nils-herrmann/clean_scirepeval huggingface dataset:
An Empirical Research of Factors Influencing the Decision-Making of Chinese Online Shoppers

As the number of Chinese online shoppers increases, it becomes increasingly important to understand their decision-making processes. The factors that influenced the purchasing behaviour of 190 online shoppers were studied. Our research showed that the ease of use of a website, the quality of product information provided the entertainment value and perceived trust all greatly enhanced the satisfaction of online shoppers and significantly influenced their buying intentions.
FluxQuery: An Execution Framework for Highly Interactive Query Workloads

Modern computing devices and user interfaces have necessitated highly interactive querying. Some of these interfaces issue a large number of dynamically changing and continuous queries to the backend. In others, users expect to inspect results during the query formulation process, in order to guide or help them towards specifying a full-fledged query. Thus, users end up issuing a fast-changing workload to the underlying database. In such situations, the user's query intent can be thought of as being in flux. In this paper, we show that the traditional query execution engines are not well-suited for this new class of highly interactive workloads. We propose a novel model to interpret the variability of likely queries in a workload. We implemented a cyclic scan-based approach to process queries from such workloads in an efficient and practical manner while reducing the overall system load. We evaluate and compare our methods with traditional systems and demonstrate the scalability of our approach, enabling thousands of queries to run simultaneously within interactive response times given low memory and CPU requirements.
Stem cell cultivation in bioreactors.

Cell-based therapies have generated great interest in the scientific and medical communities, and stem cells in particular are very appealing for regenerative medicine, drug screening and other biomedical applications. These unspecialized cells have unlimited self-renewal capacity and the remarkable ability to produce mature cells with specialized functions, such as blood cells, nerve cells or cardiac muscle. However, the actual number of cells that can be obtained from available donors is very low. One possible solution for the generation of relevant numbers of cells for several applications is to scale-up the culture of these cells in vitro. This review describes recent developments in the cultivation of stem cells in bioreactors, particularly considerations regarding critical culture parameters, possible bioreactor configurations, and integration of novel technologies in the bioprocess development stage. We expect that this review will provide updated and detailed information focusing on the systematic production of stem cell products in compliance with regulatory guidelines, while using robust and cost-effective approaches.
Economic Security Metrics

This chapter surveys economic approaches for security metrics, among which we could identify two main areas of research. One has its roots in investment and decision theory and is mainly pursued in the field of information technology-oriented business administration. It has yielded a number of quantitative metrics that can be applied as guidelines in investment decisions as well as for the evaluation of existing security measures. The second area of research has ancestors in micro-economics. It deals with market concepts to gather security-relevant information and extract quantitative indicators on information security properties. 1 Metrics for Security Investments The previous chapter has demonstrated that it is essential to measure organisations’ security at different levels of detail. This also applies to the investment perspective. In the recent years, organisations see an increasing demand for determining the cost and benefit of IT security investments. Possible reasons include compliance with regulatory requirements, emerging information security threats, or increased dependence of business processes on information technology. Apart from definitions for metrics, this section will show the motivations behind metrics as well as challenges in quantifying the value of IT security investments.
Analytical magnetic field analysis of Halbach magnetized permanent-magnet machines

We develop analytical models for predicting the magnetic field distribution in Halbach magnetized machines. They are formulated in polar coordinates and account for the relative recoil permeability of the magnets. They are applicable to both internal and external rotor permanent-magnet machines with either an iron-cored or air-cored stator and/or rotor. We compare predicted results with those obtained by finite-element analyses and measurements. We show that the air-gap flux density varies significantly with the pole number and that an optimal combination of the magnet thickness and the pole number exists for maximum air-gap flux density, while the back iron can enhance the air-gap field and electromagnetic torque when the radial thickness of the magnet is small.
A Wideband Base Station Antenna Element With Stable Radiation Pattern and Reduced Beam Squint

This paper presents the design procedure, optimization strategy, theoretical analysis, and experimental results of a wideband dual-polarized base station antenna element with superior performance. The proposed antenna element consists of four electric folded dipoles arranged in an octagon shape that are excited simultaneously for each polarization. It provides ±45° slant-polarized radiation that meets all the requirements for base station antenna elements, including stable radiation patterns, low cross polarization level, high port-to-port isolation, and excellent matching across the wide band. The problem of beam squint for beam-tilted arrays is discussed and it is found that the geometry of this element serves to reduce beam squint. Experimental results show that this element has a wide bandwidth of 46.4% from 1.69 to 2.71 GHz with ≥15-dB return loss and 9.8 ± 0.9-dBi gain. Across this wide band, the variations of the half-power-beamwidths of the two polarizations are all within 66.5° ± 5.5°, the port-to-port isolation is >28 dB, the cross-polarization discrimination is >25 dB, and most importantly, the beam squint is <4° with a maximum 10° down-tilt.
DepthNet: A Recurrent Neural Network Architecture for Monocular Depth Prediction

