Reviewerly/exHarmony huggingface dataset:
The Role of Augmented Reality and Virtual Reality in Smart Health Education: State of the Art and Perspectives

Augmented reality (AR) and virtual reality (VR) have become increasingly viable and desirable in many sectors, notably education, due to new technology advancements and the spread of economical hardware and software. In this article, we explore AR and VR for sustainable education using a comparative framework. This study aims to comprehend if using AR and VR boosts students' desire to learn. Studies from various databases and journals and those about technology systems were assessed and compared to that objective. This paper explains why the recent widespread acceptance of AR and VR in the educational sector shall be a reality in the upcoming years. It may be stated that, despite the numerous constraints and problems that face the implementation of AR and VR in educational sectors, AR/VR utilizations offer an efficient tool for enhancing the educational sector because they give immersed multimodal environments with diverse sensory elements. Nevertheless, such innovations are not without flaws, so we conclude this study by outlining some unique strategies that could be used to solve them and potential research topics for future scholars interested in using these developing technologies in education.
Privacy-Enhanced Knowledge Transfer with Collaborative Split Learning over Teacher Ensembles

Knowledge Transfer has received much attention for its ability to transfer knowledge, rather than data, from one application task to another. In order to comply with the stringent data privacy regulations, privacy-preserving knowledge transfer is highly desirable. The Private Aggregation of Teacher Ensembles (PATE) scheme is one promising approach to address this privacy concern while supporting knowledge transfer from an ensemble of "teacher" models to a "student" model under the coordination of an aggregator. To further protect the data privacy of the student node, the privacy-enhanced version of PATE makes use of cryptographic techniques at the expense of heavy computation overheads at the teacher nodes. However, this inevitably hinders the adoption of knowledge transfer due to the highly disparate computational capability of teachers. Besides, in real-life systems, participating teachers may drop out of the system at any time, which causes new security risks for adopted cryptographic building blocks. Thus, it is desirable to devise privacy-enhanced knowledge transfer that can run on teacher nodes with relatively fewer computational resources and can preserve privacy with dropped teacher nodes. In this connection, we propose a dropout-resilient and privacy-enhanced knowledge transfer scheme, Collaborative Split learning over Teacher Ensembles (CSTE), that supports the participating teacher nodes to train and infer their local models using split learning. CSTE not only allows the compute-intensive processing to be performed at a split learning server, but also protects the data privacy of teacher nodes from collusion between the student node and aggregator. Experimental results showed that CSTE achieves significant efficiency improvement from existing schemes.
Twi Machine Translation

French is a strategically and economically important language in the regions where the African language Twi is spoken. However, only a very small proportion of Twi speakers in Ghana speak French. The development of a Twi–French parallel corpus and corresponding machine translation applications would provide various advantages, including stimulating trade and job creation, supporting the Ghanaian diaspora in French-speaking nations, assisting French-speaking tourists and immigrants seeking medical care in Ghana, and facilitating numerous downstream natural language processing tasks. Since there are hardly any machine translation systems or parallel corpora between Twi and French that cover a modern and versatile vocabulary, our goal was to extend a modern Twi–English corpus with French and develop machine translation systems between Twi and French: Consequently, in this paper, we present our Twi–French corpus of 10,708 parallel sentences. Furthermore, we describe our machine translation experiments with this corpus. We investigated direct machine translation and cascading systems that use English as a pivot language. Our best Twi–French system is a direct state-of-the-art transformer-based machine translation system that achieves a BLEU score of 0.76. Our best French–Twi system, which is a cascading system that uses English as a pivot language, results in a BLEU score of 0.81. Both systems are fine tuned with our corpus, and our French–Twi system even slightly outperforms Google Translate on our test set by 7% relative.
Evaluating blockchain as a participatory organisational system: looking for transaction efficiency

Abstract The article presents a decision‐making model that can be used with blockchain technology. Blockchain is used as an alternative transaction mechanism to authority and the market, where the decision is decentralised within the organisation. Thus, the process is parameterised around the acceptance or not of a project, depending on individual levels of expertise, consensus level and including deliberation time. In addition, commission and omission errors are also evaluated. We show that this technology should be imposed naturally because, at the same level of information, it can obtain better results than any other decision mechanism in a systematic way. In addition, the blockchain ensures that both commission errors and omission errors are reduced with decision times that do not increase the expected opportunity loss on omission errors.
Novel computer aided diagnostic system using hybrid neural network for early detection of pancreatic cancer

