nils-herrmann/clean_scirepeval huggingface dataset:
Towards rain detection through use of in-vehicle multipurpose cameras

Advanced Driver Assistance Systems (ADAS) based on video camera tends to be generalized in today's automotive. However, if most of these systems perform nicely in good weather conditions, they perform very poorly under adverse weather particularly under rain. We present a novel approach that aims at detecting raindrops on a car windshield using only images from an in-vehicle camera. Based on the photometric properties of raindrops, the algorithm relies on image processing technics to highlight raindrops. Its results can be further used for image restoration and vision enhancement and hence it is a valuable tool for ADAS.
Tooth brushing Pattern Classification using Three-Axis Accelerometer and Magnetic Sensor for Smart Toothbrush

The concept of intelligent toothbrush, capable of monitoring brushing motion, orientation through the grip axis, during toothbrushing was suggested in our previous study. In this study, we describe a tooth brushing pattern classification algorithm using three-axis accelerometer and three-axis magnetic sensor. We have found that inappropriate tooth brushing pattern showed specific moving patterns. In order to trace the position and orientation of toothbrush in a mouth, we need to know absolute coordinate information of toothbrush. By applying tilt-compensated azimuth (heading) calculation algorithm, which is generally used in small telematics devices, we could find the inclination and orientation information of toothbrush. To assess the feasibility of the proposed algorithm, 8 brushing patterns were preformed by 6 individual healthy subjects. The proposed algorithm showed the detection ratio of 98%. This study showed that the proposed monitoring system was conceived to aid dental care personnel in patient education and instruction in oral hygiene regarding brushing style.
Cloak and dagger: dynamics of web search cloaking

Cloaking is a common 'bait-and-switch' technique used to hide the true nature of a Web site by delivering blatantly different semantic content to different user segments. It is often used in search engine optimization (SEO) to obtain user traffic illegitimately for scams. In this paper, we measure and characterize the prevalence of cloaking on different search engines, how this behavior changes for targeted versus untargeted advertising and ultimately the response to site cloaking by search engine providers. Using a custom crawler, called Dagger, we track both popular search terms (e.g., as identified by Google, Alexa and Twitter) and targeted keywords (focused on pharmaceutical products) for over five months, identifying when distinct results were provided to crawlers and browsers. We further track the lifetime of cloaked search results as well as the sites they point to, demonstrating that cloakers can expect to maintain their pages in search results for several days on popular search engines and maintain the pages themselves for longer still.
Rethinking Map Legends with Visualization

This design paper presents new guidance for creating map legends in a dynamic environment. Our contribution is a set ofguidelines for legend design in a visualization context and a series of illustrative themes through which they may be expressed. Theseare demonstrated in an applications context through interactive software prototypes. The guidelines are derived from cartographicliterature and in liaison with EDINA who provide digital mapping services for UK tertiary education. They enhance approaches tolegend design that have evolved for static media with visualization by considering: selection, layout, symbols, position, dynamismand design and process. Broad visualization legend themes include: The Ground Truth Legend, The Legend as Statistical Graphicand The Map is the Legend. Together, these concepts enable us to augment legends with dynamic properties that address specificneeds, rethink their nature and role and contribute to a wider re-evaluation of maps as artifacts of usage rather than statements offact. EDINA has acquired funding to enhance their clients with visualization legends that use these concepts as a consequence ofthis work. The guidance applies to the design of a wide range of legends and keys used in cartography and information visualization.
Automatic Summarization of Results from Clinical Trials

A central concern in Evidence Based Medicine (EBM) is how to convey research results effectively to practitioners. One important idea is to summarize results by key summary statistics that describe the effectiveness (or lack thereof) of a given intervention, specifically the absolute risk reduction (ARR) and number needed to treat (NNT). Manual summarization is slow and expensive, thus, with the exponential growth of the biomedical research literature, automated solutions are needed. In this paper, we present a novel method for automatically creating EBM-oriented summaries from research abstracts of randomly-controlled trials (RCTs). The system extracts descriptions of the treatment groups and outcomes, as well as various associated quantities, and then calculates summary statistics. Results on a hand-annotated corpus of research abstracts show promising, and potentially useful, results.
Code Comparison System based on Abstract Syntax Tree

