nils-herrmann/clean_scirepeval huggingface dataset:
Engineering Sustainable Blockchain Applications

Blockchain technology has attracted attention as emerging paradigm for business collaboration. Blockchain’s consensus mechanisms allow partners to cooperate in a business network. However, many applications reported in literature present merely a proof of concept from an engineering perspective. An industrialization of blockchain requires an engineering framework, which assures the sustainability of the application and in particular its network partnerships, i.e. each participant has to act as an active peer in the network rather than being a mere consumer with a wallet for participation in the blockchain. This paper presents the skeleton of such an engineering framework starting with an ideation of partnerships and collaboration patterns to clarify the incentives for participation via business model design for sustainable network operations towards the selection of an implementation platform for the business processes re-engineered. Moreover, an initial version of an interactive tool for community-oriented capturing of know-how about characteristics of blockchain platforms is presented.
Overview of NLP-TEA 2016 Shared Task for Chinese Grammatical Error Diagnosis

This paper presents the NLP-TEA 2016 shared task for Chinese grammatical error diagnosis which seeks to identify grammatical error types and their range of occurrence within sentences written by learners of Chinese as foreign language. We describe the task definition, data preparation, performance metrics, and evaluation results. Of the 15 teams registered for this shared task, 9 teams developed the system and submitted a total of 36 runs. We expected this evaluation campaign could lead to the development of more advanced NLP techniques for educational applications, especially for Chinese error detection. All data sets with gold standards and scoring scripts are made publicly available to researchers.
Pretreatment: the key to unlocking low‐cost cellulosic ethanol

New transportation fuels are badly needed to reduce our heavy dependence on imported oil and to reduce the release of greenhouse gases that cause global climate change; cellulosic biomass is the only inexpensive resource that can be used for sustainable production of the large volumes of liquid fuels that our transportation sector has historically favored. Furthermore, biological conversion of cellulosic biomass can take advantage of the power of biotechnology to take huge strides toward making biofuels cost competitive. Ethanol production is particularly well suited to marrying this combination of need, resource, and technology. In fact, major advances have already been realized to competitively position cellulosic ethanol with corn ethanol. However, although biotechno logy presents important opportunities to achieve very low costs, pretreatment of naturally resistant cellulosic mate rials is essential if we are to achieve high yields from biological operations; this operation is projected to be the single, most expensive processing step, representing about 20% of the total cost. In addition, pretreatment has pervasive impacts on all other major operations in the overall conversion scheme from choice of feedstock through to size reduction, hydrolysis, and fermentation, and on to product recovery, residue processing, and co-product potential. A number of different pretreatments involving biological, chemical, physical, and thermal approaches have been investigated over the years, but only those that employ chemicals currently offer the high yields and low costs vital to economic success. Among the most promising are pretreatments using dilute acid, sulfur dioxide, near-neutral pH control, ammonia expansion, aqueous ammonia, and lime, with signifi cant differences among the sugar-release patterns. Although projected costs for these options are similar when applied to corn stover, a key need now is to dramatically improve our knowledge of these systems with the goal of advancing pretreatment to substantially reduce costs and to accelerate commercial applications.
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods

Neural networks are known to be vulnerable to adversarial examples: inputs that are close to natural inputs but classified incorrectly. In order to better understand the space of adversarial examples, we survey ten recent proposals that are designed for detection and compare their efficacy. We show that all can be defeated by constructing new loss functions. We conclude that adversarial examples are significantly harder to detect than previously appreciated, and the properties believed to be intrinsic to adversarial examples are in fact not. Finally, we propose several simple guidelines for evaluating future proposed defenses.
Knowledge Projection for Effective Design of Thinner and Faster Deep Neural Networks

While deeper and wider neural networks are actively pushing the performance limits of various computer vision and machine learning tasks, they often require large sets of labeled data for effective training and suffer from extremely high computational complexity. In this paper, we will develop a new framework for training deep neural networks on datasets with limited labeled samples using cross-network knowledge projection which is able to improve the network performance while reducing the overall computational complexity significantly. Specifically, a large pre-trained teacher network is used to observe samples from the training data. A projection matrix is learned to project this teacher-level knowledge and its visual representations from an intermediate layer of the teacher network to an intermediate layer of a thinner and faster student network to guide and regulate its training process. Both the intermediate layers from the teacher network and the injection layers from the student network are adaptively selected during training by evaluating a joint loss function in an iterative manner. This knowledge projection framework allows us to use crucial knowledge learned by large networks to guide the training of thinner student networks, avoiding over-fitting, achieving better network performance, and significantly reducing the complexity. Extensive experimental results on benchmark datasets have demonstrated that our proposed knowledge projection approach outperforms existing methods, improving accuracy by up to 4% while reducing network complexity by 4 to 10 times, which is very attractive for practical applications of deep neural networks.
Injectable fillers for volume replacement in the aging face.

