single function approximator V (s; θ) that estimates the long-term reward from any state s, using parameters θ. In this paper we introduce universal value function approximators (UVFAs) V (s, g; θ) that generalise not just over states s but also over goals g. We develop an efficient technique for supervised learning of UVFAs, by factoring observed values into separate embedding vectors for state and goal, and then learning a mapping from s and g to these factored embedding vectors. We show how this technique may be incorporated into a reinforcement learning algorithm that updates the UVFA solely from observed rewards. Finally, we demonstrate that a UVFA can successfully generalise to previously unseen goals.
ANTISYMMETRICRNN: A DYNAMICAL SYSTEM VIEW

Recurrent neural networks have gained widespread use in modeling sequential data. Learning long-term dependencies using these models remains difficult though, due to exploding or vanishing gradients. In this paper, we draw connections between recurrent networks and ordinary differential equations. A special form of recurrent network called the AntisymmetricRNN is proposed under this theoretical framework, which is able to capture long-term dependencies thanks to the stability property of its underlying differential equation. In comparison to existing approaches for improving RNN trainability, which often incur significant computation overhead, AntisymmetricRNN achieves the same goal by design. We showcase the advantage of this new architecture through extensive simulations and experiments. AntisymmetricRNN exhibits much more predictable dynamics. It outperforms regular LSTM models on tasks requiring long-term memory, and matches the performance on tasks where short-term dependencies dominate despite being much simpler.
TRANSFORMER-XL: LANGUAGE MODELING

We propose a novel neural architecture, Transformer-XL, for modeling longerterm dependency. To address the limitation of fixed-length contexts, we introduce a notion of recurrence by reusing the representations from the history. Empirically, we show state-of-the-art (SoTA) results on both word-level and character-level language modeling datasets, including WikiText-103, One Billion Word, Penn Treebank, and enwiki8. Notably, we improve the SoTA results from 1.06 to 0.99 in bpc on enwiki8, from 33.0 to 18.9 in perplexity on WikiText-103, and from 28.0 to 23.5 in perplexity on One Billion Word. Performance improves when the attention length increases during evaluation, and our best model attends to up to 1,600 words and 3,800 characters. To quantify the effective length of dependency, we devise a new metric and show that on WikiText-103 Transformer-XL manages to model dependency that is about 80% longer than recurrent networks and 450% longer than Transformer. Moreover, Transformer-XL is up to 1,800+ times faster than vanilla Transformer during evaluation.
Profile for estimating risk of heart failure.

We devised a risk appraisal function to assess the hazard of heart failure in persons who are predisposed by coronary disease, hypertension, or valvular heart disease.



To provide general practitioners and internists with a cost-effective method to select people at high risk who are likely to have impaired left ventricular systolic function and may therefore require further evaluation and aggressive preventive measures.



The routinely measured risk factors used in constructing the heart failure profile include age, electrocardiographic left ventricular hypertrophy, cardiomegaly on chest x-ray film, heart rate, systolic blood pressure, vital capacity, diabetes mellitus, evidence of myocardial infarction, and valvular disease or hypertension. Based on 486 heart failure cases during 38 years of follow-up, 4-year probabilities of failure were computed using the pooled logistic regression model for each sex; a simple point score system was employed. A multivariate profile was also produced without the vital capacity or chest x-ray film because these may not be readily available in some clinical settings.



Using the risk factors that make up the multivariate risk formulation-derived from ordinary office procedures-the probability of developing heart failure can be estimated and compared with the average risk for persons of the same age and sex. Using this risk profile, 60% of events in men and 73% in women occurred in subjects in the top quintile of multivariate risk.



