Reviewerly/exHarmony huggingface dataset:
Machine learning methods applied to meteor detection filtering

&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;Introduction:&amp;lt;/strong&amp;gt;&amp;amp;#160;Recent expansions in fireball networks has lead to an increase in the number of events recorded each night. Due to the mechanism at the source of the detection procedure (i.e. tracking sources of light), the majority of the events is comprised of false meteors. These make their way in trajectory computations, and lead to erroneous orbits [e.g. 1].&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;Aims:&amp;lt;/strong&amp;gt;&amp;amp;#160;For this study we explore several machine learning (ML) models for their ability to filter the false detections.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;Methods:&amp;amp;#160;&amp;lt;/strong&amp;gt;First, a supervised validation was employed on the meteor detections obtained by stations within the Meteorites Orbits Reconstruction by Optical Imaging (MOROI) network [1, 2], between 2017-2020. &amp;amp;#160;Next, a set of ML models suited for classification were applied to a selection of features computed from the data. Finally, each model was tuned to their optimal hyper-parameter value, to obtain the highest score.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;Results:&amp;lt;/strong&amp;gt;&amp;amp;#160;The Neural networks method was found to best filter out the false meteors, with a recall score of 96%, followed by 95% for Gradient Boost and Random Forest algorithms. The score is expected to increase when employing a spatio-temporal filter and pixel brightness information. The results entail follow-up studies on the currently expanding FRIPON network [4].&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;References: &amp;lt;/strong&amp;gt;[1] Gural et al. (2020) &amp;lt;em&amp;gt;Planetary and Space Science&amp;lt;/em&amp;gt; 182, 104847. [2] Nedelcu D. A. et al. (2018) &amp;lt;em&amp;gt;Romanian Astronomical Journal&amp;lt;/em&amp;gt; 28, 1, 3-11. [3] Anghel S. et al. (2021&amp;lt;em&amp;gt;) in LPI Contributions&amp;lt;/em&amp;gt; &amp;lt;em&amp;gt;84&amp;lt;/em&amp;gt;, Abstract #6027. [3] Colas F. et al. (2020) &amp;lt;em&amp;gt;Astronomy &amp;amp; Astrophysics&amp;lt;/em&amp;gt; 644:A53.&amp;lt;/p&amp;gt;
Self-organisation in musicians' collective workspaces before, during, and after COVID-19

Self-organisation and co-location share a long history in the creative industries. In many of these industries, workers seek to work together and alongside each other in shared workspaces of various kinds. Yet, in the music industries, this practice is less established. This chapter discusses two (partly) self-organised initiatives of musicians in the Netherlands, examining the promises and challenges before, during, and potentially after the COVID-19 pandemic. The findings indicate that despite the potential for knowledge exchange and mutual support, most musicians valued their workspace mainly because it offered facilities (space and time to practise and perform). This was especially important during the pandemic, as it provided a daily goal and structure. As an organisational form in a post-pandemic world, this model could serve as a means to foster resilience especially for early career musicians, providing low-threshold access to essential resources (social, spatial, and symbolic), even during periods of drought.
Crypto-Litigation: An Empirical Overview for 2020-Present

This article is an empirical analysis of the past two years of litigation around cryptocurrencies and other crypto-assets. We collected almost 300 cases from the past two years that, and classified them by the various issues that were litigated in these cases. The article provides the reader with a breakdown of these issues as well as the jurisdictions from where these cases come from. We also discuss a few notable cases in order for the reader to appreciate what facts the courts are seeing. As we move into a new round of litigation due to the drop in prices, we hope that the past experiences will guide lawyers and the courts in navigating the next wave of crypto-litigation.
Semantic knowledge graph technologies in data science

