Reviewerly/exHarmony huggingface dataset:
Efficient Maximum k-plex Search via Selective Branch-and-Bound

Maximum k-plex search (MPS) finds the largest dense subgraph in a graph. Although it is a fundamental task in many AI and DB applications, MPS is computationally expensive. It searches a maximum k-plex, which is a generalization model of a clique, by removing nodes not included in the k-plex. However, handling massive graphs with numerous nodes and edges is challenging because existing MPS algorithms repeatedly compute the entire graph to remove non-k-plex nodes. Herein we propose a fast algorithm, SBnB, which outputs an exact maximum k-plex in a shorter computation time as it dynamically finds non-k-plex nodes without exploring the entire graph. Additionally, we experimentally demonstrate that SBnB outperforms state-of-the-art MPS algorithms in terms of running time by up to two orders of magnitude.
AN ANALYSIS OF ENGLISH SUMMATIVE TEST ITEMS OF TWELFTH GRADE STUDENT OF MAN 2 MERANGIN ACADEMIC YEAR 2019/2020

The purpose of this research is to evaluate the validity, reliability, level of difficulty, discriminating power, and distractor efficacy of the English summative test items for the 12th grade MAN 2 Merangin students for the 2019-2020 academic year. This is a quantitative descriptive study. The subjects of this research were the questions from the 2019/2020 MAN 2 Merangin 12th grade English summative examination. Questions, answer keys, and student responses were included in the documentation used for the data collection technique. The quantitofive technique is used to analyze the validity, reliability, level of difficulty, discriminating power, and distractor efficacy of the dofa. The obtained dofa were analyzed using manual Microsoft Excel calculations. The results indicate that there are 38 valid items (76%) and 12 invalid items (14%); the questions on the English summative test of 12th grade students of MAN 2 Merangin for the 2019-2020 academic year are unreliable with a reliability level of 0.422; there are 12 items with an easy level of difficulty, 34 with a medium level of difficulty, and 4 with a difficult level of difficulty; and there are 22 poor items.&#x0D;
Blockchain-based spam detection approach

Maintaining privacy is increasingly important for OSN (Online social network) users due to the massive quantity of sensitive shared data and the growing popularity of OSNs. Social media is now widely used and crucial for, networking, and communication. However, the widespread use of social media platforms has created numerous problems for modern society and industry. For instance, they disseminate spam data on social media because the network lacks the capability to do so. Social network platforms have revolutionized the way we communicate and interact with each other. However, the widespread use of social media has also led to an increase in spamming activities. Spamming refers to the distribution of unsolicited and irrelevant content that is intended to mislead or deceive users. In this article, we propose a blockchain-based approach for detecting spams in social media using bi-directional long short-term memory (Bi-LSTM) networks. We get a highly compelling and intriguing outcome with higher accuracy (97 percent) and lesser loss than typical learning algorithms
Exploring the Fundamentals of Python Programming: A Comprehensive Guide for Beginners

This scientific article entitled 'Exploring the Fundamentals of Python Programming: A Comprehensive Guide for Beginners' provides a comprehensive overview of the basics in Python programming, and is intended for beginners in the field of programming. The article discusses the history and importance of Python, then goes on to explain how to install Python and start writing programs with it using a text editor. It delves into the basics such as variables, data types, control flow, and functions. The article provides practical examples to illustrate how Python can be used to solve simple problems. In addition, the article deals with useful references and resources for readers to further delve into programming in Python.
A Symmetry-Based Unscented Particle Filter for State Estimation of a Ballistic Vehicle

The problem of state estimation for vehicles when an initial fix is highly uncertain and/or the number of sensors is not sufficient (and changes with time) is very relevant for both aircraft and spacecraft navigation. This work proposes a Locally Linearized Particle Filter based on a quaternion-adapted Unscented Kalman Filter to estimate the state of a vehicle with minimal sensors and uncertain initial conditions, exploiting geometrical symmetries. The algorithm is applied to a ballistic vehicle navigating towards a laser-illuminated target using on-board sensors, including a triad of accelerometers and gyroscopes, a barometric altimeter and a laser receiver. A symmetry around the vertical axis is identified; based on it, the algorithm becomes capable of solving the navigation problem, even with highly uncertain initial conditions and without enough sensor information; this second condition is particularly severe when the laser receiver is not yet obtaining data. The proposed navigation algorithm offers promising results in simulation, rapidly converging to an accurate estimate of the real trajectory when the laser receiver becomes active.
m-Health System for the Self-management of Patients with Heart Failure