Predicting the depth map of a scene is often a vital component of monocular SLAM pipelines. Depth prediction is fundamentally ill-posed due to the inherent ambiguity in the scene formation process. In recent times, convolutional neural networks (CNNs) that exploit scene geometric constraints have been explored extensively for supervised single-view depth prediction and semi-supervised 2-view depth prediction. In this paper we explore whether recurrent neural networks (RNNs) can learn spatio-temporally accurate monocular depth prediction from video sequences, even without explicit definition of the inter-frame geometric consistency or pose supervision. To this end, we propose a novel convolutional LSTM (ConvLSTM)-based network architecture for depth prediction from a monocular video sequence. In the proposed ConvLSTM network architecture, we harness the ability of long short-term memory (LSTM)-based RNNs to reason sequentially and predict the depth map for an image frame as a function of the appearances of scene objects in the image frame as well as image frames in its temporal neighborhood. In addition, the proposed ConvLSTM network is also shown to be able to make depth predictions for future or unseen image frame(s). We demonstrate the depth prediction performance of the proposed ConvLSTM network on the KITTI dataset and show that it gives results that are superior in terms of accuracy to those obtained via depth-supervised and self-supervised methods and comparable to those generated by state-of-the-art pose-supervised methods.
Implementation of the Six Core Strategies for Restraint Minimization in a Specialized Mental Health Organization.

Implementation of the Six Core Strategies to Reduce the Use of Seclusion and Restraint (Six Core Strategies) at a recovery-oriented, tertiary level mental health care facility and the resultant changes in mechanical restraint and seclusion incidents are described. Strategies included increased executive participation; enhanced staff knowledge, skills, and attitudes; development of restraint orders and decision support in the electronic medical record to enable informed debriefing and tracking of events; and implementation of initiatives to include service users and their families in the plan of care. Strategies were implemented in a staged manner across 3 years. The total number of mechanical restraint and seclusion incidents decreased by 19.7% from 2011/12 to 2013/14. Concurrently, the average length of a mechanical restraint or seclusion incident decreased 38.9% over the 36-month evaluation period. Implementation of the Six Core Strategies for restraint minimization effectively decreased the number and length of mechanical restraint and seclusion incidents in a specialized mental health care facility.
Bacterial biodegradation and bioconversion of industrial lignocellulosic streams

Lignocellulose is a term for plant materials that are composed of matrices of cellulose, hemicellulose, and lignin. Lignocellulose is a renewable feedstock for many industries. Lignocellulosic materials are used for the production of paper, fuels, and chemicals. Typically, industry focuses on transforming the polysaccharides present in lignocellulose into products resulting in the incomplete use of this resource. The materials that are not completely used make up the underutilized streams of materials that contain cellulose, hemicellulose, and lignin. These underutilized streams have potential for conversion into valuable products. Treatment of these lignocellulosic streams with bacteria, which specifically degrade lignocellulose through the action of enzymes, offers a low-energy and low-cost method for biodegradation and bioconversion. This review describes lignocellulosic streams and summarizes different aspects of biological treatments including the bacteria isolated from lignocellulose-containing environments and enzymes which may be used for bioconversion. The chemicals produced during bioconversion can be used for a variety of products including adhesives, plastics, resins, food additives, and petrochemical replacements.
DESIGN OF DUAL-FEED DUAL-POLARIZED MICROSTRIP ANTENNA WITH HIGH ISOLATION AND LOW CROSS POLARIZATION

A broadband dual-feed dual-polarized microstrip antenna with low cross polarization and high isolation is presented. The dualorthogonal linearly polarized mode is excited by two different feed mechanisms from a single circular radiating patch. One of the two modes is excited by a pair of L-shaped probes with a 180◦ phase differences, and the other is excited by an H-shaped aperture. The proposed design has a very simple antenna structure with a wide input impedance bandwidth (23.25% for Port 1 and 35% for Port 2) and also its two input port isolation is found to be as low as −40 dB. Measured results of the fabricated antenna prototypes are also carried out to verify the simulation analysis.
Know Abnormal, Find Evil: Frequent Pattern Mining for Ransomware Threat Hunting and Intelligence

