nils-herrmann/clean_scirepeval huggingface dataset:
Accurate Angle Estimator for High-Frame-Rate 2-D Vector Flow Imaging

This paper presents a novel approach for estimating 2-D flow angles using a high-frame-rate ultrasound method. The angle estimator features high accuracy and low standard deviation (SD) over the full 360° range. The method is validated on Field II simulations and phantom measurements using the experimental ultrasound scanner SARUS and a flow rig before being tested in vivo. An 8-MHz linear array transducer is used with defocused beam emissions. In the simulations of a spinning disk phantom, a 360° uniform behavior on the angle estimation is observed with a median angle bias of 1.01° and a median angle SD of 1.8°. Similar results are obtained on a straight vessel for both simulations and measurements, where the obtained angle biases are below 1.5° with SDs around 1°. Estimated velocity magnitudes are also kept under 10% bias and 5% relative SD in both simulations and measurements. An in vivo measurement is performed on a carotid bifurcation of a healthy individual. A 3-s acquisition during three heart cycles is captured. A consistent and repetitive vortex is observed in the carotid bulb during systoles.
Toward Rapid Development of Multi-Party Virtual Human Negotiation Scenarios

This paper reports on an ongoing effort to enable the rapid development of multi-party virtual human negotiation scenarios. We present a case study in which a new scenario supporting negotiation between two human role players and two virtual humans was developed over a period of 12 weeks. We discuss the methodology and development process that were employed, from storyline design through role play and iterative development of the virtual humans’ semantic and task representations and natural language processing capabilities. We analyze the effort, expertise, and time required for each development step, and discuss opportunities to further streamline the development process.
Efficient Zero-Knowledge Contingent Payments in Cryptocurrencies Without Scripts

One of the most promising innovations offered by the cryptographic currencies (like Bitcoin) are the so-called smart contracts, which can be viewed as financial agreements between mutually distrusting participants. Their execution is enforced by the mechanics of the currency, and typically has monetary consequences for the parties. The rules of these contracts are written in the form of so-called “scripts”, which are pieces of code in some “scripting language”. Although smart contracts are believed to have a huge potential, for the moment they are not widely used in practice. In particular, most of Bitcoin miners allow only to post standard transactions (i.e.: those without the non-trivial scripts) on the blockchain. As a result, it is currently very hard to create non-trivial smart contracts in Bitcoin. Motivated by this, we address the following question: “is it possible to create non-trivial efficient smart contracts using the standard transactions only?” We answer this question affirmatively, by constructing efficient Zero-Knowledge Contingent Payment protocol for a large class of NP-relations. This includes the relations for which efficient sigma protocols exist. In particular, our protocol can be used to sell a factorization (p, q) of an RSA modulus n = pq, which is an example that we implemented and tested its efficiency in practice. As another example of the “smart contract without scripts” we show how our techniques can be used to implement the contract called “trading across chains”.
Design of degrees of distribution of LDS-OFDM

In this paper, we study the design of low density signature-orthogonal frequency division multiple access (LDS-OFDM). Compare to the conventional OFDMA system, LDS system has some desirable properties that allows system operating at overloaded conditions. LDS-OFDM system has been shown that outperforms the corresponding OFDMA system. In this paper, we focus on the design of the LDS degrees distribution, which has not been addressed in the literature. The goal is to achieve the optimum system load. We propose an iterative optimization procedure by utilizing the EXIT chart. Optimized load has been given in this paper with corresponding LDS spreading parameters. The performance of the designed LDS-OFDM system is evaluated. Monte Carlo based simulations further confirm that the designed LDS-OFDM outperforms the corresponding OFDMA system by up to 10 dB.
A vision radar system for car safety driving applications

This paper presents a vision radar system (VRS) integrating Lane Departure Warning System (LDWS) and Forward Collision Warning System (FCWS) for car safety driving applications. A dynamic threshold, which improves the accuracy of lane and vehicle detection in various weather conditions, and multiple frame approval, which conquers the effect of some tough scenes, are proposed. The designed radar-map facilitates warning message issuing of safety distance for forward and potential cut-in vehicles. The proposed VRS can achieve 22 fps at 720×480 (D1) resolution on Freescale i.MX6 embedded platform.
Efficient Second-Order Gradient Boosting for Conditional Random Fields

