Reviewerly/exHarmony huggingface dataset:
From ICT Framework Compliance to Quantitative Risk Assessment: An Example of Methodology using Risk Scenarios

Cybersecurity is largely based on the use of frameworks (ISO27k, NIST, etc.) which main objective is compliance with the standard. They do not, however, address the quantification of the risk deriving from a threat scenario. This paper proposes a methodology that, having evaluated the overall capability of the controls of an ISO27001 framework, allows to select those that mitigate a threat scenario and evaluate the risk according to a Cybersecurity Risk Quantification model.
Large alphabet inference

Abstract Consider a finite sample from an unknown multinomial distribution. Inferring the underlying multinomial parameters is a basic problem in statistics and related fields. Currently known methods focus on classical regimes where the sample is large, or both the sample and the alphabet are small. In this work we study the complementary large alphabet regime, as we consider the case where the number of samples is comparable with (or even smaller than) the alphabet size. We introduce a novel inference scheme that significantly improves upon currently known methods. Our proposed scheme is robust, easy to apply and provides favourable performance guarantees.
A Comprehensive Framework for Cold Start Problem in Hybrid Recommendation Systems Extending the Efficient Multi-Mode Approach

E-Commerce is a prominent application of information technology in business, offering convenience in transactions. However, as more products and users join the platform, the complexity of E-Commerce systems increases. This necessitates the implementation of a recommendation system to enhance the user experience and address individual preferences. E-Commerce platforms often face the challenge of the cold start problem, where new products are introduced to the platform or when there is limited information available about new users. In such cases, traditional recommendation systems struggle to provide accurate recommendations due to the lack of historical data or user preferences. In this research, a comprehensive framework for an efficient multi-mode Hybrid Recommendation System is proposed, focusing on addressing the cold start problem. The system leverages user behavior tracking, specifically search history and product visit history, to capture user preferences effectively. Through experimental evaluation, the system demonstrates its adaptability to different dataset conditions, achieving a high precision rate of 90%. This research addresses the challenges of data sparsity and the cold start problem in recommendation systems, particularly in the E-Commerce context. The flexibility of the recommendation system ensures optimal recommendations in various scenarios, with potential applications beyond E-Commerce.
WheelUp! Developing an Interactive Electric-power Wheelchair Virtual Training Environment

Standard power wheelchair training is considered inadequate by clinicians and users due to insufficient means to evaluate user performance in varying environments. Virtual environment training offers a promising alternative by providing users with more practical opportunities while reducing the time demands on clinicians. However, there is a need to further explore and optimize game design elements and strategies to ensure engaging and effective training experiences for a diverse range of users. This paper presents our progress of WheelUp!, an open-source electric wheelchair virtual reality (VR) simulator. The simulator offers modularity for clinician-controlled difficulty adjustment, immediate visual and audio feedback, and a visualization tool to aid in clinical assessment and monitoring of progress. This is the first step to building an interactive platform to address the inadequacy of wheelchair training.
Invention of Line-ChatBot: An Innovative Application of ChatGPT API and LINE Bot for Enhanced Student Learning

The intelligent teaching assistant system, Line-ChatBot based on the ChatGPT API and LINE Bot is used to for homework recommendations and learning guidance. The system uses a generative language model as an accessible and cost-effective learning support tool. It has been designed to effectively comprehend and decipher difficulties in doing homework and provides tailored suggestions and guidance. Overall, Line-ChatBot is a tool for aiding students in their learning by offering an efficient, flexible, and natural learning support system. We proposed the application of this system in educational contexts for the innovation and advancement of education.
FORMATION OF COMMUNICATION SKILLS AS A PEDAGOGICAL PROBLEM

The article is one of today's current topics and is aimed at forming the communication skills of elementary school students. The importance of the use of communication in the development of independent and creative thinking of elementary school students, the problems of finding content of other psychological characteristics and volitional qualities, and their effects on the formation of communication are discussed. Also, issues of raising a spiritually mature generation through education based on a person-oriented, competency-based approach were discussed.
THEORETICAL APPROACHES TO THE DEVELOPMENT OF PROFESSIONAL PLANS OF HIGH SCHOOL STUDENTS: THE PSYCHOLOGICAL ASPECT

