Reviewerly/exHarmony huggingface dataset:
Preface

Extract This book aims to provide the would-be data scientist with a working idea of the most frequently used tools of data analysis. Our aim has been to introduce approaches to data analysis with a minimum of equations. We envisage three general classes of readers: the complete novice, those with existing statistical knowledge, and those currently employed as data scientists or using data science who wish to widen their repertoire and learn something of the underlying methodology. The entire book is relevant for the novice. The earlier chapters may be most relevant for the practitioner, since they provide the background to the methods used in later chapters, while for statisticians new to data science it will be the final chapters that are of most use. As a data scientist you will be using the computer to perform the data analysis. Any programming language should be able to carry out the analyses that we describe. We used R (because it is free); our code is available as an accompaniment to the book.
ВИКОРИСТАННЯ ДИСТАНЦІЙНИХ ОСВІТНІХ ТЕХНОЛОГІЙ У ВИКЛАДАННІ ІНОЗЕМНИХ МОВ

Nowadays, new computer technologies have a significant impact on education. The roles of teachers and students are changing, the share of self-learning is increasing, and "lifelong education" is shifting to "lifelong education". Computer technologies are
Clean Water Demand Prediction Model Using The Long Short Term Memory (LSTM) Method

Cities or districts as population centers with various service facilities, really need the provision of clean water. The agency that handles clean water in Indonesia is the Regional Drinking Water Company (PDAM). PDAMs were established in every city and district in Indonesia as agencies that serve the community's need for clean water. One of them is the Regional Public Company (Perumda) Tirta Pakuan and as time goes by the number of customers will definitely increase so that the need for clean water will also increase. The purpose of this research is to create a Clean Water Demand Prediction Model using the Long Short Term Memory (LSTM) Method to find the most optimal modeling. The data in this study were obtained from data reports is from Perumda Tirta Pakuan. The prediction model development process is carried out through Visual Studio Code tools. To find a model with the smallest error rate using various ratios, namely 80:20, 70:30, 60:40, and 50:50, then testing is also carried out based on the number of different hyperparameter values in batch sizes 5, 10, 15, 20, 25 and max epoch 50, 100, 150, 200, 250. From all the experiments that have been carried out, the most optimal is batch size 5 and epoch 50 with a ratio of 60:40 for water production to get RMSE 0.4862 and MAPE 2.5252% while for the amount of water use with a ratio of 50:50 get RMSE 0.4674 and MAPE of 2.5163%.
Web Design for Stroke Early Detection Using Decision Tree C5.0

Stroke is a disease that needs serious attention because it can cause disability and even death. According to World Health Organization (WHO) in 2022, stroke is the second leading cause of death and a leading cause of disability in the world. In Indonesia, stroke is the first leading of non-communicable disease proportion according to Riset Kesehatan Dasar in 2018. This study aims to design a web application that can help stroke early detection in a person so that people are more concerned about preventing a stroke. This study used Decision Tree (DT) C5.0 method by utilizing 10 stroke risk factors to analyze the risk of stroke in a person. Decision Tree method can break down complex datasets into several simple rules illustrated by a tree, hence the name Decision Tree. The DT C5.0 is one kind of Decision Tree method that has fast performance in classifying data compared to other methods. Therefore, this study observes how DT C5.0 works in detecting stroke risk. The output of this web application is a statement whether a person has a stroke risk or not. The secondary dataset used for model development totaled 5,109 data consisting of 249 stroke patient data and 4,860 non-stroke patient data. In this study, data balancing and cross validation were carried out so that the performance of the training results model was obtained, namely accuracy 83.54%, precision 78.67%, sensitivity 92.20%, and specificity 74.87%. Furthermore, the performance of the test results model is accuracy 84.42%, precision 79.26%, sensitivity 93.10%, and specificity 75.80%.
Tricking an AI system &amp;amp; the Cybercrime Convention

