Reviewerly/exHarmony huggingface dataset:
A novel firefly algorithm approach for efficient feature selection with COVID-19 dataset

Feature selection is one of the most important challenges in machine learning and data science. This process is usually performed in the data preprocessing phase, where the data is transformed to a proper format for further operations by machine learning algorithm. Many real-world datasets are highly dimensional with many irrelevant, even redundant features. These kinds of features do not improve classification accuracy and can even shrink down performance of a classifier. The goal of feature selection is to find optimal (or sub-optimal) subset of features that contain relevant information about the dataset from which machine learning algorithms can derive useful conclusions. In this manuscript, a novel version of firefly algorithm (FA) is proposed and adapted for feature selection challenge. Proposed method significantly improves performance of the basic FA, and also outperforms other state-of-the-art metaheuristics for both, benchmark bound-constrained and practical feature selection tasks. Method was first validated on standard unconstrained benchmarks and later it was applied for feature selection by using 21 standard University of California, Irvine (UCL) datasets. Moreover, presented approach was also tested for relatively novel COVID-19 dataset for predicting patients health, and one microcontroller microarray dataset. Results obtained in all practical simulations attest robustness and efficiency of proposed algorithm in terms of convergence, solutions' quality and classification accuracy. More precisely, the proposed approach obtained the best classification accuracy on 13 out of 21 total datasets, significantly outperforming other competitor methods.
Neural Architecture Search Survey: A Computer Vision Perspective

In recent years, deep learning (DL) has been widely studied using various methods across the globe, especially with respect to training methods and network structures, proving highly effective in a wide range of tasks and applications, including image, speech, and text recognition. One important aspect of this advancement is involved in the effort of designing and upgrading neural architectures, which has been consistently attempted thus far. However, designing such architectures requires the combined knowledge and know-how of experts from each relevant discipline and a series of trial-and-error steps. In this light, automated neural architecture search (NAS) methods are increasingly at the center of attention; this paper aimed at summarizing the basic concepts of NAS while providing an overview of recent studies on the applications of NAS. It is worth noting that most previous survey studies on NAS have been focused on perspectives of hardware or search strategies. To the best knowledge of the present authors, this study is the first to look at NAS from a computer vision perspective. In the present study, computer vision areas were categorized by task, and recent trends found in each study on NAS were analyzed in detail.
Simplified swarm optimization for hyperparameters of convolutional neural networks

Convolutional neural networks (CNNs) are widely used in image recognition. Numerous CNN models, such as LeNet, AlexNet, VGG, ResNet, and GoogLeNet, have been developed by increasing the number of layers to improve performance. However, performance deteriorates beyond a certain number of layers. Hence, hyperparameter optimization is a more efficient way to improve CNNs. To validate this concept, in the present study, an algorithm based on simplified swarm optimization was developed for optimizing the hyperparameters of the simplest CNN model: LeNet. The results of experiments involving the MNIST, Fashion-MNIST, and CIFAR-10 datasets indicated that the accuracy of the proposed algorithm was higher than those of LeNet and PSO-LeNet and that the proposed algorithm can be applied to more complex models such as AlexNet.
YoNet: A Neural Network for Yoga Pose Classification

Yoga has become an integral part of human life to maintain a healthy body and mind in recent times. With the growing, fast-paced life and work from home, it has become difficult for people to invest time in the gymnasium for exercises. Instead, they like to do assisted exercises at home where pose recognition techniques play the most vital role. Recognition of different poses is challenging due to proper dataset and classification architecture. In this work, we have proposed a deep learning-based model to identify five different yoga poses from comparatively fewer amounts of data. We have compared our model's performance with some state-of-the-art image classification models-ResNet, InceptionNet, InceptionResNet, Xception and found our architecture superior. Our proposed architecture extracts spatial, and depth features from the image individually and considers them for further calculation in classification. The experimental results show that it achieved 94.91% accuracy with 95.61% precision.
What is the Metaverse? Challenges, Opportunities, Definition, and Future Research Directions

Interest in the Metaverse has recently surged due to the transformational opportunities and experiences that it offers to customers and businesses across the globe. However, the extant literature on the Metaverse is inconsistent, with no existing literature synthesis or consensus among studies on the definition of the Metaverse. This study provides a scoping review of Metaverse research, identifying the trends, definitions, and characteristics of the Metaverse concept. After providing a review of Metaverse research in all subject areas, the study narrows its focus to the business and management field, providing a more detailed review of the trends, characteristics, opportunities, and challenges of the Metaverse in this specific context. The findings from the literature are used to propose the Metaverse Research Model which conceptualizes and problematizes the literature, providing insight into the key concepts related to the Metaverse and offering guidance in terms of future research directions.
Encoding words into interval type-2 fuzzy sets: The retained region approach

