Reviewerly/exHarmony huggingface dataset:
Social and Professional Adaptation of Students in the Educational Environment of the University in the Last Third of the 20th Century: Experience of Organization and Interpretation

The relevance of the problems of the presented research is connected with the understanding and evaluation of some forms of organization of social and professional adaptation of first-year students and their integration into the educational environment of the university, established and implemented in the practice of domestic universities in the last third of the 20th century, their influence on personal, social and professional development of future teachers. The purpose of the article is to reflect on the experience of adaptation and integration of first-year students into the educational environment of the university, which has developed in the activities of historical and pedagogical faculties that operated in the USSR from 1966 to 1996. The reflection of such experience is focused on the search for productive ideas that allow students to adapt to the norms and values of the educational environment in the modern practice of professional psychological and pedagogical education, form sustainable motives for self-development, self-education, and intensively prepare for professional activities in the Russian education system. The leading methodological ideas in the analysis of the experience of adaptation and integration of students into the educational environment of the university are the principles of objectivity, consistency, historicism, reliance on the results of practical activities, on expert judgments and assessments of direct participants and organizers of this work in the last third of the 20th century. For these purposes, the method of monographic description of the practical experience of the activities of the installation camp of the historical and pedagogical faculty of the Kursk State Pedagogical Institute, conversations with teachers and students, participant observation, study of scientific texts, press materials and Internet resources was used. Research results. The study made it possible to reveal the place and role of installation camps in ensuring the adaptation and integration of first-year students into the educational environment of the university, to substantiate the main functions of the camp in the general logic of the personal, social and professional development of future teachers, to characterize some effective types and forms of work, their practical effectiveness. In particular, the essence and content of the adaptive, integration, value-orientation functions of the camp of first-year students are revealed; presents the types and forms of work on the socio-moral, personal and professional development of first-year students; the role of senior students in organizing the life of future teachers in the educational environment of the university is characterized. Conclusion. The materials of the article can be used by heads of humanitarian faculties, teachers of psychological and pedagogical disciplines, specialists in the field of history of higher professional pedagogical education.
MMED: GEOGRAPHICAL KNOWLEDGE ENHANCEMENT FOR GRADE 7 STUDENTS AT REINA MERCEDES VOCATIONAL SCHOOL

This action research investigates the effectiveness of the MMED (Map Me Educational Booklet) in enhancing the geographical knowledge of Grade 7 students at Reina Mercedes Vocational and Industrial School. Grounded in the interdisciplinary nature of social science, particularly the symbiotic relationship between geography and history, the study addresses challenges in Araling Panlipunan, revealing disinterest due to a lack of relevance and interactive learning experiences. Inspired by research on teaching aids and supplementary materials, the researcher developed the "Map Me Educational Booklet" as an evidence-based intervention. Employing a Quasi-Experimental design, the study randomly assigned students to control and experimental groups. Results indicate a substantial improvement in both groups, with the experimental group displaying a more significant enhancement. Statistical analyses support the effectiveness of the booklet, emphasizing its positive impact on student performance. The study contributes to existing literature by aligning with real-world studies and providing a practical, evidence-based platform. Utilizing mean scores, variance analysis, t-tests, and Cohen's D, the research establishes the significance of the intervention. The findings support the booklet's role in active learning and highlight its potential for educational practices, emphasizing the need for innovative tools to address challenges in geography education. The study recommends the implementation of the "Map Me Educational Booklet" as a valuable supplement to enhance geographical knowledge, fostering implications for teaching and learning modalities.
Data Complexity: A New Perspective for Analyzing the Difficulty of Defect Prediction Tasks

Defect prediction is crucial for software quality assurance and has been extensively researched over recent decades. However, prior studies rarely focus on data complexity in defect prediction tasks, and even less on understanding the difficulties of these tasks from the perspective of data complexity. In this paper, we conduct an empirical study to estimate the hardness of over 33,000 instances, employing a set of measures to characterize the inherent difficulty of instances and the characteristics of defect datasets. Our findings indicate that: (1) instance hardness in both classes displays a right-skewed distribution, with the defective class exhibiting a more scattered distribution; (2) class overlap is the primary factor influencing instance hardness and can be characterized through feature, structural, and instance-level overlap; (3) no universal preprocessing technique is applicable to all datasets, and it may not consistently reduce data complexity, fortunately, dataset complexity measures can help identify suitable techniques for specific datasets; (4) integrating data complexity information into the learning process can enhance an algorithm’s learning capacity. In summary, this empirical study highlights the crucial role of data complexity in defect prediction tasks, and provides a novel perspective for advancing research in defect prediction techniques.
Quantifying trade-offs in satellite hardware configurations using a super-resolution framework with realistic image degradation