Conditional random fields (CRFs) are an important class of models for accurate structured prediction, but effective design of the feature functions is a major challenge when applying CRF models to real world data. Gradient boosting, which is used to automatically induce and select feature functions, is a natural candidate solution to the problem. However, it is non-trivial to derive gradient boosting algorithms for CRFs due to the dense Hessian matrices introduced by variable dependencies. Existing approaches thus use only first-order information when optimizing likelihood, and hence face convergence issues. We incorporate second-order information by deriving a Markov Chain mixing rate bound to quantify the dependencies, and introduce a gradient boosting algorithm that iteratively optimizes an adaptive upper bound of the objective function. The resulting algorithm induces and selects features for CRFs via functional space optimization, with provable convergence guarantees. Experimental results on three real world datasets demonstrate that the mixing rate based upper bound is effective for learning CRFs with non-linear potentials.
Bayesian calibration of a natural history model with application to a population model for colorectal cancer.

Cancer natural history models are essential when evaluating screening/preventative interventions or changes to diagnostic pathways. Natural history models commonly use a state transition structure, but it is often not possible to observe the state transition probabilities required for parameterization.



. The work aimed to accurately represent the uncertainty in the parameters of a state transition model for the natural history of colorectal cancer by embedding the problem in the framework of Bayesian inference.



The Metropolis-Hastings algorithm was used to estimate natural history parameters and screening test characteristics by generating multiple sets of parameters from the posterior distribution, which is the probability distribution that is compatible with the observed data. Observed data included colorectal cancer incidence categorized by age and stage, autopsy data on polyp prevalence, and cancer and polyp detection rates from the first round of screening with the fecal occult blood test in England. The approach was implemented using Visual Basic.



The results were subsequently examined for convergence using the package CODA in R 2.8.0. Outputs from fitting were samples from the joint posterior distribution of the natural history parameters given the epidemiological data. The parameter sets obtained are shown to have a good fit to all the observed data sets. These parameter sets are used when running probabilistic sensitivity analysis.



The advantages of this strategy are that it draws efficiently from a high-dimensional correlated parameter space. The algorithm is simple to code and runs overnight on a standard desktop PC. Using this method, the parameter sets are drawn according to their posterior probability given calibration data, and thus they correctly summarize the residual uncertainty in the parameter space.
Chest pathology detection using deep learning with non-medical training

In this work, we examine the strength of deep learning approaches for pathology detection in chest radiographs. Convolutional neural networks (CNN) deep architecture classification approaches have gained popularity due to their ability to learn mid and high level image representations. We explore the ability of CNN learned from a non-medical dataset to identify different types of pathologies in chest x-rays. We tested our algorithm on a 433 image dataset. The best performance was achieved using CNN and GIST features. We obtained an area under curve (AUC) of 0.87-0.94 for the different pathologies. The results demonstrate the feasibility of detecting pathology in chest x-rays using deep learning approaches based on non-medical learning. This is a first-of-its-kind experiment that shows that Deep learning with ImageNet, a large scale non-medical image database may be a good substitute to domain specific representations, which are yet to be available, for general medical image recognition tasks.
Hidden images

A hidden image is a form of artistic expression in which one or more secondary objects (or scenes) are hidden within a primary image. Features of the primary image, especially its edges and texture, are used to portray a secondary object. People can recognize both the primary and secondary intent in such pictures, although the time taken to do so depends on the prior experience of the viewer and the strength of the clues. Here, we present a system for creating such images. It relies on the ability of human perception to recognize an object, e.g. a human face, from incomplete edge information within its interior, rather than its outline. Our system detects edges of the object to be hidden, and then finds a place where it can be embedded within the scene, together with a suitable transformation for doing so, by optimizing an energy based on edge differences. Embedding is performed using a modified Poisson blending approach, which strengthens matched edges of the host image using edges of the object being embedded. We show various hidden images generated by our system.
Levofloxacin implants with predefined microstructure fabricated by three-dimensional printing technique.

A novel three-dimensional (3D) printing technique was utilized in the preparation of drug implants that can be designed to have complex drug release profiles. The method we describe is based on a lactic acid polymer matrix with a predefined microstructure that is amenable to rapid prototyping and fabrication. We describe how the process parameters, especially selection of the binder, were optimized. Implants containing levofloxacin (LVFX) with predefined microstructures using an optimized binder solution of ethanol and acetone (20:80, v/v) were prepared by a 3D printing process that achieved a bi-modal profile displaying both pulsatile and steady state LVFX release from a single implant. The pulse release appeared from day 5 to 25, followed by a steady state phase of 25 days. The next pulse release phase then began at the 50th day and ended at the 80th day. To evaluate the drug implants structurally and analytically, the microscopic morphologies and the in vitro release profiles of the implants fabricated by both the 3D printing technique and the conventional lost mold technique were assessed using environmental scanning electron microscopy (ESEM) and UV absorbance spectrophotometry. The results demonstrate that the 3D printing technology can be used to fabricate drug implants with sophisticated micro- and macro-architecture in a single device that may be rapidly prototyped and fabricated. We conclude that drug implants with predefined microstructure fabricated by 3D printing techniques can have clear advantages compared to implants fabricated by conventional compressing methods.
Design of an Optical Character Recognition System for Camera-based Handheld Devices