The first step of implementing computing with words using a perceptual computer is to establish an encoder to transform words into interval type-2 fuzzy sets. In this paper, a novel approach named the retained region approach is introduced for encoding words into interval type-2 fuzzy sets. In the retained region approach, the data part is redesigned relative to the existing approaches, and a new fuzzy set part is established in accordance with the principle of justifiable granularity. The different means and standard deviations of the embedded type-1 fuzzy sets associated with a word are recognized as the origins of its inter-uncertainty, and the relations between them are taken into account in the construction of interval type-2 fuzzy sets. Importantly, the retained region approach is a versatile approach that does not impose redundant constraints on the selection of embedded type-1 fuzzy sets, and the capacity of the retained region can be flexibly adjusted along the two dimensions of the mean and the standard deviation. Finally, the performance of the retained region approach is illustrated by several simulations and comparisons with existing approaches.
Context-aware mutual learning for semi-supervised human activity recognition using wearable sensors

With the increasing popularity of wearable sensors, deep-learning-based human activity recognition (HAR) has attracted great interest from both academic and industrial fields in recent years. Nevertheless, the performance of deep HAR methods highly depends on the quality and quantity of annotations that are not so prone to obtain in HAR. This practical concern raises broad research of semi-supervised HAR. Despite the brilliant achievements, there remain three important issues to be settled: aggravation of overfitting, deviation of distribution and ignorance of contextual information. This paper proposes a novel context-aware mutual learning method for semi-supervised HAR. Firstly, a semi-supervised mutual learning framework is introduced to alleviate the overfitting of single network. In this framework, the main and auxiliary networks are collaboratively trained with supervised information from each other. Secondly, the distribution-preserving loss, which minimizes the distance between the class distribution of predictions and that of labeled data, is proposed to hinder the deviation of the distribution. Finally, the contextual information from the neighbor sequences is adopted through a context-aware aggregation module. This module extracts richer information from a broader range of sequences. Our method is validated on four characteristic published human activity recognition datasets: UCI, WISDM, PAMAP2 and mHealth. The experimental result shows that the proposed method achieves superior performance compared with four typical methods in semi-supervised HAR.
How Does Pre-Trained Wav2Vec 2.0 Perform on Domain-Shifted Asr? an Extensive Benchmark on Air Traffic Control Communications

Recent work on self-supervised pre-training focus on leveraging large-scale unlabeled speech data to build robust end-to-end (E2E) acoustic models (AM) that can be later fine-tuned on downstream tasks e.g., automatic speech recognition (ASR). Yet, few works investigated the impact on performance when the data properties substantially differ between the pre-training and fine-tuning phases, termed domain shift. We target this scenario by analyzing the robustness of Wav2Vec 2.0 and XLS-R models on downstream ASR for a completely unseen domain, air traffic control (ATC) communications. We benchmark these two models on several open-source and challenging ATC databases with signal-to-noise ratio between 5 to 20 dB. Relative word error rate (WER) reductions between 20% to 40% are obtained in comparison to hybrid-based ASR baselines by only fine-tuning E2E acoustic models with a smaller fraction of labeled data. We analyze WERs on the low-resource scenario and gender bias carried by one ATC dataset.
Iterative clustering pruning for convolutional neural networks

Convolutional neural networks (CNNs) have shown excellent performance in numerous computer vision tasks. However, the high computational and memory demands in computer vision tasks prohibit the practical applications of CNNs on edge computing devices. Existing iterative pruning methods suffer from insufficient accuracy recovery after each pruning, which severely affects the importance evaluation of the parameters. Moreover, channel pruning based on the magnitude of parameters often results in performance loss. In this context, we propose an iterative clustering pruning method named ICP together with knowledge transfer for channels. First, channel clustering pruning is performed based on the similarity between feature maps. Then, the intermediate and output features of the original network are applied to guide the learning of the compressed network after each pruning step to quickly recover the network performance and then implement the next pruning operation. Pruning and knowledge transfer are performed alternately to achieve accurate compression of the convolutional network. Finally, we demonstrate the effectiveness of the proposed method on the CIFAR-10, CIFAR-100, and ILSVRC-2012 datasets by pruning VGGNet, ResNet, and GoogLeNet. Our pruning scheme can typically reduce parameters and Floating-point Operations (FLOPs) of the network without harming accuracy significantly. In addition, the ICP was verified to have good practical generalization by compressing the SSD network on the object detection dataset PASCAL VOC.
Joint reasoning with knowledge subgraphs for Multiple Choice Question Answering

