nils-herrmann/clean_scirepeval huggingface dataset:
Applying Core Scientific Concepts to Context-Based Citation Recommendation

The task of recommending relevant scientific literature for a draft academic paper has recently received significant interest. In our effort to ease the discovery of scientific literature and augment scientific writing, we aim to improve the relevance of results based on a shallow semantic analysis of the source document and the potential documents to recommend. We investigate the utility of automatic argumentative and rhetorical annotation of documents for this purpose. Specifically, we integrate automatic Core Scientific Concepts (CoreSC) classification into a prototype context-based citation recommendation system and investigate its usefulness to the task. We frame citation recommendation as an information retrieval task and we use the categories of the annotation schemes to apply different weights to the similarity formula. Our results show interesting and consistent correlations between the type of citation and the type of sentence containing the relevant information.
Sharding PoW-based Blockchains via Proofs of Knowledge

Blockchains based on proofs of work (PoW) currently account for more than 90% of the total market capitalization of existing digital cryptocurrencies. The security of PoWbased blockchains requires that new transactions are verified, making a proper replication of the blockchain data in the system essential. While existing PoW mining protocols offer considerable incentives for workers to generate blocks, workers do not have any incentives to store the blockchain. This resulted in a sharp decrease in the number of full nodes that store the full blockchain, e.g., in Bitcoin, Litecoin, etc. However, the smaller is the number of replicas or nodes storing the replicas, the higher is the vulnerability of the system against compromises and DoS-attacks. In this paper, we address this problem and propose a novel solution, EWoK (Entangled proofs of WOrk and Knowledge). EWoK regulates in a decentralized-manner the minimum number of replicas that should be stored by tying replication to the only directly-incentivized process in PoW-blockchains—which is PoW itself. EWoK only incurs small modifications to existing PoW protocols, and is fully compliant with the specifications of existing mining hardware—which is likely to increase its adoption by the existing PoW ecosystem. EWoK plugs an efficient in-memory hash-based proof of knowledge and couples them with the standard PoW mechanism. We implemented EWoK and integrated it within commonly used mining protocols, such as GetBlockTemplate and Stratum mining; our results show that EWoK can be easily integrated within existing mining pool protocols and does not impair the mining efficiency.
A dose dependent impact of prebiotic galactooligosaccharides on the intestinal microbiota of healthy adults.

The goal of this research was to determine the effect of different doses of galactooligosaccharide (GOS) on the fecal microbiota of healthy adults, with a focus on bifidobacteria. The study was designed as a single-blinded study, with eighteen subjects consuming GOS-containing chocolate chews at four increasing dosage levels; 0, 2.5, 5.0, and 10.0g. Subjects consumed each dose for 3 weeks, with a two-week baseline period preceding the study and a two-week washout period at the end. Fecal samples were collected weekly and analyzed by cultural and molecular methods. Cultural methods were used for bifidobacteria, Bacteroides, enterobacteria, enterococci, lactobacilli, and total anaerobes; culture-independent methods included denaturing gradient gel electrophoresis (DGGE) and quantitative real-time PCR (qRT-PCR) using Bifidobacterium-specific primers. All three methods revealed an increase in bifidobacteria populations, as the GOS dosage increased to 5 or 10g. Enumeration of bifidobacteria by qRT-PCR showed a high inter-subject variation in bifidogenic effect and indicated a subset of 9 GOS responders among the eighteen subjects. There were no differences, however, in the initial levels of bifidobacteria between the responding individuals and the non-responding individuals. Collectively, this study showed that a high purity GOS, administered in a confection product at doses of 5g or higher, was bifidogenic, while a dose of 2.5g showed no significant effect. However, the results also showed that even when GOS was administered for many weeks and at high doses, there were still some individuals for which a bifidogenic response did not occur.
A Divide and Conquer Bidirectional Search: First Results

We present a new algorithm to reduce the space complexity of heuristic search. It is most effective for problem spaces that grow polynomially wi th problem size, but contain large numbers of short cycles. For example, the problem of finding a lowest-cost corner-to-corner path in a d-dimensional grid has application to gene sequence alignment in computational biology. The main idea is to perform a bidirectional search, but saving only the Open lists and not the Closed lists. Once the search completes, we have one node on an optimal path, but don't have the solution path itself. The path is then reconstructed by recursively applying the same algorithm between the in i t ia l node and the in termediate node, and also between the intermediate node and the goal node. If n is the length of the grid in each dimension, and d is the number of dimensions, this algorithm reduces the memory requirement from to The time complexity only increases by a constant factor of in two dimensions, and 1.8 in three dimensions.
PLAR: Parallel Large-Scale Attribute Reduction on Cloud Systems

