Reviewerly/exHarmony huggingface dataset:
Organizational Debt in Large-Scale Hybrid Agile Software Development: A Case Study on Coordination Mechanisms

Abstract Software development is a complex human-centered activity, increasingly complicated by agile organizations scaling and adopting hybrid work. While technical debt has been extensively studied, other forms of debt-organizational, process, cultural, and social-have received less attention. We conducted a case study using ten semi-structured interviews, observations, and document analysis to identify coordination mechanisms used in large-scale hybrid agile. We identified organizational debt challenges such as a lack of shared mental models, team coordination, team cohesion, and team learning. Also, the hybrid working arrangement was found to create tension between increased individual autonomy and team objectives, as well as between team autonomy and inter-team coordination. We found 23 coordination mechanisms that the teams used to address challenges in their organization. We propose that implementing many of these mechanisms may help manage organizational debt.
Towards implementation of warrant-based content self-moderation

Abstract This paper recaps the issue of online mis- and disinformation as a societal problem, reviews why government roles in defining mis- and disinformation are constitutionally suspect under First Amendment jurisprudence, and builds on a content-neutral theory of disincentivizing the production of mis- and disinformation first proposed by Marshall Van Alstyne. The purpose of this paper is to develop some key implementation details that would be necessary to resolve if the theory is to be adopted operationally.
Assignment of Unexpected Tasks in Embedded System Design Process Using Genetic Programming

Embedded systems need to execute some tasks. The designer needed to predict every behaviour of the system. However the problem appears when system meets unexpected situation. In some cases the system architecture cannot be modified or such operation is too expensive. In this paper we present a novel developmental genetic programming based method for assignment of unexpected tasks in embedded system design process. Our approach does not modify the system architecture. The proposed method evolves decision trees. The new individuals are obtained during evolution process after using genetic operators: mutation, crossover, cloning and selection. After genotype to phenotype mapping the ready system is obtained.
Offline Mongolian Handwriting Identification Based on Convolutional Neural Network

Handwriting is a biometric behavioral characteristic with evident individual distinctiveness. With the rise of the deep learning trend and demands for forensic identification, handwriting identification has become one of the focal points of research in the field of pattern recognition. Research in handwriting identification for major global languages has matured. However, in China, there is limited attention in the field of writer identification for minority languages such as Mongolian, making it challenging to resolve criminal cases involving handwriting issues. This paper initiates an initial exploration of Mongolian handwriting identification by constructing a structurally simple convolutional neural network. This convolutional neural network, consisting of 12 convolution operations and designed for Mongolian handwriting identification, is referred to as MWInet-12. In this paper, the model evaluation experiments were conducted using a dataset comprising 156,372 samples contributed by 125 writers from the MOLHW dataset. The dataset was divided into training, validation, and test sets in an 8:1:1 ratio. The final results of the experiments reveal impressive accuracy on the test set, achieving a top-1 accuracy of 89.60% and a top-5 accuracy of 97.53%. Furthermore, through comparative experiments involving Resnet50, Fragnet, GRRNN, VGG16, and VGG19 models, this paper establishes that the proposed model yields the most favorable results for Mongolian handwriting identification. The exploratory research on Mongolian handwriting identification in this paper contributes to increasing awareness of information processing for minority languages. It aids in advancing research on classifying writers of Mongolian historical texts and provides technical support for judicial authentication involving handwriting issues.
A Novel Scale for Evaluating Digital Readiness toward Earthquakes: A Comprehensive Validity and Reliability Analysis

