Reviewerly/exHarmony huggingface dataset:
Using a passively stabilized Fabry-Perot etalon for determining instrumental artifacts in a spectrograph

Precision Doppler spectroscopy serves as an important tool for Radial Velocity (RV) measurements by observing Doppler shift in the stellar spectrum, which are used for various applications. Passively stabilized Fabry-Perot (FP) etalon based wavelength calibration is one of the techniques used for Doppler spectroscopy. The FP is kept in a pressure and temperature-stabilized environment for it to produce equispaced transmission lines. Since the FP is stable and the line shape is invariant across wavelength pass band, they can be used to determine the spectrograph’s instrumental artifacts and hence analyze spectrograph performance. Knowledge of instrument effects also helps in better prediction of the wavelength calibration model for the spectrograph. We have tested a passively stabilized FP on Vainu Bappu Telescope (VBT) Echelle spectrograph and Hanle Echelle spectrograph (HESP) and observed field curvature and distortion in both. We are analyzing the artifacts introduced and correcting for the same using image processing methods to compensate for the same in wavelength calibration model developed for the FP-based calibrator.
Calibration of Frequency Shift System of Wind Imaging Interferometer

In this paper, the frequency shift system calibration of the wind imaging interferometer is analyzed. By establishing the frequency shift system vibration and reflectivity models, the single factor and comprehensive factors models are used to invert the wind speed and temperature, respectively. The parameters of the frequency shift system that meet the design accuracy requirement of the instrument are determined. The conclusion of this paper provides theoretical instructions for the calibration process of wind imaging interferometer.
Textual strategies of forensic psychiatrists. A corpus-based analysis of how the language of psychiatry is reconciled with the language of law in polish forensic psychiatric opinions

The role of an expert forensic psychiatrist is likened with that of a translator: their task is to translate the language of medicine into the language of law. The aim of the article was to reconstruct the textual strategies adopted by forensic psychiatrists in terms of reconciling the discourses of law and medicine. The analysis covered 65 opinions/reports issued at a psychiatric reference centre in Poland. Thanks to the application of the innovative corpus linguistics methodology, the singularities of forensic psychiatric opinions as a genre have been captured and the degree of its conventionalisation has been assessed. The findings indicate that psychiatric opinions have not yet achieved the status of a homogenous genre, and the standardisation and formalisation processes have only reached the structural level. The expert psychiatrists constrained the presence of the author's voice and did not use the narrative form in their opinions. The analysis also captured the ethical challenges related to the dual role of forensic psychiatrists as medical doctors and representatives of the judicial system.
Pemetaan Tabel Relationship dalam Visualisasi Diagram Relasi untuk Eksplorasi Data Pada Database

Visualization is a tool that makes it easy to understand the information presented in charts and graphs. The purpose is to facilitate data exploration. Therefore, the authors use relationship diagrams as Mathematical Modeling to explore data that has a relationship between tables. The purpose of the study is to map the dataset, which found out a possibility that some errors when entering data. In the mapping process, we use the composition of relations, thus making the number of possible relationships in the mapping. Because of the problem, the table mapping use into a binary relationship diagram based on the number of attributes in a table. The conclusion obtained when using a binary relation diagram is that the more column in a table, the more mapping relationship diagrams must be drawn. Although the relationship diagram becomes very complex, it handled if the tables are correctly normalized.
Reprogramming Language Models for Molecular Representation Learning

Recent advancements in transfer learning have made it a promising approach for domain adaptation via transfer of learned representations. This is especially when relevant when alternate tasks have limited samples of well-defined and labeled data, which is common in the molecule data domain. This makes transfer learning an ideal approach to solve molecular learning tasks. While Adversarial reprogramming has proven to be a successful method to repurpose neural networks for alternate tasks, most works consider source and alternate tasks within the same domain. In this work, we propose a new algorithm, Representation Reprogramming via Dictionary Learning (R2DL), for adversarially reprogramming pretrained language models for molecular learning tasks, motivated by leveraging learned representations in massive state of the art language models. The adversarial program learns a linear transformation between a dense source model input space (language data) and a sparse target model input space (e.g., chemical and biological molecule data) using a k-SVD solver to approximate a sparse representation of the encoded data, via dictionary learning. R2DL achieves the baseline established by state of the art toxicity prediction models trained on domain-specific data and outperforms the baseline in a limited training-data setting, thereby establishing avenues for domain-agnostic transfer learning for tasks with molecule data.
Personnel matching model of K-means clustering algorithm based on spark platform

