Reviewerly/exHarmony huggingface dataset:
Can machine learning algorithms improve upon classical palaeoenvironmental reconstruction models?

Abstract. Classical palaeoenvironmental reconstruction models often incorporate biological ideas and commonly assume that the taxa comprising a fossil assemblage exhibit unimodal response functions of the environmental variable of interest. In contrast, machine learning approaches do not rely upon any biological assumptions, but instead need training with large data-sets to extract some understanding of the relationships between biological assemblages and their environment. We have developed a two-layered machine learning reconstruction model MEMLM (Multi Ensemble Machine Learning Model). The first layer applies three different ensemble machine learning models of random forests, extra random trees and lightGBM, trained on the modern taxon assemblage and associated environmental data to make reconstructions based on the three different models, while the second layer uses multiple linear regression to integrate these three reconstructions into a consensus reconstruction. We consider three versions of the model: 1) A standard version of MEMLM, which uses only taxon abundance data, 2) MEMLMe, which uses embedded assemblage information, using a natural language processing model (GLOVE) to detect associations between taxa across the training data-set and 3) MEMLMc which incorporates both taxon abundance and assemblage data. We train these MEMLM model variants with three high quality diatom and pollen training sets and compare their reconstruction performance with three weighted averaging (WA) approaches of WA-Cla (classical deshrinking), WA-Inv (inverse deshrinking) and WA-PLS (partial least squares). In general, the MEMLM approaches, even when trained on only embedded assemblage data, perform substantially better than the WA approaches under cross-validation in the larger data-sets. However, when applied to fossil data, MEMLM and WA approaches sometimes generate qualitatively different palaeoenvironmental reconstructions. We applied a statistical significance test to all the reconstructions. This successfully identified each incidence where the reconstruction is not robust with respect to the model choice. We find that machine learning approaches can outperform classical approaches, but can sometimes catastrophically fail, despite showing high performance under cross-validation, likely indicating problems when extrapolation occurs. We find that the classical approaches are generally more robust, although they can also generate reconstructions which have modest statistical significance, and therefore may be unreliable. We conclude that cross-validation is not a sufficient measure of transfer-function performance, and we recommend that the results of statistical significance tests are provided alongside the down-core reconstructions based on fossil assemblages.
Teaching during the pandemic: The case of Chilean mathematics teachers

This study sought to explore the implementation of remote teaching activities in mathematics classrooms in Chile and its outcomes in this neoliberal country during the COVID-19 pandemic. The challenges that teachers encountered when trying to carry out their remote teaching are also presented. By attending to different types of schools in the Chilean education system, the results of a comparative analysis of frequencies show differences in the use of two options of synchronous and asynchronous teaching activities, software, and in the challenges experienced by the participating teachers from three different school sectors. The results highlight a tendency and flexibility to mix synchronous and asynchronous activities coupled with all available tools from WhatsApp to Zoom. Likewise, findings suggest that the socio-economic segregation in the Chilean education system makes a difference when employing the simple and free access tools by teachers in public schools.
Research on the Impact of "Goal-centered Problem-oriented" Teaching Model Based on Mobile Platforms Concerning College English Writing Level of "Excellence Program" Students

This study focuses on the students of the "Excellence Program" for Electrical Engineering and Automation at Guangdong University of Petroleum and Chemical Technology, and compares and analyzes the impact of the "Goal-Centered Problem-Oriented" teaching mode based on mobile platforms on their English writing proficiency compared to traditional writing teaching from multiple perspectives. The aim is to use pre-test, post-test, interview, and data collection methods from the perspective of students, exploring the application of this teaching model in the English writing of "Excellence Program" students, with the aim of exploring how this model affects their writing interest and confidence, whether it can improve their English writing level, and the specific manifestations of level improvement. At the same time, this research will help teachers fully practice the new concept of English writing teaching, with students as the main body, and improve the existing teaching mode based on their actual situation, ultimately achieving the goal of increasing students' interest and confidence in English writing and improving their English writing level.
Analysis of the cultivation path of English talents in colleges and universities based on the 1+X certificate system

1+X certificate system is a system design for talent cultivation and evaluation mode in the framework of modern education system, and the proposal of 1+X certificate system can promote the reform of college education to realize the deep-level development. The article first analyzes the 1+X certificate system, then introduces the guiding significance of the 1+X certificate system for the cultivation of English talents in colleges and universities, and finally puts forward the path of cultivating English talents in colleges and universities under the 1+X certificate system, including clarifying cultivation objectives, promoting curricular reform, strengthening the construction of teaching staff, innovating the evaluation system, and strengthening the cooperation between schools and enterprises, in the hope that it can provide effective references to the relevant people.
Journal Series on Governance and Management of IT in Electronic-Based Government Systems (SPBE) in Indonesia