Knowledge graphs, the representation of data as just a semantic graph, had already garnered considerable attention in both the academia and industry worlds. Their ability to provide semantically appropriate data had already made significant reasonable solutions to several tasks, such as solving problems, guidance and knowledge representation, and has been considered to be a tremendous potential to several researchers to develop the most advanced technology. Even though numerous "Big Data" applications have been already facilitated in all kinds of commercial and science domains through information graphs but limits of effectiveness have been met by the document-centric frameworks of research. Recent controversies on the growing abundance of research journals and the issue of accuracy have stressed everything. This creates an opportunity to reconsider the prevailing view of communications of document center research scholars and turn it into a flow of information based on experience by depicting and transferring knowledge via semantic-based interconnected knowledge graphs. The development and advancement of information systems create a shared understanding of knowledge exchanged among users. The methods of query and exchange of data in the learning center of the world are at the center of the knowledge-based flow of data. By incorporating these frameworks into current and new science technology services, the knowledge structures that are now latent and profoundly concealed in documentation can be completely visible and right accessible. It would have the possibility to transform data science effort, as knowledge and analysis findings can be easily interrelated and ideally suited to diverse information requirements. In comparison, experimental findings are exactly equivalent and simpler to replicate. In this chapter, the conception of a knowledge graph for data science describes the potential framework for knowledge graph technologies and initial attempts to incorporate the framework.
Authorization Model for Securing Cloud SaaS Services (Netflix)

At the enterprise level, cloud software as a service (SaaS) is in high demand. As software as a service (SaaS) can be easily provided in a multitenant way, its execution assessment, information inactivity, and authorization are critical issues related to the cloud supplier at the time of providing administrations to sellers. Netflix is a mixed media supplier that gives online streaming from a library of movies and TV arrangement. Netflix services are exclusively based on cloud SaaS. The highlight of the study is to analyze the finest hones over secure cloud environment in authorization perspective based on behavioral biometrics. A conceptual authorization is displayed in this paper to secure cloud services, especially SaaS, when unauthorized access is supported by legitimate users. The use case is taken as Netflix.
Retraction: Trend and Risk Prevention of Accounting Information under the Background of Big Data (J. Phys.: Conf. Ser. 1345 022022)

This article has been retracted by IOP Publishing following an allegation that raises concerns this article may have been created, manipulated, and/or sold by a commercial entity. In addition, IOP Publishing has seen no evidence that reliable peer review was conducted on this article, despite the clear standards expected of and communicated to conference organisers. The authors of the article have been given opportunity to present evidence that they were the original and genuine creators of the work, however at the time of publication of this notice, IOP Publishing has not received any response. IOP Publishing has analysed the article and agrees there are enough indicators to cause serious doubts over the legitimacy of the work and agree this article should be retracted. The authors are encouraged to contact IOP Publishing Limited if they have any comments on this retraction. Retraction published: 23 September 2022
Retraction: Personal Information Security Crisis in the Era of Big Data (J. Phys.: Conf. Ser. 1486 052002)

This article has been retracted by IOP Publishing following an allegation that raises concerns this article may have been created, manipulated, and/or sold by a commercial entity. In addition, IOP Publishing has seen no evidence that reliable peer review was conducted on this article, despite the clear standards expected of and communicated to conference organisers. The authors of the article have been given opportunity to present evidence that they were the original and genuine creators of the work, however at the time of publication of this notice, IOP Publishing has not received any response. IOP Publishing has analysed the article and agrees there are enough indicators to cause serious doubts over the legitimacy of the work and agree this article should be retracted. The authors are encouraged to contact IOP Publishing Limited if they have any comments on this retraction. Retraction published: 23 September 2022
REVIEW ON COMBINING BIOMETRIC AND BEHAVIORAL AUTHENTICATION TECHNIQUES FOR SECURE MOBILE CLOUD COMPUTING