Increasingly, the use of technology is emerging as a tool to support health care.The new information and telecommunications technologies have triggered a new paradigm in health: in access, availability and management of information.Telemonitoring interventions in patients with heart failure have been shown to be cost effective in reducing the rate of hospitalisations and mortality.The development of m-Health applications focused on the centralization of care in patients, considering the perspectives of different stakeholders, promises results not only in clinical (mortality and morbidity) but also in patient outcome reports (quality of life and self-care).This work presents an integrated information system for telemonitoring and self-management of patients with heart failure.
A software package for efficient patient trajectory analysis applied to analyzing bladder cancer development

We present the Patient Trajectory Analysis Library (PTRA), a software package for explorative analysis of patient development. PTRA provides the tools for extracting statistically relevant trajectories from the medical event histories of a patient population. These trajectories can additionally be clustered for visual inspection and identifying key events in patient progression. The algorithms of PTRA are based on a statistical method developed previously by Jensen et al, but we contribute several modifications and extensions to enable the implementation of a practical tool. This includes a new clustering strategy, filter mechanisms for controlling analysis to specific cohorts and for controlling trajectory output, a parallel implementation that executes on a single server rather than a high-performance computing (HPC) cluster, etc. PTRA is furthermore open source and the code is organized as a framework so researchers can reuse it to analyze new data sets. We illustrate our tool by discussing trajectories extracted from the TriNetX Dataworks database for analyzing bladder cancer development. We show this experiment uncovers medically sound trajectories for bladder cancer.
Edukasi Bahaya Napza (Narkotika, Psikotropika, dan Zat Adiktif) bagi Remaja SMA Muhammadiyah Sumbawa

Adolescence is a transitional period between childhood and adulthood, which begins at the time of sexual maturity between the ages of 11 or 12 years to 20 years. The stages of adolescent development have tasks that must be completed. Drug abuse is becoming a problem that is increasingly concerning and a huge concern for all parties, including the academic community, government, and parents. Cases of narcotics abuse among high school youth have entered a critical stage, for this reason it is necessary to take preventive measures in the form of education. The purpose of this community service is to increase and develop youth knowledge about the dangers of drug abuse for health. The planned outcome is to increase the knowledge, understanding and awareness of adolescents about the dangers of drug abuse for adolescents.&#x0D; &#x0D;
Classroom Design and Application of Art Design Education Based on Artificial Intelligence

With the development of society, the education goal of art and design education for students is constantly changing, focusing more on students' professional and practical abilities, but the traditional teaching methods cannot provide the conditions needed for teaching. In this article, the three parts of art design classroom atmosphere before, during and after class, and according to the characteristics and needs of each part into artificial intelligence technology to build a smart classroom system. The experimental results show that AI technologies can improve the efficiency of classroom management, and the information obtained from the emotional score data can help teachers understand and master the classroom teaching situation, and analyze the shortcomings of classroom teaching content based on the comprehensive classroom emotional score. In addition, AI technology can also enrich classroom content, diversify teaching modes, increase the interaction space between teachers and students and students, and improve the teaching quality of art design classrooms.
Man in the Context of the Biological Hypothesis of Consciousness

The article analyzes the problem of human consciousness, which is commonly called the main mystery of the Universe. The authors proceed from the fact that traditional views on it do not permit to properly investigate the essence of human consciousness. According to the authors of the article, the allocation of human conscious-ness as a special autonomous object of cognition does not correspond to the idea of genetic organization of living matter between different levels. Of particular difficulty is the task of explaining the birth of mental events from neurophysiological processes occurring in the human brain. In this connection, the authors propose a new formulation of the human problem in the context of the biological hypothesis of consciousness, according to which the latter is defined as the result of the activity of special microorganisms inside man, the essence of which he knows little about, since he is accustomed to consider himself the “crown of nature” as per the con-cept of European culture. Reference is made to the consistency of the assertion that man in general and his consciousness in particular should be subject to the same biological laws of existence as other living organ-isms, since they are all part of a single physical reality.
Typed Non-determinism in Functional and Concurrent Calculi

We study functional and concurrent calculi with non-determinism, along with type systems to control resources based on linearity. The interplay between non-determinism and linearity is delicate: careless handling of branches can discard resources meant to be used exactly once. Here we go beyond prior work by considering non-determinism in its standard sense: once a branch is selected, the rest are discarded. Our technical contributions are three-fold. First, we introduce a $$\pi $$ -calculus with non-deterministic choice, governed by session types. Second, we introduce a resource $$\lambda $$ -calculus, governed by intersection types, in which non-determinism concerns fetching of resources from bags. Finally, we connect our two typed non-deterministic calculi via a correct translation.
POLITENESS STRATEGIES CONVEYED BY THE STUDENTS IN ONLINE DISCUSSIONS

