Reviewerly/exHarmony huggingface dataset:
Machine Learning to Identify Bitcoin Mining by Web Browsers

In the recent development of the online cryptocurrency mining platform, Coinhive, numerous websites have employed “Cryptojacking.” They may need the unauthorized use of CPU resources to mine cryptocurrency and replace advertising income. Web cryptojacking technologies are the most recent attack in information security. Security teams have suggested blocking Cryptojacking scripts by using a blacklist as a strategy. However, the updating procedure of the static blacklist has not been able to promptly safeguard consumers because of the sharp rise in “Cryptojacking kidnapping”. Therefore, we propose a Cryptojacking identification technique based on analyzing the user's computer resources to combat the assault technology known as “Cryptojacking kidnapping.” Machine learning techniques are used to monitor changes in computer resources such as CPU changes. The experiment results indicate that this method is more accurate than the blacklist system and, in contrast to the blacklist system, manually updates the blacklist regularly. The misuse of online Cryptojacking programs and the unlawful hijacking of users' machines for Cryptojacking are becoming worse. In the future, information security undoubtedly addresses the issue of how to prevent Cryptojacking and abduction. The result of this study helps to save individuals from unintentionally becoming miners.
Penerapan Metode Pembelajaran Permainan Edukatif Terhadap Hasil Belajar IPA di SDN 1 Sungai Miai 7 Banjarmasin

The use of various learning methods is naturally carried out by teachers as educators. Learning science in elementary schools requires the use of interesting learning methods to have a positive impact on learning outcomes. One of the learning methods that can be used by teachers for science learning is the educational game method. Several studies using the game method in learning activities show satisfactory results. However, very few studies with the title application of educational games to elementary science learning are found on the Google Scholar search page. Therefore researchers are interested in conducting research with the title "Application of Educative Game Learning Methods on Science Learning Outcomes at SDN Sungai Miai 7 Banjarmasin". Based on the title, the purpose of this research is to find out whether the application of educative game learning methods can improve learning outcomes. As per the title, this research was conducted at SDN Sungai Miai 7 Banjarmasin. This research method is classroom action research (CAR). Data was collected through test and non-test techniques by filling out a questionnaire. Tests are used to collect data on learning outcomes. The data obtained was then analyzed in a quantitative descriptive manner. The results of learning science by applying the educational game method at SDN Sungai Miai 7 Banjarmasin have increased from each meeting based on the average value and percentage of classical completeness.
Patient Clustering for Vital Organ Failure Using ICD Code With Graph Attention

Heart failure, respiratory failure and kidney failure are three severe organ failures (OF) that have high mortalities and are most prevalent in intensive care units. The objective of this work is to offer insights into OF clustering from the aspects of graph neural networks and diagnosis history.This paper proposes a neural network-based pipeline to cluster three types of organ failure patients by incorporating embedding pre-train using an ontology graph of the International Classification of Diseases (ICD) codes. We employ an autoencoder-based deep clustering architecture jointly trained with a K-means loss, and a non-linear dimension reduction is performed to obtain patient clusters on the MIMIC-III dataset.The clustering pipeline shows superior performance on a public-domain image dataset. On the MIMIC-III dataset, it discovers two distinct clusters that exhibit different comorbidity spectra which can be related to the severity of diseases. The proposed pipeline is compared with several other clustering models and shows superiority.Our proposed pipeline gives stable clusters, however, they do not correspond to the type of OF which indicates these OF share significant hidden characteristics in diagnosis. These clusters can be used to signal possible complications and severity of illness and aid personalised treatment.We are the first to apply an unsupervised approach to offer insights from a biomedical engineering perspective on these three types of organ failure, and publish the pre-trained embeddings for future transfer learning.
Towards User Experience Guidelines for Mobile Augmented Reality Storytelling with Historical Images in Urban Cultural Heritage

Augmented reality is a novel and engaging way for visitors to experience cultural heritage as locals or tourists. Mobile augmented reality applications with historical images showing landmarks or events of the past are a popular category. Using research methods such as literature review, competitive analysis, semi-structured interviews, and usability evaluations, we derived a set of practical user experience guidelines for designing mobile augmented reality storytelling applications with historical images in the context of urban cultural heritage. To demonstrate how these guidelines could be applied, we present a redesigned prototype for such an application, developed for Timisoara (Romania), European Capital of Culture in 2023.
Statistically Valid Inferences from Privacy-Protected Data

