Reviewerly/exHarmony huggingface dataset:
A Decentralized Trust Management System for Intelligent Transportation Environments

Commercialized 5G technology will provide reliable and efficient connectivity of motor vehicles that could support the dissemination of information under an intelligent transportation system. However, such service still suffers from risks or threats due to malicious content producers. The traditional public key infrastructure (PKI) cannot restrain such untrusted but legitimate publishers. Therefore, a trust-based service management mechanism is required to secure information dissemination. The issue of how to achieve a trust management model becomes a key problem in the situation. This paper proposes a novel prototype of the decentralized trust management system (DTMS) based on blockchain technologies. Compared with the conventional and centralized trust management system, DTMS adopts a decentralized consensus-based trust evaluation model and a blockchain-based trust storage system, which provide a transparent evaluation procedure and irreversible storage of trust credits. Moreover, the proposed trust model improves blockchain efficiency by only allowing trusted nodes participating in the validation and consensus process. Additionally, the designed system creatively applies a trusted execution environment (TEE) to secure the trust evaluation process together with an incentive model that is used to stimulate more participation and penalize malicious behaviours. Finally, to evaluate our new design prototype, both numerical analysis and practical experiments are implemented for performance evaluation.
Using Reachable Sets for Trajectory Planning of Automated Vehicles

The computational effort of trajectory planning for automated vehicles often increases with the complexity of the traffic situation. This is particularly problematic in safety-critical situations, in which the vehicle must react in a timely manner. We present a novel motion planning approach for automated vehicles, which combines set-based reachability analysis with convex optimization to address this issue. This combination makes it possible to find driving maneuvers even in small and convoluted solution spaces. In contrast to existing work, the computation time of our approach typically decreases, the more complex situations become. We demonstrate the benefits of our motion planner in scenarios from the CommonRoad benchmark suite and validate the approach on a real test vehicle.
Human Daily Activity Recognition Performed Using Wearable Inertial Sensors Combined With Deep Learning Algorithms

This study proposed a wearable device capable of recognizing six human daily activities (walking, walking upstairs, walking downstairs, sitting, standing, and lying) through a deep learning algorithm. Existing wearable devices are mainly watches or wristbands, and almost none are to be worn on the waist. Wearable devices in the forms of watches and wristbands are unfriendly to patients who are critically ill, such as patients undergoing dialysis. Patients undergoing dialysis have artificial blood vessels on their arm, and they cannot perform intense exercise. For this type of users, general hand wearable devices cannot correctly identify wearers' activities. Therefore, we proposed a waist wearable device and these types of daily life activities to assess their exercise. The hardware of the wearable device consisted of an inertial sensor, which included a microcontroller, a three-axis accelerometer, and a three-axis gyroscope. The activity recognition algorithm of the software used motion signals acquisition, signal normalization, and a feature learning method. The feature learning method was based on a 1D convolutional neural network that automatically performed feature extraction and classification from raw data. One part of the experimental data was from the dataset of the University of California (UCI), and the other part was recorded by this study. To capture the data recorded, the wearable inertial sensing device was attached to the waists of 21 experimental participants who performed six common movements in a laboratorial environment, and the subsequent records were collected to verify the validity of the proposed deep learning algorithm in relation to the inertial sensor of the wearable device. For the six common activities in the UCI dataset and the data recorded, the recognition rates in the training sample reached 98.93% and 97.19%, respectively, and the recognition rates in the testing sample were 95.99% and 93.77%, respectively.
Differential Evolution Mutations: Taxonomy, Comparison and Convergence Analysis

During last two decades, Differential Evolution (DE) proved to be one of the most popular and successful evolutionary algorithms for solving global optimization problems over continuous space. Proposing new mutation strategies to improve the optimization performance of (DE) is considered a significant research study. In DE, mutation operation plays a vital role in the performance of the algorithm. Therefore, in this paper, comprehensive analysis of the contributions on basic and novel mutation strategies that were proposed between 1995 and 2020 is presented. A new taxonomy based on the structure of the novel mutations is proposed. Numerical experiments on a set of 30 test problems from the CEC2017 benchmark for 10, 30, 50 and 100 dimensions, including a comparison with classical DE schemes and recent mutations schemes are executed. Furthermore, theoretical, and empirical convergence behavior analysis of all mutations is discussed. The paper also presents many recommendations, guidelines, insights, and suggestions for experienced practitioners and interested researchers in designing and developing effective and efficient DE algorithms to address various optimization problems in different fields.
Dynamic Graph Neural Networks for Sequential Recommendation

