Reviewerly/exHarmony huggingface dataset:
AIADA: Accuracy Impact Assessment of Deprecated Python API Usages on Deep Learning Models

TensorFlow is an end-to-end open-source machine learning platform including various tools, libraries, and community resources. It supports users to use many mainstream programming languages including Python. TensorFlow contains multiple abstraction layers, with APIs play significant roles in every layers. In the version iteration of TensorFlow platform development, with the release of new TensorFlow versions, because of functionality evolution, or security and performance-related changes, some APIs eventually become unnecessary. These issues cause APIs to deprecate and influence the accuracy of deep learning models results. Prior studies have investigated API evolution and its potential impact on projects. However, their studies mainly focus on API evolution instead of API deprecation, and they do not find out how the evolution affects results of deep learning models in TensorFlow. Therefore, we present a research-based prototype tool called AIADA and apply it to different revisions of the TensorFlow platform projects code for characterizing deprecated APIs. Based on the data mined by AIADA, we develop a quantitative assessment of deprecated Python APIs usages on deep learning models accuracy. We first count the amount of TensorFlow Python APIs that are deprecated, finding out that with the development of TensorFlow version, the number of deprecated APIs increases constantly. Second, we discuss the reason behind TensorFlow Python APIs become deprecated, discover that name change, weed out, and compatibility issue lead to the main cause of deprecation. Finally, we construct a deep learning project as the comparative experiment. After comparing the results between deep learning model with TensorFlow deprecated APIs and without deprecated APIs, we conclude that using deprecated APIs will cause a 10% loss on efficiency and accuracy of deep learning model.
Zur Erforschung von Korrelationen zwischen verschiedenen Fehlertypen bei der maschinellen Übersetzung aus dem Deutschen ins Slowakische

Abstract We focus on examining the impact of machine translation (MT) error rate on adequacy and fluency in machine-translated journalistic texts. German is the source language, with significant polysynthetic features in the formation of composites, and the target language is Slovak, with predominantly inflected features. We analyse twelve error categories, which are incorporated into the categorical framework for the analysis of MT errors and correspond to the four-member core MQM-DQF error typology. The results show that the most significant errors are in the categories of lexical semantics, syntactic-semantic correlativeness and grammar.
Recognizing Chinese Predicate Heads Based on Textual Bounding Boxes

Predicate head plays a central grammatical role to organize relevant syntactic elements of a sentence.Identifying the predicate is the key to understanding a sentence.However, due to the characteristics of Chinese, recognizing Chinese predicate heads is a challenging task.This paper proposes a textual bounding box-based deep architecture for Chinese predicate recognition.In this method, a sentence is first transformed into an abstract representation.Then, textual bounding boxes are generated from the abstract representation, where a bounding box represents an abstract representation of a possible predicate head.Finally, an end-to-end multi-object learning framework is designed to learn the classification confidence and the offset of a bounding box relative to a true predicate head, which simultaneously predicts the class probability and locates predicate in a sentence.Experiments show that our model achieves competitive performance in a public Chinese predicate evaluation corpus.The result shows our model has the ability to enhance model discriminability and enable more potent nonlinear function approximators for Chinese predicate heads recognition.
EFICACIA DEL PROGRAMA DE TUTORIAS EN MODALIDAD A DISTANCIA EN EL NIVEL DE EDUCACIÃ“N SUPERIOR EN UNA UNIVERSIDADMEXICANA

Tutoring consists of a mediation or facilitation work between the teacher, the students and the contents of the subjects, they integrate the academic actions oriented so that the students, in higher education, can face their adaptation to the educational system in distance mode and achieve the learning objectives. The general objective of this research is to analyze the effectiveness of the implementation of distance education strategies in university programs, in the scope of students who receive attention in the tutoring program at a University of Mexico. The research is applied, exploratory in depth, quantitative, with a non-experimental, longitudinal design. After reviewing the theoretical state of the art on tutoring and experiences in higher education in the distance modality, the results of two higher education programs were analyzed through descriptive statistics. The results show that the effectiveness of the tutorial program in the study programs analyzed is greater and that it increases as the elements of the tutorial model are adapted to the conditions of online communication. Even when being at a distance implies new challenges for tutoring programs in the university education model, the adoption of technological communication tools is much faster and accepted by the student users, who are the beneficiaries of the tutoring programs.
Photonic detector for quantum applications: DiPho

