Reviewerly/exHarmony huggingface dataset:
Document Annotation Tool for News Content Analysis

Every day, the average Internet user perceives an abundance of content that is unintentionally consumed every day. We frequently hear the seemingly obvious remark that the modern world is full of data. We are bombarded with numerous links to amusing content circulated by our friends, various news and content providers, and social media. Unfortunately, an increasing amount of this information is only loosely related to the truth. Some of the low-quality content news could be automatically detected by modern large language models (LLM). Unfortunately, we need a large number of annotated articles to train such models. In this paper, we described our tool for news content annotation. In particular, we explain our research methodology, the tool architecture, and the analysis of the quality of the annotations. In our experiments, we engaged more than 100 volunteers, who annotated almost 4000 articles.
VIRTUALITY AS A NEW PHILOSOPHICAL CATEGORY

The article describes the features of the use of virtuality ideas in social sciences. Virtuality and its possibilities in the study of existence are also considered in detail. It has been studied that a more effective approach to the study of virtuality in modern science can be implemented on the basis of a synthesis of elements of all existing methodological approaches, in particular: dialectical, informational, systemic, synergetic, etc.
Evaluating Neglected Tropical Diseases (NTDs) with ICT

The contemporary importance of Information and Communication Technologies (ICT) globally has made it to become an indispensable tool in healthcare industry. The neglected tropical diseases (NTDs) are infectious diseases that are found mainly among the world’s poorest people. They are found in several countries in Africa, Asia, and Latin America. NTDs are especially common in tropical areas where people do not have access to clean water or safe ways to dispose of human waste. This research works utilized ICT for the evaluation of the state of NTDs in Taraba state, Nigeria using SPSS version 27. The primary data were collected from Specialist Hospital, Jalingo and Primary healthcare centres across local governments in Taraba State Nigeria. The proffered recommendations will however assist in eradicating or reducing the disease in Taraba state
Hubungan Lingkar Kepala Dengan Prestasi Belajar Pada Anak SD N 1 Tambongwetan Kalikotes

Brain growth is one of the things that changes, if nutritional intake is reduced, some parts of the brain development will be disrupted. The result of malnutrition will result in obstruction of the body and brain development, where head circumference is used as a parameter to determine it. In the growth parameters, there are several body parts that are measured including body weight, height, head circumference and chest circumference, if there is one that is less than normal it can affect learning achievement. The purpose of this study was to find out "the relationship between head circumference and learning achievement in SD N 1 Tambongwetan children".&#x0D; This study used an analytic survey method with a cross sectional design. The research population comprised all grade 1 and 2 students at SD N 1 Tambongwetan as many as 46 students. Sampling using total sampling. Data analysis using Kendall Tau. The results of data analysis showed ???? = 0.337 with a P value = 0.016; P value &lt;0.05.&#x0D; The conclusion of the results of this study is that there is a relationship between head circumference and learning achievement in SD N 1 Tambongwetan children. Suggestion to parents should always pay attention to their children's nutritional intake because adequate nutrition can affect the growth of body size including head circumference.
Code Revert Prediction with Graph Neural Networks: A Case Study at J.P. Morgan Chase

Code revert prediction, a specialized form of software defect detection, aims to forecast or predict the likelihood of code changes being reverted or rolled back in software development. This task is very important in practice because by identifying code changes that are more prone to being reverted, developers and project managers can proactively take measures to prevent issues, improve code quality, and optimize development processes. However, compared to code defect detection, code revert prediction has been rarely studied in previous research. Additionally, many previous methods for code defect detection relied on independent features but ignored relationships between code scripts. Moreover, new challenges are introduced due to constraints in an industry setting such as company regulation, limited features and large-scale codebase. To overcome these limitations, this paper presents a systematic empirical study for code revert prediction that integrates the code import graph with code features. Different strategies to address anomalies and data imbalance have been implemented including graph neural networks with imbalance classification and anomaly detection. We conduct the experiments on real-world code commit data within J.P. Morgan Chase which is extremely imbalanced in order to make a comprehensive comparison of these different approaches for the code revert prediction problem.
On Effectiveness of Further Pre-training on BERT Models for Story Point Estimation

CONTEXT: Recent studies on story point estimation used deep learning-based language models. These language models were pre-trained on general corpora. However, using language models further pre-trained with specific corpora might be effective. OBJECTIVE: To examine the effectiveness of further pre-trained language models for the predictive performance of story point estimation. METHOD: Two types of further pre-trained language models, namely, domain-specific and repository-specific models, were compared with off-the-shelf models and Deep-SE. The estimation performance was evaluated with 16 project data. RESULTS: The effectiveness of domain-specific and repository-specific models were limited though they were better than the base model they further pre-trained. CONCLUSION: The effect of further pre-training was small. Large off-the-shelf models might be better to be chosen.
Mineralogical and Geochemical Characteristics of Clay Deposits, Bussaya Area, Southern Iraq