In recent years, there has been a better understanding of the aging process. In addition to changes occurring in the skin envelope, significant changes occur in the subcutaneous fat and craniofacial skeleton. This has led to a paradigm shift in the therapeutic approach to facial rejuvenation. Along with soft tissue repositioning, volumizing the aging face has been found to optimize the result and achieve a more natural appearance. Early in the aging process, when there has not been a significant change to the face requiring surgical intervention, fillers alone can provide minimally invasive facial rejuvenation through volumizing. Multiple injectable soft tissue fillers and biostimulators are currently available to provide facial volume such as hyaluronic acid, calcium hydroxylapatite, poly-L-lactic acid, polymethyl methacrylate, and silicone. A discussion of the morphological changes seen in the aging face, the properties of these products, and key technical concepts will be highlighted to permit optimum results when performing facial volumizing of the upper, middle, and lower thirds of the face. These fillers can act as a dress rehearsal for these patients considering structural fat grafting.
Ef fi ciency drivers in Brazilian insurance : A two-stage DEA meta frontier-data mining approach

This paper investigates the role of heterogeneity in the insurance sector. Here, heterogeneity is represented by different types of insurance provided and regions served. Using a balanced panel data set on Brazilian insurance companies as a case study, results corroborate this underlying hypothesis of heterogeneity's impact on performance. The implications of this research for practitioners andacademics are not only addressed in termsofmarket segmentation —which ones are the best performers—but also in terms of mergers and acquisitions—as long as insurance companies may increase their performance with the right balance of types of insurance offered and regions served.
Development of an Expert System for Dust Explosion Risk Management Based on ASP.Net and Prolog

Dust explosions always claim lives and cause huge financial losses. Dust explosion risk can be prevented by inherently safer design or mitigated by engineering protective system. Design of explosion prevention and protection needs comprehensive knowledge and data on the process, workshop, equipment, and combustible materials. The knowledge includes standards, expertise of experts, and practical experience. The database includes accidents, dust explosion characteristics, inherently safer design methods, and protective design methods. Integration of such a comprehensive knowledge system is very helpful. The developed system has the following functions: risk assessment, accident analysis, recommendation of prevention and protection solution, and computer aided design of explosion protection. The software was based on Browser/Server architecture and was developed using mixed programming of ASP.Net and Prolog. The developed expert system can be an assistant to explosion design engineers and safety engineers of combustible dust handling plants.
Resource Allocation for Underlay Cognitive Radio Networks: A Survey

For conventional wireless networks, the main target of resource allocation (RA) is to efficiently utilize the available resources. Generally, there are no changes in the available spectrum, thus static spectrum allocation policies were adopted. However, these allocation policies lead to spectrum under-utilization. In this regard, cognitive radio networks (CRNs) have received great attention due to their potential to improve the spectrum utilization. In general, efficient spectrum management and resource allocation are essential and very crucial for CRNs. This is due to the fact that unlicensed users should attain the most benefit from accessing the licensed spectrum without causing adverse interference to the licensed ones. The cognitive users or called secondary users have to effectively capture the arising spectrum opportunities in time, frequency, and space to transmit their data. Mainly, two aspects characterize the resource allocation for CRNs: 1) primary (licensed) network protection and 2) secondary (unlicensed) network performance enhancement in terms of quality-of-service, throughput, fairness, energy efficiency, etc. CRNs can operate in one of three known operation modes: 1) interweave; 2) overlay; and 3) underlay. Among which the underlay cognitive radio mode is known to be highly efficient in terms of spectrum utilization. This is because the unlicensed users are allowed to share the same channels with the active licensed users under some conditions. In this paper, we provide a survey for resource allocation in underlay CRNs. In particular, we first define the RA process and its components for underlay CRNs. Second, we provide a taxonomy that categorizes the RA algorithms proposed in literature based on the approaches, criteria, common techniques, and network architecture. Then, the state-of-the-art resource allocation algorithms are reviewed according to the provided taxonomy. Additionally, comparisons among different proposals are provided. Finally, directions for future research are outlined.
Channel Splitting Network for Single MR Image Super-Resolution

