Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence-based Hyper Activity Analysis

Sentiment analysis has emerged as a valuable tool for analyzing human behavior and measuring frustration levels. This abstract provides an overview of the sentiment analysis of human behavior in response to measuring frustration levels. By examining the emotional tone expressed in textual data, sentiment analysis techniques offer insights into individuals' frustration levels, contributing to a better understanding of their psychological wellbeing. This study focuses on the application of sentiment analysis in measuring frustration levels and understanding human behavior. It explores the limitations and challenges associated with accurately assessing frustration based on Textual data, Psychological Questionnaires, Image Processing, Tone or speech, and Augmented Reality. The study acknowledges the importance of context and the need to account for linguistic nuances, sarcasm, and individual differences in language use. It also emphasizes the significance of considering additional modalities, such as facial expressions and virtual reality, to enhance the accuracy and reliability of measuring frustration levels.
LFSphereNet: Real Time Spherical Light Field Reconstruction from a Single Omnidirectional Image

Recent developments in immersive imaging technologies have enabled improved telepresence applications. Being fully matured in the commercial sense, omnidirectional (360-degree) content provides full vision around the camera with three degrees of freedom (3DoF). Considering the applications in real-time immersive telepresence, this paper investigates how a single omnidirectional image (ODI) can be used to extend 3DoF to 6DoF. To achieve this, we propose a fully learning-based method for spherical light field reconstruction from a single omnidirectional image. The proposed LFSphereNet utilizes two different networks: The first network learns to reconstruct the light field in cubemap projection (CMP) format given the six cube faces of an omnidirectional image and the corresponding cube face positions as input. The cubemap format implies a linear re-projection, which is more appropriate for a neural network. The second network refines the reconstructed cubemaps in equirectangular projection (ERP) format by removing cubemap border artifacts. The network learns the geometric features implicitly for both translation and zooming when an appropriate cost function is employed. Furthermore, it runs with very low inference time, which enables real-time applications. We demonstrate that LFSphereNet outperforms state-of-the-art approaches in terms of quality and speed when tested on different synthetic and real world scenes. The proposed method represents a significant step towards achieving real-time immersive remote telepresence experiences.
HDR Illumination Outpainting with a Two-Stage GAN Model

In this paper we present a method for single-view illumination estimation of indoor scenes, using image-based lighting, that incorporates state-of-the-art outpainting methods. Recent advancements in illumination estimation have focused on improving the detail of the generated environment map so it can realistically light mirror reflective surfaces. These generated maps often include artefacts at the borders of the image where the panorama wraps around. In this work we make the key observation that inferring the panoramic HDR illumination of a scene from a limited field of view LDR input can be framed as an outpainting problem (whereby the original image must be expanded beyond its original borders). We incorporate two key techniques used in outpainting tasks: i) separating the generation into multiple networks (a diffuse lighting network and a high-frequency detail network) to reduce the amount to be learnt by a single network, ii) utilising an inside-out method of processing the input image to reduce the border artefacts. Further to incorporating these outpainting methods we also introduce circular padding before the network to help remove the border artefacts. Results show the proposed approach is able to relight diffuse, specular and mirror surfaces more accurately than existing methods in terms of the position of the light sources and pixelwise accuracy, whilst also reducing the artefacts produced at the borders of the panorama.
One-shot Detail Retouching with Patch Space Neural Transformation Blending

Photo retouching is a difficult task for novice users as it requires expert knowledge and advanced tools. Photographers often spend a great deal of time generating high-quality retouched photos with intricate details. In this paper, we introduce a one-shot learning based technique to automatically retouch details of an input image based on just a single pair of before and after example images. Our approach provides accurate and generalizable detail edit transfer to new images. We achieve these by proposing a new representation for image to image maps. Specifically, we propose neural field based transformation blending in the patch space for defining patch to patch transformations for each frequency band. This parametrization of the map with anchor transformations and associated weights, and spatio-spectral localized patches, allows us to capture details well while staying generalizable. We evaluate our technique both on known ground truth filters and artist retouching edits. Our method accurately transfers complex detail retouching edits.
Secure transmission of medical images in multi-cloud e-healthcare applications using data hiding scheme