Using this multivariate risk formulation, it is possible to identify high-risk candidates for heart failure who are likely to have a substantial yield of positive findings when tested for objective evidence of presymptomatic left ventricular dysfunction. The risk profile may also identify candidates who are at high risk for heart failure because of multiple, marginal risk factor abnormalities that might otherwise be overlooked.
Agonistic Properties of Cannabidiol at 5-HT1a Receptors

Cannabidiol (CBD) is a major, biologically active, but psycho-inactive component of cannabis. In this cell culture-based report, CBD is shown to displace the agonist, [3H]8-OH-DPAT from the cloned human 5-HT1a receptor in a concentration-dependent manner. In contrast, the major psychoactive component of cannabis, tetrahydrocannabinol (THC) does not displace agonist from the receptor in the same micromolar concentration range. In signal transduction studies, CBD acts as an agonist at the human 5-HT1a receptor as demonstrated in two related approaches. First, CBD increases [35S]GTPγS binding in this G protein coupled receptor system, as does the known agonist serotonin. Second, in this GPCR system, that is negatively coupled to cAMP production, both CBD and 5-HT decrease cAMP concentration at similar apparent levels of receptor occupancy, based upon displacement data. Preliminary comparative data is also presented from the cloned rat 5-HT2a receptor suggesting that CBD is active, but less so, relative to the human 5-HT1a receptor, in binding analyses. Overall, these studies demonstrate that CBD is a modest affinity agonist at the human 5-HT1a receptor. Additional work is required to compare CBD’s potential at other serotonin receptors and in other species. Finally, the results indicate that cannabidiol may have interesting and useful potential beyond the realm of cannabinoid receptors.
A Survey on Resilient Machine Learning

Machine learning based system are increasingly being used for sensitive tasks such as security surveillance, guiding autonomous vehicle, taking investment decisions, detecting and blocking network intrusion and malware etc. However, recent research has shown that machine learning models are venerable to attacks by adversaries at all phases of machine learning (e.g., training data collection, training, operation). All model classes of machine learning systems can be misled by providing carefully crafted inputs making them wrongly classify inputs. Maliciously created input samples can affect the learning process of a ML system by either slowing the learning process, or affecting the performance of the learned model or causing the system make error only in attacker’s planned scenario. Because of these developments, understanding security of machine learning algorithms and systems is emerging as an important research area among computer security and machine learning researchers and practitioners. We present a survey of this emerging area.
Exploiting Machine Learning to Subvert Your Spam Filter

Using statistical machine learning for making security decisions introduces new vulnerabilities in large scale systems. This paper shows how an adversary can exploit statistical machine learning, as used in the SpamBayes spam filter, to render it useless—even if the adversary’s access is limited to only 1% of the training messages. We further demonstrate a new class of focused attacks that successfully prevent victims from receiving specific email messages. Finally, we introduce two new types of defenses against these attacks.
Swapout: Learning an ensemble of deep architectures

We describe Swapout, a new stochastic training method, that outperforms ResNets of identical network structure yielding impressive results on CIFAR-10 and CIFAR100. Swapout samples from a rich set of architectures including dropout [20], stochastic depth [7] and residual architectures [5, 6] as special cases. When viewed as a regularization method swapout not only inhibits co-adaptation of units in a layer, similar to dropout, but also across network layers. We conjecture that swapout achieves strong regularization by implicitly tying the parameters across layers. When viewed as an ensemble training method, it samples a much richer set of architectures than existing methods such as dropout or stochastic depth. We propose a parameterization that reveals connections to exiting architectures and suggests a much richer set of architectures to be explored. We show that our formulation suggests an efficient training method and validate our conclusions on CIFAR-10 and CIFAR-100 matching state of the art accuracy. Remarkably, our 32 layer wider model performs similar to a 1001 layer ResNet model.
The MD 6 hash function A proposal to NIST for SHA-3

