Reviewerly/exHarmony huggingface dataset:
“Whoa! Incredible!:” Language Learning Experiences in Virtual Reality

Recently, immersive virtual reality (VR) environments for language learning have garnered interest from researchers and practitioners alike, based on their realistic imitations of environments where target languages might be used for “authentic-like” interactions (with other learners, bots and avatars). However, the majority of research thus far has focused on university-level students’ perceptions of VR and there is a critical need for research examining how these environments can be used effectively with kindergarten to grade 12 students. This article discusses a pilot study that aims to fill this gap in research through a three-month-long VR study conducted with 10- to 11-year-old students in Spain ( n = 24) studying English as a foreign language (EFL). VR lessons were taught using the VR language learning platform Immerse and Oculus Quest 2 VR headsets. The data corpora consist of recordings of the VR activities, comparable non-VR classroom activities and teacher-directed student output (pre-VR and post-VR activities focused on the target language). Fragments of interactions during activities in both VR and the classroom were selected and transcribed. The Common European Framework of Reference for Languages: Learning, Teaching, Assessment Companion Volume (CEFR-CV) illustrative descriptors were then used to map the participants’ language performance to the CEFR-CV scales. Findings indicate that production of the target language is less controlled and less accurate in VR compared to face-to-face “traditional” oral activities in the classroom. However, there are more examples of spontaneous language use, more examples of mediation between the learners and even moments of higher than expected levels of production and comprehension, equivalent to English language level B1 descriptors in VR.
WT-YOLOX: An Efficient Detection Algorithm for Wind Turbine Blade Damage Based on YOLOX

Wind turbine blades will suffer various surface damages due to their operating environment and high-speed rotation. Accurate identification in the early stage of damage formation is crucial. The damage detection of wind turbine blades is a primarily manual operation, which has problems such as high cost, low efficiency, intense subjectivity, and high risk. The rise of deep learning provides a new method for detecting wind turbine blade damage. However, in detecting wind turbine blade damage in general network models, there will be an insufficient fusion of multiscale small target features. This paper proposes a lightweight cascaded feature fusion neural network model based on YOLOX. Firstly, the lightweight area of the backbone feature extraction network concerning the RepVGG network structure is enhanced, improving the model’s inference speed. Second, a cascaded feature fusion module is designed to cascade and interactively fuse multilevel features to enhance the small target area features and the model’s feature perception capabilities for multiscale target damage. The focal loss is introduced in the post-processing stage to enhance the network’s ability to learn complex positive sample damages. The detection accuracy of the improved algorithm is increased by 2.95%, the mAP can reach 94.29% in the self-made dataset, and the recall rate and detection speed are slightly improved. The experimental results show that the algorithm can autonomously learn the blade damage features from the wind turbine blade images collected in the actual scene, achieve the automatic detection, location, and classification of wind turbine blade damage, and promote the detection of wind turbine blade damage towards automation, rapidity, and low-cost development.
A graph attention network utilizing multi-granular information for emotion-cause pair extraction

Emotion-cause pair extraction (ECPE) aims to extract emotion and cause clauses underlying a text and pair them. Most of the recent approaches to this problem adopt deep neural networks to model the inter-clause dependency, without making full use of information at word level and document level. In this paper, we propose a model that utilizes multi-granular information, including word-level, clause-level, and document-level information, to facilitate emotion-cause pair extraction. Our model consists of two fully-connected clause graphs, including emotion graph and cause graph, and graph attention is applied to learn emotion-specific and cause-specific representations which are then used to generate document-level representations. To exploit the mutual indication between emotion and cause, a cross-graph co-attention mechanism is proposed. Moreover, external knowledge of emotional and causal cues is incorporated to provide word-level indicative information for emotion-cause pair extraction. The proposed model is tested on both Chinese [1] and English [2] datasets, and the results show that our model achieves the state-of-the-art performance on both datasets.
Emotional Contagion in Collaborative Virtual Reality Learning Experiences: An eSports Approach

