Reviewerly/exHarmony huggingface dataset:
Solar Energy: An Analysis of Global, Direct and Diffuse Solar Irradiance for Mauritius

Information on the components of solar radiation is key to the implementation of solar energy projects. It is highly sought by policymakers, energy planners, engineers, architects, and investors, as well as researchers and practitioners in the field of solar energy. In the current study, spatial and temporal analyses of the main components of solar radiation are conducted which reveal mean global horizontal irradiance, direct normal irradiance, and diffused irradiance of 178.5 kWh/m², 187.9 kWh/m², and 58.1 kWh/m², respectively, over the 20-year period analyzed. Spatial analysis reveal that the northern plain of Mauritius favors the implementation of solar energy projects owing to high global and direct irradiances. High diffusion of incoming irradiance is observed to occur on the central plateau region and attributed to the presence of increased moisture content as a result of cloud convection occurring in the vicinity. A mean clearness index value of 0.6214 has been obtained over Mauritius, which is indicative of intermediate to clear sky conditions prevailing on the island. The clear sky conditions and high insolation are propitious conditions for solar technology implementation in Mauritius.
A Modified Logistic Map Inspired Image Encryption Scheme

In today's interconnected world, images have gained a ubiquitous presence and have become a significant component of communication, surpassing linguistic and cultural barriers. They are globally shared and have become integral to various fields such as medical imaging, defense services, scientific research, and art. However, many of these images contain personal and private information, underscoring the need for robust protection of digital images. To ensure the security of these images, it is imperative to develop innovative encryption schemes that guarantee their confidentiality and integrity. This study introduces a dependable encryption scheme that utilizes a modified logistic map. The proposed approach involves two stages: spiralization of image pixels and diffusion through a randomized key matrix. To create a randomized key matrix, image data is combined with a chaotic sequence that is derived from the modified logistic map making the resulting matrix unpredictable and secure. To restore the original image from its encrypted form, a stable decryption process is also defined. The efficacy of the suggested technique has been validated through analysis and experimental results.
Region-aware Photo Assurance System for Image Authentication

Zero-knowledge Proof (ZKP) allows active image authentication to prove image integrity after editing without revealing its source. However, existing ZKP solutions require impractical execution time, leaving a considerable gap between theory and practice. To tackle this, we present a Region-Aware Photo Assurance System based on the nature of image editing with privacy: sensitive information is usually local and relatively small, and thus by cropping and/or adding mosaic to these small regions suffices to protect privacy. Using ZKP for the locally edited region and digital signature for the others can still ensure the integrity of an image, with significantly better efficiency. We comprehensively analyzed the system's performance and showed the advantage of our system compared with the state-of-the-art ZKP-based method, PhotoProof, with 15x/60xfaster on the KeyGen/Proof operations and 25x lower in the Proof size. Furthermore, we protect several real-world images selected in the Redaction dataset with our system. Our system achieves up to 2,700x faster than PhotoProof for a proof generation. We expect the system can become a practical system for real-world applications.
Improved Hybrid Model to Text Detection by Using k-means and mser-swt with CNN

Detection of textual data from scene text images is a very thought-provoking issue in computer graphics and visualization. The challenges of text detection come from the nature of images used to detect text from them. The technology proposed is comprised of three main stages: Blurring, transformation to the frequency domain, and de-blurring in the frequency domain as preprocessing procedures; a proposed hybrid model for text detection using k-means clustering and text bounding boxes is used, and (CNN) framework used to classify into textual and non-textual regions. We used the Stroke Width Technique (SWT) when increasing the value of k by analyzing the width of the strokes in the image and thus reducing the potential textual regions resulting from the MSER, which leads to reducing the time and increasing the accuracy of detection, a convolutional neural network (CNN) algorithm is used to perform a classification of all bounding boxes. For the suggested technique’s evaluation, results are collected on three popular datasets, including SVT, IIIT5K, ICDAR 2013, and ICDAR 2015. The datasets for SVT, IIIT5K, ICDAR 2013, and ICDAR 2015 each have classification results of 87.41.
Towards a Health Recommender Tool using Machine Learning Approaches: A survey and a new idea