In recent years, medical image transmission using a multi-cloud system has played a significant role in e-Healthcare infrastructure. It allows medical practitioners to easily store, retrieve, and share patients’ medical information across multiple stakeholders. However, multi-cloud image transmission may be vulnerable to multiple security breaches, such as authentication, confidentiality, and security issues. Motivated by these issues, this paper proposes a data-hiding scheme for secure medical image transmission in a multi-cloud environment. The proposed scheme ensures imperceptible robustness and watermark security at a low computational cost. Here, the medical image is divided into a number of shares using Neighbor Mean Interpolation (NMI). To achieve confidentiality, Electronic Patient Healthcare Record (EPHR) is encrypted using Double Scan Pixel Position Shuffling (DSPPS) approach. Then, the encrypted EPHR is divided into shares and embedded in the cover medical image shares. Finally, a minimum of 50% of watermarked image shares are utilized to retrieve the original medical image and encrypted EPHR, consequently reducing multi-cloud latency and computational burden. Experimental results show that the proposed scheme shows high imperceptibility, robustness, and watermark security at a low computational cost. Comparative analysis with some of the recent popular data hiding schemes shows that the proposed scheme has improved imperceptibility and robustness by 10%–15% (approximately) with higher watermark security at a low computational cost.
Innovative activity and the volume of grants received by higher education organizations

The article is devoted to the study of the impact of grant support on the innovation activity of universities. The distribution of grants from the Russian Science Foundation and the President of the Russian Federation among universities and regions of the country is analyzed. It has been revealed that a significant part of grants is concentrated in the largest educational organizations, which may hinder the innovative development of regional universities. Correlation analysis showed a direct relationship between the number of grants received by universities and their Hirsch index, indicating scientific activity and productivity. However, despite the revealed correlation, the authors emphasize that for a more objective assessment of the effectiveness of the distribution of grant funds, it is necessary to take into account other indicators, such as the quality of scientific research, their applied potential and contribution to the socio-economic development of the regions. As a result, the study confirms the importance of grant support to stimulate innovative activity of universities and the need for further improvement of its provision mechanisms.
Innovative activity and R&amp;D volumes of higher education organizations

The article is devoted to the study of the relationship between innovation activity and the volume of research (R&amp;D) in higher education organizations. The author conducts a correlation analysis between the volume of R&amp;D per scientific and pedagogical worker and a number of indicators of universities, using data from the annual monitoring of higher education organizations. The results of the study show that the innovative activity of higher education organizations is closely related to the volume of R&amp;D. A particularly high correlation was found between the income from R&amp;D (minus budgetary funds) per scientific and pedagogical worker and the amount of R&amp;D without a state assignment per NPR. The results confirm the relevance of studying this relationship and suggest that optimizing the management of innovation processes at universities can increase the efficiency of resource use and accelerate technological progress. However, the author emphasizes that for a more complete understanding of the relationship between innovation activity and R&amp;D volumes, more extensive research is required.
Fine-grained Ethereum behavior identification via encrypted traffic analysis with serialized backward inference

Blockchain has developed rapidly in recent years. Asymmetric to the rapid development of the blockchain industry is the backwardness of blockchain regulatory technology. The existing research on blockchain security mainly explores blockchain technology and focuses on analyzing on-chain data. We argue that analyzing blockchain network traffic can provide a new perspective on building behavior patterns and blockchain regulation. Ethereum, as the most applied blockchain platform, has flourished with the maturity of blockchain technology. Monitoring fine-grained Ethereum behaviors is essential for the healthy development of Ethereum. However, different Ethereum behaviors are transmitted in an encrypted and persistent connection and are difficult to segment. Besides, the four Get class behaviors, including getBlockHeaders, getNodeData, getReceipts, and getBlockBodies, are indistinguishable from Ethereum traffic. This work proposes a Fine-grained Ethereum Behavior Identification system via encrypted traffic analysis with Serialized Backward Inference, dubbed FEBI-SBI. FEBI-SBI first performs Ethereum behavior traffic segmentation using packets with a load length of 32 Bytes and ACK values larger than their preceding packets in the same direction as the segmenting points. Then, the flow segments for Ethereum behaviors are fed to machine learning classifiers to perform coarse-grained Ethereum behavior identification. Finally, for the Get class behaviors indistinguishable by the machine learning classifiers, FEBI-SBI applies behavior serialized backward inference to identify them from their corresponding fine-grained Send class behaviors identified by the machine learning classifiers. Our experimental results demonstrate the effectiveness and efficiency of FEBI-SBI in identifying fine-grained Ethereum behaviors. FEBI-SBI can be applied to effectively identify anomaly behaviors on the Ethereum network and and thus enable the regulation of Ethereum users.
The Effect of Nutrition Education on Knowledge about Anemia Prevention Using Flipchart Media among Middle School Students at the Istiqomah Samarinda Islamic Boarding School