One of the most dangerous tumours in the world, pancreatic cancer (PC), has an unimpressive five-year survival rate of about 5%. An early PC identification is crucial for raising patient survival rates. Diagnosis of PC requires computed tomography (CT), magnetic resonance imaging (MRI) with magnetic resonance cholangiopancreatography (MRCP), or biopsy. The proposed CAD design approach includes image preprocessing, segmentation, feature extraction, and classification phases. Preprocessing is done by using Colour conversion and an isotropic diffusion filter approaches. After that, proposed Fuzzy K-NN Equality algorithm used in segmentation procedures. Deep Learning with feature extraction is used as a classification tool. Tumour cells are classified using the features collected from the pancreatic sample. Train values and testing datasets are part of the image classification criterion. For the purpose of detecting pancreatic cancer, a hybrid Deep Convolutional Neural Network with Deep Belief Network (DCNN_DBN) algorithm is used. According to the experimental findings, the current CAD system offers massive prospects as well as safety in the automated diagnosis of both benign as well as malignant cancers and produces the accuracy of 99.6%. Using this classifier, computing complexity is massively diminished. The suggested technique could be enhanced to detect more pancreatic cancer cell abnormalities.
Hate Influencers’ Mediation of Hate on Telegram: “We Declare War Against the Anti-White System”

Hate influencers play a critical role in platforming hate. In this article, we illustrate how visible (forward-facing) and invisible (faceless) hate influencers mobilize far-right hate groups in the mobile socio-sphere. Based on our digital multimodal walkthrough method and multimodal discourse analysis, we analyze 16 Telegram channels for two designated hate groups. We focus our analysis on Proud Boys content related to the 6 January attack on Capitol Hill and the White Lives Matter rallies across North America in 2021. To illustrate how hate influencers mobilize these groups, we introduce a three-part model that entails the process (mobile mobilization), means (discourses), and ends (actualizing the objective of the hate group).
Real‐time passenger flow anomaly detection in metro system

Abstract Real‐time passenger‐flow anomaly detection at all metro stations is a very critical task for advanced Internet management. Robust principal component analysis (RPCA) based method has often been employed for anomaly detection task of multivariate time series data. However, it ignores the spatio‐temporal features of regular passenger‐flow patterns, resulting in a decrease in the accuracy of anomaly detection. In this paper, RT‐STRPCA model integrating temporal periodicity and spatial similarity is proposed to address the above issues. RT‐STRPCA model detects anomalies by decomposing the observation data into normal component and anomaly component. The spatio‐temporal constraints are taken into account to extract anomalies more accurately. The real‐time anomaly detection are realized by a sliding window. The performance of RT‐STRPCA model is evaluated on synthetic datasets and real‐world datasets. The experimental results on synthetic datasets demonstrate that the method achieves more accurate real‐time anomaly detection than baseline approaches and the result on real‐world datasets verify the utility and effectiveness of the proposed method.
Efficient Attack Scheme against SKINNY-64 Based on Algebraic Fault Analysis

Lightweight block ciphers are normally used in low-power resource-constrained environments, while providing reliable and sufficient security. Therefore, it is important to study the security and reliability of lightweight block ciphers. SKINNY is a new lightweight tweakable block cipher. In this paper, we present an efficient attack scheme for SKINNY-64 based on algebraic fault analysis. The optimal fault injection location is given by analyzing the diffusion of a single-bit fault at different locations during the encryption process. At the same time, by combining the algebraic fault analysis method based on S-box decomposition, the master key can be recovered in an average time of 9 s using one fault. To the best of our knowledge, our proposed attack scheme requires fewer faults, is faster to solve, and has a higher success rate than other existing attack methods.
The performance of four domestic rooftop 7.8 kW − PV in the Kingdom of Bahrain: toward low building emission

This paper reports the performance of four domestic houses at different locations in Bahrain, each have 7.8 kW of PV on the roof, and all panels are tilted at 12°, but the azimuth of the panels (orientation) in these houses varies slightly from south (±10°); depending on roof space availability and building surroundings. The data of year 2019 have been used because all these domestic houses were monitored, maintained, and cleaned by an assigned company from the government. The annual solar electricity generated was 11,329 kWh, 11,448 kWh, 10,978 kWh, and 9995 kWh for houses # 4, # 2, #1, and # 3, respectively. The annual Specific Yield (SY) obtained was 1468 kWh/kW, 1452 kWh/kW, 1407 kWh/kW and 1254 kWh/kW for houses # 2, #4, # 1, and # 3, respectively; this makes the daily average SY equals to 4.02 kWh/kW, 3.98 kWh/kW, 3.85 kWh/kW, and 3.43 kWh/kW, respectively. The average performance ratio (PR) of each PV system were found to range from 75.1% to 65.6%. The PV system performs better in house #4 due to its azimuth PV panels' angle (orientation) which is closest to the south direction. A polynomial equation is established, relating the month number (X) and the monthly average of monthly solar electricity generated as well as the monthly specific yield (Y). This work shows that installing a 7.8 kW p of PV on the roof of all residential building in Bahrain will reduce the total CO 2 emission in Bahrain by 39.0% (4.637 tons) per year, saving 38,567 ft 3 of natural gas. This is a step towards low-carbon building; in an attempt to make Bahrain a zero carbon by 2060.
Detecting political biases of named entities and hashtags on Twitter