This paper presents a complete Optical Character Recognition (OCR) system for camera captured image/graphics embedded textual documents for handheld devices. At first, text regions are extracted and skew corrected. Then, these regions are binarized and segmented into lines and characters. Characters are passed into the recognition module. Experimenting with a set of 100 business card images, captured by cell phone camera, we have achieved a maximum recognition accuracy of 92.74%. Compared to Tesseract, an open source desktop-based powerful OCR engine, present recognition accuracy is worth contributing. Moreover, the developed technique is computationally efficient and consumes low memory so as to be applicable on handheld devices.
Intent-Aware Diversification Using a Constrained PLSA

The intent-aware diversification framework was introduced initially in information retrieval and adopted to the context of recommender systems in the work of Vargas et al. The framework considers a set of aspects associated with items to be recommended. For instance, aspects may correspond to genres in movie recommendations. The framework depends on input aspect model consisting of item selection or relevance probabilities, given an aspect, and user intents, in the form of probabilities that the user is interested in each aspect. In this paper, we examine a number of input aspect models and evaluate the impact that different models have on the framework. In particular, we propose a constrained PLSA model that allows for interpretable output, in terms of known aspects, while achieving greater performance that the explicit co-occurrence counting method used in previous work. We evaluate the proposed models using a well-known MovieLens dataset for which item genres are available.
The place of the literature review in grounded theory research

For those employing grounded theory as a research methodology, the issue of how and when to engage with existing literature is often problematic, especially for PhD students. With this in mind, the current article seeks to offer some clarity on the topic and provide novice grounded theory researchers in particular with advice on how to approach the issue of the literature review in grounded theory. This is done by reviewing the origins of grounded theory, exploring the original stance taken by the founders of the methodology with regard to the literature review, tracking how this position has changed over time, outlining the rationale associated with specific positions and discussing ideas for reconciling opposing perspectives. Coupled with this, the author draws on his own experience of using grounded theory for his PhD research to explain how extant literature may be used and discusses how the nature of engagement with existing literature may impact upon the overall written presentation of a grounded theory study.
Wideband triangle slot antenna with tuning stub

The paper presents a small triangle slot antenna fed by a coplanar waveguide. The antenna consists of a symmetrical triangle slot tuned by a metal stub and slot cap (hat) and exhibits a wide bandwidth of 53% relative to the X-band center frequency, with average gain of 7.5 dB and cross polarization level of -10 dB. In addition to being small in size, the coupling between two elements of this type of antenna is in the order of -15 dB or less, which makes it a good candidate for a phased array system.
Genome scan for meat quality traits in Nelore beef cattle.

Meat quality traits are economically important because they affect consumers' acceptance, which, in turn, influences the demand for beef. However, selection to improve meat quality is limited by the small numbers of animals on which meat tenderness can be evaluated due to the cost of performing shear force analysis and the resultant damage to the carcass. Genome wide-association studies for Warner-Bratzler shear force measured at different times of meat aging, backfat thickness, ribeye muscle area, scanning parameters [lightness, redness (a*), and yellowness] to ascertain color characteristics of meat and fat, water-holding capacity, cooking loss (CL), and muscle pH were conducted using genotype data from the Illumina BovineHD BeadChip array to identify quantitative trait loci (QTL) in all phenotyped Nelore cattle. Phenotype count for these animals ranged from 430 to 536 across traits. Meat quality traits in Nelore are controlled by numerous QTL of small effect, except for a small number of large-effect QTL identified for a*fat, CL, and pH. Genomic regions harboring these QTL and the pathways in which the genes from these regions act appear to differ from those identified in taurine cattle for meat quality traits. These results will guide future QTL mapping studies and the development of models for the prediction of genetic merit to implement genomic selection for meat quality in Nelore cattle.
Personalized PageRank Based Multi-document Summarization

