Reviewerly/exHarmony huggingface dataset:
Controlling impression: Making ruGPT3 generate sentiment-driven movie reviews

In this research paper, I investigate the controlled text generation capabilities of ruGPT3Large through fine-tuning, specifically focusing on generating movie reviews based on a designated sentiment attribute. Controlled text generation is an active area of inquiry within the domain of Natural Language Processing, particularly for the Russian language. This study exemplifies a simple approach to controllable text generation by training ruGPT3 on a textual dataset containing sentiment-marked prompts, enabling the model to recognize patterns and generate analogous texts. The research provides a comprehensive analysis of the limitations, shortcomings and merits of fine-tuning a large language model using prompts embedded in a dataset. The generated texts exhibit coherence, logical structure, abundant coreferential links, and narratives and vocabularies characteristic of film reviews. Nevertheless, ruGPT3-generated reviews exhibit certain linguistic errors. I classify the most prevalent errors, such as named entity confusion, grammatical gender inconsistencies and sentiment fluctuations. Given that the primary objective is to evaluate the efficacy of basic fine-tuning with respect to the specified attribute, both automatic sentiment analysis and human evaluation are employed for output assessment. In comparing the outputs of the fine-tuned model and the baseline ruGPT3Large, I observe that positive sentiment generation is the most successful, while neutral and negative sentiments are produced by the models less accurately.
A Fuzzy-based Simple Smart Decision Model for Assessing Information Security Risk in Public Sector Organization

The measurement of information security risk in a public sector organization is of utmost importance. This measurement serves the purpose of taking appropriate actions in response to potential risks or damaging incidents. The objective of this study is to develop a straightforward yet smart decision model capable of evaluating risks, particularly within the public sector organization. The decision support modelling (DSM) concept is employed as a framework to construct a computer model that supports decision-making in a specific case. The study comprises five stages, which are integral parts of the DSM process. These stages include analyzing the case, examining relevant documents, designing the model, constructing the model, and evaluating the finalized model. The object-oriented method serves as a fundamental approach to model design. Additionally, the fuzzy logic method, an intelligent computational technique, plays a central role in the development of this decision model. The proposed model demonstrates an average error value of 0.05 when compared to the actual risk measurement conducted. Furthermore, it reveals average risk values of 0.59 and 0.45 for the pre- and post-remediation scenarios, respectively.
A Study on The Vocabulary Knowledge of the Second Grade Students of Senior High School

This research used descriptive-quantitative design which aims to find out the vocabulary knowledge level of the second year students of SMAN 1 Kuantan Mudik. The sample of this research is class XI MIPA 3 of the second year students of SMAN 1 Kuantan Mudik which were chosen by using cluster random sampling technique. The number of the students is 32 students. The students were given The Updated Vocabulary Test (UVLT) that is adopted by Webb, et.al (2017). The result shows that the level of vocabulary knowledge of the second-grade students of SMAN 1 Kuantan Mudik is still below the threshold needed for reading or daily conversation. There were only 2 students who reached the mastery score at the 1000 level, and none of them at the 2000-5000 level. High frequency vocabulary will have a greater influence on students, according to Webb (2017). Teachers are recommended to increase students’ vocabulary knowledge through reading activities and additional vocabulary tasks.
Scene-Generalizable Interactive Segmentation of Radiance Fields

Existing methods for interactive segmentation in radiance fields entail scene-specific optimization and thus cannot generalize across different scenes, which greatly limits their applicability. In this work we make the first attempt at Scene-Generalizable Interactive Segmentation in Radiance Fields (SGISRF) and propose a novel SGISRF method, which can perform 3D object segmentation for novel (unseen) scenes represented by radiance fields, guided by only a few interactive user clicks in a given set of multi-view 2D images. In particular, the proposed SGISRF focuses on addressing three crucial challenges with three specially designed techniques. First, we devise the Cross-Dimension Guidance Propagation to encode the scarce 2D user clicks into informative 3D guidance representations. Second, the Uncertainty-Eliminated 3D Segmentation module is designed to achieve efficient yet effective 3D segmentation. Third, Concealment-Revealed Supervised Learning scheme is proposed to reveal and correct the concealed 3D segmentation errors resulted from the supervision in 2D space with only 2D mask annotations. Extensive experiments on two real-world challenging benchmarks covering diverse scenes demonstrate 1) effectiveness and scene-generalizability of the proposed method, 2) favorable performance compared to classical method requiring scene-specific optimization.
Discovering and evaluating organizational knowledge from textual data: Application to crisis management