SPBE Governance and Management is a framework that guarantees the implementation of regulation, direction and control in the implementation of SPBE in an integrated manner. SPBE Governance and Management aims to ensure that SPBE elements consist of the National SPBE Master Plan, SPBE Architecture, SPBE Roadmap, SPBE Program Plan and Budget, Business Process, Data and Information, SPBE Infrastructure, SPBE Application, SPBE Security, and Services SPBE. to achieve SPBE goals. In implementing all SPBE elements, integration and harmonization between the National SPBE and Regional SPBE are required. Therefore, this thesis research raises SPBE Governance and Management in accordance with COBIT 2019 as a governance framework that is set globally. Thus, this research will provide recommendations for the implementation of SPBE in Indonesia which can provide appropriate benefits for SPBE users, namely the community, business actors, civil servants, and other government agencies, and have detailed objectives and activities in accordance with COBIT 2019.
Strong Simulation

This book is about quantum circuit simulation on classical computers with decision diagrams. As the physical realizations of quantum computers grow more powerful, the corresponding design automation tools have to follow in order to avoid a design gap.
Measuring the Level of Satisfaction of Bachelor of Accounting Students with Distance Education in Light of the Corona Pandemic: A Field Study

This research aimed at postgraduate studies at King Abdulaziz University, male and female students at the education stage at King Abdulaziz University, and the Bachelor of Accounting program is one of the programs offered in attendance, and the university had to transfer it to study online According to the Corona pandemic, so the continuum development and the quality of the educational process requires the knowledge of the service recipients from the students who represents the department.In ways to achieve the objectives of the research from the theoretical or field side, this study relied in its procedures on the descriptive analytical approach, which depends on collecting data from the study sample consisting of (40) undergraduate students at King Abdulaziz University in the Faculty of Economics and Administration in the Accounting Department using a questionnaire. Prepared for the purposes of this study, and also studying and analyzing the responses of male and female students, and the questionnaire included six axes to determine the level of satisfaction of the students of the Accounting Department with regard to distance education.Through the study, the researcher reached a set of results that are summarized with regard to the application of the study tool (the questionnaire) by analyzing its paragraphs, with the aim of identifying the level of satisfaction of Bachelor of Accounting students about distance education at the time of the Corona pandemic, and coming up with recommendations to be implemented, and it is clear from Analysis The (Facronbach) stability coefficients for the study axes in the exploratory sample are high, which confirms the validity of the scale for application and the possibility of reliability and reliability of its results, and the study of the validity of the internal consistency through the correlation coefficients between each phrase and its total axis is strong and direct as well as statistically significant, which achieves the validity of the questionnaire. The results also showed that their satisfaction with the services related to distance education is high, which confirms their approval of the e-education services, as well as the level of their satisfaction with the electronic scientific contents and the effectiveness of the teaching aids is high, which confirms their approval of the electronic educational content and electronic educational means that are provided, and this is evident from Their satisfaction with their interaction with e-learning, which contributes to increasing work and development in raising the e-learning process provided to them. Their satisfaction with the existence of a home digital learning environment is satisfactory for the students, and this confirms the role of the family in the e-learning process in terms of providing all the prepared ways to assist students in raising their educational attainment.
Weak Simulation

Despite the initial optimism, the construction of quantum computers and the implementation of quantum algorithms turned out to be exceptionally challenging.
Approximate Simulation

The approaches to quantum circuit simulation proposed in the previous chapters provide efficient routines based on decision diagrams. However, despite the often sub-exponential memory used by decision diagrams, the worst case remains exponential.
Decision Diagrams

Classical simulation of quantum circuits is an important part of design automation for quantum computing. Moreover, conducting circuit simulation is conceptually simple as it can be done by multiplying matrices representing quantum operations to vectors representing quantum states in a straightforward fashion.
Background

In order to keep this book self-contained, this chapter reviews the concepts of quantum computing and particularly quantum circuit simulation.
FedERA: Framework for Federated Learning with Diversified Edge Resource Allocation

