Reviewerly/exHarmony huggingface dataset:
A Quantile Functions-Based Investigation on the Characteristics of Southern African Solar Irradiation Data

Exploration of solar irradiance can greatly assist in understanding how renewable energy can be better harnessed. It helps in establishing the solar irradiance climate in a particular region for effective and efficient harvesting of solar energy. Understanding the climate provides planners, designers and investors in the solar power generation sector with critical information. However, a detailed exploration of these climatic characteristics has not yet been studied for the Southern African data. Very little exploration is being done through the use of measures of centrality only. These descriptive statistics may be misleading. As a result, we overcome limitations in the currently used deterministic models through the application of distributional modelling through quantile functions. Deterministic and stochastic elements in the data were combined and analysed simultaneously when fitting quantile distributional function models. The fitted models were then used to find population means as explorative parameters that consist of both deterministic and stochastic properties of the data. The application of QFs has been shown to be a practical tool and gives more information than approaches that focus separately on either measures of central tendency or empirical distributions. Seasonal effects were detected in the data from the whole region and can be attributed to the cyclical behaviour exhibited. Daily maximum solar irradiation is taking place within two hours of midday and monthly accumulates in summer months. Windhoek is receiving the best daily total mean, while the maximum monthly accumulated total mean is taking place in Durban. Developing separate solar irradiation models for summer and winter is highly recommended. Though robust and rigorous, quantile distributional function modelling enables exploration and understanding of all components of the behaviour of the data being studied. Therefore, a starting base for understanding Southern Africa’s solar climate was developed in this study.
Neural Rendering-Based 3D Scene Style Transfer Method via Semantic Understanding Using a Single Style Image

In the rapidly emerging era of untact (“contact-free”) technologies, the requirement for three-dimensional (3D) virtual environments utilized in virtual reality (VR)/augmented reality (AR) and the metaverse has seen significant growth, owing to their extensive application across various domains. Current research focuses on the automatic transfer of the style of rendering images within a 3D virtual environment using artificial intelligence, which aims to minimize human intervention. However, the prevalent studies on rendering-based 3D environment-style transfers have certain inherent limitations. First, the training of a style transfer network dedicated to 3D virtual environments demands considerable style image data. These data must align with viewpoints that closely resemble those of the virtual environment. Second, there was noticeable inconsistency within the 3D structures. Predominant studies often neglect 3D scene geometry information instead of relying solely on 2D input image features. Finally, style adaptation fails to accommodate the unique characteristics inherent in each object. To address these issues, we propose a novel approach: a neural rendering-based 3D scene-style conversion technique. This methodology employs semantic nearest-neighbor feature matching, thereby facilitating the transfer of style within a 3D scene while considering the distinctive characteristics of each object, even when employing a single style image. The neural radiance field enables the network to comprehend the geometric information of a 3D scene in relation to its viewpoint. Subsequently, it transfers style features by employing the unique features of a single style image via semantic nearest-neighbor feature matching. In an empirical context, our proposed semantic 3D scene style transfer method was applied to 3D scene style transfers for both interior and exterior environments. This application utilizes the replica, 3DFront, and Tanks and Temples datasets for testing. The results illustrate that the proposed methodology surpasses existing style transfer techniques in terms of maintaining 3D viewpoint consistency, style uniformity, and semantic coherence.
A Deep Learning Framework for the Characterization of Thyroid Nodules from Ultrasound Images Using Improved Inception Network and Multi-Level Transfer Learning

In the past few years, deep learning has gained increasingly widespread attention and has been applied to diagnosing benign and malignant thyroid nodules. It is difficult to acquire sufficient medical images, resulting in insufficient data, which hinders the development of an efficient deep-learning model. In this paper, we developed a deep-learning-based characterization framework to differentiate malignant and benign nodules from the thyroid ultrasound images. This approach improves the recognition accuracy of the inception network by combining squeeze and excitation networks with the inception modules. We have also integrated the concept of multi-level transfer learning using breast ultrasound images as a bridge dataset. This transfer learning approach addresses the issues regarding domain differences between natural images and ultrasound images during transfer learning. This paper aimed to investigate how the entire framework could help radiologists improve diagnostic performance and avoid unnecessary fine-needle aspiration. The proposed approach based on multi-level transfer learning and improved inception blocks achieved higher precision (0.9057 for the benign class and 0.9667 for the malignant class), recall (0.9796 for the benign class and 0.8529 for malignant), and F1-score (0.9412 for benign class and 0.9062 for malignant class). It also obtained an AUC value of 0.9537, which is higher than that of the single-level transfer learning method. The experimental results show that this model can achieve satisfactory classification accuracy comparable to experienced radiologists. Using this model, we can save time and effort as well as deliver potential clinical application value.
A maturity level assessment of the use of technology by internal audit functions: a comparative analysis of the Federal Government of Canada

