Reviewerly/exHarmony huggingface dataset:
Ultra-short-term solar power forecasting based on a modified clear sky model

The ultra-short-term prediction of photovoltaic power system is of great significance to the safe operation of power system. However, the daily and annual periodicity of solar radiation brings strong non-stationary to the photovoltaic power series, which makes it difficult to predict the photovoltaic power series. In order to overcome the problem that the detailed clear sky model relies on a large number of plant parameters, this paper proposes a prediction algorithm based on a modified clear sky model. Firstly, historical data and geographical location of a certain site are utilized to build a modified clear sky model, then the theoretical clear sky power curve fitted by the modified clear sky model are divided by the actual historical power output to get a stabilized time series, finally the method of "online update" is adopted to forecast PV plant power output in the ultra-short-term time horizon (0-4 h). The test results of a photovoltaic power station in Ningxia show that the proposed model can reduce the prediction error of the 4th hour to about 3%.
Classification algorithms of strip surface defects based on PCA-ELM

The computer image technology has achieved impressive recognition rates in image classification tasks recently. In order to tackle the complex defect features in strip steel, we conduct a study of the effectiveness of principal component analysis and extreme learning machine (ELM) in the classification of strip surface in this paper. Using the principal component analysis, it extracts the characteristics of dimension reduction, obtains the characteristic quantity, reduces the dimension used as the input sample for the Extreme Learning Machine and trains the support vector machine and realizes the recognition. Compared with traditional extreme learning machine and support vector machine (SVM), PCA-ELM algorithm shows a higher accuracy and better generalization ability, which play a guiding sole on actual production.
Embodied Cognition in Performers of Large Acoustic Instruments as a Method of Designing New Large Digital Musical Instruments

We present The Large Instrument Performers Study, an interview-based exploration into how large scale acoustic instrument performers navigate the instrument’s size-related aesthetic features during the performance. Through the conceptual frameworks of embodied music cognition and affordance theory, we discuss how the themes that emerged in the interview data reveal the ways size-related aesthetic features of large acoustic instruments influence the instrument performer’s choices; how large scale acoustic instruments feature microscopic nuanced performance options; and how despite the preconception of large scale acoustic instruments being scaled up versions of the smaller instrument with the addition of a lower fundamental tone, the instruments offer different sonic and performative features to their smaller counterparts and require precise gestural control that is certainly not scaled up. This is followed by a discussion of how the study findings could influence design features in new large scale digital musical instruments to result in more nuanced control and timbrally rich instruments, and better understanding of how interfaces and instruments influence performers’ choices and as a result music repertoire and performance.
The more the better? A comparative study of the relationships among multimodal connectedness, online communication, and relational outcomes in China and the United States

Based on samples of students in China and the United States (US), this research drew on concepts in relational dialectical theory and media multiplexity theory to examine relationships among multimodal connectedness, relational communication on media, the interdependence–entrapment dialectic, and relationship satisfaction. The results indicated that multimodal connectedness had an overall negative effect on romantic relationships because it increased the feeling of entrapment and reduced relationship satisfaction. This pattern was especially evident in the Chinese sample because multimodal connectedness reduced interpersonal communication and increased conflict management online. However, social sharing on media positively mediated the effect of multimodal connectedness on satisfaction in romantic relationships. These findings suggest that media modalities and use patterns are imperative factors in examining the influence of mediated relational communication on relational outcomes.
Linear Active Disturbance Rejection Control for Propeller-Driving System based on Model Information Compensation

In order to get a better performance from the Linear Extended State Observer (LESO), a Linear Active Disturbance Rejection Control (LADRC) strategy is proposed based on the Unscented Kalman Filter (UKF) identification. The UKF, with high accuracy of estimation for nonlinear distribution, is applied to identify the system information so as to provide the design parameter b <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> of the LADRC. In order to avoid the problem that the covariance matrix is non-positive and improve the numerical robustness, singular value decomposition is usedto instead of Cholesky decomposition in the UKF. It improves the ability of the LESO, and hence the performance of LADRC by reducing the uncertainty of the controlled system. Taking into account sensor noise, a tracking-differentiator is introduced to the LESO to get a better control performance. Both the LADRC based on the UKF identification and the traditional LADRC are applied to the propeller-driving system and compared in this paper. The simulation and experiment results both show that the proposed method has better control effect compared with the traditional LADRC, and the estimation ability of the LESO is stronger.
Analysis of McEliece Public Cryptography Using Deep AutoEncoder