Anemia is a condition where the number of red blood cells or hemoglobin levels in red blood cells is below the level that is considered normal. Knowledge can be increased through nutrition education and can be assisted by the media. The aim of this research is to determine the effect of nutrition education on knowledge about anemia prevention using flipchart media among junior high school students at the Istiqomah Islamic boarding school in Samarinda. Pre-experimental research design with One Group Pretest-Posttest design with 55 respondents using purposive sampling technique. Data analysis used the Wilcoxon test with a p-value of 0.001 which shows that there is a difference in the level of female students' knowledge before and after being given education using flipchart media on female students' knowledge at the Istiqomah Samarinda Islamic Boarding School, so that there is an influence of nutrition education using flipchart media on female students' knowledge about preventing anemia.
The Influence of E-Booklet Media on Female Students' Knowledge and Attitudes Regarding Chronic Energy Deficiency

The prevalence of KEK in East Kalimantan Province reached 12.4%, the highest prevalence based on age characteristics was found in the 15-19 year age group at 36.3%. The aim of this research is to determine the effect of nutrition education using e-booklet media on increasing female students' nutritional knowledge and attitudes regarding KEK. This type of research is Quasi Experimental with Pre-Post Test with Control Group Design. The research location is MTs Negeri Samarinda. There were 36 young women who participated in this research. Knowledge and attitude data were obtained from questionnaires. The results show that there is an influence of nutrition education using e-booklet media on increasing knowledge (p-value = 0.000) and attitudes (p-value = 0.000) of young women. There is no significant difference in the influence of e-booklet and leaflet media in increasing knowledge (p-value = 0.243) and attitudes (p-value = 0.189) of young women.
Understanding Visitors’ Curiosity in a Science Centre with Deep Question Processing Network

Abstract Questions have a critical role in learning and teaching. People ask questions to obtain information and express interest in ideas. The Bristol scientific centre “We The Curious” launched “Project What If” in 2017 to inspire residents of Bristol to record their questions and pursue their curiosities. Researching these questions may help the museum better understand the curiosity of its audiences and create exhibitions or educational content that are more relevant to their interests and lives. The project managed to collect more than 10,000 questions on various topics, and more questions are being collected on a daily basis. With this large amount of data collected, it is time-consuming to process and analyse all the questions by humans. This research aims to apply artificial intelligence (AI) techniques and models in analysing these questions gathered by We The Curious. Meanwhile, in AI, there is a lack of tools that focus on processing and analysing the questions. Thus, we introduce a deep neural network called QBERT to process the questions for three tasks: question taxonomy, equivalent question detection, and question answering. Then we apply QBERT to provide an analysis of the questions collected by We The Curious, as well as comprehend Bristolians’ curiosity. Then using QBERT, we categorise the We The Curious questions into 90 themes and 5,930 communities. Moreover, 436 questions are answered by one-sentence answers extracted from Wikipedia.
Proximal Policy Optimization-Based Reinforcement Learning and Hybrid Approaches to Explore the Cross Array Task Optimal Solution

In an era characterised by rapid technological advancement, the application of algorithmic approaches to address complex problems has become crucial across various disciplines. Within the realm of education, there is growing recognition of the pivotal role played by computational thinking (CT). This skill set has emerged as indispensable in our ever-evolving digital landscape, accompanied by an equal need for effective methods to assess and measure these skills. This research places its focus on the Cross Array Task (CAT), an educational activity designed within the Swiss educational system to assess students’ algorithmic skills. Its primary objective is to evaluate pupils’ ability to deconstruct complex problems into manageable steps and systematically formulate sequential strategies. The CAT has proven its effectiveness as an educational tool in tracking and monitoring the development of CT skills throughout compulsory education. Additionally, this task presents an enthralling avenue for algorithmic research, owing to its inherent complexity and the necessity to scrutinise the intricate interplay between different strategies and the structural aspects of this activity. This task, deeply rooted in logical reasoning and intricate problem solving, often poses a substantial challenge for human solvers striving for optimal solutions. Consequently, the exploration of computational power to unearth optimal solutions or uncover less intuitive strategies presents a captivating and promising endeavour. This paper explores two distinct algorithmic approaches to the CAT problem. The first approach combines clustering, random search, and move selection to find optimal solutions. The second approach employs reinforcement learning techniques focusing on the Proximal Policy Optimization (PPO) model. The findings of this research hold the potential to deepen our understanding of how machines can effectively tackle complex challenges like the CAT problem but also have broad implications, particularly in educational contexts, where these approaches can be seamlessly integrated into existing tools as a tutoring mechanism, offering assistance to students encountering difficulties. This can ultimately enhance students’ CT and problem-solving abilities, leading to an enriched educational experience.
The Current Situation of "Three Strokes" Education in Normal Colleges and Its Solution