Cloud computing is one of the widely used technology and its use is increasing day by day due to its sharing of services over the internet. The area of concern is its security and privacy issues especially for the smart-phone users. Several security approaches had been introduced to safeguard users from unauthorized access but the security standard with the smart-phone devices are still not up to the mark and uses the typical username and passwords based authentication techniques. This paper is proposing a hybrid authentication approach combining two security modalities i.e. biometric and behavioral keystroke dynamics based user authentication which is designed to provide authentication with the existing username and password along with the biometric and behavioral keystroke dynamics based authentication with the use of biometric fingerprint scanner and keyboard thereby reducing the high cost of additional hardware devices since both are the integral part of the smart-phones these days. This approach will provide a secure access to the smart-phone users for a secure cloud environment efficiently than the existing methods.
Electron beam chiral diffraction radiation in an isosceles right triangle light-well

Electron beam diffraction radiation sources have the advantages of small size, wavelength tunability, and polarization controllability, showing great application potential in nanophotonic circuits. However, studies on optical chirality of electron beam diffraction radiation are relatively scarce. The optical chirality of electron beam diffraction radiation in isosceles right triangle light-well is realized and demonstrated in this work. The light-well consists of twelve alternating layers of gold and silicon dioxide, and the electron beam diffraction radiation originates from oscillating dipole sources within the light-well. In the case of the electron beam injecting at the geometrically asymmetric position within the structure, the diffraction radiation signal with a maximum chirality of more than 40% can be obtained. By changing the injection position of the electron beam, the state of the optical chirality can be effectively controlled, and even the inversion of the chirality can be realized.&lt;br&gt;By analyzing the vacuum electromagnetic mode in the system and the dynamic evolution of charge distribution in the gold layer, a reasonable theoretical explanation is provided for the generation of the chiral optical effect. A semi-analytical model for explaining the optical chirality of electron beam diffraction radiation is given by solving the incident electromagnetic field and the waveguide modes in the light-well. The observed difference between left-handed circularly polarized and right-handed circularly polarized signals originates from the asymmetry between left-handed and right-handed electromagnetic modes in the light-well caused by the geometrically asymmetric positional excitation, which ultimately leads to far-field chiral radiation determined by the electromagnetic field within the light-well. In addition, the simulation results of the surface charge distribution of the top gold film of the light-well reveal the correlation between the dynamic evolution of the surface charge distribution and the radiation chirality.&lt;br&gt;The advantages of nanoscale focusing and moving of electron beam excitation source make the optical chirality of electron beam diffraction radiation have more flexible adjustment potential. The proposed physical mechanism and unique experimental platform not only provide new ideas for manipulating optical chirality at the nanoscale, but also lay the foundation for binary information processing and integration in nanophotonic circuits and chiral nano-light-sources in the future.
Ontology-Driven Scientific Literature Classification Using Clustering and Self-supervised Learning

The rapid growth of scientific literature in the fields of computer engineering (CE) and computer science (CS) presents difficulties to researchers who are interested in exploring research publication records based on standard scientific categories. This urges the need for a context-aware, automatic classification of text documents into standard scientific categories. Document classification is a significant application of supervised learning which requires a labeled dataset for training the classifier. However, research publication records available on Google Scholar and dblp services are not labeled. First, manual annotation of a large body of scientific research work based on standard scientific terminology requires domain expertise and is extremely time-consuming. Second, hierarchical labeling of records facilitates a more effective and context-aware retrieval of documents. In this paper, we propose an ontology-driven classification technique based on zero-shot learning in conjunction with agglomerative clustering to automatically label a scientific literature dataset related to CE and CS. We further study and compare the effectiveness of multiple text classifiers such as logistic regression (LR), support vector machines (SVM), gradient boosting with Word2vec and bag of words (BOW) embedding, recurrent neural networks (RNN) with GloVe embedding, and feed-forward neural networks with BOW embedding. Our study showed that RNN with GloVe embedding outperforms other models with an above 0.85 F1 score on all granularity levels. Our proposed technique will help junior and experienced researchers identify new emerging technologies and domains for their research purposes.
Difficult conversations — turning dread to joy!

