Reviewerly/exHarmony huggingface dataset:
Philosophical and Pedagogical Discourse in the Postmodern Educational Space: Peculiarities of Distance Learning

The article presents a philosophical understanding and real interpretation of the existence and evolution of pedagogical (educational) discourse in the postmodern space. The results of scientific research are analyzed and the structural-semantic relationship of the concepts, terms and categories associated with the terminological field “discourse” is defined. The authors raise the problem of the postmodern significance of discourse in the period of the global transformation of the educational environment, caused by the pandemic COVID-19, which has put humanity before the fact of actualization of remote communication and receipt of educational services. The authors believe that distance learning should be provided with strong technical and technological support on the one hand and the use of this vector set of educational, pedagogical, learning and development technologies in the preparation of future specialists in the system of higher education on the other. The article deals with new approaches to the application of discourse-supporting technologies and methods. Thus, the emphasis is placed on the relevance of an open informational and educational environment of higher education institutions in the postmodern space where the role of discourse as a constructive tool of postmodernism is determined by the new interpretation, and the teacher becomes a generator of innovations in this context.
Voice Assistant as a Modern Contrivance to Acquire Oral Fluency: An Acoustical and Computational Analysis

A number of methods have been experimented to improve fluency and accuracy of second language learners in English. In acquisition of fluency in oral communication, the second language learners have many obstacles such as lack of exposure and lack of opportunities. Focus on the skills targeted and individualization of attention are needed to accomplish the objectives. The objective of this article is to enable second language learners to gain fluency in English through the use of Voice Assistant in L2 classrooms. A number of Voice Assistants were experimented to find out the suitable application for the study. The study was carried out in a university in India to find out the effectiveness of Voice Assistant for enhancing fluency. The hypothesis was tested with forty students. Pre-test and post-test were conducted to find out the fluency level of the Experimental and the Control Groups through audio analysis. The data was analyzed to indicate the significance of the method. The progress of the learners experimented with Voice Assistant was most encouraging, and they found this method quite appropriate and easy too in acquisition of oral fluency.
Unbalanced penalization: A new approach to encode inequality constraints of combinatorial problems for quantum optimization algorithms

Solving combinatorial optimization problems of the kind that can be codified by quadratic unconstrained binary optimization (QUBO) is a promising application of quantum computation. Some problems of this class suitable for practical applications such as the traveling salesman problem (TSP), the bin packing problem (BPP), or the knapsack problem (KP) have inequality constraints that require a particular cost function encoding. The common approach is the use of slack variables to represent the inequality constraints in the cost function. However, the use of slack variables considerably increases the number of qubits and operations required to solve these problems using quantum devices. In this work, we present an alternative method that does not require extra slack variables and consists of using an unbalanced penalization function to represent the inequality constraints in the QUBO. This function is characterized by larger penalization when the inequality constraint is not achieved than when it is. We evaluate our approach on the TSP, BPP, and KP, successfully encoding the optimal solution of the original optimization problem near the ground state cost Hamiltonian. Additionally, we employ D-Wave Advantage and D-Wave hybrid solvers to solve the BPP, surpassing the performance of the slack variables approach by achieving solutions for up to 29 items, whereas the slack variables approach only handles up to 11 items. This new approach can be used to solve combinatorial problems with inequality constraints with a reduced number of resources compared to the slack variables approach using quantum annealing or variational quantum algorithms.
Blockchain-based Cryptocurrency Price Prediction with Chaos Theory, Onchain Analysis, Sentiment Analysis and Fundamental-Technical Analysis

Crypto assets succeeded in making their name known to large masses with Bitcoin, which emerged as a result of the creation of the first genesis block in 2008. Until 2010, the aforementioned recognition showed itself mostly in areas such as games, but over time it managed to enter the portfolios of individual investors. Especially as of end of 2017, the rapid increases in monetary value quickly attracted the attention of corporate companies and then the (Central Banks). These assets have created different alternatives (also know as altcoins) by working and have managed to become one of the important financial instruments today. This study has examined in detail the techniques (Chaos theory, Onchain analysis and Sentiment analysis) developed on the price predictions of crypto assets, which are very important in terms of the number and quality of investors. In the study, findings were obtained that new techniques such as onchain and sentiment are more prominent in estimating crypto asset prices compared to traditional asset price estimation methods of crypto assets and that these techniques can make consistent estimations.
Multi-criteria– Recommendations using Autoencoder and Deep Neural Networks with Weight Optimization using Firefly Algorithm