In order to improve the efficiency of personnel matching system, this paper proposes a k-means clustering algorithm based on spark platform to complete the personnel matching model; spark platform completes the clustering iterative operation in the memory elastic distributed data set of all nodes in the distributed system to accelerate the clustering speed; the K-means clustering algorithm is used to complete the personnel matching model.The mean clustering algorithm is combined with clustering idea to improve the efficiency of large-scale data clustering. After the clustering results are obtained, the dynamic recommendation algorithm is used to realize the personnel matching model.
AN OVERVIEW OF LANGUAGE TESTING

Scholars have stated that it is “challenging to describe proficiency” (Goundar, 2020). One of the issues that teachers, researchers, students and language testers encounter is defining “what it actually means to be proficient in an L2”. Language proficiency “is the extent to which an individual possesses the linguistic cognition necessary to function in a given communicative situation, in a given modality such as listening, speaking, reading or writing”. It can be implied that proficiency is often discussed in the context of L2 acquisition, usually measured with test-scores. We conduct language tests in order gauge a learn proficiency. Therefore, it is crucial to first get an overview of the field of language testing. The aim of this paper is to briefly outline what has transpired in the field of language testing over the years. Article visualizations:
A New Algorithm For Reconstruction Of A Computer-Generated Hologram (CGH)

Abstract A new algorithm based on cell orientation for construction of detour Fourier Transform Computer Generated Hologram (CGH) and reconstruction of original objects from the CGH is presented here. Uniqueness of this investigation is that, besides reconstructing original objects, using this proposed algorithm, these can also be reconstructed without using monochromatic light. This investigation thus finds its importance in encryption applications. The proposed algorithms are faster as compared to Elliptic Curve Cryptography (ECC) algorithm and consumes less power.
Deep learning methods for SAR image despeckling: trends and perspectives.

Synthetic aperture radar (SAR) images are affected by a spatially-correlated and signal-dependent noise called speckle, which is very severe and may hinder image exploitation. Despeckling is an important task that aims at removing such noise, so as to improve the accuracy of all downstream image processing tasks. The first despeckling methods date back to the 1970's, and several model-based algorithms have been developed in the subsequent years. The field has received growing attention, sparkled by the availability of powerful deep learning models that have yielded excellent performance for inverse problems in image processing. This paper surveys the literature on deep learning methods applied to SAR despeckling, covering both the supervised and the more recent self-supervised approaches. We provide a critical analysis of existing methods with the objective to recognize the most promising research lines, to identify the factors that have limited the success of deep models, and to propose ways forward in an attempt to fully exploit the potential of deep learning for SAR despeckling.
Research on Modeling of Reconfiguration for Integrated Electronic System

Integrated electronic system technology has been widely used in aircraft to reduce costs and space and increase flexibility. However, with the increase of aircraft function and mission requirements, integrated electronic system becomes more and more complicated. Especially, under the requirement of system reconfigurable, the reconfiguration decisions' blueprint lacks unified modeling description, can not be quickly mapped diversified system architecture, and reconstruction configuration blueprint lacks flexible resource allocation mapping. Therefore, this paper proposes an integrated electronic system reconfigurable modeling technology based on AADL scalable architecture. By analyzing system resources and system requirements, this method constructs AADL models such as system execution platform, application program and so on, and combines AADL extensible attachments to illustrate the logical implementation architecture of the system under different blueprints. Finally, the effectiveness of this method in modeling reconfigurable integrated electronic systems is proved by experiments, and it can support the verification and analysis of reconstruction strategy.
Lightened SphereFace for face recognition

Convolutional neural networks (CNN) have immensely promoted the development of face recognition (FR) technology. In order to achieve global accuracy, CNN models tend to be deeper or multiple local facial patch ensembles, leading to excessive amounts of calculation. We address these deep FR problems and propose a lightened deep learning framework under an open-set protocol to achieve a good classification effect and streamline the model itself. To this end, we improve the SphereFace that enables the deep network to learn angularly discriminative features more efficiently. First, global average pooling (GAP) is introduced to replace the original fully connected (FC) layer, which greatly reduces the storage of the model. Compared to the widely used FC layer, GAP can reduce the number of parameters and avoid overfitting. Then multilayer perceptron is added between convolution layers, which increases the ability to characterize features. These models are trained on the CASIA-WebFace dataset and evaluated on the Labeled Faces in the Wild and YTF datasets, which show the excellent performance of lightened SphereFace (L-SphereFace) in FR tasks. At the same time, computational cost is reduced in comparison with the released SphereFace model. The storage space of the model is also greatly compressed.
Automatic Transfer Rate Adjustment for Transfer Reinforcement Learning

