nils-herrmann/clean_scirepeval huggingface dataset:
Testing the reliability and efficiency of the pilot Mixed Methods Appraisal Tool (MMAT) for systematic mixed studies review.

Systematic literature reviews identify, select, appraise, and synthesize relevant literature on a particular topic. Typically, these reviews examine primary studies based on similar methods, e.g., experimental trials. In contrast, interest in a new form of review, known as mixed studies review (MSR), which includes qualitative, quantitative, and mixed methods studies, is growing. In MSRs, reviewers appraise studies that use different methods allowing them to obtain in-depth answers to complex research questions. However, appraising the quality of studies with different methods remains challenging. To facilitate systematic MSRs, a pilot Mixed Methods Appraisal Tool (MMAT) has been developed at McGill University (a checklist and a tutorial), which can be used to concurrently appraise the methodological quality of qualitative, quantitative, and mixed methods studies.



The purpose of the present study is to test the reliability and efficiency of a pilot version of the MMAT.



The Center for Participatory Research at McGill conducted a systematic MSR on the benefits of Participatory Research (PR). Thirty-two PR evaluation studies were appraised by two independent reviewers using the pilot MMAT. Among these, 11 (34%) involved nurses as researchers or research partners. Appraisal time was measured to assess efficiency. Inter-rater reliability was assessed by calculating a kappa statistic based on dichotomized responses for each criterion. An appraisal score was determined for each study, which allowed the calculation of an overall intra-class correlation.



On average, it took 14 min to appraise a study (excluding the initial reading of articles). Agreement between reviewers was moderate to perfect with regards to MMAT criteria, and substantial with respect to the overall quality score of appraised studies.



The MMAT is unique, thus the reliability of the pilot MMAT is promising, and encourages further development.
Autonomous decision-making: a data mining approach

The researchers and practitioners of today create models, algorithms, functions, and other constructs defined in abstract spaces. The research of the future will likely be data driven. Symbolic and numeric data that are becoming available in large volumes will define the need for new data analysis techniques and tools. Data mining is an emerging area of computational intelligence that offers new theories, techniques, and tools for analysis of large data sets. In this paper, a novel approach for autonomous decision-making is developed based on the rough set theory of data mining. The approach has been tested on a medical data set for patients with lung abnormalities referred to as solitary pulmonary nodules (SPNs). The two independent algorithms developed in this paper either generate an accurate diagnosis or make no decision. The methodology discussed in the paper depart from the developments in data mining as well as current medical literature, thus creating a variable approach for autonomous decision-making.
Image contrast enhancement by automatic multi-histogram equalization for satellite images

In this paper, a new automatic histogram equalization algorithm which is based on Bi-Histogram Equalization (BHE) is proposed. The proposed method preserves the brightness and also improves the contrast. Mean value of the intensity is used for selecting the thresholds to avoid over enhancement also improving contrast of the image. The calculated mean limits the valley points to divide the histogram into small parts which guarantee equal input and output mean brightness. In addition, normalization of image brightness is applied to assure less Absolute Mean Brightness Error (AMBE). The performances of proposed algorithm, Global Histogram equalization algorithm and Bi-Histogram equalization algorithm are measured by calculating another validity parameter called Peak Signal to Noise Ratio (PSNR). The proposed method confirms the improvement in qualitative parameters as compared with the other two algorithms considered.
The effects of video game playing on attention, memory, and executive control.

Expert video game players often outperform non-players on measures of basic attention and performance. Such differences might result from exposure to video games or they might reflect other group differences between those people who do or do not play video games. Recent research has suggested a causal relationship between playing action video games and improvements in a variety of visual and attentional skills (e.g., [Green, C. S., & Bavelier, D. (2003). Action video game modifies visual selective attention. ). The current research sought to replicate and extend these results by examining both expert/non-gamer differences and the effects of video game playing on tasks tapping a wider range of cognitive abilities, including attention, memory, and executive control. Non-gamers played 20+ h of an action video game, a puzzle game, or a real-time strategy game. Expert gamers and non-gamers differed on a number of basic cognitive skills: experts could track objects moving at greater speeds, better detected changes to objects stored in visual short-term memory, switched more quickly from one task to another, and mentally rotated objects more efficiently. Strikingly, extensive video game practice did not substantially enhance performance for non-gamers on most cognitive tasks, although they did improve somewhat in mental rotation performance. Our results suggest that at least some differences between video game experts and non-gamers in basic cognitive performance result either from far more extensive video game experience or from pre-existing group differences in abilities that result in a self-selection effect.
3D printing with polymers: Challenges among expanding options and opportunities.

