nils-herrmann/clean_scirepeval huggingface dataset:
The GNOME project: a case study of open source, global software development

Many successful free/open source software (FOSS) projects start with the premise that their contributors are rarely colocated, and as a consequence, these projects are cases of global software development (GSD). This article describes how the GNOME Project, a large FOSS project, has tried to overcome the disadvantages of GSD. The main goal of GNOME is to create a GUI desktop for Unix systems, and encompasses close to two million lines of code. More than 500 individuals (distributed across the world) have contributed to the project. This article also describes the software development methods and practices used by the members of the project, and its organizational structure. The article ends by proposing a list of practices that could benefit other global software development projects, both FOSS and commercial.
0.5-V operational transconductance amplifier for CMOS bandgap reference application

A positive feedback technique that make possible the operation of operational transconductance amplifier (OTA) with very low supply voltage, down to 0.5V, is proposed. The MOSFETs providing quasi-linear temperature characteristic are used along with the proposed OTA to design a low voltage bandgap reference circuit. The 0.045-mm2 chip fabricated in SMIC 0.18-mum CMOS process generates 0.4V reference voltage with the supply down to 0.56V with a maximum temperature coefficient of 80ppm/degC, The total current is 4.8 muA at the room temperature
Stacked cylindrical dielectric resonator antenna with metamaterial as a superstrate for enhancing the bandwidth and gain

A dual segment cylindrical dielectric resonator antenna with metamaterial superstrate is presented in this paper. The metamaterial cover acting as a converging lens is sharply increasing the gain of the proposed antenna and is also responsible for increasing the bandwidth. The proposed antenna is having a better impedance matching (less than -10 dB return loss) in a wide frequency band with a bandwidth of 2.12 GHz (8.49-10.61 GHz) and peak gain of 12.86 dB. The proposed antenna is presented with parametric analysis of some key parameters. Broadside radiation pattern is obtained without metamaterial, while directive radiation pattern with narrow beamwidth is obtained with metamaterial using as a superstrate. The gain characteristics of the proposed antenna are also presented.
Three-Phase Transformerless Shunt Active Power Filter With Reduced Switch Count for Harmonic Compensation in Grid-Connected Applications

Shunt active power filter (SAPF) is the preeminent solution against nonlinear loads, current harmonics, and power quality problems. APF topologies for harmonic compensation use numerous high-power rating components and are therefore disadvantageous. Hybrid topologies combining low-power rating APF with passive filters are used to reduce the power rating of voltage source inverter (VSI). Hybrid APF topologies for high-power rating systems use a transformer with large numbers of passive components. In this paper, a novel four-switch two-leg VSI topology for a three-phase SAPF is proposed for reducing the system cost and size. The proposed topology comprises a two-arm bridge structure, four switches, coupling inductors, and sets of LC PFs. The third leg of the three-phase VSI is removed by eliminating the set of power switching devices, thereby directly connecting the phase with the negative terminals of the dc-link capacitor. The proposed topology enhances the harmonic compensation capability and provides complete reactive power compensation compared with conventional APF topologies. The new experimental prototype is tested in the laboratory to verify the results in terms of total harmonic distortion, balanced supply current, and harmonic compensation, following the IEEE-519 standard.
THE BIOLOGIC BASIS OF TRANSGENDER IDENTITY: 2D:4D FINGER LENGTH RATIOS IMPLICATE A ROLE FOR PRENATAL ANDROGEN ACTIVITY.

The biologic basis for gender identity is unknown. Research has shown that the ratio of the length of the second and fourth digits (2D:4D) in mammals is influenced by biologic sex in utero, but data on 2D:4D ratios in transgender individuals are scarce and contradictory. We investigated a possible association between 2D:4D ratio and gender identity in our transgender clinic population in Albany, New York.



We prospectively recruited 118 transgender subjects undergoing hormonal therapy (50 female to male [FTM] and 68 male to female [MTF]) for finger length measurement. The control group consisted of 37 cisgender volunteers (18 females, 19 males). The length of the second and fourth digits were measured using digital calipers. The 2D:4D ratios were calculated and analyzed with unpaired t tests.



FTM subjects had a smaller dominant hand 2D:4D ratio (0.983 ± 0.027) compared to cisgender female controls (0.998 ± 0.021, P = .029), but a ratio similar to control males (0.972 ± 0.036, P =.19). There was no difference in the 2D:4D ratio of MTF subjects (0.978 ± 0.029) compared to cisgender male controls (0.972 ± 0.036, P = .434).



Our findings are consistent with a biologic basis for transgender identity and the possibilities that FTM gender identity is affected by prenatal androgen activity but that MTF transgender identity has a different basis.