Bussaya clay deposits are located in the southern part of Iraq in Al-Muthana Governorate within the Nfayil beds (Middle Miocene), these deposits composed of palygorskite, chlorite and illite representing the clay minerals indentified using X-Ray diffraction technique data on four samples, while the non-clay minerals are quarz, dolomite and halite, twenty three samples of clay deposits were analyzed by using X-Ray Fluorescence technique for the major oxides; which demonstrates that the average values are SiO2: 39.17%, Al2O3: 9.95%, Fe2O3: 6.21%, MgO: 5.9%, CaO: 11.3%, TiO2: 0.9%, L.O.I.: 14.94%, and relativley high values of alkalines, while fifteen samples tested mechanically using Hydrometer method in which the results show a composition of clay, sandy clay, mud, and sandy mud, the chemical data showed that semi-arid to arid paleoclimate conditions predominated in the depositional environment as represented by the relationship between SiO2with Al2O3, Na2O, and K2O, While the relationship between Al2O3, CaO, Na2O, and K2O shows a low to relatively moderate chemical weathering, and due to the relationship between SiO2and (K2O / Na2O), the examined clays tectonically reflect an island arc, the intermediate igneous rocks are shown to be the source of the examined clays according to the Al2O3/TiO2 ratio.
Multithreaded parallelism for heterogeneous clusters of QPUs

In this work, we present MILQ, a quantum unrelated parallel machines scheduler and cutter. The setting of unrelated parallel machines considers independent hardware backends, each distinguished by differing setup and processing times. MILQ optimizes the total execution time of a batch of circuits scheduled on multiple quantum devices. It leverages state-of-the-art circuit-cutting techniques to fit circuits onto the devices and schedules them based on a mixed-integer linear program. Our results show a total improvement of up to 26 % compared to a baseline approach.
The Research on Aspect-Level Sentiment Analysis for Online Reviews Based on BERT-BiLSTM Model

With the development of professional online review systems, current sentiment analysis research is more focused on classifying the sentiment polarity of different aspects of the same subject, which also helps consumers to make decisions. To address the problems of multiple meanings of words, inadequate contextual semantic understanding and incomplete feature extraction in online reviews, this paper proposes an aspect-level sentiment analysis model based on BERT-BiSTM (Bidirectional Encoder Representation from Transformers and Bi-directional Long Short-Term Memory). The BERT word vectorisation can make use of the information in both directions in the text and can better learn the semantic information of the text. The results of the BERT model are fed into the BiLSTM model to fully perform feature mining for aspect extraction and sentiment polarity classification. The experimental results show that the model in this paper works well on the tasks of aspect extraction and sentiment polarity classification on the four datasets Semeval2014_restaurant (Res14), Semeval2014_laptop (Lap14), Semeval2015_restaurant (Res15) and Semeval2016_restaurant (Res16).
Data Augmentation for Genetic Programming-Driven Late Merging of HOG and Uniform LBP Features for Texture Classification

The categorization of texture images requires the identification and extraction of meaningful keypoints, a crucial step in ensuring the precise representation of textured images. The literature has introduced numerous descriptors in order to detect and capture both local and global texture characteristics. These descriptors vary in their effectiveness depending on the specific application. However, it is generally accepted that they complement each other by compensating for their strengths and weaknesses. Therefore, many approaches focused on combining different types of image descriptors generating robust features that are invariant to image transformations. These solutions mostly focused on one way to deal with information and faced more problems as they need human intervention and a large set of data. The acknowledged benefits of combining multiple types of descriptors are accompanied by challenges specially. These challenges arise due to differences in properties, such as locality and sparsity, as well as the heterogeneity exhibited by generated features. To address this issue comprehensively, we present an approach that benefits from genetic programming techniques to generate and combine two distinct texture classifiers. It incorporates histograms of oriented gradients and local binary patterns descriptors, which capture different textures. To effectively fuse the results of both classifiers, the proposed approach employs a late fusion process along with data augmentation methods while using a limited amount of training data. To evaluate the performance of the proposed approach in texture image classification tasks, we have conducted extensive experiments on six challenging datasets encompassing various variations. We have also investigated its performance in a cross dataset problem where the model has been trained on instances of a dataset before being tested on samples of another dataset. The obtained results clearly demonstrate that the proposed approach surpasses other relevant low-level approaches, as well as existing GP-based and CNN methods specifically designed for describing and classifying textures. Thanks to its ability to simultaneously leverage multiple descriptors, the suggested solution shows a high potential for real-world applications, particularly in handling various image changes with robustness.
Prelims