Modeling user preference from his historical sequences is one of the core problems of sequential recommendation. Existing methods in this field are widely distributed from conventional methods to deep learning methods. However, most of them only model users' interests within their own sequences and ignore the dynamic collaborative signals among different user sequences, making it insufficient to explore users' preferences. We take inspiration from dynamic graph neural networks to cope with this challenge, modeling the user sequence and dynamic collaborative signals into one framework. We propose a new method named Dynamic Graph Neural Network for Sequential Recommendation (DGSR), which connects different user sequences through a dynamic graph structure, exploring the interactive behavior of users and items with time and order information. Furthermore, we design a Dynamic Graph Recommendation Network to extract user's preferences from the dynamic graph. Consequently, the next-item prediction task in sequential recommendation is converted into a link prediction between the user node and the item node in a dynamic graph. Extensive experiments on four public benchmarks show that DGSR outperforms several state-of-the-art methods. Further studies demonstrate the rationality and effectiveness of modeling user sequences through a dynamic graph.
Metaverse Games: Game Changer for Healthcare?

Cyberpsychology, Behavior, and Social NetworkingVol. 25, No. 5 EditorialMetaverse Games: Game Changer for Healthcare?Brenda K. WiederholdBrenda K. WiederholdBrenda K. Wiederhold, Editor-in-ChiefSearch for more papers by this authorPublished Online:10 May 2022https://doi.org/10.1089/cyber.2022.29246.editorialAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetailsCited byDİJİTAL OYUNLARIN GELECEĞİNDE METAVERSE ETKİSİ11 January 2023 | TRT AkademiUtilizing the metaverse in anatomy and physiology5 January 2023 | Anatomical Sciences Education, Vol. 63Examining the training and education potential of the metaverse: Results from an empirical study of next generation SAFe training5 January 2023 | Journal of Software: Evolution and Process, Vol. 13The Medical Metaverse, Part 1: Introduction, Definitions, and New Horizons for NeuropsychiatryThe Journal of Neuropsychiatry and Clinical Neurosciences, Vol. 35, No. 1Pioneering the Metaverse: The Role of the Metaverse in an Aging Population20 January 2023 | JMIR Aging, Vol. 6Metaverse in Healthcare Integrated with Explainable AI and Blockchain: Enabling Immersiveness, Ensuring Trust, and Providing Patient Data Security4 January 2023 | Sensors, Vol. 23, No. 2Exploring the metaverse in the digital economy: an overview and research framework5 December 2022 | Journal of Electronic Business & Digital Economics, Vol. 1, No. 1/2The MetaPharmacy: What Happens When Big Pharma Goes Virtual? Brenda K. Wiederhold6 December 2022 | Cyberpsychology, Behavior, and Social Networking, Vol. 25, No. 12Advances in Metaverse Investigation: Streams of Research and Future Agenda29 October 2022 | Virtual Worlds, Vol. 1, No. 2The Age Beyond Sports: User Experience in the World of Metaverse30 November 2022 | Journal of MetaverseImplementation of Developed Esantem Smart Healthcare System in MetaverseApplication of Metaverse Service to Healthcare Industry: A Strategic Perspective11 October 2022 | International Journal of Environmental Research and Public Health, Vol. 19, No. 20The Trend of Metaverse and Augmented & Virtual Reality Extending to the Healthcare SystemCureus, Vol. 17The Metaverse: A New Challenge for the Healthcare System: A Scoping Review30 August 2022 | Journal of Functional Morphology and Kinesiology, Vol. 7, No. 3METAVERSE VE SAĞLIK HİZMETLERİ ÜZERİNE BİR DEĞERLENDİRME28 June 2022 | Erzincan Binali Yıldırım Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi Volume 25Issue 5May 2022 InformationCopyright 2022, Mary Ann Liebert, Inc., publishersTo cite this article:Brenda K. Wiederhold.Metaverse Games: Game Changer for Healthcare?.Cyberpsychology, Behavior, and Social Networking.May 2022.267-269.http://doi.org/10.1089/cyber.2022.29246.editorialPublished in Volume: 25 Issue 5: May 10, 2022PDF download
Enhance explainability of manifold learning

The explainability of manifold learning is rarely investigated though there is an urgent need from both AI theory and practice. In this study, we propose a novel degree of locality preservation (DLP) approach to study the interpretability of manifold learning. We estimate the DLPs of the state-of-the-art manifold learning methods: t-SNE and UMAP as well as related methods: LLE, HLLE, and LTSA along with widely used PCA across benchmark datasets classified as low-dimensional and high-dimensional data. Our study provides well-founded explanations of the manifold learning methods in terms of the DLPs. The order of their DLPs follows t-SNE> UMAP> LLE> HLLE/PCA/LTSA, though it may have some exceptions for some high-dimensional data. Both t-SNE and UMAP demonstrate an embedding distance amplification mechanism under the Euclidean distance that forces the latent local data geometry to stand out in dimension reduction. It not only explains why t-SNE and UMAP have higher DLPs than other peers, but also indicates they are not locally isometric under the Euclidean distance. Furthermore, it discovers that t-SNE and UMAP embeddings demonstrate similar nonlinear nature in dimension reduction, besides larger (smaller) data variances for low (high)-dimensional data. To the best of our knowledge, this study is the first work about the explainability of manifold learning. The proposed methods and corresponding results can be also extended to other dimension reduction techniques.
Binary Aquila Optimizer for Selecting Effective Features from Medical Data: A COVID-19 Case Study