Abstract Emotional contagion is an intriguing subject in many academic fields, and it is also relevant in collaborative learning where learners share a physical or virtual space. We aimed at exploring the possibilities of motivating, fascinating, and experiential elements of virtual reality (VR) in a collaborative learning context, with a focus on emotional contagion. We adopted the eSports mode as a competency development strategy in collaborative learning, using VR to evaluate emotional contagion that is invoked between the presenters and spectators. For this purpose, we created a VR application (HHVR) that allows freshmen students to learn about the premises and academic life at a university. We then divided 43 adult participants into presenters ( N = 9) and spectators ( N = 34); the presenters experienced the HHVR application first-hand, whereas the spectators watched the experience through a monitor. We used a questionnaire and semi-structured interviews to measure what feelings of being — existential feelings that affect the way we react to the world — the participants experienced. The collected data were analyzed by Principal Component Analysis and qualitative data coding and the results revealed emotional contagion; the spectators who followed the presenters on a monitor showed similar emotional engagement with the presenters who used the application. In conclusion, the proposed eSports mode can be a useful pedagogical technique in the context of collaborative learning with VR, as it engages emotionally both the presenters and spectators. These findings can be helpful for designing emotionally engaging collaborative learning experiences with VR and for conducting group-based UX evaluations of VR applications.
FALCoN: Detecting and classifying abusive language in social networks using context features and unlabeled data

Social networks have grown into a widespread form of communication that allows a large number of users to participate in conversations and consume information at any time. The casual nature of social media allows for nonstandard terminology, some of which may be considered rude and derogatory. As a result, a significant portion of social media users is found to express disrespectful language. This problem may intensify in certain developing countries where young children are granted unsupervised access to social media platforms. Furthermore, the sheer amount of social media data generated daily by millions of users makes it impractical for humans to monitor and regulate inappropriate content. If adolescents are exposed to these harmful language patterns without adequate supervision, they may feel obliged to adopt them. In addition, unrestricted aggression in online forums may result in cyberbullying and other dreadful occurrences. While computational linguistics research has addressed the difficulty of detecting abusive dialogues, issues remain unanswered for low-resource languages with little annotated data, leading the majority of supervised techniques to perform poorly. In addition, social media content is often presented in complex, context-rich formats that encourage creative user involvement. Therefore, we propose to improve the performance of abusive language detection and classification in a low-resource setting, using both the abundant unlabeled data and the context features via the co-training protocol that enables two machine learning models, each learning from an orthogonal set of features, to teach each other, resulting in an overall performance improvement. Empirical results reveal that our proposed framework achieves F1 values of 0.922 and 0.827, surpassing the state-of-the-art baselines by 3.32% and 45.85% for binary and fine-grained classification tasks, respectively. In addition to proving the efficacy of co-training in a low-resource situation for abusive language detection and classification tasks, the findings shed light on several opportunities to use unlabeled data and contextual characteristics of social networks in a variety of social computing applications.
Energy Efficiency in Periods of Load Shedding and Detrimental Effects of Energy Dependence in the City of Maroua, Cameroon

During the years 2021 and 2022, the city of Maroua experienced repeated power blackouts.However, this locality has significant photovoltaic energy potential.Nevertheless, the evaluation of the electrical performance showed the dependence of the population on these fluctuations, which could be bypassed or suppressed.In most cases, the blackout occurs during high energy demand.In this paper, a method for evaluating electrical efficiency is proposed and its credibility has been demonstrated on the one hand, and on the other hand, a renewable energy production system is proposed.The Homer software has made possible the analysis of the proposed system and its impact on the environment has also been carried out.The techno-economic study of the system has proved that a solar photovoltaic farm associated with an energy storage system, with a capacity of 47 MW, can meet the energy demand of the town of Maroua.This alternative is profitable for this locality which lives in a precarious situation and a continuous need.
Blockchain technology to protect label information: The effects on purchase intentions in the food industry

Blockchain technology is continuing to spread in many sectors, including the food industry, which has begun to embrace it in order to face new transformative trends. Indeed, since it is based on distributed ledgers and the encryption of stored data, blockchains may provide greater security for information delivered. It is still unclear what effects the use of blockchains has on customer behavior in the food sector. Consequently, our analyses tested the impact that blockchains have on customer purchase intentions in the food sector. Our results show that information provided to customers about the use of a blockchain to protect information throughout the supply chain can positively influence their purchase intentions. This study, consequently, offers new insights into the benefits generated by blockchains in the food sector and contributes to scientific understanding of the phenomenon overall. Moreover, our results provide insights that are also useful for managers and policymakers to further spread the use of blockchains.
The water and carbon footprint of cryptocurrencies and conventional currencies