In the last column, I wrote about clearing your plate for more purpose‐ driven work. I shared the “4 Ds” (do, delegate, delay, drop) and a few key questions to help manage workload coming in from other people.
Lightweight Model for Waifu Creation Using Deep Convolutional Generative Adversarial Network (DCGAN)

The inceptions of generative adversarial networks have made it possible for machines to mimic creativity, one of the most unique and sophisticated human characteristics. Due to the rapid advancements in the field of generative adversarial models, lots of approaches have been proposed in the past. One of the most efficient GANs is deep convolutional generative adversarial network (DCGAN), which uses convolutional layers in the generator model to generate more realistic fake images. In this paper, we propose a lightweight implementation of the DCGAN that can be productive for the animation industry. Our model can be used by animators and designers to innovate ideas about creative anime avatars that have never existed before. This novel approach not only saves a lot of time on creative thinking but also provides brand new character designs for anime and manga avatars production.
Embedded-complement and discontinuous pseudogapping in Hybrid Type-Logical Grammar: a rejoinder to Kim and Runner (2022)

Abstract In their recent paper on pseudogapping in Construction Grammar/HPSG, Kim and Runner (Kim, Jong-Bok &amp; Jeffrey T. Runner. 2022. Pseudogapping in English: A direct interpretation approach. The Linguistic Review . https://doi.org/10.1515/tlr-2022-2094 ) suggest that the analysis of pseudogapping in Hybrid Type-Logical Grammar (Hybrid TLG) presented in Kubota and Levine (Kubota, Yusuke &amp; Robert Levine. 2017. Pseudogapping as pseudo-VP ellipsis. Linguistic Inquiry 48(2). 213–257) does not explain certain complex patterns of pseudogapping for which their own proposal does offer an account. Though Kim and Runner’s (2022) remarks on Kubota and Levine (Kubota, Yusuke &amp; Robert Levine. 2017. Pseudogapping as pseudo-VP ellipsis. Linguistic Inquiry 48(2). 213–257) leave room for interpretation, we take it to be reasonably clear that they simply mean that it is difficult to see how such data could be formally accounted for in Kubota and Levine’s proposal. The primary goal of our response is to refute Kim and Runner’s claim on this interpretation. After refuting their claim on this interpretation, we consider a different interpretation of their remarks, one which merely questions the conceptual plausibility of Kubota and Levine’s (2017) broader theoretical architecture. This latter discussion leads to some interesting and important cross-theoretical comparison of different approaches to ellipsis.
STUDI PENGGUNAAN GESCHOOL DALAM PEMBELAJARAN SENI BUDAYA MATERI SENI RUPA KELAS VIII SMP NEGERI 11 PADANG

This study aims to 1) Knowing the learning process through the application of Geschool in the subjects of fine arts material art class VIII SMP Negeri 11 Padang, 2) Knowing the benefits on the subjects of art and culture fine arts material grade VIII SMP Negeri 11 Padang. This type of research is descriptive qualitative. Data types use primary and secondary data. Data collection techniques in this research are observation, interview and documentation. The steps to analyze data are data collection, data reduction, data presentation and inference. Based on the results of the research, it was concluded that the learning process through geschool application in art and culture subjects of fine arts material grade VIII SMP Negeri 11 Padang went well and was very helped, which from the geschool has helped add new menus in the application. Geschool helps teachers evaluate students about the material being studied. In the assignment, teachers have also been greatly helped by the existence of learning materials provided by MGMP in accordance with KI KD. The benefit of implementing Geschool in art and culture subjects of fine arts material grade VIII SMP Negeri 11 Padang is that teachers can directly share student material easily without taking a long time and geschool application also facilitates the tryout assessment process, teachers no longer need to check the learning results manually and made it easier to assign assignments to students. Students are disciplined in using time because the application is arranged according to the hours of the lesson, students and parents can also know directly the results of the learning and the teacher no longer checks the absence manually anymore because in the geschool application will be seen students who are following the learning and the teacher only monitors the students online.Keywords: use, geschool, cultural arts fine arts material
Developing a Ranking Problem Library (RPLIB) from a data-oriented perspective