When designing and operating Earth observation satellites, trade-offs must often be made between different hardware components, operational considerations, etc… For example, considerations include how much of the mass budget should be spent on imaging lenses?, or how long should image exposure times be?. Resulting limitations in image resolution and quality can be partially compensated for using super-resolution (SR) techniques. However, deep SR networks recently applied to satellite imagery are often trained and tested on data that lacks the typical image degrading noise present in real satellite images. In this work, we combine a method for generating realistically degraded satellite images with a deep SR network, in the context of different satellite hardware configurations and geographical types. We use this framework to assess deep SR performance given realistic remote sensing payloads across different terrain types, by evaluating payload- and terrain-dependent SR performance in reconstructing realistically degraded images. The framework allows us to model the effect of alternative satellite hardware configurations on resulting SR image quality, providing insight into optimal satellite operations and payload design in the context of SR-based image quality enhancements.
Scene Graph Lossless Compression with Adaptive Prediction for Objects and Relations

The scene graph is a novel data structure describing objects and their pairwise relationship within image scenes. As the size of scene graphs in vision and multimedia applications increases, the need for lossless storage and transmission of such data becomes more critical. However, the compression of scene graphs is less studied because of the complicated data structures involved and complex distributions. Existing solutions usually involve general-purpose compressors or graph structure compression methods, which are weak at reducing the redundancy in scene graph data. This paper introduces a novel lossless compression framework with adaptive predictors for the joint compression of objects and relations in scene graph data. The proposed framework comprises a unified prior extractor and specialized element predictors to adapt to different data elements. Furthermore, to exploit the context information within and between graph elements, Graph Context Convolution is proposed to support different graph context modeling schemes for different graph elements. Finally, an overarching framework incorporates the learned distribution model to predict numerical data under complicated conditional constraints. Experiments conducted on labeled or generated scene graphs demonstrate the effectiveness of the proposed framework for scene graph lossless compression.
A unified approach to goodness-of-fit testing for spherical and hyperspherical data

Abstract We propose a general and relatively simple method to construct goodness-of-fit tests on the sphere and the hypersphere. The method is based on the characterization of probability distributions via their characteristic function, and it leads to test criteria that are convenient regarding applications and consistent against arbitrary deviations from the model under test. We emphasize goodness-of-fit tests for spherical distributions due to their importance in applications and the relative scarcity of available methods.
Beyond the ‘critical incident’: COVID-19, data journalism and the slow road to editorial automation in Australian newsrooms

This article draws on a qualitative interview-based study and the framework of the ‘critical incident’ to explore whether, how and for whom the first year of the COVID-19 pandemic saw an increased uptake of data-driven automation in Australian newsrooms and with what implications for the field. Our findings show that, while news workers combined and adapted existing technologies to meet increased demands for rolling, data-driven coverage of the pandemic, structural and institutional factors prevented the uptake and embedding of forms of data journalism and editorial automation that may have assisted with providing more timely and effective public health information. The findings highlight the importance of COVID-19 as both an acute event and an ongoing situation that has revealed and prompted reflection on the practical and political challenges of data flows between government agencies and news organisations.
Pengenalan Fitur Free Pickup Service Pada Aplikasi “PosAja” Sebagai Upaya Mempermudah Masyarakat Dalam Mengirim Paket

Seeing the competition in the business market world is getting tighter, PT. Pos Indonesia (Persero) which provides goods or mail delivery services if it does not innovate it will be less competitive, currently PT. Pos Indonesia (Persero) is ranked number 3 nationally in the national ranking of services. Free pickup servive service or free package pick-up is an effort made by PT. Pos Indonesia (Persero) so that customers do not go to other delivery services. The PosAja Indonesia application has great potential with various services provided, but the concern of the Indonesian people for the PosAja application is still lacking because of a number of problems, including PosAja is still fairly new, so the author is very interested in introducing the application as a form of community service with the title "The introduction of the free pickup service feature in the PosAja application as an effort to make it easier for people to send packages". Application introduction activities are carried out through Instagram social media by distributing posters addressed to all Indonesian people. The community service period starts on December 14 – 18, 202. For technical implementation, it starts from planning, action, and evaluation. The results of the introduction of the PosAja application through Instagram received a positive response from the public, and simply added information to the community. With the introduction of the PosAja application, it is expected that users of this application will increase. The introduction of the free pickup service feature of the PosAja application is expected that the public can know the existence of the Pos Aja application so as to increase the adoption of the PosAja application.
Latitude-Adaptive Integer Bit Allocation for Quantization of Omnidirectional Images