The present research is inevitable revealing the politeness strategies conveyed by the students of English study program at Jambi University through online discussions. The data of the research are utterances of the research subjects (students) obtained from the students’ Whatsapp chat histories in Literature for Educational Purposes and Teaching English for Young Learners courses all through the classroom interactions. The technique of collecting the data is accomplished by means of reviewing the students’ chats as the consequence of online classes during the period of Covid-19. Every single word obtained from the students’ chats has been read conversely not all of the utterances are regarded as the data (data reduction process). The results of this research highlight four genuses of politeness strategies, namely positive politeness, negative politeness, bald on record, and off record. Briefly, the cultural issue entirely have the aftermath on the politeness strategies realized by the research subjects.
The Moderating Effect of Environmental Uncertainty on the Relationship Between Strategic Intuition, Strategic Decision-Making, and Organizational Performance: An Exploratory Study

This study explores the moderating effect of environmental uncertainty on the relationship between strategic intuition, strategic decision-making, and organizational performance in the oil sector in Iraq. Using a quantitative research design, the study collected data from 70 high-level managerial staff at the Misan Oil Company. The data was analyzed using structural equation modeling, and the results revealed that strategic intuition has a positive impact on both strategic decision-making and organizational performance. Additionally, the study found that environmental uncertainty moderates the relationship between strategic intuition, strategic decision-making, and organizational performance. The findings suggest that leaders who possess strong intuitive abilities are better equipped to navigate complex and uncertain business environments, leading to better performance outcomes. The study provides valuable insights into the intricate relationships among strategic intuition, strategic decision-making, and organizational performance, with important theoretical and practical implications for managers and organizations seeking to enhance their decision-making procedures and elevate their performance results. Future research can explore the moderating effect of environmental uncertainty in different industries and contexts, and examine the impact of other contextual factors on these relationships.
Grammatical Errors Analysis in Descriptive Text Writing

The purposes of this research were to find out studens’ grammatical errors on writing descriptive text and the possible causes of their grammatical errors. This research used descriptive qualitative method with the 20 participants of IPA MA NW Perian as the sample. Observation, interview, and questionare were used as the research instruments. The result showed that the students produce 44,87 % errors in using verb, 23,07 % errors in using noun, 16,66 % errors in using pronoun, 8,97 % errors in using preposition, and 6,41 % errors in use of adjective. The students produced 44,87 % errors from 78 of total number of errors in writing descriptive text. Based on the result interview and questionnaire the researcher found that most students knew descriptive text definition but frequently found it difficult to write descriptive text since they were still confused in grammar, vocabularies arrangement, and word orders. It was concluded that the possible causes of errors are grammatical, words selection, and word orders.
Arabic stance detection of COVID-19 vaccination using transformer-based approaches: a comparison study

Purpose Automatically extracting stance information from natural language texts is a significant research problem with various applications, particularly after the recent explosion of data on the internet via platforms like social media sites. Stance detection system helps determine whether the author agree, against or has a neutral opinion with the given target. Most of the research in stance detection focuses on the English language, while few research was conducted on the Arabic language. Design/methodology/approach This paper aimed to address stance detection on Arabic tweets by building and comparing different stance detection models using four transformers, namely: Araelectra, MARBERT, AraBERT and Qarib. Using different weights for these transformers, the authors performed extensive experiments fine-tuning the task of stance detection Arabic tweets with the four different transformers. Findings The results showed that the AraBERT model learned better than the other three models with a 70% F1 score followed by the Qarib model with a 68% F1 score. Research limitations/implications A limitation of this study is the imbalanced dataset and the limited availability of annotated datasets of SD in Arabic. Originality/value Provide comprehensive overview of the current resources for stance detection in the literature, including datasets and machine learning methods used. Therefore, the authors examined the models to analyze and comprehend the obtained findings in order to make recommendations for the best performance models for the stance detection task.
Algorithm for Detecting Small Targets Based on Upgraded YOLOv5s

