nils-herrmann/clean_scirepeval huggingface dataset:
Rapid Graph Layout Using Space Filling Curves

Network data frequently arises in a wide variety of fields, and node-link diagrams are a very natural and intuitive representation of such data. In order for a node-link diagram to be effective, the nodes must be arranged well on the screen. While many graph layout algorithms exist for this purpose, they often have limitations such as high computational complexity or node colocation. This paper proposes a new approach to graph layout through the use of space filling curves which is very fast and guarantees that there will be no nodes that are colocated. The resulting layout is also aesthetic and satisfies several criteria for graph layout effectiveness.
Quantitative Assessment of the Arm/Hand Movements in Parkinson’s Disease Using a Wireless Armband Device

We present an approach for quantitative assessment of the arm/hand movements in patients with Parkinson's disease (PD), from sensor data acquired with a wearable, wireless armband device (Myo sensor). We propose new Movement Performance Indicators that can be adopted by practitioners for the quantitative evaluation of motor performance and support their clinical evaluations. In addition, specific Movement Performance Indicators can indicate the presence of the bradykinesia symptom. The study includes seventeen PD patients and sixteen age-matched controls. A set of representative arm/hand movements is defined under the supervision of movement disorder specialist. In order to assist the evaluations, and for progress monitoring purposes, as well as for assessing the amount of bradykinesia in PD, a total set of 84 Movement Performance Indicators are computed from the sensor readings. Subsequently, we investigate whether wireless armband device, with the use of the proposed Movement Performance Indicators can be utilized: (1) for objective and precise quantitative evaluation of the arm/hand movements of Parkinson's patients, (2) for assessment of the bradykinesia motor symptom, and (3) as an adequate low-cost alternative for the sensor glove. We conducted extensive analysis of proposed Movement Performance Indicators and results are indicating following clinically relevant characteristics: (i) adequate reliability as measured by ICC; (ii) high accuracy in discrimination between the patients and controls, and between the disease stages (support to disease diagnosis and progress monitoring, respectively); (iii) substantial difference in comparison between the left-hand and the right-hand movements across controls and patients, as well as between disease stage groups; (iv) statistically significant correlation with clinical scales (tapping test and UPDRS-III Motor Score); and (v) quantitative evaluation of bradykinesia symptom. Results suggest that the proposed approach has a potential to be adopted by physicians, to afford them with quantitative, objective and precise methods and data during clinical evaluations and support the assessment of bradykinesia.
High-Efficiency Power Conversion System for Kilowatt-Level Stand-Alone Generation Unit With Low Input Voltage

This paper mainly focuses on the development of a high-efficiency power conversion system for kilowatt-level stand-alone generation units with a low output voltage, such as photovoltaic modules, fuel cells, and small-scale wind generators, and it aims at having the same output ac voltage, i.e., 110&nbsp;Vrms/ 60&nbsp;Hz as the utility power for the utilization of a stand-alone power supply. This high-efficiency power conversion system includes one high-efficiency high-step-up dc-dc converter and one soft-switching dc-ac current-source inverter. This dc-dc converter is capable of solving the voltage spike problem while the switch is turned off, and it can achieve the objectives of high efficiency and high voltage gain. Because the techniques of soft switching and voltage clamping are used in the dc-ac current-source inverter, the conversion efficiency could greatly be improved. The effectiveness of the designed circuits is verified by experimentation, and the maximum efficiency of the entire high-efficiency power conversion system is over 91% based on the experimental measurements.
A hybrid feature selection scheme for unsupervised learning and its application in bearing fault diagnosis

With the development of the condition-based maintenance techniques and the consequent requirement for good machine learning methods, new challenges arise in unsupervised learning. In the real-world situations, due to the relevant features that could exhibit the real machine condition are often unknown as priori, condition monitoring systems based on unimportant features, e.g. noise, might suffer high falsealarm rates, especially when the characteristics of failures are costly or difficult to learn. Therefore, it is important to select the most representative features for unsupervised learning in fault diagnostics. In this paper, a hybrid feature selection scheme (HFS) for unsupervised learning is proposed to improve the robustness and the accuracy of fault diagnostics. It provides a general framework of the feature selection based on significance evaluation and similarity measurement with respect to the multiple clustering solutions. The effectiveness of the proposed HFS method is demonstrated by a bearing fault diagnostics application and comparison with other features selection methods.
Help seeking behavior and the Internet: A national survey