Citation (2023), "Prelims", Iwu, C.G. and Shambare, R. (Ed.) Delivering Entrepreneurship Education in Africa, Emerald Publishing Limited, Leeds, pp. i-xv. https://doi.org/10.1108/978-1-83753-326-820231012
The Effect of Kahoot toward Students’ Reading Comprehension at the Tenth Grade Students of SMAN 8 Muaro jambi

This study aimed to find out whether or not there was any significant effect of kahoot toward students’ reading comprehension at tenth grade students of SMAN 8 Muaro Jambi. This research used quantitative research method with quasi-experimental research design which implicated experimental class and control class. The total population in this research was tenth grade students of SMAN 8 Muaro Jambi which consisted of 253 students. The samples of this research were chosen by using cluster sampling. The samples were divided into two classes, experimental class which consisted of 35 students and control class which consisted of 36 students. The technique of data collection in this research was pre-test, treatment, and post-test. The data were analyzed by using t-test. The researcher found that the mean score of pre-test at experimental class was 61,85 and the post-test was 81,71. Meanwhile the mean score of pre-test at experimental class was 60,00 and the post-test was 70,13. The result of t-test analysis presented that observation was higher than t-table (7,75 &gt; 1,994). Therefore, H1 was accepted and H0 was rejected. As the result, there was any significant effect of kahoot toward students’ reading comprehension at tenth grade students of SMAN 8 Muaro Jambi
Data Augmentation: Recoloring of the Flame Image Based on GAN

The flame’s color and texture will change in different environments, even if they are the same flame. To simulate this change and augment the data, we design a flame coloring framework, which adds different colors and textures to flame graphics. Based on GAN, a one-to-many mapping from the graphics domain to the flame image domain is established by mapping noise to the texture domain, and taking the color flame graphics without texture as the control condition. That means that not only the color and texture strength of the flame generated by our model could be controlled according to the light intensity, but also the flame’s texture is variable under the same light intensity. The variability of flame texture is directly related to input mode of the control condition. And our model is the only one which is capable of generating flames with variable textures.
Thomas Bewick — Engraver, Publisher, Innovator

The article is devoted to the problem of revising assessment of the work of the outstanding British engraver and publisher Thomas Bewick. Traditionally, it is considered in the context of the general history of engraving development, mostly not connected with either animalistics or scientific editions. In this regard, T. Bewick’s works of artistic significance belong to natural-scientific illustrations and are used in popular science editions. The erroneous assessment of his works is associated with a widespread stereotype in which scientific illustration is considered as something of low artistic value. The purpose of this research is to study the genesis of T. Bewick’s creativity by analyzing his main works. Of particular interest is the research of the artistic features of woodcut technique, which, thanks to T. Bewick, has found a new life. It is traced the evolution of the engraver’s work from his early period to mature one. The complexity of Bewick’s talent is noted not only as an engraver of book illustrations, but also as an engraver of bookplates. The characteristic of the technique of cutting a design into hardwood cut across is given, the innovative methods thanks to which Bewick has brought this type of an engraving to a new level of book illustration are revealed. Based on the memoirs of Bewick, the author traces the history of the creation of his main books — “A General History of the Quadrupeds” and “A History of British Birds”. It is noted that illustrations by G. Buffon and H. White served as additional sources for “A History of British Birds” despite the use of his own sketches. The author analyses the illustrations and engravings from these books, which are kept in the collections of the Research and Scientific Department of Rare Books (Museum of Book) of the Russian State Library. The structural features of the brief descriptions of “A General History of the Quadrupeds” are considered. Particular attention is paid to the style of the vignettes, which present an integral part of Bewick’s creative credo. Such features of Bewick’s illustrations as realism, as well as the depiction of humorous genre scenes and jokes are revealed. The author notes that at a late stage of Bewick’s art work he rarely uses black cross-hatching, using instead combinations of the wide fields and carefully engraved white spots. The most essential features of Bewick’s engraving are revealed: the principle of “white line”, hatching with crossed and parallel lines, creation of halftones with very thin strokes and tiny dots. It is emphasized that although Bewick’s illustrations have been published in popular scientific publications, they have influenced the development of scientific illustration through their highly artistic execution. It is concluded that woodcut technique allowed to simulate the volume of an image by means of a white stroke.
Solving Partially Observable 3D-Visual Tasks with Visual Radial Basis Function Network and Proximal Policy Optimization

