Reviewerly/exHarmony huggingface dataset:
Mobile learning in mathematics education: A systematic literature review of empirical research

Mobile devices (e.g., smartphones and tablets) are spreading globally, along with decreasing prices and increasing support utilities with the characteristics of availability, flexibility, portability, individuality, connectivity, and social interactivity. The rise of mobile devices/ technologies in school inspired educational researchers to introduce the term “mobile learning” to push the boundaries of traditional pedagogy. This study was a systematic review of 52 articles related to mobile learning in mathematics education in 2008-2021. It answered the questions about distribution, major research purposes, approaches, methods. The articles for review were chosen using a three-stage methodology: read, select criteria, and summarize. This review study of mobile learning presents findings, which highlighted gaps in existing literature on the topic and has provided insights, can become a platform and guidance for researchers, educators, and policy makers for future research.
Instilling the core tenets of hospitality in healthcare services: The role of service assurance and social presence

The rising elderly population around the globe together with an improved life expectancy is driving demand for effective healthcare. As healthcare technologies become increasingly innovative, various types of connected devices have been developed in order to provide high-quality healthcare services. The research question of this study is whether the presence of such devices and perceived assurance affect the satisfaction of the health service recipient. This study focuses on homecare services for elderly people and explores the effect of social presence by machines vs. humans and service assurance by machines vs. humans on customer satisfaction. This study found that using connected devices does not significantly impact customer satisfaction with the healthcare service provider. The findings of this study highlight the importance of perceived service assurance levels as well as social presence delivered by humans for homecare services.
Object Detection and Tracking for Autonomous Vehicles using Deep Learning Technique- YOLO

For self-driving cars, Optical Perception is the most important factor. By accurately detecting road signs, foottravelers and vehicles, it could help and guide auto driven vehicles to drive as safely as human beings. In recent times, several CNN (Convolutional Neural Network) centered classification- after-localization methods have increased detection results under various situations. It is not possible for the two-stage methods to be used in real-time situations as it has very slow recognition speed. In recent times, (YOLO) You Only Look Once, was anticipated, can straight forwardly degenerate to object class positions and scores from input image. This model has single network structure and has greater detection precision than present-day real-time methods. This model handles images at 45 frames per second on PASCAL VOC 2007 dataset. In this detailed study and research, YOLO model is implemented to verify its general applicability, on three unlike datasets. This helped to completely scrutinized its behavior from several features on KITTI dataset which is specifically dedicated for self-directed driving. KITTI is my key dataset. In addition, for pre-training, ImageNet dataset is used and rest three datasets are implemented for other class domains are FDDB (Face Detection Dataset & Benchmarks), Road Signs, Pascal VOC-2007/2012
A Study on Physiotherapy Exercise Corrector

An estimated 200 million individuals work in the $90 billion fitness and healthcare sector. It is crucial to carry out the exercises with proper form and vigor. Everyone is now more than ever concerned about their health due to the global pandemic and avoids crowded areas. There is a lot of incorrect information accessible on exercise posture, which adds to the information overload. Even though much of this is being addressed by technology, artificial intelligence can provide solutions to many different problems. Many people turn to physiotherapy to treat their injuries. Physiotherapy is the best approach to improving posture. The body can recover its equilibrium and correct improper posture with the help of the exercises employed in physiotherapy. There are many different types of exercises used to cure ailments, wounds, and abnormalities. Physiotherapists constantly make sure their patients are exercising appropriately and check to see if they are executing each exercise as intended. But how can you know if the patients are still exercising correctly when they do the exercises at home on their own? In this project, we propose an application that corrects your exercise posture while you view a live video feed using a deep-learning object detection model in the backend.
Exploring Deep Time-Series Imaging for Anomaly Detection of Building Energy Consumption

Although deep anomaly detection (DAD) is crucial to optimize energy management in smart buildings, there is a lack of efficient research investigating DAD of energy consumption time-series. Besides, analyzing one-dimensional (1D) energy time-series does not provide many options to detect abnormal energy con-sumption. By contrast, converting 1D signals to two-dimensional (2D) representations open the doors to benefit from the success of time-series imaging in related research fields. In this paper, we explore transfer learning in anomaly detection of energy consumption. The presented method converts time-series inputs to images, which in turn serve as inputs for pre-trained CNN models. Specifically, this helps benefit from the inherent spatial invariance since the best characteristics are efficiently provided for convolutional layers. The experimental evaluation on real and simulated datasets demonstrates the promising performance of the proposed method, primarily when the support vector machine (SVM) is used to classify the features extracted using AlexNet. Accordingly, 92.24% and 99.19% weighted F1-score rates have been reached under both scenarios, respectively.
Improving Quicksort Performance by Optimizing Branch Prediction