This report describes and analyzes the MD6 hash function and is part of our submission package for MD6 as an entry in the NIST SHA-3 hash function competition. Significant features of MD6 include: • Accepts input messages of any length up to 2 − 1 bits, and produces message digests of any desired size from 1 to 512 bits, inclusive, including the SHA-3 required sizes of 224, 256, 384, and 512 bits. • Security—MD6 is by design very conservative. We aim for provable security whenever possible; we provide reduction proofs for the security of the MD6 mode of operation, and prove that standard differential attacks against the compression function are less efficient than birthday attacks for finding collisions. We also show that when used as a MAC within NIST recommendedations, the keyed version of MD6 is not vulnerable to linear cryptanalysis. The compression function and the mode of operation are each shown to be indifferentiable from a random oracle under reasonable assumptions. • MD6 has good efficiency: 22.4–44.1M bytes/second on a 2.4GHz Core 2 Duo laptop with 32-bit code compiled with Microsoft Visual Studio 2005 for digest sizes in the range 160–512 bits. When compiled for 64-bit operation, it runs at 61.8–120.8M bytes/second, compiled with MS VS, running on a 3.0GHz E6850 Core Duo processor. • MD6 works extremely well for multicore and parallel processors; we have demonstrated hash rates of over 1GB/second on one 16-core system, and over 427MB/sec on an 8-core system, both for 256-bit digests. We have also demonstrated MD6 hashing rates of 375 MB/second on a typical desktop GPU (graphics processing unit) card. We also show that MD6 runs very well on special-purpose hardware. • MD6 uses a single compression function, no matter what the desired digest size, to map input data blocks of 4096 bits to output blocks of 1024 bits— a fourfold reduction. The number of rounds does, however, increase for larger digest sizes.) The compression function has auxiliary inputs: a “key” (K), a “number of rounds” (r), a “control word” (V ), and a “unique ID” word (U). • The standard mode of operation is tree-based: the data enters at the leaves of a 4-ary tree, and the hash value is computed at the root.  This standard mode of operation is highly parallelizable.
Team participation and online gamer loyalty

Online games have emerged as popular computer applications and gamer loyalty is vital to game providers, since online gamers frequently switch between games. Online gamers often participate in teams also. This study investigates whether and how team participation improves loyalty. We utilized a cross-sectional design and an online survey, with 546 valid responses from online game subjects. Confirmatory factor analysis was applied to assess measurement reliability and validity directly, and structural equation modeling was utilized to test our hypotheses. The results indicate that participation in teams motivates online gamers to adhere to team norms and satisfies their social needs, also enhancing their loyalty. The contribution of this research is the introduction of social norms to explain online gamer loyalty.
Converse Results to the Wiener Attack on RSA

A well-known attack on RSA with low secret-exponent d was given by Wiener about 15 years ago. Wiener showed that using continued fractions, one can efficiently recover the secret-exponent d from the public key (N, e) as long as d < N. Interestingly, Wiener stated that his attack may sometimes also work when d is slightly larger than N . This raises the question of how much larger d can be: could the attack work with non-negligible probability for d = N 1/4+ρ for some constant ρ > 0? We answer this question in the negative by proving a converse to Wiener’s result. Our result shows that, for any fixed > 0 and all sufficiently large modulus lengths, Wiener’s attack succeeds with negligible probability over a random choice of d < N δ (in an interval of size Ω(N )) as soon as δ > 1/4 + . Thus Wiener’s success bound d < N 1/4 for his algorithm is essentially tight. We also obtain a converse result for a natural class of extensions of the Wiener attack, which are guaranteed to succeed even when δ > 1/4. The known attacks in this class (by Verheul and Van Tilborg and Dujella) run in exponential time, so it is natural to ask whether there exists an attack in this class with subexponential run-time. Our second converse result answers this question also in the negative.
A review of unsupervised feature learning and deep learning for time-series modeling

This paper gives a review of the recent developments in deep learning and unsupervised feature learning for time-series problems. While these techniques have shown promise for modeling static data, such as computer vision, applying them to time-series data is gaining increasing attention. This paper overviews the particular challenges present in time-series data and provides a review of the works that have either applied time-series data to unsupervised feature learning algorithms or alternatively have contributed to modi cations of feature learning algorithms to take into account the challenges present in time-series data.
The Youth Physical Development Model