When compared to traditional fiat currencies, cryptocurrencies represent a small, but growing, fraction of financial transactions. The rapid growth of cryptocurrencies has raised concerns regarding the environmental impact of this energy-intensive type of currency. In this study, water and carbon footprints associated with cryptocurrencies are assessed. These environmental footprints are then compared to the water and carbon footprints associated with conventional currencies, which is a novelty of this study. A spatially refined, bottom-up analysis of the most energy-intensive aspects of each financial system is used in the comparison of these systems. Specifically, the energy use for printing money, bank branches, automated teller machines (ATM), and cashless transactions is evaluated at the country level and compared to the energy use of crypto mining around the world. Cryptocurrencies' electricity use of 236 × 106 megawatt hours (MWh) in 2021 surpassed that of the conventional transaction system, despite cryptocurrencies representing less than 0.5% of global cashless financial transactions. In terms of its water footprint, cryptocurrencies have an annual water consumption of 3670 × 106 cubic meters (m3). Cryptocurrencies' water footprint is more than double that of conventional currencies because crypto mining takes place in countries with higher water intensities for electricity. Crypto mining activities are also estimated to result in almost 139 × 106 tonnes carbon dioxide equivalent (CO2-eq) of global greenhouse gas emissions. While cryptocurrencies represent a small percentage of financial transactions, their environmental impact is much larger than the conventional financial transaction system. This study shows how regulatory changes, such as the 2021 crypto mining ban in China, and crypto mining location can have implications on the environmental footprint associated with cryptocurrency.
Ship detection based on multi-scale weighted fusion

Ship surveillance plays an important role in ensuring the safety of maritime transportation and navigation. Due to the influence of factors such as waves and special weather, the existing detection methods still cannot balance the accuracy, speed and the parameters of the model in the changeable and complex marine environment. To solve this problem, this paper proposes an improved real-time method based on YOLOv5, which has few parameters and achieves high detection accuracy with little memory and computation cost. Collaborative Attention (CA) mechanism is added to the network structure, which enables the model to more accurately locate and identify target regions. We also design a Spatial Pyramid Pooling module (SPP) and a weighted pyramid network called Bidirectional Feature Pyramid Network (BiFPN) based on the characteristics of the ships to better fuse feature information. Transformer encoder is introduced to capture long-distance dependencies and preserve global and local features to the greatest extent. Furthermore, the ability of our proposed structure to localize objects at each stage is improved through integrating the output of multiple modules. The experimental results show that, the comprehensive performance of this method is better than the existing technology in ship detection on different evaluation criteria.
SwinTD: Transformer-based detection network for foreign objects in the cut section of tobacco packets

This study proposes a Swin Transformer for Tobacco Detection (SwinTD) to detect foreign objects in the cut section of tobacco packets. SwinTD uses a Swin Transformer to model the relationship between foreign objects and the background. Meanwhile, a local enhancement block is proposed to enhance the global modeling ability. Then, the Swin Transformer D-Block is proposed, which introduces dense connections, increases the reuse of features, and avoids overfitting. Subsequently, the Feature Pyramid Network (FPN) is adopted to fuse the multi-stage feature maps of the Swin Transformer. Experimental results show that the proposed method achieves an AP50 of 96.4% with the backbone network Swin-T in a cut-section dataset of tobacco packets. Also, the robustness and feasibility of the network are verified on two public datasets, namely the NEU-DET dataset and the rubber lumber dataset. The proposed method achieves an AP50 of 72.2% and 88.6% with the backbone network Swin-B. The datasets and program code used in this paper are available at https://github.com/QDLGARIM.
The application of Metaverse XiRang game in the mixed teaching of art and Design in Colleges and Universities

Metaverse is a digital world created by human beings according to the physical world. Its deep integration of virtual and real features has created a new opportunity for the innovative development of game teaching in art design courses in colleges and universities under the epidemic situation. In the field of art design, the investigation and research on the learning mode of its courses show that traditional teaching can hardly bring good experience to students, which is manifested in the following aspects: the lack of "presence" in online learning caused by epidemic situation, resulting in weak display of teaching effect, and unreasonable organization of group learning in the course. Therefore, in view of these problems, this paper puts forward three paths about the innovative application of art design courses by using the teaching strategy of Xirang games, namely, interaction on the same screen and presence experience, interaction between real individuals and virtual images, and division of cooperative learning interest groups. At last, by using the research methods of semi-structured interview, eye movement experiment and scale, it is concluded that virtual game learning plays an active role in promoting the teaching reform in colleges and universities, stimulating learners to develop higher-order thinking abilities such as creativity and criticism, solving the problems of traditional teaching, and promoting learners to move from "outside the teaching scene" to "inside the teaching scene" and from "outside the knowledge" to "inside the knowledge", which leads to a new teaching direction for the future teaching mode.
Securing Healthcare Records Using Blockchain: Applications and Challenges