In a 2018 paper that was only now brought to my attention, Calo et al. discuss whether “tricking a robot” constitutes hacking. They focus on five scenarios that they analyse under the U.S. Computer Fraud and Abuse Act (CFAA). I thought it would be worthwhile to look at those same scenarios from the perspective of the Council of Europe Cybercrime Convention (also known as the Budapest Convention). I refer to AI systems rather than robots, but we mean the same. I conclude that four of the five scenarios should be criminalised under the Convention, but one should not.
An Empirical Study on the Effectiveness of Interpreting Teaching Based on the Training of Applied Talents

Interpreting talents play an increasingly critical role in the economic and social development. Applied interpreting talents generate positive effects in our country's education and cultural transmission. However, the comprehensive quality of interpretation staff still needs to be further improved. Some translators in the whole process of interpreting or engineering data translation are not very solid in professional skills, lack of on-the-scene communication ability, and have shortcomings in knowledge, which leads to low efficiency and poor practical results. Therefore, it is necessary to reform and innovate the teaching mode of interpreting personnel, and build the teaching mode of interpreting talents according to the requirements of applied talents to improve the comprehensive quality of interpreting staff.
Multi-Source Soft Labeling and Hard Negative Sampling for Retrieval Distractor Ranking

Multiple Choice Questions (MCQs) are a kind of widely adopted approaches in learning assessment. Recently the automatic generation of MCQs has become a popular research area. In this task, Distractor Ranking (DR) is one of the most meaningful and challenging sub-tasks, where the DR models learn to select high-quality distractors from numerous candidates. Currently, some DR methods adopt a two-stage ranking strategy, which brings about a complex process and error propagation. Others directly use Single-Encoder based model to improve the overall performance, which however suffers from low efficiency. To tackle these problems, we propose Retrieval Distractor Ranking (ReDR) task to meet the requirements for practical distractor retrieval scenarios, in which the models should achieve relatively high performance within an acceptable time. In this research, we develop an end-to-end way based on Dual-Encoders framework to solve ReDR task. Besides, we propose multiple kinds of relevance scores including Context-Context, Context-Distractor and Distractor-Distractor, which have been employed in two strategies: 1) Multi-source Soft Labeling (MSL), which assigns each candidate an appropriate soft label from multiple kinds of relevance scores to better simulate the sample distribution of ReDR task; 2) Multi-Source Hard Negative Sampling (MHNS), which selects the hard negative samples according to multiple kinds of relevance scores and further distinguishes the difference between them and the positive samples. The extensive experiments on two well-known MCQ benchmarks have proven the effectiveness of our method.
Self-Optimizing the Environmental Sustainability of Blockchain-Based Systems

Blockchain technology has been widely adopted in many areas to provide more dependable and trustworthy systems, including digital infrastructure. However, this technology is acknowledged to pose severe threats to the environment; its wide adoption is believed to be among the significant modern technology contributors to greenhouse gas emissions. The fundamentals of the technology and the inefficiencies of its underlying consensus algorithms (e.g., Proof of Work) are cited among the reasons for these shortcomings. In particular, trust guarantees through consensus are expensive and require excessive computational power, leading to considerable carbon emissions. In this paper, we propose a novel self-adaptive model to optimize the environmental sustainability of blockchain-based systems. It balances the systems' energy consumption and carbon emission without compromising the fundamental properties of blockchain technology. The model continuously monitors a blockchain-based system and adaptively selects miners, considering context changes and user needs. The model dynamically selects a subset of miners to perform sustainable mining processes while ensuring the decentralization and trustworthiness of the system. The aim is to minimize blockchain-based systems' energy consumption and carbon emissions while maximizing their decentralization and trustworthiness. We conduct experiments to evaluate the efficiency and effectiveness of the model. The results show that our model for self-optimizing the environmental sustainability of blockchain-based systems can reduce energy consumption by 55.49% and carbon emissions by 71.25% on average while maintaining desirable levels of decentralization and trustworthiness by more than 96.08% and 75.12%, respectively. Furthermore, these enhancements can be achieved under different operating conditions compared to similar models, including the straightforward use of Proof of Work. Also, we have investigated and discussed the correlation between these objectives and how they are related to the number of miners within the blockchain-based systems.
Modality-Invariant Structural Feature Representation for Multimodal Remote Sensing Image Matching