&lt;p&gt;Federated Learning (FL) is a distributed machine learning (ML) approach that allows multiple devices to train a model while keeping training data private on edge devices. However, existing FL libraries are incapable of: (i) supporting a wide range of existing FL algorithms, (ii) handling diverse public as well as custom datasets, (ii) discarding unreliable updates from malicious edge devices, and (iv) facilitating edge-device training. With the rapid progression in AI, there is an increment in the computation power while training the models on edge devices which results in more carbon emission (CE). However, in the existing FL frameworks, there is no module for estimating CE. In this paper, we present \textbf{FedERA}, a modular and fully customizable open-source FL framework aiming to address these issues especially also by offering extensive support on heterogeneous edge devices and by incorporating both standalone and distributed training approaches. The integration of new software modules unforeseen in existing FL frameworks not only amplifies the scope of its usability but also fosters environment-friendly FL. We believe that FedERA will be a valuable tool for researchers and practitioners working in FL. Source codes of this library along with documentation and tutorials are available at: https://github.com/anupamkliv/FedERA.&lt;/p&gt;
Multilayer Perceptron and TF-IDF in the Classification of Hate Speech on Twitter in Indonesian

Twitter nowadays is one of the popular social media which currently has over 300millions accounts, twitter is the rich source to learn about people’s opion and sentimental analysis. However, this also brings new problems where the practice of hate speech. This research classifies of hate speech on social media. Evaluation using dataset from previous research Ibrohim&amp;Budi (2019), then using classification method Multilayer Perceptron which combined with feature extraction to be able to detect negations and weighting uses Term Frequency – Inverse Document Frequency (TF-IDF). Results show that the F1 score gives an accuracy rate of up to 74.51%. This research has a reasonably good effectiveness from combining the TF-IDF and Multilayer Perceptron methods, considering the results obtained from the F1 Score evaluation value.
The Prediction of Late Tuition Fees at Muhammadiyah University of East Kalimantan Using the Logistic Regression Method

Muhammadiyah University of East Kalimantan in carrying out its operational activities relies on funds from students, one of which is tuition fees (SPP). This creates problems if students are late in making tuition payments, because tuition payments can improve the quality of education and facilitie. Therefore, the purpose of this study is to determine indicators, then implement the logistic regression algorithm by dividing the data into 70:30 and evaluating the performance results of the algorithm using the confusion matrix. The data obtained are sourced from the Academic Administration Section and the Financial Administration Section as many as 12,408 data with several attributes such as the faculty, study program, class, gender, father's income, mother's income, father's education, mother's education and label (late or not late). From the results of the data test that has been carried out, it gets the results of an accuracy of 55.89%.
The Development of a Secure Internet Protocol (IP) Network Based on Asterisk Private Branch Exchange (PBX)

The research problem described in this article is related to the security of an IP network that is set up between two cities using hosting. The network is used for transmitting telephone traffic between servers located in Germany and the Netherlands. The concern is that with the increasing adoption of IP telephony worldwide, the network might be vulnerable to hacking and unauthorized access, posing a threat to the privacy and security of the transmitted information. This article proposes a solution to address the security concerns of the IP network. After conducting an experiment and establishing a connection between the two servers using the WireShark sniffer, a dump of real traffic between the servers was obtained. Upon analysis, a vulnerability in the network was identified, which could potentially be exploited by malicious actors. To enhance the security of the network, this article suggests the implementation of the Transport Layer Security (TLS) protocol. TLS is a cryptographic protocol that provides secure communication over a computer network, ensuring data confidentiality and integrity during transmission. Integrating TLS into the network infrastructure, will protect the telephone traffic and prevent unauthorized access and eavesdropping.
Difficulties in applying augmented reality technology in teaching history from the point of view of subject teachers

The aim of the present research is to identify the difficulties of applying the technology of enhanced reality in teaching history from the standpoint of the teachers of the subject. A 20-paragraph survey measuring the most significant difficulties in applying the technology of enhanced reality in teaching has been built. The researcher applied the identification to a sample of history professors at the faculties of education and literature at Iraqi governmental and non-governmental universities, including 58 teachers. The research adopted the scoping descriptive approach, and the weight and weight of the weight were extracted to calculate the degree of difficulty in using the enhanced reality technique in teaching. The research found the following results: difficulties in applying the enhanced reality technique by history teachers; the most important of these was the sense of low importance of applying the enhanced reality technique in teaching; the lack of knowledge of using technical media from images, videos, and pamphlets; and the lack of lecture time with the density of the vocabulary. The researcher therefore recommends that the Ministry take decisions that will motivate teachers to use the technique as material and moral incentives, in addition to the need for universities to provide training courses, a comprehensive manual, and rooms equipped to use the enhanced reality technique for teaching.
Ask me anything: Sara Webb

Sara Webb is an astrophysicist and science communicator at the Swinburne University of Technology in Australia. Her research involves using AI and machine learning in astronomy, with a focus on finding and identifying anomalous sources of data in large and complex astrophysical surveys. She is also the mission director of the Swinburne Youth Space Innovation Challenge and in 2022 was named as one of the country’s “Superstars of STEM” – an initiative of Science and Technology Australia.
80: Transfer Learning Boosts Performance Of Deep Learning Model Prediction of In-Hospital Mortality In Patients Treated With Extracorporeal Membrane Oxygenation