We present an improved library for the ranking problem called RPLIB. RPLIB includes the following data and features. (1) Real and artificial datasets of both pairwise data (i.e., information about the ranking of pairs of items) and feature data (i.e., a vector of features about each item to be ranked). These datasets range in size (e.g., from small $$n=10$$ item datasets to large datasets with hundred of items), application (e.g., from sports to economic data), and source (e.g. real versus artificially generated to have particular structures). (2) RPLIB contains code for the most common ranking algorithms such as the linear ordering optimization method and the Massey method. (3) RPLIB also has the ability for users to contribute their own data, code, and algorithms. Each RPLIB dataset has an associated. JSON model card of additional information such as the number and set of optimal rankings, the optimal objective value, and corresponding figures.
SECL: Sampling enhanced contrastive learning

Instance-level contrastive learning such as SimCLR has been successful as a powerful method for representation learning. However, SimCLR suffers from problems of sampling bias, feature bias and model collapse. A set-level based Sampling Enhanced Contrastive Learning (SECL) method based on SimCLR is proposed in this paper. We use the proposed super-sampling method to expand the augmented samples into a contrastive-positive set, which can learn class features of the target sample to reduce the bias. The contrastive-positive set includes Augmentations (the original augmented samples) and Neighbors (the super-sampled samples). We also introduce a samples-correlation strategy to prevent model collapse, where a positive correlation loss or a negative correlation loss is computed to adjust the balance of model’s Alignment and Uniformity. SECL reaches 94.14% classification precision on SST-2 dataset and 89.25% on ARSC dataset. For the multi-class classification task, SECL achieves 90.99% on AGNews dataset. They are all about 1% higher than the precision of SimCLR. Experiments show that the training convergence of SECL is faster, and SECL reduces the risk of bias and model collapse.
Supplemental Material: Foreland basin response to middle Cretaceous thrust belt evolution, southwestern Montana, USA

&lt;p&gt;Files include measured stratigraphic sections, concordia diagrams for U-Pb detrital zircon data, a multidimensional scaling plot that includes arc ages, SELFRAG mineral separation methods and results, GPS coordinates for samples, R&lt;sup&gt;2&lt;/sup&gt; values for mixture model iterations, and raw point count data. &lt;/p&gt;
Context Enhanced and Data Augmented $$\mathrm W^{2}NER$$ System for Named Entity Recognition

This paper describes the system proposed by the YSF2022 team for NLPCC 2022 shared task 5 [3] on Named Entity Recognition Model for English Scientific Literature. This task needs participants to develop a named entity recognition (NER) model for domain-specific texts based on state-of-the-art NLP and deep learning techniques with the labeled domain-specific sentences corresponding to seven entity types. Without the luxury of training data, we proposed two methods to improve the performance by capturing document-level features and performing data augmentation with entity replacement. Besides, instead of using the traditional sequence labeling model, we attempted to use a novel alternative by modeling the NER as word-word relation classification. On the other hand, we apply Entity Confidence Filter (ECF) and Result Ensemble (RE) to get better performance. According to the official results, our approach ranks $$\mathrm 1^{st}$$ for the NER track in this task.
An Interview with Glenn Fulcher: Looking Back and Moving Forward

The interview took place on 16th February 2022 at 11:00am (GMT) via Zoom. The interview focused on three central themes of Professor Fulcher’s work in language assessment: (i) Reflecting on his career as an educator and scholar, (ii) Experiences and lessons learned when working with industry testing companies, and (iii) Future opportunities and challenges for the language assessment community. The interview was transcribed by Dr. Lee McCallum