Reviewerly/exHarmony huggingface dataset:
The Drivers of the Behavioral Adoption Intention of BITCOIN Payment from the Perspective of Chinese Citizens

The bitcoin payment innovation has gained wider interest around the world, but its adoption among the general population has been a challenge. Bitcoin as a peer-to-peer technology works with no central authority or banks, and the transaction management and issuing of bitcoin is carried out collectively by the network. One major debate on bitcoin development and diffusion is the critical matter of nongovernment intervention through adequate policy and regulatory framework and thus hinders people’s active participation (acceptance) in the bitcoin market. Consequently, this study is purposed to examine the role government policy and regulations (moderating impact) can have in driving the acceptance of bitcoin payment from the Chinese perspective. The UTAUT was used as the theoretical basis from which a model was developed for testing. The structural equation model (SEM) through the use of SmartPLS was employed to undertake the analysis. The results have demonstrated that government regulation moderates (significant) the influence of both performance expectancy and infrastructure support on the behavioral acceptance of bitcoin payment. However, government regulation contrary to expectations was not significant in moderating the influence of effort expectancy (EE) and security on the acceptance of bitcoin payments. Additionally, the study discovered that performance expectancy, security, EE, and infrastructure support were significant in encouraging bitcoin behavioral adoption. The practical and theoretical implications of these findings on the development and diffusion of bitcoin technology systems are dissected meticulously.
THALIS: Human-Machine Analysis of Longitudinal Symptoms in Cancer Therapy

Although cancer patients survive years after oncologic therapy, they are plagued with long-lasting or permanent residual symptoms, whose severity, rate of development, and resolution after treatment vary largely between survivors. The analysis and interpretation of symptoms is complicated by their partial co-occurrence, variability across populations and across time, and, in the case of cancers that use radiotherapy, by further symptom dependency on the tumor location and prescribed treatment. We describe THALIS, an environment for visual analysis and knowledge discovery from cancer therapy symptom data, developed in close collaboration with oncology experts. Our approach leverages unsupervised machine learning methodology over cohorts of patients, and, in conjunction with custom visual encodings and interactions, provides context for new patients based on patients with similar diagnostic features and symptom evolution. We evaluate this approach on data collected from a cohort of head and neck cancer patients. Feedback from our clinician collaborators indicates that THALIS supports knowledge discovery beyond the limits of machines or humans alone, and that it serves as a valuable tool in both the clinic and symptom research.
Evaluation of fuzzy membership functions for linguistic rule-based classifier focused on explainability, interpretability and reliability

Decision support systems focus on their interpretability with a different caution. The majority of approaches utilize reliable optimization techniques to achieve high recognition accuracy. However, accuracy improvement is done at the cost of the readability of the decision support system. This paper proposes a novel technique of linguistic rule extraction focused on: explainability, interpretability, and reliability, called EasIeR. Proposed solution performs a direct analysis of the learning set without omitting real data imperfections. The proposed algorithm processes the imprecision of data with linguistic values described by fuzzy membership functions and the uncertainty of knowledge employing a fuzzy belief function. It describes the learning set with the most appropriate fuzzy if-then rules. For this purpose, a new method of evaluation of membership functions in a set of rules and their iterative refinement is proposed. EasIeR achieves the best results of trade-off criteria between the generalization quality and the interpretability of the knowledge base when compared with state-of-art fuzzy rule-based classification approaches. Achieving the simplest rule base and competitive generalization quality is verified by cross-validation experiments with benchmark data sets. The new method of rule evaluation allows for better determination of their quality in decision support, and the selection algorithm quickly provides a clear and easy to interpret knowledge base. This rule evaluation technique will be proficient for all fuzzy rule-based classification systems. • Efficient algorithm of rule extraction provides interpretable data-driven knowledge. • Fuzzy rules and belief measure ensure classification reliability and explainability. • New rule evaluation method allows extracting effective rule-based knowledge. • Approach achieves competitive generalization quality with the simplest knowledge base.
Consensus of Linear Multivariable Discrete-Time Multiagent Systems: Differential Privacy Perspective

Differential privacy, which has been widely applied in industries, is a privacy mechanism effective in preventing malicious entities from breaching the privacy of an individual participant. It is usually achieved by adding random variables in the data. This article investigates a class of multivariable discrete-time multiagent systems with ϵ -differential privacy preserved. A novel information-masking mechanism is proposed, in which the information of each state transmitted to different neighbors is obscured by adding independent random noises. Then, the mean-square consensus conditions, and the upper bound and lower bound of the convergence rate are obtained. Moreover, the conditions for the convergence rate reaching its upper bound are established. The results can be applied to the average mean-square consensus. In addition, a necessary and sufficient condition is presented under which agents can preserve the dynamics of agents ϵ -differentially private at any time instant.
MoFaNeRF: Morphable Facial Neural Radiance Field