Demand for personalized recommendation systems elevated recently by e-commerce, news portals etc., to grab the customer interest on the sites. Collaborative filtering proves to be powerful technique but it always suffers from data sparsity, cold-start and robustness issues. These issues have been tackled by some approaches resulting in higher accuracy. Few of them take user profiles, item attributes and rating time as the side information along with ratings to give interpretative personalized recommendations. These type of approaches tries to find which factors mainly impacted the user to rate an item. Another approach extends the single-criteria ratings of collaborative filtering to multi-criteria ratings. Our approach exploits non-linear interpretative recommendations by exploring Multi-criteria ratings by combination of Autoencoders with dropout layer and firefly algorithm optimized weights for deep neural networks. Our approach solves data sparsity, scalability issues and fetch accurate recommendations. Experimental evaluations have been done using Yahoo! Movie and MovieLens datasets. Our approach outperforms in robustness and accuracy with respect to previous research works.
Joint Semantic Deep Learning Algorithm for Object Detection under Foggy Road Conditions

Current mainstream deep learning methods for object detection are generally trained on high-quality datasets, which might have inferior performances under bad weather conditions. In the paper, a joint semantic deep learning algorithm is proposed to address object detection under foggy road conditions, which is constructed by embedding three attention modules and a 4-layer UNet multi-scale decoding module in the feature extraction module of the backbone network Faster RCNN. The algorithm differs from other object detection methods in that it is designed to solve low- and high-level joint tasks, including dehazing and object detection through end-to-end training. Furthermore, the location of the fog is learned by these attention modules to assist image recovery, the image quality is recovered by UNet decoding module for dehazing, and then the feature representations of the original image and the recovered image are fused and fed into the FPN (Feature Pyramid Network) module to achieve joint semantic learning. The joint semantic features are leveraged to push the subsequent network modules ability, and therefore make the proposed algorithm work better for the object detection task under foggy conditions in the real world. Moreover, this method and Faster RCNN have the same testing time due to the weight sharing in the feature extraction module. Extensive experiments confirm that the average accuracy of our algorithm outperforms the typical object detection algorithms and the state-of-the-art joint low- and high-level tasks algorithms for the object detection of seven kinds of objects on road traffics under normal weather or foggy conditions.
The Stack: 3 TB of permissively licensed source code

Large Language Models (LLMs) play an ever-increasing role in the field of Artificial Intelligence (AI)--not only for natural language processing but also for code understanding and generation. To stimulate open and responsible research on LLMs for code, we introduce The Stack, a 3.1 TB dataset consisting of permissively licensed source code in 30 programming languages. We describe how we collect the full dataset, construct a permissively licensed subset, present a data governance plan, discuss limitations, and show promising results on text2code benchmarks by training 350M-parameter decoders on different Python subsets. We find that (1) near-deduplicating the data significantly boosts performance across all experiments, and (2) it is possible to match previously reported HumanEval and MBPP performance using only permissively licensed data. We make the dataset available at https://hf.co/BigCode, provide a tool called "Am I in The Stack" (https://hf.co/spaces/bigcode/in-the-stack) for developers to search The Stack for copies of their code, and provide a process for code to be removed from the dataset by following the instructions at https://www.bigcode-project.org/docs/about/the-stack/.
HPMA-NTRU: High-Performance Polynomial Multiplication Accelerator for NTRU

Along the rapid development of large-scale quantum computers, post-quantum cryptography (PQC) has drawn significant attention from research community recently as it is proven that the existing public-key cryptosystems are vulnerable to the quantum attacks. Meanwhile, the recent trend in the PQC field has gradually switched to the hardware acceleration aspect. Following this trend, this work presents a novel implementation of a High-performance Polynomial Multiplication hardware Accelerator for NTRU (HPMA-NTRU) under different parameter settings, one of the lattice-based PQC algorithm that is currently under the consideration by the National Institute of Standards and Technology (NIST) PQC standardization process. In total, we have carried out three layers of efforts to obtain the proposed work. First of all, we have proposed a new schoolbook algorithm based strategy to derive the desired polynomial multiplication algorithm for NTRU. Then, we have mapped the algorithm to build a high-performance polynomial multiplication hardware accelerator and have extended this hardware accelerator to different parameter settings with proper adjustment. Finally, through a series of complexity analysis and implementation based comparison, we have shown that the proposed hardware accelerator obtains better area-time complexities than the state-of-the-art one. The outcome of this work is important and will impact the ongoing NIST PQC standardization process and can be deployed further to construct efficient NTRU cryptoprocessors.
Metaverse in Tourism and Hospitality: Empirical Evidence on Generation Z from Italy

