nils-herrmann/clean_scirepeval huggingface dataset:
An approach of feature selection using graph-theoretic heuristic and hill climbing

Search-based methods that use matrix- or vector-based representations of the dataset are commonly employed to solve the problem of feature selection. These methods are more generalized and easy to apply. Recently, a set of algorithms have started using graph-based representation of the dataset instead of the traditional representations. These methods require additional modelling as the dataset needs to be represented as a graph. However, graph-based methods help in visualizing inter-feature relationship based on which graph-theoretic principles can be applied to identify good-quality feature subsets. A combination of the graph-based representation with traditional search techniques has the potential to increase model performance as well as interpretability. As per literature study, there is hardly any method which combines these approaches. In this paper, we have proposed a feature selection algorithm, which represents the dataset as a graph and then uses maximal independent sets and minimal vertex covers to improve traditional hill climbing search. The proposed method produces statistically significant improvement over (i) hill climbing, (ii) standard search-based methods and (iii) pure graph-based methods.
An Exploratory Study of User Perceptions of Payment Methods in the UK and the US

This paper presents the design and the results of a cross-cultural study of user perceptions and attitudes toward electronic payment methods. We conduct a series of semi-structured interviews involving forty participants (20 in London, UK, and 20 in Manhattan, KS, USA) to explore how individuals use the mechanisms available to them within their routine payment and banking activities. We also study their comprehension of payment processes, the perceived effort and impact of using different methods, as well as direct or indirect recollections of (suspected or actual) fraud and related interactions with banks and retailers. By comparing UK and US participants, we also elicit commonalities and differences that may help better understand, if not predict, attitudes of US customers once technologies like Chip-and-PIN are rolled out – for instance, several US participants were confused by how to use it, while UK participants found it convenient. Our results show that purchasing habits as well as the availability of rewards schemes are primary criteria influencing choices relating to payment technologies, and that inconsistencies, glitches, and other difficulties with newer technologies generate frustration sometimes leading to complete avoidance of new payment methods.
Driver-Behavior Modeling Using On-Road Driving Data: A new application for behavior signal processing

This article reviews data-centric approaches for statistical modeling of driver behavior. Modeling driver behavior is challenging due to its stochastic nature and the high degree of inter- and intradriver variability. One way to deal with the highly variable nature of driving behavior is to employ a data-centric approach that models driver behavior using large amounts of driving data collected from numerous drivers in a variety of traffic conditions. To obtain large amounts of realistic driving data, several projects have collected real-world driving data. Statistical machine-learning techniques, such as hidden Markov models (HMMs) and deep learning, have been successfully applied to model driver behavior using large amounts of driving data. We have also collected on-road data recording hundreds of drivers over more than 15 years. We have applied statistical signal processing and machine-learning techniques to this data to model various aspects of driver behavior, e.g., driver pedal-operation, car-following, and lane-change behaviors for predicting driver behavior and detecting risky driver behavior and driver frustration. By reviewing related studies and providing concrete examples of our own research, this article is intended to illustrate the usefulness of such data-centric approaches for statistical driver-behavior modeling.
Decrease in timed balance test scores with aging.

This study investigated whether 184 volunteers from 20 to 79 years of age could perform eight timed balance tests and examined the relationship between test performance and age. All subjects were able to balance with their feet together and eyes closed for 30 seconds. The ability to balance on the right and left legs did not differ significantly. Subjects over 60 years of age were unable to balance on one leg, particularly when their eyes were closed, for as long a period as younger subjects. The Pearson product-moment and Spearman correlations of age and duration of one-legged balance were -.65 and -.71 (eyes opened) and -.79 and -.75 (eyes closed). The findings suggest that when timed balance tests are performed as a part of a patient's neurologic examination, the results should be interpreted in light of the patient's age. Information is provided to assist in this interpretation.
Hybris: Robust Hybrid Cloud Storage