Attribute reduction for big data is viewed as an important preprocessing step in the areas of pattern recognition, machine learning and data mining. In this paper, a novel parallel method based on MapReduce for large-scale attribute reduction is proposed. By using this method, several representative heuristic attribute reduction algorithms in rough set theory have been parallelized. Further, each of the improved parallel algorithms can select the same attribute reduct as its sequential version, therefore, owns the same classification accuracy. An extensive experimental evaluation shows that these parallel algorithms are effective for big data.
Measures of psoriatic arthritis: Tender and Swollen Joint Assessment, Psoriasis Area and Severity Index (PASI), Nail Psoriasis Severity Index (NAPSI), Modified Nail Psoriasis Severity Index (mNAPSI), Mander/Newcastle Enthesitis Index (MEI), Leeds Enthesitis Index (LEI), Spondyloarthritis Research Co

Tender and Swollen Joint Assessment, Psoriasis Area and Severity Index (PASI), Nail Psoriasis Severity Index (NAPSI), Modified Nail Psoriasis Severity Index (mNAPSI), Mander/Newcastle Enthesitis Index (MEI), Leeds Enthesitis Index (LEI), Spondyloarthritis Research Consortium of Canada (SPARCC), Maastricht Ankylosing Spondylitis Enthesis Score (MASES), Leeds Dactylitis Index (LDI), Patient Global for Psoriatic Arthritis, Dermatology Life Quality Index (DLQI), Psoriatic Arthritis Quality of Life (PsAQOL), Fatigue (FACIT-F), Psoriatic Arthritis Response Criteria (PsARC), Psoriatic Arthritis Joint Activity Index (PsAJAI), Disease Activity in Psoriatic Arthritis (DAPSA), and Composite Psoriatic Disease Activity Index (CPDAI)
Findings of the Third Shared Task on Multimodal Machine Translation

We present the results from the third shared task on multimodal machine translation. In this task a source sentence in English is supplemented by an image and participating systems are required to generate a translation for such a sentence into German, French or Czech. The image can be used in addition to (or instead of) the source sentence. This year the task was extended with a third target language (Czech) and a new test set. In addition, a variant of this task was introduced with its own test set where the source sentence is given in multiple languages: English, French and German, and participating systems are required to generate a translation in Czech. Seven teams submitted 45 different systems to the two variants of the task. Compared to last year, the performance of the multimodal submissions improved, but text-only systems remain competitive.
FRoDO: Fraud Resilient Device for Off-Line Micro-Payments

Credit and debit card data theft is one of the earliest forms of cybercrime. Still, it is one of the most common nowadays. Attackers often aim at stealing such customer data by targeting the Point of Sale (for short, PoS) system, i.e. the point at which a retailer first acquires customer data. Modern PoS systems are powerful computers equipped with a card reader and running specialized software. Increasingly often, user devices are leveraged as input to the PoS. In these scenarios, malware that can steal card data as soon as they are read by the device has flourished. As such, in cases where customer and vendor are persistently or intermittently disconnected from the network, no secure on-line payment is possible. This paper describes FRoDO, a secure off-line micro-payment solution that is resilient to PoS data breaches. Our solution improves over up to date approaches in terms of flexibility and security. To the best of our knowledge, FRoDO is the first solution that can provide secure fully off-line payments while being resilient to all currently known PoS breaches. In particular, we detail FRoDO architecture, components, and protocols. Further, a thorough analysis of FRoDO functional and security properties is provided, showing its effectiveness and viability.
Temporal relation classification with deep neural network

We proposed neural network architecture based on Convolution Neural Network(CNN) for temporal relation classification in sentence. First, we transformed word into vector by using word embedding. In Feature Extraction, we extracted two type of features. Lexical level feature considered meaning of marked entity and Sentence level feature considered context of the sentence. Window processing was used to reflect local context and Convolution and Max-pooling operation were used for global context. We concatenated both feature vectors and used softmax operation to compute confidence score. Because experiment results didn't outperform the state-of-the-art methods, we suggested some future works to do.
Modelling infrastructures as socio-technical systems

The conceptualization of the notion of a system in systems engineering, as exemplified in, for instance, the engineering standard IEEE Std 1220-1998, is problematic when applied to the design of socio-technical systems. This is argued using Intelligent Transportation Systems as an example. A preliminary conceptualization of socio-technical systems is introduced which includes technical and social elements and actors, as well as four kinds of relations. Current systems engineering practice incorporates technical elements and actors in the system but sees social elements exclusively as contextual. When designing socio-technical systems, however, social elements and the corresponding relations must also be considered as belonging to the system.
Word Root Finder: a Morphological Segmentor Based on CRF