2D:4D = 2nd digit to 4th digit; FTM = female to male; MTF = male to female.
QoS Aware Geographic Opportunistic Routing in Wireless Sensor Networks

QoS routing is an important research issue in wireless sensor networks (WSNs), especially for mission-critical monitoring and surveillance systems which requires timely and reliable data delivery. Existing work exploits multipath routing to guarantee both reliability and delay QoS constraints in WSNs. However, the multipath routing approach suffers from a significant energy cost. In this work, we exploit the geographic opportunistic routing (GOR) for QoS provisioning with both end-to-end reliability and delay constraints in WSNs. Existing GOR protocols are not efficient for QoS provisioning in WSNs, in terms of the energy efficiency and computation delay at each hop. To improve the efficiency of QoS routing in WSNs, we define the problem of efficient GOR for multiconstrained QoS provisioning in WSNs, which can be formulated as a multiobjective multiconstraint optimization problem. Based on the analysis and observations of different routing metrics in GOR, we then propose an Efficient QoS-aware GOR (EQGOR) protocol for QoS provisioning in WSNs. EQGOR selects and prioritizes the forwarding candidate set in an efficient manner, which is suitable for WSNs in respect of energy efficiency, latency, and time complexity. We comprehensively evaluate EQGOR by comparing it with the multipath routing approach and other baseline protocols through ns-2 simulation and evaluate its time complexity through measurement on the MicaZ node. Evaluation results demonstrate the effectiveness of the GOR approach for QoS provisioning in WSNs. EQGOR significantly improves both the end-to-end energy efficiency and latency, and it is characterized by the low time complexity.
Intrusion of Overerupted Molars using Miniscrews and TMA Spring: A Case Report

Praveen Prakash1, Kuttapa Nishanth2, Nikul Jasani1, Aneesh Katyal1, US Krishna Nayak3 1Post Graduate Student, Department of Orthodontics & Dentofacial Orthopaedics, A.B. Shetty Memorial Institute of Dental Sciences, Mangalore, Karnataka, India, 2Professor, Department of Orthodontics & Dentofacial Orthopaedics, A.B. Shetty Memorial Institute of Dental Sciences, Mangalore, Karnataka, India, 3Dean Academics, Head of Department, Department of Orthodontics & Dentofacial Orthopaedics, A.B. Shetty Memorial Institute of Dental Sciences, Mangalore, Karnataka, India
Enhancement of steganography using K strange point clustering

This paper emphasis on hiding the information with all its probabilities in the Cloud Computing. We proposed the execution of steganography through clustering and implemented through K Strange Point clustering algorithm. There is a comparison done between the K Means Clustering Algorithm and our obtained result of K Strange Point Clustering Algorithm. We asset that our proposed methodology proved that it works better with the K Strange Points Clustering Algorithm. To hide data within the covering medium we use LSB algorithm. We finally proposed an enhanced scheme for best hiding capacity.
Patterns of antimicrobial resistance in lesions of hidradenitis suppurativa.

Antibiotic therapy is commonly used to treat hidradenitis suppurativa (HS). Although concern for antibiotic resistance exists, data examining the association between antibiotics and antimicrobial resistance in HS lesions are limited.



We sought to determine the frequency of antimicrobial resistance in HS lesions from patients on antibiotic therapy.



A cross-sectional analysis was conducted on 239 patients with HS seen at the Johns Hopkins Medical Institutions from 2010 through 2015.



Patients using topical clindamycin were more likely to grow clindamycin-resistant Staphylococcus aureus compared with patients using no antibiotics (63% vs 17%; P = .03). Patients taking ciprofloxacin were more likely to grow ciprofloxacin-resistant methicillin-resistant S aureus compared with patients using no antibiotics (100% vs 10%; P = .045). Patients taking trimethoprim/sulfamethoxazole were more likely to grow trimethoprim/sulfamethoxazole-resistant Proteus species compared with patients using no antibiotics (88% vs 0%; P < .001). No significant antimicrobial resistance was observed with tetracyclines or oral clindamycin.



Data on disease characteristics and antimicrobial susceptibilities for certain bacteria were limited.



Antibiotic therapy for HS treatment may be inducing antibiotic resistance. These findings highlight the importance of stewardship in antibiotic therapy for HS and raise questions regarding the balance of antibiotic use versus potential harms associated with antibiotic resistance.
Energy Saving Additive Neural Network