Humans are able to reason from multiple sources to arrive at the correct answer. In the context of Multiple Choice Question Answering (MCQA), knowledge graphs can provide subgraphs based on different combinations of questions and answers, mimicking the way humans find answers. However, current research mainly focuses on independent reasoning on a single graph for each question–answer pair, lacking the ability for joint reasoning among all answer candidates. In this paper, we propose a novel method KMSQA, which leverages multiple subgraphs from the large knowledge graph ConceptNet to model the comprehensive reasoning process. We further encode the knowledge graphs with shared Graph Neural Networks (GNNs) and perform joint reasoning across multiple subgraphs. We evaluate our model on two common datasets: CommonsenseQA (CSQA) and OpenBookQA (OBQA). Our method achieves an exact match score of 74.53% on CSQA and 71.80% on OBQA, outperforming all eight baselines.
Dynamic path planning method for headland turning of unmanned agricultural vehicles

Automatic headland turning is an important part for an unmanned agricultural vehicle at farming work. Minimize the time and travel distance and maximize the operation efficiency of the vehicles during the turning are the concerns of the current focus. This research tries to present a dynamic headland turning path planning method based on an asymmetric switch-back turning method for four-wheeled vehicles. When an agricultural vehicle slips in the field, the algorithm will re-plan the route dynamically according to the real-time position of the vehicle to improve the operation efficiency and reduce the mechanical wear caused by frequently adjusting the steering wheel. The simulation results showed that when the row width was 2 m and some noises were added in the turning process, the trajectory length planned by the proposed algorithm was reduced by 31.42 % and the lateral deviation was reduced by 95.65 % compared with that planned by the traditional switch-back algorithm. In field experiments, the vehicle approached the next working path with an average lateral deviation of 2.0 cm at a speed of 0.5 m/s during a turn. The average length of the trajectory was 11.84 m, and the average time consumed was 28.4 s. Compared with the traditional switch-back turning, the average trajectory length, average lateral deviation and average cost time were shortened by 10.51 %, 78.75 % and 10.70 %, respectively. The proposed dynamic headland turning path planning method has a significant advantage over the traditional switch-back turning method in terms of both accuracy and time efficiency. The algorithm solves the problems that when the headland width is narrow, the vehicle is difficult to turn and deviates from the predetermined trajectory due to sideslips. Moreover, it ensures the shortest path and time for the agricultural vehicles to turn in the field, which greatly shortens the overall path length and time of its field operations.
Multimodal Movie Recommendation System Using Deep Learning

Recommendation systems, the best way to deal with information overload, are widely utilized to provide users with personalized content and services with high efficiency. Many recommendation algorithms have been researched and deployed extensively in various e-commerce applications, including the movie streaming services over the last decade. However, sparse data cold-start problems are often encountered in many movie recommendation systems. In this paper, we reported a personalized multimodal movie recommendation system based on multimodal data analysis and deep learning. The real-world MovieLens datasets were selected to test the effectiveness of our new recommendation algorithm. With the input information, the hidden features of the movies and the users were mined using deep learning to build a deep-learning network algorithm model for training to further predict movie scores. With a learning rate of 0.001, the root mean squared error (RMSE) scores achieved 0.9908 and 0.9096 for test sets of MovieLens 100 K and 1 M datasets, respectively. The scoring prediction results show improved accuracy after incorporating the potential features and connections in multimodal data with deep-learning technology. Compared with the traditional collaborative filtering algorithms, such as user-based collaborative filtering (User-CF), item-based content-based filtering (Item-CF), and singular-value decomposition (SVD) approaches, the multimodal movie recommendation system using deep learning could provide better personalized recommendation results. Meanwhile, the sparse data problem was alleviated to a certain degree. We suggest that the recommendation system can be improved through the combination of the deep-learning technology and the multimodal data analysis.
A Secure and Decentralized Authentication Mechanism Based on Web 3.0 and Ethereum Blockchain Technology

Over the past decade, there has been significant evolution in the security field, specifically in the authentication and authorization part. The standard authentication protocol nowadays is OAuth 2.0-based authentication. This method relies on a third-party authentication service provider with complete control over the users’ data, which it can filter or modify at will. Blockchain and decentralization have generated much interest in recent years, and the decentralized web is considered the next significant improvement in the world wide web (also known as Web 3.0). Web3 authentication, also known as decentralized authentication, allows for the secure and decentralized authentication of users on the web. The use cases for this technology include online marketplaces, social media platforms, and other online communities that require user authentication. The advantages of Web3 authentication include increased security and privacy for users and the ability for users to have more control over their data. The proposed system implementation uses Ethereum as the blockchain and a modern web stack to enhance user interaction and usability. The solution brings benefits both to the private and the public sector, proving that it has the capability of becoming the preferred authentication mechanism for any decentralized web application.
Diagnosis of Breast Cancer using Machine Learning Techniques -A Survey