McEliece code is the first random public cryptosystem. For decades, McEliece public scheme (constructed by Goppa codes) is assumed secure against the known public cryptosystem attacks. Indistinguishability is a basic character of a public key encryption scheme. In this paper, we are aimed at proving that the classic McEliece cryptosystem is not secure against indistinguishable chosen ciphertext attack from the angle of practical discriminator in action. An online learning framework (AEMC+DISC) is designed to find ciphertext discriminative flaws automatically. In the framework, an encoding generator (AEMC) is constructed using deep autoencoder to generate legal pairs of plaintext and ciphertext without the participation of McEliece ciphers, and an vector classifier (DISC) is built to distinguish identical ciphertexts. We prove that McEliece scheme based on Goppa code (the classic one and its variants) is vulnerable under any kinds of indiscriminative attacks.
Malayalam POS Tagger—A Comparison Using SVM and HMM

Many Parts Of Speech (POS) taggers for the Malayalam language has been implemented using Support Vector Machine (SVM), Memory-Based Language Processing (MBLP), Hidden Markov Model (HMM) and other similar techniques. The objective was to find an improved POS tagger for the Malayalam language. This work proposed a comparison of the Malayalam POS tagger using the SVM and Hidden Markov model (HMM). The tagset used was the popular Bureau of Indian Standard (BIS) tag set. A manually created data set which has around 52,000 words has been taken from various Malayalam news sites. The preprocessing steps that have done for news text are also mentioned. Then POS tagging has been done using SVM and HMM. As POS tagging requires the extraction of multiple class labels, a multi-class SVM is used. It also performs feature extraction, feature selection, and classification. The word sense disambiguation and misclassification of words are the two major issues identified in SVM. Hidden Markov Model predicts the hidden sequence based on maximum observation likelihood which reduces ambiguity and misclassification rate.
OhioLINK librarians and Google Scholar over time: a longitudinal analysis of attitudes and uses

Purpose This study aims to focus on how librarians use and promote Google Scholar (GS) within their library instruction sessions. This study also examines how Google Scholar and the Google Scholar discovery layer, library links, is promoted on library websites. This information is then analyzed across the three date ranges. Design/methodology/approach This study provides a longitudinal analysis of the opinions and uses of Google Scholar by the Ohio consortium of libraries, OhioLINK. This study uses survey data that was collected in 2007, 2014 and 2019 via the OhioLINK Listserv and builds off of co-author’s previous study (2008), which examined the attitudes of OhioLINK librarians as it related to Google Scholar. Findings The results of this research suggested that there were significant changes in use and opinions of Google Scholar between 2007 and 2014 with more normalization of uses and opinions occurring between 2014 and 2019. Research limitations/implications Respondents were not asked for the type of library where they work or to identify their position within their libraries, which necessarily limits the conclusions that can be drawn from the survey responses. In retrospect, limiting the sample to instruction librarians or faculty librarians might have yielded more meaningful results. Additionally, this project did not evaluate the uses and opinions of librarians using GS at the reference desk. By providing this information, it would be easier to truly glean the uses and opinions of librarians with regard to GS. Practical implications This research will assist librarians contextualize how one of the most popular research resources has been used and promoted by those within the field and how those opinions have changed over time. This study will provide context into how Google Scholar became one of the most popular research tools and how attitudes of this unprecedented, and controversial, resource came to be accepted by librarians over the past 15 years. Social implications This study will help librarians better contextualize how other librarians use and promote Google Scholar. Furthermore, it demonstrates how a controversial information research tool became accepted by those in the field over time. Originality/value According to the authors’ knowledge, this research is the only study to provide a longitudinal analysis of the librarian opinions of Google Scholar. It targeted the same audience in identical surveys in a way no other Google Scholar research has done. It builds off of the co-author’s (2008) work, which is a well-cited study regarding librarian attitudes of Google Scholar. This research was done on the 15th year anniversary of Google Scholar.
Text Classification Using Multilingual Sentence Embeddings

Training a machine learning model always requires an ample quantity of data as the effectiveness of the model depends on how good it is trained. It is not always that easy, using Zero-shot learning this paper is concentrated on intent classification in different speeches given the model is trained only in one language. Several machine learning models were developed and their outputs were compared to understand the concept of how Zero-shot learning (ZSL) works. It is grounded on the idea that the sentence embedding of two sentences of different languages having the same sense must be similar. In order to predict the intent of sentences in different languages, every sentence must be signified in the same manner and in the same vector space. Here comes the Language Agnostic Sentence Representations or LASER which uses a single encoder to give sentence embedding vector for sentences in any language. The motive of this paper is to assess how useful these sentence representations are in the context of a particular scenario.
Design of educational games oriented to chemical literacy on petroleum material