Health-related websites have the potential to powerfully influence the attitudes and behavior of consumers. Access to reliable disease information online has been linked to reduced anxiety, increased feelings of self-efficacy, and decreases in utilization of ambulatory care. Studies report that Internet health information seekers are more likely to have health concerns; adult seekers are more likely to rate themselves as having poor health status and adolescent seekers are more likely to demonstrate clinical impairment or depressive symptomatology compared to non-seekers. Although more and more Americans are using the Internet for healthcare information, little is known about how this information affects their health behaviors. The current study extends the literature by examining characteristics associated with help seeking, either from a healthcare provider or from peers, as a direct result of health information found online. Medical care seekers appear to be using the Internet to enhance their medical care; they report using the information online to diagnose a problem and feel more comfortable about their health provider's advice given the information found on the Internet. Support seekers tend to be of slightly lower income compared to non-support seekers. They are also significantly more likely to have searched for information about a loved one's medical or health condition, signaling that many of these consumers may be caretakers.
Web Accessibility Evaluation

Web accessibility evaluation is a broad field that combines different disciplines and skills. It encompasses technical aspects such as the assessment of conformance to standards and guidelines, as well as non-technical aspects such as the involvement of end-users during the evaluation process. Since Web accessibility is a qualitative and experiential measure rather than a quantitative and concrete property, the evaluation approaches need to include different techniques andmaintain flexibility and adaptability toward different situations. At the same time, evaluation approaches need to be robust and reliable so that they can be effective. This chapter explores some of the techniques and strategies to evaluate the accessibility of Web content for people with disabilities. It highlights some of the common approaches to carry out andmanage evaluation processes rather than list out individual steps for evaluating Web content. This chapter also provides an outlook to some of the future directions in which the field seems to be heading, and outlines some opportunities for research and
An indoor autonomous guided vehicle based on electromagnetic tracking and positioning ibeacon

In order to further improve the efficiency of indoor unmanned transport, a low-power and efficient navigation and positioning system is designed. The system combines the advantages of the detecting method of electromagnetic signals and the indoor positioning technology based on Bluetooth Low Energy (BTLE). By analyzing the magnetic field model, common inductance coils are used as a sensor to detect the magnetic field, and the differential steering mechanism of the motor is controlled by the tracking characteristics of the dual-inductive electromagnetic sensor. For GPS can not provide satisfactory indoor positioning results, on the basis of the electromagnetic tracking system combined with the indoor positioning technology based on spatial information mode, a stable and independent indoor positioning system is finally realized. And with the predetermined electromagnetic track crossing point, the dead reckoning accumulated error for signpost is revised. The experimental results show that the system can achieve stable operation, and the system design is reasonable and basically meets the design goals and users' needs.
A Note on the Convergence of SOR for the PageRank Problem

A curious phenomenon when it comes to solving the linear system formulation of the PageRank problem is that while the convergence rate of Gauss–Seidel shows an improvement over Jacobi by a factor of approximately two, successive overrelaxation (SOR) does not seem to offer a meaningful improvement over Gauss–Seidel. This has been observed experimentally and noted in the literature, but to the best of our knowledge there has been no analytical explanation for this thus far. This convergence behavior is surprising because there are classes of matrices for which Gauss–Seidel is faster than Jacobi by a similar factor of two, and SOR accelerates convergence by an order of magnitude compared to Gauss–Seidel. In this short paper we prove analytically that the PageRank model has the unique feature that there exist PageRank linear systems for which SOR does not converge outside a very narrow interval depending on the damping factor, and that in such situations Gauss–Seidel may be the best choice among the relaxation parameters. Conversely, we show that within that narrow interval, there exists no PageRank problem for which SOR does not converge. Our result may give an analytical justification for the popularity of Gauss–Seidel as a solver for the linear system formulation of PageRank.
UnixMan Corpus: A Resource for Language Learning in the Unix Domain

We present a new resource, the UnixMan Corpus, for studying language learning it the domain of Unix utility manuals. The corpus is built by mining Unix (and other Unix related) man pages for parallel example entries, consisting of English textual descriptions with corresponding command examples. The commands provide a grounded and ambiguous semantics for the textual descriptions, making the corpus of interest to work on Semantic Parsing and Grounded Language Learning. In contrast to standard resources for Semantic Parsing, which tend to be restricted to a small number of concepts and relations, the UnixMan Corpus spans a wide variety of utility genres and topics, and consists of hundreds of command and domain entity types. The semi-structured nature of the manuals also makes it easy to exploit other types of relevant information for Grounded Language Learning. We describe the details of the corpus and provide preliminary classification results.
Dual wideband CPW-fed split-ring monopole antenna for WLAN applications