The article highlights theoretical approaches to understanding the concepts of “professional self-determination” and “professional plan” of the objects of study, explains their interrelation. The essence of professional self-determination of the objects of training as a dynamic process, which consists in the transition from impulsive decision-making on the choice of the future process to real self-determination, which can be realized only with a qualitative analysis of both the practical possibilities of their activities and their own internal resources, is revealed. It is noted that the professional plan specifies the self-determination of the objects of study, gives it an active and purposeful character. It specifies the self-determination of the cadet’s personality, gives it an active and purposeful character, so it can be considered as an incentive for the development of the cadet’s personality as a future officer. The emerging professional plans and longterm goals of the cadet usually do not correlate with the level of mastery of the means necessary to achieve them, which creates the basis for internal contradiction and personal and professional development. Its resolution stimulates the development and correction of new needs and plans. Professional plans of cadets are a means of realizing their professional goals, their specification in chronological and content aspects. They define the course of action necessary for the cadet to realize professional goals as the main guidelines for the future (motives for professional growth and military career, etc.). However, the cadets’ professional plans are not limited to the rational definition of their main tasks and goals, but usually contain a dynamic image of the desired professional future: the cadet models and re-enacts certain situations after completing certain tasks, imagines himself in the future as a military officer of a certain qualification.
Research on Cyber Attack Modeling and Attack Path Discovery

With the rapid development of information technology, the cyber security situation has become increasingly serious. Nowadays, cyber attacks have generally developed into multi-stage and multi-step cyberattack campaigns. As a kind of attack scene representation from the perspective of an attacker, the cyber attack model can comprehensively describe the cyber attack behavior in a complex and changeable environment. And the cyber attack model is one of the commonly used tools for cyber attack analysis and response. For enhancing network security, it is of great significance to establish a cyber attack model that is oriented to cyberattack campaigns and can be used to discover the complete attack paths. Therefore, in this paper, a cyber attack modeling method is proposed. First, we propose a meta-asset model to model asset classes, and propose to model network scenarios by instantiating network assets and combining dependency tree models, network topology and connectivity models. Then, a meta-attack model is proposed to define attack patterns, which uses behavior trees to describe the prerequisites and consequences of the attack. Finally, an ant colony optimization (ACO) algorithm adapted to the models is designed to discover the attack paths. The experimental results show that the proposed model has good descriptive capability and extensibility, and can discover potential attack paths by combining with the ACO algorithm.
Social Media Fake Account Identification Using Machine Learning

The proliferation of social media platforms has led to an increase in the creation of fake accounts. These accounts are used for various malicious activities, such as spreading false information, phishing, and identity theft. As a result, there is a growing need for effective methods to identify and eliminate fake accounts. This paper proposes a machine learning-based approach for social media fake account identification. The proposed method involves pre-processing the data, feature extraction, and training a classifier using various machine learning algorithms. The performance of the proposed method is evaluated using a publicly available dataset and compared with existing methods. The results demonstrate the effectiveness of the proposed approach in identifying fake accounts with high accuracy and low false positive rates.
Needs Analysis and Design of FlipBook-Based E-Module Development with RME Model to Improve Students' Concept Understanding Ability

This research is motivated by the lack of engaging learning resources for students and the absence of instructional materials in the form of E-Modules. The study is an observational research that focuses on the importance of developing teaching materials, particularly the development of Flipbook-Based E-Modules using the Realistic Mathematics Education (RME) instructional model, to enhance students' understanding of Trigonometry concepts. The method used is Research and Development (R&amp;D), employing the 4D development model (Define, Design, Develop, Disseminate). The research utilizes a research instrument in the form of a questionnaire that is given to each 10th-grade high school student, and the questionnaire contains questions related to the needs that will be used as a reference for the development of the E-Module. Based on the questionnaire results, data analysis of the needs is obtained, which serves as a reference for the development of the E-Module. The results are then organized based on the students' needs. This development is conducted as an effort to bring about changes to the E-Module. Therefore, it is expected that this E-Module will attract students' interest in learning and improve their understanding of concepts.
Density Estimation-based Stein Variational Gradient Descent