Abstract This study uses the stages of the Research Based Design method to produce an educational game oriented to chemical literacy on petroleum material. This research is driven by the importance of a visual learning media in order to improve the ability of chemical literacy in petroleum material so that students can connect between knowledge of concepts and phenomena that occur. The aim of the study is to describe the appearance of educational games oriented to chemical literacy on petroleum material. Based on the results of the study, it is obtained that the chemical literacy-oriented educational game products in the form of chemical monopolies and chemical energy ladders which have characteristics such as game rules that modify the monopoly and snake ladders games equipped with questions oriented to chemical literacy, obstacles in the games show the levels of difficulty and levels of thinking as well as different visualizations that illustrate the context and application of petroleum.
Locality Preserving based Motion Consensus for Endoscopic Image Feature Matching

Feature matching of endoscopic images is an important and challengeable task for many clinical applications, such as tissue surface reconstruction and object tracking. In this study, we proposed a locality preserving based motion consensus method for endoscopic image feature matching. Firstly, a local distance constraint is applied to maintain the local structure of initial matches derived from the ASIFT algorithm. Secondly, bilateral affine motion boundaries are estimated from the local structure preserving based matches to obtain precise motion constraint. Initial matches that meet the criterion of adaptive threshold of the bilateral affine motion boundaries are considered as final matches. Through considering both locality and global motion coherence of feature points, the proposed method can effectively find reliable matches from initial matches of large outlier ratios. We test our method and four state-of-the-art methods on simulated-nonrigid deformation and simulated-tool occlusion endoscopic images. The proposed method outperforms the other state-of-the-art methods in Precision, Recall, F1-Score, and Accuracy.
Quality not Quantity! A Qualitative Evaluation and Proposal for Understanding the Depth of Audience “Knowledge” Post Data Extraction

Knowledge is defined as...the result of machine extracted patterns; humans making sense of their environment; information generated and aggregated from software services or as the lowest form of human cognition. Different perspectives, different domains, but one concept. Information scientists are often concerned with retrieving knowledge from data sources and sharing that knowledge with concerned stakeholders; with such differing views on what qualifies as knowledge a cross-domain approach might prove beneficial. This work is a qualitative assessment of the layers of knowledge intended to bridge the gap between the analyst and their intended or unintended audiences. It examines the benefit of abstracting concepts used in the education discipline to justify including a post-evaluation stage to the Knowledge Discovered through Databases (KDD) framework. It also intends to promote awareness of the various human cognitive capacities and provide a useful approach for communicating and evaluating machine-extracted knowledge that supports higher order thinking.
Applying the Lens of Science Capital to Understand Learner Engagement in Informal Maker Spaces

Opportunities for young people to participate in making activities—either within school-based learning or within the growing number of makerspaces being established outside of formal education—have increased dramatically in recent years. Whilst some have advocated young people’s participation in makerspaces as an opportunity to democratise access to STEM learning, it is also acknowledged that these spaces reproduce patterns of inequitable participation found in other science-related settings. An underpinning framework that builds on the concept of science capital and the principles of the science capital teaching approach may help a better understanding of this issue. Drawing on data from observations and interviews conducted in a UK-based makerspace, we argue that science capital pedagogic principles are evident in makerspaces and, when enacted, help to create an environment where young people feel valued and better able to participate in making and coding activities. We argue that small changes to practice in the design and facilitation of makerspaces could result in such spaces being more equitable and socially just.
An Improvement of Coherent Semantic Attention for Image Inpainting

In some image restoration algorithms of the past, they often do not consider the continuity between pixels, and the internal features of the hole region. The mapping to the image semantically does not take into account the continuity of the feature, resulting in the color of the fault. Or the deformation of the edge contour of the image. In some of the current popular algorithms and models, we can clearly see the color faults and artificial repair traces from their repair results. These discontinuities are mainly because these methods ignore the semantic relevance and feature continuity of the hole region. Therefore, if we want to get a better image repair effect. We have to improve on semantic relevance and feature continuity. We validated the effectiveness of our proposed method in image restoration tasks on the CelebA and Places2 datasets, and our results yielded a better visual experience in some images than existing methods.
Dinner and Data Management: Engaging undergraduates in research data management topics outside of the curriculum

