nils-herrmann/clean_scirepeval huggingface dataset:
Climbing ring robot for inspection of offshore wind turbines

A rapid expansion of wind turbine farms for sustainable electric power production is planned in Europe by 2020. At least in the UK, these will largely be located offshore to meet growing concerns about the visual intrusiveness and noise generation produced by onshore based farms. The necessary structural integrity inspection of offshore wind turbine blades poses tremendous problems of access, danger to human operatives and costs in the event of blades having to be taken out of service and transported on shore for schedules inspections. For these reasons robotic in-situ blade inspection of offshore wind turbines has been proposed and micro/nano focus computed axial X ray tomography (MNCAT) has been identified as the optimal if not the only solution for identification of safety critical defects in the thickest blade sections. The weight of such an inspection system is very high, typically 200kg and typical cross sectional scanner dimensions of 1m x 2 m to encircle as blade, clearly involve very high destabilizing moments to be countered by the deployment robot. The solution is a climbing ring robot completely encircling a turbine tower, typically 3 meter in diameter, to provide the necessary adhesion forces and anti-destabilizing force moments. Because of the size and thus development costs of such a huge robot the optimal design path is to prototype a small scale model. First results on such a model are described and from its performance the load carrying capabilities of a full scale version can be computed and the scale model can then be refined by 'reverse engineering' to guarantee that a full scale construction is able to meet requirements. The key design innovation is that the adhesive forces between the robot and climbing surface a provided entirely by mechanical means rather than by using the usual methods of vacuum suction or magnetic force, making the system much cheaper and easier to manipulate. Furthermore the design is entirely modular.
End-to-End Text-Independent Speaker Verification with Triplet Loss on Short Utterances

Text-independent speaker verification against short utterances is still challenging despite of recent advances in the field of speaker recognition with i-vector framework. In general, to get a robust i-vector representation, a satisfying amount of data is needed in the MAP adaptation step, which is hard to meet under short duration constraint. To overcome this, we present an endto-end system which directly learns a mapping from speech features to a compact fixed length speaker discriminative embedding where the Euclidean distance is employed for measuring similarity within trials. To learn the feature mapping, a modified Inception Net with residual block is proposed to optimize the triplet loss function. The input of our end-to-end system is a fixed length spectrogram converted from an arbitrary length utterance. Experiments show that our system consistently outperforms a conventional i-vector system on short duration speaker verification tasks. To test the limit under various duration conditions, we also demonstrate how our end-to-end system behaves with different duration from 2s-4s.
Design and Experiment of a Conformal Monopulse Antenna for Passive Radar Applications

This paper proposed a design scheme of wide band monopulse antenna system for the passive radar seeker (PRS) of anti-radiation missile (ARM). To save the installation space of the PRS, the conical conformal log periodic antennas (LPDA) are employed to form the monopulse antenna system. The specific implementation scheme of PRS based on the conically conformal monopulse antennas was designed and analyzed. A practical monopulse antenna system composed of two log periodic antennas was designed and fabricated over the operating frequency range of 1GHz to 8GHz. A loaded resistor with impedance equal to 50Ω is used to reduce the return loss at low frequency. The experimental results demonstrate that acceptable wide band impedance matching and port isolation for each fabricated antenna was achieved. Furthermore, the wide band sum and difference radiation patterns were formed, which validates the proposed conformal monopulse antenna design scheme for the PRS application.
Polygon mesh repairing: An application perspective

Nowadays, digital 3D models are in widespread and ubiquitous use, and each specific application dealing with 3D geometry has its own quality requirements that restrict the class of acceptable and supported models. This article analyzes typical defects that make a 3D model unsuitable for key application contexts, and surveys existing algorithms that process, repair, and improve its structure, geometry, and topology to make it appropriate to case-by-case requirements.
 The analysis is focused on polygon meshes, which constitute by far the most common 3D object representation. In particular, this article provides a structured overview of mesh repairing techniques from the point of view of the application context. Different types of mesh defects are classified according to the upstream application that produced the mesh, whereas mesh quality requirements are grouped by representative sets of downstream applications where the mesh is to be used. The numerous mesh repair methods that have been proposed during the last two decades are analyzed and classified in terms of their capabilities, properties, and guarantees. Based on these classifications, guidelines can be derived to support the identification of repairing algorithms best-suited to bridge the compatibility gap between the quality provided by the upstream process and the quality required by the downstream applications in a given geometry processing scenario.
Organophosphate Pesticide Exposure and Attention in Young Mexican-American Children: The CHAMACOS Study

Exposure to organophosphate (OP) pesticides, well-known neurotoxicants, has been associated with neurobehavioral deficits in children.