We propose a parametric model that maps free-view images into a vector space of coded facial shape, expression and appearance with a neural radiance field, namely Morphable Facial NeRF. Specifically, MoFaNeRF takes the coded facial shape, expression and appearance along with space coordinate and view direction as input to an MLP, and outputs the radiance of the space point for photo-realistic image synthesis. Compared with conventional 3D morphable models (3DMM), MoFaNeRF shows superiority in directly synthesizing photo-realistic facial details even for eyes, mouths, and beards. Also, continuous face morphing can be easily achieved by interpolating the input shape, expression and appearance codes. By introducing identity-specific modulation and texture encoder, our model synthesizes accurate photometric details and shows strong representation ability. Our model shows strong ability on multiple applications including image-based fitting, random generation, face rigging, face editing, and novel view synthesis. Experiments show that our method achieves higher representation ability than previous parametric models, and achieves competitive performance in several applications. To the best of our knowledge, our work is the first facial parametric model built upon a neural radiance field that can be used in fitting, generation and manipulation. The code and data is available at https://github.com/zhuhao-nju/mofanerf .
Spatial information sampling: another feedback mechanism of realising adaptive parameter control in meta-heuristic algorithms

This paper innovatively proposes a spatial information sampling strategy to adaptively control the parameters of meta-heuristic algorithms (MHAs). The solutions' spatial distribution information in current iterations is used to control the parameters in the following iterations. An adaptive parameter control method requires obtaining information from the operation of MHAs and feeding it back to the adjustment of parameters. The mainstream information acquisition method is to record the changes to the solutions in the iterative process. In essence, the proposed feedback method, i.e., chaotic perceptron (CP), makes use of the temporal information arising from the change of solutions in MHAs. The wingsuit flying search algorithm and differential evolution are employed as case studies. Experimental results validate the effectiveness of the proposed strategy. The source code of CP can be found at https: //toyamaailab.github.io/.
The Role of Blockchain in Revolutionizing the Agricultural Sector

Since blockchain technology has proven to be effective in the development of a wide range of industries, its use in other fields is also being expanded. Agriculture is one such sector, where blockchain technology is being used to improve farm business operations. Today, several agribusiness firms are utilizing technology to improve food supply chain tracking. For example, Farmers Edge, the world’s leading company that revolutionized the field of digital agriculture through its work in providing advanced artificial intelligence solutions, as well as new opportunities that give agriculture a globally advanced future for all stakeholders, has taken a significant step forward. The issue of blockchain network technology and its applications in agriculture will be discussed in this study, as well as the key advantages that this technology can provide, when employed to make the lives of both producers and consumers easier. In addition, a total of 79 research papers were evaluated, with a focus on the state of blockchain technology in agriculture, related issues, and its future importance, as well as relevant contributions to this new technology and the distributions of this study by different countries.
Automatic Double-Auction Mechanism for Federated Learning Service Market in Internet of Things

In the past decade, the Artificial Intelligent & Internet of Things (AIoT) has become the disruptive force reshaping our lives and works. Billions of AIoT devices around the world are now connected to the Internet. This widespread deployment of AIoT devices presents huge challenges for the centralized AI training architecture (e.g., security issues, privacy issues). Recently, with the advance of federated learning (FL) technology, the integration of AIoT and FL is considered as a promising solution. FL enables distributed devices to collaboratively train a shared AI model while keeping all the training data contained in local. However, unlike the centralized architecture, the distributed learners are independent, uncontrollable, and self-interested. Designing effective incentive schemes to stimulate the distributed devices to actively participate in training tasks and contribute high-quality training models is the priority question. In this paper, we design a double auction mechanism for FL service market, where the trained models can be automatically traded between AIoT devices and FL platforms. Then, we propose an iterative double auction (IDA) algorithm, where a controller is implemented to guide participants to adjust their policies for the sake of maximizing individual participates utilities as well as social welfare. In addition, we propose a reinforcement learning-based scheme, termed as the Experience Weighted Attraction Learning-Double Auction (EWA-DA) algorithm. In EWA-DA, the participants can learn to improve their strategies directly by participating in the auction continuously, without the controller participating.
Object Detection using OpenCV and Python

The paper has covered topics ranging from how artificial intelligence and machine learning algorithms help in object detection to how OpenCV is such a useful tool for beginners who wish to learn how real time object identification and tracking can be done. It also shows the flexibility of a tracking system to a moving camera, ideal for automotive safety applications. Image identification makes use of techniques like detection of an object, its recognition, and segmentation. The use of artificial intelligence and machine learning enhances the rate of processing the data and maintaining the standard of the outcome. Example, by using artificial intelligence, we can very easily complete difficult tasks.
Generative Oversampling Methods for Handling Imbalanced Data in Software Fault Prediction