In this paper, the “DiPho” (Digital Photodiode) device is described. The device is used for quick verification measurements and performance monitoring of photonic sources used in quantum physics experiments. DiPho was designed as a stack of two printed circuit boards (PCB) – one contains the detection element, a silicon PIN photodiode, and analog front-end, while the other contains the necessary digital circuitry. The core of the analog front end is a programmable-gain trans-impedance amplifier which was designed to be capable of measuring photocurrents in the range of 10μA to 10mA. DiPho provides analog measurement output via SMA connector and digital data output via USB serial interface. Moreover, support for single-pair Ethernet (SPE) communication is planned.
Generative Adversarial Network-Based Improved Progressive Approach for Image Super-Resolution: ImProSRGAN

Recently, convolutional neural networks (CNNs) have been explored to achieve exceptional performance on super-resolution (SR) in terms of distortion metrics. In these methods, pixel-based loss functions have been employed to optimize their networks leading to overly smooth blurry solutions. In contrast, a generative adversarial network (GAN) has the proficiency to bring out perceptually better SR solutions. In the case of larger upscaling factors, some degradations are still discovered in the SR observations that can be reduced by increasing the number of convolution layers. However, such approach tends to increase the number of trainable parameters and additionally provides a lot of burden on resources which leads it to be unavailable for many real-world tasks. Here, we propose an improved progressive approach for SISR using GAN (i.e. ImProSRGAN). The potency of the proposed model has been seen by conducting different experiments where we observe that the introduced ImProSRGAN model performs better than existing GAN-based SISR approaches even though picking up fewer training parameters.
Present Situation and Countermeasures of the C-E Translation of the Intangible Cultural Heritage for Global Communication: Taking Foshan City as an Example

The Intangible cultural heritage is the witness of China’s historical development and a living culture with rich connotation and extraordinary value. To develop and spread it, the C-E translation of intangible cultural heritage is essential. This paper comprehensively analyzes the present situation and the existing problems of C-E translation of Foshan intangible cultural heritage, explores the reasons for its formation, and puts forward practical solutions from the perspective of relevant government departments and translators.
Implementation of CCSDS lossless compression algorithm for geomagnetic data

Abstract Long-term geomagnetic data monitoring will generate a huge amount of data, and compression algorithms will be required to reduce the amount of data to save data storage and transmission expenses. To adapt to the application with limited resources, the CCSDS lossless compression algorithm is applied to geomagnetic data measurement in our work. This compression algorithm is made up of two parts: a preprocessor and an adaptive entropy coder. In this study, the unit-delay predictor is chosen as the preprocessor based on the properties of the geomagnetic signal, and the adaptive entropy coder chooses a suitable compression algorithm for geomagnetic data. A 14-hour continuous measurement was performed. The results reveal that when the block size is set to 64, the algorithm can obtain a compression ratio in the geomagnetic field of 0.269 to 0.318. Furthermore, the number of chosen encoding options for three-axis magnetic data was counted.
Open Science – A Croatian Perspective

Jadranka Stojanovski discusses the evolution of library support for open science from a Croatian perspective. From her vantage point as (former) library director of the Ruđer Bošković Institute and associate professor at the department of information science of the University of Zadar, Stojanovski has been a pioneer in establishing services exploiting the possibilities offered by new information technologies since the 1990s. Many of her activities have been connected to broad European collaborative projects such as OpenAIRE, OASPA, and EOSC.&#x0D; The Croatian approach has been a very proactive one. Already in 1997, the CROSBI was launched, a combined national scientific bibliography and repository for Green Open Access documents. Although deposition of articles and other research documents is entirely voluntary, CROSBI now carries metadata on more than 725,000 documents, a large proportion of which are available in fulltext. Alongisde CROSBI, there are also several institutions running their own institutional repositories. There is now extensive collaboration between these services in the form of DABAR (‘beaver’ in English), aiming to enhance the interoperability and findability of documents stored in the various repositories. Stojanovski has also been involved in setting up an inventory on Who’s Who in Science in Croatia as well as a database on scientific equipment, Šestar (‘pair of compasses’).&#x0D; Set up in 2005, the HRČAK (‘hamster’) platform for Croatian scientific and professional journals has been a massive success. Less than twenty years after its inception, it now carries more than 500 scholarly journals and series of conference proceedings, nearly all of which are Diamond Open Access (i.e., free to the reader and with no author-facing publishing charges). Roughly 150 of these journals receive annual subsidies from the government, the rest are fully based on voluntary work from individual editors and the institutions or learned societies they represent. Only around 25 HRČAK journals charge Article Processing Charges. The Social Sciences and Humanities are particularly well represented on the platform, with many journals publishing in Croatian despite the lack of an official language policy in favor of Croatian as a scholarly language. The University Computing Centre in Zagreb (SRCE, ‘heart’) is responsible for the technical development of HRČAK, which is based on seamless interconnection between in-house developed software and open-source software for editorial processes, primarily Open Journal Systems.&#x0D; A national Research Data Policy or, better still, a general Open Science Policy is highly desirable, Stojanovski argues. Infrastructure is in place, but usage will undoubtedly rise significantly as soon as open science practices become mandatory.&#x0D; Alongside Dominic Tate (episode 43) and Pierre Mounier (episode 44), Jadranka was a keynote speaker at the 17th Munin Conference on Scholarly Publishing. This interview was first published online on December 13, 2022.
INFORMATION SECURITY POLICIES AND STRATEGIES AND PRACTICES ADOPTED IN IT: THE IMPORTANCE OF CONSULTANCY IN SMALL AND MEDIUM-SIZED COMPANIES