Additive manufacturing, which is more colloquially referred to as 3D printing, is quickly approaching mainstream adoption as a highly flexible processing technique that can be applied to plastic, metal, ceramic, concrete and other building materials. However, taking advantage of the tremendous versatility associated with in situ photopolymerization as well as the ability to select from a variety of preformed processible polymers, 3D printing predominantly targets the production of polymeric parts and models. The goal of this review is to connect the various additive manufacturing techniques with the monomeric and polymeric materials they use while highlighting emerging material-based developments.



Modern additive manufacturing technology was introduced approximately three decades ago but this review compiles recent peer-reviewed literature reports to demonstrate the evolution underway with respect to the various building techniques that differ significantly in approach as well as the new variations in polymer-based materials being employed.



Recent growth of 3D printing has been dramatic and the ability of the various platform technologies to expand from rapid production prototypic models to the greater volume of readily customizable production of working parts is critical for continued high growth rates. This transition to working part production is highly dependent on adapting materials that deliver not only the requisite design accuracy but also the physical and mechanical properties necessary for the application.



With the weighty distinction of being called the next industrial revolution, 3D printing technologies is already altering many industrial and academic operations including changing models for future healthcare delivery in medicine and dentistry.
Nonparametric Scene Parsing via Label Transfer

While there has been a lot of recent work on object recognition and image understanding, the focus has been on carefully establishing mathematical models for images, scenes, and objects. In this paper, we propose a novel, nonparametric approach for object recognition and scene parsing using a new technology we name label transfer. For an input image, our system first retrieves its nearest neighbors from a large database containing fully annotated images. Then, the system establishes dense correspondences between the input image and each of the nearest neighbors using the dense SIFT flow algorithm [28], which aligns two images based on local image structures. Finally, based on the dense scene correspondences obtained from SIFT flow, our system warps the existing annotations and integrates multiple cues in a Markov random field framework to segment and recognize the query image. Promising experimental results have been achieved by our nonparametric scene parsing system on challenging databases. Compared to existing object recognition approaches that require training classifiers or appearance models for each object category, our system is easy to implement, has few parameters, and embeds contextual information naturally in the retrieval/alignment procedure.
A Comparison of Controllers for Balancing Two Wheeled Inverted Pendulum Robot

One of the challenging tasks concerning two wheeled inverted pendulum (TWIP) mobile robot is balancing its tilt to upright position, this is due to its inherently open loop instability. This paper presents an experimental comparison between model based controller and non-model based controllers in balancing the TWIP mobile robot. A Fuzzy Logic Controller (FLC) which is a non-model based controller and a Linear Quadratic Controller (LQR) which is a model-based controller, and the conventional controller, Proportional Integral Derivative (PID) were implemented and compared on a real time TWIP mobile robot. The FLC controller performance has given superior result as compared to LQR and PID in terms of speed response but consumes higher energy. Index Term-Two Wheeled Inverted Pendulum (TWIP), Fuzzy Logic Controller (FLC), Linear Quadratic Controller (LQR), Euler Lagrange equations.
Towards a Decentralized Magnetic Indoor Positioning System

Decentralized magnetic indoor localization is a sophisticated method for processing sampled magnetic data directly on a mobile station (MS), thereby decreasing or even avoiding the need for communication with the base station. In contrast to central-oriented positioning systems, which transmit raw data to a base station, decentralized indoor localization pushes application-level knowledge into the MS. A decentralized position solution has thus a strong feasibility to increase energy efficiency and to prolong the lifetime of the MS. In this article, we present a complete architecture and an implementation for a decentralized positioning system. Furthermore, we introduce a technique for the synchronization of the observed magnetic field on the MS with the artificially-generated magnetic field from the coils. Based on real-time clocks (RTCs) and a preemptive operating system, this method allows a stand-alone control of the coils and a proper assignment of the measured magnetic fields on the MS. A stand-alone control and synchronization of the coils and the MS have an exceptional potential to implement a positioning system without the need for wired or wireless communication and enable a deployment of applications for rescue scenarios, like localization of miners or firefighters.
Systematic Risk in Supply Chain Networks