In recent years, machine learning techniques based on neural networks for mobile computing become increasingly popular. Classical multi-layer neural networks require matrix multiplications at each stage. Multiplication operation is not an energy efficient operation and consequently it drains the battery of the mobile device. In this paper, we propose a new energy efficient neural network with the universal approximation property over space of Lebesgue integrable functions. This network, called, additive neural network, is very suitable for mobile computing. The neural structure is based on a novel vector product definition, called ef-operator, that permits a multiplierfree implementation. In ef-operation, the ”product” of two real numbers is defined as the sum of their absolute values, with the sign determined by the sign of the product of the numbers. This ”product” is used to construct a vector product in R . The vector product induces the l1 norm. The proposed additive neural network successfully solves the XOR problem. The experiments on MNIST dataset show that the classification performances of the proposed additive neural networks are very similar to the corresponding multi-layer perceptron and convolutional neural networks (LeNet).
Evaluating CoBlox: A Comparative Study of Robotics Programming Environments for Adult Novices

A new wave of collaborative robots designed to work alongside humans is bringing the automation historically seen in large-scale industrial settings to new, diverse contexts. However, the ability to program these machines often requires years of training, making them inaccessible or impractical for many. This project rethinks what robot programming interfaces could be in order to make them accessible and intuitive for adult novice programmers. We created a block-based interface for programming a one-armed industrial robot and conducted a study with 67 adult novices comparing it to two programming approaches in widespread use in industry. The results show participants using the block-based interface successfully implemented robot programs faster with no loss in accuracy while reporting higher scores for usability, learnability, and overall satisfaction. The contribution of this work is showing the potential for using block-based programming to make powerful technologies accessible to a wider audience.
The N-Tuple bandit evolutionary algorithm for automatic game improvement

This paper describes a new evolutionary algorithm that is especially well suited to AI-Assisted Game Design. The approach adopted in this paper is to use observations of AI agents playing the game to estimate the game's quality. Some of best agents for this purpose are General Video Game AI agents, since they can be deployed directly on a new game without game-specific tuning; these agents tend to be based on stochastic algorithms which give robust but noisy results and tend to be expensive to run. This motivates the main contribution of the paper: the development of the novel N-Tuple Bandit Evolutionary Algorithm, where a model is used to estimate the fitness of unsampled points and a bandit approach is used to balance exploration and exploitation of the search space. Initial results on optimising a Space Battle game variant suggest that the algorithm offers far more robust results than the Random Mutation Hill Climber and a Biased Mutation variant, which are themselves known to offer competitive performance across a range of problems. Subjective observations are also given by human players on the nature of the evolved games, which indicate a preference towards games generated by the N-Tuple algorithm.
An Evolutionary Multiobjective Approach to Sparse Reconstruction

This paper addresses the problem of finding sparse solutions to linear systems. Although this problem involves two competing cost function terms (measurement error and a sparsity-inducing term), previous approaches combine these into a single cost term and solve the problem using conventional numerical optimization methods. In contrast, the main contribution of this paper is to use a multiobjective approach. The paper begins by investigating the sparse reconstruction problem, and presents data to show that knee regions do exist on the Pareto front (PF) for this problem and that optimal solutions can be found in these knee regions. Another contribution of the paper, a new soft-thresholding evolutionary multiobjective algorithm (StEMO), is then presented, which uses a soft-thresholding technique to incorporate two additional heuristics: one with greater chance to increase speed of convergence toward the PF, and another with higher probability to improve the spread of solutions along the PF, enabling an optimal solution to be found in the knee region. Experiments are presented, which show that StEMO significantly outperforms five other well known techniques that are commonly used for sparse reconstruction. Practical applications are also demonstrated to fundamental problems of recovering signals and images from noisy data.
On full text download and citation distributions in scientific-scholarly journals

A statistical analysis of full text downloads of articles in Elsevier's ScienceDirect covering all disciplines reveals large differences in download frequencies, their skewness, and their correlation with Scopus-based citation counts, between disciplines, journals, and document types. Download counts tend to be two orders of magnitude higher and less skewedly distributed than citations. A mathematical model based on the sum of two exponentials does not adequately capture monthly download counts. The degree of correlation at the article level within a journal is similar to that at the journal level in the discipline covered by that journal, suggesting that the differences between journals are to a large extent discipline-specific. Despite the fact that in all study journals download and citation counts per article positively correlate, little overlap may exist between the set of articles appearing in the top of the citation distribution and that with the most frequently downloaded ones. Usage and citation leaks, bulk downloading, differences between reader and author populations in a subject field, the type of document or its content, differences in obsolescence patterns between downloads and citations, different functions of reading and citing in the research process, all provide possible explanations of differences between download and citation distributions.
Are anthropomorphic persuasive appeals effective? The role of the recipient's motivations.