Objective: Companies in the cybersecurity consulting market are expanding their services in periodic training to business stakeholders and enforcement of security policies. The objective of this work is to demonstrate the importance of consulting in information security policies aimed at small and medium-sized companies. Methods: It is characterized as a narrative literature review with a qualitative approach, which does not use explicit and systematic criteria for the search and critical analysis of the selected literature. Results: Tools needed for cybersecurity include endpoint detection and response (EDR), antivirus software, next-generation firewalls, Domain Name System (DNS) protection, email gateway security, intrusion detection and prevention, logging and log monitoring, endpoint protection, authentication and virtual private network (VPN) services, cloud-based security, web application firewalls (WAFs), software-defined wide area networks (SD-WAN), enterprise password management , privileged access management (GAP), vulnerability and threat management, and threat detection. Conclusions: In summary, SMBs seem to implement some of the basic cybersecurity measures only as part of their overall IT implementation. However, it appears that unless cybersecurity controls are included as part of an IT solution, many SMBs do not realize the resulting potential risks to their business.
The solution of inverse matrix by using unary polynomial ring theory

Matrix is in the core position in algebra, and the inverse matrix is particularly important. So far, there are adjoint matrix method, elementary transformation method, block matrix method and so on in solving the inverse matrix. In this paper, we will introduce a method of seeking the inverse matrix by using unary polynomial ring theory. This method can reverse the matrix equation into the algebra equation, cleverly avoiding the complex matrix equation.
EE550 The Potential Cost and Cost-Effectiveness Impact of Using Machine Learning Sepsis Prediction Algorithm for Early Detection of Sepsis in Intensive Care Units in Sweden and the United Kingdom

Early diagnosis of sepsis has been shown to reduce treatment delays, increase appropriate care, improve patient outcomes including reduced mortality. The machine learning prediction algorithm uses variables routinely collected at intensive care units (ICUs) to predict sepsis and has shown excellent predictive properties in clinical settings. This research aims to estimate the potential cost and cost-effectiveness impact of a machine learning algorithm forecasting the onset of sepsis, in an ICU setting in Sweden and the UK.
SA39 RWE Generation: Have We Reached the Tipping Point to Drive Mainstream Adoption of Automated Electronic Health Record (EHR) Extraction Technology?

Due to backlogs created by the COVID-19 pandemic on patient healthcare delivery coupled with an increase in the number of studies and breadth and depth of research questions, there are significant challenges with the timely conduct of real-world data (RWD) studies relying on data abstraction from electronic health records (EHRs). Many healthcare providers (HCPs) are increasingly unable to participate in RWD studies due to time and resource constraints and the need to prioritize clinical work and trials. Therefore, identifying robust solutions to facilitate and streamline HCP participation in RWD studies and address broad and diverse stakeholder research needs is of paramount importance to future direction and success of RWD research.
RWD121 Assessing the Use of Variational Bayes for Large Real-World Data

Bayesian approaches to real-world observational analyses has been limited by the computational challenges applying the Markov-chain Monte-carlo (MCMC) approach to large real-world data. MCMC is considered the gold standard for Bayesian inference because, in the limit, MCMC is guaranteed to converge to the true posterior distribution. Approximate Bayesian inference via optimization of the variational evidence lower bound, usually called Variational Inference (VI), has been successfully demonstrated for other applications. We investigate the performance and characteristics of currently available R and Python VI software implementations for real-world observational data.
Linear regression models

Linear regression aims to learn a linear model that can predict the target using the features as accurately as possible. The assumption of linear regression models is that the target is linearly correlated with the features, i.e., the regression function E(y|x) is linear in x, where E(⋅) denotes expectation. If the assumption can (almost) be satisfied, linear regression can be comparable or can even outperform fancier non-linear models. The linear regression model is one of the most classic models for prediction tasks, and it is still widely used in the computer and big data era, thanks to its intuitiveness and interpretability in particular. In the following sections, we first introduce simple linear regression models (with a single feature) and the least squares method, which aims to find the optimal parameters of a linear regression model by minimizing the sum of squares of the residuals. Then, we discuss multiple linear regression (with more than one feature) and its extension. Finally, we introduce shrinkage linear regression models.
Artificial neural network

