Reviewerly/exHarmony huggingface dataset:
Upaya Meningkatkan Keterampilan Berbicara Di Depan Umum

Speaking in general can be interpreted as conveying one's intentions (ideas, thoughts, thoughts, or feelings) to others by using spoken language so that these intentions can be understood by others. The development of speaking is the stage where the child has begun to be able to convey the message he wants. in the form of sound sequences in the form of two or three words. Good language development, especially in speaking Makes children able to express their thoughts and feelings intelligently according to the context and situation when he is speaking. The goal is for students to be involved realistically rather than just practicing linguistic material. In the learning process at school, students' speaking skills still need to be guided, especially with good learning modeling from the teacher. And speaking skills are skills possessed by a person to convey ideas, feelings, and ideas to other people orally. The method used in this study uses the library research method or approach, library research or literature. In an effort to develop public speaking skills, the teacher can provide motivation so that students can improve their communication skills in speaking.
The Effect of Discussion Strategy to Improve Students’ Speaking Fluency In the XI class of Ashabul Yamin Boarding School

The study was carried out in response to a number of issues that were discovered in the field. There were issues with fluency among the students. When speaking in front of the class, the pupils appeared nervous because they lacked confidence in their ability to communicate their views in English. They were frightened of making grammatical, pronunciation, or fluency errors. The researcher employed a conversation method to help students improve their speaking fluency in this study. The methodology of this research was quantitative research, and the type of research was experimental research. This study used a pre-experimental design using a one-group pretest-posttest design. The participants in this study were all of the students of Ashabul Yamin Boarding School's grade XI B. The researcher employed the purposive sampling technique to collect the sample, with 16 students from XI B class serving as the study's sample. In other words, the Ha was accepted, which said that adopting discussion approach to improve students' speaking fluency in the XI class of Ashabul Yamin Boarding School had a substantial effect. It may be stated that employing the Discussion Strategy aided students in learning to communicate, particularly in speaking fluency.
The Influence Of Picture Series In Teaching Speaking Recount Text

This research is a research to test whether the use of picture series media is influence in teaching writing. The purpose of this study was to determine whether there was a significant difference in the writing ability of students who were taught using the picture series. The population of this research is the eighth grade students of MTs Nurul Qur'an Jogoroto in the academic year 2022/2023. The total population is 215 students divided into six classes, each class contains between 35-38 students. The sample in this study was class VIII 5 and VIII 6 MTs Nurul Quran Jogoroto, totaling 70 students. The researcher used Quasi Experimental Class method to obtain score data. The researcher conducted a normality test to find out whether the data was normal or not. Based on the data normality test that has been carried out by the researcher, it shows that sig. 2-tailed is 0.000 meaning that data is normally distributed. Then, it will be continued with a mann whitney test. Based on the testing the hypothesis terms, if score of t is sig. &lt; 0.05, Ha is accepted and H0 is rejected. In the mann whitney test calculation, the data shows sig.(2-tailed) 0.000. The data lower than 0.5, it means that H0 is rejected and Ha is accepted.
Author Index: Volume 33 (2023)

International Journal of Neural SystemsVol. 33, No. 12, 2399001 (2023) Free AccessAuthor Index: Volume 33 (2023)https://doi.org/10.1142/S0129065723990010Cited by:0 (Source: Crossref) Previous AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Remember to check out the Most Cited Articles! Check out our titles in neural networks today! FiguresReferencesRelatedDetails Recommended Vol. 33, No. 12 Metrics History PDF download
Binance case worsens crypto's image problem

Headline US: Binance case worsens crypto's image problem
Modeling Educational Tradition at a Modern University

The article is devoted to the problem of correlation between the educational tradition of Russian society and the educational models formed in the course of historical development. According to the author, the center for the creation of these models is the university environment, in which these models are saturated with certain con-tent, and an educational tradition is created. It is there that the personnel capable of transmitting the educational model into all spheres of public life are trained. Thus, the model turns out to be primary in relation to the trans-formation of the educational tradition. Therefore, universities remain unique repositories of historical memory, providing acceptable forms of its transmission in the educational environment. The Russian educational tradi-tion can be characterized by three main educational models, the analysis of which is still a scientific problem. The article highlights the main characteristics of the pre-revolutionary and Soviet educational models, and ex-amines the problems of development of the post-Soviet model of education. As conclusions of the study, such a basis for a new educational model as the domestic concept of “common cause” is proposed for develop-ment. It is expected that this will allow uniting the efforts of all interested social strata and population groups in the transformation of the Russian educational tradition.
Complex porphyry copper ore-magmatic systems of the andesitoid and basaltoid volcano-plutonic belts

