Reviewerly/exHarmony huggingface dataset:
Digitalization and the Information Society in Algeria: Digital Transformation Actors and Key Variables

The information society is a product of the intersection of socio-historical-technological contexts, where the development of science and industry has a global reach. The pervasiveness of new information and communication technologies makes it an essential component of a new civilization, affecting every country in varying degrees. Although this techniques approach is grounded in some truth, it neglects other vital aspects that challenge the idea of an information society that prioritizes human needs. The leading nations have achieved this through active research and development, driven by government involvement and the significant contributions of universities. Furthermore, the collaboration of diverse economic, institutional, social, and civic actors has played an essential role in its advancement. However, constructing and promoting an information society transcends infrastructure. It involves political actions that consider the socio-technological nature of this development and its impact on society and other sectors of activity. The objective of this article is twofold. On the one hand, the study analysed the situation of Algeria and its position in relation to other countries regarding the information society, and the other examined the factors that influence the development of the information society in Algeria, trying to identify the most important ones. Finally, the authors proposed a development strategy in this area. The research thesis was formulated as follows: What are the key variables that have an impact on the development of digitalization and the information society in Algeria? Thus, the regulatory frameworks of the most advanced countries are central to the initiatives aimed at its development. To foster the emergence of an information society in Algeria, solidarity must be strengthened, diversity promoted, and the potential of all citizens catalysed.
Pedagogical and educational aspect of the historical work of M. Arkas “History of Ukraine-Rusʺ

77and multifactoriality.Particular emphasis is placed on the use of general scientific methods (biographical analysis, synthesis, and classification), as well as interdisciplinary (structural and systematic approach) and historical (retrospective, problem-chronological, comparative-historical, and descriptive) methods.The scientific novelty of this study is that an attempt was made to expand the pedagogical and educational aspect of the historical work of M. Arkas "History of Ukraine-Rus".The value of using M. Arkas's creative work as a significant factor in educating patriotic feelings, civic position, and humanity is determined.The researchers are convinced that the key to becoming a true patriot is to study the historical heritage of one's proper motherland and local people, which served as the basis for writing the book "History of Ukraine-Rus".M. Arkas brought to the fore the study of the history of the people, which is one of the most valuable spiritual treasures.This is one of the main principles of its educational potential.In addition, there is every reason to consider the book "History of Ukraine-Rus" to be a full-fledged textbook.And it is not just about the popular presentation of material and illustrations; there are such important principles of learning as interest, consistency, and scientific content.
Masking Strategies for Background Bias Removal in Computer Vision Models

Models for fine-grained image classification tasks, where the difference between some classes can be extremely subtle and the number of samples per class tends to be low, are particularly prone to picking up background-related biases and demand robust methods to handle potential examples with out-of-distribution (OOD) backgrounds. To gain deeper insights into this critical problem, our research investigates the impact of background-induced bias on fine-grained image classification, evaluating standard backbone models such as Convolutional Neural Network (CNN) and Vision Transformers (ViT). We explore two masking strategies to mitigate background-induced bias: Early masking, which removes background information at the (input) image level, and late masking, which selectively masks high-level spatial features corresponding to the background. Extensive experiments assess the behavior of CNN and ViT models under different masking strategies, with a focus on their generalization to OOD backgrounds. The obtained findings demonstrate that both proposed strategies enhance OOD performance compared to the baseline models, with early masking consistently exhibiting the best OOD performance. Notably, a ViT variant employing GAP-Pooled Patch token-based classification combined with early masking achieves the highest OOD robustness.Our code and models are available at: GitHub
Analysis of Foundation Application Results Using Airbrush on Combination Facial Skin Types

This research is a descriptive study that aims to find out the results of applying foundation using an airbrush on combination facial skin types. The number of respondents in this study was 20 trained panelists. Data collection techniques used were observation, Focus Group Discussion (FGD), documentation, questionnaires, and interviews. The data analysis technique used is descriptive and Likert scale. The results of this study indicate that with the application of foundation using an airbrush on combination facial skin, the evenness of the foundation is considered to be able to cover pores and acne scars, but the selection of the foundation color shade is not quite right so the skin color looks uneven. The durability of the foundation is considered to be able to last when left for 2 hours. Based on the opinion of the panelists regarding the results of applying airbrush, it is in the very good category at 85%. And in the aspect of resilience assessment, namely 98.75%.
Algorithm for planning shelters in oil and gas energy resource-based cities based on artificial intelligence resilient city model