The code comparison technology plays a very important part in the work of plagiarism detection and software evaluation. Software plagiarism mainly appears as copy-and-paste or with a little modification after this, which will not change the function of the code, such as replacing the name of methods or variables, reordering the sequence of the statements etc. This paper introduces a plagiarism detection tool named CCS (Code Comparison System) which is based on the Abstract Syntax Tree (AST). According to the syntax tree's characteristics, CCS calculates their hash values, transforms their storage forms, and then compares them node by node. As a result, the efficiency improves. Moreover, CCS preprocesses a large amount of source code in its database for potential use, which also accelerate the course of plagiarism detection. CCS also takes special measurement to reduce mistakes when calculating the hash values of the operations like subtraction and division. It performs well in the code comparison field, and is able to help with the copyright protecting of the source code.
A Novel Grid Synchronization PLL Method Based on Adaptive Low-Pass Notch Filter for Grid-Connected PCS

The amount of distributed energy resources (DERs) has increased constantly worldwide. The power ratings of DERs have become considerably high, as required by the new grid code requirement. To follow the grid code and optimize the function of grid-connected inverters based on DERs, a phase-locked loop (PLL) is essential for detecting the grid phase angle accurately when the grid voltage is polluted by harmonics and imbalance. This paper proposes a novel low-pass notch filter PLL (LPN-PLL) control strategy to synchronize with the true phase angle of the grid instead of using a conventional synchronous reference frame PLL (SRF-PLL), which requires a d-q-axis transformation of three-phase voltage and a proportional-integral controller. The proposed LPN-PLL is an upgraded version of the PLL method using the fast Fourier transform concept (FFT-PLL) which is robust to the harmonics and imbalance of the grid voltage. The proposed PLL algorithm was compared with conventional SRF-PLL and FFT-PLL and was implemented digitally using a digital signal processor TMS320F28335. A 10-kW three-phase grid-connected inverter was set, and a verification experiment was performed, showing the high performance and robustness of the proposal under low-voltage ride-through operation.
Assessing the effectiveness of a large database of emotion-eliciting films : A new tool for emotion researchers

'Assessing the effectiveness of a large database of emotion-eliciting films: A new tool for emotion researchers', This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution , reselling , loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material.
A Two-Location Inventory Model with Transshipment and Local Decision Making

In situations where a seller has surplus stock and another seller is stocked out, it may be desirable to transfer surplus stock from the former to the latter. We examine how the possibility of such transshipments between two independent locations affects the optimal inventory orders at each location. If each location aims to maximize its own profits—we call this local decision making—their inventory choices will not, in general, maximize joint profits. We find transshipment prices which induce the locations to choose inventory levels consistent with joint-profit maximization. (Transshipments; Newsvendor Model; Nash Equilibrium)
Handwritten isolated Bangla compound character recognition: A new benchmark using a novel deep learning approach

In this work, a novel deep learning technique for the recognition of handwritten Bangla isolated compound character is presented and a new benchmark of recognition accuracy on the CMATERdb 3.1.3.3 dataset is reported. Greedy layer wise training of Deep Neural Network has helped to make significant strides in various pattern recognition problems. We employ layerwise training to Deep Convolutional Neural Networks (DCNN) in a supervised fashion and augment the training process with the RMSProp algorithm to achieve faster convergence. We compare results with those obtained from standard shallow learning methods with predefined features, as well as standard DCNNs. Supervised layerwise trained DCNNs are found to outperform standard shallow learning models such as Support Vector Machines as well as regular DCNNs of similar architecture by achieving error rate of 9.67% thereby setting a new benchmark on the CMATERdb 3.1.3.3 with recognition accuracy of 90.33%, representing an improvement of nearly 10%.
Dilated Convolutional Neural Networks for Cardiovascular MR Segmentation in Congenital Heart Disease

We propose an automatic method using dilated convolutional neural networks (CNNs) for segmentation of the myocardium and blood pool in cardiovascular MR (CMR) of patients with congenital heart disease (CHD). Ten training and ten test CMR scans cropped to an ROI around the heart were provided in the MICCAI 2016 HVSMR challenge. A dilated CNN with a receptive field of 131 × 131 voxels was trained for myocardium and blood pool segmentation in axial, sagittal and coronal image slices. Performance was evaluated within the HVSMR challenge. Automatic segmentation of the test scans resulted in Dice indices of 0.80±0.06 and 0.93±0.02, average distances to boundaries of 0.96±0.31 and 0.89±0.24 mm, and Hausdorff distances of 6.13±3.76 and 7.07±3.01 mm for the myocardium and blood pool, respectively. Segmentation took 41.5 ± 14.7 s per scan. In conclusion, dilated CNNs trained on a small set of CMR images of CHD patients showing large anatomical variability provide accurate myocardium and blood pool segmentations.
A software engineering approach to ontology building

