Reviewerly/exHarmony huggingface dataset:
The Implication of Metaverse in the Traditional Medical Environment and Healthcare Sector: Applications and Challenges

There are a lot of studies that have been presenting the idea of the metaverse since 2021. It's the term for the next-generation mobile computing platform, which will be extensively utilized in the future and refers to the internet accessed through VR and AR glasses. The range of illnesses people face today is different from what it was decades ago. Cancer, COPD, diabetes, heart disease, and asthma are just few of the many non-communicable diseases that pose a serious risk to human health in the modern world. As a result, efforts toward chronic disease prevention and management need to be bolstered. The emergence of metaverse theory may point healthcare in novel directions. Uneven allocation of medical resources, difficulties in follow-up, overburdening of experts, and so on remain obstacles in the management of chronic diseases in the existing healthcare system. However, advanced artificial intelligence (AI) integrated into metaverse healthcare platforms could solve these problems. The article presents the potential of Metaverse technology in the context of modern digital medicine and the future of the medical metaverse. Some related applications of Metaverse in the healthcare sector are presented. Problems and challenges are highlighted .
Artificial Intelligence and Blockchain Technology in Insurance Business

With the use of cutting-edge technology like artificial intelligence, the Internet of Things, and blockchain, the insurance industry is about to enter a new era. Financial innovations will have a wide range of effects on economic activity. The future of the AI and blockchain-based insurance industry appears bright. The expansion of the insurance industry will be considerably aided by AI and blockchain technology. The corona situation has recently brought to light some inefficiencies of the conventional system, including fewer client–insurer interaction capabilities, challenges in targeting consumers, difficulties working from home, a lack of IT support, a lack of transparency, and sparse management. This article examines the impact of cutting-edge financial technology on the insurance industry, including AI technology and blockchain, and describes the phenomena from several angles. Insightful and scientific, ICT-based insurance makes considerable use of electrical and IT equipment for technology resolution without the consumer providing the insurance company with any natural resources. Digital insurer seems to be growing more popular at the moment, but it’s essential to understand the problems and challenges it faces, which result in poor penetration. Although there are many advantages for the insured and insurer, internet insurance providers are nevertheless subject to operational, regulatory, and company reputation, and their customers worry about the safety of their transactions and identity theft. This article aims to illustrate the fundamentals and significance of AI and blockchain technology in the insurance business, looking at benefits that accrue to both the provider and the customer. Although there are many studies already published, the focus of this research is the insurance industry. By fusing cutting-edge technical demands with established company competencies, this paper assists the management in expanding their understanding of competing for advantage.
Orthogonal-Moment-Based Attraction Measurement With Ocular Hints in Video-Watching Task

Pupil dilation and eye movements are closely related to human emotional and cognitive processes. Visual stimulus, especially video clips, is widely used in computer-assisted experimental paradigms as emotional inducers. However, the level of attraction as a critical factor to such visual stimulus still needs comprehensive investigation. This article conducts a novel study of attraction assessment with pupil diameter and eye movements. We collected high temporal resolution ocular variation data from 50 subjects while they viewed a variety of emotional video stimuli. Besides, this article proposes two orthogonal-moment-based feature extraction methods for emotion classification, i.e., Legendre moment and Krawtchouk moment. The results of experiments show that our proposed feature sets achieve better classification performance compared with conventional time- or frequency-domain feature sets. The accuracy of predicting attraction level reached 87.4% and 91.0% when new features were used alone and combined with conventional features, respectively. Compared with the conventional features with an accuracy of 86.6%, our proposed features can improve the accuracy by 4.4%.This study conducts a ground investigation of quantitative attraction assessment for video clips, which might provide reference for paradigm design of affective computing research.
Towards a competence-based polytomous knowledge structure theory

The present article lays out the foundations of an axiomatic theory of attribute maps, an extension of skill maps to polytomous knowledge structure theory. A deterministic relationship between the available attributes and the observable item responses is established by means of two mappings denoted attribute map and item–response function. The attribute map assigns to each item–response pair the set of attributes that are instrumental for observing that particular response to the item. The item–response function assigns to each set of attributes the set of item responses that, according to the attribute map, can be obtained with those attributes. The proposed approach is shown to be rather general and capable of handling a multitude of polytomous items that can be encountered in practice. Examples are provided that cover the analysis of responses on Likert scales, responses awarded partial credits, and the investigation of misconceptions.
Adaptive Multivariate Time-Series Anomaly Detection