Cities based on oil and gas energy resources are crucial to energy production and economic development, but they also face various disasters and security risks. To ensure the safety and well-being of urban residents during disaster events, the planning of urban shelters is crucial. In this paper, comprehensively considering multiple factors such as disaster risk, population distribution, and convenient transportation, the artificial bee colony algorithm is used to optimize the site selection and capacity planning of shelters. By comprehensively evaluating the disaster resistance capacity of urban refuges, the response speed of residents and other related indicators, the planning algorithm of refuges is continuously optimized to better meet the needs of oil and gas energy resource-based cities. The results of the study showed that the average overall disaster resilience of AI-based urban shelters reached 0.64. When the distance to the shelter was 4 km, the average response speed of residents reached 10.22 min, and other indicators also improved. The research shows that the oil and gas energy urban refuge planning algorithm based on the artificial intelligence elastic city model provides an innovative approach for urban planners and disaster managers. Further research and practice will help promote the application of this algorithm in real cities, improving the resilience and disaster resistance of cities and the safety and security level of residents.
PB1855: USE OF MACHINE LEARNING MODEL TO IDENTIFY PATIENTS RECEIVING OPTIMAL TREATMENT AND THE ASSOCIATED NON-BIOLOGICAL FACTORS: A POPULATION-BASED STUDY

Topic: 4. Acute myeloid leukemia - Clinical Background: AML patients not admitted to specialised haematology unit (SHU) during their care pathway have less access to curative treatment. Aims: Our aim is to determine whether access to optimal curative treatment is based on the patient’s clinico-biological characteristics or affected by non-biological factors. Methods: Patients were described according to their treatment modalities (curative, non-curative and untreated). In 1000 times bootstrapping process, we trained machine learning (ML) algorithms to learn the clinico-biological characteristics that were used to decide the treatment modalities of AML gold standard patient (treated in academic hospitals by haematologists within 5 days of their diagnosis, n=297). Based on the ML model predictions, we create two groups of patients: optimal treatment patient (predicted as treated and effectively treated) versus non-optimal treatment patients (predicted as treated but effectively untreated). Then after, a multivariate logistic regression model was used to determine the non-biological factors associated with non-optimal treatments. Finally, the mean of the 1000 estimates was used as the Odds Ratio. Results: In our study, 499 patients (n=48.03%), 273 (26.28%) and 261 (25.12%) had respectively received curative, non-intensive and no treatment. The 3 most influential factors in the choice of treatment modalities were age (61.24% influence), secondary aspect of AML/MDS (21.43%) and presence of severe comorbidities (4.95%). In total, n=460 (44.5%) had received optimal treatment and n=101 (9.8%) received non-optimal treatment. Factors associated with non-optimal treatment were: living in Basse-Normandie (0.63 times) and living in a municipality located between 30 and 100km of an academic hospital. Summary/Conclusion: We show that non-biological factors, in particular geographical disparities have an impact on access to optimal curative treatment. This may be related to geographical disparities in the organisation of the AML care pathway or potentially to the situation of a geographical medical desert, which needs to be studied in more detail.Keywords: Artificial intelligence, Population, Acute myeloid leukemia, Epidemiology
A Calibration-Free Hybrid Approach Combining SSVEP and EOG for Continuous Control

While SSVEP-BCI has been widely developed to control external devices, most of them rely on the discrete control strategy. The continuous SSVEP-BCI enables users to continuously deliver commands and receive real-time feedback from the devices, but it suffers from the transition state problem, a period the erroneous recognition, when users shift their gazes between targets. To resolve this issue, we proposed a novel calibration-free Bayesian approach by hybridizing SSVEP and electrooculography (EOG). First, canonical correlation analysis (CCA) was applied to detect the evoked SSVEPs, and saccade during the gaze shift was detected by EOG data using an adaptive threshold method. Then, the new target after the gaze shift was recognized based on a Bayesian optimization approach, which combined the detection of SSVEP and saccade together and calculated the optimized probability distribution of the targets. Eighteen healthy subjects participated in the offline and online experiments. The offline experiments showed that the proposed hybrid BCI had significantly higher overall continuous accuracy and shorter gaze-shifting time compared to FBCCA, CCA, MEC, and PSDA. In online experiments, the proposed hybrid BCI significantly outperformed CCA-based SSVEP-BCI in terms of continuous accuracy (77.61 ± 1.36%vs. 68.86 ± 1.08% and gaze-shifting time (0.93 ± 0.06s vs. 1.94 ± 0.08s). Additionally, participants also perceived a significant improvement over the CCA-based SSVEP-BCI when the newly proposed decoding approach was used. These results validated the efficacy of the proposed hybrid Bayesian approach for the BCI continuous control without any calibration. This study provides an effective framework for combining SSVEP and EOG, and promotes the potential applications of plug-and-play BCIs in continuous control.
Logistic Regression Modeling with A Bayesian Approach to The Risk Factors of Colorectal Cancer Patients