High resolution magnetic resonance (MR) imaging is desirable in many clinical applications due to its contribution to more accurate subsequent analyses and early clinical diagnoses. Single image super resolution (SISR) is an effective and cost efficient alternative technique to improve the spatial resolution of MR images. In the past few years, SISR methods based on deep learning techniques, especially convolutional neural networks (CNNs), have achieved state-of-the-art performance on natural images. However, the information is gradually weakened and training becomes increasingly difficult as the network deepens. The problem is more serious for medical images because lacking high quality and effective training samples makes deep models prone to underfitting or overfitting. Nevertheless, many current models treat the hierarchical features on different channels equivalently, which is not helpful for the models to deal with the hierarchical features discriminatively and targetedly. To this end, we present a novel channel splitting network (CSN) to ease the representational burden of deep models. The proposed CSN model divides the hierarchical features into two branches, i.e., residual branch and dense branch, with different information transmissions. The residual branch is able to promote feature reuse, while the dense branch is beneficial to the exploration of new features. Besides, we also adopt the merge-and-run mapping to facilitate information integration between different branches. Extensive experiments on various MR images, including proton density (PD), T1 and T2 images, show that the proposed CSN model achieves superior performance over other state-of-the-art SISR methods.
The International Physical Activity Questionnaire (IPAQ): a study of concurrent and construct validity.

The International Physical Activity Questionnaire (IPAQ) was developed to measure health-related physical activity (PA) in populations. The short version of the IPAQ has been tested extensively and is now used in many international studies. The present study aimed to explore the validity characteristics of the long-version IPAQ.



Forty-six voluntary healthy male and female subjects (age, mean +/- standard deviation: 40.7 +/- 10.3 years) participated in the study. PA indicators derived from the long, self-administered IPAQ were compared with data from an activity monitor and a PA log book for concurrent validity, and with aerobic fitness, body mass index (BMI) and percentage body fat for construct validity.



Strong positive relationships were observed between the activity monitor data and the IPAQ data for total PA (rho = 0.55, P < 0.001) and vigorous PA (rho = 0.71, P < 0.001), but a weaker relationship for moderate PA (rho = 0.21, P = 0.051). Calculated MET-h day(-1) from the PA log book was significantly correlated with MET-h day(-1) from the IPAQ (rho = 0.67, P < 0.001). A weak correlation was observed between IPAQ data for total PA and both aerobic fitness (rho = 0.21, P = 0.051) and BMI (rho = 0.25, P = 0.009). No significant correlation was observed between percentage body fat and IPAQ variables. Bland-Altman analysis suggested that the inability of activity monitors to detect certain types of activities might introduce a source of error in criterion validation studies.



The long, self-administered IPAQ questionnaire has acceptable validity when assessing levels and patterns of PA in healthy adults.
Deep learning and its application to medical image segmentation

One of the most common tasks in medical imaging is semantic segmentation. Achieving this segmentation automatically has been an active area of research, but the task has been proven very challenging due to the large variation of anatomy across different patients. However, recent advances in deep learning have made it possible to significantly improve the performance of image recognition and semantic segmentation methods in the field of computer vision. Due to the data driven approaches of hierarchical feature learning in deep learning frameworks, these advances can be translated to medical images without much difficulty. Several variations of deep convolutional neural networks have been successfully applied to medical images. Especially fully convolutional architectures have been proven efficient for segmentation of 3D medical images. In this article, we describe how to build a 3D fully convolutional network (FCN) that can process 3D images in order to produce automatic semantic segmentations. The model is trained and evaluated on a clinical computed tomography (CT) dataset and shows stateof-the-art performance in multi-organ segmentation.
Factors Influencing Consumers ’ Online Purchase Intention : A Study among University Students in Malaysia

E-Commerce is one of the crucial trading methods worldwide. Hence, it is important to understand consumers’ online purchase intention. This research aims to examine factors that influence consumers’ online purchase intention among university students in Malaysia. Quantitative research approach has been adapted in this research by distributing online questionnaires to 250 Malaysian university students aged between 20-29 years old, who possess experience in online purchases. Findings of this research have discovered that trust, perceived usefulness and subjective norm are the significant factors in predicting online purchase intention. However, perceived ease of use and perceived enjoyment are not significant in predicting the variance in online purchase intention. The findings also revealed that subjective norm is the most significant predicting factor on online purchase intention among university students in Malaysia. Findings of this research will provide online marketers with a better understanding on online purchase intention which enable them to direct effective online marketing strategies.
A Network Pharmacology Approach to Uncover the Pharmacological Mechanism of XuanHuSuo Powder on Osteoarthritis.