This paper proposes a novel parameter for transfer reinforcement learning to avoid over-fitting when an agent uses a transferred policy from a source task. Learning robot systems have recently been studied for many applications, such as home robots, communication robots, and warehouse robots. However, if the agent reuses the knowledge that has been sufficiently learned in the source task, deadlock may occur and appropriate transfer learning may not be realized. In the previous work, a parameter called transfer rate was proposed to adjust the ratio of transfer, and its contribution include avoiding dead lock in the target task. However, adjusting the parameter depends on human intuition and experiences. Furthermore, the method for deciding transfer rate has not discussed. Therefore, an automatic method for adjusting the transfer rate is proposed in this paper using a sigmoid function. Further, computer simulations are used to evaluate the effectiveness of the proposed method to improve the environmental adaptation performance in a target task, which refers to the situation of reusing knowledge.
Project technology as a means of schoolchildren communication skills development

Abstract Abstarct. Project technology is the basis for creating a practical research environment in the framework of school education and is seen as a master unit in the formation of a teenager’s communication skills, which requires the development of a framework, both educational and scientific. Research projects involve collaboration between students, as well as a student and a teacher, which develops interpersonal skills, contributes to the development of emotional intelligence, conscientiousness, citizenship in children and, at the same time, innovation, identity. The purpose of this article is to study the main contributions of the practical application of design technologies for the development of the communication skills of the school audience through the formation of cognitive, motivational and activity-practical components. In the article, the authors present the results of the correlation between the data on the application of design technology in the experimental group, using the Fisher criterion (φ-criterion), proving the positive dynamics of improving self-determination, empathy, tolerance and creativity as the main characteristics of the criteria for the selected components of the formation of the communicative abilities of schoolchildren.
Finite key effects in satellite quantum key distribution

Global quantum communications will enable long-distance secure data transfer, networked distributed quantum information processing, and other entanglement-enabled technologies. Satellite quantum communication overcomes optical fibre range limitations, with the first realisations of satellite quantum key distribution (SatQKD) being rapidly developed. However, limited transmission times between satellite and ground station severely constrains the amount of secret key due to finite-block size effects. Here, we analyse these effects and the implications for system design and operation, utilising published results from the Micius satellite to construct an empirically-derived channel and system model for a trusted-node downlink employing efficient BB84 weak coherent pulse decoy states with optimised parameters. We quantify practical SatQKD performance limits and examine the effects of link efficiency, background light, source quality, and overpass geometries to estimate long-term key generation capacity. Our results may guide design and analysis of future missions, and establish performance benchmarks for both sources and detectors.
Semantic Interoperability to Enable Smart, Grid-Interactive Efficient Buildings

Author(s): Bergmann, Harry; Mosiman, Cory; Saha, Avijit; Haile, Selam; Livingood, William; Bushby, Steve; Fierro, Gabe; Bender, Joel; Poplawski, Michael; Granderson, Jessica; Pritoni, Marco | Abstract: Achieving a widespread transition to grid-interactive, efficient buildings (GEBs) depends critically on there being sufficient interoperability among connected building systems. While many critical elements already exist at the technical interoperability level (TCP/IP, BACnet, etc.), a lack of interoperability in the semantic level hinders streamlined integration of interdependent applications. Semantics refers to expressing information about “things” in a way that can be consistently understood by applications. Key components of formalized semantics include identifying what a “thing” is (its “type”), defining general information about that “thing” (its characteristics or properties), and defining the appropriate relationships of that “thing” to other “things” (its function or role in a larger system). Although this might seem initially trivial, the success of smart building applications is highly dependent on maintaining consistent self-descriptive notions of the “things”. Without semantic interoperability, it is technically difficult, labor-intensive, and cost-prohibitive to enable three key objectives of GEBs: optimizing performance, automatically identifying and diagnosing faults, and delivering grid services. Industry, academia, and standards bodies have invested effort in developing information models to facilitate semantic interoperability, however, they have not been widely adopted across the U.S. commercial building portfolio. This paper will present a pathway to drive semantic interoperability through a three-pronged approach to be led by the DOE Building Technologies Office in partnership with NIST and multiple national laboratories comprising: 1) industry engagement and coordination across existing efforts; 2) a semantic interoperability standard that empowers building owners to identify and require interoperable attributes when procuring equipment and applications; 3) tools to assist in implementation and a test framework to verify compliance of products with semantic interoperability specifications. This approach is designed to accelerate the timeline for adoption of semantic interoperability specifications. The intent is to reduce soft costs associated with implementing advanced controls, fault detection and diagnostics, and other smart building technologies and use cases as a necessary step in achieving an energy efficient smart grid future.
Means for organizing project activities in informatics for the development of universal educational actions of upper secondary school students

