nils-herrmann/clean_scirepeval huggingface dataset:
Anchoring Data Quality Dimensions in Ontological Foundations

of an organization. A leading computer industry information service firm indicated that it “expects most business process reengineering initiatives to fail through lack of attention to data quality.” An industry executive report noted that more than 60% of surveyed firms (500 medium-size corporations with annual sales of more than $20 million) had problems with data quality. The Wall Street Journal also reported that, “Thanks to computers, huge databases brimming with information are at our fingertips, just waiting to be tapped. They can be mined to find sales Anchoring Data Quality Dimensions Ontological Foundations
Reentrant Polygon Clipping

A new family of clipping algorithms is described. These algorithms are able to clip polygons against irregular convex plane-faced volumes in three dimensions, removing the parts of the polygon which lie outside the volume. In two dimensions the algorithms permit clipping against irregular convex windows.
Polygons to be clipped are represented as an ordered sequence of vertices without repetition of first and last, in marked contrast to representation as a collection of edges as was heretofore the common procedure. Output polygons have an identical format, with new vertices introduced in sequence to describe any newly-cut edge or edges. The algorithms easily handle the particularly difficult problem of detecting that a new vertex may be required at a corner of the clipping window.
The algorithms described achieve considerable simplicity by clipping separately against each clipping plane or window boundary. Code capable of clipping the polygon against a single boundary is reentered to clip against subsequent boundaries. Each such reentrant stage of clipping need store only two vertex values and may begin its processing as soon as the first output vertex from the preceeding stage is ready. Because the same code is reentered for clipping against subsequent boundaries, clipping against very complex window shapes is practical.
For perspective applications in three dimensions, a six-plane truncated pyramid is chosen as the clipping volume. The two additional planes parallel to the projection screen serve to limit the range of depth preserved through the projection. A perspective projection method which provides for arbitrary view angles and depth of field in spite of simple fixed clipping planes is described. This method is ideal for subsequent hidden-surface computations.
Enhanced Visibility of MoS 2 , MoSe 2 , WSe 2 and Black-Phosphorus : Making Optical Identification of 2 D Semiconductors Easier

We explore the use of Si3N4/Si substrates as a substitute of the standard SiO2/Si substrates employed nowadays to fabricate nanodevices based on 2D materials. We systematically study the visibility of several 2D semiconducting materials that are attracting a great deal of interest in nanoelectronics and optoelectronics: MoS2, MoSe2, WSe2 and black-phosphorus. We find that the use of Si3N4/Si substrates provides an increase of the optical contrast up to a 50%–100% and also the maximum contrast shifts towards wavelength values optimal for human eye detection, making optical identification of 2D semiconductors easier.
Trends in augmented reality tracking, interaction and display: A review of ten years of ISMAR

Although Augmented Reality technology was first developed over forty years ago, there has been little survey work giving an overview of recent research in the field. This paper reviews the ten-year development of the work presented at the ISMAR conference and its predecessors with a particular focus on tracking, interaction and display research. It provides a roadmap for future augmented reality research which will be of great value to this relatively young field, and also for helping researchers decide which topics should be explored when they are beginning their own studies in the area.
Web API growing pains: Stories from client developers and their code

Web APIs provide a systematic and extensible approach for application-to-application interaction. Developers using web APIs are forced to accompany the API providers in their software evolution tasks. In order to understand the distress caused by this imposition on web API client developers we perform a semi-structured interview with six such developers. We also investigate how major web API providers organize their API evolution, and we explore how this affects source code changes of their clients. Our exploratory study of the Twitter, Google Maps, Facebook and Netflix web APIs analyzes the state of web API evolution practices and provides insight into the impact of service evolution on client software. Our study is complemented with a set of observations regarding best practices for web API evolution.
Posterior vertebral column resection for severe pediatric deformity: minimum two-year follow-up of thirty-five consecutive patients.

Retrospective review of a prospectively accrued patient cohort.



The ability to treat severe pediatric spinal deformity through an all-posterior vertebral column resection (VCR) has obviated the need for a circumferential approach in both primary and revision settings. We examined indications, correction rates, and complications of this challenging procedure in the pediatric population.



Traditionally, severe pediatric spinal deformities were treated through a combined anterior/posterior spinal fusion.