This work presents the results of the empirical study conducted on internal audit (IA) functions in the Federal Government of Canada (after this Federal Government) to measure generalized audit software (GAS) use practices. The study empirically gauged the function maturity of the Federal Government Internal Audit. It sought to provide information on the current state and usage of GAS and the future needs of audit functions across the federal government. The current maturity assessment (2022) is phase two; phase one (2017) was completed five years ago. This work enables us to see if progress has been made in data analytics and provides valuable information on where to focus efforts to achieve best practices. People, processes and technology form the foundation of effective internal auditing. It is essential to continue assessing progress in these areas. This paper focuses on these three aspects, which contribute equally to the overall assessment of the maturity of GAS use by internal auditors in the Federal Government. The comparison drawn from the empirical findings indicates that there has not been significant progress in any area or overall maturity levels since the initial study in 2017. A comprehensive discussion of the results leads to policy recommendations for shaping the maturity-level assessment of future GAS use. At the same time, by considering Canada as an advanced country case study, the research aims to provide a lessons-learned experience from an organizational learning perspective for other countries and organizations while contributing to decision-making processes.
Leveraging 4D Golf Apparel Wear Simulation in Online Shopping: A Promising Approach to Minimizing the Carbon Footprint

As fashion e-commerce grows, the online return rates are running higher than ever before. Online customers buy the same product in multiple sizes or colors with the intention of returning what is not necessary as they are unable to have a tactile experience during their purchase. In terms of sustainability, returns have a huge negative impact on the environment, causing waste sent to landfills and carbon emissions. In the United States alone, over 15 million metric tons of carbon dioxide are annually emitted from transporting returned inventory. This study explored an innovative way to help reduce online returns due to fit and sizing issues using four-dimensional (4D) golf apparel wear simulation. The study observed how online customers reacted to an apparel wear simulation where they could see the body–clothing interactions, such as dynamic changes in the drape of a garment and cloth deformations caused by different body movements, with a focus on golf apparel. Female customers (n = 13) with experience playing golf and purchasing golf apparel online participated in randomized experiments where three different e-commerce demo websites embedded with simulations were shown. In-depth interviews were followed to collect qualitative data, and surveying was used to quantitatively assess the perceived usefulness of 4D golf apparel wear simulations. The findings of the study indicated that the wear simulation has the potential to help customers find the correct fit and size when shopping online. By exploring the idea of providing a more accurate representation of how apparel fits and interacts with the body, this study sheds light on the promising approach of leveraging 4D golf apparel wear simulations in online shopping to enhance sustainable fashion and potentially contribute to reducing the carbon footprint by minimizing returns.
Aircraft Target Detection from Remote Sensing Images under Complex Meteorological Conditions

Taking all-day, all-weather airport security protection as the application demand, and aiming at the lack of complex meteorological conditions processing capability of current remote sensing image aircraft target detection algorithms, this paper takes the YOLOX algorithm as the basis, reduces model parameters by using depth separable convolution, improves feature extraction speed and detection efficiency, and at the same time, introduces different cavity convolution in its backbone network to increase the perceptual field and improve the model’s detection accuracy. Compared with the mainstream target detection algorithms, the proposed YOLOX-DD algorithm has the highest detection accuracy under complex meteorological conditions such as nighttime and dust, and can efficiently and reliably detect the aircraft in other complex meteorological conditions including fog, rain, and snow, with good anti-interference performance.
A Set of Rules for Function-Oriented Automatic Multi-Sentence Analysis in Patents

This study proposes some rules for performing a function-oriented search (providing function and object) to extract technical systems from patents, using syntax and dependency patterns to analyse multiple sentences. Unlike the most common inter-sentence analysis methods, the proposed method does not use context information or distance to link the elements of several sentences, but generic terms from patent ontology. The content provided by the rules was entirely derived from a statistical analysis of many patents from different domains, in order to provide a general validity for the rules. The application of the method in two case studies, related to metal cutting and manure processing, highlighted its main advantages. Its degree of automation is such that the expert is almost exclusively excluded, except in the definition of the function on which to build the document pool. The precision and the recall of the results during the tests exceeded 90%. The current limitation concerns the manual control of some results, about 25%, which derive from an additional set of dependency patterns that are difficult to automate and deserve further investigation. The technical systems are many more in number and are more detailed with regard to structural aspects than those obtainable by analysing only single sentences and/or syntax.
Representation of Observations in Reinforcement Learning for Playing Arcade Fighting Game

