nils-herrmann/clean_scirepeval huggingface dataset:
Pothole Detection System using Machine Learning on Android

This paper investigates an application of mobile sensing: detection of potholes on roads. We describe a system and an associated algorithm to monitor the pothole conditions on the road. This system, that we call the Pothole Detection System, uses Accelerometer Sensor of Android smartphone for detection of potholes and GPS for plotting the location of potholes on Google Maps. Using a simple machine-learning approach, we show that we are able to identify the potholes from accelerometer data. The pothole detection algorithm detects the potholes in real-time. A runtime graph has been shown with the help of a charting software library ‘AChartEngine’. Accelerometer data and pothole data can be mailed to any email address in the form of a ‘.csv’ file. While designing the pothole detection algorithm we have assumed some threshold values on x-axis and z-axis. These threshold values are justified using a neural network technique which confirms an accuracy of 90%-95%. The neural network has been implemented using a machine learning framework available for Android called ‘Encog’. We evaluate our system on the outputs obtained using two, three and four wheelers.  Machine Learning, Context, Android, Neural Networks, Pothole, Sensor
Statistics of Speckle in Ultrasound B-Scans

In the ultrasound imaging process, the complex summation at the transducer face is assumed to be linear. The envelope detection process in B-scanning is a nonlinear step which yields essentially the magnitude of the complex field or voltage. (A square law detector, as in the analogous laser case, yields the square of the magnitude, i.e., the intensity, of the field.) I t is shown that Rayleigh statistics govern the fist-order behavior of the magnitude; and the autocorrelation f the resulting image speckle is obtained by the method of Middleton. The corresponding power spectrum follows immediately by Fourier transformation. Theoretical and experimentally determined autocorrelation functions and power spectra derived from B-scans of a scattering phantom containing many scatterers per resolution cell are presented. These functions lead naturally to the definition of the average speckle spot or cell sue, and this in turn is comparable to the resolution cell. Each independent speckle serves as a degree of freedom that determines the number of samples of tissue available over a target. As the speckle cell size decreases this number increases in a manner predictable from the physical parameters of the cell size. However, it is found that the speckle cell is broadened, the degrees of freedom diminished, when the object structure is correlated. This yields the possibility of deducing information about the object structure from the second-order statistics of the speckle texture, in addition to that obtainable from the fiistorder statistics.
AN IMPROVED DEFECT CLASSIFICATION ALGORITHM FOR SIX PRINTING DEFECTS AND ITS IMPLEMENTATION ON REAL PRINTED CIRCUIT BOARD IMAGES

Because decisions made by human inspectors often involve subjective judgment, in addition to being intensive and therefore costly, an automated approach for printed circuit board (PCB) inspection is preferred to eliminate subjective discrimination and thus provide fast, quantitative, and dimensional assessments. In this study, defect classification is essential to the identification of defect sources. Therefore, an algorithm for PCB defect classification is presented that consists of well-known conventional operations, including image difference, image subtraction, image addition, counted image comparator, flood-fill, and labeling for the classification of six different defects, namely, missing hole, pinhole, underetch, short-circuit, open-circuit, and mousebite. The defect classification algorithm is improved by incorporating proper image registration and thresholding techniques to solve the alignment and uneven illumination problem. The improved PCB defect classification algorithm has been applied to real PCB images to successfully classify all of the defects.
Universal Accumulators with Efficient Nonmembership Proofs

Based on the notion of accumulators, we propose a new cryptog raphic scheme called universal accumulators. This scheme enables one to commit to a set of values using a short accumulator and to efficiently com pute a membership witness of any value that has been accumulated. Unlike tradi tional accumulators, this scheme also enables one to efficiently compute a nonmemb ership witness of any value that has not been accumulated. We give a construc tion for universal accumulators and prove its security based on the strong RSA a ssumption. We further present a construction for dynamic universal accumula tors; this construction allows one to dynamically add and delete inputs with constan t computational cost. Our construction directly builds upon Camenisch and L ysyanskaya’s dynamic accumulator scheme. Universal accumulators can be se en as an extension to dynamic accumulators with support of nonmembership witn ess. We also give an efficient zero-knowledge proof protocol for proving that a committed value is not in the accumulator. Our dynamic universal accumulator c onstruction enables efficient membership revocation in an anonymous fashion.
PWM Based Automatic Closed Loop Speed Control of DC Motor

