Reviewerly/exHarmony huggingface dataset:
PIRAP: Medical Cancer Rehabilitation Healthcare Center Data Maintenance Based on IoT-Based Deep Federated Collaborative Learning

Medical cancer rehabilitation healthcare center data maintenance is a global challenge with increased mortality risk. The Internet of Things (IoT)-based applications in healthcare were implemented through sensors and various connecting devices. The main problem of this procedure is the privacy of data, which is the biggest challenge with IoT, as all the connected devices transfer data in real time, the integration of multiple and other protocols can be hacked by the end-to-end connection, and it is not secure, security issues may crop up due to handling of such massive data in real time. Recent studies showed that a more structured risk assessment is needed to secure the medical cancer rehabilitation healthcare center data maintenance. In this respect, collaborative learning frameworks, such as Deep Federated Collaborative Learning (DFCL), are implemented for the study of medical cancer rehabilitation healthcare center data maintenance based on IoT-based systems and are proposed with smart short-term Bayesian convolution network systems for data analysis. This DFCL approach has been preferred in this context, strengthening privacy by allowing sensitive data to be retained. Experiments on benchmark datasets demonstrate that the federated model balances fairness, privacy, and accuracy. In this paper, we analyze administrative data count by medical stages taken from 2016 to 2022, the administrative data include data for routine operations. It is frequently used to assess by achieving an accuracy range of 19.8%. The leading diagnoses taken as per the patient’s cost and stay count identifying a disease, illness, or problem by examining the unusual combination of symptoms made an accurate diagnosis which is 26% more efficient than the leading diagnosis. The hospital dictionary analysis is based on dictionary analysis count and data visualization summary; accuracy is 50% higher than the existing data visualization summary. By comparing the hospital dictionary, home health care analysis shows a 44.5% efficient analysis rate for patient data maintenance. Moreover, the adult day-care centers analyzed 88.6% efficient analysis rate for patient data maintenance with 750 patients.
Optimization of backpropagation neural network models for reliability forecasting using the boxing match algorithm: Electro-mechanical case

Abstract Presenting a robust intelligent model capable of making accurate reliability forecasts has been an attractive topic to most industries. This study mainly aims to develop an approach by utilizing backpropagation neural network (BPNN) to predict the reliability of engineering systems, such as industrial robot systems and turbochargers, with reasonable computing speed and high accuracy. Boxing match algorithm (BMA), as an evolutionary metaheuristic algorithm with a new weight update strategy, is proposed to bring about performance improvements of the ANN in reliability forecast. Consequently, the hybrid model of BMA-BPNN has been provided to gain a significant level of accuracy in optimizing the weight and bias of BPNN using three sets of function approximation data to benchmark the proposed approach’s performance. Then, the BMA is utilized to improve reliability forecasting accuracy in engineering problems. The obtained results reveal that the presented algorithm delivers exceptional performance in function approximation, and its performance in forecasting engineering systems’ reliability is about 20% better than further compared algorithms. Similarly, rapid convergence rate, reasonable computing time, and well-performing are additional characteristics of the presented algorithm. Given the BMA-BPNN characteristics and the acquired findings, we can conclude that the proposed algorithm can be applicable in forecasting engineering problems’ reliability.
Secure Medical Data Abstraction Using Convolutional Neural Network

One of the most crucial components of medicine is data security and privacy which is vital while transferring it over the internet. There are some situations where the patient is not satisfied with the disclosure of his medical information to outsiders. Steganography is the technique where secret information can be hidden inside another ordinary file or document. The problem here is that by using traditional methods, the data may not be extracted properly at the destination. Consequently, this study suggests a methodology for image steganography based on convolutional neural networks (CNN) that hide the patient’s confidential data like identity, medical condition, treatment, and comments of doctors inside an MRI scan of a patient with Dicom image format. The data is encrypted before being incorporated into the cover image for an additional layer of security, and the obtained information can be deciphered at the destination to retrieve the original information. The result in terms of performance will be an increase in hiding capacity and level of abstraction of data.
Chatbot Enabled Smart Tourism Service for Indian Cities: An AI Approach