A sanitary crisis is a series of health events that can affect a large number of persons. Late events (COVID 19) have shown how unprepared we are to such situations. This issue raises the need to propose a Health Recommender Tool (HRT) to help both patients and medical professionals make more efficient and accurate health-related decisions. This paper presents an overview of recommendation systems (RS) methods that can be classified into categories: content-based method, collaborative filtering and hybrid method. We provide a literature review on machine learning (ML) based RS mainly in the medical domain. This work proposes a multi-phase process where the first phase is the data pre-processing and the second is the use of ML based recommendation techniques to get predictions on the relevance of documents to the user. The last step is the comparison between each technique. Our study concerns especially Algeria where the need of this kind of tools is greatest.
Simulation of Anisoplanatic Turbulence for Images and Videos

Turbulence is a common phenomenon in the atmosphere and can generate a variety of distortions in an image. This can cause further image processing tasks to struggle due to lack of detail in the resulting turbulence affected imagery. It is therefore useful to attempt to remove such distortions as a post processing step. However, the development of such algorithms is difficult due to the complex nature of turbulence data acquisition. To alleviate these issues, this paper presents the development of a turbulence simulator that is capable of imparting the effects of a turbulent atmosphere onto clean images and videos. This work also provides a large, publicly available dataset that can be used as a benchmark. The simulator and dataset will be valuable resources in the field of turbulence mitigation. Indeed, the simulator allows researchers to simulate specific turbulent conditions for any application as required; while the dataset provides the ability to make use of turbulent data without the expensive time commitment of simulation.
EasyColor: Interactive Colorization by User's Edge-Masks and Strokes

Semi-automated manual interactive colorization tasks aim to efficiently overcome the issue of color bleeding, which is mainly addressed by edge constraints in recent works. For example, Photoshop takes local colorization with manually annotated selection regions. However, the approach requires very precise edges thus it requires experienced professionals and costs an amount of time. To ease the processing process, we conduct a Krita plugin namely ‘EasyColor’ for more convenient interactive colorization. Specifically, EasyColor is built on the Krita software and incorporates our specially developed algorithm. Users only need to roughly select an area around the color bleeding and paint color strokes. The algorithm automatically can recognize the edges within the selected area and utilizes the strokes to reapply colors to the entities within the selection. This allows users to quickly rectify images affected by color bleeding. This article primarily introduces the EasyColor plugin.
Fuzzy-Based Modeling with Semi-Automatic Rule Learning for Eye-Tracking Data Analysis: A Case Study on Raven's Progressive Matrices

Eye-tracking allows for the identification of events inherent to human vision, capable of revealing implicit aspects of one's behavior. Identifying and correlating eye-tracking data with domain-specific knowledge can be a complex task. In this context, intelligent systems can be employed to combine specialist's knowledge and experience with eye-tracking data, to better understand the implicit information contained in human visual behavior. This works proposes a framework for developing eye-tracking-based systems that enhance assessment accuracy for specific tasks. This framework result in a model that captures the specialist's knowledge of subjective aspects. The focus of this study is to evaluate the correlation between visual behavior and efficacy in solving tests inspired by Raven's Progressive Matrices. We chose an approach based on fuzzy rules, as it allows us to represent knowledge more legibly to end-users. The model's rules were developed and validated alongside a psychology expert. The system was tested with user data and showed promising results.
Preventing Financial Statement Fraud with Blockchain-based Verifiable Accounting System

This paper proposes a blockchain-based anti-fraud verifiable accounting information system (AIS) approach for maintaining verifiable financial statements. The approach utilizes triple-entry transaction records and digital signatures to ensure the authenticity and integrity of financial data. It also provides a balance between confidentiality and verifiability by encrypting sensitive information and requiring the transparency of crucial financial statement-related information. The proposed approach provides a practical and secure solution for preventing financial statement fraud and offers a more efficient and reliable alternative to traditional trusted third party (TTP) servers. Overall, this paper contributes to the field of accounting and auditing by providing a novel blockchain-based approach to enhance transparency, trust, and accountability in financial reporting.
A novel Data and Model Centric artificial intelligence based approach in developing high-performance Named Entity Recognition for Bengali Language