A division of the porphyry copper family ore deposits into formational types associated with the basaltoid and andesitoid volcano-plutonic belts is proposed. It is shown that differences in the structure of the basement of these belts and in the composition of the ore-bearing magmatic chambers are reflected not only in the productive plutonogenic association petrology, alteration-mineralization zonality, and mineral composition of the mineral deposits, but also in the metallogenic zonality of the complex ore-magmatic systems centered by these deposits. The established differences should be taken into account for elaboration of the forecasiting and prospecting models of potential porphyry copper ore regions and clusters.
Improvement of the global positioning accuracy with miniaturized strap-down INS systems through wavelet filtering of data from MEMS inertial sensors

In this chapter, the correlation between the issue of perturbed data acquired from miniaturized inertial sensors and the wavelet filtering technique is investigated. The market growth of micro-electro-mechanical systems (MEMS) has impacted various fields, and its potential application in strap-down inertial navigation systems (INS) could not be overlooked. Despite the apparent benefits of dimension and price reduction, the utilization of miniaturized inertial sensors for manufacturing the inertial measurement unit (IMU) entails certain drawbacks: the output signals are usually corrupted with different types of errors, which distort the real navigation information. The proposed case focuses on the suitability of wavelets for denoising the perturbed IMU signals before being erroneously processed by the navigation algorithm. The applicative part consisted in implementing sensor software models in Simulink and testing various wavelet filters. Furthermore, to fully assess the efficacy of the wavelet denoising technique, the model of a SDINS employing a MEMS-based IMU was established in Simulink. The evaluation involved comparing the attitude, position and speed components obtained before and after the denoising procedure with those of an ideal model linked to constant inputs. The results demonstrated the effectiveness of the proposed association in terms of positioning accuracy, signal characteristics improvement and computation complexity.
Ore deposit prospecting using the chlorine and bromine secondary dispersion halos

The Cland Brions, that form complex compounds with metals, produce secondary dispersion halos in the hypergenesis zone at Au, Ag, Cu, Pb, and Zn ore deposits which ore bodies are controlled by fracturing and fault structures. The appearance of anomalies of the highly mobile water-soluble ions, manifested on the surface and in the wall rocks, is explained by their transfer from the deep sources to the surface by a gas flow. The migration of the ions to the earth's surface and further into the atmosphere is influenced by many factors (atmospheric pressure, temperature, humidity, rock porosity, etc.) that complicate the observation results.
Quantum simulation of ODEs, PDEs and related problems. Part II: Nonlinear problems and applications

In the second part of the lectures on quantum simulation for partial differential equations, we will focus on nonlinear partial differential equations and applications of Schrodingerisation. 1) For (nonlinear) Hamilton-Jacobi equation and scalar nonlinear hyperbolic equations, we use the level set method to map them exactly to phase space linear transport PDEs so they can be implemented with quantum algorithms. We gain quantum advantage for various physical and numerical parameters. 2) We show how to apply Schrodingerisation to a system of (nonlinear) ordinary differential equations. 3) For (both artificial and physical) boundary value and interface problems of linear PDEs, we show how one can Schrodingerise them so they become suitable for quantum simulations. 4) We give an example of Schrodingerisation for Maxwell’s equations.
A Privacy and Energy-Aware Federated Framework for Human Activity Recognition

Human activity recognition (HAR) using wearable sensors enables continuous monitoring for healthcare applications. However, the conventional centralised training of deep learning models on sensor data poses challenges related to privacy, communication costs, and on-device efficiency. This paper proposes a federated learning framework integrating spiking neural networks (SNNs) with long short-term memory (LSTM) networks for energy-efficient and privacy-preserving HAR. The hybrid spiking-LSTM (S-LSTM) model synergistically combines the event-driven efficiency of SNNs and the sequence modelling capability of LSTMs. The model is trained using surrogate gradient learning and backpropagation through time, enabling fully supervised end-to-end learning. Extensive evaluations of two public datasets demonstrate that the proposed approach outperforms LSTM, CNN, and S-CNN models in accuracy and energy efficiency. For instance, the proposed S-LSTM achieved an accuracy of 97.36% and 89.69% for indoor and outdoor scenarios, respectively. Furthermore, the results also showed a significant improvement in energy efficiency of 32.30%, compared to simple LSTM. Additionally, we highlight the significance of personalisation in HAR, where fine-tuning with local data enhances model accuracy by up to 9% for individual users.
Outlier Detection Based on Autoencoder Ensembles with Denoising layer and Attention Mechanism