We investigated whether OP exposure, as measured by urinary dialkyl phosphate (DAP) metabolites in pregnant women and their children, was associated with attention-related outcomes among Mexican-American children living in an agricultural region of California.



Children were assessed at ages 3.5 years (n = 331) and 5 years (n = 323). Mothers completed the Child Behavior Checklist (CBCL). We administered the NEPSY-II visual attention subtest to children at 3.5 years and Conners' Kiddie Continuous Performance Test (K-CPT) at 5 years. The K-CPT yielded a standardized attention deficit/hyperactivity disorder (ADHD) Confidence Index score. Psychometricians scored behavior of the 5-year-olds during testing using the Hillside Behavior Rating Scale.



Prenatal DAPs (nanomoles per liter) were nonsignificantly associated with maternal report of attention problems and ADHD at age 3.5 years but were significantly related at age 5 years [CBCL attention problems: β = 0.7 points; 95% confidence interval (CI), 0.2-1.2; ADHD: β = 1.3; 95% CI, 0.4-2.1]. Prenatal DAPs were associated with scores on the K-CPT ADHD Confidence Index > 70th percentile [odds ratio (OR) = 5.1; 95% CI, 1.7-15.7] and with a composite ADHD indicator of the various measures (OR = 3.5; 95% CI, 1.1-10.7). Some outcomes exhibited evidence of effect modification by sex, with associations found only among boys. There was also limited evidence of associations between child DAPs and attention.



In utero DAPs and, to a lesser extent, postnatal DAPs were associated adversely with attention as assessed by maternal report, psychometrician observation, and direct assessment. These associations were somewhat stronger at 5 years than at 3.5 years and were stronger in boys.
Anime Artist Analysis based on CNN

Artist recognition is not easy for human beings if the artworks are general and 1 share many similarities, but modern machine learning or deep learning models may 2 help people identify the artwork authors. In this project, we trained Convolutional 3 Neural Networks (CNN) identifying Japanese anime artists. Our dataset was 4 crawled from Pixiv, a well-known Japanese website containing lots of fine anime 5 artworks. Specifically, the dataset has 9 authors and each one has 200 paintings. 6 We trained different kinds of networks, from a very basic Conv2D net to more 7 advanced networks, in order to carry out the identification. It turned out that 8 traditional neural networks can in some way identify different authors, but the 9 accuracy could be improved to a better level by tuning hyperparameters or using 10 more advanced networks such as ResNet and VGGNet.
Understanding the Dynamic Interplay of Social Buzz and Contribution Behavior within and between Online Platforms - Evidence from Crowdfunding

Motivated by the growing interconnection between online platforms, we examine the dynamic interplay between social buzz and contribution behavior in the crowdfunding context. Since the utility of crowdfunding projects is usually difficult to ascertain, prospective backers draw on quality signals, such as social buzz and prior-contribution behavior, to make their funding decisions. We employ the panel vector autoregression (PVAR) methodology to investigate both intraand cross-platform effects based on data collected from three platforms: Indiegogo, one of the largest crowdfunding platforms on the web, Twitter and Facebook. Our results show a positive influence of social buzz on project backing, but a negative relationship in the reverse direction. Furthermore, we observe strong positive feedback cycles within each platform. Our results are supplemented by split-sample analyses for project orientation (Social, Cause and Entrepreneurial) and project success (Winners vs. Losers), in which Facebook shares were identified as a critical success factor.
YACCLAB - Yet Another Connected Components Labeling Benchmark

The problem of labeling the connected components (CCL) of a binary image is well-defined and several proposals have been presented in the past. Since an exact solution to the problem exists and should be mandatory provided as output, algorithms mainly differ on their execution speed. In this paper, we propose and describe YACCLAB, Yet Another Connected Components Labeling Benchmark. Together with a rich and varied dataset, YACCLAB contains an open source platform to test new proposals and to compare them with publicly available competitors. Textual and graphical outputs are automatically generated for three kinds of test, which analyze the methods from different perspectives. The fairness of the comparisons is guaranteed by running on the same system and over the same datasets. Examples of usage and the corresponding comparisons among state-of-the-art techniques are reported to confirm the potentiality of the benchmark.
Proposal Manipulation of 3 D Objects in Immersive Virtual Environments

Interactions within virtual environments (VE) often require manipulation of 3D virtual objects. For this purpose, several research have been carried out focusing on mouse, touch and gestural input. While existing mid-air gestures in immersive VE (IVE) can offer natural manipulations, mimicking interactions with physical objects, they cannot achieve the same levels of precision attained with mouse-based applications for computer-aided design (CAD). Following prevailing trends in previous research, such as degrees-offreedom (DOF) separation, virtual widgets and scaled user motion, we intend to explore techniques for IVEs that combine the naturalness of mid-air gestures and the precision found in traditional CAD systems. In this document we survey and discuss the state-of-the-art in 3D object manipulation. With challenges identified, we present a research proposal and corresponding work plan.
The Complexity of Relational Query Languages (Extended Abstract)