Abstract Ideological divisions in the United States have become increasingly prominent in daily communication. Accordingly, there has been much research on political polarization, including many recent efforts that take a computational perspective. By detecting political biases in a text document, one can attempt to discern and describe its polarity. Intuitively, the named entities (i.e., the nouns and the phrases that act as nouns) and hashtags in text often carry information about political views. For example, people who use the term “pro-choice” are likely to be liberal and people who use the term “pro-life” are likely to be conservative. In this paper, we seek to reveal political polarities in social-media text data and to quantify these polarities by explicitly assigning a polarity score to entities and hashtags. Although this idea is straightforward, it is difficult to perform such inference in a trustworthy quantitative way. Key challenges include the small number of known labels, the continuous spectrum of political views, and the preservation of both a polarity score and a polarity-neutral semantic meaning in an embedding vector of words. To attempt to overcome these challenges, we propose the P olarity-aware E mbedding M ulti-task learning ( PEM ) model. This model consists of (1) a self-supervised context-preservation task, (2) an attention-based tweet-level polarity-inference task, and (3) an adversarial learning task that promotes independence between an embedding’s polarity component and its semantic component. Our experimental results demonstrate that our PEM model can successfully learn polarity-aware embeddings that perform well at tweet-level and account-level classification tasks. We examine a variety of applications—including a study of spatial and temporal distributions of polarities and a comparison between tweets from Twitter and posts from Parler—and we thereby demonstrate the effectiveness of our PEM model. We also discuss important limitations of our work and encourage caution when applying the PEM model to real-world scenarios.
An Exploratory Case Study on Student Teachers’ Experiences of Using the AR App Seek by iNaturalist When Learning About Plants

In this case study we explore the use of Augmented Reality (AR) as a pedagogical tool to promote student teachers learning about plants in a biology course module within teacher education. Traditionally, when studying plants in science education, flora books are used. However, digital technology has contributed to new ways of exploring plants, and AR is suggested as an important tool. This requires teacher educators who are familiar with AR and the pedagogical implications of using AR when teaching about plants. Therefore, the aim of this study is to investigate student teachers’ experiences of using the AR app Seek by iNaturalist during an excursion to identify and learn about plants, as well as discuss the pedagogical implications of integrating the app into a biology course module. The student teachers took part in the excursion using the app to practice plant identification. Afterwards, semi-structured interviews were conducted with three student teachers. A thematic analysis approach was used to explore the student teachers’ experiences when using Seek. The findings indicate that Seek increases student teachers’ awareness of plants, which promotes their interest and engagement in plants. The easy accessibility in their phone makes them use Seek in their spare time. The increased interest and easy access support learning. By knowing the name of plants, the student teachers establish relationships with plants, which increases awareness of the importance of caring for nature. Hence, Seek could be seen as a pedagogical tool that promotes student teachers’ learning and interest about plants.
Some thoughts on dynamic systems modeling of L2 learning

The aim of this article is to present a very basic dynamic systems model of L2 learning based on a number of basic principles 1) at any moment in time, a learner’s L2 proficiency is a distribution of potentialities (possible levels of L2 production), 2) the distribution changes as a result of experienced L2-events such as conversations, 3) L2 proficiency and L2 events are represented on the same underlying array of linguistic proficiency (from 0, i.e., inexistent, to 1, i.e., maximal under the currently available linguistic resources); 4) learning processes are “normative” in the sense that they are governed by a process of convergence on the language spoken by a particular L2 community, this process depends on an optimum between familiarity and novelty; 5) the parameters governing the systems dynamic differ among individual learners and L2 learning contexts (e.g., highly adaptive versus non-adaptive communicative interactions with native L2 speakers).
Metallogenesis and Formation of the Maliping Pb-Zn Deposit in Northeastern Yunnan: Constraints from H-O Isotopes, Fluid Inclusions, and Trace Elements