This paper presents a dual wideband CPW-fed monopole antenna based on split-ring elements for WLAN (2.4/5 GHz) applications. The proposed split-ring monopole antenna (SRMA) has a compact novel design which provides 13.8% and 55.5% impedance bandwidth at operational frequency bands. Moreover, the antenna exhibits good radiation pattern with predicted gains of 4–6 dBi in the related bands. The analysis and design of the proposed microstrip antenna have been carried out by means of Ansoft HFSS and an antenna prototype was then constructed and tested. Numerical analyses using the simulator and measurement results are also presented in this paper.
A Simple Instrument to Measure IT-Business Alignment Maturity

The challenge of aligning Information Technology (IT) to business has often been cited as a key issue by IT executives. This paper presents a simple, flexible, and easy-to-use instrument that measures the alignment maturity between business and IT and identifies major gaps. The proposed instrument is based on Luftman’s “Strategy Alignment Maturity Model” (SAMM); it directly encodes all attributes of SAMM alignment areas using a unidimensional framework. The instrument supports multiple levels of analysis with minimum assumptions about data using non-parametric statistical tools. In addition, the instrument provides an aggregation procedure to summarize the alignment maturity level for high-level executives. The instrument can also be customized to incorporate the contextual parameters of a company. In addition to the development of the instrument, this paper also shows how this instrument was applied to assess the alignment maturity level between IT and business in a rapidly growing company that had recently been publicly listed. The instrument was successful in identifying six major gaps for the company across the various alignment areas. These gaps were benchmarking, business metrics, strategic business planning, inter / intra organizational learning, architectural integration, and the impact of IT on business processes.
Accurate SHRG-Based Semantic Parsing

We demonstrate that an SHRG-based parser can produce semantic graphs much more accurately than previously shown, by relating synchronous production rules to the syntacto-semantic composition process. Our parser achieves an accuracy of 90.35 for EDS (89.51 for DMRS) in terms of ELEMENTARY DEPENDENCY MATCH, which is a 4.87 (5.45) point improvement over the best existing data-driven model, indicating, in our view, the importance of linguistically-informed derivation for data-driven semantic parsing. This accuracy is equivalent to that of English Resource Grammar guided models, suggesting that (recurrent) neural network models are able to effectively learn deep linguistic knowledge from annotations.
BIRNet: Brain Image Registration Using Dual-Supervised Fully Convolutional Networks

In this paper, we propose a deep learning approach for image registration by predicting deformation from image appearance. Since obtaining ground-truth deformation fields for training can be challenging, we design a fully convolutional network that is subject to dual-guidance: (1) Coarse guidance using deformation fields obtained by an existing registration method; and (2) Fine guidance using image similarity. The latter guidance helps avoid overly relying on the supervision from the training deformation fields, which could be inaccurate. For effective training, we further improve the deep convolutional network with gap filling, hierarchical loss, and multi-source strategies. Experiments on a variety of datasets show promising registration accuracy and efficiency compared with state-of-the-art methods.
Health information systems: Failure, success and improvisation

The generalised assumption of health information systems (HIS) success is questioned by a few commentators in the medical informatics field. They point to widespread HIS failure. The purpose of this paper was therefore to develop a better conceptual foundation for, and practical guidance on, health information systems failure (and success).



Literature and case analysis plus pilot testing of developed model.



Defining HIS failure and success is complex, and the current evidence base on HIS success and failure rates was found to be weak. Nonetheless, the best current estimate is that HIS failure is an important problem. The paper therefore derives and explains the "design-reality gap" conceptual model. This is shown to be robust in explaining multiple cases of HIS success and failure, yet provides a contingency that encompasses the differences which exist in different HIS contexts. The design-reality gap model is piloted to demonstrate its value as a tool for risk assessment and mitigation on HIS projects. It also throws into question traditional, structured development methodologies, highlighting the importance of emergent change and improvisation in HIS.



The design-reality gap model can be used to address the problem of HIS failure, both as a post hoc evaluative tool and as a pre hoc risk assessment and mitigation tool. It also validates a set of methods, techniques, roles and competencies needed to support the dynamic improvisations that are found to underpin cases of HIS success.
BrainID: Development of an EEG-based biometric authentication system

Authentication is a crucial consideration when securing data or any kind of information system. Though existing approaches for authentication are user-friendly, they have vulnerabilities such as the possibility & criminally threatening a user. We propose a novel approach which uses Electroencephalogram (EEG) brain signals for an authentication process. Unique features of EEG data for distinguishing brain activities can potentially be used to authenticate a user. Compared to other biometric systems, this approach is very robust and more secure because response is significantly changed according to instantaneous mental condition. In the proposed approach, the system user is asked to visualize a number while corresponding EEG signals are captured. Captured signals are used to train the system, compared in the authentication process. This approach mainly focuses on the 8 to 30 Hz Alpha and Beta combined frequency band across all EEG channels, since it is the most appropriate band for EEG signals in Brain Computer Interfaces (BCI). Common Spatial Patterns (CSP) values were used as main features to train the model. Linear discriminant analysis (LDA) was used as a classification algorithm for a given set of user data. A trained set of models for each user is embedded in the system as a parameter database. Each user selects the profile attached to their trained model before the authentication session. Eventually, a trained model can authenticate a user after memorization of pre-determined four digits which the user is asked to think at the first stage of the process. The maximum accuracy recorded with the existing data was 96.97%.
Faster Smith-Waterman database searches with inter-sequence SIMD parallelisation