Abstract In the field of outlier detection, two common challenges have persisted. Firstly, outlier detection datasets are often small in size, which can lead to overfitting issues when using deep learning models such as autoencoders. Secondly, as the dimensionality of datasets increases, many dimensions may be irrelevant or noisy, which can adversely affect the model’s ability to learn meaningful features. This phenomenon is known as “the curse of dimensionality.” To address these challenges, this study proposes a solution using an ensemble of autoencoders with denoising layers to mitigate overfitting. Additionally, a novel attention mechanism is introduced to predict the importance of each feature, thereby addressing the curse of the dimensionality problem. The proposed approach is evaluated on five datasets, including BreastW and Vowels, and compared with existing methods. Experimental results demonstrate that the proposed method outperforms existing methods on four out of the five datasets, showcasing its effectiveness.
Overcoming Catastrophic Forgetting with Detail-Degradation Decoupling Networks for Super-Resolution

Abstract Super-resolution reconstruction is a crucial technology that can significantly enhance image resolution and reconstruct high-frequency details. Existing methods are fine-tuned based on the application target during their usage, which enables them to be applied to new data domains. Nevertheless, the extent to which the fine-tuned model can generalize to the original data domain remains unclear, and therefore, additional research is warranted in this particular aspect. To solve the above problems, we analyzed the forgetting phenomenon of super-resolution methods under different degradation kernels, scenarios, and modalities. Then, we introduced Detail-Degradation Decoupling Networks, which separate the inverse process of degradation and the process of recovering high-frequency details in super-resolution. During the incremental learning process, the network parameters can be adjusted based on the task’s characteristics. Our experimental results demonstrate that our approach can significantly reduce catastrophic forgetting in super-resolution reconstruction and enhance the network’s ability to generalize and remain robust.
DualGGAN: A New Facial Style Transfer Network

Abstract In response to the background penetration problem of unsupervised style transfer algorithms in most cases, a Transformer style transfer network DualGGAN based on dual generators and fusion of relative position encoding is proposed. The network is trained using the least squares generative adversarial network, and the neural network is used as the image feature extractor to generate feature maps to obtain facial image features with attention weights from the feature maps, utilizing relative position encoding and mask loss to jointly constrain feature region style transfer. The experimental results show that the DualGGAN network effectively reduces artifact generation when implementing facial style transfer, maintains good background consistency, and has good generalization ability. Experiments have shown that the FID and KID indicators on the cat2dog and older2adult datasets are significantly improved compared to other algorithms.
Analisis Multi Slice Computed Tomography (MSCT) Nasofaring Dengan Klinis Karsinoma Nasofaring Di Instalasi Radiodiagnostik RSUP Dr. Hasan Sadikin Bandung

Background: Nasopharyngeal carcinoma is cancer that occurs in the nasopharyngeal mucosa which shows squamous cell differentiation. MSCT has become a reliable imaging technique for assessing the extent of nasopharyngeal carcinoma. Nasopharyngeal MSCT examination procedure with clinical nasopharyngeal carcinoma at the Radiodiagnostic Installation of Dr. RSUP. Hasan Sadikin Bandung uses a head protocol and slice thickness of 3-5 mm. This study aims to determine the Nasopharyngeal MSCT examination procedure for clinical nasopharyngeal carcinoma using a head protocol with manual intravenous contrast injection.&#x0D; Method: The type of research used is descriptive qualitative research with a case study approach. This research was conducted at the Radiodiagnostic Installation of RSUP Dr. Hasan Sadikin Bandung The subjects in this study consisted of three patients, two radiographers and three radiologists. Data collection methods are carried out through observation, interviews and documentation. The data analysis technique used is an interactive analysis model.
Tinjauan Pelaksanaan Penyusutan Rekam Medis Di Rumah Sakit Umum Daerah Kembangan

Shrinkage is the reduction of medical records on storage shelves by moving inactive medical records to a different room from the active shelf seen from the last patient visit. The purpose of this study was to determine the implementation of shrinkage at the Kembangan Regional General Hospital. This research method uses a qualitative descriptive method by means of interviews and observations in the medical records unit. So it can be concluded from the results of the study that the Kembangan Regional General Hospital already has SOP related to shrinkage which is divided into SOP sorting, media transfer and destruction. In the implementation of shrinkage, officers carry out the process of sorting, transferring and transferring media every day. But officers do not conduct an assessment based on use value. The destruction process is carried out when the number of medical records has reached 2 tons by using a third party to make medical records in the form of pulp. The main obstacles to the implementation of shrinkage are officers who do not have a decree on duties and teams so that officers often delay the implementation of shrinkage, paper quality that makes it difficult for officers to see medical record sheets in the past year and scan machines that heat up quickly due to continuous use. Therefore, the Kembangan Regional General Hospital should complete the SOP related to shrinkage, make a decree of duties and conduct an assessment stage so that the implementation of shrinkage is carried out properly, and add a scan machine so that the implementation of media transfer is not delayed.
Penerapan Machine Learning Untuk Prediksi Masa Studi Mahasiswa di Perguruan Tinggi X