Visual Reinforcement Learning (RL) has been largely investigated in recent decades. Existing approaches are often composed of multiple networks requiring massive computational power to solve partially observable tasks from high-dimensional data such as images. Using State Representation Learning (SRL) has been shown to improve the performance of visual RL by reducing the high-dimensional data into compact representation, but still often relies on deep networks and on the environment. In contrast, we propose a lighter, more generic method to extract sparse and localized features from raw images without training. We achieve this using a Visual Radial Basis Function Network (VRBFN), which offers significant practical advantages, including efficient and accurate training with minimal complexity due to its two linear layers. For real-world applications, its scalability and resilience to noise are essential, as real sensors are subject to change and noise. Unlike CNNs, which may require extensive retraining, this network might only need minor fine-tuning. We test the efficiency of the VRBFN representation to solve different RL tasks using Proximal Policy Optimization (PPO). We present a large study and comparison of our extraction methods with five classical visual RL and SRL approaches on five different first-person partially observable scenarios. We show that this approach presents appealing features such as sparsity and robustness to noise and that the obtained results when training RL agents are better than other tested methods on four of the five proposed scenarios.
The Future of Education

This research aims to design an interactive e-book containing Kastem and its development guide. Kastem acronyms for character, science, technology, engineering, and mathematics. The research design used is ADDIE at the analysis and design stages. The population consisted of all elementary school students in Bengkulu City in grades 1, 2, and 3. The research sample was conducted by purposive random sampling of as many as 820 students. Data collection using questionnaires, interviews, and tests. Data analysis was done descriptively. The results of the design of the interactive booklet containing Kastem and its guide have the characteristics of an interesting, familiar, concrete, specific theme. They are equipped with guiding questions or trigger questions at the beginning of the sub-chapter, as well as combining elements of student character and six media types.
Improving Students’ Vocabulary Mastery through Fun Thinkers Media

This research examines the vocabulary knowledge abilities of autistic students in English subjects at SMP Negeri 3 Hutabayuraja. According to Zuchdi (1995: 3-7) vocabulary mastery is a person's ability to recognize, understand and use words well and correctly by listening, speaking, reading and writing.The research's formulation of the issue is "Does vocabulary knowledge skills be improved through Fun Thinkers media for the first year Autistic students of SMP Negeri 3 Hutabayuraja". The purpose of the current research is to ascertain whether linguistic abilities among autistic pupils have increased as a result of exposure to Playful Philosophers materials. The method of gathering information is being tested. Five autistic children in class VII at SMP Negeri 3 Hutabayuraja served as the study's participants. This research uses an experimental method, namely using Single Subject Research (SSR) with an AB design. The usage of Fun Thinkers media can enhance vocabulary knowledge abilities for the first-year Autistic students at SMP Negeri 3 Hutabayuraja, according to the findings of the data analysis and.discussion above. It.can be seen that.the.provision of interventions in.the form.of Vocabulary Learning through fun thinkers’ media shows an increase in scores for RH students and the improvement obtained is quite good.
Lingvistiskais eksperiments: marķētāju vienprātība latviešu valodas WordNet semantisko saišu veidošanā

This article describes the experiment for determining inter-annotator agreement in the semantic annotation of the Latvian WordNet, which was carried out within the project „Latvian Wordnet and Word Sense Disambiguation”. The semantic relations used in the Latvian WordNet are synonymy, near-synonymy, hyponymy, antonymy, meronymy, gradation sets and similarity (defined as „see also”). These semantic relations are determined for four main parts of speech: nouns, verbs, adjectives, and adverbs. During the experiment, 20 words were selected from the online dictionary Tēzaurs.lv, and the team of linguists individually formed semantic links between the senses of these words and other word senses in the dictionary. The obtained data was analysed using both statistical and qualitative analysis methods to determine the level of inter-annotator agreement and potential causes of differing word sense interpretation. The results of the experiment have led to the conclusion that individual annotation yields a medium level of inter-annotator agreement, whereas collective discussions improve this level significantly, even though certain cases remained where the annotators could not reach a consensus, illustrating the subjective nature of the semantic analysis. The inter-annotator agreement levels for adjectives and adverbs are lower, possibly due to the lack of more precise processing guidelines for these parts of speech. Overall, collective discussions during annotation appear to improve the methodology of semantic annotation significantly.
Improved generalization of cyclist detection on security cameras with the OpenImages Cyclists dataset

Most large public datasets containing cyclists for training detectors based on Deep Learning have annotations for bicycles and people, but not for cyclists. Even when it is not the case, the quality and quantity of the images are limited. To overcome these limitations, we propose the new OpenImages Cyclists dataset, built through the pre-selection of images from the OpenImages set and a new algorithm for semiautomatic generation of cyclist annotation aided by people and bicycle detectors. A cyclist detector trained with this dataset achieved identification rates up to 78% and 89% in two different sets of images obtained from security cameras at USP, Campus São Paulo - Capital.