Abstract Approximating a target distribution is important in many statistical problems, such as Bayesian inference. We introduce a variant of Stein variational gradient descent, called the density estimation-based Stein variational gradient descent (DESVGD). DESVGD utilizes kernel density estimation techniques to replace the empirical measure in SVGD with its continuous counterpart, which allows direct computation of the KL divergence between the current approximation and the target and thereby helps monitoring the numerical convergence of the iterative optimization process. DESVGD also offers derivatives of the KL divergence, which can be used to better design learning rates and thus to achieve faster convergence. By simply replacing the kernel used in SVGD with its weighted average, the implementation of DESVGD reduces to that of SVGD. Our numerical experiments demonstrate that DESVGD well approximates the target distribution and that it outperforms the original SVGD in terms of approximation quality.
Visible-infrared person re-identification via specific and shared representations learning

Abstract The primary goal of visible-infrared person re-identification (VI-ReID) is to match pedestrian photos obtained during the day and night. The majority of existing methods simply generate auxiliary modalities to reduce the modality discrepancy for cross-modality matching. They capture modality-invariant representations but ignore the extraction of modality-specific representations that can aid in distinguishing among various identities of the same modality. To alleviate these issues, this work provides a novel specific and shared representations learning (SSRL) model for VI-ReID to learn modality-specific and modality-shared representations. We design a shared branch in SSRL to bridge the image-level gap and learn modality-shared representations, while a specific branch retains the discriminative information of visible images to learn modality-specific representations. In addition, we propose intra-class aggregation and inter-class separation learning strategies to optimize the distribution of feature embeddings at a fine-grained level. Extensive experimental results on two challenging benchmark datasets, SYSU-MM01 and RegDB, demonstrate the superior performance of SSRL over state-of-the-art methods.
From simulation to reality: development of a small-scale autonomous electric vehicle for educational and research purposes

Robotics is a constantly evolving field, making it possible to perform various tasks that help people every day. Among these tasks, robot safe navigation through different environments is one of the challenges of mobile robotics. This work presents the development of a small autonomous vehicle designed for navigation using predefined coordinates. The chosen navigation strategy uses virtual vector fields that exploit an interaction of virtual attractive and repulsive forces. The proposed strategy has been validated through simulations and in real scenarios. Furthermore, this work presents the development of the physical prototype of the robot, emphasizing the practical and educational aspects of using autonomous robots, providing valuable insights and practical learning experiences in robotics and navigation.
Integrating Adam Optimizer to Enhance Efficiency of Transfer Learning Model for Diagnosing Cancers

Despite huge advancements in medical fields, diseases like cancer continue to plague people since we are still prone to them. The main purpose is to design a critical analysis of cancers, including breast, pancreatic, colon, skin, lung, and other cancers from the year 1990 to 2019, to identify some of the rising factors of both lung and colon cancers that cause unnecessary deaths. Thus, early detection on the other hand greatly improves the chances of survival rates in humans. At that place, Transfer Learning (TL) techniques have made significant improvements these days.&#x0D; Material and Methods: We herein implemented the exploratory data analysis (EDA) technique to analyze 29 types of cancer deaths worldwide since 1990th century. Between them, we have emphasized lung cancer (LC) and colon cancers (CC) for our study and taken 25,000 histopathology image data from the publicly available Kaggle repository. Further, we proposed a novel methodology using the EfficientNetB7 model defining each step to classify five types of lung and colon tissues (two benign and three malignant) from the histopathological images. Although, an algorithm has been designed that clearly describes the workflow of our proposed method. This experiment has been done through input_shape=(X, Y, 3) on the ‘ImageNet’ dataset, Adam optimizer, and accuracy metrics using Python 3.8.8 software on Jupyter 6.4.3 environment.&#x0D; Results: We evaluated the model progress using 50 epochs during the training phase, which resulted in more than 98% accuracy and less than 2% loss in both the training/ validation phases. Besides that, we designed a classification report that describes the performance of our model, where we achieved more than 98.07% accuracy score, 98% precision score, 98% of recall score, and 98% of f1-score. These results signify that implementing an optimization technique on the EfficientNetB7 model improves the overall performance as compared to 93.19% for LC 1 and 93.91% for CC 2 for the RestNet50 model.&#x0D; Conclusion: A novelty of this research specifies the methodology to predict LC and CC by applying the EfficientNetB7 model to the cancer dataset which might be beneficial for both doctors as well as healthcare firms.
UPAYA MENULIS SURAT LAMARAN KERJA BAHASA INDONESIA MENGGUNAKAN MODEL THINK PAIRE SHARE