Imbalanced software fault datasets, having fewer faulty modules than the nonfaulty modules, make accurate fault prediction difficult. It is challenging for software practitioners to handle imbalanced fault data during software fault prediction (SFP). Earlier, several researchers have applied oversampling techniques such as synthetic minority oversampling techniques and others for imbalanced learning in SFP. However, most of these techniques resulted in overfitted prediction models. This article presents generative oversampling methods to handle imbalanced data problems in the SFP. Using the generative adversarial network (GAN) based approach, the presented methods generate synthetic samples of the faulty modules to balance the proportion of faulty and nonfaulty modules in the fault datasets. Further, SFP models are built on the processed fault datasets using different machine learning techniques. Experimental validation of the presented oversampling methods is done on 18 fault datasets gathered from PROMISE, JIRA, Eclipse data repositories, and precision, recall, f1-score, and AUC are used as evaluation measures. We extensively compared presented oversampling methods with various state-of-the-art class imbalance techniques and baseline models. The experimental results evidenced that the presented methods improved fault prediction performance and yielded better performance than the state-of-the-art class imbalance techniques.
Cross-Modal 360° Depth Completion and Reconstruction for Large-Scale Indoor Environment

In a large-scale epidemic, reducing direct contact among medical personnel, attendants and patients has become a necessary means of epidemic prevention and control. Intelligent vehicles and mobile robots in the hospital environment, such as disinfection vehicles, logistics vehicles, nursing robots, and guiding robots, play an important role in improving the operational efficiency of the medical system and promoting epidemic prevention and governance. Powerful capabilities of environmental spatial perception and reconstruction are the keys to accurate localization, navigation, and obstacle avoidance for intelligent vehicles and autonomous robots in such operations. Omnidirectional perception is becoming increasingly important and proliferative in autonomous vehicles and robots since its wide field of view significantly enhances the perception ability. However, the lack of dense and accurate 360° depth datasets has brought the challenge to the omnidirectional perception. In this paper, we propose a depth-sensing and reconstruction system to address this challenge in the large-scale indoor environment. First, we design an omnidirectional depth completion convolutional neural network model, in which a spherical normalized convolutional and the unit sphere area-based loss are introduced to extract features from cross-modal omnidirectional input with unequal sparsity and deal with the imbalanced data distribution and distortion in the panoramic input. In addition, we present a 3D reconstruction system by integrating our depth completion into omnidirectional localization and dense mapping. We evaluate our method on 360D large-scale indoor datasets and real-world sequences of a challenging hospital scene. Extensive experiments show that the proposed method outperforms the other state-of-the-art (SoTA) approaches in terms of depth completion and 3D reconstruction.
A new efficient hierarchical multi-secret sharing scheme based on linear homogeneous recurrence relations

Hierarchical secret sharing is an important key management technique since it is specially customized for hierarchical organizations with different departments allocated with different privileges, such as the government agencies or companies. Hierarchical access structures have been widely adopted in secret sharing schemes, where efficiency is the primary consideration for various applications. How to design an efficient hierarchical secret sharing scheme is an important issue. A famous hierarchical secret sharing (HSS) scheme was proposed by Tassa based on Birkhoff interpolation. Later, based on the same method, many other HSS schemes were proposed. However, these schemes all depend on Polya’s condition, which is a necessary condition, not a sufficient condition. It cannot guarantee that Tassa’s HSS scheme always exists. Furthermore, this condition needs to check the non-singularity of many matrices. We propose a hierarchical multi-secret sharing scheme based on the linear homogeneous recurrence (LHR) relations and the one-way function. In our scheme, we select m linearly independent homogeneous recurrence relations. The participants in the highly-ranked subsets γ1,γ2,⋯,γj-1 join in the jth subset to construct the jth LHR relation. In addition, the proposed hierarchical multi-secret sharing scheme just requires one share for each participant. Besides, our scheme is both perfect and ideal. Furthermore, our scheme avoids many checks of the non-singularity of many matrices in the presented hierarchical secret sharing schemes. Although we need to publish more public values, our scheme reduces the computational complexity of the hierarchical secret sharing schemes from exponential time to polynomial time, i.e., O(nkm-1logn), which is relatively more efficient than schemes in the literature.
Change Detection Meets Visual Question Answering