Emergence of crypto-ransomware has significantly changed the cyber threat landscape. A crypto ransomware removes data custodian access by encrypting valuable data on victims’ computers and requests a ransom payment to reinstantiate custodian access by decrypting data. Timely detection of ransomware very much depends on how quickly and accurately system logs can be mined to hunt abnormalities and stop the evil. In this paper we first setup an environment to collect activity logs of 517 Locky ransomware samples, 535 Cerber ransomware samples and 572 samples of TeslaCrypt ransomware. We utilize Sequential Pattern Mining to find Maximal Frequent Patterns (MFP) of activities within different ransomware families as candidate features for classification using J48, Random Forest, Bagging and MLP algorithms. We could achieve 99% accuracy in detecting ransomware instances from goodware samples and 96.5% accuracy in detecting family of a given ransomware sample. Our results indicate usefulness and practicality of applying pattern mining techniques in detection of good features for ransomware hunting. Moreover, we showed existence of distinctive frequent patterns within different ransomware families which can be used for identification of a ransomware sample family for building intelligence about threat actors and threat profile of a given target.
A Comprehensive Study of Features and Algorithms for URL-Based Topic Classification

Given only the URL of a Web page, can we identify its topic? We study this problem in detail by exploring a large number of different feature sets and algorithms on several datasets. We also show that the inherent overlap between topics and the sparsity of the information in URLs makes this a very challenging problem. Web page classification without a page’s content is desirable when the content is not available at all, when a classification is needed before obtaining the content, or when classification speed is of utmost importance. For our experiments we used five different corpora comprising a total of about 3 million (URL, classification) pairs. We evaluated several techniques for feature generation and classification algorithms. The individual binary classifiers were then combined via boosting into metabinary classifiers. We achieve typical F-measure values between 80 and 85, and a typical precision of around 86. The precision can be pushed further over 90 while maintaining a typical level of recall between 30 and 40.
Evaluating Story Generation Systems Using Automated Linguistic Analyses

Story generation is a well-recognized task in computational creativity research, but one that can be difficult to evaluate empirically. It is often inefficient and costly to rely solely on human feedback for judging the quality of generated stories. We address this by examining the use of linguistic analyses for automated evaluation, using metrics from existing work on predicting writing quality. We apply these metrics specifically to story continuation, where a model is given the beginning of a story and generates the next sentence, which is useful for systems that interactively support authors’ creativity in writing. We compare sentences generated by different existing models to human-authored ones according to the analyses. The results show some meaningful differences between the models, suggesting that this evaluation approach may be advantageous for future research.
Feminism and Economic Inequality

Economic inequality was a key issue in the 2016 presidential campaign1 and probably influenced the election of Donald Trump.2 It is an issue that is profoundly significant to the growing number of individuals—disproportionately women and minorities—who find themselves on the wrong end of the increasingly bi-modal economic spectrum, and raises serious concerns about the erosion of the “American dream”3 and the stability and viability of our democracy.4 Economists, political theorists, sociologists, and
Improved Fully Convolutional Network with Conditional Random Fields for Building Extraction

Building extraction from remotely sensed imagery plays an important role in urban planning, disaster management, navigation, updating geographic databases and several other geospatial applications. Several published contributions are dedicated to the applications of Deep Convolutional Neural Network (DCNN) for building extraction using aerial/satellite imagery exists; however, in all these contributions a good accuracy is always paid at the price of extremely complex and large network architectures. In this paper, we present an enhanced Fully Convolutional Network (FCN) framework especially molded for building extraction of remotely sensed images by applying Conditional Random Field (CRF). The main purpose here is to propose a framework which balances maximum accuracy with less network complexity. The modern activation function called Exponential Linear Unit (ELU) is applied to improve the performance of the Fully Convolutional Network (FCN), resulting in more, yet accurate building prediction. To further reduce the noise (false classified buildings) and to sharpen the boundary of the buildings, a post processing CRF is added at the end of the adopted Convolutional Neural Network (CNN) framework. The experiments were conducted on Massachusetts building aerial imagery. The results show that our proposed framework outperformed FCN baseline, which is the existing baseline framework for semantic segmentation, in term of performance measure, the F1-score and Intersection Over Union (IoU) measure. Additionally, the proposed method stood superior to the pre-existing classifier for building extraction using the same dataset in terms of performance measure and network complexity at once.
Endoscopic submucosal dissection: European Society of Gastrointestinal Endoscopy (ESGE) Guideline.

This Guideline is an official statement of the European Society of Gastrointestinal Endoscopy (ESGE). The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system 1 2 was adopted to define the strength of recommendations and the quality of evidence.