The article discusses the feasibility of organizing project activity in informatics, aimed at developing universal educational actions of upper secondary school students. The possibility of development of regulatory, cognitive and communicative universal educational actions in the context of project activity is revealed. Various means of organizational and methodological support of project activity on informatics are proposed, contributing to the development of universal educational actions of upper secondary school students. An algorithm for constructing a Gantt chart is described, and an example of upper secondary school students compiling a cyclogram of an informatics project based on this algorithm is considered. The possibility of using the Trello online resource to manage upper secondary school students in informatics project activity is demonstrated. The analysis of the formation of universal educational actions of upper secondary school students as a result of the use of organizational and methodological support of compulsory project activity in informatics is presented.
Predictive Models on the Rise, But Do They Work for Health Care?

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Predictive models are</b> designed to remove some of the subjectivity inherent in medical decision-making and to automate certain health-related services with the idea of improving the accuracy of diagnosis, providing personalized treatment options, and streamlining the health care industry overall. More and more of these models using approaches including machine learning are showing up for use in doctor’s offices and hospitals, as well as in telemedicine applications, which have become prevalent with the growing demand for online alternatives to office visits.
PREVENTIVE EFFORTS AGAINST KONJO LANGUAGE EXTINCTION THROUGH MULTILINGUAL LEARNING

The objectives of this study are: 1) to explore strategies for teaching Indonesian and English to integrate local languages, 2) to identify the factors that support the revitalization of local languages in multilingual learning, 3) to determine the perceptions of teachers and students towards the use of local languages in language learning.The results showed that Indonesian language learning at SMP Satap Kajang was carried out in accordance with the applicable curriculum, namely the K-13 curriculum. Meanwhile, learning English has been taught to students since grade I. Local languages have never been taught to students because there is no direction or order from the Bulukumba district education office about what local languages to teach, who teaches them, and how to learn local languages.The observation also found that SMP 1 Satap Kajang had the opportunity to carry out learning the Konjo language. This is supported by the existence of several native teachers who master the Konjo language (one science teacher, social studies, Indonesian, mathematics, religious education, and one English teacher) the teacher also welcomes if multilingual integrated learning is carried out besides the majority of junior high school students. is a native of Kajang who in fact speaks Konjo in general, although most 92% of students know Konjo language. This potential needs to be utilized for learning local languages, especially Konjo language, as an introduction to regional languages and prevent extinction.
Digital Watermarking using Dragonfly Optimization Algorithm

In this paper a novel digital watermarking algorithm is proposed. The proposed method comprises of a watermarking embedding and extraction algorithm using bio inspired optimization technique. Dragonfly algorithm (DA) is based on the static and dynamic swarming behaviors of dragonflies in nature. The dragonfly algorithm is used to optimize the scaling factor of the watermarking so that an optimal watermark is embedded. Watermarking algorithms take as input a cover image and the message. The cover image in the proposed method is decomposed into sub bands using discrete wavelet transform (DWT). Thereafter, it is converted to discrete cosine blocks (DCT). An optimal scaling factor is required for performing the watermarking. In this paper, DA is used for computing the scaling factor. The DA generated scaling factor is optimal and improves the performance of the watermarking. The inverse DWT and DCT are computed to extract the watermarked image from the cover image. The proposed method is applied on different images to evaluate the performance. The results obtained are compared with other state of the art methods.
Password-Based Authentication

Among today’s factors and methods of authentication, the something-you-know factor, more specifically password, is the most commonly used. It remains the main method for proving user’s identity for email accounts, Internet banking accounts, and other online services. This is understandable given the fact that every user is only required to remember their password, instead of having to carry an authentication token or an additional smart card.