Establishing feature correspondences between multimodal remote sensing images is an essential task for realizing diverse applications. Conventional matching methods, which employ gradient or phase congruency (PC) for feature detection and description, produce limited performance when images suffer from strong noises and intensity differences. In this study, we propose a novel modality-invariant structural feature representation (MISFR) method for multimodal remote sensing image matching. First, a maximal/minimal enhanced PC moment (EPCM) representation is designed by incorporating the PC with a multiscale relative total variation (RTV) model for feature detection and description. The EPCM integrates the advantages of these two models to exploit intrinsic structural features while providing robustness against modality variations. Then, to improve the feature stability and repeatability, an aggregation structural feature detector (ASFD) is proposed, in which the corner and edge points are separately extracted on the minimal and maximum EPCMs. Moreover, an adaptive binning-based log-polar descriptor is constructed on the maximal EPCM, named enhanced features of orientated PC (EOPC), to robustly characterize feature points. Various experiments on a series of multimodal remote sensing images demonstrate that our MISFR significantly improves the matching performance compared with four state-of-the-art approaches.
The Illusion of Visual Security: Reconstructing Perceptually Encrypted Images

Perceptual image encryption degrades image quality by selectively encrypting some key information of the plain images. The encrypted images are partially perceptible according to the security or quality requirements. Although several types of attacks have tried to infer privacy information from the encrypted images, they can only either extract statistical information or enhance image sketch. In this paper, we take one step further and fully recover the plain images from perceptually encrypted counterparts by designing a non-local attack network (NL-ANet). NL-ANet is composed of densely cascaded multiscale non-local modules (MSNL) and a hierarchical attention fusion module (HAFM). In particular, to better reconstruct encryption distortion, we introduce MSNL to capture powerful hierarchical features from different scales, and propose HAFM to adaptively aggregate and enhance informative hierarchical features for reconstruction. We also propose a new instantiation of the multi-head non-local block with channel attention (MHCA) to explore the long-range dependencies of global contextual information. Extensive experiments show that NL-ANet is encryption-agnostic and superior on different perceptual encryption schemes under different encryption strengths. NL-ANet also achieves better performance than state-of-the-art image restoration methods.
Accelerating and Securing Blockchain-enabled Distributed Machine Learning

In the Internet of Things (IoT) employing centralized machine learning, security is a major concern due to the heterogeneity of end devices. Malicious devices could launch poisoning attacks to degrade machine learning models. Distributed machine learning (DML) with blockchain provides a potential solution. Once local weights are recorded on the blockchain, model aggregation with defensive schemes can be executed on smartphones to prevent attacks. However, blockchain with the proof-of-work (PoW) consensus mechanism wastes computing resources and adds latency to DML. Computing resources can be utilized more efficiently with proof-of-useful-work (uPoW), which secures transactions by solving relevant real-world problems. We propose a novel uPoW method to minimize per-round latency of DML. The uPoW mining process schedules DML instances among multi-access edge computing (MEC) servers by solving a multi-way number partitioning problem. Moreover, poisoning attacks on heterogeneous training data pose significant challenges to blockchain-based DML. To address this problem, we propose a novel aggregation protocol, named <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathit{Corrected Krum}$</tex-math></inline-formula> , to counter such attacks and improve the convergence speed of DML. By leveraging the mean-field approximation method, training errors are corrected to reduce the negative impact of poisoning attacks. Simulation results show that our proposed blockchain approach can significantly speed up DML compared with benchmarks.
Improving Knowledge Distillation via Head and Tail Categories