Crisis management effectiveness relies mainly on the quality of the distributed human organization deployed for saving lives, limiting damage and reducing risks. Organizations set up in this context are not always predefined and static; they could evolve and new forms could emerge since actors, such as volunteers or NGO, could join dynamically to collaborate. To improve crisis resolution effectiveness, it is first important to understand, analyze and evaluate such dynamic organizations in order to adjust crisis management plans and ease coordination among actors. Giving a textual experience feedback from past crisis, the objective of this paper is to discover the organizational structure deployed in the considered crisis and then evaluate it according to a set of criteria. For that purpose, we combine in a coherent framework text and association rule mining for pattern discovery and annotation, and multi-agent system models and techniques for formally building and evaluating organizational structures. We present the OSminer algorithm that discovers association rules based on relevant textual patterns and then builds an organizational structure including three main relations between actors: power, control and coordination. A real-life case study, a flood crisis hitting the south west of France, serves as a basis for testing/experimenting our solution. The organizational structure, discovered in this case study, has 24 actors. Its evaluation indicates its efficiency, but shows that it is neither robust nor flexible. Our findings highlight the potential of our approach to discover and evaluate organizational structures from a text recording interactions between stakeholders in a crisis context.
Reservoir computing allows recovering hidden network dynamics

In this study, we examined reservoir computing (RC)as a tool for predicting the macroscopic dynamics of a subset of oscillators in a network based on the macroscopic dynamics of other parts of it. As a model network, we utilized a network of 300 Kuramoto oscillators with adaptation. Our results demonstrate that reservoir computing effectively addresses this task. Additionally, a similar reservoir computing model was applied to experimental neurovisualization data and exhibited high accuracy in reconstructing damaged EEG channels compared to classical methods like spatial interpolation.
Text Independent Speaker Recognition Using Fuzzy Based Neural Network

In the present scenario, the field of biometrics has arisen as an applied science with the goal of automatically measuring individuals' unique distinguishing traits and exploiting the data for forensic, security, and surveillance purposes. In text-independent speaker recognition, the speaker is recognized only on the basis of their voice signals, without reference to the content of the speech. Our solution to this is a fuzzy-based neural network [1], [15] architecture that learns feature vectors from training samples that contain MFCC. Three layers make up our suggested architecture: (i) the input layer, which accepts feature vectors as input; (ii) the hidden layer, which is in charge of employing k-means clustering to cluster the feature vectors; fuzzification; and computing effective information. and (iii) the output layer where the weighted information is converted into an objective function which is helpful for the unsupervised learning of the proposed network using the particle swarm optimization (PSO) [12]. The unknown parameters in the objective function to be optimized by PSO include the fuzzification parameters and the weights on the information computed at the hidden layer. In our work, we used a database with 100 speakers and 10 samples per speaker, and we created a novel architecture. 8 of these samples were used for testing, and the remaining 2 were used for training. After that, the trained network's performance was assessed using the test samples. In noise-free voice samples, our proposed network performed admirably with 87% accuracy, outperforming the Gaussian Mixture Model (GMM) at 86%.
The Implementation of 15-Minute Extensive Reading Approach As School Literacy Movement Activity: Students' Perceptions

The low rate of Indonesian students’ literacy skills need to be deliberated more. Responding to this issue, the government created the school literacy movement program to improve Indonesian students’ literacy. The aim of this study is to investigate the implementation as well as the students' perceptions towards 15-minute extensive reading as school literacy movement (GLS) activity in the classroom. This study employed a qualitative method which applied descriptive case study design. The research was conducted at SMP Brawijaya Smart School Malang. The instruments were observation notes and interview guidelines. The interview involved two students who have been undergoing extensive reading activity at school. The collected data were analysed by using interactive analysis model which was carried out in four stages, namely collecting data, data reduction, data display, and conclusion drawing. The findings showed that the implementation of 15-minute extensive reading approach as school literacy movement in SMP Brawijaya Smart School Malang was held quite well by involving and reflecting 9 of the 10 principle of extensive reading as well as two stages of the school literacy program (GLS) namely habituation and development. The findings also revealed that the implementation of 15-minute extensive reading approach as school literacy movement activity is categorically helpful for SMP Brawijaya Smart School Malang students’ literacy and second language acquisition.
Autonomous Driving Research Trend Analysis Using Topic Modeling

With the recent development of the fourth industry, there is a lot of interest in research related to artificial intelligence, the Internet of Things, and big data, and various convergence studies are being conducted on one technology. Among them, autonomous driving research is increasing exponentially, and countries around the world are investing not only in research support but also in autonomous driving by changing legal regulations. This study examines annual research topic trends in LDA and TF-IDF from papers related to autonomous driving and vehicles in the Scopus journal and extracted promising topics in 2023 using Week Signal process. In LDA, it is not limited to 'vehicle' in autonomous vehicle analysis, but has been studied in various ways such as aviation and underwater, and various research algorithms have been extracted without focusing on 'vehicle' in autonomous driving analysis. In TF-IDF, we can find out the new topics that appeared in the latest year, through the Week Signal. It is possible to guess that research such as artificial intelligence research and environmental factors would be promising, as well as research on stability and accuracy regarding safety.
Multiple spheres detection problem—Center based clustering approach