Besides well-known benefits, commodity cloud storage also raises concerns that include security, reliability, and consistency. We present Hybris key-value store, the first robust hybrid cloud storage system, aiming at addressing these concerns leveraging both private and public cloud resources.
 Hybris robustly replicates metadata on trusted private premises (private cloud), separately from data which is dispersed (using replication or erasure coding) across multiple untrusted public clouds. Hybris maintains metadata stored on private premises at the order of few dozens of bytes per key, avoiding the scalability bottleneck at the private cloud. In turn, the hybrid design allows Hybris to efficiently and robustly tolerate cloud outages, but also potential malice in clouds without overhead. Namely, to tolerate up to f malicious clouds, in the common case of the Hybris variant with data replication, writes replicate data across f + 1 clouds, whereas reads involve a single cloud. In the worst case, only up to f additional clouds are used. This is considerably better than earlier multi-cloud storage systems that required costly 3f + 1 clouds to mask f potentially malicious clouds. Finally, Hybris leverages strong metadata consistency to guarantee to Hybris applications strong data consistency without any modifications to the eventually consistent public clouds.
 We implemented Hybris in Java and evaluated it using a series of micro and macrobenchmarks. Our results show that Hybris significantly outperforms comparable multi-cloud storage systems and approaches the performance of bare-bone commodity public cloud storage.
Power factor improvement of a transverse flux machine with high torque density

Design of a permanent magnet transverse flux machine optimized for high torque density and high power factor is presented. The optimization process focuses on performance enhancement through improvement of power factor without sacrificing torque density. Simple magnetostatic simulation based method is proposed for the optimization process. Magnetic couplings among axially separated phases are also discussed in the article. Experimental results demonstrating the correlation of measured inductances with that of simulation results are provided.
METAMEMORY : A THEORETICAL FRAMEWORK AND NEW FINDINGS

Although there has been excellent research by many investigators on the topic of metamemory, here we will focus on our own research program. This article will begin with a description of a theoretical framework that has evolved out of metamemory research, followed by a few remarks about our methodology, and will end with a review of our previously unpublished findings. (Our published findings will not be systematically reviewed here; instead, they will be mentioned only when necessary for continuity .
Automatic Spine Segmentation using Convolutional Neural Network via Redundant Generation of Class Labels for 3D Spine Modeling

There has been a significant increase from 2010 to 2016 in the number of people suffering from spine problems. The automatic image segmentation of the spine obtained from a computed tomography (CT) image is important for diagnosing spine conditions and for performing surgery with computer-assisted surgery systems. The spine has a complex anatomy that consists of 33 vertebrae, 23 intervertebral disks, the spinal cord, and connecting ribs. As a result, the spinal surgeon is faced with the challenge of needing a robust algorithm to segment and create a model of the spine. In this study, we developed an automatic segmentation method to segment the spine, and we compared our segmentation results with reference segmentations obtained by experts. We developed a fully automatic approach for spine segmentation from CT based on a hybrid method. This method combines the convolutional neural network (CNN) and fully convolutional network (FCN), and utilizes class redundancy as a soft constraint to greatly improve the segmentation results. The proposed method was found to significantly enhance the accuracy of the segmentation results and the system processing time. Our comparison was based on 12 measurements: the Dice coefficient (94%), Jaccard index (93%), volumetric similarity (96%), sensitivity (97%), specificity (99%), precision (over segmentation; 8.3 and under segmentation 2.6), accuracy (99%), Matthews correlation coefficient (0.93), mean surface distance (0.16 mm), Hausdorff distance (7.4 mm), and global consistency error (0.02). We experimented with CT images from 32 patients, and the experimental results demonstrated the efficiency of the proposed method.
PESTO: Parameter EStimation TOolbox

PESTO is a widely applicable and highly customizable toolbox for parameter estimation in MathWorks MATLAB. It offers scalable algorithms for optimization, uncertainty and identifiability analysis, which work in a very generic manner, treating the objective function as a black box. Hence, PESTO can be used for any parameter estimation problem, for which the user can provide a deterministic objective function in MATLAB.


Availability and implementation
PESTO is a MATLAB toolbox, freely available under the BSD license. The source code, along with extensive documentation and example code, can be downloaded from https://github.com/ICB-DCM/PESTO/.



jan.hasenauer@helmholtz-muenchen.de.



Supplementary data are available at Bioinformatics online.
Does Transcranial Direct Current Stimulation Improve Healthy Working Memory?: A Meta-analytic Review