Ontologies are the backbone of the Semantic Web, a semantic-aware version of the World Wide Web. The availability of large-scale high quality domain ontologies depends on effective and usable methodologies aimed at supporting the crucial process of ontology building. Ontology building exhibits a structural and logical complexity that is building methodology that capitalizes the large experience drawn from a widely used standard in software engineering: the Unified Software Development Process or Unified Process (UP). In particular, we propose UP for ONtology (UPON) building, a methodology for ontology building derived from the UP. UPON is presented with the support of a practical example in the eBusiness domain. A comparative evaluation with other methodologies and the results of its adoption in the context of the Athena EU Integrated Project are also discussed. & 2008 Elsevier B.V. All rights reserved.
Finite-Time Analysis of Kernelised Contextual Bandits

We tackle the problem of online reward maximisation over a large finite set of actions described by their contexts. We focus on the case when the number of actions is too big to sample all of them even once. However we assume that we have access to the similarities between actions’ contexts and that the expected reward is an arbitrary linear function of the contexts’ images in the related reproducing kernel Hilbert space (RKHS). We propose KernelUCB, a kernelised UCB algorithm, and give a cumulative regret bound through a frequentist analysis. For contextual bandits, the related algorithm GP-UCB turns out to be a special case of our algorithm, and our finite-time analysis improves the regret bound of GP-UCB for the agnostic case, both in the terms of the kerneldependent quantity and the RKHS norm of the reward function. Moreover, for the linear kernel, our regret bound matches the lower bound for contextual linear bandits.
Skill Squatting Attacks on Amazon Alexa

The proliferation of the Internet of Things has increased reliance on voice-controlled devices to perform everyday tasks. Although these devices rely on accurate speechrecognition for correct functionality, many users experience frequent misinterpretations in normal use. In this work, we conduct an empirical analysis of interpretation errors made by Amazon Alexa, the speech-recognition engine that powers the Amazon Echo family of devices. We leverage a dataset of 11,460 speech samples containing English words spoken by American speakers and identify where Alexa misinterprets the audio inputs, how often, and why. We find that certain misinterpretations appear consistently in repeated trials and are systematic. Next, we present and validate a new attack, called skill squatting. In skill squatting, an attacker leverages systematic errors to route a user to malicious application without their knowledge. In a variant of the attack we call spear skill squatting, we further demonstrate that this attack can be targeted at specific demographic groups. We conclude with a discussion of the security implications of speech interpretation errors, countermeasures, and future work.
From opencl to high-performance hardware on FPGAS

We present an OpenCL compilation framework to generate high-performance hardware for FPGAs. For an OpenCL application comprising a host program and a set of kernels, it compiles the host program, generates Verilog HDL for each kernel, compiles the circuit using Altera Complete Design Suite 12.0, and downloads the compiled design onto an FPGA.We can then run the application by executing the host program on a Windows(tm)-based machine, which communicates with kernels on an FPGA using a PCIe interface. We implement four applications on an Altera Stratix IV and present the throughput and area results for each application. We show that we can achieve a clock frequency in excess of 160MHz on our benchmarks, and that OpenCL computing paradigm is a viable design entry method for high-performance computing applications on FPGAs.
Driver Inattention Monitoring System for Intelligent Vehicles: A Review

In this paper, we review the state-of-the-art technologies for driver inattention monitoring, which can be classified into the following two main categories: 1) distraction and 2) fatigue. Driver inattention is a major factor in most traffic accidents. Research and development has actively been carried out for decades, with the goal of precisely determining the drivers' state of mind. In this paper, we summarize these approaches by dividing them into the following five different types of measures: 1) subjective report measures; 2) driver biological measures; 3) driver physical measures; 4) driving performance measures; and 5) hybrid measures. Among these approaches, subjective report measures and driver biological measures are not suitable under real driving conditions but could serve as some rough ground-truth indicators. The hybrid measures are believed to give more reliable solutions compared with single driver physical measures or driving performance measures, because the hybrid measures minimize the number of false alarms and maintain a high recognition rate, which promote the acceptance of the system. We also discuss some nonlinear modeling techniques commonly used in the literature.
Mining Association Rules with Item Constraints

The problem of discovering association rules has received considerable research attention and several fast algorithms for mining association rules have been developed. In practice, users are often interested in a subset of association rules. For example, they may only want rules that contain a specific item or rules that contain children of a specific item in a hierarchy. While such constraints can be applied as a postprocessing step, integrating them into the mining algorithm can dramatically reduce the execution time. We consider the problem of integrating constraints that n..,, l.....l,.... ,....,,....:,,, -1.~.. cl., -..s..a..-m e.. ..l.“,“, CUG Y”“Ac;Qu GnpLz:I)DIVua “YGI “Us: pGYaLcG “I OLJDciliLG of items into the association discovery algorithm. We present three integrated algorithms for mining association rules with item constraints and discuss their tradeoffs.
Theory and simulation in neuroscience.