In this paper, we propose an adaptation of the well-known k-means algorithm for solving the multiple spheres detection problem when data points are homogeneously scattered around several spheres. We call this adaptation the k-closest spheres algorithm. In order to choose good initial spheres, we use a few iterations of the global optimizing algorithm DIRECT , resulting in the high efficiency of the proposed k-closest spheres algorithm. We present illustrative examples for the case of non-intersecting and for the case of intersecting spheres. We also show a real-world application in analyzing earthquake depths.
2OC: A General Automated Orientation and Orthorectification Method for Corona KH-4B Panoramic Imagery

Due to a lack of geographical reference information, complex panoramic camera models, and intricate distortions, including radiation, geometric, and land cover changes, it can be challenging to effectively apply the large number (800,000+) of high-resolution Corona KH-4B panoramic images from the 1960s and 1970s for surveying-related tasks. This limitation hampers their significant potential in the remote sensing of the environment, urban planning, and other applications. This study proposes a method called 2OC for the automatic and accurate orientation and orthorectification of Corona KH-4B images, which is based on generalized control information from reference images such as Google Earth orthophoto. (1) For the Corona KH-4B panoramic camera, we propose an adaptive focal length variation model that ensures accuracy and consistency. (2) We introduce a robust multi-source remote sensing image matching algorithm, which includes an accurate primary orientation estimation method, a multi-threshold matching enhancement strategy based on scale, orientation, and texture (MTE), and a model-guided matching strategy. These techniques are employed to extract high-accuracy generalized control information for Corona images with significant geometric distortions and numerous weak texture areas. (3) A time-iterative Corona panoramic digital differential correction method is proposed. The orientation and orthorectification results of KH-4B images from multiple regions, including the United States, Russia, Austria, Burkina Faso, Beijing, Chongqing, Gansu, and the Qinghai–Tibet Plateau in China, demonstrate that 2OC not only achieves automation but also attains a state-of-the-art level of generality and accuracy. Specifically, the standard deviation of the orientation is less than 2 pixels, the mosaic error of orthorectified images is approximately 1 pixel, and the standard deviation of ground checkpoints is better than 4 m. In addition, 2OC can provide a longer time series analysis of data from 1962 to 1972, benefiting various fields such as environmental remote sensing and archaeology.
Collecting Meta-Data from the OpenML Public Repository

In Machine Learning (ML), selecting the most suitable algorithm for a problem is a challenge. Meta-Learning (MtL) offers an alternative approach by exploring the relationships between dataset characteristics and ML algorithmic performance. To conduct a MtL study, it is necessary to create a metadataset comprising datasets of varying characteristics and defying the ML algorithms at different levels. This study analyzes the information available in the OpenML public repository for building such meta-datasets, which provides a Python API for easy data importation. Assessing the content currently available in the platform, there is still no extensive meta-feature characterization for all datasets, limiting their complete characterization.
Real-Time 3D Object Detection on Crowded Pedestrians

In the field of autonomous driving, object detection under point clouds is indispensable for environmental perception. In order to achieve the goal of reducing blind spots in perception, many autonomous driving schemes have added low-cost blind-filling LiDAR on the side of the vehicle. Unlike point cloud target detection based on high-performance LiDAR, the blind-filling LiDARs have low vertical angular resolution and are mounted on the side of the vehicle, resulting in easily mixed point clouds of pedestrian targets in close proximity to each other. These characteristics are harmful for target detection. Currently, many research works focus on target detection under high-density LiDAR. These methods cannot effectively deal with the high sparsity of the point clouds, and the recall and detection accuracy of crowded pedestrian targets tend to be low. To overcome these problems, we propose a real-time detection model for crowded pedestrian targets, namely RTCP. To improve computational efficiency, we utilize an attention-based point sampling method to reduce the redundancy of the point clouds, then we obtain new feature tensors by the quantization of the point cloud space and neighborhood fusion in polar coordinates. In order to make it easier for the model to focus on the center position of the target, we propose an object alignment attention module (OAA) for position alignment, and we utilize an additional branch of the targets’ location occupied heatmap to guide the training of the OAA module. These methods improve the model’s robustness against the occlusion of crowded pedestrian targets. Finally, we evaluate the detector on KITTI, JRDB, and our own blind-filling LiDAR dataset, and our algorithm achieved the best trade-off of detection accuracy against runtime efficiency.
Improving Sickle Cell Disease Classification: A Fusion of Conventional Classifiers, Segmented Images, and Convolutional Neural Networks