The Maliping large-scale Pb-Zn deposit is located in the Sichuan-Yunnan-Guizhou Pb-Zn polymetallic metallogenic triangle area (SYGT), where the Pb-Zn ore body is hosted in the interlayer fracture zone at the interface between siliceous cataclastic dolomite and clastic rocks in the Lower Cambrian Yuhuchun Formation and is tectonically driven. Unlike other Pb-Zn de-posits hosted in the Sinian and Carboniferous carbonate rocks in the area, the metallogenic mechanism and deep and peripheral ore prospecting prediction research require further exploration. In this study, representative samples of a typical orebody profile were systematically collected, and microthermometry of fluid inclusions and H-O isotopes and metal sulfide trace element analyses were performed. The main findings were as follows: (1) The fluid inclusion study showed that the ore-forming fluids have vapor-rich phase reduction characteristics of medium-low temperature, salinity, and density. (2) H-O isotopic studies showed that the ore-forming fluids are derived from the mixing of deep-source fluids flowing through the deep fold basement (Kunyang Group) and or-ganic-containing basin brine. (3) Rare earth element (REE) characteristics indicate that the ore-forming materials were primarily derived from the folded basement (Kunyang Group). (4) The trace element study showed that sphalerite is relatively enriched in Cu, Cd, Ga, and Ge, while depleted in Fe, Mn, Sn, and Co, similar to the typical Huize-type (HZT) Pb-Zn deposit in the area. Therefore, it is suitable to explore the deposit using a large-scale “four step style” ore prospecting method for ore prospecting and prediction. Moreover, the results provide a reference for the study of Pb-Zn metallogenic systems and new ideas for the deep and peripheral prospecting of Pb-Zn deposits in this area.
CNN feature and classifier fusion on novel transformed image dataset for dysgraphia diagnosis in children

Dysgraphia is a neurological disorder that hinders the acquisition process of normal writing skills in children, resulting in poor writing abilities. Poor or underdeveloped writing skills in children can negatively impact their self-confidence and academic growth. This work proposes various machine learning methods, including transfer learning via fine-tuning, transfer learning via feature extraction, ensembles of deep convolutional neural network (CNN) models, and fusion of CNN features, to develop a preliminary dysgraphia diagnosis system based on handwritten images. In this work, an existing online dysgraphia dataset is converted into images, encompassing various writing tasks. Transfer learning is applied using a pre-trained DenseNet201 network to develop four distinct CNN models separately trained on word, pseudoword, difficult word, and sentence images. Soft voting and hard voting strategies are employed to ensemble these CNN models. The pre-trained DenseNet201 network is used for CNN feature extraction from each task-specific handwritten image data. The extracted CNN features are then fused in different combinations. Three machine learning algorithms support vector machine (SVM), AdaBoost, and Random forest are employed to assess the performance of the CNN features and fused CNN features. Among the task-specific models, the SVM trained on word data achieved the highest accuracy of 91.7%. In the case of ensemble learning, soft voting ensembles of task-specific CNNs achieved an accuracy of 90.4%. The feature fusion approach substantially improved the classification accuracy, with the SVM trained on fused features from the task specific-data achieving an accuracy of 97.3%. This accuracy surpasses the performance of state-of-the-art methods by 16%.
NASCTY: Neuroevolution to Attack Side-Channel Leakages Yielding Convolutional Neural Networks

Side-channel analysis (SCA) is a class of attacks on the physical implementation of a cipher, which enables the extraction of confidential key information by exploiting unintended leaks generated by a device. In recent years, researchers have observed that neural networks (NNs) can be utilized to perform highly effective SCA profiling, even against countermeasure-hardened targets. This study investigates a new approach to designing NNs for SCA, called neuroevolution to attack side-channel traces yielding convolutional neural networks (NASCTY-CNNs). This method is based on a genetic algorithm (GA) that evolves the architectural hyperparameters to automatically create CNNs for side-channel analysis. The findings of this research demonstrate that we can achieve performance results comparable to state-of-the-art methods when dealing with desynchronized leakages protected by masking techniques. This indicates that employing similar neuroevolutionary techniques could serve as a promising avenue for further exploration. Moreover, the similarities observed among the constructed neural networks shed light on how NASCTY effectively constructs architectures and addresses the implemented countermeasures.
A role of functional morphemes in Korean categorial grammars

Abstract This study discusses a role of functional morphemes in Korean categorial grammars, providing the reviews of various types of Korean categorial grammars that have never been conducted so far, notwithstanding many previous studies on them. Previous work has presented different morphological segmentation because of Korean’s agglutinative characteristics, implying that Korean words may contain a different segmentation sequence of morphemes. We focus on functional morphemes in Korean categorial grammars, which have been explored in different ways by previous work. We present detailed analyses for postpositions and verbal endings in categorial grammars, insisting that the functional morphemes in Korean should be treated as part of a word, with the result that their categories do not require to be assigned individually in a syntactic level, and also that it would be more efficient to assign the syntactic categories on the fully inflected lexical word derived by the lexical rule of the morphological processes in the lexicon.