Existing Multivariate time-series anomaly detection methods aim to calculate the anomaly scores of observed sequences and learn a threshold to judge whether the input data is abnormal. However, they neglected the temporal covariate shift problem, which leads to the learned thresholds cannot be generalized in the test set, resulting in suboptimal detection performance in practical cases. We propose the Adaptive Multivariate Time-series Anomaly Detection framework in this paper, namely DATECT, to address the above challenging problems. Specifically, to enhance the robustness of anomaly measurement, DATECT adopts the dilated convolution based AutoEncoder to integrate both prediction errors and reconstruction errors into the output anomaly scores. Meanwhile, a novel Adaptive Window Normalization method is put forth to reduce the diversity of the distribution of anomaly scores in the test set, hence effectively improving the generalization capability of the detection model. Finally, to further reduce the side-effect of domain-specific dynamic noise, DATECT utilizes Non-parametric Scan Statistics to select the subsets of significantly abnormal signals and highlight the anomaly segments. Experiments on five datasets show that our method can significantly alleviate the performance drop caused by the temporal covariate shift problem, outperforms the baseline in terms of detection performance and generalization, averagely improving the F1-score by 8.66% and the F1∗-score (upper bound) by 1.18%.
Improved estimation of hourly direct normal solar irradiation (DNI) using geostationary satellite visible channel images over moderate albedo areas

An accurate knowledge of the direct solar irradiance at normal incidence (DNI) is required to size solar energy systems, specially those using solar concentration technologies. In the absence of measurements, DNI can be estimated over an arbitrary site with a dedicated satellite model or by using satellite-derived global horizontal irradiance (GHI) and a phenomenological diffuse-direct separation model. This second procedure is error-prone, specially under partial cloudiness conditions and low solar elevation angles. A novel and simple semi-empirical satellite-based model to estimate DNI in moderate albedo regions using a physical clear sky model and a cloudiness index obtained from visible satellite imagery is proposed and assessed. An ergodic assumption in which the images are spatially averaged to better represent the hourly time basis is used. Over the target region (Southeastern South America, SESA), the new DNI satellite model outperforms the alternative strategy at the hourly level, with an average uncertainty of 20% (versus 25%) when compared with three measurement station's data sets located in center Argentina, southern Brazil and Uruguay.
Identifying personal physiological data risks to the Internet of Everything: the case of facial data breach risks

Abstract Personal physiological data is the digital representation of physical features that identify individuals in the Internet of Everything environment. Such data includes characteristics of uniqueness, identification, replicability, irreversibility of damage, and relevance of information, and this data can be collected, shared, and used in a wide range of applications. As facial recognition technology has become prevalent and smarter over time, facial data associated with critical personal information poses a potential security and privacy risk of being leaked in the Internet of Everything application platform. However, current research has not identified a systematic and effective method for identifying these risks. Thus, in this study, we adopted the fault tree analysis method to identify risks. Based on the risks identified, we then listed intermediate events and basic events according to the causal logic, and drew a complete fault tree diagram of facial data breaches. The study determined that personal factors, data management and supervision absence are the three intermediate events. Furthermore, the lack of laws and regulations and the immaturity of facial recognition technology are the two major basic events leading to facial data breaches. We anticipate that this study will explain the manageability and traceability of personal physiological data during its lifecycle. In addition, this study contributes to an understanding of what risks physiological data faces in order to inform individuals of how to manage their data carefully and to guide management parties on how to formulate robust policies and regulations that can ensure data security.
Chaos and Cellular Automata-Based Substitution Box and Its Application in Cryptography

