nils-herrmann/clean_scirepeval huggingface dataset:
Analysis and comparison study of PWM and Phase-Shifted PWM full-bridge inverter fed high-voltage High-Frequency Ozone Generator

This paper presents the analysis and the comparison study of a High-voltage High-frequency Ozone Generator using PWM and Phase-Shifted PWM full-bridge inverter as a power supply. The circuits operations of the inverters are fully described. In order to ensure that zero voltage switching (ZVS) mode always operated over a certain range of a frequency variation, a series-compensated resonant inductor is included. The comparison study are ozone quantity and output voltage that supplied by the PWM and Phase-Shifted PWM full-bridge inverter. The ozone generator fed by Phase-Shifted PWM full-bridge inverter, is capability of varying ozone gas production quantity by varying the frequency and phase shift angle of the converter whilst the applied voltage to the electrode is kept constant. However, the ozone generator fed by PWM full-bridge inverter, is capability of varying ozone gas production quantity by varying the frequency of the converter whilst the applied voltage to the electrode is decreased. As a consequence, the absolute ozone quantity affected by the frequency is possibly achieved.
An evaluation of GUI and kinesthetic teaching methods for constrained-keyframe skills

Keyframe-based Learning from Demonstration has been shown to be an effective method for allowing end-users to teach robots skills. We propose a method for using multiple keyframe demonstrations to learn skills as sequences of positional constraints (c-keyframes) which can be planned between for skill execution. We also introduce an interactive GUI which can be used for displaying the learned c-keyframes to the teacher, for altering aspects of the skill after it has been taught, or for specifying a skill directly without providing kinesthetic demonstrations. We compare 3 methods of teaching c-keyframe skills: kinesthetic teaching, GUI teaching, and kinesthetic teaching followed by GUI editing of the learned skill (K-GUI teaching). Based on user evaluation, the K-GUI method of teaching is found to be the most preferred, and the GUI to be the least preferred. Kinesthetic teaching is also shown to result in more robust constraints than GUI teaching, and several use cases of K-GUI teaching are discussed to show how the GUI can be used to improve the results of kinesthetic teaching.
Coverage and Capacity Analysis of Sigfox, LoRa, GPRS, and NB-IoT

In this paper the coverage and capacity of SigFox, LoRa, GPRS, and NB-IoT is compared using a real site deployment covering 8000 km2 in Northern Denmark. Using the existing Telenor cellular site grid it is shown that the four technologies have more than 99 % outdoor coverage, while GPRS is challenged for indoor coverage. Furthermore, the study analyzes the capacity of the four technologies assuming a traffic growth from 1 to 10 IoT device per user. The conclusion is that the 95 %-tile uplink failure rate for outdoor users is below 5 % for all technologies. For indoor users only NB-IoT provides uplink and downlink connectivity with less than 5 % failure rate, while SigFox is able to provide an unacknowledged uplink data service with about 12 % failure rate. Both GPRS and LoRa struggle to provide sufficient indoor coverage and capacity.
Low-rank matrix factorization with attributes

We develop a new collaborative filtering (CF) method that combines both previously known users’ preferences, i.e. standard CF, as well as product/user attributes, i.e. classical function approximation, to predict a given user’s interest in a particular product. Our method is a generalized low rank matrix completion problem, where we learn a function whose inputs are pairs of vectors – the standard low rank matrix completion problem being a special case where the inputs to the function are the row and column indices of the matrix. We solve this generalized matrix completion problem using tensor product kernels for which we also formally generalize standard kernel properties. Benchmark experiments on movie ratings show the advantages of our generalized matrix completion method over the standard matrix completion one with no information about movies or people, as well as over standard multi-task or single task learning methods.
Estimation of the use of voip server resources by neural networks

No researches on VoIP servers have highlighted their analytical models to study their behavior depending on the number of users. This is complex because the scheduling algorithms to solve it have limitations. The aim of our paper is to present a VoIP server modeling by neural network, having as input the number of calls and output as the use of hardware resources of the server. A generalization of this model is given at the end of our research.
Analysis of the reputation system and user contributions on a question answering website: StackOverflow