The electric drive systems used in many industrial applications require higher performance, reliability, variable speed due to its ease of controllability. The speed control of DC motor is very crucial in applications where precision and protection are of essence. Purpose of a motor speed controller is to take a signal representing the required speed and to drive a motor at that speed. Microcontrollers can provide easy control of DC motor. Microcontroller based speed control system consist of electronic component, microcontroller and the LCD. In this paper, implementation of the ATmega8L microcontroller for speed control of DC motor fed by a DC chopper has been investigated. The chopper is driven by a high frequency PWM signal. Controlling the PWM duty cycle is equivalent to controlling the motor terminal voltage, which in turn adjusts directly the motor speed. This work is a practical one and high feasibility according to economic point of view and accuracy. In this work, development of hardware and software of the close loop dc motor speed control system have been explained and illustrated. The desired objective is to achieve a system with the constant speed at any load condition. That means motor will run at a fixed speed instead of varying with amount of load. KeywordsDC motor, Speed control, Microcontroller, ATmega8, PWM.
Human Action Recognition without Human

The objective of this paper is to evaluate “human action recognition without human”. Motion representation is frequently discussed in human action recognition. We have examined several sophisticated options, such as dense trajectories (DT) and the two-stream convolutional neural network (CNN). However, some features from the background could be too strong, as shown in some recent studies on human action recognition. Therefore, we considered whether a background sequence alone can classify human actions in current large-scale action datasets (e.g., UCF101). In this paper, we propose a novel concept for human action analysis that is named “human action recognition without human”. An experiment clearly shows the effect of a background sequence for understanding an action label.
On the Robustness of Mobile Device Fingerprinting: Can Mobile Users Escape Modern Web-Tracking Mechanisms?

Client fingerprinting techniques enhance classical cookie-based user tracking to increase the robustness of tracking techniques. A unique identifier is created based on characteristic attributes of the client device, and then used for deployment of personalized advertisements or similar use cases. Whereas fingerprinting performs well for highly customized devices (especially desktop computers), these methods often lack in precision for highly standardized devices like mobile phones.
 In this paper, we show that widely used techniques do not perform well for mobile devices yet, but that it is possible to build a fingerprinting system for precise recognition and identification. We evaluate our proposed system in an online study and verify its robustness against misclassification.
 Fingerprinting of web clients is often seen as an offence to web users' privacy as it usually takes place without the users' knowledge, awareness, and consent. Thus, we also analyze whether it is possible to outrun fingerprinting of mobile devices. We investigate different scenarios in which users are able to circumvent a fingerprinting system and evade our newly created methods.
Discursive Meaning Creation in Crowdfunding: A Socio-material Perspective

Crowdfunding is an exciting new phenomenon with the potential to disrupt early-stage capital markets. Enabled through specialized internet websites and social media, entrepreneurs now have a new source for start-up capital (estimated at $2.8 billion in 2012). Currently, entrepreneurs need to network through intermediaries to have access to wealthy investors. Crowdfunding bypasses these intermediaries and brings the ability to raise capital to the crowd. Consequently, decisions to fund an entrepreneurial endeavor are not made through ‘who you know’ and back-room deals, but through the discourse that occurs through the crowdfunding project page. The purpose of this research is to analyze and understand this discourse and the meaning it creates over the course of a crowdfunding campaign. The lens of sociomateriality in conjunction with discourse analysis is used to identify how meaning is created and its influence on the IS artifact.
Robust Sclera Recognition System With Novel Sclera Segmentation and Validation Techniques

Sclera blood veins have been investigated recently as a biometric trait which can be used in a recognition system. The sclera is the white and opaque outer protective part of the eye. This part of the eye has visible blood veins which are randomly distributed. This feature makes these blood veins a promising factor for eye recognition. The sclera has an advantage in that it can be captured using a visible-wavelength camera. Therefore, applications which may involve the sclera are wide ranging. The contribution of this paper is the design of a robust sclera recognition system with high accuracy. The system comprises of new sclera segmentation and occluded eye detection methods. We also propose an efficient method for vessel enhancement, extraction, and binarization. In the feature extraction and matching process stages, we additionally develop an efficient method, that is, orientation, scale, illumination, and deformation invariant. The obtained results using UBIRIS.v1 and UTIRIS databases show an advantage in terms of segmentation accuracy and computational complexity compared with state-of-the-art methods due to Thomas, Oh, Zhou, and Das.
Smart integrated temperature sensor - mixed-signal circuits and systems in 32-nm and beyond

Integrating smart temperature sensors into digital platforms facilitates information to be processed and transmitted, and open up new applications. Furthermore, temperature sensors are crucial components in computing platforms to manage power-efficiency trade-offs reliably under a thermal budget. This paper presents a holistic perspective about smart temperature sensor design from system- to device-level including manufacturing concerns. Through smart sensor design evolutions, we identify some scaling paths and circuit techniques to surmount analog/mixed-signal design challenges in 32-nm and beyond. We close with opportunities to design smarter temperature sensors.
Glomerular filtration rate, proteinuria, and the incidence and consequences of acute kidney injury: a cohort study

Low values of estimated glomerular filtration rate (eGFR) predispose to acute kidney injury, and proteinuria is a marker of kidney disease. We aimed to investigate how eGFR and proteinuria jointly modified the risks of acute kidney injury and subsequent adverse clinical outcomes.