Transcranial direct current stimulation (tDCS) has been reported to improve working memory (WM) performance in healthy individuals, suggesting its value as a means of cognitive enhancement. However, recent meta-analyses concluded that tDCS has little or no effect on WM in healthy participants. In this article, we review reasons why these meta-analyses may have underestimated the effect of tDCS on WM and report a more comprehensive and arguably more sensitive meta-analysis. Consistent with our interest in enhancement, we focused on anodal stimulation. Thirty-one articles matched inclusion criteria and were included in four primary meta-analyses assessing the WM effects of anodal stimulation over the left and right dorsolateral pFC (DLPFC) and right parietal lobe as well as left DLPFC stimulation coupled with WM training. These analyses revealed a small but significant effect of left DLPFC stimulation coupled with WM training. Left DLPFC stimulation alone also enhanced WM performance, but the effect was reduced to nonsignificance after correction for publication bias. No other effects were significant, including a variety of tested moderators. Additional meta-analyses were undertaken with study selection criteria based on previous meta-analyses, to reassess the findings from these studies using the analytic methods of this study. These analyses revealed a mix of significant and nonsignificant small effects. We conclude that the primary WM enhancement potential of tDCS probably lies in its use during training.
Correction of deep bite in adults using the Invisalign system.

719 D overbite is commonly treated by molar extrusion, incisor intrusion, or both. Careful diagnostic assessment is needed to determine the best treatment approach. Patients with structural deep bites may require orthognathic surgery for full correction. The amount of incisor exposure in smiling is an important consideration in deep-bite cases. When the maxillary incisor display is correct, maxillary incisor intrusion is unnecessary; treatment should involve intrusion and leveling of the mandibular anterior teeth to avoid flattening of the smile arc. Any maxillary incisor intrusion or mandibular leveling should be carefully monitored during treatment to maintain proper incisor exposure while effectively correcting the deep bite. After the development of thermoformed appliances that could be used for minor tooth movements, the Invisalign* system was introduced for treatment of moderate malocclusions. In 2004, Wheeler reported that Invisalign could be used in patients with anterior and posterior crossbites, deep bites, anterior open bites or shallow overbites, and periodontal problems. The present article describes the use of Invisalign appliances for the correction of deep bite in adult patients with normal skeletal patterns.
Ontology-Based Automatic Text Summarization Using FarsNet

To summarize a text means to compress the text source into a shorter text in a way that the informational content is kept the same. With regard to the irregular volume of information available on the internet, manual summarization of huge volume of information by humans will be very arduous and difficult. There have been many activities in the field of automatic summarization so far. However, a lack of having methods capable of achieving a semantic hierarchy available in the documents is still felt. In this article we will propose a method for summarizing Persian documents which uses ontology for recognizing the semantic relationship between different parts of a text and extracting important sentences. For this purpose, mapping an input document with ontology brings about a graph whose vertices are the concepts available in ontology and its edges are the relationships among these concepts. This graph which is a graph-based representation of the input text comprises the necessary computational base for recognizing the important sentences in the input document and the production of a summary. The achieved results indicate the acceptable capability of the proposed method in obtaining semantic relationships available in documents and automatic text summarization. The FarsNet ontology is the base for this article.
Tuning of neural networks using particle swarm optimization to model MIG welding process

Particle swarm optimization technique has been used for tuning of neural networks utilized for carrying out both forward and reverse mappings of metal inert gas (MIG) welding process. Four approaches have been developed and their performances are compared to solve the said problems. The first and second approaches deal with tuning of multi-layer feed-forward neural network and radial basis function neural network, respectively. In the third and fourth approaches, a back-propagation algorithm has been used along with particle swarm optimization to tune radial basis function neural network. Moreover, in these two approaches, two different clustering algorithms have been utilized to decide the structure of the network. The performances of hybrid approaches (that is, the third and fourth approaches) are found to be better than that of the other two.
Students' social media engagement and fear of missing out (FoMO) in a diverse classroom

With the growing attention paid to fear of missing out (FoMO) psychological phenomenon in explaining social media engagement (SME), this mixedmethod research measured the relative impact of FoMO on students’ SME for personal reasons during lectures. The moderating effect of culture (minority vs. nonminority students) on the connection between FoMO and SME was also considered. Quantitative data were gathered from 279-undergraduate students. The structural equation modeling results showed a positive moderate connection between the FoMO and SME variables. The bootstrapping result showed a significant indirect effect between the minority group of students and SME through increased levels of FoMO. A sequential explanatory strategy was used to refine and interpret the quantitative results. Accordingly, qualitative data were gathered by using semistructured interviews to assist in explaining the findings of the quantitative phase. The qualitative data suggested several explanations for students’ distractive behavior enabled by technology during class. The main recurrent theme was the frequently used instructional activities based on the teacher-centered pedagogical approach. This approach imposed greater challenges for minority students as they tend to grapple with a host of language barriers. These students reported using social media tools to seek help from friends during lectures and feared missing out a useful assistance. Another finding showed that mainly non-minority students who experienced FoMO admitted using social media during lessons regardless of the teaching method implemented.
An Evaluation of the Challenges of Multilingualism in Data Warehouse Development