Question answering (Q&A) communities have been gaining popularity in the past few years. The success of such sites depends mainly on the contribution of a small number of expert users who provide a significant portion of the helpful answers, and so identifying users that have the potential of becoming strong contributers is an important task for owners of such communities.
 We present a study of the popular Q&A website StackOverflow (SO), in which users ask and answer questions about software development, algorithms, math and other technical topics. The dataset includes information on 3.5 million questions and 6.9 million answers created by 1.3 million users in the years 2008--2012. Participation in activities on the site (such as asking and answering questions) earns users reputation, which is an indicator of the value of that user to the site.
 We describe an analysis of the SO reputation system, and the participation patterns of high and low reputation users. The contributions of very high reputation users to the site indicate that they are the primary source of answers, and especially of high quality answers. Interestingly, we find that while the majority of questions on the site are asked by low reputation users, on average a high reputation user asks more questions than a user with low reputation. We consider a number of graph analysis methods for detecting influential and anomalous users in the underlying user interaction network, and find they are effective in detecting extreme behaviors such as those of spam users. Lastly, we show an application of our analysis: by considering user contributions over first months of activity on the site, we predict who will become influential long-term contributors.
MDP and Machine Learning-Based Cost-Optimization of Dynamic Resource Allocation for Network Function Virtualization

The introduction of Network Functions Virtualization (NFV) enables service providers to offer software-defined network functions with elasticity and flexibility. Its core technique, dynamic allocation procedure of NFV components onto cloud resources requires rapid response to changes on-demand to remain cost and QoS effective. In this paper, Markov Decision Process (MDP) is applied to the NP-hard problem to dynamically allocate cloud resources for NFV components. In addition, Bayesian learning method is applied to monitor the historical resource usage in order to predict future resource reliability. Experimental results show that our proposed strategy outperforms related approaches.
Security Issues in Wireless Sensor Networks: A Survey

Due to a wide range of applications, wireless sensor networks (WSNs) have recently attracted a lot of interest to the researchers. Limited computational capacity and power usage are two major challenges to ensure security in WSNs. Recently, more secure communication or data aggregation techniques have discovered. So, familiarity with the current research in WSN security will benefit researchers greatly. In this paper, security related issues and challenges in WSNs are investigated. We identify the security threats and review proposed security mechanisms for WSNs. Moreover, we provide a brief discussion on the future research direction in WSN security.
A Case Study of the Health of an Augmented Reality Software Ecosystem: Vuforia

Augmented Reality is becoming increasingly popular. The success of a platform is typically observed by measuring the health of the software ecosystem surrounding it. In this paper, we take a closer look at the Vuforia ecosystem’s health by mining the Vuforia platform application repository. It is observed that the developer ecosystem is the strength of the platform. We also determine that Vuforia could be the biggest player in the market if they lay its focus on specific types of app
Test Case Prioritization Using Test Similarities

A classical heuristic in software testing is to reward diversity, which implies that a higher priority must be assigned to test cases that differ the most from those already prioritized. This approach is commonly known as similarity-based test prioritization (SBTP) and can be realized using a variety of techniques. The objective of our study is to investigate whether SBTP is more effective at finding defects than random permutation, as well as determine which SBTP implementations lead to better results. To achieve our objective, we implemented five different techniques from the literature and conducted an experiment using the defects4j dataset, which contains 395 real faults from six real-world open-source Java programs. Findings indicate that running the most dissimilar test cases early in the process is largely more effective than random permutation (Vargha–Delaney A [VDA]: 0.76–0.99 observed using normalized compression distance). No technique was found to be superior with respect to the effectiveness. Locality-sensitive hashing was, to a small extent, less effective than other SBTP techniques (VDA: 0.38 observed in comparison to normalized compression distance), but its speed largely outperformed the other techniques (i.e., it was approximately 5–111 times faster). Our results bring to mind the well-known adage, “don’t put all your eggs in one basket”. To effectively consume a limited testing budget, one should spread it evenly across different parts of the system by running the most dissimilar test cases early in the testing process.
Industrial robot capability models for agile manufacturing

This paper aims to represent a capability model for industrial robot as they pertain to assembly tasks.  The architecture of a real kit building application is provided to demonstrate how robot capabilities can be used to fully automate the planning of assembly tasks. Discussion on the planning infrastructure is done with the Planning Domain Definition Language (PDDL) for heterogeneous multi robot systems.  The paper describes PDDL domain and problem files that are used by a planner to generate a plan for kitting. Discussion on the plan shows that the best robot is selected to carry out assembly actions.  The author presents a robot capability model that is intended to be used for helping manufacturers to characterize the different capabilities their robots contribute to help the end user to select the appropriate robots for the appropriate tasks, selecting backup robots during robot’s failures to limit the deterioration of the system’s productivity and the products’ quality and limiting robots’ failures and increasing productivity by providing a tool to manufacturers that outputs a process plan that assigns the best robot to each task needed to accomplish the assembly.
An exploration of climate data using complex networks

