nils-herrmann/clean_scirepeval huggingface dataset:
Extracting and matching authors and affiliations in scholarly documents

We introduce Enlil, an information extraction system that discovers the institutional affiliations of authors in scholarly papers. Enlil consists of two steps: one that first identifies authors and affiliations using a conditional random field; and a second support vector machine that connects authors to their affiliations. We benchmark Enlil in three separate experiments drawn from three different sources: the ACL Anthology Corpus, the ACM Digital Library, and a set of cross-disciplinary scientific journal articles acquired by querying Google Scholar. Against a state-of-the-art production baseline, Enlil reports a statistically significant improvement in F_1 of nearly 10% (p << 0.01). In the case of multidisciplinary articles from Google Scholar, Enlil is benchmarked over both clean input (F_1 > 90%) and automatically-acquired input (F_1 > 80%).
 We have deployed Enlil in a case study involving Asian genomics research publication patterns to understand how government sponsored collaborative links evolve. Enlil has enabled our team to construct and validate new metrics to quantify the facilitation of research as opposed to direct publication.
An Eecient Oo-line Electronic Cash System Based on the Representation Problem

We present a new o -line electronic cash system based on a problem, called the representation problem, of which little use has been made in literature thus far. Our system is the rst to be based entirely on discrete logarithms. Using the representation problem as a basic concept, some techniques are introduced that enable us to construct protocols for withdrawal and payment that do not use the cut and choose methodology of earlier systems. As a consequence, our cash system is much more e cient in both computation and communication complexity than previously proposed systems. Another important aspect of our system concerns its provability. Contrary to previously proposed systems, its correctness can be mathematically proven to a very great extent. Speci cally, if we make one plausible assumption concerning a single hash-function, the ability to break the system seems to imply that one can break the Di e-Hellman problem. Our system o ers a number of extensions that are hard to achieve in previously known systems. In our opinion the most interesting of these is that the entire cash system (including all the extensions) can be incorporated straightforwardly in a setting based on wallets with observers, which has the important advantage that doublespending can be prevented in the rst place, rather than detecting the identity of a double-spender after the fact. In particular, it can be incorporated even under the most stringent requirements conceivable about the privacy of the user, which seems to be impossible to do with previously proposed systems. Another bene t of our system is that framing attempts by a bank have negligible probability of success (independent of computing power) by a simple mechanism from within the system, which is something that previous solutions lack entirely. Furthermore, the basic cash system can be extended to checks, multi-show cash and divisibility, while retaining its computational e ciency. Although in this paper we only make use of the representation problem in groups of prime order, similar intractable problems hold in RSA-groups (with computational equivalence to factoring and computing RSAroots). We discuss how one can use these problems to construct an e cient cash system with security related to factoring or computation of RSA-roots, in an analogous way to the discrete log based system. Finally, we discuss a decision problem (the decision variant of the Di e-Hellman problem) that is strongly related to undeniable signatures, which to our knowledge has never been stated in literature and of which we do not know whether it is inBPP . A proof of its status would be of interest to discrete log based cryptography in general. Using the representation problem, we show in the appendix how to batch the con rmation protocol of undeniable signatures such that polynomially many undeniable signatures can be veri ed in four moves. AMS Subject Classi cation (1991): 94A60 CR Subject Classi cation (1991):
A 0.01–26 GHz single-chip SiGe reflectometer for two-port vector network analyzers

This paper presents a single-chip 0.01–26 GHz reflectometer for two-port vector network analyzers (VNA). The reflectometer consists of a bridge coupler integrated together with two wideband heterodyne receivers. For wideband operation, a resistive bridge coupler is used with a directivity of 33 dB up to 26 GHz. Also, a high-linearity receiver channel is designed so as to accommodate 10 dBm of RF input power to the reflectometer. The SiGe chip is 1.8 mm2 and consumes 640 mW from a 3.3 V supply. The dynamic range of the reflectometer chip is 127±2 dB at 0.01–26 GHz with an IF resolution bandwidth (RBW) of 10 Hz. The chip is placed on a printed circuit board (PCB), and RF, LO and DC bias are connecterized. Measurements of several device-under-test units (DUTs) with a two-port 0.01–26 GHz VNA composed of two reflectometer chips shows excellent agreement with a commercial VNA, with a magnitude and phase difference of < 0.2 dB and < 2° respectively for Su. Two-port measurements also show an 821 dynamic range of 80 dB, limited by the measurement setup and not by the chip. To our best knowledge, this is the first demonstration of a VNA operating from 10 MHz to mm-wave frequencies with the capability of measuring −80 dB of S21 as well as minimal magnitude and phase difference compared to state-of-the-art VNA measurements.
FINANCIAL ACCOUNTING FRAUD DETECTION USING BUSINESS INTELLIGENCE