The Earth’s surface is continually changing, and identifying changes plays an important role in urban planning and sustainability. Although change detection techniques have been successfully developed for many years, these techniques are still limited to experts and facilitators in related fields. In order to provide every user with flexible access to change information and help them better understand land-cover changes, we introduce a novel task: change detection-based visual question answering (CDVQA) on multi-temporal aerial images. In particular, multi-temporal images can be queried to obtain high level change-based information according to content changes between two input images. We first build a CDVQA dataset including multi-temporal image-question-answer triplets using an automatic question-answer generation method. Then, a baseline CDVQA framework is devised in this work, and it contains four parts: multi-temporal feature encoding, multi-temporal fusion, multi-modal fusion, and answer prediction. In addition, we also introduce a change enhancing module to multi-temporal feature encoding, aiming at incorporating more change-related information. Finally, effects of different backbones and multi-temporal fusion strategies are studied on the performance of CDVQA task. The experimental results provide useful insights for developing better CDVQA models, which are important for future research on this task. The dataset will be available at https://github.com/YZHJessica/CDVQA.
Deepfake detection in digital media forensics

With the development of technology and ease of creation of fake content, the manipulation of media is carried out on a large scale in recent times. The rise of AI altered videos or Deepfake media has posed a great threat to media integrity and is being produced and spread widely across social media platforms, the detection of which is seen to be a major challenge. In this paper, an approach for Deepfake detection has been provided. ResNext, a Convolutional Neural Network (CNN) algorithm and Long Short-Term Memory (LSTM) is used as an approach to detect the Deepfake videos. The approach and its steps are discussed in this paper. The accuracy obtained for the developed Deep-Learning (DL) model over the Celeb-Df dataset is 91%.
Wrapper and Hybrid Feature Selection Methods Using Metaheuristic Algorithms for English Text Classification: A Systematic Review

Feature selection (FS) constitutes a series of processes used to decide which relevant features/attributes to include and which irrelevant features to exclude for predictive modeling. It is a crucial task that aids machine learning classifiers in reducing error rates, computation time, overfitting, and improving classification accuracy. It has demonstrated its efficacy in myriads of domains, ranging from its use for text classification (TC), text mining, and image recognition. While there are many traditional FS methods, recent research efforts have been devoted to applying metaheuristic algorithms as FS techniques for the TC task. However, there are few literature reviews concerning TC. Therefore, a comprehensive overview was systematically studied by exploring available studies of different metaheuristic algorithms used for FS to improve TC. This paper will contribute to the body of existing knowledge by answering four research questions (RQs): 1) What are the different approaches of FS that apply metaheuristic algorithms to improve TC? 2) Does applying metaheuristic algorithms for TC lead to better accuracy than the typical FS methods? 3) How effective are the modified, hybridized metaheuristic algorithms for text FS problems?, and 4) What are the gaps in the current studies and their future directions? These RQs led to a study of recent works on metaheuristic-based FS methods, their contributions, and limitations. Hence, a final list of thirty-seven (37) related articles was extracted and investigated to align with our RQs to generate new knowledge in the domain of study. Most of the conducted papers focused on addressing the TC in tandem with metaheuristic algorithms based on the wrapper and hybrid FS approaches. Future research should focus on using a hybrid-based FS approach as it intuitively handles complex optimization problems and potentiality provide new research opportunities in this rapidly developing field.
An AI-Enabled Hybrid Lightweight Authentication Scheme for Intelligent IoMT Based Cyber-Physical Systems

In the era of smart healthcare, Internet of Medical Things-based Cyber-Physical Systems (IoMT-based-CPS) play an important role in acquiring, evaluating, monitoring, tracking, and prescribing patients ubiquitously. For these applications, trustworthy authentication and unassailable communication are the most noteworthy impediments to be considered to attain the trust of patients, healthcare experts, nursing staff, pharmacologists, and other involved commodities. To address these security concerns, in this paper, we present a lightweight hybrid authentication, data privacy and preservation model that is constituted from supervised machine learning (SML) algorithm and a Cryptographic Parameter Based Encryption and Decryption (CPBE&D) algorithm to guarantee the authentication of legitimate IoMT-based-CPS accompanied by encrypted data transmission over the wireless communication channel. To accomplish promising results, we have facilitated a decentralized verification and authentication among legitimate IoMT-based-CPS in the network with an objective to reduce the authentication time, computation cost, and communication overhead with the assistance of the SML algorithm to predicate and forward the authentication parameters of these devices to the next concerned trusted authority when a patient is moving from one hospital (region) to another hospital (region). During the simulation results analysis, SML and CPBE&D authentication scheme demonstrated impressive security features in terms of cost-effective authentication throughout the legitimate patient wearable IoMT-based-CPS validation process, accompanied by profitable communication metrics in the comparison of predecessor works.