Omnidirectional images have gained significant popularity and drawn great attention nowadays, which poses challenges to omnidirectional image processing in solving the bottleneck of storage and transmission. Projecting onto a two-dimensional image plane is generally used to compress an omnidirectional image. However, the most commonly used projection format, the equirectangular projection (ERP), results in a significant amount of redundant samples in the polar areas, thus incurring extra bitrate and geometric distortion. We derive the optimal latitude-adaptive bit allocation for each image tile. Subsequently, we propose a greedy algorithm for non-negative integer bit allocation (NNIBA) for non-uniform quantization under an omnidirectional image quality metric WMSE. In our experiment, we design quantization tables based on JPEG and compare our approach with other sampling-related methods. Our method achieves an average bit saving of 7.9% compared with JPEG while outperforming other sampling-related methods. Besides, we compare our non-uniform quantization approach with two proposed bit allocation methods, achieving an average improvement of 0.35 dB and 2.66 dB under WS-PSNR, respectively. The visual quality assessment also confirms the superiority of our method.
Prosody in linguistic journals: a bibliometric analysis

Abstract The present study provides a systematic review of prosody research in linguistic journals through a bibliometric analysis. Using the bibliographic data from 2001 to 2021 in key linguistic journals that publish prosody-related research, this study adopted co-citation analysis and keyword analysis to investigate the state of the intellectual structure and the emerging trends of research on prosody in linguistics over the past 21 years. Additionally, this study identified the highly cited authors, articles and journals in the field of prosody. The results offer a better understanding of how research in this area has evolved and where the boundaries of prosody research might be pushed in the future.
A Study on Developmental Strategies for Improving the Quality of Secondary Special Education for Students with Disabilities

In the era of the fourth industrial revolution, addressing the multifaceted learning needs of students with special needs has become increasingly pivotal. This study aims to explore and enhance the quality of secondary special education, adapting to new educational methodologies in tandem with technological advancements while fostering the development of teachers and educational stakeholders. To identify the key areas necessitating development in secondary special education, interviews were conducted with special education teachers. These interviews informed the creation of a questionnaire, which was subsequently disseminated through a Google survey to special education teachers in various special schools and classes nationwide. The survey responses were analyzed using descriptive statistics to gain comprehensive insights. This study's findings integrate the perspectives from the interviews and survey data, leading to the formulation of strategies for the advancement of secondary special education. The analysis categorized the developmental needs into four primary domains: teacher development, curriculum enhancement, school infrastructure, and external factors related to the school environment. The paper concludes with a discussion that synthesizes these findings, offering a pathway for future improvements in the quality of secondary special education, and highlighting the importance of adapting to the changing educational landscape in the era of technological progress.
Teaching Process for Modules in the Information Technology Major for Higher Students According to the Competency Approach

The first part of the article studies the concept of “competency” and approaches to competency, setting goals and output standards of the teaching process. The main part of the article focuses on analyzing the teaching process according to the competency approach, introducing the program content of the Information Technology knowledge block. The content of this program includes basic information technology, programming, operating systems, networks, databases, software, information security, artificial intelligence, web and mobile development, and management. projects, and cloud computing. The teaching process according to the competency approach is described in detail, starting from determining learning goals and output standards, followed by learning and practice. Using AI Chatbot and Website is recommended as an effective means of learning. Special emphasis is placed on student support through AI Chatbot, helping students to be autonomous and confident in the learning process. Diverse exercises and practices are provided to develop students’ abilities. Next is the testing and assessment step, emphasizing the importance of multi-dimensional assessment to ensure objectivity and fairness to test students’ knowledge and abilities. The last part of the article is to perfect the teaching process based on the competency approach in the Information Technology industry, with benefits such as the competency to personalize learning and develop learning competency for students.
RoboBoat: A robotic boat for 3D mapping of partially flooded underground sites