Modeling work in neuroscience can be classified using two different criteria. The first one is the complexity of the model, ranging from simplified conceptual models that are amenable to mathematical analysis to detailed models that require simulations in order to understand their properties. The second criterion is that of direction of workflow, which can be from microscopic to macroscopic scales (bottom-up) or from behavioral target functions to properties of components (top-down). We review the interaction of theory and simulation using examples of top-down and bottom-up studies and point to some current developments in the fields of computational and theoretical neuroscience.
Corporate Governance of Big Data: Perspectives on Value, Risk, and Cost

Finding data governance practices that maintain a balance between value creation and risk exposure is the new organizational imperative for unlocking competitive advantage and maximizing value from the application of big data. The first Web extra at http://youtu.be/B2RlkoNjrzA is a video in which author Paul Tallon expands on his article "Corporate Governance of Big Data: Perspectives on Value, Risk, and Cost" and discusses how finding data governance practices that maintain a balance between value creation and risk exposure is the new organizational imperative for unlocking competitive advantage and maximizing value from the application of big data. The second Web extra at http://youtu.be/g0RFa4swaf4 is a video in which author Paul Tallon discusses the supplementary material to his article "Corporate Governance of Big Data: Perspectives on Value, Risk, and Cost" and how projection models can help individuals responsible for data handling plan for and understand big data storage issues.
Lexicon-Based Methods for Sentiment Analysis

We present a lexicon-based approach to extracting sentiment from text. The Semantic Orientation CALculator (SO-CAL) uses dictionaries of words annotated with their semantic orientation (polarity and strength), and incorporates intensification and negation. SO-CAL is applied to the polarity classification task, the process of assigning a positive or negative label to a text that captures the text's opinion towards its main subject matter. We show that SO-CAL's performance is consistent across domains and in completely unseen data. Additionally, we describe the process of dictionary creation, and our use of Mechanical Turk to check dictionaries for consistency and reliability.
Non-rigid range-scan alignment using thin-plate splines

We present a nonrigid alignment algorithm for aligning high-resolution range data in the presence of low-frequency deformations, such as those caused by scanner calibration error. Traditional iterative closest points (ICP) algorithms, which rely on rigid-body alignment, fail in these cases because the error appears as a nonrigid warp in the data. Our algorithm combines the robustness and efficiency of ICP with the expressiveness of thin-plate splines to align high-resolution scanned data accurately, such as scans from the Digital Michelangelo Project [M. Levoy et al. (2000)]. This application is distinguished from previous uses of the thin-plate spline by the fact that the resolution and size of warping are several orders of magnitude smaller than the extent of the mesh, thus requiring especially precise feature correspondence.
A Pedestrian Detector Using Histograms of Oriented Gradients and a Support Vector Machine Classifier

This paper details filtering subsystem for a tetra-vision based pedestrian detection system. The complete system is based on the use of both visible and far infrared cameras; in an initial phase it produces a list of areas of attention in the images which can contain pedestrians. This list is furtherly refined using symmetry-based assumptions. Then, this results is fed to a number of independent validators that evaluate the presence of human shapes inside the areas of attention. Histogram of oriented gradients and Support Vector Machines are used as a filter and demonstrated to be able to successfully classify up to 91% of pedestrians in the areas of attention.
A scalable hash ripple join algorithm

Recently, Haas and Hellerstein proposed the hash ripple join algorithm in the context of online aggregation. Although the algorithm rapidly gives a good estimate for many join-aggregate problem instances, the convergence can be slow if the number of tuples that satisfy the join predicate is small or if there are many groups in the output. Furthermore, if memory overflows (for example, because the user allows the algorithm to run to completion for an exact answer), the algorithm degenerates to block ripple join and performance suffers. In this paper, we build on the work of Haas and Hellerstein and propose a new algorithm that (a) combines parallelism with sampling to speed convergence, and (b) maintains good performance in the presence of memory overflow. Results from a prototype implementation in a parallel DBMS show that its rate of convergence scales with the number of processors, and that when allowed to run to completion, even in the presence of memory overflow, it is competitive with the traditional parallel hybrid hash join algorithm.
Modeling Semantic Expectation: Using Script Knowledge for Referent Prediction

Recent research in psycholinguistics has provided increasing evidence that humans predict upcoming content. Prediction also affects perception and might be a key to robustness in human language processing. In this paper, we investigate the factors that affect human prediction by building a computational model that can predict upcoming discourse referents based on linguistic knowledge alone vs. linguistic knowledge jointly with common-sense knowledge in the form of scripts. We find that script knowledge significantly improves model estimates of human predictions. In a second study, we test the highly controversial hypothesis that predictability influences referring expression type but do not find evidence for such an effect.