Universities play a role in producing quality resources from their graduate students, so that the quality and accreditation of tertiary institutions are things that need attention. One indicator of higher education accreditation is student graduation on time, so student graduation must be an important concern for tertiary institutions. Based on the results of the documentation, the percentage of students graduating on time is lower than students who are not completing their studies on time, therefore it is necessary to analyze the student's study period to overcome the study period that graduates are not on time using the machine learning method with the Naïve Bayes Classifier algorithm to predict student study period. The research method uses the Naïve Bayes Classifier algorithm method which is part of Artificial Intelligence (AI), which consists of preprocessing, input, process and output. because this method has high accuracy and can work better in real-world cases. The results of predicting the timeliness of student study time at college X with 3553 data using the Naïve Bayes Classifier algorithm method, using WEKA tools succeeded in predicting student study time with 70% data taining and 30% as random testing data with the system. Using 11 attributes, namely study program, GPA, mother's occupation, mother's income, entry period, father's occupation, father's income, route of entry, gender, and school of origin, obtained a percentage of precision value of 54.545%, recall value of 67.220%, and the accuracy level reaches 79.925% which is categorized as good, using the ROC curve calculation to form almost close to (0.1) with an AUC value of 0.844 which is categorized as very good. Based on the results of the percentage accuracy rate, ROC curve and AUC value, the Naïve Bayes Classifier predicts student graduation in the "Good" category
CYBER SECURITY AND DATA PRIVACY LAW IN PAKISTAN: PROTECTING INFORMATION AND PRIVACY IN THE DIGITAL AGE

The importance of cybersecurity and data privacy cannot be overstated in today's digital age. As Pakistan continues to advance its digital infrastructure and reliance on technology, the need for robust cyber security and data privacy measures has become increasingly urgent. The country has seen a surge in cyber attacks and data breaches in recent years, highlighting the need for a comprehensive legal framework to protect peoples' and organizations' sensitive information. This research paper endeavors to furnish a thorough and up-to-date examination of the existing state of cybersecurity measures and data privacy concerns in Pakistan, with a focus on the challenges and limitations that need to be addressed in order to enhance the country's cyber resilience and protect its citizens' personal information. It will examine the existing legal framework, including the Pakistan Computer Emergency Response Team (Pak-CERT) Act, 2017, the PECA Act, 2016, the Data Protection Act, 2018, and the Cybercrime Act, 2019. The paper will also analyze the various regulations and guidelines related to cybersecurity and data privacy, such as the National Cybersecurity Policy, 2018, and the Data Protection Regulations, 2018. CERT rules 2023 gives a legislative framework to deal with cyber-attacks and vulnerabilities that come up from time to time at the national, industry, and organizational levels. It sets up a working structure for technical support, operational equipment, and capacity building services. The paper will also identify and discuss the challenges and limitations of the current legal framework, including inadequate legal provisions, lack of awareness and enforcement, limited found capacity of law enforcement agencies, and insufficient coordination between government agencies. Furthermore, the paper will provide recommendations for improving the legal framework, such as strengthening legal provisions, increasing awareness and enforcement, building strength for law enforcement agencies, and enhancing coordination between government agencies. The study will employ a qualitative research approach, using a combination of literature review, legal analysis, and expert opinions. The data collection methods will include a review of relevant laws, regulations, and guidelines, as well as interviews with experts in the field of cybersecurity and data privacy. The research paper's conclusions will further the current discussion on cyber security and data privacy in Pakistan., and provide valuable insights for policymakers, regulators, and stakeholders. The paper will also serve as a resource for individuals, businesses, and organizations seeking to understand the lawful framework for cyber security and data privacy in Pakistan. Ultimately, the paper aims to provide a comprehensive knowledge of the present state of cybersecurity and data privacy law in Pakistan and to offer recommendations for improvement, with the goal of enhancing the country's cyber security and data privacy posture.
Digital Media as English Language Learning Facilities and Infrastructure

Technological developments in the era of the industrial revolution 4.0 affect everything, including the education sector. The national education sector must be ready for major changes in the face of current technological developments. MTs Nurul Qomar Tawangrejo are very supportive of utilizing digital media as English language learning so that they provide complete facilities and infrastructure, especially internet access as a major component in the sustainability of digital-based learning. With the complete facilities and infrastructure, it makes it easy for teachers and students to do learning. This research uses a qualitative descriptive method, data analysis is carried out through three stages, namely data reduction, data presentation, and conclusion drawing. The purpose of this research was to find out and analyze the utilization of digital media as English language learning facilities and infrastructure at MTs Nurul Qomar Tawangrejo. After adapting to distance learning which uses digital media, it has now become commonplace and even makes it easier for teachers to carry out the learning process, so MTs Nurul Qomar Tawangrejo is increasingly maximizing the utilization of digital media as English language learning facilities and infrastructure. Through digital media, it can provide various facilities for teachers and students to carry out learning.