Sickle cell anemia, which is characterized by abnormal erythrocyte morphology, can be detected using microscopic images. Computational techniques in medicine enhance the diagnosis and treatment efficiency. However, many computational techniques, particularly those based on Convolutional Neural Networks (CNNs), require high resources and time for training, highlighting the research opportunities in methods with low computational overhead. In this paper, we propose a novel approach combining conventional classifiers, segmented images, and CNNs for the automated classification of sickle cell disease. We evaluated the impact of segmented images on classification, providing insight into deep learning integration. Our results demonstrate that using segmented images and CNN features with an SVM achieves an accuracy of 96.80%. This finding is relevant for computationally efficient scenarios, paving the way for future research and advancements in medical-image analysis.
K-Hyperparameter Tuning in High-Dimensional Space Clustering: Solving Smooth Elbow Challenges Using an Ensemble Based Technique of a Self-Adapting Autoencoder and Internal Validation Indexes

<i>k</i>-means is a popular clustering algorithm because of its simplicity and scalability to handle large datasets. However, one of its setbacks is the challenge of identifying the correct <i>k</i>-hyperparameter value. Tuning this value correctly is critical for building effective <i>k</i>-means models. The use of the traditional elbow method to help identify this value has a long-standing literature. However, when using this method with certain datasets, smooth curves may appear, making it challenging to identify the <i>k</i>-value due to its unclear nature. On the other hand, various internal validation indexes, which are proposed as a solution to this issue, may be inconsistent. Although various techniques for solving smooth elbow challenges exist, <i>k</i>-hyperparameter tuning in high-dimensional spaces still remains intractable and an open research issue. In this paper, we have first reviewed the existing techniques for solving smooth elbow challenges. The identified research gaps are then utilized in the development of the new technique. The new technique, referred to as the ensemble-based technique of a self-adapting autoencoder and internal validation indexes, is then validated in high-dimensional space clustering. The optimal <i>k</i>-value, tuned by this technique using a voting scheme, is a trade-off between the number of clusters visualized in the autoencoder’s latent space, <i>k</i>-value from the ensemble internal validation index score and one that generates a value of 0 or close to 0 on the derivative , at the elbow. Experimental results based on the Cochran’s Q test, ANOVA, and McNemar’s score indicate a relatively good performance of the newly developed technique in <i>k</i>-hyperparameter tuning.
Coordination within Conversational Agents with Multiple Sources

Conversational agents can now operate with language models, rules, ontologies and varied other sources to provide smooth dialogue. However, the coordination of multiple sources in conversational agents is a challenge. We present a mechanism to effectively orchestrate multiple sources in a conversational agent, by relying on a client-server approach with an associated prompt generation module that deals with heterogeneous domain-oriented modules. As a detailed use case, we describe the architecture of a chatbot specialised in topics related to the Brazilian coast, and we study the benefits of our approach.
Teachers' Professional Socialization in the System of Higher Education

The development of the education system is closely connected with scientific progress, the country's economic growth, social evolution, and individuals' intellectual capital. Teachers play the most critical role in the modernization of educational processes in Russia, the authors argue. The professional socialization of future teachers contributes to their successful inclusion in labor markets, strengthens hidden resources, forms an original style of thinking, and helps to assimilate ethical standards. The essence of the concept of “professional socialization of the future teacher” is discussed in the chapter. Furthermore, the authors focus on critical aspects within the process of professional socialization; and the pedagogical conditions for the success of its implementation are analyzed and discussed. The research demonstrates that the modernization of Russian society has significantly changed the nature of the socio-cultural influence on the personality of students. The inclusion of students in various types of interactive dialogue interactions creates real prerequisites for developing personal and professional qualities, raising professional maturity of an individual, and high contribution to the assimilation of social values. The authors conclude that the professional socialization of students mastering the profession of a teacher goes through two main stages in their development, namely adaptation and professional identification.
Effects of mobile learning games on students’ self-regulated learning

This paper investigates the impact of using a mobile learning game on students’ self-regulated learning (SRL). This research study uses a research and development approach by adapting the 4D model (defined, designed, developed, and disseminated). Data analysis was carried out using descriptive statistics. This study explored 120 students from 2 schools in Surakarta, Indonesia. Data collection was carried out using questionnaires and interviews to see the effectiveness of the mobile learning game on SRL. This research shows that the application of the mobile learning game is quite effective as a learning medium and supports distance learning. The results indicate that the mobile learning game positively promotes SRL and has a significant increase in each indicator of SRL. The game provides a different atmosphere in learning so that students are enthusiastic when participating in learning with games. This study contributes to the concept of self-regulated studying with games. Keywords: Games; game-based learning; mobile learning game; self-regulated learning.