We did a cohort study of 920,985 adults residing in Alberta, Canada, between 2002 and 2007. Participants not needing chronic dialysis at baseline and with at least one outpatient measurement of both serum creatinine concentration and proteinuria (urine dipstick or albumin-creatinine ratio) were included. We assessed hospital admission with acute kidney injury with validated administrative codes; other outcomes were all-cause mortality and a composite renal outcome of end-stage renal disease or doubling of serum creatinine concentration.



During median follow-up of 35 months (range 0-59 months), 6520 (0·7%) participants were admitted with acute kidney injury. In those with eGFR 60 mL/min per 1·73 m(2) or greater, the adjusted risk of admission with this disorder was about 4 times higher in those with heavy proteinuria measured by dipstick (rate ratio 4·4 vs no proteinuria, 95% CI 3·7-5·2). The adjusted rates of admission with acute kidney injury and kidney injury needing dialysis remained high in participants with heavy dipstick proteinuria for all values of eGFR. The adjusted rates of death and the composite renal outcome were also high in participants admitted with acute kidney injury, although the rise associated with this injury was attenuated in those with low baseline eGFR and heavy proteinuria.



These findings suggest that information on proteinuria and eGFR should be used together when identifying people at risk of acute kidney injury, and that an episode of acute kidney injury provides further long-term prognostic information in addition to eGFR and proteinuria.



The study was funded by an interdisciplinary team grant from Alberta Heritage Foundation for Medical Research.
Six dangerous myths about pay.

Every day, executives make decisions about pay, and they do so in a landscape that's shifting. As more and more companies base less of their compensation on straight salary and look to other financial options, managers are bombarded with advice about the best approaches to take. Unfortunately, much of that advice is wrong. Indeed, much of the conventional wisdom and public discussion about pay today is misleading, incorrect, or both. The result is that business people are adopting wrongheaded notions about how to pay people and why. In particular, they are subscribing to six dangerous myths about pay. Myth #1: labor rates are the same as labor costs. Myth #2: cutting labor rates will lower labor costs.  labor costs represent a large portion of a company's total costs. Myth #4: keeping labor costs low creates a potent and sustainable competitive edge. Myth #5: individual incentive pay improves performance. Myth #6: people work primarily for the money. The author explains why these myths are so pervasive, shows where they go wrong, and suggests how leaders might think more productively about compensation. With increasing frequency, the author says, he sees managers harming their organizations by buying into--and acting on--these myths. Those that do, he warns, are probably doomed to endless tinkering with pay that at the end of the day will accomplish little but cost a lot.
VR Juggler: a virtual platform for virtual reality application development

Today, scientists and engineers are exploring advanced applications and uses of immersive systems that can be cost-effectively applied in their fields. However, one of the impediments to the widespread use of these technologies is the extensive technical expertise required of application developers. A software environment that provides abstractions from specific details of hardware configurations and low-level software tools is needed to provide a common base for the prototyping, development, testing and debugging of applications. This paper describes VR Juggler, a virtual platform for the creation and execution of immersive applications, that provides a virtual reality system-independent operating environment. We focus on the approach taken to specify, design and implement VR Juggler and the benefits derived from our approach.
Comparison of laser beam propagation at 785 nm and 1550 nm in fog and haze for optical wireless communications

There is currently a misconception among designers and users of free space laser communication (lasercom) equipment that 1550 nm light suffers from less atmospheric attenuation than 785 or 850 nm light in all weather conditions. This misconception is based upon a published equation for atmospheric attenuation as a function of wavelength, which is used frequently in the free-space lasercom literature. In hazy weather (visibility > 2 km), the prediction of less atmospheric attenuation at 1550 nm is most likely true. However, in foggy weather (visibility < 500 m), it appears that the attenuation of laser light is independent of wavelength, ie. 785 nm, 850 nm, and 1550 nm are all attenuated equally by fog. This same wavelength independence is also observed in snow and rain. This observation is based on an extensive literature search, and from full Mie scattering calculations. A modification to the published equation describing the atmospheric attenuation of laser power, which more accurately describes the effects of fog, is offered. This observation of wavelength-independent attenuation in fog is important, because fog, heavy snow, and extreme rain are the only types of weather that are likely to disrupt short (<500 m) lasercom links. Short lasercom links will be necessary to meet the high availability requirements of the telecommunications industry.
Optimization in dynamic environments: a survey on problems, methods and measures