New digital technology trends have the potential to mitigate the effects of earthquakes and improve response and recovery efforts such as earthquake prediction, emergency call-out, and earthquake location notification. Earthquake readiness makes it necessary to have a digitally prepared society. However, no scale has been found in the literature that can measure the digital attitudes and skills of individuals regarding earthquakes. For this reason, to fill this gap in the literature, this descriptive research study aimed to develop an original, valid, and reliable scale to determine the digital readiness of individuals toward earthquakes. Data were collected from 621 adult individuals with different socioeconomic characteristics using the convenience sampling method. An item pool was created through a comprehensive literature review, followed by a series of analyses encompassing content validity, construct validity, convergent validity, discriminant validity, criterion-related validity, reliability analysis, and item analysis in the course of the scale’s development process. For the digital readiness toward earthquakes, the Draft scale, with 21 items remaining, a KMO value (0.894), and a Bartlett sphericity test result of χ2 (91) = 2407.76 (p &lt; 0.001) showed the suitability of the data for exploratory factor analysis (EFA). As a result of the EFA, 14 items were categorized into two dimensions based on whether their eigenvalues exceeded 1. The explained variance was 46.823% (eigenvalue = 6.555) in the first factor, while it was 12.832% (eigenvalue = 1.796) in the second factor, and the total variance was 59.655%. After analyzing the scale’s items within these dimensions, the first dimension was named “Technological Skills”, comprising eight items, and the second dimensions was named “Digital Attitudes”, consisting of six items. The factor loadings for these items ranged from 0.562 to 0.900. Confirmatory factor analysis (CFA) affirmed the factorial structure identified by the EFA. For the five-point Likert-type Digital Readiness toward Earthquakes scale, named the DRE scale, with 14 items after validity analyses, Cronbach’s alpha value was obtained as 0.910, demonstrating high internal consistency. Cronbach’s alpha values of the two factors were 0.910 for the “Technological Skills” dimension and 0.837 for the “Digital Attitudes” dimension. It was concluded that the developed scale is a valid and reliable measurement in evaluating the digital readiness of individuals toward earthquakes.
Geochemical features of host rocks of the Porokhovskoe tungsten deposit (South Urals) and their role in concentration of ore matter

The article contains the results of statistical processing of geochemical data for rocks of the Porokhovskoe tungsten deposit (South Urals) obtained during systematic geological exploration. Statistical samplings were formed for various types of host rocks: andesites and their tuffs, shales, metasomatites (including sericite-quartz, epidote and chlorite-bearing) and skarns. It is established that all rocks of the Porokhovskoe deposit are characterized by increased contents of W and Mo and, to a lesser extent, Be, Cu, Li, and Zn and by stable groups of correlated elements, both associated with greisenization process and those inherited from the&#x0D; protolith. No zoning is revealed in the distribution of elements in relation to the Yugo-Konevsky leucogranitic pluton, which is considered the ore-generating for the deposit. No correlation between W and Mo was identifed in all types of host rocks. A number of statistical methods is proposed for the identifcation of promising areas with unclear W-Mo mineralization in rocks.
SOCIALIZATION OF FIRE PREVENTION AND CONTROL IN SRIGONCO VILLAGE, MALANG

The causes of fires are not only natural factors but also human factors, especially negligence and unpreparedness in protecting against fire. This community service is carried out because fires often occur in residential areas. Socialization on fire prevention and control measures in Srigonco Village, Malang is a form of education and practice which aims to provide an understanding of the factors that cause fires, how to prevent and control them so that it is hoped that participants can prevent fires from occurring and extinguish fires using modern or traditional fire extinguishers. The activity was held on August 24th, 2023 at the Srigonco Malang Village Hall. The targets are health cadres, UKK cadres and rescue teams. Before carrying out the activity, 2 month preparation process was carried out, starting from selecting materials to submitting permits to the relevant parties. The results of the evaluation of activities attended by 24 participants showed that participants participated in the activities enthusiastically and conducively, and the activities were carried out smoothly and on time. There was an increase in participants' knowledge from an average score of 60 to 95. Most participants can also use traditional (APT) and modern fire extinguishers (APAR).
Geographic Knowledge Base Question Answering over OpenStreetMap

In recent years, question answering on knowledge bases (KBQA) has emerged as a promising approach for providing unified, user-friendly access to knowledge bases. Nevertheless, existing KBQA systems struggle to answer spatial-related questions, prompting the introduction of geographic knowledge ba se question answering (GeoKBQA) to address such challenges. Current GeoKBQA systems face three primary issues: (1) the limited scale of questions, restricting the effective application of neural networks; (2) reliance on rule-based approaches dependent on predefined templates, resulting in coverage and scalability challenges; and (3) the assumption of the availability of a golden entity, limiting the practicality of GeoKBQA systems. In this work, we aim to address these three critical issues to develop a practical GeoKBQA system. We construct a large-scale, high-quality GeoKBQA dataset and link mentions in the questions to entities in OpenStreetMap using an end-to-end entity-linking method. Additionally, we develop a query generator that translates natural language questions, along with the entities predicted by entity linking into corresponding GeoSPARQL queries. To the best of our knowledge, this work presents the first purely neural-based GeoKBQA system with potential for real-world application.
FinKENet: A Novel Financial Knowledge Enhanced Network for Financial Question Matching