Two complexity measures for query languages are proposed. Data complexity is the complexity of evaluating a query in the language as a function of the size of the database, and expression complexity is the complexity of evaluating a query in the language as a function of the size of the expression defining the query. We study the data and expression complexity of logical languages - relational calculus and its extensions by transitive closure, fixpoint and second order existential quantification - and algebraic languages - relational algebra and its extensions by bounded and unbounded looping. The pattern which will be shown is that the expression complexity of the investigated languages is one exponential higher then their data complexity, and for both types of complexity we show completeness in some complexity class.
Robust acoustic echo cancellation in the short-time fourier transform domain using adaptive crossband filters

This paper presents a robust acoustic echo cancellation (AEC) system in the short-time Fourier transform (STFT) domain using adaptive crossband filters. The STFT-domain AEC allows for a simpler system structure compared to the traditional frequency-domain AEC, which normally requires several applications of the discrete Fourier transform (DFT) and the inverse DFT, while the robust AEC (RAEC) allows for continuous and stable filter updates during double talk without freezing the adaptive filter. The RAEC and the STFT-domain AEC have been investigated in the past in separate studies. In this work we propose a novel algorithm that combines the advantages of both approaches for robust update of the adaptive crossband filters even during double talk. Experimental results confirm the benefit of incorporating the robustness constraint for the adaptive crossband filters and show improved performance in terms of the echo reduction and the predicted sound quality.
LSTM Model to Forecast Time Series for EC2 Cloud Price

With the widespread use of spot instances in Amazon EC2, which utilize unused capacity with unfixed price, predicting price is important for users. In this paper, we try to forecast spot instance price by using long short-term memory (LSTM) algorithm to predict time series of the prices. We apply cross validation technique to our data set, and extract some features; this help our model to learn deeply. We make the prediction for 10 regions, and measure the performance using root-mean-square error (RMSE). We apply our data to other statistical models to compare their performance; we found that our model works more efficiently, also the error is decreasing more with cross validation and the result is much faster. Based on our result we try to find the availability zone that less become over on-demand price and less changing price over time, which help users to choose the most stable availability zone.
JaVerT 2.0: compositional symbolic execution for JavaScript

We propose a novel, unified approach to the development of compositional symbolic execution tools, bridging the gap between classical symbolic execution and compositional program reasoning based on separation logic. Using this approach, we build JaVerT 2.0, a symbolic analysis tool for JavaScript that follows the language semantics without simplifications. JaVerT 2.0 supports whole-program symbolic testing, verification, and, for the first time, automatic compositional testing based on bi-abduction. The meta-theory underpinning JaVerT 2.0 is developed modularly, streamlining the proofs and informing the implementation. Our explicit treatment of symbolic execution errors allows us to give meaningful feedback to the developer during whole-program symbolic testing and guides the inference of resource of the bi-abductive execution. We evaluate the performance of JaVerT 2.0 on a number of JavaScript data-structure libraries, demonstrating: the scalability of our whole-program symbolic testing; an improvement over the state-of-the-art in JavaScript verification; and the feasibility of automatic compositional testing for JavaScript.
3-Dimensional Localization via RFID Tag Array

In this paper, we propose 3DLoc, which performs 3-dimensional localization on the tagged objects by using the RFID tag arrays. 3DLoc deploys three arrays of RFID tags on three mutually orthogonal surfaces of each object. When performing 3D localization, 3DLoc continuously moves the RFID antenna and scans the tagged objects in a 2-dimensional space right in front of the tagged objects. It then estimates the object's 3D position according to the phases from the tag arrays. By referring to the fixed layout of the tag array, we use Angle of Arrival-based schemes to accurately estimate the tagged objects' orientation and 3D coordinates in the 3D space. To suppress the localization errors caused by the multipath effect, we use the linear relationship of the AoA parameters to remove the unexpected outliers from the estimated results. We have implemented a prototype system and evaluated the actual performance in the real complex environment. The experimental results show that 3DLoc achieves the mean accuracy of 10cm in free space and 15.3cm in the multipath environment for the tagged object.
Automatic license plate detection and recognition using OpenCV

License plate recognition (LPR) has always been one of the crucial predicaments faced due to in numerous reasons such as severe lighting conditions, complex background, unpredicted weather conditions, low light and more. This paper is to enlight the above-mentioned muddles through a model based on OpenCV for enhancing details of edge information in license plate with improved text detection and recognition methods. LPR is a cutting-edge, next-generation system with imminent technological application in almost every field of the transportation industry.  Automatic number plate recognition,
Accelerated Convolutions for Efficient Multi-Scale Time to Contact Computation in Julia