The paper investigates the inherent problems of financial fraud detection and proposes a forensic accounting framework using business intelligence as a plausible means of addressing them. The paper adopts an empirical case study approach to present how business intelligence could be used effectively in the detection of financial accounting fraud. The proposed forensic accounting framework using business intelligence (BI) provides a three-phase model via novel knowledge discovery technique to perform the financial analysis such as ratio analysis for a business case scenario. The implementation of the framework practically demonstrates by using their accounting data how the technologies and the investigative methods of trend analysis could be adopted in order to investigate fraudulent financial reporting unlike traditional methods of vertical and horizontal analysis for the business case study. Finally, the results justify the effectiveness of the proposed BI model in proactively identifying, classifying and evaluating financial fraud in the organisation. This research further leads to practical follow-up steps that would serve as guidelines for the forensic accounting auditors and management to focus on the prime areas of financial fraud present in the case study. Overall, the proposed model caters to detecting various types of accounting fraud as well as aids in continuous improvement of an organisation’s accounting, audit, systems and policies through the feedback loop.
The effects of scapular stabilization based exercise therapy on pain, posture, flexibility and shoulder mobility in patients with shoulder impingement syndrome: a controlled randomized clinical trial

Dysfunction in the kinetic chain caused by poor scapula stabilization can contribute to shoulder injuries and Shoulder Impingement Syndrome (SIS). The purpose of this study was to compare the effectiveness of two treatment approaches scapular stabilization based exercise therapy and physical therapy in patients with SIS.



The study is a randomized clinical trial in which 68 patients with SIS were randomly assigned in two groups of exercise therapy (ET) and physical therapy (PT) and received 18 sessions of treatment. Pain, shoulders' range of abduction and external rotation, shoulder protraction, scapular rotation and symmetry as well as postural assessment and Pectoralis minor length were evaluated pre and post intervention. The paired-sample t test and the independent sample t test were applied respectively to determine the differences in each group and between two groups.



Our findings indicated significant differences in abduction and external rotation range, improvement of forward shoulder translation and increase in the flexibility of the involved shoulder between the two groups (respectively ; p=0.024, p=0.001, p<0/0001, p<0/0001). No significant difference was detected in pain reduction between the groups (p=0.576). Protraction of the shoulder (p<0.0001), forward head posture (p<0/0001) and mid thoracic curvature (p<0.0001) revealed a significant improvement in the ET group. Apparent changes occurred in scapular rotation and symmetry in both groups but no significant differences were observed between the two groups (respectively; p=0.183, p=0.578).



The scapular stabilization based exercise intervention was successful in increasing shoulder range, decreasing forward head and shoulder postures and Pectoralis minor flexibility.
Biosynthesis And Production Of Sophorolipids

Sophorolipids are biosurfactants belonging to the class of the glycolipid, produced mainly by the osmophilic yeast Candida bombicola. Structurally they are composed by a disaccharide sophorose (2’-O-β-D-glucopyranosyl-β-D-glycopyranose) which is linked β -glycosidically to a long fatty acid chain with generally 16 to 18 atoms of carbon with one or more unsaturation. They are produced as a complex mix containing up to 40 molecules and associated isomers, depending on the species which produces it, the substrate used and the culture conditions. They present properties which are very similar or superior to the synthetic surfactants and other biosurfactants with the advantage of presenting low toxicity, higher biodegradability, better environmental compatibility, high selectivity and specific activity in a broad range of temperature, pH and salinity conditions. Its biological activities are directly related with its chemical structure. Sophorolipids possess a great potential for application in areas such as: food; bioremediation; cosmetics; pharmaceutical; biomedicine; nanotechnology and enhanced oil recovery.
A New Design of High-Performance Large-Scale GIS Computing at a Finer Spatial Granularity : A Case Study of Spatial Join with Spark for Sustainability

Sustainability research faces many challenges as respective environmental, urban and regional contexts are experiencing rapid changes at an unprecedented spatial granularity level, which involves growing massive data and the need for spatial relationship detection at a faster pace. Spatial join is a fundamental method for making data more informative with respect to spatial relations. The dramatic growth of data volumes has led to increased focus on high-performance large-scale spatial join. In this paper, we present Spatial Join with Spark (SJS), a proposed high-performance algorithm, that uses a simple, but efficient, uniform spatial grid to partition datasets and joins the partitions with the built-in join transformation of Spark. SJS utilizes the distributed in-memory iterative computation of Spark, then introduces a calculation-evaluating model and in-memory spatial repartition technology, which optimize the initial partition by evaluating the calculation amount of local join algorithms without any disk access. We compare four in-memory spatial join algorithms in SJS for further performance improvement. Based on extensive experiments with real-world data, we conclude that SJS outperforms the Spark and MapReduce implementations of earlier spatial join approaches. This study demonstrates that it is promising to leverage high-performance computing for large-scale spatial join analysis. The availability of large-sized geo-referenced datasets along with the high-performance computing technology can raise great opportunities for sustainability research on whether and how these new trends in data and technology can be utilized to help detect the associated trends and patterns in the human-environment dynamics.
Long-term recovery of left ventricular function after primary angioplasty for acute myocardial infarction.