By detecting arrays with a low degree of presort-edness, an adaptive quicksort algorithm can switch to a branch free implementation in order to reduce the runtime by up to 2.5 times compared to traditional implementations and outperform strictly branch free algorithms on arrays with a high degree of presortedness. By using a fast method of detecting the degree of presortedness, no prior knowledge about the array composition is required for the algorithm.
AlgoCert: Adopt Non-transferable NFT for the Issuance and Verification of Educational Certificates using Algorand Blockchain

In addition to tanking the economy and society, credential fraud frequently damages the credibility of higher education. As the majority of conventional verification and issuance procedures are arduous, expensive, and obsolete, fraud has proliferated in numerous forms, ranging from paper-based counterfeit certificates to replicas enabled by database manipulation, and has reached exponential quantities. In this paper, the authors propose AlgoCert, a comprehensive credential verification solution that incorporates blockchain, self-sovereign identity (SSI), and decentralized identifier (DID) to introduce a more proficient, attack-resistant system that is user-friendly as well as effective at reducing widespread credential fraud. In addition, AlgoCert leverages stateful smart contracts on the Algorand blockchain and non-transferable non-fungible tokens (NFT), and neither students nor organizations are required to handle credentials in order to use the system. This paper provides a prototype of our solution and detail our attempts to develop a verification service with features, including data security, transcript authentication, and selective data disclosure.
Resilient Distributed Classification Learning Against Label Flipping Attack: An ADMM-Based Approach

Distributed classification learning (DCL) is a promising solution to establish Internet of Things-based smart applications, especially due to its strong ability in dealing with large-scale and high-concurrency data. However, the performance of DCL may be seriously affected by the label flipping attack (LFA). Regarding the LFA-resilient learning problem, most existing works are built in more centralized settings. The work addressing the secure DCL issue makes an assumption that the label flipping rates are symmetric and available for scheme design. In this article, we remove this assumption and propose an LFA-resilient DCL scheme, named FENDER, without knowing the asymmetric flipping rates. The challenge is to guarantee both attack resilience and algorithm convergence. We carefully integrate a resilient loss and the alternating direction method of the multiplier scheme, making FENDER resilient to LFA. Further, we systematically analyze the performance of FENDER according to a metric reflecting the models obtained by all the servers at different iterations. In addition, we discuss and compare FENDER with some existing methods from the aspects of algorithm establishment and performance guarantee. Finally, extensive experiments with multiple real-world data sets are performed to validate the developed theory and evaluate the performance of the trained models.
Blockchain-Assisted Secure Data Sharing Protocol With a Dynamic Multiuser Keyword Search in IIoT

The Industrial Internet of Things (IIoT) and cloud computing have developed rapidly in recent years. Many enterprises are willing to outsource lightweight devices’ industrial data via the cloud to lower manufacturing costs and enhance production efficiency. The data sharers, however, usually have concerns about the security and privacy of their data stored in cloud outsourcing systems. Traditional certificateless searchable encryption primitives are tough to realize dynamically revocable and high-efficiency decryption. Furthermore, data sharing on untrusted devices may cause a single-key exposure problem. To address these issues, we propose a blockchain-assisted secure data-sharing protocol with a dynamic multiuser keyword search (DMUKS) in IIoT, which utilizes blockchain-assisted techniques to solve a single-key exposure problem and to realize fast certificateless keyword search as well as dynamic user and key management. The proposed DMUKS scheme enjoys minor computation and communication overheads. It maintains a constant ciphertext and trapdoor query size as users increase and supports user addition and revocation. Moreover, it periodically supports key and ciphertext updating and is secure against keyword-guessing attacks under the random oracle model. The performance comparison and analysis demonstrate that it is more efficient and flexible than the existing data sharing with keyword search schemes.
Attention Map Guided Transformer Pruning for Occluded Person Re-Identification on Edge Device