I production output is generally correlated with the state of the economy. Nonetheless, during times of economic downturn, some industries take the biggest hit, whereas at times of economic boom they reap most benefits. To provide insight into this phenomenon, we map supply networks of industries and firms and investigate how the supply network structure mediates the effect of economy on industry or firm sales. Previous research has shown that retail sales are correlated with the state of the economy. Since retailers source their products from other industries, the sales of their suppliers can also be correlated with the state of the economy. This correlation represents the source of systematic risk for an industry that propagates through a supply chain network. Specifically, we identify the following mechanisms that can affect the correlation between sales and the state of the economy in a supply chain network: propagation of systematic risk into production decisions, aggregation of orders from multiple customers in a supply chain network, and aggregation of orders over time. We find that the first effect does not amplify the correlation; however, the latter two intensify correlation and result in the amplification of correlation upstream in supply networks. We demonstrate three managerial implications of this phenomenon: implications for the cost of capital, for the risk-adjusted valuation of supply chain improvement projects, and for supplier selection and risk. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2015.2187.
Critical Success Factors for a Customer Relationship Management Strategy

Most organizations have perceived the customer relationship management (CRM) concept as a technological solution for problems in individual areas, accompanied by a great deal of uncoordinated initiatives. Nevertheless, CRM must be conceived as a strategy, due to its human, technological, and processes implications, at the time an organization decides to implement it. On this basis, the main goal stated in this research is to propose, justify, and validate a model based on critical success factors (CSFs) that will constitute a guide for companies in the implementation and diagnosis of a CRM strategy. The model is conformed by a set of 13 CSFs with their 55 corresponding metrics, which will serve as a guide for organizations wishing to apply this type of strategy. These factors cover the three key aspects of every CRM strategy (human factor, processes, and technology); giving a global focus and propitiating success in the implementation of a CRM strategy. These CSFs – and their metrics – were evaluated by a group of internationally experts allowing determining guidelines for a CRM implementation as well as the probable causes of the deWciencies in past projects.
ELMoLex : Connecting ELMo and Lexicon features for Dependency Parsing

In this paper, we present the details of the neural dependency parser and the neural tagger submitted by our team ‘ParisNLP’ to the CoNLL 2018 Shared Task on parsing from raw text to Universal Dependencies. We augment the deep Biaffine (BiAF) parser (Dozat and Manning, 2016) with novel features to perform competitively: we utilize an indomain version of ELMo features (Peters et al., 2018) which provide contextdependent word representations; we utilize disambiguated, embedded, morphosyntactic features from lexicons (Sagot, 2018), which complements the existing feature set. Henceforth, we call our system ‘ELMoLex’. In addition to incorporating character embeddings, ELMoLex leverage pre-trained word vectors, ELMo and morphosyntactic features (whenever available) to correctly handle rare or unknown words which are prevalent in languages with complex morphology. ELMoLex1 ranked 11th by Labeled Attachment Score metric (70.64%), Morphology-aware LAS metric (55.74%) and ranked 9th by Bilexical dependency metric (60.70%). In an extrinsic evaluation setup, ELMoLex ranked 7th for Event Extraction, Negation Resolution tasks and 11th for Opinion Analysis task by F1 score.
The cognitive underpinnings of effective teamwork: a meta-analysis.

Major theories of team effectiveness position emergent collective cognitive processes as central drivers of team performance. We meta-analytically cumulated 231 correlations culled from 65 independent studies of team cognition and its relations to teamwork processes, motivational states, and performance outcomes. We examined both broad relationships among cognition, behavior, motivation, and performance, as well as 3 underpinnings of team cognition as potential moderators of these relationships. Findings reveal there is indeed a cognitive foundation to teamwork; team cognition has strong positive relationships to team behavioral process, motivational states, and team performance. Meta-analytic regressions further indicate that team cognition explains significant incremental variance in team performance after the effects of behavioral and motivational dynamics have been controlled. The nature of emergence, form of cognition, and content of cognition moderate relationships among cognition, process, and performance, as do task interdependence and team type. Taken together, these findings not only cumulate extant research on team cognition but also provide a new interpretation of the impact of underlying dimensions of cognition as a way to frame and extend future research.
MTPA Trajectory Tracking Control for Interior PMSM Based on Adaptive Parameter Identification

For an interior permanent magnet synchronous motor (IPMSM) control system, the actual maximum torque per ampere (MTPA) trajectory may deviate from the ideal one with parameter variances, and thus the system may not obtain the maximum torque output. In view of this problem, a novel parameter identification method based on the model reference adaptive system (MRAS) is proposed in this paper. In this method, the deviations of MTPA trajectories with parameter variances are analyzed and evaluated firstly, and then the q-axis inductance and the rotor flux linkage are identified on-line in a full rank MRAS model according to Popov Hyper Stability Theorem. Finally, the feasibility and validity of the designed method are proved through simulation results.
A Search/Crawl Framework for Automatically Acquiring Scientific Documents