As the most familiar type of arthritis and a chronic illness of the joints, Osteoarthritis (OA) affects a great number of people on the global scale. XuanHuSuo powder (XHSP), a conventional herbal formula from China, has been extensively applied in OA treatment. Nonetheless, its pharmacological mechanism has not been completely expounded. In this research, a network pharmacology approach has been chosen to study the pharmacological mechanism of XHSP on OA, and the pharmacology networks were established based on the relationship between four herbs found in XHSP, compound targets, and OA targets. The pathway enrichment analysis revealed that the significant bioprocess networks of XHSP on OA were regulation of inflammation, interleukin-1β (IL-1β) production and nitric oxide (NO) biosynthetic process, response to cytokine or estrogen stimuli, and antiapoptosis. These effects have not been reported previously. The comprehensive network pharmacology approach developed by our research has revealed, for the first time, a connection between four herbs found in XHSP, corresponding compound targets, and OA pathway systems that are conducive to expanding the clinical application of XHSP. The proposed network pharmacology approach could be a promising complementary method by which researchers might better evaluate multitarget or multicomponent drugs on a systematic level.
A Nonlinear Analysis Method for Performance Based Seismic Design

A relatively simple nonlinear method for the seismic analysis of structures (the N2 method) is presented. It combines the pushover analysis of a multidegree-of-freedom (MDOF) model with the response spectrum analysis of an equivalent single-degree-of-freedom (SDOF) system. The method is formulated in the acceleration displacement format, which enables the visual interpretation of the procedure and of the relations between the basic quantities controlling the seismic response. Inelastic spectra, rather than elastic spectra with equivalent damping and period, are applied. This feature represents the major difference with respect to the capacity spectrum method. Moreover, demand quantities can be obtained without iteration. Generally, the results of the N2 method are reasonably accurate, provided that the structure oscillates predominantly in the first mode. Some additional limitations apply. In the paper, the method is described and discussed, and its basic derivations are given. The similarities and differences between the proposed method and the FEMA 273 and ATC 40 nonlinear static analysis procedures are discussed. Application of the method is illustrated by means of an example.
Broadband CPW-Fed Circularly Polarized Square Slot Antenna With Lightening-Shaped Feedline and Inverted-L Grounded Strips

A novel design is described for a circularly polarized square slot antenna (CPSSA). Circular polarization (CP) operations can be attained using a lightening-shaped feedline protruded from the signal line of the feeding coplanar waveguide (CPW). The CP bandwidth can be significantly enhanced by implanting a pair of inverted-L grounded strips into the slot and adjusting the dimensions of the lightening-shaped feedline, whereas the impedance bandwidth can be greatly enlarged through tuning embedded vertical and horizontal stubs. The designed antenna was measured to exhibit a CP bandwidth of as high as 48.8%.
Blockchain exhumed

A blockchain is a distributed, decentralised database of records of digital events (transactions) that took place and were shared among the participating parties. Each transaction in the public ledger is verified by consensus of a majority of the participants in the system. Bitcoin may not be that important in the future, but blockchain technology's role in Financial and Non-financial world can't be undermined. In this paper, we provide a holistic view of how Blockchain technology works, its strength and weaknesses, and its role to change the way the business happens today and tomorrow.
A Brief Review on Leading Big Data Models

Today, science is passing through an era of transformation, where the inundation of data, dubbed data deluge is influencing the decision making process. The science is driven by the data and is being termed as data science. In this internet age, the volume of the data has grown up to petabytes, and this large, complex, structured or unstructured, and heterogeneous data in the form of “Big Data” has gained significant attention. The rapid pace of data growth through various disparate sources, especially social media such as Facebook, has seriously challenged the data analytic capabilities of traditional relational databases. The velocity of the expansion of the amount of data gives rise to a complete paradigm shift in how new age data is processed. Confidence in the data engineering of the existing data processing systems is gradually fading whereas the capabilities of the new techniques for capturing, storing, visualizing, and analyzing data are evolving. In this review paper, we discuss some of the modern Big Data models that are leading contributors in the NoSQL era and claim to address Big Data challenges in reliable and efficient ways. Also, we take the potential of Big Data into consideration and try to reshape the original operationaloriented definition of “Big Science” (Furner, 2003) into a new data-driven definition and rephrase it as “The science that deals with Big Data is Big Science.” Disciplines Agriculture | Bioresource and Agricultural Engineering | Computer Sciences | Statistics and Probability Comments This article is from Data Science Journal. 13, pp.138–157.  This work is licensed under a Creative Commons Attribution 4.0 License.  sugamsha@iastate.edu 2 Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, IA, 50010, USA 3 Department of Computer Science, Iowa State University, Ames, IA, 50010, USA 4 Electronics & Computer Discipline, DPT, Indian Institute of Technology, Roorkee, 247001, INDIA