Medical technological advancements have led to the creation of various large datasets with numerous attributes. The presence of redundant and irrelevant features in datasets negatively influences algorithms and leads to decreases in the performance of the algorithms. Using effective features in data mining and analyzing tasks such as classification can increase the accuracy of the results and relevant decisions made by decision-makers using them. This increase can become more acute when dealing with challenging, large-scale problems in medical applications. Nature-inspired metaheuristics show superior performance in finding optimal feature subsets in the literature. As a seminal attempt, a wrapper feature selection approach is presented on the basis of the newly proposed Aquila optimizer (AO) in this work. In this regard, the wrapper approach uses AO as a search algorithm in order to discover the most effective feature subset. S-shaped binary Aquila optimizer (SBAO) and V-shaped binary Aquila optimizer (VBAO) are two binary algorithms suggested for feature selection in medical datasets. Binary position vectors are generated utilizing S- and V-shaped transfer functions while the search space stays continuous. The suggested algorithms are compared to six recent binary optimization algorithms on seven benchmark medical datasets. In comparison to the comparative algorithms, the gained results demonstrate that using both proposed BAO variants can improve the classification accuracy on these medical datasets. The proposed algorithm is also tested on the real-dataset COVID-19. The findings testified that SBAO outperforms comparative algorithms regarding the least number of selected features with the highest accuracy.
Color image encryption and authentication using dynamic DNA encoding and hyper chaotic system

• A hyperchaotic system is used to shuffle image at block-level and bit-level. • Row-level dynamic and random DNA encoding of every color plane of image. • Hardness of Elliptic curve discrete logarithmic problem. • A novel digital signature, Mutual authentication and key exchange scheme. • Low computational complexity and robustness to various types of security attacks. This paper takes a holistic approach to propose a comprehensive framework for color image encryption with some novel features. The framework is designed around a secure encryption and decryption scheme leveraging dynamic DNA encoding, hyperchaotic system and elliptic curve cryptography. The novel features being mutually authenticated key generation and exchange based on split share and digital signature for image authentication. A color image is encoded into a DNA sequence using randomly selected row-level encoding rules. A novel 4D-Hyperchaotic system is used to generate pseudo-random sequences to permutate image information at bit-level and block-level. The multidimensional Hyperchaotic system increases non-periodicity, ergodicity, and unpredictability than a simple chaotic system. An elliptic curve-based substitution system is employed to achieve computationally efficient encryption and authentication. Different subkeys have been employed to increase the key space and embed confusion and diffusion into the proposed scheme. Results and analysis show that the proposed framework is computationally efficient and robust to different types of attacks and cryptanalysis carried over the images
Cloud-IIoT-Based Electronic Health Record Privacy-Preserving by CNN and Blockchain-Enabled Federated Learning

Industrial cloud computing and Internet of Things have transformed the healthcare industry with the rapid growth of distributed healthcare data. Security and privacy of healthcare data are crucial challenges in the healthcare industry. This article proposes a novel technique using deep learning and blockchain techniques for electronic health record privacy-preservation. The processed dataset classified normal and abnormal users using the convolutional neural network approach. Then, by using blockchain integrated with a cryptography-based federated learning module, the abnormal users have been processed and removed from the database along with the accessibility for the health records. The simulation has been done in the Python tool and experimental results show that the model’s classification results and performance are better than other existing techniques.
X-Invariant Contrastive Augmentation and Representation Learning for Semi-Supervised Skeleton-Based Action Recognition

Semi-supervised skeleton-based action recognition is a challenging problem due to insufficient labeled data. For addressing this problem, some representative methods leverage contrastive learning to obtain more features from the pre-augmented skeleton actions. Such methods usually adopt a two-stage way: first randomly augment samples, and then learn their representations via contrastive learning. Since skeleton samples have already been randomly augmented, the representation ability of the subsequent contrastive learning is limited due to the inconsistency between the augmentations and representations. Thus, we propose a novel X-invariant Contrastive Augmentation and Representation learning (X-CAR) framework to thoroughly obtain rotate-shear-scale (X for short) invariant features by learning augmentations and representations of skeleton sequences in a one-stage way. In X-CAR, a new Adaptive-combination Augmentation (AA) mechanism is designed to rotate, shear, and scale the skeletons by learnable controlling factors in an adaptive way rather than a random way. Here, such controlling factors are also learned in the whole contrastive learning process, which can facilitate the consistency between the learned augmentations and representations of skeleton sequences. In addition, we relax the pre-definition of positive and negative samples to avoid the confusing allocation of ambiguous samples, and present a new Pull-Push Contrastive Loss (PPCL) to pull the augmenting skeleton close to the original skeleton, while push far away from the other skeletons. Experimental results on both NTU RGB+D and North-Western UCLA datasets show that the proposed X-CAR achieves better accuracy compared with other competitive methods in the semi-supervised scenario.
Momentum Contrastive Voxel-Wise Representation Learning for Semi-supervised Volumetric Medical Image Segmentation