Knowledge distillation (KD) is a technique that transfers “dark knowledge” from a deep teacher network (teacher) to a shallow student network (student). Despite significant advances in KD, existing work has not adequately mined two crucial types of knowledge: 1) the knowledge of head categories, which represents the relationship between the target category and its similar categories. Our findings reveal that this highly similar (complex) knowledge is essential for improving student’s performance; and 2) the effectively utilized knowledge of tail categories. Existing studies often treat the non-target categories collectively without sufficiently considering the effectiveness of knowledge from tail categories. To tackle these challenges, we reformulate classical KD (ReKD) into two components: Top- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> Inter-class Similar Distillation (TISD) and Non-Top- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> Inter-class Discriminability (NTID). Firstly, TISD captures and imparts the knowledge of head categories to the student. Our experimental results have verified that TISD is particularly effective in transferring the knowledge of head categories, even in fine-grained dataset classification. Secondly, we theoretically show that the weighting coefficient of NTID increases with the probability of Top- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> , leading to stronger suppression of knowledge transfer for tail categories. This observation explains why difficult samples are more informative than simple ones. To better utilize both types of knowledge, we optimize both TISD and NTID using different weighting coefficients, thereby enhancing the student’s ability to learn this valuable knowledge from both head and tail categories. Furthermore, our extensive experimental results demonstrate that ReKD achieves state-of-the-art performance on various image classification datasets, including CIFAR-100, Tiny-ImageNet, and ImageNet-1K, as well as object detection and instance segmentation using the MS-COCO dataset.
The Relationship of The Level of Family Knowledge With Compliance With Medication In Glaucoma Clients At Poli Rumah Eye Undaan Surabaya

Glaucoma is the second largest cause of blindness after cataract. Blindness due to glaucoma is irreversible. However, blindness in patiens due to glaucoma can be prevented by increasing patient compliance in drug use. This research was using an observational analytic study with a cross-sectional study approach. The research respondents were 50 glaucoma patients who were undergoing treatment and fulfilling the inclution criteria at Eye Hospital of Undaan Surabaya. Data collected in this study are primary data. Data was collected by interviewing with a closed questionnaire guide. Statistical tests performed were validity, realibily, and spearman correlation tests. Result A significant relathionship was optained between knowledge and adherence to treatment (p=0,000). Strong statitiscal relathionship of 0.728 range of values berween variables including the strong category (0,6 – 0,8), and the direction of correlation is positive which means the higher the level of knowledge, the impact on the higher level of treatment adherence.
The Creativity of Pre-Service Mathematics Teachers in Digital Learning

&lt;p class="JRPMAbstractBodyEnglish"&gt;&lt;span lang="IN"&gt;Implementing teaching assistants for students of the Education Study Program in schools during distance learning makes them have to be creative in using technology in learning. So the purpose of this research is to find out the forms of creativity of &lt;/span&gt;pre-service&lt;span lang="IN"&gt; teachers who use technology in distance learning during the &lt;/span&gt;COVID&lt;span lang="IN"&gt;-19 pandemic. This study uses a qualitative method with a case study approach to Mathematics Education students at &lt;/span&gt;&lt;span lang="EN-ID"&gt;a private Islamic &lt;/span&gt;university in Semarang, Indonesia &lt;span lang="IN"&gt;who carry out teaching assistance at partner schools. The data collection used in this research is observation and interviews. &lt;/span&gt;According to t&lt;span lang="IN"&gt;he results&lt;/span&gt; obtained&lt;span lang="IN"&gt;, the creativity of prospective mathematics teachers in applying learning media is &lt;/span&gt;shown in making&lt;span lang="IN"&gt; learning videos, interactive multimedia, and presentation materials using &lt;/span&gt;PowerPoint&lt;span lang="IN"&gt; (PPT) containing material and sample questions. Second, creativity in utilizing the potential of schools is the use of online learning platforms, including Microsoft Teams, Zoom meeting, Learning Management System (e-learning), &lt;/span&gt;G&lt;span lang="IN"&gt;oogle &lt;/span&gt;C&lt;span lang="IN"&gt;lassroom, &lt;/span&gt;G&lt;span lang="IN"&gt;oogle &lt;/span&gt;M&lt;span lang="IN"&gt;eet, and Whats&lt;/span&gt;A&lt;span lang="IN"&gt;pp Groups. Creativity in designing learning that can foster creativity and innovation for students includes &lt;/span&gt;developing&lt;span lang="IN"&gt; lesson plans that utilize the platforms and media created and applying learning approaches such as constructivism and contextual. In addition, it also applies game learning strategies and group discussions through breakout rooms.&lt;/span&gt;&lt;/p&gt;&lt;p class="JRPMAbstractBodyEnglish"&gt;&lt;span lang="IN"&gt; &lt;/span&gt;&lt;/p&gt;
Measuring Uncertainty to Extract Fuzzy Membership Functions in Recommender Systems