Morphological segmentation of words is a subproblem of many natural language tasks, including handling out-of-vocabulary (OOV) words in machine translation, more effective information retrieval, and computer assisted vocabulary learning. Previous work typically relies on extensive statistical and semantic analyses to induce legitimate stems and affixes. We introduce a new learning based method and a prototype implementation of a knowledge light system for learning to segment a given word into word parts, including prefixes, suffixes, stems, and even roots. The method is based on the Conditional Random Fields (CRF) model. Evaluation results show that our method with a small set of seed training data and readily available resources can produce fine-grained morphological segmentation results that rival previous work and systems.
East African climate pulses and early human evolution

Current evidence suggests that all of the major events in hominin evolution have occurred in East Africa. Over the last two decades, there has been intensive work undertaken to understand African palaeoclimate and tectonics in order to put together a coherent picture of how the environment of East Africa has varied in the past. The landscape of East Africa has altered dramatically over the last 10 million years. It has changed from a relatively flat, homogenous region covered with mixed tropical forest, to a varied and heterogeneous environment, with mountains over 4 km high and vegetation ranging from desert to cloud forest. The progressive rifting of East Africa has also generated numerous lake basins, which are highly sensitive to changes in the local precipitation-evaporation regime. There is now evidence that the presence of precession-driven, ephemeral deep-water lakes in East Africa were concurrent with major events in hominin evolution. It seems the unusual geology and climate of East Africa created periods of highly variable local climate, which, it has been suggested could have driven hominin speciation, encephalisation and dispersal out of Africa. One example is the significant hominin speciation and brain expansion event at ~1.8 Ma that seems to have been coeval with the occurrence of highly variable, extensive, deep-water lakes. This complex, climatically very variable setting inspired first the variability selection hypothesis, which was then the basis for the pulsed climate variability hypothesis. The newer of the two suggests that the long-term drying trend in East Africa was punctuated by episodes of short, alternating periods of extreme humidity and aridity. Both hypotheses, together with other key theories of climate-evolution linkages, are discussed in this paper. Though useful the actual evolution mechanisms, which led to early hominins are still unclear and continue to be debated. However, it is clear that an understanding of East African lakes and their palaeoclimate history is required to understand the context within which humans evolved and eventually left East Africa.
Drug-target interaction prediction using ensemble learning and dimensionality reduction.

Experimental prediction of drug-target interactions is expensive, time-consuming and tedious. Fortunately, computational methods help narrow down the search space for interaction candidates to be further examined via wet-lab techniques. Nowadays, the number of attributes/features for drugs and targets, as well as the amount of their interactions, are increasing, making these computational methods inefficient or occasionally prohibitive. This motivates us to derive a reduced feature set for prediction. In addition, since ensemble learning techniques are widely used to improve the classification performance, it is also worthwhile to design an ensemble learning framework to enhance the performance for drug-target interaction prediction. In this paper, we propose a framework for drug-target interaction prediction leveraging both feature dimensionality reduction and ensemble learning. First, we conducted feature subspacing to inject diversity into the classifier ensemble. Second, we applied three different dimensionality reduction methods to the subspaced features. Third, we trained homogeneous base learners with the reduced features and then aggregated their scores to derive the final predictions. For base learners, we selected two classifiers, namely Decision Tree and Kernel Ridge Regression, resulting in two variants of ensemble models, EnsemDT and EnsemKRR, respectively. In our experiments, we utilized AUC (Area under ROC Curve) as an evaluation metric. We compared our proposed methods with various state-of-the-art methods under 5-fold cross validation. Experimental results showed EnsemKRR achieving the highest AUC (94.3%) for predicting drug-target interactions. In addition, dimensionality reduction helped improve the performance of EnsemDT. In conclusion, our proposed methods produced significant improvements for drug-target interaction prediction.
Design and Characterization of a Low-Cost Self-Oscillating Fluxgate Transducer for Precision Measurement of High-Current

Precision measurement of dc high current is usually realized by second harmonic fluxgate current transducers, but the complicated modulation and demodulation circuits with high cost have been limiting their applications. This paper presents a low-cost transducer that can substitute the traditional ones for precision measurement of high current. The new transducer, based on the principle of zero-flux, is the combination of an improved self-oscillating fluxgate sensor with a magnetic integrator in a common feedback loop. The transfer function of the zero-flux control strategy of the transducer is established to verify the validity of the qualitative analysis on operating principle. Origins and major influence factors of the modulation ripple, respectively, caused by the useful signal extraction circuit and the transformer effect are studied, and related suppression methods are proposed, which can be considered as one of the major technical modifications for performance improvement. As verification, a prototype is realized, and several key specifications, including the linearity, small-signal bandwidth, modulation ripple, ratio stability under full load, power-on repeatability, magnetic error, and temperature coefficient, are characterized. Measurement results show that the new transducer with the maximum output ripple 0.3 μA can measure dc current up to ±600 A with a relative accuracy 1.3 ppm in the full scale, and it also can measure ac current and has a -3 dB bandwidth greater than 100 kHz.
No more beating about the bush : A Step towards Idiom Handling for Indian Language NLP