Tourism is one of the significant factors that deter-mines the growth of any city. Tourism-based growth is possible only if the requirements and challenges of tourists are well addressed. A boost in the tourism sector is witnessed in the last decade due to the proliferation of Information communication technologies and smart devices in society. Tourists frequently encounter many challenges when they visit new cities such as the language barriers, food, affordability, transportation, guide and security concerns. Technology enabled information services are highly necessary for smart tourism. To address this, the tourism sector has to focus on Information Dissemination services for tourists and other visitors, especially in cities. In this work, we propose an Artificial Intelligence-based chatbot as an information dissemination system specific to Indian cities that are diverse in their culture and language. This work presents the design of a dynamic chatbot that gives real-time information to tourists and various city stakeholders. We also examine how Natural Language Processing can help to provide customized output to the users based on their queries. The proposed AI-based chatbot gives the user an optimum response and enhances the user experience.
A Multidimensional Parallel Convolutional Connected Network Based on Multisource and Multimodal Sensor Data for Human Activity Recognition

Human activity recognition (HAR) technology based on wearables has received increasing attention in recent years. The traditional methods have used hand-crafted features to recognize human activities, resulting in shallow feature extraction. With the development of deep learning, an increasing number of researchers have focused on studying deep learning methods. To achieve higher recognition accuracy, the majority of the current HAR research involves multisource and multimodal sensors (MMSs) data. However, due to the limitations in the receptive fields of single-dimensional convolutional kernels, these networks are still infeasible for extracting spatiotemporal features. In this study, a multidimensional parallel convolutional connected (MPCC) deep learning network based on MMS data for HAR is proposed that fully utilizes the advantages of multidimensional convolutional kernels. Moreover, multiscale residual convolutional squeeze-and-excitation (MRCSE) modules are proposed to enrich the diversity of feature information by combining squeeze-and-excitation (SE) blocks. A daily home activity (DHA) data set is constructed based on the requirements for HAR in certain scenarios, such as smart home, and we conduct experiments on the optimal combination of sensor locations on the DHA data set according to a weighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{F}1~({\mathrm{ F}}_{\mathrm{ W}})$ </tex-math></inline-formula> -score. Both tenfold and leave-one-subject-out (LOSO) cross-validations (CVs) are used to evaluate the performance of the proposed network. The MPCC-MRCSE network achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm{ F}}_{\mathrm{ W}}$ </tex-math></inline-formula> -scores of 98.33% and 95.42% on the physical activity monitoring for aging people (PAMAP2) and OPPORTUNITY data sets using tenfold CVs, respectively, and achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm{ F}}_{\mathrm{ W}}$ </tex-math></inline-formula> -scores of 81.47% on the PAMAP2 when applying an LOSO CV.
Ship Detection in SAR Images Based on Multilevel Superpixel Segmentation and Fuzzy Fusion

Superpixel can maintain the boundary of the target and reduce the influence of speckle noise, which has been widely applied to synthetic aperture radar (SAR) image target detection. But the size of the superpixel has a great impact on the performance of superpixel-based SAR target detection algorithms. To solve this problem, we propose a multi-level ship target detection algorithm based on superpixel segmentation. Firstly, the SAR images are segmented in different levels with different superpixel sizes. Different descriptions of the SAR images are obtained in different levels. Secondly, we determine the feature of the superpixels in each level. And in order to enhance the adaptability of the proposed algorithm, we propose an adaptive distance calculation method to select the contrast superpixels in each level. Thirdly, the soft detection results are realized in each level by using the fuzzy C-means (FCM) algorithm. At last, the soft detection results obtained in different levels are fused by a new fusion strategy to achieve the final ship target detection result. The influences caused by different superxiel sizes can be effectively eased by fusion. Experiments in different SAR images have verified the effectiveness of the proposed algorithm in accurately detecting ship targets and insensitivity to the superpixel size.
Improving the performance of automatic short answer grading using transfer learning and augmentation

The task of grading answers ranging from one phrase to one paragraph using computational techniques is known as Automated Short Answer Grading (ASAG). The performance of existing systems is not good enough due to limited data and the lack of availability of data in many domains. Many ASAG systems were developed as an outcome of the active research in this field. This study builds an effective system for grading short answers in the programming domain by leveraging Pre-trained Language Models and Text Augmentation. We fine-tuned three-sentence transformer models on the SPRAG corpus with five different augmentation techniques: viz., Random Deletion, Synonym Replacement, Random Swap, Backtranslation, and NLPAug. The SPRAG corpus contains student responses involving keywords and special symbols. We experimented with four different data sizes with the augmented data to determine the impact of training data on the fine-tuned sentence transformer model. this paper provides an exhaustive analysis of fine-tuning pretrained sentence transformer models with varying sizes of data by applying text augmentation techniques. we found that applying random swap and synonym replacement techniques together while fine-tuning has given a significant improvement, With a 4.91% increase in accuracy and a 3.36% increase in the F1-score. All the trained models are publicly available1.
A Survey on Multimodal Knowledge Graphs: Construction, Completion and Applications