Despite the advancements in search engine features, ranking methods, technologies, and the availability of programmable APIs, current-day openaccess digital libraries still rely on crawl-based approaches for acquiring their underlying document collections. In this paper, we propose a novel search-driven framework for acquiring documents for scientific portals. Within our framework, publicly-available research paper titles and author names are used as queries to a Web search engine. Next, research papers and sources of research papers are identified from the search results using accurate classification modules. Our experiments highlight not only the performance of our individual classifiers but also the effectiveness of our overall Search/Crawl framework. Indeed, we were able to obtain approximately 0.665million research documents through our fully-automated framework using about0.076 million queries. These prolific results position Web search as an effective alternative to crawl methods for acquiring both the actual documents and seed URLs for future crawls.
TriggerSync: A time synchronisation tool

This paper presents a framework for synchronising multiple triggered sensors with respect to a local clock using standard computing hardware. Providing sensor measurements with accurate and meaningful timestamps is important for many sensor fusion, state estimation and control applications. Accurately synchronising sensor timestamps can be performed with specialised hardware, however, performing sensor synchronisation using standard computing hardware and non-real-time operating systems is difficult due to inaccurate and temperature sensitive clocks, variable communication delays and operating system scheduling delays. Results show the ability of our framework to estimate time offsets to sub-millisecond accuracy. We also demonstrate how synchronising timestamps with our framework results in a tenfold reduction in image stabilisation error for a vehicle driving on rough terrain. The source code will be released as an open source tool for time synchronisation in ROS.
Generic Torque-Maximizing Design Methodology of Surface Permanent-Magnet Vernier Machine

A surface permanent-magnet vernier machine (SPMVM) has a toothed-pole structure, and its highly nonlinear relationship between its dimensions and magnetic field makes its design cumbersome. This paper presents a generic design methodology for the machine which brings out its torque-maximizing structure in a convenient manner. Various suggestions concerning the design of the SPMVM are also set forth.
High Performance Transactions via Early Write Visibility

In order to guarantee recoverable transaction execution, database systems permit a transaction’s writes to be observable only at the end of its execution. As a consequence, there is generally a delay between the time a transaction performs a write and the time later transactions are permitted to read it. This delayed write visibility can significantly impact the performance of serializable database systems by reducing concurrency among conflicting transactions. This paper makes the observation that delayed write visibility stems from the fact that database systems can arbitrarily abort transactions at any point during their execution. Accordingly, we make the case for database systems which only abort transactions under a restricted set of conditions, thereby enabling a new recoverability mechanism, early write visibility, which safely makes transactions’ writes visible prior to the end of their execution. We design a new serializable concurrency control protocol, piece-wise visibility (PWV), with the explicit goal of enabling early write visibility. We evaluate PWV against state-of-the-art serializable protocols and a highly optimized implementation of read committed, and find that PWV can outperform serializable protocols by an order of magnitude and read committed by 3X on high contention workloads.
A Novel Algorithm for the Precise Calculation of the Maximal Information Coefficient

Measuring associations is an important scientific task. A novel measurement method maximal information coefficient (MIC) was proposed to identify a broad class of associations. As foreseen by its authors, MIC implementation algorithm ApproxMaxMI is not always convergent to real MIC values. An algorithm called SG (Simulated annealing and Genetic) was developed to facilitate the optimal calculation of MIC, and the convergence of SG was proved based on Markov theory. When run on fruit fly data set including 1,000,000 pairs of gene expression profiles, the mean squared difference between SG and the exhaustive algorithm is 0.00075499, compared with 0.1834 in the case of ApproxMaxMI. The software SGMIC and its manual are freely available at http://lxy.depart.hebust.edu.cn/SGMIC/SGMIC.htm.
The Cascade and Cascode Resistive Feedback Low Noise Amplifier for UWB Applications

this paper present two different resistive feedback low noise amplifiers. The benefits of using mentioned resistive feedback is to enhance the bandwidth and provide wideband input matching characteristics. These two low noise amplifiers (LNAs) are compared with each other and compared with previous designs. Results of simulations show that our LNAs have better performance than conventional works. The first resistive feedback LNA demonstrates a flat 11 dB voltage gain and S11<-14 dB over frequency range of 3.1-10.6 GHz. Noise figure in this range is 2.4-4 dB and this circuit consumes 26 mW. The second resistive feedback LNA has a noise figure of 2.7 dB, 17 dB flat gain and input impedance matching parameter, S11, is lower than -10 dB over the UWB frequency band. It consumes 26 mW. Keywords— Low Noise Amplifier; Resistive Feedback; Ultra Wide Band; Cascade; Cascode