Named Entity Recognition (NER) plays a significant role in enhancing the performance of all types of domain specific applications in Natural Language Processing (NLP). According to the type of application, the goal of NER is to identify target entities based on the context of other existing entities in a sentence. Numerous architectures have demonstrated good performance for high-resource languages such as English and Chinese NER. However, currently existing NER models for Bengali could not achieve reliable accuracy due to morphological richness of Bengali and limited availability of resources. This work integrates both Data and Model Centric AI concepts to achieve a state-of-the-art performance. A unique dataset was created for this study demonstrating the impact of a good quality dataset on accuracy. We proposed a method for developing a high quality NER dataset for any language. We have used our dataset to evaluate the performance of various Deep Learning models. A hybrid model performed with the exact match F1 score of 87.50%, partial match F1 score of 92.31%, and micro F1 score of 98.32%. Our proposed model reduces the need for feature engineering and utilizes minimal resources.
THE MEASURE OF TARGET SALIENCY FOR TARGET-ORIENTED IMAGE FUSION

Our analysis of the available literature on quantitative quality indices characterizing the visibility, conspicuity, and saliency of the objects in an image reveals that the used approaches do not fit the military needs, in particular those required by the target sightseeing systems of armored vehicles. Their main drawback is that these indices are not target-oriented. The long elapsed time needed for their evaluation is another inconvenience, making them inapplicable for military purposes, which in most cases require a real-time response. Special target-oriented image quality indices are needed for military applications. In this paper, we propose the very such target-oriented normalized visibility, conspicuity, and saliency indices and corresponding methods for their measurements. Comparison of the times elapsed for the generation of saliency maps by the analyzed literature methods and the adequacy of the saliency scores produced by them with those for the saliency index, proposed in this paper, reveals that the computation with our approach is several orders of magnitude faster than for the prototype methods available in the literature while providing comparable adequacy of the measured target-oriented image quality indices.
Estimating text regressions using txtreg_train

In this article, I introduce new commands to estimate text regressions for continuous, binary, and categorical variables based on text strings. The command txtreg_train automatically handles text cleaning, tokenization, model training, and cross-validation for lasso, ridge, elastic-net, and regularized logistic regressions. The txtreg_predict command obtains the predictions from the trained text regression model. Furthermore, the txtreg_analyze command facilitates the analysis of the coefficients of the text regression model. Together, these commands provide a convenient toolbox for researchers to train text regressions. They also allow sharing of pretrained text regression models with other researchers.
An Optimization-Based Guidance System for Tactical Multi-Rotor UAVs Mapping Contested Environments

This paper presents an original guidance system for autonomous multi-rotor unmanned aerial vehicles (UAVs) tasked with creating maps of environments while operating in a tactical manner. An original algorithm is developed to produce a sequence of goal points whose locations enable exploration in a systematic or greedy manner, a path planner allows to interpolate goal points while exploiting obstacles for shelter, and a trajectory planner designed within the model predictive control framework enables collision-avoidance and accounts for the output feedback linearized dynamics, thrust saturation, and attitude constraints. This system assumes no prior information on the environment or threats, does not rely on external sources of information, and is equipped with forward-facing cameras to enable low altitude and indoor operations. Numerical simulations validate the proposed guidance system.
Resistance to the Postdigital

Much publicly available resistance to the postdigital appears in work by authors who themselves contribute to postdigital research and scholarship. The entry considers issues of terminology, negative aspects of contemporary postdigital contexts, and critiques relating to theory and research. People who openly resist the postdigital are also likely to want to supplement its themes with another theoretical perspective in order to clarify or to provide more rigour. Many theoretical positions are compatible with the postdigital though not necessarily with each other. Some people who identify as outsiders may find the postdigital too complex or esoteric.
The construction of teaching quality evaluation system of health management majors in universities based on information fusion technology

Abstract This paper begins with the multi-level processing of instructional multi-source data using information fusion techniques, with varying degrees of abstraction of the raw data for each level of processing. The set of all possible outcomes that an instructional framework can recognize for a given instructional problem is identified through D-S evidence theory. Secondly, the m factors that affect the object of teaching evaluation are grouped into the set of factors affecting the evaluation by fuzzy evaluation method, and their factor set weight values are calculated. Finally, the teaching quality evaluation system was constructed based on the factor set weight values. The results show that the experimental obtained Kendall’s harmony coefficient reaches 0.9028, and the evaluation is reliable. The overall score rate in teaching and assessment evaluation is 0.71 to reach the expected value, which indicates that the teaching quality evaluation index system established this time has relatively good internal consistency. The evaluation index system constructed in this paper makes the classroom teaching evaluation highly operable, which can be accepted by students and teachers in the evaluation process and ensures the accuracy of the evaluation results.
The Postdigital University Teacher