As an essential part of artificial intelligence, a knowledge graph describes the real-world entities, concepts and their various semantic relationships in a structured way and has been gradually popularized in a variety practical scenarios. The majority of existing knowledge graphs mainly concentrate on organizing and managing textual knowledge in a structured representation, while paying little attention to the multimodal resources (e.g., pictures and videos), which can serve as the foundation for the machine perception of a real-world data scenario. To this end, in this survey, we comprehensively review the related advances of multimodal knowledge graphs, covering multimodal knowledge graph construction, completion and typical applications. For construction, we outline the methods of named entity recognition, relation extraction and event extraction. For completion, we discuss the multimodal knowledge graph representation learning and entity linking. Finally, the mainstream applications of multimodal knowledge graphs in miscellaneous domains are summarized.
Experimental Examination of Entanglement Estimates

Recently, a proper genuine multipartite entanglement measure has been found for three-qubit pure states [see Xie and Eberly, Phys. Rev. Lett. 127, 040403 (2021)PRLTAO0031-900710.1103/PhysRevLett.127.040403], but capturing useful entanglement measures for mixed states has remained an open challenge. So far, it requires not only a full tomography in experiments, but also huge calculational labor. A leading proposal was made by Gühne, Reimpell, and Werner [Phys. Rev. Lett. 98, 110502 (2007)PRLTAO0031-900710.1103/PhysRevLett.98.110502], who used expectation values of entanglement witnesses to describe a lower bound estimation of entanglement. We provide here an extension that also gives genuine upper bounds of entanglement. This advance requires only the expectation value of any Hermitian operator. Moreover, we identify a class of operators A_{1} that not only give good estimates, but also require a remarkably small number of experimental measurements. In this Letter, we define our approach and illustrate it by estimating entanglement measures for a number of pure and mixed states prepared in our recent experiments.
Authentication Technology in Internet of Things and Privacy Security Issues in Typical Application Scenarios

This paper mainly summarizes three aspects of information security: Internet of Things (IoT) authentication technology, Internet of Vehicles (IoV) trust management, and IoV privacy protection. Firstly, in an industrial IoT environment, when a user wants to securely access data from IoT sensors in real-time, they may face network attacks due to the data being transmitted through an open channel. In order to solve this problem, we innovatively propose a user and device authentication model integrated with cloud computing, introduce an algorithm related to protocol design, and summarize the research direction of developing a more lightweight algorithm when designing security protocols in the future. Secondly, for mobile IoT applications, such as IoV, information collection and distribution is realized by establishing a network between vehicles and infrastructure. IoV will face security threats such as information insecurity and privacy disclosure. We introduce a typical trust management model for the IoV, which solves the problem of information unreliability by storing vehicle trust values. In the future, we are committed to making the process of computing node credibility using a trust model more robust. Finally, aiming at the privacy protection of the IoV, we propose a cross-domain anonymous authentication system model based on blockchain. The user’s auxiliary authentication information is stored in the blockchain, and the auxiliary authentication information of any registered user can be obtained from the blockchain. The privacy protection of cross-domain authentication can be realized through anonymous authentication, which greatly saves the communication cost of cross-domain authentication. In the future, we will try to use deep learning or federated learning to integrate with blockchain for actual deployment.
Roman Urdu Hate Speech Detection Using Transformer-Based Model for Cyber Security Applications

Social media applications, such as Twitter and Facebook, allow users to communicate and share their thoughts, status updates, opinions, photographs, and videos around the globe. Unfortunately, some people utilize these platforms to disseminate hate speech and abusive language. The growth of hate speech may result in hate crimes, cyber violence, and substantial harm to cyberspace, physical security, and social safety. As a result, hate speech detection is a critical issue for both cyberspace and physical society, necessitating the development of a robust application capable of detecting and combating it in real-time. Hate speech detection is a context-dependent problem that requires context-aware mechanisms for resolution. In this study, we employed a transformer-based model for Roman Urdu hate speech classification due to its ability to capture the text context. In addition, we developed the first Roman Urdu pre-trained BERT model, which we named BERT-RU. For this purpose, we exploited the capabilities of BERT by training it from scratch on the largest Roman Urdu dataset consisting of 173,714 text messages. Traditional and deep learning models were used as baseline models, including LSTM, BiLSTM, BiLSTM + Attention Layer, and CNN. We also investigated the concept of transfer learning by using pre-trained BERT embeddings in conjunction with deep learning models. The performance of each model was evaluated in terms of accuracy, precision, recall, and F-measure. The generalization of each model was evaluated on a cross-domain dataset. The experimental results revealed that the transformer-based model, when directly applied to the classification task of the Roman Urdu hate speech, outperformed traditional machine learning, deep learning models, and pre-trained transformer-based models in terms of accuracy, precision, recall, and F-measure, with scores of 96.70%, 97.25%, 96.74%, and 97.89%, respectively. In addition, the transformer-based model exhibited superior generalization on a cross-domain dataset.
Going Beyond Observable Actions