To investigate changes in left ventricular function in the first 6 months after acute myocardial infarction treated with primary angioplasty. To assess clinical variables, associated with recovery of left ventricular function after acute myocardial infarction.



Changes in left ventricular function were studied in 600 consecutive patients with acute myocardial infarction, all treated with primary angioplasty. Left ventricular ejection fraction was measured by radionuclide ventriculography in survivors at day 4 and after 6 months. Patients with a recurrent myocardial infarction within the 6 months were excluded.



Successful reperfusion (TIMI 3 flow) by primary angioplasty was achieved in 89% of patients. The mean ejection fraction at discharge was 43.7%+/-11.4, whereas the mean ejection fraction after 6 months was 46.3%+/-11.5 (P<0.01). During the 6 months, the mean relative improvement in left ventricular ejection fraction was 6%. An improvement in left ventricular function was observed in 48% of the patients; 25% of the patients had a decrease, whereas in the remaining patients there was no change. After univariate and multivariate analysis, an anterior infarction location, an ejection fraction at discharge < or =40% and single-vessel disease were significant predictors of left ventricular improvement during the 6 months.



After acute myocardial infarction treated with primary angioplasty there was a significant recovery of left ventricular function during the first 6 months after the infarction. An anterior myocardial infarction, single-vessel coronary artery disease, and an initially depressed left ventricular function were independently associated with recovery of left ventricular function. Multivessel disease was associated with absence of functional recovery. Additional studies, investigating complete revascularization are needed, as this approach may potentially improve long-term left ventricular function.
Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer's Disease

Graphs are widely used as a natural framework that captures interactions between individual elements represented as nodes in a graph. In medical applications, specifically, nodes can represent individuals within a potentially large population (patients or healthy controls) accompanied by a set of features, while the graph edges incorporate associations between subjects in an intuitive manner. This representation allows to incorporate the wealth of imaging and non-imaging information as well as individual subject features simultaneously in disease classification tasks. Previous graph-based approaches for supervised or unsupervised learning in the context of disease prediction solely focus on pairwise similarities between subjects, disregarding individual characteristics and features, or rather rely on subject-specific imaging feature vectors and fail to model interactions between them. In this paper, we present a thorough evaluation of a generic framework that leverages both imaging and non-imaging information and can be used for brain analysis in large populations. This framework exploits Graph Convolutional Networks (GCNs) and involves representing populations as a sparse graph, where its nodes are associated with imaging-based feature vectors, while phenotypic information is integrated as edge weights. The extensive evaluation explores the effect of each individual component of this framework on disease prediction performance and further compares it to different baselines. The framework performance is tested on two large datasets with diverse underlying data, ABIDE and ADNI, for the prediction of Autism Spectrum Disorder and conversion to Alzheimer's disease, respectively. Our analysis shows that our novel framework can improve over state-of-the-art results on both databases, with 70.4% classification accuracy for ABIDE and 80.0% for ADNI.
A Comparison of Crossover and Mutation in Genetic Programming

This paper presents a large and systematic body of data on the relative effectiveness of mutation, crossover, and combinations of mutation and crossover in genetic programming (GP). The literature of traditional genetic algorithms contains related studies, but mutation and crossover in GP differ from their traditional counterparts in significant ways. In this paper we present the results from a very large experimental data set, the equivalent of approximately 12,000 typical runs of a GP system, systematically exploring a range of parameter settings. The resulting data may be useful not only for practitioners seeking to optimize parameters for GP runs, but also for theorists exploring issues such as the role of “building blocks” in GP.
Training Deep Networks with Structured Layers by Matrix Backpropagation

Deep neural network architectures have recently produced excellent results in a variety of areas in artificial intelligence and visual recognition, well surpassing traditional shallow architectures trained using hand-designed features. The power of deep networks stems both from their ability to perform local computations followed by pointwise non-linearities over increasingly larger receptive fields, and from the simplicity and scalability of the gradient-descent training procedure based on backpropagation. An open problem is the inclusion of layers that perform global, structured matrix computations like segmentation (e.g. normalized cuts) or higher-order pooling (e.g. log-tangent space metrics defined over the manifold of symmetric positive definite matrices) while preserving the validity and efficiency of an end-to-end deep training framework. In this paper we propose a sound mathematical apparatus to formally integrate global structured computation into deep computation architectures. At the heart of our methodology is the development of the theory and practice of backpropagation that generalizes to the calculus of adjoint matrix variations. The proposed matrix backpropagation methodology applies broadly to a variety of problems in machine learning or computational perception. Here we illustrate it by performing visual segmentation experiments using the BSDS and MSCOCO benchmarks, where we show that deep networks relying on second-order pooling and normalized cuts layers, trained end-to-end using matrix backpropagation, outperform counterparts that do not take advantage of such global layers.
Hands in action: real-time 3D reconstruction of hands in interaction with objects