THE DEVELOPMENT OF PHYSICAL FITNESS IN YOUNG ATHLETES IS A RAPIDLY EXPANDING FIELD OF INTEREST FOR STRENGTH AND CONDITIONING COACHES, PHYSICAL EDUCATORS, SPORTS COACHES, AND PARENTS. PREVIOUS LONG-TERM ATHLETE DEVELOPMENT MODELS HAVE CLASSIFIED YOUTH-BASED TRAINING METHODOLOGIES IN RELATION TO CHRONOLOGIC AGE GROUPS, AN APPROACH THAT HAS DISTINCT LIMITATIONS. MORE RECENT MODELS HAVE ATTEMPTED TO BRIDGE MATURATION AND PERIODS OF TRAINABILITY FOR A LIMITED NUMBER OF FITNESS QUALITIES, ALTHOUGH SUCH MODELS APPEAR TO BE BASED ON SUBJECTIVE ANALYSIS. THE YOUTH PHYSICAL DEVELOPMENT MODEL PROVIDES A LOGICAL AND EVIDENCE-BASED APPROACH TO THE SYSTEMATIC DEVELOPMENT OF PHYSICAL PERFORMANCE IN YOUNG ATHLETES.
HyperPower: Power- and memory-constrained hyper-parameter optimization for neural networks

While selecting the hyper-parameters of Neural Networks (NNs) has been so far treated as an art, the emergence of more complex, deeper architectures poses increasingly more challenges to designers and Machine Learning (ML) practitioners, especially when power and memory constraints need to be considered. In this work, we propose HyperPower, a framework that enables efficient Bayesian optimization and random search in the context of power- and memory-constrained hyperparameter optimization for NNs running on a given hardware platform. HyperPower is the first work (i) to show that power consumption can be used as a low-cost, a priori known constraint, and (ii) to propose predictive models for the power and memory of NNs executing on GPUs. Thanks to HyperPower, the number of function evaluations and the best test error achieved by a constraint-unaware method are reached up to 112.99× and 30.12× faster, respectively, while never considering invalid configurations. HyperPower significantly speeds up the hyper-parameter optimization, achieving up to 57.20× more function evaluations compared to constraint-unaware methods for a given time interval, effectively yielding significant accuracy improvements by up to 67.6%.
Locating Complex Named Entities in Web Text

Named Entity Recognition (NER) is the task of locating and classifying names in text. In previous work, NER was limited to a small number of predefined entity classes (e.g., people, locations, and organizations). However, NER on the Web is a far more challenging problem. Complex names (e.g., film or book titles) can be very difficult to pick out precisely from text. Further, the Web contains a wide variety of entity classes, which are not known in advance. Thus, hand-tagging examples of each entity class is impractical. This paper investigates a novel approach to the first step in Web NER: locating complex named entities in Web text. Our key observation is that named entities can be viewed as a species of multiword units, which can be detected by accumulating n-gram statistics over the Web corpus. We show that this statistical method’s F1 score is 50% higher than that of supervised techniques including Conditional Random Fields (CRFs) and Conditional Markov Models (CMMs) when applied to complex names. The method also outperforms CMMs and CRFs by 117% on entity classes absent from the training data. Finally, our method outperforms a semi-supervised CRF by 73%.
Generating Text via Adversarial Training

Generative Adversarial Networks (GANs) have achieved great success in generating realistic synthetic real-valued data. However, the discrete output of language model hinders the application of gradient-based GANs. In this paper we propose a generic framework employing Long short-term Memory (LSTM) and convolutional neural network (CNN) for adversarial training to generate realistic text. Instead of using standard objective of GAN, we match the feature distribution when training the generator. In addition, we use various techniques to pre-train the model and handle discrete intermediate variables. We demonstrate that our model can generate realistic sentence using adversarial training.