Convolutions have long been regarded as fundamental to applied mathematics, physics and engineering. Their mathematical elegance allows for common tasks such as numerical differentiation to be computed efficiently on large data sets. Efficient computation of convolutions is critical to artificial intelligence in real-time applications, like machine vision, where convolutions must be continuously and efficiently computed on tens to hundreds of kilobytes per second. In this paper, we explore how convolutions are used in fundamental machine vision applications. We present an accelerated n-dimensional convolution package in the high performance computing language, Julia, and demonstrate its efficacy in solving the time to contact problem for machine vision. Results are measured against synthetically generated videos and quantitatively assessed according to their mean squared error from the ground truth. We achieve over an order of magnitude decrease in compute time and allocated memory for comparable machine vision applications. All code is packaged and integrated into the official Julia Package Manager to be used in various other scenarios.
Complications of injectable fillers, part I.

Dermal filling has rapidly become one of the most common procedures performed by clinicians worldwide. The vast majority of treatments are successful and patient satisfaction is high. However, complications, both mild and severe, have been reported and result from injection of many different types of dermal fillers. In this Continuing Medical Education review article, the author describes common technical errors, the signs and symptoms of both common and rare complications, and management of sequelae in clear, easily adaptable treatment algorithms.
The Asymptotic Decider: Resolving the Ambiguity in Marching Cubes

A method for computing isovalue or contour surfaces of a trivariate function is discussed. The input data are values of the trivariate function, Fijk, at the cuberille grid points (Xi, yj, zk) and the output is a collection of triangles representing the surface consisting of all points where F(x, y, z) is a constant value. The method described here is a modification that is intended to correct a problem with a previous method.
Word Segmentation in Sanskrit Using Path Constrained Random Walks

In Sanskrit, the phonemes at the word boundaries undergo changes to form new phonemes through a process called as sandhi. A fused sentence can be segmented into multiple possible segmentations. We propose a word segmentation approach that predicts the most semantically valid segmentation for a given sentence. We treat the problem as a query expansion problem and use the path-constrained random walks framework to predict the correct segments.
Assessing The Feasibility Of Self-organizing Maps For Data Mining Financial Information

Analyzing financial performance in today’s information-rich society can be a daunting task. With the evolution of the Internet, access to massive amounts of financial data, typically in the form of financial statements, is widespread. Managers and stakeholders are in need of a data-mining tool allowing them to quickly and accurately analyze this data. An emerging technique that may be suited for this application is the self-organizing map. The purpose of this study was to evaluate the performance of self-organizing maps for analyzing financial performance of international pulp and paper companies. For the study, financial data, in the form of seven financial ratios, was collected, using the Internet as the primary source of information. A total of 77 companies, and six regional averages, were included in the study. The time frame of the study was the period 1995-00. An example analysis was performed, and the results analyzed based on information contained in the annual reports. The results of the study indicate that self-organizing maps can be feasible tools for the financial analysis of large amounts of financial data.
A Modified Secure Remote Password (SRP) Protocol for Key Initialization and Exchange in Bluetooth Systems

This paper presents a novel solution to previously published weaknesses identified within the Bluetooth initialization key generation process. The current initialization key generating protocol will be replaced by a more robust technique, the Secure Remote Password protocol (SRP); the proposed algorithm is adapted to Bluetooth’s constrained environment by replacing the exponentiation operations with elliptic curve multiplications. This is followed by an analytical performance evaluation for the new protocol. The analysis suggests the suitability of the proposed BT-EC-SRP solution for the constrained environment of Bluetooth devices.
The Quest for Scalable Blockchain Fabric: Proof-of-Work vs. BFT Replication

Bitcoin cryptocurrency demonstrated the utility of global consensus across thousands of nodes, changing the world of digital transactions forever. In the early days of Bitcoin, the performance of its probabilistic proof-of-work (PoW) based consensus fabric, also known as blockchain, was not a major issue. Bitcoin became a success story, despite its consensus latencies on the order of an hour and the theoretical peak throughput of only up to 7 transactions per second. The situation today is radically different and the poor performance scalability of early PoW blockchains no longer makes sense. Specifically, the trend of modern cryptocurrency platforms, such as Ethereum, is to support execution of arbitrary distributed applications on blockchain fabric, needing much better performance. This approach, however, makes cryptocurrency platforms step away from their original purpose and enter the domain of database-replication protocols, notably, the classical state-machine replication, and in particular its Byzantine fault-tolerant (BFT) variants. In this paper, we contrast PoW-based blockchains to those based on BFT state machine replication, focusing on their scalability limits. We also discuss recent proposals to overcoming these scalability limits and outline key outstanding open problems in the quest for the “ultimate” blockchain fabric(s).