To discover patterns in historical data, climate scientists have applied various clustering methods with the goal of identifying regions that share some common climatological behavior. However, past approaches are limited by the fact that they either consider only a single time period (snapshot) of multivariate data, or they consider only a single variable by using the time series data as multi-dimensional feature vector. In both cases, potentially useful information may be lost. Moreover, clusters in high-dimensional data space can be difficult to interpret, prompting the need for a more effective data representation. We address both of these issues by employing a complex network (graph) to represent climate data, a more intuitive model that can be used for analysis while also having a direct mapping to the physical world for interpretation. A cross correlation function is used to weight network edges, thus respecting the temporal nature of the data, and a community detection algorithm identifies multivariate clusters. Examining networks for consecutive periods allows us to study structural changes over time. We show that communities have a climatological interpretation and that disturbances in structure can be an indicator of climate events (or lack thereof). Finally, we discuss how this model can be applied for the discovery of more complex concepts such as unknown teleconnections or the development of multivariate climate indices and predictive insights.
Automated NMR Assignment and Protein Structure Determination using Sparse Dipolar Coupling Constraints.

0079-6565/$ see front matter  NMR, Nuclear Magnetic Resonan RMSD, mean square deviation; HSQC, heteronuclea spectroscopy; NOE, Nuclear Overhauser Effect; RDC, re Protein Data Bank; pol g, zinc finger domain of the hu CH, C Ha; hSRI, human Set2-Rpb1 interacting do human transcription elongation factor CA150 (RN domain interacting protein); POF, principal order fram MD, molecular dynamics; SSE, secondary structure e WPS, well-packed satisfying; vdW, van der Waals; DO * Corresponding author.  E-mail address: brd+pnmrs09@cs.duke.edu (B.R. D URL: http://www.cs.duke.edu/brd (B.R. Donald).
Semiconductor-based galvanic isolation

This paper presents the study on the semiconductor-based galvanic isolation. This solution delivers the differential-mode (DM) power via semiconductor power switches during their on states, while sustaining the common-mode (CM) voltage and blocking the CM leakage current with those switches during their off states. While it is impractical to implement this solution with Si devices, the latest SiC devices and the coming vertical GaN devices, however, provide unprecedented properties and thus can potentially enable the practical implementation. An isolated dc/dc converter based on the switched-capacitor circuit is studied as an example. The CM leakage current caused by the line input and the resulted touch current (TC) are quantified and compared to the limits in the safety standard IEC60950. To reduce the TC, low switch output capacitance and low converter switching frequency are needed. Then, discussions are presented on the TC reduction approaches and the design considerations to achieve high power density and high efficiency. A 400-V, 400-W prototype based on 1.7-kV SiC MOSFETs is built to demo the DM power delivery performance and showcase the CM leakage current problem. Further study on the CM leakage current elimination is needed to validate this solution.
Delivering Deep Learning to Mobile Devices via Offloading

Deep learning has the potential to make Augmented Reality (AR) devices smarter, but few AR apps use such technology today because it is compute-intensive, and front-end devices cannot deliver sufficient compute power. We propose a distributed framework that ties together front-end devices with more powerful back-end "helpers" that allow deep learning to be executed locally or to be offloaded. This framework should be able to intelligently use current estimates of network conditions and back-end server loads, in conjunction with the application's requirements, to determine an optimal strategy.
 This work reports our preliminary investigation in implementing such a framework, in which the front-end is assumed to be smartphones. Our specific contributions include: (1) development of an Android application that performs real-time object detection, either locally on the smartphone or remotely on a server; and (2) characterization of the tradeoffs between object detection accuracy, latency, and battery drain, based on the system parameters of video resolution, deep learning model size, and offloading decision.
Detecting DNA cytosine methylation using nanopore sequencing

In nanopore sequencing devices, electrolytic current signals are sensitive to base modifications, such as 5-methylcytosine (5-mC). Here we quantified the strength of this effect for the Oxford Nanopore Technologies MinION sequencer. By using synthetically methylated DNA, we were able to train a hidden Markov model to distinguish 5-mC from unmethylated cytosine. We applied our method to sequence the methylome of human DNA, without requiring special steps for library preparation.
Literature review on wearable systems in upper extremity rehabilitation