Question matching is the fundamental task in retrieval-based dialogue systems which assesses the similarity between Query and Question. Unfortunately, existing methods focus on improving the accuracy of text similarity in the general domain, without adaptation to the financial domain. Financial question matching has two critical issues: (1) How to accurately model the contextual representation of a financial sentence? (2) How to accurately represent financial key phrases in an utterance? To address these issues, this paper proposes a novel Financial Knowledge Enhanced Network (FinKENet) that significantly injects financial knowledge into contextual text. Specifically, we propose a multi-level encoder to extract both sentence-level features and financial phrase-level features, which can more accurately represent sentences and financial phrases. Furthermore, we propose a financial co-attention adapter to combine sentence features and financial keyword features. Finally, we design a multi-level similarity decoder to calculate the similarity between queries and questions. In addition, a cross-entropy-based loss function is presented for model optimization. Experimental results demonstrate the effectiveness of the proposed method on the Ant Financial question matching dataset. In particular, the Recall score improves from 73.21% to 74.90% (1.69% absolute).
Types of domain and <scp>task‐solving</scp> information in media scholars' data interaction

Abstract The purpose of this study is to examine what types of domain and task‐solving information media scholars need while interacting with research data to create new knowledge. The study is situated within information interaction research in information science. The approach is user‐oriented and qualitative. The research data consist of interviews of 25 media scholars about their interactions with research data. In the analysis, deductive and inductive approaches were combined to identify domain and task‐solving information types from the interview data. The results show that media scholars needed two domain information types and three task‐solving information types while interacting with research data. The domain information types were (1) earlier research information and (2) experience‐based domain information. The task‐solving information types were (1) information about methods and tools, (2) information about rules and norms, and (3) self‐created task‐supporting information. Of these, experience‐based domain information and self‐created task‐supporting information have been less considered in prior research on domain and task‐solving information. The findings of this study are useful for providing support for data interaction. Furthermore, the study sheds light on the concepts of domain information and task‐solving information in the context of interacting with research data.
Analisis Kesalahan dalam Penggunaan Huruf Kapital dan Tanda Baca pada Teks Karangan Sederhana Siswa Kelas III SDN Banjarsari

This research is motivated by the errors seen when students write capital letters and punctuation marks in a sentence that is made. This research was conducted using a method that is descriptive qualitative, where this method involves direct research, in observing an object under study, which aims to examine errors when using capital letters and punctuation marks in an essay that has been made by students and female students. The data collection from the research results is explained together with the location of the errors and explained in words. In this study, the subjects studied were third grade students of SDN Banjarsari 113 with a total of 32 students and third grade teachers of SDN Banjarsari 113. The research employs documentation, interviews, and observation as methods for collecting data. Data review was carried out by collecting data, data selection process, data presentation, and conclusion drawing. The results of the research conducted found several errors in the application of capital letters and punctuation marks. These errors include the writing and placement of capital letters in a sentence, mistakenly placing punctuation marks in a sentence made, and errors in the application of capital letters usually occur in the initial word which is the first sentence, in new sentences, in sentence substitutions, place names and names of people. The cause of these errors is because students still write casually, do not pay attention to the proper application of capital letters and punctuation marks, do not understand the use of capital letters and punctuation marks themselves, and are unfocused or do not pay attention when the teacher explains, then forget the proper writing of capital letters and punctuation marks, so it is wrong when applying them.
Experimental Analysis of Feature-Based Image Registration Methods in Combination with Different Outlier Rejection Algorithms for Histopathological Images

Registration involves aligning two or more images by transforming one image into the coordinate system of another. Registration of histopathological slide images is a critical step in many image analysis applications including disease detection, classification, and prognosis. It is very useful in Computer-Aided Diagnosis (CAD) and allows automatic analysis of tissue images, enabling more accurate detection and prognosis than manual analysis. Due to the complexity and heterogeneity of histopathological images, registration is challenging and requires the careful consideration of various factors, such as tissue deformation, staining variation, and image noise. There are different types of registration and this work focuses on feature-based image registration specifically. A qualitative analysis of different feature detection and description methods combined with different outlier rejection methods is conducted. The four feature detection and description methods experimentally analyzed are Oriented FAST and rotated BRIEF (ORB), Binary Robust Invariant Scalable Key points (BRISK), KAZE, and Accelerated KAZE, and the three outlier rejection methods examined are Random Sample Consensus (RANSAC), Graph cut RANSAC (GC-RANSAC), and Marginalizing Sample Consensus (MAGSAC++). The results are visually and quantitively analyzed to select the method that gives the most accurate and robust registration of the histopathological dataset at hand. Several evaluation metrics, the number of key points detected, and a number of inliers are used as parameters for evaluating the performance of different feature detection–description methods and outlier rejection algorithm pairs. Among all the combinations of methods analyzed, BRISK paired with MAGSAC++ generates the most optimal registration results.
Role of Blockchain Technology in Industry 4.0