This research is a descriptive qualitative research. Data collection was done using observation, essay tests, and documentation methods. This study analyzes student errors based on test results on students' ability to write job application letters. Errors in writing job application letters often occur in the writing stages, such as content, word choice, sentences, paragraphs, and spelling on job application letters. Researchers used the think pair share model. This type of learning model is very suitable for students and helps researchers carry out learning activities in writing application letters. The subjects of this study were students of class XII TBSM and XII TKJ, totaling 60 people. In this study, students were given an essay test in writing a job application letter in the form of a description, then analyzed for errors based on the Indonesian correspondence guide with different types of errors for each category of errors. Obtained from the conclusion, the problem that arises from the background of the problem is in what way the efforts of class XII students of SMK PAB 1 Helvetia in writing essential parts using spelling on job application letters. The results of the data analysis show the control class with an average score of 66 and the experimental results with an average score of 81. Students using the think pair share model are more active in learning, asking questions, and answering questions posed by researchers using the think pair share model.
Three-Stream Convolutional Neural Network for Depression Detection With Ocular Imaging

Depression is a prevalent and severe mental disorder that significantly affects both mind and body, leading to persistent feelings of sadness, despair, and impaired functionality. Diagnosis of depression primarily relies on clinical assessment and observation of symptoms. However, due to the lack of objective indicators, the experience and skills of doctor may lead to misdiagnosis. Current researches indicate that eye movement patterns and pupil dilation can serve as potential biomarkers for emotional and cognitive dysregulation in individuals with depression. However, most studies are based on manually extracted eye movement features, overlooking a significant portion of information available in ocular imaging. This paper proposes Three-Stream Convolutional Neural Network (TSCNN) for detecting depression, leveraging both spatio-temporal information of raw ocular imaging and paradigmatic semantic features. We suggest using optical flow with different sampling intervals to capture temporal features. In the third stream, we employ an encoder to learn semantic information from paradigm images and use it as prior knowledge. Finally, we utilize a fully connected network for classification, achieving an accuracy of 79.3% on our self-collected dataset. The proposed method may demonstrate significant clinical utility in the future.
Supporting the Next Generation of Leaders in the Geosciences: Barnard’s Environmental Science Pathways Scholar Program

To address the national need to diversify the geosciences, we created the cohort-based Environmental Science Pathways Scholars Program, or E(SP)2, for undergraduates in the Environmental Science Department at Barnard College who identify as Black, Indigenous, and people of color (BIPOC), first generation, or low income. By fostering a more inclusive community in the particularly formative stages of academic and early professional development, E(SP)2 aims to support and retain a diverse cohort of students who have developed confidence in their scientific skills and identities, and who share a passion for environmental science. E(SP)2 is modeled on the Science Pathways Scholar Program [(SP)2] at Barnard College, an active and successful multidisciplinary opportunity at Barnard College.
REVERSE DOMAIN ADAPTATION FOR INDOOR CAMERA POSE REGRESSION

Abstract. Synthetic images have been used to mitigate the scarcity of annotated data for training deep learning approaches, followed by domain adaptation that reduces the gap between synthetic and real images. One such approach is using Generative Adversarial Networks (GANs) such as CycleGAN to bridge the domain gap where the synthetic images are translated into real-looking synthetic images that are used to train the deep learning models. In this article, we explore the less intuitive alternate strategy for domain adaption in the reverse direction; i.e., real-to-synthetic adaptation. We train the deep learning models with synthetic data directly, and then during inference we apply domain adaptation to convert the real images to synthetic-looking real images using CycleGAN. This strategy reduces the amount of data conversion required during the training, can potentially generate artefact-free images compared to the harder synthetic-to-real case, and can improve the performance of deep learning models. We demonstrate the success of this strategy in indoor localisation by experimenting with camera pose regression. The experimental results indicate an improvement in localisation accuracy is observed with the proposed domain adaptation as compared to the synthetic-to-real adaptation.