Abstract Small targets are discovered despite the challenges posed by the limited number of features, low resolution, difficulty in extracting discriminative characteristics, and ease of interference from external influences. A modest target detection technique based on enhanced YOLOv5s is suggested in this research. First, a test storey dedicated to tiny objects is appended to the characteristic fusion part of the algorithm to help the model efficiently acquire features with smaller sensory fields; second, to help the model find features more correctly, a cooperative attention method module is integrated into the characteristic extraction section to eliminate trait redundancy without sacrificing feature information; finally, the Bi-FPN network construction is combined to help the model to locate features more accurately. With the Bi-FPN construction, the characteristic combination process is modified to strengthen the examining capability of small objectives with a characteristic combination. The experimental results demonstrate that the enhanced algorithm increases average detection accuracy over the baseline YOLOv5s method by 6.3%, while also increasing small target detection accuracy and decreasing false and missed detection situations.
Hardening of a Continuous Behavior-based Authentication Distributed System

"Password-based traditional authentication systems are increasingly insufficient when it comes to providing security and checking the identity of the authenticated user. What happens when the password of an user has been stolen or an active user is not the same user who authenticated firstly? A distributed system using AI (Artificial Intelligence) acting as a second factor authentication method by analyzing user's mouse events has to provide confidentiality and integrity in order to protect against different attacks such as Man-In-The-Middle that allow sniffing or data tampering, resulting in an identity spoof. In order to grant integrity and confidentiality, encryption and authentication must be implemented. Authentication is used to allow one node to produce or consume data from an existent message stream and encryption in order to avoid exposing these data to external agents. PKI (Public Key Infrastructure) system is widely used over the internet, so it is a trusty authentication and encryption framework. By using PKI in this project, hardening is performed by creating with OpenSSL a trusted Certificate Authority that issues and signs the certificates used by each node in the distributed system. Trust in this Certificate Authority is implemented by creating keystores and truststores for each node with keytool. This project resulted in a secure communication system preventing data from being sniffed or tampered."
Towards a FAIR Dataset for Spanish Non-Functional Requirements

Supervised Machine Learning algorithms (ML) have enhanced the performance of the automatic non-functional requirements (NFR) classification in the Requirements Engineering domain. However, the lack of public datasets, dealing with imbalanced datasets and reproducibility are current concerns in ML experiments. We conducted a quasi-experiment to generate a dataset of NFR in the Spanish Language, following the FAIR Principles. We collected 109 requirements from an open access repository of the University of A Coruña, and performed a labeling process based in the categories and subcategories of the ISO/IEC 25010 quality model. Using a Fleiss’ Kappa test we obtained a substantial agreement (0.78) at the category level and a moderate agreement (0.48) when the classification is per subcategory.
Point of Sale Fraud Detection Methods via Machine Learning

Restaurant cash registers frequently experience fraudulent transactions, leading to substantial financial losses for operators. Despite several methods aimed at preventing fraud at the cash register, addressing this issue remains an ongoing concern. In this study, machine learning methods are used to detect fraudulent transactions at the cash register in fast-food restaurants. By using POS logs, transactions in restaurants are recorded and these logs are analyzed to detect fraudulent transactions on an unbalanced dataset. Random forest, XGBoost and LGBM algorithms are used in the study and different resampling techniques (ADASYN etc.) are applied to improve the performance of these algorithms. In addition, it is aimed to find the best parameters with the randomized search method. In conclusion, this study offers a solution for detecting fraudulent transactions at the cash register in fast-food restaurants. The results of the study are promising in its current state.
Compact Dynamic Inner Canonical Correlation Analysis for Nonstationary Dynamic Feature Extraction and Prediction

In this paper, a novel compact dynamic inner canonical correlation analysis (DiCCA) algorithm with autoregressive integrated moving average (ARIMA) inner models is proposed to better capture the latent dynamics of high dimensional time series. It can extract latent factors that capture the underlying dynamics of the data and model them using the ARIMA structure, which has fewer parameters and more flexibility than the potentially high-order AR structure used in the original DiCCA algorithm. The proposed algorithm can also handle nonstationary latent factors by explicitly modeling the unit roots. The algorithm integrates the extraction and the modeling of a latent factor in one step, resulting in a consistent inner and outer model. The algorithm is applied to an industrial dataset and shows better performance than the original DiCCA algorithm.
Echo State Networks for anomaly detection in industrial systems

In the field of industrial engineering, it is extremely interesting and useful to develop models that faithfully represent the behaviour of real physical systems, which allow to understand their dynamics, to optimise them or to detect anomalous or faulty situations. This paper proposes the modelling of the dynamics of water tanks within an industrial plant with real instrumentation, using Echo State Networks. The developed models have been used to explore the detection of anomalies in the system by analysing the differences between the estimated system state and the actual state after the introduction of faults.