This paper presents a method for vision based estimation of the pose of human hands in interaction with objects. Despite the fact that most robotics applications of human hand tracking involve grasping and manipulation of objects, the majority of methods in the literature assume a free hand, isolated from the surrounding environment. Our hand tracking method is non-parametric, performing a nearest neighbor search in a large database (100000 entries) of hand poses with and without grasped objects. The system operates in real time, it is robust to self occlusions, object occlusions and segmentation errors, and provides full hand pose reconstruction from markerless video. Temporal consistency in hand pose is taken into account, without explicitly tracking the hand in the high dimensional pose space.
Sentiment analysis of Bengali comments with Word2Vec and sentiment information of words

The vector representation of Bengali words using word2vec model (Mikolov et al. (2013)) plays an important role in Bengali sentiment classification. It is observed that the words that are from same context stay closer in the vector space of word2vec model and they are more similar than other words. In this article, a new approach of sentiment classification of Bengali comments with word2vec and Sentiment extraction of words are presented. Combining the results of word2vec word co-occurrence score with the sentiment polarity score of the words, the accuracy obtained is 75.5%.
A Bayesian approach to covariance estimation and data fusion

In this paper, we address the fusion problem of two estimates, where the cross-correlation between the estimates is unknown. To solve the problem within the Bayesian framework, we assume that the covariance matrix has a prior distribution. We also assume that we know the covariance of each estimate, i.e., the diagonal block of the entire co-variance matrix (of the random vector consisting of the two estimates). We then derive the conditional distribution of the off-diagonal blocks, which is the cross-correlation of our interest. The conditional distribution happens to be the inverted matrix variate t-distribution. We can readily sample from this distribution and use a Monte Carlo method to compute the minimum mean square error estimate for the fusion problem. Simulations show that the proposed method works better than the popular covariance intersection method.
Design of solar power semiconductor refrigerator

A solar energy semiconductor cooling box is presented in the paper. The cooling box is compact and easy to carry, can be made a special refrigeration unit which is smaller according to user needs. The characteristics of the cooling box are its simple use and maintenance, safe performance, decentralized power supply, convenient energy storage, no environmental pollution, and so on. In addition, compared with the normal mechanical refrigeration, the semiconductor refrigeration system which makes use of Peltier effect does not require pumps, compressors and other moving parts, and so there is no wear and noise. It does not require refrigerant so it will not produce environmental pollution, and it also eliminates the complex transmission pipeline. The concrete realization form of power are “heat - electric - cold”, “light - electric - cold”, “light - heat - electric - cold”. In order to achieve the purpose of cooling, solar cells generate electricity to drive the semiconductor cooling devices. The working principle is mainly photovoltaic effect and the Peltier effect.
Upregulation of the GEF-H1 pathway after transient cerebral ischemia

The microtubule-dependent GEF-H1 pathway controls synaptic re-networking and overall gene expression via regulating cytoskeleton dynamics. Understanding this pathway after ischemia is essential to developing new therapies for neuronal function recovery. However, how the GEF-H1 pathway is regulated following transient cerebral ischemia remains unknown. This study employed a rat model of transient forebrain ischemia to investigate alterations of the GEF-H1 pathway using Western blotting, confocal and electron microscopy, dephosphorylation analysis, and pull-down assay. The GEF-H1 activity was significantly upregulated by: (i) dephosphorylation and (ii) translocation to synaptic membrane and nuclear structures during the early phase of reperfusion. GEF-H1 protein was then downregulated in the brain regions where neurons were destined to undergo delayed neuronal death, but markedly upregulated in neurons that were resistant to the same episode of cerebral ischemia. Consistently, GTP-RhoA, a GEF-H1 substrate, was significantly upregulated after brain ischemia. Electron microscopy further showed that neuronal microtubules were persistently depolymerized in the brain region where GEF-H1 protein was downregulated after brain ischemia. The results demonstrate that the GEF-H1 activity is significantly upregulated in both vulnerable and resistant brain regions in the early phase of reperfusion. However, GEF-H1 protein is downregulated in the vulnerable neurons but upregulated in the ischemic resistant neurons during the recovery phase after ischemia. The initial upregulation of GEF-H1 activity may contribute to excitotoxicity, whereas the late upregulation of GEF-H1 protein may promote neuroplasticity after brain ischemia.