This paper provides a survey of the research done on optimization in dynamic environments over the past decade. We show an analysis of the most commonly used problems, methods and measures together with the newer approaches and trends, as well as their interrelations and common ideas. The survey is supported by a public web repository, located at http://www.dynamic-optimization. org where the collected bibliography is manually organized and tagged according to different categories.
Instant Espresso: Interactive Analysis of Relationships in Knowledge Graphs

We demonstrate InstantEspresso, a system to explain the relationship between two sets of entities in knowledge graphs. InstantEspresso answers questions of the form «Which European politicians are related to politicians in the United States, and how?» or «How can one summarize the relationship between China and countries from the Middle East?» Each question is specified by two sets of query entities. These sets (e. g. European politicians or United States politicians) can be determined by an initial graph query over a knowledge graph capturing relationships between real-world entities. InstantEspresso analyzes the (indirect) relationships that connect entities from both sets and provides a user-friendly explanation of the answer in the form of concise subgraphs. These so-called relatedness cores correspond to important event complexes involving entities from the two sets. Our system provides a user interface for the specification of entity sets and displays a visually appealing visualization of the extracted subgraph to the user. The demonstrated system can be used to provide background information on the current state-of-affairs between realworld entities such as politicians, organizations, and the like, e. g. to a journalist preparing an article involving the entities of interest. InstantEspresso is available for an online demonstration at the URL http://espresso.mpi-inf.mpg.de/.
Efficient Neural Audio Synthesis

Sequential models achieve state-of-the-art results in audio, visual and textual domains with respect to both estimating the data distribution and generating high-quality samples. Efficient sampling for this class of models has however remained an elusive problem. With a focus on text-to-speech synthesis, we describe a set of general techniques for reducing sampling time while maintaining high output quality. We first describe a single-layer recurrent neural network, the WaveRNN, with a dual softmax layer that matches the quality of the state-of-the-art WaveNet model. The compact form of the network makes it possible to generate 24 kHz 16-bit audio 4× faster than real time on a GPU. Second, we apply a weight pruning technique to reduce the number of weights in the WaveRNN. We find that, for a constant number of parameters, large sparse networks perform better than small dense networks and this relationship holds for sparsity levels beyond 96%. The small number of weights in a Sparse WaveRNN makes it possible to sample high-fidelity audio on a mobile CPU in real time. Finally, we propose a new generation scheme based on subscaling that folds a long sequence into a batch of shorter sequences and allows one to generate multiple samples at once. The Subscale WaveRNN produces 16 samples per step without loss of quality and offers an orthogonal method for increasing sampling efficiency.
Sensing muscle activities with body-worn sensors

We demonstrate that simple, unobtrusive sensors attached to the lower arm can be used to capture muscle activations during specific hand and arm activities such as grasping. Specifically, we investigate the use of force sensitive resistors and fabric stretch sensors, that can both be easily integrated into clothing. We use the above sensors to detect the contractions of arm muscles. We present and compare the signals that both sensors produce for a set of typical hand actions. We finally argue that they can provide important information for activity recognition
Predicting Parking Lot Occupancy in Vehicular Ad Hoc Networks

The search for free parking places is a promising application for vehicular ad hoc networks (VANETs). In order to guide drivers to a free parking place at their destination, it is necessary to estimate the occupancy state of the parking lots within the destination area at time of arrival. In this paper, we present a model to predict parking lot occupancy based on information exchanged among vehicles. In particular, our model takes the age of received parking lot information and the time needed to arrive at a certain parking lot into account and estimates the future parking situation at time of arrival. It is based on queueing theory and uses a continuous-time homogeneous Markov model. We have evaluated the model in a simulation study based on a detailed model of the city of Brunswick, Germany.
Heuristics Evaluation Strategy for Mobile Game-Based Learning

Evaluation of learning media is necessary for determining the effectiveness of the produced media. When evaluating mobile game-based learning (mGBL), conventional usability heuristics evaluations lack comprehension and difficult to be directly implemented. Therefore, heuristics evaluation strategy is proposed to evaluate specifically for mGBL. The strategy consists of four components: Game Usability, Mobility, Game Play, and Learning Content. Each of the components represents the issues to be considered and evaluated for mGBL. Additionally, in this study, a prototype of mGBL was developed and evaluated by utilizing the proposed strategy. The results indicate that the strategy is useful and potential to be implemented for similar mGBL applications.
Data Augmentation for Low-Resource Neural Machine Translation

The quality of a Neural Machine Translation system depends substantially on the availability of sizable parallel corpora. For low-resource language pairs this is not the case, resulting in poor translation quality. Inspired by work in computer vision, we propose a novel data augmentation approach that targets low-frequency words by generating new sentence pairs containing rare words in new, synthetically created contexts. Experimental results on simulated low-resource settings show that our method improves translation quality by up to 2.9 BLEU points over the baseline and up to 3.2 BLEU over back-translation.