Anthropomorphic persuasive appeals are prevalent. However, their effectiveness has not been well studied. The present research addresses this issue with two experiments in the context of environmental persuasion. It shows that anthropomorphic messages, relative to non-anthropomorphic ones, appear to motivate more conservation behaviour and elicit more favourable message responses only among recipients who have a strong need for effectance or social connection. Among recipients whose such need is weak, anthropomorphic appeals seem to backfire. These findings extend the research on motivation and persuasion and add evidence to the motivational bases of anthropomorphism. In addition, joining some recent studies, the present research highlights the implications of anthropomorphism of nature for environmental conservation efforts, and offers some practical suggestions for environmental persuasion.
Noncontact Measurement of Complex Permittivity and Thickness by Using Planar Resonators

This paper presents a novel noncontact measurement technique that entails using a single-compound triple complementary split-ring resonator (SC-TCSRR) to determine the complex permittivity and thickness of a material under test (MUT). The proposed technique overcomes the problem engendered by the existence of air gaps between the sensor ground plane and the MUT. In the proposed approach, a derived governing equation of the resonance frequencies is used to estimate the thickness and complex permittivity of the MUT by calculating the resonant frequency (f r) and magnitude response in a single-step noncontact measurement process. This study theoretically analyzed and experimentally verified a simple and low-cost SC-TCSRR measurement method for assessing materials in a noncontact method. For a 0.2-mm air gap, the experiments yielded average measurement errors of 4.32% and 5.05% for the thickness and permittivity, respectively. The proposed SC-TCSRR technique provides excellent solutions for reducing the effect of air-gap conditions on permittivity, thickness, and loss tangent in noncontact measurements.
Super resolution using edge prior and single image detail synthesis

Edge-directed image super resolution (SR) focuses on ways to remove edge artifacts in upsampled images. Under large magnification, however, textured regions become blurred and appear homogenous, resulting in a super-resolution image that looks unnatural. Alternatively, learning-based SR approaches use a large database of exemplar images for “hallucinating” detail. The quality of the upsampled image, especially about edges, is dependent on the suitability of the training images. This paper aims to combine the benefits of edge-directed SR with those of learning-based SR. In particular, we propose an approach to extend edge-directed super-resolution to include detail from an image/texture example provided by the user (e.g., from the Internet). A significant benefit of our approach is that only a single exemplar image is required to supply the missing detail – strong edges are obtained in the SR image even if they are not present in the example image due to the combination of the edge-directed approach. In addition, we can achieve quality results at very large magnification, which is often problematic for both edge-directed and learning-based approaches.
Balance recovery of a quadruped robot

Maintaining the balance or stability of legged robots in natural terrains is a challenging problem. Besides the inherent unstable characteristics of legged robots, the sources of instability are the irregularities of the ground surface and also the external pushes. In this paper, a push recovery framework for restoring the robot balance against external unknown disturbances will be demonstrated. It is assumed that the magnitude of exerted pushes is not large enough to use a reactive stepping strategy. In the comparison with previous methods, which a simplified model such as point mass model is used as the model of the robot for studying the push recovery problem, the whole body dynamic model will be utilized in present work. This enhances the capability of the robot to exploit all of the DOFs to recover its balance. To do so, an explicit dynamic model of a quadruped robot will be derived. The balance controller is based on the computation of the appropriate acceleration of the main body. It is calculated to return the robot to its desired position after the perturbation. This acceleration should be chosen under the stability and friction conditions. To calculate main body acceleration, an optimization problem is defined so that the stability, friction condition considered as its constraints. The simulation results show the effectiveness of the proposed algorithm. The robot can restore its balance against the large disturbance solely through the adjustment of the position and orientation of main body.
Non-photorealistic rendering of portraits

We describe an image-based non-photorealistic rendering pipeline for creating portraits in two styles: The first is a somewhat "puppet" like rendering, that treats the face like a relatively uniform smooth surface, with the geometry being emphasised by shading. The second style is inspired by the artist Julian Opie, in which the human face is reduced to its essentials, i.e. homogeneous skin, thick black lines, and facial features such as eyes and the nose represented in a cartoon manner. Our method is able to automatically generate these stylisations without requiring the input images to be tightly cropped, direct frontal view, and moreover perform abstraction while maintaining the distinctiveness of the portraits (i.e. they should remain recognisable).
Human hand gesture recognition using a convolution neural network

Automatic human gesture recognition from camera images is an interesting topic for developing intelligent vision systems. In this paper, we propose a convolution neural network (CNN) method to recognize hand gestures of human task activities from a camera image. To achieve the robustness performance, the skin model and the calibration of hand position and orientation are applied to obtain the training and testing data for the CNN. Since the light condition seriously affects the skin color, we adopt a Gaussian Mixture model (GMM) to train the skin model which is used to robustly filter out non-skin colors of an image. The calibration of hand position and orientation aims at translating and rotating the hand image to a neutral pose. Then the calibrated images are used to train the CNN. In our experiment, we provided a validation of the proposed method on recognizing human gestures which shows robust results with various hand positions and orientations and light conditions. Our experimental evaluation of seven subjects performing seven hand gestures with average recognition accuracies around 95.96% shows the feasibility and reliability of the proposed method.