Unprecedented quantities of data that could help social scientists understand and ameliorate the challenges of human society are presently locked away inside companies, governments, and other organizations, in part because of privacy concerns. We address this problem with a general-purpose data access and analysis system with mathematical guarantees of privacy for research subjects, and statistical validity guarantees for researchers seeking social science insights. We build on the standard of “differential privacy,” correct for biases induced by the privacy-preserving procedures, provide a proper accounting of uncertainty, and impose minimal constraints on the choice of statistical methods and quantities estimated. We illustrate by replicating key analyses from two recent published articles and show how we can obtain approximately the same substantive results while simultaneously protecting privacy. Our approach is simple to use and computationally efficient; we also offer open-source software that implements all our methods.
Students’ Attitude Toward E- Learning During Covid 19 Pandemic At Informatics Department Of Universitas Pgri Sumatera Barat

This study examined how students felt about online learning during the COVID 19 Pandemic at the Informatics Department of the University of Sumatera Barat. Students' ways of thinking, experiencing, or understanding the learning process might be influenced by their attitude. Teachers must therefore be aware of pupils' attitudes. Using empirical data, the researcher used this study to analyze how pupils think, feel, and behave. At the Informatics Department of Universiatas PGRI Sumatera Barat, the goal was to determine how the students' attitudes about thinking, feeling, and acting during the Covid 19 Pandemic influenced their usage of online learning. Research was conducted using a quantitative approach using either a survey or a descriptive design. Cross-sectional research was the study's research design. All students at Informatics Department session 2020 A are examined in this study of the twenty-three students of Universitas PGRI Sumatera Barat. The information was gathered using a survey. According to the study's findings, students exhibit a favorable attitude toward online learning, with a mean score of 3,5 (N = 23, st.d = 0.9). They behave negatively toward online learning, with a mean average of 2,9 (N=23, st.d = 1,0). Therefore, it is advised that teachers help their students transform their negative attitudes into good ones.
Comparing Data Mining Classification for Online Fraud Victim Profile in Indonesia

Classification is one of the most often employed data mining techniques. It focuses on developing a classification model or function, also known as a classifier, and predicting the class of objects whose class label is unknown. Categorizing applications include pattern recognition, medical diagnosis, identifying weaknesses in organizational systems, and classifying changes in the financial markets. The objectives of this study are to develop a profile of a victim of online fraud and to contrast the approaches frequently used in data mining for classification based on Accuracy, Classification Error, Precision, and Recall. The survey was conducted using Google Forms, which is an online platform. Naive Bayes, Decision Tree, and Random Forest algorithms are popular models for classification in data mining. Based on the sociodemographics of Indonesia's online crime victims, these models are used to classify and predict. The result shows that Naïve Bayes and Decision Tree are slightly superior to the Random Forest Model. Naive Bayes and Decision Tree have an accuracy value of 77.3%, while Random Forest values 76.8%.
A Graph-Based Soft Actor Critic Approach in Multi-Agent Reinforcement Learning

Multi-Agent Reinforcement Learning (MARL) is widely used to solve various real-world problems. In MARL, the environment contains multiple agents. A good grasp of the environment can guide agents to learn cooperative strategies. In Centralized Training Decentralized Execution (CTDE), a centralized critic is used to guide cooperative strategies learning. However, having multiple agents in the environment leads to the curse of dimensionality and influence of other agents’ strategies, resulting in difficulties for centralized critics to learn good cooperative strategies. We propose a graph-based approach to overcome the above problems. It uses a graph neural network, which uses partial observations of agents as input, and information between agents is aggregated by graph methods to extract information about the whole environment. In this way, agents can improve their understanding of the overall state of the environment and other agents in the environment while avoiding dimensional explosion. Then we combine a dual critic dynamic decomposition method with soft actor-critic to train policy. The former uses individual and global rewards for learning, avoiding the influence of other agents’ strategies, and the latter help to learn an optional policy better. We call this approach Multi-Agent Graph-based soft Actor-Critic (MAGAC). We compare our proposed method with several classical MARL algorithms under the Multi-agent Particle Environment (MPE). The experimental results show that our method can achieve a faster learning speed while learning better policy.
Osservazioni tassiane a margine dell’opera di Boccaccio

The essay proposes a study of the relationship between Tasso and Boccaccio, analysing the references to the ancient auctoritas present in Tasso's theoretical production. The sixteenth-century poet's interest in Boccaccio's works can be found in the author's epistolary, in the letters in which Tasso claims to be in possession of some of his texts or in those in which he explicitly requests them. As with Dante and Petrarch, the reading is careful and inclined to take the ancient source as a model for observations of a rhetorical, linguistic-grammatical and metrical nature. These observations form a non-systematic corpus of reflections on poetic language, an authentic reflection of an intimate dialogue between the author and Boccaccio.
Implementing Video OCR along with SWT Technique for Video indexing and Analysis