Due to its significant capability of modeling long-range dependencies, vision transformer (ViT) has achieved promising success in both holistic and occluded person re-identification (Re-ID) tasks. However, the inherent problems of transformers such as the huge computational cost and memory footprint are still two unsolved issues that will block the deployment of ViT based person Re-ID models on resource-limited edge devices. Our goal is to reduce both the inference complexity and model size without sacrificing the comparable accuracy on person Re-ID, especially for tasks with occlusion. To this end, we propose a novel attention map guided (AMG) transformer pruning method, which removes both redundant tokens and heads with the guidance of the attention map in a hardware-friendly way. We first calculate the entropy in the key dimension and sum it up for the whole map, and the corresponding head parameters of maps with high entropy will be removed for model size reduction. Then we combine the similarity and first-order gradients of key tokens along the query dimension for token importance estimation and remove redundant key and value tokens to further reduce the inference complexity. Comprehensive experiments on Occluded DukeMTMC and Market-1501 demonstrate the effectiveness of our proposals. For example, our proposed pruning strategy on ViT-Base enjoys <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">29.4%</b> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FLOPs</b> savings with <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.2%</b> drop on Rank-1 and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.4%</b> improvement on mAP, respectively. Code and models have been made available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/NUST-Machine-Intelligence-Laboratory/AMG</uri> .
Contextualized Relation Predictive Model for Self-Supervised Group Activity Representation Learning

Group activity analysis has attracted remarkable attention recently due to the widespread applications in security, entertainment and military. This paper targets at learning group activity representations with self-supervision, which differs from the majorities relying heavily on manually annotated labels. Moreover, existing Self-Supervised Learning (SSL) methods for videos are sub-optimal to generate such representations because of the complex context dynamics in group activities. In this paper, an end-to-end framework termed Contextualized Relation Predictive Model (Con-RPM) is proposed for self-supervised group activity representation learning with predictive coding. It involves the Serial-Parallel Transformer Encoder (SPTrans-Encoder) to model the context of spatial interactions and temporal variations, and the Hybrid Context Transformer Decoder (HConTrans-Decoder) to predict the future spatio-temporal relations guided by holistic scene context. Additionally, to improve the discriminability and consistency of prediction, we introduce a united loss integrating group-wise and person-wise contrastive losses in frame-level as well as the adversarial loss in global sequence-level. Consequently, our Con-RPM learns robust group representations via describing temporal evolutions of individual relationships and scene semantics explicitly. Extensive experimental results on downstream tasks indicate the effectiveness and generalization of our model in self-supervised learning, and present state-of-the-art performance on the Volleyball, Collective Activity, VolleyTactic, and Choi's New datasets.
Dynamic Measurement Assessments of Sensory Gloves Based on Resistive Flex Sensors and Inertial Measurement Units

Sensory gloves are devices capable of measuring finger movements and are useful in numerous applications, many of which require real-time data acquisition. However, the procedures explored in the literature to assess measurement repeatability and reliability mainly rely on static or quasi-static conditions. To overcome this limitation, here we propose a testing procedure for assessing measurements under dynamic conditions (slow, medium, and rapid finger joint movements). To this aim, we used two sensory gloves, one based on resistive flex sensors (RFSs) and another based on inertial measurement units (IMUs) – as two of the most adopted types. Our study demonstrated the feasibility of measuring dynamic finger movements and the differences in dynamic measurement repeatability and reliability between RFS- and IMU-based gloves when considering the angles (in degrees) of each finger joint. The RFS-based glove scored with an average range ± standard deviation (SD) of 6.84 ± 2.77°, and an intraclass correlation coefficient (ICC) of 0.77 ± 0.14, whereas the IMU-based glove scored with an average range of 8.49 ± 2.72°, and an ICC of 0.75 ± 0.14. Both gloves exhibited better repeatability and reliability at the slowest speed, with the RFS-based glove having an higher repeatability than the IMU-based one ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> < 0.001). Moreover, when compared to previous studies, the results (in terms of reliability and repeatability) here obtained under dynamic conditions are comparable to those obtained under static or quasi-static conditions. In summary, our results indicate that both proposed sensory gloves are suitable for most applications that require dynamic interactions.
Applications of Deep Learning and Machine Learning in Computational Medicine

Computational medicine has emerged due to the advances in medical technology in parallel with big data and artificial intelligence.A new way of treating complex diseases is evolving called 'Precision Medicine' fueled by big data extracting meaningful information from individual variability.At the forefront is biomedical research aiming to promote the area of precision medicine.Though traditional machine learning methods have built successful models for cancer diagnosis to sars-cov2 pulmonary infection, the advent of modern deep learning methods has had phenomenal growth in genomics, electronic health records, and drug development.The challenges in Deep learning applications in medicine include lack of data, privacy, heterogeneity of data, and interpretability.Analysis and discussion on these problems provide a reference to improve the application of deep learning in medical health.
The Use of Augmented Reality and Virtual Reality Technologies in Teaching Foreign Languages