The current state of education for students in normal colleges to write well in pen, chalk and brush (Abbreviated as "three strokes ") has always been a hotly debated topic, mainly due to the many problems encountered in the teaching of the three strokes. Firstly, the recruitment and training of teachers of the three strokes, which is of utmost importance; secondly, a reasonable curriculum; and thirdly, a sound assessment mechanism. Only when these problems are solved at the root of the problem will the other problems be solved.
Postdigital

Since 2000, the concept of the postdigital has rapidly spread throughout diverse fields of human interest. Depending on the context, the postdigital has developed in various directions and carries different meanings. While there are good reasons for why the postdigital should not be firmly defined, there is a need to describe its many facets and their implications. This entry describes the main uses of the postdigital in the arts, humanities, and social sciences.
A systematic review of SVVR in language education in terms of the ADDIE model

Spherical video-based virtual reality (SVVR) is a form of virtual reality that employs 360-degree videos to immerse learners in virtual environments. As SVVR can simulate realistic experiences, the current trend of language education is towards more use of it. To examine its current status, this study systematically reviewed 31 articles on SVVR-enhanced language education from the perspectives of Analysis, Design, Development, Implementation, and Evaluation (i.e. the ADDIE model). The results indicate that the main affordances of SVVR include providing learners with authentic and immersive learning experiences, enabling content visualization, and giving learners a sense of freshness. The most popular pedagogical mode is to immerse and situate students in scenes, landscapes, city views, and real-life scenarios in SVVR. The associated language learning activities can be writing, speaking, and listening practices, as well as grammar, vocabulary, and culture learning. Scaffolding strategies such as providing guided questions for students’ exploration and engagement in SVVR and conducting collaborative or problem-based learning, as evidenced by several studies, seem effective. The effects of SVVR-enhanced language education are mainly evaluated through tests, questionnaires, interviews, and reflection papers. Based on the results, we propose future research and development directions for researchers and practitioners interested in SVVR-enhanced language education.
The devil is in the detail: reconceptualising bystander reactions to online political hostility

Online content moderation is often equated with platform-initiated take-downs and account suspensions. Counterspeech and other types of pro-social bystander behaviours present alternative approaches. They rely on the reactions of online bystanders to crowd-moderate hostile content. However, the current conceptual understanding of bystander reactions to hostility on social media is inadequate. What constitutes a reaction; what functions do specific reactions serve; and what audiences are reactions aimed at? Conceptual clarity is a prerequisite for studying the prevalence and consequences of pro-social bystander reactions. We therefore offer a novel unifying framework for conceptualising bystander reactions to online hostility: The Online Bystander Repertoire of Action (OBRA) Framework. This provides a foundation for future research on crowd-moderation, as it (1) combines insights across multiple research fields, (2) explicates the complex possible reactions beyond simplistic ‘reaction/no reaction´ distinctions and (3) draws attention to the degree of pro-sociality of bystander reactions – something that is often taken for granted.
Methodologies to Distinguish Locomotive Types Based on the Measured Geometry

This chapter presents methods to sort locomotives and other certain types of vehicles by types using geometry measured by a measurement station. The approach is implemented using a series of artificial locomotives created by adding normally distributed noise to each distance between adjustment wheels of locomotives that are in exploitation in Norway. Two approaches are studied. The first approach, Gaussian Mixture Clustering, is presented and shown to be suitable to obtain geometries of the main locomotives in the set. The second approach, based on the comparison of norms of differences between geometries of measured and known locomotives, is found quite accurate to use for sorting measured locomotives by the predefined types. The methods are presented on geometries of locomotives, but they also can be used for distinguishing different types of wagons and multiple units.
Secure Cloud-Based Media Sharing: Scalable Access Control and Privacy-Preserving Deduplication

This research delves into the realm of secure deduplication schemes, strategically designed to enhance storage efficiency within cloud environments. An initial focus is placed on an advanced AES encryption scheme utilizing a message-derived key, resulting in a consistent mapping of identical plaintexts to ciphertexts. The proposed AES framework not only incorporates convergent encryption but also provides meticulous security definitions. While the landscape of cloud computing boasts numerous techniques for data security, existing methodologies fall short in addressing nuanced aspects related to ciphertext. In response to this gap, our study introduces a pioneering information management paradigm. This comprehensive framework encompasses data gathering, sharing, and restrictive distribution, with a particular emphasis on multi-owner privacy preservation within the cloud. Under this innovative framework, data owners gain the capability to securely disseminate private information to predefined groups of clients through the cloud infrastructure. The research presented herein serves as a significant contribution to the ongoing discourse on secure deduplication in cloud storage. By combining advanced encryption techniques with a forward-looking information sharing strategy, our approach seeks to elevate the effectiveness of data security protocols in cloud environments