This research aims to create a model, determine the factors that affect the probability of someone suffering from colorectal cancer also design a web application to do prediction using the probability logit function which has been made. There are 4 independent variables and 1 dependent variable which are used in this research. The independent variables used in this research are heredity-pedigree value, age, Adenomatous Polyposis Coli, and MutS Homolog 2. And the dependent variable used in this research is someone’s status of suffering from colorectal cancer symbolized with Y. The stages of designing a web application include making use case diagrams, use case descriptions, activity diagrams, sequence diagrams, class diagrams, entity relationship diagrams, and designing the application interface. The results of this research will be a logit probability model which can be used to calculate the probability value of someone suffering from colorectal cancer. Model constructed using significant 3 independent variables which are heredity-pedigree, age, and Adenomatous Polyposis Coli.
Retraction Notice: Intelligentization of Tourism Japanese Environmental Scenic Spots Based on Intelligent Translation Hardware System

Based on the intelligent translation hardware system, this article discusses the existing situation of the Japanese explanation of the 5A-level scenic spots in Jiangxi Province from the perspective of the Japanese explanation of the scenic spot, combined with the relevant information of the Lushan Scenic Area in Jiangxi Province, and raises and analyzes the problems to achieve the purpose of improving the problem., I hope to provide reference materials for the actual Japanese explanation work of the scenic spot. Establish a variety of translation projects that suit students' academic abilities and interests, and obtain better teaching results. Practice has shown that the practical, collaborative, and interactive features of the translation workshop teaching model have significant meanings for cultivating students' translation ability and comprehensive literacy, and are worthy of being tried and promoted in teaching activities.
Retraction Notice: Numerical Analysis of Synchronous Dislocation Scheduling Algorithm Optimization in Shared Cloud Computing Environment

This paper studies the numerical analysis of the optimization of the synchronization misalignment scheduling algorithm under the shared cloud computing environment. First, the user submitted job is initialized, and then the job is inserted into the execution queue according to the appropriate strategy of the user synchronization misalignment scheduling algorithm. Establish multiple simultaneous multiple vacations Queuing model. The steady-state distribution of the system model is given by the quasi-birth-death process and the matrix geometric solution method, and the expressions of the average response time of the task and the average power of the system are derived. The test results show that in the case of multiple virtual machines, compared with Xen's Credit scheduling algorithm, the synchronization optimization scheduling system CCHybrid improves the performance of parallel loads by 15% to 50%, and the performance impact on non-parallel loads does not exceed 5%.
Retraction Notice: Application of Probability Optimization Model in Advanced Mathematics in Data Mining

With the continuous deepening of education informatization, the advanced mathematics auxiliary teaching system has been gradually put into use, but from the perspective of the existing advanced mathematics auxiliary teaching system functions, the significance and function of the application of over-analysis probability thought in advanced mathematics will be further analyzed. Probabilistic thinking methods to solve problems. Based on this, this article first explains the importance of designing advanced mathematics data mining system based on probability optimization model, and proposes the design process of advanced mathematics auxiliary teaching system based on data mining from different directions.
STUDENTS' DIFFICULTY IN USING PARTS OF SPEECH IN SPOOF TEXT IN STUDENTS OF ENGLISH LANGUAGE EDUCATION PROGRAM

Grammar is a language to talk about language. Just as one cannot explain how a motor engine functions (or is falling to function) without naming words for its parts and their specific actions, so it is impossible to explore the function of words and the part they play in forming meaningful language without a naming procedure. The purpose of this research is to know the students’ difficulties in using Parts of Speech of a text. This research is a qualitative method with a phenomenological approach. This research was conducted on the first semester student namely 95 students in 2 classess at HKBP Nommensen University Pematangsiantar. The instruments used written test. The results showed the highest one is very good category with 68 students with 71,5% and the fair category with 2 students on the 2,1%. It found no Interjection included. The text used the determiner/ article out of the box of Parts of Speech with 13 words. The dominantly applied by the students in finding the difficulties is stated to the Pronoun, Adjective, and Adverb. The last stated the word of article or determiner into the Adjective.
Label-consistent Kernel Transform Learning based Sparse Hashing for cross-modal retrieval