The main purpose of this paper is to expand the usage of OCR (Optical character recognition) as this is only implemented over images and to extend this Video OCR is introduced in a way to help to retrieve the information from the video without playing the video.Video OCR is executed with the assistance of OpenCv2 module and PyTesseract [7] at the side of SWT approach which all pretty collectively make an ideal aggregate to offer an appropriate content from the video (i.e., Lecture video or any kind of video which has slides or text on the background of the video) [2,4].This technique is performed in a well-designed along with easy steps to provide us an correct end result of the facts from the video into textual files.In addition to this we also added Speech Recognition module within the project to support the video along with the text file.This speech delivered by the faculty (i.e., instructor/educator/teacher), or an educator will be also resulted in a text file.
UDDIPOK: A reading comprehension based question answering dataset in Bangla language

The popularity of reading comprehension (RC) is increasing day-to-day in Bangla Natural Language Processing (NLP) research area, both in machine learning and deep learning techniques. However, there is no original dataset from various sources in the Bangla language except translated from foreign RC datasets, which contain abnormalities and mismatched translated data. In his paper, we present UDDIPOK, a novel wide-ranging, open-domain Bangla reading comprehension dataset. This dataset contains 270 reading passages, 3636 questions, and answers from diverse origins, for instance, textbooks, exam questions from middle and high schools, newspapers, etc. Furthermore, this dataset is formated in CSV, which contains three columns: passages, questions, and answers. As a result, data can be handled expeditiously and easily for any machine learning research.
Acceptance of Artificial Intelligence in Human Resource Practices by Employees

Introduction: Many organisations nowadays use artificial intelligence (AI) in human resource (HR) activities like talent acquisition, onboarding of new employees, learning and development, succession planning, retention of employees, and automation of administrative tasks. When AI is integrated with HR practices, it helps HR personnel to focus more on the strategic aspects of the HR function and relieve them from routine HR activities.Purpose: The readiness of employees to accept any change depends on organisational facilitation to change, employee willingness to accept the change, the requirement for change, situational factors, etc. This research studies the factors influencing employees’ change readiness towards acceptance of AI in HR practices. The researchers also strive to develop a conceptual technology adoption model for AI in HR practices by studying the earlier models. Finally, the research explores the acceptance of AI by various service sector employees and identifies whether there is any difference in their acceptance of AI based on demographic variables.Methodology: A conceptual framework was derived using a combination of previous models, including the Technology Readiness Index (TRI), Change Readiness Scale, Technology Acceptance Model (TAM), Technology, Organization, and Environment (TOE) model, and change readiness scale. A structured questionnaire was designed and distributed to 228 respondents from the service sector based on the conceptual framework. An exploratory factor analysis (EFA) was used to determine the elements that influence employees’ level of change readiness.Findings: The exploratory results on data collected from 228 respondents show that the model can be used for further research if a confirmatory factor analysis and validity and reliability test are performed. Employees are aware of AI and how it is used in HR practices, based on the study results. Moreover, while most respondents favour using AI in their company’s HR practices, they are wary of some aspects of AI.
A multimodal evolutionary algorithm with multi-niche cooperation

Multimodal optimization problems, which involve multiple global optima, are common in real-world applications. So far, plenty of multimodal evolutionary algorithms (MMEAs) have been proposed, where niching techniques are widely utilized to locate different optima by trying to cover each modality with an exclusive niche. However, most existing MMEAs deal with niches independently without considering their similarity and redundancy, which greatly limits the performance of the algorithms. Directing against this issue, this study proposes a multi-niche cooperation based MMEA, where a knowledge transfer strategy (KTS) and a collaborative search mechanism (CSM) are designed. Specifically, given the high similarity shared by different modalities, KTS cooperatively evolves the corresponding niches by transferring knowledge among them, thereby accelerating their convergence. For niches possibly covering the same modality, CSM explicitly measures the search intensity on the modality and adaptively deactivates redundant niches, so that excessive searches on the modality can be avoided. This study incorporates the above two strategies into a classic MMEA named NEA2, and thus leads to a multi-niche cooperation based NEA (MNC-NEA). Experiments conducted on 20 benchmark functions demonstrate that KTS and CSM are efficient and complementary, and they together endow MNC-NEA with a significant competitive advantage over 11 state-of-the-art MMEAs.
Cross-modal multi-label image classification modeling and recognition based on nonlinear