Substitution boxes are the key factor in symmetric-key cryptosystems that determines their ability to resist various cryptanalytic attacks. Creating strong substitution boxes that have multiple strong cryptographic properties at the same time is a challenging task for cryptographers. A significant amount of research has been conducted on S-boxes in the past few decades, but the resulting S-boxes have been found to be vulnerable to various cyberattacks. This paper proposes a new method for creating robust S-boxes that exhibit superior performance and possess high scores in multiple cryptographic properties. The hybrid S-box method presented in this paper is based on Chua’s circuit chaotic map, two-dimensional cellular automata, and an algebraic permutation group structure. The proposed 16×16 S-box has an excellent performance in terms of security parameters, including a minimum nonlinearity of 102, the absence of fixed points, the satisfaction of bit independence and strict avalanche criteria, a low differential uniformity of 5, a low linear approximation probability of 0.0603, and an auto-correlation function of 28. The analysis of the performance comparison indicates that the proposed S-box outperforms other state-of-the-art S-box techniques in several aspects. It possesses better attributes, such as a higher degree of inherent security and resilience, which make it more secure and less vulnerable to potential attacks.
Flexible and Efficient Multi-Keyword Ranked Searchable Attribute-Based Encryption Schemes

Currently, cloud computing has become increasingly popular and thus, many people and institutions choose to put their data into the cloud instead of local environments. Given the massive amount of data and the fidelity of cloud servers, adequate security protection and efficient retrieval mechanisms for stored data have become critical problems. Attribute-based encryption brings the ability of fine-grained access control and can achieve a direct encrypted data search while being combined with searchable encryption algorithms. However, most existing schemes only support single-keyword or provide no ranking searching results, which could be inflexible and inefficient in satisfying the real world’s actual needs. We propose a flexible multi-keyword ranked searchable attribute-based scheme using search trees to overcome the above-mentioned problems, allowing users to combine their fuzzy searching keywords with AND–OR logic gates. Moreover, our enhanced scheme not only improves its privacy protection but also goes a step further to apply a semantic search to boost the flexibility and the searching experience of users. With the proposed index-table method and the tree-based searching algorithm, we proved the efficiency and security of our schemes through a series of analyses and experiments.
Robust Human Face Emotion Classification Using Triplet-Loss-Based Deep CNN Features and SVM

Human facial emotion detection is one of the challenging tasks in computer vision. Owing to high inter-class variance, it is hard for machine learning models to predict facial emotions accurately. Moreover, a person with several facial emotions increases the diversity and complexity of classification problems. In this paper, we have proposed a novel and intelligent approach for the classification of human facial emotions. The proposed approach comprises customized ResNet18 by employing transfer learning with the integration of triplet loss function (TLF), followed by SVM classification model. Using deep features from a customized ResNet18 trained with triplet loss, the proposed pipeline consists of a face detector used to locate and refine the face bounding box and a classifier to identify the facial expression class of discovered faces. RetinaFace is used to extract the identified face areas from the source image, and a ResNet18 model is trained on cropped face images with triplet loss to retrieve those features. An SVM classifier is used to categorize the facial expression based on the acquired deep characteristics. In this paper, we have proposed a method that can achieve better performance than state-of-the-art (SoTA) methods on JAFFE and MMI datasets. The technique is based on the triplet loss function to generate deep input image features. The proposed method performed well on the JAFFE and MMI datasets with an accuracy of 98.44% and 99.02%, respectively, on seven emotions; meanwhile, the performance of the method needs to be fine-tuned for the FER2013 and AFFECTNET datasets.
An Enhanced Ensemble Deep Neural Network Approach for Elderly Fall Detection System Based on Wearable Sensors

Fatal injuries and hospitalizations caused by accidental falls are significant problems among the elderly. Detecting falls in real-time is challenging, as many falls occur in a short period. Developing an automated monitoring system that can predict falls before they happen, provide safeguards during the fall, and issue remote notifications after the fall is essential to improving the level of care for the elderly. This study proposed a concept for a wearable monitoring framework that aims to anticipate falls during their beginning and descent, activating a safety mechanism to minimize fall-related injuries and issuing a remote notification after the body impacts the ground. However, the demonstration of this concept in the study involved the offline analysis of an ensemble deep neural network architecture based on a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN) and existing data. It is important to note that this study did not involve the implementation of hardware or other elements beyond the developed algorithm. The proposed approach utilized CNN for robust feature extraction from accelerometer and gyroscope data and RNN to model the temporal dynamics of the falling process. A distinct class-based ensemble architecture was developed, where each ensemble model identified a specific class. The proposed approach was evaluated on the annotated SisFall dataset and achieved a mean accuracy of 95%, 96%, and 98% for Non-Fall, Pre-Fall, and Fall detection events, respectively, outperforming state-of-the-art fall detection methods. The overall evaluation demonstrated the effectiveness of the developed deep learning architecture. This wearable monitoring system will prevent injuries and improve the quality of life of elderly individuals.
Multi-angle face expression recognition based on integration of lightweight deep network and key point feature positioning