One of the major challenges in the field of Natural Language Processing (NLP) is the handling of idioms; seemingly ordinary phrases which could be further conjugated or even spread across the sentence to fit the context. Since idioms are a part of natural language, the ability to tackle them brings us closer to creating efficient NLP tools. This paper presents a multilingual parallel idiom dataset for seven Indian languages in addition to English and demonstrates its usefulness for two NLP applications Machine Translation and Sentiment Analysis. We observe significant improvement for both the subtasks over baseline models trained without employing the idiom dataset.
Acute mountain sickness: influence of susceptibility, preexposure, and ascent rate.

We examined the interaction of various established risk factors for acute mountain sickness (AMS) in a setting relevant for mountaineering and trekking.



Questionnaires were obtained from 827 mountaineers on the day of arrival at the Capanna Margherita (4559 m) and on the next morning. Susceptibility to AMS was assessed by type and frequency of symptoms during previous exposures. AMS was defined as an AMS-C score >/= 0.7 (environmental symptom questionnaire) on the morning after arrival at 4559 m. Preexposure was defined as having spent more than 4 d above 3000 m during the 2 months preceding the climb to the Capanna Margherita. Slow ascent was defined as ascending in more than 3 d. A risk model was developed by multiple logistic regression and prospectively validated on independent data.



In susceptible individuals, the prevalence of AMS was 58% with rapid ascent and no preexposure, 29% with preexposure only, 33% with slow ascent only, and 7% with both preexposure and slow ascent. The corresponding values for nonsusceptible individuals were 31%, 16%, 11%, and 4%. The overall odds ratio for developing AMS in susceptible versus nonsusceptible individuals was 2.9 (95% confidence interval, 2.1-4.1). Age, gender, training, body mass index, alcohol intake, and cigarette smoking had no significant effect in bivariate analyses or in multivariate logistic regressions.



Individual susceptibility, rate of ascent, and preexposure are the major, independent determinants for prevalence of AMS.
Process Mining: Discovery, Conformance and Enhancement of Business Processes

Looking for qualified reading resources? We have process mining discovery conformance and enhancement of business processes to check out, not only review, yet also download them or even read online. Discover this great publication writtern by now, simply right here, yeah just right here. Obtain the data in the sorts of txt, zip, kindle, word, ppt, pdf, as well as rar. Once again, never ever miss out on to read online as well as download this publication in our site here. Click the link. Our goal is always to offer you an assortment of cost-free ebooks too as aid resolve your troubles. We have got a considerable collection of totally free of expense Book for people from every single stroll of life. We have got tried our finest to gather a sizable library of preferred cost-free as well as paid files.
Finite-Dimensional Variational Inequality and Nonlinear Complementarity Problems: A Survey of Theory, Algorithms and Applications

Over the past decade, the field of finite-dimensional variational inequality and complementarity problems has seen a rapid development in its theory of existence, uniqueness and sensitivity of solution(s), in the theory of algorithms, and in the application of these techniques to transportation planning, regional science, socio-economic analysis, energy modeling, and game theory. This paper provides a state-of-the-art review of these developments as well as a summary of some open research topics in this growing field.
A Study on Deep Learning Based Sauvegrain Method for Measurement of Puberty Bone Age

This study applies a technique to expand the number of images to a level that allows deep learning. And the applicability of the Sauvegrain method through deep learning with relatively few elbow X-rays is studied. The study was composed of processes similar to the physicians’ bone age assessment procedures. The selected reference images were learned without being included in the evaluation data, and at the same time, the data was extended to accommodate the number of cases. In addition, we adjusted the X-ray images to better images using U-Net and selected the ROI with RPN + so as to be able to perform bone age estimation through CNN. The mean absolute error of the Sauvegrain method based on deep learning is 2.8 months and the Mean Absolute Percentage Error (MAPE) is 0.018. This result shows that X ray analysis using the Sauvegrain method shows higher accuracy than that of the age group of puberty even in the deep learning base. This means that deep learning of the Suvegrain method can be measured at a level similar to that of an expert, based on the extended X-ray image with the image data extension technique. Finally, we applied the Sauvegrain method to deep learning for accurate measurement of bone age at puberty. As a result, the present study is based on deep learning, and compared with the evaluation results of experts, it is possible to overcome limitations of the method of measuring bone age based on machine learning which was in TW3 or Greulich & Pyle due to lack of XI confirmed the fact. And we also presented the Sauvegrain method, which is applicable to adolescents as well.