In this paper we discuss Business Intelligence and define what is meant by support for Multilingualism in a Business Intelligence reporting context. We identify support for Multilingualism as a challenging issue which has implications for data warehouse design and reporting performance. Data warehouses are a core component of most Business Intelligence systems and the star schema is the approach most widely used to develop data warehouses and dimensional Data Marts. We discuss the way in which Multilingualism can be supported in the Star Schema and identify that current approaches have serious limitations which include data redundancy and data manipulation, performance and maintenance issues. We propose a new approach to enable the optimal application of multilingualism in Business Intelligence. The proposed approach was found to produce satisfactory results when used in a proof-of-concept environment. Future work will include testing the approach in an enterprise environment.
An analysis of ontology engineering methodologies : a literature review.

It is now widely accepted that ontologies play a critical role in achieving the goal of machine understandable web, also known as semantic web. In order to develop ontologies, several methodologies have been proposed during the last two decades. Despite the fact, that quite a number of ontology engineering methodologies have been proposed, still the field lacks widely accepted and mature methodologies. Most methodologies lack sufficient details of techniques and activities employed in them. However, some methodologies provide sufficient details including METHONTOLOGY. This article discusses and reports a critical analysis and comparison of these methodologies. The analysis is performed based on a criterion, derived from related literature, trends and needs which evolved over the years. The results of the analysis showed that there is no completely mature methodology. Therefore, this research may act as a preliminary guide to come with a state of art ontology engineering methodology, bridging up the existing gaps and shortfalls.
Assessing It/Business Alignment

Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content.
Multiple ant-bee colony optimization for load balancing in packet-switched networks

One of the important issues in computer networks is “Load Balancing” which leads to efficient use of the network resources. To achieve a balanced network it is necessary to find different routes between the source and destination. In the current paper we propose a new approach to find different routes using swarm intelligence techniques and multi colony algorithms. In the proposed algorithm that is an improved version of MACO algorithm, we use different colonies of ants and bees and appoint these colony members as intelligent agents to monitor the network and update the routing information. The survey includes comparison and critiques of MACO. The simulation results show a tangible improvement in the aforementioned approach.
Metaheuristic and Evolutionary Methods for Feature Selection in Sentiment Analysis (a Comparative Study)

With the accelerated evolution of World Wide Web and the widespread of on-line collaborative tools, there is an increasing care towards automatic tools for Sentiment Analysis to provide a quantitative measure of “positivity” or “negativity” about opinions or social comments. But there are many challenges faced the sentiment analysis and evaluation process. These challenges become obstacles in analyzing the accurate meaning of sentiments and detecting the suitable sentiment polarity. The most important challenge is to identify and extract features we will use in our model. In this paper, we hand over an overview of the last spread out techniques for features selection in sentiment analysis based on metaheuristics and evolutionary algorithms as a quick reference guide in the choice of the most suitable methods for solving a specific problem in the sentiment analysis field more precisely in the feature selection stage.
Multi-robot long-term persistent coverage with fuel constrained robots

In this paper, we present an algorithm to solve the Multi-Robot Persistent Coverage Problem (MRPCP). Here, we seek to compute a schedule that will allow a fleet of agents to visit all targets of a given set while maximizing the frequency of visitation and maintaining a sufficient fuel capacity by refueling at depots. We also present a heuristic method to allow us to compute bounded suboptimal results in real time. The results produced by our algorithm will allow a team of robots to efficiently cover a given set of targets or tasks persistently over long periods of time, even when the cost to transition between tasks is dynamic.
Document classification through image-based character embedding and wildcard training

Languages such as Chinese and Japanese have a significantly large number (several thousands) of alphabets as compared to other languages, and each of their sentences consists of several concatenated words with wide varieties of inflected forms; thus appropriate word segmentation is quite difficult. Therefore, recently proposed sophisticated language-processing methods designed for languages such as English cannot be applied. In this paper, we address those issues and propose a new and efficient document classification technique for such languages. The proposed method is characterized into a new “image-based character embedding” method and character-level convolutional neural networks method with “wildcard training.” The first method encodes each character based on its pictorial structures and preserves them. Further, the second method treats some of the input characters as wildcards in the classification stage and functions as efficient data augmentation. We confirmed that our proposed method showed superior performance when compared conventional methods for Japanese document classification problems.