Reviewerly/exHarmony huggingface dataset:
The Model of the Digital Competence Profile of Pedagogical University Students

Authors:
raw_author_name: Ruslan M. Chudinsky
raw_author_name: Alla A. Maleva
raw_author_name: Vasily V. Malev
raw_author_name: Vladislav M. Dubov

Abstract: The article actualizes the assessment of the competencies of students of a pedagogical university through the systematic collection and analysis of the level of competence formation in the process of mastering the main professional educational program. The need to monitor and evaluate the development of students’ competencies makes it possible to identify the absence or insufficient level of competence formation among students and make appropriate managerial and pedagogical decisions to improve the quality of the educational process. One of such means is the digital competence profile of a student of a pedagogical university, by which the authors understand the system of collecting, analyzing and visualizing a set of competencies that are formed and developed by students as a result of mastering the basic educational program and extracurricular activities necessary for the implementation of professional pedagogical activity, presented digitally in the information and educational environment of the university. The article presents a model of the digital competence profile of pedagogical university students, identifies structural elements and functional relationships between them. The purpose of the article is to develop a model of the digital competence profile of pedagogical university students, to identify objects and subjects of the digital profile, to determine the structural elements of the digital profile of a pedagogical university student, to identify functional relationships and relationships between the structural elements of the model. Methodology and methods of research. The study was carried out on the basis of the modeling method and the structural and functional method, which provides identification of the structural elements of the digital profile of a student of a pedagogical university, determination of functional relationships and relationships between structural elements and their functions relative to each other. Conclusion. The model proposed by the authors is aimed at tracking the level of formation of the digital profile of competencies during the transition from formal assessment of knowledge to informal assessment of abilities, forming an individual route of formation in the context of a successful individual career trajectory of a student of a pedagogical university. The model is the basis for the development of an information system of a digital tool and can be used to monitor and evaluate the professional competencies of students of a pedagogical university.
Data Complexity: A New Perspective for Analyzing the Difficulty of Defect Prediction Tasks

Authors:
raw_author_name: Xiaohui Wan
raw_affiliation_string: School of Automation Science and Electrical Engineering Beihang University, Beijing, China
raw_author_name: Zheng Zheng
raw_affiliation_string: School of Automation Science and Electrical Engineering Beihang University, Beijing, China
raw_author_name: Fangyun Qin
raw_affiliation_string: College of Information Engineering Capital Normal University, Beijing, China
raw_author_name: Xuhui Lu
raw_affiliation_string: School of Automation Science and Electrical Engineering Beihang University, Beijing, China

Abstract: 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

Authors:
raw_author_name: Colm Lynch
raw_affiliation_string: Payload Group, Planet Labs Inc, San Francisco, CA, USA
raw_author_name: Giovanni Marchisio
raw_affiliation_string: Payload Group, Planet Labs Inc, San Francisco, CA, USA
raw_author_name: Kai Qin
raw_affiliation_string: Space Technology and Industry Institute, Swinburne University of Technology, Hawthorn, VIC, Austrialia
raw_author_name: Alan R. Duffy
raw_affiliation_string: Space Technology and Industry Institute, Swinburne University of Technology, Hawthorn, VIC, Austrialia

Abstract: 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

Authors:
raw_author_name: Weiyao Lin
raw_affiliation_string: Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China
raw_author_name: Yufeng Zhang
raw_affiliation_string: Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China
raw_author_name: Wenrui Dai
raw_affiliation_string: Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
raw_author_name: Huabin Liu
raw_affiliation_string: Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China
raw_author_name: John See
raw_affiliation_string: School of Mathematical and Computer Sciences, Heriot-Watt Unversity, Edinburgh, Malaysia
raw_author_name: Hongkai Xiong
raw_affiliation_string: Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China

Abstract: 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

Authors:
raw_author_name: Simos Meintanis
raw_affiliation_string: Department of Economics, National and Kapodistrian University of Athens, Athens, Greece

Abstract: 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

Authors:
raw_author_name: Jean Burgess
raw_affiliation_string: Queensland University of Technology, Australia

Abstract: 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.
Latitude-Adaptive Integer Bit Allocation for Quantization of Omnidirectional Images

Authors:
raw_author_name: Hui Feng
raw_affiliation_string: School of Information Science and Technology, Fudan University, Shanghai 200433, China
raw_author_name: Bo Hu
raw_affiliation_string: Fudan-Zhuhai Innovation Institute, Zhuhai 519031, China; School of Information Science and Technology, Fudan University, Shanghai 200433, China

Abstract: 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

Authors:
raw_author_name: Xue Wu
raw_affiliation_string: School of Foreign Languages, Huazhong University of Science and Technology, 1037 Luoyu Road, Hongshan District, 430074, Wuhan, Hubei, China

Abstract: 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.
RoboBoat: A robotic boat for 3D mapping of partially flooded underground sites