This paper presents a novel multi-document summarization approach based on personalized pagerank (PPRSum). In this algorithm, we uniformly integrate various kinds of information in the corpus. At first, we train a salience model of sentence global features based on Naive Bayes Model. Secondly, we generate a relevance model for each corpus utilizing the query of it. Then, we compute the personalized prior probability for each sentence in the corpus utilizing the salience model and the relevance model both. With the help of personalized prior probability, a Personalized PageRank ranking process is performed depending on the relationships among all sentences in the corpus. Additionally, the redundancy penalty is imposed on each sentence. The summary is produced by choosing the sentences with both high query-focused information richness and high information novelty. Experiments on DUC2007 are performed and the ROUGE evaluation results show that PPRSum ranks between the 1st and the 2nd systems on DUC2007 main task.
ShoVAT: Shodan-based vulnerability assessment tool for Internet-facing services

Shodan has been acknowledged as one of the most popular search engines available today, designed to crawl the Internet and to index discovered services. This paper expands the features exposed by Shodan with advanced vulnerability assessment capabilities embedded into a novel tool called ShoVAT. ShoVAT takes the output of traditional Shodan queries and performs an in-depth analysis of service-specific data, i.e., service banners. It embodies specially crafted algorithms which rely on novel in-memory data structures to automatically reconstruct Common Platform Enumeration (CPE) names and to proficiently extract vulnerabilities from National Vulnerability Database (NVD). Compared to the state-of-the-art, ShoVAT brings several novel and significant contributions, since it encompasses automated vulnerability identification techniques, it can return highly accurate results with customized and even purposefully modified service banners, and it supports historical service vulnerability analysis without the need to deploy additional monitoring infrastructures. Experiments performed on 1501 services in twelve different institutions across different sectors revealed high accuracy of results and a total of 3922 known vulnerabilities. This is the accepted version of the following article: B. Genge and C. Enăchescu: ShoVAT: Shodan-based vulnerability assessment tool for Internet-facing services. Security and communication networks, Wiley, 2015, . SECURITY AND COMMUNICATION NETWORKS Security  Networks 2014; 00:1–18
Kernel-Based Learning of Hierarchical Multilabel Classification Models

We present a kernel-based algorithm for hierarchical text c lassification where the documents are allowed to belong to more than one category at a time. The clas sification model is a variant of the Maximum Margin Markov Network framework, where the classifi cation hierarchy is represented as a Markov tree equipped with an exponential family defined o n the edges. We present an efficient optimization algorithm based on incremental conditi onal gradient ascent in single-example subspaces spanned by the marginal dual variables. The optim ization is facilitated with a dynamic programming based algorithm that computes best update dire ctions in the feasible set. Experiments show that the algorithm can feasibly optimize t raining sets of thousands of examples and classification hierarchies consisting of hundreds of nodes. Training of the full hierarchical model is as efficient as training independent SVM-light clas sifiers for each node. The algorithm’s predictive accuracy was found to be competitive with other r ecently introduced hierarchical multicategory or multilabel classification learning algorithms .
Spatial graphlet matching kernel for recognizing aerial image categories

This paper presents a method for recognizing aerial image categories based on matching graphlets(i.e., small connected subgraphs) extracted from aerial images. By constructing a Region Adjacency Graph (RAG) to encode the geometric property and the color distribution of each aerial image, we cast aerial image category recognition as RAG-to-RAG matching. Based on graph theory, RAG-to-RAG matching is conducted by matching all their respective graphlets. Towards an effective graphlet matching process, we develop a manifold embedding algorithm to transfer different-sized graphlets into equal length feature vectors and further integrate these feature vectors into a kernel. This kernel is used to train a SVM [8] classifier for aerial image categories recognition. Experimental results demonstrate our method outperforms several state-of-the-art object/scene recognition models.
Deep Learning Improves Template Matching by Normalized Cross Correlation

Template matching by normalized cross correlation (NCC) is widely used for finding image correspondences. We improve the robustness of this algorithm by preprocessing images with "siamese" convolutional networks trained to maximize the contrast between NCC values of true and false matches. The improvement is quantified using patches of brain images from serial section electron microscopy. Relative to a parameter-tuned bandpass filter, siamese convolutional networks significantly reduce false matches. Furthermore, all false matches can be eliminated by removing a tiny fraction of all matches based on NCC values. The improved accuracy of our method could be essential for connectomics, because emerging petascale datasets may require billions of template matches to assemble 2D images of serial sections into a 3D image stack. Our method is also expected to generalize to many other computer vision applications that use NCC template matching to find image correspondences.