Between 2000 and 2005, 35 consecutive patients underwent a posterior-only VCR by 1 of 2 surgeons at a single institution. Patients were divided into 5 diagnostic categories: (1) severe scoliosis (S) (n = 2; mean, 115 degrees; range, 79-150 degrees; average flexibility, 12%); (2) global kyphosis (GK) (n = 3; mean, 101 degrees; range, 91-113 degrees; average flexibility, 16%); (3) angular kyphosis (AK) (n = 10; mean, 86 degrees; range, 45-135 degrees, average flexibility, 23%); (4) kyphoscoliosis (KS) (n = 8; mean kyphosis, 103 degrees/scoliosis 87 degrees; mean combined, 190 degrees; range, 144-237 degrees); (5) congenital scoliosis (CS) (n = 12; mean, 43 degrees; range, 23-69 degrees; average flexibility, 20%). There were 20 primary/15 revision surgeries. There were 20 one-level, 11 two-level, and 4 three-level resections.



The major curve correction averaged: Group S = 61 degrees/51%, Group GK = 56 degrees/55%, Group AK = 51 degrees/58%, Group KS = 98 degrees/54%, and Group CS = 24 degrees/60%. The average OR time was 460 minutes (range, 210-822), with an average EBL of 691 mL (range, 125-2200). There were no spinal cord-related complications; however, 2 patients (8.5%) lost intraoperative neuromonitoring data during correction with data returning to baseline following prompt surgical intervention. Two patients had implant revisions, 1 for a delayed deep infection at 2 years and the other for implant prominence at 3-year follow-up.



A posterior-based VCR is a safe but challenging technique to treat severe primary or revision pediatric spinal deformities. Intraoperative SCM (especially motor-evoked potentials) is mandatory to prevent spinal cord-related neurologic complications. Dramatic radiographic and clinical correction of these deformities can be obtained via a posterior-only approach.
Social entrepreneurship research : A source of explanation , prediction , and delight

Social entrepreneurship, as a practice and a field for scholarly investigation, provides a unique opportunity to challenge, question, and rethink concepts and assumptions from different fields of management and business research. This article puts forward a view of social entrepreneurship as a process that catalyzes social change and addresses important social needs in a way that is not dominated by direct financial benefits for the entrepreneurs. Social entrepreneurship is seen as differing from other forms of entrepreneurship in the relatively higher priority given to promoting social value and development versus capturing economic value. To stimulate future research the authors introduce the concept of embeddedness as a nexus between theoretical perspectives for the study of social entrepreneurship.
Honeypot Frameworks and Their Applications: A New Framework

Honeypot is a decoy system or a simulated application which simulates an entire network to lure attacker by disguising itself with popular vulnerabilities. There are different types of honeypots. For instance, a research honeypot can assist researchers to monitor and analyse the activities of the attacker that are captured in the honeypot. Usually, honeypot can be categorised into three different sub-types based on its purpose, interaction and form. It is then further categorised according to its nature, specialization and framework. Honeypot, however, is not a foolproof concept; often it can be detected by experienced attacker. The information about the features of honeypot and anti-honeypot tools are widely available online to educate attackers. This book will cover the honeypot to detect some of the more popular and damaging attacks such as worm, DDoS, APT, phishing and insider breaches. It will also cover the application of forensics work in honeypot and proposed concept from honeypot researchers to enhance the features of honeypot so as to make it difficult distinguish between a real host and honeypot.
An Improved k-Nearest Neighbor Classification Using Genetic Algorithm

k-Nearest Neighbor (KNN) is one of the most popular algorithms for pattern recognition. Many researchers have found that the KNN algorithm accomplishes very good performance in their experiments on different data sets. The traditional KNN text classification algorithm has three limitations: (i) calculation complexity due to the usage of all the training samples for classification, (ii) the performance is solely dependent on the training set, and (iii) there is no weight difference between samples. To overcome these limitations, an improved version of KNN is proposed in this paper. Genetic Algorithm (GA) is combined with KNN to improve its classification performance. Instead of considering all the training samples and taking k-neighbors, the GA is employed to take k-neighbors straightaway and then calculate the distance to classify the test samples. Before classification, initially the reduced feature set is received from a novel method based on Rough set theory hybrid with Bee Colony Optimization (BCO) as we have discussed in our earlier work. The performance is compared with the traditional KNN, CART and SVM classifiers.
Evaluation of LoRa receiver performance under co-technology interference

LoRa networks achieve a remarkably wide coverage as compared to that of the conventional wireless systems by employing the chirp spread spectrum (CSS). However, single-hop LoRa networks have limited penetration in indoor environments and cannot efficiently utilize the multiple-access dimensions arising from different spreading factors (SF) because the star topology is typically used for LoRa. On the other hand, a multi-hop LoRa network has the potential to realize extensive network coverage by utilizing packet relaying and improving the network capacity by utilizing different SFs. We evaluated the LoRa receiver performance under co-technology interference via simulation and experiments to realize these potentials. The results show that LoRa can survive interference from time-synchronized packets transmitted by multiple transmitters with an identical SF. Furthermore, we examined the orthogonality between different SFs by evaluating the required signal-to-interference ratio (SIR). Finally, we demonstrated the possibility of employing different SFs to construct the pipeline in a multi-hop relay network to increase network efficiency.
Comparison of Wavelet and Short Time Fourier Transform Methods in the Analysis of EMG Signals