Challenges in the industrial world are evolving rapidly. The game-changing technologies for overcoming these challenges on the path to Industry 4.0 are digitization and automation. We can control and improve every area of supply chain and manufacturing processes with the aid of Industry 4.0 technologies. It offers access to the real-time data and insights we need to run a company more profitably and efficiently. As a result, one can make better, quicker business choices. Supply chains and manufacturing processes may be built with the use of Blockchain technology. In this chapter, the connection between Blockchain and Industry 4.0 is made clear. The nine pillars of Industry 4.0 are defined as the core value drivers of the manufacturing process. The next part provides a quick overview of the characteristics of Blockchain technology. The final section investigates the function of Blockchain in Industry 4.0 through the use of different applications.
AESTHETIC VS ARTISTIC CONSCIOUSNESS: TYPOLOGY OF AESTHETICS AND LITERATURE INTERACTION

The objective of the work is to study the specifics of aesthetic and artistic consciousness in their interaction in terms of literary comprehension of philosophical concepts; to highlight the levels of interaction between aesthetics and literature as a factor in the formation of aesthetic dominants of the literary trend and artistic method. The focus of the work determines the use of cultural-historical, historicalliterary, philosophical and aesthetic research methods. Aesthetic consciousness is understood as a mode of intellectual-sensory perception (and assessment) of empirical reality, which determines the type of artistic consciousness and is reflected in a work of fiction. Three levels of correlation between aesthetic consciousness and reality are distinguished: the syncretic, at which the aesthetic consciousness merges with the empirical, dissolving into reality, and the ideal of beauty corresponds to scientific ideas about the surrounding world and man; the discrete level emphasises a certain autonomy of aesthetic consciousness from empirical one, its orientation towards the expression of some ideological or ideal essence; the synthetic, representing dialectical interaction and mutual influence, giving rise to harmony and aesthetic value of the unity of content and form. The specificity of the relationship between aesthetic and artistic consciousness is determined. The process of interaction between aesthetic and artistic consciousness as the interaction between aesthetics and literature takes place at two levels. The first level represents the transformation of the system of aesthetic-philosophical theories and characterises the state of aesthetic consciousness as a set of aesthetic views of the epoch. The second level is characterised by the process of “the transition of the artistic to the general cultural”, involving a change in worldview stereotypes when literature, producing its own spiritual values and anti-values, turns out to be able to reorient public consciousness (in some instances, even scientific) and public psychology, to shape the mindset, tastes, and specifics of everyday culture. The findings note that the convergence of aesthetic consciousness and literature, along with the actualisation of the principle of possibilities that began in the Romantic era, largely predetermined the development of aesthetic consciousness at the turn of the 19th and 20th centuries, when their interaction became so intertwined that it formed a unity. The distinction between aesthetics and literature practically disappeared, ushering in a new stage of cultural reorientation for aesthetics and art.
An Evolutionary Approach to Automated Class-Specific Data Augmentation for Image Classification

Convolutional neural networks (CNNs) can achieve remarkable performance in many computer vision tasks (e.g. classification, detection and segmentation of images). However, the lack of labelled data can significantly hinder their generalization capabilities and limit the scope of their applications. Synthetic data augmentation (DA) is commonly used to address this issue, but uniformly applying global transformations can result in suboptimal performance when certain changes are more relevant to specific classes. The success of DA can be improved by adopting class-specific data transformations. However, this leads to an exponential increase in the number of combinations of image transformations. Finding an optimal combination is challenging due to a large number of possible transformations (e.g. some augmentation libraries offering up to sixty default transformations) and the training times of CNNs required to evaluate each combination. Here, we present an evolutionary approach using a genetic algorithm (GA) to search for an optimal combination of class-specific transformations subject to a feasible time constraint. Our study demonstrates a GA finding augmentation strategies that are significantly superior to those chosen randomly. We discuss and highlight the benefits of using class-specific data augmentation, how our evolutionary approach can automate the search for optimal DA strategies, and how it can be improved.