The paper has been conducted to analyze the change taking place in tourism marketing due to the metaverse innovation through tastes and needs of today's new generations of tourists, especially generation Z, that will be the future customers of the next decades. The goal of this study has been to analyze the opportunities concerning the metaverse and its immersive technology, through a survey conducted on a sample of generation Z people and understand which are the practical implications that can guide tourism companies and territorial governments' decisions during the nearest future, to satisfy new market demand and optimize their communication in the metaverse.
Multi-object tracking by multi-feature fusion to associate all detected boxes

Multi-object tracking (MOT) aims to estimate object trajectory in videos using either a public or private detection approach. Current trackers trained in private mode on diverse datasets make the comparison with cutting-edge methods unfair. Instead of relying exclusively on a single feature, integrating several features is more effective as it helps in data association in various conditions such as occlusion and fast Motion. Driven by this, we propose an improvised data association method by employing public detection tracker built on a CenterNet detector. In contrast to prior arts that merely used high score boxes, we improvise the data association in two stages by considering all the detections. The first stage is enforced by fusing feature embedding, IoU, and Motion features extracted from high-score boxes, followed by unmatched trajectories associated with low-score boxes using IoU similarity in the second stage. Our approach greatly surpasses cutting-edge methods in terms of excellent track quality, fewer ID switches, and high accuracy on MOT challenge datasets. The tracklet interpolation is used as a post-processing approach to fill the gap left by missing detections that improve performance even more.
Facial biotype classification for orthodontic treatment planning using an alternative learning algorithm for tree augmented Naive Bayes

Abstract Background When designing a treatment in orthodontics, especially for children and teenagers, it is crucial to be aware of the changes that occur throughout facial growth because the rate and direction of growth can greatly affect the necessity of using different treatment mechanics. This paper presents a Bayesian network approach for facial biotype classification to classify patients’ biotypes into Dolichofacial (long and narrow face), Brachyfacial (short and wide face), and an intermediate kind called Mesofacial, we develop a novel learning technique for tree augmented Naive Bayes (TAN) for this purpose. Results The proposed method, on average, outperformed all the other models based on accuracy, precision, recall, $$F_{1}\hbox {-score}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>F</mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:mtext>-score</mml:mtext> </mml:mrow> </mml:math> , and kappa, for the particular dataset analyzed. Moreover, the proposed method presented the lowest dispersion, making this model more stable and robust against different runs. Conclusions The proposed method obtained high accuracy values compared to other competitive classifiers. When analyzing a resulting Bayesian network, many of the interactions shown in the network had an orthodontic interpretation. For orthodontists, the Bayesian network classifier can be a helpful decision-making tool.
Problems with the implementation of blockchain technology for decentralized IoT authentication

Security and scalability issues impede the implementation of the Internet of Things (IoT). Because of the diversity of the surroundings, overcoming these obstacles is not an easy task. The decentralized and secure nature of blockchain technology has been recognized as a potential solution to these issues. However, the sharp contrast between resource-hungry blockchain technology and resource-starved IoT devices introduces a new challenge. The goal of this systematic literature review was to uncover issues that developed while attempting to blend various technologies, with a focus on authentication. In many cases, smart contracts have made it possible to implement at least some of the solutions that have been suggested. Many systems have figured out how to authenticate a user, but there are still fundamental issues with blockchain adoption for the IoT. While the rest of the solutions merely strive to do the best with what is available, three stand out as genuine attempts to overcome these issues by fundamentally altering the blockchain technology
INVESTIGATING THE IMPORTANCE OF ETHICS AND SECURITY ON INTERNET OF MEDICAL THINGS (IoMT)