Researchers are faced with unprecedented challenges due to the size and complexity of data, and libraries are stepping in to help by providing guidance on research data management primarily to graduate students and faculty. Currently, many universities are encouraging an undergraduate research experience where students engage in research projects in the classroom and in research labs, yet research data management is often not included as part of these opportunities. At UW-Madison, we piloted researchERS (Emerging Research Scholars), a program for undergraduates from all disciplines to learn data management skills. Focusing on core concepts as well as data ethics, reproducibility, and research workflows, the format of the program included seven evening workshops, two networking events, and one field trip. Each workshop invited campus and community speakers relevant to the workshop’s theme as a way to introduce the students to the network of available resources and data expertise and provided food for attendees. The workshops also built in customized activities to show students how to incorporate best practices into their work. Local businesses provided a tour of their facilities as well as a talk on how they leverage data. This paper will describe this program as well as the benefits and drawbacks of tailoring a research data management program toward undergraduates.
Knee monitoring device based on optical sensor embedded in mechanical compartment assembly

Development of various health monitoring devices for health monitoring applications such as spine monitoring, respiration rate, heart rate, as well as upper and lower limb motion detections are at increasing rate. The use of optical fiber-based devices provides significant contribution in this area due to their advantages such as immunity to electromagnetic interference, lightweight and possibly small sensor setup. However, the application of intensity-based optical fiber sensor for human joint motion detections resulted in limited detection angle, where most sensor are not able to detect more than 90 deg. angle. To improve this limitation, we propose an optical sensor approach with mechanical-assisted components assembly that translates angular movement to linear movement. The light detection on the photodiode array at different pixels is analyzed to represent the angle movement of the knee. Based on our study, the proposed device can be applied to detect angle between 0−160° with an accuracy of 0.08°. The obtained results make it possible to apply this technique as knee monitoring device.
Not the Time for Central Bank Digital Currency

Abstract This short article addresses several important (but less discussed) aspects of the introduction of central bank digital currency that give cause for concern, no matter whether such a currency is intended as a substitute or a complement to cash. It discusses potential effects, such as bank runs and capital flight, and analyzes possible interactions between central bank digital currency and the limits on cash payments that already exist in several European countries. What are the structural characteristics that still make paper money and coins (the only means of payment directly issued by central banks) irreplaceable? These and other issues (including effects of COVID-19 on cash payment limits) are explored through a discursive approach that is simultaneously grounded in rigorous macroeconomic analysis. JEL Classification: E51, E58, E71
Nonlinear Filtering with a Polynomial Series of Gaussian Random Variables

Filters relying on the Gaussian approximation typically incorporate the measurement linearly, i.e., the value of the measurement is pre-multiplied by a matrix-valued gain in the state update. Nonlinear filters that relax the Gaussian assumption, on the other hand, typically approximate the distribution of the state with a finite sum of point masses or Gaussian distributions. In this work, the distribution of the state is approximated by a polynomial transformation of a Gaussian distribution, allowing for all moments, central and raw, to be rapidly computed in closed form. Knowledge of the higher-order moments is then employed to perform a polynomial measurement update, i.e., the value of the measurement enters the update function as a polynomial of arbitrary order. A filter employing a Gaussian approximation with linear update is, therefore, a special case of the proposed algorithm when the order of the update is set to one. At the cost of more computations, the new methodology guarantees performance better than the linear/Gaussian approach for nonlinear systems. This work employs monomial basis functions and Taylor series, but it is readily extendable to an orthogonal polynomial basis.
Efficient locality-sensitive hashing over high-dimensional streaming data

Approximate nearest neighbor (ANN) search in high-dimensional spaces is fundamental in many applications. Locality-sensitive hashing (LSH) is a well-known methodology to solve the ANN problem. Existing LSH-based ANN solutions typically employ a large number of individual indexes optimized for searching efficiency. Updating such indexes might be impractical when processing high-dimensional streaming data. In this paper, we present a novel disk-based LSH index that offers efficient support for both searches and updates. The contributions of our work are threefold. First, we use the write-friendly LSM-trees to store the LSH projections to facilitate efficient updates. Second, we develop a novel estimation scheme to estimate the number of required LSH functions, with which the disk storage and access costs are effectively reduced. Third, we exploit both the collision number and the projection distance to improve the efficiency of candidate selection, improving the search performance with theoretical guarantees on the result quality. Experiments on four real-world datasets show that our proposal outperforms the state-of-the-art schemes.