Background: The application of deep learning – a powerful but data-intensive subset of machine learning – to smaller cohorts including extracorporeal membrane oxygenation (ECMO) datasets is challenging. Transfer learning can boost the performance of deep learning models trained on smaller datasets. Methods: We applied deep learning to routinely collected electronic health record data within the Medical Information Mart for Intensive Care IV database to predict in-hospital mortality in a small cohort of subjects treated with ECMO, 24 hours prior to death. Model performance was assessed with and without pre-training on data for subjects not treated with ECMO. Results: The study dataset included 27,192 subjects admitted to one of six intensive care unit (ICUs) at a single medical center between 2008 and 2019. Seventy five (0.3%) subjects were treated with ECMO, including 58 (77%) treated with veno-arterial ECMO. In-hospital mortality in subjects treated and not treated with ECMO were 57.3% and 16.1%, respectively. The model performed poorly without pre-training, AUC 0.65 (95% CI 0.59-0.73). However, pre-training significantly improved model performance, AUC 0.82 (95% CI 0.81-0.83). Pre-training on smaller cohorts of subjects not treated with ECMO in select ICUs resulted in similar strong performances. Conclusions: A deep learning model performed well at predicting in-hospital mortality in a small cohort of patients treated with ECMO after first pre-training on data from patients not treated with ECMO. Transfer learning allows pre-trained deep learning models to be applied to smaller clinical datasets, including datasets of patients undergoing highly specialized treatments, resulting in better overall performance.
Detection of obfuscated Tor traffic based on bidirectional generative adversarial networks and vision transform

The Onion Router (TOR) network is a decentralized system of volunteer-run servers that aims to protect the anonymity and privacy of users by routing their internet traffic through a series of nodes. Individuals who use the TOR network may employ obfuscated traffic to conceal their internet activity from network administrators or security systems attempting to block or monitor them. Furthermore, some may use obfuscated Tor traffic to hide illegal activities, such as buying and selling illegal goods or accessing illegal services on the dark web. Despite efforts to identify and block Tor traffic, challenges remain, such as a limited set of features for identification, leading to false positives and negatives. To address these challenges, this paper proposes a novel approach using Visual Transformation (ViT), and augmentation by Bidirectional Generative Adversarial Networks (BiGAN). The proposed approach demonstrates superior performance on the ISCX-Tor2016 dataset, achieving 99.59% accuracy, 99.83% recall, 99.72% precision, and 99.78% F-score, thereby outperforming current state-of-the-art techniques.
Intel releases 12-qubit silicon quantum chip

Intel has released its newest quantum chip. Called Tunnel Falls, the chip contains a 12-qubit array and is based on silicon spinqubit technology.
IBM achieve quantum advantage without error correction

An international team of researchers has used a 127-qubit quantum processor to calculate the magnetic properties of a model 2D material.
STUDENTS' SPEAKING DIFFICULTIES IN ESP: ENGLISH FOR ACCOUNTING

This study aims to find problems that influenced the student’s speaking difficulties in English Specific Purposes especially in the accounting field. The results are necessary to find some solutions so it can graduate qualified students who are ready for facing some futures career challenge. Nowadays interpersonal communications skill is required for employees. This research conducted in the accounting study program and the students as key informants. This study uses a descriptive qualitative approach. According to the findings, internal and external factors both have an influence on students’ speaking difficult. The recommendations from this research are by creating good emotional relationships between teacher and students, conducting students cantered learning, and fun cooperative learning.
THE EFFECTIVENESS OF USING SIMON SAYS GAME TO TEACH LISTENING

The purpose of this study was to know the effectiveness of Simon says game on students’ listening ability at class VIII I of SMPN 1 Jombang. The method used in this research was quantitative method with a pre- experimental design. Population of the research was eighth grade of SMPN 1 Jombang, and the researcher used class VIII I as the sample of this research with total 30 students which consisted of 12 boys and 18 girls. The instrument of this study was listening test and it was conducted before (pre-test) and after (post- test) the treatment. The result of the data was analyzed using SPSS for windows ver.25. The mean score of students in the pre- test is 76,67 and the mean score of students in post- test is 95,17. Therefore, the paired sample t-test is 0,000 which is lower than 0,05. That means H0 is rejected (null hypothesis) and Ha is accepted (alternative hypothesis). To sum it up, the results show that Simon says game was effective on students’ listening ability.&#x0D; Keywords: effectiveness, listening ability, Simon says game