1 ESGE recommends endoscopic en bloc resection for superficial esophageal squamous cell cancers (SCCs), excluding those with obvious submucosal involvement (strong recommendation, moderate quality evidence). Endoscopic mucosal resection (EMR) may be considered in such lesions when they are smaller than 10 mm if en bloc resection can be assured. However, ESGE recommends endoscopic submucosal dissection (ESD) as the first option, mainly to provide an en bloc resection with accurate pathology staging and to avoid missing important histological features (strong recommendation, moderate quality evidence). 2 ESGE recommends endoscopic resection with a curative intent for visible lesions in Barrett's esophagus (strong recommendation, moderate quality evidence). ESD has not been shown to be superior to EMR for excision of mucosal cancer, and for that reason EMR should be preferred. ESD may be considered in selected cases, such as lesions larger than 15 mm, poorly lifting tumors, and lesions at risk for submucosal invasion (strong recommendation, moderate quality evidence). 3 ESGE recommends endoscopic resection for the treatment of gastric superficial neoplastic lesions that possess a very low risk of lymph node metastasis (strong recommendation, high quality evidence). EMR is an acceptable option for lesions smaller than 10 - 15 mm with a very low probability of advanced histology (Paris 0-IIa). However, ESGE recommends ESD as treatment of choice for most gastric superficial neoplastic lesions (strong recommendation, moderate quality evidence). 4 ESGE states that the majority of colonic and rectal superficial lesions can be effectively removed in a curative way by standard polypectomy and/or by EMR (strong recommendation, moderate quality evidence). ESD can be considered for removal of colonic and rectal lesions with high suspicion of limited submucosal invasion that is based on two main criteria of depressed morphology and irregular or nongranular surface pattern, particularly if the lesions are larger than 20 mm; or ESD can be considered for colorectal lesions that otherwise cannot be optimally and radically removed by snare-based techniques (strong recommendation, moderate quality evidence).
Efficient manifold ranking for image retrieval

Manifold Ranking (MR), a graph-based ranking algorithm, has been widely applied in information retrieval and shown to have excellent performance and feasibility on a variety of data types. Particularly, it has been successfully applied to content-based image retrieval, because of its outstanding ability to discover underlying geometrical structure of the given image database. However, manifold ranking is computationally very expensive, both in graph construction and ranking computation stages, which significantly limits its applicability to very large data sets. In this paper, we extend the original manifold ranking algorithm and propose a new framework named Efficient Manifold Ranking (EMR). We aim to address the shortcomings of MR from two perspectives: scalable graph construction and efficient computation. Specifically, we build an anchor graph on the data set instead of the traditional k-nearest neighbor graph, and design a new form of adjacency matrix utilized to speed up the ranking computation. The experimental results on a real world image database demonstrate the effectiveness and efficiency of our proposed method. With a comparable performance to the original manifold ranking, our method significantly reduces the computational time, makes it a promising method to large scale real world retrieval problems.
Vulvar necrobiotic xanthogranuloma.

An 89-year-old Caucasian woman with a 1.5-year history of IgGmonoclonal gammopathy of undetermined significance presented for evaluation of a large ulceration on the left buttock and an asymptomatic lesion on the vulva. On physical examination, she had an extensive well-defined indurated ulcer obliterating the clitoris and encompassing both labia majora  and an 83 8-cm circular ulcer extending down to muscle on the left buttock with a necrotic rim . A 1.5-cm yellow nodule was present on the left lower eyelid, and a 4-cm oval yellow-pink plaque on the lateral distal aspect of her right thigh . The patient was given the diagnosis of monoclonal gammopathy of undetermined significance 1.5 years before presentation with serum protein electrophoresis demonstrating 0.58 g/dL of monoclonal IgG paraprotein and elevated free
Minimally invasive porcelain veneers: indications for a conservative esthetic dentistry treatment modality.

Patients have many restorative options for changing the appearance of their teeth. The most conservative restorative treatments for changing the appearance of teeth include tooth bleaching, direct composite resin veneers, and porcelain veneers. Patients seeking esthetic treatment should undergo a comprehensive clinical examination that includes an esthetic evaluation. When selecting a conservative treatment modality, the use of minimally invasive or no-preparation porcelain veneers should be considered. As with any treatment decision, the indications and contraindications must be considered before a definitive treatment plan is made. Long-term research has demonstrated a 94% survival rate for minimally invasive porcelain veneers. While conservation of tooth structure is important, so is selecting the right treatment modality for each patient based on clinical findings.