Numerous opportunities are emerging due to the Internet of Things' (IoT) rapid development, which has the potential to significantly improve human quality of life in many areas. The healthcare industry is the main place where IoT can enhance our quality of lives. Security and privacy concerns, however, take center stage in electronic health (eHealth) systems, and the incorporation of IoT makes them increasingly difficult to address. Due to the numerous stakeholders in its broader ecosystem, the majority of IoMT which raises security concerns. The IoT-based healthcare system has various security features. This search provides an Identity-Based Cryptography cornerstone management technique that uses collaborative authentication and a secret passcode to protect transformation of data between any IoT health device and any other entity from a different company or domain (IBC).
Language models and brain alignment: beyond word-level semantics and prediction

Pretrained language models that have been trained to predict the next word over billions of text documents have been shown to also significantly predict brain recordings of people comprehending language. Understanding the reasons behind the observed similarities between language in machines and language in the brain can lead to more insight into both systems. Recent works suggest that the prediction of the next word is a key mechanism that contributes to the alignment between the two. What is not yet understood is whether prediction of the next word is necessary for this observed alignment or simply sufficient, and whether there are other shared mechanisms or information that is similarly important. In this work, we take a first step towards a better understanding via two simple perturbations in a popular pretrained language model. The first perturbation is to improve the model's ability to predict the next word in the specific naturalistic stimulus text that the brain recordings correspond to. We show that this indeed improves the alignment with the brain recordings. However, this improved alignment may also be due to any improved word-level or multi-word level semantics for the specific world that is described by the stimulus narrative. We aim to disentangle the contribution of next word prediction and semantic knowledge via our second perturbation: scrambling the word order at inference time, which reduces the ability to predict the next word, but maintains any newly learned word-level semantics. By comparing the alignment with brain recordings of these differently perturbed models, we show that improvements in alignment with brain recordings are due to more than improvements in next word prediction and word-level semantics.
Placing inclusive education in coversation with digital education

This article places inclusive education in conversation with digital education, and it does so for two reasons. The first reason is that the increased use of digital education will need to be inclusive and should not result in increasing inequalities. The second reason is that the experience within inclusive education provides valuable insights which could benefit the development of digital education. This article is conceptually based, and its aim is to show how experiences in inclusive education could benefit the development of digital education and vice versa. It argues that like inclusive education, the increased use of digital education in the future will require a redefinition and revisioning of education. While policies and legislation as well as training in digital education are important, this article recommends that digital education will need to engage with the deep-seated values, beliefs and assumptions held by teachers, students, parents, and society at large, and will require careful attention being paid to the systematic organisation of online learning in terms of curriculum, pedagogy, and assessment just as inclusive education.
High-Performance Actionable Knowledge Miner for Boosting Business Revenue

This research proposes a novel strategy for constructing a knowledge-based recommender system (RS) based on both structured data and unstructured text data. We present its application to improve the services of heavy equipment repair companies to better adjust to their customers’ needs. The ultimate outcome of this work is a visualized web-based interactive recommendation dashboard that shows options that are predicted to improve the customer loyalty metric, known as Net Promoter Score (NPS). We also present a number of techniques aiming to improve the performance of action rule mining by allowing to have convenient periodic updates of the system’s knowledge base. We describe the preprocessing-based and distributed-processing-based method and present the results of testing them for performance within the RS framework. The proposed modifications for the actionable knowledge miner were implemented and compared with the original method in terms of the mining results/times and generated recommendations. Preprocessing-based methods decreased mining by 10–20×, while distributed mining implementation decreased mining timesby 300–400×, with negligible knowledge loss. The article concludes with the future directions for the scalability of the NPS recommender system and remaining challenges in its big data processing.
The Usage of MALL in Learners’ Readiness to Speak English

Having proficiency in English macro skills has been of foremost importance among English language learners to have a sustainable education. Evidently, knowing English corresponds with being able to articulate the language. However, many language learners still struggle to successfully master speaking skills. Previous studies advocated mobile-assisted language learning (MALL) as an innovative pedagogical practice that has the potential to be utilized in the acquisition of the speaking skills. Nevertheless, current studies that specify the usage of MALL in the readiness of speaking English are still limited. Therefore, this paper plans to fill in the gap by propounding a systematic review of published studies from the year 2013 to 2022 that are relevant to the implementation of MALL in the readiness to speak English. The findings of this review indicated that the usage of MALL fosters affective variables such as motivation, self-confidence, less anxiety, and learners’ control in the readiness of speaking skills, but most of the reviewed studies’ focal point were on older or adult learners, which created a gap in the literature. Hence, this requires the study of the usage of MALL in primary and secondary levels of education for the benefits of English language learners of all ages in the acquisition of speaking English.