Abstract In the current advancement of science and technology, robot and machine vision technology have significant application value. Due to their mimetic structure, quadruped robots are better equipped to adapt to the requirements of walking over complex terrain. This paper introduce lightweight deep network and combining key point feature positioning for multi-angle face expression recognition. Using robot dog to recognize facial expressions will be affected by distance and angle. To solve this problem, this paper proposes a method for facial expression recognition at different distances and angles,which solved the larger distance and deflection angle of face expression recognition accuracy and real-time issues. The dataset used in this study was collected by adjusting the camera mounted in an AI robotic dog in a laboratory scenario. The result shows a common and fast and intuitive visual representation of human beings to convey emotions or mental states, facial expression is an effective way for robots to obtain information and interact with people through visual images.
A survey of deep learning-based 3D shape generation

Abstract Deep learning has been successfully used for tasks in the 2D image domain. Research on 3D computer vision and deep geometry learning has also attracted attention. Considerable achievements have been made regarding feature extraction and discrimination of 3D shapes. Following recent advances in deep generative models such as generative adversarial networks, effective generation of 3D shapes has become an active research topic. Unlike 2D images with a regular grid structure, 3D shapes have various representations, such as voxels, point clouds, meshes, and implicit functions. For deep learning of 3D shapes, shape representation has to be taken into account as there is no unified representation that can cover all tasks well. Factors such as the representativeness of geometry and topology often largely affect the quality of the generated 3D shapes. In this survey, we comprehensively review works on deep-learning-based 3D shape generation by classifying and discussing them in terms of the underlying shape representation and the architecture of the shape generator. The advantages and disadvantages of each class are further analyzed. We also consider the 3D shape datasets commonly used for shape generation. Finally, we present several potential research directions that hopefully can inspire future works on this topic.
Label Propagation and Contrastive Regularization for Semisupervised Semantic Segmentation of Remote Sensing Images

Remarkable progress based on deep neural networks has been achieved on the semantic segmentation in remote sensing images. However, pixel-level labeling is expensive for remote sensing images. Semi-supervised semantic segmentation becomes an alternative approach to reduce the cost of annotation, and it is crucial to utilize efficiently a large number of unlabeled data. Nevertheless inevitably, there is the unbalanced class distribution between labeled and unlabeled data of remote sensing scene. Existing semi-supervised methods train unlabeled images in isolation from labeled images and only learn reliable pixel pseudo-labels, leading to underutilization of unlabeled images. This article proposes a novel semi-supervised semantic segmentation approach based on label propagation and contrastive regularization for remote sensing images. Specifically, the unlabeled images are augmented by randomly copy-pasting the class regions from labeled images. A prototype feature constraint module is used to enforce the constraint on the pixel features of unlabeled images relying on the prototype features from labeled images, achieving feature alignment on the entire dataset. Furthermore, we present the region contrastive learning module that guides the model to learn feature consistency under different perturbations and compact feature representations over class regions on unlabeled images. Extensive experimental results on multiple remote sensing datasets demonstrate that our proposed approach achieves superior performance compared with state-of-the-art semi-supervised semantic segmentation methods.
Applying Transformer-Based Text Summarization for Keyphrase Generation

Keyphrases are crucial for searching and systematizing scholarly documents. Most current methods for keyphrase extraction are aimed at the extraction of the most significant words in the text. But in practice, the list of keyphrases often includes words that do not appear in the text explicitly. In this case, the list of keyphrases represents an abstractive summary of the source text. In this paper, we experiment with popular transformer-based models for abstractive text summarization using four benchmark datasets for keyphrase extraction. We compare the results obtained with the results of common unsupervised and supervised methods for keyphrase extraction. Our evaluation shows that summarization models are quite effective in generating keyphrases in the terms of the full-match F1-score and BERTScore. However, they produce a lot of words that are absent in the author's list of keyphrases, which makes summarization models ineffective in terms of ROUGE-1. We also investigate several ordering strategies to concatenate target keyphrases. The results showed that the choice of strategy affects the performance of keyphrase generation.