The COVID-19 virus shook the entire world and only technology was permitted in many fields, including healthcare, to sustain a minimal degree of existence. In countries where patients have trouble using technology, this is a problem. Not to mention problems like cyber-attacks that compromise patient privacy by disclosing their medical information and jeopardise crucial patient records. Advancements in the IT sector are digitising the healthcare sector with the aim to boost the security and privacy of Personal Health Records. The healthcare system is facing major concerns owing to clarity of data, compliance, persistence, confidentiality, extensibility, and investigations. In addition, due to the centralisation of many existing systems of data management in the healthcare industry, in the case of a natural disaster, there is a probability of a single point of failure (SPOF). Central server-based systems, cloud-based systems, and blockchain-based systems are just a few of the solutions available. The way data is managed in the healthcare sector could be fundamentally remodelled, reshaped, and transformed by blockchain, a ground-breaking decentralised technology. While in healthcare systems, blockchain technology is used to share and store records of patients across various hospitals, laboratories, doctors, or pharmacies, in the medical areas blockchain applications can reliably detect serious and even life-threatening errors. This could lead to an improvement in the system’s efficiency, transparency, and security of the exchange of medical data. Medical institutions can use this technology to get insight into medical records and improve their analyses. The current work discusses the benefits of adopting blockchain technology for securing healthcare data and emphasises its important characteristics. The work also lists the challenges that are impeding blockchain implementation in the healthcare sector.
Backward-Leak Uni-Directional Updatable Encryption from (Homomorphic) Public Key Encryption

The understanding of directionality for updatable encryption (UE) schemes is important, but not yet completed in the literature. We show that security in the backward-leak uni-directional key updates setting is equivalent to the no-directional one. Combining with the work of Jiang (ASIACRYPT 2020) and Nishimaki (PKC 2022), it is showed that the backward-leak notion is the strongest one among all known key update notions and more relevant in practice. We propose two novel generic constructions of UE schemes that are secure in the backward-leak uni-directional key update setting from public key encryption (PKE) schemes: the first one requires a key and message homomorphic PKE scheme and the second one requires a bootstrappable PKE scheme. These PKE can be constructed based on standard assumptions (such as the Decisional Diffie-Hellman and Learning With Errors assumptions).
A Survey of Artificial Intelligence in Fashion

The fashion industry is on the verge of an unprecedented change. Fashion applications are benefiting greatly from the development of machine learning, computer vision, and artificial intelligence. In this article, we present an overview of three major topics of fashion and associated state-of-the-art techniques: 1) fashion analysis, including popularity prediction and fashion trend analysis; 2) fashion recommendation, including fashion compatibility and outfit matching; and 3) fashion synthesis, including makeup transfer and virtual try-on. Problem formulations, method comparisons, and evaluation metrics are illustrated for each topic of fashion research. Additionally, promising directions for development in each area are outlined to inspire future research.
Local nonlinear dimensionality reduction via preserving the geometric structure of data

Dimensionality reduction has many applications in data visualization and machine learning. Existing methods can be classified into global ones and local ones. The global methods usually learn the linear relationship in data, while the local ones learn the manifold intrinsic geometry structure, which has a significant impact on pattern recognition. However, most of existing local methods obtain an embedding with eigenvalue or singular value decomposition, where the computational complexities are very high in a large amount of high-dimensional data. In this paper, we propose a local nonlinear dimensionality reduction method named Vec2vec, which employs a neural network with only one hidden layer to reduce the computational complexity. We first build a neighborhood similarity graph from the input matrix, and then define the context of data points with the random walk properties in the graph. Finally, we train the neural network with the context of data points to learn the embedding of the matrix. We conduct extensive experiments of data classification and clustering on nine image and text datasets to evaluate the performance of our method. Experimental results show that Vec2vec is better than several state-of-the-art dimensionality reduction methods, except that it is equivalent to UMAP on data clustering tasks in the statistical hypothesis tests, but Vec2vec needs less computational time than UMAP in high-dimensional data. Furthermore, we propose a more lightweight method named Approximate Vec2vec (AVec2vec) with little performance degradation, which employs an approximate method to build the neighborhood similarity graph. AVec2vec is still better than some state-of-the-art local dimensionality reduction methods and competitive with UMAP on data classification and clustering tasks in the statistical hypothesis tests.