This chapter aims to introduce a widely-used machine learning model for both regression and classification tasks called artificial neural network (ANN). Basic concepts of ANNs are first covered, and then typical algorithms and tricks for ANN training are introduced. Finally, deep learning models, as an important type of neural network, are briefly discussed.
Support vector machine

This chapter first introduces one of the most popular machine learning models for classification tasks called support vector machine (SVM). Then, kernel trick to improve its prediction accuracy while reducing the computation burden is discussed. The extension of SVM to address regression tasks, which is called support vector regression, is then presented.
Cluster analysis

Cluster analysis, or clustering, is a general task aiming to group a given set of examples into several groups (i.e., clusters) following given criteria, such that the examples in the same group are as close as to each other, and the examples in different groups are as different as from each other. Generally, clustering works in the context of unsupervised learning, where only the features of the examples are known while there is no target defined or targets are unknown. It aims to divide the whole data set into several mutually exclusive and complementary clusters, so as to mine the properties of the clusters formulated where such properties are represented by cluster labels.
Geochemistry and Petrographic Study of Abu Shaybah Formation NW Libya

This paper studies the geochemistry, petrography and diagenesis of sandstone of Abu Shaybah formation in the northwest Libya.The study describes the geology of the formation and the petrographic analysis shows that the Abu Shaybah sandstone are detrital grains of monocrystalline and polycrystalline quartz, the grains were range from poorly to moderately sorted with some subangular grains.The Abu Shaybah sandstone are very pure quartz arenites with little feldspar or rock fragments.The geochemical analysis shows that all major elements has a minor variation in concentration values and the high concentration of all elements are SiO₂.The chemical alteration index in the studied samples ranges from 63.55% to 72.04 % and that is indicate the weathering in the sandstone source area is low to moderate.Digenetic analysis shows that there are chemical and physical compaction which can be seen from dissolution of grains and straight grains contact.Late cementation has reduced the pore spaces such as quartz overgrowth, while in some sample the pore space has been increased due to grains alteration.
Potret analisis difusi inovasi dalam publikasi artikel ilmiah di Lentera Pustaka

Introduction. The relationship between the diffusion of innovation and the scientific publication is in the process of scientific communication through media that records previous knowledge from research. The purpose of this study is to show stages of innovation adoption from the articles in one academic journal namely Lentera Pustaka.&#x0D; Data Collection Methods. This study used qualitative research approach with descriptive analysis. Data collection techniques were conducted through observations, interviews, and document study. Informants were selected using a purposive sampling technique.&#x0D; Data Analysis. We analysed the data by reducing, presenting, and drawing conclusions or verification.&#x0D; Results and Discussion. Innovations or new ideas from articles that have been adopted and implemented, are key points. The stage of adopting innovations from scientific articles starts from the published findings, then the adopters produce new knowledge from pre-existing knowledge.&#x0D; Conclusion. The stages of adoption of article innovation in Lentera Pustaka include knowledge, persuasion, decision making, implementation, and confirmation. The editorial board of Lentera Pustaka can ask eaders to participate in the survey related to the interests of proposed research topics. This research shows that articles in scientific journals can be a solution to scientific problems.
Manajemen koleksi museum dan problematika pengelolaannya di Kalimantan Selatan pada era new normal

Introduction. Museum collection management is interesting to discuss because it is one of the main activities in museum management as an information institution with problems, especially in the new normal era of famous museums in South Kalimantan (Lambung Mangkurat Museum, Wasaka Museum, and Sultan Suriansyah Museum).&#x0D; Research methods. This study uses a qualitative method by applying data collection techniques are: interviews, observations, and documents.&#x0D; Data Analysis. The data analysis process includes data reduction, data presentation, and conclusions/verification.&#x0D; Results and Discussion. In the new normal era, the Lambung Mangkurat Museum and Wasaka Museum have implemented the concept of museum collection management. In contrast, the Sultan Suriansyah Museum has not properly implemented the collection management concept. There were various problems in the management of museum collections and the solutions sought by each museum.&#x0D; Conclusions and Suggestions. Lambung Mangkurat Museum, Wasaka Museum, and the Sultan Suriansyah Museum continue to manage museum collections in the new normal era according to their capacity. Therefore, it is recommended that the government pay more attention to coaching and competency development for museum collection managers and the need for an e-catalog system in all museums.