The Smith-Waterman algorithm for local sequence alignment is more sensitive than heuristic methods for database searching, but also more time-consuming. The fastest approach to parallelisation with SIMD technology has previously been described by Farrar in 2007. The aim of this study was to explore whether further speed could be gained by other approaches to parallelisation. A faster approach and implementation is described and benchmarked. In the new tool SWIPE, residues from sixteen different database sequences are compared in parallel to one query residue. Using a 375 residue query sequence a speed of 106 billion cell updates per second (GCUPS) was achieved on a dual Intel Xeon X5650 six-core processor system, which is over six times more rapid than software based on Farrar's 'striped' approach. SWIPE was about 2.5 times faster when the programs used only a single thread. For shorter queries, the increase in speed was larger. SWIPE was about twice as fast as BLAST when using the BLOSUM50 score matrix, while BLAST was about twice as fast as SWIPE for the BLOSUM62 matrix. The software is designed for 64 bit Linux on processors with SSSE3. Source code is available from http://dna.uio.no/swipe/ under the GNU Affero General Public License. Efficient parallelisation using SIMD on standard hardware makes it possible to run Smith-Waterman database searches more than six times faster than before. The approach described here could significantly widen the potential application of Smith-Waterman searches. Other applications that require optimal local alignment scores could also benefit from improved performance.
A 12.77-MHz 31 ppm/°C On-Chip RC Relaxation Oscillator With Digital Compensation Technique

The design of a 12.77-MHz on-chip RC relaxation oscillator with digital compensation technique is presented. To maintain the frequency stability versus temperature and supply voltage variations, loop delay tuning by a digital feedback loop is developed in this system. In order to generate an on-chip reference for digital calibration, a replica comparator is added. The on-chip relaxation oscillator is fabricated in 0.18-μm CMOS process. The measured output frequency variation is 31 ppm/°C across -30 to 120 °C temperature range after compensation. The frequency variation over the supply voltage from 0.6 V to 1.1 V is ±0.5%/V. The measured total power consumption is 56.2 μW at 0.9-V supply voltage when the digital compensation blocks are enabled. After digital compensation, the compensation blocks can be shutdown for power saving, and the main oscillator consumes only 12.8 μW.
Social media and internet-based data in global systems for public health surveillance: a systematic review.

The exchange of health information on the Internet has been heralded as an opportunity to improve public health surveillance. In a field that has traditionally relied on an established system of mandatory and voluntary reporting of known infectious diseases by doctors and laboratories to governmental agencies, innovations in social media and so-called user-generated information could lead to faster recognition of cases of infectious disease. More direct access to such data could enable surveillance epidemiologists to detect potential public health threats such as rare, new diseases or early-level warnings for epidemics. But how useful are data from social media and the Internet, and what is the potential to enhance surveillance? The challenges of using these emerging surveillance systems for infectious disease epidemiology, including the specific resources needed, technical requirements, and acceptability to public health practitioners and policymakers, have wide-reaching implications for public health surveillance in the 21st century.



This article divides public health surveillance into indicator-based surveillance and event-based surveillance and provides an overview of each. We did an exhaustive review of published articles indexed in the databases PubMed, Scopus, and Scirus between 1990 and 2011 covering contemporary event-based systems for infectious disease surveillance.



Our literature review uncovered no event-based surveillance systems currently used in national surveillance programs. While much has been done to develop event-based surveillance, the existing systems have limitations. Accordingly, there is a need for further development of automated technologies that monitor health-related information on the Internet, especially to handle large amounts of data and to prevent information overload. The dissemination to health authorities of new information about health events is not always efficient and could be improved. No comprehensive evaluations show whether event-based surveillance systems have been integrated into actual epidemiological work during real-time health events.



The acceptability of data from the Internet and social media as a regular part of public health surveillance programs varies and is related to a circular challenge: the willingness to integrate is rooted in a lack of effectiveness studies, yet such effectiveness can be proved only through a structured evaluation of integrated systems. Issues related to changing technical and social paradigms in both individual perceptions of and interactions with personal health data, as well as social media and other data from the Internet, must be further addressed before such information can be integrated into official surveillance systems.