Nowadays, due to the high volume of choices for customers which causes confusion, the use of recommender systems is strongly growing. Of course, existing systems have two problems, one is complexity and the other is failure to consider uncertainty. In this article, we have reduced the complexity of the system by using a fuzzy innovative system and solved the problem of the uncertainty of users' ratings regarding goods. For that purpose, this research attempts to extract fuzzy membership functions from the Yahoo movie dataset for recommendation applications. In the proposed method, a type I fuzzy system with low numbers of membership functions is designed. The uncertainty in users' ratings is handled by clustering users and movies. Moreover, repeated user evaluations of the same movies are used to determine the uncertainty in improved type 1 membership functions. To evaluate the proposed strategy, MAE, confusion matrix, and Classification-report are used. The result demonstrates the superiority of the introduced strategy.
Design and Implementation of Facets of Dynamic Policies

Information Flow Control (IFC) in dynamic contexts is challenging due to different interpretations of security that arise. This paper introduces a modular framework to address this challenge. We present a dynamic floating-label enforcement mechanism that can be instantiated based on the intended security. Our approach formalizes a simply typed λ-calculus, extended with IFC operations, and adopts an epistemic perspective on security definition.
Multi-Label Classification with Single Positive Label for Remote Sensing Image

In the field of Remote Sensing Scene Classification (RSSC), multi-label classification has become necessary. However, the creation of a multi-label dataset is a laborious process due to the higher annotation costs compared to multi-class classification. In this study, we conducted a pioneering experiment in the context of partial-label classification on remote sensing datasets and aim to discuss the differences and limitations. In partial-label classification, each image is assigned some "positive" labels, which means it is annotated, and other "unknown" labels which are not determined as positive or negative. Consequently, the model is trained with limited information. We evaluated the classification performance on MLRSNet and AID multilabel datasets, using the method and loss functions that have shown excellent performance in previous studies on ground-level view datasets. Our code is available at https://github.com/Kf-7070/ IGARSS2023_partial_label.
A Specific Emitter Identification Method Based on Time-Frequency Feature Extraction

With the rapid growth of the Internet of Things (IoT), fundamental security measures of wireless networks have become a basic requirement. Aiming at the identification of wireless transmitters with the same parameters, this paper proposes a specific emitter identification (SEI) method based on time-frequency feature extraction. Received signals go through preprocessing, i.e., multipath effect estimation and Doppler frequency compensation, to mitigate the channel effect. Then the time-frequency spectrum is generated and a time-frequency feature extraction network is constructed to achieve feature extraction and identification task. Real-world data are used to verify the effectiveness of the proposed method. The overall identification accuracy for stationary emitters reaches 92.9%. Besides, the proposed preprocessing method improves moving emitter identification accuracy by 12%.