Authors:
raw_author_name: Danilo Tardioli
raw_affiliation_string: Centro Universitario de la Defensa  Zaragoza Spain; Information and Communication Technology, Aragón Institute for Engineering Research (I3A) Universidad de Zaragoza Zaragoza Spain
raw_author_name: Luis Riazuelo
raw_affiliation_string: Information and Communication Technology, Aragón Institute for Engineering Research (I3A) Universidad de Zaragoza  Zaragoza Spain
raw_author_name: Luis Montano
raw_affiliation_string: Information and Communication Technology, Aragón Institute for Engineering Research (I3A) Universidad de Zaragoza  Zaragoza Spain

Abstract: 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.
Text‐based sentiment analysis in finance: Synthesising the existing literature and exploring future directions

Authors:
raw_author_name: James Bowden
raw_affiliation_string: Strathclyde Business School University of Strathclyde Glasgow UK
raw_author_name: Yashar Moshfeghi
raw_affiliation_string: NeuraSearch Laboratory University of Strathclyde Glasgow UK

Abstract: 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

Authors:
raw_author_name: Ayoung Kim
raw_affiliation_string: Division of Software Yonsei University Wonju Republic of Korea
raw_author_name: Soon‐heung Jung
raw_affiliation_string: Media Research Division Electronics and Telecommunications Research Institute Deajeon Republic of Korea
raw_author_name: Hyon‐Gon Choo
raw_affiliation_string: Media Research Division Electronics and Telecommunications Research Institute Deajeon Republic of Korea
raw_author_name: Jeongil Seo
raw_affiliation_string: Department of Computer Engineering Dong-A University Busan Republic of Korea

Abstract: 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

Authors:
raw_author_name: Margarita Gamarra
raw_affiliation_string: Department of Computer Science, Universidad del Norte, Colombia
raw_author_name: Eduardo Zurek
raw_affiliation_string: Department of Computer Science, Universidad del Norte, Colombia

Abstract: 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.
Joint Incentive Mechanism Design and Energy-Efficient Resource Allocation for Federated Learning in UAV-Assisted Internet of Vehicles

Authors:
raw_author_name: Zhibo Han
raw_affiliation_string: Beijing Key Laboratory of Work Safety Intelligent Monitor, Beijing University of Posts and Telecommunications, Beijing 100876, China
raw_author_name: Bei Zhuang
raw_affiliation_string: Beijing Key Laboratory of Work Safety Intelligent Monitor, Beijing University of Posts and Telecommunications, Beijing 100876, China
raw_author_name: Ji Ma
raw_affiliation_string: School of Network Security, Jinling Institute of Technology, Nanjing 211169, China
raw_author_name: Huaglory Tianfield
raw_affiliation_string: Department of Computing, Glasgow Caledonian University, Scotland G4 0BA, UK

Abstract: With the increasing demand for application development of task publishers (e.g., automobile enterprises) in the Internet of Vehicles (IoV), federated learning (FL) can be used to enable vehicle users (VUs) to conduct local application training without disclosing data. However, the challenges of VUs’ intermittent connectivity, low proactivity, and limited resources are inevitable issues in the process of FL. In this paper, we propose a UAV-assisted FL framework in the context of the IoV. An incentive stage and a training stage are involved in this framework. UAVs serve as central servers, which assist to incentivize VUs, manage VUs’ contributed resources, and provide model aggregation, making sure communication efficiency and mobility enhancement in FL. The numerical results show that, compared with the baseline algorithms, the proposed algorithm reduces energy consumption by 50.3% and improves model convergence speed by 30.6%.
Hardware–Software Co-Design of an Audio Feature Extraction Pipeline for Machine Learning Applications

Authors:
raw_author_name: Jure Vreča
raw_affiliation_string: Jožef Stefan Institute, 1000 Ljubljana, Slovenia; Jožef Stefan International Postgraduate School (IPS), 1000 Ljubljana, Slovenia
raw_author_name: Ratko Pilipović
raw_affiliation_string: Faculty of Computer and Information Science, University of Ljubljana, 1000 Ljubljana, Slovenia
raw_author_name: Anton Biasizzo
raw_affiliation_string: Jožef Stefan Institute, 1000 Ljubljana, Slovenia

Abstract: Keyword spotting is an important part of modern speech recognition pipelines. Typical contemporary keyword-spotting systems are based on Mel-Frequency Cepstral Coefficient (MFCC) audio features, which are relatively complex to compute. Considering the always-on nature of many keyword-spotting systems, it is prudent to optimize this part of the detection pipeline. We explore the simplifications of the MFCC audio features and derive a simplified version that can be more easily used in embedded applications. Additionally, we implement a hardware generator that generates an appropriate hardware pipeline for the simplified audio feature extraction. Using Chisel4ml framework, we integrate hardware generators into Python-based Keras framework, which facilitates the training process of the machine learning models using our simplified audio features.