Abstract: Pauses represented in process data captured from digital learning and assessment tasks are defined as the time elapsed between two subsequent events. Pauses are used as a marker of unobservable cognitive processes, such as encoding, problem-solving, and planning, that underlie test takers’ subsequent observable actions in educational assessment. To make valid inferences about the underlying cognitive processes represented by pauses, we argue that applying a task-specific cognitive modeling approach is required. We discuss and demonstrate how to apply a task-specific, theoretical cognitive modeling approach for interpreting pauses. We believe that this approach will have value for educational researchers seeking to make valid, task-general inferences about test-taker cognition from pauses represented in process data.
MEDICAL METAVERSE: TECHNOLOGIES, APPLICATIONS, CHALLENGES AND FUTURE

Currently, medical technology is developing rapidly and more medical problems are being solved. However, the shortage of medical resources and the high cost of medical care have become severe medical problems in response to the needs of patients. The continuous improvement of medical experience and quality is one of the most critical issues and is widely valued. The metaverse is an emerging digital space that integrates all kinds of technologies, including artificial intelligence, virtual reality, augmented reality, internet technology, blockchain and digital twin, etc. These technologies can ensure an immersive experience in the metaverse for humans to complete various tasks efficiently. Many fields, including video games, industrial production and medical service, have started to pay attention to the application of the metaverse. In the medical field, medical personnel can use the metaverse to achieve efficient diagnosis, education and treatments. And the interaction between medical personnel and patients in digital space can be strong. In this paper, we review the technologies and the applications of the metaverse and explore the potential of the metaverse in healthcare from several application scenarios. Based on this, the current challenges are analyzed and the possible solutions are proposed as the future direction of medical metaverse.
Machine Learning-Based Mobile Applications Using Python and Scikit-Learn

This chapter gives a broad outline of machine learning on Android mobile phones using the Scikit-learn module. The first section introduces the reader to Python language; next, Python on Android is introduced with a brief historical note on implementations of Python on Android mobile phones. Pydroid3 is introduced in the subsequent section. This is followed by instructions on setting up an Android phone for machine learning. This is followed by a description of supportive modules for machine learning that are available for Pydroid3, and some example codes, namely: os, pathlib, Pandas, NumPy, SciPy, Matplotlib, Seaborn, PySimpleGUI, NetworkX, Biopython, WordCloud, Kivy, and Jupyter Notebook. The last section of this compilation describes the Scikit-learn library, basic concepts of the Scikit-learn module, and algorithms available with this module, namely: Linear Regression, Logistic Regression, Principal Component Analysis (PCA), XGBoost, K-nearest neighbors, and support vector machine.
An Examination of Quantum Information Processing Through Quantum Cryptography; A study

"Along with these developments, personal microwave technology has enabled strong non-linear effects at the photon level, leading to readily observable novel parameter regimes in quantum optics. Circuit QED has opened up new opportunities to explore the rich physics of quantum information processing (QIP) and quantum optics (QO), making them scalable on the road to quantum computing. However, we must also discuss some of the challenges involved. Quantum Technologies (QT) is a cross-disciplinary field that has made great progress in recent years. Technologies that can explicitly represent individual quantum states, as well as superposition and entanglement, are now being developed to exploit the 'strange' properties of quantum mechanics. In quantum communication, individual or entangled photons are used to securely send data, while quantum simulation utilizes well-controlled quantum systems that are less accessible. Interest is growing in higher dimensional quantum states and quantum communication, as the extended availability of Hilbert space and greater information capacity, along with increased noise elasticity, offer many advantages and new research possibilities. Let's focus our attention on the benefits of higher dimensional quantum states for quantum communication, as shown by Kuditz and others. Nevertheless, it has been demonstrated that higher dimensional quantum states can also provide improvements in many other areas."