Abstract Recently, it has become a popular strategy in multi-label image recognition to predict those labels that co-occur in a picture. Previous work has concentrated on capturing label correlation but has neglected to correctly fuse picture features and label embeddings, which has a substantial influence on the model’s convergence efficiency and restricts future multi-label image recognition accuracy improvement. In order to better classify labeled training samples of corresponding categories in the field of image classification, a cross-modal multi-label image classification modeling and recognition method based on nonlinear is proposed. Multi-label classification models based on deep convolutional neural networks are constructed respectively. The visual classification model uses natural images and simple biomedical images with single labels to achieve heterogeneous transfer learning and homogeneous transfer learning, capturing the general features of the general field and the proprietary features of the biomedical field, while the text classification model uses the description text of simple biomedical images to achieve homogeneous transfer learning. The experimental results show that the multi-label classification model combining the two modes can obtain a hamming loss similar to the best performance of the evaluation task, and the macro average F 1 value increases from 0.20 to 0.488, which is about 52.5% higher. The cross-modal multi-label image classification algorithm can better alleviate the problem of overfitting in most classes and has better cross-modal retrieval performance. In addition, the effectiveness and rationality of the two cross-modal mapping techniques are verified.
GAN-Based Robust Motion Planning for Mobile Robots Against Localization Attacks

Motion planning (MP) is essential but challenging for mobile robots. Most of the existing MP methods, at each instant, compute an action based on the states of the robot and the surrounding obstacles, assuming that the robot's localization module is attack-free. Unfortunately, the localization module is vulnerable to sensor attacks, such as GPS spoofing attacks. In this letter, we propose a novel robust framework, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GAN-MP</monospace> , where a generative adversarial network (GAN) is exploited to mitigate the localization attacks, and the state-of-the-art MP methods are applied to generate collision-free actions. Specifically, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GAN-MP</monospace> aims to learn a Generator to compute the potential positions of the robot. Consequently, it can reserve the robot's benign states while correcting the attacked states. Hence, it is suitable for benign and attacked scenarios without any attack detector. In addition, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GAN-MP</monospace> is method-agnostic and can be easily integrated with any existing MP method. We instantiate <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GAN-MP</monospace> with a deep reinforcement learning method to demonstrate its design and training processes. Comprehensive experiments show that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GAN-MP</monospace> can mitigate localization attacks and guarantee safe motion. We also demonstrate the robustness and generalization of <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GAN-MP</monospace> .
Machine Learning Based Resilience Testing of an Address Randomization Cyber Defense

Moving target defenses (MTDs) are widely used as an active defense strategy for thwarting cyberattacks on cyber-physical systems by increasing diversity of software and network paths. Recently, machine Learning (ML) and deep Learning (DL) models have been demonstrated to defeat some of the cyber defenses by learning attack detection patterns and defense strategies. It raises concerns about the susceptibility of MTD to ML and DL methods. In this article, we analyze the effectiveness of ML and DL models when it comes to deciphering MTD methods and ultimately evade MTD-based protections in real-time systems. Specifically, we consider a MTD algorithm that periodically randomizes address assignments within the MIL-STD-1553 protocol—a military standard serial data bus. Two ML and DL-based tasks are performed on MIL-STD-1553 protocol to measure the effectiveness of the learning models in deciphering the MTD algorithm: 1) determining whether there is an address assignments change i.e., whether the given system employs a MTD protocol and if it does 2) predicting the future address assignments. The supervised learning models (random forest and k-nearest neighbors) effectively detected the address assignment changes and classified whether the given system is equipped with a specified MTD protocol. On the other hand, the unsupervised learning model (K-means) was significantly less effective. The DL model (long short-term memory) was able to predict the future addresses with varied effectiveness based on MTD algorithm's settings.
The Method of Structural Adaptation of the Compartmental Spiking Neuron Model

A method of structural training of a compartmental spike model of a neuron is proposed. The training task was to form a response to a pattern represented by a vector of single spikes with time coding, where each component of the vector enters a separate dendrite of the neuron. Depending on the given pattern, the algorithm automatically selects such parameters of the neuron model as: the size of the soma, the length of the dendrites, and the number of synapses on each of the dendrites. A sign of pattern recognition by the trained model is the generation of an output spike by the model. The quality of pattern recognition has been studied. Experimentally, the universal value of the neuron activation threshold is determined, which does not depend on the size of the vector describing the input pattern.