The electromyographic (EMG) signal observed at the surface of the skin is the sum of thousands of small potentials generated in the muscle fiber. There are many approaches to analyzing EMG signals with spectral techniques. In this study, the short time Fourier Transform (STFT) and wavelet transform (WT) were applied to EMG signals and coefficients were obtained. In these studies, MATLAB 7.01 program was used. According to obtained results, it was determined that WT is more useful than STFT in the fields of eliminating of resolution problem and providing of changeable resolution during analyze.
An in-depth study of the promises and perils of mining GitHub

With over 10 million git repositories, GitHub is becoming one of the most important sources of software artifacts on the Internet. Researchers mine the information stored in GitHub’s event logs to understand how its users employ the site to collaborate on software, but so far there have been no studies describing the quality and properties of the available GitHub data. We document the results of an empirical study aimed at understanding the characteristics of the repositories and users in GitHub; we see how users take advantage of GitHub’s main features and how their activity is tracked on GitHub and related datasets to point out misalignment between the real and mined data. Our results indicate that while GitHub is a rich source of data on software development, mining GitHub for research purposes should take various potential perils into consideration. For example, we show that the majority of the projects are personal and inactive, and that almost 40 % of all pull requests do not appear as merged even though they were. Also, approximately half of GitHub’s registered users do not have public activity, while the activity of GitHub users in repositories is not always easy to pinpoint. We use our identified perils to see if they can pose validity threats; we review selected papers from the MSR 2014 Mining Challenge and see if there are potential impacts to consider. We provide a set of recommendations for software engineering researchers on how to approach the data in GitHub.
Mining Public Transport User Behaviour from Smart Card Data

In urban public transport, smart card data is made of millions of observations of users boarding vehicles over the network across several days. The issue addresses whether data mining techniques can be used to study user behaviour from these observations. This must be done with the help of transportation planning knowledge. Hence, this paper presents a common “transportation planning/data mining” methodology for user behaviour analysis. Experiments were conducted on data from a Canadian transit authority. This experience demonstrates that a combination of planning knowledge and data mining tool allows producing travel behaviours indicators, mainly regarding regularity and daily patterns, from data issued from operational and management system. Results show that the public transport users of this study can rapidly be divided in four major behavioural groups, whatever type of ticket they use.
Kinematic and dynamic vehicle models for autonomous driving control design

We study the use of kinematic and dynamic vehicle models for model-based control design used in autonomous driving. In particular, we analyze the statistics of the forecast error of these two models by using experimental data. In addition, we study the effect of discretization on forecast error. We use the results of the first part to motivate the design of a controller for an autonomous vehicle using model predictive control (MPC) and a simple kinematic bicycle model. The proposed approach is less computationally expensive than existing methods which use vehicle tire models. Moreover it can be implemented at low vehicle speeds where tire models become singular. Experimental results show the effectiveness of the proposed approach at various speeds on windy roads.
gNUFFTW : Auto-Tuning for High-Performance GPU-Accelerated Non-Uniform Fast Fourier Transforms by Teresa

Non-uniform sampling of the Fourier transform appears in many important applications such as magnetic resonance imaging (MRI), optics, tomography and radio interferometry. Computing the inverse often requires fast application of the non-uniform discrete Fourier transform (NUDFT) and its adjoint operation.  and localized interpolations. These approaches require selecting several parameters, such as interpolation and FFT grid sizes, which affect both the accuracy and runtime. In addition, different implementations lie on a spectrum of precomputation levels, which can further speed up repeated computations, with various trade-offs in planning time, execution time and memory usage. Choosing the optimal parameters and implementations is important for performance speed, but difficult to do manually since the performance of NUFFT is not well-understood for modern parallel processors. Inspired by the FFTW library, we demonstrate an empirical auto-tuning approach for the NUFFT on General Purpose Graphics Processors Units (GPGPU). We demonstrate order-of-magnitude speed improvements with autotuning compared to typical default choices. Our auto-tuning is implemented in an easy to use proof-of-concept library called gNUFFTW, which leverages existing open-source NUFFT packages, cuFFT and cuSPARSE libraries, as well as our own NUFFT implementations for high performance. non-uniform, non-Cartesian, FFT, NUFFT, GPU, auto-tuning, image processing.
Robust Image Registration Using Log-Polar Transform

This paper describes a hierarchical image registration algorithm for affine motion recovery. The algorithm estimates the affine transformation parameters necessary to register any two digital images misaligned due to rotation, scale, shear, and translation. The parameters are computed iteratively in a coarse-to-fine hierarchical framework using a variation of the Levenberg-Marquadt nonlinear least squares optimization method. This approach yields a robust solution that precisely registers images with subpixel accuracy. A log-polar registration module is introduced to accommodate arbitrary rotation angles and a wide range of scale changes. This serves to furnish a good initial estimate for the optimization-based affine registration stage. We demonstrate the hybrid algorithm on pairs of digital images subjected to large affine motion.
Intelligent Wireless Patient Monitoring and Tracking System (Using Sensor Network and Wireless Communication).