Breast cancer is a disease in which the cells of the breast develop unnaturally and uncontrollably, resulting in a mass called a tumor. If lumps in the breast are not addressed, they can spread to other regions of the body, including the bones, liver, and lungs. Men and women are both affected by breast cancer, albeit men are at a lower risk. This research investigates the detection of breast cancer by applying machine learning algorithms, deep learning algorithms, and hybrid machine learning approaches to a variety of datasets. These datasets include breast cancer databases from Wisconsin as well as mammography imaging datasets. The goal of this research is to find the best model for breast cancer diagnosis.
A Survey on Human Activity Recognition Using Deep Learning Techniques and Wearable Sensor Data

HAR has attained major attention because of its significant use in real-life scenarios like activity and fitness monitoring, rehabilitation, gaming, prosthetic limbs, healthcare, smart surveillance systems, etc. HAR systems provide ways for monitoring human behaviors and detecting body movements and various activities by using sensor data. The collection of sensors available in the mobile and other wearable devices has made most of these HAR applications easily possible. Moreover, Deep Learning (DL) has further accelerated the research on HAR using the data obtained via wearable devices. In this paper, we have discussed the overview of HAR, its applications, and popular benchmark datasets available publicly. Further, we discussed various DL techniques applied for HAR applications. We have also presented the challenges associated with the field and the future directions for performing more vital research in HAR.
Blockchain based efficient tamper-proof EHR storage for decentralized cloud-assisted storage

In recent years, the rapid and wide-ranging implementation of a cloud-based electronic healthcare record (EHR) storage system has shown significant advantages in effectively managing EHR for healthcare organizations and patients. However, in the cloud-based EHR storage model, the patients no longer have direct control of their EHR, whereas healthcare organizations may access the outsourced EHR whenever necessary. It may always cause severe security issues, specifically when healthcare organizations collude with the cloud service provider (CSP) to conceal any medical malpractice. Therefore, to deal with these significant concerns, we have introduced a novel blockchain based efficient tamper-proof model for EHR storage in decentralized InterPlanetary File System (IPFS) storage in the cloud - “TAC-EHR”. The key idea of the model is that every operation involves outsourcing EHRs and integrating these EHRs into a transaction on the public blockchain provides computationally unforgeability to the outsourced EHRs. Moreover, the proposed EHR storage model can also manage batch outsourcing, i.e., numerous EHR outsourcing for multiple patients by multiple doctors simultaneously, in an effective manner. The experimental and security analysis demonstrates that the proposed blockchain based cloud-assisted EHR storage model efficiently assures intractability computationally and outperforms the existing models in terms of computational and communication overhead.
Cooperative multi-camera vehicle tracking and traffic surveillance with edge artificial intelligence and representation learning

Traffic surveillance cameras are the eyes of the Intelligent Transportation Systems (ITS). However, they are currently isolated and can only extract information from each of their fixed views. To track vehicles across multiple cameras and help public agencies collect link travel time and speed information, an Edge-empowered Cooperative Multi-camera Sensing (ECoMS) System is proposed. ECoMS system presents a novel algorithmic and edge-server cooperative system construct to push edge computing and multi-camera re-identification workflow serving for traffic sensing based on Internet of Things (IoT) architecture. On the algorithm side, ECoMS system proposes a featherlight edge-based computer vision framework for vehicle detection, tracking, and features selection process in a real-time manner. Then, by only sending the objects’ representations to the server, the high-bandwidth data transmission and the heavy post-processing system can be abandoned. Furthermore, a hierarchical clip-based deep vehicle re-identification framework is proposed and integrated into the ECoMS system, and significantly outperforms other state-of-the-art methods by 4%–8% on Rank-1 accuracy. Finally, to balance the accuracy level of different camera pairs, a collaborative cross-camera traffic information estimation framework based on kernel density estimation with kernel smoother is implemented, which can get the precise link and region traffic information together with distributions (less than 1.01 KL distance). By maximizing the cooperation of the computational resources, orchestrating the data transmission and integrating road network graph features in the system, the ECoMS can precisely model the network-scale traffic information in a flexible, cost-effective, and scalable workflow. To the author’s best knowledge, ECoMS is the first multi-camera vehicle tracking and traffic monitoring system based on cooperative IoT architecture.