Contrastive learning (CL) aims to learn useful representation without relying on expert annotations in the context of medical image segmentation. Existing approaches mainly contrast a single positive vector (i.e., an augmentation of the same image) against a set of negatives within the entire remainder of the batch by simply mapping all input features into the same constant vector. Despite the impressive empirical performance, those methods have the following shortcomings: (1) it remains a formidable challenge to prevent the collapsing problems to trivial solutions; and (2) we argue that not all voxels within the same image are equally positive since there exist the dissimilar anatomical structures with the same image. In this work, we present a novel Contrastive Voxel-wise Representation Learning (CVRL) method to effectively learn low-level and high-level features by capturing 3D spatial context and rich anatomical information along both the feature and the batch dimensions. Specifically, we first introduce a novel CL strategy to ensure feature diversity promotion among the 3D representation dimensions. We train the framework through bi-level contrastive optimization (i.e., low-level and high-level) on 3D images. Experiments on two benchmark datasets and different labeled settings demonstrate the superiority of our proposed framework. More importantly, we also prove that our method inherits the benefit of hardness-aware property from the standard CL approaches. Codes will be available soon.
The Biases of Pre-Trained Language Models: An Empirical Study on Prompt-Based Sentiment Analysis and Emotion Detection

Thanks to the breakthrough of large-scale pre-trained language model (PLM) technology, prompt-based classification tasks, e.g., sentiment analysis and emotion detection, have raised increasing attention. Such tasks are formalized as masked language prediction tasks which are in line with the pre-training objects of most language models. Thus, one can use a PLM to infer the masked words in a downstream task, then obtaining label predictions with manually defined label-word mapping templates. Prompt-based affective computing takes the advantages of both neural network modeling and explainable symbolic representations. However, there still remain many unclear issues related to the mechanisms of PLMs and prompt-based classification. We conduct a systematic empirical study on prompt-based sentiment analysis and emotion detection to study the biases of PLMs towards affective computing. We find that PLMs are biased in sentiment analysis and emotion detection tasks with respect to the number of label classes, emotional label-word selections, prompt templates and positions, and the word forms of emotion lexicons.
Boosted sooty tern optimization algorithm for global optimization and feature selection

Feature selection (FS) represents an optimization problem that aims to simplify and improve the quality of highly dimensional datasets through selecting prominent features and eliminating redundant and irrelevant data to classify results better. The goals of FS comprise dimensionality reduction and enhancing the classification accuracy in general, accompanied by great significance in different fields like data mining applications, pattern classification, and data analysis. Using powerful optimization algorithms is crucial to obtaining the best subsets of information in FS. Different metaheuristics, such as the Sooty Tern Optimization Algorithm (STOA), help to optimize the FS problem. However, such kind of techniques tends to converge in sub-optimal solutions. To overcome this problem in the STOA, an improved version called mSTOA is introduced. It employs the balancing exploration/exploitation strategy, self-adaptive of the control parameters strategy, and population reduction strategy. The proposed approach is proposed for solving the FS problem, but also it has been validated over benchmark optimization problems from the CEC 2020. To assess the performance of the mSTOA, it has also been tested with different algorithms. The experiments in terms of FS provide qualitative and quantitative evidence of the capabilities of the mSTOA for extracting the optimal subset of features. Besides, statistical analyses and no-parametric tests were also conducted to validate the result obtained by the mSTOA in optimization.
Neural Rays for Occlusion-aware Image-based Rendering

We present a new neural representation, called Neural Ray (NeuRay), for the novel view synthesis task. Recent works construct radiance fields from image features of input views to render novel view images, which enables the generalization to new scenes. However, due to occlusions, a 3D point may be invisible to some input views. On such a 3D point, these generalization methods will include inconsistent image features from invisible views, which interfere with the radiance field construction. To solve this problem, we predict the visibility of 3D points to input views within our NeuRay representation. This visibility enables the radiance field construction to focus on visible image features, which significantly improves its rendering quality. Meanwhile, a novel consistency loss is proposed to refine the visibility in NeuRay when finetuning on a specific scene. Experiments demonstrate that our approach achieves state-of-the-art performance on the novel view synthesis task when generalizing to unseen scenes and outperforms perscene optimization methods after finetuning. Project page:https://liuyuan-pal.github.io/NeuRay/