This paper reports a structured literature survey of research in wearable technology for upper-extremity rehabilitation, e.g., after stroke, spinal cord injury, for multiple sclerosis patients or even children with cerebral palsy. A keyword based search returned 61 papers relating to this topic. Examination of the abstracts of these papers identified 19 articles describing distinct wearable systems aimed at upper extremity rehabilitation. These are classified in three categories depending on their functionality: movement and posture monitoring; monitoring and feedback systems that support rehabilitation exercises, serious games for rehabilitation training. We characterize the state of the art considering respectively the reported performance of these technologies, availability of clinical evidence, or known clinical applications.
Digital Service Innovation from Open Data: Exploring the Value Proposition of an Open Data Marketplace

Open data marketplaces have emerged as a mode of addressing open data adoption barriers. However, knowledge of how such marketplaces affect digital service innovation in open data ecosystems is limited. This paper explores their value proposition for open data users based on an exploratory case study. Five prominent perceived values are identified: lower task complexity, higher access to knowledge, increased possibilities to influence, lower risk and higher visibility. The impact on open data adoption barriers is analyzed and the consequences for ecosystem sustainability is discussed. The paper concludes that open data marketplaces can lower the threshold of using open data by providing better access to open data and associated support services, and by increasing knowledge transfer within the ecosystem.
A Single Shot Text Detector with Scale-adaptive Anchors

Currently, most top-performing text detection networks tend to employ fixed-size anchor boxes to guide the search for text instances. ey usually rely on a large amount of anchors with different scales to discover texts in scene images, thus leading to high computational cost. In this paper, we propose an end-to-end boxbased text detector with scale-adaptive anchors, which can dynamically adjust the scales of anchors according to the sizes of underlying texts by introducing an additional scale regression layer. e proposed scale-adaptive anchors allow us to use a few number of anchors to handle multi-scale texts and therefore significantly improve the computational efficiency. Moreover, compared to discrete scales used in previous methods, the learned continuous scales are more reliable, especially for small texts detection. Additionally, we propose Anchor convolution to beer exploit necessary feature information by dynamically adjusting the sizes of receptive fields according to the learned scales. Extensive experiments demonstrate that the proposed detector is fast, taking only 0.28 second per image, while outperforming most state-of-the-art methods in accuracy.
Data center networking with multipath TCP

Recently new data center topologies have been proposed that offer higher aggregate bandwidth and location independence by creating multiple paths in the core of the network. To effectively use this bandwidth requires ensuring different flows take different paths, which poses a challenge.
 Plainly put, there is a mismatch between single-path transport and the multitude of available network paths. We propose a natural evolution of data center transport from TCP to multipath TCP. We show that multipath TCP can effectively and seamlessly use available bandwidth, providing improved throughput and better fairness in these new topologies when compared to single path TCP and randomized flow-level load balancing. We also show that multipath TCP outperforms laggy centralized flow scheduling without needing centralized control or additional infrastructure.
Designing Recommender Systems for Tourism

In this paper, we argue that the process of developing travel recommender systems (TRS) can be simplified. By studying the application domain of tourism information systems, and examining the algorithms and architectures available for recommender systems today, we discuss the dependencies and present a methodology for developing TRS, which can be applied at very early stages of TRS development. The methodology aims to be insightful without overburdening the project team with the mathematical basis and technical detail of the state of the art in recommender systems and give guidance on design choices to the project team.
Lesions and Neoplasms of the Penis: A Review.

In addition to practitioners who care for male patients, with the increased use of high-resolution anoscopy, practitioners who care for women are seeing more men in their practices as well. Some diseases affecting the penis can impact on their sexual partners. Many of the lesions and neoplasms of the penis occur on the vulva as well. In addition, there are common and rare lesions unique to the penis. A review of the scope of penile lesions and neoplasms that may present in a primary care setting is presented to assist in developing a differential diagnosis if such a patient is encountered, as well as for practitioners who care for their sexual partners. A familiarity will assist with recognition, as well as when consultation is needed.
Conflicts and complements between eastern cultures and agile methods: an empirical investigation

Department of Computer Information Systems, J. Mack Robinson College of Business, Georgia State University, Atlanta, USA; Department of Information Technology & Decision Sciences, Strome College of Business, Old Dominion University, Norfolk, USA; Management Science and Information Systems Department, College of Management, University of Massachusetts Boston, 100 Morrissey Blvd., Boston, MA 02125, USA; Department of Computer Information Systems, Zicklin School of Business, The City University of New York, New York, USA; Department of Business Information Technology, Pamplin College of Business, Virginia Polytechnic Institute and State University, Blacksburg, USA