Abstract Reinforcement learning (RL) is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning algorithms have become very popular in simple computer games and games like chess and GO. However, playing classical arcade fighting games would be challenging because of the complexity of the command system (the character makes moves according to the sequence of input) and combo system. In this paper, a creation of a game environment of The King of Fighters ’97 (KOF ’97), which implements the open gym env interface, is described. Based on the characteristics of the game, an innovative approach to represent the observations from the last few steps has been proposed, which guarantees the preservation of Markov’s property. The observations are coded using the “one-hot encoding” technique to form a binary vector, while the sequence of stacked vectors from successive steps creates a binary image. This image encodes the character’s input and behavioural pattern, which are then retrieved and recognized by the CNN network. A network structure based on the Advantage Actor-Critic network was proposed. In the experimental verification, the RL agent performing basic combos and complex moves (including the so-called “desperation moves”) was able to defeat characters using the highest level of AI built into the game.
PARALLEL CORPUS OF THE KAZAKH AND RUSSIAN LANGUAGES: DEVELOPMENT, OPERATION AND PROBLEMS

The research paper gives a brief overview of the history of the creation of linguistic corpora, describes their classification according to various criteria and types of parallel subcorpuses. The original Kazakh text of M. Auezov's epic novel «Abai Zholy» and its Russian translation, made by A. Kim, were manually aligned at the level of a paragraph (sentence) in a parallel subcorpus being developed as part of the national corpus of the Kazakh language.During the development of the parallel subcorpus, Microsoft Office Excel, Notepad++, Python, Django, MySQL software tools were used. The software architecture and the order of operation of the parallel subcorpus can be represented as follows: 1) texts in two languages were collected using the Excel office program and aligned manually at the paragraph (sentence) level; 2) aligned texts were loaded directly from an Excel file into the MySQL database management system; 3) the downloaded texts were sorted using the Notepad++ word processor program, their statistics were obtained; 4) the Django web server was used to publish the sorted texts on the Internet and provide user requests; 5) the Processing.py program written in Python and equipped with a search function was used to connect the Django web server to the MySQL database management system; 6) the parallel subcorpus software architecture was developed using client-server and MVC (Model-View-Controller) technologies.The parallel subcorpus consists of a database of aligned texts, markups, metamarkups and a search engine, information about the text entered into the subcorpus (metamarkup) includes the following parameters: author, translator, work title, translation title, publication date of the work, translation period, original language, translation language. The search engine allows users to find the desired word by parameters: word, phrase, sentence, and capital letters (in Kazakh and Russian). The paper describes the interface of the parallel subcorpus in Kazakh and Russian and the interface of the results after searching for the desired word through one of the search parameters, the total and non-repeating number of words used in the text in two languages, the number of sentences, as well as numerical and percentage values of the ten most commonly used words in both languages were determined.In addition, in the process of aligning the original Kazakh text of the epic novel with the Russian translated version at the paragraph (sentence) level, the following features were identified: 1) from the point of view of structure, that is, the words used in the paragraph (sentence) are approximately equivalent in number; 2) from the point of view of content, they approximately coincide; 3) do not coincide in structure and content: some paragraphs (sentences) in the original text in Kazakh are translated into Russian incorrectly, superficially or briefly, their approximate meaning is given.
Difficulties and Strategies in Producing English Writing Text: What do EFL Students’ Perceive?

This study aimed to discover the EFL students' viewpoints on the difficulties they encountered when writing in English, as well as their strategies for overcoming these hurdles. This study was conducted on EFL students in a senior high school in Medan. This research involved the involvement of twenty-nine eleventh-graders. The researchers utilized a questionnaire and an interview to collect data following their research themes. The questionnaire aims to identify the barriers students face when writing in English. The questionnaire was sent using a Google form with closed-ended questions based on Bryne's (1988) categories and a five-point Likert scale (strongly agree, agree, neutral, disagree, and strongly disagree). Subsequently, an interview was conducted with a subset of participants (n = 5) specifically chosen to elicit further information about their strategies for overcoming writing difficulties. The interview was recorded using a cell phone by the researchers. Before data processing, the audio cassette was transcribed word-for-word. Thus, data collected with three instruments were analyzed using three approaches: frequency count for questionnaire data and content analysis for interview data. According to the findings, students encountered linguistic (grammar, vocabulary, language use, sentence construction) and psychological issues when composing English texts (lack of teacher instruction and feedback). There are no cognitive appearance issues. The students use mind mapping, translating materials from Indonesian to English, maintaining practice, and peer evaluation to overcome their writing difficulties. This research was expected to contribute to minimizing the difficulties experienced by students in writing English through the strategy revealed in this study
Towards Gestural Interaction with 3D Industrial Measurement Data Using HMD AR