University teachers have a complex history with digital (and other) technologies, which can offer opportunities for creative approaches to teaching but are also associated with instrumental teaching and constraints on practice. The Covid-19 pandemic ensured that all university teachers have engaged at some level with technology for teaching; shared experiences from this time have fed into the research on postdigital knowledge ecologies. A key message for university teachers from the postdigital literature is that there are alternative approaches to the way things are. Postdigital writers and editors are themselves using alternative methods to make this point.
Postdigital Citizenship

This entry provides some prevalent considerations, as emerging from the literature, towards (re)defining citizenship under a postdigital view. These considerations include the definition of a postdigital city, the challenge of (re)defining civic rights, duties, and identities, the centrality of social participation and action, as well as the definition and importance of digital justice. The entry concludes with a reflection on the social and digital interdependence of human and non-human agents, and the role of postdigital civic identities and performativity towards making such interdependence a positive and productive enterprise.
California Sunshine

How sunny is sunny California? Just like everything else about California, sun and clouds vary tremendously by region and by season. The state has some of the foggiest places in the country, while it also has the sunniest locations. In summer, a short distance away from the foggy central coast regions, the Central ValleyCentral Valley may be sunny and hot. This chapter shows the wide variations of sun and clouds from the foggy coasts to the cloudless deserts. We illustrate the large diurnal and seasonal changes in solar radiationSolar radiation from the north to the southern border. Summer tends to be cloudy and relatively cool in coastal regions while it is warm and sunny inland. During winter, southern regions are sunnier largely due to a shorter rainy season than is seen in the north. We also show how different cities in the state vary in sunshine throughout the year. California is a leader in the solar energy industry. The chapter looks at how regions of the state hold great potential in capturing the sun’s energy. California is the United States’ leader in the use of solar power with most thermalThermal facilities located in the southeastern part of the state.
Postdigital Architecture

Architecture is the practice of designing buildings, and the mastery of the knowledge and skills to do so. Architectural authorship is often ascribed to individual architects. However, since the adoption of technologies such as Computer-Aided Design (CAD) and Building Information Management (BIM), the design of buildings has become more dynamic and collaborative. Postdigital architecture is characterised, therefore, by the ways in which the skills and knowledge employed in the design and construction of buildings are no longer the exclusive preserve of the architect.
Automatic proficiency scoring for early-stage writing

In this work, we study the feasibility of using machine learning and natural language processing methods for assessing writing proficiency in Danish with respect to text construction, sentence construction, and use of modifiers. Our work is based on the analytical framework for scoring early writing proposed by Kabel et al. (2022), where each text is first annotated by a human expert according to a predefined coding scheme and subsequently scored using statistical Rasch modelling (Rasch, 1960). We investigate two different strategies for estimating these scores automatically: 1) we propose a system for identifying the central linguistic features automatically mimicking the role of the human experts and 2) we train state-of-the-art discriminative machine learning models to predict the proficiency scores directly from the texts. We conduct a number of experiments to evaluate and compare the two approaches. Our results show strong and statistically significant correlations between the scores generated using the automatic system and scores based on human experts. We also estimate and report the reliability of the individual linguistic features in the automatic annotation system. Finally, we also propose and evaluate an extension of the statistical model, which allows the model to compensate for potential systematic errors in the automatic annotations. The article thereby contributes to the area of automated essay scoring (AES) and shows that it is possible to provide teachers with automated valid and reliable knowledge about the development of their students' writing competences, which they can use in their feedback to students.
Postdigital Embodiment

This entry focuses on the topic of postdigital embodiment and identifies key ideas to connect online and offline embodied performances and the consequences thereof. The synopsis starts with an examination of selected literature on the perceptions of the body to expand them through a postdigital lens and emphasise the entanglements of human and non-human embodied experiences. The entry presents eight challenges for a postdigital analysis of embodiment.
Postdigital Phenomenology

Phenomenology studies lived experience and phenomena that are given to us in our everyday world which we take for granted in the way they appear to us. For phenomenology, our lived experience is intentional, embodied, and intersubjective. It is formed by what is present in experience and also by what is absent in experience. From different phenomenological perspectives, we can either aim to find essences of phenomena or to interpret our lifeworld. Thus, phenomenology poses interesting questions and opportunities for postdigital research. These are for example what is the role of our living active body in forming postdigital experiences and what aspects of postdigital world are present or absent in our experience.