Abstract Currently, there are various widely used methods for hashing-based cross modal retrieval to achieve promising performance in the information retrieval task. However, the challenge of learning discriminative representations against a heterogeneous gap in the case of multi-modal data still needs to be explored. Most existing methods directly project raw heterogeneous data linearly onto a common projection space, leading to high projection error because the semantic similarity of multi-modal data is not captured. We need the efficient feature representation of heterogeneous and multi-modal data to learn high-quality discrete hash codes and improve retrieval performance. This paper proposes a label-consistent kernel transform learning-based sparse hashing (LcKTLSH) for cross modal retrieval. Instead of projecting raw data samples, LcKTLSH learns transform and sparse coefficients suited for projection onto the kernel space, assuming each modality of a single instance has a unique hash code. The learned sparse coefficients are embedded into pre-learned hash codes, and semantic information from the similarity matrix is embedded into pre-learned hash codes to generate final hash codes. Kernel trick accounts for non-linearity in the data, and label-consistency constraint acts as the supervision term. Extensive experimental results on four benchmark datasets significantly advance over previous investigations.
Specxai - Spectral Interpretability of Deep Learning Models

Abstract Deep learning is becoming increasingly adopted in almost all sectors of business due to its ability to transform large quantities of data into high-performing models. The power of deep learning is that it can capture non-trivial and non-linear phenomena from data that even humans can find hard to interpret. Despite this, these models are generally regarded as black boxes, which, despite their performance, could hinder their adoption in some industries. Trusting these models for essential engineering applications such as automotive, energy, healthcare, and finance is difficult without any reasoning. In this context, the field of eXplainable AI attempts to develop techniques that temper the impenetrable nature of the models and promote a level of understanding of their behavior. Here, we present a novel framework, SpecXAI, based on the spectral characterization of the entire network. We show how this framework can be used to not only understand the model but also manipulate it into a more interpretable and linear symbolic representation, which is useful for better understanding the model's behavior.
Making a Grammar Checker with Autocorrect Options Using NLP Tools

Our natural language approach concerns syntactic analysis using a dedicated Javascript library - wink-nlp - and semantic analysis based on Prolog programming language, facilitated by another Javascript library - tau-prolog - that allows defining logical programs, declaring rules and checking for goals inside Javascript language. Firstly, our program splits the original text into sentences, than into tokens and identifies each part of the sentence, dynamically maps entities into Prolog rules, then check the spelling accordingly to the definite clause grammar (DCG) by querying the pre-defined program for initial goals (the sentence itself). Basically, we let the parser infer its own rules from the syntactic point of view, then check the grammar from a semantic perspective against the DCG inside the same workflow or pipeline of steps.The provided article demonstrates the usage of the wink-nlp and tau-prolog packages for natural language processing (NLP) and logical programming, respectively.
Retracted: Design of Service Robot Based on User Emotion Recognition and Environmental Monitoring

[This retracts the article DOI: 10.1155/2022/3517995.].
Retraction Notice: Computer-Aided "Learning Community" Pathophysiology Cloud Laboratory Design and Hardware Implementation

Pathology is highly intuitive and practical. Pathology experiment teaching is an important way to exercise students' practical ability, a breakthrough point to stimulate students' interest in exploration, cultivate students' innovative spirit, and one of the important ways and means to acquire knowledge of pathology. This paper focuses on the design and implementation of the computer-aided "learning community" pathophysiology cloud laboratory. In response to such problems, this paper proposes an integrated framework of the Web-based Online Learning Community Knowledge Innovation Platform (OLCKIS) according to the learning process of the online learning community, analyzes the requirements of the system, designs the architecture of the system, and clarifies the key technologies of the system.
Retraction Notice: Design of Sports Competition Information Issuing System Based on Fuzzy Website Generation System

Aiming at the problems of inconsistency of format, incompatibility of information release software, duplication of development work and other issues in the information release of various competitions, this paper designs and develops a general information release system for sports competitions. Through the fuzzy website generation system, the individual competitions can be completed. Design of information release system. The software design of the system is based on the BS architecture, and a large number of grouping rule tables are built-in for regular games, which can give the best grouping in most cases. The dynamic expression analysis function in the scoring algorithm basically covers all numerical calculation scoring competition systems.
Retracted: An Evaluation Model for the Influence Factors of Interest in Literature Courses Based on Data Analysis and Association Rules in a Small-Sample Environment

[This retracts the article DOI: 10.1155/2022/1900509.].
Retracted: Online Labor Education Optimization Method Based on Computer Intelligent Algorithm

[This retracts the article DOI: 10.1155/2022/8740978.].
Foreword

This collection of reflective essays is a treasure trove of advice, reflection and hard-won experience from experts in the field of open and distance education. Each chapter offers tried-and-tested advice for nascent academic writers, delivered with personal, rich, and wonderful stories of the authors’ careers, their process, their research and their writing, and the struggles and triumphs they have encountered in the course of their careers.