Abstract Despite the spread of augmented reality (AR) systems and its applications onto a number of various areas, the adoption of AR in industrial context is relatively limited. We decided to conduct an exploratory user study to define the eventual singularities that might be associated with the barriers for HMD AR technology adoption in the industrial settings, as recent works presented potential benefits of its applications with regard to specific 3D measurement data interpretation. The task-based study was designed to engage users with interaction of volumetric data of static and time series nature. We compared actions of users performed in lab vs. in situ conditions simulating real, process tomography measurement data visualisations for granular bulk solids flow in large containers. Study results revealed concrete directions for further work that might eventually enable wider adoption of HMD AR systems in the industrial context in terms of specific gestural interaction and visualisation techniques development.
Integrated Behavioural Design Framework to Encourage COVID-19-Appropriate Behaviour in University Canteens and Eateries

Behavioural design is a significant area of research due to its potential to solve problems by facilitating desirable human behaviour in areas such as healthcare and sustainability. Unlike conventional design domains, behavioural design distinguishes itself by prioritising behaviours as the primary objects of design instead of artefacts. However, the operationalisation of behavioural design suffers from its inability to cohesively integrate behavioural science-led and design-led methods to characterise problematic and target behaviours in a systemic context. The domain also lacks a structured framework to bridge the gap between target behaviours and design interventions. Thus, there is a perceived need for an integrative behavioural design framework that helps designers address complex human behaviours in a social setup like university canteens. With the resumption of in-person classes, there are high chances for university canteens to be overcrowded, potentially leading to the spread of the COVID-19 virus. We address this challenge by proposing an integrated framework for facilitating behaviours like social distancing, wearing of masks, and effective lane movement. Further, the application of the framework is demonstrated in a university canteen setup using defined behavioural parameters.
Study and Evaluation of User Interaction and User Experience Design for the Development of a Fully Autonomous Passenger Drone Interior Cabin for India

Passenger drones are currently being developed to solve the purpose of point-to-point commute. The user interaction for futuristic vehicles will play a vital role in enhancing the experience and making the passenger feel safe. The interior of the drone cabin should be passenger-centric, giving the user various experiences to choose from on the fly. This research paper addresses the user interaction and behavior within a fully autonomous passenger drone interior cabin in India. Brainstorming, mind mapping, and user survey were the methodology used to collect data. The data were analyzed using qualitative and quantitative tools. Brainstorming led to the mapping of user interaction from entry (ingress) to exit (egress), which was used as an input to develop an online survey to validate the design parameters. The paper predicts the various user experience design parameters for user interaction, including voice, gesture, and touch, within a fully autonomous passenger interior cabin. The results from this paper can be taken as recommendations for designing user interaction in the interior cabin space of passenger drones in India.
On Defining Interactive Visual Narratives (IVNs)

To arrive at a definition of Interactive Visual Narratives (IVN), we begin our inquiry by looking at its hypernym, the Visual Narratives. We then proceed to gather a better understanding of the fields that influenced and fuelled the growth of IVNs, the multifarious experiments and implementations that have assisted in intensifying the scope of the field. The comprehensive examination of the varied spectrum of IVNs revealed that they are not a discrete phenomenon that needs isolated review but a specialised field that demands scrutiny as a unified whole. There exists a lacuna in the investigation of what constitutes an IVN. What are its components? How do they function? The definition of an IVN as a conceptual entity having its definitive structure will lead to the examination of its structure. This definition will help answer the questions sighted and further the knowledge on its construction, making IVNs an intelligible phenomenon. This understating will be of immense benefit to practitioners of design, researchers and students. The review of terms used to denote IVNs brought to light that all of the categories mentioned share certain commonalities. With this endeavour, we present a definition of Interactive Visual Narratives and enumerate the characteristic features.