*  Ch.Sandeep Kumar Subudhi Abstract Aim of our work is to monitor the human body temperature, blood pressure (BP), Pulse Rate and ECG and tracking the patient location. The human body temperature, BP, Pulse Rate and ECG are detected in the working environment; this can be sensed by using respective sensors. The sensed information is send to the PIC16F877 microcontroller through signal conditioning circuit in the patient unit. A desired amount of sensor value is set and if it is exceeded preliminary steps should be taken by the indicating by buzzer.The sensor information will be transmitted from the patient unit to the main controller unit with the help of Zigbee communication system which is connected with the microcontrollers in the both units. The main controller unit will send those sensed data as well as the location of that patient by the help of GPS Module to the observer/doctor. The observer/doctor can receive the SMS sent by GSM module and further decision can be taken. The message is sent to a mobile phone using Global system mobile (GSM) Modem. MAX232 was a driver between microcontroller and modem.
A soft, bistable valve for autonomous control of soft actuators

Almost all pneumatic and hydraulic actuators useful for mesoscale functions rely on hard valves for control. This article describes a soft, elastomeric valve that contains a bistable membrane, which acts as a mechanical “switch” to control air flow. A structural instability—often called “snap-through”—enables rapid transition between two stable states of the membrane. The snap-upward pressure, ΔP1 (kilopascals), of the membrane differs from the snap-downward pressure, ΔP2 (kilopascals). The values ΔP1 and ΔP2 can be designed by changing the geometry and the material of the membrane. The valve does not require power to remain in either “open” or “closed” states (although switching does require energy), can be designed to be bistable, and can remain in either state without further applied pressure. When integrated in a feedback pneumatic circuit, the valve functions as a pneumatic oscillator (between the pressures ΔP1 and ΔP2), generating periodic motion using air from a single source of constant pressure. The valve, as a component of pneumatic circuits, enables (i) a gripper to grasp a ball autonomously and (ii) autonomous earthworm-like locomotion using an air source of constant pressure. These valves are fabricated using straightforward molding and offer a way of integrating simple control and logic functions directly into soft actuators and robots.
Multilevel thresholding for satellite image segmentation with moth-flame based optimization

In this paper, an improved version of the moth-flame optimization (MFO) algorithm for image segmentation is proposed to effectively enhance the optimal multilevel thresholding of satellite images. Multilevel thresholding is one of the most widely used methods for image segmentation, as it has efficient processing ability and easy implementation. However, as the number of threshold values increase, it consequently becomes computationally expensive. To overcome this problem, the nature-inspired meta-heuristic named multilevel thresholding moth-flame optimization algorithm (MTMFO) for multilevel thresholding was developed. The improved method proposed herein was tested on various satellite images tested against five different existing methods: the genetic algorithm (GA), the differential evolution (DE) algorithm, the artificial bee colony (ABC) algorithm, the particle swarm optimization (PSO) algorithm, and the moth-flame optimization (MFO) algorithm for solving multilevel satellite image thresholding problems. Experimental results indicate that the MTMFO more effectively and accurately identifies the optimal threshold values with respect to the other state-of-the-art optimization algorithms.
Local Feature Based Multiple Object Instance Identification Using Scale and Rotation Invariant Implicit Shape Model

In this paper, we propose a Scale and Rotation Invariant Implicit Shape Model (SRIISM), and develop a local feature matching based system using the model to accurately locate and identify large numbers of object instances in an image. Due to repeated instances and cluttered background, conventional methods for multiple object instance identification suffer from poor identification results. In the proposed SRIISM, we model the joint distribution of object centers, scale, and orientation computed from local feature matches in Hough voting, which is not only invariant to scale changes and rotation of objects, but also robust to false feature matches. In the multiple object instance identification system using SRIISM, we apply a fast 4D bin search method in Hough space with complexity O(n), where n is the number of feature matches, in order to segment and locate each instance. Futhermore, we apply maximum likelihood estimation (MLE) for accurate object pose detection. In the evaluation, we created datasets simulating various industrial applications such as pick-and-place and inventory management. Experiment results on the datasets show that our method outperforms conventional methods in both accuracy (5%-30% gain) and speed (2x speed up).