The article is devoted to analyzing the educational potential of augmented reality (AR) and virtual reality (VR) technologies in teaching foreign languages, specifically in the professional training of future teachers. The possibility and benefits of introducing AR technologies in the system of teachers’ professional training are based on the experience of the discipline «Modern Technologies of Teaching a Foreign Language to Preschool Children», which is taught during the first (bachelor's) level of education to students of the 012 Preschool Education Specialty at the Faculty of Pedagogical Education in Borys Grinchenko Kyiv University. The literature analysis and the description of the empirical experience of the interaction and implementation of AR and VR technologies in the course content are the major research methods used in this article. The article performs an analysis of the advantages and disadvantages of AR and VR technologies in foreign language teaching. The didactic potential of both aforementioned technologies is identified, including the improved learning environment, increased motivation, interaction, and content retention, the richness of multimedia modes, better visualization, and suitability for special learning needs. The article emphasizes the influence on multiple language skills and vocabulary development (vocabulary acquisition, phonics, pronunciation, comprehension, writing, reading, speaking, improved communication, and general learning skills). The data received proves the suitability and benefits of implementing current AR technologies in the professional training of future teachers and language teaching in general, as opposed to VR technologies that are limited due to their drawbacks that outweigh their current advantages, but still allow them to be considered a very powerful tool in the near future. Still, their partial accessibility by learners gives them the right to be used in terms of the realization of individual approaches. As a result of this article, a list of AR and VR technologies that could be used in foreign language teaching is revealed. The perspective of future research could focus on the analysis of specific technologies from AR or VR groups
Online Learning Quality Evaluation in Higher Education During COVID-19 Pandemic

The COVID-19 pandemic has drastically altered people’s lives, including university education. COVID-19 has significantly impacted the transition from offline to online learning. Furthermore, education in Indonesia must be able to accelerate education 4.0. The learning system is carried out remotely using information technology. There will be numerous major challenges in implementing the online learning model. During the COVID-19 pandemic, Universitas Islam 45 needed to assess student satisfaction with online learning to maintain service quality. This study aimed to assess student satisfaction during online learning during the COVID-19 pandemic using the Service Quality (Servqual) and Importance Performance Analysis (IPA) methods. Servqual and IPA are methods for measuring customer satisfaction and service quality. According to the Servqual results, all 17 measurements have a negative gap value. This demonstrates a gap between students’ perceptions and expectations of online learning. The IPA Matrix indicates the priority of improving online-based learning, which includes responding quickly and efficiently to students’ needs in online-based learning, encouraging students’ motivation throughout the learning process, and understanding students’ impediments during the process of online-based learning.
Semi-supervised Learning in Distributed Split Learning Architecture and IoT Applications

In the era of big data, new learning techniques are emerging to solve the difficulties of data collection, storage, scalability, and privacy. To overcome these challenges, we propose a distributed learning system that merges the hybrid edge-cloud split-learning architecture with the semi-supervised learning scheme. The proposed system based on three semisupervised learning algorithms (FixMatch, Virtual Adversarial Training, and MeanTeacher) is compared to the supervised learning scheme and trained on different datasets and data distributions (IID and non-IID) and with a variable number of clients. The new system could efficiently utilize the local unlabeled samples on the client side and gave a performance encouragement that exceeds 30% in most cases even with small percentage of labelled data. Additionally, certain Split-SSL algorithms showed performance that was on par with or occasionally even better than more resource-intensive algorithms, although requiring less processing power and convergence time.
Teaching Quantum Machine Learning in Computer Science

The field of quantum computing is rapidly growing, with near term applications immediately available for use. The application of quantum computing to machine learning (i.e., quantum machine learning) is similarly growing rapidly. The presence of noisy intermediate-scale quantum (NISQ) era computers is further enabling research in the area. Historically, the barrier to entry of quantum computing has been nearly insurmountable for computer science students, or any other students who lack a strong physics background. However, quantum computing and quantum machine learning are becoming increasingly accessible, regardless of background. The goal of this paper is to present and demonstrate that the field is accessible to computer science students and to provide a sample curriculum. This curriculum can be used in a standalone class or as part of another machine learning class, as the authors have done.