Abstract This paper presents the development and field testing of RoboBoat, an unmanned surface vehicle designed to acquire three‐dimensional (3D) point cloud representations of narrow flooded galleries in caves, mines, and other similar environments where conventional surveying and mapping procedures cannot be applied. We developed techniques tailored to these scenarios, including a contour‐following reactive navigation method and an iterative closest point‐based simultaneous localization and mapping method. Custom metrics have been defined to iteratively validate and improve the 3D model of the passageways. The successful field tests in natural caves and mining galleries demonstrate the accuracy of the 3D representation, making it suitable for digital preservation of large‐volume cultural heritage sites.
Personalizing Government Services through Artificial Intelligence: Opportunities and Challenges

Using artificial intelligence (AI) to customize government services brings advantages and challenges. On the one side, artificial intelligence (AI) can assist government organizations in better comprehending the needs and preferences of citizens, improving service delivery and raising citizen happiness. On the other hand, there are concerns around privacy, security, and ethical considerations related to the use of AI in government services. This article reviews the existing literature on the use of AI in personalizing government services, identifies key opportunities and challenges, and presents case studies of successful AI implementations in government services. The article concludes with recommendations for future research and practice in the area of AI and government services.
Text‐based sentiment analysis in finance: Synthesising the existing literature and exploring future directions

Summary Advances in Deep Learning have drastically improved the abilities of Natural Language Processing (NLP) research, creating new state‐of‐the‐art benchmarks. Two research streams at the forefront of NLP analysis are transformer architecture and multimodal analysis. This paper critically evaluates the extant literature applying sentiment analysis techniques to the financial domain. We classify the financial sentiment analysis literature according to the most used techniques in the area, with a focus on methods used to detect sentiment within corporate earnings conference calls, because of their dual modality (text‐audio) nature. We find that the financial literature follows a similar path to NLP sentiment literature, in that more advanced techniques to define sentiment are being used as the field progresses. However, techniques used to determine financial sentiment currently fall behind state‐of‐the‐art techniques used within NLP. Two future directions stem from this paper. Firstly, we propose that the adoption of transformer architecture to create robust representations of textual data could enhance sentiment analysis in academic finance. Secondly, the adoption of multimodal classifiers in finance represents a new, currently underexplored area of study that offers opportunities for finance research.
Suboptimal video coding for machines method based on selective activation of in‐loop filter

Abstract A conventional codec aims to increase the compression efficiency for transmission and storage while maintaining video quality. However, as the number of platforms using machine vision rapidly increases, a codec that increases the compression efficiency and maintains the accuracy of machine vision tasks must be devised. Hence, the Moving Picture Experts Group created a standardization process for video coding for machines (VCM) to reduce bitrates while maintaining the accuracy of machine vision tasks. In particular, in‐loop filters have been developed for improving the subjective quality and machine vision task accuracy. However, the high computational complexity of in‐loop filters limits the development of a high‐performance VCM architecture. We analyze the effect of an in‐loop filter on the VCM performance and propose a suboptimal VCM method based on the selective activation of in‐loop filters. The proposed method reduces the computation time for video coding by approximately 5% when using the enhanced compression model and 2% when employing a Versatile Video Coding test model while maintaining the machine vision accuracy and compression efficiency of the VCM architecture.
A distance-based kernel for classification via Support Vector Machines

Support Vector Machines (SVMs) are a type of supervised machine learning algorithm widely used for classification tasks. In contrast to traditional methods that split the data into separate training and testing sets, here we propose an innovative approach where subsets of the original data are randomly selected to train the model multiple times. This iterative training process aims to identify a representative data subset, leading to improved inferences about the population. Additionally, we introduce a novel distance-based kernel specifically designed for binary-type features based on a similarity matrix that efficiently handles both binary and multi-class classification problems. Computational experiments on publicly available datasets of varying sizes demonstrate that our proposed method significantly outperforms existing approaches in terms of classification accuracy. Furthermore, the distance-based kernel achieves superior performance compared to other well-known kernels from the literature and those used in previous studies on the same datasets. These findings validate the effectiveness of our proposed classification method and distance-based kernel for SVMs. By leveraging random subset selection and a unique kernel design, we achieve notable improvements in classification accuracy. These results have significant implications for diverse classification problems in Machine Learning and data analysis.